Reviewerly/exHarmony huggingface dataset:
Prediction of Inference Energy on CNN Accelerators Supporting Approximate Circuits

Design methodologies developed for optimizing hardware implementations of convolutional neural networks (CNN) or searching for new hardware-aware neural architectures rely on the fast and reliable estimation of key hardware parameters, such as the energy needed for one inference. Utilizing approximate circuits in hardware accelerators of CNNs faces the designers with new problems during their simulation — commonly used tools (TimeLoop, Accelergy, Maestro) do not support approximate arithmetic operations. This work addresses the fast and efficient prediction of consumed energy in hardware accelerators of CNNs that utilize approximate circuits such as approximate multipliers. First, we extend the state-of-the-art software frameworks TimeLoop and Accelergy to predict the inference energy when exact multipliers are replaced with various approximate implementations. The energies obtained using the modified tools are then considered the ground truth (reference) values. Then, we propose and evaluate, using two accelerators (Eyeriss and Simba) and two types of networks (CNNs generated by EvoApproxNAS and standard ResNet CNNs), two predictors of inference energy. We conclude that a simple predictor based on summing the energies needed for all multiplications highly correlates with the reference values if the CNN’s architecture is fixed. For complex CNNs with variable architectures typically generated by neural architecture search algorithms, a more sophisticated predictor based on a machine learning model has to be employed. The proposed predictors are 420-533× faster than reference solutions.
Diagnosis of Quantum Circuits in the NISQ Era

Currently, noisy intermediate-scale quantum (NISQ) circuits may not always generate correct outputs due to noise and faults. In this work, we propose a diagnosis technique for NISQ circuits. The proposed technique contains static diagnosis and dynamic diagnosis. Static diagnosis uses a fault dictionary that contains output probability distribution for each fault. Dynamic diagnosis uses binary search to find the accurate fault locations of faulty quantum circuits. We demonstrate our technique using the Qiskit simulator with realistic noise models. We evaluate 15 benchmarks with unitary and non-unitary faults injected. Simulation results show that the average accuracy and resolution are 97.70% and 1.81. Experiments on the IBM Q devices have also been performed, and results show that our technique is feasible on real quantum circuit devices.
Hybrid Binary Neural Networks: A Tutorial Review

In this article, we review neural networks which have neurons with binary operations or networks that use binary transforms such as the Walsh-Hadamard transform (WHT). Neural networks with binary neurons or binary layers can be used in edge applications and/or applications requiring energy-efficient decision-making. WHT-based network is as accurate as the regular neural networks in the CIFAR-10 and the Tiny ImageNet image databases.
ISLR: Indian Sign Language Recognition

Normal people can readily connect and communicate with one another, however, those with hearing and speech impairments find it difficult to converse with normal-hearing people without the assistance of a translator. The only way deaf and dumb people can communicate is through Sign Language. Indian Sign Language has its own grammar, syntax, vocabulary, and unique language features. We propose two methods, namely Bidirectional LSTM and BERT Transformer to address the problem of sign language translation. The proposed work is validated on standard datasets and provides promising results. The INCLUDE-50 dataset is used to validate the performance of the proposed algorithm. The deep neural network is evaluated using a combination of approaches for augmentation of the data, features extraction using the mediapipe.On the Dataset INCLUDE 50 the best performing model obtained an accuracy of 89.5%. This model employs a feature extractor that has been pre-trained, as well as an encoder and a decoder.
Optimizing MRC Tasks: Understanding and Resolving Ambiguities

The attention model allows paying flexible attention to only those components of the input that contribute to the effective execution of the task at hand. An artificial intelligence competition known as Machine Reading Comprehension (MRC) asks machines to respond to questions based on passages that they have been provided with. The primary purpose of this research is to provide responses to questions that were taken from the Stanford Question Answering Dataset (SQUAD), which includes paragraphs along with questions and the answers that correlate to those questions. This study focuses on the implementation of various approaches that take advantage of the attention mechanism. A thorough examination of emerging methods for producing word embeddings, feature extraction, attention mechanisms, and answer selection. The flaws and concerns with the model’s fairness and trustworthiness have also been noted.
Enhancing Image Denoising Performance through a Family of Algorithms

Images are an integral and indispensable aspect of various disciplines, such as medicine, surveillance, and the entertainment industry. However, the quality of images can be severely compromised by the presence of sensor noise, quantization errors, or transmission errors. This research proposes a novel approach that combines wavelet thresholding and BM3D (Block-Matching and 3D Filtering) techniques for effective image denoising.The efficacy of the methodologies is evaluated and compared to cutting-edge denoising techniques, demonstrating superior performance in both quantitative metrics and visual quality. Furthermore, the study delves into the intricate mechanisms underlying the denoising process and the impact of various parameters on the denoising performance, contributing significantly to the field of image denoising.
Survey, Analysis and Association Rules derivation using Apriori Method for buying preference amongst kids of age-group 5 to 9 in India

what used to be an annual or bi-annual phenomenon of toys/sports/utility item purchase nearly two decades back, is a weekly/bi-weekly/daily transaction now-adays. Toys purchase by kids in a very frequent transaction that happens almost every alternate day in a big segment of society and thus produce high volumes of data. Consequently, there is rising scope to apply data mining methods to obtain toys/items_of_interest buying patterns amongst kids. In the present piece of research, we have applied Apriori algorithm to perform data mining using the data collected through a Google form after circulating children’s (age group 5-9) acquaintance of toys, across the country; the survey got carried out through students of two engineering colleges where diverse group of students from different parts of the country are studying. Nine association rules were achieved after applying Apriori Algorithm on the data set of the Toys/Sports items thus formed. Further, accuracy of framed rules has also been manually validated by the store owner; Beyblades and Carom are the most preferred toys/sports items; whereas Bicycle and Bat-Ball falls at second position in the list. The results provide very useful association amongst toys/Sports.
Analysis of Adversarial Attacks on Support Vector Machine

This paper investigates the use of Support Vector Machines (SVMs) in sleep stage classification and their sensitivity to adversarial assaults. It illustrates the power of machine learning (ML) for precise sleep stage classification, while also emphasizing the security risks posed by adversarial attacks on ML models. Using the secML module in Python, the study investigates defense mechanisms and the robustness of SVMs against adversarial attacks. The findings highlight the significance of taking security into account when designing and deploying ML models for safety-critical applications, such as autonomous driving, cyber-security systems, healthcare, etc.
Multilingual Indian Language Neural Machine Translation System Using mT5 Transformer

This paper demonstrates the development and evaluation of a multilingual neural machine translation system for Indian languages based on the mT5 transformer, successfully utilized to develop multiple state-of-the-art NLP models. We used the modified Asian Language Treebank multilingual dataset to train the system for developing a Machine Translation model capable of translating text in English, Hindi and Bengali amongst each other. Our system was able to achieve acceptable BLEU scores of over 20 in five of the six language pairs, with the English to Bengali system achieving a maximum BLEU score of 49.87 and the Bengali to English system achieving an average BLEU score of 42.43.
Stratification of Hacker Forums and Predicting Cyber Assaults for Proactive Cyber Threat Intelligence

Cyber hazards have emerged as a significant cause of worry for society. Firms are beginning to invest heavily in developing Cyber Threat Intelligence in recent years in order to combat the rising threat of cyber-attacks (CTI). Predominantly, many businesses gathered and analyzed data from internal log files, leading in reactive CTI, which is essentially a data-driven process. The internet hacker community may provide significant proactive CTI value by alerting enterprises about risks that they were previously unaware of. Forums, more than any other platform, give the most metadata, data persistence, and tens of thousands of publicly available Tools, Techniques, and Procedures. Anticrawling techniques, including as authentication, throttling, and obfuscation, are commonly employed in forums. This study intends to create a unique web crawler, as well as use machine learning and deep learning approaches with neural networks to automatically categorize hacker forum data into predetermined categories and anticipate probable future cyber risks for proactive and timely CTI.
Multifactor Authentication System

Authentication plays a crucial role in our day-to-day life. Whether it is in your office, college, university, hospitals, banks, social media account, and whatnot. In the present day, most authentication systems are using a single metric to determine authenticity (i.e., password). Although there are some systems that use Multi-Factor Authentication which includes OTPs, unique codes such as Time-based codes, etc., they require an additional device to carry. In our project, we want to develop a Multi-modal Authentication system where we use the unique metrics of a person to identify him such as the face, and fingerprint. In order to develop such a system, we made use of Raspberry Pi, OpenCV, and AWS services.
Cross Platform Mobile Application for solving Calculus

Mathematics is widely used in engineering and education domains. The ubiquitous use of smartphones makes it easy for a user to digitally look for a math solution. Manually typing a mathematical expression is a cumbersome process; many a time, even basic mathematical expressions with a superscript and subscript get difficult to interpret. As a result, this paper describes a cross-platform mobile scanner application integrated with optical character recognition (OCR) to extract the math expression from a handwritten or printed image, as well as the final solution to the problem. The mathematical equation scanner proposed is an efficient and time effective solution for solving polynomial and calculus equations.
Deaf and Mute Sign Language Translator on Static Alphabets Gestures using MobileNet

Sign language is the language used by deaf and dumb people to communicate with others. Deaf and mute people express their thoughts and ideas through hand movements or facial expressions or gestures. However, interpreting sign language can be challenging for individuals who are not fluent in it. The current sign language recognition methods often rely on expensive hardware such as depth cameras or specialized gloves, which can be a barrier to widespread adoption. In this paper, we propose a low-cost solution for sign language recognition using MobileNet, a lightweight convolutional neural network architecture. This Paper deals with the static American Sign alphabet (j and z dynamic). The proposed model extracts the features and classifies them. The Model is able to predict the alphabet successfully corresponding to the sign. A finger Spelling dataset is used to train and test the model. The proposed model was successfully recognized with an accuracy of 99.93%. The obtained results and graphs show that the system is able to predict the sign correctly.
Improved American Sign Language Recognition and Correction Using Inception Network, MediaPipe and PyEnchant

Sign language recognition through image processing presents challenges related to the requirement of real time applicability and high accuracy. Though previous work adopting methodologies from deep convolutional neural network architectures have shown to achieve good performance, they lack a consummate solution in terms of accuracy due to consideration of word based recognition. Recent development of Inception Network based architectures have shown promising classification accuracy with relatively less computational demand. Hence in this paper we propose a methodology that adopts Inception Network for the task of Sign Language Recognition. We considered the American Sign Language Recognition and Correction model. The correction and suggestion tools are implemented in the model to rectify any incorrect sign detection. The results from our approach achieves accuracy in the order of 99 percent.
Human Fall Detection using Skeleton Features

Unintentional falls of people, when left without serve may cause severe injuries and in extreme cases, they may even lead to loss of lives. In order to provide timely medication, detection of fall events when occurred is necessary.Any action can be considered as the specific motion of various bone key points. So, in our work, we considered bone key points as feature extractors. The MediaPipe framework developed by Google is used to detect the bone key features and its coordinates on the human skeleton. The data obtained is then normalized with respect to the boundary box drawn around humans. Machine learning classifiers, RF, SVM and Deep Learning model, DNN are then used individually to recognise and classify the action into fall or non-fall events. NTU-RGB+D dataset is used in our work. Real time detection using a webcam is also tested. The accuracy achieved by DNN model is 97.63% and that of SVM and RF classifiers is 83.3% and 99.34% respectively. Thus, the highest accuracy is achieved by RF classifier which is 99.34%.
Classifying Human Activities using CNN and ConvLSTM in Video Sequences

Video surveillance plays an important role to analyze any anomaly activity in the given premises. However, cameras can only capture the video information but cannot determine the type of activity on its own. Therefore, such systems require regular human intervention and monitoring. This requires a lot of time and manual efforts. This calls for the need of automatic human activity recognition (HAR) system. This is possible using latest technologies like computer vision and deep learning based systems. Recognizing human activities in videos is a challenging task in computer vision. The main function of intelligent video systems is to automatically identify and tag the actions performed by people in video sequences accurately. The objective of this research is to develop a model that can accurately recognize and classify human activities from video footage. The information captured by the cameras i.e., videos can be used to determine the type of activity using deep learning based networks. Such a network should be capable of classifying the videos using the available spatial and temporal information. In this paper, a framework is proposed where the data is pre-processed initially to reject redundant information. This data is fed then into deep network to predict the event. In this paper, for HAR, two different network models are presented based on the size of the sequence of frames. One network takes in just the most significant frame and the other uses a longer sequence of frames for predicting the behavior as a time domain parameter.
Multimodal Autonomous Verbal Assessment With Visual Inspection

This paper proposes an autonomously assessing system for the verbal examination of candidates. The system uses audio-video inputs and processes them to detect the candidate’s spoken answer, and compares it to the model answer in the dataset with the corresponding question. The semantic similarity score will be calculated and used to recommend the next question from the database using various types of recommendation systems discussed in the paper. Additionally, the system employs video analysis techniques to detect and prevent modern malpractices like multiple faces and reading from notes during the examination process. The proposed system aims to improve the efficiency and fairness of verbal examinations by eliminating human bias and accurately evaluating the candidate’s understanding of the subject. The system performance will be evaluated using a dataset of spoken answers and the results will demonstrate its effectiveness in improving the efficiency and fairness of the verbal examination process.
A Comprehensive Product Review System for Improved Customer Satisfaction

Digital reviews now have a significant impact on how consumers communicate globally and how they make purchases. When a buyer looks at the product’s ratings and reviews, they are frequently confused by the sheer volume of them. In the proposed study, product reviews are classified into positive and negative sentiments using the VADER (Valence Aware Dictionary for Sentiment Reasoning), a machine learning model that classifies reviews into positive and negative sentiments based on attributes discovered by the model that is used in the proposed work to categories product reviews into positive and negative categories. We provide the consumer with a graph of the number of good and negative reviews for the product they are interested in, as well as the total positive and negative review polarity for the item. To save clients’ time, a graphical representation of the analysis is also given.
Genetic Algorithm-based Framework for Layer-Fused Scheduling of Multiple DNNs on Multi-core Systems

Heterogeneous multi-core architectures are becoming a popular design choice to accelerate the inference of modern deep neural networks (DNNs). This trend allows for more flexible mappings onto the cores, but shifts the challenge to keeping all cores busy due to limited network parallelism. To this extent, layer-fused processing, where several layers are mapped simultaneously to an architecture and executed in a depth-first fashion, has shown promising opportunities to maximize core utilization. However, SotA mapping frameworks fail to efficiently map layer-fused DNNs onto heterogeneous multi-core architectures due to ignoring 1.) on-chip weight traffic and 2.) inter-core communication congestion. This work tackles these shortcomings by introducing a weight memory manager (WMM), which manages the weights present in a core and models the cost of re-fetching weights. Secondly, the inter-core communication (ICC) of feature data is modeled through a limited-bandwidth bus, and optimized through a contention-aware scheduler (CAS). Relying on these models, a genetic algorithm is developed to optimally schedule different DNN layers across the different cores. The impact of our enhanced modeling, core allocation and scheduling capabilities is shown in several experiments and demonstrates a decrease of 52% resp. 38% in latency, resp. energy when mapping a multi-DNN inference, consisting of ResNet-18, MobileNet-V2 and Tiny YOLO V2, on a heterogeneous multi-core platform compared to iso-area homogeneous architectures.
Performance Comparison of FPGA based Linear SVMS Classifier and Computer Simulation

Machine learning algorithms are the turf of artificial intelligence which handle the learning aspect of computers/machines due to advancement in technologies that permitted these binary machines to build a better understanding of patterns and logic; also help in finding solutions for real-world problems. As a machine learning tool, support vector machines (SVMs) are a prominent supervised algorithm that deals with the classification and regression of the observed datasets. In this paper, support Vector Machine algorithm has been implemented over Field Programmable Gate Array (FPGA) for classification in linear mode to accelerate the computations by the aid of the parallel nature of FPGAs to acquire high prediction accuracy at a cost of its high computational complexity.
Butterfly Effect Attack: Tiny and Seemingly Unrelated Perturbations for Object Detection

This work aims to explore and identify tiny and seemingly unrelated perturbations of images in object detection that will lead to performance degradation. While tininess can naturally be defined using <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{p}$</tex> norms, we characterize the degree of “unrelatedness” of an object by the pixel distance between the occurred perturbation and the object. Triggering errors in prediction while satisfying two objectives can be formulated as a multi-objective optimization problem where we utilize genetic algorithms to guide the search. The result successfully demonstrates that (invisible) perturbations on the right part of the image can drastically change the outcome of object detection on the left. An extensive evaluation reaffirms our conjecture that transformer-based object detection networks are more susceptible to butterfly effects in comparison to single-stage object detection networks such as YOLOv5.
Mitigating Heterogeneities in Federated Edge Learning with Resource- independence Aggregation

Heterogeneities have emerged as a critical challenge in Federated Learning (FL). In this paper, we identify the cause of FL performance degradation due to heterogeneous issues: the local communicated parameters have feature mismatches and feature representation range mismatches, resulting in ineffective global model generalization. To address it, Heterogeneous mitigating FL is proposed to improve the generalization of the global model with resource-independence aggregation. Instead of linking local model contributions to its occupied resources, we look for contributing parameters directly in each node's training results.
Class-based Quantization for Neural Networks

In deep neural networks (DNNs), there are a huge number of weights and multiply-and-accumulate (MAC) operations. Accordingly, it is challenging to apply DNNs on resource- constrained platforms, e.g., mobile phones. Quantization is a method to reduce the size and the computational complexity of DNNs. Existing quantization methods either require hardware overhead to achieve a non-uniform quantization or focus on model-wise and layer-wise uniform quantization, which are not as fine-grained as filter-wise quantization. In this paper, we propose a class-based quantization method to determine the minimum number of quantization bits for each filter or neuron in DNNs individually. In the proposed method, the importance score of each filter or neuron with respect to the number of classes in the dataset is first evaluated. The larger the score is, the more important the filter or neuron is and thus the larger the number of quantization bits should be. Afterwards, a search algorithm is adopted to exploit the different importance of filters and neurons to determine the number of quantization bits of each filter or neuron. Experimental results demonstrate that the proposed method can maintain the inference accuracy with low bit-width quantization. Given the same number of quantization bits, the proposed method can also achieve a better inference accuracy than the existing methods.