Reviewerly/exHarmony huggingface dataset:
Crowdsourced Quality Assessment of Enhanced Underwater Images - a Pilot Study

Underwater image enhancement (UIE) is essential for a high-quality underwater optical imaging system. While a number of UIE algorithms have been proposed in recent years, there is little study on image quality assessment (IQA) of enhanced underwater images. In this paper, we conduct the first crowdsourced subjective IQA study on enhanced underwater images. We chose ten state-of-the-art UIE algorithms and applied them to yield enhanced images from an underwater image benchmark. Their latent quality scales were reconstructed from pair comparison. We demonstrate that the existing IQA metrics are not suitable for assessing the perceived quality of enhanced underwater images. In addition, the overall performance of 10 UIE algorithms on the benchmark is ranked by the newly proposed simulated pair comparison of the methods.
Adaptive Multi-branch Feature Fusion Attention Network for Few-Shot Image Classification

Learning from the limited labeled examples, denoted as few-shot, has been successfully applied into the image detection and classification. However, their ability to learn new concepts quickly is limited. Meta-learning is one approach to address this issue, by enabling the network to learn how to learn. However, most of metric-based meta-learning few-shot models low recognition accuracy and due to the large difference in data distribution in few-shot learning, the importance of model feature extraction is more reflected. Especially, some existing methods only utilize high-level semantic feature, thus ignore the underlying information that is essential for few-shot learning. In addition, these methods adopted the fixed distance functions for feature measurement, thus they cannot fully integrate features. To solve these problems, an end-to-end, few-shot classification model (AMBR) with the adaptive multi-branch feature fusion attention is proposed in this paper. The multi-path feature residual module is designed to extract and fuse the features for making full use of the underlying information. Then, while utilizing the metric module to learn the metric between the support set and query set features, the attention module is employed to enhance the learned features. Finally, a non-linear classifier is proposed for image classification. We evaluate our method on two benchmark datasets, namely Omniglot and Mini-imagenet. Experimental results demonstrate our proposed AMBR algorithm achieves the excellent performance in these two datasets through a shallow feature extraction network, and is superior to the state-of-the-art.
The Energy Potential Assessment for a Road Power Plant with the Slider-Rocker Converter

According to Rosavtostat, poor visibility is the main cause of traffic accidents involving pedestrians. It is hard for drivers to notice pedestrians and pedestrian crossings at dusk and after dark even if there is a road sign. Currently, autonomous wind-and-solar plants are used to increase traffic safety. That being said, the global research community is studying the prospects of using road power plants in practice. The operating principle of such plants is converting the motion energy of cars into elec-tricity. The goal of this research is to determine the forces applied to the road power plant mecha-nisms when a car passes through it. The forces occurring in the mechanical components converting the energy of a passing car into electricity were determined during the force study of the road power plant. The calculations showed that a passage of an average car weighing 1600 kg through the road power plant creates a torque effect of 393 Nm on the generator shaft. Simultaneously, a moment of resistance occurs in the generator whose value depends on the speed of the passing car: the moment of resistance equals 115 Nm at 20 kmph, 260 Nm at 30 kmph, 390 Nm at 40 kmph. The amount of energy accumulated by the flywheel is proportional to the speed value, and it takes the following values: 94 J at 20 kmph, 210 J at 30 kmph, and 330 J at 40 kmph. The calculation data show that the road power plant is effective at the set parameters of struc-tural components.
Video Encoding Complexity Characterization

Spatial/temporal information (SI/TI) is used to characterize the scene-complexity of videos. It is often hypothesized that SI/TI can also be used to measure the encoding-complexity of a video; although higher scene-complexity may not always mean a higher encoding-complexity for videos. In this paper, two alternative views on the video encoding-complexity are proposed. In the first view, a ground-truth of encoding-complexity is derived using the area-under-the-curve of a rate-quality plot. In the second view, constant rate-factor (CRF) video encoding is used to synthesize a measure of video encoding-complexity. Experimental evaluation on 734 UHD video sequences showed that the CRF based method, compared to SI/TI, yields better complexity characterization w.r.t. the ground-truth.
MP<sup>2</sup>-Method: A Visual Analysis Method for Material Performance Prediction Supporting Model Selection

With the advent of Industry 4.0, the traditional way of fluorine material research by adjusting parameters through multiple trials can no longer meet the demand of multi-source heterogeneous and large amount of complex data processing and analysis, the visual analysis technology based on machine learning models brings opportunities for material science research. However, due to the high confidentiality of fluorine material data, it is not convenient for factories to trust the data to third-party professionals for processing and analysis. Therefore, this paper introduces a visual analysis method for material performance prediction supporting model selection, MP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Method, for different data sets, which supports researchers’ autonomous selection and comparison of different levels of prediction models, and enables performance prediction of fluorine materials using visual analysis by adjusting control parameters. In addition, the method introduces uncertainty visual analysis to reduce the uncertainty of the control parameter data and improve the prediction accuracy. Finally, the usefulness and reliability of the MP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Method is demonstrated by case studies and interviews with domain experts.
A Novel Gradient Accumulation Method for Calibration of Named Entity Recognition Models

The adoption of deep learning models has brought significant performance improvements across several research fields, such as computer vision and natural language processing. However, their “black-box” nature yields the downside of poor explainability: in particular, several real-world applications require - to varying extents - reliable confidence scores associated to a model's prediction. The relation between a model's accuracy and confidence is typically referred to as calibration. In this work, we propose a novel calibration method based on gradient accumulation in conjunction with existing loss regularization techniques. Our experiments on the Named Entity Recognition task show an improvement of the performance/calibration ratio compared to the current methods.
SV-DEMR: An Electronic Medical Record Data Sharing Scheme Based on Searchable and Verifiable Encryption via Consortium Blockchain

Nowadays, electronic medical records(EMR) have became a new type of digital medical service instead of handwritten paper medical records. However, due to different medical institutions belong to different trustworthy domains, there exites barriers to health data exchang, interoperability, and so on. Considering that it was difficult to share medical record data among different medical institutions, we propose an blockchain based EMR data sharing scheme with searchable and verifiable encryption, i.e. SV-DEMR. In SV-DEMR, we propose a hybrid chain which includes consortium chain and private chain. To acheive the secure storage and sharing of EMR, we use the medical institution server to store the EMR ciphertext, the private chain to store the ciphertext hash value, and the consortium chain to share the ciphertext and key parameters. Moreover, we also propose a CP-ABE algorithm that ciphertext searchable and identity verifiable to realize access control and guarantee authenticity of data and efficient index of ciphertext on the blockchain, named SV-CPABE. Finally, the security of our SV-DEMR is proved theoretically. The extensive experiments are made to demonstrate the better performance in SV-DEMR.
Reducing Revocation Latency in IoV using Edge Computing and Permissioned Blockchain

In Internet of Vehicles (IoV), authentication technology provides a basic security means to achieve trusted communication between legitimate vehicles. Revocation checking for vehicle certificates is an indispensable procedure in the process of authentication to protect vehicular networks from attacks by non-legitimate vehicles. However, revocation checking introduces procedures that require additional time to process which challenges latency-sensitive applications in vehicular networks. This challenge grows more evidently if taking into consideration the factor of privacy preservation. In this paper, we propose to offload partial revocation tasks to network edges to lighten the revocation process in vehicles. Particularly, we design a dual-certificates model for the revocation offloading process and employ blockchain to achieve decentralized Certificate Revocation List (CRL) management. Finally, we build a prototype of our proposed solution based on Hyperledger Fabric using permission blockchain, and compare it with Proof-of-Work scheme in terms of blockchain synchronization latency performance.
A Secure and Efficient Fine-Grained Deletion Approach over Encrypted Data

Documents are a common method of storing infor-mation and one of the most conventional forms of expression of ideas. Cloud servers store a user's documents with thousands of other users in place of physical storage devices. Indexes corresponding to the documents are also stored at the cloud server to enable the users to retrieve documents of their interest. The index includes keywords, document identities in which the keywords appear, along with Term Frequency-Inverse Document Frequency (TF-IDF) values which reflect the keywords' relevance scores of the dataset. Currently, there are no efficient methods to delete keywords from millions of documents over cloud servers while avoiding any compromise to the user's privacy. Most of the existing approaches use algorithms that divide a bigger problem into sub-problems and then combine them like divide and conquer problems. These approaches don't focus entirely on fine-grained deletion. This work is focused on achieving fine-grained deletion of keywords by keeping the size of the TF-IDF matrix constant after processing the deletion query, which comprises of keywords to be deleted. The experimental results of the proposed approach confirm that the precision of ranked search still remains very high after deletion without recalculation of the TF-IDF matrix.
Numerical Investigation of Short Pulse Propagation in a Semiconductor Optical Amplifier for Application to Optical Reservoir Computing

The propagations of three similar pulses through a gain-saturated SOA were numerically investigated. They showed large difference in power waveforms at the output of SOA. The results imply its possibility of application to reservoir computing.
Transferring Facial Expressions to Digital Character

In computer graphics, any attempt to realistically visualize movement presents a major challenge. This is especially true for the visualization of facial movements because of a large number of muscles on the face that are responsible for micro mimicry on which the realism of facial movements depends. The paper presents a low-cost end to end system for estimating and transmitting human facial expressions to a digital character in real time. It comprises a station for analysis and processing of data, in which detected facial features obtained by recording a person with a camera are processed, as well as a station for those displaying movements on a digital character.
AI Driven User Authentication

As our society is becoming increasingly more mobile and digitally connected, the need for accurately identifying an individual and to prevent the problem of identity theft is of paramount importance. How can we trust that an individual is whom he or she professed to be? As it turned out, user authentication is a vast research area involving computer vision and AI. It also involved a huge amount of data processing. There are multiple digital metrics about an individual that are being continuously extracted and evaluated to cross check the person’s identity. This chapter provides an introduction into the authentication technology that employs some of the latest in AI technology in order to help us establish trust in a modern digital society.
Cyberbullying Vs Freedom of Speech and Right to Privacy

The purpose of this article is to examine the dispute among cyber bulling and freedom of speech and right to privacy. The study highlights the law relating to freedom of speech , cyberbullying , right to privacy and restrictions that are imposed .This study elucidates the most recent scholarly contributions by employing a systematic literature review method. The findings of this research reveal that reform of laws are required to combat cyber bulling in Sri Lanka inorder to safeguard freedom of speech and Right to Privacy. In terms of originality, to the best of the authorâ€™s knowledge, this is the first research article that explores the use of branding and trademark system in the tourism sector in Sri Lanka.
Research on Robustness of Network Security Vulnerability Detection Algorithm based on Recurrent Bayesian Networks

Aiming at the problems of low detection accuracy and poor robustness of the existing zero-speed detection methods, an effective gait cycle segmentation method is adopted and a Bayesian network model based on inertial sensor measurements and kinematics knowledge is introduced to infer the zero-speed interval; The method can effectively reduce the ambiguity of the zero velocity (ZV) boundary. Support vector machine has the advantages of versatility, simple calculation, high operation efficiency and perfect theory. It is a relatively mature and efficient algorithm in the current network security situation algorithm. And a looped Bayesian network model for probabilistic safety assessment of simple feedback control systems is established.
STEM-EDUCATION AS A BASIS FOR IMPROVING THE QUALITY OF NATURAL EDUCATION IN THE CONDITIONS OF THE NEW UKRAINIAN SCHOOL

The article considers the problem and prospects of introducing the use of STEM-education as one of the main factors in improving the quality of natural education. Today, the ideas of the STEM approach to learning are supported by many educational systems around the world. Thus, according to the Concept of the New Ukrainian School, a school graduate must be an individual, a patriot and an innovator. To analyze the potential and degree of implementation of STEM education, we conducted a survey in September–November 2021, by surveying 99 teachers. Questionnaire for teachers, consisted of 2 thematic sections. The first thematic section included questions about the general information of the interviewer and his/her work environment. The second section included questions about STEM education and its impact on the interest of students in the learning process; comprehensive knowledge of natural processes and phenomena by students; for the development of general competencies by students, etc. The analysis of the questionnaires allows the authors to form two general trends of the study, the first one in the powerful potential of STEM technology to improve the quality of natural education. Thus, 92.2% of surveyed teachers believe that the involvement of STEM education will increase interest among students in natural sciences. The second trend is characterized by the slow accumulation of STEM didactics and teaching methodics. Only 18.18% of teachers were satisfied with the equipment of their classroom. Thus, seeing the potential of STEM-education in the promotion of natural education we can predict a significant expansion of STEM-didactics and the introduction of STEM-laboratories in the implementation of the Concept of the New Ukrainian School, which will further positively affect the quality of natural education.
Tor vs Yggdrasil: A Comparative Study of Two Different Communication System

The Internet is one of the most reliable sources of communication in today’s world, but the identities of senders and receivers are not completely secure. The most suitable and widely used choice is to use communication method which has a strong encryption for someone to hide the identity. In this paper, we analyze and compare the most widely used communication system, i.e., Tor, alongside Yggdrasil Network, the most advanced and decentralized communication system. Tor is the most used anonymous communication system worldwide, while Yggdrasil was created to be a future-proof decentralized anonymous alternative routing system.
A Node-labeling-based Method for Evaluating the Anonymity of Tor Network

In order to better evaluate the anonymity of the Tor network, this paper evaluates the degree of the Tor network anonymity by calculating the probability of routing links being hijacked when users communicate under different node labels. According to the official routing node algorithm, we discuss the results of link establishment from the perspective of node labeling separately and calculate the hijacked rate of different links based on the established evaluation model. The experimental results based on the real data of nodes provided by the official CollecTor website show that the Tor network can resist the intrusion of 10% of malicious nodes, and the anonymity of the Tor network decreases rapidly when the malicious nodes in the routed nodes exceed 20%. In a collection of nodes with different labels, nodes with exit labels have a greater impact on system anonymity when subjected to the same level of attack.
Trolley Remote Control System Using Eye Motion Tracking

In this paper, a trolley remote control system based on eye tracking technology is proposed that controls the direction of the trolley's movement according to the gaze direction of the human eye. The system's graphical user interface contains eight arrows pointing in different directions that function as the gaze targets for moving the trolley in the corresponding direction. This paper compares two algorithms for detecting fixation points. One method clusters the gaze position of the human eye recorded by the eye tracker in the preset time window, and the other method directly uses the fixation point determined by the eye tracker's own software to construct a fixation heat map and then perform image processing. Through experiments, it was found that the average target detection accuracy of the system using the clustering algorithm (99.78%) is 3.13% higher than that of the system using the second method and the difference between the performances of the algorithms was found to be significant. The results demonstrate that in the trolley remote control scenario, the use of eye tracking technology with a clustering algorithm can realize efficient and natural human-computer interaction. Moreover, such a system has good sensitivity and stability.
U-CAN: A Convolutional Neural Network Based Intrusion Detection for Controller Area Networks

The Controller area network (CAN) is the most extensively used in-vehicle network. It is set to enable communication between a number of electronic control units (ECU) that are widely found in most modern vehicles. CAN is the de facto in-vehicle network standard due to its error avoidance techniques and similar features, but it is vulnerable to various attacks. In this research, we propose a CAN bus intrusion detection system (IDS) based on convolutional neural networks (CNN). U-CAN is a segmentation model that is trained by monitoring CAN traffic data that are preprocessed using hamming distance and saliency detection algorithm. The model is trained and tested using publicly available datasets of raw and reverse-engineered CAN frames. With an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1} {Score}$</tex> of 0.997, U-CAN can detect DoS, Fuzzy, spoofing gear, and spoofing RPM attacks of the publicly available raw CAN frames. The model trained on reverse-engineered CAN signals that contain plateau attacks also results in a true positive rate and false-positive rate of 0.971 and 0.998, respectively.
Building Blocks for a Virtual Time Machine

An abstract is usually brief, it must do almost as much work as Time travel is an old dream of mankind that is fueled by fascination and curiosity. Of course, such a journey through time goes far beyond our physical possibilities today. Historical science largely recapitulates the past using text, while the creative industry reproduces it as a more or less well-researched fiction. The Time Machine Initiative has now taken on the task of digitizing and processing the cultural heritage on a large scale in order to create new virtual accesses to the past, which - taking into account the fragmentary tradition - come close to a journey through time. In a large-scale interdisciplinary and trans-European research project, a kind of edition of European history is to be created which, as a data-saturated reconstruction, can create a new form of comprehensibility and experience. The time machine would therefore be a virtual research environment. In order to reach this ambitious goal, digitization and automated analysis of history and art has to be taken one step at a time. In the presentation, we shortly describe the project and present concrete research results that have been obtained in this direction ranging from book CT, i.e., the scanning of a whole book in a single scan, over writer and font identification up to first results on automated art analysis and understanding.
PSINet

RGB-D Salient Object Detection (RGB-D SOD) is a pixel-level dense prediction task that can highlight the prominent object in the scene by combining color information and depth constraints. Attention mechanisms have been widely employed in SOD due to their ability to capture important cues. However, most existing attentions (\textite.g., spatial attention, channel attention, self-attention) mainly exploit the pixel-level attention maps, ignoring the region properties of salient objects. To remedy this issue, we propose a progressive saliency iteration network (PSINet) with a region-wise saliency attention to improve the regional integrity of salient objects in an iterative manner. Specifically, two-stream Swin Transformers are first employed to extract RGB and depth features. Second, a multi-modality alternate and inverse module (AIM) is designed to extract complementary features from RGB-D images in an interleaved manner, which breaks down the barriers of inconsistency existing in the cross-modal data and also sufficiently captures the complementarity. Third, a triple progressive iteration decoder (TPID) is proposed to optimize the salient objects, where a coarse saliency map, generated by integrating multi-scale features with a U-Net, is viewed as region-wise attention maps to construct a region-wise saliency attention module(RSAM), which can emphasize the prominent region of features. Finally, the regional integrity of salient objects can be gradually optimized from coarse to fine by iterating the above steps on TPID. Quantitative and qualitative experiments demonstrate that the proposed model performs favorably against 19 state-of-the-art (SOTA) saliency detectors on five benchmark RGB-D SOD datasets.