Reviewerly/exHarmony huggingface dataset:
FS-BAN: Born-Again Networks for Domain Generalization Few-Shot Classification

Conventional Few-shot classification (FSC) aims to recognize samples from novel classes given limited labeled data. Recently, domain generalization FSC (DG-FSC) has been proposed with the goal to recognize novel class samples from unseen domains. DG-FSC poses considerable challenges to many models due to the domain shift between base classes (used in training) and novel classes (encountered in evaluation). In this work, we make two novel contributions to tackle DG-FSC. Our first contribution is to propose Born-Again Network (BAN) episodic training and comprehensively investigate its effectiveness for DG-FSC. As a specific form of knowledge distillation, BAN has been shown to achieve improved generalization in conventional supervised classification with a closed-set setup. This improved generalization motivates us to study BAN for DG-FSC, and we show that BAN is promising to address the domain shift encountered in DG-FSC. Building on the encouraging findings, our second (major) contribution is to propose Few-Shot BAN (FS-BAN), a novel BAN approach for DG-FSC. Our proposed FS-BAN includes novel multi-task learning objectives: Mutual Regularization, Mismatched Teacher, and Meta-Control Temperature, each of these is specifically designed to overcome central and unique challenges in DG-FSC, namely overfitting and domain discrepancy. We analyze different design choices of these techniques. We conduct comprehensive quantitative and qualitative analysis and evaluation over six datasets and three baseline models. The results suggest that our proposed FS-BAN consistently improves the generalization performance of baseline models and achieves state-of-the-art accuracy for DG-FSC. Project Page: yunqing-me.github.io/Born-Again-FS/.
Designing a cross-organizational identity management system: Utilizing SSI for the certification of retailer attributes

Abstract The introduction of blockchain offers new opportunities to rethink enterprise identity management. Recently, a new concept has emerged in the blockchain community called self-sovereign identity. Self-sovereign identity combines several existing decentralized identity management approaches, promising new ways to promote more convenient, connected, and secure identity services for the private and public sector. Nevertheless, research in this area is still in its infancy. Most of the very few articles focus either on the opportunities self-sovereign identity might offer or on very specific technical features. Studies on real-world applications of organizations using modern self-sovereign identity implementations and design theory are very rare. To fill this gap, we follow the design science research approach to design, implement, and evaluate a self-sovereign identity system to present tax attributes of online retailers. We present four design principles and conclude that the use of self-sovereign identity and blockchain offers opportunities to improve verification processes.
Secure Control Using Homomorphic Encryption and Efficiency Analysis

In order to enhance the cyber-security of networked control systems, Kogiso and Fujita (2015) proposed a concept of controller encryption using homomorphic encryption for the first time. Encrypted linear controllers using a homomorphic encryption scheme could conceal the information processed inside the controller device and maintain the original functions of controllers. In this paper, we propose a scheme to encrypt the linear controller using the BGN encryption, which supports homomorphic addition and multiplication. We also compare the efficiency of this scheme with the scheme with an encrypted controller using RSA encryption and Paillier encryption.
Anti-noise and denoising performance of global rainbow processing

This study simulated and experimentally evaluates the effect of signal noise of 5–60 dB on the inversion accuracy of the global rainbow signals of droplets with three typical size distributions (normal, log-normal, and bimodal normal), using a local minimum-based algorithm for parameter retrieval and empirical mode decomposition for denoising. For the simulated noise-free global rainbow signals of droplets with normal size distributions, the inversion algorithm has the highest accuracy, with the maximum relative error of droplet mean diameter D and the maximum absolute error of refractive index n being only 0.34% and 1.3 × 10−5. The empirical mode decomposition provides the best denoising and stability when preprocessing global rainbow signals of bimodal-normal size distributed droplets, reducing the errors of D and n from 51.4%, 9 × 10−4 to 7.9%, 2.4 × 10−4 even at a signal-to-noise ratio of 5 dB. Experiments were conducted based on a typical global rainbow measurement system. The errors of D and the n retrieved before and after denoising the noisy experimental signal are less than 5.2%, 1.4 × 10−4 and 2.4%, 0.51×10−4 respectively. Experimental results verify the feasibility and effectiveness of the noisy global rainbow processing procedures consisting of an original inversion algorithm and EMD denoising.
The c-differential uniformity and boomerang uniformity of three classes of permutation polynomials over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math>

Permutation polynomials with low c-differential uniformity and boomerang uniformity have wide applications in cryptography. In this paper, by utilizing the Weil sums technique and solving some certain equations over F2n, we determine the c-differential uniformity and boomerang uniformity of these permutation polynomials: (1) f1(x)=x+Tr1n(x2k+1+1+x3+x+ux), where n=2k+1, u∈F2n with Tr1n(u)=1; (2) f2(x)=x+Tr1n(x2k+3+(x+1)2k+3), where n=2k+1; (3) f3(x)=x−1+Tr1n((x−1+1)d+x−d), where n is even and d is a positive integer. The results show that the involutions f1(x) and f2(x) are APcN functions for c∈F2n﹨{0,1}. Moreover, the boomerang uniformity of f1(x) and f2(x) can attain 2n. Furthermore, we generalize some previous works and derive the upper bounds on the c-differential uniformity and boomerang uniformity of f3(x).
A survey on implementations of homomorphic encryption schemes

With the increased need for data confidentiality in various applications of our daily life, homomorphic encryption (HE) has emerged as a promising cryptographic topic. HE enables to perform computations directly on encrypted data (ciphertexts) without decryption in advance. Since the results of calculations remain encrypted and can only be decrypted by the data owner, confidentiality is guaranteed and any third party can operate on ciphertexts without access to decrypted data (plaintexts). Applying a homomorphic cryptosystem in a real-world application depends on its resource efficiency. Several works compared different HE schemes and gave the stakes of this research field. However, the existing works either do not deal with recently proposed HE schemes (such as CKKS) or focus only on one type of HE. In this paper, we conduct an extensive comparison and evaluation of homomorphic cryptosystems' performance based on their experimental results. The study covers all three families of HE, including several notable schemes such as BFV, BGV, FHEW, TFHE, CKKS, RSA, El-Gamal, and Paillier, as well as their implementation specification in widely used HE libraries, namely Microsoft SEAL, PALISADE, and HElib. In addition, we also discuss the resilience of HE schemes to different kind of attacks such as indistinguishability under chosen plaintext attack and integer factorization attacks on classical and quantum computers.
Historical Text Image Enhancement Using Image Scaling and Generative Adversarial Networks

Historical documents such as newspapers, invoices, contract papers are often difficult to read due to degraded text quality. These documents may be damaged or degraded due to a variety of factors such as aging, distortion, stamps, watermarks, ink stains, and so on. Text image enhancement is essential for several document recognition and analysis tasks. In this era of technology, it is important to enhance these degraded text documents for proper use. To address these issues, a new bi-cubic interpolation of Lifting Wavelet Transform (LWT) and Stationary Wavelet Transform (SWT) is proposed to enhance image resolution. Then a generative adversarial network (GAN) is used to extract the spectral and spatial features in historical text images. The proposed method consists of two parts. In the first part, the transformation method is used to de-noise and de-blur the images, and to increase the resolution effects, whereas in the second part, the GAN architecture is used to fuse the original and the resulting image obtained from part one in order to improve the spectral and spatial features of a historical text image. Experiment results show that the proposed model outperforms the current deep learning methods.
Multi-label classification via closed frequent labelsets and label taxonomies

Multi-label classification (MLC) is a very explored field in recent years. The most common approaches that deal with MLC problems are classified into two groups: (i) problem transformation which aims to adapt the multi-label data, making the use of traditional binary or multiclass classification algorithms feasible, and (ii) algorithm adaptation which focuses on modifying algorithms used into binary or multiclass classification, enabling them to make multi-label predictions. Several approaches have been proposed aiming to explore the relationships among the labels, with some of them through the transformation of a flat multi-label label space into a hierarchical multi-label label space, creating a tree-structured label taxonomy and inducing a hierarchical multi-label classifier to solve the classification problem. This paper presents a novel method in which a label hierarchy structured as a directed acyclic graph (DAG) is created from the multi-label label space, taking into account the label co-occurrences using the notion of closed frequent labelset. With this, it is possible to solve an MLC task as if it was a hierarchical multi-label classification (HMC) task. Global and local HMC approaches were tested with the obtained label hierarchies and compared with the approaches using tree-structured label hierarchies showing very competitive results. The main advantage of the proposed approach is better exploration and representation of the relationships between labels through the use of DAG-structured taxonomies, improving the results. Experimental results over 32 multi-label datasets from different domains showed that the proposed approach is better than related approaches in most of the multi-label evaluation measures and very competitive when compared with the state-of-the-art approaches. Moreover, we found that both tree and in specially DAG-structured label hierarchies combined with a local hierarchical classifier are more suitable to deal with imbalanced multi-label datasets.
Cooperative Swarm Learning for Distributed Cyclic Edge Intelligent Computing

Emerging technologies and applications including the Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. Recently, the emerging Federated Learning (FL) systems mitigates some of those concerns, by keeping data private to cater the confidentiality issues. Although FL has drawn the attention of researchers and developers, the involvement of an aggregator server for global model updates, makes the system prone to privacy and security breaches. To resolve this issue Swarm Learning (SL) securely onboards edge clients and dynamically elects the swarm leader, to perform collaborative model training in an extremely decentralized, private and secured manner. However, the conventional blockchain-enabled dynamic selection of leader at every communication round is resource intensive and degrades SL’s effectiveness. In this paper, we propose a lightweight communication and computation efficient cooperative swarm learning framework for cyclic edge intelligent computing. The proposed cooperative swarm learning provides cyclic learning based on Deep Neural Networks procedure for training a global model in a sequential ring topology in one iteration which reduces the computational and communication overhead. The global model update method trains a shared global thermal comfort prediction model. Since there is model updating being performed in each participant of the cyclic edge intelligent computing network, bandwidth and computing power demand are reduced. An empirical investigation on Non-IID thermal comfort dataset demonstrates the superiority of the proposed framework.
Twitter Bot Detection Using Diverse Content Features and Applying Machine Learning Algorithms

A social bot is an intelligent computer program that acts like a human and carries out various activities in a social network. A Twitter bot is one of the most common forms of social bots. The detection of Twitter bots has become imperative to draw lines between real and unreal Twitter users. In this research study, the main aim is to detect Twitter bots based on diverse content-specific feature sets and explore the use of state-of-the-art machine learning classifiers. The real-world data from Twitter is scrapped using Twitter API and is pre-processed using standard procedure. To analyze the content of tweets, several feature sets are proposed, such as message-based, part-of-speech, special characters, and sentiment-based feature sets. Min-max normalization is considered for data normalization and then feature selection methods are applied to rank the top features within each feature set. For empirical analysis, robust machine learning algorithms such as deep learning (DL), multilayer perceptron (MLP), random forest (RF), naïve Bayes (NB), and rule-based classification (RBC) are applied. The performance evaluation based on standard metrics of precision, accuracy, recall, and f-measure reveals that the proposed approach outperforms the existing studies in the relevant literature. In addition, we explore the effectiveness of each feature set for the detection of Twitter bots.
Experimental Study: Deep Learning-Based Fall Monitoring among Older Adults with Skin-Wearable Electronics

Older adults are more vulnerable to falling due to normal changes due to aging, and their falls are a serious medical risk with high healthcare and societal costs. However, there is a lack of automatic fall detection systems for older adults. This paper reports (1) a wireless, flexible, skin-wearable electronic device for both accurate motion sensing and user comfort, and (2) a deep learning-based classification algorithm for reliable fall detection of older adults. The cost-effective skin-wearable motion monitoring device is designed and fabricated using thin copper films. It includes a six-axis motion sensor and is directly laminated on the skin without adhesives for the collection of accurate motion data. To study accurate fall detection using the proposed device, different deep learning models, body locations for the device placement, and input datasets are investigated using motion data based on various human activities. Our results indicate the optimal location to place the device is the chest, achieving accuracy of more than 98% for falls with motion data from older adults. Moreover, our results suggest a large motion dataset directly collected from older adults is essential to improve the accuracy of fall detection for the older adult population.
Research on Identification and Location of Charging Ports of Multiple Electric Vehicles Based on SFLDLC-CBAM-YOLOV7-Tinp-CTMA

With the gradual maturity of autonomous driving and automatic parking technology, electric vehicle charging is moving towards automation. The charging port (CP) location is an important basis for realizing automatic charging. Existing CP identification algorithms are only suitable for a single vehicle model with poor universality. Therefore, this paper proposes a set of methods that can identify the CPs of various vehicle types. The recognition process is divided into a rough positioning stage (RPS) and a precise positioning stage (PPS). In this study, the data sets corresponding to four types of vehicle CPs under different environments are established. In the RPS, the characteristic information of the CP is obtained based on the combination of convolutional block attention module (CBAM) and YOLOV7-tinp, and its position information is calculated using the similar projection relationship. For the PPS, this paper proposes a data enhancement method based on similar feature location to determine the label category (SFLDLC). The CBAM-YOLOV7-tinp is used to identify the feature location information, and the cluster template matching algorithm (CTMA) is used to obtain the accurate feature location and tag type, and the EPnP algorithm is used to calculate the location and posture (LP) information. The results of the LP solution are used to provide the position coordinates of the CP relative to the robot base. Finally, the AUBO-i10 robot is used to complete the experimental test. The corresponding results show that the average positioning errors (x, y, z, rx, ry, and rz) of the CP are 0.64 mm, 0.88 mm, 1.24 mm, 1.19 degrees, 1.00 degrees, and 0.57 degrees, respectively, and the integrated insertion success rate is 94.25%. Therefore, the algorithm proposed in this paper can efficiently and accurately identify and locate various types of CP and meet the actual plugging requirements.
CMA-ES with exponential based multiplicative covariance matrix adaptation for global optimization

Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is one of the proven evolutionary algorithms to solve complex optimization problems. However, CMA-ES is plagued with the computational overload that is associated with the unstable matrix decomposition process. In the current work, the computationally expensive covariance matrix decomposition is replaced with a multiplicative update of the mutation matrix which is a result of first-order exponential approximation. In addition, we incorporate the Heaviside function into the mutation matrix update to appropriately control the mutation step size. The proposed mutation matrix update scheme and the incorporation of the Heaviside function result in a modified evolution path. The performance of the proposed framework, referred to as Exponential Simplified CMA-ES (xSCMA-ES) is favorably compared with the state-of-the-art CMA-ES-based algorithms on — (a) IEEE CEC 2014 benchmark suite (b) with different DE variants on CoCo Framework and (c) hybrid active power filter design problem where the objective is to minimize the harmonic distortions.
Direction-aware attention aggregation for single-stage hazy-weather crowd counting

Crowd counting in adverse hazy weather is inevitable and significant to the scene understanding in real-world application. For the crucial and challenging hazy-weather crowd counting issue, this paper proposes a single-stage hazy-weather crowd counting method based on direction-aware attention aggregation. The method develops a Directional Context Attention (DCA) block to realize embedding the positional information to the channel attention aggregation and to capture the long-range dependencies about the crowd structure, both of which guide the counting method readily locate the crowd of interest. Due to the lack of the hazy-weather crowd counting benchmark datasets, we also generate the hazy-weather crowd counting datasets to evaluate the proposed method. Experimental results and the discussions demonstrate our method is effective greatly on the hazy-weather crowd counting task, especially the design of the core DCA block. In this research work, both the proposed method and the generated benchmark datasets in hazy-weather scene facilitate the development of the crowd counting in the adverse weather conditions and the universal intelligent surveillance technologies. The generated hazy-weather crowd counting benchmarks datasets (Hazy-ShanghaiTechRGBD and Hazy-JHU) in this paper would be released in https://github.com/312524/Hazy-CC after peer-review process of this work to facilitate further research.
Synthesising 3D solid models of natural heterogeneous materials from single sample image, using encoding deep convolutional generative adversarial networks

Three-dimensional solid computational representations of natural heterogeneous materials are challenging to generate due to their high degree of randomness and varying scales of patterns, such as veins and cracks, in different sizes and directions.In this regard, this paper introduces a new architecture to synthesise 3D solid material models by using encoding deep convolutional generative adversarial networks (EDCGANs).DCGANs have been useful in generative tasks in relation to image processing by successfully recreating similar results based on adequate training.While concentrating on natural heterogeneous materials, this paper uses an encoding and a decoding DCGAN combined in a similar way to auto-encoders to convert a given image into marble, based on patches.Additionally, the method creates an input dataset from a single 2D high-resolution exemplar.Further, it translates of 2D data, used as a seed, into 3D data to create material blocks.While the results on the Z-axis do not have size restrictions, the X-and Y-axis are constrained by the given image.Using the method, the paper explores possible ways to present 3D solid textures.The modelling potentials of the developed approach as a design tool is explored to synthesise a 3D solid texture of leaf-like material from an exemplar of a leaf image.
PGF-BIQA: Blind image quality assessment via probability multi-grained cascade forest

Blind image quality assessment (BIQA) aims to automatically predict the perceptual quality of a digital image without accessing its pristine reference and plays an important role in computer vision and image analysis. In fact, the difference in the subjective perception of image quality by evaluators greatly affected quality scores of the images. Aiming at the problem that the existing models cannot take into account the subjective perception differences in people, a blind image quality assessment algorithm via probability gcForest (PGF-BIQA) is proposed. Specifically, we use five qualitative labels to replace the specific quality score and extract image color and texture features to represent image quality. Unlike the widely used neural network, gcForest is ensemble by decision trees which randomly extract different features of various modes to learn the relationship between label and quality features. Therefore, a probability gcForest is designed, combining the frequency probability model and the gcForest, using probability confidence to represent the multiple decision modes results, and describing the subjective perception differences in evaluators. Next, through quality anchors and probability confidences of different classes, PGF-BIQA achieves image quality assessment. Moreover, this method proposes an image classification method that resolves the problem of an unbalanced number of images of different classes and the calculation process of the algorithm is simple and effective. Extensive experiments demonstrate that the proposed method is highly consistent with human perception and outperforms many state-of-the-art BIQA algorithms.