Reviewerly/exHarmony huggingface dataset:
Density-Based Clustering by Means of Bridge Point Identification

Density-based clustering focuses on defining clusters consisting of contiguous regions characterized by similar densities of points. Traditional approaches identify core points first, whereas more recent ones initially identify the cluster borders and then propagate cluster labels within the delimited regions. Both strategies encounter issues in presence of multi-density regions or when clusters are characterized by noisy borders. To overcome the above issues, we present a new clustering algorithm that relies on the concept of bridge point. A bridge point is a point whose neighborhood includes points of different clusters. The key idea is to use bridge points, rather than border points, to partition points into clusters. We have proved that a correct bridge point identification yields a cluster separation consistent with the expectation. To correctly identify bridge points in absence of a priori cluster information we leverage an established unsupervised outlier detection algorithm. Specifically, we empirically show that, in most cases, the detected outliers are actually a superset of the bridge point set. Therefore, to define clusters we spread cluster labels like a wildfire until an outlier, acting as a candidate bridge point, is reached. The proposed algorithm performs statistically better than state-of-the-art methods on a large set of benchmark datasets and is particularly robust to the presence of intra-cluster multiple densities and noisy borders.
An Application of Deep Learning YOLOv5 Framework to Intelligent Radio Spectrum Monitoring

In this paper, we propose an intelligent radio spectrum monitoring system based on spectrogram image detection. The system utilizes the You Only Look Once 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> version (YOLOv5) as the framework's core. YOLOv5 is a widely-known, powerful, and efficient deep learning framework for object detection. We use Electrosense devices as the spectrum sensor to collect the dataset for training YOLOv5 model. The spectrum sensor connects to the Electrosense server and retrieves the Signal to Noise Ratio (SNR) to present the spectrogram. The trained YOLOv5 then detects the frequency bands from spectrogram images by bounding boxes. The trained YOLOv5 performance achieves 99.3% precision and 100% recall (sensitivity) on the training dataset. Compared with paper [1], which has 99.6% accuracy, the proposed model seems a little less accurate, but this is an object detection model with more complexity than classification.
Function Grouping &amp; Visualization Through Machine Learning to Aid and Automate Reverse Engineering of Malware

Modern malware analysis is stymied by dependence on the manual components of reverse engineering, which require skilled reverse engineers to perform static analysis. Machine learning and statistical analysis allow for augmentation of static analysis, detection of common benign code in malicious samples, and grouping similar bodies of low-level code. In this work four malware campaigns along with a dataset of known benign executables were utilized to test a process for grouping nearly identical functions to find similarities across executables and identify common code. In addition, those groups were collated to create sets of shared common code which could be used to better understand malware sample variants.
Proposal for Portable Fluid Mechanics Laboratory for Project-based Learning

In engineering education, it is important to improve students’ ability to think and make decisions when solving engineering problems. The measurement and observation of fluid phenomena are useful for this task. However, these activities are difficult to conduct in an educational setting because large equipment is often required. To overcome this problem, we propose and implement a portable fluid mechanics laboratory that consists of inexpensive, safe, and portable visualization and measurement equipment. Many of the components are designed using 3D CAD and can be fabricated using a 3D printer, reducing cost. The fabrication of the portable laboratory and fluid experiments using the laboratory can also be incorporated into project-based learning. Feedback from students suggests that the design and fabrication of the laboratory and the experiments improved their basic engineering skills and skills related to fluid mechanics.
Tree-structured Auxiliary Online Knowledge Distillation

Traditional knowledge distillation adopts a two-stage training process in which a teacher model is pre-trained and then transfers the knowledge to a compact student model. To overcome the limitation, online knowledge distillation is proposed to perform one-stage distillation when the teacher is unavailable. Recent researches on online knowledge distillation mainly focus on the design of the distillation objective, including attention or gate mechanism. Instead, in this work, we focus on the design of the global architecture and propose Tree-Structured Auxiliary online knowledge distillation (TSA), which adds more parallel peers for layers close to the output hierarchically to strengthen the effect of knowledge distillation. Different branches construct different views of the inputs, which can be the source of the knowledge. The hierarchical structure implies that the knowledge transfers from general to task-specific with the growth of the layers. Extensive experiments on 3 computer vision and 4 natural language processing datasets show that our method achieves state-of-the-art performance without bells and whistles. To the best of our knowledge, we are the first to demonstrate the effectiveness of online knowledge distillation for machine translation tasks. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code is available at https://github.com/Linwenye/Tree-Supervised.
Hybrid Confidentiality Framework for Secured Cloud Computing

Cloud computing has emerged as a time changing technology provides variety of applications in shared mode. It provides an anytime, anywhere, anything over the Internet with one-stop service to end users. Open nature and public network make it vulnerable and security prone for several security threats. This paper investigates that single security algorithm may be easy target to compromise communication and intercept messages. Subsequently wide use and heavy traffic also raise concern of overload and performance. To keep the tradeoff between performance and security, a hybrid encryption model has been proposed based on two symmetric key cryptographic techniques. RC6 and Blowfish algorithms with 256 bit key long to create hard secure ecosystem. To associate originality and integrity, it involves MD5 and message digest solution for individual chunk. File chunking system has been implemented as like Google File System to consider large file with high performance. Proposed solution has been implemented using java technology through REST API and deployed on Amazon Web Server -EC2 instance. Performance evolution has been done in terms of computation time, memory complexity for different file size. Experimental analysis concludes that hybrid model enhances the encryption time but symmetric algorithms help to keep tradeoff between security and performance in comparison of asymmetric key cryptographic hybrid model.
GNN-Detective: Efficient Weakly Correlated Neighbors Distinguishing and Processing in GNN

In the field of various downstream tasks of graph learning, graph neural networks (GNNs) have achieved the state-of-the-art (SOTA) performance benefits from its special propagation mechanism. The propagation mechanism aggregates attributes from neighbor nodes to obtain expressive node representations, which is pivotal for achieving SOTA performance in various downstream tasks. However, in most graph datasets, the neighborhood of each node may contain weakly correlated neighbors (WCNs), whose attributes may impair the expressiveness of central node representations. Though efforts have been devoted to solving such problem, they merely focus on aggregating fewer or even subtracting the attributes of WCNs. However, WCNs still share some correlated information with the central node, thus the correlated information provided by WCNs is underutilized. In this work, we devote to leveraging the correlated information provided by WCNs with our proposed method, namely GNN-detective. This detective can efficiently and automatically distinguish WCNs, as well as dig out their correlated information in the graph. It is realized by a semi-supervised learning framework, where the Differential Propagation (DP) module is designed specially for information triage and utilization. This module can fully leverage the correlated information provided by WCNs, and eliminate interference of uncorrelated information. We have conducted semi-supervised node classification tasks on 9 benchmark datasets. Our proposed method is proven to achieve the best performance in processing WCNs. The problems such as over-smoothing and overfitting are also mitigated as evaluated.
Construction and Analysis of Higher Vocational College Teaching Evaluation System Resources Based on Data Mining Function

In order to make an objective evaluation on the teaching quality of the teachers in the course, and promote the teaching reform and the improvement of the teaching quality, a Municipal Public Vocational College in Beijing organizes students'evaluation, supervision and peer evaluation of teaching at the end of each semester. The data entry contains information such as teacher number, faculty, Peer Review, supervised evaluation, student evaluation, and weighted total score, all of which are in the 100-point scale. In order to analyze the difference of teachers'evaluation scores among peers, supervisors and students, the three attribute scores were sorted out and analyzed. Based on the analysis, peer and supervisory assessment scores are more professional than student assessment scores, and can reflect teachers' real teaching performance.
Learned Variational Video Color Propagation

In this paper, we propose a novel method for color propagation that is used to recolor gray-scale videos (e.g. historic movies). Our energy-based model combines deep learning with a variational formulation. At its core, the method optimizes over a set of plausible color proposals that are extracted from motion and semantic feature matches, together with a learned regularizer that resolves color ambiguities by enforcing spatial color smoothness. Our approach allows interpreting intermediate results and to incorporate extensions like using multiple reference frames even after training. We achieve state-of-the-art results on a number of standard benchmark datasets with multiple metrics and also provide convincing results on real historical videos – even though such types of video are not present during training. Moreover, a user evaluation shows that our method propagates initial colors more faithfully and temporally consistent.
SEMACC: Efficient multistep attention crowd counting network based on shuffle module

At present, crowd counting is a hot issue, although there have been many studies. However, most of the research is oriented toward improving detection accuracy, ignoring the constraints of monitoring computing power in practical application scenarios. In this paper, we give three challenges to crowd counting and propose a dense crowd counting network called SEMACC based on a deep neural network. SEMACC refers to the lightweight neural network ShuffleNetV2 to design the backbone network ShuffleUnit, and designs and proposes the lightweight feature extractor EMA module with multi-step attention integration centered on dilated separable convolution. The EMA module can effectively extract the mixed features of the channel, spatial and branch attention. Finally, the Decoder module is used to parse the feature into a crowd density map. The experimental results show that the parameters of SEMACC are only 2.85M, and the average accuracy is 91.3% in ShanghaiTech datasets, and satisfactory accuracy is obtained with less calculation.
WAPITI – Web-based Assignment Preparation and Instruction Tool for Interpreters

This paper proposes a framework to ease the workload of preparation for interpreters through quick and efficient discovery of relevant material. We describe a software architecture and present arguments why this combination of components and functionalities will result in an ideal assignment preparation tool for interpreters, which they currently are lacking. We draw from the rich professional experience from interpretation experts and teaching staff gathered through interviews and feedback over an extended period of time. We use this experience to add functionalities to enrich, share, and store data; anywhere, be it at home, or on a mobile device while on the go. This results in a multimodal, flexible, easy to use, mobile-ready Web application. The framework allows for incremental extension, export and import of the data collection, keeping in mind accessibility, mobility, interoperability, reusability, and sustainability.
Rethinking and Improving Few-Shot Segmentation From a Contour-Aware Perspective

Existing few-shot segmentation approaches basically adopt the idea of comparing the semantic prototype vector of the query image and support images, and then obtaining the segmentation result. However, recent studies have shown that a single feature vector in feature map cannot accurately represent pixel-level categories, thus leading to poor segmentation of object boundary and semantic ambiguity. To address this common problem, we propose a novel contour-aware network (CTANet) for few-shot segmentation in this paper. Unlike the usual practice of classifying each pixel separately, CTANet regards all pixels within the same contour as a whole, which can take advantage of the internal consistency of objects to obtain a more accurate representation of category information. To obtain the accurate object contour, our network consists of a contour generation module and a contour refinement module, where the former exploits multiple levels of features to generate a primary contour map and the latter learns to refine the primary contour map. Furthermore, a novel contour-aware mixed loss is proposed to fuse the common BCE loss and our contour-aware loss to supervise the training process on two levels, pixel-level and contour-level. Extensive experiments demonstrate that our CTANet achieves a new state-of-the-art performance on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \text{PASCAL-5}^{i}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \text{COCO-20}^{i}$</tex-math></inline-formula> . Hopefully, our new perspective could provide more clues for future research on few-shot segmentation. Our code is freely available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hardtogetA/CTANet</uri> .
The Capture and Evaluation System of Student Actions in Physical Education Classroom Based on Deep Learning

Nowadays, it is essential to capture and evaluate student action in the physical education classroom to assess their behavior. Every student’s performance is unique in physical activity. Every time, the staff or trainer cannot watch and evaluate the students individually. At the university level, the use of classroom capture systems is becoming more widespread. However, due to technology’s recent growth and application, the research on classroom capture systems’ efficacy in university classrooms has been minimal. This paper is proposed for the student action capture and evaluation system. Image preprocessing is the process of preparing pictures for use in model training and inference. This covers resizing, orienting, and color adjustments, among other things. As a result, a change that can be an augmentation in certain cases can be better served as a pretreatment step in others. The DL-IF uses cloud technology for data storage and evaluation. DL-IF uses the imaging technology to monitor students’ actions and responses in the classroom. The image data are evaluated based on the trained set of data provided in DL-IF’s Artificial Neural Network (ANN). The evaluation of unique individuality in every student’s performance is reported to the respective trainer. The simulation analysis of the proposed method DL-IF proves that it can monitor, capture and evaluate every student’s action in all physical activity classrooms. Hence, it proved that this framework could work with high accuracy and minimized mean square error rate.
Preface

Extract In questions of Science, the authority of a thousand is not worth the humble reasoning of a single individual.Galileo Galilei (1564−1642) MemComputing is a portmanteau word I introduced in a Nature Physics article of 20131 to represent ‘any physical system that computes in and with memory’. In other words, it is a type of computation performed by physical units able to concomitantly process and store information in the same region of space.2 However, although the prefix ‘mem’ stands precisely for mem-ory, I do not necessarily mean ‘long-term memory’ or ‘storage’. Rather, I have in mind the following equivalence: A MemComputing machine then exploits time non-locality (the ability to remember its past dynamics) to perform the necessary tasks involved, whether this memory is short- or long-term.3 As such, the MemComputing paradigm is a radical departure from the accepted Turing paradigm of computation, and from its traditional implementation in a von Neumann architecture, where a central processing unit (CPU) is physically distinct from its storage unit.4 It is also fundamentally different from another computing paradigm of current interest: Quantum Computing.
A Comparative Study of COVID-19 CT Image Synthesis using GAN and CycleGAN

Generative adversarial networks (GANs) have been very successful in many applications of medical image synthesis, which hold great clinical value in diagnosis and analysis tasks, especially when data is scarce. This study compares the two most adopted generative modelling algorithms in recent medical image synthesis tasks, namely the traditional Generative Adversarial Network (GAN) and Cycle-consistency Generative Adversarial Network (CycleGAN) for COVID-19 CT image synthesis. Experiments show that very plausible synthetic COVID-19 images with a clear vision of artificially generated ground glass opacity (GGO) can be generated with CycleGAN when trained using an identity loss constant at 0.5. Moreover, it is found that the synthesis of the synthetic GGO features is generalized across images with different chest and lung structures, which suggests that diverse patterns of GGO can be synthesized using a conventional Image-to- Image translation setting without additional auxiliary conditions or visual annotations. In addition, similar experiment setting achieves encouraging perceptual quality with a Fréchet Inception Distance score of 0.347, which outperforms GAN at 0.383 and CycleGAN at 0.380 with an identity loss constant of 0.005. The experiment outcomes postulate a negative correlation between the strength of the identity loss and the significance of the synthetic instances manifested on the generated images, which highlights an interesting research path to improve the quality of generated images without compromising the significance of synthetic instances upon the image translation.
Explaining noise effects in CNN: a practical case study on volcano signals

The study presented in this work was devoted to understanding the effect of noises in classifiers created to model signals, a particular case of data analysis characterized for presenting temporal dependencies. In this sense, we have considered a real-world application related to predicting seismic volcano events based on its temporal monitoring. Firstly, we created a convolutional neural network (CNN) to model and predict such signals. Then, we have used statistical tests to confirm the noise influence on our network. Finally, we have used an eXplainable AI (XAI) approach to describe the noise behavior visually. Besides developing a CNN with an outstanding performance to deal with this real-world application, the theoretical contribution of our work is twofold: firstly, the adoption of an XAI approach, originally designed to explain image classification, to describe the influence of noise in signals; and, secondly, we show an approach to reconstruct signals after this explaining process to smooth their observations in the time domain.
Style changing of the Real human face to cartoon face by Cycle-GAN

Research the Cycle-GAN work theory and find different learning rates how to affect the result of Cycle-GAN. Using the different combinations of learning rates and epochs to train the model, observing and comparing results generated by different combinations, finding which kind of combination will generate the best result. It uses 0.0002 as the learning rate and trains 31 epochs as our stander data. In the stander data’s results, real-face to cartoon-face, some of them look well, but some of them look terrible. For example, some faces are half-baked. Cartoon-face to real-face, the generated real faces have the fundamental face shape but the part of the eyes is a black hole. The outcomes show that when using 0.0001 as the learning rate and training 31 epochs generated results are much better than the stander data’s result. The cartoon human face generated by the 0.0001 learning rate, is more complete than the 0.0002 learning rate. Moreover, the result of real-face, the zone of eyes also better than the 0.0002 learning rate. The eyes are more clear and bright. But when this paper increases the number of epochs to 47 and keeps 0.0001 as the learning rate, the result is overfitting. The real-face to cartoon-face, generated cartoon faces hair colors cannot fit with train data and all the yellow. The cartoon face to the real face, the background is weird and the shape of the faces is not very well. In summary, the best combination of earning rates and the number of epochs is the 0.0001 learning rate and 31 epochs.