Reviewerly/exHarmony huggingface dataset:
Deep Learning and Sensor Fusion Technique for Indigenous Intelligent Vision System for Theft Detection

This paper proposes an indigenous system with modified architecture to classify different kinds of objects used in burglary and certain suspicious activities identified in thievery. The classifier used is a part of the security system to detect the images/ movements pertaining to theft or unusual acts while shoplifting valuable objects from jewellery stores. A sequence of integrated real-time computer vision algorithms and sensor fusion technology are implemented. The system adopts the transfer learning method (has 68 layers, 75 connections) as a pre-trained neural network to identify the unusual objects. Here, a person can take suitable action to aid the situation. The classifier algorithm was trained on a dataset of 144 cases of images and augmented appropriately belonging to seven different classes. It provides an accuracy of 97.73% during the validation phase and the confusion matrix resulted in an accuracy close to 95% and an F1-score of 84%.
An RNN-Based Resource Allocation Strategy in Cloud Computing

Cloud computing has become an essential technology for businesses and individuals, providing a flexible and scalable way to store, process and access data and applications. As cloud usage continues to grow, there is a need to optimize resource allocation and minimize costs. The auction mechanism in cloud computing is important because it ensures that cloud resources are allocated to customers based on their needs and budget. Cloud providers set the initial price for the resources and customers can bid above or below this price. The auction typically lasts for a specified time, and the resources are allocated to the highest bidder at the end. This paper proposes an RNN-based resource allocation strategy for cloud computing. The proposed strategy leverages the power of recurrent neural networks to predict future resource demands of cloud applications and dynamically allocate resources accordingly. The RNN model is trained on historical data of resource usage and can capture the temporal patterns and correlations in the data. The proposed strategy is evaluated through experiments on a real-world cloud platform, and the results demonstrate that it outperforms existing resource allocation strategies regarding both resource utilization and application performance. The Proposed approach outperforms existing mechanisms in terms of enhancing the efficiency and effectiveness of cloud computing systems.
Co-Design of Algorithm and FPGA Accelerator for Conditional Independence Test

Conditional independence (CI) testing is a critical statistical method that determines conditional independence between variables using data. It is useful for various data mining applications, such as causal discovery, Bayesian inference, and agent-based model validation. However, the high volume of CI test queries and the large data sizes make CI testing computationally intensive. This paper proposes a hardware-oriented residual-based CI testing algorithm, co-designed with an FPGA accelerator, to address this issue. Our system accelerates CI tests by skipping least-squares computations algorithmically, enabling fixed-point operations in correlation evaluation and parallelization of permutation tests. Our experimental evaluation demonstrates that our method is as accurate as state-of-the-art CI testing approaches. Furthermore, our experimental implementation on an Intel Arria 10 FPGA delivers up to 32 times higher performance compared to state-of-the-art CI test tools running on eight Intel Xeon Silver 4110 CPU cores.
Devanagari Character Recognition Using MLP-Mixer and CNN Extracted Features

Devanagari is an ancient script that is used to write Hindi, Nepali, Marathi, Maithili, Awadhi, Newari, and Bhojpuri, among other Indo-Aryan languages. Thousands of individuals in India use this script to write documents in Marathi and Hindi. Indian mythology is based on this script. Because of the script's prominence, handwritten Devanagari character identification has grown in popularity over time. In this paper, we present a comparative analysis of different machine learning and deep learning models for Devanagari character recognition. MultiLayer Perceptron(MLP) Mixer is the technology we're using because extensive research has been done in Devanagari Text Recognition using Convolutional Neural Networks(CNN). This paper aims to provide a comparison between traditional models and a newer model(MLP Mixer). We have used CNN as a standalone deep learning model for our problem and used it for feature extraction. We have also developed some traditional machine learning classifiers using these learned complex features.
Surveillance Robot with Object Detection System

Surveillance is an integral part of life nowadays. In every organization, firms, workplace and home, surveillance is required. Automatic surveillance can be achieved with the use of sensor technology, processor module and embedded cameras. Here a solution is presented with the integrated technology of ultrasonic sensor, ESP-32 embedded camera, dc motor & arduino processor module to design a fully automotive surveillance and obstacle avoiding robot in order to provide free movement within a premises. Hardware prototype is developed with the use of aforesaid components and tested successfully.
SONA: An Accelerator for Transform-Domain Neural Networks with Sparse-Orthogonal Weights

Recent advances in model pruning have enabled sparsity-aware deep neural network accelerators that improve the energy-efficiency and performance of inference tasks. We introduce SONA, a novel transform-domain neural network accelerator in which convolution operations are replaced by element-wise multiplications with sparse-orthogonal weights. SONA employs an output stationary dataflow coupled with an energy-efficient memory organization to reduce the overhead of sparse-orthogonal transform-domain kernels that are concurrently processed without any conflicts. Weights in SONA are non-uniformly quantized with bit-sparse canonical-signed-digit representations to reduce multiplications to simple additions. Moreover, for sparse fully-connected layers (FCLs), SONA introduces column-based-block structured pruning, which is integrated into the same architecture that maintains full multiply-and-accumulate (MAC) array utilization. Compared to prior dense and sparse neural networks accelerators, SONA can reduce inference energy by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5.1\times$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.4 \times$</tex> and increase performance by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5.2\times$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.1\times$</tex> , respectively, for convolution layers. For sparse FCLs, SONA can reduce inference energy by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.4\times$</tex> and increase performance by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\times$</tex> compared to prior work.
Content Richness Evaluation Method For Abstractive Summarization

The abstractive summarization model can generate summaries with factual errors, providing error correction models can solve this general problem. Deleting erroneous facts is the most commonly used important strategy in the error correction process. However, we found through experiments that when using this strategy, the error correction model is more inclined to delete all suspicious content, resulting in a significant reduction in information volume. To ensure both the information richness and factual consistency of summaries, this paper proposes a content richness evaluation method named ASCRE. It evaluates the richness of a summary by measuring the quantity and effectiveness of the information contained within it. This paper demonstrates through experiments that ASCRE can effectively evaluate the quality of abstractive summarization system and error correction models. At the same time, we propose a factual consistency and content richness trade off line as the quality evaluation benchmark.
Revolutionizing Renewable Sources of Energy using IoT: Impact and Challenges

The use of Internet of Things (IoT) technology in renewable energy is examined in this study. The efficiency and productivity of renewable power plants must be increased to meet the rising demand for renewable energy. By enabling real-time monitoring of numerous parameters like dust collection, temperature, weather, and so forth, IoT offers a solution to this issue. The production of energy can be improved using these statistics. The paper also addresses the many IoT sensors that can be used to construct such a system and the difficulties that must be overcome for its adoption. The study's findings show how the Internet of Things (IoT) has the power to revolutionise renewable energy sources and open the door to greater effectiveness, financial success, and environmental sustainability.
Evaluation of Credit Bank Information Management System Model Based on Blockchain Technology

With the continuous development of blockchain technology, its application in the field of credit supervision is becoming increasingly widespread. This article took bank credit as an example to analyze the credit risk management system constructed based on blockchain technology. The system used blockchain as an information sharing platform for data storage and management, and established corresponding security measures for transaction processes, payment and settlement, so as to provide online monitoring functions. By establishing a dynamic supervision mechanism for the entire process of financial business, the automation of credit default warning and processing work was achieved. Afterwards, this article also tested the performance of the model. The test results showed that the memory usage of the credit bank information management system model based on blockchain technology was between 1% and 5%.
Traditional Mongolian-to-Cyrillic Mongolian Conversion Method Based on the Combination of Rules and Transformer

Mongolian words are composed of stems and suffixes, resulting in a large vocabulary of Mongolian and many Out-of-vocabulary (OOV) words. Using only a dictionary cannot convert Traditional Mongolian OOV words. Therefore, this paper proposes a Traditional Mongolian to Cyrillic Mongolian conversion method based on combining rules and Transformer. This method uses rules based approach for converting in-vocabulary words and the Transformer model to convert OOV words. Experimental results show that using a Transformer outperforms the previous best OOV words conversion model, and the conversion method based on the combination of rules and Transformer has WER of 13.18%.
An efficient container bin-packing method for large scale cloud-native clusters

Scheduling tens of thousands of containers in large scale cloud-native clusters faces great challenges. Imbalanced resource utilization and fragmentation is a key challenge. To address this, we propose an efficient resource scheduling optimization method for large-scale data center. By exploiting the similarities in resource usage among instances within each cloud-native application, our approach aims to find a set of optimal application colocation hints for each application under different types of physical machines. The optimized colocation hints can be used in bin packing algorithms, allowing for efficient and effective resource management in data centers. Experiment results show that our proposed solution outperforms the classic FFD and DP algorithms in ter ms of the number of saved physical machines and the resource usage standard deviations of cluster nodes.
An Efficient approach of True Random Number Generation using A Data encryption method in Security &amp; cryptography algorithm

True random number generators (TRNGs) have drawn a lot of focus due to their numerous uses in data encryption and transfer. The encryption method or privacy protocols of the data protection centre often use the real random numbers produced by the true random number generators. TRNGs were already being used to save energy in the recent times developed stochastic computing paradigm. This methodology utilizes the extended symmetric key cryptography technique, which utilizes the very same framework for both the encryption and decryption processes. The encryption and decryption keys used in traditional cryptography techniques are identical and are kept private. The technique employs a randomized numbers generator to generate keys, which increases technique performance. The algorithm uses 512-bit keys for increased security and offers the idea of intrinsic key creation at the receiving end using the 512-bit key provided by the transmitter. To avoid conventional warfare assaults as well as any damaging cyber threats, this private key will be stored in the transmitter side databases and sent to the side of the receiver in a different way. This approach performs encryption and decryption more effectively for huge information than previous techniques do. With huge documents with reduced latency, this technique offers a higher pace compared to previous methods.
Design and Implementation of Snake Detection Cum Repellent Device

Each year, approximately 5.4 million snake bites occur. Between 81,410 to 1,37,880 people die every year, and there are around three times as many amputations and other types of lasting disability. Up to 2 million individuals in Asia are bitten by snakes each year, necessitating medical attention. Just, by warning people about the existence of snakes nearby, most fatalities and severe effects from snake bites can be avoided. Since majority of government universities are situated on forested areas, snake concerns affect practically every institution. Every year during the monsoon, residents at our hostel experience several snake encounters which make them feel uneasy. Therefore, we suggest creating a prototype that can quickly identify the existence of snakes and inform the students living in that hostel so that they can take precautions. The suggested system's major goal is to be able to recognize the presence of snakes based on the computer vision technique. To accomplish the stated, the system uses the YOLO (YOU ONLY LOOK ONCE) model which is a popular object detection model. The system consists of three modules: sensing and detection, alert, repellent. Motion sensors and Pi-camera make up the sensing module. The Raspberry Pi has Open CV loaded for YOLO object detection. Input fed from the Pi-camera will be analyzed using the YOLO algorithm and snake will be recognized using our pretrained custom dataset. Based on the output from the detecting module, GSM & GPS module sends the alert along with location and the repellent module will generate ultrasonic vibrations.
EKIGCN: External Knowledge Injected Graph Convolutional Networks for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a challenging subtask in the natural language processing community, which aims to determine the sentiment polarity about the corresponding specific aspect terms. In existing works, external knowledge has been proven effective to improve the ABSA tasks’ performance. However, it is still essential to study further to find a universal method for ABSA tasks. Therefore, we propose an external knowledge-injected graph convolutional network (EKIGCN) to enhance the sentiment representation with commonsense. Specifically, Sentic Net is leveraged to provide external knowledge to construct an affective dependency graph. In order to fully exploit the effectiveness of affective knowledge, a shallow mutual interaction is utilized to fuse the learned contextual representation and the graph. Besides, to make EKIGCN sensitive to aspect terms, an aspect-aware multi-head attention module is also employed to enhance the corresponding relations between aspect and contextual words. Extensive experiments are conducted on three popular datasets to validate the effectiveness of our proposed model, and the results outperform the mentioned state-of-the-art methods in ABSA tasks.
Review on Mobile App Development: Tools and Techniques

In today's world, mobile phones play an important role in improving our lives. Various applications for mobile banking, travel ticket booking, and health monitoring are making life easier and more convenient. With the use of mobile phones and their applications, we have entered a digital age where almost everything is available at our fingertips. A major task for mobile app developers is to choose the correct tool for developing apps. There are three basic technologies i.e. Native, Web, and Hybrid for developing mobile apps. The choice of mobile app development tool or technology will depend on the specific needs of the project. Each approach has its strengths and weaknesses, and developers must consider factors such as budget, timeline, and performance requirements when selecting a development approach. The most outstanding performance and user experience are provided by native apps, but their development can be costly and time-consuming. Web applications are the least expensive and easiest to develop, but they may not offer the same level of performance or access to device features as native or hybrid apps. Hybrid apps offer an acceptablecompromise between performance and development costs. In this paper, we mainly focus on different mobile app development tools and techniques e.g. React Native, Flutter, Ionic, Xamarin, and programming languages used e.g. HTML, CSS, and J avaScript along with their applications and limitations. Apart from this, we have also done a comparative analysis of these tools and techniques and programming languages that are beneficial to developers and researchers working in the area of mobile app development.
The Least Square QR Method Improves Extreme Learning Machine

The Extreme Learning Machine (ELM) is a fast and straightforward learning algorithm designed for single hidden layer feed-forward neural networks (SLFNs). The ELM randomly assigns input weights and calculates the output weights analytically using the Moore-Penrose generalized inverse (MP) (also called Pseudo inverse). However, the MP is not preferable for ill-posed systems as it is sensitive to perturbation and may produce inaccurate solutions. Furthermore, it is computationally expensive as it involves matrix inversion. Regularized ELM (RELM) was proposed to address the limitations of the MP. In RELM, the regularization parameter is the primary factor that controls the classifier's performance. Though the RELM is stable and produces accurate results, finding a n a ppropriate regularization parameter value is tedious and increases the computation. This work proposes a variant of ELM named LSQR-ELM algorithm, which balances the ELM and RELM by maintaining stability and accuracy and avoiding the regularization entirely. The LSQR-ELM is based on the Least Squares QR (LSQR) iterative technique. Since the LSQR finds p a rtial Q R rather than full QR, it reduces unnecessary computation. To verify the proposed algorithm's ability, comparisons are made with several existing methods including ELM, Minimum Residual Method (MT-ELM), SVD-based SG-ELM, Optimally Pruned ELM (OP-ELM), and K-nearest neighbor (KNN). Extensive experimen-tation on various datasets demonstrates that the LSQR-ELM algorithm outperforms ELM, SG-ELM, OP-ELM, and KNN on most datasets. Moreover, it exhibits comparable performance to MT-ELM while requiring significantly f e wer computations.
Adversarial Defense based on Image Style Transfer

Recent research shows that deep neural network is vulnerable to the attack of adversarial samples, which leads to the incorrect output of the model. Therefore, the defense against adversarial attacks on deep learning models has become the hotspot of artificial intelligence security research. This paper proposes an image-processing-based defense method ADIST (Adversarial Defense Based on Image Style Transfer), which removes the adversarial perturbation from the input image by wavelet image restoration, multi-template image style transfer, and feature modulation before the classification by the target classifying model. Specifically, the input image is style-transferred multiple times using different style templates. In each style-transfer branch, the input image is denoised by wavelet decomposed and reconstructed after randomly dropping a certain amount of high-frequency components. Then a feature encoder is applied to the reconstructed image and the style template to extract image features. The image features are further pruned by adaptive truncation to eliminate part of the antagonistic disturbance. Then, the semantic information of the input image is fused with the style-template part to generate a new image by the style-transfer module. Finally, the classification results for these multiple new images are combined to produce the final prediction. We have tested the proposed method using ResNet34 and WideResNet50 as the classification network on the Mini-ImageNet dataset. Under white box attack, the proposed method is 2.2% and 2.7% better than the most advanced RAIN method based on image preprocessing, respectively.
Security Enhancement of QR Code Using S-Boxes and Hyper-Chaotic System

QR code is widely used in numerous applications, as they are easy to create and scan. The crucial data inside the QR code are widely used by mobile devices. So, it becomes essential to protect associated data inside the QR code from a third party. This manuscript proposed a method to protect the data of QR codes using a reconfigurable hyperchaotic system and subbyte image encryption and decryption algorithm. To demonstrate this approach, consider the QR code image and modify this image using an encryption process and further decode the original QR code, in all cases analyzed their histogram. This analysis has been done on the MATLAB tool.
Post Quantum Blockchain Assisted Privacy Preserving Protocol for Internet of Medical Things

Internet of Medical Things (IoMT) is an advanced ecosystem made with the help of Internet of Things technology, smart sensors, medical equipment, and connected end users. Internet of Medical Things is very helpful to enhance efficiency, accuracy, and qualities traditional healthcare system. A smart IoMT contains end users, heterogeneous network, and private data is transferred through public channel. Therefore, it becomes hard to achieve both security and privacy in the IoMT system. In this article, we have focused on designing privacy preserving authenticated key mechanism between two parties. We have proposed a blockchain assisted authenticated key exchange mechanism for IoMT systems supporting fog computing paradigm. The proposed privacy preserving authenticated key exchange protocol is based post quantum assumption “ring learning with errors” and physical unclonable function (PUF), respectively. A detailed security analysis ensures that proposed protocol is secure against most of the existing attacks. This protocol is also secure against quantum attacks based on the assumption.