Reviewerly/exHarmony huggingface dataset:
Research on Multi-Dimensional Trust Evaluation Mechanism of FinTech Based on Blockchain

New technologies such as Internet, cloud computing, artificial intelligence (AI) and blockchain have greatly promoted the innovation of financial industry structure and paradigm, improved the efficiency of financial services and brought the spillover of the financial technology (FinTech) risk. The existing financial regulation methods cannot meet the needs of the development of FinTech, therefore, there is an urgent need to improve the trust mechanism. Blockchain can effectively solve the problems of security and trust in FinTech. This paper will explore the expression method of trust index in blockchain, and build based on blockchain a multi-dimensional trust index system and evaluation mechanism (MDTEM) for FinTech. Firstly, a four level blockchain structure has been built, including cloud level blockchain, Internet level blockchain, contract level blockchain and application level blockchain in the FinTech ecological environment to ensure the security, reliability and trustworthiness of financial services; Secondly, according to the trust structure of blockchain on FinTech payment behavior, the index system and evaluation mechanism of direct trust, indirect trust, recommendation trust and feedback trust of FinTech based on blockchain are designed. Finally, the trust simulation experiment of FinTech mechanism is carried out from three aspects: data sending, data transmission, data reception and delivery success rate. Simulation results show that the proposed MDTEM trust mechanism can better improve the safe and reliable application of FinTech trust mechanism.
Fuzzy Clustering of Single-View Incomplete Data Using a Multiview Framework

We propose four frameworks for clustering data with missing values. We first use a very simple method to impute the missing values and generate multiple imputed versions of the data. These views are then clustered together to obtain a common partition matrix and a common set of centroids. As the clustering framework, we use a multiview version of the Fuzzy-c-Means (MVFCM) and a multiview version of Kernelized Fuzzy-c-Means (MVKFCM). To find the importance (weights) of different views, we use an entropic regularization term using the weights. After obtaining the optimal weights, the final imputation is done as a weighted sum (convex combination) of the imputed values used to generate the views. The final clustering is done on this imputed data set. We compare the performance of the proposed algorithms with several algorithms using Normalized Mutual Information, Adjusted Rand Index, and cluster accuracy on 12 benchmark data sets. Of these algorithms, the MVKFCM is found to perform the best. The MVFCM and MVKFCM use <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$5 \times r$</tex-math></inline-formula> views, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r$</tex-math></inline-formula> is the number of classes (note that the class labels are not used). However, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r$</tex-math></inline-formula> may not be known, and also for large <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r$</tex-math></inline-formula> , there will be too many views. So we propose two variants of MVKFCM: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$MVKFCM_{FV}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$MVKFCM_{RFV}$</tex-math></inline-formula> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FV</i> stands for a fixed number of views and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RFV</i> stands for a robust version with fixed views). The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$MVKFCM_{RFV}$</tex-math></inline-formula> generates views in a manner that helps to obtain robust performance. As expected, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$MVKFCM_{RFV}$</tex-math></inline-formula> is found to be the best performing algorithm.
Context-Based Semantic Communication via Dynamic Programming

Standard digital communication techniques allow us to set aside the meaning of the messages to concentrate on the transmission of bits efficiently and reliably. However, with the integration of artificial intelligence into communications technology and the merging of communication and computation within devices, increasing evidence suggests that the semantic aspect of communication cannot be set aside. We propose a part-of-speech-based encoding strategy and context-based decoding strategies, in which various deep learning models are presented to learn the semantic and contextual features as background knowledge. With the background knowledge, our strategies can be applied to some non-jointly-designed communication scenarios with uncertainty. We compare the performances of two proposed decoding strategies, the deep learning models of which are different, to provide model-choice design guidelines in accordance with specific communication conditions. Further, we discuss the impact of several parameters on the performance of our strategies, such as the size of the context window and the size of the feature window. Simulation results indicate the effectiveness and the reliability of our strategies in terms of decreasing the number of bits used to transmit messages and increasing the semantic accuracy between transmitted messages and recovered messages.
Toward the Development of Large-Scale Word Embedding for Low-Resourced Language

Word embedding is possessed by Natural language processing as a key procedure for semantically and syntactically manipulating the unlabeled text corpus.While this process represents the extracted features of corpus on vector space that enables to perform the NLP tasks such as summary generation, text simplification, next sentence prediction, etc.There exist some approaches for word embedding that consider co-occurrence and word frequency, such as Matrix Factorization, skip-gram, hierarchical-structure regularizer, and noise contrastive estimation.These approaches have created mature word vectors for most spoken languages in the world, on the other hand, the research community turned their minor attention towards the Urdu language having 231.3 million speakers.This paper focuses on creating Urdu word embedding.To perform this task, we used a dataset covering different categories of News such as Business, Sports, Health, Politics, Entertainment, Science, world, and others.This dataset was tokenized while creating 288 million tokens.Further, for word vector formation we utilized skip-gram also known as the word2vec model.The embedding was performed while limiting the vector dimensions to 100, 200, 300, 400, 500, 128, 256, and 512.For evaluation Wordsim-353 and Lexsim-999 annotated datasets were utilized.The proposed work achieved a 0.66 Spearman correlation coefficient value for wordsim-353 and 0.439 for Lexsim-999.The results were compared with state-of-the-art and were observed better.
Real-World Image Super-Resolution by Exclusionary Dual-Learning

Real-world image super-resolution is a practical image restoration problem that aims to obtain high-quality images from in-the-wild input, has recently received considerable attention with regard to its tremendous application potentials. Although deep learning-based methods have achieved promising restoration quality on real-world image super-resolution datasets, they ignore the relationship between L1- and perceptual- minimization and roughly adopt auxiliary large-scale datasets for pre-training. In this paper, we discuss the image types within a corrupted image and the property of perceptual- and Euclidean- based evaluation protocols. Then we propose a method, Real-World image Super-Resolution by Exclusionary Dual-Learning (RWSR-EDL) to address the feature diversity in perceptual- and L1- based cooperative learning. Moreover, a noise-guidance data collection strategy is developed to address the training time consumption in multiple datasets optimization. When an auxiliary dataset is incorporated, RWSR-EDL achieves promising results and repulses any training time increment by adopting the noise-guidance data collection strategy. Extensive experiments show that RWSR-EDL achieves competitive performance over state-of-the-art methods on four in-the-wild image super-resolution datasets.
Stacking Sigmas: A Framework to Compose $$\varSigma $$-Protocols for Disjunctions

Zero-Knowledge (ZK) Proofs for disjunctive statements have been a focus of a long line of research. Classical results such as Cramer et al. [CRYPTO'94] and Abe et al. [AC'02] design generic compilers that transform certain classes of ZK proofs into ZK proofs for disjunctive statements. However, communication complexity of the resulting protocols in these results ends up being proportional to the complexity of proving all clauses in the disjunction. More recently, Heath et al. [EC'20] exploited special properties of garbled circuits to construct efficient ZK proofs for disjunctions, where the proof size is only proportional to the length of the largest clause in the disjunction. However, these techniques do not appear to generalize beyond garbled circuits. In this work, we focus on achieving the best of both worlds. We design a general framework that compiles a large class of unmodified $$\varSigma $$ -protocols, each for an individual statement, into a new $$\varSigma $$ -protocol that proves a disjunction of these statements. Our framework can be used both when each clause is proved with the same $$\varSigma $$ -protocol and when different $$\varSigma $$ -protocols are used for different clauses. The resulting $$\varSigma $$ -protocol is concretely efficient and has communication complexity proportional to the communication required by the largest clause, with additive terms that are only logarithmic in the number of clauses. We show that our compiler can be applied to many well-known $$\varSigma $$ -protocols, including classical protocols (e.g. Schnorr [JC'91] and Guillou-Quisquater [CRYPTO'88]) and modern MPC-in-the-head protocols such as the recent work of Katz, Kolesnikov and Wang [CCS'18] and the Ligero protocol of Ames et al. [CCS'17]. Finally, since all of the protocols in our class can be made non-interactive in the random oracle model using the Fiat-Shamir transform, our result yields the first generic non-interactive zero-knowledge protocol for disjunctions where the communication only depends on the size of the largest clause.
RISC-V Galois Field ISA Extension for Non-Binary Error-Correction Codes and Classical and Post-Quantum Cryptography

Due to the recent advances in new communication standards, such as 5G New Radio and beyond 5G, and in quantum computing and communications, new requirements for integrating processors into nodes have appeared. These requirements are meant to provide flexibility in the network to reduce operational costs and support diversity in services and load balancing. They are also designed to integrate both new and classical algorithms into efficient and universal platforms, execute specific operations, and attend to tasks with lower latency. Furthermore, some cryptographic algorithms (classical and post-quantum), which are essential to portable devices, share the same arithmetic with error-correction codes. For example, Advanced Encryption Standard (AES), elliptic curve cryptography, Classic McEliece, Hamming Quasi-Cyclic, and Reed-Solomon codes use GF(2^m) arithmetic. As this arithmetic is the basis of many algorithms, a versatile RISC-V Galois field ISA extension is proposed in this work. The RISC-V instruction set extension is implemented and validated using SweRV-EL2 1.3 on a Nexys A7 FPGA. In addition, a five-times acceleration is achieved for AES, Reed-Solomon codes, and Classic McEliece (post-quantum cryptography) at the expense of increasing the logic utilization by 1.27%.
Fair Contrastive Learning on Graphs

Node representation learning plays a critical role in learning over graphs. Specifically, the success of contrastive learning methods in unsupervised node representation learning has been demonstrated for various tasks, which has led to increase in attention towards the field. Despite the increasing popularity, fairness is widely under-explored in the area. Motivated by this, this study proposes novel fairness-aware graph augmentations based on adaptive feature masking and edge deletion, in order to mitigate the bias in graph contrastive learning. Different fairness notions on graphs are introduced in the study to guide the designs of the proposed adaptive augmentation schemes. Moreover, it is quantitatively shown that the proposed feature masking scheme can reduce the intrinsic bias. Experimental results on four real-world networks are presented to show that the introduced augmentation frameworks can improve group fairness measures together with comparable classification accuracy to state-of-the-art graph contrastive learning studies for node classification.
A Parking Sharing Network Over Blockchain With Proof-of-Planned-Behavior Consensus Protocol

Parking problem has become a bottle neck of urban traffic management. On one hand, there is no effective way to get timely information of available parking lots. On the other hand, owners of private parking spaces are less willing to share their spare lots. To deal with the parking problem, a new parking sharing network named ParkChain is firstly proposed using blockchain to build a decentralized but trustworthy network. Herein, ParkChain provides a distributed autonomous infrastructure for both vehicle drivers and park granters, which can promote the extensive parking resource sharing and relieve the parking problem. A new blockchain consensus protocol named Proof-of-Planned-Behavior (PoPB) is proposed. It builds the model of the autonomous consensus process based on the theory of planned behavior (TPB), derives a computable threshold for qualification of block data authorizers, and then develops a dynamic authorizer group mechanism for creditability and decentrality considerations. Furthermore, various smart contracts are developed to carry out the parking services transparently. Finally, the prototype has been successfully implemented in a university campus setting to demonstrate the effectiveness of ParkChain.
MSTCGAN: Multiscale Time Conditional Generative Adversarial Network for Long-Term Satellite Image Sequence Prediction

Satellite image sequence prediction is a crucial and challenging task. Previous studies leverage optical flow methods or existing deep learning methods on spatial-temporal sequence models for the task. However, they suffer from either oversimplified model assumptions or blurry predictions and sequential error accumulation issue, for a long-term forecast requirement. In this paper, we propose a novel Multi-Scale Time Conditional Generative Adversarial Network (MSTCGAN). To address the sequential error accumulation issue, MSTCGAN adopts a parallel prediction framework to produce the future image sequences by a one-hot time condition input. In addition, a powerful multi-scale generator is designed with the multi-head axial attention, which helps to carefully preserve the fine-grained details for appearance consistency. Moreover, we develop a temporal discriminator to address the blurry issue and maintain the motion consistency in prediction. Extensive experiments have been conducted on FengYun-4A satellite data set, and the results demonstrate the effectiveness and superiority of the proposed method over state-of-the-art approaches.
What Do Participants Expect to Experience in the Rubber Hand Illusion? A Conceptual Replication of Lush (2020)

The sense of body ownership (the feeling that the body belongs to the self) is commonly believed to arise through multisensory integration. This is famously shown in the rubber hand illusion (RHI), where touches applied synchronously to a fake hand and to the participant’s real hand (which is hidden from view) can induce a sensation of ownership over the fake one. Asynchronous touches weaken or abolish the illusion, and are typically used as a control condition. Subjective experience during the illusion is measured using a questionnaire, with some statements designed to capture illusory sensation and others designed as controls. However, recent work by Lush (2020, Collabra: Psychology) claimed that participants may have different expectations for questionnaire items in the synchronous condition compared to the asynchronous condition, and for the illusion-related items compared to the control items. This may mean that the classic RHI questionnaire is poorly controlled for demand characteristics. In the current work a conceptual replication of Lush (2020) was performed. Participants were presented with a video of the RHI procedure and reported the sensations they would expect to experience, both in free response and by rating questionnaire items. Participants had greater expectations for illusion statements in the synchronous condition compared to the asynchronous condition, and for illusion statements compared to control statements. However, free responses suggested that such expectations may be at least partially driven by exposure to the questionnaire items. Further work is necessary to understand whether similar expectations exist for the true RHI procedure, what might drive them, and whether they have an impact on reported RHI experience.
SimVODIS++: Neural Semantic Visual Odometry in Dynamic Environments

Accurate estimation of 3D geometry and camera motion enables a wide range of tasks in robotics and autonomous vehicles. However, the lack of semantics and the performance degradation due to dynamic objects hinder its application to real-world scenarios. To overcome these limitations, we design a novel neural semantic visual odometry (VO) architecture on top of the simultaneous VO, object detection and instance segmentation (SimVODIS) network. Next, we propose an attentive pose estimation architecture with a multi-task learning formulation for handling dynamic objects and VO performance enhancement. The extensive experiments conducted in our work attest that the proposed SimVODIS++ improves the VO performance in dynamic environments. Further, SimVODIS++ focuses on salient regions while excluding feature-less regions. Performing the experiments, we have discovered and fixed the data leakage problem in the conventional experiment setting followed by numerous previous works—which we claim as one of our contributions. We make the source code public.
The Complexity of Approximate Pattern Matching on de Bruijn Graphs

Aligning a sequence to a walk in a labeled graph is a problem of fundamental importance to Computational Biology. For finding a walk in an arbitrary graph with |E| edges that exactly matches a pattern of length m, a lower bound based on the Strong Exponential Time Hypothesis (SETH) implies an algorithm significantly faster than $$\mathcal {O}(|E|m)$$ time is unlikely [Equi et al., ICALP 2019]. However, for many special graphs, such as de Bruijn graphs, the problem can be solved in linear time [Bowe et al., WABI 2012]. For approximate matching, the picture is more complex. When edits (substitutions, insertions, and deletions) are only allowed to the pattern, or when the graph is acyclic, the problem is again solvable in $$\mathcal {O}(|E|m)$$ time. When edits are allowed to arbitrary cyclic graphs, the problem becomes NP-complete, even on binary alphabets [Jain et al., RECOMB 2019]. These results hold even when edits are restricted to only substitutions. Despite the popularity of de Bruijn graphs in Computational Biology, the complexity of approximate pattern matching on de Bruijn graphs remained open. We investigate this problem and show that the properties that make de Bruijn graphs amenable to efficient exact pattern matching do not extend to approximate matching, even when restricted to the substitutions only case with alphabet size four. Specifically, we prove that determining the existence of a matching walk in a de Bruijn graph is NP-complete when substitutions are allowed to the graph. In addition, we demonstrate that an algorithm significantly faster than $$\mathcal {O}(|E|m)$$ is unlikely for de Bruijn graphs in the case where only substitutions are allowed to the pattern. This stands in contrast to pattern-to-text matching where exact matching is solvable in linear time, like on de Bruijn graphs, but approximate matching under substitutions is solvable in subquadratic $$\tilde{O}(n\sqrt{m})$$ time, where n is the text's length [Abrahamson, SIAM J. Computing 1987].
Route Coordination of UAV Fleet to Track a Ground Moving Target in Search and Lock (SAL) Task Over Urban Airspace

Drone has become more and more popular in various civil applications due to the open of the low-altitude airspace and its easy operation. Unlike the common search and track task for the target, the new search and lock (SAL) task is focused on in this article. In this task, multiple drones first try to detect the moving ground target cooperatively. Then, they must lock the target by covering all the surrounding area of it at a low flight altitude, which indicates that the target can be watched clearly in all directions from then on. The SAL task can be applied in the panorama shot for the moving ground target. First, the low-altitude urban airspace is discretized into cubes, based on which the flight rules of drone are defined. The field of view (FOV) of drone is modeled considering the flight altitude and the block of buildings. For the cooperation among multiple drones, the constraints on the patten of waypoint, the communication distance, and the collision avoidance are all included. The goal in the search phase is to cover more area, which have not been visited recently to increase the probability of detecting the target, and it is expected to lock the target as soon as possible in the lock phase. A new swarm-based imitative learning optimization (SBILO) algorithm is proposed to determine the waypoint of drone in the search phase. To have a quick response to the escape behavior of the target in the lock phase, the waypoint of drone is generated in a distributed way to cover more surrounding area of the target and lock it gradually. The case of losing the target in the FOV of all drones is also addressed by covering more possible places where the target may appear. Simulation results demonstrate that the SAL task can be performed efficiently by the drones with the flight routes obtained by the proposed SBILO algorithm and the distributed asynchronous decision-make (DADM) approach.