Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Weighted K-Means Clustering and Artificial Neural Network for an Anomaly-Based Network Intrusion Detection System

Abstract Despite the rapid developments in data technology, intruders are among the most revealed threats to security. Network intrusion detection systems are now a typical constituent of network security structures. In this paper, we present a combined weighted K-means clustering algorithm with artificial neural network (WKMC+ANN)-based intrusion identification scheme. This paper comprises two modules: clustering and intrusion detection. The input dataset is gathered into clusters with the usage of WKMC in clustering module. In the intrusion detection module, the clustered information is trained with the utilization of ANN and its structure is stored. In the testing process, the data are tested by choosing the most suitable ANN classifier, which corresponds to the closest cluster to the test data, according to distance or similarity measures. For experimental evaluation, we used the benchmark database, and the results clearly demonstrated that the proposed technique outperformed the existing technique by having better accuracy.
Hybrid forests for left ventricle segmentation using only the first slice label

Machine learning models produce state-of-the-art results in many MRI images segmentation. However, most of these models are trained on very large datasets which come from experts manual labeling. This labeling process is very time consuming and costs experts work. Therefore finding a way to reduce this cost is on high demand. In this paper, we propose a segmentation method which exploits MRI images sequential structure to nearly drop out this labeling task. Only the first slice needs to be manually labeled to train the model which then infers the next slice's segmentation. Inference result is another datum used to train the model again. The updated model then infers the third slice and the same process is carried out until the last slice. The proposed model is an combination of two Random Forest algorithms: the classical one and a recent one namely Mondrian Forests. We applied our method on human left ventricle segmentation and results are very promising. This method can also be used to generate labels.
Central Limit Model Checking

We consider probabilistic model checking for continuous-time Markov chains (CTMCs) induced from Stochastic Reaction Networks against a fragment of Continuous Stochastic Logic (CSL) extended with reward operators. Classical numerical algorithms for CSL model checking based on uniformisation are limited to finite CTMCs and suffer from exponential growth of the state space with respect to the number of species. However, approximate techniques such as mean-field approximations and simulations combined with statistical inference are more scalable but can be time-consuming and do not support the full expressiveness of CSL. In this article, we employ a continuous-space approximation of the CTMC in terms of a Gaussian process based on the Central Limit Approximation, also known as the Linear Noise Approximation, whose solution requires solving a number of differential equations that is quadratic in the number of species and independent of the population size. We then develop efficient and scalable approximate model checking algorithms on the resulting Gaussian process, where we restrict the target regions for probabilistic reachability to convex polytopes. This allows us to derive an abstraction in terms of a time-inhomogeneous discrete-time Markov chain (DTMC), whose dimension is independent of the number of species, on which model checking is performed. Using results from probability theory, we prove the convergence in distribution of our algorithms to the corresponding measures on the original CTMC. We implement the techniques and, on a set of examples, demonstrate that they allow us to overcome the state space explosion problem, while still correctly characterizing the stochastic behaviour of the system. Our methods can be used for formal analysis of a wide range of distributed stochastic systems, including biochemical systems, sensor networks, and population protocols.
The Impact of Combination Therapy on Infliximab Levels and Antibodies in Children and Young Adults With Inflammatory Bowel Disease.

Goal The aim of this study was to evaluate the effect of combination therapy with methotrexate or 6-mercaptopurine on infliximab levels (IFXL) and antibodies to infliximab (ATI). Background Infliximab (IFX) is a highly effective therapy for inflammatory bowel disease (IBD). Unfortunately, 25%-50% of patients will lose response to IFX. Loss of response is correlated with low IFXL and ATI formation which accelerates drug clearance. Combination therapy is thought to decrease ATI formation. Methods We performed a cross-sectional analysis of 223 pediatric and young adult patients with IBD on IFX. IFXL and ATI were measured and compared between subjects on current combination therapy, prior combination therapy, and IFX monotherapy. Results Eighty-four (37.7%) patients were on combination therapy and 139 (62.3%) were on IFX monotherapy. Within the current monotherapy group, 112 (80.6%) had previously been on combination therapy, while 27 (19.4%) had never been on a concomitant immunomodulator. Patients currently on combination therapy had a higher IFXL (17.00 ± 1.33 μg/mL) than those currently on IFX monotherapy (13.18 ± 1.26 μg/mL), P < 0.01. IFXL was lowest in patients who had never been on combination therapy (11.53 ± 2.05 μg/mL) and highest in patients currently on combination therapy (17.00 ± 1.33 μg/mL). Patients currently on combination therapy had a lower rate of detectable ATI (9.5%) compared with those on monotherapy (20.0%) in multivariate analysis (odds ratio [OR]: 0.3; 95% confidence interval (CI), 0.1-0.7, P < 0.01). Conclusions Current or prior combination therapy is associated with higher IFXL and lower rates of ATI formation.
Managing mastitis in dairy heifers to improve overall herd health

Replacement heifers are critical to dairy herd productivity because they represent the future milking and breeding stock of all dairy operations. The goal should be to provide an environment for heifers to develop their full lactation potential at the desired age with minimal expense. Animal health and wellbeing play vital roles in achieving this potential. One disease that can influence future productivity is heifer mastitis caused by Staphylococcus aureus, the coagulase-negative staphylococci (CNS), and the environmental streptococci. The presence of mastitis in young dairy heifers is generally not observed until freshening or until the first clinical flare-up in early lactation. Thus, animals may carry intramammary infections (IMI) for a year or more before they are diagnosed with mastitis, resulting in reduced milk yield (Boddie et al., 1987). The greatest development of milk-producing tissue in the udder occurs during the first pregnancy, so it is important to protect the heifer mammary gland from pathogenic microorganisms to ensure maximum milk production during the first and future lactations.
Unchained Cellular Obfuscation Areas for Location Privacy in Continuous Location-Based Service Queries

To access location-based service (LBS) and query surrounding points of interest (POIs), smartphone users typically use built-in positioning functions of their phones when traveling at unfamiliar places. However, when a query is submitted, personal information may be leaked when they provide their real location. Current LBS privacy protection schemes fail to simultaneously consider real map conditions and continuous querying, and they cannot guarantee privacy protection when the obfuscation algorithm is known. To provide users with secure and effective LBSs, we developed an unchained regional privacy protection method that combines query logs and chained cellular obfuscation areas. It adopts a multiuser anonymizer architecture to prevent attackers from predicting user travel routes by using background information derived from maps (e.g., traffic speed limits). The proposed scheme is completely transparent to users when performing continuous location-based queries, and it combines the method with actual road maps to generate unchained obfuscation areas that conceal the actual locations of users. In addition to using a caching approach to enhance performance, the proposed scheme also considers popular tourist POIs to enhance the cache data hit ratio and query performance.
Adaptive Uniformization

Uniformization has been shown to be, in many cases, a good method to compute transient state probabilities of a continuous-time Markov chain. However, two issues limit its use: uniformization can be computationally very intensive, for instance, on sti models, and uniformization cannot be used for all model classes, e.g., models with not uniformly bounded transition rates. In this paper we introduce adaptive uniformization, a variation on standard uniformization, which can overcome these problems for some models. Adaptive uniformization di ers from standard uniformization in that it uses a uniformization rate that adapts depending on the set of states that the process can be in after a particular number of jumps. Doing this can sometimes signi cantly reduce the computational cost required to obtain a solution. A formal de nition of adaptive uniformization is rst given, along with a proof that adaptive uniformization yields correct results. Characteristics of models that can facilitate solution and alternative methods for computing the required \jump probabilities" are then discussed. Finally, the computational cost of adaptive uniformization (relative to standard uniformization) is illustrated, through its application to an extended machine-repairman model.
A Spatial Basis Coverage Approach For Uplink Training And Scheduling In Massive MIMO Systems

Massive multiple-input multiple-output (massive MIMO) can provide large spectral and energy efficiency gains. Nevertheless, its potential is conditioned on acquiring accurate channel state information (CSI). In time division duplexing (TDD) systems, CSI is obtained through uplink training which is hindered by pilot contamination. The impact of this phenomenon can be relieved using spatial division multiplexing, which refers to partitioning users based on their spatial information and processing their signals accordingly. The performance of such schemes depend primarily on the implemented grouping method. In this paper, we propose a novel spatial grouping scheme that aims at managing pilot contamination while reducing the required training overhead in TDD massive MIMO. Herein, user specific decoding matrices are derived based on the columns of the discrete Fourier transform matrix (DFT), taken as a spatial basis. Copilot user groups are then formed in order to obtain the best coverage of the spatial basis with minimum overlapping between decoding matrices. We provide two algorithms that achieve the desired grouping and derive their respective performance guarantees. We also address inter-cell copilot interference through efficient pilot sequence allocation, leveraging the formed copilot groups. Various numerical results are provided to showcase the efficiency of the proposed algorithms.
Comparing Online Behavior in Korea and the Us Yahoo! Answers versus Naver Knowledge-in

This study compares online behavior of the leading Q&A online sites in Korea and the United States: Yahoo! Answers(US) and Naver Knowledge-iN (South Korea). We found that Yahoo! Answers users communicate more actively than Naver Knowledge-iN users, while Naver Knowledge-iN users seem more concerned about their social reputations. Among three ties (i.e., “providing answers” tie, co-answering tie and “getting answers” tie) at Q&A sites, “providing answers” ties show a positive influence on the user’s quality of answers. Interestingly, co-answering ties show a negative influence on the quality of answers. This implies that while an answerer’s successful communications with askers are beneficial, relationships among co-answerers may be competitive. It also seems that in online knowledge sharing, individualistic US users are more sociable than collectivistic Korean users.
Potentials and Limitations of Deep Neural Networks for Cognitive Robots

Although Deep Neural Networks reached remarkable performance on several benchmarks and even gained scientific publicity, they are not able to address the concept of cognition as a whole. In this paper, we argue that those architectures are potentially interesting for cognitive robots regarding their perceptual representation power for audio and vision data. Then, we identify crucial settings for cognitive robotics where deep neural networks have as yet only contributed little compared to the challenges in cognitive robotics. Finally, we argue that the rather unexplored area of Reservoir Computing qualifies to be an integral part of sequential learning in this context.
Isoflavones, calcium, vitamin D and inulin improve quality of life, sexual function, body composition and metabolic parameters in menopausal women: result from a prospective, randomized, placebo-controlled, parallel-group study

Introduction Menopause results in metabolic changes that contribute to increase risk of cardiovascular diseases: increase in low density lipoprotein (LDL) and triglycerides and decrease in high density lipoprotein (HDL), weight gain are associated with a correspondent increase in incidence of hypertension and diabetes. The aim of this study was to evaluate the effect of a preparation of isoflavones, calcium vitamin D and inulin in menopausal women. Material and methods We performed a prospective, randomized, placebo-controlled, parallel-group study. A total of 50 patients were randomized to receive either oral preparations of isoflavones (40 mg), calcium (500 mg) vitamin D (300 UI) and inulin (3 g) or placebo (control group). Pre- and post-treatment assessment of quality of life and sexual function were performed through Menopause-Specific Quality of Life Questionnaire (MENQOL) and Female Sexual Function Index (FSFI); evaluations of anthropometric indicators, body composition through bioelectrical impedance analyser, lumbar spine and proximal femur T-score and lipid profile were performed. Results After 12 months, a significant reduction in MENQOL vasomotor, physical and sexual domain scores (p < 0.05) and a significant increase in all FSFI domain scores (p < 0.05) were observed in treatment group. Laboratory tests showed significant increase in serum levels of HDL (p < 0.05). No significant changes of lumbar spine and femur neck T-score (p > 0.05) were found in the same group. Conclusions According to our data analysis, isoflavones, calcium, vitamin D and inulin may exert favourable effects on menopausal symptoms and signs.
Adaptive Sensing for Learning Nonstationary Environment Models

Most environmental phenomena, such as wind profiles, ozone concentration and sunlight distribution under a forest canopy, exhibit nonstationary dynamics i.e. phenomenon variation change depending on the location and time of occurrence. Non-stationary dynamics pose both theoretical and practical challenges to statistical machine learning algorithms aiming to accurately capture the complexities governing the evolution of such processes. In this paper, we address the sampling aspects of the problem of learning nonstationary spatio-temporal models, and propose an efficient yet simple algorithm - LISAL. The core idea in LISAL is to learn two models using Gaussian processes (GPs) wherein the first is a nonstationary GP directly modeling the phenomenon. The second model uses a stationary GP representing a latent space corresponding to changes in dynamics, or the nonstationarity characteristics of the first model. LISAL involves adaptively sampling the latent space dynamics using information theory quantities to reduce the computational cost during the learning phase. The relevance of LISAL is extensively validated using multiple real world datasets.
Lost landscapes of healing: the decline of therapeutic mental health landscapes

Abstract Therapeutic landscapes feature prominently in contemporary discourse as they relate to mental health and well-being. Yet this engagement belies the long history of such settings in the therapeutic regimen for those with mental illness. Notable here are the asylum landscapes of the nineteenth century which included airing courts, ornamental gardens, orchards, vegetable plots and recreational grounds aimed at providing meaningful activity for inhabitants, natural beauty to assist them in their recovery, as well as provide food and income for the institution. With the emergence of deinstitutionalisation, modern pharmacotherapy and the reductionist biomedical approach to health, among other factors, these landscapes lost their therapeutic place during the twentieth century. Lacking however, is a critical examination of this transformation. Through the conceptual framework of therapeutic landscapes, this article explores this nexus with a focus on changes which transpired during this period eventuating in the demise of institutional landscapes for mental illness.
Empirical Welfare Analysis under Social Interactions ( Preliminary , Comments Welcome )

We develop tools for empirical welfare-analysis in binary-choice models with social spillover or preference for conformity. We show that under spillover, distribution of consumer welfare resulting from tax/subsidy induced price changes is generically not point-identified from demand (i.e. choice probabilities) even under parametric preferences and a unique equilibrium. In fact, the same structural demand function can correspond to both positive and negative aggregate welfare effects. Such non-identification results from dependence of non-purchase utility on aggregate purchase-rate. In Brock-Durlauf-type single-index settings, welfare-distributions can be bounded; the bounds’width increases with the strength of net spillover and with the impact of price-change on average choice. We illustrate our method using experimental data on mosquito-net adoption in rural Kenya. We show that under spillover, means-tested subsidies may substantially reduce welfare of ineligibles and increase deadweight-loss, compared to settings with no spillover. Ignoring spillover would also imply over (respectively, under)-estimation of demand at less (resp., more) generous eligibility-rules.
Towards the Automation of Metamorphic Testing in Model Transformations

Model transformations are the cornerstone of Model-Driven Engineering, and provide the essential mechanisms for manipulating and transforming models. Checking whether the output of a model transformation is correct is a manual and error-prone task, this is referred to as the oracle problem in the software testing literature. The correctness of the model transformation program is crucial for the proper generation of its output, so it should be tested. Metamorphic testing is a testing technique to alleviate the oracle problem consisting on exploiting the relations between different inputs and outputs of the program under test, so-called metamorphic relations. In this paper we give an insight into our approach to generically define metamorphic relations for model transformations, which can be automatically instantiated given any specific model transformation.
Design and provability of a statically configurable hypervisor

In this thesis we develop a novel, minimalist design for Type I hypervisors and present a fully working prototype for the ARMv7 and ARMv8 architectures. We introduce its key design paradigm, the Principle of Staticity, elaborate on its consequences for the resulting implementation, and discuss scenarios where not all dynamicity can be eliminated. We also describe, implement, and evaluate the configuration framework necessary to build this specific kind of hypervisor. Even though the compiled hypervisor images are fully static, we show that the framework is powerful enough to support various system-on-chip target platforms, different address translation regimes, and even completely unrelated architectures. We further demonstrate its versatility by extending the framework to even support embedded processors which only contain a memory protection unit. After performing a selection of benchmarks to substantiate the competitiveness of our implementation, we continue by subjecting our hypervisor to an analysis using a specialized form of symbolic model checking. We succeed in deriving integrity properties for the hypervisor as well as among virtual machines with surprisingly little effort, but show that complexity immediately ramps up once dynamic components like a shadow paging unit are added to our design. Finally we demonstrate the extensibility of our design despite our postulated Principle of Staticity. As an optimization example, we introduce the concept of ”lightweight VMs” and present a novel use case for a recent feature of the ARM architecture, thereby reducing the switch latency between virtual machines. We conclude with an overview over the published research efforts based on or heavily inspired by our hypervisor and discuss future research questions.
A Review on Human Body Communication: Signal Propagation Model, Communication Performance, and Experimental Issues

Human body communication (HBC), which uses the human body tissue as the transmission medium to transmit health informatics, serves as a promising physical layer solution for the body area network (BAN). The human centric nature of HBC offers an innovative method to transfer the healthcare data, whose transmission requires low interference and reliable data link. Therefore, the deployment of HBC system obtaining good communication performance is required. In this regard, a tutorial review on the important issues related to HBC data transmission such as signal propagation model, channel characteristics, communication performance, and experimental considerations is conducted. In this work, the development of HBC and its first attempts are firstly reviewed. Then a survey on the signal propagation models is introduced. Based on these models, the channel characteristics are summarized; the communication performance and selection of transmission parameters are also investigated. Moreover, the experimental issues, such as electrodes and grounding strategies, are also discussed. Finally, the recommended future studies are provided.
A longitudinal study of quality of life among people living with a progressive neurological illness

This study investigated predictors of quality of life (QOL) of people with progressive neurological illnesses. Participants were 257 people with motor neurone disease (MND), Huntington’s disease (HD), multiple sclerosis (MS), or Parkinson’s. Participants completed questionnaires on two occasions, 12 months apart. There was an increase in severity of symptoms for people withMND, negative mood for people with HD and Parkinson’s, and social support satisfaction for people with MS. Regression analyses were conducted to determine predictors of QOL for each group. Predictor variables were length of illness, symptoms (physical symptoms, control over body, cognitive symptoms and psychological symptoms), mood, relationship satisfaction and social support. Predictors of QOL were severity of symptoms for people withMND, HD and MS; negative mood for people withMNDand Parkinson’s; and social support satisfaction for people with MS. These results demonstrate the importance of illness severity and mood in predicting QOL, but also indicate differences between illness groups. The limited role played by social support and relationship is a surprising finding from the current study.