Reviewerly/exHarmony huggingface dataset:
DASLog: Decentralized Auditable Secure Logging for UAV Ecosystems

Rapid technological advancements in unmanned aerial vehicles (UAVs) have revolutionized intelligent platforms such as smart cities. These advancements have paved the way for an emerging class of Internet of Things (IoT) systems called the Internet of Drones (IoD), which has noteworthy security and privacy challenges. In this article, we tackle the problem of secure logging and design a novel secure logging scheme—DASLog—for the use case of aerial transport of (medical) goods via drones. Our logging scheme provides public auditability of logging records in the setting where all logging components are managed by a single entity. Our secure logging system relies on hash chains and a Merkle tree to generate proofs for stored logging records. These proofs get written on a private blockchain and can be used later by data consumers to verify the integrity and completeness of a set of logging records. We demonstrate the feasibility of our approach via a proof-of-concept prototype relying on Hyperledger Besu and implemented on multiple Amazon EC2 instances. The performance evaluation of our demonstrator shows that up to 8000 logging records per second can be processed.
Temporal–Spatial Dynamic Convolutional Neural Network for Human Activity Recognition Using Wearable Sensors

The Sensor-based Human Activity Recognition (SHAR) task seeks to recognize signals collected by various sensors embedded in intelligent devices to assist people in their daily lives. Motivated by the success of deep learning, many researchers are studying combining deep learning with SHAR. The key to implementing sensor-based human activity recognition with deep learning lies in facilitating model performance and maintaining efficiency when the model is performed on resource-constrained devices. To address this challenge, we present an effective sensor signal representation method, termed the Temporal-Spatial Dynamic Convolutional (TS-DyConv) network, to recognize human activity. TS-DyConv aims to dynamically learn the convolutional kernels weighted with attention generated along the temporal and spatial kernel spaces. This way, the TS-DyConv can diversify the kernel to enhance the sensor signal’s recognition capabilities without raising complexity and maintaining efficiency. Extensive experiments conducted on three benchmark SHAR datasets, e.g., OPPORTUNITY, PAMAP2, and USC-HAD, demonstrate the superiority of our method over the deep learning baselines and existing SHAR works.
LNOA: A Real-time Obstacle Avoidance Motion Planning Method for Redundant Manipulator Based on Reinforcement Learning

Aiming at the redundant manipulator operation task that needs to ensure the end-effector trajectory tracking as much as possible in the dynamic obstacle scene, a loose null-space obstacle avoidance (LNOA) method based on reinforcement learning (RL) is proposed. Firstly, the joint motion is decomposed into trajectory tracking motion and loose null-space obstacle avoidance motion, and the latter is further decomposed into joint null-space motion and end-effector slack motion; on this basis, LNOA framework for obstacle avoidance is designed. Secondly, the RL method is introduced to learn the loose null-space obstacle avoidance motion generation strategy, so as to generate the end-effector slack component and joint null-space component autonomously, which is then combined with the trajectory tracking component to realize obstacle avoidance and end-effector trajectory maintenance simultaneously. Finally, the simulation is conducted to verify the effectiveness of the proposed LNOA method.
The Experience of Using Virtua Integrated Library System in Nigerian University Libraries

The paper explores the experience of using Virtua Integrated Library System by some university libraries in Nigeria for automating their operations throughout a decade. It also accounts for the challenges encountered by the libraries and reveals their switch over to other software. Survey method was used to collect data from systems' staff and librarians of the six universities that adopted Virtua software via self-distributed online questionnaire. Purposive sampling technique was employed to select the respondents. The findings revealed that majority of the libraries used Virtua for years more than any of their previous software. The initial source of funding the software and staff training was provided by the Carnegie Corporation and MacArthur Foundation. The modules implemented by the libraries include cataloguing, circulation, administration and Online Public Access Catalogue (OPAC) and serials control and acquisition were partially used. The system was friendly used by the university libraries with few challenges which included increase in subscription fees. At different periods of few years back, the libraries dropped the software and switched over to Koha and locally developed library system respectively. It is recommended that the University Management of the libraries should provide adequate funds to enhance the implementation of library automation software.
Internet of Things and Artificial Intelligence Enabled Smart Wheel Chair

A smart wheelchair with IoT based interactive health monitoring system is proposed. IoT based live monitoring of elderly people health parameters like temperature, pressure, fall detection and emergency notification button to intimate the emergency situation to the care taker so that the care taker can give instructions from a IoT app to an Alexa device connected with a camera and monitor in the wheelchair system and it also helps elderly people is sitting and getting up from a chair using hydraulics. This smart system can support the elderly and disabled people with high accuracy and efficiency. The proposed Smart Wheelchair consists of various sensors to monitor the body parameters from the wheelchair connected to the central Raspberry Pi 4. In addition to this an emergency notification system is included to intimate the caretaker so that the care taker can give instructions from their Android mobile to an Alexa device, which in turn connected to a display with camera in the wheelchair system. For easy seating and getting off from the chair a mechanical hydraulics system is designed to lift the seat while sitting and to get up from the seat.
A Smart Monitoring of Solar PV Panel with Performance Amplification using IoT

In order to meet the increasing energy demands, renewable energy sources are considered as the most dependable and suitable solution. Solar photovoltaic energy is one of the most potential renewable energy sources available today, with no carbon emissions. Solar energy is generated as a result of the atomic fusion reactions that occur at the sun's core. It represents all electromagnetic radiation (visible light, infrared, ultraviolet, X-rays, and radio waves). Recently, Solar panels are monitored by using an Internet of Things (IoT)-based system to gather and evaluate solar energy characteristics in order to ensure a reliable power output and performance. Based on its ability to evaluate peak performance (photovoltaic), one important advantage of the technology is enhanced solar PV system maintenance. PV monitoring systems intend to provide a low-cost solution by continuously presenting remote energy yields and performance on a computer or smart phone. Environmental conditions such as voltage, current, temperature, light intensity, and dust are measured and recorded by the IoT-based system. Furthermore, the Wireless monitoring system ensures the highest operational dependability of a PV system at a lowest system cost. Solar radiation may be converted into useful power using photovoltaic (PV) devices. The high initial cost is a significant obstacle to the wider adoption of solar energy systems. Monitoring and control systems are becoming more common as a way to optimize PV system performance and extend the usable life of connected equipment. Data collection, display, and archiving are all handled by the IoT system. The system's sophisticated features can also estimate and detect solar panel damage.
Recognition of Indian Classical Dance Hand Gestures

The use of hand gestures to convey events or objects visually is one of the most distinctive features of Indian classical dance. These gestures can be performed either single handedly or with both hands. This work investigates the potential of recognizing hand gestures, or mudras, of the various classical dance forms of India. By applying a variety of pre-trained CNN models to train the dataset, it seeks to test the efficiency of convolutional neural networks at distinguishing and categorizing hand gestures from images. It also involves evaluating the accuracy of each model and analyzing the performance metrics of the same. The article also entails the lesser explored domain of recognizing mudras from dance videos using object detection techniques, and combining it with the results obtained from the CNN model.
Research on automatic test paper generating method based on improved differential evolution algorithm

To further improve the speed and efficiency, this paper proposed the improved differential evolution algorithm combined with the adaptive technology and simplex search algorithm. The mathematical model was designed to represent an intelligent test paper-generating process in quantitative form. Combined with the mathematical model, the improved differential evolution algorithm was applied to automatically extract the optimal paper that meets the user's needs of multiple goals. The efficiency of the differential evolution algorithm was improved by improving the initial population, the evolutionary strategy, the worst individuals using the simplex search algorithm, and the adaptive calculation of the scale factor F and the cross-probability factor CR. Experiments show that the improved algorithm is faster and better than the improved algorithm and can meet the needs of online examination systems from the question bank.
Recognizing small-data Chinese invoices through pretrained models

Detection and recognition of text instances from invoice data is an important topic in optical character recognition (OCR). For conventional OCR models, large training datasets are usually required to achieve a high-quality learning performance. However, when only small training data are available, how to achieve a sufficiently low error rate is crucial for practical real-life applications. Here, we propose to combine the two neural networks, PSENet and TrOCR, to implement OCR for small invoice data in Chinese. Specifically, due to the fact that PSENet and TrOCR are pre-trained text detection and recognition models, they are expected to perform well even on small datasets. Simulation results show that our method can reach a low character error ratio (CER) of 0.0267 with a small training data of size 202.
Design of Noise Covariance Adaptive Federated Filter Based on Variational Bayesian Theory

In order to solve the problem that the measurement noise covariance of the navigation measurement components in the integrated navigation system of unmanned aerial vehicle (UAV) in close formation flight is time-varying or difficult to detect accurately, which leads to the decline of filtering accuracy, the zero-reset federated filter is adopted. Different filter calculation periods and fusion periods are designed to fuse the navigation information with unequal intervals. Based on the idea of variational Bayesian inference in the subfilter, the real posterior distribution is approximated by a simple distribution, and the unknown measurement noise covariance is estimated adaptively. The mathematical simulation shows that the algorithm can effectively improve the relative navigation accuracy of UAV formation flight, and can better adapt to the situation of measurement noise covariance mutation.
EDoG: Adversarial Edge Detection For Graph Neural Networks

Graph Neural Networks (GNNs) have been widely applied to different tasks such as bioinformatics, drug design, and social networks. However, recent studies have shown that GNNs are vulnerable to adversarial attacks which aim to mislead the node (or subgraph) classification prediction by adding subtle perturbations. In particular, several attacks against GNNs have been proposed by adding/deleting a small amount of edges, which have caused serious security concerns. Detecting these attacks is challenging due to the small magnitude of perturbation and the discrete nature of graph data. In this paper, we propose a general adversarial edge detection pipeline EDoG without requiring knowledge of the attack strategies based on graph generation. Specifically, we propose a novel graph generation approach combined with link prediction to detect suspicious adversarial edges. To effectively train the graph generative model, we sample several sub-graphs from the given graph data. We show that since the number of adversarial edges is usually low in practice, with low probability the sampled sub-graphs will contain adversarial edges based on the union bound. In addition, considering the strong attacks which perturb a large number of edges, we propose a set of novel features to perform outlier detection as the preprocessing for our detection. Extensive experimental results on three real-world graph datasets including a private transaction rule dataset from a major company and two types of synthetic graphs with controlled properties (e.g., Erdos-Renyi and scale-free graphs) show that EDoG can achieve above 0.8 AUC against four state-of-the-art unseen attack strategies without requiring any knowledge about the attack type (e.g., degree of the target victim node); and around 0.85 with knowledge of the attack type. EDoG significantly outperforms traditional malicious edge detection baselines. We also show that an adaptive attack with full knowledge of our detection pipeline is difficult to bypass it. Our results shed light on several principles to improve the robustness of GNNs.
Reprogrammable-FL: Improving Utility-Privacy Tradeoff in Federated Learning via Model Reprogramming

Model reprogramming (MR) is an emerging and powerful technique that provides cross-domain machine learning by enabling a model that is well-trained on some source task to be used for a different target task without finetuning the model weights. In this work, we propose Reprogrammable-FL, the first framework adapting MR to the setting of differentially private federated learning (FL), and demonstrate that it significantly improves the utility-privacy tradeoff compared to standard transfer learning methods (full/partial finetuning) and training from scratch in FL. Experimental results on several deep neural networks and datasets show up to over 60% accuracy improvement given the same privacy budget. The code repository can be found at https://github.com/IBM/reprogrammble-FL.
What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel

A wide breadth of research has devised data augmentation approaches that can improve both accuracy and generalization performance for neural networks. However, augmented data can end up being far from the clean training data and what is the appropriate label is less clear. Despite this, most existing work simply uses one-hot labels for augmented data. In this paper, we show re-using one-hot labels for highly distorted data might run the risk of adding noise and degrading accuracy and calibration. To mitigate this, we propose a generic method AutoLabel to automatically learn the confidence in the labels for augmented data, based on the transformation distance between the clean distribution and augmented distribution. AutoLabel is built on label smoothing and is guided by the calibration-performance over a hold-out validation set. We successfully apply AutoLabel to three different data augmentation techniques: the state-of-the-art RandAug, AugMix, and adversarial training. Experiments on CIFAR-10, CIFAR-100 and ImageNet show that AutoLabel significantly improves existing data augmentation techniques over models' calibration and accuracy, especially under distributional shift. Additionally, AutoLabel improves adversarial training by bridging the gap between clean accuracy and adversarial robustness.
Development Of A Classification Architecture For Images Represented Using Quantum Theory : *using IBM QISKIT Liberaries

Quantum image processing is paramount in image processing-based research areas nowadays due to the less computational time. This paper presents the quantum mechanic-based image denoising model. Also, This article discusses the quantum boolean mean filter and new quantum-boolean image denoising that are connected to computational basis states. This paper describes basic quantum information processing principles, new and better image representation approaches, quantum image processing implementation problems, quantum boolean mean filtering approach for image denoising observation using appropriate matrice, and finally, a work proposal for future work and conclusion. A comparison table of different classification techniques is also included in the paper.
Indian Coin Detection and Recognition Using Deep Learning Algorithm

Coins play a very important role in our daily transactions whereas manually counting them is a bit of a tiring task. There is a need for automatic coin detection and recognition systems. The main objective of this paper is to detect and recognize Indian coins of various denominations based on their features including texture, colour, shape and so on. A very popular object detection algorithm, YOLOv5 is employed to perform this object detection task. CSPDarkNet and PANet in YOLOv5 are used to extract rich informative features and solve the memory and time requirement problem. YOLO layer in the YOLOv5 model generates a vector that includes anchor boxes, class labels and class scores. We demonstrated the performance of YOLOv5 with other popular objects such as Fast-RCNN, Faster-RCNN and SSD. However, the YOLOv5 model comprehensively outperforms in terms of accuracy and speed. Additionally, it also compared with current state-of-the-art methods still the YOLOv5 surpass those methods.
Application of Artificial Intelligence in Software Development Life Cycle: A Systematic Mapping Study

Building a quality maintainable software product is the necessity of any organization and it depends on various factors. The selection of an appropriate model is one of the factors to achieve this objective. Primarily any Software Development Life cycle (SDLC) model consists of seven phases: requirement gathering, planning, designing, coding, testing, implementation, and maintenance. Each phase has its own importance and will have to optimize those to get an optimal result. And for that, artificial intelligence (AI) offers a lot of potential for improving all aspects of the software development life cycle phases. The revolution of AI in software engineering (SE) is showing an upsurge in research. It becomes evident that a variety of AI paradigms might be used to improve the process and address many of the fundamental issues that the SE sector has been dealing with. Incorporation of AI in SE significantly impacts the developer’s productivity and product reliability which leads to achieving quality maintainable software products. This work presents a comprehensive study on AI methods in SDLC (model-wise) and AI methods in SDLC (phase-wise) with the help of different parameters. The author’s aim is to provide insights to the researchers and practitioners belonging to the field of SE, who will benefit from this study and get the future direction as well.
A Matching Recommendation Mechanism Based on Deep Learning and Topic Model

In recent years, text recommendation has been widely used in various APPs as a key technology for users to quickly and accurately obtain relevant information. Traditional text recommendation cannot obtain the internal relationship between users and articles, and ignores the information generated by users. Therefore, this paper proposes a matching recommendation mechanism based on articles and comments. First introduce the word2vec word vector model, use the vector to measure the relative meaning between words, and construct the document vector and user distribution vector based on the word vector. Then, under the framework of the topic model, a joint deep learning method—long and short-term memory network LSTM, makes full use of the new model before and after the sentence to learn the document to update the word vector expression of the sentence and document vector. Among them, the conditional random field (CRF) model is added to train the tags to solve the problem of insufficient attention to key words. Finally, in the matching mechanism, the similar relationship among the topic distributions, the constructed document vector and the user vector are used for training. Compared with the current popular topic model TopicRNN method, topic word vector model LF-LDA method, topic vector-based text representation method and four methods of LF-LDA combined with Word2vec text representation, the experimental results show that the matching recommendation classification is obtained Improved and very robust, training time is greatly shortened, the algorithm in this paper is effective.
A fuzzy logic based explicit declustering technique

During the spatial estimation of geoscience resource variables, the quantity or quality of minerals and hydrocarbons can be represented by a broad range of properties, including geochemical, geotechnical, or other physical measures. Preferential sampling within the region of interest causes biased global parameters due to clustered sampling patterns. Unbiased sample distribution is essential for conducting conditional simulations to model uncertainty of spatially distributed attributes, e.g. geochemical content of metal or porosity. Therefore, declustering procedures are applied during resource estimation to estimate an unbiased statistical distribution of the measured variables. Traditional techniques such as cell declustering do not consider grade clustering, i.e., the similarity of measured variables within a spatially clustered neighbourhood. This paper presents a declustering technique that explicitly accounts for spatial clustering and the similarity of measured samples' attributes within these spatially clustered samples. In the proposed method, samples were first classified explicitly into spatial and geochemical (grade) clusters using the Fuzzy c-means algorithm. Declustering weights were derived using the Mamdani based Fuzzy Inference System using various T-norm operations. The technique was applied to the publicly available GSLib and Walker Lake datasets. It was shown that the proposed scheme produced more accurate results than those obtained with the traditional declustering technique.