Miracle-dz/UniFAR-FTUs huggingface dataset:
Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark

: Today, deep learning approaches are widely used to build Intrusion Detection Systems for securing IoT environments. However, the models’ hidden and complex nature raises various concerns, such as trusting the model output and understanding why the model made certain decisions. Researchers generally publish their proposed model’s settings and performance results based on a speciﬁc dataset and a classiﬁcation model but do not report the proposed model’s output and ﬁndings. Similarly, many researchers suggest an IDS solution by focusing only on a single benchmark dataset and classiﬁer. Such solutions are prone to generating inaccurate and biased results. This paper overcomes these limitations in previous work by analyzing various benchmark datasets and various individual and hybrid deep learning classiﬁers towards ﬁnding the best IDS solution for IoT that is efﬁcient, lightweight, and comprehensive in detecting network anomalies. We also showed the model’s localized predictions and analyzed the top contributing features impacting the global performance of deep learning models. This paper aims to extract the aggregate knowledge from various datasets and classiﬁers and analyze the commonalities to avoid any possible bias in results and increase the trust and transparency of deep learning models. We believe this paper’s ﬁndings will help future researchers build a comprehensive IDS based on well-performing classiﬁers and utilize the aggregated knowledge and the minimum set of signiﬁcantly contributing features. output would help researchers improve their model’s design to reduce the size, increase processing speed, and improve attack detection capability. This work reveals future opportunities towards building a comprehensive online IDS based on minimum but signiﬁcantly contributing features. Future researchers can also gain insight into good-performing classiﬁers and benchmark datasets to ﬁnd an optimal fusion of best classiﬁers and related settings.
Emotion Detection Using Physiological Signals

The emotion detection employing physiological signals is more unfailing as related to imaging and audio techniques. The biosignals such as ECG, GSR, used for emotion detection. The raw physiological signals contain distinct noises attributable to the power-line interference, motion of the electrodes, muscle artifacts, baseline wander. Hence pre-processing and filtering the signals makes the data compatible with the human-computer interaction systems. We have filtered and pre-processed the ECG, GSR signals of the AMIGOS database. Also eliminated the baseline drift using the baseline wandering pathfinding algorithm, extracted the peaks in the GSR signal, and interbeat measurements of the ECG signals. The various time-domain parameters, computed which can differentiate between the various dimensions of Russell's model. Human-computer interaction systems can use these methods for diverse applications.
Towards Artificial Robotic Skin: Highly Sensitive Flexible Tactile Sensing Arrays with 3D Sensing Capabilities

Advanced tactile sensing will be one of the key enablers for more interactive and flexible robotics or prosthetics. We proposed a sensing technology, which aims for the replication of the human sense of touch by manufacturing dense arrays (100 sensors/cm2) of highly sensitive tactile sensors on a flexible substrate. In this work, we present the characterization of the sensor array with the developed readout electronics, which can resolve capacitance changes of 1.4 fF at an update rate of 5 kS/s and without crosstalk between different sensing cells. Based on those findings we study the sensor response upon actuation with 3D forces and show that they can be resolved.
Translating Intrusion Alerts to Cyberattack Stages using Pseudo-Active Transfer Learning (PATRL)

Intrusion alerts continue to grow in volume, variety, and complexity. Its cryptic nature requires substantial time and expertise to interpret the intended consequence of observed malicious actions. To assist security analysts in effectively diagnosing what alerts mean, this work develops a novel machine learning approach that translates alert descriptions to intuitively interpretable Action-Intent-Stages (AIS) with only 1% labeled data. We combine transfer learning, active learning, and pseudo labels and develop the Pseudo-Active Transfer Learning (PATRL) process. The PATRL process begins with an unsupervised-trained language model using MITRE ATT&CK, CVE, and IDS alert descriptions. The language model feeds to an LSTM classifier to train with 1% labeled data and is further enhanced with active learning using pseudo labels predicted by the iteratively improved models. Our results suggest PATRL can predict correctly for 85% (top-1 label) and 99% (top-3 labels) of the remaining 99% unknown data. Recognizing the need to build confidence for the analysts to use the model, the system provides Monte-Carlo Dropout Uncertainty and Pseudo-Label Convergence Score for each of the predicted alerts. These metrics give the analyst insights to determine whether to directly trust the top-1 or top-3 predictions and whether additional pseudo labels are needed. Our approach overcomes a rarely tackled research problem where minimal amounts of labeled data do not reflect the truly unlabeled data’s characteristics. Combining the advantages of transfer learning, active learning, and pseudo labels, the PATRL process translates the complex intrusion alert description for the analysts with confidence.
On the Number of Arithmetic Operations in NTT-based Polynomial Multiplication in Kyber and Dilithium Cryptosystems

National Institute of Standards and Technology (NIST) initiated a post-quantum standardization process in 2016, and as of July 2020, Round 3 candidates were announced. Among these candidates, Crystals-Kyber and Crystals-Dilithium are the most promising lattice-based key encapsulation mechanism (KEM) and signature algorithm that rely on the module learning with errors (Module-LWE) problem. In general, polynomial multiplication is one of the most time-consuming operations in Module-LWE based cryptosystems. There are several polynomial multiplication methods for multiplying two polynomials effectively. One of the most efficient methods is Number Theoretic Transform (NTT). This paper analyzes the number of arithmetic operations occupied in NTT multiplication for Kyber and Dilithium cryptosystems. The general formula on the number of multiplications and additions used in NTT operation for the lattice-based algorithms which have a ring structure similar to Kyber and Dilithium is given for $q < 2^{w-1}$ where $w$ is the word size and $q$ is the modulus. Also, cycle counts of arithmetic operations of Kyber and Dilithium are calculated on reference implementations to determine the relationship between our formulations and cycle counts.
Can pose classification be used to teach Kickboxing?

Kickboxing is a combat sport, based on kicking, punching, Knee and elbow strikes and defence moves. Every kickboxing technique needs to be preformed a specific way, As there is correct postures and wrong postures to every technique. In this paper, we offer a system that can facilitate the beginners trainees to learn kickboxing. The system uses a camera to estimate poses and then, classify them into “correct techniques” and their common mistakes or “wrong pose” using ANN. Live feedback is offered by the system. Whenever the classifier recognize a wrong pose, a message is shown to indicate how to correct the posture. Our hypothesis is that, when trainees have the ability to see and recognize their wrong posters, they learn faster. We evaluate the progress of the trainees based on the time it takes to complete a simple kickboxing exercise. Two types of experiments were conducted. The first calculated the progress of trainees everyday, the other calculated the progress of trainees through three training sessions in the span on two hours. Our results show that time taken by users to preform the moves decrease with each time they use our system. This paper focuses on 3 kickboxing techniques, which are slipping, jab and front kick.
A Deep Reinforcement Learning-based Resource Management Scheme for SDN-MEC-supported XR Applications

The Multi-Access Edge Computing (MEC) paradigm provides a promising solution for efficient computing services at edge nodes, such as base stations (BS), access points (AP), etc. By offloading highly intensive computational tasks to MEC servers, critical benefits in terms of reducing energy consumption at mobile devices and lowering processing latency can be achieved to support high Quality of Service (QoS) to many applications. Among the services which would benefit from MEC deployments are eXtended Reality (XR) applications which are receiving increasing attention from both academia and industry. XR applications have high resource requirements, mostly in terms of network bandwidth, computation and storage. Often these resources are not available in classic network architectures and especially not when XR applications are run by mobile devices. This paper leverages the concepts of Software Defined Networking (SDN) and Network Function Virtualization (NFV) to propose an innovative resource management scheme considering heterogeneous QoS requirements at the MEC server level. The resource assignment is formulated by employing a Deep Reinforcement Learning (DRL) technique to support high quality of XR services. The simulation results show how our proposed solution outperforms other state-of-the-art resource management-based schemes.
LDPC-based multi-relay lossy forwarding for correlated source transmission over orthogonal Rayleigh fading channels

In this paper, we design a communication and coding strategy for Lossy Forwarding (LF) systems with multiple relay nodes or helpers based on Low-Density Parity-check Codes (LDPC) with message passing decoding applied on a Tanner-graph that maps all the network. The system performance is investigated under the cases of a fixed number of helpers, and a random multiple shifted-Poisson distributed helpers for orthogonal Rayleigh fading channels. All the practical results will be also compared and validated with respect to theoretical outage probabilities.
High Driving Frequency (>54 kHz) and Wide Scanning Angle (>100 Degrees) MEMS Mirror Applying Secondary Resonance For 2K Resolution AR/MR Glasses

We have successfully realized the MEMS mirror which can scan the wide optical angle of over 100 degrees at the driving frequency of 58 kHz by applying the secondary resonance mode driving design and the new stress dispersion structure to our “Double-Tuning-Fork type MEMS mirror” which was reported in “MEMS 2020” [1]. This MEMS mirror paves the way for realizing a high resolution “2K (2560 × 1440@60 fps)” in the laser beam scanning system employing the two-scanning-mirror architecture where each mirror in responsible for one axis [2], which was impossible with conventional MEMS mirrors.
Hierarchical Learning-based Particle Swarm Optimizer

Particle Swarm Optimization (PSO) is an optimization technique that has been applied to solve various optimization problems. Its traditional strategy adopts elitism, in which only the personal and global best positions are utilized as leaders to guide all particles’ update and discard other potentially excellent positions around which the global optimum may be found. In a human society, people fall into different classes. They tend to learn from better ones, not just from the best ones in the whole society. Inspired from the learning behavior in a human society, this work considers particles in a swarm as people belonging to different classes and proposes a hierarchical learning-based particle swarm optimizer (HLPSO). In it, particles hierarchically learn from the ones in either the same or upper level ones. The levels of particles are updated according to their fitness after each iteration. Since all particles determine respective leaders according to their own levels, the population hierarchically learns from a large number of potentially excellent positions, which greatly maintains the diversity of population and brings HLPSO a powerful exploration capability. The diversity analyses of HLPSO reveal that the hierarchical utilization of diversified leaders maintains population diversity. HLPSO and eight popular PSO contenders are tested on 28 CEC2013 benchmark functions. Experimental results indicate its high effectiveness and efficiency.
Severity Prediction of Software Vulnerabilities based on their Text Description

Software vulnerabilities represent a real challenge nowadays, often resulting in disruption of vital systems and data loss. Due to the multitude of software applications used within a company, system administrators often end up in the situation of facing multiple vulnerabilities at the same time, having no choice but to prioritize the most critical ones. Administrators commonly use vulnerability databases and metric systems to rank vulnerabilities; however, it usually takes from days to weeks for the metrics to be published since these metrics are established by human security analysts and the number of daily discovered exploits is constantly increasing. Therefore, newly discovered vulnerabilities, especially those without an available patch, represent the largest problem. In this paper, we propose a deep learning approach to predict the severity score and other metrics of a vulnerability using only its text description, which is available on discovery. We use a Multi-Task Learning architecture with a pre-trained BERT model for computing vector-space representations of words. Our best configuration achieves a mean absolute error of 0.86 for the severity score and an accuracy of 71.55% for the severity level.
RESHAPING THE E-HEALTH LANDSCAPE HOW INTERDISCIPLINARY RESEARCH DRIVES INNOVATION IN BROADBAND APPLICATIONS

High-speed broadband provides a platform for a wide range of applications, including e-health. The development of e-health applications has to take into account diverse bodies of knowledge, from the appropriate configuration of network technologies through to the clinical deployment of new treatments. Thus, interdisciplinary research is central to e-health. The Institute for a Broadband-Enabled Society (IBES) was established at the University of Melbourne to support interdisciplinary research that leverages the benefits of ubiquitous high-speed broadband networks, such as the emerging National Broadband Network. Research into health and wellbeing is central to the Institute. This paper discusses how interdisciplinary research drives the development of e-health solutions focusing specifically on three case studies — stroke rehabilitation, mental health, and education and training.
Mobile LiDAR Deployment Optimization: Towards Application for Pavement Marking Stained and Worn Detection

Clear and distinctive pavement markings play a critical role in providing traffic information and avoiding traffic crashes at night and in adverse weather conditions. Periodically inspecting pavement marking is essential but requires vast human and material resources currently. This paper attempts to use the low-channel LiDAR sensor to detect and evaluate the stain, wear, and cracks of pavement marking based on the research of Pike and Che that the retroreflectivity of pavement markings is highly correlated with the laser intensity of LiDAR. Thus, the deployment location of mobile LiDAR based on its built-in characteristics and mechanical structure is optimized. An optimization model is formed and solved with the elitist preservation genetic algorithm (EGA). The results show that the best installation height is 0.5 m. The rotation angle of LiDAR is 128.4°, 148.0° in the urban and highway scenarios, respectively. The field test results show that the pavement marking can obtain best the coverage, density, and effective use of LiDAR point cloud with the above setting. The field test’s mean relative error (MRE) is less than 5% and 10% in vertical installation and vertical installation with an inclination, respectively.
RCS reduction by 1-bit coding metasurface

A thin coding metasurface was produced with $8\times 8$ random configurations formed of two elements, encoded as element “0” and element “1”, with a phase difference equals to $180\pm 30^{\circ}$ in a broad frequency band from 28 to 40GHz. An optimal coding metasurface allowed to reduce the monostatic and bistatic Radar Cross Section (RCS) below -10 dB from 28 to 40GHz when compared to a perfect electric conductor $(PEC)$ plate with same size. The coupling effect produced between the elements has been reduced. The optimal random coding metasurface was designed by using an efficient method of minimization called “pattern search” with the help of planar array theory, and simulated by means of the CST Microwave Studio software.
More Reliable Test Suites for Dynamic APR by using Counterexamples

Dynamic automated program repair (APR) techniques, which use test suites for bug localization and evaluating candidate patches, have promising results. However, many studies show that machine-generated patches with dynamic APR tools are not always reliable. Recent studies show that enhancing test suites by adding tests will help dynamic APR tools generate more reliable patches. We evaluate the effectiveness of minimally enhancing test suites by adding counterexamples for repaired programs that suffer from test overfitting. We use formal methods as an independent standard for evaluating patches' correctness and for generating counterexamples. Techniques for evaluating patch correctness (both with human reviewers and formal methods) can create false negatives, meaning that the repaired program is correct but is deemed incorrect. A counterexample is a good way to check on reviewer decisions about correctness. Our study evaluated 256 repaired but not verified programs (from the buggy Java+JML dataset); the repairs were generated by seven state-of-the-art dynamic APR tools. Our results show that the counterexample generated by the OpenJML tool could correctly classify all these programs into the categories of “test overfitting” and “false negatives. ” After adding tests based on the counterexamples to the test suites, we ran the APR tools on the original buggy programs again and found that: (1) the APR tools were able to generate about 27.3% more correct patches with the enhanced test suite, and (2) the enhanced test suite resulted in the APR tools generating about 83.6% fewer overfitted patches.
Effect of magnet types on performance of high speed spoke interior permanent magnet machines designed for traction applications

Interior permanent magnet machines are considered the state-of-the art for traction motors especially in light-duty hybrid and electrical vehicles. These motors usually use Neodymium Iron Boron (NdFeB) permanent magnets. These magnets include both light rare earth materials such as Neodymium (Nd) as well as heavy rare-earth materials such as Dysprosium (Dy). The main purpose of Dy is to enhance the magnet coercivity to avoid demagnetization under both high temperatures as well as flux-weakening. One of the key risks in terms of using these rare-earth magnets is the significant fluctuation/increase in their prices over the past few years. Applications that use large quantities of these magnets, such as traction motors and wind generators, are the most affected by these fluctuations. There has been an ongoing global effort to try to reduce or eliminate the use of rare earth materials (especially Dy which is the most expensive) without sacrificing too much performance. This paper will focus on advanced spoke designs targeting traction applications. The goal of this paper is to come up with new spoke designs using various grades of Dy-free magnets as well as ferrites targeting the same set of specifications. The paper will provide a detailed comparison between the various designs highlighting the key tradeoffs in terms of power density, efficiency, flux-weakening capability and magnet susceptibility to demagnetization. Also, a prototype using ferrites has been built and tested and the experimental results will be presented.
The Function of Performance Appraisal in Career Development

The design of a performance appraisal system may be considered an unusual topic in the field of career development. However, it is considered most relevant in the current employment climate for graduates given the repeated emphasis on career development at various stages of the employment process. There has been a growing emphasis since the mid-1970s on employees' needs rather than just organisational needs, and a recognition of the strategic value of employees being developed to their best potential. Organisations have become increasingly aware that the effective development of their employees' skills and knowledge has benefits for the whole organisation. Career development (or employee or professional development) is a thread that runs through each stage of this discussion of the design of a performance appraisal system. This issue of career development may also be observed in promotional brochures and information sessions given by recruiters of graduates of tertiary institutions. Much is made of training and development opportunities as organisations seek to attract the brightest and best graduates to their graduate management trainee programs. Performance appraisal can be a crucial factor in the setting of career goals and the perception of job satisfaction leading to increased motivation and productivity.
OTT (Over-The-Top) in 2015

OTT (Over the Top) is the name given to media entertainment services delivered over the Internet. It usually refers to “premium” content services and is typically used in connection with services offered by traditional broadcast operators and content production houses over the Internet. The definition of “premium” is of course unclear and it may be taken as a purely technical catch-all e.g., high-production values, high-resolution pictures, and sound. Perhaps a better definition is “media that customers are willing to pay for” or “media for which access is managed by some kind of authentication. ” This article reports on the state of the OTT sector to date.
Deployment Options of 5G Network Slicing for Smart Healthcare

Network slicing enables the creation of multiple logical independent networks on physical networking infrastructure. Network slice deployment in a Fifth Generation (5G) mobile network can be classified as vertical and horizontal slicing. The paper compares the performance of the two slicing methods through solving two convex optimization problems, considering several smart hospital scenarios that differ from each other based on their medical speciality. The results are used to draw insights on the most appropriate slicing approach for each setup.
Clustering and Transmit Power Control for Social Assisted D2D Cellular Networks

Device-to-Device (D2D) is a novel communication architecture that is expected to significantly improve the performance of next-generation 5G cellular networks by efficient reuse of licensed spectrum. However, excessive and uncontrolled spectrum reuse comes with the cost of co-channel interference, which deteriorates the overall network’s performance. To maximize the gains of D2D deployments, it is crucial to develop interference-aware spectrum reuse techniques. This paper proposes a social-aware clustering technique to maximize spectrum reuse and minimize co-channel interference for parallel D2D transmissions. Furthermore, we propose a Particle Swarm Optimisation based transmit power control scheme to maximize the system throughput and energy efficiency. Our simulation results show a significant improvement in network performance for different activation probabilities.