Reviewerly/exHarmony huggingface dataset:
A Decision-Making Method for Blockchain Platforms Using Axiomatic Design

For companies using blockchain technology, it is critical to select the most suitable blockchain platform to develop enterprise applications. However, it is still a challenge for enterprises. As an important part of modern decision science, multi-criteria decision-making can well solve the problem of blockchain platform selection. Blockchain platforms integrate various blockchain technologies, and enterprises also need to consider multiple different criteria in the decision-making process. Therefore, this paper will use a heterogeneous multi-criteria decision-making method to solve the blockchain platform selection problem. First, the blockchain platform alternatives and evaluation criteria used for decision-making are identified. Second, blockchain platform alternatives are evaluated with appropriate fuzzy numbers based on defined evaluation criteria. Then, the original evaluations are consistency and normalized to obtain a normalized evaluation. Next, the improved information content formulas of the axiomatic design is proposed to obtain the information content of each normalized evaluation. Then, the weights of all evaluated criteria are obtained using the entropy weight method. Finally, the total weighted information content of each blockchain platform alternative is obtained. With validation, the decision-making model of blockchain platform proposed in this paper has a strong reference value.
A Human-Centred Technology Approach to Pedestrian Safety in Smart Cities

Road safety, and in particular pedestrian safety, represents a growing global concern as urban populations continue to rise and age. Motorists and passengers have benefited from technological improvements for their safety, but pedestrian safety remains largely dependent on simple traffic lights, physical interventions in the built environment, and the behaviour of drivers. At the same time, advancements in smart technologies offer opportunities for improving the safety of pedestrians in cities. The chapter describes how such emerging opportunities can be identified through a human-centred technology approach, which combines (a) human-computer interaction (HCI) research as a reference point to better understand what is technologically possible and (b) human-centred methods to gain insights about people’s lived experiences. The chapter demonstrates this approach by linking current pedestrian safety solutions to HCI research, presenting an online ethnographic study of user comments, and developing avenues for smart technology interventions to improve pedestrian safety in cities. Beyond pedestrian safety, the chapter presents recommendations for how to implement a human-centred technology approach in future smart city initiatives.
Taylor – Impersonation of AI for Audiovisual Content Documentation and Search

While AI-based audiovisual analysis tools have without doubt made huge progress, integrating them in media production and archiving workflows is still challenging, as the provided annotations may not match needs in terms of type, granularity and accuracy of metadata, and do not well align with existing workflows. We propose a system for annotation and search in media archive applications, using a range of AI-based analysis methods. In order to facilitate communication of explanations and collect relevance feedback, an impersonation of the systems’ intelligence, named Taylor, is included as an element of the user interface.
Interval Matching Algorithm for Task Scheduling with Time Varying Resource Constraints

The co-location of online services and offline tasks has become very popular in data centers, which can largely improve resource utilization. Scheduling co-located offline tasks is challenging due to the interference with online services. Existing co-location scheduling algorithms try to find the best combination of different workloads to avoid performance interference and maximize the utilization of data centers, but few of them take the time varying resource constraints into account. We propose a heuristic algorithm named interval matching scheduling algorithm based on the idea that the time series of available resources and task scheduling can be regarded as interval endpoints. The proposed scheduling algorithm makes decisions based on a scoring method that calculates the matching degrees of the tasks and the changing resource series. The experimental results show that the proposed algorithm has achieved better performance under different parameter settings comprehensively.
Research on Action Recognition Based on Zero-shot Learning

At present, the research on human action recognition has achieved remarkable results and is widely used in various industries. Among them, human action recognition based on deep learning has developed rapidly. With sufficient labeled data, supervised learning methods can achieve satisfactory recognition performance. However, the diversification of motion types and the complexity of the video background make the annotation of human motion videos a lot of labor costs. This severely restricts the application of supervised human action recognition methods in practical scenarios. Since the zero-shot learning method can realize the recognition of unseen action categories without relying on a large amount of labeled data. In recent years, action recognition methods based on zero-shot learning have received great attention from researchers. In this paper, we propose an attention-based zero-shot action recognition model ADZSAR. We design a novel attention-based mechanism feature extraction method that introduces the current state-of-the-art semantic embedding model (Word2Vec). Experiments show that this method performs the best among similar zero-shot action recognition methods based on spatio-temporal features.
Highly Accurate Dynamic Gesture Recognition Method Based on Edge Intelligence

In recent years, gestures have been widely used in many fields. For example, human-computer interaction, virtual reality, gesture translation, etc. The reason for its rapid development is due to the emergence of deep learning and artificial intelligence under today’s society. Due to dynamic gestural interactions, such large intelligent models are often characterized by many parameters, large sample size, frequent parameter updates, and high communication volume. Based on this feature, we propose a cloud-based edge design architecture approach for gesture recognition based on an improved YOLOv5 network model optimization by changing different gestures, background interference, which uses a 21-layer model for the neural network. With the use of edge intelligence, the computational accuracy can be improved by 10.6% over the traditional YOLOv5 with a MAP value of 93.3%. The final recall rate is also improved by 3.6%. The parameter model is only 43.6% of the original one. This shows the practicality and operability of using edge computing as a technique in gesture recognition, as well as the small improvement cost and obvious effect.
Blockchains with Five Merkle Trees to Support Financial Transactions

Traditional blockchain systems the consensus protocol and transaction management is performed together. In other words, the completion of the consensus protocol means the completion of a transaction. Furthermore, the transaction is considered as settled. This scheme is not compatible with modern financial transaction rules where transaction is distinct from settlement, and thorough regulation compliance should be performed to ensure that no money laundering. This paper proposes using five Merkle trees to maintain data in a blockchain system to separate consensus protocol from transaction management.
Lattice-Based Fine-grained Data Access Control and Sharing Scheme in Fog and Cloud Computing Environments for the 6G Systems

The 6G (the sixth generation mobile communication) network is a heterogeneous network, which includes the cloud computing and the fog computing environment. And, the data securities in the 6G Systems may face great challenges. On the one hand, fine-grained data access control in fog and cloud computing environments is essential to the 6G network. On the other hand, quantum computing attacks may cause great security threats for the 6G network. In order to solve the above problems, we propose a lattice-based fine-grained data access control and sharing scheme in fog and cloud computing environments (LB-DACSS) for the 6G network. Firstly, our LB-DACSS scheme can achieve secure data sharing and fine-grained access control in fog and cloud computing environments. Secondly, we apply a lattice encryption algorithm to ensure our LB-DACSS scheme can withstand quantum computing attacks. Thirdly, our LB-DACSS scheme can achieve secure attribute revocation. Fourthly, we bind a unique secret value related to the user's attribute to construct the user's secret key. Finally, both formal security analysis and performance analysis show that our LB-DACSS scheme is efficient and secure.
Fbereum: A Novel Distributed Ledger Technology System

Over the past several years, due to the progression toward data-driven scientific disciplines, the field of Big Data has gained significant importance. These developments pose certain challenges in the area of efficient, effective, and secure management and transmission of digital information. This paper presents and evaluates a novel Distributed Ledger Technology (DLT) system, Fibereum, in a variety of use-cases, including a DLT-based system for Big Data exchange, as well as the fungible and non-fungible exchange of artwork, goods, commodities, and digital currency. Fibereum’s innovations include the application of non-linear data structures and a new concept of Lazy Verification. We demonstrate the benefits of these novel features for DLT system applications’ cost performance and their added resilience towards cyber-attacks via the consideration of several use cases.
Repeated-root constacyclic codes of length $ p_1p_2^t p^s $ and their dual codes

&lt;abstract&gt;&lt;p&gt;Let $ \mathbb{F}_q $ be the finite field with $ q = p^{k} $ elements, and $ p_{1}, p_{2} $ be two distinct prime numbers different from $ p $. In this paper, we first calculate all the $ q $-cyclotomic cosets modulo $ p_1p_2^t $ as a preparation for the following parts. Then we give the explicit generator polynomials of all the constacyclic codes of length $ p_1p_2^tp^s $ over $ \mathbb{F}_q $ and their dual codes. In the rest of this paper, we determine all self-dual cyclic codes of length $ p_1p_2^t p^s $ and their enumeration. This answers a question recently asked by B. Chen, H.Q.Dinh and Liu. In the last section, we calculate the case of length $ 5\ell p^{s} $ as an example.&lt;/p&gt;&lt;/abstract&gt;
ARID—An Augmented Reality Mobile Application for Interior Design

Interior design is important for the interior of a space to achieve a polished and more aesthetically pleasing environment. Engaging the services of an interior designer can be costly. Therefore, individuals who are not able to engage an interior designer tend to design their room or space by visiting furniture showrooms and social media sites for design ideas. However, individuals who lack experience in interior design might find it difficult to visualize the overall feel and look of the space and may lack the skills to estimate the dimensions of the space. Augmented-reality (AR)-enabled interior design applications offer the potential for individuals to design their room or space by enabling individuals to visualize and estimate the dimensions of the room or space. Although there are several AR-enabled applications for interior design, some limitations exist. As such, this research aims to develop a collaborative AR-enabled application for interior design (ARID) that overcomes the limitations of existing applications. Rapid Application Development Methodology (RAD) was used to develop the proposed AR-enabled application for interior design. The resulting ARID application for interior design overcomes the limitations of existing applications by allowing the user to view the furniture on both horizontal and vertical planes, change the wall and floor color, and enable multiple users to collaborate when designing the room or space. In addition, the fundamental features of allowing the users to select furniture and decoration from a menu of items and previewing the furniture or decoration for the room or space using AR were also developed. Feedback gathered from user acceptance tests revealed that the ARID application was easy to use and beneficial for individuals when designing their rooms or space. The ARID application benefits the field of interior design by overcoming the limitations of existing interior design applications and enables individuals to visualize the room or space they wish to design.
WOTSwana: A Generalized $$\mathcal {S}_{\text{ leeve }}$$ Construction for Multiple Proofs of Ownership

The $$\mathcal {S}_{\text{ leeve }}$$ construction proposed by Chaum et al. (ACNS’21) introduces an extra security layer for digital wallets by allowing users to generate a “back up key” securely nested inside the secret key of a signature scheme, i.e., ECDSA. The “back up key”, which is secret, can be used to issue a “proof of ownership”, i.e., only the real owner of this secret key can generate a single proof, which is based on the WOTS+ signature scheme. The authors of $$\mathcal {S}_{\text{ leeve }}$$ proposed the formal technique for a single proof of ownership, and only informally outlined a construction to generalize it to multiple proofs. This work identifies that their proposed construction presents drawbacks, i.e., varying of signature size and signing/verifying computation complexity, limitation of linear construction, etc. Therefore we introduce WOTSwana, a generalization of $$\mathcal {S}_{\text{ leeve }}$$ , which is, more concretely, a more general scheme, i.e. an extra security layer that generates multiple proofs of ownership, and put forth a thorough formalization of two constructions: (1) one given by a linear concatenation of numerous WOTS+ private/public keys, and (2) a construction based on tree like structure, i.e., an underneath Merkle tree whose leaves are WOTS+ private/public key pairs. Furthermore, we present the security analysis for multiple proofs of ownership, showcasing that this work addresses the early mentioned drawbacks of the original construction. In particular, we extend the original security definition for $$\mathcal {S}_{\text{ leeve }}$$ . Finally, we illustrate an alternative application of our construction, by discussing the creation of an encrypted group chat messaging application.
InstaVarjoLive: An Edge-Assisted 360 Degree Video Live Streaming for Virtual Reality Testbed

Virtual Reality (VR) challenges us with the requirements of ultra-low latency and ultra-high bandwidth. Existing methods that rely on cloud computing systems to improve the latency and bandwidth problems cannot satisfy the high computation and fast communication requirements in VR. Edge computing has emerged as a promising solution that can be applied in VR to optimize the latency and bandwidth prob-lems. However, another challenge is applying edge computing technology to improve the seamless for the VR users. Based on this, this paper proposes an edge-end collaboration testbed called Insta VarjoLive and conducts the experiments on the real-time 360° video live streaming seamless using VR headsets. We compared our experiments with the 360° videos watched by users from the cloud through VR headset and obtained the results showing that the edge-assisted 360° videos live streaming method has three major advantages: better real-time delivery, lower response time, and higher bandwidth guaranteed. Furthermore, we tested our experiments and discussed the other possible optimization methods in the future.
Research on DAG Based Consensus Mechanism for Adjustable Load Metering Data

The distributed ledger technology of blockchain can provide an efficient solution for the trustworthy problem of adjustable load metering data. However, due to the issues caused by nodal constrained resource of computation and storage, high concurrent transactions, and ledger data expansion with elapsed time, etc., it faces non-trivial challenges to design a consensus mechanism for second-level transaction data. This paper presents a shard-based DAG blockchain architecture for large-scale and distributed internet of things scenarios, where each device is a node of the network. These devices construct a multi-hop network, and by using the shard technology, some neighboring nodes belong to the same shard. The preference relationship between transactions is generated based on the shard-based DAG blockchain, and a scalable high throughput block consensus is designed such that using parallel treatment of transactions the throughput of blockchain can be significantly improved. Finally, to improve the limited computation and storage capability, we use container edge service engine in system design.
Adapitch: Adaption Multi-Speaker Text-to-Speech Conditioned on Pitch Disentangling with Untranscribed Data

In this paper, we proposed Adapitch, a multi-speaker TTS method that makes adaptation of the supervised module with untranscribed data. We design two self supervised modules to train the text encoder and mel decoder separately with untranscribed data to enhance the representation of text and mel. To better handle the prosody information in a synthesized voice, a supervised TTS module is designed conditioned on content disentangling of pitch, text, and speaker. The training phase was separated into two parts, pretrained and fixed the text encoder and mel decoder with unsupervised mode, then the supervised mode on the disentanglement of TTS. Experiment results show that the Adaptich achieved much better quality than baseline methods.
Toward Source-Free Cross Tissues Histopathological Cell Segmentation via Target-Specific Finetuning

Recognition and quantitative analytics of histopathological cells are the golden standard for diagnosing multiple cancers. Despite recent advances in deep learning techniques that have been widely investigated for the automated segmentation of various types of histopathological cells, the heavy dependency on specific histopathological image types with sufficient supervised annotations, as well as the limited access to clinical data in hospitals, still pose significant challenges in the application of computer-aided diagnosis in pathology. In this paper, we focus on the model generalization of cell segmentation towards cross-tissue histopathological images. Remarkably, a novel target-specific finetuning-based self-supervised domain adaptation framework is proposed to transfer the cell segmentation model to unlabeled target datasets, without access to source datasets and annotations. When performed on the target unlabeled histopathological image set, the proposed method only needs to tune very few parameters of the pre-trained model in a self-supervised manner. Considering the morphological properties of pathological cells, we introduce two constraint terms at both local and global levels into this framework to access more reliable predictions. The proposed cross-domain framework is validated on three different types of histopathological tissues, showing promising performance in self-supervised cell segmentation. Additionally, the whole framework can be further applied to clinical tools in pathology without accessing the original training image data. The code and dataset are released at: https://github.com/NeuronXJTU/SFDA-CellSeg.
Safe and Robust Transfer Learning

In this chapter, we discuss the safety and robustness of transfer learning. By safety, we refer to its defense and solutions against attack and data privacy misuse. By robustness, we mean the transfer mechanism that prevents the model from learning from spurious relations. Therefore, we introduce the related topics from three levels: (1) framework level, which is the safe fine-tuning process against defect inheritance, (2) data level, which is the transfer learning system against data privacy leakage, and (3) mechanism level, which is causal learning.
Analytic Correlation Penalty with Variable Window in Multi-task Learning Disease Progression Model

Alzheimer's Disease (AD) is the most common reason of dementia that causes serious problems in patients' congnitive functions. Multi-task learning (MTL) has performed well in studies of longitudinal processes in Alzheimer's disease for revealing the progression of AD. Combined with prior knowl-edges in disease progression or medical science, regularization MTL framework could introduce empirical constraints more flexibly. Meanwhile, it brings higher cost during optimization. While it shown that most of formulations could not define the disease progression precisely. Existing regression methods with temporal smoothness method eliminated abnormal fluctuation of cognitive scores, and neglected the sophisticated progression in disease. In this article, we proposed an analytic method to define the progression of AD, and a flexible bandwidth method to encourage the points of disease time sequence temporal smoothness in an appropriate way. To solve three non-smooth penalties in our method, we proposed an optimization method combined accelerated gradient descent (AGD) and alternating direction method of multipliers (ADMM).
A Novel Machine Learning-Based Model for Reentrant Vulnerabilities Detection

Machine learning-based models are one of the main methods for detecting reentrant vulnerabilities. However, these models extract smart contract features only from a single form, resulting in incompleteness and inaccuracy of features. To address this problem, we propose a novel machine learning-based model for reentrant vulnerabilities detection. We extract and fuse features from abstract syntax trees, opcodes, control flow graph basic blocks, and combine machine learning algorithms for reentrant vulnerabilities detection. Additionally, to address the time-consuming problem of manual labeling, we also propose an approach for automatically adding dataset labels. We perform experiments on Smartbugs and SolidiFi-benchmark datasets and results show that our model outperforms existing models.
Incremental Unsupervised Adversarial Domain Adaptation for Federated Learning in IoT Networks

Federated learning, as an effective machine learning paradigm, can collaboratively training an efficient global model by exchanging the network parameters between edge nodes and the cloud server without sacrificing data privacy. Unfortunately, the obtained global model cannot generalize to newly collected unlabeled data since the unlabeled data collected by different edge devices are diverse. Furthermore, the distributions of collected labeled data and unlabeled data are also different for edge devices. In this paper, we propose a method named Incremental Unsupervised Adversarial Domain Adaptation (IUADA) for federated learning, which aims to reduce the domain shift between the labeled data and unlabeled data in the edge nodes and enhance the performance of the personalized target domain models based on the local unlabeled data. Finally, we evaluate the performance of the proposed method by using three real-world datasets. Extensive experimental results demonstrate that the proposed method is efficient to solve the problem of domain shift and achieves a better performance for unlabeled data for federated learning.