Reviewerly/exHarmony huggingface dataset:
[Digital pathology].

Digitalisation of pathology slides allows pathologists to make diagnoses using a high-resolution computer screen instead of a conventional microscope. In 2020/21, the four pathology departments in the Region of Southern Denmark implemented digital pathology for all histologic samples. Going digital necessitated optimisation of workflows and training of pathologists, avoiding a reduction in diagnostic quality. This review describes the process for realisation of digital pathology and its future perspectives, including artificial intelligence algorithms to be implemented.
Fine-Grained Software Defect Prediction Based on the Method-Call Sequence

Currently, software defect-prediction technology is being extensively researched in the design of metrics. However, the research objects are mainly limited to coarse-grained entities such as classes, files, and packages, and there is a wide range of defects that are difficult to predict in actual situations. To further explore the information between sequences of method calls and to learn the code semantics and syntactic structure between methods, we generated a method-call sequence that retains the code context structure information and the token sequence representing semantic information. We embedded the token sequence into the method-call sequence and encoded it into a fixed-length real-valued vector. We then built a defect-prediction model based on the transformer, which maps the code-vector representation containing the method-call sequences to a low-dimensional vector space to generate semantic features and syntactic structure features and also predicts the defect density of the method-call sequence. We conducted experiments on 10 open-source projects using the ELFF dataset. The experimental results show that the method-call sequence-level prediction effect is better than the class-level effect, and the prediction results are more stable than those of the method level. The mean absolute error (MAE) value of our approach was 8% lower than that of the other deep-learning methods.
HFGNN-Proto: Hesitant Fuzzy Graph Neural Network-Based Prototypical Network for Few-Shot Text Classification

Few-shot text classification aims to recognize new classes with only a few labeled text instances. Previous studies mainly utilized text semantic features to model the instance-level relation among partial samples. However, the single relation information makes it difficult for many models to address complicated natural language tasks. In this paper, we propose a novel hesitant fuzzy graph neural network (HFGNN) model that explores the multi-attribute relations between samples. We combine HFGNN with the Prototypical Network to achieve few-shot text classification. In HFGNN, multiple relations between texts, including instance-level and distribution-level relations, are discovered through dual graph neural networks and fused by hesitant fuzzy set (HFS) theory. In addition, we design a linear function that maps the fused relations to a more reasonable range in HFGNN. The final relations are used to aggregate the information of neighbor instance nodes in the graph to construct more discriminative instance features. Experimental results demonstrate that the classification accuracy of the HFGNN-based Prototypical Network (HFGNN-Proto) on the ARSC, FewRel 5-way 5-shot, and FewRel 10-way 5-shot datasets reaches 88.36%, 94.45%, and 89.40%, respectively, exceeding existing state-of-the-art few-shot learning methods.
Potential Impact of CV-QKD Integration on Classical WDM Network Capacity

Continuous-variable quantum key distribution (CV-QKD) could allow QKD and classical optical signals physically sharing the same optical fibers in existing networks. However, Raman scattering imposes a limit on the optical power, which in turn impacts the network capacity for classical traffic in presence of CV-QKD. Network-planning simulations indicate that maxing out the CV-QKD capacity in an optical link can adversely impact its classical capacity. Although preliminary, these results show that designing a mixed classical and CV-QKD network will require dedicated planning heuristics and tools that specifically seek a compromise between classical and CV-QKD traffics.
USS Directed E2E Speech Synthesis For Indian Languages

The state-of-the-art end-to-end (E2E) text-to-speech synthesis systems produce highly intelligible speech. But they lack the timbre of Unit Selection Synthesis (USS) and do not perform well in a low-resource environment. Moreover, the high synthesis quality of E2E is limited to read speech. But for conversational speech synthesis, we observe the problem of missing words and the creation of artifacts. On the other hand, USS not only produces the exact speech according to the text but also preserves the timbre. Combining the advantages of USS and the continuity property of E2E, this paper proposes a technique to combine the classical USS with the neural-network-based E2E system to develop a hybrid model for Indian languages.The proposed system guides the USS system using the E2E system. Syllable-based USS and character-based E2E TTS systems are built. Mel spectrograms of syllable-like units generated in the USS and E2E frameworks are compared, and the mel-spectrogram of the better unit is used in the waveglow vocoder. A dataset of 5 Indian languages is used for the experiments. DMOS scores are obtained for conversational speech utterances improperly synthesized in the vanilla E2E and USS frameworks using the Hybrid system and an average absolute improvement of 0.3 is observed over the E2E system.
A Multi-MAV System for the Autonomous Elimination of Multiple Targets in the MBZIRC 2020 Competition

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) is a prestigious, biennial competition aimed at furthering the state of the art in the field of autonomous robotics. In this paper, we present our solution to one of the tasks in the MBZIRC 2020 competition, which design won second place in Challenge 1 and first place in the Grand Challenge of the competition. This paper focuses specifically on the popping task of multiple balloons by multiple Micro Aerial Vehicles (MAVs). This task required a rapid and robust performance to compete with systems from other expert robotic teams from around the world. In this task, a team of autonomous MAV’s had to seek and attack several balloons positioned throughout the competition arena. The novel fast autonomous searching for multiple targets in 3D, their reliable detection, precise relative state estimation, and agile motion planning algorithms are presented in this paper, together with an application for general tasks of 3D target capturing. With a primary focus on reliability, the methods reported in this paper and the entire, complex multi-agent system were successfully verified in both extreme conditions of the desert and the MBZIRC competition. An evaluation of the proposed methods using data from the competition and additional separate datasets is presented. The relevant code of our implementation has been made publicly available for the robotics community.
Training Strategies of Practical Ability of Building Materials Professionals in Higher Vocational Colleges Under the Guidance of “Three Education Reform” Concept

The main purpose of the implementation of the three-education reform concept is to improve the teaching quality and talent training level of higher vocational colleges. Through the reform, one can promote the optimization of teaching methods, provide more space for student’s self-development, and improve the quality of talent training. Building materials in higher vocational colleges are a high requirement for practical ability, which emphasizes the cultivation of students’ practical ability. Under the three-education reform concept, the traditional teaching mode should be replaced to provide more practical opportunities for students, in return give more attention to the development of students’ practical ability. This paper mainly explores the current situation and training strategies in building material professionals in higher vocational colleges under the reform of the three education systems for a reference.
Scalar is Not Enough

Unbiased learning to rank (ULTR) aims to train an unbiased ranking model from biased user click logs. Most of the current ULTR methods are based on the examination hypothesis (EH), which assumes that the click probability can be factorized into two scalar functions, one related to ranking features and the other related to bias factors. Unfortunately, the interactions among features, bias factors and clicks are complicated in practice, and usually cannot be factorized in this independent way. Fitting click data with EH could lead to model misspecification and bring the approximation error.
Applications of the linear canonical transform to digital image processing

In this paper, an existing approximation of discrete linear canonical transform (DLCT) is analyzed, and constraints are derived to fulfill some paramount properties as inversibility and additivity or the possibility to perform classical image operations in the frequency domain as image filtering. Giving some special values to the DLCT parameters and taking advantage of the division of the image spectrum in four zones of different significance, an application of image feature classifications is successfully investigated. Also, the required constraints are obtained to determine the suitability of the selected approximation when working with digital images.
Generalized Likelihood Ratio Test for Adversarially Robust Hypothesis Testing

Machine learning models are known to be susceptible to adversarial attacks which can cause misclassification by introducing small but well designed perturbations. In this paper, we consider a classical hypothesis testing problem in order to develop fundamental insight into defending against such adversarial perturbations. We interpret an adversarial perturbation as a nuisance parameter, and propose a defense based on applying the generalized likelihood ratio test (GLRT) to the resulting composite hypothesis testing problem, jointly estimating the class of interest and the adversarial perturbation. While the GLRT approach is applicable to general multi-class hypothesis testing, we first evaluate it for binary hypothesis testing in white Gaussian noise under $\ell_{\infty}$ norm-bounded adversarial perturbations, for which a known minimax defense optimizing for the worst-case attack provides a benchmark. We derive the worst-case attack for the GLRT defense, and show that its asymptotic performance (as the dimension of the data increases) approaches that of the minimax defense. For non-asymptotic regimes, we show via simulations that the GLRT defense is competitive with the minimax approach under the worst-case attack, while yielding a better robustness-accuracy tradeoff under weaker attacks. We also illustrate the GLRT approach for a multi-class hypothesis testing problem, for which a minimax strategy is not known, evaluating its performance under both noise-agnostic and noise-aware adversarial settings, by providing a method to find optimal noise-aware attacks, and heuristics to find noise-agnostic attacks that are close to optimal in the high SNR regime.
CNN-Based Cross-Modal Residual Network for Image Synthesis

This study attempts to address the issue that present cross-modal image synthesis algorithms do not capture the spatial and structural information of human tissues effectively. As a consequence, the resulting photos include flaws including fuzzy edges and a poor signal-to-noise ratio. The authors offer a cross-sectional technique that combines residual modules with generative adversarial networks. The approach incorporates an enhanced residual initial module and attention mechanism into the generator network, reducing the number of parameters and improving the generator's feature learning capabilities. To boost discriminant performance, the discriminator employs a multiscale discriminator. A multilevel structural similarity loss is included in the loss function to improve picture contrast preservation. On the ADNI data set, the algorithm is compared to the mainstream algorithms. The experimental findings reveal that the synthetic PET image's MAE index has dropped while the SSIM and PSNR indexes have improved. The experimental findings suggest that the proposed model may maintain picture structural information while improving image quality in both visual and objective measures. The residue initial module and attention mechanism are employed to increase the generator's capacity for learning, while the multiscale discriminator is utilized to improve the model's discriminative performance. The enhanced method in this study can maintain the structure and contrast information of the picture, according to comparative experimental findings using the ADNI dataset. The produced picture is hence more aesthetically similar to the genuine print.
Implementation of Blockchain for Secured Criminal Records

Criminal activities are occurring every day in society. People all over the world are either victims, witnessing or criminals. As a citizen of a nation, anyone has the right to fill a case against anyone based on their instant evidence (either an audio, video, or image). Although it should be done perfectly but, in many countries, especially in developing countries local criminals are powerful in the society and thus the victims cannot fill a case due to fear of losing their lives. In the digital era, it is easier to raise the voice against any antisocial activity using digital platforms. Not only this problem is an issue but a way of organized and secured data management system is required to help the police. As it is a huge amount of national data, the main concern in this study is to ensure that no one can hack, steal or manipulate the data in any way. According to Shukla, P., Tyagi, R., & Tyagi, A. the system architecture was different for police officers and general people, yet the system perspective were not narrowed down for further implementation [1]. Along with other technologies Blockchain has been used in order to produce a criminal data record management in other system. The concept of the research is based on the confidentiality of criminal data and maintenance from the local Police Officers perspective. The study aims to contribute to the security protocol of the criminal record data through Blockchain. The work focuses over both the security protocols and the system architecture from both the user and implementers’ side.
QoE-based Situational Awareness-Centric Decision Support for Network Video Surveillance

Control room video surveillance is an important source of information for ensuring public safety. To facilitate the process, a Decision-Support System (DSS) designed for the security task force is vital and necessary to take decisions rapidly using a sea of information. In case of mission critical operation, Situational Awareness (SA) which consists of knowing what is going on around you at any given time plays a crucial role across a variety of industries and should be placed at the center of our DSS. In our approach, SA system will take advantage of the human factor thanks to the reinforcement signal whereas previous work on this field focus on improving knowledge level of DSS at first and then, uses the human factor only for decision-making. In this paper, we propose a situational awareness-centric decision-support system framework for mission-critical operations driven by Quality of Experience (QoE). Our idea is inspired by the reinforcement learning feedback process which updates the environment understanding of our DSS. The feedback is injected by a QoE built on user perception. Our approach will allow our DSS to evolve according to the context with an up-to-date SA.
Human–Object Interaction Detection with Ratio-Transformer

Human–object interaction (HOI) is a human-centered object detection task that aims to identify the interactions between persons and objects in an image. Previous end-to-end methods have used the attention mechanism of a transformer to spontaneously identify the associations between persons and objects in an image, which effectively improved detection accuracy; however, a transformer can increase computational demands and slow down detection processes. In addition, the end-to-end method can result in asymmetry between foreground and background information. The foreground data may be significantly less than the background data, while the latter consumes more computational resources without significantly improving detection accuracy. Therefore, we proposed an input-controlled transformer, “ratio-transformer” to solve an HOI task, which could not only limit the amount of information in the input transformer by setting a sampling ratio, but also significantly reduced the computational demands while ensuring detection accuracy. The ratio-transformer consisted of a sampling module and a transformer network. The sampling module divided the input feature map into foreground versus background features. The irrelevant background features were a pooling sampler, which were then fused with the foreground features as input data for the transformer. As a result, the valid data input into the Transformer network remained constant, while irrelevant information was significantly reduced, which maintained the foreground and background information symmetry. The proposed network was able to learn the feature information of the target itself and the association features between persons and objects so it could query to obtain the complete HOI interaction triplet. The experiments on the VCOCO dataset showed that the proposed method reduced the computational demand of the transformer by 57% without any loss of accuracy, as compared to other current HOI methods.
Research on the Current Situation and Strategy of “Internet Plus” Online Chinese Education

The era of Industry 4.0 brings about qualitative changes to the economy and society as well as poses new challenges to Chinese education, personnel training, and future development. It is the responsibility of Chinese educational institutions, researchers, government departments, and enterprises to adapt to the changes in science and technology, the market, and the society; to meet the needs of teachers, parents, and students; to understand the current situation of online Chinese education; to connect with the outside world; and ultimately to create a connection with a broader ecosystem of Chinese education to meet the learning needs of Chinese learners around the world.
An Efficient Revocable Identity-Based Encryption with Equality Test Scheme for the Wireless Body Area Network

With the rapid development and popularization of cloud computing, people are willing to upload their own data to the cloud to enjoy the services. However, some personal and private data are not suitable for uploading directly to the cloud. Therefore, these data must be encrypted before uploading to the cloud to ensure the confidentiality. To achieve the confidentiality of data and enjoy cloud services, a notion of identity-based encryption with equality test (IBEET) was proposed. Using IBEET, two ciphertexts encrypted under different public keys can be tested to confirm whether they contain the same plaintext. The equality test can be applied to the wireless body area network system in which the cloud can utilize ciphertexts from patients and medical institutions to perform equality tests to determine whether which patient’s status is abnormal. Indeed, revoking illegal or expired users on any cryptosystem is an important issue. To the best of our knowledge, there is little research on the design mechanism of user revocation in the IBEET. In this paper, we propose a novel notion of revocable identity-based encryption with an equality test, called RIBEET. Based on the notion, we present the first RIBEET scheme. Meanwhile, the proposed scheme will be proven to be secure under the bilinear Diffie-Hellman (BDH) assumption.
Integrated 3d flow-based multi-atlas brain structure segmentation

MRI brain structure segmentation plays an important role in neuroimaging studies. Existing methods either spend much CPU time, require considerable annotated data, or fail in segmenting volumes with large deformation. In this paper, we develop a novel multi-atlas-based algorithm for 3D MRI brain structure segmentation. It consists of three modules: registration, atlas selection and label fusion. Both registration and label fusion leverage an integrated flow based on grayscale and SIFT features. We introduce an effective and efficient strategy for atlas selection by employing the accompanying energy generated in the registration step. A 3D sequential belief propagation method and a 3D coarse-to-fine flow matching approach are developed in both registration and label fusion modules. The proposed method is evaluated on five public datasets. The results show that it has the best performance in almost all the settings compared to competitive methods such as ANTs, Elastix, Learning to Rank and Joint Label Fusion. Moreover, our registration method is more than 7 times as efficient as that of ANTs SyN, while our label transfer method is 18 times faster than Joint Label Fusion in CPU time. The results on the ADNI dataset demonstrate that our method is applicable to image pairs that require a significant transformation in registration. The performance on a composite dataset suggests that our method succeeds in a cross-modality manner. The results of this study show that the integrated 3D flow-based method is effective and efficient for brain structure segmentation. It also demonstrates the power of SIFT features, multi-atlas segmentation and classical machine learning algorithms for a medical image analysis task. The experimental results on public datasets show the proposed method's potential for general applicability in various brain structures and settings.
LTPS - Service for long-term preservation of digital signatures

Digital transformation of public or private services, although it brings many improvements for our societies, it also raises security issues, mainly when focusing on the migration of physical signed documents in the digital environment. Digital signatures have proven to be reliable, thus they have seen a higher adoption in the last few years, especially in the context of the Covid-19 pandemic. Yet, a disadvantage is that digital signatures are volatile, being valid only for a maximum of a few years, depending on the validity of the asymmetric private key used to create that digital signature. Thus, validating a signed document after a few years could prove to be troublesome. In order to solve this problem, legislative and standardization efforts have been made and the legal and technical frameworks for long-term preservation services have been issued. In this paper we focus on the presentation of our implementation of a long-term preservation service for digital signatures, that is part of an entire long-term preservation system composed of multiple services, as mentioned in the paper. Our implementation is compliant with ETSI standards and uses the capabilities of current PKI infrastructures, while also adding in the resilience and trust of the blockchain.