Reviewerly/exHarmony huggingface dataset:
Staged compilation with two-level type theory

The aim of staged compilation is to enable metaprogramming in a way such that we have guarantees about the well-formedness of code output, and we can also mix together object-level and meta-level code in a concise and convenient manner. In this work, we observe that two-level type theory (2LTT), a system originally devised for the purpose of developing synthetic homotopy theory, also serves as a system for staged compilation with dependent types. 2LTT has numerous good properties for this use case: it has a concise specification, well-behaved model theory, and it supports a wide range of language features both at the object and the meta level. First, we give an overview of 2LTT's features and applications in staging. Then, we present a staging algorithm and prove its correctness. Our algorithm is "staging-by-evaluation", analogously to the technique of normalization-by-evaluation, in that staging is given by the evaluation of 2LTT syntax in a semantic domain. The staging algorithm together with its correctness constitutes a proof of strong conservativity of 2LLT over the object theory. To our knowledge, this is the first description of staged compilation which supports full dependent types and unrestricted staging for types.
Blind Restoration of Images Distorted by Atmospheric Turbulence Based on Deep Transfer Learning

Removing space-time varying blur and geometric distortions simultaneously from an image is a challenging task. Recent methods (including physical-based methods or learning-based methods) commonly default the turbulence-degraded operator as a fixed convolution operator. Obviously, the assumption does not hold in practice. According to the situation that the real turbulence distorted operator has double uncertainty in space and time dimensions, this paper reports a novel deep transfer learning (DTL) network framework to address this problem. Concretely, the training process of the proposed approach contains two stages. In the first stage, the GoPro Dataset was used to pre-train the Network D1 and freeze the bottom weight parameters of the model; in the second stage, a small amount of the Hot-Air Dataset was employed for finetuning the last two layers of the network. Furthermore, residual fast Fourier transform with convolution block (Res FFT-Conv Block) was introduced to integrate both low-frequency and high-frequency residual information. Subsequently, extensive experiments were carried out with multiple real-world degraded datasets by implementing the proposed method and four existing state-of-the-art methods. In contrast, the proposed method demonstrates a significant improvement over the four reported methods in terms of alleviating the blur and distortions, as well as improving the visual quality.
A Quantum Algorithm for RF-based Fingerprinting Localization Systems

Fingerprinting is one of the mainstream technologies for localization. However, it needs significant storage overhead and running time, preventing it from scaling to support world-wide indoor/outdoor localization.Quantum computing has the potential to revolutionize computation by making some classically intractable problems solvable on quantum computers. In this paper, we propose a quantum fingerprint-based localization algorithm for enabling large-scale location tracking systems, envisioning future era of location tracking and spatial systems. Specifically, we propose a quantum algorithm that provides an exponential enhancement of both the space and running time complexity compared to the traditional classical systems. We give the details of how to build the quantum fingerprint, how to encode the received signal strength (RSS) measurements in quantum particles, and finally; present a quantum algorithm for calculating the cosine similarity between the online RSS measurements and the fingerprint ones.Results from deploying our algorithm in three real testbeds on IBM Quantum Experience machines confirm the ability of our quantum system to get the same accuracy as the classical one but with the potential exponential saving in both space and running time.
A novel label-based multimodal topic model for social media analysis

Extracting useful knowledge from multimodal data is the core of many multimedia applications, such as recommendation systems, and cross-modal retrieval. In this paper, we propose a label-based multimodal topic (LB-MMT) model to jointly model text and image data tagged with multiple labels. Specifically, we use the labels as supervised information to generate the text and image data. In the LB-MMT model, we assume that the textual words and visual words related to each text and image are drawn from a mixture of latent topics, where each topic is represented as a group of textual words and visual words. Moreover, we introduce multiple topics for each label, to build the top-down relationship from label to text and image. To investigate the effectiveness of the proposed approach, we conduct extensive experiments on a real-world multimodal dataset with labels. The results show the proposed approach obtains superior performances on topic coherence and label prediction compared with previous competitors. In addition, we show that our model yields interesting insights about multimodal topics. The proposed model provides important practical implications, e.g., designing more attractive multimodal contents for marketers.
HDPoA: Honesty-based distributed proof of authority via scalable work consensus protocol for IoT-blockchain applications

Applying blockchain technology to the Internet of Things (IoT) provides several advantages when compared to conventional systems, including improving the security by ensuring data integrity and accountability while enabling reliable control over many devices. However, integrating blockchain into IoT systems presents some challenges. A main challenges is designing a consensus protocol that is suitable for the IoT systems, where some devices may lack adequate resources, such as computation power. This paper introduces a novel consensus protocol called honesty-based distributed proof of authority (HDPoA) via scalable work. HDPoA is based on proof of authority (PoA) and proof of work (PoW), with the integration of PoW, HDPoA is able to realize the security advantages provided by PoW. This is achieved by utilizing the IoT devices’ collective computation power to mine and generate a new block. HDPoA was analyzed and then deployed and tested utilizing a purposely built testbed incorporating commercial devices that are low-cost. A performance measurements and evaluation along with the security analyses of HDPoA was conducted using a total of 30 different IoT devices comprise of Raspberry Pis, ESP32, and ESP8266. These measurements included energy consumption, devices’ battery life, devices’ hash power, and the mining time. The measured values of hash per joule (h/J) for mining were 13.8 Kh/J, 54 Kh/J, and 22.4 Kh/J when using the Raspberry Pis, the ESP32 devices, and the ESP8266 devices, respectively. The measured devices’ hash power, measured energy consumption, and the security analyses showed that HDPoA is secure and suitable for utilization into IoT-blockchain systems.
WTEYE: On-chain wash trade detection and quantification for ERC20 cryptocurrencies

Wash trade is a common form of volume manipulation used to attract investors into the market and mislead them into making wrong investment judgments. Wash trade transactions are even more prominent in ERC20 cryptocurrencies. In this paper, we proposed two kinds of algorithms to reserve direct evidence of wash trade based on the on-chain transaction data of ERC20 cryptocurrencies. After labeling the wash trade, we continued to obtain features of the wash trade and quantify the volume of the wash trade. Our experiments show that for most ERC20 cryptocurrencies, the rate of wash trade reached over 15%. Specifically, over 30% of UNI token transactions ​were labeled as wash trade. It is demonstrated that the activations of most ERC20 cryptocurrencies are unreal, and restoring real data is necessary for market regulation.
Applying blockchain-based method to smart contract classification for CPS applications

Smart contract has been the core of blockchain systems and other blockchain-based systems since Blockchain 2.0. Various operations on blockchain are performed through the invocation and execution of smart contracts. This leads to extensive combinations between blockchain, smart contract, Internet of Things (IoT) and Cyber-Physical System (CPS) applications, and then many blockchain-based IoT or CPS applications emerge to provide multiple benefits to the economy and society. In this case, obtaining a better understanding of smart contracts will contribute to the easier operation, higher efficiency and stronger security of those blockchain-based systems and applications. Many existing studies on smart contract analysis are based on similarity calculation and smart contract classification. However, smart contract is a piece of code with special characteristics and most of smart contracts are stored without any category labels, which leads to difficulties of smart contract classification. As the back end of a blockchain-based Decentralized Application (DApp) is one or several smart contracts, DApps with labeled categories and open source codes are applied to achieve a supervised smart contract classification. A three-phase approach is proposed to categorize DApps based on various data features. In this approach, 5,659 DApps with smart contract source codes and pre-tagged categories are first obtained based on massive collected DApps and smart contracts from Ethereum, State of the DApps and DappRadar. Then feature extraction and construction methods are designed to form multi-feature vectors that could present the major characteristics of DApps. Finally, a fused classification model consisting of KNN, XGBoost and random forests is applied to the multi-feature vectors of all DApps for performing DApp classification. The experimental results show that the method is effective. In addition, some positive correlations between feature variables and categories, as well as several user behavior patterns of DApp calls, are found in this paper.
End-to-End Incomplete Multiview Fuzzy Clustering With Adaptive Missing View Imputation and Cooperative Learning

The purpose of multiview fuzzy clustering is to integrate fuzzy partitions of multiple complete views and obtain the optimal clustering partition. However, multiview data collected in the real world are often incomplete. To address this problem, partial view-based methods and missing view imputation-based methods have been proposed in recent years and achieved some success. However, these two groups of methods still have the following issues. First, since missing view processing and clustering are handled separately, the processed data lack relevance for clustering. Second, either hidden or visible information is explored for clustering, which precludes cooperative learning between these two types of information. Third, the within view and between view information is not fully explored. This article proposes a new end-to-end incomplete multiview fuzzy clustering method to deal with the issues. Based on traditional multiview fuzzy clustering, we construct a new end-to-end clustering framework to integrate the three tasks—missing view imputation, hidden view learning and clustering—as a single process. The framework not only enables mutual coordination of the three tasks, but also cooperative learning between the hidden and visible views. Next, we explore the within and between view information and propose two enhanced learning mechanisms to improve the quality of the imputed missing views and the learned hidden views. Finally, we introduce an adaptive view weighting mechanism to further improve the robustness of the model. Experiments on real-world datasets demonstrate the superiority of the proposed method.
Research on Intangible cultural heritage education inheritance based on augmented reality technology

The intangible cultural heritage is exposed to the risk of being lost or forgotten forever. As a result, importance should be attached to the protection of intangible cultural heritage. This paper introduces design concepts and working prototypes for augmented reality (AR) applications, which makes users know more about the process of Chinese lanterns. 3D images are overlapped a real environment through AR technology, giving intangible cultural heritage learning more fun. Visitors can experience this app during the lantern festival. Studies show that it is feasible to use AR technology for intangible cultural heritage education, which can transfer knowledge and improve learning interests.
The potential of Google Classroom web service for lecturers of higher educational establishments under pandemic conditions

Researches and publications on using Google Classroom web service for lecturers of higher educational establishments under pandemic conditions are analysed. The current state of higher education under pandemic conditions is characterised. Features of Google Classroom web service have been identified. The methodical development for lecturers of higher educational establishments “Potential of using Google Classroom web service” is described. Criteria and levels of using Google Classroom web service are defined. Initial diagnostic of the levels of using Google Classroom web service was conducted. During the diagnostic was revealed that most respondents have insufficient levels of theoretical knowledge and practical skills. In order to increase the level of using Google Classroom web service, the methodological development was implemented. As a result, positive dynamics in the levels of theoretical knowledge and practical skills of using Google Classroom web service was revealed. The effectiveness of the obtained results was confirmed by Fisher’s criterion.
Robust multi-label feature selection with shared label enhancement

Feature selection has attracted considerable attention due to the wide application of multi-label learning. However, previous methods do not fully consider the relationship between feature sets and label sets but devote attention to either of them. Furthermore, conventional multi-label learning utilizes logical labels to estimate relevance between feature sets and label sets so that the importance of corresponding labels cannot be well reflected. Additionally, numerous irrelevant and redundant labels degrade the classification performance of models. To this end, we propose a multi-label feature selection method named Robust multi-label Feature Selection with shared Label Enhanced (RLEFS). First, we obtain a robust label enhancement term by reconstructing labels from logical labels to numerical labels and imposing $$l_{2,1}$$ -norm onto the label enhancement term. Second, RLEFS utilizes the robust label enhancement term to share the similar latent semantic structure between feature matrix and label matrix. Third, local structure is considered to ensure the consistency of label information during the feature selection process. Finally, we integrate the above terms into one joint learning framework, and then, a simple but effective optimization method with provable convergence is proposed to solve RLEFS. Experimental results demonstrate the classification superiority of RLEFS in comparison with seven state-of-the-art methods.
A Hardware-Friendly Low-Bit Power-of-Two Quantization Method for CNNs and Its FPGA Implementation

To address the problems of convolutional neural networks (CNNs) consuming more hardware resources (such as DSPs and RAMs on FPGAs) and their accuracy, efficiency, and resources being difficult to balance, meaning they cannot meet the requirements of industrial applications, we proposed an innovative low-bit power-of-two quantization method: the global sign-based network quantization (GSNQ). This method involves designing different quantization ranges according to the sign of the weights, which can provide a larger quantization-value range. Combined with the fine-grained and multi-scale global retraining method proposed in this paper, the accuracy loss of low-bit quantization can be effectively reduced. We also proposed a novel convolutional algorithm using shift operations to replace multiplication to help to deploy the GSNQ quantized models on FPGAs. Quantization comparison experiments performed on LeNet-5, AlexNet, VGG-Net, ResNet, and GoogLeNet showed that GSNQ has higher accuracy than most existing methods and achieves "lossless" quantization (i.e., the accuracy of the quantized CNN model is higher than the baseline) at low-bit quantization in most cases. FPGA comparison experiments showed that our convolutional algorithm does not occupy on-chip DSPs, and it also has a low comprehensive occupancy in terms of on-chip LUTs and FFs, which can effectively improve the computational parallelism, and this proves that GSNQ has good hardware-adaptation capability. This study provides theoretical and experimental support for the industrial application of CNNs.
Privacy Protection of Edge Computing Using Homomorphic Encryption

The emergence in the field of Internet of Things and the success of the cloud providers have extended the horizon of the new technological model that is Edge computing. This technology has got the ability to handle problems such as improving the response time, increasing battery life, cost saving of the network, along with ensuring protection and privacy of the data. To manifest the idea of Edge Computing, here a detailed case study is presented starting from cloud architecture to the security issues, further extending to its security countermeasures using Homomorphic encryption. This article explores the security challenges and the smart way to handle them and ensure privacy to the data with nicely explained algorithms. The algorithms perform computation on the encrypted form without decryption of the ciphertext. This does not require the sharing of a secret key. The opportunities in the field of edge computing is also emphasize with the expectation that this paper will pique interest and encourage further study in this area.
Vulnerabilities of the Open Platform Communication Unified Architecture Protocol in Industrial Internet of Things Operation

Recently, as new threats from attackers are discovered, the damage and scale of these threats are increasing. Vulnerabilities should be identified early, and countermeasures should be implemented to solve this problem. However, there are limitations to applying the vulnerability discovery framework used in practice. Existing frameworks have limitations in terms of the analysis target. If the analysis target is abstract, it cannot be easily applied to the framework. Therefore, this study proposes a framework for vulnerability discovery and countermeasures that can be applied to any analysis target. The proposed framework includes a structural analysis to discover vulnerabilities from a scenario composition, including analysis targets. In addition, a proof of concept is conducted to derive and verify threats that can actually occur through threat modeling. In this study, the open platform communication integrated architecture used in the industrial control system and industrial Internet of Things environment was selected as an analysis target. We find 30 major threats and four vulnerabilities based on the proposed framework. As a result, the validity of malicious client attacks using certificates and DoS attack scenarios using flooding were validated, and we create countermeasures for these vulnerabilities.
Quantum sensing of supersensitivity for the Ohmic quantum reservoir

We propose an approach to implement a supersensitive estimation of the key parameters of the Ohmic-family spectral density with coherent spin states as the quantum sensor. This method can dramatically improve the estimation precision of the reservoir coupling strength as well as the cutoff frequency of the spectral density, by using both the number of spins $N$ and encoding time $t$ as effective resources. The quantum Fisher information indicates that the estimation sensitivity of the spectral density can surpass the shot-noise limit for all the sub-Ohmic, Ohmic, and super-Ohmic reservoirs. In particular, for super-Ohmic reservoirs, the precision can achieve a scaling $\ensuremath{\propto}1/(Nt)$. We also present the measurement scheme which can saturate the quantum Cram\'er-Rao bound.
Chaotic Stochastic Paint Optimizer (CSPO)

Optimization of engineering problems requires addressing several common difficulties in the optimization problem, including but not limited to a large number of decision variables, multiple often conflicting objectives, constraints, locally optimal solutions, and expensive objective functions. It is pretty common that an algorithm performs very well on test functions but struggles when applying to real-world problems. This paper proposes a chaotic version of the recently proposed algorithm called chaotic stochastic paint optimizer (CSPO). A comparative study with other meta-heuristics demonstrates the merits of this algorithm and the change applied in this work.
A Mutual Information Domain Adaptation Network for Remotely Sensed Semantic Segmentation

Although deep learning has made semantic segmentation of very-high-resolution (VHR) remote sensing (RS) images practical and efficient, its large-scale application is still limited. Given the diversity of imaging sensors, acquisition conditions, and regional styles, a deep learning network well-trained on one source domain dataset often suffers from drastic performance drops when applied to other target domain datasets. Thus, we propose a novel end-to-end mutual information domain adaptation network (MIDANet) that can shift between semantic segmentation domains by integrating multitask learning in the convolutional neural networks within an entropy adversarial learning (EAL) framework. Through the joint learning of semantic segmentation and elevation estimation, the features extracted by MIDANet can concentrate more on the elevation clues while dropping the domain-variant information (i.e., texture, spectral information). First, one encoder is applied to excavate general semantic features. Two decoders that share the same architecture are used to perform pixel-level classification and digital surface model (DSM) regression. Second, feature interaction modules (FIMs) and a mutual information attention unit (MIAU) are designed to mine the latent relationships between the two tasks and enhance their feature representations. Finally, a final MIDANet is obtained for semantic segmentation that does not require any semantic segmentation labels in the target domain after the adversarial learning of the classification entropy at the output level. Extensive comparative experiments and ablation studies were conducted on the International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam and Vaihingen test datasets. The results show that MIDANet outperforms other state-of-the-art domain adaptation (DA) methods in both evaluation metrics and visual assessment.