Reviewerly/exHarmony huggingface dataset:
A Real TimeVehicle Detection Based on Modified Convolutional Neural Networks in Different Climate

In order to effectively manage and monitor traffic on the roads, intelligent technologies must be able to detect and tally the number of cars on the road. The technology may be used for controlling and monitoring traffic in both urban areas and on highways, especially in times of heavy congestion or bad weather. It has traditionally been accomplished using sensor data and the standard toolkit for image processing. Recently however, with the help of deep learning-based smart computer vision systems, this process has become highly scalable and dependable. For intelligent traffic monitoring and management of issues like traffic congestion, the information gathered from various surveillance cameras may be utilized to train models that can recognize and track on-road cars, even in low-light and blurry situations brought on by bad weather. Although they all aim to solve the same problem, vehicle identification algorithms all have to deal with one or two very distinct cases. Vehicle detection under haze, dust, sandstorms, snow, and rain, as well as daytime and nocturnal conditions, is the focus of this study. The suggested design is based on the CNN architecture as a starting point, but it adds a spatial pyramid pooling layer and removes several of the Batch Normalization levels. During training, the system achieved an average accuracy of 81% and was able to identify even the tiniest vehicles in the scene.
Inferring User Image-Search Goals Using SVM and Re-ranking process

Inferring user image search primarily creates pure images from search engine results. The current image-searched re-ranking algorithms needed to be more reliable because of the assumption that the first text-oriented image-search result is used in the re-ranking technique. The visualized re-ranking method had been intended to refine the search results from the text-oriented picture program by incorporating the knowledge supplied by the imagined sense to increase the precision of the search ranking. Visualized re-ranking has become a favorite research topic in the multimedia retrieving and laptop vision sectors since it allows for the consideration of visualized sense in previous picture search engines in a lightweight manner without causing acquisition measurability issues. Furthermore, visual re-ranking may be utilised to increase the quality of the acquired information in the technique of mechanically creating online coaching information for object identification, in addition to the picture search situation using SVM classification for the different image datasets. The study of a question's user search aims will be extremely useful in improving computer program connection and user experience, i.e., 91% accuracy. Although much focus has been on inferring user objectives or intents for text searches, more must be predicted for image searches.
Traffic Sign Classification using Le-Net Architecture

Roadside traffic signs are crucial to our safety while we are travelling. Similar to human drivers, classification of traffic signs is a crucial component in self-driving car applications. Self-driving cars must use camera data to make judgments based on what they observe. Self-driving cars must be able to recognize things and classify traffic signs in order for us to know when they cease yielding and if they will go at a speed of 30 or 50 kilometers per hour. To categorize traffic signs, utilize the traffic sign classification system. so that they can be informed and warned in advance to prevent rule violations. The suggested approach substantially addresses certain drawbacks of the current classification systems, such as inaccurate predictions, hardware cost, and maintenance. The suggested method uses LE-NET architecture to provide a categorization of traffic signs. This will enable autonomous vehicles to decide depending on what they perceive.
Classification of Blood Cell Data using Deep Learning Approach

Machine Learning is an established branch of artificial intelligence that comprises of algorithms and mathematical relationships and is rapidly being used to clinical research. Machine Learning enables computers to be programmed without explicit information and to learn from it. The outcomes of using these technologies in medical data processing have been amazing, with great effectiveness in detecting illnesses. According to studies, Machine Learning techniques significantly improve complex medical decision-making processes in medical image processing by extracting and then evaluating the features of these pictures. An urgent demand for more sophisticated data analysis approaches has arisen as the number of medical diagnosis tools rose and a significant amount of high-quality data was produced. Traditional methodologies are incapable of examining such vast amounts of data. One of the critical diseases, Leukemia can be found in the blood cell data image of patients by applying machine learning algorithms. Here in this paper, the blood cell images available in the open literature are analyzed using the different algorithm networks for the refinement of images to identify the diseases like leukemia.
Intelligent diagnosis system of cardiovascular disease based on case-based reasoning

Cardiovascular disease has the characteristics of high morbidity, high disability rate and high mortality, that seriously endangers people's health and life. It is of great significance for the prevention and early diagnosis of cardiovascular disease. With the assistant diagnosis method of cardiovascular disease based on case-based reasoning (CBR), the cardiovascular status represented by the current pulse waveform can be diagnosed by the historical pulse waveform. Firstly, the case base is constructed by the existing cases of cardiovascular disease. When there are new cases to be diagnosed, the similar cases will be retrieved by case retrieval, and the recommended diagnostic results of the new case will be obtained by the case reuse. In order to improve the accuracy of case diagnosis, three weight allocation and optimization methods, namely, average weight, genetic algorithm optimal allocation weight and introspective learning dynamic adjustment weight, were compared. The experimental results show that weight optimization method with introspective learning can significantly improve the accuracy and the ROC (Receiver Operating Characteristic) performance of diagnosis.
Detection Of Snowball and Jersey Colour And Jersey Number Using CNN

A snowball is spherical item composed of snow that is often formed by scooping snow into the hands and compacting it into a ball. Games like snowball battles frequently involve the use of snowballs. A larger snowball can also be created by rolling a smaller snowball across a snowy surface. As it rolls, the smaller snowball gathers more snow, which causes it to grow. Tracking separate performers on ice as well as team strategic decision-making depend on the ability of coaches and other analysts to accurately identify snowball performers and squads in a game. In contrast, snowball is a fluid sport because of the complicated situations and frequent team substitutions, which cause the players to adopt different postures while playing. In this proposed system, individual ice snowball players are distinguished using a 2-phase cascaded convolution neural network (CNN) prototypical, jersey color of discovered individuals is recovered to additional ascertain team connections. Because we used a dataset we created from real images, our algorithm is able to remove most of disturbing details, which results in an overall detection accuracy as well as recall amount of over 95% for separate performers, an average detection accuracy of over 96% for teams. With our own dataset, we are able to reach maximum degree of accuracy in our player detection algorithm.
Potential usage of AI in Blockchain Technology

In recent Times, Artificial intelligence (AI) and blockchain have become two of the most exciting and disruptive technologies. Block chain technology has the ability to automate payment in cryptocurrency and to provide access to a shared ledger of data, transactions, and logs in a decentralized, secure, and trusted manner. AI, on the other hand, offers intelligence and decision-making capabilities for machines similar to humans Also with Smart contracts or self-executing contracts with the terms of the agreement written into code. By using blockchain to track and secure these contracts, and AI to help automate the execution of their terms, it is possible to create more efficient and trustworthy business processes. In this paper, we present a detailed of on block chain applications for AI.
A Blockchain-based Model for Maternal Health Information Exchange and Prediction of Health Risks using Machine Learning

Proper antenatal and postnatal care are necessary to safeguard a woman's health as well as the health of her unborn child. The field of maternal healthcare is witnessing a dramatic shift as a result of recent advancements and new initiatives in medical technology. Still the issues of data security, reliability, transparency, informed decisions, and accountability in providing health care services to name a few. In order to address the aforementioned issues and challenges and reduce the rate of maternal mortality, adoption of medical technology will be essential. In this study, an attempt has been made to combine blockchain and predictive data analytics using an ensemble machine learning algorithm in order to collect, analyze, and store maternal health data securely and reliably and predict the risk level of pregnancy complications. The data stored on the blockchain cannot be altered; it serves as a secure setting for storing patient information as well as an authentic source for learning data. The publicly accessible dataset has been used to train and test the proposed blockchain-based pregnancy complications prediction system. The effectiveness of the proposed system has also been evaluated.
TEA-S: A Tiny and Efficient Architecture for PLAC-Based Softmax in Transformers

With the popularity of Transformer neural networks, it is inevitable for hardware accelerators to perform nonlinear computation mainly based on the softmax operation. However, a better compromise between the algorithm performance and hardware overhead is always a constant challenge. Hence, this brief advances a tiny and efficient architecture named TEA-S to implement the softmax function with the universal approximation scheme based on Piecewise Linear Approximation Computation (PLAC). With the first co-optimization of calculation and memory, TEA-S can better achieve the design goals of the tiny area and high efficiency. The implementation results show that the peak efficiency of processing 8-bit quantized data will be 487.51 Gps/(mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{{2}}{\cdot }$ </tex-math></inline-formula> mW) with the tiny area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3052.43~{\mu }{\mathrm {m}}^{2}$ </tex-math></inline-formula> at the frequency of 0.5 GHz under 90-nm CMOS technology. Moreover, TEA-S can offer the universal solution to any lengths of input sequences, providing negligible accuracy loss in Transformers compared to the quantized baselines.
Cross-Domain Alignment for Zero-Shot Sketch-Based Image Retrieval

Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a rising theme with broad application prospects. Given the sketch image as a query, the goal of ZS-SBIR is to correctly retrieve the semantically similar images under the zero-shot scenario. The key is to project images from photo and sketch domains into a shared space, where the domain gap and semantic gap are effectively bridged. Most previous studies have approached ZS-SBIR as a classification problem and used classification loss to obtain discriminative features. However, these methods do not explicitly encourage the alignment of features, degrading the retrieval performance. To address this issue, this paper proposes a novel method called Cross-domain Alignment (CA) for ZS-SBIR. Specifically, we present a Large-margin Cross-domain Contrastive (LCC) loss to stimulate intra-class compactness and inter-class separability from both domains, motivated by the relationships of pairwise distances in metric learning. The loss boosts features’ alignment and enjoys more discrimination. Moreover, based on the “embedding stability” phenomenon of the neural network, we elaborate a Cross-batch Semantic Metric (CSM) mechanism for boosting the performance of ZS-SBIR. Extensive experiments demonstrate that the proposed CA achieves encouraging performance on the challenging Sketchy and TU-Berlin benchmarks.
Proximity-Aware Clinical Passage Retrieval Framework by Exploiting Knowledge Structure

Clinicians have minimal time to search for and absorb the information needed while performing the duties of their medical practice. Their time-pressured situations requires the relevant information in search to be retrieved and presented in a more succinct form, such as in a short passage, rather than a whole page or document. In this context, clinical decision support (CDS) searches are beneficial when used to retrieve critical medical passages that can assist the practice of medical experts by offering appropriate medical information relevant to the medical case at hand. We present a novel CDS search framework designed for passage retrieval in order to support clinical decision-making using laboratory test results by incorporating proximity information. To do so, we use a knowledge structure that graphically visualizes key concepts and the corresponding relationships in a specific domain where nodes denote with their associative relationships. Furthermore, unlike previous studies that exploit knowledge structures during a re-ranking step, only dealing with initially highly retrieved passages, we utilize a knowledge structure for the purpose of query expansion. By doing so, our approach can unveil passages that are not retrieved during the initial retrieval process by including latent terms in the query list. We compared two models with/without edge pruning to capture a more latent relationship between terms. Our experiment showed that the embedded-based knowledge structure outperformed previous knowledge structure building approaches and other proximity-aware state-of-the-art models.
MD-Pose: Human Pose Estimation for Single-Channel UWB Radar

Human pose estimation based on optical sensors is difficult to solve the situation under harsh environments and shielding. In this paper, a Micro-Doppler (MD) based human pose estimation for the single-channel ultra-wideband (UWB) radar, called MD-Pose, is proposed. The MD characteristic reflects the kinematics of the human and provides a unique method for identifying the target’s posture, which offers a more comprehensive perception of human posture. We explore the relationship between the human skeleton and the MD signature, which reveals the fundamental origins of these previously unexplained phenomena. The single-channel UWB radar is widely used because of its small size, low cost, and portability. In contrast, its resolution is lower than that of the MIMO UWB radar. Therefore, this paper reveals how to implement fine-grained human posture based on the MD signature with fewer channels. The MD spectrogram of the human target is obtained by the short-time Fourier transform (STFT), which is the input data of the proposed MD-Pose. A quasi-symmetric U-Net neural network is trained with the UWB radar MD spectrogram, which can estimate the human keypoints. The experiments show comparable quantitative results with the state-of-the-art human pose estimation method and provide the underlying insights needed to guide the design of radar-based human pose estimation.
ASPIRE: An Intermediate Representation for Abstract Security Policies

Modern System-on-Chip (SoC) architectures include various Intellectual Property (IP) cores that are security-sensitive. In order to protect the design against malicious attacks, every new SoC has to undergo the laborious process of identifying the security assets that are relevant to the existing security policies and rules. Unfortunately, very little research has been done in exploiting the potential of abstraction for specification of security policies. We introduce ASPIRE, an Abstract Security Policy Inter-mediate Representation, which specifies Temporal Logic and Information Flow security policies in a generalized format. In the ASPIRE methodology, we develop an abstract architecture and formulate ASPIRE for Abstract Security Policies (ASP) based on the abstract architecture. This abstract architecture is developed using the security assets that are identified from the abstract security policies. Further, an equivalence check is conducted for the ASPIRE representations related to abstract and concrete architectures in order to generate System Verilog Assertions (SVAs) to verify the concrete architectures. We demonstrate this methodology using several security policies for multiple test cases including OpenRISC-SoC, CVA6, Wishbone Bus, MUX, and AES-T100.
Inside the Windows Security Push: A Twenty-Year Retrospective

This article is a follow-up to an article on the Windows security push in the first issue of IEEE Security and Privacy (January 2003). It provides additional detail on the security push and its results, and describes the creation and evolution of the Security Development Lifecycle (SDL) that integrated software security into Microsoft's development process. The article concludes with a summary of lessons learned about effective ways of creating secure software at scale.
Hierarchical Multi-Label Attribute Classification With Graph Convolutional Networks on Anime Illustration

In this study, we present a hierarchical multi-modal multi-label attribute classification model for anime illustrations using graph convolutional networks (GCNs). The focus of this study is multi-label attribute classification, as creators of anime illustrations frequently and deliberately emphasize subtle features of characters and objects. To analyze the connections between attributes, we develop a multi-modal GCN-based model that can use semantic features of anime illustrations. To create features representing the semantic information of anime illustrations, we construct a novel captioning framework by combining real-world images with their animated style transformations. In addition, because the attributes of anime illustrations are hierarchical, we introduce a loss function that considers the hierarchy of attributes to improve classification accuracy. The proposed method has two main contributions: 1) By introducing a GCN with semantic features into the multi-label attribute classification task of anime illustrations, we capture more comprehensive relationships between attributes. 2) By following certain rules to build a hierarchical structure of attributes that appear frequently in anime illustrations, we further capture subordinate relationships between attributes. In addition, we demonstrate the effectiveness of the proposed method by experiments.
Efficient FPGA Implementations of Lifting based DWT using Partial Reconfiguration

Discrete Wavelet Transform (DWT) is an ubiquitous mathematical technique used in a wide range of applications. The fundamental advantages of DWT are its high compression ratio, lack of blocking artifacts, and strong time & frequency domain localization. This proposed work is an efficient implementation of (9,7) lifting based 1D, 2D, and 3D DWTs, where the hardware software codesign with partial reconfiguration (PR) has made a significant reduction in resource utilization. Additionally, the suggested approach yields a very low mean square error (MSE), resulting in a substantially higher peak signal to noise ratio (PSNR). The constant multiply-add unit based proposed work with single core implementation achieves 80% decrease in the number of look up tables (LUT) and 32% decrease in number of slices utilized when compared with the variable multiply-add unit based implementation. To prove the architecture's originality, it is tested for six distinct applications such as mono audio, stereo audio, grayscale image, colour image, gray scale video, and colour video. The implementation of the work is done on Zynq 7000 (XC7Z020CLG484-1) field programmable gate array (FPGA) with Xilinx Vivado.
Automatic Implementation and Evaluation of Error-Correcting Codes for Quantum Computing: An Open-Source Framework for Quantum Error Correction

Due to the fragility of quantum mechanical effects, real quantum computers are plagued by frequent noise effects that cause errors during computations. Quantum error-correcting codes address this problem by providing means to identify and correct corresponding errors. However, most of the research on quantum error correction is theoretical or has been evaluated for specific hardware models only. Moreover, the development of corresponding codes and the evaluation of whether they indeed solve the problem for a particular hardware model, still often rests on tedious trial-and-error thus far. In this work, we propose an open-source framework that supports engineers and researchers in these tasks by automatically applying error-correcting codes for a given application followed by an automatic noise-aware quantum circuit simulation. Case studies showcase that this allows for a substantially more efficient implementation and evaluation of error-correcting codes.
Analysis of the Effect of the Indoor Environment on L-shaped Movement of Small Size Four Rotor Drone

In this paper, L-shaped movement control experiment assuming the narrow passage movement inside the building of four rotor helicopter was confirmed and the control stability was evaluated in a small size room (4× 4m, 3m height). In this experiment, a small 4-rotor drone was used and I made measurements in an L-shaped corridor, creating an environment with walls by means of partition board. To verify the problem of the orbit control with an L-shaped bend passage, an autonomous flight movement control experiment was conducted by 10 times with two different control strategies. Experimental result shows that (1) the stability of the L-shaped movement of the drone was seen the S.D. of the between trajectory of two 90 degree curves, and the overshoot fashion of the second 90 degree curves position. (2) The stability depends on the control method the PID and the proposed V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> control methods. (3) In an environment with obstacles, the overshoot is larger than when there are no obstacles, and the V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> control works effectively. This means that in such a case of drone flight of the L-shaped corridor in the building, the above two evaluation methods are useful, and stable flight can be achieved by reducing overshoot in corridors with walls. Our proposed the V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> condition control would be more useful in these case. As a result, it is necessary to carefully select the control method of the drone according to the usage scene.