Reviewerly/exHarmony huggingface dataset:
CASDC: A Cryptographically Secure Data System Based on Two Private Key Images

Colored digital images are one of the most important types of digital data to be used in many vital applications, which require a safe way to protect them from hacking operations and the danger of intruders and data thieves. This paper presents an effective and safe method for storing digital colored images (CASDC). A high level of protection is provided through a complex secret key agreed upon between the sender and the receiver. The secret key consists of nine decimal digits (and can be increased as needed). These digits are processed to extract three values for each color of the three color channels. A left rotation process is performed for the value of each color to produce three new values, where an exclusion process is performed between them to obtain the encrypted value for the color. CASDC is evaluated against a wide range of images to calculate its throughput to show the extent to which this method fulfills encryption and decryption requirements. The Mean Square Error (MSE) values, Peak Signal to Noise Ratio (PSNR), and Correlation Coefficient for the three primary channels of the RGB coloring system were analyzed. The practical results of the proposed method are compared with other standard methods such as Data Encryption Standard (DES), Tripple-DES (3DES), Advanced Encryption Standard (AES), and Blow Fish (BF). According to the obtained results, CASDC outperforms all standard methods in terms of efficiency by reducing the time of encryption and decryption and increasing the throughput of the corresponding process. Besides, CASDC is robust against breaks, as the attempts to break the private key will require hundreds of years in the best case.
Dynamic and bibliometric analysis of terms identifying the combating financial and cyber fraud system

The main purpose of this study is to conduct a dynamic and bibliometric analysis of the main terms that identify the system for combating financial and fraud to identify trends in the formation of social and scientific thought. The review of the scientific literature indicates an increase in the number of scientific publications over the past ten years. It was revealed that the most cited works cover the problems associated with cyber threats in everyday life, among which are botnets, cyber bullying, as well as financial fraud implemented through cryptocurrencies, smart contracts, and the black market on the Internet. Cloud forensics, technical and intellectual analysis are proposed as countermeasures. The research tools were a dynamic analysis of global network user requests, implemented using Google Trends, and a bibliometric analysis of scientific publications by the world’s leading scientists, performed using the VOSviewer analytical package. The search terms “Fraud”, “Finance Fraud”, “Cyber Fraud”, “Finance Cyber Fraud”, “Money Laundering”, “Anti-Money Laundering” and “Anti-Fraud” for the period from 08/07/2017 to 08/07/2022. For bibliometric analysis, two datasets with a length of 2,000 observations were formed based on queries in the Scopus database regarding the terms “Cyber Crime” and “Anti-money Laundering”. The results of the dynamic analysis revealed a decrease in the level of interest in fraud and financial fraud since the beginning of 2021, while the trend of cyber fraud is increasing. This led to the conclusion that there was an impact of the pandemic, which caused an increase in cybercrime. The results of the analysis of requests for “Fraud” and “Finance Fraud” by geographical distribution showed that they interested users belonging to countries with a significant difference in economic development. That is, representatives of poor countries are potential cyber fraudsters, and developed countries are potential victims of fraud. Conducting a bibliometric analysis made it possible to obtain clusters of promising areas of scientific research in the field of cybercrimes, among which mathematical and network tools for combating them, general concepts, digitalization and digital forensics, cyber protection, data protection, authentication and encryption of data, etc. are highlighted. At the same time, the focus of research is shifting towards methods of countering cybercrimes. Promising directions in the field of Money Laundering are mathematical methods and information technologies, cryptocurrencies and blockchains, corruption, financial terrorism, etc. The greatest potential belongs to money laundering through cryptocurrencies and blockchains. The lessons learned can be useful for improving the strategy of combating financial and cybercrimes and forming an analytical basis for the scientific community and practitioners.
Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness

Neural architectures based on attention such as vision transformers are revolutionizing image recognition. Their main benefit is that attention allows reasoning about all parts of a scene jointly. In this paper, we show how the global reasoning of (scaled) dot-product attention can be the source of a major vulnerability when confronted with adversarial patch attacks. We provide a theoretical understanding of this vulnerability and relate it to an adversary's ability to misdirect the attention of all queries to a single key token under the control of the adversarial patch. We propose novel adversarial objectives for crafting adversarial patches which target this vulnerability explicitly. We show the effectiveness of the proposed patch attacks on popular image classification (ViTs and DeiTs) and object detection models (DETR). We find that adversarial patches occupying 0.5% of the input can lead to robust accuracies as low as 0% for ViT on ImageNet, and reduce the mAP of DETR on MS COCO to less than 3%.
WDBench: A Wikidata Graph Query Benchmark

We propose WDBench: a query benchmark for knowledge graphs based on Wikidata, featuring real-world queries extracted from the public query logs of the Wikidata SPARQL endpoint. While a number of benchmarks for graph databases (including SPARQL engines) have been proposed in recent years, few are based on real-world data, even fewer use real-world queries, and fewer still allow for comparing SPARQL engines with (non-SPARQL) graph databases. The raw Wikidata query log contains millions of diverse queries, where it would be prohibitively costly to run all such queries, and difficult to draw conclusions given the mix of features that these queries use. WDBench thus focuses on three main query features that are common to SPARQL and graph databases: (i) basic graph patterns, (ii) optional graph patterns, (iii) path patterns, and (iv) navigational graph patterns. We extract queries from the Wikidata logs specifically to test these patterns, clean them of non-standard features, remove duplicates, classify them into different structural subsets, and present them in two different syntaxes. Using this benchmark, we present and compare performance results for evaluating queries using Blazegraph, Jena/Fuseki, Virtuoso and Neo4j.
Evaluating the effectiveness of local explanation methods on source code-based defect prediction models

Interpretation has been considered as one of key factors for applying defect prediction in practice. As one way for interpretation, local explanation methods has been widely used for certain predictions on datasets of traditional features. There are also attempts to use local explanation methods on source code-based defect prediction models, but unfortunately, it will get poor results. Since it is unclear how effective those local explanation methods are, we evaluate such methods with automatic metrics which focus on local faithfulness and explanation precision. Based on the results of experiments, we find that the effectiveness of local explanation methods depends on the adopted defect prediction models. They are effective on token frequency-based models, while they may not be effective enough to explain all predictions of deep learning-based models. Besides, we also find that the hyperparameter of local explanation methods should be carefully optimized to get more precise and meaningful explanation.
Maximizing fair content spread via edge suggestion in social networks

Content spread inequity is a potential unfairness issue in online social networks, disparately impacting minority groups. In this paper, we view friendship suggestion, a common feature in social network platforms, as an opportunity to achieve an equitable spread of content. In particular, we propose to suggest a subset of potential edges (currently not existing in the network but likely to be accepted) that maximizes content spread while achieving fairness. Instead of re-engineering the existing systems, our proposal builds a fairness wrapper on top of the existing friendship suggestion components. We prove the problem is NP-hard and inapproximable in polynomial time unless P = NP. Therefore, allowing relaxation of the fairness constraint, we propose an algorithm based on LP-relaxation and randomized rounding with fixed approximation ratios on fairness and content spread. We provide multiple optimizations, further improving the performance of our algorithm in practice. Besides, we propose a scalable algorithm that dynamically adds subsets of nodes, chosen via iterative sampling, and solves smaller problems corresponding to these nodes. Besides theoretical analysis, we conduct comprehensive experiments on real and synthetic data sets. Across different settings, our algorithms found solutions with near-zero unfairness while significantly increasing the content spread. Our scalable algorithm could process a graph with half a million nodes on a single machine, reducing the unfairness to around 0.0004 while lifting content spread by 43%.
CNN-Attention-DNN Design for CFO Estimation of Non-Pilot-Assisted OFDM System

In this letter, we propose a novel attention mechanism inspired convolutional neural network (CNN)-deep neural network (DNN) called CAD model architecture for carrier frequency offset (CFO) estimation in orthogonal frequency division multiplexing (OFDM) systems. The hyperparameters used in the proposed CAD model are optimized using the Bayesian approach. The proposed CFO estimation scheme is robust to small symbol timing offset errors and does not require apriori knowledge of modulation format, channel, and transmitted OFDM signal parameters. Simulations performed over the realistic frequency-selective channel models with and without power amplifier non-linearity indicate that the proposed CAD architecture outperforms the conventional statistical-based and recurrent neural network (RNN)-based models. Lastly, the proposed CAD model is validated on the radio frequency testbed.
Handwritten Signature Verification System using Deep Learning

Every person has a unique signature, which is primarily used for identity documents and the authentication of significant documents or legal documentation. Static and dynamic signature verification are the two types of signature verification. The basic parameters that must be addressed when creating a signature verification system are user acceptance, the level of security necessary, accuracy, cost, and implementation. The objective of Signature verification used to determine whether a signature is real or a fake. This has proven to be a difficult endeavor, particularly in the offline scenario, which employs images of scanned signatures and does not have access to dynamic information about the signing process. The offline handwritten signature is one of the most essential biometrics used in banking systems, administrative, and financial applications. The study is to evaluate the performance of a few well-known deep convolutional neural networks as feature extractors in Handwritten Signature Verification (HSV) using transfer learning with activation function, as well as to review the available convolutional neural network signature verification methods. This is achieved by three pretrained models, VGG16, VGG19, and ResNet50, which are most often used generic models in computer vision tasks. Obtained experimental results, by comparing three models with SigComp2009 datasets and adding different parameters to each model. And conclude that VGG19 is best suitable for dataset, which has 97.83 percent accuracy than another model. These findings will help academics construct more effective Deep Learning-based signature verification methods.
Performance Evaluation of Ethereum and Hyperledger Fabric Blockchain Platforms

The evolution of blockchain has revolutionized the field of security and decentralization. Blockchain enables secure information storage and transfer because to its strong security features and the trust it offers. The users can have authenticity and trust because of the immutability. The two popular open-source blockchain platforms are Ethereum and Hyperledger Fabric. Ethereum is a public blockchain platform that enables secure peer-to-peer applications like crypto-currency and smart contracts. Ethereum has grown a reputation for dependability as a result of big developers taking an interest in it and contributing over time. Hyperledger is a permissioned blockchain platform used by enterprises to mainly induce the power of blockchain in creating immutable ledgers and keep transactions discrete among business entities. The effectiveness and scalability of both private and public networks must be evaluated and assessed. In this paper, we compare and analyse the performance of the hyperledger and ethereum networks. To further fine-tune the configuration parameters and comprehend the performance variances that occur, we developed real-time simulations and built blockchain networks on the Ubuntu OS. By changing the blockchain parameters, it was possible to measure the time it took to group the transactions into a block, the size of the overall block that comprises the bundled transactions, and the latency to construct the blocks. The findings show that when compared to public blockchain networks, private blockchain networks are more robust in performance, have lower latency, and charge no transaction fees
JIRA: Joint Incentive Design and Resource Allocation for Edge-Based Real-Time Video Streaming Systems

Edge computing has been introduced as a promising technology for real-time video streaming systems. However, due to the lack of automatic incentives and the limitation of resources, traditional edge computing performs poorly in nowadays scenarios. To handle these two challenges, we propose a framework of <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J</u> oint <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</u> ncentive design and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> esource <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> llocation (JIRA) for edge-based real-time video streaming systems. Technically, to ensure the trust and automatic distribution of incentives, we develop a novel smart contract based incentive mechanism and implement a prototype. Meanwhile, we propose an efficient online algorithm, i.e., JIRA, which dynamically adjusts compression ratio, offloading decision, and resource allocation to achieve performance optimization for video streaming under long-term latency and resource constraints. Specifically, JIRA is based on Lyapunov optimization, which decomposes the challenging long-term decision problem into a series of real-time optimization problems. Then we propose a multi-cut Generalized Benders Decomposition based algorithm (MGA) to tackle the non-convexity of the decomposed problem. Through rigorous theoretical analysis, we prove the performance bound of JIRA. Extensive simulations demonstrate that the proposed schemes can achieve an efficient trade-off between accuracy performance and energy consumption.
Adaptive Image Transformations for Transfer-Based Adversarial Attack

Adversarial attacks provide a good way to study the robustness of deep learning models. One category of methods in transfer-based black-box attack utilizes several image transformation operations to improve the transferability of adversarial examples, which is effective, but fails to take the specific characteristic of the input image into consideration. In this work, we propose a novel architecture, called Adaptive Image Transformation Learner (AITL), which incorporates different image transformation operations into a unified framework to further improve the transferability of adversarial examples. Unlike the fixed combinational transformations used in existing works, our elaborately designed transformation learner adaptively selects the most effective combination of image transformations specific to the input image. Extensive experiments on ImageNet demonstrate that our method significantly improves the attack success rates on both normally trained models and defense models under various settings.
Fast-MoCo: Boost Momentum-Based Contrastive Learning with Combinatorial Patches

Contrastive-based self-supervised learning methods achieved great success in recent years. However, self-supervision requires extremely long training epochs (e.g., 800 epochs for MoCo v3) to achieve promising results, which is unacceptable for the general academic community and hinders the development of this topic. This work revisits the momentum-based contrastive learning frameworks and identifies the inefficiency in which two augmented views generate only one positive pair. We propose Fast-MoCo - a novel framework that utilizes combinatorial patches to construct multiple positive pairs from two augmented views, which provides abundant supervision signals that bring significant acceleration with neglectable extra computational cost. Fast-MoCo trained with 100 epochs achieves $$\mathbf {73.5\%}$$ linear evaluation accuracy, similar to MoCo v3 (ResNet-50 backbone) trained with 800 epochs. Extra training (200 epochs) further improves the result to $$\mathbf {75.1\%}$$ , which is on par with state-of-the-art methods. Experiments on several downstream tasks also confirm the effectiveness of Fast-MoCo. (Code and pretrained models are available at https://github.com/orashi/Fast-MoCo .)
Fusionize: Improving Serverless Application Performance through Feedback-Driven Function Fusion

Serverless computing increases developer productivity by removing operational concerns such as managing hardware or software runtimes. Developers, however, still need to partition their application into functions, which can be error-prone and adds complexity: Using a small function size where only the smallest logical unit of an application is inside a function maximizes flexibility and reusability. Yet, having small functions leads to invocation overheads, additional cold starts, and may increase cost due to double billing during synchronous invocations. In this paper we present Fusionize, a framework that removes these concerns from developers by automatically fusing the application code into a multi-function orchestration with varying function size. Developers only need to write the application code following a lightweight programming model and do not need to worry how the application is turned into functions. Our framework automatically fuses different parts of the application into functions and manages their interactions. Leveraging monitoring data, the framework optimizes the distribution of application parts to functions to optimize deployment goals such as end-to-end latency and cost. Using two example applications, we show that Fusionizecan automatically and iteratively improve the deployment artifacts of the application.
Model-Free Unsupervised Anomaly Detection of a General Robotic System Using a Stacked LSTM and Its Application to a Fixed-Wing Unmanned Aerial Vehicle

With the growing application of various robots in real life, the need for an automatic anomaly detection system for robots is necessary for safety. In this paper, we develop an anomaly detection method using a stacked LSTM that can be applied to any robot controlled by a feedback control. Our method does not need installation of additional sensors. Our method is model-free and unsupervised because it does not require the analytical model of the system and the training data does not require faulty operation conditions. We validate our method on real fixed-wing unmanned aerial vehicle flight data containing control surface failure scenarios. We demonstrate the superiority of the proposed algorithm over existing anomaly detection methods in the literature. Our code is available at https://github.com/superhumangod/Model-free-unsupervised-anomaly-detection.
A Comprehensive Machine Learning Based Pipeline for an Accurate Early Prediction of Sepsis in ICU

Sepsis is a lethal infection-related illness that has an extremely high fatality rate, especially among intensive care unit patients. Early and precise recognition of sepsis is critical as delayed treatment increases the mortality rate dramatically. System inflammatory response syndrome, quick sequential organ failure assessment, and modified early warning score are the traditional clinical score systems in practice to detect sepsis. But the scoring systems fail in the early prediction of sepsis, a stage in which if a patient is treated immediately, the mortality rate will reduce significantly. The proposed classifier can accurately predict sepsis up to six hours before the disease is clinically diagnosed. The patient’s electronic medical records, demographics, and vital signs are used to achieve this. The study uses data set adaptive data preprocessing strategies. The proposed method adds value to existing literature by introducing a novel outlier-based mean-median data imputation technique that enhances the prediction’s overall accuracy. The primary factors that influence the classifier’s predictions have been outlined, making the model easier to understand for medical professionals. For the classification of patients as sepsis positive or negative, four algorithms were investigated: Random Forest, Logistic Regression, Gradient Boosting, and Decision Tree. Of all the prediction algorithms, Random Forest gives the best results with an accuracy of 99.01%, F1-score of 99%, and an area under the receiver operator characteristic curve of 99.99%. Even for a 24-hour early prediction of sepsis, the random forest method is proven to provide greater prediction accuracy while logistic regression provides the least prediction accuracy. We attribute this to the fact that, unlike regression models, random forests do not require that the model have a linear relationship between the dependent and independent variables. The evaluation measures produced are useful and can be tremendously valuable in predicting sepsis in a timely and accurate manner.