Reviewerly/exHarmony huggingface dataset:
Privacy-Aware Artificial Intelligence with Homomorphic Encryption using Machine Learning

Along with the expansion of machine learning (ML) applications, the amount of data required to create predictions increases. Big-data ML has always been limited by off-chip memory capacity and computational speed. Considerably, privacy is one of the limitations of big data, which can be solved by homomorphic encryption (HE). Due to the combination of HE and ML, the multi-party privacy-protected ML suggested in this research may assist numerous users in doing artificial intelligence (AI) without disclosing private data. The technique may train common models in situations of data abuse, particularly in private data protection. The model trained using the ML technique named Artificial Neural Network (ANN) has a similar impact to the model developed using all data on a single computer, according to experiments using the algorithm. The gradient data is simply transmitted by all parties, and homomorphic procedures in the main computing system combine the gradient data. Besides, the optimal key is selected using the significance of the Lion Algorithm (LA). After homomorphic procedures, the learning model is modified depending on the new gradient data.
Augmented Reality Application for Sustainable Development – A Grounded Theory Approach

Augmented Reality combines three-dimensional virtual items with reality. It is one of the valuable tools in promoting sustainable development. Companies from manufacturing, packaging, defense, and other areas are adopting AR technology to achieve sustainable development goals. This study explores the application of Augmented reality in attaining sustainable development from the expert’s point of view. An open-ended semi-structured questionnaire was prepared, and the interviews were conducted with 15 experts from academics and industry. With the grounded theory approach, five broad themes were explored. The findings revealed that AR application across industries is in its nascent stage. Although the application of AR is vast, there are a few challenges, including-hardware updates, availability of skilled employees, data privacy issues, and others.
A Convolved Self-Attention Model for IMU-based Gait Detection and Human Activity Recognition

This paper presents a convolved self-attention neural network model for gait detection and human activity recognition (HAR) tasks using wearable inertial measurement unit (IMU) sensors. By embedding a convolved window inside the self-attention module, prior time step knowledge is utilized by self-attention layer to improve accuracy. Moreover, a streamlined fully connected (FC) layer without hidden layers is proposed for the feature mixer. This arrangement enables significant reduction of overall network parameters, since hidden layers occupy the majority of the parameters in a transformer encoder. Compared to the other state-of-art neural networks, the proposed method achieved better accuracy of 95.83% and 96.01% with the smallest network size on HAR datasets UCI-HAR and MHEALTH respectively,
GPIL: Gradient with PseudoInverse Learning for High Accuracy Fine-Tuning

PseudoInverse learning (PIL) is proposed to increase the convergence speed of conventional gradient descent. PIL can be trained with fast and reliable convolutional neural networks (CNNs) without a gradient using a pseudoinverse matrix. However, PIL has several problems when training a network. First, there is an out-of-memory problem because all batches are required during one epoch of training. Second, the network cannot be deeper because more unreliable input pseudoinverse matrices are used as the deeper PIL layer is stacked. Therefore, PIL has not yet been effectively applied to widely used deep models. Inspired by the limitation of the existing PIL, we propose a novel error propagation methodology that allows the fine-tuning process, which is often used in a resource-constrained environment, to be performed more accurately. In detail, by using both PIL and gradient descent, we not only enable mini-batch training, which was impossible in PIL, but also achieve higher accuracy through more accurate error propagation. Moreover, unlike the existing PIL, which uses only the pseudoinverse matrix of the CNN input, we additionally use the pseudoinverse matrix of weights to compensate for the limitations of PIL; thus, the proposed method enables faster and more accurate error propagation in the CNN training process. As a result, it is efficient for fine-tuning in resource-constrained environments, such as mobile/edge devices that require an accuracy comparable to small training epochs. Experimental results show that the proposed method improves the accuracy after ResNet-101 fine-tuning on the CIFAR-100 dataset by 2.78% compared to the baseline.
An Integrated CPU-GPU Frequency Scaling Governor Based on Deep Recurrent Q-Network for Partially Observable Rendering Applications

Dynamic voltage and frequency scaling (DVFS) is an effective approach to balance the performance and power consumption of the CPU-GPU-based video rendering applications. Operating System (OS) based DVFS governors are general in nature and thus lack of regulation effectiveness for workload with different characteristics. Reinforcement learning (RL) enhances power management ability for application-specific DVFS, whereas the regulation robustness is limited due to partial observability of complex SoCs. To mitigate this issue, in this paper, a deep recurrent Q-Network (DRQN) based DVFS governor for rendering applications is proposed where a recurrent neural network (RNN) exploiting historical information is embedded in a Deep Q-Network (DQN). Evaluated on the Nvidia Jetson NX platform with various settings, the proposed DRQN policy achieves an up to ×2.11 improvement in Valid Performance Per Watt (VPPW) compared with Linux default governors and shows superior regulation stability compared with other RL-based state-of-the-art.
AI-assisted ISP hyperparameter auto tuning

Images and videos are vital visual information carriers, and the image signal processor (ISP) is an essential hardware component for capturing and processing these visual signals. ISPs convert raw data into high-quality color images, which requires various function modules to control different aspects of image quality. However, the results of these modules are interdependent and have crosstalk with each other, making it tedious and time-consuming for manual tuning to obtain a set of ideal parameter configurations to achieve stable performance. In this paper, we introduce xkISP, a self-developed open-source ISP project which includes both a C model and hardware implementation of an 8-stage ISP pipeline. Most importantly, we present a novel proxy function-based AI-assisted ISP tuning solution that is demonstrated to accelerate the ISP parameter configuration process and improve performance for both human vision and computer vision tasks.
Applications of Machine Learning in Cloud Forensics

Cloud computing has recently experienced rapid expansion, making it a major target for cybercrimes. Though cloud computing has many advantages, there are also substantial security issues related to confidentiality, integrity, privacy, and availability. Being a relatively new discipline, cloud forensics faces numerous challenges and issues with interpreting and analyzing data. Forensic investigators and law enforcement confront numerous difficulties in data collection, data protection, and evidence access. Several sophisticated models have been proposed in recent years aiming to accelerate the entire investigation process or address several issues that arise frequently in forensic investigations. This review paper seeks to comprehend the significance of various machine learning models which would help cloud forensics advance significantly. It also provides a detail information on cloud forensics as well as the research trends that have been going on in this field over the past few years. A detailed comparative analysis of different approaches of machine learning on development of cloud forensics has been presented in this research study.
Landmark-Based Adversarial Network for RGB-D Pose Invariant Face Recognition

Even though numerous studies have been conducted, face recognition still suffers from poor performance in pose variance. Besides fine appearance details of the face from RGB images, we use depth images that present the 3D contour of the face to improve recognition performance in large poses. At first, we propose a dual-path RGB-D face recognition model which learns features from separate RGB and depth images and fuses the two features into one identity feature. We add associate loss to strengthen the complementary and improve performance. Second, we proposed a landmark-based adversarial network to help the face recognition model extract the pose-invariant identity feature. Our landmark-based adversarial network contains a feature generator, pose discriminator, and landmark module. After we use 2-stage optimization to optimize the pose discriminator and feature generator, we removed the pose factor in the feature extracted by the generator. We conduct experiments on KinectFaceDB, RealSense <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">test</inf> and LiDAR <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">test</inf> . On KinectFaceDB, we achieve a recognition accuracy of 99.41%, which is 1.31% higher than other methods. On RealSense <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">test</inf> , we achieve a classification accuracy of 92.57%, which is 30.51% higher than other methods. On LiDAR <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">test</inf> , we achieve 98.21%, which is 21.88% higher than other methods.
Temporal Similarity-Based Computation Reduction for Video Transformers in Edge Camera Nodes

Recognizing human actions in video sequences has become an essential task in video surveillance applications. In such applications, transformer models have rapidly gained wide interest thanks to their performance. However, their advantages come at the cost of a high computational and memory cost, especially when they need to be incorporated in edge devices. In this work, temporal similarity tunnel insertion is utilized to reduce the overall computation burden in video transformer networks in action recognition tasks. Furthermore, an edge-friendly video transformer model is proposed based on temporal similarity, which substantially reduces the computation cost. Its smaller variant EMViT achieves 38% computation reduction under the UCF101 dataset, while keeping the accuracy degradation insignificant (<0.02%). Also, the larger variant CMViT reduces computation by 14% (13%) with an accuracy degradation of 2% (3%) in scaled Kinetic400 and Jester datasets.
F-CNN: Faster CNN Exploiting Data Re-Use with Statistical Analysis

Many of the current edge computing devices need efficient implementation of Artificial Intelligence (AI) applications due to strict latency, security and power requirements. Nonetheless, such devices, face various challenges when executing AI applications due to their limited computing and energy resources. In particular, Convolutional Neural Networks (CNN) is a popular machine learning method that derives a high-level function from being trained on various visual input examples. This paper contributes to enabling the use of CNN on resource-constrained devices offline, where a trade-off between accuracy, running time and power efficiency is verified. The paper investigates the use of minimum pre-processing methods of input data to identify nonessential computations in the convolutional layers. In this work, Spatial locality of input data is considered along with an efficient pre-processing method to mitigate the accuracy loss caused by the computational re-use approach. This technique was tested on LeNet and CIFAR-10 structures and was responsible for 1.9% and 1.6% accuracy loss while reducing the processing time by 38.3% and 20.9% and reducing the energy by 38.3%, and 20.7%, respectively. The models were deployed and verified on Raspberry Pi 4 B platform using the MATLAB coder to measure time and energy.
Hardware-Friendly Activation Function Designs and Its Efficient VLSI Implementations for Transformer-Based Applications

The activation function is one of key elements in modern machine learning algorithms. However, some broadly-used activation functions are exceptionally complex, e.g., GELU in Transformer-based algorithms, which makes their precise yet efficient VLSI implementations extremely hard. In this paper, two series of hardware-friendly activation function designs, DNR and PWL, and their VLSI implementations are proposed. Both are specifically designed to replace GELU, which is widely used in Transformer-related applications. Instead of utilizing traditional lookup-table (LUT)-based approximation methods, this paper introduces new activation functions that are not only hardware-friendly but successfully alleviate the dying neuron issue. Besides, each series includes a number of members, which can be freely selected through programming to best fit a given application. Experimental results indicate that the proposed new activation functions achieve comparable or even better model accuracy as compared to GELU. Moreover, the highly efficient and flexible VLSI implementations support 16 different Q-formats to maximize the output precision under various input scales. Compared with approximation-based implementation strategies, the proposed activation function designs and the corresponding LUT-free hardware implementations do achieve a significant improvement in speed, area, and power.
Enhancing Fault Resilience of QNNs by Selective Neuron Splitting

The superior performance of Deep Neural Networks (DNNs) has led to their application in various aspects of human life. Safety-critical applications are no exception and impose rigorous reliability requirements on DNNs. Quantized Neural Networks (QNNs) have emerged to tackle the complexity of DNN accelerators, however, they are more prone to reliability issues.In this paper, a recent analytical resilience assessment method is adapted for QNNs to identify critical neurons based on a Neuron Vulnerability Factor (NVF). Thereafter, a novel method for splitting the critical neurons is proposed that enables the design of a Lightweight Correction Unit (LCU) in the accelerator without redesigning its computational part.The method is validated by experiments on different QNNs and datasets. The results demonstrate that the proposed method for correcting the faults has a twice smaller overhead than a selective Triple Modular Redundancy (TMR) while achieving a similar level of fault resiliency.
FPGA-Based High-Speed and Resource-Efficient 3D Reconstruction for Structured Light System

To achieve high-speed and low-resource consumption 3D measurement, we propose a parallel and full-pipeline FPGA architecture for the phase measuring profilometry algorithm. The proposed system uses four-step phase-shifting and gray code decoding to generate accurate 3D point clouds. Experimental results show that the proposed architecture can process 12 frames of images with a resolution of 720 × 540 in just 12.2 ms, which is 110 times faster than the same implementation in software, and has the smallest resource consumption compared with other similar FPGA systems. This makes the proposed system very suitable for high-speed embedded 3D shape measurement applications.
Searching Tiny Neural Networks for Deployment on Embedded FPGA

Embedded FPGAs have become increasingly popular as acceleration platforms for the deployment of edge-side artificial intelligence (AI) applications, due in part to their flexible and configurable heterogeneous architectures. However, the complex deployment process hinders the realization of AI democratization, particularly at the edge. In this paper, we propose a software-hardware co-design framework that enables simultaneous searching for neural network architectures and corresponding accelerator designs on embedded FPGAs. The proposed framework comprises a hardware-friendly neural architecture search space, a reconfigurable streaming-based accelerator architecture, and a model performance estimator. An evolutionary algorithm targeting multi-objective optimization is employed to identify the optimal neural architecture and corresponding accelerator design. We evaluate our framework on various datasets and demonstrate that, in a typical edge AI scenario, the searched network and accelerator can achieve up to a 2.9% accuracy improvement and up to a 21 speedup compared to manually designed networks based on× common accelerator designs when deployed on a widely used embedded FPGA (Xilinx XC7Z020).
Deep-learning-based X-ray CT Slice Analysis for Layout Verification in Printed Circuit Boards

3D X-ray Computational Tomography (CT) systems have been employed to inspect Printed Circuit Boards (PCB) for security analysis, considering the heightened trustworthiness concern on the globalized supply chain. In this paper, we propose a deep-learning-based layout verification (DELVer) framework to automatically extract PCB layout information from X-ray CT slices and verify against the design files. Leveraging on geometrical projective transformation, our proposed DELVer framework aligns the acquired CT slice of each PCB layer with their corresponding design file, to train state-of-the-art deep learning models for layout extraction and verification. It thus alleviates the laborious manual data labeling for deep learning models. With a cross-device evaluation on 4 multi-layer satellite PCBs of board size around 90 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , our proposed DELVer framework demonstrates how deep learning models can generalize to unseen target PCBs for layout verification, establishing an efficient solution for PCB assurance and industrial failure analysis.
Efficient Parameter Learning of Bayesian Network with Latent Variables from High-Dimensional Data

Bayesian network with latent variables (BNLV) plays an important role in the representation of dependence relations and inference of uncertain knowledge with unobserved variables. The variables with large cardinalities in high-dimensional data make it challenging to efficiently learn the large-scaled probability parameters as the conditional probability distributions (CPDs) of BNLV. In this paper, we first propose the multinomial parameter network to parameterize the CPDs w.r.t. latent variables. Then, we extend the M-step of the classic EM algorithm and give the efficient algorithm for parameter learning of BNLV. Experimental results show that our proposed method outperforms some state-of-the-art competitors.
Cheer Up: An Advanced Mobile Application Featuring Chef Selection, Recipe Search, and Chef Assistance facilities for the Citizens of Bangladesh

A suitable chef selection and assistance system are crucial to accomplish different food cooking, recipe selection, and food delivery-related activity in different social and official programs. Presently available literary works on chef selection and assistance rely on traditional offline-based chef search or recipe delivery processes rather than advanced mobile application-based online systems. The current offline-based chef assistance services are suffered from some problems such as inefficient chef selection, high time and cost-consuming, and very limited information and resources. To conquer the problems, this paper delivers an advanced chef assistance mobile application that selects suitable chefs based on the chef’s price, rating, and experience. The proposed chef assistance mobile application confers some interesting features like a chef job application feature, selected chef confirmation, suitable recipe search, reporting misconduct, adding a review, and a chef rating feature. The results regarding user judgments evaluation guaranteed that up to 85% users rated the proposed application as more suitable than existing schemes.
Software fault prediction using a differential evolution-based wrapper approach for feature selection

Software fault prediction (SFP) aids in the early sensing of software flaws, hence improving the quality of software. The prediction process utilizes previously used software measurements and faulty data as autonomous characteristics to determine whether or not there is an issue in the software. Aboriginal detection of faults in software saves a significant amount of money space and labor costs which is necessary to remedy those flaws. However, because the magnitude of data is so large, feature selection(FS) is critical in order to obtain relevant information. This work provides an FS perspective based on the Differential Evolutionary Algorithm(DE) that uses the difference between pairs of randomly selected objective vectors for the mutation process in contrast to traditional Evolutionary Algorithms(EA) that rely on the preset probability distribution function for the same. For our tests, we employed a mix of DE, NBC, KNN, and DTC Classifiers. Our findings indicate that applying the DE for FS enhanced our accuracy in all three classifiers for the SFP data.
Entity Hallucination Index in Abstractive Summarization – A Metric

This paper focuses on the issue of entity hallucination in abstractive summarization, which refers to generating summary sentences that mentions entities or events that do not appear in the original document. The article highlights the importance of faithfulness in abstractive summarization and studies various models to demonstrate evaluation of entity hallucination. The article emphasizes that while these models have shown promising results, entity hallucination remains an active area of research in abstractive summarization. A novel Metric for evaluating Hallucination of entity is proposed which evaluates the entities in a summary specifically for hallucination levels. The metric gives a gist of quantitative presence of types of hallucination that exists with respect to entities in the generated summary.
Video Anomaly Detection using Variational Convolutional LSTM Autoencoder

Unintentional, inadvertent, unanticipated, or unplanned events are referred to as anomalies or abnormal events. Anomaly detection in surveiiance video has been a topic of active research for several years and is a very challenging task. In this research, we investigate the performance of the variational autoencoder taking into account convolutional LSTM. To improve model performance, the model makes use of a latent space that follows a Gaussian distribution. Instead of just employing mean square error, the loss function used in this study accounts for both mean square error and KL divergence loss. Combining both losses results in a more effective regularisation impact of the model. To show the model’s resilience, this study takes into account two publicly accessible datasets: the Avenue dataset and the UCSD Ped1 dataset. Additionally, a performance comparison is made between convolutional LSTM and variational convolutional LSTM autoencoder.