Miracle-dz/UniFAR-FTUs huggingface dataset:
Facial Expression Recognition System with Local Binary Features of Neural Network

The use of accurate and faster algorithms for real-time automatic facial expression detection is a challenge. Facial expression recognition (FER) has three important steps: face detection, facial feature extraction, and emotion classification based on the extracted features. Face detection comes under the pre- processing stage, where the face of the object can be recovered for easy extraction of the features. But, the most important part of FER is carefully selecting the features responsible for detecting the expressions and their extraction from the face. Nowadays, mostly advanced neural networks are used for the classification of expressions based on the extracted features, which are also very complex and time-consuming. Further, the decision trees and the neural networks increase the recognition rate of facial expressions into many folds. The requirement of the number of extracted features also increases to achieve better accuracy. Hence, the storage requirement also increases along with an increase in the complexity and cost of the complete system. This work tries to reduce the number of features by focusing only on local binary features, reducing the storage, complexity, and overall cost of the complete FER system. An improved pre-processing stage is used in the proposed work. Further, it has an important local feature- based learning step that includes the extraction of local binary features for expression classification. These feature vectors are concatenated and used in Shallow neural networks with minimum complications and a smaller number of layers for optimizing the expression recognition process along with gentle boost decision trees. This method of local binary features-based neural network (LBF-NN) is applied to three different popular databases, and it yielded favorable results of more than 93% accuracy, even in comparison with various complex and advanced algorithms.
Towards Hardware-Software Self-Adaptive Acceleration of Spiking Neural Networks on Reconfigurable Digital Hardware

Self-adaptive systems can dynamically and autonomously modify their parameters or structure to adjust their behavior in response to changes in their environment or input. The resulting flexibility and robustness can be a great advantage in various fields like robotics or industrial processes. Here, we demonstrate the feasibility of combining field-programmable gate arrays (FPGAs) and spiking neural networks (SNNs) to create self-adaptive systems capable of dynamic and autonomous modification of their structure in both software and hardware, allowing them to learn and evolve over time. We present a proof-of-concept implementation of an SNN accelerator that can adapt its network topology using dynamic partial reconfiguration and its weight parameters using online learning methods, such as Spike-Timing Dependent Plasticity (STDP) and anti-STDP. We evaluated different configurations of the accelerator in terms of resource utilization, power consumption, and runtime and tested its online learning capabilities in a classification task using the N-MNIST dataset. In our experiments, the accelerator saved 33% dynamic power by adapting to changing task conditions, and achieved a speedup factor of 5.3 with respect to the input data time resolution. Our findings suggest that combining FPGAs and SNNs can lead to the effective instantiation of SNN hardware resources to meet changing task conditions, thus paving the way for future research on self-adaptive SNNs that use diverse online learning algorithms to improve themselves both in software and hardware.
CapsuleGAN for Sound Event Detection

Polyphonic sound event detection (SED) refers to the detection and identification of multiple sound events in complex environments, such as car noise, ocean waves, bird calls, etc., and locating the start and end of each sound time occurrence. In practice, polyphonic SED usually requires a large amount of annotation data, however, it is difficult to annotate a large number of sounds in natural environments. Therefore, weakly annotated polyphonic SED has become one of the current research hotspots. In this paper, we propose a weakly annotated multi-voice event detection method based on generative adversarial capsule networks (CapsuleGAN). The aim is to solve the problem of difficult annotation in polyphonic SED and to improve the detection accuracy and robustness of the model. Specifically, we adopt a CapsuleGAN structure that combines a Generative Adversarial Network (GAN) [1] with a Capsule Network (CapsNet) to improve the robustness of the model and enable the model to better handle complex datasets of sound event representation. In addition, we employ streamwise regularisation, adversarial loss, and cross-entropy loss as the classifier's objective function to improve the classification accuracy of the model. To evaluate our approach, we conducted experiments on the DCASE2017 Task 4 dataset. Among them, the F-score of the audio annotation subtask reached 58.9% and the error rate of sound event localisation reached 0.75. The results show that our method has an advantage in weakly annotated polyphonic SED, with better robustness and higher detection accuracy.
Evaluation of Color Saturation and Hue Effects on Image Quality

Saturation and hue are image characteristics that significantly affect the perception of a scene. This paper observes the impact of changing saturation and hue on image quality. Concepts of subjective image quality assessment methods are explained. Reasons for developing objective image quality evaluation methods are provided together with a short overview of the methods used in the paper. A brief interpretation of color spaces in which the objective evaluation was carried out is given. The created image database containing images with modified saturation and hue with regard to original images is described. The settings of the performed subjective testing are stated. A subjective and objective evaluation of image quality was executed. Kendall's tau and Spearman's rho correlation coefficients are calculated for the comparison of subjective and objective evaluation results. Efficiency of the methods is discussed.
Exploring the Impact of a Gamified Exercise Platform to Support Healthy Ageing: Home-Based Study with Older Adults

Active ageing is an increasingly important concept within society due to the ageing of the world population and search for enhanced quality of life. Technological resources such as serious games can be used as a means to promote healthier lifestyle and ageing, particularly in older adults. This study intended to explore the effects of using a digital approach composed of serious games to support physical and cognitive exercise at home, to support healthy ageing. Eleven older adults voluntarily participated in an eight-day home-based study. The aim of the study was to explore the feasibility of the digital approach in autonomous sessions at home, by measuring satisfaction and motivation, and assessing the impact of different gamification strategies. The results showed that participants exercised autonomously on average on 5.5 out of the 8 days. Each session had an average duration of 35 minutes. Moreover, participants reported an average satisfaction of 80.0% throughout the sessions. Yet, the findings indicate that system usability should still be improved. Participants provided suggestions towards an easier user experience, pointing for example to the importance of personalized exercise plans. The findings suggest that gamification strategies focused on multi-joint and dynamic exercises were preferred over single limb and more monotonous exercises. The current study highlights the potential of home-based digital solutions to support healthy ageing in older adults and provide suggestions for future endeavors in this area.
A Radio Modulation Data Augmentation Method Based on Improved CGAN

In recent years, the rapid development of 6G, edge computing, Internet of Things and other technologies has made wireless communication technology an important direction of modern scientific and technological progress. The research of wireless communication technology based on machine learning is facing the common pain point problem of lacking large-scale and high-quality data sets. The application of data-driven learning algorithms is restricted by its inherent characteristics, and wireless communication data has problems such as high collection difficulty, high cleaning cost, and large label data. Therefore, in order to develop wireless communication technology based on machine learning, we first need to meet the needs of high-quality wireless communication data sets. This paper proposes a radio modulation data augmentation method based on improved conditional generative adversarial networks (RMDA-CGAN), and verifies the effectiveness of the augmented data set through classification tasks.
Application of Deep Learning for Intrusion Detection and Cyber-Attack Detection Using Internet of Things Networks

The ability to deploy such systems throughout a region, or perhaps a nation, will increase as IoT infrastructure develops. Although Internet of Things (IoT) devices are capable of offering intelligent services, the massive amounts of data they gather and analyze pose a severe security threat. Researchers have spent a lot of work building sophisticated NIDS to prevent the exploitation of IoT data in smart applications. However, present methods may be vulnerable to attack, particularly new attacks, due to a lack of sufficient and evenly distributed attack data used to train the detection strategy. This study introduces a original hierarchical adversarial attack (HAA) generation strategy, which in turn targets the convolutional neural network-based intrusion detection in IoT devices with a limited budget, and therefore realizes the level-aware black-box adversarial attack approach. To generate adversarial instances with little disturbance, an intelligent approach is developed that relies on a saliency map methodology. To do this, a shadow CNN framework has been constructed. Using a random walk having restart based hierarchical node selection approach, a subset of the targeted IoT network's nodes with the lowest structural strength are selected. The proposed HAA generation technique is evaluated in comparison to three reference methods using the publicly available data set UNSW-SOSR2019. In a comparison of the two most sophisticated convolutional neural network (CNN) designs, GCN and JK-Net, it was shown that the classification accuracy for NIDS in IoT settings may be reduced by more than 30%.
Quantifying Faulty Assumptions in Heterogeneous Multi-Agent Systems *

We study the problem of analyzing the effects of inconsistencies in perception, intent prediction, and decision making among interacting agents. When accounting for these effects, planning is akin to synthesizing policies in uncertain and potentially partially-observable environments. We consider the case where each agent, in an effort to avoid a difficult planning problem, does not consider the inconsistencies with other agents when computing its policy. In particular, each agent assumes that other agents compute their policies in the same way as it does, i.e., with the same objective and based on the same system model. While finding policies on the composed system model, which accounts for the agent interactions, scales exponentially, we efficiently provide quantifiable performance metrics in the form of deltas in the probability of satisfying a given specification. We showcase our approach using two realistic autonomous vehicle case-studies and implement it in an autonomous vehicle simulator.
Interpretable Credit Card Fraud Detection Using Machine Learning Leveraging SHAP

More and more companies are taking their services online as a result of how widely accessible the internet is. Besides, because of the growth of E-commerce websites, both individuals and businesses that deal in finances are more dependent on internet administrations to handle their business. Since more and more people are using online banking and making purchases online, credit card fraud has increased. These days, fraudsters use a variety of cutting-edge ways to thwart the methodical operation of the fraud detection system that is already in place. In this research, we have implemented six different machine learning (ML) models to develop an efficient fraud detection system. The techniques in our research include Logistic Regression, SVM, Random Forest, AdaBoost, and AdaBoost with Random Forest and Logistic Regression as the base estimators. Furthermore, to illustrate the classification results, we employed SHapley Additive exPlanations (SHAP) to demonstrate the explainability of the two top-performing models. This research shows that the Random Forest and AdaBoost with Random Forest as a base estimator models, are the most effective algorithm with a test accuracy of 99.999% each. Besides, these two models have performed very well in terms of Precision, Recall, and F1-Score than the other ML models.
A Developer Recommendation Algorithm Based on Multi-Relationship Knowledge Enhancement

In recent years, collaborative software development has gradually become an emerging software development model. However, with the continuous development of crowdsourcing platforms, the problem of information overload has become increasingly serious, and it has become crucial to recommend suitable developers for tasks. Traditional recommendation methods face two major challenges: first, the text features of tasks and developers are highly concise and contain a large number of entities; second, tasks are one-time and explicit interactive data is sparse. To solve these challenges, this paper proposes a developer recommendation algorithm based on multi-relationship knowledge enhancement. We identify entities and contextual entities from text features and link the relationship between tasks and developers from a knowledge perspective. In addition, we treat developers' participation in registering, submitting, and winning tasks as different preferences, and assign different weights to each relationship. Finally, we enhance developers' feature representation using multi-relationship neighborhood aggregation. We conducted extensive experiments on a real Topcoder dataset, and the results show that our method is significantly better than the other four comparison methods in accuracy and MRR.
A comparative experimental study of LiDAR, camera, and LiDAR-camera localization algorithms for autonomous mobile robots on urban sidewalks

To navigate autonomously, mobile robots require an accurate and drift-free source of localization. Many algorithmic solutions have been proposed in the literature, exploiting sensors like LiDARs and cameras. This paper proposes a benchmark of some open-source localization algorithms on a self-acquired, sidewalk-focused dataset. The dataset was collected by an instrumented robotic platform traveling more than 5 kilometers on public sidewalks. The analysis targets the use case of map-based localization exploiting an a-priori known localization map. Results are reported as mean position errors with respect to a high quality ground truth trajectory, as computed by the Absolute Trajectory Error (ATE) method.
Comparison of Plain Network and ResNet Classifiers for Fruit Image Classification

As deep learning techniques continue to advance, image recognition and classification have become research hotspots in the field of computer vision. This study aims to investigate the application of deep learning classifier in fruit image recognition problems, and we have compared two distinct deep neural network architectures: Plain Network and ResNet. In addition, this study primarily focuses on the practical application of deep learning neural networks in classification problems. Therefore, we use fruit classification as an example and build a deep learning classifier for fruits. The introduction and popularization of deep learning neural networks hold significant meaning. The contributions of this study are as follows: 1) Popularization of deep learning neural networks through educational efforts. 2) Experimental evidence demonstrates that the deep network learning architecture ResNet outperforms the traditional Plain Network in terms of learning efficiency and accuracy in classification problems.
A Novel Low Common-Mode Voltage DAC Switching Strategy for SAR ADCs

This paper presents a 10-bit successive approximation analog-to-digital converter (ADC) with a conversion rate of 50 MS/s and proposes a low common-mode voltage fully differential switch switching scheme. The ADC employs a non-binary weighted non-split Sub-DAC capacitor structure and incorporates redundant bits to improve switch conversion speed and accuracy. During the switching process, only the reference voltage Vcm is utilized, with a variation range between 0 and Vcm. A comparison with traditional switching schemes demonstrates a 93% reduction in switch energy and an 83% decrease in area.In the SMIC 180nm CMOS process, utilizing an initial signal frequency of 24.26MHz, the ADC achieved a SNDR of 57.98dB and ENOB of 9.34 bits. Furthermore, the FOM for this ADC was measured to be 179.95 fJ/conversion-step.
A Simple Design of Wideband Filtering Square Patch Antenna Based on Triple Modes via Loading of Slots

This paper presents a simple design of the wideband filtering antenna based on the square patch resonator via slit-loading. By properly incorporating two pairs of slots along both radiation edges of the square patch resonator, two additional resonant modes are produced around the conventional TM10 mode. Moreover, these two modes both perform inverse electric field at two sides of the etched slot, which provide the potential for creation of radiation nulls. In this context, by taking fully utilization of these two new modes together with the TM10,a wideband filtering antenna with three resonances and two radiation nulls is constructed. The simulated results indicate that the proposed antenna is operating from 3.34 to 3.66 GHz with a factional bandwidth of 9.14%. Besides, two zeros exist in the boresight gain frequency response at 3.29 and 3.75 GHz, leading to the high frequency selectivity in both passband edges.
Exploring Effective Outlier Detection in IoT: A Systematic Survey of Techniques and Applications

This paper presents a survey of outlier detection techniques used in the analysis of IoT data. We conducted a comprehensive review of existing research studies and identified the most popular techniques for detecting outliers in IoT data, including statistical techniques, machine learning algorithms, and hybrid methods. We also discuss the challenges of IoT, such as data quality, scalability, and security issues, and highlight the importance of outlier detection in various IoT applications, such as healthcare, smart cities, and industrial automation. The results of our survey can be used as a useful resource for researchers and practitioners who are interested in exploring the state-of-the-art techniques in outlier detection for IoT data analysis as it's a very simple guide for any beginner.
Membership Information Leakage in Well-Generalized Auto Speech Recognition Systems

Membership inference attack (MIA) attempts to determine whether a particular data sample has participated in the training process of the target model, and is regarded as basic form of privacy breach in machine learning. This paper provides a label-only membership inference attack to well-generalized automatic speech recognition (ASR) systems, while only being granted black-box access permissions for the target system. Initially, we develop shadow models as substitution of the target ASR model. Subsequently, data augmentation techniques are employed to generate additional speech samples for the member and non-member sets elements of the shadow models. These augmentation techniques include modifying speech speed, cropping, adding noise, and so on. We query the shadow models with all the samples above, and we can derive a series of features from the model's output. All the features and their corresponding membership labels are then utilized to train an audit model. This audit model will be used to judge the membership of a given sample. The experimental results indicate that the performance metrics of this attack are significantly higher when the adversary has knowledge about the data distribution of target model's training set and the target model's architecture. However, if the adversary is lack of this knowledge, the probability of successful MIA is comparable to random guessing. The main contribution is that we investigate the membership leakage in well-generalized auto speech recognition system, and propose a data augmentation-based membership inference attack. We also discuss the transferability of the proposed scheme under various attack uncertainty.
Variational Autoencoder Based Anomaly Detection for AIOps of Power Grid Supercomputing Center

With the fast development of power system and big data, the power grid supercomputing center faces the problem that index anomaly detection is not timely and accurate, which affects safe and stable operation of the power system. To solve the above problems, we propose to use a small variational autoencoder network for index anomaly detection of power grid supercomputing center for the first time. First, the index data is preprocessed and normalized, and then the normalized data is fed into the proposed small variational autoencoder for training and testing. Finally, the best F1-Score of 0.9091 is obtained on the publicly available GAIA index anomaly detection dataset, which can timely and accurately detect whether indexes of the power grid supercomputing center are abnormal.
Efficient DOA Estimation Based on Cauchy Loss Variable Step Size Algorithm

The Cauchy loss function is robust to large outliers and has successful applications in signal processing. In this paper, a DOA estimation algorithm based on adaptive zero technology is proposed, which updates the weight of the filter by applying the Cauchy loss function, and further improves the algorithm performance by using the variable step size method. The algorithm step size is adjusted by accumulating errors and establishing a nonlinear function with instantaneous errors. The DOA estimation ability, estimation error and accuracy of the improved Cauchy algorithm in the impulse noise environment are analyzed. The algorithm is verified by simulation experiments.
Zero-Vuln: Using deep learning and zero-shot learning techniques to detect zero-day Android malware

The prevalence of cyber security threats, such as the Android Zero-day vulnerability, is becoming increasingly worrisome. With the widespread use of Android-powered mobile devices, attackers are leveraging zero-day vulnerabilities to infect Android software at an alarming rate. Detecting zero-day vulnerabilities in Android applications is particularly challenging due to their unpatched and undiscovered nature, resulting in a lack of reference points for identification. In response to this issue, we propose a novel and effective system called Zero-Vuln, which is designed to classify and identify zero-day Android malware. Zero-Vuln leverages deep learning and zero-shot learning techniques, as well as established data-sets, to identify previously unknown malware. Our approach achieves a remarkable performance of 83% accuracy, as well as high precision and recall, and represents a significant contribution to the field of cyber security.