Reviewerly/exHarmony huggingface dataset:
DCCL: Dual-channel hybrid neural network combined with self-attention for text classification

Text classification is a fundamental task in natural language processing. The Chinese text classification task suffers from sparse text features, ambiguity in word segmentation, and poor performance of classification models. A text classification model is proposed based on the self-attention mechanism combined with CNN and LSTM. The proposed model uses word vectors as input to a dual-channel neural network structure, using multiple CNNs to extract the N-Gram information of different word windows and enrich the local feature representation through the concatenation operation, the BiLSTM is used to extract the semantic association information of the context to obtain the high-level feature representation at the sentence level. The output of BiLSTM is feature weighted with self-attention to reduce the influence of noisy features. The outputs of the dual channels are concatenated and fed into the softmax layer for classification. The results of the multiple comparison experiments showed that the DCCL model obtained 90.07% and 96.26% F1-score on the Sougou and THUNews datasets, respectively. Compared to the baseline model, the improvement was 3.24% and 2.19%, respectively. The proposed DCCL model can alleviate the problem of CNN losing word order information and the gradient of BiLSTM when processing text sequences, effectively integrate local and global text features, and highlight key information. The classification performance of the DCCL model is excellent and suitable for text classification tasks.
Improving Performance of Large-Scale MIMO Detector Via A Proposed Two-Step Deep-Learning Architecture

A large-scale multiple-input multiple-out (LS-MIMO) transmission technique with low-resolution analog-to-digital converters (ADCs) has become one of the promising techniques for 5G and future wireless networks. In this paper, we first present the mathematical derivation to fit the high-order 4-ary Amplitude Shift-Keying (ASK) superposition modulation scheme in the conventional deep-learning LS-MIMO signal detector designed previously for the binary phase-shift-keying (BPSK) modulation scheme. Importantly, we also propose a two-step deep-learning detection network that does not require training the network again when changing the modulation order, for example, from BPSK to 4-ary ASK and more, provided that the MIMO configuration is kept unchanged. The two-step model offers a low computing requirement and shorter processing time and still achieves better bit error rate (BER) performance over the one-step architecture. The essential attribute of the proposed two-step detector is that no further training is needed makes it promising for the Internet of Things, where devices with a low computing capacity are embedded. Moreover, the advantages of the two-step deep-learning detector also open up a great possibility to deploy the adaptive modulation transmission scheme to enjoy the channel gain variation of wireless fading channels.
MLCFNet: Multi-Level Context Fusion Network for 3D Object Tracking

Due to exhaustive proposal generations, conventional 3D object tracking methods based on template matching are time-consuming. Some recent works that leverage template clues to directly obtain 3D boxes are more efficient, but they don’t take full advantage of the template context. In this work, we propose a novel Multi-Level Context Fusion Network (MLCFNet) to track objects robustly. Our main idea is to fuse template context in multiple levels (point, local, and global features) into the search area and utilize the joint information to predict the final box. Specifically, a 3D Siamese Network firstly extracts multi-level features in the search area and template. Then, to promote the guidance of the template, a Context Fusion Network fuses these features into the search area and generates guided points. Finally, these points are used to regress potential object centers and cluster 3D object proposals. Experiments on KITTI and nuScenes tracking datasets demonstrate that MLCFNet outperforms other state-of-the-art methods by a large margin.
Non-small cell lung cancer (NSCLC) classification using convolutional neural network (CNN)

Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC) are types of Lung cancer. SCLC usually occurs in patients who have a history of heavy smoking and spreads more rapidly than NSCLC. However, about 80−85% of all lung cancer cases are NSCLC types that mostly attack men and women. This study aimed to classify NSCLC into squamous cell carcinoma, adenocarcinoma, large cell carcinoma, and normal lung and to compare the architecture VGG19 and ResNet50 for classification NSCLC. 1000 images data for each class was used in this study from CT Scan images. Three processes were used for classification processes, such as preprocessing, classification, and validation. The resizing and grayscale process was conducted for preprocessing step to ensure all input images are uniform. The significant result was achieved in the ResNet50 architecture, with an accuracy of 98.35% on testing data, 99.87% on training data, and 96% on validation data. Meanwhile, the best performance on the validation data in the normal class was that the results of precision, sensitivity, F1-score, and specificity were 100%, 100%, 100%, and 100%, respectively.
Image Forgery Detection Using Structural Similarity and Morphological Tools

Recent advancements in digital imaging technology and computer vision have brought forth a multitude of new possibilities in every field ranging from manufacturing to healthcare and graphic designing to virtual reality. With the many benefits that these advancements have brought when this technology falls into unscrupulous and malicious hands its effects can be adverse. Nowadays, it is possible for anyone to easily manipulate images to an extent that makes it seamless to the human eye, making it impossible to distinguish between an original and a counterfeit image. An immensely popular example of this technology is deepfake technology, which has the potential to create entirely false narratives of people as the results of deepfake media have started to look stunningly believable. To be able to distinguish what is real and what is not, we need to be able to measure the structural similarity between both real and fake images. For this purpose, we need a metric that can effectively distinguish different structures in an image. Two such widely used metrics are Mean Squared Error (MSE) and Structural Similarity Index Measure (SSIM). These metrics can also be used in assessing the quality of an image by comparing it with a lossy and compressed version of itself to understand the degradation in quality. This research applies morphological tools over MSE and SSIM and provides a series of experiments. The intent is to compare both metrics and conclude which of the two provides us with a more accurate description of the structural differences between two images. Our result shows that using a combination of morphological tools i.e., Erosion and Dilation (collectively called Opening), with SSIM provides better results to find the differences or similarities among images.
Accelerating Graph Neural Network Training on ReRAM-Based PIM Architectures via Graph and Model Pruning

Graph neural networks (GNNs) are used for predictive analytics on graph-structured data, and they have become very popular in diverse real-world applications. Resistive random-access memory (ReRAM)-based PIM architectures can accelerate GNN training. However, GNN training on ReRAM-based architectures is both compute- and data intensive in nature. In this work, we propose a framework called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SlimGNN</i> that synergistically combines both graph and model pruning to accelerate GNN training on ReRAM-based architectures. The proposed framework reduces the amount of redundant information in both the GNN model and input graph(s) to streamline the overall training process. This enables fast and energy-efficient GNN training on ReRAM-based architectures. Experimental results demonstrate that using this framework, we can accelerate GNN training by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ {4}. {5} {\times }$ </tex-math></inline-formula> while using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ {6}. {6} {\times }$ </tex-math></inline-formula> less energy compared to the unpruned counterparts.
Design and implementation of education service platform for innovation and entrepreneurship based on cloud computing technology

In the wave of development in the new era, major colleges and universities have included innovation and entrepreneurship education as one of the compulsory courses. This paper analyses the functional and performance requirements of the innovation and entrepreneurship education service platform, and puts forward the basic idea of building an innovation and Entrepreneurship Education Internet service platform based on cloud computing. This paper analyses the main system architecture of the platform, and explores the specific implementation of the platform based on cloud computing technology, providing a reference for the design and research of similar platforms. Under high concurrency pressure, we compared the average CPU utilization with that of TTFB before and after virtualization. The results show that the performance of the innovation and entrepreneurship education service platform has been significantly improved after cloud deployment.
Arbitrage Attack: Miners of the World, Unite!

Blockchain oracles are introduced to mitigate the gap between blockchain-based applications and real-world information. To solve the centralization problem of current oracle systems, many decentralized protocols have been designed. In this paper, we define the basic model for decentralized oracles that rely on unencrypted transactions for verification and adjustment tasks. Furthermore, we introduce Arbitrage attack against such decentralized oracles carried out by rational miners and mining pools. We analyze the attack based on game-theoretic methods. Moreover, we briefly discuss the price of anarchy to demonstrate the characteristic of attackers’ cooperation union under different circumstances.
A Domain Adaptation Network for Cross-Imaging Satellites Sar Image Ship Classification

Aiming at the problem of ship classification in Synthetic Aperture Radar (SAR) images crossing different imaging satellites, we propose a novel domain adaptation (DA) network. For the proposed DA network, we consider one labeled SAR image dataset as source domain and another unlabeled dataset acquired by a different imaging satellite as target do-main. First, the structural regularization of the source domain is achieved by jointly training the feature classifier and the domain classifier. Then, by minimizing the KL-divergence between the label distribution predicted by the network and the introduced auxiliary distribution, the cluster alignment of the target domain is further realized. The experimental results on the datasets obtained from different satellites verify the performance of proposed method is better than state-of-the-art DA method.
On the Integration of Acoustics and LiDAR: a Multi-Modal Approach to Acoustic Reflector Estimation

Having knowledge on the room acoustic properties, e.g., the location of acoustic reflectors, allows to better reproduce the sound field as intended. Current state-of-the-art methods for room boundary detection using microphone measurements typically focus on a two-dimensional setting, causing a model mismatch when employed in real-life scenarios. Detection of arbitrary reflectors in three dimensions encounters practical limitations, e.g., the need for a spherical array and the increased computational complexity. Moreover, loudspeakers may not have an omnidirectional directivity pattern, as usually assumed in the literature, making the detection of acoustic reflectors in some directions more challenging. In the proposed method, a LiDAR sensor is added to a loudspeaker to improve wall detection accuracy and robustness. This is done in two ways. First, the model mismatch introduced by horizontal reflectors can be resolved by detecting reflectors with the LiDAR sensor to enable elimination of their detrimental influence from the 2D problem in pre-processing. Second, a LiDAR-based method is proposed to compensate for the challenging directions where the directive loudspeaker emits little energy. We show via simulations that this multi-modal approach, i.e., combining microphone and LiDAR sensors, improves the robustness and accuracy of wall detection.
Dual Disentangled Attention for Multi-Information Utilization in Sequential Recommendation

Sequential recommendation systems predict the next item that a user will interact with according to the sequential patterns inferred from historical interacted items. Benefiting from the self-attention mechanism, BERT has achieved success in sequential recommendation. Nevertheless, it is still challenging to leverage multiple types of information (e.g., item IDs, attributes, timestamps) effectively under the BERT framework. Existing methods fuse different types of information as the input of BERT by directly adding or concatenating their embedding vectors. This way of fusion ignores the differences of different types of information in sequential pattern inference, which can cause the mutual interference problem and hinder the effective utilization of multi-information. In this work, we propose a Dual Disentangled Attention (DDA) based BERT model, called DDA-BERT, for better leveraging multi-information. Our model performs disentangled information modeling from two aspects: 1) different views to describe items using content information (e.g., using IDs or using attributes) are modeled in different attention heads, respectively; 2) different sequential information (time intervals, relative position distances) modeling is disentangled from content information modeling while calculating the attention scores. As a result, DDA-BERT can effectively prevent the mutual interference problem to improve the prediction accuracy. Extensive experiments on three benchmark datasets show that DDA-BERT consistently outperforms the state-of-the-art baselines by up to 30%.
An Analysis of Content Gaps Versus User Needs in the Wikidata Knowledge Graph

Content gaps in knowledge graphs impact downstream applications. Semantic Web researchers have studied them mainly in relation to data quality or ontology evaluation, for instance by proposing frameworks to capture various quality dimensions or methods to assess these dimensions, such as completeness, accuracy, or consistency. Less work has been done in framing these gaps in the context of user needs. This limits our ability to design processes and tools to help knowledge engineers tackle such gaps effectively. We propose a framework that: (i) captures core types of content gaps, informed by a literature review on peer-production systems; and, in the areas with such gaps, (ii) quantitatively compares the imbalances in the work on the knowledge graph with the imbalances in users' information needs to clarify the origin of the gaps. We operationalize the framework with gender, recency, geographic, and socio-economic gaps, and apply it to Wikidata by comparing edit metrics with Wikipedia pageviews between 2018 and 2021. We did not find gender or recency gaps endogenous to Wikidata's production. Only exceptionally, Wikidata editors work on under-represented entities (e.g. people from countries with lower Human Development Index) less than they should according to the volume of requests. We hope this study will provide a foundation for knowledge engineers to explore the causes of content gaps and address them if and when needed.
The algorithm of pedagogical support for the formation of flexible competencies

The atlas with a list of new professions confirms the idea that system thinking is one of the most sought-after skills of the future, as a result, system thinking is related to 4K competencies. Flexible competencies (softskills) are especially important in modern education, in achieving personal, subject, meta-subject results by schoolchildren, along with skills that will be related to professional activity in a particular field, the definition of spiritual and moral, general cultural, general intellectual value orientations of a growing personality in the future. The analysis of the relationship of 4K with the requirements of personal results of the federal state standards of primary general education and basic general education allowed us to identify the actual vector of the practical value of flexible competencies, namely, the ability to build communication (communication), creatively (creatively) perform certain tasks, the ability to critically evaluate information and work effectively in a team, interacting with each other in real, sometimes in crisis conditions of society. The formation of flexible competencies among students based on the methods used to study, analyze information sources, experiment, modeling, design, planning, design, updated format of extracurricular activities, the algorithm proposed in this paper, made it possible to make the process of achieving results as effective as possible.
Automated Knowledge Extraction of Liver Cysts From CT Images Using Modified Whale Optimization and Fuzzy C Means Clustering Algorithm

In this study, the integrated modified whale optimization and modified fuzzy c-means clustering algorithm using morphological operations are developed and implemented for appropriate knowledge extraction of a cyst from computer tomography (CT) images of the liver to facilitate modern intelligent healthcare systems. The proposed approach plays an efficient role in diagnosing the liver cyst. To evaluate the efficiency, the outcomes of the proposed approach have been compared with the minimum cross entropy based modified whale optimization algorithm (MCE and MWOA), teaching-learning optimization algorithm based upon minimum cross entropy (MCE and TLBO), particle swarm intelligence algorithm (PSO), genetic algorithm (GA), differential evolution (DE) algorithm, and k-means clustering algorithm. For this, various parameters such as uniformity (U), mean structured similarity index (MSSIM), structured similarity index (SSIM), random index (RI), and peak signal-to-noise ratio (PSNR) have been considered. The experimental results show that the proposed approach is more efficient and accurate than others.
Role of electronic gadgets in removing inequalities

Electronic gadgets can be seen as a learning tool and a means of communication. Within learning situations, multimedia products and online services can be used creatively and reflectively. One of the side effects of India's internal diversities is the lack of equal opportunities for education for all. That is, inequality of educational opportunities. Education of students in India has been affected by caste, gender, religion, region, economic reasons. The effect of inequality exists on the entire education system of India. In addition, multimedia can be used to promote subject and cross-curricular learning. Multimedia is very helpful and fruitful in education due to its characteristics of interactivity, flexibility, and integration of various media that can support learning, taking into account individual differences among learners and enhancing their motivation.
Negotiation game to introduce non-linear utility

Much prior work in automated negotiation makes the simplifying assumption of linear utility functions. As such, we propose a framework for multilateral repeated negotiations in a complex game setting---to introduce non-linearities---where negotiators can choose with whom they negotiate in subsequent games. This game setting not only creates non-linear utility functions, but also motivates the negotiation.
A Brief History of Programming Languages

This chapter will give a very brief and incomplete history of programming languages, focusing mainly on the early history up to the end of the 1960s. The early history of programming languages is closely linked to the early history of computers, so this will also be covered, though in lesser detail.
Improving Bug Localization With Effective Contrastive Learning Representation

Automated localization of buggy files can accelerate developers' efficiency of software maintenance, improving the quality of software products.State-of-the-art approaches for bug localization is based on neural networks, e.g., RNN or CNN, and can learn semantic feature from the given bug report.However, these simple neural architectures are difficult to learn the deep contextual feature from bug reports, which hurts the semantic mapping between bug reports and their corresponding buggy files.To resolve the above problem, in this paper we propose a bug localization approach that combines pre-trained language models and contrastive learning, namely CoLoc.Specifically, CoLoc first is pre-trained on a large-scale bug report corpus in an unsupervised way, to learn the deep contextual feature of each token in the bug report according to its context.Afterward, CoLoc is further pre-trained by a contrastive learning objective to learn the contrastive learning representations both of bug reports and buggy files.Contrastive learning can help CoLoc to learn the semantic differences between different bug reports and buggy files.To evaluate the effectiveness of CoLoc, we choose five baseline approaches and compare their performance on a public dataset.The experimental results show that CoLoc outperforms all baseline approaches by up to 76.00% in terms of MRR, achieving new results for bug localization.
Research on Defect Rotation Detection of Aircraft Flared Tube Based on Improved YOLOv4

Aircraft Flared Tube (AFT) is an important part of the aircraft system, and its surface defect detection has become a prerequisite for meeting the long-term normal operation of the aircraft. Although the existing deep learning defect detection methods have made great progress, there are still problems such as difficulty in detecting tiny defect, insufficient model generalization ability and harsh detection environment. Therefore, based on the analysis of the YOLOv4 model, we first utilize the Multistage Attention Module (MAM) to enhance the feature expression ability of the shallow network. Secondly, we exploit rotation detection to accommodate large aspect ratio defects on AFT surfaces. Finally, we convert the pytorch model into a tensorRT engine and deploy it on Agx xavier to achieve high efficiency, high-stability, and low-power inference. Experimental results show that the mean Average Precision (MAP) of our improved model reaches 97.87%, and the single image detection speed reaches 223.23ms, which further proves the good performance of our model on the AFT detection task.