Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Intrusion Detection System Based on Decision Tree and Support Vector Machine

As the use of network services increase, security is considered as a crucial and major issue in the network. Several computers connected with the network play an essential role in business and other applications running over the network to provide services. Therefore, we need to search out the best ways to protect the system. One of the methods is to provide security to the system and analyze network traffic through intrusion detection or intrusion prevention. In this paper, a hybrid intrusion detection framework is suggested. Proposed hybrid IDS is a combination of two machine learning algorithms J48 DT and SVM. To select relevant features from the KDD CUP dataset Particle Swarm Optimization is used. WEKA is used to implement classification on the KDD CUP dataset. The dataset is divided into ratios of 60:40, 70:30, and 80:20 for training and testing purpose. The experiment result showed 99.1% accuracy, 99.6% detection rate and 1.0% FAR for 60:40 datasets whereas accuracy, detection rate and FAR for 70:30 datasets are 99.2%, 99.6% and 0.9% respectively for 80:20 datasets 99.1%, 99.6% and 0.9% respectively.
Human Moving Behavior Recognition Based on the D-S Evidence Theory and SVM utilizing Smartphone

With the popularization of smartphones, the technology of moving human behavior recognition utilizing smartphones has drawn wide attention of scholars. However, due to the limited computing power of smartphones, it is difficult for them to stably run recognition systems with highly complex. In addition, when a behavior switches, the transitional action is also difficult to recognize. To address these defects, this paper first proposes a feature extraction method using gait periodicity to extract characteristics of moving human behavior quickly and effectively. Secondly, a recognition framework combining SVM and the D-S evidence theory is proposed to recognize transitional action. A series of experiments and their results show that the both proposed methods can improve accuracy rate of moving human behavior recognition to 95.5%.
Gasification of Biomass: An Investigation of Key Challenges to Advance Acceptance of the Technology

Although the general principles of biomass gasification are broadly understood, at a larger scale of operation (e.g. > 200 kg/h) there is a lack of confidence in the translation of the basic scientific concepts into a financially viable operation that satisfies regulatory requirements. Looking in particular at the operation of a down-draft type of gasifier, a number of challenges were identified and studied in greater detail. Gasification experiments were performed on wood and straw pellets in a small scale, 21 mm i.d. quartz-tube reactor. These provided useful insight into what was occurring inside the gasifier, and the complexity and roles of the various reaction zones. In order to perform on-line gas analysis measurements in real time, a method was developed which enabled a quadrupole mass spectrometer (QMS) to be used. This was tested in a laboratory environment, and then used on a commercial pilot-plant gasifier (150 to 250 kg/h). This enabled the composition of the gas to be monitored while the plant was started up, and then operated at various levels of gas flow through the plant. In general the concentrations measured during a stable operation were as follows: CO = 16.0 vol.%, H2 = 11.9 vol.%, CO2 = 15.8 vol.%, N2 = 54.1 vol.%, CH4 = 1.9 vol.%, O2 = 0.3 vol.%. Measurements of O2 concentrations in the gas stream on start-up provide useful information on conditions when a flammable atmosphere could exist in the lines/vessels. To help with the development of suitable gas clean-up strategies, the presence of two key sulphur species, H2S and carbonyl sulphide (COS), was studied in more detail. Experimental measurements were taken on the laboratory reactor (e.g. H2S = 286 ppmv, COS = 28 ppmv for gasification of refuse-derived fuel (RDF) pellets), and the commercial pilot-scale gasifier (e.g. H2S = 332 ppmv, COS = 12 ppmv). This data was also compared with theoretical thermodynamic predictions. The steam gasification of char was also studied in a laboratory 9.5 mm i.d. reactor, and kinetic expressions were determined for RDF-derived char. It was shown that high concentrations of H2 (20 vol.%) and CO (15 vol.%) can be achieved, and the temperature at which reactions were initiated was > 700 oC, and significant at 900 oC. Interestingly, the RDF-derived char (at carbon conversion from 10 to 70 %) appears to be more reactive than other biochars reported in the literature. However, at high conversion (> 50 %), its apparent reactivity decreases with carbon conversion, behaving in a similar manner to coal chars.
Design and Optimization of Edge Computing for Data Fusion in V2I Cooperative Systems

Real-time data fusion combining with information from vehicles and roadside unit (RSU) is a promising solution to promote traffic safety and efficiency. In this paper, we design a multi-source data fusion scheme in edge computing-enabled vehicle-to-infrastructure (V2I) cooperative systems, where data fusion can be processed at RSU or vehicle. In order to balance the tradeoff between the vehicle speed and the fusion range, we define a new performance metric, namely fusion gain. We formulate the jointly data offloading decision, fusion range and computing resource allocation problem for maximizing the system fusion gain while minimizing local and edge computational resource consumption. We reformulate the stated problem and design a substitution-knapsack algorithm to reach a sub-optimal solution. Numerical results show that the proposed scheme has a significant performance gain and effectively promotes system utility in varying traffic environments.
Identifying and Prioritizing Chaos Experiments by Using Established Risk Analysis Techniques

The prevalence of microservice architectures and container orchestration technologies increases the complexity of assessing such systems’ resilience. Chaos engineering is an emerging approach for resilience assessment by testing hypotheses after intentionally injecting faults into a distributed system and observing customer- and business-affecting metrics. As the number of potential risks within a complex system is high, the identification and prioritization of effective and efficient chaos experiments are non-trivial. In the scope of an industrial case study, this work investigates means to identify and prioritize chaos experiments by using established risk analysis techniques known from engineering safety-critical systems, namely i) Fault Tree Analysis, ii) Failure Mode and Effects Analysis, iii) and Computer Hazard and Operability Study. We conducted semi-structured interviews to elicit architectural information and resilience requirements of the case study system. The extracted knowledge was leveraged during the application of the risk analysis techniques. A subset of the identified and prioritized risks was used to create and execute chaos experiments. The risk analysis resulted in over 100 findings and revealed that the system is rather fragile as it comprises a high amount of single points of failure. The chaos experiments revealed further weaknesses for formerly unknown system behavior.
Inflammatory Response and Phenotyping in Severe Acute Respiratory Infection From the Middle East Respiratory Syndrome Coronavirus and Other Etiologies.

OBJECTIVES In this study, we evaluated the inflammatory response in patients with severe acute respiratory infection due to the Middle East respiratory syndrome and non-Middle East respiratory syndrome and assessed the presence of distinct inflammatory subphenotypes using latent class analysis. DESIGN Prospective cohort study. SETTING A tertiary care ICU in Riyadh, Saudi Arabia. PATIENTS Consecutive critically ill patients with laboratory-confirmed Middle East respiratory syndrome severe acute respiratory infection and non-Middle East respiratory syndrome severe acute respiratory infection. INTERVENTION None. MEASUREMENTS AND MAIN RESULTS We measured cytokines on days 1, 3, 7, and 14 of ICU stay. We included 116 patients (40 with Middle East respiratory syndrome severe acute respiratory infection and 76 with non-Middle East respiratory syndrome severe acute respiratory infection). On ICU day 1, both patients with Middle East respiratory syndrome severe acute respiratory infection and non-Middle East respiratory syndrome severe acute respiratory infection had higher levels of interleukin-3, interleukin-4, interleukin-6, interleukin-8, interleukin-17A, eotaxin, and epidermal growth factor compared with healthy controls. There were no differences in cytokines over time between patients with Middle East respiratory syndrome severe acute respiratory infection and non-Middle East respiratory syndrome severe acute respiratory infection. Using day 1 cytokine levels, latent class analysis categorized patients into two subphenotypes: subphenotype 1 (n = 74 [64%]) and subphenotype 2 (n = 42 [36%]); the latter had significantly higher levels of interleukin-1β, interleukin-1ra, interleukin-2, interleukin-6, interleukin-7, interleukin-8, interleukin-10, interleukin-12p70, interleukin-15, interleukin-17A, inducible protein-10, monocyte chemoattractant protein-1, macrophage inflammatory protein-1α, macrophage inflammatory protein-1β, tumor necrosis factor-α, granulocyte-macrophage colony-stimulating factor, granulocyte-colony stimulating factor, interferon-α, and interferon-γ. Although baseline characteristics were not different between the two subphenotypes, patients in the subphenotype 2 had higher ICU mortality compared with the subphenotype 1 (18/42 [43%] vs 17/74 [23%]; p = 0.03). CONCLUSIONS One third of critically ill patients with Middle East respiratory syndrome severe acute respiratory infection and non-Middle East respiratory syndrome severe acute respiratory infection demonstrated a subphenotype characterized by increased proinflammatory cytokines, consistent with cytokine storm. Further research is needed to examine whether immunomodulators have differential effects based on inflammatory subphenotypes.
A Cognitive Analytics based Approach for Machine Health Monitoring, Anomaly Detection, and Predictive Maintenance

Traditionally, there are two major limitations for machine learning (ML)-assisted manufacturing applications. First, it would require a tremendous amount of manual data annotations for ML models. Second, ML models are often learned offline and unable to capture the machine dynamism and adapt to changes over the time. In this paper, we propose a framework based on the concept of cognitive analytics with unsupervised learning for machine health monitoring, anomaly detection and predictive maintenance. The experimental results on an industrial robot demonstrates the effectiveness of the proposed framework in the identified use case.
Joint Access and Backhaul Link Optimization in Multiple UAV-Assisted Emergency Network

With the development of unmanned aerial vehicles (UAVs) in both hardware technology and theoretical approaches, UAVs begin to emerge as important potential equipment for future communication networks, where UAV-assisted emergency communication attracts the most attention due to the flexibility and mobility of UAVs. In this paper, we consider a remote emergency scenario with the ground base stations (BSs) being destroyed and study the access and backhaul link optimization problem. We propose a joint user association, UAV positioning, and resource allocation (UPR) algorithm to maximize the system throughput. We deploy UAVs not only as BSs to provide access links but also as relays to provide backhaul links, where the transmission rate of the system depends on the minimum of all the transmission links. We theoretically analyze the maximization problem of the system throughput and derive the optimal resource allocation for all the transmission links. We also prove the optimality of the equidistant alignment of UAV relays and their linear alignment with the emergency vehicle and solve the resulting optimization problem by alternating between the positioning of UAVs and user association. Simulation results verify the effectiveness of the proposed UPR algorithm.
A highly nonlinear substitution-box (S-box) design using action of modular group on a projective line over a finite field

Cryptography is commonly used to secure communication and data transmission over insecure networks through the use of cryptosystems. A cryptosystem is a set of cryptographic algorithms offering security facilities for maintaining more cover-ups. A substitution-box (S-box) is the lone component in a cryptosystem that gives rise to a nonlinear mapping between inputs and outputs, thus providing confusion in data. An S-box that possesses high nonlinearity and low linear and differential probability is considered cryptographically secure. In this study, a new technique is presented to construct cryptographically strong 8×8 S-boxes by applying an adjacency matrix on the Galois field GF(28). The adjacency matrix is obtained corresponding to the coset diagram for the action of modular group PSL(2,Z) on a projective line PL(F7) over a finite field F7. The strength of the proposed S-boxes is examined by common S-box tests, which validate their cryptographic strength. Moreover, we use the majority logic criterion to establish an image encryption application for the proposed S-boxes. The encryption results reveal the robustness and effectiveness of the proposed S-box design in image encryption applications.
Computational imaging during video game playing shows dynamic synchronization of cortical and subcortical networks of emotions

Emotions are multifaceted phenomena affecting mind, body, and behavior. Previous studies sought to link particular emotion categories (e.g., fear) or dimensions (e.g., valence) to specific brain substrates but generally found distributed and overlapping activation patterns across various emotions. In contrast, distributed patterns accord with multi-componential theories whereby emotions emerge from appraisal processes triggered by current events, combined with motivational, expressive, and physiological mechanisms orchestrating behavioral responses. According to this framework, components are recruited in parallel and dynamically synchronized during emotion episodes. Here, we use functional MRI (fMRI) to investigate brain-wide systems engaged by theoretically defined components and measure their synchronization during an interactive emotion-eliciting video game. We show that each emotion component recruits large-scale cortico-subcortical networks, and that moments of dynamic synchronization between components selectively engage basal ganglia, sensory-motor structures, and midline brain areas. These neural results support theoretical accounts grounding emotions onto embodied and action-oriented functions triggered by synchronized component processes.
Health and Wellness Impacts of Traditional Physical Activity Experiences on Indigenous Youth: A Systematic Review

Traditional physical activities have numerous physiological and psychosocial benefits for Indigenous youth around the world. Little is known about the positive health and wellness impacts of traditional physical activity experiences on Indigenous youths. The aim of this systematic review is to explore the holistic health and wellness impacts of traditional physical activities on Indigenous youth from certain North American and Oceania geographic areas. A systematic search of four electronic databases (PubMed, ERIC, Scopus and Web of Science) was conducted to identify peer-reviewed publications of qualitative research exploring the diverse health experiences of traditional physical activities for Indigenous youth in Canada, the United States of America, New Zealand and Australia. A qualitative synthesis of studies between 2006 and 2018 were included, and findings were synthesized using an integrated Indigenous-ecological model, which broadly captures health and wellness impacts under intrapersonal, interpersonal, organizational, community and policy level outcomes using medicine wheel teachings. In total, nine studies were identified via this search. Overall, the literature described numerous emotional, mental and spiritual benefits of traditional physical activity, and youth experiences were affected by familial and communal relationships, and systemic factors. Among Indigenous youth, this research shows the importance of including traditional physical activity in future programs and partnerships with community expertise.
Electrical Properties of Cells: Patch Clamp for Biologists

Basics. Van de Graaff. Capacitors. Dielectric Constant. Capacititive Current. Batteries. Bioelectricity. Bioelectricity. Diffusion. Gravity and Diffusion Compared. Membrane Potential. Membrane Capacitance. Ion Selectivity. Channel Density. Channel Conductance. The Total Membrane. Applications. Current. The Boltzmann Equation. Fluctuations in the Current. The Double-Sided Boltzmann Equation. Boltzmann Distribution when a Population of Channels is Always Open. ACh Receptor-Channel Complexes. Ion Channels in Sperm. Appendix I. Operation Amplifiers. Appendix II. Solution to Nernst-Planck Equation. Appendix III. There Are All Kinds of Averages. Appendix IV. How Money Goes. Index.
Validation of the Nepali versions of the Neck Disability Index (NDI) and the Numerical Rating Scale (NRS) for Neck Pain.

STUDY DESIGN A cross-sectional study with a test-retest design. OBJECTIVE Translation, cross-cultural adaption, and assessing measurement properties of the numerical rating scale (NRS) for neck pain intensity and the Neck Disability Index (NDI) in a Nepalese neck pain population. SUMMARY OF BACKGROUND DATA Neck pain is one of the most common musculoskeletal disorders in Nepal. Research on neck pain disorders has been hampered by lack of standardized patient reported outcome measures (PROMs) in Nepali language. Therefore, we aimed at validating a Nepali version of the NDI and NRS neck pain. METHODS At Dhulikhel hospital in Nepal 150 patients with neck pain and/or cervical radiculopathy completed the translated self-administered questionnaires. We had made one cultural adaption of the NDI driving item in the final Nepali version. Relative reliability was analyzed with intraclass correlation coefficient (ICC 2.1) and absolute reliability with the smallest detectable change (SDC). Internal consistency was assessed by Cronbach's Alpha. Construct and discriminative validity was assessed by Spearman's correlation for a-priori hypotheses, Receiver Operating Characteristics curves and analysis of variance. Time spent and assistance needed to complete the questionnaires were used to assess feasibility. RESULTS Test-restest reliability was excellent with ICC (95% Confidence Intervals) of 0.87 (0.66, 0.94) for NDI and 0.97 (0.94, 0.99) for NRS neck pain. The absolute reliability was acceptable (a SDC of 1.6 for NRS and 9.3 for NDI) and a Cronbach's alpha (internal consistency) of 0.70 for NDI, as well as acceptable construct validity, discriminative validity and feasibility. CONCLUSION The Nepali versions of the NRS neck pain and NDI can be recommended for assessing pain and disability among patients with neck pain and cervical radiculopathy, but their responsiveness to change remains to be tested.Evidence Level: 2.
A simple quasi-LPV approach to control design of an industrial wind turbine

This paper presents a simple quasi-LPV approach to the controller design of a variable-speed variable-pitch wind turbine working at rated power. Using the polynomial approximation for the power coefficient, it is shown that a simple decomposition will lead to a stable quasi-LPV model for which the pitch control design procedure is straightforward with optimal robust techniques. As the designed controller works satisfactorily in a local region around the selected operating point, a simple controller scheduling method using wind speed is therefore applied to extend the operational zone to the entire full load region. Simulations with a verified model of the wind turbine developed in the FAST code demonstrate the performance superiority of the designed controller in comparison with the baseline design.
On the Impact of User Mobility on the Performance of Wireless Receivers

In this paper we study the effect of user mobility on the performance of wireless digital receivers operating in the presence of fading, shadowing, and path loss. Recently, it was shown in (Lopez-Fernandez et al., 2018) that the distribution of the received power of a mobile receiver is given by the incomplete moment of the underlying static fading distribution. In this paper, we consider the random waypoint mobility model and show that, in fact, other performance metrics of the mobile receiver can be expressed in terms of statistical characterizations of the fading distribution in the static case. We consider the moment generating function of the received signal-to-noise ratio (SNR), the average bit error rate and the average channel capacity and present analytical results for these performance metrics in different generalized fading environments. Asymptotic expressions are also derived that converge to the exact performance at high values of the SNR. Numerical results are provided to quantify the effects of network dimension, path loss coefficient and fading model parameters on the performance metrics of the mobile receiver.
A Microwave Coincidence Imaging Method for Complex Target Based on Sparse Representation

Microwave coincidence imaging (MCI) has great potential in super-resolution imaging realms, which has achieved a superior performance for sparse targets. However, its performance for complex target will degrade severely due to the weak sparsity. In this paper, the sparse representation method is applied to MCI and a reconstruction-based imaging method for complex target is proposed. Numerical experiments illustrate the validity of proposed method.
How Adolescents Use Social Media to Cope with Feelings of Loneliness and Anxiety During COVID-19 Lockdown

Next to physical health problems and economic damage, the coronavirus disease 2019 (COVID-19) pandemic and associated lockdown measures taken by governments of many countries are expected to cause mental health problems. Especially for adolescents, who highly rely on social contacts with peers, the prolonged period of social isolation may have detrimental effects on their mental health. Based on the mood management theory, the current study examines if social media are beneficial for adolescents to cope with feelings of anxiety and loneliness during the quarantine. A survey study among 2,165 (Belgian) adolescents (13-19 years old) tested how feelings of anxiety and loneliness contributed to their happiness level, and whether different social media coping strategies (active, social relations, and humor) mediated these relations. Structural equation modeling revealed that feelings of loneliness had a higher negative impact on adolescents' happiness than feelings of anxiety. However, anxious participants indicated to use social media more often to actively seek for a manner to adapt to the current situation, and to a lesser extent as a way to keep in touch with friends and family. The indirect effect of anxiety on happiness through active coping was significantly positive. Participants who were feeling lonely were more inclined to use social media to cope with lacking social contact. However, this coping strategy was not significantly related to their happiness feelings. Humorous coping was positively related with feelings of happiness, but not influenced by loneliness or anxiety. To conclude, social media can be used as a constructive coping strategy for adolescents to deal with anxious feelings during the COVID-19 quarantine.