Reviewerly/exHarmony huggingface dataset:
Visual and quantitative evaluation of microcalcifications in mammograms with deep learning-based super-resolution

To evaluate visually and quantitatively the performance of a deep-learning-based super-resolution (SR) model for microcalcifications in digital mammography.Mammograms were consecutively collected from 5080 patients who underwent breast cancer screening from January 2015 to March 2017. Of these, 93 patients (136 breasts, mean age, 50 ± 7 years) had microcalcifications in their breasts on mammograms. We applied an artificial intelligence model known as a fast SR convolutional neural network to the mammograms. SR and original mammograms were visually evaluated by four breast radiologists using a 5-point scale (1: original mammograms are strongly preferred, 5: SR mammograms are strongly preferred) for the detection, diagnostic quality, contrast, sharpness, and noise of microcalcifications. Mammograms were quantitatively evaluated using a perception-based image-quality evaluator (PIQE).All radiologists rated the SR mammograms better than the original ones in terms of detection, diagnostic quality, contrast, and sharpness of microcalcifications. These ratings were significantly different according to the Wilcoxon signed-rank test (p <.001), while the noise score of the three radiologists was significantly lower (p <.001). According to PIQE, SR mammograms were rated better than the original mammograms, showing a significant difference by paired t-test (p <.001).An SR model based on deep learning can improve the visibility of microcalcifications in mammography and help detect and diagnose them in mammograms.
Comparative Analysis Between Macro and Micro-Accuracy in Imbalance Dataset for Movie Review Classification

Classification for multi-class dataset provides exciting and explorative domain to be studied in data science domain. And yet, the challenges of measuring the accuracy of multi-class performance rise an issue worth detailed research to be explored. Due to multi-class accuracy may be lower due to imbalance dataset, this paper aimed to analyze the usage of macro and micro-accuracy in classifying text data with multi-class label. This research focused on text data of movie reviews being classified by three multi-class classifier which are Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF). We set five performance measure to be analyzed; recall, precision, f-score, sensitivity and specificity with regards of micro and macro-accuracy. We successfully yielded a significant result of comparative analysis where average micro-accuracy (87.3%) produced 14.8% higher than macro-accuracy (72.5%) for imbalance dataset. Result also shown a significant gap between balanced and imbalanced dataset. For further analysis, the flexibility of class label in multi-class may be studied to obtain the changing of learning behavior of the classifier as future work.
Intracranial vessel wall segmentation with deep learning using a novel tiered loss function incorporating class inclusion

Abstract Purpose To develop an automated vessel wall segmentation method on T1‐weighted intracranial vessel wall magnetic resonance images, with a focus on modeling the inclusion relation between the inner and outer boundaries of the vessel wall. Methods We propose a novel method that estimates the inner and outer vessel wall boundaries simultaneously, using a network with a single output channel resembling the level‐set function height. The network is driven by a unique tiered loss that accounts for data fidelity of the lumen and vessel wall classes and a length regularization to encourage boundary smoothness. Results Implemented with a 2.5D UNet with a ResNet backbone, the proposed method achieved Dice similarity coefficients (DSC) in 2D of 0.925 ± 0.048, 0.786 ± 0.084, Hausdorff distance (HD) of 0.286 ± 0.436, 0.345 ± 0.419 mm, and mean surface distance (MSD) of 0.083 ± 0.037 and 0.103 ± 0.032 mm for the lumen and vessel wall, respectively, on a test set; compared favorably to a baseline UNet model that achieved DSC 0.924 ± 0.047, 0.794 ± 0.082, HD 0.298 ± 0.477, 0.394 ± 0.431 mm, and MSD 0.087 ± 0.056, 0.119 ± 0.059 mm. Our vessel wall segmentation method achieved substantial improvement in morphological integrity and accuracy compared to benchmark methods. Conclusions The proposed method provides a systematic approach to model the inclusion morphology and incorporate it into an optimization infrastructure. It can be applied to any application where inclusion exists among a (sub)set of classes to be segmented. Improved feasibility in result morphology promises better support for clinical quantification and decision.
Birds of a Feather Flock Together: Generating Pornographic and Gambling Domain Names Based on Character Composition Similarity

Cybercriminals often register many pornographic or gambling domains (known as abusive domains) with similar character compositions in bulk to reduce their investment in buying domains and make it easier for clients to remember and spread them. Therefore, this study combines the ideas of text similarity and text generation and proposes an abusive domain generation model based on GRU for rapidly generating new abusive domain names from known ones. Additionally, we develop a two-layer detection system for pornography and gambling domains using fastText and CNN models to obtain an abusive domain dataset for model training and validation. In the end, our detection system identifies pornographic and gambling domains with 99% precision while balancing correctness and speed. By inputting 40,000 random keywords into the abusive domain generation model, we obtained 130,220 online domains that served web pages, of which about 66% were pornographic or gambling domains. The results show that by exploiting cybercriminals’ behaviors in registering abusive domain names, such as bulk registration of similar domain names, we can prospectively acquire a large number of new abusive domains based on known ones. This study demonstrates that predicting new abusive domains not only expands the domain blacklist but also allows researchers to target the generated suspicious domains and dispose of them in time before they show abusive behavior.
User-Centric Blockchain for Industry 5.0 Applications

The forthcoming Industry 5.0 era reinforces the role of the “human( user )-centric” approach for future technologies, services and applications, where the key objective is to satisfy the quality of service (QoS), security and privacy requirements of each individual user. To meet this objective, blockchain is considered as one of the prime enablers, as it allows secure, reliable, verifiable, and transparent management of private user data. However, contemporary blockchains are not suitable for Industry 5.0 applications due to their inability to ensure high throughput while maintaining reasonable security levels. Hence, in this paper, we design a user-centric sharded blockchain that enables generating, verifying, and storing blocks of data related to individual users with the aim to satisfy their QoS, security and privacy requirements. By analyzing the impact of user allocations to shards on the block period, we devise the user-shard allocation algorithm to minimize the block period or, equivalently, maximize the system throughput, and demonstrate the superior performance of our framework via simulations.
Gender Dimension of the European Union’s Communication Ecology Problems in High-Technology Sectors

The article sheds light on the representation of women in the EU STEM education in 2021 in general and information and communication technologies education in particular via the lenses of analysis of social prejudices and negative communication practices. Modern social priorities substantiate the special focus on the communicative environment. Stereotypical approaches to a women's place in the EU society significantly affect their involvement in STEM. The authors suggest that relatively low participation of women in this area is due to educational prejudices. The influence of stereotypes about the weak potential of women in STEM leads to the refusal of girls and their parents to choose this profile in education. The article discloses problems connected with female involvement in the STEM education using quantitative indicators, the study of environmental principles of communication including social experience, empathetic behavior and emotional intelligence.
Application of Smooth Fuzzy Model in Image Denoising and Edge Detection

In this paper, the bounded variation property of fuzzy models with smooth compositions have been studied, and they have been compared with the standard fuzzy composition (e.g., min–max). Moreover, the contribution of the bounded variation of the smooth fuzzy model for the noise removal and edge preservation of the digital images has been investigated. Different simulations on the test images have been employed to verify the results. The performance index related to the detected edges of the smooth fuzzy models in the presence of both Gaussian and Impulse (also known as salt-and-pepper noise) noises of different densities has been found to be higher than the standard well-known fuzzy models (e.g., min–max composition), which demonstrates the efficiency of smooth compositions in comparison to the standard composition.
Recognition of Hand Gesture Using Electromyography Signal: Human-Robot Interaction

Recognition of hand gestures has been developed in various research domains and proven to have significant benefits in improving the necessity of human-robot interaction (HRI). The introduction of intelligent statistics knowledge methodologies, such as big data and machine learning, has ushered in a new era of data science and made it easier to classify hand motions accurately using electromyography (EMG) signals. However, the collecting and labelling of the vast dataset enforces a significant workload; resulting in implementations takes a long time. As a result, a unique strategy for combining the advantages of depth vision learning with EMG-based hand gesture detection was developed. It is accomplished of automatically categorizing the class of the obtained EMG data using ensemble learning without considering the hand motion sequence. The models were built and interpreted using the SVM with RBF kernel, Random Forest, and Catboost with the best hyperparameters. The resultant value states that Catboost produces the best accuracy of around 0.95 as compared with other models. This demonstrates that the suggested technique can recognize hand gestures with better performance rate.
Color Image Feature Matching Method Based on the Improved Firework Algorithm

An improved ORB feature point purification method has been proposed to address the problem of large feature point matching error and low image registration rate in the oriented FAST and rotated BRIEF (ORB) feature point color image matching algorithm. Pure virtual quaternions were used to represent the color image pixels in this method, and an improved FAST algorithm was used to detect the color feature points at first. The firework algorithm has been used to divide the detected feature points into key areas, auxiliary areas, and small influence areas, depending on the degree of attachment. The feature points of the key area and the subsidiary area are the required feature points. In order to further improve the efficiency of matching the color image feature points, the firework explosion radius formula and the explosion number formula in the firework algorithm have been improved, and an improved firework algorithm is proposed. This improved algorithm purifies the quaternion-represented color image feature points. For feature matching, the hamming distance was used. Experiments show that, when compared to the traditional ORB algorithm, the improved algorithm retains the key feature points of the color image with high accuracy, removes a large number of irrelevant feature points and noise points, and provides significantly higher accuracy and efficiency of color image matching.
Analysis on Image Compression for Multimedia Communication Using Hybrid of DWT and DCT

Image compression is a technique to compress information that converts the original image with limited bits and reconstructs it with some distortion. This compression leads to native disc memory saving; conjointly, it helps in speedy multimedia system files transfer. This analysis presents a projected technique for the compression of multimedia system pictures victimization hybrid compression technique (discrete trigonometric function remodel and separate moving ridge transform). DWT and DCT Area unit applied initial on individual RGB parts. Once applying this, image is amount for every distinctive amount. Therefore, on verifying the distinctive code for every distinctive image for his or her cryptography, the standard of the new image compression technique is evaluated victimization the height signal to noise magnitude relation (PSNR) through simulations victimization MATLAB. The simulation results show that the projected JPEG codec delivers increased performance over the standard JPEG code in terms of PSNR.
English Teaching Quality Evaluation Based on Analytic Hierarchy Process and Fuzzy Decision Tree Algorithm

China's economy has become more globalized in the twenty-first century. English has become the primary means of communication and trade between people and other countries as the economy has rapidly developed. As a result, colleges and universities have increased the English proficiency and teaching quality criteria for college students. The success of college instructors' English instruction is judged using teaching quality as a primary metric. The procedure of assessing the quality of English instruction is complicated. Using appropriate assessment indexes and procedures, a scientific and sensible evaluation system for English education should be developed. This study uses the AHP and fuzzy decision tree algorithms to investigate the quality of teaching (preferably English) and gives teaching evaluation data to university administrators, which is useful to enhance the quality of teaching (preferably English) in colleges. First, a hierarchical analysis-based evaluation index system (EIS) for English teaching is established, followed by a detailed description of the decision tree algorithm's calculation process and the application of the fuzzy decision tree algorithm to the evaluation of teaching (preferably English) quality. Finally, the practical application effect of the algorithm is tested. The results show that the accuracy of this algorithm is higher than other algorithms, and it is helpful to improve the efficiency of evaluating English teaching quality.
A Reinforcement Learning Approach to Speech Coding

Speech coding is an essential technology for digital cellular communications, voice over IP, and video conferencing systems. For more than 25 years, the main approach to speech coding for these applications has been block-based analysis-by-synthesis linear predictive coding. An alternative approach that has been less successful is sample-by-sample tree coding of speech. We reformulate this latter approach as a multistage reinforcement learning problem with L step lookahead that incorporates exploration and exploitation to adapt model parameters and to control the speech analysis/synthesis process on a sample-by-sample basis. The minimization of the spectrally shaped reconstruction error to finite depth manages complexity and serves as an effective stand in for the overall subjective evaluation of reconstructed speech quality and intelligibility. Different control policies that attempt to persistently excite the system states and that encourage exploration are studied and evaluated. The resulting methods produce reconstructed speech quality competitive with the most popular speech codec utilized today. This new reinforcement learning formulation provides new insights and opens up new directions for system design and performance improvement.
Multi-hop quantum operation teleportation via different high-level partially entangled channels

Abstract To implement remote control operations in quantum networks, two schemes for the multi-hop quantum operation teleportation are proposed by using different partially entangled states. One is to adopt high-level partially Bell states as quantum channels, the other is to use high-level partially GHZ-Bell states. By comparing the two schemes, it is found that although both schemes have the same communication delay, the latter has less classical information cost and higher success probability of the communication.
Construction of International Education Talents Training Mechanism Based on Data Fusion Algorithm

With the increasing trend of economic globalization and the continuous evolution of economic forms, the role of science and technology in promoting the development of human civilization has become more and more obvious. With the improvement of comprehensive national strength, the knowledge economy has brought new opportunities and challenges. With the increasing internationalization of higher education, it has not only driven the economic exchanges between countries but also led to the exchange of educational resources among transnational countries. However, China’s international education talent cultivation started late, with poor teaching awareness, imperfect discipline construction, and low quality of international education talents. A series of problems have seriously hindered the establishment of the talent training mechanism. In order to establish a reasonable and perfect talent training mechanism, optimize the international education talent training mechanism, make the international education talent training mechanism keep up with the development trend of science, technology, and education in the contemporary world, promote the internationalization of international education talent training, and strengthen the exchange of educational resources among countries. This paper uses various algorithms of fusion algorithms to accurately calculate the talent data. The algorithm results show that the optimized grouped quadratic combined Kalman filter has a shorter running time than the traditional Kalman filter, which decreased about 20%. The S-LEACH algorithm outperforms the traditional algorithm in the simulation results of the number of surviving nodes, relative energy consumption, and data transmission. In response to the national strategy of rejuvenating the country through science and education, cultivating international talents and building a modern talent training mechanism are particularly important.
A cooperative LiDAR-camera scheme for extrinsic calibration

In this paper, a method for LiDAR-camera extrinsic calibration from multiple static scenes is proposed, using a simple design for calibration target with an ArUco marker. Towards this end, a novel LiDAR-camera cooperative scheme is employed. At first, the camera-based detection of the marker guides a processing of the LiDAR point cloud to detect the 3D marker in it. Once the marker has been accurately localized in the LiDAR point cloud, further correction takes place regarding the pose estimation of the marker. In this way, the advantages of each sensor are used to improve marker localization. The improved accuracy achieved in the computation of the extrinsic calibration parameters has been experimentally shown in both quantitative and qualitative terms.
Federated Dictionary Learning from Non-IID Data

The problem of learning a common dictionary by following a federated learning framework, in a network where each edge user may have statistically different data, is considered. In such a challenging setting, the Federated Averaging solution is shown to exhibit poor performance. To alleviate the drawbacks of this approach, two more elaborate schemes are proposed. The new schemes are designed so as to offer increased performance in the case of non-i.i.d. data, that is the focus in this work. Extensive simulation results are presented for an image processing scenario, that demonstrate the effectiveness of the proposed methods.
FLAD

Widely adopted digital cameras and smartphones have generated a large number of videos, which have brought a tremendous workload to video editors. Recently, a variety of automatic/semi-automatic video editing methods have been proposed to tackle this issue in some specific areas. However, for the production of meeting recordings, the existing studies highly depend on additional conditions of conference venues, like infrared camera or special microphone, which are not practical. Moreover, current video quality assessment works mainly focus on the quality loss after compression or encoding rather than the human-centered video content flaws. In this paper, we design and implement FLAD, a human-centered video content flaw detection system for meeting recordings, which could build a bridge between subjective sense and objective measures from a human-centered perspective. The experimental results illustrate the proposed algorithms could achieve the state-of-the-art video content flaw detection performance for meeting recordings.
Applying VertexShuffle toward 360-degree video super-resolution

With the recent successes of deep learning models, the performance of 2D image super-resolution has improved significantly. Inspired by recent state-of-the-art 2D super-resolution models and spherical CNNs, in this paper, we design a novel spherical superresolution (SSR) approach for 360-degree videos. To address the bandwidth waste problem associated with 360-degree video transmission/streaming and save computation, we propose the Focused Icosahedral Mesh to represent a small area on the sphere and construct matrices to rotate spherical content to the focused mesh area. We also propose a novel VertexShuffle operation on the mesh, motivated by the 2D PixelShuffle operation. We compare our SSR approach with state-of-the-art 2D super-resolution models. We show that SSR has the potential to achieve significant benefits when applied to spherical signals.
Power-efficient live virtual reality streaming using edge offloading

This paper aims to address the significant power challenges in live virtual reality (VR) streaming (a.k.a., 360-degree video streaming), where the VR view rendering and the advanced deep learning operations (e.g., super-resolution) consume a considerable amount of power draining the battery-constrained VR headset. We develop EdgeVR, a power optimization technique for live VR streaming, which offloads the on-device VR rendering and deep learning operations to an edge server for power savings. To address the significantly increased motion-to-photon (MtoP) latency due to the edge offloading, we develop a live VR viewport prediction method to pre-render the VR views on the edge server and compensate for the round-trip delays. We evaluate the effectiveness of EdgeVR using an end-to-end live VR streaming system with an empirical VR head movement dataset involving 48 users watching 9 VR videos. The results reveal that EdgeVR achieves power-efficient live VR streaming with low MtoP latency.
Exploring non-verbal cues and user attention in IVR with eye tracking technologies

This study is a between-groups preliminary evaluation of how Non-Verbal Cues (NVCs) impact participant attention and degree of social presence with an NPC. Participants are divided in two groups and witness an agent's monologue who features high-fidelity NVCs (motion-captured gaze, blinking, and lower facial expressions) in one group, but who has those "turned off" for the other. This study aims at establishing appropriate data collection methodology and scenario-related guidelines for follow-up experimentation with a higher volume of interaction variables, particularly real-time interaction between remotely located users. Initial results indicate that real-time tracked NVCs enhance engagement and social presence to variant degrees compared to low-fidelity automated cues.