Reviewerly/exHarmony huggingface dataset:
RPMG-FSS: Robust Prior Mask Guided Few-Shot Semantic Segmentation

Few-shot semantic segmentation (FSS) has been developed to perform pixel-level segmentation with only a few dense labeled examples for training, which relieves the expensive annotation problem in traditional segmentation models. Current researches on FSS generally act the labeled masks on the corresponding support images to obtain the class-specific embeddings, and predict the pixel-level masks for query images by matching their pixels to these class-specific embeddings. Their performance is difficult to further break through because of the limited supervision from single-view support images and the neglect of position information from similar pixels between query and support images. To solve these issues, we propose a novel robust prior mask guided model named RPMG-FSS for the challenging FSS task. The core of RPMG-FSS is to produce a robust prior mask with good generalization ability on novel classes to better assist the following query mask prediction. Note that each element in the prior mask corresponds to one pixel in query image. It not only considers the interaction within one view and between multiple views of the support image, but also fuses the top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> similarity values to all support pixels and these pixels’ position information. The parameters in RPMG-FSS are optimized with the combination of segmentation loss and multi-view contrastive loss. Comprehensive experiments on two datasets show that our RPMG-FSS achieves outstanding performance comparing with the current popular baselines. The code is released on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/dxzxy12138/RPMG-FSS/tree/master</uri>
Knowledge Distillation With Feature Self Attention

With the rapid development of deep learning technology, the size and performance of the network continuously grow, making network compression essential for commercial applications. In this paper, we propose a Feature Self Attention (FSA) module that extracts correlation information between the hidden features of a network and a new method for distilling the correlation features to compress the model. FSA does not require a special module or network to match features between the teacher model and the student model. By removing the multi-head structure and the repeated self-attention blocks in the existing self-attention mechanism, it minimizes the addition of parameters. Based on ResNet-18, 34, the added parameters are only 2.00M and the training speed is also the fastest in comparison to benchmark models. It was demonstrated through experiments that the use of interrelationship loss between features can be beneficial for training student models, indicating the importance of considering correlation information in deep neural network compression. And it was verified through training from scratch on the vanilla without the pre-trained weight of the student model.
2D LiDAR Based Reinforcement Learning for Multi-Target Path Planning in Unknown Environment

Global path planning techniques have been widely employed in solving path planning problems, however they have been found to be unsuitable for unknown environments.Contrarily, the traditional Q-learning method, which is a common reinforcement learning approach for local path planning, is unable to complete the task for multiple targets.To address these limitations, this paper proposes a modified Q-learning method, called Vector Field Histogram based Q-learning (VFH-QL) utilized the VFH information in state space representation and reward function, based on a 2D LiDAR sensor.We compared the performance of our proposed method with the classical Q-learning method (CQL) through training experiments that were conducted in a simulated environment with a size of 400 square pixels, representing a 20-meter square map.The environment contained static obstacles and a single mobile robot.Two experiments were conducted: experiment A involved path planning for a single target, while experiment B involved path planning for multiple targets.The results of experiment A showed that VFH-QL method had 87.06% less training time and 99.98% better obstacle avoidance compared to CQL.In experiment B, VFH-QL method was found to have an average training time that was 95.69% less than that of the CQL method and 83.99% better path quality.The VFH-QL method was then evaluated using a benchmark dataset.The results indicated that the VFH-QL exhibited superior path quality, with efficiency of 94.89% and improvements of 96.91% and 96.69% over CQL and SARSA in the task of path planning for multiple targets in unknown environments.
Fast and Privacy-Preserving Attribute-Based Keyword Search in Cloud Document Services

Currently, various encryption techniques have been employed to protect the documents in cloud storage. In particular, attribute-based keyword search (ABKS) is a practical encryption primitive that can realize fine-grained access control and keyword based searching over encrypted documents. However, the search time in most of the existing ABKS schemes increases linearly with the size of document collection, which hinders the wide application of ABKS in cloud computing. To this end, we propose FAKS, a fast and privacy-preserving attribute-based keyword search system for cloud document services. Specifically, FAKS builds a Bloom filter tree structure from the document collection, which avoids matching keywords by traversing the entire collection. Then we introduce an attribute-based authenticated index retrieval (ABAIR) scheme to encrypt the Bloom filters in the tree node and retrieve the documents with the encrypted Bloom filters of the query keywords. Further, we give a concrete construction of FAKS from ABAIR to execute the keyword matching operations sublinearly in a top-down manner, and prove the security of FAKS against chosen keyword attack and keyword guessing attack. Finally, we conduct extensive experiments over the Wikipedia dataset, which show better and more stable search efficiency of FAKS compared to existing schemes.
A Model-Based Analysis-Design Approach for Virtual Binocular Vision System With Application to Vision-Based Tactile Sensors

The virtual binocular vision system (VBVS) that consists of a single camera and multiple mirrors has been applied to the stereo vision. Several VBVS configurations have been proposed, showing the advantages including the compact structure, simple calibration, and flexible adjustment. However, due to the high complexity of the optical system, few researchers have studied the comprehensive analyzing and designing of the VBVS. To fill this gap, we propose a universal model and the design criteria, and then construct a model-based analysis-design approach of the VBVS with mirrors. First, based on a novel understanding of the reflective optical system, the imaging cone model (ICM) is introduced to describe the geometry properties of an arbitrary VBVS completely, and the VBVS equations are constructed to calculate the imaging cones. Second, the equivalent descriptions of the design criteria are proposed as the expected field of view (FOV) and performances, deriving the analysis method of the VBVS. Then, the analysis method is combined with traversal calculation to establish a design process and its algorithm, which is applied to a novel vision-based tactile sensor with greatly improved design efficiency. Finally, we conduct the measurement of 3D tactile information to demonstrate the performances of the designed sensors.
Quantum-Resistant MACsec and IPsec for Virtual Private Networks

Despite considerable progress in theoretical post-quantum cryptography we have yet to see significant advances in its practical adoption. The necessary protocol modifications need to be identified, implemented and tested; good solutions need to be standardized and finally adopted in the real world. This work executes the first steps needed to standardize quantum-proof Virtual Private Networks (VPNs) on Layers 2 and 3 of the OSI model employing the MACsec/MKA and IPsec/IKEv2 protocols, respectively. We identify requirements and assemble a list of ideal features, discuss difficulties and possible solutions, point out our standardization efforts, and provide the results of some sample implementations for both layers.
Decentralized Insurance Subrogation Using Blockchain

Subrogation in insurance is the legal right that an insurance company holds to recover the amount paid to the insured from the at-fault party. As of now, subrogation is a relatively manual, laborious, and time-consuming process, requiring many physical checks and much communication between the insurance companies. Moreover, the involvement of multiple stakeholders in the process leads to a high administration cost and a long delay in processing the claims. This even increases when multiple claimants or third parties are involved. Although there are several blockchain-based proposals for general insurance, the topic of subrogation is still untouched. This paper introduces a novel blockchain-based solution to the subrogation process in the auto-insurance industry. We consider all existing stakeholders, including the insured, adjuster, lawyer, agency, and special investigation unit (SIU), and we design smart contracts aimed at making the subrogation process more efficient, transparent, and secure. The use of blockchain saves a lot of unnecessary expenditure, time, and manual labor, facilitating easy data-sharing while reducing the possibility of human errors. Moreover, the absence of centralized authority eliminates any fraudulent activities and thus increases trust in the system. To the best of our knowledge, this proposal is the first of its kind using blockchain technology. It will cover a broader perspective of insurance subrogation, bringing all associated stakeholders under the same platform.
ChainDB: Ensuring Integrity of Querying Off-Chain Data on Blockchain

Enterprise blockchain applications have been widely adopted and the data scale on blockchain increases rapidly in recent years. In practical application, participants prefer to store most of the business data in local database systems and only provide when queried by others due to privacy concerns, high storage cost, or constraints of on-chain space. However, this makes that the integrity of query results cannot be guaranteed and participants cannot fully trust others, and data owner has the opportunity to provide falsified records. In this paper, we present ChainDB, a relational database that can ensure integrity of querying off-chain data on blockchain. The key insight behind is using Merkle B+ tree as index structure, thus has being able to provide Merkle proof and keep all the records in order. Since the index structure is extended on B+ tree, ChainDB is compatible with common SQL operations and it is easy for enterprises to migrate from classic database systems to ChainDB. The experimental results show that ChainDB not only provides the ability to ensure integrity of querying off-chain data on blockchain, but also performs better than existing solutions.
A Smart and Private Blockchain-enabled Framework for Digital Assets

Nowadays, many digital assets are generated all the time, and managing these digital assets has become an important issue. The traditional digital asset management system manages data by the service provider, and data is not processed transparently. Furthermore, centralized storage makes Single Point of Failure (SPOF), and it is also challenging to ensure that data backed up by all nodes are synchronized. Rapid improvement of blockchain technology, the above two problems are resolved by leveraging blockchain’s decentralization and tamper resistance characteristics. The EU General Data Protection Rules (GDPR) state the principles for protecting data owners in 2018. Several principles for deleting personal data conflict with the blockchain, such as Storage Limit and the Right to Be Forgotten. The existing blockchain-enabled digital asset management methods do not apply to the GDPR. Although some methods are proposed for storing digital assets in off-chain storage and storing the hash address of storage in the blockchain, the problem of SPOF has not been resolved yet. This motivates to propose SPChain, a smarter and private GDPR-compliant digital asset framework enabled by blockchain and decentralized InterPlanetary File System is implemented to solve the problem of SPOF. The proposed SPChain to digital art management provides an implementation using Hyperledger Fabric. The proposed framework consists of a model developer, art creator, collector, service provider and securely assessed AI model for managing digital assets. The experiments measured throughput, latency and resource consumption of various functions in the smart contract’s artwork_CC, walle_CC, 3A_CC, log_CC, model_CC. This proposed work is the first novel work to combine digital asset management with detailed design of the decentralized off-chain storage and artificial intelligence models.
Understanding Security Audits on Blockchain

Hacks on blockchains are prevalent nowadays because a wide range of vulnerabilities exists in smart contracts. To deal with the vulnerabilities, security auditing has been emerging and widely adopted for manual code inspection. However, little is known about how manual security auditing impacts the blockchain community. In this work, we investigated security audits on blockchain by answering three research questions covering the type, severity, and resolve status of issues in the audit reports. We in total collected 2,421 security audit reports consisting of the details of 28,782 security issues. We found that manual security auditing has superior merit over static analysis tools in identifying logic-related issues, considering 27.65% of all findings are logical issues. We also found that 87.28% of 2,421 projects have at least one centralization issue that breaks the decentralization primitive of blockchain. Moreover, while 21.17% of issues are of major or critical severity, we surprisingly found that 61.83% of all issues were unresolved or partially resolved. We concluded that while manual security auditing can help identify security vulnerabilities uniquely, project developers need to take serious actions to address and secure their smart contracts.
Data Mining of Uniswap Decentralized Exchange

The data from decentralized finance services is openly available once they operate in public blockchains. Although data encryption techniques for transaction exist, they are challenging, increase costs and decrease trust. Therefore, everyone can use the DeFi data. Natively, the data is stored as a transaction log, where we have a byte code of contracts and history of their method calls with parameters. So the problem is to represent it conveniently for analysis. In the paper, we consider a decentralized exchange called UniSwap, overview its operation principles, create a database with the related data and share scripts with the elementary examples. The results help overcome the entering threshold for further data analysis.
A Distributed Authenticity Verification Scheme Using Deep Learning for NFT Market

With the recent proliferation of blockchains, identifying security risks to them has become an important issue. Among the various types of cyberattacks against blockchains, the blockchain poisoning attack involves the storing of malicious data in the blockchain to compromise it. One scenario is an attack that distributes forgeries of digital content traded and managed using Non-Fungible Token (NFT) on the blockchain. Currently, concomitant with the growing interest in NFT-based content trading, blockchain poisoning attacks on NFT trading and their effects have also increased. In this study, we examined the issues that may lead to attacks in the process from generation to distribution of digital content using NFT from the viewpoint of flexibility and interoperability of the content. Consequently, we discovered that there are two types of attack risks in NFT trading using malicious content: fake attacks and reuse attacks. As a countermeasure against these attacks, we propose a method for verifying the authenticity of the content itself using a decentralized scheme. The proposed method ensures the confidentiality of contents by using deep learning as an irreversible transformation operation in the distributed scheme and for privacy protection.
AmIOnline: Blockchain-based Availability Testing Service

Information technology has penetrated into all spheres of activity, and the business sphere is not an exception. The uninterrupted and efficient operation of these technologies guarantees the efficiency of business processes and the entire work of the company. The most significant way to improve the business is by impressing customers and keeping them satisfied. If customers cannot get the service, they turn to the competitors. The first step to their satisfaction is the fulfillment of the promises made by the business. The customers come to the service with certain expectations. And one of the most crucial expectations is getting the service when they need it. Any compromise with availability has serious effects on business. Therefore, it is important to control and ensure the availability of the company’s service from necessary places on the planet. For checking the availability of the service, the organization can implement the approach of using special internet services, but they do not look reliable. To increase trust between the parties, it is necessary to implement blockchain technology. The paper presents the blockchain-based availability testing service. The service allows organizations not only to verify the accuracy of the work done but also allows checking the availability from the special places.
PShard: A Practical Sharding Protocol for Enterprise Blockchain

Enterprise blockchain is commonly a permissioned blockchain that processes complex and massive transactions among enterprises for cooperation. With the continuous increase of business scale, the performance of a single blockchain is difficult to meet the demand and sharding is considered as a potential technology that can solve the problem. However, existing sharding protocols either process transactions only on a subset of blockchain nodes or needs to modify consensus algorithm, making the protocols difficult to apply in enterprise blockchain systems. In this paper, we propose PShard, a practical sharding protocol for enterprise blockchain. PShard adopts a two-layer data model for sharded enterprise blockchain where each enterprise participates in every shard and uses a two-phase method to execute cross-shard transactions to guarantee the safety and liveness of the protocol. We implement a prototype of enterprise blockchain system based on PShard and evaluate it in the experiments. The experimental results show that the performance of the system grows linearly as the number of shards increases.
An Online Model for Detecting Attacks in Wireless Sensor Networks

The Internet of Things (IoT) has been employed in a variety of critical fields, including healthcare, geriatric surveillance, self-driving vehicles, and energy management. The most prevalent infrastructure for these applications is Wireless Sensor Networks (WSNs). Although WSNs have intriguing characteristics, the security of such networks is a major concern, especially for applications where confidentiality is extremely crucial. In order to set up WSNs securely, any type of intrusion should be identified before attackers potentially harm the network. However, research findings have demonstrated that existing approaches are ineffective, particularly in detecting attacks in real-time, mostly owing to the accumulation of enormous amounts of data through interconnected devices. Within this interpretation, our intention is to construct a robust Intrusion Detection System (IDS) for analyzing real-time network traffic of WSNs using online learning algorithm while taking into account the network’s resource constraints. To achieve this goal, we investigated the use of an online classifier, namely the Hoeffding Adaptive Tree (HAT), along with the selection of relevant attributes to distinguish four kinds of DoS attacks among normal network traffic: the attacks considered are Blackhole, Grayhole, Flooding, and Scheduling attacks. Among the experimental findings, we determined that utilizing the HAT classifier along with the Chi-squared method of feature selection, detection rate percentages were 86.75%, 80.02%, 94.92%, 99.12% and 99.03% respectively for Flooding, Scheduling, Grayhole, Blackhole and normal case attacks. With an overall accuracy of 99.03%. Based on these findings, it is indeed possible to infer that the HAT classifier is extremely beneficial for categorizing attacks, as it has managed to secure a high detection rate despite the existence of many threats.
Robustness of Neural Rankers to Typos: A Comparative Study

Recent advances in passage retrieval have seen the introduction of pre-trained language models (PLMs) based neural rankers. While generally very effective, little attention has been paid to the robustness of these rankers. In this paper, we study the effectiveness of state-of-the-art PLM rankers in presence of typos in queries, as an indication of the rankers’ robustness. As of PLM rankers, we consider the two most promising directions explored in previous work: dense retrievers vs. sparse retrievers. We find that both types of rankers are very sensitive to queries with typos. We then apply an existing augmentation-based typos-aware training technique with the aim of creating typo-robust dense and sparse retrievers. We find that this simple technique only works for dense retrievers, while it hurts effectiveness when used on sparse retrievers.
Pengaruh Penguasaan Ilmu Nahwu Dan Sharaf Terhadap Kemampuan Membaca Kitab Fathul Qarib

The purpose of this research was to determine the effect of mastery of nahwu and sharaf on the ability to read the book of Fathul Qarib. The research method used in this study is a quantitative method. Data analysis was carried out with correlational data that had been collected. The data consists of the independent variable (X), namely the mastery of nahwu and sharaf. And the dependent variable (Y), namely the ability to read the book Fathul Qarib. Based on the results of data analysis conducted by researchers, it can be concluded that: mastery of nahwu shorof science has a very good value, ability to read the book of Fathul Qorib has a fairly good value, there is a close relationship between mastery of nahwu and shorof science with ability to read the book of Fathul Qorib, and there is a significant positive effect between mastery of nahwu and shorof science with the ability to read the book of Fathul Qorib by 22.73% and there are still 77.27% other things that affect the Y variable.