Reviewerly/exHarmony huggingface dataset:
The Key Technologies of Blockchain and the Design of Smart Education Platform

Blockchain technology has the characteristics of decentralization, immutability, traceability, high transparency, high credibility, etc., using key technologies such as blockchain identity authentication, learning records, resource innovation, copyright protection, etc., to cope with the challenges faced in the design process of the current smart education platform, and puts forward the overall design framework that reflects the resource layer, data layer, network layer, consensus layer, contract layer, service layer, and application layer, forming a blockchain to empower smart education resource protection, sharing management, property rights management, knowledge currency, credit conversion, The design ideas of certificate certification and other aspects will surely provide strong support for the design of the smart education platform, and also provide a better path choice for the agglomeration and development of private higher education in the Guangdong-Hong Kong-Macao Greater Bay Area.
Biogeography Based optimization with Salp Swarm optimizer inspired operator for solving non-linear continuous optimization problems

In this paper, a novel attempt is made to incorporate the two effective algorithm strategies, where BBO has a strong exploration and Salp Swarm Algorithm (SSA) is used for exploitation of the search space. The proposed algorithm is tested on IEEE CEC 2014 and statistical, convergence graphs are given. The proposed algorithm is also applied to 10 real life problems and compared with its counterpart algorithm. Results obtained by above experiments have demonstrated the outperformance of the hybrid version of BBO over other algorithms.
Tridiagonal maximum-entropy sampling and tridiagonal masks

The NP-hard maximum-entropy sampling problem (MESP) seeks a maximum (log-) determinant principal submatrix, of a given order, from an input covariance matrix C. We give an efficient dynamic-programming algorithm for MESP when C (or its inverse) is tridiagonal and generalize it to the situation where the support graph of C (or its inverse) is a spider graph with a constant number of legs (and beyond). We give a class of arrowhead covariance matrices C for which a natural greedy algorithm solves MESP. A mask M for MESP is a correlation matrix with which we pre-process C, by taking the Hadamard product M∘C. Upper bounds on MESP with M∘C give upper bounds on MESP with C. Most upper-bounding methods are much faster to apply, when the input matrix is tridiagonal, so we consider tridiagonal masks M (which yield tridiagonal M∘C). We make a detailed analysis of such tridiagonal masks, and develop a combinatorial local-search based upper-bounding method that takes advantage of fast computations on tridiagonal matrices.
Block verifiable dynamic searchable encryption using redactable blockchain

To solve the problems of low efficiency, inflexible updates and the high storage cost of existing result verifiable searchable encryption schemes, we propose a dynamic searchable encryption scheme with block verification using redactable blockchain. First, the inverted index is divided into blocks, the verification tag corresponding to the block index is uploaded to the redactable blockchain, and smart contracts are used to perform block verification of the query results to improve the query and verification performance. Furthermore, we use the blockchain rewriting technology to update the tags in the result checklist, improving the data update performance and scalability of the blockchain, and ensuring the constant storage overhead of the blockchain. Security analysis confirms that our proposals guarantee the correctness and completeness of the query results. Experimental results show that our proposed approach can improve the query efficiency and result verification efficiency under the premise of the low-speed growth of the blockchain data scale. In particular, the advantages become more significant as the data collection scale grows.
A robust and lossless commutative encryption and watermarking algorithm for vector geographic data

As an organic integration of encryption and watermarking technology, commutative encryption and watermarking (CEW) overcomes the limitation that a single security protection technology cannot provide full-process data protection. Existing CEW algorithms for vector geographic data, however, only take attack robustness into account, making them inappropriate for copyright protection of high-precision vector geographic data since the influence of embedding watermark on data accuracy. This study presents a CEW algorithm that utilizes geometric features and the initial vertex order of features to address this issue. The minimum bounding rectangle (MBR) is chosen as the geometric characteristic in this method to provide independent operating space for watermark and encryption. The encryption is completed by randomly shifting the coordinates of the features. At the same time, the watermark information is embedded by modifying the features' starting vertex order. The watermark can be extracted either from the plaintext or the ciphertext, as the order of the element's starting points remains unchanged regardless of the coordinates. Finally, the commutative, robustness, and lossless ness of the algorithm in this study are experimentally validated. As can be demonstrated by the results, the proposed algorithm is more robust, lossless, and secure than some state-of-the-art algorithms.
Measurement optimization of variational quantum simulation by classical shadow and derandomization

Simulating large quantum systems is the ultimate goal of quantum computing. Variational quantum simulation (VQS) gives us a tool to achieve the goal in near-term devices by distributing the computation load to both classical and quantum computers. However, as the size of the quantum system becomes large, the execution of VQS becomes more and more challenging. One of the most severe challenges is the drastic increase in the number of measurements; for example, the number of measurements tends to increase by the fourth power of the number of qubits in a quantum simulation with a chemical Hamiltonian. This work aims to dramatically decrease the number of measurements in VQS by recently proposed shadow-based strategies such as classical shadow and derandomization. Even though previous literature shows that shadow-based strategies successfully optimize measurements in the variational quantum optimization (VQO), how to apply them to VQS was unclear due to the gap between VQO and VQS in measuring observables. In this paper, we bridge the gap by changing the way of measuring observables in VQS and propose an algorithm to optimize measurements in VQS by shadow-based strategies. Our theoretical analysis not only reveals the advantage of using our algorithm in VQS but theoretically supports using shadow-based strategies in VQO, whose advantage has only been given numerically. Additionally, our numerical experiment shows the validity of using our algorithm with a quantum chemical system.
LP-BT: A location privacy protection algorithm based on ball trees

With the maturity of global positioning technology and the massive popularity of mobile terminals, location-based services can provide people with convenient and efficient assistance. To use such services, mobile users need to provide location information and request query content. However, this process inevitably leads to the leakage of users’ privacy information, which poses a great threat to their property and personal safety. To address the privacy leakage in location services, this paper proposes a location privacy protection method based on ball tree (LP-BT). We first use the ball tree as a spatial index structure, and then do fuzzification on the location information of end users to obtain the maximum primary anonymous entropy, and combine the neural network learning algorithm to predict the corresponding entropy value. Finally, the final entropy is obtained based on the average entropy of the two stages. Experimental results on public dataset manifest that our model is superior to other models such as random selection model and path-based fake location generation model in terms of privacy protection level, user density and anonymization time overhead.
Autonomous battery-changing system for UAV’s lifelong flight

Unmanned aerial vehicles (UAVs) lifelong flight is essential to accomplish various tasks, e.g., aerial patrol, aerial rescue, etc. However, traditional UAVs have limited power to sustain their flight and need skilled operators manually control their charging process. Manufacturers and users are eagerly seeking a reliable autonomous battery-changing solution. To address this need, we propose and design an autonomous battery-changing system for UAVs using the theory of inventive problem solving (TRIZ) and user-centered design (UCD) methods. For practical application, we employ UCD to thoroughly analyze user requirements, identify multiple pairs of technical contradictions, and solve these inconsistencies using TRIZ theory. Furthermore, we design an autonomous battery-changing hardware system that meets user requirements and realizes the battery change process after the automatic landing of UAVs. Finally, we conduct experiments to validate our system's effectiveness. The experimental results show that our battery-changing system can implement the autonomous battery-changing task, and our system has high efficiency and robustness in the real environment.
Secure and private data sharing in CPS e-health systems based on CB-SMO techniques

In recent years, security and privacy preservation are key problems in the systems, electronic health data exchange can help to improving diagnostic accuracy. Due to its immutability, blockchain has been presented as a potential method for achieving Global Network Record (GNR) sharing with security and privacy preservation in recent days. This paper offers a safe and privacy-preserving GNR sharing CB-SMO method for diagnostic improvements in CPS (Cyber Physical System) e-Health systems based on blockchain technology. To begin, two types of approaches are employed, such as consortium blockchain, which are built by inventing data structures as well as consensus processes. The SMO is in charge of storing GNR, while the consortium blockchain maintains track of the GNR's secured indicators. To protect the information, access control, privacy preservation, as well as secure search, all documents, including the GNR, keywords, and the patients' identify, is public key encrypted with keyword search. Moreover, while developing extra blocks to the blockchains, block producers must show evidence of compliance, which ensures data consistency. The suggested protocol can satisfy the security goals, according to the security analysis. In addition, we test the performance of the suggested method using Apache JMeter. The prediction accuracy rate of the proposed work is 99% at a reasonable cost of time.
Unsupervised object discovery with pseudo label generated using K-means and self-supervised transformer

Instance segmentation is a fundamental task in the field of computer vision. It aims to assign every pixel to an appropriate class and localize objects within bounding boxes. However, expensive pixel-level segmentation labels are essential for training current state-of-the-art instance segmentation models. In this paper, we propose IMST, a simple method for generating instance-level pseudo-object mask labels without any form of human annotation. IMST leverages the fact that self-supervised transformers embed background and foreground into distinct cluster spaces. This characteristic allows us to discover class-agnostic object masks from unlabeled image datasets using cosine distance K-means clustering. We also present an object mask refinement method that employs ensemble results of K-means in a single image. Despite its simplicity, IMST achieves state-of-the-art performance in the unsupervised object mask discovery task. In unsupervised class-agnostic instance segmentation, IMST outperforms concurrent works by margins of 5.3 AP and 4.0 AP, respectively, on COCO20k and COCO val2017. Our proposed method can be extended to object box discovery tasks, such as unsupervised class-agnostic object detection and unsupervised single object discovery, surpassing previous works.
Density-based approach for fuzzy rule interpolation

Sparse rule base is one of the common problems in fuzzy rule-based systems, and fuzzy rule interpolation (FRI) could derive interpolated results for the input based on the neighbor fuzzy rules when the input is not matched by any of the fuzzy rules. The core idea of FRI is that similar inputs would lead to similar results, and several FRI methods that use a pre-defined number of closest rules to obtain the interpolated results have been presented. However, this could lead to the loss of some information as selecting a given number of rules without considering the exact distance between them and the input could lead to the selection of unwanted rules or the ignoring of similar rules. This paper presents a density-based fuzzy rule interpolation method that uses a density-based approach to search and select the closest rules for unmatched inputs. Instead of selecting a given number of rules, the proposed method adaptively selects the closest rules that are within a certain range of the unmatched inputs, thus assuring the selected rules are with high similarity to the inputs. The performance of the proposed method is verified through fifteen classification benchmarks, showing the effectiveness and efficiency of the proposed method.
TIVE: A toolbox for identifying video instance segmentation errors

In this paper, we introduce TIVE, a Toolbox for Identifying Video instance segmentation Errors. By directly operating output prediction files, TIVE defines isolated error types and weights each type's damage to mAP, for the purpose of distinguishing model characters. By decomposing localization quality in spatial–temporal dimensions, model's potential drawbacks on spatial segmentation and temporal association can be revealed. TIVE can also report mAP over instance temporal length for real applications. We conduct extensive experiments by the toolbox to further illustrate how spatial segmentation and temporal association affect each other. We expect the analysis of TIVE can give the researchers more insights, guiding the community to promote more meaningful explorations for video instance segmentation. The proposed toolbox is available at https://github.com/wenhe-jia/TIVE.
Effective website fingerprinting attack based on the first packet direction only

Tor is an anonymity protocol that utilizes multi-layers onion encryption to protect the privacy and anonymity of clients during crawling websites. However, Tor is vulnerable to website fingerprinting attacks in that the adversary analyzes the transferred traffic patterns passively and detects visited websites without decryption. The core of fingerprinting attacks depends on flow-based network information such as packet direction information that cannot be concealed using encryption. In the real world, the packet streams observed during a website fetch may be affected by network events like changes in download rate, queuing, retransmission, and packet loss. In this paper, we propose a correlation-based website fingerprinting attack based on only the packet direction information as a set of observations of random variables with an unknown probability distribution. In the proposed attack, instead of the whole of the traffic instances for each website, only the first fraction of transmitted packets are used for fingerprinting. The main focus of the proposed attack is on the preprocessing and classification parts and aims to evaluate the importance of direction information in website detection and mitigate the adverse effects of unpredicted network noises in attack performance. The preprocessing phase preprocesses background noises and unpredicted network events that a real-world adversary may confront. The CorrClassifier model is designed as a prototype of a classifier tailored to the attack conditions to calculate the similarity distance of observed traffic patterns to detect visited websites and reduce the complexity of the attack using a low-dimension correlation method. Preprocessing the noises before feeding them to the classifier helps the accuracy keep up more robustly while the polarity of the target website grows gradually because of the unexpected network conditions. Experiments in a closed-world setting indicate the effectiveness of the proposed method in detecting the targeted websites with 95% accuracy using only the first fraction of direction information.
Polyp segmentation with distraction separation

In clinical practice, automatic polyp segmentation in colonoscopy images is important for computer-aided clinical diagnosis of colorectal cancer. Existing polyp segmentation methods still suffer from the challenges of false positive/negative distractions to distinguish polyps and normal tissues. In this paper, we propose a novel Distraction Separation Network (DSNet) that mines potential polyp regions from the low-level semantic features while segregating background regions. To support the proposed framework, we propose two modules, including the neighbor fusion module (NFM) and the distraction separation module (DSM). The neighbor fusion module first integrates high-level features to obtain initial segmentation results as the prior guidance map. Guided by the prior results, multiple distraction separation modules are then employed to capture multi-scale contextual information for eliminating distraction. By separating distractions on different levels, DSNet can progressively refine segmentation results. Extensive experiments show that DSNet outperforms state-of-the-art methods on six challenging benchmark datasets.
Enhancing chaos in multistability regions of Duffing map for an image encryption algorithm

This paper investigates and analyzes the dynamics of the two-dimensional Duffing map. Multistability behavior has been observed from the system numerically. Such behavior, especially the coexistence of chaotic and periodic attractors, is undesirable in the applications of chaos-based cryptography. Therefore, we design and implement a Sine–Cosine chaotification technique to enhance chaos in the multi-stable regions. Furthermore, this paper proposes a new image encryption algorithm to examine the performance of the generalized Duffing map in cryptography applications. Simulation results and security analysis reveal that the proposed algorithm can effectively encrypt and decrypt several image types with a high level of security.
Toward Efficient Multicodec Streaming

One of the biggest challenges in modern-era media delivery systems is the fragmentation of the population of receiving devices in terms of codec capabilities. For example, modern Apple devices can decode and seamlessly switch between H.264/Advanced Video Coding (AVC) and high efficiency video encoding (HEVC) streams. Most new TVs or set-top boxes can also decode HEVC, but they cannot switch between HEVC and H.264/AVC streams in the same streaming session. And there are still plenty of older devices/streaming clients that can only receive and decode H.264/AVC streams. With the arrival of next-generation codecs, such as AV1 and Versatile Video Coding (VVC), the fragmentation of codec support across devices becomes even more complex. This situation prompts the question—how can we serve such a population of devices most efficiently by using codecs delivering the best performance in all cases yet producing the minimum possible number of streams such that the overall cost of media delivery is minimal? In this article, we explain how this problem can be formalized and solved at the stage of dynamic generation of encoding profiles for adaptive bitrate (ABR) streaming. The proposed solution effectively generalizes the per-title or context-aware encoding (CAE) class of techniques, considering multiple sets of renditions generated using each codec and codec usage distributions by the population of the receiving devices. We also discuss additional system-level means (proper manifest generation, HLS and Dynamic Adaptive Streaming over HTTP (DASH) quality annotations, device detection, and edge logic) needed to make the proposed solution practically deployable.
AGRONER: An unsupervised agriculture named entity recognition using weighted distributional semantic model

In this work, we propose a novel weighted distributional semantic model for unsupervised Named Entity Recognition (NER) in domain specific texts, specifically focusing on agricultural domain. Developing accurate agriculture NER models requires overcoming several challenges, including the lack of annotated data, domain-specific vocabulary, entity ambiguity, and contextual variation. The proposed approach is completely unsupervised and utilizes an extended BERT model with LDA topic modeling (exBERT_LDA+) for NER. The proposed Agricultural Named Entity Recognition (AGRONER) model, focuses on identifying six major entities, disease, soil, pathogen, pesticide, crops, and place. The existing four entities are recognized using the proposed algorithm while we utilize the AGROVOC dictionary for crops and Geocoding APIs for Place entities. Due to the absence of a benchmark dataset in the agriculture domain, we created a corpus of 30,000 sentences extracted from recognized agriculture sites. For the evaluation, we used a test corpus with 700 sentences that include 1690 entity names. The labeled entities were then manually checked to evaluate the prediction accuracy. The proposed approach presents a macro average F-measure of 80.43%, which is quite promising for an unsupervised domain specific entity labeling. We performed ablations studies, where the proposed model exhibited a relative percentage improvement of 31.56%, 26.11% F-measure when compared to BERT without LDA (BERT_LDA−) and extended BERT without LDA (exBERT_LDA−)models, respectively. Experimental results show the efficacy of the proposed approach in labeling the named entities in an unsupervised set-up for the agricultural domain. Further, the approach can be easily extended to recognize more domain-specific entities.1
Bitcoin transaction pattern recognition based on semi-supervised learning

In the complex, anonymous and decentralized Bitcoin network, there exist many types of transaction patterns each of which exhibits its own unique structure. It therefore becomes very useful to analyze transaction records to recognize transaction patterns as well as the various roles that traders play in each type of transactions, which can help better understand the operations inside the network. In this paper, we propose a semi-supervised learning based method for the recognition of Bitcoin transaction patterns after defining Bitcoin transaction patterns and describing some of the existing patterns. The proposed method performs transaction pattern recognition following the clustering approach after completing several key tasks including community classification, transaction structure creation and feature construction. Results of the experiment using real data show that for known transaction patterns, the proposed method can achieve recall rate of at least 70%. Compared to some existing methods, the proposed method can achieve better recognition results by depending not only on the clustering results, but also on the recognition and clustering results of embedded data, which can enhance the effectiveness and the practicality of Bitcoin transaction pattern recognition.