Reviewerly/exHarmony huggingface dataset:
Optimized Machine Learning: Training and Classification Performance Using Quantum Computing

Quantum Computing hardware in great extent to optimize algorithm performance of various domains of classical and high-performance computing. Optimization in any category of algorithm results in improved performance and new method suggestion for existing standards. Initially machine learning from classical to parallel computing have given very good platform for researcher's to optimize algorithm performance if data size will be huge and complex. Quantum mechanics such as superposition and entanglement will make quantum computing more successful over classical standard. Quantum Computers operates on quantum superposition and entanglement mechanism of quantum physics. Quantum machine learning integrates machine learning and quantum computing together for exponential speedup. Classical machines are enables to perform several tasks because of computational complexity and operational ability of hardware. Quantum computing definitely justice the operational ability for complex task and pattern formation. Quantum machine learning will optimize existing machine learning methods based upon performance outcome of algorithm. This paper discussed optimization of supervised machine learning using variational quantum circuits. Discussion extended to various methods and subroutines to justify classifier's performance for big data processing on quantum hardware.
Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

Compared with single-band remote sensing images, multispectral images can obtain information on the same target in different bands. By combining the characteristics of each band, we can obtain clearer enhanced images; therefore, we propose a multispectral image enhancement method based on the improved dark channel prior (IDCP) and bilateral fractional differential (BFD) model to make full use of the multiband information. First, the original multispectral image is inverted to meet the prior conditions of dark channel theory. Second, according to the characteristics of multiple bands, the dark channel algorithm is improved. The RGB channels are extended to multiple channels, and the spatial domain fractional differential mask is used to optimize the transmittance estimation to make it more consistent with the dark channel hypothesis. Then, we propose a bilateral fractional differentiation algorithm that enhances the edge details of an image through the fractional differential in the spatial domain and intensity domain. Finally, we implement the inversion operation to obtain the final enhanced image. We apply the proposed IDCP_BFD method to a multispectral dataset and conduct sufficient experiments. The experimental results show the superiority of the proposed method over relative comparison methods.
Navigating the Blockchain Trilemma: A Supply Chain Dilemma

Supply chain practitioners are constantly searching for optimization methods to improve the effectiveness of the supply chain. The emergence of blockchain technology has promised high hopes for the industries to solve many prevalent issues such as tedious documentation trails, lack of transparency, counterfeit products, and many others. The inherent immutability and decentralized nature of the blockchain technology has allowed developers to come out with ways to potentially improve these issues. One of the major issues faced by blockchain companies is the blockchain trilemma, which dictates that no blockchain can be simultaneously decentralized, secured, and scaled. Blockchains should only be able to achieve two of these elements at a high level and somewhat sacrifice the remaining element. This has been a major criticism toward the technology, hindering mass adoption among users. This paper studies this phenomenon and its effects from the standpoint of supply chain users. This paper also proposes a diagram to plot a supply chain's position within the blockchain trilemma, which should lead to different course of actions. Ultimately, this paper sheds light into the effects of the blockchain trilemma on the supply chain mass adoption.
Pyramid Bayesian Method for Model Uncertainty Evaluation of Semantic Segmentation in Autonomous Driving

Abstract With the fast-developing deep learning models in the field of autonomous driving, the research on the uncertainty estimation of deep learning models has also prevailed. Herein, a pyramid Bayesian deep learning method is proposed for the model uncertainty evaluation of semantic segmentation. Semantic segmentation is one of the most important perception problems in understanding visual scene, which is critical for autonomous driving. This study to optimize Bayesian SegNet for uncertainty evaluation. This paper first simplifies the network structure of Bayesian SegNet by reducing the number of MC-Dropout layer and then introduces the pyramid pooling module to improve the performance of Bayesian SegNet. mIoU and mPAvPU are used as evaluation matrics to test the proposed method on the public Cityscapes dataset. The experimental results show that the proposed method improves the sampling effect of the Bayesian SegNet, shortens the sampling time, and improves the network performance.
Communicating Patient Health Data: A Wicked Problem

Designing patient-collected health data visualizations to support communicating patient data during clinical visits is a challenging problem due to the heterogeneity of the parties involved: patients, healthcare providers, and healthcare systems. Designers must ensure that all parties' needs are met. This complexity makes it challenging to find a definitive solution that can work for every individual. We have approached this research problem-communicating patient data during clinical visits-as a wicked problem. In this article, we outline how wicked problem characteristics apply to our research problem. We then describe the research methodologies we employed to explore the design space of individualized patient data visualization solutions. Lastly, we reflect on the insights and experiences we gained through this exploratory design process. We conclude with a call to action for researchers and visualization designers to consider patients' and healthcare providers' individualities when designing patient data visualizations.
Fake Review Detection Of E-Commerce Electronic Products Using Machine Learning Techniques

The rapid growth of internet access has given rise to a digital era. The availability of internet access has pushed almost 70% of the population to switch to internet for their daily needs and accessories. Mainly, E-commerce platforms are being used at a much higher rate than ever before. People who buy from these e-commerce platforms make decisions on whether to buy a product or not solely based on the ratings and reviews of a product that are provided by these platforms. Due to the simple nature of this review system, sellers and even individuals tend to exploit it by writing dishonest reviews with an intention of either boosting its ratings or simply to sabotage it. These fake reviews are aimed at deceiving customers and convince them to buy/deter a certain product. Due to the lack of a robust system to identify real and fake reviews, these spams manage to show up on top. To avoid this problem and provide a more efficient way to filter and provide a more efficient way to reviews. This work focus on designing machine learning model for fake review detection and compare the performance of three different algorithms. As a result of this research work random forest algorithm outperform than other two algorithms. Web based User Interface(UI) designed to remove fake review and display trusted review based on the ranking.
Deep Learning-based Intelligent Reading Recognition Method of the Digital Multimeter

Digital multimeter, as a multi-purpose electronic measuring instrument, has basic fault diagnosis function and is widely used in laboratories and industries. In order to solve the problems of manual calibration wasting manpower, manual reading is error-prone, and to improve the accuracy and robustness of automatic reading recognition of digital meters. In this paper, a deep learning-based reading recognition method of the digital multimeter is proposed. Firstly, the YOLO target detection method is used to extract the digital display area of multimeter. Then, the CNN classification method is used to identify 0~9 numbers, negative signs and blanks in the reading area. Simultaneously, the connected domain method is adopted to locate the decimal point based on its location characteristics. Finally, the complete reading information can be obtained by merging the reading recognition result and the decimal point positioning result. Experiments were conducted and the results shown that the overall characters recognition accuracy reaches 99.85%. Besides, only one error occurred in the final test with an accuracy of 98%. Meanwhile, this proposed method can adapt to various complex environments, and meet the real-time requirements. Specifically, compared with the traditional method, the proposed modified deep learning method has high robustness and practicability.
A Federated Adversarial Learning Method for Biomedical Named Entity Recognition

Identifying medical terms with specific meaning and information with semantic attribute is the prerequisite of conducting semantic analysis in medical field. However, the problem of medical data island restricts the development of entity recognition in a great extent. In addition to the ban of data sharing between different hospitals, different departments in the same hospital also can not exchange data due to privacy security concerns and ethical issues. To solve these problems, in the federated learning framework, the server trains a global model collaboratively through aggregating the encrypted or noised model parameters of the local participated clients without data leakage. In this paper, to well apply federated learning on biomedical named entity recognition (BioNER), we propose the federated adversarial learning (FAL) method with consideration of the training cost and model performance. FAL not only makes use of a modified structured pruning scheme to reduce the number of model parameters but also exploits an improved adversarial learning approach named protected fast gradient method (PFGM) to enhance the robustness and generalization of the model. In the experiment, we use the datasets of five departments in the same tumor hospital, such as gynecology department and gastric surgery department. Results show that the proposed FAL framework achieves expected effect with high efficiency.
Moving-Object-Aware Anomaly Detection in Surveillance Videos

Video anomaly detection plays a crucial role in automatically detecting abnormal actions or events from surveillance video, which can help to protect public safety. Deep learning techniques have been extensively employed and achieved excellent anomaly detection results recently. However, previous image-reconstruction-based models did not fully exploit foreground object regions for the video anomaly detection. Some recent works applied pre-trained object detectors to provide local context in the video surveillance scenario for anomaly detection. Nevertheless, these methods require prior knowledge of object types for the anomaly which is somewhat contradictory to the problem setting of unsupervised anomaly detection. In this paper, we propose a novel framework based on learning the moving-object feature prediction based on a convolutional autoencoder architecture. We train our anomaly detector to be aware of moving-object regions in a scene without using an object detector or requiring prior knowledge of specific object classes for the anomaly. The appearance and motion features in moving objects regions provide comprehensive information of moving foreground objects for unsupervised learning of video anomaly detector. Besides, the proposed latent representation learning scheme encourages the convolutional autoencoder model to learn a more convergent latent representation for normal training data, while anomalous data exhibits quite different representations. We also propose a novel anomaly scoring method based on the feature prediction errors of moving foreground object regions and the latent representation regularity. Our experimental results demonstrate that the proposed approach achieves competitive results compared with SOTA methods on three public datasets for video anomaly detection.
loT-based temperature and humidity monitoring system for smart garden

Abstract The main objective of this paper is to design an IoT (Internet of Things) to monitor temperature and humidity for smart gardens. Temperature sensors and humidity sensors measure environmental conditions and are processed by a microcontroller. The actuator used is a spray pump that is used to spray water into the air to lower the temperature. Data from the sensors and status from the actuators are sent to the server and can be monitored via a smartphone. The data collected can be analyzed for various purposes. The result obtained is the effect of spraying on temperature reduction.
Improve Sensing and Communication Performance of UAV via Integrated Sensing and Communication

The unmanned aerial vehicle (UAV) needs to sense the environment to ensure safe flight, and the sensing accuracy and communication delay performance are two important indicators of safe flight. The strategy of using integrated sensing and communication (ISAC) technology to improve the sensing and communication performance is proposed in this paper. On the one hand, the extended kalman filter (EKF) algorithm is adopted to achieve the fusion of communication location information and sensing information to improve the accuracy of target sensing. On the other hand, a Identification Friend or Foe (IFF) method based on ISAC is proposed to reduce communication delay. Compared with the traditional IFF method, the integrated technology used for IFF can realize the radar sensing and communication interrogating functions in parallel, greatly shortening the sensing time. Simulation results show that using ISAC technology, the sensing performance of UAV has been greatly improved, the communication delay can be reduced by up to 50%, the accuracy of target sensing can be improved by 24.2 % when communication location information and radar sensing information have the same sensing accuracy.
A Fast Keccak Hardware Design for High Performance Hashing System

The growth of cryptocurrencies has attracted much attention from a variety of fields, especially in academic research. Their protocol is built and developed on blockchain technology. One of them is Ethereum which is a popular and highest valued cryptocurrency on the market. Therefore, the need for space to store transactions information increases, leading to the creating of blocks is extremely urgent. This work is called Ethereum mining. Miners have to solve a problem called Ethash (Ethereum’s Proof-of-Work) to find the results satisfied the requirements of the problem. In this paper, we proposed a hardware design architecture to perform the Ethash mining system. To obtain the high performance for the Ethashed block mining system, the fast design of the Keccak function is also proposed. The design is implemented on Xilinx Virtex 7 FPGA-VC707. The throughput of our proposed Keccak function reaches 8 Gbps with efficiency of 5.5 Mbps/Slice. The hashrate of the mining system reaches 88KH/s at frequency of 19.231MHz.
Weakly Correlated Knowledge Integration for Few-shot Image Classification

Abstract Various few-shot image classification methods indicate that transferring knowledge from other sources can improve the accuracy of the classification. However, most of these methods work with one single source or use only closely correlated knowledge sources. In this paper, we propose a novel weakly correlated knowledge integration (WCKI) framework to address these issues. More specifically, we propose a unified knowledge graph (UKG) to integrate knowledge transferred from different sources (i.e., visual domain and textual domain). Moreover, a graph attention module is proposed to sample the subgraph from the UKG with low complexity. To avoid explicitly aligning the visual features to the potentially biased and weakly correlated knowledge space, we sample a task-specific subgraph from UKG and append it as latent variables. Our framework demonstrates significant improvements on multiple few-shot image classification datasets.
Never-Stop Context-Free Learning

In this paper, we revisit the concept of never-stop learning, a combination of active automata learning and runtime monitoring. Published research focuses on regular systems and became practical with the development of the TTT algorithm and its redundancy-free approach of storing information. With the recent development of our active learning algorithm for systems of procedural automata (SPAs), we can infer instrumented context-free/procedural systems via a simultaneous inference of individual (regular) procedures. In this paper, we combine these two concepts to lift the concept of never-stop (or life-long) learning to the level of context-free/procedural systems. In an empirical evaluation we show that using the TTT algorithm for procedural learning allows us to tackle internal (procedural) redundancy whereas the inherent compositional structure and instrumentation of our SPA approach allows us to tackle external (global) redundancy. A comparison with the alternative formalism of visibly pushdown automata (VPAs) shows that our approach performs better by multiple orders of magnitude, making it a valuable choice for practical never-stop context-free learning.
The Complexity Analysis Matrix

This paper outlines an approach to describing complexity representation in interactive narratives, in order to understand and evaluate how narrative serious games and similar forms might scaffold cognitive reduction of complexity. We adapt Yoon et al.'s media-agnostic framework for complexity learning to the interactive narrative context. Using the userly text model, we distinguish between first-order (observing) and second-order (enactive) subject positions and the distinct types of knowledge that they afford. The resulting matrix allows us to comparatively describe three components of a complexity triad - complexity in the (story)world, in the artefact and in subjective understanding. This makes it possible to assess and compare differences between the complexity representations within each component and thus evaluate complexity reduction as a function of the distance between the complexity representations within the artefact and within the subject. With this matrix, we begin to address the challenge of interactively narrating complexity as a problem of learning effectiveness, integrating insights from narratology, complexity theory and the learning sciences.
BGRA-Net: Boundary-Guided and Region-Aware Convolutional Neural Network for the Segmentation of Breast Ultrasound Images

In this paper, we propose a novel convolutional neural network based on boundary-guided and region-aware (BGRA-Net) for breast tumor segmentation in ultrasound images. In particular, in the encoding stage, we propose a boundary-guided module (BGM) to guide the learning of boundary features in the decoding stage by explicitly strengthening the extracted boundary information. Meanwhile, in the decoding stage, we propose a region-aware module (RAM) to integrate different levels of detailed and semantic features to improve the comprehensive representation of tumor regional features. Besides, a scale-adaptive module (SAM) is further proposed to capture the characteristics of tumors with different sizes between the encoding and decoding stages. To evaluate the effectiveness of our BGRA-Net, we conduct extensive experiments on the UDIAT dataset and compare it with eight state-of-the-art methods. The experimental results show that our BGRA-Net outperforms the state-of-the-art methods and can achieve accurate segmentation of breast tumors with ambiguous boundaries.
CC-DenseUNet: Densely Connected U-Net with Criss-Cross Attention for Liver and Tumor Segmentation in CT Volumes

The automatic segmentation of liver and tumor is important for hepatic tumor surgery. In this paper, we propose a novel densely connected U-Net (CC-DenseUNet), which integrates criss-cross attention (CCA) module, to segment the liver and tumor in computed tomography (CT) volumes. The dense interconnections in CC-DenseUNet ensure the maximum information flow between encoder layers when extracting intraslice features of liver and tumors. Moreover, the CCA module is used in CC-DenseUNet to efficiently capture only the necessary and meaningful non-local contextual information of CT images containing liver or tumors. We evaluated the proposed CCDenseUNet on the Liver Tumor Segmentation Challenge and 3DIRCADb datasets. Experimental results show that our method outperformed the state-of-the-art methods in liver tumor segmentation and achieved a highly competitive performance in liver segmentation.
Efficient Threshold-Optimal ECDSA

This paper proposes a threshold-optimal ECDSA scheme based on the first threshold signature scheme by Gennaro et al. with efficient non-interactive signing for any \(t+1\) signers in the group, provided the total group size is more than twice the threshold t. The scheme does not require any homomorphic encryption or zero-knowledge proofs and is proven to be robust and unforgeable with identifiable aborts tolerating at most t corrupted participants. The security of the scheme is proven in a simulation-based definition, assuming DDH and that ECDSA is existentially unforgeable under chosen message attack. To evaluate the performance of the protocol, it has been implemented in C++ and the results demonstrate the non-interactive signing phase takes 0.12 ms on average meaning over 8000 signatures can be created per second. With pre-signing phase, it takes 3.35 ms in total, which is over 144 times faster than the current state of the art.
Development of Quantum Annealer Using Josephson Parametric Oscillators

A Josephson parametric oscillator (JPO) is an interesting system from the viewpoint of quantum optics because it has two stable self-oscillating states and can deterministically generate quantum cat states. A theoretical proposal has been made to operate a network of multiple JPOs as a quantum annealer, which can solve adiabatically combinatorial optimization problems at high speed. Proof-of-concept experiments have been actively conducted for application to quantum computations. This article provides a review of the mechanism of JPOs and their application as a quantum annealer.