Reviewerly/exHarmony huggingface dataset:
A Novel Homomorphic Computing Approach of Edge Internet of Things

Edge Internet of Things (EIoT) involves computing at edge servers rather than at the cloud, thus primarily reducing latency and bandwidth issues. A traditional EIoT system often ignores the security issues that prevail due to power and computational limitations. Additionally, a hierarchical EIoT system essentially acts like a cloud system requiring security access to be given to a third party for data analysis. The concept of homomorphic encryption can be introduced in this scenario where computation can be done on encrypted data thus removing the need to give third-party access to sensitive data. A novel homomorphic encryption is proposed by enhancing the EIGamal algorithm with the capability of learning with errors to adapt it as a post-quantum cryptographic algorithm. This enhanced algorithm cannot be decrypted purely using the knowledge needed to decrypt the original algorithm, with the same key pair.
A Critical Investigation into the Blockchain Technology and Its Present-Day Uses

The IoT is a new technology that allows for the networking and communication of digital and physical things to provide novel digital services that enhance our daily lives. Though the Internet of Things (IoT) has many benefits, its centralized design raises several problems, such as a lack of redundancy, privacy concerns, transparency, and compromised data integrity. These issues are a roadblock to further advancements in IoT software. Since Bitcoin's introduction as the first and largest cryptocurrency, blockchain technology has been widely recognized as a foundation for digital money. So yet, it has to be utilized primarily to decentralize monetary or payment systems. Blockchain's distributed transaction ledger might be used to store and transmit contracts over a network. This study provides a comprehensive overview of the most recent blockchain-related applications in the academic literature. Several research papers from the canon of literature were hand-picked for inclusion in the blockchain for their potential to advance human understanding. Several observations are examined and addressed in the latter part of the work.
Fast Encoding of Extended Integrated Interleaved Codes Based on Two-Dimensional LCH-FFT

In this paper, we present fast encoding algorithms for extended integrated interleaved (EII) codes by re-defining them as a class of bivariate polynomial evaluation codes. Based on the recently proposed two-dimensional LCH-FFT algorithm, fast algorithms are derived for nonsystematic and systematic encoding. For an EII code of length N, both the encoding algorithms achieve the computational complexity O(N log (N)).
Industrial Anomaly Detection via Teacher Student Network

This paper proposes a teacher-student network for industrial anomaly detection tasks using knowledge distillation techniques, where the feature representation capabilities of the teacher network model are utilized to guide the student model in efficiently identifying anomalous images. In detail, the VGG model is introduced as the backbone network for anomaly detection. Meanwhile, a residual attention mechanism is proposed to improve the feature output capability of the student network. The student network is able to learn good feature extraction abilities due to the robust feature representation of the teacher network. At the same time, the residual attention mechanism enhances the student network's ability to characterize the output of normal data features, amplifying the feature differences between the student and teacher networks for abnormal data. In summary, this paper addresses the problem of difficult detection of small targets in anomaly detection. Experimental validation has been performed on the industrial anomaly detection dataset MVTec-AD, which ultimately achieved 96.70% and 97.94% results for detecting and localizing, respectively. Compared with the other anomaly detection methods, the method proposed in this paper achieves a significant performance improvement.
Software Test Automation of Power Window Functionality

In the automotive industries, there is a constant development in control strategy with the use of electronic hardware and software for advanced functionality. Software test automation plays a crucial role in ensuring the quality and reliability of power window functionality in automotive systems. This paper aims to automate power window testing by prioritizing crucial functionalities like obstacle detection, thermal and voltage protection, and precise position tracking. The power window is a mechatronic application and its operation is controlled through software algorithm. To establish the experimental setup for this research, essential components such as the power window Electronic Control Unit (ECU), an external DC power supply, PCB layout design and software tools like the vector CANoe tool and Communication Access Programming Language (CAPL) browser were utilized. The automated testing methodology proposed in this paper helps to reduce the damaging of ECU and human injuries while testing. At present, the testing process is performed manually, requiring six weeks to complete 400 scenario checks. However, by automating this testing, the time frame can be significantly reduced to just two weeks. A comprehensive report is generated that includes detailed information such as the start time, end time, and recorded values.
LSTM-Based Model for Classification of Tweets

In today's age of technology, the whole world is in a state of rapid change, and as Internet use is worldwide, it is becoming an essential requirement for everyone. Social media on the internet is getting a lot of attention these days. Many people are using social networking applications to voice their opinions on everyday issues, and different ideas of individuals on various topics are expressed and constantly disseminated via numerous social media outlets. It's no secret that social media is one of the largest sources of unstructured data and analyzing this data and extracting meaning from it is a tedious task. In this paper we have not only explored the various machine learning algorithms which may be applied for the sentiment analysis but we have proposed a LSTM based model for sentiment analysis. The experimental results show that LSTM has the highest accuracy among all the models.
HashCache: Accelerating Serverless Computing by Skipping Duplicated Function Execution

Serverless computing is a leading force behind deploying and managing software in cloud computing. One inherent challenge in serverless computing is the increased overall latency due to duplicate computations. Our initial investigation into the function invocations of serverless applications reveals an abundance of duplicate invocations. Inspired by this critical observation, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HashCache</i> , a system designed to cache duplicate function invocations, thereby mitigating duplicate computations. In HashCache, serverless functions are classified into three categories, namely, computational functions, stateful functions, and environment-related functions. On the grounds of such a function classification, HashCache associates the stateful functions and their states to build an adaptive synchronization mechanism. With this support, HashCache exploits the cached results of computational and stateful functions to serve upcoming invocation requests to the same functions, thereby reducing duplicate computations. Moreover, HashCache stores remote files probed by stateful functions into a local cache layer, which further curtails invocation latency. We implement HashCache within the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Apache OpenWhisk</i> to forge a cache-enabled serverless computing platform. We conduct extensive experiments to quantitatively evaluate the performance of HashCache in terms of invocation latency and resource utilization. We compare HashCache against two state-of-the-art approaches - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FaaSCache</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OpenWhisk</i> . The experimental results unveil that our HashCache remarkably reduces invocation latency and resource overhead. More specifically, HashCache curbs the 99-tail latency of FaaSCache and OpenWhisk by up to 91.37% and 95.96% in real-world serverless applications. HashCache also slashes the resource utilization of FaaSCache and OpenWhisk by up to 31.62% and 35.51%, respectively.
CamRaDepth: Semantic Guided Depth Estimation Using Monocular Camera and Sparse Radar for Automotive Perception

Our research aims to generate robust, dense 3-D depth maps for robotics, especially autonomous driving applications. Since cameras output 2-D images and active sensors such as LiDAR or radar produce sparse depth measurements, dense depth maps need to be estimated. Recent methods based on visual transformer networks have outperformed conventional deep learning approaches in various computer vision tasks, including depth prediction, but have focused on the use of a single camera image. This article explores the potential of visual transformers applied to the fusion of monocular images, semantic segmentation, and projected sparse radar reflections for robust monocular depth estimation. The addition of a semantic segmentation branch is used to add object-level understanding and is investigated in a supervised and unsupervised manner. We evaluate our new depth estimation approach on the nuScenes dataset where it outperforms existing state-of-the-art camera-radar depth estimation methods. We show that models can benefit from an additional segmentation branch during the training process by transfer learning even without running segmentation at inference. Further studies are needed to investigate the usage of 4-D-imaging radars and enhanced ground-truth generation in more detail. The related code is available as open-source software under <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/TUMFTM/CamRaDepth</uri> .
ISL Animated Translator and Desktop Virtual Assistant

Sign language is a visual language that uses hand gestures to exchange information between their community and with other people. There has always been difficulty in communication between a verbally impaired person and a normal person. The Sign Language Recognition system is a breakthrough for helping deaf-mute people to communicate with others. Various Indian Sign Language Systems have been developed by many developers around the world but they are neither flexible nor cost-effective for the end users. The main reason behind the lack of research is that there is no proper grammar of ISL and another reason is Sign language is not universal and it varies from country to country and region to region [1]. The aim of this project is to develop a software system that can facilitate communication between normal individuals and deaf/mute individuals by translating Hindi or English text into Indian Sign Language (ISL) gestures, and vice versa using the MediaPipe model. The system will consist of a translator component to generate ISL gestures from input text, an animator component that uses computer graphics techniques to create realistic representations of ISL gestures, and an AI virtual assistant that interprets ISL gestures made by the user to trigger corresponding actions. The system will be integrated with the MediaPipe model to enable translation of ISL gestures to text using computer vision techniques. The overall goal is to provide an advanced system that bridges the communication gap between normal individuals and deaf/mute.
Residual Dense Blocks and Contrastive Regularization Integrated Underwater Image Enhancement Network

Due to the influence of optical properties and water quality conditions in underwater environments, underwater images often encounter challenges such as light attenuation, scattering, and absorption, resulting in low image quality, including blurriness, dimness, and lack of details. These limitations impose restrictions on the successful implementation of underwater vision tasks. Nonetheless, the significance of original underwater images in the context of feature extraction and learning tends to be disregarded by the majority of existing methodologies. This paper addresses this oversight by introducing techniques known as Residual Dense Blocks (RDB) and Contrastive Regularization (CR). These techniques serve to amplify the influence of the original underwater images, facilitating the construction of an end-to-end fully convolutional network tailored for the purpose of enhancing underwater image quality. This innovative network is denoted as RDCR. By leveraging the local and global feature fusion of RDB and the contrastive learning of CR, this model effectively extracts multi-level features from the original images, adaptively preserving hierarchical features, and achieves high-quality underwater image enhancement through learning from the original images. The superiority of our proposed model over alternative comparative algorithms is evidenced by the experimental results obtained across four distinct datasets, encompassing assessments of both subjective visual perception and objective evaluation.
Enhancing DETR with Attention-Based Thresholding for Efficient Early Japanese Book Reorganization

Analysis of early Japanese books is an essential clue for researching the history and culture of the period, however, the books are written in pre-modern Japanese called Kuzushiji, which is difficult to read unless one is an expert. Kuzushiji by OCR has been popular in recent years, although recognition of Kuzushiji by end-to-end object detection is difficult with conventional CNN-based models such as YOLO. This paper uses the Transformer-based object detection model DETR, which outperforms CNNs in the field of object detection and improves DETR performance using threshold processing. The threshold processing for Self-Attention can reduce redundant dependencies between each patch and accelerate DETR training. In this experiment, DN-DAB-DETR-R50 with thresholding achieved +2.0%F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> in total and up to +7.3%F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> per book compared to the vanilla model and prove the efficiency of the thresholding. However, threshold processing increases computational costs slightly, and future work is improving the problem by patch pooling according to attention weights using its sparsity with thresholding.
Ensemble-Based Context-Aware Recommender System Using Clustering And Singular Value Decomposition

The recommendation system is facing the problem of how to overcome sparsity, scalability, and cold start issues. Numerous studies have been done to increase recommendation accuracy and address the aforementioned issues. But there is a need for improvement. Therefore, this research proposed a context-aware recommendation system that tries to improve accuracy, scalability, and sparsity issues of recommendation by integrating ensemble clustering for clustering the dataset, SVD for dimensionality reduction, and context-aware for improving the accuracy and sparsity issue. The research also will create a new similarity metric that will be used to find similarities among user-user and/or item-item so that the sparsity of the data is decreased. we evaluate our approach on MovieLens 100k and 1M real-world datasets. The findings indicate that the SVD has performed better with high-accuracy rating prediction using the reconstructed low-dimensional matrices in comparison to the KNN by splitting the sparse and high-dimensional rating matrix into two low-dimensional matrices. While the context-aware uses the item context such as movie ontology and user contexts such as age, location, and gender to improve scalability. On comparison with the state-of-the-art approach so far, we noticed that the proposed method based on (User and Item+SVD+ensemble+Context) was the best-performing technique with an average MAE score of 0.6031. F-measure, Precision, and throughput results demonstrated that the CF recommender systems' performance could be enhanced by using context awareness in conjunction with clustering and dimensionality reduction techniques. Our analysis of findings showed that recommender systems' scalability and sparsity problems can be resolved using the suggested recommendation method.
Synthetic Sensor Measurement Generation With Noise Learning and Multi-Modal Information

Deep learning has transformed data generation, particularly in creating synthetic sensor data. This capability is invaluable in fields like autonomous driving, robotics, and computer science. To achieve this, we train models using real data, enabling them to replicate sensor data closely. These models introduce variations and noise, enhancing diversity and realism. Prominent techniques, including generative adversarial networks (GANs), variational autoencoders (VAEs), and recurrent neural networks (RNNs), excel in generating synthetic sensor data. Our paper focuses on Autoregressive Convolutional Recurrent Neural Networks (CRNN) for Multivariate Time Series Prediction. We incorporate Denoising Autoencoders (DAE) to mimic real-world noise characteristics. Our model is trained and validated using Ultra Wide Band (UWB) and Ultra High-Frequency Radio Frequency Identification (UHF-RFID) sensor data. It integrates sensor measurements and diverse information sources to produce synthetic data complementing real-world data. While demonstrated with UHF-RFID and UWB sensors, these techniques extend to industrial automation, healthcare and environmental monitoring. While our methodology exhibits broad potential, we present practical demonstrations with UHF-RFID and UWB sensors. Our deep neural network model allows researchers to construct datasets for algorithm validation, eliminating the need for costly and time-consuming data collection.
Improved Double N-Step Phase Shift Method(DNPSM) Based on Unwrapping Before Fusion

In the field of fringe projection profile, the DNPSM becomes a research hotspot because of its small phase error and high detection accuracy. However, the DNPSM is affected by periodic phase error(PPE) in the wrapped phase fusion process, and it produces spurious tones at the edge position of the wrapped phase. To solve this problem, this paper proposes an improved DNPSM based on unwrapping before fusion. Firstly, we get wrapped phase from the two sets of phase shift fringe images(PSFI) captured by camera, and then we unwrap the two groups of wrapped phase with the phase unwrapping method. Finally, we fuse the two unwrapped phase. We use the proposed method compared with complementary Gray code(CGC) N-step phase shift method(NPSM) and CGC-DNPSM that wrapping after fusing respectively. Experimental results show our method could effectively eliminate spurious tones in phase and improve the detection accuracy.
Quantum Cryptography: Mathematical Modelling and Security Analysis

This paper offers a thorough investigation into quantum cryptography, a security paradigm based on the principles of quantum mechanics that provides exceptional guarantees for communication and information protection. The study covers the fundamental principles of quantum cryptography, mathematical modelling, practical applications, and future prospects. It discusses the representation of quantum states, quantum operations, and quantum measurements, emphasising their significance in mathematical modelling. The paper showcases the real-world applications of quantum cryptography in secure communication networks, financial systems, government and defence sectors, and data centres. Furthermore, it identifies emerging domains such as IoT, 5G networks, blockchain technology, and cloud computing as promising areas for implementing quantum cryptographic solutions. The paper also presents avenues for further research, including post-quantum cryptography, quantum cryptanalysis, multi-party quantum communication, and device-independent quantum cryptography. Lastly, it underscores the importance of developing robust infrastructure, establishing standards, and ensuring interoperability to facilitate widespread adoption of quantum cryptography. This comprehensive exploration of quantum cryptography contributes to the advancement of secure communication, information protection, and the future of information security in the era of quantum technology.
A Novel Framework for Detection of Digital Face Video Manipulation using Deep Learning

Digital face manipulation and classification have recently attracted the attention of academia and industry worldwide. Researchers have developed deep learning and computer vision techniques for detecting face manipulations, and it has become a challenging task to differentiate between authentic and manipulated face images manually. The challenge results in the decline of authenticity in digital media content. In this paper, we propose a framework for the classification of manipulating face images using the EfficientNet learning model. The proposed framework takes four digital facial forgeries: Face-Swap, Face-2-Face, DeepFakes, and neural textures. Multiple manipulation techniques are used to process manipulated faces, such as the Blaze-face tracking method, to determine the locations of the face images and pixel coordinates. The proposed framework is used first to identify the type of face manipulation and then to perform detection of the tampered regions in the face images. The proposed framework provided an automated benchmark that considers all four modification techniques in a realistic situation. The results show that the proposed framework outperforms existing approaches regarding accuracy and efficiency. Furthermore, the proposed framework is suitable for detecting digital face video manipulation in various applications, including forensics and security.
Pilot Design and Channel Estimation Using Deep Learning for Downlink Massive MIMO Systems

Our research paper presents a new method for designing pilot signals and channel estimation in massive multiple-input multiple-output (MIMO) systems applying two-stage deep learning (DL), Artificial Neural Networks (ANN) and Wide ResNet (WRN). Our proposed DL framework leverages the compressibility of massive MIMO channels in the angular domain to reconstruct high-dimensional channels from underdetermined measurements accurately. The end-to-end ANN-WRN architecture imitates the pilot signals and channel estimation through deep learning. A fully connected layer is utilized to compress the high-dimensional massive MIMO channel vector into low-dimensional received measurements, treating the weights as the pilot signals to design the pilot signals. Unlike traditional methods that increase the depth of the network, WRN allows for adjusting the network width while maintaining high performance, this adjustment improves channel estimation. A fully connected layer and Wide ResNet are employed to reconstruct the high-dimensional channel for the channel estimator. We trained the end-to-end deep neural network using the Adam algorithm, which optimizes a loss function defined as the mean square error between the input and output. By training with extensive channel samples, we could simultaneously obtain the pilot signals and the channel estimator. Our simulation results demonstrate that our proposed solution achieves a superior normalized mean square error (NMSE) of approximately 1 to 2 dB compared to the previous DNN network.