Reviewerly/exHarmony huggingface dataset:
CNN-based image small-angle rotation angle estimation

Digital image rotation angle estimation is an essential research branch of resampling detection, and many methods have been proposed for the resampling detection problem. However, the angle factor estimation for small-angle image rotation has not been adequately studied. This paper proposes a digital image rotation angle estimation algorithm based on a convolutional neural network (CNN). The angle range of small-angle rotations is defined as an integer interval from 1° to 9°. Thus, the problem of estimating the rotation angle of a digital image is converted into a multiclassification problem. The rotation image is first preprocessed with a high-pass filter, and the resampling trace of the image is enhanced. The two-dimensional cyclic spectrum of the filtered image is then calculated. As an input to the network model, the two-dimensional spectrum map suppresses the effect of image content on rotation angle estimation. Next, a series of convolution layers are used to automatically extract the resampling features caused by rotation from the two-dimensional cyclic spectrum. Finally, the rotation angle of the image is estimated in the output layer. Experimental results show the superiority of the proposed method in small-angle rotation image estimation.
The Research Persona Method: Figuring and Reconfiguring Personalised Information Flows

Abstract This chapter explores the prospects of assembling “research personas” as a qualitative method that uses the positionality of the researcher situated amidst algorithmically suggested content for studying how digital personalisation is produced, encountered and experienced. Building on past and ongoing digital methods projects concerning the networked flow of misinformation, disinformation and authenticity, the research persona method looks not only inside but also across algorithms and digital methods approaches that repurpose the methods of the medium. As an immersive method that is both a product and mode of studying “figuration”, configuring research personas can show how personalisation is produced and accomplished by the interplay of various actors, devices, interfaces, infrastructures, user practices and data flows.
Research on Power Grid Fault Diagnosis Based on a Quantitative Representation of Alarm Information

Power grid operation is trending toward digitalization and intelligence, and data-driven power grid fault diagnosis has become a realistic demand for smart grid dispatching. At present, power grid fault diagnosis is based on text content analysis and alarm information feature extraction. With a lack of models and methods for alarm information quantitative representation, current models cannot adapt to complex and changeable engineering field practices. Based on the quantity and time-series distribution characteristics of alarm information during faults, in this article, we propose a quantitative representation method for alarm information and use the quantified alarm information as classification features. The classification algorithm is used to obtain the classification results based on a power grid fault diagnosis framework. A fault diagnosis strategy is designed to integrate fault classification and faulty device discrimination model results, and fault types and faulty devices are determined. Finally, the simulation system-generated fault cases and actual fault cases collected by a power grid are used to verify the calculation example. The experiments and model comparisons show that the method, with the advantages of simple modeling, stronger practicability, and better diagnosis accuracy than previous methods, can handle different forms of alarm information and perform power grid fault diagnosis based on the data.
TriggerBench: A Performance Benchmark for Serverless Function Triggers

Serverless computing offers a scalable event-based paradigm for deploying managed cloud-native applications. Function triggers are essential building blocks in serverless, as they initiate any function execution. However, function triggering is insufficiently studied and inherently hard to measure given the distributed, ephemeral, and asynchronous nature of event-based function coordination. To address this gap, we present TriggerBench, a cross-provider benchmark for evaluating serverless function triggers based on distributed tracing. We evaluate the trigger latency (i.e., time to transition between two functions) of eight types of triggers in Microsoft Azure and three in AWS. Our results show that all triggers suffer from long tail latency, storage triggers introduce variable multi-second delays, and HTTP triggers are most suitable for interactive applications. Our insights can guide developers in choosing optimal event or messaging triggers for latency-sensitive applications. Researchers can extend TriggerBench to study the latency, scalability, and reliability of further trigger types and cloud providers.
COCAS+: Large-Scale Clothes-Changing Person Re-Identification With Clothes Templates

Recent years person re-identification (ReID) has been developed rapidly due to its broad practical applications. Most existing benchmarks assume that the same person wears the same clothes across captured images, while, in real-world scenarios, person may change his/her clothes frequently. Thus the Clothes-Changing person ReID (CC-ReID) problem is introduced and several related benchmarks are established. CC-ReID is a very difficult task as the main visual characteristics of a human body, clothes, are different between query and gallery, and clothes-irrelevant features are relatively weak. To promote the research and applications of person ReID in clothes-changing scenarios, in this paper, we introduce a new task called Clothes Template based Clothes-Changing person ReID (CTCC-ReID), where the query image is enhanced by a clothes template which shares similar visual patterns with the clothes of the target person image in the gallery. So, ReID methods are encouraged to jointly consider the original query image and the given clothes template for retrieval in the proposed CTCC-ReID setting. To facilitate research works on CTCC-ReID, we construct a novel large-scale ReID dataset named ClOthes ChAnging person Set Plus (COCAS+), which contains both realistic and synthetic clothes-changing person images with manually collected clothes templates. Furthermore, we propose a novel Dual-Attention Biometric-Clothes Transfusion Network (DualBCT-Net) for CTCC-ReID, which can effectively learn to extract biometric features from the original query person image and clothes features from the given clothes template and then fuse them through a Dual-Attention Fusion Module. Extensive experimental results show that the proposed CTCC-ReID setting and COCAS+ dataset can help greatly push the performance of clothes-changing ReID toward practical applications, and synthetic data is impressively effective for CTCC-ReID. What’s more, the proposed DualBCT-Net shows significant improvements over state-of-the-art methods on the CTCC-ReID task. COCAS+ and code of DualBCT-Net will be released in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Chenhaobin/COCAS-plus</uri> .
The ARgus Designer: Supporting experts while conducting user studies of AR/MR applications

Augmented reality (AR) and mixed reality (MR) applications have gained momentum the past couple of years among researchers. Even though many approaches have addressed the challenges and opportunities of evaluating such applications and some have even used these technologies as evaluation methods, only a few attempts have been made in creating tools to support their overall evaluation process. This work aims to contribute to this gap by proposing the ARgus Designer System whose objective is to assist evaluators in conducting user studies for assessing the usability and User Experience of AR/MR applications. In the proposed evaluation tool, the experts' cognition is enhanced in real-time during testing with various metrics and features, while wearing an MR device (specifically the HoloLens 2), in an effort to facilitate the evaluation process. Focusing primarily on the domain of prototyping for Intelligent Environments (IEs), the first instantiation of the envisioned system augments the user's surroundings by overlaying virtual interactive artifacts, 3D objects and digital user interfaces (UIs) on top of the real-world environment, while at the same time enhancing the evaluators' field of view by projecting experiment-related data.
Joint Reinforcement and Contrastive Learning for Unsupervised Video Summarization

This letter presents a joint Reinforcement and Contrastive Learning framework termed RCL for unsupervised video summarization, aiming at addressing the existing two shortcomings: (i) poor feature representation, and (ii) inefficient context modeling capability. Concretely, the proposed framework consists of an Optimized Coding Module (OCM) and a Dissimilarity-Guided Attention Graph (DGAG). The OCM is grounded on Gate Recurrent Unit (GRU), which encodes the content within shots into concise representations. Different from the existing approaches, contrastive learning is introduced to promote discriminative and informative feature learning. Afterward, the DGAG adaptively performs feature aggregation by evaluating the semantic dissimilarity across shots to eliminate chaotic message passing for accurate context modeling. Finally, the procedure of importance score prediction is formulated as a node classification task, and these scores are utilized for a summary generation. Extensive experiments on the benchmark datasets demonstrate the superior performance of the proposed method.
A WoZ Study for an Incremental Proficiency Scoring Interview Agent Eliciting Ratable Samples

To assess the conversational proficiency of language learners, it is essential to samples that are representative of the learner’s full linguistic ability. This is realized through the adjustment of oral interview questions to the learner’s perceived proficiency level. An automatic system eliciting ratable samples must incrementally predict the approximate proficiency from a few turns of dialog and employ an adaptable question generation strategy according to this prediction. This study investigates the feasibility of such incremental adjustment of oral interview question difficulty during the interaction between a virtual agent and learner. First, we create an interview scenario with questions designed for different levels of proficiency and collect interview data using a Wizard-of-Oz virtual agent. Next, we build an incremental scoring model and analyze the accuracy. Finally, we discuss the future direction of automated adaptive interview system design.
Machine Learning-Based Anomaly Detection for Multivariate Time Series With Correlation Dependency

Recent advances in data collection facilitate the acquisition of large quantities of multivariate time series (MTS) data from various real-world systems.Anomaly detection in high-dimensional MTS data is essential to improving the productivity and safety of such systems; however, capturing the complex intercorrelations between different pairs of time series related to anomalous patterns is challenging.In this study, two different anomaly detection problems-mean shift and structural change-were defined based on the correlation dependency of MTS.Existing algorithms were experimentally analyzed and compared based on their correlation dependency encoding methods using synthetic datasets, with the results revealing that the explicit encoding of correlation dependency improves the predictive performance of anomaly detection in MTS data.
Comparison of random forest algorithm, support vector machine and neural network for classification of student satisfaction towards higher education services

Students are customers of a higher education institution. Various types of services and facilities are provided by universities for students, such as academic services, student services, and infrastructure including information technology services. Student satisfaction with the quality of the university services is one of the factors that affect the academic performance of the university. This study aims to compare the level of accuracy between the Random Forest method, Support Vector Machine and Neural Network for the classification of student satisfaction with the services provided by Higher Education. The dataset consisted of 430 respondents from private universities in Vietnam taken and adapted from previous research. The training and testing dataset uses a split proportion of 80:20 of the total available datasets. Model performance measurements using matrix confusion include accuracy, sensitivity (recall), and precision. The results showed that the Random Forest method can produce the best accuracy rate of 76.47%, followed by Neural Network and Support Vector Machine algorithms with an accuracy rate of 74.12% on each.
Meta-Self-Training Based on Teacher–Student Network for Industrial Label-Noise Fault Diagnosis

The success of deep-learning-based fault diagnosis methods usually relies on a large amount of high-quality data annotation, which is difficult or/and expensive to obtain in many practical industrial applications. When label noise exists, the performance of these methods will be severely affected. Self-training-based label correction is a possible approach to address the label-noise problem. It uses the pseudolabels produced by deep neural network (DNN) to gradually replace the original noisy labels, thereby reducing the impact of label noise. However, due to lack of guidance, self-training methods are often prone to confirmation bias, which can lead to many incorrect pseudolabels. In this work, we aim to address the confirmation bias issue of self-training in the label-noise scenario, so as to obtain better label correction results. A meta-self-training method is proposed, which adopts a self-training mechanism to train a teacher network and uses the pseudolabels generated by the teacher to train a student network. On this basis, the test loss of the student on a small amount of clean data is used to measure the quality of these pseudolabels, and a meta-learning framework is developed to feed this test loss back to the teacher to guide the generation of better pseudolabels. Experimental results show that the proposed method can effectively improve the label correction results of self-training, so that both teacher and student models can achieve good performance in label-noise fault diagnosis problems.
Software failure time series prediction with RBF, GRNN, and LSTM neural networks

The important task of software quality assurance is failure prediction. Time series forecasting methods can be successfully used for this purpose. This paper aims to study and compare the effectiveness of software failure prediction using different types of neural networks, in particular RBF neural network and recurrent neural networks (GRNN and LSTM). The Chromium browser failure dataset was used in this study. The dataset contains information about 11,001 bug reports for 759 consecutive days. Predictions were performed for 150-day, 500-day, and 759-day time series. Long time series of software failures cannot be approximated with satisfactory accuracy by an RBF neural network. Both GRNN and LSTM neural networks were able to predict the long software failures time series with RMSE values of 0.321 and 0.241 correspondingly.
Optical Flow Training Under Limited Label Budget via Active Learning

Supervised training of optical flow predictors generally yields better accuracy than unsupervised training. However, the improved performance comes at an often high annotation cost. Semi-supervised training trades off accuracy against annotation cost. We use a simple yet effective semi-supervised training method to show that even a small fraction of labels can improve flow accuracy by a significant margin over unsupervised training. In addition, we propose active learning methods based on simple heuristics to further reduce the number of labels required to achieve the same target accuracy. Our experiments on both synthetic and real optical flow datasets show that our semi-supervised networks generally need around 50% of the labels to achieve close to full-label accuracy, and only around 20% with active learning on Sintel. We also analyze and show insights on the factors that may influence active learning performance. Code is available at https://github.com/duke-vision/optical-flow-active-learning-release .
UniNet: Unified Architecture Search with Convolution, Transformer, and MLP

Recently, transformer and multi-layer perceptron (MLP) architectures have achieved impressive results on various vision tasks. However, how to effectively combine those operators to form high-performance hybrid visual architectures still remains a challenge. In this work, we study the learnable combination of convolution, transformer, and MLP by proposing a novel unified architecture search approach. Our approach contains two key designs to achieve the search for high-performance networks. First, we model the very different searchable operators in a unified form, and thus enable the operators to be characterized with the same set of configuration parameters. In this way, the overall search space size is significantly reduced, and the total search cost becomes affordable. Second, we propose context-aware downsampling modules (DSMs) to mitigate the gap between the different types of operators. Our proposed DSMs are able to better adapt features from different types of operators, which is important for identifying high-performance hybrid architectures. Finally, we integrate configurable operators and DSMs into a unified search space and search with a Reinforcement Learning-based search algorithm to fully explore the optimal combination of the operators. To this end, we search a baseline network and scale it up to obtain a family of models, named UniNets, which achieve much better accuracy and efficiency than previous ConvNets and Transformers. In particular, our UniNet-B5 achieves 84.9% top-1 accuracy on ImageNet, outperforming EfficientNet-B7 and BoTNet-T7 with 44% and 55% fewer FLOPs respectively. By pretraining on the ImageNet-21K, our UniNet-B6 achieves 87.4%, outperforming Swin-L with 51% fewer FLOPs and 41% fewer parameters. Code is available at https://github.com/Sense-X/UniNet .
Large-Scale Spectral Analysis for Element Quantification using Deep Neural Networks

Spectral analysis is an essential part in many ap-plication domains where the element concentrations must be determined quickly and precisely. Classic analytical methods are powerful, yet still face some major challenges. Recently, neural-network-based approaches have been introduced. However, their efforts and scope are still limited due to the lack of sufficient data and proper usage of neural network techniques. In this paper, we proposed a general neural network baseline framework for large-scale spectral analysis based on simulation data. To set up a proper baseline, we conduct extensive experiments to examine the required data size, select feasible network architecture and discuss problem-specific configurations. We find that CNN and DenseNet perform well in terms of overall performance and generalization ability. Compared to analytical methods, our approach is more robust, much faster and achieves competitive performance.
Semantic and Structural View Fusion Modeling for Social Recommendation

Existing studies have shown that user-item interaction data and social relation data can be jointly used for enhancing the performance of social recommendation. However, limited research has a focus on investigating how to deeply exploit different views of social interaction structures and rating behavior differences for further improving social recommendation. To this end, in this paper, we propose to integrate information from both semantic and structural views for social recommendation. Specifically, we first design a collective intelligence-based strategy to reveal high-quality implicit relations for both users and items. Then, by reformulating all available nodes and relations as a heterogeneous graph, we define multiple semantic metapaths to capture diverse preferences for comprehensive user and item representations. While various metapaths enlarge the representation capacity of users and items, they also introduce noise and irrelevant information. We recall that, for the user-item interaction graph, different structure sizes (e.g., local and global structures) provide diverse and complementary information for recommendation. Motivated by this, we propose a semantic and structural view fusion framework for social recommendation (S4Rec), which consists of a deep graph model and a wide attentive SVD (Singular Value Decomposition) model for rating prediction by taking the local and global structure as input and aggregating messages along the predefined metapaths. Finally, the two predicted results are adaptively fused to achieve the final both accurate and stable prediction. In addition, we treat the user's rating behavior difference as the relative position difference problem in the embedding space, and model it with TransH to improve the generalization ability of the main rating model. Extensive experiments on three open datasets demonstrate the superiority of our framework compared with state-of-the-art methods. Particularly, our model outperforms other baselines under different sparsity conditions, further validating the effectiveness on cold-start users. We release the source code at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lcwy220/Social-Recommendation</uri> .
StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation

Recent advances in text-to-image synthesis have led to large pretrained transformers with excellent capabilities to generate visualizations from a given text. However, these models are ill-suited for specialized tasks like story visualization, which requires an agent to produce a sequence of images given a corresponding sequence of captions, forming a narrative. Moreover, we find that the story visualization task fails to accommodate generalization to unseen plots and characters in new narratives. Hence, we first propose the task of story continuation, where the generated visual story is conditioned on a source image, allowing for better generalization to narratives with new characters. Then, we enhance or ‘retro-fit’ the pretrained text-to-image synthesis models with task-specific modules for (a) sequential image generation and (b) copying relevant elements from an initial frame. We explore full-model finetuning, as well as prompt-based tuning for parameter-efficient adaptation, of the pretrained model. We evaluate our approach StoryDALL-E on two existing datasets, PororoSV and FlintstonesSV, and introduce a new dataset DiDeMoSV collected from a video-captioning dataset. We also develop a model StoryGANc based on Generative Adversarial Networks (GAN) for story continuation, and compare with the StoryDALL-E model to demonstrate the advantages of our approach. We show that our retro-fitting approach outperforms GAN-based models for story continuation. We also demonstrate that the ‘retro-fitting’ approach facilitates copying of visual elements from the source image and improved continuity in visual frames. Finally, our analysis suggests that pretrained transformers struggle with comprehending narratives containing multiple characters, and translating them into appropriate imagery. Our work encourages future research into story continuation and large-scale models for the task (Code and data are available at https://github.com/adymaharana/storydalle ).