Reviewerly/exHarmony huggingface dataset:
FOCUS: Fatigue-Reducing IOCs Through Prioritization and Scoring

“In recent years, the field of Cyber Threat Intelligence (CTI) has undergone rapid development, primarily due to the emergence of various organizations and platforms dedicated to threat intelligence. Through automated analysis and real-time information sharing, companies can now more effectively identify and manage the risks associated with cyber threats and attacks. One of the most commonly used types of threat data is Indicators of Compromise (IOCs), which play a crucial role in early cyber intrusion detection and prevention. However, the quality of the collected data is often limited, and the sheer volume of real-time data can overwhelm security analysts, leading to IOC data fatigue. To address this challenge, a new scoring mechanism called FOCUS has been proposed. FOCUS utilizes various attributes and relationships provided by the VirusTotal online scanning engine analysis results to prioritize a set of IOCs. By concentrating on properly analyzed and ranked IOCs from a vast dataset, FOCUS can assist organizations in allocating resources and efforts more effectively. Ultimately, FOCUS can enhance the accuracy and efficiency of IOC analysis, strengthening an organization's ability to respond to cybersecurity threats.
A Secure and Scalable Peer-to-Peer Federated Learning Approach for Handling Veracity in Big Data

A massive source of terabytes of data is produced consistently from advanced data frameworks and innovation such as distributed/cloud computing, mobile network devices, and the Internet of Things. However, maintaining data veracity in handling such vast and diverse datasets presents significant challenges. In response to these challenges, Federated Learning (FL) emerges as a promising candidate to address the veracity issues in big data analytics. The distributed approach of FL not only ensures data privacy but also mitigates the risk of data breaches and unauthorized access, enhancing data security and veracity. In this paper, we present a Secure and Scalable Peer-to-Peer FL (P2P-FL), a novel approach that tackles the challenges of maintaining data veracity while simultaneously enhancing security and scalability through the utilization of multi-level encryption-decryption processes. This approach eliminates the need for a central server and allows federates to communicate directly, optimizing the efficiency of FL. Differential privacy techniques further ensure individual data privacy. Overall, our approach advances the effectiveness and security of machine learning models in privacy-sensitive environments. In our study, we conducted a comprehensive analysis traditional FL and Peer-to-Peer P2P-FL. We evaluated the performance based on accuracy, loss, and F1 score metrics. The results showcased the effectiveness of our Secure and Scalable Peer-to-Peer FL approach.
Energy Aware Federated Learning with Application of Activity Recognition

Human activity recognition (HAR) based on smart devices is gaining increasing attention from the pervasive computing research community due to its wide application in smart healthcare. Modern deep learning models for recognizing human activities rely heavily on sensor data to attain great accuracy. However, leveraging data gathered from smart devices to train such models in a data center results in significant energy consumption and potential privacy violations. By merging numerous local models that are trained on data coming from various clients, federated learning can be used to address the aforementioned problems. By creating a federated learning-based convolutional neural network (CNN) model, which is trained on both public and real-world datasets, we analyze federated learning (FL) to train a human activity identification classifier and compare its performance to centralized learning. The global model achieves accuracy comparable to centralized learning when trained using federated learning on skewed datasets. Additionally, we discover that the selection of clients is a significant problem and suggest a federated learning algorithm that selects the maximum number of clients to increase the convergence rate and model correctness of FL.
Optimization and Performance Analysis of Shor's Algorithm in Qiskit

Shor's algorithm is widely renowned in the field of quantum computing due to its potential to efficiently break RSA encryption in polynomial time. In this paper, we optimized an end-to-end library-based implementation of Shor's algorithm using the IBM Qiskit quantum library and derived a speed-of-light (i.e., theoretical peak) performance model that calculates the minimum runtime required for executing Shor's algorithm with input size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> on a certain machine by counting the total operations as a function of different numbers of gates. We evaluated our model by running Shor's algorithm on CPUs and GPUs, and simulated the factorization of a number up to 4,757. Comparing the speed-of-light runtime to our real-world measurement, we are able to quantify the margin for future quantum library improvements.
A Design Method for Golomb Coding in Layered Scrambled JPEG XS Scheme

In this paper, we propose two-layer lossless coding compatible with JPEG XS that can process in the scrambled domain. For encoding scrambled residuals of the enhancement layer, we introduce a design method for Golomb coding. Efficient Golomb coding can be achieved by setting a coding parameter on the basis of a base-layer JPEG XS parameter. Lightweight and efficient encoding can be realized without additional pro-cessing for coding parameter optimization. We demonstrated its performance via simulation for 8K ultra-high-definition images.
Sentence Level Language Identification in Code-mix Gujarati Language with Transformers

Code-mix is gaining popularity due to its widespread usage on social media platforms. Even bilingual users converse in code-mix in their day-to-day life due to the popularity of the English language mix with the user’s mother tongue. Codemix phenomena come under NLP(Natural Language Processing), again a subpart of Artificial Intelligence. Code-mix language means grammatically following one language but has used another language’s words. Identification of code-mix is challenging due to using two languages in one sentence. Our motive is to identify the code-mix language at the sentence level. Already machine learning algorithms have been applied to identify sentence-level code-mix languages. In this article, we have applied Deep learning and a transformer-based framework for the sentence-level language identification task. We have used BERT in the Gujarati code-mix data set and received 96.2%, which is a more accurate result than the machine learning and Deep learning algorithms have achieved.
Improvisation in SHA Algorithm

SHA-2 is a secure hash function and a successor to the SHA-l algorithm. It is designed to be more secure and resistant to attack. SHA-2 is resistant to a variety of attacks, including collisions, preimages, and second-preimages. The security of SHA-2 is based on its design and implementation. The design of SHA-2 includes a large message block size, large number of rounds, and a complex round function design. These features make it very difficult for an attacker to find collisions or preimages for SHA-2. The implementation of SHA-2 must be careful to avoid side-channel attacks, which are a type of attack that exploits information that is leaked during the execution of a cryptographic algorithm. The security of SHA-2 has been evaluated by a number of independent organizations. These organizations have concluded that SHA-2 is a secure hash function. However, it is important to note that no hash function is completely secure. As technology advances, it is possible that new attacks will be developed against SHA-2. This paper analyses the advantages and disadvantages of SHA-2 algorithm and proposes some mechanisms to improve the security further.
Generating High-Performance Number Theoretic Transform Implementations for Vector Architectures

Fully homomorphic encryption (FHE) offers the ability to perform computations directly on encrypted data by encoding numerical vectors onto mathematical structures. However, the adoption of FHE is hindered by substantial overheads that make it impractical for many applications. Number theoretic transforms (NTTs) are a key optimization technique for FHE by accelerating vector convolutions. Towards practical usage of FHE, we propose to use SPIRAL, a code generator renowned for generating efficient linear transform implementations, to generate high-performance NTT on vector architectures. We identify suitable NTT algorithms and translate the dataflow graphs of those algorithms into SPIRAL's internal mathematical representations. We then implement the entire workflow required for generating efficient vectorized NTT code. In this work, we target the Ring Processing Unit (RPU), a multitile long vector accelerator designed for FHE computations. On average, the SPIRAL-generated NTT kernel achieves a 1.7x speedup over naive implementations on RPU, showcasing the effectiveness of our approach towards maximizing performance for NTT computations on vector architectures.
An Analysis of Energy Requirement for Computer Vision Algorithms

The energy requirements of neural network learning are growing at a rapid rate. Increased energy demands have caused a global need to seek ways to improve energy efficiency of neural network learning. This paper aims to establish a baseline on how adjusting basic parameters can affect energy consumption in neural network learning on Computer Vision tasks. In this article, we catalog the effects of various adjustments, from simple batch size adjustments to more complicated hardware settings (e.g., power capping). Based on our characterizations, we have found numerous avenues to adjust computer vision algorithm energy expenditure. For example, switching from a single precision model to mixed precision training can result in energy reductions of nearly 40 %. dditionally, power capping the Graphical Processing Unit (GPU) can reduce energy cost by an additional 10%.
Fake Profile Detection in Online Social Networks Using Machine Learning Models

A new era of networking has emerged due to the easy access of social networks. People can share a lot of information using online social networks. Some of the widely used social networks are Twitter, Facebook, Google+, Instagram, Pinterest and LinkedIn. The majority of social network users are ignorant of the security threats that OSNs pose. There are several issues with modern online social networks, such as fraudulent profiles and online impersonation. False profiles spread inaccurate information about a certain individual or make fraudulent attempts with bad intentions. Fake profiles can be identified using machine learning techniques. Various machine learning models, such as Support Vector Machine, Decision Tree, Neural Networks, Random Forest, Naive Bayes, Logistic Regression, and K-nearest Neighbor can be employed for the detection of fake profiles. The different machine learning techniques for identifying fake profiles are compared in this paper.
Toward Understanding Psychiatric and Cognitive Characteristics: A Deep Generative Model for Extracting Shared and Private Representations and Its Evaluation with Synthetic Multimodal Data

Latent psychiatric and cognitive characteristics of individuals may be present in multiple data sources, such as subjective self-report measures and objective behavioral data. Therefore, integrative learning of multimodal data from individuals has the potential to contribute to understanding these characteristics. In this study, we propose a novel generative model and evaluate it with synthetically generated multimodal data. In multimodal data, each modality is considered to have low-dimensional latent representations, but these representations are not always fully shared with another modality. Therefore, we assume that latent representations of multimodal data consist of shared representations across all the modalities and private representations unique to each modality. Under this assumption, we develop a deep generative model that can learn to extract these different latent representations from both non-time-series and time-series data in an end-to-end manner. To evaluate our proposed model, we conducted simulation experiments with a synthetic multimodal dataset containing shared and private information. Experimental results on the inference of shared and private latent representations, as well as on self-modal and cross-modal reconstructions, demonstrate the potential of our proposed model to better understand latent psychiatric and cognitive characteristics in multimodal data.
Robust Fine-Tuning of Vision-Language Models for Domain Generalization

Transfer learning enables the sharing of common knowledge among models for a variety of downstream tasks, but traditional methods suffer in limited training data settings and produce narrow models incapable of effectively generalizing under distribution shifts. Foundation models have recently demonstrated impressive zero-shot inference capabilities and robustness under distribution shifts. However, zero-shot evaluation for these models has been predominantly confined to benchmarks with simple distribution shifts, limiting our understanding of their effectiveness under the more realistic shifts found in practice. Moreover, common fine-tuning methods for these models have yet to be evaluated against vision models in few-shot scenarios where training data is limited. To address these gaps, we present a new recipe for few-shot fine-tuning of the popular vision-language foundation model CLIP and evaluate its performance on challenging benchmark datasets with realistic distribution shifts from the WILDS collection. Our experimentation demonstrates that, while zero-shot CLIP fails to match performance of trained vision models on more complex benchmarks, few-shot CLIP fine-tuning outperforms its vision-only counterparts in terms of in-distribution and out-of-distribution accuracy at all levels of training data availability. This provides a strong incentive for adoption of foundation models within few-shot learning applications operating with real-world data.
File Fragment Analysis Using Machine Learning

Files on a storage device, irrespective of the file system, when deleted, are unallocated and marked for garbage collection by the system. Once unallocated, the storage locations they once occupied are now available for use by newer files created in the system. This often leads to remnants of the deleted files persisting on the storage medium. Unless forensically erased by rewriting random bits on the storage medium, one can confidently expect to find fragments of deleted files on any modern digital storage medium. Often, suspects also delete files in a desperate attempt to destroy evidence. In these instances, it becomes imperative that a forensic examiner can identify any remnant file fragments and, with some luck, assemble them into a composite whole to conduct a sound investigation of a digital crime. Other scenarios where fragments are commonplace include network traffic, infection by malware, and other related circumstances. Most file fragment analysis and restoration techniques illustrated in the literature are based on offline algorithms, with hardcoded file signature (header/footer) analysis, or in the absence of header/footer information, byte frequency analysis, and similar processes. We contend that file fragment analysis is critical to a sound forensic examination and, as such, demands a more sophisticated recovery strategy. To this end, in this research, we propose to apply machine learning techniques to solve this problem. We show the efficacy of byte frequency analysis and grayscale imaging in improving the classification of the accuracy of file fragments over the traditional techniques in the existing body of work. We also demonstrate the comparative effectiveness of these two analysis methods and their applicability to this problem.
Enhancing Trust and Reliability in AI and ML: Assessing Blockchain's Potential to Ensure Data Integrity and Security

The increasing use of data-centric approaches in the fields of Machine Learning and Artificial Intelligence (ML/ AI) has raised substantial issues over the security, integrity, and trustworthiness of data. In response to this challenge, Blockchain technology offered a promising and practical solution as its inher-ent characteristics as a decentralized distributed ledger, coupled with cryptographic processes, offer an unprecedented level of data confidentiality and immutability. This study examines the mutually beneficial connection between Blockchain technology and ML/ AI, using Blockchain's inherent capacity to protect against unauthorized alterations of data during the training phase of ML models. The technique includes the development of data blocks obtained from the training dataset and subsequent submission to the mining process via the use of smart contracts and the adoption of the Proof-of-Federated-Learning (PoFL) consensus method. The aforementioned procedure is enhanced by the use of Secure Multi-Party Computation (SMPC) in order to generate a cryptographic signature for every individual data block. These signatures are securely stored within a cloud-based architecture and serve a crucial role in the data verification process during the training phase of ML models. This Work-in-Progress (WiP) research investigates the potential collaboration between Blockchain technology and ML/ AI, bolstering data qual-ity and trust to enhance data-driven decision-making fortifying the models' ability to provide precise and dependable results.
A Lightweight Encryption Method For Privacy-Preserving in Process Mining

Novel technological achievements in the fields of business intelligence, business management and data science are based on real-time and complex virtual networks. Sharing data between a large number of organizations that leads to a system with high computational complexity is one of the considerable characteristics of the current business networks. Discovery, conformance and enhancement of the business processes are performed using the generated event logs. In this regard, one of the overlooked challenges is privacy-preserving in the field of process mining in the industry. To preserve the data-privacy with a low computational complexity structure that is a necessity for the current digital business technology, a novel lightweight encryption method based on Haar transform and a private key is proposed in this paper. We compare the proposed method with the well-known homomorphic cryptosystem and Walsh-Hadamard encryption (WHE) in terms of cryptography, computational complexity and the structure vulnerability. The analyses show that the proposed method anonymizes the event logs with the lower complexity and more accuracy compared with two aforementioned cryptosystems, significantly.
Commodity Personalized Recommendation Algorithm Based on the Knowledge Graph

Personalized recommendation systems have become an important part of e-commerce, social media, and other applications. However, the traditional collaborative filtering algorithm is only based on the user's scoring history of the product, ignoring the attributes and characteristics of the product itself. To solve this problem, this paper proposes a personalized recommendation algorithm based on knowledge graph, which can combine the similarity between goods and user preferences to make recommendations and add the scoring mechanism, thus improving the accuracy and practicability of the recommendation system. Experimental results show that our algorithm outperforms the traditional user-based and item-based co-filtering algorithms in evaluation indexes such as accuracy, recall and F1 value, demonstrating the effectiveness and feasibility of this algorithm in the field of personalized recommendation.
Dual Watermarking Based on DCT with Human Visual Characteristics for Authentication and Copyright Protection

Nowadays, multimedia contents like images are easily distributed globally because of the widespread usage of information and communication technologies. Digital watermarking is an approach to prevent attacks or modification on images that might lead to serious problems like spreading of fake news, image piracy and illegal distribution of images. This research presents dual image watermarking based on human visual characteristics for authentication and copyright protection. The scheme aims to embed a watermark by modifying <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$U$</tex> coefficients of DCT-SVD with minimal distortion to the human eye. Additionally, the scheme demonstrates embedding authentication bits in the LSB for efficient tamper localization, enabling the detection of modifications made to the original image. The experimental results validate the effectiveness of the proposed scheme, showcasing superior performance in terms of imperceptibility, robustness, and tamper localization compared to existing watermarking schemes subjected to the same attacks. The scheme's ability to embed undetectable watermarks ensures copyright protection and authenticity verification, while its robustness against various image processing attacks. The proposed scheme offers a reliable approach for securing digital image content by ensuring imperceptibility, robustness, and tamper localization.
A Massively Parallel BWP Algorithm for Solving Large-Scale Systems of Nonlinear Equations

This paper presents a GPU-based massively parallel implementation of the Best- Worst-Play (BWP) metaphor-less optimization algorithm, which results from the combination of two other simple and quite efficient population-based algorithms, Jaya and Rao-l, that have been used to solve a variety of prob-lems. The proposed parallel GPU version of the algorithm is here used for solving large nonlinear equation systems, which have enormous importance in different areas of science, engineering, and economics and are usually considered the most difficult class of problems to solve by traditional numerical methods. The proposed parallelization of the BWP algorithm was implemented using the Julia programming language on a GeForce RTX 3090 GPU with 10496 CUDA cores and 24 GB of VRAM and tested on a set of challenging scalable systems of nonlinear equations with dimensions between 500 and 2000. Depending on the tested problem and dimension, the GPU-based implementation of BWP exhibited a speedup up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$283.17\times$</tex> , with an average of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$161.21\times$</tex> , which shows the efficiency of the proposed G PU - based parallel version of the BWP algorithm.
A Composable Just-In-Time Programming Framework with LLMs and FBP

This paper introduces a computing framework that combines Flow-Based Programming (FBP) and Large Language Models (LLMs) to enable Just-In-Time Programming (JITP). JITP empowers users, regardless of their programming expertise, to actively participate in the development and automation process by leveraging their task-time algorithmic insights. By seamlessly integrating LLMs into the FBP workflow, the framework allows users to request and generate code in real-time, enabling dynamic code execution within a flow-based program. The paper explores the motivations, principles, and benefits of JITP, showcasing its potential in automating tasks, orchestrating data workflows, and accelerating software development. Through a fully implemented JITP framework using the Composable platform, we explore several examples and use cases to illustrate the benefits of the framework in data engineering, data science and software development. The results demonstrate how the fusion of FBP and LLMs creates a powerful and user-centric computing paradigm.