Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrusion Detection System Using Machine Learning

— With the approaching era of web, the network security has become the key foundation for ton of economic and business net applications. Incursion detection is one of the looms to resolve the matter of network security. Imperfection of incursion detection systems (IDS) has given a chance for data processing to make many vital contributions to the sphere of incursion detection. In recent years, several researchers are mistreatment data processing techniques for building IDS. Here, we propose a brand new approach by utilizing data processing techniques like neuro-fuzzy and radial basis support vector machine (SVM) for serving to IDS to achieve higher detection rate. The projected technique has four major steps: primarily, k-means bunch is employed to get totally different coaching subsets. Then, supported the obtained coaching subsets, totally different neuro-fuzzy models are trained. Later, a vector for SVM classification is made and within the finish, classification mistreatment radial SVM is performed to notice incursion went on or not. Maybe the applicability and capability of the new approach, the results of experiments on KDD CUP 1999 dataset is incontestable. Experimental results show that our proposed new approach does higher than BPNN, multiclass SVM and different well-known strategies like call trees and Columbia model in terms of sensitivity, specificity and specifically detection accuracy.
Malware Family Classification Model using Xception Network

This paper proposes a malware family classification model using CNN (Convolutional Neural Network). Malware uses different attack techniques and has different characteristics depending on malware families, and various analysis techniques must be applied for malware analysis. If a malware sample cannot be classified into a malware family nor categorized, the analysis time of the malware sample may be increased dramatically because the security analyst does not have any prior knowledge. In order to reduce analysis time of a malware sample, malware family classification must be preceded so that damages can be minimized through appropriate analysis and prompt responses to malware. The proposed model of this paper is composed of two major modules: an imaging module to generate images of malware families and an artificial intelligence module to classify malware samples into malware families. As a result of experimenting with the proposed malware family classification model, it was confirmed that unknown malware could be classified as a family with an accuracy of 96.08%.
Achieving High End-to-End Availability in VNF Networks

As software programs, Virtual Network Functions (VNFs) introduce new challenges to network availability due to their potential software failures. Existing models on the availability of VNF networks did not consider all the possible hardware and software failures, making them incapable of analyzing the end-to-end availability of a path. Furthermore, they did not capture the correlation between repeating nodes and links in the path, resulting in inaccurate analytical results. In this paper, we propose a new analytical model, which considers all hardware and software failures as well as the effect of repeating components, to effectively analyze the end-to-end availability of a flow path in VNF networks. On top of the analytical model, we formulate the Highest Availability Path (HAP) problem that finds the flow path with the highest end-to-end availability, and prove its NP-hardness by reduction from the Node-Weighted Steiner Tree problem. Next, we propose two algorithms for HAP: the first one based on a Steiner Tree approximation algorithm, having high time complexity and serving as a performance benchmark; the second one using a dynamic programming approach to search a multi-layer graph in polynomial time. Finally, we present extensive evaluation data to demonstrate the effectiveness of the Layered Search algorithm, which achieves comparable performance as that of the Steiner Tree based algorithm and runs faster by four orders of magnitude.
Distribution and motion of hydrogen gas in the Milky Way galaxy

We explored methods of observing hydrogen gas within the Milky Way galaxy and its motion. We derive expression for velocity of observed gas relative to local standard of rest to model the motion of the interstellar matter and show how we use this model to find the distribution of neutral hydrogen gas throughout the galaxy. We use the LAB data of the 21-cm neutral hydrogen line and apply this model to the data. This model is quite simple and we are ignoring things like turbulence of the gas and we assume the gas moves around the galactic center in perfect circular motion. We calculate the column density of the gas of annuli and find that the distribution of the gas is most concentrated in the galactic plane. As we move out from the center the gas distributes more above and below the plane. We also find that the galactic plane warps at the outer edges of the Milky Way.
An Interactive and Intelligent Tool for Microphone Array Design

This engineering brief will present a new microphone array design app named MARRS (microphone array recording and reproduction simulator). Developed based on a novel psychoacoustic time-level trade-off algorithm, MARRS provides an interactive, object-based workflow and graphical user interface for localization prediction and microphone array configuration. It allows the user to predict the perceived positions of multiple sound sources for a given microphone configuration. The tool can also automatically configure suitable microphone arrays for the user’s desired spatial scene in reproduction. Furthermore, MARRS overcomes some of the limitations of existing microphone array simulation tools by taking into account microphone height and vertical orientations as well as the target loudspeaker base angle. The iOS and Android app versions of MARRS can be freely downloaded from the Apple App Store and the Resources section of the APL website: https://www.hud. ac.uk/apl, respectively.
Analysis of Efficient Net CNN model on luna16 extracted lung nodule patches

—Convolutional neural networks are used in many applications giving excellent predictions. These networks with their ability to make external predictions, when used on medical data can help in the detection of cancer. Lung cancer is a very dangerous and life-ending disease. By training the convolutional neural networks on the lung CT scan data. The trained model can be used to classify the nodules as benign or malignant.
Magnitude and Associated Factor of Acute Appendicitis among Adult Patient Admitted At Hawassa University Comprehensive Specialized Hospital in Surgical Ward

Introduction: Acute Appendicitis (AA) refers to severe inflammation of the vermiform appendix, which is because of obstruction of the lumen of the appendix due to the inefficient emptying into the colon. In most parts of the world, including Ethiopia, acute appendicitis is one of the major causes for which many patients undergo surgical intervention. Objectives: The study aims to assess magnitude and associated factors of acute appendicitis among adult patients admitted to the surgical ward at Hawassa University Comprehensive Specialized Hospital, (HUCSH) Sidema region Southern Ethiopia. Materials and Methods: Retrospective cross sectional study was conducted at HUCSH surgical ward and participants the magnitude of AA was 36 (15.2%). The associated factors such as being male (AOR=0.39; 95% CI (0.18-0.83), Study participant having fecal impaction (AOR=0.02; 95% CI (0.01-0.06) and having intestinal parasite AOR=0.14; 95% CI (0.06-0.32) were significantly associated with the magnitude of AA. Conclusion: The study revealed that the magnitude of AA from study participants was 36 (15.2%). In the current study associated factors like sex, residence fecal impaction and intestinal parasite were significant predictors of AA. So it requires timely designed and implemented medical intervention to reduce the incidence of AA.
The Impact of Temperature on Grape Prices: Evidence from Australia

The potential impact of climate change has motivated the Australian wine sector to fund the development of a Climate Atlas that provides information on how climate will change in the various Australian wine regions (i.e., Remenyi et al. (2019)). This Climate Atlas projects that precipitation patterns will change in different directions across wine regions, but temperatures will increase in all regions. The aim of this study is to estimate the impact of growing season temperature (GST) on grape prices in Australia and to quantify the potential impact of future changes in GST projected in the Climate Atlas. GST is the most widely used thermal-based bioclimatic index in viticulture and is highly related to grape quality (Liles and Verdon‐Kidd, 2020).
A SYSTEMATIC APPROACH TO THE DEVELOPMENT OF MATHEMATICAL COMPETENCE AMONG STUDENTS OF TECHNICAL UNIVERSITIES

The basis of the methodology of the presented scientific research is composed of systemic and activity-based approaches that are focused on a holistic study of objects by identifying the diverse elements and relationships between them. This approach, reflecting the dialectical unity of continuous and discrete, holistic and structural, allows you to create models of complex processes, facilitating their study, makes their study more simplified. A holistic process, divided into parts, is easier to analyze.
Deep IV in Law: Appellate Decisions and Texts Impact Sentencing in Trial Courts

Do U.S. Circuit Courts’ decisions on criminal appeals inﬂuence sentence lengths imposed by U.S. District Courts? We explore the use of high-dimensional instrumental variables to estimate this causal relationship. Using judge characteristics as instruments, we implement two-stage models on court sentencing data for the years 1991 through 2013. We ﬁnd that Democratic, Jewish judges tend to favor criminal defendants, while Republican, Catholic judges tend to rule against them. We also ﬁnd from our experiment that prosecutors backlash to Circuit Court rulings while District Court judges comply. Methodologically, we demonstrate the applicability of deep instrumental variables to legal data.
SecleGAN: Segementation of CycleGAN

In the ﬁeld of Deep Learning, Generative Adversarial Networks, or GANs, has gained wide success over the pass few years, and one of the notable extension of GANs model is Cycle-Consistent Adversarial Networks, or CycleGAN. which the algorithm has been practical in solving image-to-image translation problem. However, despite the fact that CycleGAN algorithm gained a lot success of in transforming images, it also has some limitations and failed some cases. In this paper, we would like to propose a new up-grade from the orginal CycleGAN that we combine segmentation with the CycleGAN to potentially extend and improve the performance of the CycleGAN algorithm. We call our upgrade as SecleGAN algorithm, which is a combination of segmentation and CycleGAN.
INFLUENCE OF PLANT GROWTH REGULATORS ON ANATOMICAL OF FODDER BEAN LEAVES

It was studied the mesostructure organization of leaf apparatus and pigment content of leaves under application of growthregulators with different mechanism of action – esfon (2-HEFC) (0,2%) (ethylene producer) and Epin (0,1 ml/l) (growth stimulator) on broad bean plants. The plant growth regulators treatment on broad beans led to the thickening of leavesdue to increase in the growth of columnar and spongy leaf parenchyma. Growth regulators influenced on the formation of stomatal apparatus of broad beans leaves in different ways. The inhibitory compound esfon caused a decrease in the number of lower epidermal cells and stomata with a simultaneous increase in the stomatal cells area. The application of stimulator compound Epin led to an increase in epidermal cells, without differences in the number of stomata but the area of stomatal cells increased significantly. It has been established the enhancement of photosynthetic processes due to increase in the chlorophyll content in assimilative cells of leaves under esfon on broad beans.
Asymptotic behaviors of solutions to semilinear wave equations with initial data of slow decay

Some useful and remarkable property are derived from a representation formula of a radially symmetric solution to the Cauchy problem for a homogeneous wave equation in odd space dimensions. These properties provide us with enough information to consider the semilinear case, namely, the associated integral equation with the problem will be considered on a weighted L∞-space. This formulation enables us to deal with the problem for slowly decaying initial data.
Comparison of two frailty screening tools for acutely admitted elderly patients.

INTRODUCTION Frailty is a clinical syndrome that arises due to age-related decline, diseases, malnutrition and lifestyle. Two major perspectives on frailty exists: frailty as a phenotype and frailty as an accumulation of deficits. The two types are measured by Fried's Phenotype (FP) and the Clinical Frailty Scale (CFS), respectively. The aim of this study was to investigate which model best predicts 90-day mortality in elderly patients acutely admitted to an emergency department in Denmark. METHODS This study comprised a prospective cohort with the following inclusion criteria: age > 65 years, acute admission and admission >24 h. Bispebjerg Hospital, Odense University Hospital and Hospital of Southwest Jutland participated in the study. The FP and the CFS were measured in all patients. Descriptive statistics, relative risk (RR), odds ratio (OR), risk difference and receiver-operating characteristics (ROC) analysis were performed. The outcome was 90-day mortality. RESULTS A total of 1,030 patients participated (mean age: 78.2 years, 54% female). Among these, 221 were frail by the FP (score > 3) and 555 participants were frail by the CFS (score > 5). Within 90 days, 128 died. The analyses revealed significant associations between frailty and 90-day mortality. For the FP, the RR was 2.67 (95% confidence interval (CI): 1.93-3.69), p less-than 0.001; and for the CFS, the RR was 4.12 (95% CI: 2.65-6.42), p less-than 0.001. The adjusted OR for the CFS was 4.38 (95% CI: 2.68-7.13); for the FP, 3.88 (95% CI: 2.51-6.01). CONCLUSION A significant association existed between frailty and 90-day mortality in the Danish cohort. However, the CFS is a better predictor of 90-day mortality the FP. Even so, the CFS still has a lack of sensitivity and specificity. FUNDING none TRIAL REGISTRATION. not relevant.
5G NETWORK SLICING FOR SMART DISTRIBUTION GRID OPERATIONS

The power distribution grid has to undergo substantial modifications in order to meet the future electricity needs. The increased flexibility in power consumption, local production and storage insist on smarter supervisory control and data acquisition (SCADA) operations which can restrain dynamic conditions of the power distribution grid. Furthermore, the grid connected and islanded operations of microgrids imply that advanced protection and control schemes are of utmost importance. This calls for high reliable, secure and low-latency communication services. 5G can be foreseen as a strong candidate to provide such services. Network slicing is a new paradigm in 5G which is intended to benefit many industries by slicing one physical network infrastructure into many virtual end-to-end networks. This paper proposes a scalable communication architecture which can provide customized communication services according to the diverse performance requirements demanded by the future smart distribution grid. It recommends the use of 5G network slicing to provide for the necessary communication services, proposing a network slice template to comprehend with respective requirements.
Chatbots: An Overview Types, Architecture, Tools and Future Possibilities

— With social media penetration and internet connectivity poised to increase accompanied by advances in natural language processing and artificial intelligence, chatbots are expected to dominate the market. For a developer it is important to understand what the chatbot will offer and what category the chatbot falls into. This would help pick the algorithms or platforms and tools to use to build the bot. It also helps the end-users understand what to expect. Here we discuss the types of chatbots, the tools and algorithms that can be used for different types of chatbots and provide a general architecture that can be followed while building bots. We also address the areas where in chatbots are lacking and identify the research areas hence needing attention.
Effects of judo participation in children: a systematic review “IDO MOVEMENT FOR CULTURE. Journal of Martial Arts Anthropology”, Vol. 18, no. 4 (2018), pp.

Background and aim. Judo is a popular sport worldwide. It was created as a means for holistic education and it is regularly practiced by hundreds of thousands of youngsters. This systematic review is aimed at summarizing the evidence on the physical, physiolog-ical/motor and social/psychological effects of judo participation in children. Methodology. The review was conducted according to the PRISMA-P protocol. The Cochrane Library, PubMed, PsycINFO, Scopus, SportDiscus, Web of Science, DOAJ, LILACS and SCIELO databases were consulted for document retrieval by using the terms “judo” and “child” and other related terms, and the snowballing technique was also used. The Newcastle-Ottawa Scale was used to assess the quality of the selected studies. The studied variables were study type and aims, sample, interventions and procedures, measurements and outcomes. Results. Nine studies, all cohort studies, were selected for inclusion. Judo participation improved participants’ arms bone mineral and fitness in variables such as flexibility, muscular endurance or agility, and also avoided the increase of their subcutaneous fat levels, but did not prove to be superior to participation in other sports. On the contrary, judo participants showed higher levels of anger that their peers. is sorely participation be of the standards paid at the psychological outcomes of this participation as negative effects on anger have been reported.
The role of PTEN in puromycin aminonucleoside-induced podocyte injury

Podocytes are specialized cells of the glomerulus that play important structural and functional roles in maintaining the filtration barrier. Loss and injury of podocytes are leading factors of glomerular disease and kidney failure. Recent studies found that phosphatase and tensin homolog (PTEN) may play a critical role in maintaining the normal structure and function in podocytes. However, we still understand very little about how PTEN is regulated under podocyte injury conditions. In this study, We therefore investigated whether PTEN could play a role in podocyte injury induced by puromycin aminonucleoside (PAN), and whether dexamethasone (DEX) alleviates podocyte injury by PTEN/PI3K/Akt signaling. Our results showed that PI3K/Akt pathway was activated in podocytes exposed to PAN conditions, accompanied by down-regulation of the PTEN and microtubule-associated light chain 3 (LC3) expression.podocyte-specific knockout of PTEN significantly promoted podocyte injury, The potential renoprotection of overexpressed PTEN in podocytes was partly attributed with an improvement in autophagy and the inhibition of apoptosis.These novel findings also suggest that targeting PTEN might be a novel and promising therapeutic strategy against podocyte injury.
Hybrid Fusion based Multimodal Biometric System for Accurate Personal Identification

---In the recent years, biometric authentication has become popular in modern society. The recognition accuracy of unimodal biometric systems has to contend with a variety of issues such as background noise, noisy data, non-universality, spoof attacks, intra-class variations, inter-class similarities or distinctiveness, interoperability problems. To overcome the limitation of a single biometrics, information from multiple biometrics can be integrated to achieve more reliable and robust performance. In existing system, score level fusion method is introduced to achieve better identification result using Left and Right Palmprint Images. However, various normalization methods of the matching scores cause different decision boundaries. Also, a too small training set of scores might easily overfits the data, especially in methods with flexible boundaries. To solve this problem, the proposed system introduced a hybrid fusion approach which integrate both score level and decision level fusion. In this proposed system, left and right palmprint of the same subject is correlated and crossing matching score of the left and right palmprint is computed for improving the efficiency of identity identification. Then ROC is derived from the component matching scores and the score-level fused matching scores. Finally combined both score level and decision level results to achieve hybrid fusion. The experimental results show that the proposed system achieves better performance compared with existing system in terms of detection rate and false acceptance rate.
Parallel Self-Assembly with SMORES-EP, a Modular Robot

—Self-assembly of modular robotic systems enables the construction of complex robotic conﬁgurations to adapt to different tasks. This paper presents a framework for SMORES types of modular robots to efﬁciently self-reconﬁgure into tree topologies. These modular robots form kinematic chains that have been shown to be capable of a large variety of manipulation and locomotion tasks, yet they can reconﬁgure using a mobile reconﬁguration. A desired kinematic topology can be mapped onto a planar pattern with optimal module assignment based on the modules’ location, then the mobile reconﬁguration assembly process can be executed in parallel. The framework is demonstrated on the SMORES-EP platform.
Exploring open innovation in social enterprises in Turkey: A case study of Mikado Consulting

This project examines how Mikado Consulting implements open innovation within the social innovation ecosystem in Turkey. Nowadays, open innovation is significant for any organization because individuals from the outside can be creative to contribute to sharing knowledge and be useful in finding ideas. Therefore, open innovation potentially advances the company because individuals or new partners can create value for the organization. On the other hand, open innovation is a new topic in Turkey. We realized that one of the biggest successes that Mikado Consulting achieves is implementing open innovation in their company. Mikado Consulting is a consulting company that engages with social problems and gets outside help with experts in their field when it is needed. To understand the social innovation ecosystem in Turkey and what open innovation is, we interviewed the founder of Mikado Consulting. Serra Titiz (Titiz) helped us to explore how Mikado Consulting integrates knowledge from internal and external actors. Additionally, we discussed how Mikado Consulting co-create a network for the social innovation ecosystem. Our findings show that Mikado Consulting implements open innovation by integrating internal and external knowledge within its networks and uses different methods to maintain the knowledge flow in Mikado. Consequently, it has an impact on Turkey’s entire social innovation ecosystem.