Reviewerly/exHarmony huggingface dataset:
Supervised Contrastive Learning with Term Weighting for Improving Chinese Text Classification

With the rapid growth of information retrieval technology, Chinese text classification, which is the basis of information content security, has become a widely discussed topic. In view of the huge difference compared with English, Chinese text task is more complex in semantic information representations. However, most existing Chinese text classification approaches typically regard feature representation and feature selection as the key points, but fail to take into account the learning strategy that adapts to the task. Besides, these approaches compress the Chinese word into a representation vector, without considering the distribution of the term among the categories of interest. In order to improve the effect of Chinese text classification, a unified method, called Supervised Contrastive Learning with Term Weighting (SCL-TW), is proposed in this paper. Supervised contrastive learning makes full use of a large amount of unlabeled data to improve model stability. In SCL-TW, we calculate the score of term weighting to optimize the process of data augmentation of Chinese text. Subsequently, the transformed features are fed into a temporal convolution network to conduct feature representation. Experimental verifications are conducted on two Chinese benchmark datasets. The results demonstrate that SCL-TW outperforms other advanced Chinese text classification approaches by an amazing margin.
Few-Shot Document-Level Relation Extraction

We present FREDo, a few-shot document-level relation extraction (FSDLRE) benchmark.As opposed to existing benchmarks which are built on sentence-level relation extraction corpora, we argue that document-level corpora provide more realism, particularly regarding none-ofthe-above (NOTA) distributions.Therefore, we propose a set of FSDLRE tasks and construct a benchmark based on two existing supervised learning data sets, DocRED and sciERC.We adapt the state-of-the-art sentence-level method MNAV to the document-level and develop it further for improved domain adaptation.We find FSDLRE to be a challenging setting with interesting new characteristics such as the ability to sample NOTA instances from the support set.The data, code, and trained models are available online 1 .
Forgery-Domain-Supervised Deepfake Detection With Non-Negative Constraint

Fake faces produced by deepfake techniques have attracted public concerns in recent years. Deepfake detection is a binary classification task that distinguishes fake faces from real ones. As the training data for deepfake detection is usually generated from real faces via various face forgery methods, it is difficult for a single binary decision boundary to distinguish fake faces. Besides that, the learned features often involve irrelevant information for identifying fake faces that are generated from diverse forgery methods. To deal with such challenges, unlike existing approaches that regard fake detection as a binary classification, we re-model the task as a multiclass forgery-domain classification task, where each forgery method is treated as a distinct class. This simplifies the complex decision boundary brought by the diversity of forgery patterns and provides more forgery-relevant information for the learning process. In addition, we introduce a non-negative constrained learning framework composed of non-negative features and a non-negative constrained classifier (NCC) to block irrelevant features with zero weights and enhance forgery-relevant features with positive weights, leading to a sparse structure of the classifier. Furthermore, to capture subtle and discriminative forgery-relevant features, we propose an integration module over augmented faces based on cross-attention. We demonstrate that our approach achieves competitive performance and generalization ability on widely-used benchmarks through extensive experiments.
Analyzing Hate Speech Data along Racial, Gender and Intersectional Axes

Warning: This work contains strong and offensive language, sometimes uncensored.To tackle the rising phenomenon of hate speech, efforts have been made towards data curation and analysis.When it comes to analysis of bias, previous work has focused predominantly on race.In our work, we further investigate bias in hate speech datasets along racial, gender and intersectional axes.We identify strong bias against African American English (AAE), masculine and AAE+Masculine tweets, which are annotated as disproportionately more hateful and offensive than from other demographics.We provide evidence that BERT-based models propagate this bias and show that balancing the training data for these protected attributes can lead to fairer models with regards to gender, but not race.
Combining Spatial and Frequency Information for Image Deblurring

This paper aims to combine spatial and frequency information for single image deblurring. Although some methods have tried to use frequency information to perform deblurring, they only simply process the different frequencies information separately or concatenate the real part and imaginary part of frequency features but ignore the strong correlation between them. To address this problem, we propose a simple but effective frequency interaction pipeline to realize the mutual conversion of the real part and the imaginary part. Then, we construct a spatial-frequency conversion module (SFCM) to promote the mutual conversion between the frequency information and the spatial information. Based on the proposed components, we build a multi-scale deblurring network, dubbed SFDNet, which can fully exploit coarse and middle-level information in spatial and frequency domains for finer scale image deblurring. Extensive experiments on the GoPro and HIDE datasets demonstrate that the proposed network outperforms the state-of-the-art methods both quantitatively and visually.
A multi-flexible video summarization scheme using property-constraint decision tree

Video summarization (VS) technology presents a multi-tendency development. However, there are still certain challenges in shortening a long video into several diverse and concise versions, such as generating various versions and variable lengths of summaries depending on user requirements. In this context, it is critical to extract the spatial–temporal features of video frames. To address these issues, this paper proposes a multi-flexible video summarization scheme using a property-constraint decision. Due to the difficulty of directly transforming user requirements into summary algorithm factors, this paper employs property-constraints as a bridge between user requirements and the video summary algorithm. The specific implementation method is to aggregate and analyze existing research results on summary-oriented properties. It begins with the establishment of a property-constraints library, followed by the translating and mapping of user requirements into VS property-constraints, and ultimately constructing a property-constraints tree for flexible decision versions of VS. In addition, a hybrid cascade bidirectional network (CB-ConvLSTM) based on the Convolutional LSTM Network (ConvLSTM) is designed to extract the spatial–temporal features of the video. On this basis, the VS generator is configured. The goal of this scheme, which combines the VS property constraints and CB-ConvLSTM, is to “analyze once, satisfy multiple factors, generate multiple levels”. Verification experiments and a comparative analysis are conducted on benchmark datasets to evaluate the algorithm in this paper. The results indicate that the proposed algorithm is highly rational, effective, and usable.
A hybrid recommendation technique using topic embedding for rating prediction and to handle cold-start problem

Recommender systems aim to estimate item ratings and recommend items based on the users’ interests. The traditional recommender systems generally consider user–item rating information for rating prediction, but they suffer from various limitations, such as data sparsity, black-box recommendation, and cold-start problems. As a result, researchers have proposed amalgamating contextual information with rating data to provide effective recommendations. Although user-generated data in the form of reviews are a rich source of contextual information, they are rarely utilized in recommender algorithms. This study presents a hybrid recommendation technique, called RecTE, using rating data and topic embedding, which is an amalgamation of word embedding and topic modeling techniques. The novelty of RecTE lies in predicting item ratings using topic embeddings learned by incorporating local and global contextual information and integrating them with user-based collaborative filtering. RecTE is empirically evaluated over three real-world datasets – YelpNYC, YelpZip and TripAdvisor. This technique performs significantly better in comparison to nine baselines and five state-of-the-art recommendation techniques. On empirical analysis, we found that incorporating topic embedding in RecTE makes it capable of performing significantly better and handle cold-start problems effectively in comparison to the existing recommendation approaches.
A Novel Criticality Analysis Technique for Detecting Dynamic Disturbances in Human Gait

The application of machine learning (ML) has made an unprecedented change in the field of medicine, showing a significant potential to automate tasks and to achieve objectives that are closer to human cognitive capabilities. Human gait, in particular, is a series of continuous metabolic interactions specific for humans. The need for an intelligent recognition of dynamic changes of gait enables physicians in clinical practice to early identify impaired gait and to reach proper decision making. Because of the underlying complexity of the biological system, it can be difficult to create an accurate detection and analysis of imbalanced gait. This paper proposes a novel Criticality Analysis (CA) methodology as a feasible method to extract the dynamic interactions involved in human gait. This allows a useful scale-free representation of multivariate dynamic data in a nonlinear representation space. To quantify the effectiveness of the CA methodology, a Support Vector Machine (SVM) algorithm is implemented in order to identify the nonlinear relationships and high-order interactions between multiple gait data variables. The gait features extracted from the CA method were used for training and testing the SVM algorithm. The simulation results of this paper show that the implemented SVM model with the support of the CA method increases the accuracy and enhances the efficiency of gait analysis to extremely high levels. Therefore, it can perform as a robust classification tool for detection of dynamic disturbances of biological data patterns and creates a tremendous opportunity for clinical diagnosis and rehabilitation.
An Efficient Hybrid Model for Low-light Image Enhancement in Mobile Devices

With the help of continuous optimizations in hardware and software, smartphones can now capture vivid, detailed macro pictures as well as high-resolution videos. How-ever, taking photos/videos in a low-light environment with smartphones would still result in underexposed and bad-quality photos/videos due to their physical limitations — small sensor size, compact lenses, and the lack of specific hardware and software. A variety of low-light enhancement techniques have been proposed, but their effectiveness is limited by their high complexity and the limited computational resources of smartphones. In this paper, we present an efficient hybrid solution, named as LLNet, to generate a high-resolution enhanced image given the corresponding high-resolution low-light image on mobile de-vices. LLNet consists of two main parts: 1) a lightweight convolutional neural network for features restoration that takes a low-resolution low-light image scaled down from the high-resolution input and predicts an enhanced low-resolution output; 2) a non-trainable transformation estimation model that approximates a linear transformation between the low-resolution input and predicted low-resolution output. By applying the estimated transformation on a high-resolution low-light image, the corresponding enhanced image can be predicted efficiently. To sup-port the development of this learning-based solution, we introduce a dataset of normal-exposure low-light images, with corresponding long-exposure reference images, and all the images were captured by smartphones under real-world low-light scenes. Experiments demonstrate that LL-Net can provide a real-time (around 32ms) smartphone pre-view (1440*1080 resolution) with outstanding image enhancement under low-light environments with affordable re-sources consumption. One real viewfinder video demo is attached as supplementary material to indicate the practicality of LLNet on real smartphones.
Multi-Camera Multi-Vehicle Tracking with Domain Generalization and Contextual Constraints

In this paper, we propose a system for Multi-Camera Multi-Target (MCMT) Vehicle Tracking in Track 1 of AI City Challenge 2022. There are many technical difficulties to the MCMT problem such as a common lack of labeled data in real scenarios, a distortion of vehicle detailed appearances in recording, and ambiguity between highly similar vehicles. Taking those into account, we develop a 3-component MCMT system that exploits vehicle behavior, leverages synthetic data and multiple augmentation techniques, and enforces contextual constraints. Specifically, our system involves a motion-driven vehicle tracker for obtaining robust trajectories, applying MixStyle domain generalization on the TransReID model to exploit as much labeled data as possible, and experimenting with contextual constraints such as our proposed neighbor matching to address ambiguity in terms of vehicle appearances. Overall, our system achieved an IDF1 score of 0.7255.
Geochemical Indication of Functional Zones at the Archaeological Sites of Eastern Europe

The article considers a new approach for determining the functional zones of the prehistoric archaeological sites in Eastern Europe by the method of geochemical indication: the use of mathematical statistics for processing the geochemical data of cultural deposits at archaeological sites, and the identification of groups of interrelated chemical elements and compounds that reflects the processes of natural sedimentation and anthropogenic activity. It makes it possible to separate the lithological and anthropogenic components. This approach is important for the identification of geochemical element groups associated with different functional zones. The reconstructions were conducted at the Neolithic, Early Metal Age, and the Bronze-Early Iron Age sites in Eastern Europe. Abnormal concentrations of the association (P2O5antr, CaOantr and Srantr) in sediments are attributed to zones of accumulation of bone remains. Anomalous concentrations of a group of elements (K2Oantr, Rbantr) in deposits are associated with wood ash and fireplaces, ash residues from ritual activities, and fires. The group of elements (Ba, MnO, Corg) reflects the accumulation of humus and organic remains, and can characterize areas with food residues, skins, and rotten wood. With the help of the distribution of the main lithological elements (SiO2, Al2O3) in sediments, it is possible to reconstruct the paleorelief at the sites.
TS360: A Two-Stage Deep Reinforcement Learning System for 360-Degree Video Streaming

360-degree video provides the users with immersive experience of the designated scene. However, the challenge is that delivery of the full high-quality 360-degree videos may exhaust the bandwidth leading to degraded user experience. Moreover, unreasonable number of tiles results in uneven distribution of resources during transmitting. In this paper, we propose a Two-Stage deep reinforcement learning system for 360-degree video streaming, named TS360, which determines the division of tiles and the quality allocation in two successive stages. In the tile division stage, the tiles are divided according to the viewing region weights so that the bandwidth can be utilized more efficiently in the quality allocation stage. On the other hand, we employ the Asynchronous Advantage Actor-Critic (A3C) with the aid of ResNet for optimizing the Quality of Experience (QoE) and the bandwidth efficiency. Simulation results compared with the state-of-the-art benchmarkers indicate that TS360 increases the bandwidth efficiency by 56% while improving the user QoE by 10% on average.
What Makes the Identity of a Scientific Method? A History of the “Structural and Analytical Typology” in the Growth of Evolutionary and Digital Archaeology in Southwestern Europe (1950s–2000s)

Usual narratives among prehistoric archaeologists consider typological approaches as part of a past and outdated episode in the history of research, subsequently replaced by technological, functional, chemical, and cognitive approaches. From a historical and conceptual perspective, this paper addresses several limits of these narratives, which (1) assume a linear, exclusive, and additive conception of scientific change, neglecting the persistence of typological problems; (2) reduce collective developments to personal work (e.g. the “Bordes’” and “Laplace’s” methods in France); and (3) presuppose the coherence and identity of these “methods” over time. It explores the case of the “Structural and analytical typology” method, developed in France, Spain, and Italy from the 1950s to the 2000s by Georges Laplace and his collaborators for lithic implements. This paper (1) provides a detailed historical account of the evolving content of this collective endeavour over five decades; (2) it addresses the epistemological question of what makes the identity and unity of a scientific method, demonstrating that the core component of the “analytical typology” lies in its particular way to represent real-world phenomena through its notation system; and (3) it reveals how this little known but significant episode of advances in the methods and theory of archaeology, contemporary but independent of the “New Archaeology” trend in English-speaking archaeology, was instrumental in the continuation of evolutionary perspectives in France and in the development of quantitative and formal methods in archaeology in southwestern Europe, foreseeing crucial knowledge representation issues raised today by digital methods in archaeology and data curation.
An Efficient Center-Based Method With Multilevel Auxiliary Supervision for Multiscale SAR Ship Detection

<b>The problem of multi-scale ship detection in synthetic aperture radar (SAR) images has received much attention with the development of deep convolutional neural networks (DCNNs). However, existing DCNN-based multi-scale SAR ship detection methods often lead to time-consuming detection process due to the massive parameters therein. To address this issue, a lightweight center-based detector with the multi-level auxiliary supervision (MLAS) structure is proposed in this paper. First, an extremely lightweight backbone network is designed to improve the computation efficiency and extract SAR image features in a bottom-up manner. Then, a feature fusion network (FFN) containing three multi-scale feature fusion modules is introduced to combine semantic features with different levels. Finally, a novel MLAS-based framework is proposed to train our DCNN with multi-level auxiliary detection subnets. MLAS improves the performance of multi-scale ship detection benefiting from the guidance of multi-level attention. Experimental results on the open SAR image dataset SSDD show that our proposed detector achieves a similar average precision (AP) for the problem of multi-scale SAR ship detection but significantly reduces the computation burden of state-of-the-art methods. The required number of floating points of operations (FLOPs) of our method is only 21.70&#x0025;, 19.30&#x0025;, and 4.81&#x0025; of those of CenterNet, YOLOv3, and RetinaNet, respectively, and the number of learnable weights in our method is only 0.68 million that is 5.63&#x0025;, 1.10&#x0025;, 2.98&#x0025; of those of the aforementioned three existing methods, respectively.</b>
A framework for adapting online prediction algorithms to outlier detection over time series

This study introduces a novel framework that eases the adoption of any online prediction algorithm for outlier detection over time series data. The proposed framework comprises both streaming data normalization and online anomaly scoring and identification based on prediction errors. To demonstrate the utility of the proposed framework, a novel neural-network-based online time series anomaly detection algorithm called EORELM-AD is developed by implementing the steps of the proposed framework over an ensemble of online recurrent extreme learning machines. Extensive experiments on well-known benchmark datasets for time series outlier detection are presented and discussed, yielding two main conclusions. First, the performance of the proposed EORELM-AD detector is competitive in comparison to several state-of-the-art outlier detection algorithms. Second, the proposed framework is a useful tool for adapting an online time series prediction algorithm to outlier detection.
Towards Generalized Methods for Automatic Question Generation in Educational Domains

Students learn more from doing activities and practicing their skills on assessments, yet it can be challenging and time consuming to generate such practice opportunities. In our work, we examine how advances in natural language processing and question generation may help address this issue. In particular, we present a pipeline for generating and evaluating questions from text-based learning materials in an introductory data science course. The pipeline includes applying a text-to-text transformer (T5) question generation model and a concept hierarchy extraction model on the text content, then scoring the generated questions based on their relevance to the extracted key concepts. We further evaluated the question quality with three different approaches: information score, automated rating by a trained model (Google GPT-3) and manual review by human instructors. Our results showed that the generated questions were rated favorably by all three evaluation methods. We conclude with a discussion of the strengths and weaknesses of the generated questions and outline the next steps towards refining the pipeline and promoting natural language processing research in educational domains.
Classification of data on stacked autoencoder using modified sigmoid activation function

A Neural Network is one of the techniques by which we classify data. In this paper, we have proposed an effectively stacked autoencoder with the help of a modified sigmoid activation function. We have made a two-layer stacked autoencoder with a modified sigmoid activation function. We have compared our autoencoder to the existing autoencoder technique. In the existing autoencoder technique, we generally use the logsigmoid activation function. But in multiple cases using this technique, we cannot achieve better results. In that case, we may use our technique for achieving better results. Our proposed autoencoder may achieve better results compared to this existing autoencoder technique. The reason behind this is that our modified sigmoid activation function gives more variations for different input values. We have tested our proposed autoencoder on the iris, glass, wine, ovarian, and digit image datasets for comparison propose. The existing autoencoder technique has achieved 96% accuracy on the iris, 91% accuracy on wine, 95.4% accuracy on ovarian, 96.3% accuracy on glass, and 98.7% accuracy on digit (image) dataset. Our proposed autoencoder has achieved 100% accuracy on the iris, wine, ovarian, and glass, and 99.4% accuracy on digit (image) datasets. For more verification of the effeteness of our proposed autoencoder, we have taken three more datasets. They are abalone, thyroid, and chemical datasets. Our proposed autoencoder has achieved 100% accuracy on the abalone and chemical, and 96% accuracy on thyroid datasets.