Miracle-dz/UniFAR-FTUs huggingface dataset:
Internet of Things Intrusion Detection: A Deep Learning Approach

The Internet of Things (IoT) is a shifting paradigm that allows the integration of billions of devices with the Internet. With its wide range of application domains, including smart cities, smart homes, and e-health, the IoT has created new challenges, particularly security threats. Traditional security solutions, such as firewalls and intrusion detection systems, need amending to fit the new networking paradigm. Given the recent advances in machine learning, we investigated the use of deep learning algorithms for anomaly detection. The IoT collects a massive amount of data from the environment, and deep learning is based on a set of algorithms striving for the data. Intrusion detection systems are used to expose network threats and are an effective means of protecting network assets. Anomaly detection is a conventional intrusion detection approach that separates normal and abnormal network traffic using statistical, rule-based, or machine learning models. Of the machine learning models, deep learning is a neural network algorithm that has provided breakthroughs in domains such as object and voice recognition. However, there are limitations in applying deep learning to network anomaly detection. This paper proposes a novel anomaly detection framework based on unsupervised deep learning algorithms for revealing network threats. Our research explores the applicability of deep learning to detect anomalies by evaluating the use of Restricted Boltzmann machines as generative energy-based models against Autoencoders as non-probabilistic algorithms. The study provides an in-depth analysis of unsupervised deep learning algorithms. The simulations studies show $\approx$ 99% detection accuracy, which is significantly improved compared to the closely related work.
Solving Ontology Meta-Matching Problem Through an Evolutionary Algorithm With Approximate Evaluation Indicators and Adaptive Selection Pressure

Ontology applies commonly to solve the problem of heterogeneity of data in the Semantic Web, but the heterogeneity problem between two ontologies seriously affects their communication. As an effective method, ontology matching can address the problem above, whose core technique is the similarity measure. A single similarity measure calculates the similarity value about a feature between two concepts, but none of the similarity measures can ensure their effectiveness in all context due to the diverse heterogeneous features between two ontologies. Therefore, multiple similarity measures are usually aggregated to improve the result’s confidence. The problem that how to determine the optimal aggregating weights for the different similarity measures to obtain a high-quality alignment is called the meta-matching problem of ontology, which is modeled as a nonlinear problem with many local optimal solutions. Evolutionary Algorithm (EA) can represent an efficient methodology to address the ontology meta-matching problem, but EA-based ontology matching techniques suffer from the premature convergence and the requirement of a reference alignment to evaluate the solutions. To overcome the defects mentioned above, in this work, an improved EA-based matching approach is proposed, where two approximate evaluation indicators, i.e. pseudo-recall and pseudo-precision, are presented to evaluate the solution’s quality, and an adaptive selection pressure is utilized to overcome the premature convergence. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)’s benchmark, and the experimental results will prove the effectiveness of our proposed method.
A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories

With the rapid development of mobile internet technology, mobile applications (apps) have been rapidly popularized. To facilitate users’ choice of apps, app recommendation is becoming a research hotspot in academia and industry. Although traditional app recommendation approaches have achieved certain results, these methods only mechanically consider the user’s current context information, ignoring the impact of the user’s previous related context on the user’s current selection of apps. We believe this has hindered the further improvement of the recommendation effect. Based on this fact, this paper proposes a novel context-aware mobile application recommendation approach based on user behavior trajectories. We named this approach CMARA, which is the initials acronym of the proposed approach. Specifically, 1) CMARA integrates the heterogeneous information of the target users such as the user’s app, time, and location, into users behavior trajectories to model the users’ app usage preferences; 2) CMARA constructs the context Voronoi diagram using the users’ contextual point and leverages the context Voronoi diagram to build a novel user similarity model; 3) CMARA uses the target user’s current contextual information to generate an app recommendation list that meets the user’s preferences. Through experiments on large-scale real-world data, we verified the effectiveness of CMARA.
Building a smart city 4.0 ecosystem platform: an overview and case study

This paper presents an overview of a smart city 4.0 framework in accelerating digital transformation, especially during the COVID-19 pandemic, using Jakarta as a case study. The findings of this study provide new insights how to translate a vision into a reality in the form of smart city 4.0 framework that offers a significant opportunity to advance the understanding of building a smart city ecosystem with technologies, innovations and collaborations. This paper applies four principles of the framework, namely mobile-first, system-and-data driven, digital experience, and smart collaboration in building a smart city 4.0 ecosystem platform. Part of the aim of this paper is to examine Jakarta’s super-app called JAKI that is compatible with such principles as a use case in the time of the pandemic. It provides a better understanding of common elements in building a new concept of a smart city. The results will inspire and give a contribution to other cities to consider the framework in building a smart city 4.0 ecosystem platform to foster quality of life, economic growth and sustainability.
PSubCLUS: A Parallel Subspace Clustering Algorithm Based On Spark

Clustering is one of the most important unsupervised machine learning tasks. It is widely used to solve problems of intrusion detection, text analysis, image segmentation etc. Subspace clustering is the most important method for high-dimensional data clustering. In order to solve the problem of parallel subspace clustering for high-dimensional big data, this paper proposes a parallel subspace clustering algorithm based on spark named PSubCLUS which is inspired by SubCLU, a classical subspace clustering algorithm. While Spark is the most popular big data parallel processing platform currently, PSubCLUS uses the Resilient Distributed Datasets (RDD) provided by Spark to store data points in a distributed way. The two main performing stages of this algorithm, one-dimensional subspace clustering and iterative clustering, can be executed in parallel on each worker node of cluster. PSubCLUS also uses a repartition method based on the number of data points to achieve load balancing. Experimental results show that PSubCLUS has good parallel speedup and ideal load balancing effect, which is suitable for solving the parallel subspace clustering of high-dimensional big data.
RSX: Reproduction Scenario Extraction Technique for Business Application Workloads in DBMS

Reproducing software defects plays a crucial role in the process of diagnosing and resolving bugs. While reproducing bugs, programmers may apply additional analysis tools to locate the point at which the program has gone astray. In a database management system for handling massive concurrent workloads from business application, developing a reproduction scenario may be challenging due to complex transactional dependency among workload executions. In this paper, for SAP HANA Quality assurance, we introduce a technique to extract a lightweight reproduction scenario which contains the relevant sequence of database operations for retriggering a target system failure. Our approach comprises two phases: (1) transforming a historical transactional information into a compact data structure, (2) extracting a reproduction scenario to isolate the symptom. We evaluate our technique against real functional bugs detected during the SAP HANA release validation and highlight its effectiveness for handling complex symptom analysis.
Big Data Analytics for Power Distribution Systems using AMI and Open Source Tools

Advanced meters now account for almost half of the electric meters in the United States. In addition to reducing the costs of meter reading, these meters provide a vast increase in the amount of consumption data available to the utility. Many organizations, however, lack the expertise to analyze this data, so it languishes unused. This paper describes steps taken to make AMI data usable for day-to-day planning tasks at a medium-sized distribution utility. The proposed approach can be implemented on commodity hardware, providing a cost effective and secure platform for advanced analysis tasks.
Machine Learning Performance for Radio Localization under Correlated Shadowing

Utilizing machine learning methods for radio localization is gaining popularity in recent years. This is because of the current technology trends in better connectivity, cloud database, and cheaper processing power. Received signal strength (RSS) fingerprinting is one of the common localization methods because of its relative simplicity and ability to produce well-distinct patterns at different locations. In this paper, we compare the performance of different machine learning algorithms in terms of their mean localization error using RSS fingerprinting. The comparison is based on two key parameters, namely; (i) the correlation distance of the radio shadowing, and (ii) the standard deviation of the shadowing. The studied machine learning methods are the linear regression (LR), k-nearest neighbour regression (kNR), decision tree regression (DTR) and random forest regression (RFR), where extensive simulation demonstrates the performance of these methods under the correlated shadowing scenarios.
Optimizing the Performance of Organic-Based Perovskite on Crystalline Silicon

Organic-based perovskite provide low-cost deposition onto silicon solar cells. Due to the thermal mismatch between the silicon/perovskite layers, thermal stresses are induced during processing and operating temperatures. Two types of methylammonium lead perovskite CH3NH3PbI3 and CH3NH3PbBr3 are chosen as candidate. Thermo-elasticity coupled with FEM to analyze the stress distribution and at the mismatch interface. CH3NH3PbBr3 shows higher stress distribution during deposition compared with CH3NH3PbI3. At elevated operating temperatures both CH3NH3PbI3 and CH3NH3PbBr3 show recovery of the stresses, reaching similar stress distributions. This can be attributed to the transition in the CH3NH3PbI3 crystal structure from tetragonal to crystal symmetry.
Automatic Diagnosis of Diabetic Retinopathy using Machine Learning: A Review

Diabetic Retinopathy is a popular cause of diabetes, causing vision-impacting lesions of the retina. Blindness may be avoided by early detection. The ophthalmologist’s manual approach of diagnosing diabetic retinopathy is expensive and time consuming. At the same time, unlike computer assisted diagnostic systems, it may cause misdiagnosis. Deep learning has recently become one of the most effective approaches that has obtained better efficiency in the analysis and classification of medical images. In medical image analysis, convolutional neural networks are more commonly used as a deep learning approach and they are extremely effective. This paper assessed and addressed the new state-of-the-art Diabetic Retinopathy color fundus image classification and detection methodologies using deep learning and machine learning techniques. Additionally, various challenging issues that need further study are also discussed.
Photovoltaic Array Power Prediction Model Based on EEMD and PSO-KELM

In order to improve the accuracy of short-term photovoltaic (PV) power prediction, the PV array power prediction model based on ensemble empirical mode decomposition (EEMD) and kernel extreme learning machine (KELM) optimized by particle swarm optimization algorithm (PSO-KELM) is proposed in this paper. The EEMD algorithm is used to decompose the power sequence of the PV array into a series of intrinsic mode functions (IMFs) with different features, the IMFs with similar features are divided into new components by sample entropy (SE), and establishing the KELM power prediction model corresponding to the new component. The PSO is used to optimize the output weight of KELM, seeking the optimal solution of upper and lower limits, and improving the performance of KELM. The actual data is used to compare the proposed prediction model with the traditional back-propagation (BP) and extreme learning machine (ELM) prediction models. The results validate that the proposed model can predict power of PV array with good accuracy and calculation speed.
Four-Wheel Vehicle Driving by using a Spatio-Temporal Characterization of the P300 Brain Potential

In this work a P300-based Brain Computer Interface (BCI) for the remote control of a four wheels vehicle, is presented. The proposed BCI exploits the P300 signal, an event-related potential (ERP) typically induced by visual/audio oddball paradigm stimulation protocol. For the driving purpose, in our application a four-choice synchronous BCI has been implemented. The neural interface architecture is made up by (i) the acquisition unit, (ii) the processing unit and (iii) the navigation unit. The former unit collects brain signals by 6 smart wireless electrodes from the parietal-cortex area. The processing unit is composed of a dedicated µPC (Raspberry Pi, RPi) performing stimuli delivery, the machine learning (ML) stage and the real-time classification. Specifically, the processing unit bases its ML stage working on a typical classification problem approach (i.e., feature extraction and classification). In this context, the main contribution of the work lies in the introduction of a P300 spatio-temporal characterization approach (t-RIDE), which allows to analyze all the available choices in a one-vs-all discrimination scenarios. It permits the implementation of very common binary classifiers despite the hyper dimensionality of the classification problem. Finally, the RPi-based navigation unit actuates the received commands and supports the vehicle by using peripheral sensors. As a proof of concept, the BCI operation has been tested on 7 subjects (aged 26 ± 3), using an acrylic prototype car. The experimental results showed that in the online free-drive mode (testing set), the BCI accuracy reached 84.28 ± 0.87% all over 4 choices, on single-trials.
Deep hierarchical reinforcement learning in a markov game applied to fishery management decision making

In this article, we propose an approach based on deep hierarchical reinforcement learning to help the management of a fishery formalized by a markov game. Two actors, the fishermen seeking to maximize their profit and the decisionmakers seeking to manage the exploitation in the best possible way through the introduction of quotas. For each, we test two learning approaches, one based only on observations, the other on pre-processing leading to beliefs. Several learning algorithms are tested and the results are compared. This has allowed us to identify a minimum amount of knowledge to have for optimal quota setting that also leads to significant and sustainable gains without prior knowledge.
Impact of the Ball Grid Array Connection Failures on Signal Integrity

Ball grid arrays (BGA) are widely used in micro assemblies to provide both the mechanical connection between the chip substrate and the circuit board, and the electrical connection between the chip circuit and the peripheral matching circuit. Connection failures of a ball grid array may result in deterioration of signal integrity and low communication quality. In the present work, the impact of a degraded ball grid array connection on high-frequency characteristics was studied. A 3-D electromagnetic field model and an equivalent circuit model of a degraded ball grid array were developed. The electrical performance of the degraded ball grid array was analyzed and simulated. It was found that the 3-D electromagnetic field model results were in good agreements with the predicted behaviors from equivalent circuit model simulations. The characteristics of the ball grid array for different degradation levels were then compared and analyzed. This research serves to provide a better understanding of the effects of ball grid array deterioration on signal performance for application in fault detection strategies for electronic systems.
Iterative Design of a 60 kW All-Si Modular LLC Converter for Electric Vehicle Ultra-Fast Charging

This paper proposes an iterative design procedure for a high-power LLC resonant converter, taking part in a 60 kW modular DC/DC conversion stage for an electric vehicle (EV) ultra-fast battery charger. The basics of operation of the LLC converter are briefly recalled and the most relevant analytical expressions are reported. Due to the high-power requirement and the wide output battery voltage range (i.e. 250-1000 V), a modular design approach is adopted, leveraging the split input DC-link structure provided by a 3-level active front-end. A total of four modules, with at 15 kW nominal power and a 250-500 V output voltage regulation capability, are designed with a straightforward iterative procedure based on the first-harmonic approximation (FHA). Finally, the proposed methodology is verified experimentally on a 15 kW LLC converter prototype directly resulting from the design procedure.
A Study on the Impact of Alcoholism on EEG-based Cryptographic Key Generation Systems

Alcoholism is one of the brain disorders that involves in electroencephalogram (EEG) signals and have impact on EEG-based systems. However, this issue has not been investigated. In this paper, we propose an EEG-based cryptographic key generation system using EEG signals and present experiments performed on two groups of subjects, alcoholic and non-alcoholic groups in the Alcoholism database. This EEG-based cryptographic key generation system is based on an assumption that EEG signal is quasi-stationary if the time window is sufficiently short. With this assumption, stable EEG features are extracted to generate cryptographic keys. The impact of alcoholism on performance of the system is analysed based on our experimental results.
Stopping Criteria for Iterative Compressive Sensing of Sparse Angular-Frequency Spectrum

Recent trends have shown that spectrum regulators are opening up more shared spectrum bands for fifth generation mobile communications system (5G) and Beyond 5G (B5G) networks. The possibility for spectrum sharing in the angular domain as well as the frequency domain simultaneously increases with the use of array antennas in 5G systems. An efficient technique for joint sensing of the angular-frequency spectrum called the Wideband Group Simultaneous Orthogonal Matching Pursuit (WGSOMP) algorithm was previously proposed based on compressive sensing (CS) assuming a sparse spectrum. In this paper, several stopping criteria is proposed for the WGSOMP algorithm, which aims to maximize the probability of detection while minimizing the probability of false alarm in unoccupied bands. Performance of the proposed stopping criteria is evaluated through Monte Carlo simulation, and results show that the probability of false alarm can be reduced by more than half without reducing the probability of detection at SNRs larger than 0 dB, especially when the transmit signal parameters are not confined to the angular-frequency grid defined in the overcomplete dictionaries.
Light irradiance and spectral distribution effects on microalgal bioreactors

The irradiance and light spectral distribution affect the growth and productivity of microalgal cultures. In extensive open pond cultures, the light control has limited options, mainly the culture depth. In photobioreactors, besides the culture depth, the light source, configuration of the reactor, light pathway, and flow rate can be used to control the characteristics of the light available to the cultures. The change of light conditions can also be used to modify the composition of the microalgal biomass produced to optimize the production of bioproducts of interest. Additionally, in mixed cultures, the species composition can be influenced by the light quantity and quality. Determining the effect of the light quantity and quality in photosynthetic cultures will help to develop strategies to optimize the production of biomass, lipids, pigments, proteins, and other compounds of interest in photosynthetic microorganisms. Information obtained from bench scale cultures can rarely be applied directly to large‐scale bioreactors. Nonetheless, determining the kinetic parameters of microalgal cultures at bench scale will reduce the time needed to optimize the cultures in photobioreactors. In this work, a review of the main factors is presented, along with specific examples of the effect of light quality and quantity in cultures with single and multiple species. Additionally, some models to predict the effect of light on microalgal productivity are discussed.
Vision-based Vehicle Detection and Distance Estimation

Real-time vehicle detection is one of the most important topics under the Autonomous Vehicles (AVs) research paradigm and traffic surveillance. Detecting vehicles and estimating their distances are essential to ensure that the vehicles can keep a safe distance and run safely on the roads. The technology can also be utilized to determine traffic flow and estimate vehicle speed. In this paper, we apply two different deep learning models and compare their performances in detecting vehicles such as cars and trucks for deployment on the self-driving cars to ensure road safety. Our models are based on YOLOv4 and Faster R-CNN which are efficient and accurate in object detection within a given distance. We also propose a vision-based distance estimation algorithm to estimate other vehicles’ distances. In detecting vehicles within 100 meters, the two variations of our models, YOLOv4 and Faster R-CNN, achieved 99.16% and 95.47% mean precision, and 79.36% and 85.54% Fl-measure respectively on a two-way road. The detection speed is 68 fps and 14 fps for YOLOv4 and Faster R-CNN respectively.
Interfacial Processes and Tribocharging: Effect of Plasma Surface Modification and Physisorption

The tribocharging characteristics of polymer powders have a significant impact on several industrial processes such as electrostatic powder coating and polymer powder processing. The nature of chemical species on the surface of powder influences the triboelectric charging characteristics. An atmospheric-pressure helium plasma was used to modify tribocharging properties of polystyrene microspheres and acrylic powders. Plasma surface modification reduced the magnitude of tribocharging for the test polymer powders. When tribocharged against stainless steel, the net charge-to-mass ratio (Q/M) for both polystyrene microspheres and acrylic powders was less than that for the control samples. Plasma surface modification also reduced the tribocharging of mannitol, a pharmaceutical powder, against stainless steel. Further, Mars simulant dust (JSC Mars 1) was used as a test sample to study the effect of carbon dioxide physisorption on tribocharging. The Q/M of the sample with physisorbed CO2 was less than that of the control in all size ranges.