Reviewerly/exHarmony huggingface dataset:
Reliable DNN Partitioning for UAV Swarm

Recently deep neural networks (DNNs) are widely used in various fields. These intelligence applications, such as target recognition, are often computation-intensive and latency-sensitive. Since a single UAV's computing resource is limited, it is difficult to complete the DNN inference task independently. Partitioning the deep neural work into numerals subtasks and distributing them to multiple UAVs for collaborative computing seems a better way to finish the task. However, UAV usually works in a harsh environment, such as battlefield, disaster area, etc., and the link interruption or node failure in the inference process caused by uncertain factors may lead to failure of inference task. Hence, the reliability of DNN inference is of high importance. In this paper, we propose a deep Q learning-based DNN partitioning strategy for minimizing the energy consumption of DNN collaborative inference among multi-UAVs within latency and reliability requirements. To validate the effectiveness of the proposed strategy, a series of experiments are conducted on four kinds of typical DNNs (i.e., AlexNet, VGG19, GoogleNet, and ResNet). The simulation results prove the proposed strategy can effectively reduce the DNN inference cost under constraints.
A blockchain-based storage intelligent

A blockchain is a distributed and decentralized database that allows users to securely store and exchange data without requiring the intervention of a third party. Information, called transactions, is collected in blocks, which are then linked together via cryptographic processes in an irreversible way. The registry has served as a historical record of all actions taken by participants in the network since its launch. An increase in the number of transactions, leading to a considerable increase in the size of the primary blockchains, making them more difficult to maintain, perhaps discouraging certain nodes from storing the entire blockchain and therefore weakening decentralization. In this study, we introduce the low-storage node, a new type of node that stores chunks of blocks rather than whole blocks and is encoded with an erasure code. A low-storage node recovers the initial block by downloading and decoding a enough encoded fragments from other nodes in the network. This strategy has the advantage of allowing certain nodes to keep a reduced version of the blockchain while contributing to its decentralization. This simplifies the scaling of blockchains, which is one of the main flaws of the technology. BlockStock is a complete system that allows nodes to rent out its additional storage space to others. The main innovation is the use of blockchain-based smart contracts that enable frequent, automated and secure payments based on proofs of recovery provided by storage servers.
Parasite Chain Attack Detection in the IOTA Network

Distributed ledger technologies (DLTs) based on Directed Acyclic Graphs (DAGs) have been gaining much attention due to their performance advantage over the traditional blockchain. IOTA is an example of DAG-based DLT that has shown its significance in the Internet of Things (IoT) environment. Despite that, IOTA is vulnerable to double-spend attacks, which threaten the immutability of the ledger. In this paper, we propose an efficient yet simple method for detecting a parasite chain, which is one form of attempting a double-spend attack in the IOTA network. In our method, a score function measuring the importance of each transaction in the IOTA network is employed. Any abrupt change in the importance of a transaction is reflected in the 1st and 2nd order derivatives of this score function, and therefore used in the calculation of an anomaly score. Due to how the score function is formulated, this anomaly score can be used in the detection of a particular type of parasite chain, characterized by sudden changes in the in-degree of a transaction in the IOTA graph. The experimental results demonstrate that the proposed method is accurate and linearly scalable in the number of edges in the network.
Recurring distributed software maintenance meetings

Meetings have always been a significant part of working life and software development is no exception, with meetings of all kinds taking place daily. One type of meeting that is critical to software development that has not been widely studied to date is the recurring software maintenance meeting: a regularly scheduled meeting during which the primary product leads consider emerging issues and new directions for an already deployed and functioning software system. In this paper, we describe preliminary results of our ongoing analysis of ten consecutive recurring maintenance meetings of the architecture committee of a major healthcare software company. The results provide a foundation for further analysis in terms of first building a categorization of the kinds of discussions that take place. We present the setting, our approach to analysis, and preliminary observations. A primary outcome is that the kinds of discussions are much more varied than one might have initially expected.
Non-frontal face recognition method with a side-face-correction generative adversarial networks

Frontal face image recognition is the main target of traditional face recognition.The deflection of the human face often causes the dislocation of the facial features,which leads to the reduction of the recognition accuracy of the non-frontal face.To solve the above problems,a non-frontal face recognition model based on generative adversarial network is proposed.In this model,the angle information is encoded separately by using a two-channel generator and auto-coding network,and the non-frontal face image in natural environment is corrected to obtain the frontal face image.Through the multi-discriminator mechanism of facial attention,we set discriminators in the eye, eyebrow, nose, mouth and the whole area of the face image so as to retain the details of the face to the maximum extent while ensuring the clarity of image.Then the corrected face features are extracted by Facenet and MTCNN to obtain the non-frontal face recognition results.The model is validated on multi-PIE dataset and CFP dataset.The results show that the accuracy of non-frontal face recognition is improved by 1% in CFP dataset compared with VGG-FACE, TP- CNN and HPN.
Research on Internet Text Sentiment Classification Based on BERT and CNN-BiGRU

In order to classify Internet text according to different sentiments, a new text sentiment classification model BERT-CBA is proposed, which combines BERT pretrained model, Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU) and Attention Mechanism. First, the text data is input into BERT to generate word vector representation of the fusion context text context, the output of BERT is simultaneously input into CNN and BiGRU to perform parallel operations, and the operation results are fused and spliced into the attention mechanism layer. Finally, the Softmax excitation function is used to obtain emotional probability distribution of the text. The experimental results on the test set show that the model proposed in this paper has obtained the expected results on various evaluation indicators, and has certain feasibility and effectiveness for text sentiment classification.
YONO: Modeling Multiple Heterogeneous Neural Networks on Microcontrollers

Internet of Things (IoT) systems provide large amounts of data on all aspects of human behavior. Machine learning techniques, especially deep neural networks (DNN), have shown promise in making sense of this data at a large scale. Also, the research community has worked to reduce the computational and resource demands of DNN to compute on low-resourced micro controllers (MCUs). However, most of the current work in embedded deep learning focuses on solving a single task efficiently, while the multi-tasking nature and applications of IoT devices demand systems that can handle a diverse range of tasks (such as activity, gesture, voice, and context recognition) with input from a variety of sensors, simultaneously. In this paper, we propose YONO, a product quantization (PQ) based approach that compresses multiple heterogeneous models and enables in-memory model execution and model switching for dissimilar multi-task learning on MCUs. We first adopt PQ to learn codebooks that store weights of different models. Also, we propose a novel network optimization and heuristics to maximize the com-pression rate and minimize the accuracy loss. Then, we develop an online component of YONO for efficient model execution and switching between multiple tasks on an MCU at run time without relying on an external storage device. YONO shows remarkable performance as it can compress multiple heterogeneous models with negligible or no loss of accuracy up to 12.37x. Furthermore, YONO's online component enables an efficient execution (latency of 16–159 ms and energy consumption of 3.8-37.9 mJ per operation) and reduces modelloading/switching la-tency and energy consumption by 93.3-94.5% and 93.9-95.0%, respectively, compared to external storage access. Interestingly, YONO can compress various architectures trained with datasets that were not shown during YONO's offline codebook learning phase showing the generalizability of our method. To summarize, YONO shows great potential and opens further doors to enable multi-task learning systems on extremely resource-constrained devices.
Exploratory Data Analysis Based on Micro grids Generation for Control Communication and Monitoring via Wireless Sensor Network

Modern-day scientists have experienced a wide utilization of wireless sensor networking benefits for offering better cyber-security measures. With the application of WSNs over a smart micro-grid generation, critical security protocols, controlling communication, and monitoring the entire smart power grid system have become easier these days. Due to the application of WSNs systems in recent times, all the complex controlling and monitoring issues have been resolved with enhanced cyber security scopes for a sustainable future. In order to examine the particular research topic, researchers have performed a primary data gathering method by quantitative analysis. Through processing three relevant survey questions, they have conducted a probability sampling method for gathering the responses of almost 70 participants regarding the research topic. All the research outcomes reflect that around 52% of the participants have strongly agreed with the use of WSNs for better communication controlling and monitoring facilities. Apart from this, around 55 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> of the people have supported that WSNs can aid in more accurate cyber security measures for protecting authentic information. On the other hand, almost 50% of the participants have expressed their negative responses over the use of WSNs towards more error reduction rate in controlling and measuring the overall smart-grid system. Researchers, therefore, are going to investigate the contributions of WSNs to understand their advantages for better smart micro-grid generation management in the sustainable future.
Development of Computer Vision and Deep Learning based Algorithm to Improve Waste Management System

Noteworthy steps are being taken towards improving the level of hygiene and cleanliness in cities. Even with these efforts littering and non-recyclable waste is still a serious maj or issue socially and ecologically. Locating, identifying, and picking up the waste by staff can be a tiresome and inefficient task requiring long hours of manpower. Computer Vision and Deep Learning techniques can help solve this major problem by applying detection algorithms which can ease the task of analyzing such images for the benefit of humans. To address the problem of locating and identification of the waste objects we propose a cost-effective method which is based on application detection algorithms on low altitude imagery from streets, sidewalks, houses, and landfills taken by surveillance camera or Unmanned aerial vehicles or UAVs. The deep learning-based framework consists of deep convolutional neural networks which perform the localization and the classification task which can then be processed and put through into automated pickup planning system. A dataset was also compiled and annotated to train and evaluate the model. The model achieved a mAP score of 85&#x0025; when tested on 5 classes while testing on all classes a mAP score of 78&#x0025; was obtained.
Attendance Management System Based on Face Recognition Using Haar-Cascade

Smart Attendance with Real-Time Face Recognition is a practical option for managing student attendance systems on a daily basis. An attendance system based on facial recognitionis a method of identifying people by their faces. The regularity of student attendance is now a maj or problem for educational institutions. This is primarily owing to the fact that a student's total academic success is influenced by their attendance at the institute. Calling out the roll call or having students sign a piece of paper are the two most common methods of recording attendance. Face biometrics based on a high-definition monitor are used to recognise students' faces for the purpose of taking attendance. They were both more difficult and time-consuming. video, as well as other forms of information technology. Faces will be found by a computer system in my face recognition project. can distinguish human faces in photos or videos acquired by a surveillance system quickly and precisely camera. For enhancing the performance of face recognition, a variety of algorithms and strategies have been developed.As a result, a computer-based student attendance management system is required, which will assist instructors in automatically keeping attendance data.
A Critical Review of Applications of Artificial Intelligence (AI) and its Powered Technologies in the Financial Industry

The present research shed light on the applications of AI technologies for the financial industry of the UK. The research has also investigated the different types of powered technologies of AI and their impact on finance operations and activities. This research possesses the tools and techniques used by the researcher in gathering the research evidence for the proper completion of the research work.
An Approach on Cyber Crime Prediction Using Prophet Time Series

The advancement of technology in every facet of human existence has moulded a far broader crime-solving strategy. Extensive medical study has been conducted on the origins and shape of crime, as well as its intensity and dynamics, with the help of researchers from several scientific disciplines. Government agencies and police departments now have more options for tracking crime events, including the capacity to gather and preserve specific data as well as spatial and temporal information. Prophet is an additive model-based approach for forecasting time series data that matches non-linear patterns with yearly, weekly, and daily seasonality, as well as the holiday effect. It employs a decomposable model that consists of three primary components: trend, seasonality, and holiday effects. The Prophet model is used in this paper to predict crimes.
Focal-WNet: An Architecture Unifying Convolution and Attention for Depth Estimation

Extracting depth information from a single RGB image is a fundamental and challenging task in computer vision with wide-ranging applications. This task cannot be solved using traditional methods like multi-view geometry but only by deep learning. Existing methods using convolutional neural nets produce inconsistent and blurry results due to the lack of long-range dependencies. With the recent success of Transformer networks in computer vision, which can process information locally and globally, we leverage this idea to propose a novel architecture named Focal-WNet in this paper. This architecture consists of two separate encoders and a single decoder. The main aim of this network is to learn most monocular depth cues like relative scale, contrast differences, texture gradient, etc. We incorporate focal self-attention instead of vanilla self-attention to reduce the computational complexity of the network. Along with the focal transformer layers, we leverage a convolutional architecture to learn depth cues that cannot be learned by a transformer alone as some cues like occlusion require a local receptive field and are easier for a conv-net to learn. Extensive experiments show that the proposed Focal-WNet achieves competitive results on two challenging datasets. Our code and pre-trained weights are available at https://github.com/Goubeast/Focal-WNet
Performance Analysis of Light-weight Cryptographic Algorithms for Internet of Things (IoT) Applications: A Systematic Review

Internet of things IoT is the interconnected objects or things that allows to facilitate a ubiquitous activity of smart devices. This study aims to present the current state of lightweight cryptography algorithms used in Internet of Things environment. This study performs a systematic review of literature to identify the lightweight cryptography algorithms, extracts relevant data such as level of security, encryption and decryption performance, execution time, memory usage, clock, speed, latency, and frequency. Comparison tables shows these data for further analysis, assessment and evaluation. This study contributes by providing an understanding of the performance of the lightweight cryptography algorithms used in Internet of Things environment and devices. This study also presents future work to guide researchers in extending the contribution of this study.
A configurable and efficient implementation of Number Theoretic Transform (NTT) for lattice based Post-Quantum-Cryptography

Advancement in the quantum computer has imposed huge threat to modern cryptography techniques. Post quantum cryptography (PQC) based on lattice structure provides quantum-resistance and facilitates efficient hardware implementation compared to other solutions. Ring learning with errors (RLWE) is a basic primitive of lattice-based cryptosystem, defined on a polynomial quotient ring with time and resource consuming multiplication operations. Number Theoretic Transform (NTT) is an extensively used polynomial multiplier to achieve AreaX-Time trade-off. In this paper, we proposed a fully configurable NTT which supports different RLWE and design parameters to produce highly optimized hardware. Our design can be customized for higher throughput or lightweight applications. The proposed high performance design achieves 9X improvement in execution time compared to High-Level synthesis, pure software and various hardware implementations. The result shows that our lightweight architecture consumes 16X lesser resource than other FPGA solutions targeted for deeply embedded systems. The proposed design is experimented in the NewHope PQC key exchange application and the execution time is enhanced by 37% at 133 MHz clock frequency compared to recent implementations.
Drone Detection using YOLOV4 on Images and Videos

The paper covers the capstone project that I have been working on for last 5 months (June 2021 - October 2021).The Topic "Drone Detection using YOLOV4 on images and videos" speaks about training model using YOLOV4 algorithm resulting into detecting the drones in given images and videos for testing. It also speaks about how CCTV cameras could be operated to detect the drones and alert the concerned people. The idea behind this project to make CCTVs capable of detecting such kinds of drones. We could make use of OpenCV and develop a model to make it work accordingly. At this stage, we make use of yolov4 to generate a robust model to detect drones.The model could be made more robustly by adding up images and even by classifying different kinds of drones. It has got future scope because it could be even applied on video clips. Yolov4 offers the potential to produce accurate results if an optimised amount of data is provided to the model. Hence, this is just a start as it can be made more reliable and powerful by updating the model. The future would be more secure and safe by implementing such projects on a real-time basis with the help of such algorithms and technologies making it easier for people who keep gazing for hours at a computer screen to provide best safety.
MARG-UNet: A Single Image Dehazing Network Based on Multimodal Attention Residual Group

In real life, fog is generated under specific weather conditions, which reduces the color fidelity and information integrity of outdoor images. Although dehazing methods based on the convolutional neural network (CNN) had been rapid and significant progress, there are still problems of non-homogeneous haze that cannot be completely removed in the image, and the image after dehazing appears conspicuous color and structure migration between the hazy-free image and the clean image, which cannot be consistent with the human visual system. Based on the above problems, this paper proposes a dehazing model with encoder-decoder architecture, which enhances the feature extraction ability of non-homogeneous by embedding channel attention and pixel attention in the residual blocks, enhancing the dehazing performance of the model. At the same time, the multiscale structural similarity index (MS-SSIM) loss function and Mean Absolute Error (MAE) loss function is introduced to make inconspicuous color and structure deviation between the dehazing image and the clean image. Experiment results show that the Peak Signal to Noise Ratio (PSNR) is improved by 1.27% without reducing the Structural Similarity (SSIM). The image after dehazing is more matched with the human visual system, which effectively solves the problem of incomplete image dehazing.
Lightweight PM-YOLO Network Model for Moving Object Recognition on the Distribution Network Side

For the problems of large size and high computation of deep convolutional neural network (CNN) models, which make it difficult to achieve real-time detection on resource-limited mobile or embedded devices, and the existing lightweight models are not sufficient for detection of small targets on the distribution side, a model compression algorithm that combines multi-scale detection and lightweight target recognition PM-YOLO (Prune-MobileNetv3-YOLOv5s) is proposed to achieve efficient detection. Based on the YOLOv5s model, feature information detection is performed at different scales by adding a network processing layer for small targets to improve model recognition accuracy. Target recognition backbone network is reconfigured based on improved MobileNetv3, which reduces model parameters and convolutional operations and improves target recognition rate. Based on iterative sparsity training, the pruning method eliminates redundant parameters, thus compressing model volume and computation volume to the limit. Fine-tuning techniques and related optimization means are used to ensure that the accuracy error of the model before and after compression is within an acceptable range. The experimental results show that under the same test set and test environment, in compared with the YOLOv5s target recognition algorithm, the PM-YOLO algorithm compresses the YOLOv5s model volume by 85.4&#x0025;, reduces the floating-point type computation by 84.6&#x0025;, increases the prediction speed by 3.7 times, and achieves 90.6&#x0025; accuracy of the compressed model recognition with only 1.5&#x0025; accuracy loss. The proposed method has high detection accuracy and real-time detection speed while reducing the platform volume power consumption storage and computing power requirements, which has obvious advantages over existing lightweight models and is more conducive to the deployment of the model in resource-limited mobile, thus, making the supervision function of moving targets more accessible.