Reviewerly/exHarmony huggingface dataset:
An Evaluation and Architecture Exploration Engine for CNN Accelerators through Extensive Dataflow Analysis

Systolic array is one of the popular convolutional neural network accelerator architectures due to its high computation efficiency. Nevertheless, the huge design space and complicated interactions among different design parameters make it hard to find the best configuration for various applications. To overcome this issue, this paper presents an evaluation and design space exploration engine, NNeed, for systolic-array CNN accelerators through extensive dataflow analysis. It uses a highly configurable hardware template to describe accelerator operations in detail. The rapid evaluation provides PPA results, pipeline stage analysis, external memory access statistics, and so on. NNeed explores the 9-dimensional design space and supports multiple objective functions for design optimization. Experimental results show that NNeed can generate an accelerator configuration with up to 23% and 50% improvement in performance and energy as compared with a typical handcrafted design.
Investigating Security and Privacy Concerns in Deep-Learning-based Pervasive Health Monitoring Architectures

Pervasive Health Monitoring (PHM) uses sensors and wearable devices and data analytics for real-time health monitoring. It enables early detection and personalized care interventions. This technology has the potential to revolutionize healthcare by improving proactive and preventive care.Besides, Deep learning (DL) based PHM is even more promising as it improves the discovery of complex patterns and correlations. This leads to precise health monitoring and personalized care, enhances diagnostics, and ultimately improves patient outcomes in the field of healthcare.However, privacy and security considerations must be addressed for successful implementation. This paper investigates the security and privacy concerns in Pervasive Health Monitoring architectures. It discusses through an illustrative DL-based PHM architecture the potential threats and attacks during the inference and training phases, and identifies key security and privacy issues. It also gives insights on countermeasures and technological solutions that can address security and privacy concerns in PHM architectures.
Security Issues in Cloud-based IoT Systems

Cloud computing has transformed the IT landscape by offering benefits like scalability, flexibility, and cost reduction. However, the security challenges in cloud environments have raised concerns for organizations adopting cloud services. In this paper, an overview of cloud computing is provided, including its main characteristics, deployment models, service models, and associated benefits. The focus is on categorizing cloud threats and proposing an improved taxonomy to better organize and classify security concerns specific to cloud computing. Through a literature survey, this research presents detailed findings and analyses from relevant papers, contributing to a more comprehensive understanding of the challenges and solutions in cloud computing security. By addressing these knowledge gaps, organizations can enhance their understanding and effectively mitigate risks associated with cloud services.
Web APIs composition based on Knowledge Graph Word Embedding and Entity Linking

Relying on ontological relationships which ensure the matching between parameters of Web API operations (inputs and outputs) is not enough to achieve semantic composition in the context of a Knowledge base (KB). In this paper, these parameters of Web API operations are related to entities of a given KB and the word embedding is integrated with the semantic ontological relationships between these entities to extend the user query to their similar entities. Extending the query by equivalent, plugIn, and contextual entities guides the process of generating the dependency graph composition. Our composition approach adds semantics and enhances the matchmaking algorithm within the KB context. Grounded on a developed dataset, our experiments show the accuracy of our composition algorithm.
Hardware-Optimized Deep Learning Model for FPGA-Based Character Recognition

Deep neural networks (DNNs) are widely used algorithms in machine learning. Even though most of the deep learning applications are driven by software solutions, there has been significant research and development aimed at optimizing these algorithms over the years. However, when considering hardware implementation applications, it becomes essential to optimize the design not only in software but also in hardware. In this paper, we present a straightforward yet effective Convolutional Neural Network architecture that is meticulously optimized both in hardware and software for char-acter recognition applications. The implemented accelerator was realized on a Xilinx Zynq XC7Z020CLG484 FPGA using a high-level synthesis tool. To enhance performance, the accelerator employs an optimized fixed-point data type and applies loop parallelization techniques combining 2D convolution and 2D max pooling operations. The hardware efficiency of the proposed DNN is compared with some of the existing architectures in terms of hardware utilization.
Content-Based Recommender System using Word Embeddings for Pedagogical Resources

Recommender Systems are important systems operating within a system to ensure how certain types of data are managed on the internet. These systems help users with overwhelming data and provide a better user navigation experience. This paper presents a content-based recommender system for online resources using deep learning. We have included some deep learning techniques to allow a good semantic understanding of educational resources. However, we have used a pre-trained word2vec model owned by Google for the following three reasons: (1) Google is reliable; (2) the content of the Google news dataset is close to the content of the shared articles dataset; and (3) training a word2vec model is time-consuming and domain-independent itself. We have also used techniques for dimensionality reduction like t-distributed Stochastic Neighbor Embedding and Principal Component Analysis to reduce the dimensions of users and items vectors. Our approach aims to ameliorate the recommendations’ accuracy and better satisfy the requirements of users. The results obtained when we tested our system are encouraging.
Uncovering Linguistic Patterns: Exploring Ensemble Learning and Low-Level Features for Identifying Spoken Arabic, English, Spanish, and German

This paper presents a novel approach to spoken language identification in Arabic, English, Spanish, and German languages using ensemble learning techniques. The study compares the performance of two well-known ensemble learning algorithms (Random Forest and XGBoost). Next, a Stacking Ensemble method with Logistic Regression is used as a meta-model, which combines the predictions of two configurations of Random Forest and two other configurations of XGBoost. We explore the utilization of the audio Low-Level Descriptors features, which have previously received limited attention in spoken language identification. Experimental results demonstrate the effectiveness of the ensemble learning algorithms, achieving high accuracy in accurately classifying spoken languages. Notably, the Stacking Ensemble method showcases its ability to reduce misclassification rate, emphasizing its potential for performance improvement. The stacking Ensemble method achieved the highest recorded classification rate of 97.22%, outperforming individual methods.
Optimizing Constrained-Modulus Barrett Multiplier for Power and Flexibility

Fully Homomorphic Encryption (FHE) promises data protection by computing on encrypted data, but demands resource-intensive computation. FHE hardware accelerators, which improve FHE scheme performance with densely packed computing units, could potentially damage the chip with excessive heat dissipation because of high power consumption. Therefore, it is necessary to reduce the power consumption of the accelerator and its most critical module, i.e., modular multiplier. In this work, we extend the idea of allowing a specific form of modulus to achieve a low-power Barrett modular multiplier (BM). BM with constraint width can reduce power consumption by 15% and area by 20%. We propose an approximation for the number of moduli available with the discussed constraints on the modulus.
TRAPDOOR: Repurposing neural network backdoors to detect dataset bias in machine learning-based genomic analysis

Use of Machine Learning (ML) to understand underlying patterns in gene mutations (genomics) has far-reaching results in diagnosis and treatment for life-threatening diseases like cancer. Success and sustainability of ML algorithms depends on the quality and diversity of training data, and under-representation of groups (gender, race, etc.) can lead to exacerbation of systemic discrimination issues. In this work, we propose TRAPDOOR, a methodology for the identification of biased datasets by repurposing, otherwise malicious, neural backdoors. Our methodology can leak potential bias information about the cloud’s dataset which is collected in a collaborative setting, without hampering the genuine performance. Using a real-world cancer genomics dataset, we analyze feasibility of leaking bias for gender and race attributes. Our experimental results show that TRAPDOOR can detect the presence of dataset bias with 100% accuracy, and furthermore can also extract the extent of bias by recovering the percentage with a small error.
Efficient, Error-Resistant NTT Architectures for CRYSTALS-Kyber FPGA Accelerators

The dawn of cost-effective miniaturised satellites is currently attracting venture capital in a never seen before ratio to launch mega-constellations of satellites for a diverse range of applications.These satellites are vulnerable to attacks by high-capability cyber-criminals (including quantum enabled adversaries), due to the critical data they transmit.Additionally, space missions have long lifespan and a long lead time in terms of development process, requiring a pre-emptive outlook to ensuring their safety.In 2016, National Institute of Standards and Technology (NIST) initiated the competition to standardise the post-quantum cryptography (PQC) schemes, announcing the first portfolio of chosen schemes in 2022.This work targets the only public key exchange (PKE) scheme among the winners of the NIST-PQC standardisation process, CRYSTALS-Kyber, and implements its core bottleneck operation, i.e., number theoretic transform (NTT) extensively used for the polynomial multiplication.To avoid data corruption due to space based radiations, a novel error-resistant model for NTT is presented based on hybrid protection mechanisms, i.e., the use of hamming codes for detection and correction of errors in the twiddle factors and the use of parity computed for all NTT coefficients for error detection.Benchmarking error protection overheads on a Xilinx Virtex-7 FPGA reports 16.4% and 10.8% degradation on the hardware efficiency when the hamming codes for twiddle factors and parity bit for NTT coefficients are used to mitigate errors, respectively.A total of 29.2% area overhead is benchmarked when compared to the standard unprotected NTT implementations.
Reducing Depth of Quantum Adder using Ling Structure

Improving the performance of quantum adder is an important technical challenge with major impact on the implementation of efficient, large-scale quantum computing. Continuing along this research direction, we propose a novel parallel-prefix quantum adder based on Ling expansion. We systematically explored classical structures for parallel-prefix adders assessing their suitability to be realized in quantum domain. Furthermore, Ling adder enforces Logical OR and large fan-out, which require innovative solutions. We addressed these challenges to realize the quantum Ling adder, which results in a T-depth of only $O\left( {\log \frac{n}{2}} \right)$. This represents a substantial improvement over the previous quantum adders based on parallel prefix structure, which require O(log n) T-depth. We present extensive theoretical and simulation-based studies to establish our claims.
Quantitative Explainable AI For Face Recognition

Face recognition is widely adopted in our daily life in recent years. It usually relies on sophisticated techniques to achieve high accuracy in identifying or verifying the identities of given face images. Artificial intelligence (AI), especially deep learning, is a popular technique used in face recognition due to its high accuracy, known as the deep face recognition. However, the reliability of the deep face recognition models becomes a concern, especially in security-critical applications. The main challenge is the "black-box" nature of the sophisticated internal structure of the models. Explainable AI has emerged as a solution that provides meaningful explanations to help humans understand the complicated internal structure of the deep learning models, and increase the transparency and interpretability of the "black box". However, this is often at the cost of model accuracy. In this paper, we propose an approach to increasing both the accuracy and interpretation of quantitatively explainable AI models for face recognition. It increases the accuracy of the explainable face recognition models by applying improved loss functions and enhances quantitative interpretability by adding a new visualisation feature. The proposed approach is validated using advanced deep face recognition models and is compared with existing approaches to demonstrate its better performance.
Securing Supply Chain: A Comprehensive Blockchain-based Framework and Risk Assessment

Cyber attacks on data, networks, and software have become a crucial problem for supply chain management due to the globalization, decentralization, and digitalization. Blockchain provides an ideal platform for business stakeholders to address issues with modern supply chains, such as traceability, interoperability, and transparency. However, adopting blockchain is challenging as it introduces risks to the supply chain.In this paper, we propose a blockchain-based framework to manage the supply chain and enable a trust-based feedback mechanism, fostering trust among supply chain stakeholders. Moreover, we perform a qualitative risk assessment for adopting blockchain in the supply chain management process, based on standards provided by the National Institute of Standards and Technology (NIST). Our assessment shows that if a threat is imminent, the risk associated with the consensus, limited fixed verification capacity, and inter-autonomous system communication is high in a blockchain-based supply chain that uses proof of authority.
Extended k<sup>m</sup>-Anonymity for Randomization Applied to Binary Data

Various models for evaluating anonymity have been proposed so far. Among them, k-anonymity is widely known as a typical anonymity measure, which guarantees that at least k individuals in a database have the same values. However, it is difficult to create highly useful anonymized data satisfying k-anonymity for high-dimensional data because of the curse of dimensionality. To overcome the problem, several approaches relaxing k-anonymity have been proposed, such as k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> -anonymity and σ-k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> -anonymity. Unfortunately, they can only evaluate deterministic anonymization methods.We propose Pk <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> -anonymity, a variant of k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> -anonymity, and prove that k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> -anonymity and Pk <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> -anonymity are equivalent in a deterministic privacy mechanism. This suggests that our Pk <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m-</sup> anonymity is an extension of k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> anonymity. Also, we propose a k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> -anonymization method for binary data, unlike the previous approaches for non-binary data. The success probability and utility of the proposed method are examined with the number of attributes as a parameter. Our experiments show that the "curse of dimensionality" does not occur up to a dimensionality of 45 and that usefulness does not deteriorate in the range of dimensionality from 10 to 40.
Utilizing Risk Number and Program Slicing to Improve Human-Machine Pair Inspection

Human-Machine Pair Inspection (HMPI) is a novel code inspection technology proposed in our previous work, which is the style that machine will intelligently guide the programmer to carry out inspections of the program code during programming. For large-scale software projects, the efficiency of HMPI needs to be improved due to the inaccurate measurement of the code structure and the excessive inspection scope. In this paper, to alleviate the above deficiencies, we propose the Risk Number, a code evaluation metric generated based on historical error data. The Risk Number is calculated by a statistical tool called regression analysis, which more accurately indicates the relationship between the nested structure of the code and the likelihood of containing bugs than Cognitive Complexity. Additionally, HMPI is supported by utilizing Risk Number to point out high-risk code and program slicing techniques to extract statements that have dependencies on the code to generate checklists, thereby reducing the scope of inspection. We describe a case study to evaluate the performance of this method by comparing its inspection time and number of detected errors with our previous work. The result shows that the method is likely to guide the programmer to inspect the faulty code earlier and be more efficient in detecting defects than HMPI based on Cognitive Complexity.
GhostBuy : An All-Steps Anonymous Purchase Platform (ASAPP) based on Separation of Data

In recent years – and especially since the beginning of the COVID-19 pandemic – online shopping has become a part of everyday life for many people. Yet, in contrast to buying at a traditional retail store, staying anonymous is at least difficult if not impossible when shopping online – in particular, when physical goods are to be delivered. From the customer perspective, reasons for seeking to anonymously shop online can be manifold, for example they do not want their data to be used by Big Data-enabled online retailers or simply want no one to know about their purchases. From the point of view of online retailers, the prospect of anonymous online shopping should therefore not only be seen as a threat to their data-driven business models, but also as an opportunity to attract new customers.In this paper we review system architecture designs that were proposed by other authors for potentially realizing what we call All-Steps Anonymous Purchase Platforms (ASAPP). We propose a new design that improves earlier work by realizing the concept of Separation of Data within a single platform and - in contrast to many previous works - allows for anonymous shopping from existing marketplaces: GhostBuy.We implement a working prototype of this platform that demonstrates not only the fundamental feasibility of the architecture but also that such a platform can be realized with a look-and-feel similar to that of common online shops. We also propose solutions for certain related aspects that are particularly important in the context of such a platform, as for example a guaranteed use of secure user passwords or application-level database encryption.We evaluate to what extent the proposed architecture and prototype preserve the customers’ anonymity/privacy, showing that the prototype provides it to the maximum possible extent that can be achieved based on the proposed architecture. We also show that the system provides 256-bit security against all but one considered cryptographic and mis-authentication attack vectors and discuss how this can also be achieved for the remaining attack vector. Closing our evaluation, we show how well the platform could presumably be deployed in the real world. Finally, limitations, possible improvements, and potential further future work are discussed and proposed.
Privacy-Preserving Publication of GWAS Statistics using Smooth Sensitivity

With the recent increase in the medical data and health awareness, the use of genomic data to promote personalized medicine has been widely considered. Simultaneously, privacy concerns have arisen with the publication of statistics obtained from large-scale genomic statistical analysis such as GWAS. All existing differentially private methods for GWAS statistics protect privacy by adding noise based on global sensitivity, considering the worst-case scenario of possible datasets. However, the amount of noise required in practical cases is considerably smaller, and these methods do not achieve the desired accuracy in private statistics. In this study, we propose a privacy-preserving method for publishing much more accurate statistics using smooth sensitivity, which generates tailored noise for each dataset. We first introduce a more rigorous theorem on the properties of the noise distribution than was known previously and propose a new ϵ-differentially private method for publishing GWAS statistics. We also provide theoretical proof of the privacy guarantee. Thereafter, we present novel theorems for computing the smooth sensitivity significantly faster than conventional approaches. This enables the application of smooth sensitivity to GWAS statistics, which would otherwise be impossible because of the exceedingly high computational complexity. Based on these theorems, we performed detailed analyses of key GWAS statistics and developed efficient algorithms to obtain their smooth sensitivities. Experimental results demonstrate that our proposed methods achieve at least 3 times higher accuracy than existing global sensitivity-based methods. Furthermore, the execution time is sufficiently short, and the accuracy increases when the dataset becomes larger, suggesting that our methods are suitable for the publication of statistics in large-scale analysis. Because our method is expected to be applicable to other general statistics, this study is an important step toward highly accurate statistical analysis using smooth sensitivity. The supplemental materials are available at https://github.com/ay0408/SS-based-Stats.