Reviewerly/exHarmony huggingface dataset:
On the Limitations of Combining Sentiment Analysis Tools in a Cross-Platform Setting

A positive working climate is essential in modern software development. It enhances productivity since a satisfied developer tends to deliver better results. Sentiment analysis tools are a means to analyze and classify textual communication between developers according to the polarity of the statements. Most of these tools deliver promising results when used with test data from the domain they are developed for (e.g., GitHub). But the tools’ outcomes lack reliability when used in a different domain (e.g., Stack Overflow). One possible way to mitigate this problem is to combine different tools trained in different domains. In this paper, we analyze a combination of three sentiment analysis tools in a voting classifier according to their reliability and performance. The tools are trained and evaluated using five already existing polarity data sets (e.g. from GitHub). The results indicate that this kind of combination of tools is a good choice in the within-platform setting. However, a majority vote does not necessarily lead to better results when applying in cross-platform domains. In most cases, the best individual tool in the ensemble is preferable. This is mainly due to the often large difference in performance of the individual tools, even on the same data set. However, this may also be due to the different annotated data sets.
Intelligent Face Mask and Body Temperature Detection System using Machine Learning Algorithm

Face mask and body temperature detection is necessary for current pandemic period. Detecting face mask and body temperature helps in decreasing or to avoid spreading of COVID cases especially in crowded areas. The main purpose of face mask recognition and temperature prediction system is to find whether a person is wearing a mask or not and to check the body temperature. With the help of deep neural network based Convolution Neural Network algorithm, face mask has been recognized. For body temperature, LM35 temperature sensor is used. This system undergoes data pre-processing, training, detecting face mask and temperature. By using MobileNet V2, Frontcascadexml file, tensor flow and keras software library the face mask is detected. Then, the result is send to the Arduino microcontroller and displays that the face mask is detected or not by using LED. If the mask is not weared by the person, buzzer will be alarmed. Similar procedure was carried out for monitoring the temperature of a person using LM35 temperature sensor. The main advantages of MobileNet V2 are higher performance, lesser network size and minimum number of parameter are required.
Chinese Psychological QA Database and its Research Problems

In the past, with the development of the psychological industry in China, a large number of psychological databases have emerged. This paper surveys the psychological repositories currently available at home and abroad and researches the commonly used repositories, based on which a professional question-and-answer (Q&A) psychological repository is established and provides a researchable corpus of question and answer matching and psychological classification of research paper for suicide, depression and insomnia classification. Finally, the problems faced by machine learning when applying knowledge graphs or deep learning are discussed.
Variable Depth Bayesian Neural Networks Using Reversible Jumps

Bayesian approaches to Neural Architecture Search (NAS) offer several benefits when compared to typical grid-search methods. Marginalization of a joint posterior distribution of network parameters as well as network architecture specification allows for a principled analysis of predictive and model uncertainty. In this paper, we propose a method of applying Reversible Jump Markov Chain Monte Carlo (RJMCMC) as inference over a distribution of neural network depths. We refer to these models as Reversible Jump Bayesian Neural Networks (RJBNN), and show that a tenable acceptance probability for across-dimension proposals can be achieved through the use of a composite sampler featuring the Hamiltonian Monte Carlo (HMC) based No U-Turn Sampler (NUTS) algorithm. We compare the results of an RJBNN model with standard Bayesian Neural Networks (BNN) of comparable architecture sizes on a regression task for the Boston Housing dataset.
Single Image Self-Learning Super-Resolution with Robust Matrix Regression

The similarity measure plays the key role in the self-learning framework for single image super-resolution. This paper involves matrix regression with properties of robustness and two-dimensional structure to measure the similarity between image blocks and enhance the effect of super-resolution. Specifically, we use the minimal nuclear norm of representation error as a criterion, and the alternating direction method of multipliers (ADMM) to calculate the similarity between high- and low-resolution image blocks. Evaluation on several images with different interference and experimental results of super-resolution images clearly demonstrate the advantages of our proposed method in visual robustness and super-resolution effects.
On the Velocity of Massive Waves

In this chapter we show how waves can become massive. A simple, but powerful model is used that originates from quantum field theory. It shows how interactions with other fields (such as the Higgs field) slow waves down and give them the properties we see in matter. Based on this, we can show why massive waves can only approach light speed, but never reach it. We show how this picture gives us the dispersion relation for matter and why massive waves have inertia. We discuss the mass shell and the fact that it is not only the Higgs field that gives mass. We show how massless waves such as photons can be made massive and slowed down, and we discuss experiments in which this is achieved. Then, we discuss why and how massive waves experience length contraction and time dilation compared to massless waves and why this is a purely relative property. We discuss the Doppler effect as an example and give further insight into the way waves get adjusted in length and time. Finally, we summarize how waves perceive each other as contracted and dilated and we show how this leads to the postulates of special relativity.
Research and Improvement of Blockchain DPoS Consensus Mechanism

Aiming at the two major problems existing in the delegated proof of stake (Delegated Proof of Stake, DPoS) consensus mechanism, namely, the low enthusiasm of nodes to participate in voting and the inability of malicious nodes to deal with them quickly, the corresponding optimization scheme is proposed. First, establish a voting incentive mechanism to encourage token holders to participate in the consensus mechanism to vote and improve efficiency. Secondly, select representative nodes from the campaign nodes to perform bookkeeping and chaining in turn. These nodes supervise and inspect each other, which can detect and deal with malicious nodes in time, and ensure the security of consensus nodes. The experimental results show that the improved DPoS consensus mechanism improves the processing speed of malicious nodes, with faster operation efficiency and higher stability, and has certain application value.
Distributed fusion filter for stochastic uncertain system with time-correlated noises and packet dropout compensations

In this paper, the distributed fusion filtering problem for stochastic uncertain systems with packet-dropping compen-sation and time-correlated noises is studied. The time-correlated process and measurement noises are described by the first-order Gaussian- Markov process. The uncertainties of model parameters are described by correlated white multiplicative noises. When the sensor measurement signal is lost over network transmission, its one-step prediction is used for compensation. Based on the local filters proposed in the recent literature, the filtering error cross-covariance matrices between any two local filters are derived. Further, a distributed fusion filter weighted by matrices is presented based on the distributed weighted fusion algorithm in the linear minimum variance (LMV) sense. The simulation results verify the effectiveness of the proposed algorithm.
A Bio-inspired Dark Adaptation Framework for Low-light Image Enhancement

In low light conditions, image enhancement is critical for vision-based artificial systems since details of objects in dark regions are buried. Moreover, enhancing the low-light image without introducing too many irrelevant artifacts is important for visual tasks like motion detection. However, conventional methods always have the risk of “bad” enhancement. Nocturnal insects show remarkable visual abilities at night time, and their adaptations in light responses provide inspiration for low-light image enhancement. In this paper, we aim to adopt the neural mechanism of dark adaptation for adaptively raising intensities whilst preserving the naturalness. We propose a framework for enhancing low-light images by implementing the dark adaptation operation with proper adaptation parameters in R, G and B channels separately. Specifically, the dark adaptation in this paper consists of a series of canonical neural computations, including the power law adaptation, divisive normalization and adaptive rescaling operations. Experiments show that the proposed bio-inspired dark adaptation framework is more efficient and can better preserve the naturalness of the image compared to existing methods.
Self-Supervised Depth Estimation Leveraging Global Perception and Geometric Smoothness

Self-supervised depth estimation has drawn much attention in recent years as it does not require labeled data but image sequences. Moreover, it can be conveniently used in various applications, such as autonomous driving, robotics, realistic navigation, and smart cities. However, extracting global contextual information from images and predicting a geometrically natural depth map remain challenging. In this paper, we present DLNet for pixel-wise depth estimation, which simultaneously extracts global and local features with the aid of our depth Linformer block. This block consists of the Linformer and innovative soft split multi-layer perceptron blocks. Moreover, a three-dimensional geometry smoothness loss is proposed to predict a geometrically natural depth map by imposing the second-order smoothness constraint on the predicted three-dimensional point clouds, thereby realizing improved performance as a byproduct. Finally, we explore the multi-scale prediction strategy and propose the maximum margin dual-scale prediction strategy for further performance improvement. In experiments on the KITTI and Make3D benchmarks, the proposed DLNet achieves performance competitive to those of the state-of-the-art methods, reducing time and space complexities by more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$62\%$</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">$56\%$</tex-math> </inline-formula> at a resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$416 \times 128$</tex-math> </inline-formula> , respectively. Extensive testing on various real-world situations further demonstrates the strong practicality and generalization capability of the proposed model.
Improvement of lottery scheduling algorithm based on machine learning algorithm

CPU process scheduling is essential to reduce idle time and improve the efficiency of processes and processors. Process scheduling determines which process will be executed, and the execution time. The operating system performs reasonable process scheduling to maximize the use of resources. This not only improves the operating efficiency of the operating system, but also makes users feel better about interacting with the computer. This article introduces the process scheduling technology and the memory layout of the process. Machine learning (ML) algorithms consider multiple attributes of a process to predict its turnaround-time (TaT).[1] They are used to allocate appropriate time slices in the Linux scheduler. Experiments can successfully reduce TaT through this method. And this article has made certain improvements to the lottery scheduling algorithm. Its average waiting time is reduced to a certain extent, and the operating efficiency of the operating system and the user's experience are improved.
Sant: Creating virtual scenarios by smart devices

This paper presents a demonstration of a developed mobile application, an authoring tool, which let people create simple AR games and educational content with our free 3D scientific models. In the AR game, the application supports interaction between virtual objects and physical toys (our cardboard toy kits), which enhance users' experience.
Adaptive Versus Static Multi-oracle Algorithms, and Quantum Security of a Split-Key PRF

In the first part of the paper, we show a generic compiler that transforms any oracle algorithm that can query multiple oracles adaptively, i.e., can decide on which oracle to query at what point dependent on previous oracle responses, into a static algorithm that fixes these choices at the beginning of the execution. Compared to naive ways of achieving this, our compiler controls the blow-up in query complexity for each oracle individually, and causes a very mild blow-up only. In the second part of the paper, we use our compiler to show the security of the very efficient hash-based split-key PRF proposed by Giacon, Heuer and Poettering (PKC 2018), in the quantum random-oracle model. Using a split-key PRF as the key-derivation function gives rise to a secure KEM combiner. Thus, our result shows that the hash-based construction of Giacon et al. can be safely used in the context of quantum attacks, for instance to combine a well-established but only classically-secure KEM with a candidate KEM that is believed to be quantum-secure. Our security proof for the split-key PRF crucially relies on our adaptive-to-static compiler, but we expect our compiler to be useful beyond this particular application. Indeed, we discuss a couple of other, known results from the literature that would have profitted from our compiler, in that these works had to go though serious complications in order to deal with adaptivity.
A Lightweight Automatic Modulation Recognition Algorithm Based on Deep Learning

Automatic modulation recognition (AMR) plays a critical role in modern communication systems. Owing to the recent advancements of deep learning (DL) techniques, the application of DL has been widely studied in AMR, and a large number of DL-AMR algorithms with high recognition rates have been developed. Most DL-AMR algorithm models have high recognition accuracy but have numerous parameters and are huge, complex models, which make them hard to deploy on resource-constrained platforms, such as satellite platforms. Some lightweight and low-complexity DL-AMR algorithm models also struggle to meet the accuracy requirements. Based on this, this paper proposes a lightweight and high-recognition-rate DL-AMR algorithm model called Lightweight Densely Connected Convolutional Network (DenseNet) Long Short-Term Memory network (LDLSTM). The model cascade of DenseNet and LSTM can achieve the same recognition accuracy as other advanced DL-AMR algorithms, but the parameter volume is only 1/12 that of these algorithms. Thus, it is advantageous to deploy LDLSTM in resource-constrained systems.
Ensemble Image Super-Resolution CNNs for Small Data and Diverse Compressive Models

Convolutional neural networks (CNN) have become some of the most powerful tools for image reconstruction problems thanks to the availability of very large data sets. Implementations of deep residual structures, adversarial generation networks and attention mechanisms have made great accomplishment. However, the good performance from complex and deep network architecture is not guaranteed when the training data set is small and not well preventative for the entire population. There are many real-world image reconstruction tasks where large and diverse training data is unavailable, such as problems in the physical sciences and engineering for which the data set generation process is complicated and large data sets are expensive to construct. For example, herein we discuss the application of deep-learning to challenging problems in material science. Inspired by compressive sensing and ensemble learning, we propose a method using ensemble image super-resolution CNNs in transform domains to overcome the challenges of small training data in image reconstruction problems. Ensemble methods provide a more robust approach when CNNs are trained with less representative data. Transform domains could support the CNNs with multiple sparse representations of the original image data which enrich the information so that the CNNs can be sufficiently trained even using small data sets. Particularly, we report here a successful application of CNN ensembles to the reconstruction of areal density maps of carbon nano-tube sheet materials. We show that applying the ensemble CNNs in transform domains can reveal finer details in the material texture and help to improve the quality control capabilities for carbon nano-tube sheet production with only a small collection of training data.
NEWSFARM: A Large-Scale Chinese Corpus of Long News Summarization

Recently, the field of natural language processing (NLP) has grown rapidly, driven by massive datasets. At the same time, the need for automatic summarization systems has been rapidly increasing as the amount of textual information on the web and in large data centers became intractable for human readers. However, the lack of large-scale and high-quality Chinese datasets remain a critical bottleneck for further research on automatic text summarization. To close this gap, we searched domestic and foreign Chinese news websites and designed the FCFS (Format and Content Filtering System) to crawl and filter these records to construct NEWSFARM. The NEWSFARM is a large-scale Chinese long news summarization corpus, containing more than 220K Chinese long news and summaries written by professional editors or authors, all of which are released to the public <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . We calculated the static metrics and designed many experiments with the baseline models to evaluate the dataset. By comparing with the common datasets, the results not only demonstrate the usefulness and challenges of the proposed corpus for automatic text summarization but also validate the superiority of FCFS.
AitoloakarnaniaFortifications: An AR application for the promotion of the fortifications of Aitoloakarnania

Throughout the last years technology has developed at a blistering pace and more and more augmented reality applications are being used. Therefore, cultural heritage sites have started to utilize AR technology in order to improve the visitor’s experience of visiting a site. This paper attempts to analyze the creation of the first AR application which features the main archaeological sites of the prefecture of Aitoloakarnania.
Semantic-Aware Registration with Weakly-Supervised Learning

Medical image registration is a fundamental task for many clinical applications. Most deep learning-based image registrations methods have achieved brilliant performance owing to the incorporation of mask information. However, existing mask-guided registration methods only focus on volumetric registration inside the paired masks, ignoring the inherent attributes of the anatomical structure in other dimensions (e.g., smoothness of organ surface, connectivity of tubular structures, etc.). To address this problem, we proposed a novel semantic-aware registration network suitable for multi-organs registration with different anatomical structures. By analyzing the structural characteristics of various organs or tissues, semantic constraints are directly imposed on the deformation field from two geometrically meaningful dimensions (surface, contour) to preserve the topology of organs or tissues. To ensure the versatility of our proposed network, only a randomly selected segmentation mask is used for supervision at each iteration during training. Such a training strategy can satisfy the accurate registration of an individual organ at the inference stage. Experiments on a pelvic CT dataset with 112 subjects show that our method can achieve higher registration accuracy and preserve the anatomical structure more effectively than state-of-the-art methods.
Sparse Dense Neural Network Architecture for Turbo Decoding

The process of decoding data in communication systems is among the most computationally expensive processes. As we move towards cloud based architectures, there is a need for faster data decoding. We propose a novel Sparse Dense Neural Network (SDNN) solution for the turbo decoder module. We show that our proposed solution outperforms both the conventional decoders and the other ML-based decoding algorithms in the literature, in terms of computational complexity. Specifically, a computational gain of about 10% to 20% is achieved, with the BER performance kept comparable to ML-based state-of-the-art turbo decoders. We then show that our solution is also scalable with the order of modulation scheme.
Towards Reproducible Evaluations for Flying Drone Controllers in Virtual Environments

Research attention on natural user interfaces (NUIs) for drone flights are rising. Nevertheless, NUIs are highly diversified, and primarily evaluated by different physical environments leading to hard-to-compare performance between such solutions. We propose a virtual environment, namely VRFlightSim, enabling comparative evaluations with enriched drone flight details to address this issue. We first replicated a state-of-the-art (SOTA) interface and designed two tasks (crossing and pointing) in our virtual environment. Then, two user studies with 13 participants demonstrate the necessity of VRFlightSim and further highlight the potential of open-data interface designs.
Underwater Image Classification via Multiview-based Auxiliary Learning

In this study, we considered the problem of under-water image classification in the context of underwater pipeline inspection. This task is particularly more difficult than many classification tasks due to the subtle differences between classes, the inherent imbalance in the occurrence of the classes and the variability caused by water clarity. We experimented with both transformer and CNN architectures while for the latter, we also employed auxiliary learning as well as capitalizing on the multiview aspect on the underwater pipeline capturing setup. Additionally, we also adopted a DNN interpretability approach to locate the regions relevant to the predictions using solely image-level annotations. Finally, the extracted explainability cues were integrated into the training process with the purpose of producing more robust predictions and more complete explanations.