Reviewerly/exHarmony huggingface dataset:
Research on Mental Health Monitoring Scheme of Migrant Children Based on Convolutional Neural Network Based on Deep Learning

In recent years, with the acceleration of urbanization and the implementation of compulsory education, the pressure on students' study and life has increased, and the phenomenon of psychological and behavioral problems has become increasingly prominent. Therefore, the school has regarded students' mental health education as the top priority in teaching work. Effective expression classification can assist psychology researchers to study psychology and other disciplines and analyze children's psychological activities and mental states by classifying expressions, thereby reducing the occurrence of psychological behavior problems. Most of the current mainstream methods focus on the exploration of text explicit features and the optimization of representation models, and few works pay attention to deeper language expressions. Metaphors, as language expressions often used in daily life, are closely related to an individual's emotion, cognition, and psychological state. This paper studies children's smiling face recognition based on deep neural network. In order to obtain a better identification effect of mental health problems of children, this paper attempts to use multisource data, including consumption data, access control data, network logs, and grade data, and proposes a multisource data-based mental health problem identification algorithm. The main research focus is feature extraction, trying to use one-dimensional convolutional neural network (1D-CNN) to mine students' online patterns from online behavior sequences, calculate abnormal scores based on students' consumption data in the cafeteria, and describe the dietary differences among students. At the same time, this paper uses the students' psychological state data provided by the psychological center as a label to improve the deficiencies caused by the questionnaire. This paper uses the training set to train five common classification algorithms, evaluates them through the validation set, and selects the best classifier as our algorithm and uses it to identify students with mental health problems in the test set. The experimental results show that precision reaches 0.68, recall reaches 0.56, and F1-measure reaches 0.67.
Multilingual Semantic Sourcing using Product Images for Cross-lingual Alignment

In online retail stores with ever-increasing catalog, product search is the primary means for customers to discover products of their interest. Surfacing irrelevant products can lead to poor customer experience and in extreme situations loss in engagement. With the recent advances in NLP, Deep Learning models are being used to represent queries and products in shared semantic space to enable semantic sourcing. These models require a lot of human annotated (query, product, relevance) tuples to give competitive results which is expensive to generate. The problem becomes more prominent in the emerging marketplaces/languages due to data paucity problem. When expanding to new marketplaces, it becomes imperative to support regional languages to reach a wider customer base and delighting them with good customer experience. Recently, in the NLP domain, approaches using parallel data corpus for training multilingual models have become prominent, but they are expensive to generate. In this work, we learn semantic alignment across languages using product images as an anchor between them. This overcomes the necessity of parallel data corpus. We use the human annotated data from established marketplace to transfer relevance classification knowledge to new/emerging marketplaces to solve the data paucity problem. Our experiments performed on datasets from Amazon reveal that we outperform state-of-the-art baselines with 2.4%-3.65% ROC-AUC lifts on relevance classification task across non-English marketplaces, 34.69%-51.67% [email protected] lifts on language-agnostic retrieval task and 6.25%-13.42% [email protected] lifts on semantic neighborhood quality task, respectively. Our models demonstrate efficient transfer of relevance classification knowledge from data rich marketplaces to new marketplaces by achieving ROC-AUC lifts of 3.74%-6.25% for the relevance classification task in the zero-shot setting where the human annotated relevance data of target marketplace is unavailable during training.
ACM-NDC study 2021--2022

research-article Free AccessACM-NDC study 2021--2022: tenth annual study of non-doctoral-granting departments in computing Authors: Stuart Zweben The Ohio State University The Ohio State UniversityView Profile , Jodi L. Tims Northeastern University Northeastern UniversityView Profile , Cindy Tucker Bluegrass Community and Technical College Bluegrass Community and Technical CollegeView Profile , Yan Timanovsky Association for Computing Machinery Association for Computing MachineryView Profile Authors Info & Claims ACM InroadsVolume 13Issue 3September 2022 pp 38–54https://doi.org/10.1145/3544304Published:23 August 2022Publication History 0citation158DownloadsMetricsTotal Citations0Total Downloads158Last 12 Months158Last 6 weeks60 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Explanation of Deep Learning–Based Radioisotope Identifier for Plastic Scintillation Detector

Radioisotope identification (RIID) is a representative application of deep learning for radiation measurements. Deep learning-based RIID models have been implemented in various types of radiation detectors; however, very few of these models have been interpreted using explainable artificial intelligence (XAI) methods. This paper presents an explanation of a deep learning–based RIID model for a plastic scintillation detector. The RIID task is defined as a multilabel binary classification problem, and the dataset is generated using a random sampling procedure. The identification performance is verified using experimental data. The experimental results demonstrate that the performance of the RIID models increased with the increase in the total counts of the dataset. Additionally, XAI methods are implemented, and their explanatory performance is verified for the spectral input. The domain knowledge of RIID for the plastic scintillation detector is that patterns near the Compton edge can be used as evidence for the existence of radioisotopes. Among the implemented XAI methods, integrated gradient and layerwise relevance propagation exhibited concurrence with the domain knowledge, with the Shapley value explanation method presenting the most reliable results.
LMix: regularization strategy for convolutional neural networks

Deep convolutional neural networks perform well in the field of computer vision, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. Therefore, proper regularization strategies are needed to alleviate these problems. Currently, regularization strategies with mixed sample data augmentation perform very well, and these algorithms allow the network to generalize better, improve the baseline performance of the model. However, interpolation-based mixed sample data augmentation distorts the data distribution, while masking-based mixed sample data augmentation results in excessive information loss for overly regular shapes of masks. Although mixed sample data augmentation is proven to be an effective method to improve the baseline performance, generalization ability and robustness of deep convolutional models, there is still room for improvement in terms of maintaining the of image local consistency and image data distribution. In this paper, we propose a new mixed sample data augmentation-LMix, which uses random masking to increase the number of masks in the image to maintain the data distribution, and high-frequency filtering to sharpen the image to highlight recognition regions. We applied the method to train CIFAR-10, CIFAR-100, SVHN, and Tiny-ImageNet datasets under the PreAct-ResNet18 model to evaluate the method, and obtained the latest results of 96.32, 79.85, 97.01, and 64.16%, respectively, which are 1.70, 4.73, and 8.06% higher than the optimal baseline accuracy. The LMix algorithm improves the generalization ability of the state-of-the-art neural network architecture and enhances the robustness to adversarial samples.
Towards Scalable Blockchains Using Service-Oriented Architectures

In recent years, blockchain applications beyond cryptocurrency has received tremendous attention due to its salient features which includes distributed management, security, anonymity, and immutability. However, conventional blockchains suffer from lack of scalability, high complexity, privacy, and governance. In this paper, we study the existing solutions introduced to address these limitations. We categorize these solutions into four groups which are: i) grouping nodes where the participating nodes are formed into smaller groups, ii) side channels where selected nodes form a child ledger attached to the main ledger to communicate privately, iii) optimized consensus algorithms that aim to reduce the overheads associated with committing new blocks, and iv) Blockchain-as-a-Service (BaaS) that employ service computing concepts and offload the blockchain management overheads to the cloud. A detailed discussion on BaaS is proposed along with a study of the existing cloud architectures. We elaborate on the advantages of employing blockchain to address challenges in service computing such as service recommendation. Finally, we discuss future research directions.
CNS Flight Stack for Reproducible, Customizable, and Fully Autonomous Applications

While low-level auto pilot stacks for aerial vehicles focus on robust control, sensing, and estimation, the continuous advancement of higher-level autonomy for aerial vehicles requires much more complex higher-level flight stacks in order to enable safe, fully autonomous long-duration missions. Rather than focusing on the low-level control, high-level flight stacks are required to monitor the system's integrity continuously, initiate contingency plans, execute mission plans and adapt them in non-nominal situations, allow for proper data logging, and provide standardized interfaces and integrity verification for external mission planners and localization modules. To that end, we present our freely available, high-level flight stack dubbed CNS Flight Stack that meets the above requirements and at the same time a) is platform-agnostic through a generalized (embedded) hardware abstraction layer, b) uses low compute complexity for online use on embedded hardware, and c) can be extended with other sensor modalities, integrity checks, and mission modules. These additional properties make it reproducible on a variety of different platforms for safe and fully autonomous applications. We tested the proposed flight stack in over 450 real-world flights and report the failure modes our framework detected and also mitigated to avoid crashes of the aerial system.
Scaling Multimodal Planning: Using Experience and Informing Discrete Search

Robotic manipulation is inherently continuous, but typically has an underlying discrete structure, such as if an object is grasped. Many problems like these are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multimodal</i> , such as pick-and-place tasks where every object grasp and placement is a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mode</i> . Multimodal problems require finding a sequence of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transitions</i> between modes—for example, a particular sequence of object picks and placements. However, many multimodal planners fail to scale when motion planning is difficult (e.g., in clutter) or the task has a long horizon (e.g., rearrangement). This work presents solutions for multimodal scalability in both these areas. For motion planning, we present an experience-based planning framework <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">alef</small> which reuses experience from similar modes both online and from training data. For task satisfaction, we present a layered planning approach that uses a discrete <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lead</i> to bias search toward useful mode transitions, informed by weights over mode transitions. Together, these contributions enable multimodal planners to tackle complex manipulation tasks that were previously infeasible or inefficient, and provide significant improvements in scenes with high-dimensional robots.
SAR-to-Optical Image Translation With Hierarchical Latent Features

Due to the all-weather and all-time imaging capability of Synthetic Aperture Radar (SAR), SAR remote sensing analysis has attracted much attention recently. However, compared with optical images, SAR images are more difficult to be interpreted. If a SAR image could be translated into its corresponding optical image, then the generated optical image would be helpful for assisting the interpretation. Addressing this issue, we investigate how to translate SAR images to optical ones in this work, and propose a parallel generative adversarial model for SAR-to-optical image translation, called Parallel-GAN, consisting of a backbone image translation sub-network and an adjoint optical image reconstruction sub-network. Under the proposed model, the backbone image translation sub-network is designed to translate SAR images to optical ones, and simultaneously some of its intermediate layers are required to output similar latent features to those from the corresponding layers of the adjoint image reconstruction sub-network. Thanks to the imposed hierarchical latent optical features, the proposed Parallel-GAN could achieve the SAR-to-optical image translation effectively. Extensive experimental results on three public datasets demonstrate that the proposed method outperforms ten state-of-the-art methods for SAR-to-optical image translation.
Online Anomaly Detection for Aeroengine Gas Path Based on Piecewise Linear Representation and Support Vector Data Description

The online anomaly detection of an aeroengine gas path system is very important to ensure the safety of aircraft. The traditional online anomaly detection method based on a sliding window has many shortcomings, which leads to the lack of accuracy of anomaly detection. In addition, the extreme lack of abnormal samples is also a difficult problem for engine anomaly detection. Therefore, this article proposes an anomaly detection method based on piecewise linear representation (PLR) and support vector data description (SVDD). First, the monitoring data of the aeroengine gas path are segmented and linearly represented so that the data in each segment have basically consistent changing trends, which can make up for the defects of the traditional sliding window. And an improved online segmentation method is proposed that increases the efficiency of the segmentation algorithm. Then, segment features are extracted, and input samples for the anomaly detection model are constructed. The classification model employs SVDD that requires only normal samples. The SVDD model is trained on the offline normal samples and anomaly detection is performed on the online samples. Experimental results show the superiority of this method in aeroengine gas path anomaly detection.
deepPIC: Deep Perceptual Image Clustering For Identifying Bias In Vision Datasets

Dataset bias in manually collected datasets is a known problem in computer vision. In safety-critical applications such as autonomous driving, these biases can lead to catastrophic errors from models trained on such datasets, jeopardizing the safety of users and their surroundings. Being able to unpuzzle the bias in a given dataset, and across datasets, is an essential tool for building safe and responsible AI. In this paper, we present deepPIC: deep Perceptual Image Clustering, a novel hierarchical clustering pipeline that leverages deep perceptual features to visualize and understand bias in unstructured and unlabeled datasets. It does so by effectively highlighting nuanced subcategories of information embedded within the data (such as multiple but repetitive shadow types) that typically are hard and/or expensive to annotate. Through experiments on a variety of image datasets, both open-source and internal, we demonstrate the effectiveness of deepPIC in (i) singling out errors in metadata from open-source datasets such as BDD100K; (ii) automatic nuanced metadata annotation; (iii) mining for edge cases; (iv) visualizing inherent bias both within and across multiple datasets; and (v) capturing synthetic data limitations; thus highlighting the wide variety of applications this pipeline can be applied to. All clustering results included here have been uploaded with image thumbnails on our project website - https://alchemz.github.io/unpuzzle_dataset_bias/. We recommend zooming in for best impact.
Unsupervised Multi-View Gaze Representation Learning

We present a method for unsupervised gaze representation learning from multiple synchronized views of a person’s face. The key assumption is that images of the same eye captured from different viewpoints differ in certain respects while remaining similar in others. Specifically, the absolute gaze and absolute head pose of the same subject should be different from different viewpoints, while appearance characteristics and gaze angle relative to the head coordinate frame should remain constant. To leverage this, we adopt a cross-encoder learning framework, in which our encoding space consists of head pose, relative eye gaze, eye appearance and other common features. Image pairs which are assumed to have matching subsets of features should be able to swap those subsets among themselves without any loss of information, computed by decoding the mixed features back into images and measuring reconstruction loss. We show that by applying these assumptions to an unlabelled multi-view video dataset, we can generate more powerful representations than a standard gaze cross-encoder for few-shot gaze estimation. Furthermore, we introduce a new feature-mixing method which results in higher performance, faster training, improved testing flexibility with multiple views, and added interpretability with learned confidence.
Towards Real-world Shadow Removal with a Shadow Simulation Method and a Two-stage Framework

Shadow removal is an important yet challenging restoration task. State-of-the-art shadow removal methods usually require paired datasets for training. Existing shadow removal datasets lack large-scale quantity and scene diversity. Hence, models trained on such datasets have poor generalization ability. This paper proposes a simple yet robust shadow simulation method to simulate shadow on the grayscale. The proposed shadow simulation method can be applied to arbitrary shadow-free images and masks to generate corresponding shadow images. With our shadow simulation method, we can generate a large-scale and diverse paired shadow removal dataset. Besides, we introduce a two-stage framework, Gray-to-Color Shadow Removal Network (G2C-DeshadowNet), for shadow removal. The first stage is a Grayscale Enhancement Network, which attempts to remove shadows on the grayscale. The second stage is a Colorization Network, which attempts to colorize the grayscale shadow-free image. Extensive experiments on ISTD+, SRD, and SBU datasets show that G2C-DeshadowNet outperforms state-of-the-art methods and has better generalization ability. We will release our code at https://github.com/jianhaogao/Shadow-Removal-with-Two-stage-Framework.
Cross-modal Target Retrieval for Tracking by Natural Language

Tracking by natural language specification in a video is a challenging task in computer vision. Distinct from initializing the target state only by the bounding box in the first frame, language specification has a strong potential to assist visual object trackers to capture appearance variation and eliminate semantic ambiguity of the tracked object. In this paper, we carefully design a unified local-global-search framework from the perspective of cross-modal retrieval, including a local tracker, an adaptive retrieval switch module, and a target-specific retrieval module. The adaptive retrieval switch module aligns semantics from the visual signal and the lingual description of the target using three sub-modules, i.e., object-aware attention memory, part-aware cross-attention, and vision-language contrast, which achieve an automatic switch between local search and global search. When booting the global search mechanism, the target-specific retrieval module re-localizes the missing target in the image-wide range via an efficient vision-language guided proposal selector and target-text match. Numerous experimental results on three prevailing benchmarks show the effectiveness and generalization of our framework.
A Coarse-to-Fine Boundary Localization method for Naturalistic Driving Action Recognition

Naturalistic driving action recognition plays an important role in understanding drivers’ distracted behaviors in the traffic environment. The main challenge of this task is the accurate localization of the temporal boundary for each distracted driving behavior in the video. Although many temporal action localization methods can identify action categories, it is difficult to predict accurate temporal boundaries for this task since the driving actions of the same category usually present large intra-class variation. In this paper, we introduce a Coarse-to-Fine Boundary Localization method called CFBL, which obtains fine-grained temporal boundaries progressively through three stages. Concretely, in the first coarse boundary generation stage, we adopt a modified anchor-free model Anchor-Free Saliency-based Detector (AFSD) to make an interval estimation of the temporal boundaries of distracted behaviors. In the second boundary refinement stage, we use the Dense Boundary Generation (DBG) model to adjust the estimated interval of the temporal boundaries. In the final boundary decision stage, we build a Localization Boundary Refinement Module to determine the final boundaries of different actions. Besides, we adopt a voting strategy to combine the results of different camera views to enhance the model’s distracted driving action classification ability. The experiments conducted on the Track 3 validation set of the 2022 AI City Challenge demonstrate competitive performance of the proposed method.
Detecting Objects in Less Response Time for Processing Multimedia Events in Smart Cities

Due to increase in multimedia traffic in smart cities, we are facing the problem of processing unseen classes in real-time. Existing neural-network based object detectors may support this growing demand of multimedia data but have the limitation of availability of trained classifiers for unseen concepts. This results in a long waiting time for users who want to detect unseen classes. In this paper, we proposed three approaches where we can utilize existing object detection models and can train unseen classes within short training time. Our approaches are based on similarity of unseen classes with seen classes, and availability (presence or absence) of bounding boxes. Our results indicate that the proposed framework can achieve accuracy between 95.14% to 98.53% within response time of ∼0.01 min to ∼30 min for seen and partially unseen classes. Moreover we achieve state of the art results (68.78 mAP within 10 min) for unseen classes that have only image-level labels for training and no bounding boxes. Our qualitative results indicate that our approaches can work well for any unseen class (not only for conventional object detection datasets).