Reviewerly/exHarmony huggingface dataset:
LPPDA: A Light-Weight Privacy-Preserving Data Aggregation Protocol for Smart Grids

Smart meters are continuously being deployed in several countries as a step in the direction of modernizing the power grid. Smart meters allow for automatic electricity consumption reporting to energy providers to facilitate billing and demand-based power generation. However, research has shown that such high resolution reporting to suppliers can potentially be used to invade consumers’ privacy; by identifying and predicting their behavior based on their consumption readings. This work presents a new protocol to preserve users’ privacy while maintaining the benefits of smart grids. The proposed method utilizes different techniques like randomization, masking, and differential privacy to build the scheme. The proposed method is shown to be more efficient compared to previouswork in terms of performance and communication overhead. The implementation, simulation, and analysis are performed on datasets of real smart meters readings of households and electric vehicle chargers.
Multimodal Assessment of Interest Levels in Reading: Integrating Eye-Tracking and Physiological Sensing

Interest in the context of reading holds special significance as it serves as a driving force for learning and education. By understanding and leveraging students’ interests, educators can create more effective and enjoyable learning environments that promote personalized learning experiences, enhanced comprehension, deep understanding, and motivation. A multimodal approach integrating gaze and physiological data could provide a more comprehensive and accurate assessment of interest levels. The goal of this study is to measure the level of interest experienced by users when reading newspaper articles by integrating gaze data and physiological responses. An experiment was conducted which recorded the gaze and physiological data from 13 university students reading 18 newspaper articles collected from the BBC news database. An SMI eye-tracker and an Empatica E4 wristband were used synchronously to capture the user’s eye movements and physiological data. To predict the interest levels of the participants, a manual feature extraction-based approach and a deep learning-based approach were employed. The interest levels were divided into four-class and binary based on the responses from the participants. A CNN-LSTM model using the gaze features outperformed other models in terms of accuracy and F1-score with 52.8% and 51.8 for four-class and 82.3% and 81.7 for binary classification using leave-one-document-out and leave-one-participant-out cross-validation, respectively.
Video Dehazing Network Based on Multiscale Attention

Learning-based methods are mostly performed on images. However, less attention has been paid to these methods for video dehazing. This is because of the challenges involved in finding ways to better extract and fuse the spatial and temporal features between consecutive frames, as well as the reconstruction of a more realistic latent frame. This study proposes a multiscale attention video dehazing network (MAVDN) to recover clear dehazed videos. In terms of feature extraction, the proposed method used a feature extractor based on multiscale attention to extracting features at different levels comprehensively. A pixel attention-guided multi-frame fusion module was designed for temporal modeling to aggregate complementary information between adjacent frames. Additionally, a reconstruction module based on cascaded dilated convolutions was designed to better reconstruct the latent frames in the reconstruction stage. Extensive experiments conducted on real-world video dehazing datasets show that the proposed MAVDN achieved superior dehazing performance compared to state-of-the-art methods.
Autoscaled-Wavelet Convolutional Layer for Deep Learning-Based Side-Channel Analysis

Continuous Wavelet Transform (CWT) is rarely used in the field of side-channel analysis due to problems related to parameter (wavelet scale) selection; There is no way to find the optimal wavelet scale other than an exhaustive search, and the resulting spectrogram analysis can introduce significant analysis complexity. However, a well-scaled CWT can improve the signal-to-noise ratio of side-channel signals, which can lead to better attack performance. And our insights suggest that there is scope for CWT and deep learning approaches to be combined, which could help the models to train more effectively while overcoming the problems of CWT. In this context, we propose a novel feature extraction layer that combines a CWT with a Convolutional Neural Network (CNN). The proposed method can leverage neural network training to automatically adjust a wavelet scale, which is a critical parameter of CWT. Furthermore, the proposed method can lead to performance improvements by enabling a deep learning model to perform on-the-fly multi-frequency analysis without any pre-processing. By bringing the two approaches together, we were able to overcome the limitations of CWT and improve the performance of deep learning-based side-channel analysis. As an experimental result using open dataset ASCAD, a de facto standard in deep learning-based side-channel analysis, we confirmed that the proposed method could improve the performance by inserting the proposed layer into existing state-of-the-art deep learning models.
Automatic Camera Selection, Shot Size, and Video Editing in Theater Multi-Camera Recordings

In a non-professional environment, multi-camera recordings of theater performances or other stage shows are difficult to realize, because amateurs are usually untrained in camera work and in using a vision mixing desk that mixes multiple cameras. This can be remedied by a production process with high-resolution cameras where recordings of image sections from long shots or medium-long shots are manually or automatically cropped in post-production. For this purpose, Gandhi et al. presented a single-camera system (referred to as Gandhi Recording System in the paper) that obtains close-ups from a high-resolution recording from the central perspective. The proposed system in this paper referred to as "Proposed Recording System" extends the method to four perspectives based on a Reference Recording System from professional TV theater recordings from the Ohnsorg Theater. Rules for camera selection, image cropping, and montage are derived from the Reference Recording System in this paper. For this purpose, body and pose recognition software is used and the stage action is reconstructed from the recordings into the stage set. Speakers are recognized by detecting lip movements and speaker changes are identified using audio diarization software. The Proposed Recording System proposed in this paper is practically instantiated on a school theater recording made by laymen using four 4K cameras. An automatic editing script is generated that outputs a montage of a scene. The principles can also be adapted for other recording situations with an audience, such as lectures, interviews, discussions, talk shows, gala events, award ceremonies, and the like. Test persons confirm in an online study the added value of the perspective diversity of four cameras of the Proposed Recording System versus the single-camera method of Gandhi et al.
An IEEE21451-001 Compliant Smart Sensor for Early Earthquake Detection

This paper introduces a novel smart sensor that employs an advanced algorithm for earthquake early warning (EEW). The sensor utilizes a smart sampling technique to extract significant signal information, simplifying the process of inferring knowledge. The main objective is to assess the potential destructiveness of an incoming earthquake by analysing the initial moments of the P-wave, and to generate an alert for prompt action, if necessary. The study includes the development and presentation of the proposed method, as well as performance evaluations using real seismic data obtained from freely accessible databases. These evaluations confirm the effectiveness of the proposed method in accurately estimating earthquake magnitudes. Furthermore, the paper includes a comparison with a widely used EEW algorithm. The real-time functionality and interoperability of devices are crucial considerations in earthquake detection applications. The suitability and compatibility of the proposed method with the IEEE1451 family of standards are demonstrated and emphasized in the paper.
Lossless Neural Network Model Compression Through Exponent Sharing

Artificial intelligence (AI) on the edge has emerged as an important research area in the last decade to deploy different applications in the domains of computer vision and natural language processing on tiny devices. These devices have limited on-chip memory and are battery-powered. On the other hand, neural network (NN) models require large memory to store model parameters and intermediate activation values. Thus, it is critical to make the models smaller so that their on-chip memory requirements are reduced. Various existing techniques like quantization and weight-sharing reduce model sizes at the expense of some loss in accuracy. We propose a lossless technique of model size reduction by focusing on the sharing of exponents in weights, which is different from the sharing of weights. We present results based on generalized matrix multiplication (GEMM) in NN models. Our method achieves at least a 20% reduction in memory when using Bfloat16 and around 10% reduction when using IEEE single-precision floating point, for models, in general, with a very small impact (up to 10% on the processor and less than 1% on FPGA) on the execution time with no loss in accuracy. On specific models from HLS4ML, about 20% reduction in memory is observed in single precision with little execution overhead.
Efficient-Adam: Communication-Efficient Distributed Adam

Distributed adaptive stochastic gradient methods have been widely used for large-scale nonconvex optimization, such as training deep learning models. However, their communication complexity on finding <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> -stationary points has rarely been analyzed in the nonconvex setting. In this work, we present a novel communication-efficient distributed Adam in the parameter-server model for stochastic nonconvex optimization, dubbed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Efficient-Adam</i> . Specifically, we incorporate a two-way quantization scheme into Efficient-Adam to reduce the communication cost between the workers and server. Simultaneously, we adopt a two-way error feedback strategy to reduce the biases caused by the two-way quantization on both the server and workers, respectively. In addition, we establish the iteration complexity for the proposed Efficient-Adam with a class of quantization operators, and further characterize its communication complexity between the server and workers when an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> -stationary point is achieved. Finally, we apply Efficient-Adam to solve a toy stochastic convex optimization problem and train deep learning models on real-world vision and language tasks. Extensive experiments together with a theoretical guarantee justify the merits of Efficient Adam.
Comparative Analysis of ASV Spoofing Countermeasures: Evaluating Res2Net-Based Approaches

Popular topics in the field of countermeasures include feature engineering and neural-network-based models, which involve neural network architectures and loss criteria. This study focuses on Res2Net and its variant models to examine the impact of model generalization on countermeasure performance in the ASVspoof 2019 logical access and physical access scenarios. Results reveal that while Res2Net exhibits superior generalization compared to its variants, the most effective countermeasure combines both feature engineering and model optimization. The proposed dynamic modulated-Res2Net utilizes channel-wise soft attention to recalibrate feature maps, offering adaptive adjustments to spoofing cues of varying scales. Evaluation on the logical access dataset demonstrates dynamic modulated-Res2Net's relative improvement of over 38% compared to Res2Net. Furthermore, we exploit low-frequency features and combine them with dynamic modulated-Res2Net to achieve in an equal error rate of 1.21% under logical access and 0.41% under physical access, establishing our proposed dynamic modulated-Res2Net as one of the top-performing single systems. Additionally, we compare the best countermeasures in different scenarios, highlighting the ongoing challenge of achieving generalization.
Simplifying Handwritten Medical Prescription: OCR Approach

Our approach in this paper offers the application of Optical Character Recognition for the digitalization of handwritten medical prescriptions. It is determined that Doctor’s handwritten prescriptions create a two-fold problem, one of the problems is unreadable writing and another inability of pharmacists to understand the medicine name. Our study will help in making pharmacist life easy at one end and making digitalized medical records maintenance easy. The field of OCR is still in the evolution phase. OCR is a branch of computer vision in computer science. Even after a lot of studies and related papers mentioning the procedures for translating handwritten textual content into computer-legible form, still it requires more research. OCR is a development process in which the tools are trained to recognize the image of any handwritten text/photo/scanned doc and transfer the same into character classification. This research can be used as a document helpful for researchers working in the Character Recognition research area. Identification of an optimal feature mining process might be the single-most valuable factor in attaining high character acknowledgment. The accuracy of the model is one of the important measures for the success of this research.
ARM-FT Autonomic Randomized Cloudlet Management Through Fault Tolerance

Autonomic computing is one of the emerging technology which provides some self-characteristics to manage the applications over cloud computing. Cloud computing platforms are being involved in many businesses to provide the business to business and business to customer services. This leads to the load over servers, and due to this, the automatic services monitoring and maintenance have become a big challenging issue over the internet. However, many researchers are focusing on this gap to overcome with their different research idea, but still, the need is to showcase the research gaps to the community for better understanding. Machine learning and optimization of Quality of Service QoS are the key parameters to automate the technology through self-learning activities. To automate the technological applications in industry demands, the use of a self-learning system is playing a vital role, and the knowledge of autonomic computing is required to produce to the research community. This paper is focused on randomized cloudlets management through autonomic computing characteristics and fault-tolerance technique (ARM-FT), and the experimental results found significant records to improve the systems based on resource utilization.
Analyzing the Online Customer Post-purchasing Behavior: Sentimental Analysis Approach

As the entire world is growing rapidly with innovative and improvised technologies, all become more dependent upon internet-based services. E-commerce is one of the major sectors which already have an immense impact on everyday lifestyle. The e-commerce giants like Amazon and Flipkart are now competing in Indian market with a neck-on-neck market share of 31.2% and 31.9%, respectively. As availability, transparency and logistics are becoming almost homogenous for all the competitors, everyone tries to focus on the market capturing by investing more time on the customer needs. To analyze the customer needs, it is very important to understand the sentiment of them so that can get an extra advantage over other competitors. In this study, customer sentiment was analyzed in the form of customer feedback from the portals of Amazon and Flipkart. As the feedbacks are in text format and qualitative in nature, Natural Language Processing (NLP) method was applied using R software. Different codes for sentiment analysis were applied to understand that if there is any difference in customer sentiments in those two portals.
Deep Learning-Based Framework for URL Phishing Detection

The modern digital world is full of breakthroughs and innovations due to the exponential rising of digital devices, user numbers, and the internet that connects people to those gadgets. Because the number of internet users is increasing at an exponential rate, cybercrimes are also growing at an exponential rate. The weaknesses or vulnerabilities employed by the criminal in the user's device are the primary reason for the hacking. Because the user is unaware of the vulnerabilities in their equipment, hackers are more likely to exploit those weaknesses and carry out cybercrime. Phishing is the most frequent cybercrime, with criminals using it to carry out illicit operations such as obtaining unauthorized access and passwords, fraudulent transactions, identity theft, etc. As a result, when it comes to the user being the weakest link for Hackers to carry out illicit acts, Phishing detection is and will continue to be a trending and hot topic in the digital world. There are various available techniques for the detection of the Phishing URLs like the blacklist approach, machine learning-based approach, visual features extraction, etc. These methods were popular, and some of these provided improved accuracy when trained and tested using a machine learning model and classification algorithms. However, when the model is refined, new datasets and hacking techniques are developed daily. As a result, the model must be trained using updated datasets and various methodologies. The machine learning techniques used for the detection of Phishing websites may or may not be effective when applying it to real-world scenarios. However, using this approach, we can build an Artificial intelligence-based model to check for the website to be a Phishing or Benign webpage (URL). The main idea behind the AI model to build is to extract the website features and use them for training the models. This paper explores and exploits the neural network models and machine learning classification models. A detailed comparative analysis of performance evaluation is also explored, examined, and discussed. After training the model using the features collected from the websites, we have predicted the outputs from the test data and given the prediction that a website is benign or Phishing.
BERT Based Language Identification in Code-Mixed English-Assamese Social Media Text

Language identification in code-mixed language pairs has progressively gained research interest in recent times. Due to the extensive use of social media, it has become necessary to identify languages in code-mixed text for dealing with tasks such as detection of hate speeches, misinformation, and disinformation. Recent transformer models such as BERT have shown very good results in many NLP tasks including language identification. This work uses a transfer learning approach by applying a BERT model for language identification at a word level in a code-mixed Assamese-English language pair. Experimental results performed with an available data set show that BERT performs better than using word-level features or semantic word embeddings with an accuracy of 94%.
Deep Learning Based World Coin Currency Detection

Detection and recognition of coin currency play an important role in a lot of banking systems and for collectors of ancient or discontinued coins to recognize or detect fake ones from real ones. Current coin recognition systems are established based on the physical characteristics of the coin, on the other hand, image recognition models are utilized for shape, engravings, and edge detection. For the detection of world coin currency, we proposed a Deep learning-based convolutional neural network with MobileNet V2. CNN is the most advanced and efficient image processing deep learning technique. The model is trained on more than 6000 pictures for training data, validation data and testing data and categorized the data as per the country using a JSON file. The model obtained a training accuracy of 86.87%, a validation accuracy of 82.65%, and a testing accuracy of 84.24%. The model predicts one incorrectly out of the 10 predictions which indicate how well-trained the model is. To identify coin currencies in real-time, this model must be integrated with cell phones in the future.
Blockchain Supported Sustainable Supply Chain in Industry 4.0

Complex and globalized supply chains can only be managed through multi-echelon supply chains connecting with technology. Sustainability is safeguarded by practicing digitization using technology with adequate transparency. The seamless coordination across supply chain partners augments the value chain. Uninterrupted connectivity guards against disruptions, agility, and resiliency. Innovative technology like Blockchain technology which is a distributed ledger that shares data on the peer-to-peer network in supply chain operations offers transparency, trust, and consensus among stakeholders with flexibility in operations. The use of these technologies benefits in the form of reducing the Bullwhip effect across the supply chain. The study provides technological advancements for sustainable supply chain management. Literature is studied for evaluating Industry 4.0 and available technologies like the internet of things, IoT-enabled manufacturing, blockchain-managed transportation and logistics, and smart models for business operations. The study proceeds with Industry 4.0 and beyond to onlooking blockchain capabilities for a sustainable supply chain. The area covered is (a) green supply chain practices, (b) improved product lifecycle, monitoring, and reporting sustainability, and (c) improved monitoring and reporting sustainability.
Image Retrieval Technique Using Visual Features in Content-Based Image Retrieval System

The rapid growth and widespread availability of visual data on the internet has resulted in a growth in image search and retrieval research. In the last years, Content-based image retrieval has gotten a lot of attention which uses visual content depiction to retrieve the relevant images. Methods for visual retrieval using text search methods may suffer from incompatibility between text words and visual content due to the lack of visual content as a grading hint. There are some major challenges in this research like semantic gap, intention gap. To overcome this issue many research has been done. This research focused on the semantic gap between the images for which histogram equalization technique is used after converting the images into grey. It has been applied on color images by applying the same procedure to the RGB components of the image's RGB color values individually. However, because the procedure affects the relative ranges of the color balance, using the same technique to change the RGB components of an RGB image could result in significant changes to the image’s color balance and allow you to recover the intended image with greater accuracy.