Reviewerly/exHarmony huggingface dataset:
ZeroMT: Multi-transfer Protocol for Enabling Privacy in Off-Chain Payments

The privacy problem in public blockchains is a well-know challenge. Despite the robustness and decentralisation properties of the blockchain, transaction information remains visible to everyone in the network. Several proposals aim at solving these issues with advanced cryptographic techniques, such as zero-knowledge proofs, which turned out to be the best candidates. However, previous works are not aimed at off-chain sessions, which often end with a transfer that involves multiple payees, coming from a single payer or from more than one. With this paper, we introduce ZeroMT, a protocol for multiple confidential balance transfers that occur in a single transaction. With this novel approach, parties of an off-chain session gain privacy for their balances and transfer amounts. In addition, all transfers occur within a single transaction that benefits scalability, reducing the number of transactions to be validated in the main-chain. We provide the generic construction of a confidential multi-transfer transaction that can be assembled off-chain and verified by smart contract platforms. As a part of our protocol, we design the multi-transfer proof system by combining the aggregate version of Bulletproofs and an extended $$\Sigma $$ -Protocol to $$n$$ ciphertexts, generalising the Zether- $$\Sigma $$ -Bullets protocol .
Implicit Motion-Compensated Network for Unsupervised Video Object Segmentation

Unsupervised video object segmentation (UVOS) aims at automatically separating the primary foreground object(s) from the background in a video sequence. Existing UVOS methods either lack robustness when there are visually similar surroundings (appearance-based) or suffer from deterioration in the quality of their predictions because of dynamic background and inaccurate flow (flow-based). To overcome the limitations, we propose an implicit motion-compensated network (IMCNet) combining complementary cues ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , appearance and motion) with aligned motion information from the adjacent frames to the current frame at the feature level without estimating optical flows. The proposed IMCNet consists of an affinity computing module (ACM), an attention propagation module (APM), and a motion compensation module (MCM). The light-weight ACM extracts commonality between neighboring input frames based on appearance features. The APM then transmits global correlation in a top-down manner. Through coarse-to-fine iterative inspiring, the APM will refine object regions from multiple resolutions so as to efficiently avoid losing details. Finally, the MCM aligns motion information from temporally adjacent frames to the current frame which achieves implicit motion compensation at the feature level. We perform extensive experiments on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit {DAVIS}_{\textit {16}}$ </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">$\textit {YouTube-Objects}$ </tex-math></inline-formula> . Our network achieves favorable performance while running at a faster speed compared to the state-of-the-art methods. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xilin1991/IMCNet</uri> .
Direct Estimation of Diagnostic Classification Model Attribute Mastery Profiles via a Collapsed Gibbs Sampling Algorithm

This paper proposes a novel collapsed Gibbs sampling algorithm that marginalizes model parameters and directly samples latent attribute mastery patterns in diagnostic classification models. This estimation method makes it possible to avoid boundary problems in the estimation of model item parameters by eliminating the need to estimate such parameters. A simulation study showed the collapsed Gibbs sampling algorithm can accurately recover the true attribute mastery status in various conditions. A second simulation showed the collapsed Gibbs sampling algorithm was computationally more efficient than another MCMC sampling algorithm, implemented by JAGS. In an analysis of real data, the collapsed Gibbs sampling algorithm indicated good classification agreement with results from a previous study.
Blockchain-Based Robust Data Security Scheme in IoT-Enabled Smart Home

The recent surge in development of smart homes and smart cities can be observed in many developed countries.While the idea to control devices that are in home (embedded with the Internet of Things (IoT) smart devices) by the user who is outside the home might sound fancy, but it comes with a lot of potential threats.There can be many attackers who will be trying to take advantage of this.So, there is a need for designing a secure scheme which will be able to distinguish among genuine/authorized users of the system and attackers.And knowing about the details of when and what IoT devices are used by the user, the attacker can trace the daily activities of user and can plan an attack accordingly.Thus, the designed security scheme should guarantee confidentiality, anonymity and un-traceability.Most of the schemes proposed in the literature are either non-blockchain based which involves inherent problems of storing data in a single-server or assuming weaker attack models.In this work, we propose a novel scheme based on blockchain technology, assuming a stronger Canetti and Krawczyk (CK)-threat model.Through the formal and informal security, and comparative analysis, we show that the proposed scheme provides a superior security and more functionality features, with less communication cost and comparable computational cost as compared to other competent schemes.Moreover, the blockchain based simulation study on the proposed scheme has been conducted to show its feasibility in real-life application.
An island-based hybrid evolutionary algorithm for caloric-restricted diets

The most popular and successful way to maintain a healthy body is to have a rich and balanced diet combined with physical exercise. Since the diet dilemma was proposed, several works in the literature suggested calculating a diet that respects the nutritional needs of each person. In the Caloric-Restricted Diet Problem (CRDP), the goal is to find a reduced-calorie diet that meets these nutritional needs, enabling weight loss. This paper proposes an Island-Based Hybrid Evolutionary Algorithm (IBHEA) that uses a Genetic Algorithm (GA) and a Differential Evolution (DE) Algorithm with different parameters settings in different islands communicating through several migration policies to solve the CRDP. Computational experiments showed that IBHEA outperformed more than 5% compared with non-distributed and non-hybrid implementations, generating a greater variety of diets with a small calorie count.
Visual Object Tracking Based on Light-Field Imaging in the Presence of Similar Distractors

Visual object tracking is of great importance in the field of computer vision. One of the main challenges is the difficulty of identifying moving targets from nearby similar distractors with a single-view image of the scene. To overcome this challenge, in this article, we acquire multiview images of the scenes by using a light-field camera. The multiview images are able to capture the 4-D structure instead of the 2-D plane of the objects but are more difficult to process. Therefore, we propose a novel representation for multiview images, i.e., the macro-epipolar plane image (macro-EPI), which highlights both spatial topological and angular information of the target and distractors. It is obtained by slicing the original multiview images into pieces and properly restacking these pieces in an ordinal manner. The resulting macro-EPI is mapped into the 2-D space; therefore, we adapt a modified autoencoder network to train a macro-EPI feature extractor. Thereafter, we design a composite framework of two-pattern convolution filters based on a discriminative correlation filter for object tracking, which successfully discriminates the target from the distractors by merging the macro-EPI features and the single-view image features. The experiments also show that our method outperforms the state-of-the-art methods in the presence of similar distractors.
Abbreviation-Expansion Pair Detection for Glossary Term Extraction

Context and motivation: Providing precise definitions of all project specific terms is a crucial task in requirements engineering. In order to support the glossary building process, many previous tools rely on the assumption that the requirements set has a certain level of quality. Question/problem: Yet, the parallel detection and correction of quality weaknesses in the context of glossary terms is beneficial to requirements definition. In this paper, we focus on detection of uncontrolled usage of abbreviations by identification of abbreviation-expansion pair (AEP) candidates. Principal ideas/results: We compare our feature-based approach (ILLOD) to other similarity measures to detect AEPs. It shows that feature-based methods are more accurate than syntactic and semantic similarity measures. The goal is to extend the glossary term extraction (GTE) and synonym clustering with AEP-specific methods. First experiments with a PROMISE data-set extended with uncontrolled abbreviations show that ILLOD is able to extract abbreviations as well as match their expansions viably in a real-world setting and is well suited to augment previous term clusters with clusters that combine AEP candidates. Contribution: In this paper, we present ILLOD, a novel feature-based approach to AEP detection and propose a workflow for its integration to clustering of glossary term candidates.
Research on facial expression recognition based on an improved fusion algorithm

Abstract This article puts forward a facial expression recognition (FER) algorithm based on multi-feature fusion and convolutional neural network (CNN) to solve the problem that FER is susceptible to interference factors such as non-uniform illumination, thereby reducing the recognition rate of facial expressions. It starts by extracting the multi-layer representation information (asymmetric region local binary pattern [AR-LBP]) of facial expression images and cascading them to minimize the loss of facial expression texture information. In addition, an improved algorithm called divided local directional pattern (DLDP) is used to extract the original facial expression image features, which not only retains the original texture information but also reduces the time consumption. With a weighted fusion of the features extracted from the above two facial expressions, new AR-LBP-DLDP facial local features are then obtained. Later, CNN is used to extract global features of facial expressions, and the local features of AR-LBP-DLDP obtained by weighted fusion are cascaded and fused with the global features extracted by the CNN, thereby producing the final facial expression features. Ultimately, the final facial expression features are input into Softmax for training and classification. The results show that the proposed algorithm, with good robustness and real-time performance, effectively improves the recognition rate of facial expressions.
A Complementary Fusion Strategy for RGB-D Face Recognition

RGB-D Face Recognition (FR) with low-quality depth maps recently plays an important role in biometric identification. Intrinsic geometry properties and shape clues reflected by depth information significantly promote the FR robustness to light and pose variations. However, the existing multi-modal fusion methods mostly lack the ability of complementary feature learning and establishing correlated relationships between different facial features. In this paper, we propose a Complementary Multi-Modal Fusion Transformer (CMMF-Trans) network which is able to complement the fusion while preserving the modal-specific properties. In addition, the proposed novel tokenization and self-attention modules stimulate the Transformer to capture long-range dependencies supplementary to local representations of face areas. We test our model on two public datasets: Lock3DFace and IIIT-D which contain challenging variations in pose, occlusion, expression and illumination. Our strategy achieves the state-of-the-art performance on them. Another meaningful contribution in our work is that we have created a challenging RGB-D FR dataset which contains more kinds of difficult scenarios, such as, mask occlusion, backlight shadow, etc.
On k-universal quadratic lattices over unramified dyadic local fields

Let $k$ be a positive integer and let $F$ be a finite unramified extension of $\mathbb{Q}_2$ with ring of integers $\mathcal{O}_F$. An integral (resp. classic) quadratic form over $\mathcal{O}_F$ is called $k$-universal (resp. classically $k$-universal) if it represents all integral (resp. classic) quadratic forms of dimension $k$. In this paper, we provide a complete classification of $k$-universal and classically $k$-universal quadratic forms over $\mathcal{O}_F$. The results are stated in terms of the fundamental invariants associated to Jordan splittings of quadratic lattices.
Few-Shot Semantic Segmentation via Frequency Guided Neural Network

Prototype learning is extensively used in few-shot semantic segmentation due to its excellent capability of semantic information extraction and effective prevention of overfitting. The previous prototype based methods ignore the frequency discrepancy inside the object, thereby leading to semantic confusion of the object. In this paper, we propose a frequency guided network (FGNet) which explicitly models the semantic information of different frequencies and precisely guides the semantic alignment of the object. Specifically, the proposed FGNet consists of two modules: a frequency separation module (FSM) and a multi-guided feature enrichment module (MG-FEM) to complete the multi-frequency semantic information extraction and alignment, respectively. Experiments on PASCAL- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$5^{i}$</tex-math></inline-formula> dataset show that our FGNet achieves mIoU score of 61.2% in 1-shot which surpasses the state-of-the-art methods.
Bayes-Based Distributed Estimation in Adversarial Multitask Networks

Recently, the problem of security distributed estimation in adversarial multitask wireless sensor networks has attracted extensive attention. For example, malicious attackers always affect signal processing and reduce network estimation performance by destroying data information. To address this issue, by using task similarity, this article proposes a diffusion least-mean square algorithm based on Bayes (BDLMS) in multitask adversarial network environment. The BDLMS algorithm can divide the wireless sensor system into two subsystems: Noncooperative LMS (NCLMS) subsystem and distributed diffusion LMS (DLMS) subsystem. Among them, the NCLMS subsystem ensures the state consistency of neighboring nodes by task similarity and calculates the posterior probability of node being attacked by Bayes rule to achieve attack detection. The DLMS subsystem performs parameter estimation by combining safe and reliable information. In addition, the mean and mean-square stability of the BDLMS algorithm are analyzed. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments in different network environments.
On the Latency Performance in Private Blockchain Networks

Blockchain technologies have been emerging with the potential to disrupt many fields (e.g., cryptocurrencies replacing the traditional ones, enabling trustworthy voting, etc.). The Internet of Things (IoT) has been predictably strengthened when integrating to the private blockchain, such as Ethereum. In an IoT deployment with private Ethereum, a thorough understanding of the latency is a critical issue that has not been adequately understood in the literature. Motivated by that, this work aims to comprehend the latency performance in the IoT Ethereum with two popular consensus algorithms: Proof of Work (PoW) and Proof of Authority (PoA). Initially, we clarify different latency segments from transaction submission to execution, namely, the transaction lifecycle in a private blockchain. We then consider the three related latency metrics: 1) transaction-oriented latency; 2) mining time; and 3) block-oriented latency in the PoW case. With PoA, the mining time’s consideration is omitted since the mining process is not necessary. After that, we construct a realistic private Ethereum IoT network (i.e., using a laptop and seven Raspberry Pi 3b+ nodes) and a large-scale emulated one with 30 nodes. We write and deploy a smart contract to read and write data to the blockchain and measure the latencies in various scenarios. The measurement results reveal the values of transaction-oriented and block-oriented latency with PoW and PoA in both the actual and emulated networks. Moreover, we derive the expected value for the PoW’s mining time by fitting the probabilities to an exponential curve.
Data Source Selection in Federated Learning: A Submodular Optimization Approach

Federated learning is a new learning paradigm that jointly trains a model from multiple data sources without sharing raw data. For the practical deployment of federated learning, data source selection is compulsory due to the limited communication cost and budget in real-world applications. The necessity of data source selection is further amplified in presence of data heterogeneity among clients. Prior solutions are either low in efficiency with exponential time cost or lack theoretical guarantees. Inspired by the diminishing marginal accuracy phenomenon in federated learning, we study the problem from the perspective of submodular optimization. In this paper, we aim at efficient data source selection with theoretical guarantees. We prove that data source selection in federated learning is a monotone submodular maximization problem and propose FDSS, an efficient algorithm with a constant approximate ratio. Furthermore, we extend FDSS to FDSS-d for dynamic data source selection. Extensive experiments on CIFAR10 and CIFAR100 validate the efficiency and effectiveness of our algorithms.
An Orthogonal Subspace Decomposition Method for Cross-Modal Retrieval

As a general characteristic observed in the real-world datasets, multimodal data are usually partially associated, which comprise the commonly shared information across modalities (i.e., modality-shared information) and the specific information only exists in a single modality (i.e., modality-specific information). Cross-modal retrieval methods typically use these information in multimodal data as a whole and project them into a common representation space to calculate the similarity measure. In fact, only modality-shared information can be well aligned in the learning of common representations, whereas modality-specific information usually brings about interference term and decreases the performance of cross-modal retrieval. The explicit distinction and utilization of these two kinds of multimodal information are important to cross-modal retrieval, but rarely studied in previous research. In this article, we explicitly distinguish and utilize modality-shared and modality-specific features for learning better common representations, and propose an orthogonal subspace decomposition method for cross-modal retrieval, named orthogonal subspace decomposition method. Specifically, we introduce a structure preservation loss to ensure modality-shared information to be well preserved, and optimize the intramodal discrimination loss and intermodal invariance loss to learn the semantic discriminative features for cross-modal retrieval. We conduct comprehensive experiments on four widely used benchmark datasets, and the experimental results demonstrate the effectiveness of our proposed method.
Research and Implementation of Embedded Real-time Target Detection Algorithm Based on Deep Learning

Abstract In view of the poor detection effect and low robustness of traditional target detection algorithms, this paper studies target detection algorithms based on deep learning, and designs an embedded real-time target detection evaluation board based on AI chip. It is realized that most of the common deep convolutional neural network models represented by YOLOv3 can be transplanted to the board. Although there is a certain accuracy loss within the acceptable range, this implementation achieves a faster speed to meet the needs of real-time detection. Meanwhile, a complete set of video component calling and network transmission schemes are proposed. By designing a unified standard interface accessed to the program framework, the implementation board can be flexibly extended to meet the needs of various artificial intelligence applications.
CB-DA: Lightweight and Escrow-Free Certificate-Based Data Aggregation for Smart Grid

Recent development of smart cities includes advanced and necessary use of modern smart grid (SG), than the traditional power grid. The paradigm of SG has also transformed houses into a home area networks (HAN). In HAN, several smart devices and appliances are connected to the electricity control centers (ECC). Appliances share their load and consumption related information to ECC through smart meters. The consumption data may be used for supply-demand management, for example, by ramping production up or down as needed. However, security and privacy of the consumers data are greatly important, since fine-grained smart meter data may reveal an users presence/absence in his/her house. To address this issue, several public-key-based or identity-based data aggregation schemes have been proposed in the literature. However, most of such schemes either suffer from the complexity of certificate management or key escrow problem. To eliminate these issues, in this paper we propose an efficient certificate-based data aggregation (CB-DA) scheme. In the proposed CB-DA scheme, the owner selects a secret key and then use the secret key along with certificates as decryption/signing keys.
Comparison of Deep Q-Learning, Q-Learning and SARSA Reinforced Learning for Robot Local Navigation

This paper presents a performance comparison of mobile robot obstacle avoidance between using Deep Reinforcement Learning (DRL) and two classical Reinforcement Learning (RL). For the DRL-based method, Deep Q-Learning (DQN) algorithm was used whereas for the RL-based method, Q-Learning and Sarsa algorithms were used. In our experiments, we have used the extended OpenAI Gym ToolKit to compare the performances of DQN, Q-Learning, and Sarsa algorithms in both simulated and real-world environments. Turtlebot3 Burger was used as the mobile robot hardware to evaluate the performance of the RL models in the real-world environment. The average rewards, episode steps, and rate of successful navigation were used to compare the performance of the navigation ability of the RL agents. Based on the simulated and real-world results, DQN has performed significantly better than both Q-Learning and Sarsa. It has achieved 100% success rates during the simulated and real-world tests.