Reviewerly/exHarmony huggingface dataset:
A Target Detection and Tracking Method for Multiple Radar Systems

Multiple radar systems represent an attractive option for target tracking because they can significantly enlarge the area coverage and improve both the probability of trajectory detection and the localization accuracy. The presence of multiple extended targets or weak targets is a challenge for multiple radar systems. Moreover, their performance may be severely deteriorated by regions characterized by a high clutter density. In this paper, an algorithm for detection and tracking of multiple targets, extended or weak, based on measurements provided by multiple radars in an environment with heavily cluttered regions, is proposed. The proposed method features three stages. In the first stage, past measurements are exploited to build a spatio-temporal clutter map in each radar; a weight is then assigned to each measurement to assess its significance. In the second stage, a track-before-detect algorithm, based on a weighted three-dimensional Hough transform, is applied to obtain target tracklets. In the third stage, a low-complexity tracklet association method, exploiting a lion reproduction model, is applied to associate tracklets of the same target. Three experiments are presented to illustrate the effectiveness of the proposed approach. The first experiment is based on synthetic data, the second one on actual data from a radar network with two homogeneous air surveillance radars, and the third one on actual data from a radar network with four different marine surveillance radars. The results reveal that the proposed method can outperform competing approaches.
Detection of Counterfeit Products using Blockchain

Supply chain management frequently faced issues such as service redundancy, poor coordination between several departments, and lack of standardization as a result of the lack of transparency. Product counterfeiting is something which is very common now-a-days and it’s almost impossible to detect a counterfeit product just by looking at it. Counterfeiters cause significant challenges for legitimate firms, yet far too many people have no idea of the entire amount of counterfeit items’ influence on brands. There are several methods devised in the past to get away with this problem of product counterfeiting. The most popular methods are using RFID tags, Artificial Intelligence, QR code based systems, etc. But each of them had few disadvantages such as the QR code can be copied from a genuine product and placed on a fake product, artificial intelligence uses CNN and machine learning which needs heavy computational power and so on. The idea of this project is to improve detection of fake products by tracking its supply chain history. This is achieved with Blockchain technology which ensures the identification and traceability of real products throughout the supply chain. Blockchain based system, makes everything decentralized that may be accessed by several parties at the same time. One of its main advantages is that the recorded data is difficult to change without the consent of all parties concerned which makes the data extremely secure and protect from all vulnerabilities. This paper presents system designed using blockchain technology for detection of counterfeit products.
An Edge Intelligent Blockchain-Based Reputation System for IIoT Data Ecosystem

Industrial Internet of Things (IIoT) devices generate and collect massive amounts of industrial data. Monetizing the flood of data generated by the IIoT devices has enabled the creation of the IIoT data ecosystem, where individuals and businesses may trade data. With the rapid expansion of the online data trading industry, the necessity for an edge intelligent reputation system is becoming increasingly important as more individuals and services connect online. In recent years, researchers have proposed blockchain-based reputation systems as a means of offering anonymity, security, transparency, and mutual trust for both providers and customers in Industry 4.0. Unfortunately, they focus on the decentralized reputation system with a single certificate authority, which creates the concern of a single point of failure (SPOF). Moreover, researchers paid little attention to the performance measures of these blockchain-based reputation systems to demonstrate their usability in a real IIoT data ecosystem. This article proposes a robust edge intelligent blockchain-based reputation system capable of avoiding failures by enhancing the Raft consensus mechanism. We provide extensive security analysis and simulation experiments to demonstrate the performance of the blockchain-based reputation system for the IIoT data ecosystem using different metrics, such as transaction throughput, latency, and resource consumption.
Quito: a Coverage-Guided Test Generator for Quantum Programs

Automation in quantum software testing is essential to support systematic and cost-effective testing. Towards this direction, we present a quantum software testing tool called Quito that can automatically generate test suites covering three coverage criteria defined on inputs and outputs of a quantum program coded in Qiskit, i.e., input coverage, output coverage, and input-output coverage. Quito also implements two types of test oracles based on program specifications, i.e., checking whether a quantum program produced a wrong output or checking a probabilistic test oracle with statistical test. We describe the architecture and methodology of the tool. We also validated the tool with one quantum program and one faulty version of it. Results indicate that Quito can generate test suites and perform test assessments that detect faults, and produce test results with a good time performance.Quito’s code: https://github.com/Simula-COMPLEX/quitoQuito’s video: https://youtu.be/kuI9QaCo8A8Artifact Available: https://doi.org/10.5281/zenodo.5288665
Financial Impact of Internet Access Infrastructure of Online Learning Mode on Tertiary Students in Covid-19 Era in Ghana

The Internet technology has had profound impact on our lives as well as changed the ways in which businesses interact with customers and has immense potential to improve the quality of education, a sustainable development goal set by the United Nations. Access to the Internet helps both educational administrators and students to reduce the costs of learning and improve the quality of education in schools and colleges. In developing countries, especially in Africa, the cost of accessing the Internet by students continues to be high and out of reach of many as one considers other key elements such as affordable broadband, computers, smartphone devices and the knowledge, skills, and competency to use the Internet technology. This paper examined the financial burden placed on students in accessing the Internet for their education at the tertiary level during the COVID-19 pandemic period. The results showed that tertiary students were financially impaired during the period as the cost of accessing the Internet (Internet data/subscription, Internet devices etc.) generally increased during the period.
S‐shaped and V‐shaped binary African vulture optimization algorithm for feature selection

Abstract The African vulture optimization algorithm (AVOA) is a recently developed metaheuristic algorithm that imitates the eating and movement patterns of authentic African vultures. AVOA is developed to address the continuous optimization problem. However, AVOA is unable to solve the discrete search space, this inspires us to develop the binary AVOA for feature selection problems in classification tasks. The suggested BAVOA incorporates an eight‐transfer function (S‐shaped and V‐shaped) for transforming a continuous variable to a binary one. Using 14 benchmark data sets, the proposed technique is compared against 15 conventional binary metaheuristics algorithms in terms of classification accuracy, fitness function, number of selected features and converging ability. Furthermore, the results are statistically analysed using Wilcoxon test. The comparative findings of S‐Shaped and V‐shaped transfer functions indicate the superior performance of BAVOA methods, particularly S2‐BAVOA, in contrast to other transfer function. Based on results, it turns out that the suggested technique converges to the global minimum in several iterations based on the selection of optimal characteristics, fitness values and higher classification accuracy as compared to the classical binary metaheuristic algorithms.
A Virtual Tour for the Promotion of Tourism of the City of Bari

The use of information technology in the field of cultural heritage makes it possible to involve more and more people in the promotion of cultural heritage, fostering social, cultural, economic and community growth. This work stems from the interest in using Virtual Reality (VR) in the field of cultural heritage, creating a tour of the city of Bari that tells its evolution over the years. To this end, a low-cost VR360 application has been developed which, by means of a cardboard, allows the user to experience a virtual journey through time. It tells the story of the city, focusing on its urban expansion and the evolution of its architectural styles, influenced by various dominations over the centuries, up to the current state. The virtual environment was created from spherical images of the city, captured through 360° cameras and enriched with various types of information content. The user experience was assessed by means of a questionnaire derived from previous work that was generalised and adapted to the considered scenario: the results showed a very good level of satisfaction, usability, engagement, immersion and sense of presence; the highest score was obtained for the visual quality of the images of the virtual environment.
QMutPy: a mutation testing tool for Quantum algorithms and applications in Qiskit

There is an inherent lack of knowledge and technology to test a quantum program properly. In this paper, building on the definition of syntactically equivalent quantum gates, we describe our efforts in developing a tool, coined QMutPy, leveraging the well-known open-source mutation tool MutPy. We further discuss the design and implementation of QMutPy, and the usage of a novel set of mutation operators that generate mutants for qubit measurements and gates. To evaluate QMutPy’s performance, we conducted a preliminary study on 11 real quantum programs written in the IBM’s Qiskit library. QMutPy has proven to be an effective quantum mutation tool, providing insight into the current state of quantum tests. QMutPy is publicly available at https://github.com/danielfobooss/mutpy. Tool demo: https://youtu.be/fC4tOY5trqc.
A large-scale study of usability criteria addressed by static analysis tools

Static analysis tools support developers in detecting potential coding issues, such as bugs or vulnerabilities. Research on static analysis emphasizes its technical challenges but also mentions severe usability shortcomings. These shortcomings hinder the adoption of static analysis tools, and in some cases, user dissatisfaction even leads to tool abandonment.
An Image Classification Algorithm Based on Hybrid Quantum Classical Convolutional Neural Network

Quantum machine learning is emerging as a strategy to solve real-world problems. As a quantum computing model, parameterized quantum circuits provide an approach for constructing quantum machine learning algorithms, which may either realize computational acceleration or achieve better algorithm performance than classical algorithms. Based on the parameterized quantum circuit, we propose a hybrid quantum-classical convolutional neural network (HQCCNN) model for image classification that comprises both quantum and classical components. The quantum convolutional layer is designed using a parameterized quantum circuit. It is used to perform linear unitary transformation on the quantum state to extract hidden information. In addition, the quantum pooling unit is used to perform pooling operations. After the evolution of the quantum system, we measure the quantum state and input the measurement results into a classical fully connected layer for further processing. We demonstrate its potential by applying HQCCNN to the MNIST dataset. Compared to a convolutional neural network in a similar architecture, the results reveal that HQCCNN has a faster training speed and higher testing set accuracy than a convolutional neural network.
Phase-matching quantum key distribution with advantage distillation

Abstract Phase-matching quantum key distribution (PM-QKD) provides a promising solution to surpass the fundamental rate–distance bound without quantum repeaters. In this paper, we insert an additional advantage distillation (AD) step after quantum communication to improve the performance of PM-QKD. Simulation results show that, by splitting the raw key into blocks of only a few bits so as to identify highly correlated bit pairs, the AD method can tolerate high system misalignment errors and improve the secret key rate and transmission distance significantly, which is very promising in current PM-QKD systems.
Statistical Drift Detection Ensemble for batch processing of data streams

Among the difficulties being considered in data stream processing, a particularly interesting one is the phenomenon of concept drift. Methods of concept drift detection are frequently used to eliminate the negative impact on the quality of classification in the environment of evolving concepts. This article proposes Statistical Drift Detection Ensemble (sdde), a novel method of concept drift detection. The method uses drift magnitude and conditioned marginal covariate drift measures, analyzed by an ensemble of detectors, whose members focus on random subspaces of the stream's features. The proposed detector was compared with state-of-the-art methods on both synthetic data streams and the semi-synthetic streams generated based on the real-world concepts. A series of computer experiments and a statistical analysis of the results, both for the classification accuracy and Drift Detection errors were carried out and confirmed the effectiveness of the proposed method.
DotMat: Solving Cold-Start Problem and Alleviating Sparsity Problem for Recommender Systems

Cold-start and sparsity problem are two key intrinsic problems to recommender systems. During the past two decades, researchers and industrial practitioners have spent considerable amount of efforts trying to solve the problems. However, for cold-start problem, most research relies on importing side information to transfer knowledge. A notable exception is ZeroMat, which uses no extra input data. Sparsity is a lesser noticed problem. In this paper, we propose a new algorithm named DotMat that relies on no extra input data, but is capable of solving cold-start and sparsity problems. In experiments, we prove that like ZeroMat, DotMat can achieve competitive results with recommender systems with full data, such as the classic matrix factorization algorithm.
Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection

Camera-LiDAR fusion provides precise distance measurements and fine-grained textures, making it a promising option for 3D vehicle detection in autonomous driving scenarios. Previous camera-LiDAR based 3D vehicle detection approaches mainly focused on employing image-based pre-trained models to fetch semantic features. However, these methods may perform inferior to the LiDAR-based ones when lacking semantic segmentation labels in autonomous driving tasks. Motivated by this observation, we propose a novel semantic augmentation method, namely Sem-Aug, to guide high-confidence camera-LiDAR fusion feature generation and boost the performance of multimodal 3D vehicle detection. The key novelty of semantic augmentation lies in the 2D segmentation mask auto-labeling, which provides supervision for semantic segmentation sub-network to mitigate the poor generalization performance of camera-LiDAR fusion. Using semantic-augmentation-guided camera-LiDAR fusion features, Sem-Aug achieves remarkable performance on the representative autonomous driving KITTI dataset compared to both the LiDAR-based baseline and previous multimodal 3D vehicle detectors. Qualitative and quantitative experiments demonstrate that Sem-Aug provides significant improvements in challenging <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hard</i> detection scenarios caused by occlusion and truncation.
Cross-Image Region Mining With Region Prototypical Network for Weakly Supervised Segmentation

Weakly supervised image segmentation trained with image-level labels usually suffers from inaccurate coverage of object areas during the generation of the pseudo groundtruth. This is because the object activation maps are trained with the classification objective and lack the ability to generalize. To improve the generality of the object activation maps, we propose a region prototypical network ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RPNet</b> ) to explore the cross-image object diversity of the training set. Similar object parts across images are identified via region feature comparison. Object confidence is propagated between regions to discover new object areas while background regions are suppressed. Experiments show that the proposed method generates more complete and accurate pseudo object masks while achieving state-of-the-art performance on PASCAL VOC 2012 and MS COCO. In addition, we investigate the robustness of the proposed method on reduced training sets. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/liuweide01/RPNet-Weakly-Supervised-Segmentation</uri> .
CONCEAL: A robust dual-color image watermarking scheme

Compared with binary and grayscale watermark, the color watermark has a larger amount of information, which makes color blind watermarking algorithms more challenging in terms of robustness, watermark capacity and computational complexity under the limitation of distortion. To better meet these challenges, a robust dual-COlor image watermarking scheme exploiting COmpressive seNsing and inter-bloCk approximatE mAximum eigenvaLue, namely CONCEAL, is proposed in this work. Specifically, the Compressive Sensing (CS) is first executed on a watermark, which can effectively compress the watermark information to one-half of the original. Secondly, the carrier image is separated into 4×4 non-overlapping pixel blocks, and the standard deviation of each pixel block is calculated. Then, the pixel blocks with smaller standard deviations are subdivided into four 2×2 non-overlapping pixel sub-blocks (the upper-left, lower-left, upper-right, and lower-right pixel sub-blocks), and the Approximate Maximum Eigenvalue (AME) of four sub-pixel blocks are directly calculated in the spatial-domain at the same time. Finally, every 3-bit watermark data is embedded in a 4×4 pixel block using the AME of the upper-left pixel sub-block and the other three pixel sub-blocks. The imperceptibility and robustness of CONCEAL are assessed experimentally. The experimental results show that all Peak Signal-to-Noise Ratios (PSNRs) are above 47 dB, Structural Similarity Index Measures (SSIMs) are above 0.98, and Normalized Correlations (NCs) are above 0.9. Compared with the state-of-the-art schemes, the proposed CONCEAL possesses large capacity, high imperceptibility, comparable robustness.
Telugu Optical Character Recognition Using Deep Learning

In a current generation many of researches developed to convert photogenic data into digital information. But this development consider only for popular languages like English, Latin and Chinese etc., but only few researches on religion languages. Coming to this project about conversion of Telugu handwritten data into digital form. It is not only printed conversion also handwritten data of different writing modules with different styles of writing. To convert into digital form here chosen two programming languages those are Matlab and Python. Considering the results and drawbacks finalise the python. It is mainly aimed towards to decrease time of user and increase the productivity and perfection outcome of data. An important features of this project is convert the Telugu handwritten into digital form using of pre-trained models also it works on offline.
Reconfigurable Hardware Design Approach for Economic Neural Network

Hardware-based neural networks are becoming attractive because of their superior performance. One of the research challenges is to design such hardware using less area to minimize the cost of on-chip implementation. This brief proposes an area-efficient implementation of an Artificial Neural Network (ANN). The proposed method reduces the number of layers in the ANN by nearly half through a novel, dual use of some layers denoted as hidden layers. The hidden layers are non-traditional layers proposed in this brief. They are adaptable, and each such layer performs two separate functions through judicious use of different weights. Thus, each hidden or flexible layer does the work of two traditional ANN layers. The other type of layers used in the proposed design is the fixed layers that are used traditionally. The fixed layers are not flexible and serve the functionality of a single layer. The proposed design keeps the number of the fixed layers as low as possible. One or more fixed layers may still be needed for some applications besides the proposed flexible layers. The proposed method is implemented in Verilog HDL on Altera Arria 10 GX FPGA. Its area usage is only 41% of the state-of-the-art method, while its power consumption and speed overheads are small.
Model-Based Occlusion Disentanglement for Image-to-Image Translation

Image-to-image translation is affected by entanglement phenomena, which may occur in case of target data encompassing occlusions such as raindrops, dirt, etc. Our unsupervised model-based learning disentangles scene and occlusions, while benefiting from an adversarial pipeline to regress physical parameters of the occlusion model. The experiments demonstrate our method is able to handle varying types of occlusions and generate highly realistic translations, qualitatively and quantitatively outperforming the state-of-the-art on multiple datasets.
One Quadrillion Triangles Queried on One Million Processors

We update our prior 2017 Graph Challenge submission [7] on large scale triangle counting in distributed memory by demonstrating scaling and validation on trillion-edge scale-free graphs. We incorporate recent distributed communication optimizations developed for irregular communication workloads [1], and demonstrate scaling up to 1.5 million cores of IBM BG/Q Sequoia at LLNL. We validate our implementation using nonstochastic Kronecker graph generation where ground-truth local and global triangle counts are known, and model our Kronecker graph inputs after the Graph500 [5] R-MAT inputs. To our knowledge, our results are the largest triangle count experiments on synthetic scale-free graphs to date.