Reviewerly/exHarmony huggingface dataset:
A survey of application research based on blockchain smart contract

Nowadays, blockchain technology and industry has developed rapidly all over the world, which is inseparable from continuous innovation and improvement on smart contract technology. Therefore, by summarizing the working principle and application research status of blockchain smart contract, this paper analyzes the development and challenges of smart contract. Firstly, we introduce the model and operation principle of blockchain smart contract for the overall architecture, analyze the deployment process of smart contract with Ethereum, Hyperledger Fabric and EOSIO, and make a comparative analysis from the technical level. And taking Byteball, InterValue and IOTA platforms as examples, we introduce the deployment process and application potential for DAG-based blockchain smart contract. Additionally, we also summarize the application research of smart contract for international and Blockchain Oracle, and discuss its innovative application and development trend in the future. Secondly, we introduce the application status of smart contract with Ethereum and Hyperledger Fabric platforms from the aspects of financial transactions, Internet of things, medical applications, and supply chain, and further discuss EOS (enterprise operation system), Blockchain Oracle and other application fields. Furthermore, we introduce the application advantages and challenges to smart contract for industrial Internet from the fields of manufacturing, food industry, industrial Internet of things and industry 4.0. Finally, we discuss the challenges faced by smart contract with technical issues, analyzes the impact on large-scale applications and mining system on the sustainable development of smart contract, and looks forward to the future research direction of blockchain smart contract.
Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies' major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MCC value is required. To overcome prior research limitations, a modified network of YOLOv5 is used in this study to detect and classify breast tumors. Our research is conducted using publicly available datasets Curated Breast Imaging Subset of DDSM (CBIS-DDSM). The first step is to perform preprocessing, which includes image enhancing techniques and the removal of pectoral muscles and labels. The dataset is then annotated, augmented, and divided into 60% for training, 30% for validation, and 10% for testing. The experiment is then performed using a batch size of 8, a learning rate of 0.01, a momentum of 0.843, and an epoch value of 300. To evaluate the performance of our proposed model, our proposed model is compared with YOLOv3 and faster RCNN. The results show that our proposed model performs better than YOLOv3 and faster RCNN with 96% mAP, 93.50% MCC value, 96.50% accuracy, 0.04 FPR, and 0.03 FNR value. The results show that our suggested model successfully identifies and classifies breast tumors while also overcoming previous research limitations by lowering the FPR and FNR and boosting the MCC value.
A Neural Network Ensemble With Feature Engineering for Improved Credit Card Fraud Detection

Recent advancements in electronic commerce and communication systems have significantly increased the use of credit cards for both online and regular transactions. However, there has been a steady rise in fraudulent credit card transactions, costing financial companies huge losses every year. The development of effective fraud detection algorithms is vital in minimizing these losses, but it is challenging because most credit card datasets are highly imbalanced. Also, using conventional machine learning algorithms for credit card fraud detection is inefficient due to their design, which involves a static mapping of the input vector to output vectors. Therefore, they cannot adapt to the dynamic shopping behavior of credit card clients. This paper proposes an efficient approach to detect credit card fraud using a neural network ensemble classifier and a hybrid data resampling method. The ensemble classifier is obtained using a long short-term memory (LSTM) neural network as the base learner in the adaptive boosting (AdaBoost) technique. Meanwhile, the hybrid resampling is achieved using the synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) method. The effectiveness of the proposed method is demonstrated using publicly available real-world credit card transaction datasets. The performance of the proposed approach is benchmarked against the following algorithms: support vector machine (SVM), multilayer perceptron (MLP), decision tree, traditional AdaBoost, and LSTM. The experimental results show that the classifiers performed better when trained with the resampled data, and the proposed LSTM ensemble outperformed the other algorithms by obtaining a sensitivity and specificity of 0.996 and 0.998, respectively.
<i>CP-BDHCA:</i> Blockchain-Based Confidentiality-Privacy Preserving Big Data Scheme for Healthcare Clouds and Applications

Healthcare big data (HBD) allows medical stakeholders to analyze, access, retrieve personal and electronic health records (EHR) of patients. Mostly, the records are stored on healthcare cloud and application (HCA) servers, and thus, are subjected to end-user latency, extensive computations, single-point failures, and security and privacy risks. A joint solution is required to address the issues of responsive analytics, coupled with high data ingestion in HBD and secure EHR access. Motivated from the research gaps, the paper proposes a scheme, that integrates blockchain (BC)-based confidentiality-privacy (CP) preserving scheme, CP-BDHCA, that operates in two phases. In the first phase, elliptic curve cryptographic (ECC)-based digital signature framework, HCA-ECC is proposed to establish a session key for secure communication among different healthcare entities. Then, in the second phase, a two-step authentication framework is proposed that integrates Rivest-Shamir-Adleman (RSA) and advanced encryption standard (AES), named as HCA-RSAE that safeguards the ecosystem against possible attack vectors. CP-BDAHCA is compared against existing HCA cloud applications in terms of parameters like response time, average delay, transaction and signing costs, signing and verifying of mined blocks, and resistance to DoS and DDoS attacks. We consider 10 BC nodes and create a real-world customized dataset to be used with SEER dataset. The dataset has 30,000 patient profiles, with 1000 clinical accounts. Based on the combined dataset the proposed scheme outperforms traditional schemes like AI4SAFE, TEE, Secret, and IIoTEED, with a lower response time. For example, the scheme has a very less response time of 300 ms in DDoS. The average signing cost of mined BC transactions is 3,34 seconds, and for 205 transactions, has a signing delay of 1405 ms, with improved accuracy of ≈ 12% than conventional state-of-the-art approaches.
Faster ground state preparation and high-precision ground energy estimation with fewer qubits

We propose a general-purpose quantum algorithm for preparing ground states of quantum Hamiltonians from a given trial state. The algorithm is based on techniques recently developed in the context of solving the quantum linear system problem. We show that, compared to algorithms based on phase estimation, the runtime of our algorithm is exponentially better as a function of the allowed error, and at least quadratically better as a function of the overlap with the trial state. We also show that our algorithm requires fewer ancilla qubits than existing algorithms, making it attractive for early applications of small quantum computers. Additionally, it can be used to determine an unknown ground energy faster than with phase estimation if a very high precision is required.
Entanglement negativity of fermions: Monotonicity, separability criterion, and classification of few-mode states

We study quantum information aspects of the fermionic entanglement negativity recently introduced in [Phys. Rev. B 95, 165101 (2017)] based on the fermionic partial transpose. In particular, we show that it is an entanglement monotone under the action of local quantum operations and classical communications--which preserves the local fermion-number parity-- and satisfies other common properties expected for an entanglement measure of mixed states. We present fermionic analogs of tripartite entangled states such as W and Greenberger-Horne-Zeilinger states and compare the results of bosonic and fermionic partial transpose in various fermionic states, where we explain why the bosonic partial transpose fails in distinguishing separable states of fermions. Finally, we explore a set of entanglement quantities which distinguish different classes of entangled states of a system with two and three fermionic modes. In doing so, we prove that vanishing entanglement negativity is a necessary and sufficient condition for separability of $N\geq 2$ fermionic modes with respect to the bipartition into one mode and the rest. We further conjecture that the entanglement negativity of inseparable states which mix local fermion-number parity is always non-vanishing.
Comments Mining With TF-IDF: The Inherent Bias and Its Removal

Text mining have gained great momentum in recent years, with user-generated content becoming widely available. One key use is comment mining, with much attention being given to sentiment analysis and opinion mining. An essential step in the process of comment mining is text pre-processing; a step in which each linguistic term is assigned with a weight that commonly increases with its appearance in the studied text, yet is offset by the frequency of the term in the domain of interest. A common practice is to use the well-known tf-idf formula to compute these weights. This paper reveals the bias introduced by between-participants' discourse to the study of comments in social media, and proposes an adjustment. We find that content extracted from discourse is often highly correlated, resulting in dependency structures between observations in the study, thus introducing a statistical bias. Ignoring this bias can manifest in a non-robust analysis at best and can lead to an entirely wrong conclusion at worst. We propose an adjustment to tf-idf that accounts for this bias. We illustrate the effects of both the bias and correction with with seven Facebook fan pages data, covering different domains, including news, finance, politics, sport, shopping, and entertainment.
A text summarization method based on fuzzy rules and applicable to automated assessment

In the last two decades, the text summarization task has gained much importance because of the large amount of online data, and its potential to extract useful information and knowledge in a way that could be easily handled by humans and used for a myriad of purposes, including expert systems for text assessment. This paper presents an automatic process for text assessment that relies on fuzzy rules on a variety of extracted features to find the most important information in the assessed texts. The automatically produced summaries of these texts are compared with reference summaries created by domain experts. Differently from other proposals in the literature, our method summarizes text by investigating correlated features to reduce dimensionality, and consequently the number of fuzzy rules used for text summarization. Thus, the proposed approach for text summarization with a relatively small number of fuzzy rules can benefit development and use of future expert systems able to automatically assess writing. The proposed summarization method has been trained and tested in experiments using a dataset of Brazilian Portuguese texts provided by students in response to tasks assigned to them in a Virtual Learning Environment (VLE). The proposed approach was compared with other methods including a naive baseline, Score, Model and Sentence, using ROUGE measures. The results show that the proposal provides better f-measure (with 95% CI) than aforementioned methods.
A Real-Time Convolutional Neural Network for Super-Resolution on FPGA With Applications to 4K UHD 60 fps Video Services

In this paper, we present a novel hardware-friendly super-resolution (SR) method based on a convolutional neural network (CNN) and its dedicated hardware (HW) on field programmable gate array (FPGA). Although CNN-based SR methods have shown very promising results for SR, their computational complexities are prohibitive for hardware implementation. To the best of our knowledge, we are the first to implement a real-time CNN-based SR HW that upscales 2K full high-definition video to 4K ultra high-definition (UHD) video at 60 frames per second (fps). In our dedicated CNN-based SR HW, low-resolution input frames are processed line-by-line, and the number of convolutional filter parameters is reduced significantly by incorporating depth-wise separable convolutions with a residual connection. Our CNN-based SR HW incorporates a cascade of 1D convolutions having large receptive fields along horizontal lines while keeping vertical receptive fields minimal, which allows us to save required line memory space in achieving comparable SR performance against full 2D convolution operations. For efficient HW implementation, we use a simple and effective quantization method with little peak signal-to-noise ratio (PSNR) degradation. Also, we propose a compression method to efficiently store intermediate feature map data to reduce the number of line memories used in HW. Our HW implementation on the FPGA generates 4K UHD frames of higher PSNR values at 60 fps and shows better visual quality, compared with conventional CNN-based SR methods that are trained and tested in software.
Convolutional Neural Network Architecture for Geometric Matching

We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine, homography or thin-plate spline transformation, and estimating its parameters. The contributions of this work are three-fold. First, we propose a convolutional neural network architecture for geometric matching. The architecture is based on three main components that mimic the standard steps of feature extraction, matching and simultaneous inlier detection and model parameter estimation, while being trainable end-to-end. Second, we demonstrate that the network parameters can be trained from synthetically generated imagery without the need for manual annotation and that our matching layer significantly increases generalization capabilities to never seen before images. Finally, we show that the same model can perform both instance-level and category-level matching giving state-of-the-art results on the challenging PF, TSS and Caltech-101 datasets.
Secure Phrase Search for Intelligent Processing of Encrypted Data in Cloud-Based IoT

Phrase search allows retrieval of documents containing an exact phrase, which plays an important role in many machine learning applications for cloud-based Internet of Things (IoT), such as intelligent medical data analytics. In order to protect sensitive information from being leaked by service providers, documents (e.g., clinic records) are usually encrypted by data owners before being outsourced to the cloud. This, however, makes the search operation an extremely challenging task. Existing searchable encryption schemes for multikeyword search operations fail to perform phrase search, as they are unable to determine the location relationship of multiple keywords in a queried phrase over encrypted data on the cloud server side. In this paper, we propose P3, an efficient privacy-preserving phrase search scheme for intelligent encrypted data processing in cloud-based IoT. Our scheme exploits the homomorphic encryption and bilinear map to determine the location relationship of multiple queried keywords over encrypted data. It also utilizes a probabilistic trapdoor generation algorithm to protect users' search patterns. Thorough security analysis demonstrates the security guarantees achieved by P3. We implement a prototype and conduct extensive experiments on real-world datasets. The evaluation results show that compared with existing multikeyword search schemes, P3 can greatly improve the search accuracy with moderate overheads.
HybridNet: A fast vehicle detection system for autonomous driving

For autonomous driving system, vehicle detection is an import part as well as a challenging problem due to the large intra-class differences caused by occlusion, truncation and different viewpoints. The detection system should be fast and accurate enough to support real-world applications. Most of the existing deep convolution neural network (CNN) based object detection methods can be roughly categorized into two streams: single-stage and two-stage modes. These single-stage methods are usually extremely fast and easy to train with losing some precision. As for two-stage methods, they often get high performance in object detection competitions, however, they are not competitive for real-world applications because of the speed limits. The detection system with high degree of precision and fast computation speed is desirable. In this paper, a new two-stage regression based cascade object detection system is proposed. This system can be fast detection of the vehicles which concentrated the advantages of the two aforementioned methods, denoted by HybridNet. In our design, the first and the second stage are both regression modes. We add a transitional stage to map proposals (generated in the first stage) on high resolution feature maps to get exact features for decision refinement in the second stage. The challenging KITTI and PASCAL VOC2007 data sets are used to evaluate our proposed method. The experimental results show that our approach is more fast and more accurate in vehicle detection than other state-of-the-art methods.
Enabling Reliable Keyword Search in Encrypted Decentralized Storage with Fairness

Blockchain has led the trend of decentralized applications and shown great use beyond cryptocurrencies. Decentralized storage such as Storj and Sia leverages blockchain to establish an open platform for sharing economy, which provides private and reliable file-outsourcing services. However, the ubiquitous keyword search function over encrypted files is yet to be supported. To enable this function, we first apply searchable encryption techniques to the decentralized setting. But this primitive can hardly ensure the service integrity. The reason is that decentralized storage commonly faces severe threats from both clients and service peers. Service peers may return partial or incorrect results, while clients may intentionally slander the service peers to avoid payments. To address these threats, we utilize the smart contract to record the logs of encrypted search (aka evidence) on the blockchain, and devise a fair protocol to handle disputes and issue fair payments. Using a dynamic-efficient searchable encryption scheme as an instantiation, we craft a concrete scheme that preserves encrypted search capability and enforces ecosystem healthiness, so that service peers are incentivized to make real efforts and jointly guarantee service reliability. We implement our scheme in Python and Solidity, and test its search performance and transaction costs on Ethereum.
MOWS: Multi-objective workflow scheduling in cloud computing based on heuristic algorithm

Cloud computing is emerging with growing popularity in workflow scheduling, especially for scientific workflow. Deploying data-intensive workflows in the cloud brings new factors to be considered during specification and scheduling. Failure to establish intermediate data security may cause information leakage or data alteration in the cloud environment. Existing scheduling algorithms for the cloud disregard the interaction among tasks and its effects on application security requirements. To address this issue, we design a new systematic method that considers both tasks security demands and interactions in secure tasks placement in the cloud. In order to respect security and performance, we formulate a model for task scheduling and propose a heuristic algorithm which is based on task’s completion time and security requirements. In addition, we present a new attack response approach to reduce certain security threats in the cloud. To do so, we introduce task security sensitivity measurement to quantify tasks security requirements. We conduct extensive experiments to quantitatively evaluate the performance of our approach, using WorkflowSim, a well-known cloud simulation tool. Experimental results based on real-world workflows show that compared with existing algorithms, our proposed solution can improved the overall system security in terms of quality of security and security risk under a wide range of workload characteristics. Additionally, our results demonstrate that the proposed attack response algorithm can effectively reduce cloud environment threats.
A cost-sensitive three-way combination technique for ensemble learning in sentiment classification

Deep neural networks (DNN) have achieved remarkable results in sentiment classification. Some ensemble methods of DNN models and traditional feature-based models are proposed recently. However, to the best of our knowledge, most of the works use traditional ensemble combination techniques, e.g. voting and stacking, which are designed for weak base classifiers. So far many base classifiers, e.g. DNN, have been able to achieve good results in sentiment classification tasks, so there should be a new ensemble combination technique designed for strong base classifiers. To address this issue, we proposed a cost-sensitive combination technique using sequential three-way decisions (3WD), which is named S3WC. In S3WC, base classifiers are arranged in a linear arrangement, and a gate mechanism is constructed in each step to divide the objects into three groups, i.e., positive region, negative region and boundary region, which respectively correspond to acceptance, rejection and deferment in sequential 3WD. Each object is grouped by minimizing its total cost consisting of misclassification cost and time cost. The objects in boundary region require more information to decrease the misclassification cost, so they are reclassified by the subsequent base classifiers in order to obtain more information, while the time cost increases. In the experiment, we apply S3WC to DNN models and traditional feature-based models on five benchmark datasets, and compare its performance with traditional ensemble combination techniques. The experimental results show that S3WC outperforms any of its base classifiers in terms of classification accuracy, and the total cost of S3WC is lower than that of the existing ensemble combination techniques (e.g. majority-voting, weighted-voting, meta-learning).