Reviewerly/exHarmony huggingface dataset:
Reducing Over-smoothing in Graph Neural Networks Using Relational Embeddings

Graph Neural Networks (GNNs) have achieved a lot of success with graph-structured data. However, it is observed that the performance of GNNs does not improve (or even worsen) as the number of layers increases. This effect has known as over-smoothing, which means that the representations of the graph nodes of different classes would become indistinguishable when stacking multiple layers. In this work, we propose a new simple, and efficient method to alleviate the effect of the over-smoothing problem in GNNs by explicitly using relations between node embeddings. Experiments on real-world datasets demonstrate that utilizing node embedding relations makes GNN models such as Graph Attention Network more robust to over-smoothing and achieves better performance with deeper GNNs. Our method can be used in combination with other methods to give the best performance. GNN applications are endless and depend on the user's objective and the type of data that they possess. Solving over-smoothing issues can potentially improve the performance of models on all these tasks.
A Divide-Align-Conquer Strategy for Program Synthesis

A major bottleneck in search-based program synthesis is the exponentially growing search space which makes learning large programs intractable. Humans mitigate this problem by leveraging the compositional nature of the real world: In structured domains, a logical specification can often be decomposed into smaller, complementary solution programs. We show that compositional segmentation can be applied in the programming by examples setting to divide the search for large programs across multiple smaller program synthesis problems. For each example, we search for a decomposition into smaller units which maximizes the reconstruction accuracy in the output under a latent task program. A structural alignment of the constituent parts in the input and output leads to pairwise correspondences used to guide the program synthesis search. In order to align the input/output structures, we make use of the Structure-Mapping Theory (SMT), a formal model of human analogical reasoning which originated in the cognitive sciences. We show that decomposition-driven program synthesis with structural alignment outperforms Inductive Logic Programming (ILP) baselines on string transformation tasks even with minimal knowledge priors. Unlike existing methods, the predictive accuracy of our agent monotonically increases for additional examples and achieves an average time complexity of $\mathcal{O}(m)$ in the number $m$ of partial programs for highly structured domains such as strings. We extend this method to the complex setting of visual reasoning in the Abstraction and Reasoning Corpus (ARC) for which ILP methods were previously infeasible.
An Impartial Transformer for Story Visualization

Story Visualization is an advanced task of computed vision that targets sequential image synthesis, where the generated samples need to be realistic, faithful to their conditioning and sequentially consistent. Our work proposes a novel architectural and training approach: the Impartial Transformer achieves both text-relevant plausible scenes and sequential consistency utilizing as few trainable parameters as possible. This enhancement is even able to handle synthesis of 'hard' samples with occluded objects, achieving improved evaluation metrics comparing to past approaches.
Face Detection System for Smart Security Application

Face detection and recognition can be applied to numerous fields, and it is primarily used for improving security. For security purposes, facial recognition is considered to be the most reliable and accurate technology for identifying a person. Improvements in security systems can be made through this technology without causing any inconvenience. This article discusses several systems, such as smart home security systems, autonomous face detection systems, automotive security-based systems, face detection for surveillance applications, and multi-face recognition systems. Various detection mechanisms include such as Local Binary Pattern Histogram (LBPH), Support Vector Machine (SVM), AdaBoost learning algorithm, Haar Classifier Algorithm, and Principal component Analysis (PCA). A detailed study is carried out with these advanced techniques and their advantages, disadvantages and accuracies are compared and contrasted. According to the investigations, the Haar classifier appears to be superior to other techniques due to its accuracy and features.
Radio Frequency Identification for Drones Using Spectrogram and CNN

Over the past few years, commercial drones have grown in popularity. However, the pervasive use of drones may pose a range of secure risks to sensitive areas such as airports and military bases. Hence, drone detection and identification are critical and necessary for governments and security agencies. This paper proposes a radio frequency identification (RFI) system for drones based on spectrogram and convolutional neural network (CNN). Specifically, spectrogram is used to represent fine-grained time-frequency characteristics of drone signals. Then CNN is designed to infer drone types by identifying their spectrograms. In practice, drones have different operating channels, and any one of them can be selected for signal transmission. It means that the carrier frequencies of their signals are unknown, which may result in misclassifications. To address this problem, we collect drone signals from all potential frequency bands, and demonstrate that carrier frequency offset (CFO) compensation can significantly improve the system performance. Experimental evaluation is performed in real wireless environments involving 6 drones and a Universal Software Radio Peripheral (USRP) X310 platform. Moreover, the proposed spectrogram-based CNN can reach the best performance compared with the IQ-based and FFT-based CNNs. The classification accuracy is beyond 98% for drones operating on arbitrary channels.
Adaptive Quantization based on Ensemble Distillation to Support FL enabled Edge Intelligence

Federated learning (FL) has recently become one of the most acknowledged technologies in promoting the development of intelligent edge networks with the ever-increasing computing capability of user equipment (UE). In traditional FL paradigm, local models are usually required to be homogeneous for aggregation to achieve an accurate global model. Moreover, considerable communication cost and training time may be incurred in resource-constrained edge networks due to a large number of UEs participating in model transmission and the large size of transmitted models. Therefore, it is imperative to develop effective training schemes for heterogeneous FL models, while reducing communication cost as well as training time. In this paper, we propose an adaptive quantization scheme based on ensemble distillation (AQeD) for FL to facilitate personalized quantized model training over heterogeneous local models with different size, structure, and quantization level, etc. Specifically, we design an augmented loss function by jointly considering distillation loss function, quantization values and available wireless resources, where UEs train their local personalized machine learning models and send the quantized models to a server. Based on local quantized models, the server first performs global aggregation for cluster ensembles and then sends the aggregated model of the cluster back to the participating UEs. Numerical results show that our proposed AQeD scheme can significantly reduce communication cost as well as training time in comparison with some known state-of-the-art solutions.
Advanced Pattern-Mining System for Fake News Analysis

Decomposition MapReduce mining for fake news analysis (DMRM-FNA), a novel generic parallel pattern-mining framework, is developed in this article to solve difficulties in social network analysis using big data exploration. The first difficulty faced by existing techniques is the inability to retrieve actionable insights into the structure of fake news data. This can be solved by extracting patterns from fake news and matching them with real news data. The second difficulty is the computational time of existing pattern-mining solutions. This might be solved by combining both decomposition and MapReduce mining techniques to extract relevant patterns from fake news data. The multiobjective <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means algorithm is used to first aggregate fake news data. To generate useful patterns, a parallel pattern-mining method based on MapReduce structures is applied. To evaluate the created DMRM-FNA framework (DFAST) and demonstrate the high performance of sequential pattern-mining challenges on massive social network data, several tests were conducted. Our results show that the proposed DMRM-FNA performs well in terms of memory usage and efficiency. Moreover, DMRM-FNA outperforms existing models in terms of accuracy and feasibility.
Protection from A Quantum Computer Cyber-Attack

Encryption is required to address the ongoing issue of information security within communication networks. In the second part of the 20th century, quantum computing was first utilized to crack encryption protocols with the SHOR algorithm's development. The transformation from traditional to computational has the possibility of jeopardizing the protection of the current transaction system, even though recent improvements in QC functionality have increased the privacy and security, integrity, and accessibility of networks by shielding them from attacks like eavesdropping. This research adopts a comprehensive literature review approach to clarify the effects of quantum computers on information security. In addition, the study provides a summary of current efforts to guard against quantum attacks. Examining all suggested information security and privacy safeguards, this study examines the additional issue that quantum computers would severely damage information security.
An Overview of Copy Move Forgery Detection Approaches

Images have greater expressive power than any other forms of documents. With the Internet, images are widespread in several applications. But the availability of efficient open-source online photo editing tools has made editing these images easy. The fake images look more appealing and original than the real image itself, which makes them indistinguishable and hence difficult to detect. The authenticity of digital images like medical reports, scan images, financial data, crime evidence, legal evidence, etc. is of high importance. Detecting the forgery of images is therefore a major research area. Image forgery is categorized as copy-move forgery, splicing, and retouching. In this work, a review of copy-move forgery is discussed along with the existing research on its detection and localization using both conventional and deep-learning mechanisms. The datasets used and challenges towards improving or developing novel algorithms are also presented.
Analysis of Seismic Signal and Detection of Abnormalities

Seismic signals are ground vibrations used to detect seismic events. However, seismic signal captured from sensors is distorted signal contains noise and makes actual event detection difficult. In most cases, external noise such as manmade or any heavy vehicle vibration always overlaps with the seismic reflections over time. The presence of noise in the seismic signal makes it difficult to determine the magnitude at which the seismic events have occurred. The aim of our study is to process the signals received from seismic sensor and identify it as seismic events signal and non-seismic events signal based on the magnitude. The authors propose a robust noise suppression method using bandpass filter, IIR Wiener filter and event detection using recursive Short-Term Average (STA)/Long Term Average (LTA) and Carl Short Term Average (STA)/Long Term Average (LTA). The proposed study determines reference magnitude to distinguish seismic and non-seismic activity. The projected study is based on the analysis of seismic signal received from single sensor and sensor networks (SN) and determines the magnitude to distinguish seismic and nonseismic events and time of an actual earthquake event. The experimental dataset is a broadband seismic signal from BSVK and CUKG station sensors located at Basavakalyan, Karnataka, and the Central University of Karnataka respectively. The proposed approach helps to extract the information about preseismic event, actual seismic event, post-seismic event activities and identify the abnormal pattern that supports to detect heearth’s activities before the actual seismic event.
<i>NAUTILUS</i>: A CASE STUDY IN HOW A DIGITAL SCORE CAN TRANSFORM CREATIVITY

Abstract This article discusses Nautilus (2022), a composition for solo bass flute created using machine-learning techniques and a Unity game engine. We consider the approaches we adopted and how they enhanced creativity and musicianship for those involved. We reflect on Unity's potential as a novel and flexible driver for the creation of a musical score in which traditional elements of compositional design are presented to a performer as a co-creator for interpretation and communication inside the act of musicking. Through this we offer insights into performer agency and how a performer decodes media, sound, images and AI through their instrument, personal skills and musical aesthetic. We describe how the notion of a music score was re-conceptualised, transforming our understanding of the activities of composition, collaboration and performance.
A Review of Blockchain Technology Based Techniques to Preserve Privacy and to Secure for Electronic Health Records

Research has been done to broaden the block chain’s use cases outside of finance since Bitcoin introduced it. One sector where block chain is anticipated to have a big influence is healthcare. Researchers and practitioners in health informatics constantly struggle to keep up with the advancement of this field's new but quickly expanding body of research. This paper provides a thorough analysis of recent studies looking into the application of block chain based technology within the healthcare sector. Electronic health records (EHRs) are becoming a crucial tool for health care practitioners in achieving these objectives and providing high-quality treatment. Technology and regulatory barriers, such as concerns about results and privacy issues, make it difficult to use these technologies. Despite the fact that a variety of efforts have been introduced to focus on the specific privacy and security needs of future applications with functional parameters, there is still a need for research into the application, security and privacy complexities, and requirements of block chain based healthcare applications, as well as possible security threats and countermeasures. The primary objective of this article is to determine how to safeguard electronic health records (EHRs) using block chain technology in healthcare applications. It discusses contemporary HyperLedgerfabrics techniques, Interplanar file storage systems with block chain capabilities, privacy preservation techniques for EHRs, and recommender systems.
BENDLET TRANSFORM BASED OBJECT DETECTION SYSTEM USING PROXIMITY LEARNING APPROACH

This study presents a Bendlet Transform-based Object Detection (BTOD) system that recognizes an object in the image.Finding a specific object in images or videos is the goal of the field of object recognition.Though humans are able to identify a large number of objects, it is very difficult for computer vision systems in general.The appearance of the objects may change depending on the perspective, the size or scale, or translation and rotation.This work extracts Bendlet transform-based features from the images at different levels, and then the discriminant features are selected by employing genetic algorithms.The performance of the BTOD system is analyzed with different nearest neighbours for classifying objects in the Columbia Object Image Library (COIL-100) in terms of classification accuracy.It is observed from the results that the BTOD system with a one-nearest neighbour provides better performance than the two-nearest neighbour classifier.The former classifier gives 99.47% accuracy, whereas the later classifier gives 99.19%.
Natural Backdoor Attacks on Speech Recognition Models

With the rapid development of deep learning, its vulnerability has gradually emerged in recent years. This work focuses on backdoor attacks on speech recognition systems. We adopt sounds that are ordinary in nature or in our daily life as triggers for natural backdoor attacks. We conduct experiments on two datasets and three models to validate the performance of natural backdoor attacks and explore the effects of poisoning rate, trigger duration and blend ratio on the performance of natural backdoor attacks. Our results show that natural backdoor attacks have a high attack success rate without compromising model performance on benign samples, even with short or low-amplitude triggers. It requires only 5% of poisoned samples to achieve a near 100% attack success rate. In addition, the backdoor will be automatically activated by the corresponding sound in nature, which is not easy to be detected and will bring severer harm.
Ethical Hacking: White Hat Hackers

Nowadays the demand for Computers and Smart phones has increased so much that people cannot complete their work except these two things. Whether we do our own business or work in a company or bank, computers are used everywhere. To run a company or to run a business, it is necessary to use a computer because with its help we do a lot of work and calculations in a few minutes. Similarly, to how minor and large obstacles must be overcome in order to complete any assignment, many problems must be overcome when dealing with computers. For decades, hacking has been a huge part of the computing world. It is a large field that encompasses a wide range of issues. If we first determine when this hacking occurred, we will learn that it was first utilized in 1960 by MIT, and that at the same time, the term "hacker" was coined and later became well-known.
StyleID: Identity Disentanglement for Anonymizing Faces

Privacy of machine learning models is one of the remaining challenges that hinder the broad adoption of Artificial Intelligent (AI). This paper considers this problem in the context of image datasets containing faces. Anonymization of such datasets is becoming increasingly important due to their central role in the training of autonomous cars, for example, and the vast amount of data generated by surveillance systems. While most prior work de-identifies facial images by modifying identity features in pixel space, we instead project the image onto the latent space of a Generative Adversarial Network (GAN) model, find the features that provide the biggest identity disentanglement, and then manipulate these features in latent space, pixel space, or both. The main contribution of the paper is the design of a feature-preserving anonymization framework, StyleID, which protects the individuals’ identity, while preserving as many characteristics of the original faces in the image dataset as possible. As part of the contribution, we present a novel disentanglement metric, three complementing disentanglement methods, and new insights into identity disentanglement. StyleID provides tunable privacy, has low computational complexity, and is shown to outperform current state-of-the-art solutions.
SoK: Secure E-Voting with Everlasting Privacy

Vote privacy is a fundamental right, which needs to be protected not only during an election, or for a limited time afterwards, but for the foreseeable future. Numerous electronic voting (e-voting) protocols have been proposed to address this challenge, striving for everlasting privacy. This property guarantees that even computationally unbounded adversaries cannot break privacy of past elections. The broad interest in secure e-voting with everlasting privacy has spawned a large variety of protocols over the last three decades. These protocols differ in many aspects, in particular the precise security properties they aim for, the threat scenarios they consider, and the privacy-preserving techniques they employ. Unfortunately, these differences are often opaque, making analysis and comparison cumbersome. In order to overcome this non-transparent state of affairs, we systematically analyze all e-voting protocols designed to provide everlasting privacy. First, we illustrate the relations and dependencies between all these different protocols. Next, we analyze in depth which protocols do provide secure and efficient approaches to e-voting with everlasting privacy under realistic assumptions, and which ones do not. Eventually, based on our extensive and detailed treatment, we identify which research problems in this field have already been solved, and which ones are still open. Altogether, our work offers a well - founded reference point for conducting research on secure e - voting with everlasting privacy as well as for future - proofing privacy in real - world electronic elections.
Vehicle Control on an Uninstrumented Surface With an Off-the-Shelf Smartwatch

Advances in wearable and machine learning technologies have caused smartwatches to emerge as promising input devices across disciplines. In this study, we propose a user interface, designed for an automotive environment for recognizing input commands. As the use of extra elements to extend the scope of interactions may visually distract users during driving, we focus on using body parts as an interaction space. Specifically, we utilized the lap as an interaction hyperplane due to its near-flat surface, which provides users interaction analogous to a touchpad. To substantiate the proposed approach, we collected motion signals using an off-the-shelf smartwatch and trained them in supervised learning settings. As target gestures, we defined ten different hand gestures, such as tapping and folding/spreading all fingers on the lap. The experimental results exhibited a test accuracy of 94%, thereby validating the feasibility of the proposed approach.
Adaptive model predictive control of mobile robot with local path refitting

Path planning and tracking control are important modules of mobile robots, which have outstanding advantages in improving the flexibility of mobile robots. However, in the existing applications, these two modules are usually processed separately, which makes the obstacle avoidance process of the mobile robot uncontrollable and reduces the adaptability of the application scenario. In order to realize collision-free high-precision trajectory tracking control of mobile robots in dynamic scenes, this paper proposes an adaptive MPC control method based on curvature constraints, which can improve the safety of operation and reduce the chattering caused by unreasonable paths in the process of operation. Firstly, according to the kinematic constraints of general mobile robots, the constraint model of mobile robot path planning is constructed. At the same time, the adaptive parameter adjustment rules are adopted to realize the balance between the calculation efficiency and the control cycle. Finally, a smooth safe path is obtained by re-fitting the planned path and reprocessing the nodes with excessive curvature. Compared with traditional methods, the proposed method has better advantages.