Reviewerly/exHarmony huggingface dataset:
LogoMix: A Data Augmentation Technique for Object Detection Applied to Logo Recognition

Automatic logo detection on images is a challenging task in computer vision with many applications in social media platforms. Recent works use generic deep-learning based object detectors, but they require huge databases for optimal recognition performance, which are very costly to obtain, especially in logo detection applications where the number of classes can be very high. For this reason, LogoMix, a new data augmentation method for object detection tasks, is proposed in this paper. It creates new sample images by combining logo entities with different degrees of overlapping. A state-of-the-art object detector trained with LogoMix has been evaluated on two popular logo datasets, QMUL-OpenLogo and FlickrLogos-32, and compared with other state-of-the-art works to prove its high performance.
Reliability Aware Cost Driven Mechanism for Scientific Workflows through Optimal Duplication Strategy

In recent years, all the computation intensive application especially scientific application has migrated to the cloud computing environment due to the diverse requirement. Moreover, diverse requirement of computing can be fulfilled through the cloud computing as it provides the on demand scalable service including resource and platform. Furthermore, in scientific application, it is very important for computing model to fulfill the QoS requirement which includes the fault tolerance and optimal cost; also, this has been one of the major challenging issues nowadays. Thus, in this research work, we develop a ODS (Optimal Duplication Strategy) for fault tolerance and cost driven mechanism also named as ODS-FTC; ODS-FTC uses the iterative based approach that selects VM and its duplicates that has minimum makespan in case of individual task. Moreover, this provides the utility against failure occurrence and optimal selection with optimal redundancy causes the cost to be optimal. ODS-FTC is evaluated considering the scientific workflow. Furthermore, in case of all instances, ODS-FTC is proved to be marginally improvised than the existing model.
Ideal kernel tuning: Fast and scalable selection of the radial basis kernel spread for support vector classification

A simple and fast method, named ideal kernel tuning, is proposed to select the radial basis kernel spread of the support vector machine for classification. The spread is selected directly from data, without any training nor test, in order to bring the kernel matrix nearer to the ideal kernel matrix, with values one for patterns of the same class and zero otherwise. To avoid scaling with the training set size, the kernel matrix is calculated for a small set of class prototypes. The selected spread can be used also for multi-class classification problems considering the two most populated classes. Compared to other 5 popular tuning algorithms, the proposed approach is a smart and efficient strategy whose performance is very near to the state-of-the-art, is 2–4 orders of magnitude faster and requires very little memory, scales better with the dataset size both in time and memory, and is able to classify medium-size datasets up to 70,000 training patterns, where other methods fail.
Research on knowledge modeling and knowledge push method for space engineering research and development based on ontology

In the design process of space engineering R & D , engineers need to use a lot of existing knowledge to assist design. A modeling method of space engineering knowledge network based on ontology is proposed relying on existing Aerospace Thesaurus. Through knowledge resource supply-demand matching technology and knowledge network map, a knowledge push method for space engineering is designed combined with specific tasks. It has been preliminarily applied in the Space Engineering Data Center Platform. The tests shows that this technology can effectively improve the standardization of aerospace terminology and the accuracy of knowledge service. It also promotes space engineering R&D knowledge’s effective organization and utilization.
Computational Time Reduction in Stream Field Navigation Through Dynamic Path Search and Selection in Kinematic Environments

Path planning algorithms based on physical models have been in development to allow navigating an environment in a way that mimics nature. Contrary to combinatorial and sampling-based algorithms, which represent the field by a set of nodes then use searching techniques to find a path, the artificial potential field method solves this problem by attracting the robot to the target and repelling it from the obstacles While the Stream Field Navigation (SFN) algorithm solves the problem by simulating a fluid field. This research proposes a dynamic path search and selection schema to allow the SFN algorithm to deal with moving obstacles. This research shows how the algorithm deals with a kinematic (variable) environment using the discretized path calculations. The dynamic search and selection algorithm is also capable of updating the robot's path when the goal is re-located. The developed approach is shown to greatly reduce the computational time required to obtain a path compared to re-simulating the field when a change is introduced to the environment.
Image Retrieval through sketches based on Descriptor with Data Retrieval using Reversibility Method

The various analysis of Image retrieval makes the Query by sketches as an important branch of image retrieval. It also acts as supplementary to Content Based Image Retrieval. Whereas Sketch based image retrieval plays a vital role in critical applications like military and crime systems, as accuracy becomes the major factor. Here loss can be any data or information hidden in images. The data hiding technique uses reversibility to extract the data, as well as the real images, which can be extracted from sketches. The message embedding and extracting process uses all 3 color channels and a difference histogram. The proposed work of SBIR adopts different processes like pre-processing for extraction of strong edges, feature extraction by descriptor, and constraint&#x0027;s application for the selection of shaping edges. In the proposed work, the framework will significantly improve the SBIDR performance by exploring the method: Image Retrieval with Data Retrieval simultaneously.
A Hand Gesture Recognition Algorithm Based on Multi-scale Hybrid Features

Abstract A human hand gesture recognition algorithm based on multi-scale hybrid features is pro-posed. A large number of gesture data images are organized and annotated by automatic im-age annotation algorithm. Based on dense sampling, the multi-scale Gaussian pyramid is taken into account for enhancing the image details; it can realize the recognition of both large and small target gestures. Meanwhile, according to the aggregation and distribution of gestures, statistical analysis is used to optimize the size and aspect ratio of anchors, which can further improve the accuracy of recognition. The experiment results show that the mean average precisions of this algorithm can be improved to 99.7% and 93.3% for the simple gesture and complex gesture, respectively, which are higher than that obtained by these traditional methods, such as faster region-convolution neural network (Faster R-CNN), You Only Look Once (YOLO), and SSD.
DyCo: Dynamic, Contextualized AI Models

Devices with limited computing resources use smaller AI models to achieve low-latency inferencing. However, model accuracy is typically much lower than the accuracy of a bigger model that is trained and deployed in places where the computing resources are relatively abundant. We describe DyCo, a novel system that ensures privacy of stream data and dynamically improves the accuracy of small models used in devices. Unlike knowledge distillation or federated learning, DyCo treats AI models as black boxes. DyCo uses a semi-supervised approach to leverage existing training frameworks and network model architectures to periodically train contextualized, smaller models for resource-constrained devices. DyCo uses a bigger, highly accurate model in the edge-cloud to auto-label data received from each sensor stream. Training in the edge-cloud (as opposed to the public cloud) ensures data privacy, and bespoke models for thousands of live data streams can be designed in parallel by using multiple edge-clouds. DyCo uses the auto-labeled data to periodically re-train, stream-specific, bespoke small models. To reduce the periodic training costs, DyCo uses different policies that are based on stride, accuracy, and confidence information. We evaluate our system, and the contextualized models, by using two object detection models for vehicles and people, and two datasets (a public benchmark and another real-world proprietary dataset). Our results show that DyCo increases the mAP accuracy measure of small models by an average of 16.3% (and up to 20%) for the public benchmark and an average of 19.0% (and up to 64.9%) for the real-world dataset. DyCo also decreases the training costs for contextualized models by more than an order of magnitude.
Vulnerability and Transaction Behavior Based Detection of Malicious Smart Contracts

Smart Contracts (SCs) in Ethereum can automate tasks and provide different functionalities to a user. Such automation is enabled by the ‘Turing-complete’ nature of the programming language (Solidity) in which SCs are written. This also opens up different vulnerabilities and bugs in SCs that malicious actors exploit to carry out malicious or illegal activities on the cryptocurrency platform. In this work, we study the correlation between malicious activities and the vulnerabilities present in SCs and find that some malicious activities are correlated with certain types of vulnerabilities. We then develop and study the feasibility of a scoring mechanism that corresponds to the severity of the vulnerabilities present in SCs to determine if it is a relevant feature to identify suspicious SCs. We analyze the utility of the severity score towards detection of suspicious SCs using unsupervised machine learning (ML) algorithms across different temporal granularities and identify behavioral changes. In our experiments with on-chain SCs, we were able to find a total of 1094 benign SCs across different granularities which behave similar to malicious SCs, with the inclusion of the smart contract vulnerability scores in the feature set.
Progressive GAN-Based Transfer Network for Low-Light Image Enhancement

Images captured in low-light conditions usually suffer from very low contrast and underexpose, which cannot be directly utilized in the subsequent computer vision tasks, such as object recognition, detection, identification and tracking. Existing methods include HE based method, Retinex theory based method and deep learning method which may generate undesirable enhanced results including amplified noise, biased colors and extreme boundary. To address this problem, we utilize prior knowledge of Retinex theory and GAN based on data statistic to propose a progressive GAN-based Transfer network to realize the low-light enhancement. In this paper, the image is decomposed by JieP method based on the Retinex model to obtain the reflection and light components, and learn the relationship between the reflection component of the low-light image and normal light image via a reflection decomposition on network (RefDecN), and then generate the reflection component of the low-light image. Then, another illumination transfering network (IllumTransN) is utilized to transfer the light of normal light image to the reflection component to realize low-light enhancement. Experimental results of low-light image enhancement on RAISE, LOL and MEF datasets demonstrate our ProGAN can outperform state-of-the-art methods in terms of objective and subjective quality.
Object Detection Edge Performance Optimization on FPGA-Based Heterogeneous Multiprocessor Systems

Object detection tasks implemented using complex convolutional neural network (CNN) algorithms are both computational and memory intensive, making them difficult to deploy on CPU-only embedded systems due to their limited edge computing capabilities. Heterogeneous multiprocessor systems come in handy to perform these tasks. These systems usually integrate CPU and other processing units like GPU, DSP and FPGA such that each task is preferably executed by the unit that is able to perform that task efficiently with superior energy efficiency. This paper proposes a workflow with a series of optimization approaches such as model pruning, model quantization and multi-threading design in implementing an object detection task based on YOLOv4-CSP on a FPGA-based heterogeneous multiprocessor system. The YOLOv4-CSP network architecture is the state-of-the-art one-stage detection model. It is widely known for its fast inference time in object detection task. The experiments show that we can achieve a significant edge performance with lesser computing resources to implement object detection with complex CNN algorithms.
Optimal Algorithm for Credit Card Fraud Detection

Fraudulent transactions made in a credit card can be discovered using various algorithms of ML, which are a process of information or a thorough check by a data science group, that supports high-quality results in revealing and preventing deceptive undertakings. CC scam unmasking is currently one of the most repeatedly occurring issue in the globe. This is mainly caused due to online payments. These fraudulent activities occur when online remittance is done without any secured procedures. This project aims to focus on machine intelligence algorithms to discover the deceptive CC discharge. The results of algorithms happen to establish precision or correctness, accuracy, recall, and F1-score. The Logistic Regression, Naive Bayes, and Random Forest classifiers are high-quality inventions used to detect fraud.
Spatial- and time- division multiplexing in CNN accelerator

With the widespread use of real-time data analysis by artificial intelligence (AI), the integration of accelerators is attracting attention from the perspectives of their low power consumption and low latency. The objective of this research is to increase accelerator resource efficiency and further reduce power consumption by sharing accelerators among multiple users while maintaining real-time performance. To achieve the accelerator-sharing system, we define three requirements: high device utilization, fair device utilization among users, and real-time performance. Targeting the AI inference use case, this paper proposes a system that shares a field-programmable gate array (FPGA) among multiple users by switching the convolutional neural network (CNN) models stored in the device memory on the FPGA, while satisfying the three requirements. The proposed system uses different behavioral models for workloads with predictable and unpredictable data arrival timing. For the workloads with predictable data arrival timing, the system uses spatial-division multiplexing of the FPGA device memory to achieve real-time performance and high device utilization. Specifically, the FPGA device memory controller of the system transparently preloads and caches the CNN models into the FPGA device memory before the data arrival. For workloads with unpredictable data arrival timing, the system transfers CNN models to the FPGA device memory upon data arrival using time-division multiplexing of FPGA device memory. In the latter case of unpredictable workloads, the switch cost between CNN models is non-negligible to achieve real-time performance and high device utilization, so the system integrates a new scheduling algorithm that considers the switch time of the CNN models. For both predictable and unpredictable workloads, user fairness is achieved by using an ageing technique in the scheduling algorithm that increases the priority of jobs in accordance with the job waiting time. The evaluation results show that the scheduling overhead of the proposed system is negligible for both predictable and unpredictable workloads providing practical real-time performance. For unpredictable workloads, the new scheduling algorithm improves fairness by 24%–94% and resource efficiency by 31%–33% compared to traditional algorithms using first-come first-served or round-robin. For predictable workloads, the system improves fairness by 50.5 % compared to first-come first-served and achieves 99.5 % resource efficiency.
Gcn Recommendation Model Based on the Fusion of Dynamic Multiple-View Latent Interest Topics

Graph neural network-based representation models have shown extraordinary potential in numerous recommender system applications. Previous studies mainly considered high-order connectivity information in a single view from an interaction graph but ignored the individualized information of the users or items, making them vulnerable to over-smoothing problems. In this study, we proposed a novel dynamic multi-view fusion-based graph convolution network model for recommendation systems. First, we adopted matrix decomposition to obtain individualized features. Second, multiple views were generated for learning on the basis of latent user interest topics from the decomposed matrix, and a continuous awareness mechanism was proposed to maintain the model’s focus on the individualized features. Lastly, the different features from multiple views were dynamically fused through a principal component control mechanism to predict the similarities between users and items. Experimental results of three popular datasets of recommendation systems demonstrated that our method could effectively alleviate the over-smoothing problem and achieved better performance than four state-of-the-art baselines.
A variant of RSA using continued fractions

In the modern world, the need for secure communication is of utmost importance and in that cryptography plays a significant role. The RSA algorithm is one of the most widely used public key cryptosystems and mathematicians throughout the world are proposing new variants for RSA in order to overcome its drawbacks. In our system, we introduce a new variant for the textbook RSA algorithm using continued fractions. We have developed the key generation using continued fractions and further improved the encryption using a technique similar to that of padding. We have also proved that our system is secure against the meet-in-the-middle-attack in contrast to the textbook RSA algorithm.
Design of Children’s Motor Skills Training Management System Based on Human Movement Recognition Algorithm of Depth Information

With the deepening of the concept of all-round development, the training of children's motor skills is becoming more and more popular. Children's motor skills training management system is based on the product of children's motor skills training combined with modern technology. This article aims to design a management system for children's motor skills training based on human motion recognition methods. This article proposes a human motion recognition algorithm based on depth information. The traditional human motion recognition algorithm is more biased towards children's motor skills. For the design of the system, this article also pays great attention to children's experience. After analyzing the performance of the system in this study, it is found that the system is not ideal for multitarget human motion recognition. Therefore, this article optimizes the recognition. After the optimization, the recognition rate of the head, upper body, and lower body of the multiobjective human motion recognition is more than 80%.
Reversibly stealth QR code based on N-methylmaleimide-vinyl acetate copolymers under the condition of acid-base change

The Quick Response (QR) code has been widely used in many fields. While the information security of users will be endangered, if the QR code is illegally duplicated. So, the stealth QR code has the potential application in security printing. In this paper, a kind of stealth QR code that can be reversibly displayed by N-methylmaleimide (MMI) and vinyl acetate (VAc) copolymers as inkpad under the condition of acid-base change. The PVAc/MMI copolymers were synthesized by solution polymerization in methanol using VAc and different amount of MMI (MMI:VAc = 1, 2, 3 and 4 mol%). Because there is a big difference of the reactivity ratio of MMI (rMMI = 1.16) and VAc (rVAc = 1.90 × 10−3), the MMI were starved feeding into the polymerization system at a fixed rate over a period of 3 h. The actual mole ratio of MMI units in copolymers are 0.70 mol%, 1.91 mol%, 3.03 mol% and 5.83 mol% corresponding to the feeding ratios of MMI:VAc from 1 mol% to 4 mol%, respectively. The PVAc/MMI copolymers can be become to PVA/MMI by alcoholysis. The thermal properties of PVAc/MMI and PVA/MMI were investigated. The introduction of MMI in copolymers are helpful for improving the glass transition temperature (Tg) of PVAc/MMI and PVA/MMI, decreasing melting points (Tm) of PVA/MMI. The interesting thing is that the colorless PVAc/MMI showed color change under base condition in the alcoholysis process, further, the color showed reversibly change under acid/base condition change. This feature inspires us that using PVAc/MMI copolymer solution as inks, a kind of QR code patterns can be realized with fast color response, from blue color in alkaline condition to achromatous in acidic condition, which confirms the superior information encryption ability of the chemical-based QR pattern. Based on this feature, the QR code stamped by PVAc/MMI copolymers can be reversibly displayed in the actual information encryption application.
Multi-Script Text Detection from Image Using FRCNN

Textual information is the most common type of way by which we can determine what text/texts we are looking for. In order to retrieve text from images the first and foremost step is text detection from the image. Text detection has a wide range of applications such as translation, smart car driving system, information retrieval, indexing of multimedia archives, sign board reading, and countless. Multilingual text detection from images adds an extra complication to a computer vision problem. As India is a multilingual country and therefore multi-script texts can be found almost everywhere. A multi-script text differs in terms of formats, strokes, width, and height. Also, universal features for such an environment are unknown and difficult to determine as well. Therefore, detecting multi-script text from images is an important yet unsolved problem. In this work, we proposed a faster RCNN-based method for detecting English, Hindi, and Gujarati text from Images. Faster RCNN is the state-of-the-art approach for object detection. As it works for objects which are of large size and texts are of smaller size, the parameters are tuned to meet the objective of multi-script text detection. The dataset is created by collecting images as there is no standard dataset available that includes English, Gujarati, and Hindi texts in the public domain.