Reviewerly/exHarmony huggingface dataset:
Image Forgery Localization using Image Patches and Deep Learning

Due to the broad accessibility of camera systems, photographing has grown in popularity. Photos seem to be significant in our daily lives since they carry a plethora of data, and it is frequently necessary to improve photographs to acquire greater information. Although many technologies are available to enhance the images, these are also often utilized to fabricate photos, leading to the spreading of disinformation. Image forgeries are becoming a serious topic of concern. To locate image forgeries, several conventional frameworks have been developed in the past. Convolutional neural networks (CNNs) have garnered much popularity in recent years, and CNN’s have influenced image forgery localization. One of the most difficult images forgery types is image splicing, in which a part of an image is copied into another image. Image forgery localization techniques that exist in the literature have some limitations. Hence, it is essential to develop a technique for effectively and accurately locating forgeries in the tampered images. We present a strong deep learning-based approach for detecting forgery in an image by using image patches. A patch is taken around it to classify a pixel in an image, which is passed to a CNN to predict whether the pixel belongs to the tampered region. The proposed method efficiently predicts the boundary pixels of the tampered region and the background image. The technique has been rigorously evaluated, and the experiment results obtained are extremely encouraging on CASIA 2.0 database.
A Convolutional Neuro-Fuzzy Network Using Fuzzy Image Segmentation for Acute Leukemia Classification

Acute lymphoblastic leukemia (ALL) is a kind of blood cell malignancy that is distinguished by the presence of many lymphoid blasts in circulation. ALL accounts for over 80% of juvenile leukemia and is most common in children aged 3 to 7. The conventional approach to ALL classification requires meticulous examination of cell images by expert pathologists, which is time-consuming and results in non-standardized reports. Automation in ALL prognoses is an essential but challenging procedure. Fuzzy reasoning rules are integrated into connectionist networks when fuzzy systems and neural networks are combined to generate neuro-fuzzy systems. Current neuro-fuzzy systems, on the other hand, are created with shallow structures that have poor generalization capacity. This article automatically detects ALL from microscopic cell images, employing a deep convolutional neuro-fuzzy network. In order to reduce overfitting, data augmentations were utilized, and the number of samples got increased before training the model. Image preprocessing includes a two-stage fuzzy color segmentation technique is used to separate leukocytes from the rest of the blood components. After applying image preprocessing, the size of images decreased to $64 \times64$ such that containing a single nucleus per image. We propose a convolutional neuro-fuzzy network based on Takagi-SugenoKang (TSK) fuzzy model for acute lymphoblastic leukemia diagnosis. Our suggested model achieved an accuracy of 97.31% on average for acute lymphoblastic leukemia detection.
Teacher-student knowledge distillation for real-time correlation tracking

The performance of correlation filter (CF) based visual trackers has been greatly improved with pretrained deep convolutional neural networks. However, these networks limit the application scope of CF based trackers because of high feature dimension, high time consumption of feature extraction and huge memory storage. To alleviate this problem, we introduce a teacher-student knowledge distillation framework to obtain a lightweight network to speed up CF based trackers. Specifically, we take a pretrained deep convolutional neural network from the image classification task as a teacher network, and distill this teacher network into a lightweight student network. During offline distillation training process, we propose an attention transfer loss to ensure the lightweight student network maintains feature representation of the large-capacity teacher network. Meanwhile, we propose a correlation tracking loss to transfer the student network from image classification task to correlation tracking task, which improves the discriminant ability of the student network. Experiments on OTB, VOT2017 and Temple Color show that, using the learned lightweight network model as the feature extractor, the state-of-the-art CF based tracker achieves real-time speed on a single CPU, while maintaining almost the same tracking performance.
Representation Learning: Recommendation With Knowledge Graph via Triple-Autoencoder

The last decades have witnessed a vast amount of interest and research in feature representation learning from multiple disciplines, such as biology and bioinformatics. Among all the real-world application scenarios, feature extraction from knowledge graph (KG) for personalized recommendation has achieved substantial performance for addressing the problem of information overload. However, the rating matrix of recommendations is usually sparse, which may result in significant performance degradation. The crucial problem is how to extract and extend features from additional side information. To address these issues, we propose a novel feature representation learning method for the recommendation in this paper that extends item features with knowledge graph via triple-autoencoder. More specifically, the comment information between users and items is first encoded as sentiment classification. These features are then applied as the input to the autoencoder for generating the auxiliary information of items. Second, the item-based rating, the side information, and the generated comment representations are incorporated into the semi-autoencoder for reconstructed output. The low-dimensional representations of this extended information are learned with the semi-autoencoder. Finally, the reconstructed output generated by the semi-autoencoder is input into a third autoencoder. A serial connection between the semi-autoencoder and the autoencoder is designed here to learn more abstract and higher-level feature representations for personalized recommendation. Extensive experiments conducted on several real-world datasets validate the effectiveness of the proposed method compared to several state-of-the-art models.
An Empirical Study of Integrating Information Technology in English Teaching in Artificial Intelligence Era

Affordance of information technology in artificial intelligence era is accelerating its increasing integration into English teaching all over the world. However, the implementation of integrating information and communication technology requires the transformation of teaching concepts, the optimization of teaching strategies, and the innovation of teaching methods.This empirical study, which is based on analysis of status quo of college English teaching modes in China, aims to explore pedagogical challeges and motivate English teachers to take initiative to transform their clarssrooms with technology. It further proposes a framework as well as some innovative strategies in implementing the reform in college English classrooms. An exploratory study was conducted using the framework, indicating that integrating information communication technology into education can greaty enhance teaching efficiency, effectively improve learning outcomes, and promote students’ positive attitude towards learning.
Income diversification and bank performance nexus: Does corruption matter?

This study examines the impact of income diversification and corruption on banks’ performance. In particular, we focus on the impact of the extent of corruption in a country on the relationship between banks’ income diversification and performance. We utilize annual data on 715 banks from 52 countries in Africa over a period of eight years, 2011–2018. The results reveal that income diversification enhances banks' profit and risk-adjusted profit. On the other hand, corruption significantly reduces bank performance. We find that the positive impact of income diversification on performance is undermined in countries with a high level of corruption. We also examine the effect of corruption on the diversification–performance nexus across bank ownership groups using subsamples of local, regional African, and non-African banks. We find that corruption affects especially the operation of local and regional African banks, and affects less the operation of non-African banks. Our findings have essential implications for the regulation of banks and financial stability in general in African countries.
Using a Deep Quantum Neural Network to Enhance the Fidelity of Quantum Convolutional Codes

The fidelity of quantum states is an important concept in quantum information. Improving quantum fidelity is very important for both quantum communication and quantum computation. In this paper, we use a quantum neural network (QNN) to enhance the fidelity of [6, 2, 2] quantum convolutional codes. Towards the circuit of quantum convolutional codes, the target quantum state |0⟩ or |1⟩ is turned into entangled quantum states, which can defend against quantum noise more effectively. As the quantum neural network works better for quantum states with low dimension, we divide the quantum circuits into two parts. Then we apply the quantum neural network to each part of the circuit. The results of the simulation show that the network performs well in enhancing the fidelity of the quantum states. Through the quantum neural network, the fidelity of the first part is enhanced from 95.2% to 99.99%, and the fidelity of the second part is enhanced from 93.88% to 94.57%.
Towards Education 4.0: Enhancing Traditional Textbooks with Augmented Reality and Quick Response codes

By 2025, two billion people will be part of Generation Alpha, which refers to children born between 2010 and 2025. These children, who have had the greatest access to technology up to this point, naturally use smartphones, tablets, computers, and other devices. Students of this generation will be able to benefit from the incorporation of emerging technologies and innovations into classrooms. The present paper aims to investigate the potential of these technologies in education to facilitate the leap towards Education 4.0 as part of the overall picture of Industry 4.0. The first section of the paper deals with a review of the scientific literature on the use of Quick Response (QR) codes and Augmented Reality (AR) in the development of learning materials. Then, the paper proposes a solution that supports and facilitates the integration of such technologies in regular textbooks. Connecting learning materials with the technologies that students use every day can lead to a better understanding and involvement in the classroom. However, traditional textbooks and other learning materials need to be updated to meet the expectations of students who use smartphones and other technologies as extensions of their bodies.
MetricMask: Single category instance segmentation by metric learning

In this paper, we introduce a novel single category instance segmentation method, termed MetricMask, which can be used by easily embedding it into most off-the-shelf detection and segmentation methods. Currently, instance segmentation methods usually segment each instance after completing the object detection. Our method can implement segmentation for all instances at once by fusing object detection, semantic segmentation, and metric learning. The contributions are threefold, 1. The instance segmentation is converted to three parallel tasks, including bounding box regression, mask regression, and embedding vector of pixel regression. 2. Random sampling metric loss is proposed to optimize embedding vectors, saving GPU memory without losing instance segmentation accuracy. 3. Based on the model output(bounding box, embedding vector of pixel, and the category-level mask), we propose a Metric Operation to segment each instance. Finally, we conduct experiments on two standard datasets, including the COCO person dataset and the ISOD dataset. The experiment results show that our method has excellent competitiveness with other methods. In particular, our method has an excellent performance on the large object and outperforms the existing state-of-the-art competitors on the ISOD dataset. We hope the MetricMask of our proposed will provide a new method for instance segmentation.
Deep Transfer Learning-Based Fall Detection Approach Using IoMT-Enabled Thermal Imaging-Assisted Pervasive Surveillance and Big Health Data

People’s need for healthcare capacity has become increasingly critical as the elderly population continues to grow in most communities. Approximately 25–47% of seniors fall annually, and early detection of poor balance can significantly reduce their risk. Automated fall detection with big data analytics is key to maintaining the safety of the elderly in smart cities. Visible image systems (VIS) in smart buildings, on the other hand, visible image systems (VIS) in smart buildings may compromise the privacy of seniors by enabling technologies for intelligent big data analytics (IBDA). Thermal imaging (TI) is less obtrusive than visual imaging and can be used in combination with machine vision to perform a wide range of IBDAs. In this study, we present a novel two-step method for detecting falls in TI frames using deep learning (DL). As the first step, tracking tools are used to locate people’s locations. A novel modified deep transfer learning (TL) technique is used to classify the trajectory created by the tracking approach for people who are at risk of falling. Fall detection by the IBDA will be connected to the Internet of medical things (IoMT) and used as smart technology in the process of big data-assisted pervasive surveillance and health analytics. According to an analysis of the publicly available thermal fall dataset, our method outperforms traditional fall detection methods, with an average error of less than 3%. Additionally, IoMT platforms facilitate data processing, real-time monitoring and healthcare management. Our smart scheme for using big data analytics to enable intelligent decisions is compatible with the various spaces and provides a comfortable and safe environment for current and future elderly people.
Validation of a Speech Database for Assessing College Students’ Physical Competence under the Concept of Physical Literacy

This study developed a speech database for assessing one of the elements of physical literacy-physical competence. Thirty-one healthy and native Cantonese speakers were instructed to read a material aloud after various exercises. The speech database contained four types of speech, which were collected at rest and after three exercises of the Canadian Assessment of Physical Literacy 2nd Edition. To show the possibility of detecting each exercise state, a support vector machine (SVM) was trained on the acoustic features. Two speech feature sets, the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) and Computational Paralinguistics Challenge (ComParE), were utilized to perform speech signal processing. The results showed that the two stage four-class SVM were better than the stage one. The performances of both feature sets could achieve 70% accuracy (unweighted average recall (UAR)) in the three-class model after five-fold cross-validation. The UAR result of the resting and vigorous state on the two-class model running with the ComParE feature set was 97%, and the UAR of the resting and moderate state was 74%. This study introduced the process of constructing a speech database and a method that can achieve the short-time automatic classification of physical states. Future work on this corpus, including the prediction of the physical competence of young people, comparison of speech features with other age groups and further spectral analysis, are suggested.
Learning semantic alignment from image for text-guided image inpainting

In this paper, we propose a method called LSAI (learning semantic alignment from image) to recover the corrupted image patches for text-guided image inpainting. Firstly, a multimodal preliminary (MP) module is designed to effectively encode global features for images and textual descriptions, where each local image patch and word are taken into account via multi-head self-attention. Secondly, non-Euclidean semantic relations between images and textual descriptions are captured with graph structure by building a semantic relation graph (SRG). The constructed SRG is able to obtain meaningful words describing the image content and alleviate the impact of distracting words, which is achieved by aggregating the semantic relations with graph convolution. In addition, a text-image matching loss is devised to penalize the restored images for diverse textual and visual semantics. Quantitative and qualitative experiments conducted on two public datasets show the outperformance of our proposed LSAI (e.g., FID value is reduced from 30.87 to 16.73 on CUB-200-2011 dataset).
Practitioners’ best practices to Adopt, Use or Abandon Model-based Testing with Graphical models for Software-intensive Systems

Model-based testing (MBT) has been extensively researched for software-intensive systems but, despite the academic interest, adoption of the technique in industry has been sparse. This phenomenon has been observed by our industrial partners for MBT with graphical models. They perceive one cause to be a lack of evidence-based MBT guidelines that, in addition to technical guidelines, also take non-technical aspects into account. This hypothesis is supported by a lack of such guidelines in the literature. Objective: The objective of this study is to elicit, and synthesize, MBT experts' best practices for MBT with graphical models. The results aim to give guidance to practitioners and aspire to give researchers new insights to inspire future research. Method: An interview survey is conducted using deep, semi-structured, interviews with an international sample of 17 MBT experts, in different roles, from software industry. Interview results are synthesised through semantic equivalence analysis and verified by MBT experts from industrial practice. Results: 13 synthesised conclusions are drawn from which 23 best-practice guidelines are derived for the adoption, use and abandonment of the technique. In addition, observations and expert insights are discussed that help explain the lack of wide-spread adoption of MBT with graphical models in industrial practice. Conclusions: Several technical aspects of MBT are covered by the results as well as conclusions that cover process- and organizational factors. These factors relate to the mindset, knowledge, organization, mandate and resources that enable the technique to be used effectively within an organization. The guidelines presented in this work complement existing knowledge and, as a primary objective, provide guidance for industrial practitioners to better succeed with MBT with graphical models.
Measurement for encrypted open resolvers: Applications and security

Encrypted DNS has been proposed to mitigate the vulnerability of traditional DNS to surveillance and tampering. Some encrypted DNS protocols, like DNS over HTTPS (DoH) and DNS over TLS (DoT), have been promoted by the community and supported by the industry. However, although encrypted DNS are proposed to protect users' privacy and security, the security of their application in practice is still unknown. In this study, we focus on DoH and DoT to study the application and security of encrypted DNS from the perspective of open resolvers. We first propose a novel encrypted open resolvers discovery method. It enables us to implement a comprehensive discovery of encrypted open resolvers across the IPv4 network. Furthermore, we conduct security measurements on encrypted open resolvers for the recursive and iterative resolution they perform. In our measurements, we conduct the most comprehensive discovery to date and detect 5.7k open DoH resolvers and 9.6k open DoT resolvers in the IPv4 network. Moreover, we have observed several security issues of the encrypted open resolvers. For example, we find 10.2% of the open DoH resolvers and 60.7% of the open DoT resolvers use invalid certificates; 19.2% of the DNSSEC-supporting open DoH resolvers do not actually implement DNSSEC validation in resolution, including those from famous providers Facebook and Alidns. Our research reveals pervasive misconfigurations of the encrypted open resolvers in the wild. We recommend that resolver administrators need carefully check and maintain the DNS security configurations on their encrypted resolvers.
DRHDR: A Dual branch Residual Network for Multi-Bracket High Dynamic Range Imaging

We introduce DRHDR, a Dual branch Residual Convolutional Neural Network for Multi-Bracket HDR Imaging.To address the challenges of fusing multiple brackets from dynamic scenes, we propose an efficient dual branch network that operates on two different resolutions.The full resolution branch uses a Deformable Convolutional Block to align features and retain high-frequency details.A low resolution branch with a Spatial Attention Block aims to attend wanted areas from the non-reference brackets, and suppress displaced features that could incur on ghosting artifacts.By using a dual branch approach we are able to achieve high quality results while constraining the computational resources required to estimate the HDR results.
Observation of supersymmetry and its spontaneous breaking in a trapped ion quantum simulator

Supersymmetry (SUSY) helps solve the hierarchy problem in high-energy physics and provides a natural groundwork for unifying gravity with other fundamental interactions. While being one of the most promising frameworks for theories beyond the Standard Model, its direct experimental evidence in nature still remains to be discovered. Here we report experimental realization of a supersymmetric quantum mechanics (SUSY QM) model, a reduction of the SUSY quantum field theory for studying its fundamental properties, using a trapped ion quantum simulator. We demonstrate the energy degeneracy caused by SUSY in this model and the spontaneous SUSY breaking. By a partial quantum state tomography of the spin-phonon coupled system, we explicitly measure the supercharge of the degenerate ground states, which are superpositions of the bosonic and the fermionic states. Our work demonstrates the trapped-ion quantum simulator as an economic yet powerful platform to study versatile physics in a single well-controlled system.
A Novel Collision-Free Homotopy Path Planning for Planar Robotic Arms

Achieving the smart motion of any autonomous or semi-autonomous robot requires an efficient algorithm to determine a feasible collision-free path. In this paper, a novel collision-free path homotopy-based path-planning algorithm applied to planar robotic arms is presented. The algorithm utilizes homotopy continuation methods (HCMs) to solve the non-linear algebraic equations system (NAES) that models the robot's workspace. The method was validated with three case studies with robotic arms in different configurations. For the first case, a robot arm with three links must enter a narrow corridor with two obstacles. For the second case, a six-link robot arm with a gripper is required to take an object inside a narrow corridor with two obstacles. For the third case, a twenty-link arm must take an object inside a maze-like environment. These case studies validated, by simulation, the versatility and capacity of the proposed path-planning algorithm. The results show that the CPU time is dozens of milliseconds with a memory consumption less than 4.5 kB for the first two cases. For the third case, the CPU time is around 2.7 s and the memory consumption around 18 kB. Finally, the method's performance was further validated using the industrial robot arm CRS CataLyst-5 by Thermo Electron.