Reviewerly/exHarmony huggingface dataset:
Contextual in situ help for visual data interfaces

As the complexity of data analysis increases, even well-designed data interfaces must guide experts in transforming their theoretical knowledge into actual features supported by the tool. This challenge is even greater for casual users who are increasingly turning to data analysis to solve everyday problems. To address this challenge, we propose data-driven, contextual, in situ help features that can be implemented in visual data interfaces. We introduce five modes of help-seeking: (1) contextual help on selected interface elements, (2) topic listing, (3) overview, (4) guided tour, and (5) notifications. The difference between our work and general user interface help systems is that data visualization provide a unique environment for embedding context-dependent data inside on-screen messaging. We demonstrate the usefulness of such contextual help through two case studies of two visual data interfaces: Keshif and POD-Vis. We implemented and evaluated the help modes with two sets of participants, and found that directly selecting user interface elements was the most useful.
Neuro‐fuzzy Elman wavelet control for nonlinear uncertain systems with fuzzy input and unknown fuzzy disturbances: Application to robotics

Summary Keeping into view the irregular existence of uncertainties and disturbances in nonlinear systems, a novel adaptive control scheme based on neuro‐fuzzy Elman wavelet neural network (NFEWNN) has been proposed in this work which is capable of accurately modeling system uncertainties. In addition to this, an improvement in an evolutionary algorithm named particle swarm optimization has also been introduced to optimize the initial parameters of the controller. Uncertain non‐linear discrete‐time systems in the non‐strict feedback form with unknown disturbances are taken into consideration. This work aims to design an efficient adaptive control scheme for the proposed systems in the case of fuzzy input and fuzzy external disturbance which makes the control of the system more complex and difficult. The proposed NFEWNN control scheme is capable of tracking the desired trajectory successfully along with accurately modeling system uncertainties with high computational power, low computational burden, and rapid convergence rate. The stability of the controller has been proved using Lyapunov theory and guaranteed convergence of the controller is proved. Finally, real‐world applications of the work have been found in robotics. The controller has been implemented on the robotic manipulator and the dynamic model of aerial robotic vehicles to prove its effectiveness and efficiency.
Recognition of characters on curved metal workpiece surfaces based on multi-exposure image fusion and deep neural networks

Accurate industry online scene text recognition techniques for character on curved metal-workpieces are investigated. To increase the contrast between the characters and their backgrounds, and to reduce the interactions of corrosion et al., image pyramid fusion method is proposed to synthesize high-quality detail-enhanced images from image sequences with multi-exposure times. With this method, the three most important reflection-related image quality properties: contrast, saturation, and exposure, can be enhanced simultaneously. A Resnet-50 network is then used as a feature extraction network to fuse features from different scales of feature maps, the masks and the envelope contour coordinates of character text lines can then be obtained by combining global contextual information through semantic segmentation. Finally, characters included in the irregular text lines are recognized with an attention mechanism-based text rectification recognition algorithm. The advantages and effectiveness of the proposed method are statistically analyzed with the data from different production lines. Code will be available in https://github.com/HuaxiongWu-ZJLG/CSCM-Recognition.git.
A Characterization of Entangled Two‐Qubit States via Partial‐Transpose‐Moments

Abstract Although quantum entanglement is an important resource, its characterization is quite challenging. The partial transposition is a common method to detect bipartite entanglement. In this paper, the authors study the partial‐transpose(PT)‐moments of two‐qubit states, and completely describe the whole region, composed of the second and third PT‐moments, for all two‐qubit states. Furthermore, they determine the accurate region corresponding to all entangled two‐qubit states. The states corresponding to those boundary points of the whole region, and to the border lines between separable and entangled states are analyzed. As an application, they characterize the entangled region of PT‐moments for the two families of Werner states and Bell‐diagonal states. The relations between entanglement and the pairs of PT‐moments are revealed from these typical examples. They also numerically plot the whole region of possible PT‐moments for all two‐qubit X ‐states, and find that this region is almost the same as the whole region of PT‐moments for all two‐qubit states. Moreover, they extend their results to detect the entanglement of multiqubit states. By utilizing the PT‐moment‐based method to characterize the entanglement of the multiqubit states mixed by the GHZ and W states, they propose an operational way of verifying the genuine entanglement in such states.
Software Development for Educational Information Services Using Multilayering Semantics Adaptation

Electronic information services are robust platforms that impact daily life and facilitate new research. Increasing software modularity and reusability saves time and money. Numerous designs and programming are challenging when developing a complex educational information system. This article proposes a new paradigm of multi-dimensional information layers, aspects, functional data, and composition rules in software development to create practical educational information platforms. The proposed approach uses aspect orientation throughout educational information's software development life cycle, from planning to implementation and evaluation. Finally, the authors demonstrate and evaluate the design model's modularity and adaptation through fine-granularity design and class reuse. The result reduces invasive changes, promotes modularity and reuse, and eliminates duplication in component-based software design.
On-demand inference acceleration for directed acyclic graph neural networks over edge-cloud collaboration

As the development of deep neural network (DNN) tasks that feature a directed acyclic graph (DAG) surges in modern industries, simultaneously meeting the latency and accuracy requirements of these key DNN tasks has remained elusive. The general feasible solution is to find an optimal DNN partition that combines cloud with edge computing to implement edge–cloud collaboration for DNN inference. Nevertheless, the dynamicity of network conditions and the uncertain availability of cloud computing resources pose formidable obstacles to this collaborative inference. In this study, we formulate the optimization of DNN partition as a minimum cut problem in DAG derived from DNN and then propose an early-exit DAG-DNN inference (EDDI) framework that supports synergistically on-demand inference acceleration for DAG DNN. This framework introduces two novel components: (1) Evaluator that derives an approximately optimal solution for constructing DNN into appropriate DAG, assisting in solving the optimization problem; and (2) Optimizer that enables collaborative optimization of the early exit and DNN partition strategy at run time to improve performance while meeting user-defined latency requirements. Quantitative evaluations show that EDDI outperforms state-of-the-art schemes by 10.6%, 3.3%, and 3%, on average, in terms of model accuracy, inference latency, and throughput under diverse latency constraints, respectively. Meanwhile, latency speedup ratio increases by an average of 8% and 4% under varying network conditions and cloud server capacities, respectively.
Corrosion characterization of steel wires based on persistent homology theory for magnetostrictive guided wave testing signal

Corrosion of steel wires in cables is a frequent occurrence and is a major factor affecting the durability and safety of cables. Magnetostrictive guided waves have been employed to detect steel wire corrosion. However, due to the complexity of the geometry of the corrosion area, it is difficult to interpret the magnetostrictive guided wave testing signals. Hence, extracting features from testing signals to characterize the corrosion condition is a great challenge. This study proposes a topological feature for corrosion characterization of steel wire, which is extracted from the two-dimensional point cloud with optimal geometry structure of the testing signal based on persistent homology theory. Corrosion experiments were carried out on 30 galvanized steel wires. The experimental results indicate that the topological feature increases roughly linearly with the corrosion depth. Furthermore, compared to other features obtained from the time domain, frequency domain, and joint time–frequency domain, this topological feature can reflect the progression of corrosion more accurately.
Galois self-dual extended duadic constacyclic codes

Galois inner product is a generalization of the Euclidean inner product and Hermitian inner product. The theory on linear codes under Galois inner product can be applied in the constructions of MDS codes and quantum error-correcting codes. In this paper, we construct Galois self-dual codes and MDS Galois self-dual codes from extensions of constacyclic codes. First, we explicitly determine all the Type II splittings leading to all the Type II duadic constacyclic codes in two cases. Second, we propose methods to extend two classes of constacyclic codes to obtain Galois self-dual codes, and we also provide existence conditions of Galois self-dual codes which are extensions of constacyclic codes. Finally, we construct some (almost) MDS Galois self-dual codes using the above results. Some Galois self-dual codes and (almost) MDS Galois self-dual codes obtained in this paper turn out to be new.
Discerning potable water sources using Monte Carlo based simulation for health risk assessment in Omu-Aran, Nigeria

Human health is vulnerable upon exposure to persistent heavy metals (HMs) in environmental media such as groundwater even at trace levels. Therefore, this study aims to estimate the health risk posed by exposure to HMs using deterministic and probabilistic methods. Water samples were collected from 8 Hand dug wells (HWs) and 8 Boreholes (BHs) within Omu-Aran, Nigeria. The order of magnitude of the average contents for heavy metals are Zn > Fe > Al > Cu > Cr > Mn > Pb in HWs and Zn > Fe > Cu > Al > Cr > Pb > Mn for BHs. Pollution indices reveal that BHs were of better quality while HWs were more polluted. PCA shows contamination of HWs come from anthropogenic sources. Monte Carlo Simulation (MCS) technique was used to evaluate non-carcinogenic and carcinogenic risk in children and adults. The computed Hazard Index (HI) values for children and adults show the likelihood of exceeding the HI value of 1 is greater in children (90%) than in adults (16%) upon exposure to Cr in HWs. Computed lifetime carcinogenic risk (LTCR) values via oral pathways for Pb exposure are well below the 10−4, hence, no carcinogenic risk is expected upon exposure to Pb. Conversely, for HWs, LTCR values for Cr exposure in children ranged from 0 to 2.14*10−4, indicating that the current Cr levels pose a risk, especially for children. Hence, prolonged duration to HMs exposure via ingestion increases the possibility of having cancer. Sensitivity analysis reveal Exposure frequency and HMs concentration are the most significant input factors on the risk estimate. Overall, BHs represent a safe water source for both adults and children, while HWs could pose health risk in children due to elevated HMs levels.
A novel method for optimizing spectral rotation embedding K-means with coordinate descent

Lloyd's heuristic K-Means, one of the most widely used clustering methods, plays a vital role in a variety of downstream tasks in machine learning owing to its simplicity. However, Lloyd's heuristic sometimes performs poorly in finding the local minimum and is heavily influenced by the initial points. To address these issues, we propose a novel optimization method for the K-Means model. First, we establish that the K-Means minimization problem can be reformulated as a trace maximization problem, which can be seen as a unified view of spectral clustering. Then, we relax the constraint of the scaled cluster matrix and implement an improved spectral rotation to bring the cluster matrix infinitely close to the binary indicator matrix. To this end, an efficient and redundancy-free coordinate descent (CD) method is used to optimize the spectral rotation. Extensive experiments including a hybrid test on several different datasets showed that the proposed algorithm achieved better local objective values compared to Lloyd's heuristic under different initialization strategies (random or K-Means++). In the hybrid test, the proposed algorithm could further decrease the convergence value of the objective function obtained by Lloyd's heuristic; conversely, Lloyd's heuristic did not work. Moreover, statistical hypothesis and comparison tests further validated the superiority of the proposed algorithm.
A Novel Construction Of Certificateless Aggregate Signature Scheme For Healthcare Wireless Medical Sensor Networks

Abstract To ensure privacy and security of healthcare wireless medical sensor networks (HWMSNs), several concrete constructions of efficient certificateless aggregate signature (CLAS) scheme without bilinear pairing were proposed in the last few years. However, many previous constructions of CLAS scheme were found to be impractical, which either fail to meet the claimed security or contain design flaws. For example, in some of the previous proposals, any adversary can forge a valid signature on any new message. In this paper, we first demonstrate some security issues and design flaws in the previous proposals of CLAS scheme. As follows, to further address the above deficiencies, a new construction of CLAS scheme with improved security is presented, and the formal security proof is given using Forking Lemma in the random oracle model, assuming that the discrete logarithm problem is hard. Compared with the previous CLAS schemes, our construction has similar computational costs, and it provides better security guarantees. Therefore, compared with the existing solutions, our proposal with strong security and high computational efficiency is more suitable for use in HWMSNs.
L0 image smoothing via iterating truncated L1 gradient regularization

Edge-preserving image smoothing is a fundamental tool in computational photography and graphics. It aims to suppress insignificant details while maintaining salient structures. The classical L0 filter provides an elegant framework for a variety of applications. However, it inclines to sharpen the salient edges, thus suffering from gradient reversals and color deviations. We propose a solution toward the objective of optimizing summed squared error regularized by the L0-norm of the gradients. The proposed solution explores an iterative strategy, where each iteration is an optimization problem based on the truncated L1 regularization, which can be solved efficiently with the combination of alternating direction method of multipliers and Fourier domain optimization. Leveraging L1-norm limits the aggressive modifications of gradients during the iterative process, which alleviates various artifacts in classical L0 filter. Experiment results indicate that the proposed method achieves superior performance in various applications, including texture removal, detail enhancement, HDR tone mapping, and compression artifact removal. The proposed filter can be conveniently implemented on GPU. It takes our filter 0.51 s to process a 720P color image on a NVIDIA GTX 1080 GPU.
Non-Interactive Decision Trees and Applications with Multi-Bit TFHE

Machine learning classification algorithms, such as decision trees and random forests, are commonly used in many applications. Clients who want to classify their data send them to a server that performs their inference using a trained model. The client must trust the server and provide the data in plaintext. Moreover, if the classification is done at a third-party cloud service, the model owner also needs to trust the cloud service. In this paper, we propose a protocol for privately evaluating decision trees. The protocol uses a novel private comparison function based on fully homomorphic encryption over the torus (TFHE) scheme and a programmable bootstrapping technique. Our comparison function for 32-bit and 64-bit integers is 26% faster than the naive TFHE implementation. The protocol is designed to be non-interactive and is less complex than the existing interactive protocols. Our experiment results show that our technique scales linearly with the depth of the decision tree and efficiently evaluates large decision trees on real datasets. Compared with the state of the art, ours is the only non-interactive protocol to evaluate a decision tree with high precision on encrypted parameters. The final download bandwidth is also 50% lower than the state of the art.
MIND: Maximum Mutual Information Based Neural Decoder

We are assisting at a growing interest in the development of learning architectures with application to digital communication systems. Herein, we consider the detection/decoding problem. We aim at developing an optimal neural architecture for such a task. The definition of the optimal criterion is a fundamental step. We propose to use the mutual information (MI) of the channel input-output signal pair, which yields to the minimization of the a-posteriori information of the transmitted codeword given the communication channel output observation. The computation of the a-posteriori information is a formidable task, and for the majority of channels it is unknown. Therefore, it has to be learned. For such an objective, we propose a novel neural estimator based on a discriminative formulation. This leads to the derivation of the mutual information neural decoder (MIND). The developed neural architecture is capable not only to solve the decoding problem in unknown channels, but also to return an estimate of the average MI achieved with the coding scheme, as well as the decoding error probability. Several numerical results are reported and compared with maximum a-posteriori and maximum likelihood decoding strategies.
Few-Shot Fine-Grained Image Classification: A Survey

With the development of deep learning, fine-grained image classification task has made remarkable achievements, but it largely depends on a large number of annotated data samples. However, in practical applications, such as public safety, medicine, endangered species and other professional fields, such fine-grained samples with annotations are difficult to obtain. When the samples are insufficient, the traditional deep learning training method is easy to yield the over fitting phenomenon, which greatly limits the applicability of such algorithms. The general few-shot algorithm is not designed for the fine-grained characteristics of the image, so it can not effectively extract the subtle differences in the image, which results in poor performance. Therefore, fine-grained image classification task based on few-shot learning has attracted more and more attentions in recent years. This paper introduces the task of few-shot fine-grained image classification, and summarizes the literatures in this field over the recent years. According to the few-shot learning methods, the existing algorithms are divided into the following three categories: meta learning based methods, multimodal based methods, and data augmentation based methods. In addition, the common datasets and performance evaluation indexes in the field of few-shot fine-grained image classification are summarized. Finally, the technical difficulties and research trends in this field are discussed.
A Garbage Classification Method Based on Improved YOLOv5

With the development of national economy, people's daily garbage is increasing day by day. Relying on manpower to sort garbage is a heavy workload and low efficiency. In this paper, an automatic garbage classification system model based on computer vision is proposed to solve the above problems. Firstly, the YOLOv5 object detection algorithm is improved, and abandoning small object detection to obtain faster detection speed. Secondly, shortcut the complex network layer in the YOLOv5 algorithm framework to speed up the recognition speed while ensuring the recognition accuracy. Finally, the traditional loss function is used to improve the detection speed of large objects in raspberry pie. We collect various types of garbage data set, trains the model on the improved algorithm and tests it by embedding raspberry pie. The experimental results show that the proposed algorithm framework has the characteristics of fast speed and high precision, which can complete the garbage classification at the source of garbage.
Security Verification of Key Exchange in Ciphertext-Policy Attribute Based Encryption

Attribute-based encryption (ABE) is an extension scheme of identity-based encryption and public-key encryption. It can achieve fine-grained access control and one-to-many encryption mode, suitable for practical applications. In addition to mathematical proofs, it is essential to verify its security properties using different protocols. This paper proposes two key exchange protocols to securely exchange the user secret keys in ABE. ProVerif is an automated cryptographic protocol verifier that we have used during protocol verification. We specifically aim to protect the confidentiality of the generated keys. The proposed protocols are formally analysed and their security property has been formally proved.
Proposal of a smart contract-based security token management system

The blockchain was designed as a management system of cryptocurrencies in cyberspace. Its use cases spread to “Tokenization,” which transforms various assets in the real world into digitally represented ones. Tokenization removes physical restrictions and allows its users to enjoy benefits such as online completed ownership transfer and sharing of ownership of a high-value asset such as real estate. One use case for tokenization is a security token offering (STO), a popular usage among investors. Still, ordinary tokenization methods could not convince most investors to participate in an STO by achieving secure asset transfer only. In this paper, we design the operational model of security tokens from the analysis of given use cases to clarify the potential functionalities for improving coverage of smart contracts in STOs. We also propose the smart contract-based security token management system based on an extended smart contract system interworking with multiple blockchain ledgers.
An Intelligent Garbage Classification System Using a Lightweight Network MobileNetV2

In response to the current problems of low garbage disposal efficiency and high labor cost, this paper proposes an intelligent and low-cost garbage classification system. By making a dataset, the system is trained with the MobileNetV2 network for migration learning under the Keras framework to recognize and classify garbage types, and deployed in embedded equipment to achieve accurate classification of individual garbage. The system has functions such as full load detection, voice broadcast, and recognition of garbage count. When the system detects the light intensity below a certain threshold, the lighting system above the system automatically turns on to make the system suitable for the night environment. The experimental results show that the system can operate normally, with a prediction time of 62ms for garbage recognition, using 292.9K RAM and 212.9K Flash, and an accuracy rate of 98.7% for garbage recognition.