Reviewerly/exHarmony huggingface dataset:
Invisible Adversarial Attacks on Deep Learning-Based Face Recognition Models

Deep learning technology has grown rapidly in recent years and achieved tremendous success in the field of computer vision. At present, many deep learning technologies have been applied in daily life, such as face recognition systems. However, as human life increasingly relies on deep neural networks, the potential harms of neural networks are being revealed, particularly in terms of deep neural network security. More and more studies have shown that existing deep learning-based face recognition models are vulnerable to attacks by adversarial samples, resulting in misjudgments that could have serious consequences. However, existing adversarial face images are rather easy to identify with the naked eye, so it is difficult for attackers to carry out attacks on face recognition systems in practice. This paper proposes a method for generating adversarial face images that are indistinguishable from the source images based on facial landmark detection and superpixel segmentation. First, the eyebrows, eyes, nose, and mouth regions are extracted from the face image using a facial landmark detection algorithm. Next, the superpixel segmentation algorithm is used to include the pixels neighboring the extracted facial landmarks with similar pixel values. Lastly, the segmented regions are used as masks to guide existing attack methods to insert adversarial noise within the masked areas. Experimental results show that our method can generate adversarial samples with high Structural Similarity Index Measure (SSIM) values at the cost of a small percentage of attack success rate. In addition, to simulate real-time physical attacks, printouts of the adversarial images generated by the proposed method are presented to the face recognition system via a camera and are still able to fool the face recognition model. Experimental results indicated that the proposed method can successfully perform adversarial attacks on face recognition systems in real-world scenarios.
A Data Analytics Methodology to Visually Analyze the Impact of Bias and Rebalancing

Data Analytics have become a key component of many business processes which influence several aspects of our daily life. Indeed, any misinterpretation or flaw in the outputs of Data Analytics results can cause significant damage, specialy when dealing with one of the most often overlooked issues, namely the unaware use of biased data. When data bias goes unadverted, it warps the meaning of data, having a devastating effect on Data Analytics results. Although it is widely argued that the most common manner to deal with data bias is to rebalance biased datasets, it is not an aseptic transformation, leading to several potentially undesired side-effects that will probably harm the result of data analyses. Therefore, in order to analyze the underlying bias in datasets, in this work we present (i) a comprehensive methodology based on visualization techniques, which assists users in the definition of their analytical requirements to detect and visually represent the data bias automatically helping them to find out whether it is appropriate to artificially rebalance their dataset or not; (ii) a novel metamodel for visually representing bias; (iii) a motivating real-world running example used to analyze the impact of bias in Data Analytics and (iv) an assessment of the improvements introduced by our proposal through a complete real-world case study by using a Fire Department Calls for Service dataset, thus demonstrating that rebalancing datasets is not always the best option. It is crucial to study the context where the decisions are going to be taken. Moreover, it is also important to do a pre-analysis with the aim of knowing the nature of the datasets and how biased they are.
Caputo-Hadamard fractional chaotic maps

Abstract In this paper, we proposed a new fractional two dimensional trigonometric combined discrete chaotic mapping (2D-TCDCM) and a fractional 2-D Kawakami map within Caputo-Hadamard fractional difference. We observed the dynamic behaviours of the proposed Caputo-Hadamard fractional maps, including fractal graph, maximum lyapunov exponent, phase trajectory and randomness test. We illustrate the advantage of using Caputo-Hadamard fractional difference. As a conclusion, we get the condition of the proposed fractional map to behave chaotically with physics background.
Federated Synthetic Data Generation with Stronger Security Guarantees

Synthetic data generation plays a crucial role in many areas where data is scarce and privacy/confidentiality is a significant concern. Generative Adversarial Networks (GANs), arguably one of the most widely used data synthesis techniques, allow for the training of a model (i.e., generator) that can generate real-looking data by playing a min-max game with a discriminator model. When multiple organizations are reluctant to share their sensitive data, GANs models can be trained in a federated manner, commonly with the use of differential privacy (DP). In order to achieve a reasonable level of model utility, DP trades privacy exhibiting vulnerability to various attacks (e.g., membership inference attack). In this paper, we propose a hybrid solution, PP-FedGAN, to the asynchronous federated, privacy-preserving training of GANs models by combining the CKKS homomorphic encryption (HE) scheme with differential privacy. The addition of HE results in around 10 seconds of overhead on the client side per round and 115 seconds on the entire training procedure. We also analyze the security of PP-FedGAN under the honest-but-curious security model. Where stronger security guarantees are required, our proposal presents a better alternative to solutions that only employ DP.
SEAL: Capability-Based Access Control for Data-Analytic Scenarios

Data science is the basis for various disciplines in the Big-Data era. Due to the high volume, velocity, and variety of big data, data owners often store their data in data servers. Past few years, many computation techniques have emerged to protect the security and privacy of such shared data while enabling analysis thereon. Hence, access-control systems must provide a fine-grained, multi-layer mechanism to protect data. However, the existing systems and frameworks fail to satisfy all these requirements and resolve the trust issue between data owners and analysts.
SGDA: A Saliency-Guided Domain Adaptation Network for Nighttime Semantic Segmentation

Nighttime semantic segmentation has attracted considerable attention due to its crucial status in the smart city. However, it is challenging to handle poor illumination and indiscernible information. To tackle these problems, a saliency-guided domain adaptation network, SGDA, is proposed via adapting daytime models to nighttime scenes. Firstly, a saliency guidance branch is attached to the segmentation network to enrich the spatial features and guide the model to better perceive detail information. Secondly, to embed the saliency guidance to the segmentation network, a pyramid attention architecture is designed to fuse the features from the two branches. Thirdly, an illumination adaptation module is constructed to close the intensity distributions via adversarial learning, with an elaborately designed loss function to improve the performance. Extensive experiments on Dark Zurich dataset and Nighttime Driving dataset validate the effectiveness of SGDA, and indicate that our method improves the accuracy on small object categories,
Encoding Techniques on Multivariate Time Series Signals for Failure Prevention of Industrial Assets with Unsupervised Deep Anomaly Detection

This paper addresses the use of encoding techniques of time series data in a Predictive Maintenance (PdM) system for failure prevention of industrial assets. Baseline is a dataset of an electric motor from an industrial application. Goal of this work is to compare the effectiveness of the different encoding techniques with the raw time series using an unsupervised deep learning approach for anomaly detection in multivariate time series based on a Convolutional Autoencoder (CAE). The encoding techniques investigated here are Gramian Angular Field, Markov Transition Field and Recurrence Plot. For this task, 37 experiments have been realized. It has been demonstrated that anomaly detection with almost all of the encoding techniques performed better than with the use of raw time series. Finally, the proposed approaches have been evaluated and further potential research work in this area has been pointed out.
A dedicated benchmark for contour-based corner detection evaluation

Numerous contour-based corner detection (CBCD) algorithms have been proposed recently, necessitating effective and practical evaluation. Most existing methods evaluate corner detection accuracy through metrics between the testing image and its attacked versions or rely on image-specific ground truth for corner evaluation. These methods use images as input, failing to solely evaluate the corner detection performance but combining it with contour extraction evaluation. Since contour extraction is another important research topic and existing CBCD algorithms almost have no contribution to contour extraction, this intertwining may negatively impact the evaluation results, hindering corner detection development. Furthermore, most evaluation methods directly provide simple statistical scores of evaluation metrics, such as the mean value, which are inadequate to reflect the overall performance distribution. This study presents a novel benchmark that is specifically designed for assessing CBCD methods, which includes two major contributions. Firstly, we design two dedicated datasets, one with the ground-truth corner and the other without them. The dedicated contours instead of images are employed as input to evaluate numerous CBCD methods, eliminating the impact of the extracted contour quality. When the ground-truth corners are unavailable, we employ additional contour attacks, including Gaussian noise, projective, and combined geometry on contours, to simulate real-world complex image processes compared with the attacks in existing evaluation methods. Secondly, we evaluate the performance of twelve CBCD methods using six distinct metrics based on the constructed contour datasets. To gain a deep insight into the overall performance distribution, the sign test method for hypothesis testing is utilized alongside some simple statistical measures for evaluation metric analysis. Experimental results demonstrated that no individual method performs the best across all six evaluation metrics, while different CBCD algorithms have their positive scenarios. The evaluation code will be publicly available at https://github.com/roylin1229/CBCD_eva.
Learning smooth dendrite morphological neurons for pattern classification using linkage trees and evolutionary-based hyperparameter tuning

The current learning approach for smooth dendrite morphological neurons (DMNs) determines dendrite parameters using k-means clustering, which is non-reproducible due to its stochastic nature, risking falling into local minima. To overcome this issue, we introduce a DMN learning approach based on a deterministic hierarchical clustering method, which builds a linkage tree for each class of patterns. In addition, a micro genetic algorithm automatically tunes the cut-off points in the linkage trees hierarchy to create suitable clusters of dendrites. The classification experiments consider 40 real-world datasets. The proposed approach outperforms three DMN models in classification performance and is quite competitive with a hybrid morphological-linear perceptron, multilayer perceptron, random forest, and support vector machine. Therefore, the proposed method is a suitable alternative for pattern classification applications.
ELICE: Embedding Language through Informative Contrastive-Encoder

In recent years, pre-trained transformer encoders have achieved successful milestones in natural language understanding (NLU). However, they still suffer from the representations with insufficient semantics, the sequences with finite lengths, and the need for a considerable number of parameters. These issues lead to difficulties not only in practical deployment under limited computational resources but also in downstream applications. To address such issues, this paper presents ELICE, a method of embedding language through an informative contrastive-encoder. Through the strategies of unordered composition and dropout-based contrastive learning, ELICE can enhance long documents embedding under the desirable model size and producing generic textual representations by condensing multiple unstructured features, such as titles, metadata, and segments of content. Furthermore, the contrastive learning by the NT-Xent objective is experimentally proven to ameliorate the document-level embedding space for a more isotropic one. ELICE was evaluated on four tasks of the CLUE benchmark (Mandarin NLU tasks). With the basic version, the ELICE-base has 12 million parameters and achieves 99% of the performance of the BERT-base (108 million) on average. Furthermore, on the IMDb sentiment classification (English NLU task), ELICE-base (13 million) achieves 95% of the accuracy of XLNet (110 million). Additionally, we deployed ELICE in a practical system for real-time ad-hoc document retrieval regarding legal compliance in the financial industry, and the fine-tuned ELICE outperformed the pre-trained baseline and the whitening representations. These results demonstrate the effectiveness, the robustness, and the flexibility of ELICE in the field of language understanding.
EfficientNet Models for Detection of Anemia Disorder using Palm Images

Anemia is a disorder that causes the body to have insufficient red blood cells or hemoglobin resulting in a decrease in the blood's ability to carry oxygen to the body's tissues. Elevated pulse, weariness, skin pallor, shortness of breath, lightheadedness, and dizziness are a few symptoms that may appear on the onset of Anemia. The underlying diagnosis affects the course of treatment. To treat an iron deficit patient, iron supplements are utilized. Low vitamin levels may be treated with vitamin B supplements. This study explains the use of the Convolutional Neural Networks (CNN) to categorize hemoglobin levels using patient palm photographs. Anemia and non-Anemia are classified according to the hemoglobin levels. ML-assisted illness diagnosis is more accurate, less time-consuming, and requires less additional labour than traditional methods of diagnosis. EfficientNetB0 model with softmax activation function and Adam optimizer achieved the best results, with an accuracy of 97.52% and an F1 score of 97%. The full family of EfficientNet models, from B0 to B7 performed well with both sigmoid and softmax activation functions and Adam optimizer, with results consistently exceeding 92%. In contrast, the same models with linear activation function underperformed.
Blockchain-based Secure Health Records in the Healthcare Industry

Global healthcare systems are going through substantial changes in access, data processing, monitoring, and healthcare. By 2020, it's anticipated that connected and data-capturing equipment will have produced around 2314 exabytes of healthcare data. Cybercriminals work very hard to get access to medical information. Due to this challenge, the market for cybersecurity is predicted to reach $27.1 billion in 2026. To collect data for clinical research, a central repository will be formed with blockchain technology. This article proposes a secure mechanism utilizing Blockchain to guarantee the security of electronic health records (EHR). Databases, the Internet of Things, sensors, and other computing resources are part of the architecture. In comparison to the conventional healthcare system, the security and privacy of EHR will be improved by this framework for security
Vision Based Detection and Analysis of Human Activities

These days, it's not uncommon to see video cameras installed to keep an eye on pedestrians and motorists alike in a variety of public spaces. The proliferation of camera footage necessitates the creation of some means of deducing the nature of the activity captured on film. Due to the widespread availability of acquisition devices like handsets & camcorders, HAR may be used in a wide variety of contexts. The proliferation of electronic gadgets and software has been matched by a revolution in data extraction made possible by breakthroughs in AI. Difficulties such backdrop congestion, partly blockage, variations in size, perspective, illumination, & look, make it difficult to recognize human actions in video frame or still photographs. Multimodal recognition is necessary in several fields, such as CCTV, human computer interaction for characterizing human behavior. This study surveys the most current and cutting-edge findings from the study of how to categorize human actions. This research proposes a taxonomy for studying human activity and explore the benefits and drawbacks of this diverse set of approaches. Disabled people's daily routines, including resting, moving, traveling down & up the stairs, speaking, & laying, have been frequently tracked using cellphones. Human motion detection often makes use of well-established ML & DL techniques include CNN, & LSTM Network.
Innovative Method for Face Emotion Recognition using Hybrid Deep Neural Networks

Automatic facial expression recognition opens up new possibilities for human-computer interaction. To a large extent, face detection is the crux of many systems that aim to recognize people. Face detection is a challenging task because of the wide variety of human faces in terms of color, stance, expression, location, and orientation. It is therefore convenient to distinguish different facial expressions using various modelling techniques. Recognizing human gestures in images is a fascinating area of research because of the variety of expressions that may be conveyed by the position and movement of the eyes, mouth, nose, etc. Any facial expression-recognition neural network can also be used to classify faces, identify faces, and match faces to tokens. The FER system is entirely automated, with separate modules for finding faces, finding faces, extracting features, choosing the best features, and classifying data. After a face has been detected using the AdaBoost algorithm, the frame with the highest intensity of emotion is extracted using the inter-frame mutual information criterion. Several techniques, such as the Gabor filter and GAN-CNN model training are applied to the selected frames to extract distinctive characteristics. According to the findings of this study, a GAN-CNN based method can be used to identify the face expressions. As compared to the CNN and SVM models, this approach achieves higher accuracy (about 99.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ).
Face Detection based Secured ATM System with Two Step Verification using Fisher Face Method

Automated teller machines (ATMs) are utilizedby almost everyone today. Due to the inconvenience of carrying an ATM card everywhere, people might forget to bring their card or PIN code. The ATM card could be broken, whichwould restrict the user from having access to theirmoney. An actual security solution is offered in this proposal. Technologies like Face recognition and Mobile app confirmation to increase the security of accounts and the privacy of users are included. When a user attempts to make a transaction after having their face recorded and stored in the bank's database, the system performs face detection using the A TM’ s camera and performs user face verification. If the invalid user needs to continue the transaction process, the OTP authentication should be made by the valid user in the Mobile application, so that the unauthorized person would continue the transaction.
Explainable Machine Translation for Intelligent E-Learning of Social Studies

Social Studies (SST) fosters civic knowledge of topics such as crime, economic development, and health, allowing students to make informed decisions in a culturally diverse society. Although SST is the subject with the highest performance in the Primary Leaving Examination (PLE), the Ministry of Education reported that pupils who fail do so because they are unable to interpret the questions or respond to them in a timely manner. This is due in part to a linguistic barrier, as children in areas such as rural Central Uganda speak Luganda and only utilize English at school, which affects learning. This prevents them from properly learning to understand the dialogue and exercises provided in English. Machine Translation (MT) is a powerful tool for overcoming language barriers, but the black-box structure of most MT models makes understanding the reasoning behind the translations challenging. Therefore, this paper illustrates the application of Explainable Artificial Intelli-gence(XAI) to support the translation of SST notes from Luganda to English and vice versa. Two transformer models(opus-mt-en-lg and opus-mt-lg-en models) were trained and evaluated using the BiLingual Evaluation Understudy (BLEU) score. The English-Luganda model obtained a 21.43% BLEU score while the Luganda-English model obtained a 16.87% BLEU score, which was higher than the model reference score. This work can be used as an innovative approach to Intelligent E-Learning to overcome barriers to traditional classroom-based language instruction, such as geographical distance and lack of qualified teachers, and can be used to reach a wider audience of children.
Controlling PowerPoint Presentation using Hand Gestures in Real-Time

PowerPoint Presentations in the Microsoft play an important role in today's world for delivering the professional presentations to the audience. The presentation may often interrupted with when ever presenter want to change slides, zoom into the theme. Another common problem is the audience in the last row of the hall may sometimes can not reach the speech of the presenter. The objective of this research is to control the power point using Hand Gestures and as well as convert the presenter's speech signal into text. Python interface is used with the microsoft power point that gives the presenter a lot of flexibility in controlling the slides of power point with hand gestures.
A Comprehensive Analysis of Recent Advances in Deep Learning based Solar Irradiance Forecasting

Powering remote mobile communication towers is essential for establishing reliable communication. If the backup diesel generators in the remote mobile towers are replaced by renewable energy source such as solar PV system, the environmental pollution is reduced. Solar irradiance (SI) forecast is crucial for effective planning and operation of solar energy systems. Deep learning (DL) algorithms have become an optimistic method for predicting SI in recent years owing to their ability for handling intricate non-linear relationships and sizable data sets. This study reviews the most prominent Deep learning models (DLM) for predicting SI, which include Convolution Neural Networks (CNNs), Long Short term Memory (LSTM), Deep Belief Networks (DBNs), Recurrent Neural Networks (RNNs) and Hybrid Models. The study discusses each model's benefits and drawbacks as well as how well it was applied to different SI predicting issues. The review also highlights the importance of carefully selecting the model architecture and hyperparameters, and the training data quantity and quality, for the success of DLM for solar irradiance forecasting. The models are compared based on their accuracy and efficiency in predicting solar irradiance for different time horizons, from few seconds to several hours. The study provides insights into the suitability of different DLM for solar irradiance forecasting and outlining the future paths in the area of DL for solar irradiance predictions.