Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Optimized Focal Loss for Imbalanced Dataset on Network Intrusion Detection System

The demand for safe internet access will increase in the future, as a variety of malicious attacks are targeting fragile devices in the network. For this reason, a network intrusion detection system (NIDS) is utilized to detect and notify attacks. Various algorithms employing pattern analysis techniques have been successfully implemented in NIDS, such as shallow machine learning and deep learning models. However, the problem of an imbalanced dataset is often negligible in intrusion detection. Often, the results achieved by machine learning tend to majority classes and ignore minority classes. Therefore, we propose the focal loss network intrusion detection system (FL-NIDS), an improved classification model, to address the issue of an imbalanced dataset. This paper focuses on finding the most appropriate hyperparameter values for focal loss in intrusion detection systems. To show the effectiveness of the proposed FL-NIDS, we utilize the UNSW-NB15 dataset, which suffers the imbalance classes. Our experiment shows that the FL-NIDS using the best combination parameters of focal loss obtain higher detection accuracy in the majority classes as well as in the minority classes, compared to the general model such as Deep Neural Network (DNN) and Convolutional Neural Network (CNN).
Continuous Prediction of Lower-Limb Joint Torque Based on IPSO-LSTM

Predicting joint torque is increasingly important for wearable devices, especially exoskeleton robots. Continuous joint torque prediction based on surface electromyography (sEMG) signals and joint angles can be used for human-machine cooperative control of exoskeletons. Improved particle swarm optimization (IPSO) algorithm was proposed to optimize long short-term memory (LSTM) neural network, which was trained with lower-limb joint angles and sEMG signals of ten muscles to predict hip flexion/extension, knee flexion/extension and ankle dorsiflexion/plantarflexion torques. We used root mean square error (RMSE) and coefficient of determination between predicted and measured joint torques to evaluate the prediction performance. According to the results, compared with LSTM and PSO-LSTM (Particle Swarm Optimization-based LSTM) model, the mean RMSE of IPSO-LSTM (Improved Particle Swarm Optimization-based LSTM) decreases by 21.5% and 12.7%, respectively, and the mean coefficient of determination increases 0.013 and 0.0057, respectively. Therefore, IPSO-LSTM has higher accuracy in continuous prediction of joint torque of lower limbs.
Island Detection based on voltage ratio and artificial neural network for inverter-based distributed generation

Integrated power system provides operational reliability and economic viability with improved power system management and control due to the presence of power electronics converters (PECs). However, the nonlinear characteristics of modern PECs may create power quality degradation at the point of common coupling (PCC). Moreover, the conventional detection fails or takes a large operating time to generate a trip signal under severe fault conditions with incompetent anti-islanding detection capability. A passive method of island detection is proposed based on the combination of voltage ratio and artificial neural network (ANN). The goal is to provide detection accuracy under severe conditions and effective anti-islanding against commonly appearing uncertainties like frequency variation, voltage sag/swell, multi-DG operation, power quality issues, etc. This methodology is validated under a solar PV inverter-based IEEE-13 standard bus grid-connected power system under severe disturbances to test the islanding detection capability and non-severe conditions to check the proper anti-islanding protection shield. The accuracy of the proposed method is found to be 100% with zero NDZ.
Event Classification with Imbalanced and Missing Data for an Air-Handling Unit

Prediction of faults reliably for Air Handling Units (AHU) is a key aspect of correcting errors and eliminating non-optimal functionality. Machine learning classification methods with data sampling are widely utilized to forecast these kinds of events, which, by their nature, seldom occur in equipment. The model proposed in this paper harnesses seven years of data from an air handling unit that contains information about, for example, temperature, humidity, CO2content, and fan speed. This paper contributes to the field of imbalanced classification problems by proposing a novel data undersampling algorithm to enhance the classification model results in the presence of imbalanced and missing data. Moreover, this paper compares several oversampling methods, undersampling methods, probability calibration, and machine learning methods. Then, the paper reports on the proposed final model (proposed undersampling Algorithm 1, Tomek Links, and Logistic Regression) to forecast imperfect heat recovery events in an air handling unit that occur relatively seldom. The precision of the final model was 0.93 for the unseen data; this result was reasonable considering the imbalance of data concurring with missing data sequences.
HFF-Net: Hierarchical Feature Fusion Network for Point Cloud Generation with Point Transformers

Estimating the complete 3D point cloud from a partial input is a key challenge in 3D vision. Existing point cloud completion networks overlook the long-range, hierarchical features and object details of the incomplete point cloud. To this end, we propose Hierarchical Feature Fusion Network (HFF-Net) for precise and detailed point cloud completion. To succeed at this task, HFF-Net estimates the missing Point Agents (PAs) by designing a topology-aware transformer-based encoder-decoder network with Multi-level Feature Learning (MFL), which hierarchically exploits the various regional and detailed information. Further, to make better utilization of the hierar-chical information captured from MFL, we devise the Hier-archical Features Fusion (HFF) module to convert them into cross-regional features. Besides, the predicted PAs is utilized by a multi-resolution output module to recover the missing point cloud in a coarse-to-fine manner. Experiments indi-cate that HFF-Net performs favorably against state-of-the-art (SOTA) approaches on both the new-proposed and existing datasets.
On-Shell Recursion Relations in General Relativity

This thesis is a study of the validity and application of the on-shell recursion relations within the theory of General Relativity. These relations are also known as the BrittoCachazo-Feng-Witten (BCFW) relations. They reduce the calculation of a tree-level graviton scattering amplitude into the evaluation of two smaller physical amplitudes and of a propagator. With multiple applications of the recursion relations, amplitudes can be uniquely constructed from fundamental three-graviton amplitudes. The BCFW prescriptions were first applied to gauge theory. We thus provide a self-contained description of their usage in this context. We then generalize the proof of their validity to include gravity. The BCFW recursion relations can then be used to reconstruct the full theory from cubic vertices. We finally describe how these threegraviton vertices can be determined uniquely from Poincare symmetries.
Big Biological Data: Need for a Reorientation of the Governance Framework

Biological data (biodata) has become a buzzword for innovations in this century. With pioneering advances in machine learning techniques and artificial intelligence, biological datasets have brought about revolutionary changes in many areas, particularly in healthcare. In this process, a host of ethical and legal issues have also come to the fore. The purpose of this paper is to address the need for a reorientation of the governance framework for human biodata. Using the case of Dinerstein v. Google, this paper argues that the data governance framework must take into account and balance interests of both fostering innovation and protecting societal values.
Fronthaul Load-Reduced Scalable Cell-Free massive MIMO by Uplink Hybrid Signal Processing

This paper proposes hybrid signal processing schemes for the uplink cell-free massive MIMO; these schemes serve to reduce fronthaul loads to obtain a scalable centralized processing architecture. In this architecture, received signals of multiple receive antennas at the access points (APs) are compressed into fewer streams by local spatial signal processing and then the streams are forwarded to a central processing unit (CPU) via fronthaul, and the CPU performs scalable processing for channel estimation and signal detection based on partial minimum mean squared error (PMMSE). We propose two kinds of concrete local signal processing methods for this hybrid processing architecture: one is based on MMSE, and the other is based on principal component analysis (PCA) with eigenvalue decomposition (EVD). For the EVD, a local vector selection based EVD (LVS-EVD) that selects uniform number of eigenvectors for each AP in a standalone way, and a global vector selection based EVD (GVS-EVD) that determines the dimensions of the weight vector of each AP in the CPU, are further considered. Computer simulations verify the approaches and compare their effectiveness. In addition, we show that the GVS-EVD scheme can be operated with significantly reduced fronthaul loads without severe performance degradation.
Role of Materials for Energy Storage in Renewable Systems: A Quick Review

Intermittent nature of renewable energy sources is one of the main causes which create the doubt in mind of project planner. Probably this is one of the key barriers to damp in their huge penetration. Now days, energy storage system are in spotlight to produce electricity from renewable energy resources as they may be resolve problems of the oscillated power supply by energy resources which are irregular by nature. The energy storage system can reduce frequent islanding from load as well as smoothen out the balance of supply and demand supply, if any mismatch occurs. In this paper a simulation based work is done to observe the crucial role of storage. It comes out that with DC voltage 412V and output RMS 242V. The voltage THD is 2.87%. But at time 0.2 sec when radiation is reduced to 200W/m2, the output comes down to 292V and output RMS 177V with voltage THD 3.97%.
On Asymmetric Game for NOMA-ALOHA under Fading

We consider a non-orthogonal multiple access aided ALOHA (NOMA-ALOHA) that can enhance the spectral efficiency by sending uplink packets of different power levels in wireless multiuser systems. In particular, we develop an asymmetric game theory model for NOMA-ALOHA that involves two different groups of users and analyze the mean rewards and payoffs of actions made by the users. While taking into account not only collisions, but also fading, the derived theoretical results are utilized to formulate a general-sum game with two groups of users and find mixed strategy Nash equilibrium (NE). Interestingly, the analytical and numerical results clearly show that, when the NOMA-ALOHA runs at an NE, a far-user can exploit an improved channel gain of a near-user in the other group in terms of the throughput.
Local Spectral for Multiresolution Community Search in Attributed Graph

Community search aims to identify all latent members of a particular community from given nodes. Though previous studies have been proven effective, some of them ignore two insights. First, node attributes provide side information to describe features of nodes. It contributes to optimal results. Second, multiresolution community affords latent information to depict the hierarchical relation of network and ensure that one of them is closest to the real one. These aspects motivate us to propose LSMSA, which utilizes Local Spectral for Multiresolution community Search in Attributed graph. Specifically, inspired by the local modularity and graph wavelets, we propose Multiresolution Local modularity (MLQ) in node-attribute graph. Furthermore, to detect multiscale local communities, a sparse indicator-vector is developed based on MLQ via solving a linear programming problem. Extensive experimental results on real-world attributed graphs have demonstrated the detected community is meaningful and its scale can be changed reasonably.
Implementation of Common Ground Five-Level Transformerless Inverter using Virtual DC Link

With the continuous rise in environmental sustainability, PV resources are playing a wide role in the upbringing of newer technologies. TLMLI is one such advancement that has the advantages of low cost and higher efficiency, but they suffer from high-frequency common-mode voltage and leakage current. To mitigate this problem the neutral of the grid is directly connected to the negative node of the PV. This paper presents a CG-based inverter topology that uses a virtual DC-link to produce a 5-level voltage. The inherent feature of this topology lies in the self-voltage balancing of the DC-link. Different levels of the topology are explained along with figures. Level shift PWM is used for pulse generation to the switches and also a generalized detail of the voltage stress, current stress, and power loss equations are presented. Finally, the simulation and hardware synchronized results are demonstrated along with voltage and current waveforms.
Adaptive Fuzzy Asymptotic Tracking Control for Fractional-order Nonlinear Systems with Nonstrict-feedback Structure

This paper solves the adaptive dynamic surface asymptotic tracking control problem of fractional order nonlinear systems(FONSs) with non-strict feedback structure. Firstly, the fuzzy logic systems(FLSs) is utilized to deal with unknown nonlinear functions to eliminate the influence of uncertainties. Secondly, the problem of “explosion of complexity” is solved by designing dynamic surface control(DSC) algorithm. The stability and asymptotic tracking performance of the system are proved by using fractional Lyapunov function theory. Finally, a simulation example is supplied to verify the effectiveness of the control method.
A novel miniaturized FSS based electromagnetic shield for SATCOM applications

A miniaturized frequency selective surface (FSS) based electromagnetic shield having wide band response is presented in this paper. A novel square FSS (SFSS) is designed and optimized over a single‐layer dielectric and achieves electromagnetic shielding for SATCOM in X‐band. This SFSS also achieves a stable frequency response at normal and oblique angles of incidences in both TE and TM wave modes. Simulated and measured results verify shielding effectiveness of the proposed FSS over a wide band.
Aftercare Strategy for Investors in the Occupied Palestinian Territory

* This study was prepared by the UNCTAD secretariat (the Assistance to the Palestinian People Unit of the Division on Globalization and Development Strategies and the Investment Facilitation Section of the Division on Investment and Enterprise). The study benefited from the contributions of Mr. Antoine Basile (UNCTAD consultant). The designations employed and the presentation of the material in this document do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory city or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries.
Hypnotherapy in management of pain in children with cancer: a case report

Introduction and importance: Pain is the most common symptom in cancer patients that can affect their performance and quality of life. The present study aimed to determine the effect of hypnotherapy on the intensity of pain in children with cancer. Case presentation: This is a case study with a single-subject research design, in which a total of 3 female children with cancer were recruited using voluntary response sampling. Classical hypnotherapy was conducted for patients in 8 treatment sessions with a 1-month postintervention follow-up. Participants completed the Oucher Pain Scale (OPS) at 5 stages including before the intervention, third, fifth, and eighth hypnotherapy sessions, and 1 month after the intervention. Data analysis was conducted using a statistical report on the recovery rate and effect size. Clinical discussion: The results showed that hypnotherapy had a significant effect on the intensity of pain, in the treatment and follow-up stages. Conclusions: It can be stated that the use of hypnotherapy has been highly effective in reducing the intensity of pain in children with cancer.
Near-Data Processing in Database Systems on Native Computational Storage under HTAP Workloads

Today's Hybrid Transactional and Analytical Processing (HTAP) systems, tackle the ever-growing data in combination with a mixture of transactional and analytical workloads. While optimizing for aspects such as data freshness and performance isolation, they build on the traditional data-to-code principle and may trigger massive cold data transfers that impair the overall performance and scalability. Firstly, in this paper we show that Near-Data Processing (NDP) naturally fits in the HTAP design space. Secondly, we propose an NDP database architecture, allowing transactionally consistent in-situ executions of analytical operations in HTAP settings. We evaluate the proposed architecture in state-of-the-art key/value-stores and multi-versioned DBMS. In contrast to traditional setups, our approach yields robust, resource- and cost-efficient performance.
A Comparison of Models and Methods for Spatial Interpolation in Statistics and Numerical Analysis

Interpolation of spatial data is a very general mathematical problem with many applications, such as surface reconstruction, the numerical solution of partial differential equations, learning theory, and the prediction of environmental variables, to name a few. One important statistical approach to this problem is known as Kriging, the geostatistical term for optimal linear prediction of spatial processes. It is identical to a method called kernel interpolation used in numerical analysis for the same problem, but derived under completely different modelling assumptions. Despite their similarity, these two approaches have so far been developed largely independently within the two different mathematical communities.Synthesizing results from both statistical and numerical analysis literature with many new results, this monograph presents and contrasts the two modelling paradigms, yielding an understanding of the different notions of optimality and the different concepts to quantify the interpolation error. New results are presented which allow for a comprehensive characterization of the sample path regularity of second-order random fields (the common model in geostatistics), showing that the typical modelling assumptions in numerical analysis are also made implicitly in the statistical model. Finally we explore both theoretically and in simulation studies in how far methods for identifying the covariance parameters of a random field and for selecting a good interpolation kernel can be used in the respective other framework.This PhD thesis is entirely self-contained providing a concise introduction to both probability theory and to the theory of reproducing kernel Hilbert spaces. It is addressed to researchers, lectures and students with a background and interest in either statistics or numerical analysis and may serve as a lecture note as well as a reference manual for questions concerning models and methods for spatial interpolation.
Electrode erosion properties of gas spark switches for fast linear transformer drivers

Fast linear transformer drivers (FLTDs) are a popular and potential route for high-power devices employing multiple “bricks” in series and parallel, but they put extremely stringent demands on gas switches. Electrode erosion of FLTD gas switches is a restrictive and unavoidable factor that degrades performance and limits stability. In this paper, we systematically investigated the electrode erosion characteristics of a three-electrode field distortion gas switch under the typical working conditions of FLTD switches, and the discharge current was 7–46 kA with 46–300 ns rise time. A high speed frame camera and a spectrograph were used to capture the expansion process and the spectral emission of the spark channel was used to estimate the current density and the spark temperature, and then the energy fluxes and the external forces on the electrode surface were calculated. A tens of kilo-ampere nanosecond pulse could generate a 1011 W/m2 energy flux injection and 1.3–3.5 MPa external pressure on the electrode.
Heads-Up Green in Connected Traffic Signals

Information-sharing between traffic signals and connected vehicles can enhance the traffic conditions in signalized intersections. We propose a straightforward application, heads-up green, with the aim to inform queuing connected vehicles about a switch from red to green. In this way, the drivers’ reaction time is expected to be reduced and the number of vehicles that can pass the intersection during a cycle is thereby increased. Unlike the applications Green Light Optimal Speed Advisory (GLOSA), time-to-green and time-to-red, there is no need for predictions of future traffic signal states. Therefore, heads-up green can be used in traffic actuated and adaptive control strategies in signalized intersections without loss in precision. A simulation-based evaluation of a typical Swedish intersection with a traffic actuated controller is performed to evaluate the traffic performance. The main goal as been to show on the potential to use heads-up green to increase traffic efficiency in signalized intersections. Additionally, different reaction times to the information from heads-up green has been investigated. The results show that with heads-up green it is possible to improve the travel time by up to 15% at high demand levels when queues are frequently observed. The improvements are largest with 100% connected vehicles, but significant improvements of 5-15% are observed also for lower shares of connected vehicles. At high demand levels, travel times are decreased already at small reductions in the reaction time. Hence, there is a potential to improve traffic efficiency already when small changes in reaction time can be achieved with heads-up green.
Adaptive Model Predictive Control of Wave Energy Converters

In this paper, we propose an adaptive hierarchical model predictive control (AHMPC) scheme for wave energy converters (WECs). This AHMPC enables adaptive tuning mechanism for a model predictive control (MPC) strategy by estimating the dynamics of a WEC online, so that it can recover from performance degradation of a WEC due to the dynamics variations at different sea conditions. The proposed AHMPC consists of two layers: On the top layer, an efficient cascaded estimation algorithm is developed to online identify and update the WEC model adaptively according to the change of sea states; on the bottom layer, a specially tailored MPC controller is implemented based on the updated WEC model to maximize the energy output subject to constraints for safe operation requirements. Numerical simulations are provided to show the efficacy of the proposed AHMPC scheme.