Reviewerly/exHarmony huggingface dataset:
Autoencoders - A Comparative Analysis in the Realm of Anomaly Detection

We applied convolutional versions of a "standard" autoencoder (CAE), a variational autoencoder (VAE) and an adversarial autoencoder (AAE) to two different publicly available datasets and compared their anomaly detection performances. We used the MNIST dataset [14] as a simple anomaly detection scenario. The CIFAR10 dataset [13] was used to examine the autoencoders in a more complex anomaly detection task. The anomaly detection performance of our different autoencoder types is compared in a qualitative and quantitative manner. The time needed for training the models is measured to capture their complexity. The simplest model demanding the simplest training, the CAE, computes results which are nearly as accurate and for some cases even better than results achieved by the VAE and AAE. We show that all three autoencoder types computed convincing anomaly detection results for the more simple-structured MNIST scenario. However, none of the autoencoder types proved to capture a good representation of the relevant features of the more complex CIFAR10 dataset, leading to moderately good anomaly detection performances.
Surveillance Video Anomaly Detection with Feature Enhancement and Consistency Frame Prediction

Surveillance video anomaly detection is a challenging problem because of the diversity of abnormal events. The current prediction-based methods outperform reconstruction-based methods. But the former has the following issues: 1) Using optical flow to represent motion will affect real-time detection. 2) Distinguishing abnormal events only by local relationships will lead to ambiguity. 3) Semantic information and spatiotemporal constraint are not fully utilized. To address these problems, we propose FECP-Net: a network with feature enhancement and consistency frame prediction for surveillance video anomaly detection. We use the RGB difference between consecutive frames rather than optical flow to realize real-time detection. Meanwhile, we design a feature enhancement module to enrich semantics and global context information in features. In addition, we add spatiotemporal consistency constraint and consistency loss to strengthen consistency predictions. Extensive experiments on standard benchmarks demonstrate the effectiveness of our method.
Research of Vertical Domain Entity Linking Method Fusing Bert-Binary

To solve the problem of unclear entity boundaries and low recognition accuracy in Chinese text, we construct the crop dataset and propose a Bert-binary-based entity link method. Candidate entity sets are generated through entity matching in multiple data sources. The Bert-binary model is called to calculate the correct probability of the candidate entity, and the entity with the highest score is screened for linking. In comparative experiments with three models on the crop dataset, the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>F</mi> <mn>1</mn> </math> value is increased by 2.5% on the best method or by 8.8% on average. The experimental results show the effectiveness of Bert-binary method in this paper.
Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection

Key Performance Indicators (KPI), which are essentially time series data, have been widely used to indicate the performance of telecom networks. Based on the given KPIs, a large set of anomaly detection algorithms have been deployed for detecting the unexpected network incidents. Generally, unsupervised anomaly detection algorithms gain more popularity than the supervised ones, due to the fact that labeling KPIs is extremely time- and resource-consuming, and error-prone. However, those unsupervised anomaly detection algorithms often suffer from excessive false alarms, especially in the presence of concept drifts resulting from network re-configurations or maintenance. To tackle this challenge and improve the overall performance of unsupervised anomaly detection algorithms, we propose to use active learning to introduce and benefit from the feedback of operators, who can verify the alarms (both false and true ones) and label the corresponding KPIs with reasonable effort. Specifically, we develop three query strategies to select the most informative and representative samples to label. We also develop an efficient method to update the weights of Isolation Forest and optimally adjust the decision threshold, so as to eventually improve the performance of detection model. The experiments with one public dataset and one proprietary dataset demonstrate that our active learning empowered anomaly detection pipeline could achieve performance gain, in terms of F1-score, more than 50% over the baseline algorithm. It also outperforms the existing active learning based methods by approximately 6%-10%, with significantly reduced budget (the ratio of samples to be labeled).
Automatic Image Annotation Using Quantization Reweighting Function and Graph Neural Networks

This paper investigates the issues in image annotation, which automatically assigns appropriate tags to a given image describing its content the best. Due to the introduction of deep learning methods and the use of graph neural networks (GNNs), automatic image annotation has made significant progress in recent years. An image may have multiple tags associated with it, and a tag may appear in several images within the dataset; therefore, it is inefficient to study each tag individually. Some studies have attempted to model the dependencies between tags using vocabulary to improve the performance of automatic image annotation. However, it remains unclear how to create an appropriate vocabulary graph. We propose to construct this graph by modeling the relationship between tags. In the tag graph, edges are reweighted based on cosine similarity and a quantization function. To represent each node in the graph, we use two methods of word embedding. We then use graph neural networks to extract graph features. From the graph and image features, we obtain our output vector (set of class probabilities). The proposed approach is evaluated using precision, recall, $$F_1$$ , and $$N^+$$ performance measures on two public benchmark datasets (Corel5k, and ESP Game). Results of experiments show that our method is superior to current state-of-the-art methods. On Corel5k, we achieved the best performance with $$N^+$$ and recall, the second-best performance with $$F_1$$ . The second-best performance with $$N^+$$ and precision and the best $$F_1$$ are also achieved on ESP Game.
Human-in-the-Loop Optimization for Artificial Intelligence Algorithms

Numerous organisations use artificial intelligence algorithm-based products in their different activities. These solutions help with a wide range of jobs, from operational task automation to augmentation-based strategic decision making. The users' trust in the truth and fairness of a product's outputs must be built before it can be completely integrated and embedded in an organization's daily functioning. They would be burdened with more work if Artificial Intelligence (AI) products did not have this feature. A human-in-loop decision-making process is important for building confidence and producing a successful AI-powered solution. In this research, a novel interactive system was created to explore the behaviour of AI-powered products. When designing our framework, we considered the necessity of integrating a human-in-the-loop technique in the design stage, something that had been missed in prior research of a comparable scale. The proposed software can optimise and monitor the AI-powered product process and outputs to involve people directly in the optimisation loop to identify and avoid likely and diverse failures. The Local Interpretable Model-agnostic Explanations (LIME) heatmap was utilised to illustrate decision-making features and mistake details more effectively throughout the improvement phase. The literature highlights the need of taking these issues into account throughout the design stage of an AI-powered product. This article describes how a human-in-loop AI-powered product is created by combining technologies from the AI, risk management, and human-computation domains. The designed system is based on deep learning as its decision-making engine, LIME as its approach explanation module, and the human aspect of knowledge workers. For real-world applications, we show how the created system improves product dependability and understandability by using real data and benchmark datasets.
Can we trust bounding box annotations for object detection?

Object detection is a classical problem in computer vision, and the vast majority of approaches require large annotated datasets for training and evaluation purposes. The most popular representations are bounding boxes (BBs), usually defined as the minimal-area rectangle that encompasses the whole object region. However, the annotation process presents some subjectiveness (particularly when occlusions are present), and its quality might get degraded when the annotators get tired. Comparing BBs is crucial for evaluation purposes, and the Intersection-over-Union (IoU) is the standard similarity metric. In this paper, we provide theoretical and experimental results indicating that the IoU can be strongly affected even by small annotation discrepancies in popular datasets used for object detection. As a consequence, the Average Precision (AP) value commonly used to evaluate object detectors is also influenced by annotation bias or noise, particularly for small objects and tighter IoU thresholds.
Visual Goal-Directed Meta-Imitation Learning

The goal of meta-learning is to generalize to new tasks and goals as quickly as possible. Ideally, we would like approaches that generalize to new goals and tasks on the first attempt. Requiring a policy to perform on a new task on the first attempt without even a single example trajectory is a zero-shot problem formulation. When tasks are identified by goal images, the tasks can be considered visually goal-directed. In this work, we explore the problem of visual goal-directed zero-shot meta-imitation learning. Inspired by several popular approaches to Meta-RL, we composed several core ideas related to task-embedding and planning by gradient descent to attempt to explore this problem. To evaluate these approaches, we adapted the Meta-world benchmark tasks to create 24 distinct visual goal-directed manipulation tasks. We found that 7 out of 24 tasks could be successfully completed on the first attempt by at least one of the approaches we tested. We demonstrated that goal-directed zero-shot approaches can translate to a physical robot with a demonstration based on Jenga block manipulation tasks using a Kinova Jaco robotic arm.
Linear Combination Approximation of Feature for Channel Pruning

Network pruning is an effective method that reduces the computation of neural networks while maintaining high performance. This enables the operation of deep neural networks in resource-limited environments. In a general large network, the roles of each channel often inevitably overlap with those of others. Therefore, for more effective pruning, it is important to observe the correlation between features in the network. In this paper, we propose a novel channel pruning method, namely, the linear combination approximation of features (LCAF). We approximate each feature map by a linear combination of other feature maps in the same layer, and then remove the most approximated one. Additionally, by exploiting the linearity of the convolution operation, we propose a supporting method called weight modification, to further reduce the loss change that occurs during pruning. Extensive experiments show that LCAF achieves state-of-the-art performance in several benchmarks. Furthermore, ablations on the LCAF demonstrate the effectiveness of our approach in a variety of ways.
Revisiting the Receptive Field of Conv-GRU in DROID-SLAM

This work focuses on improving the Conv-GRU-based optical flow update within a DROID-SLAM framework. Prior optical flow models typically follow a UNet or coarse-to-fine architecture in order to extract long-range cross-correlation and context cues. This helps flow estimation in the presence of large motion and challenging image regions, e.g., textureless regions. We propose modifications to the Conv-GRU module which follows the rationale of these prior models by integrating (Atrous) Spatial Pyramid Pooling and global self-attention into the Conv-GRU block. By enlarging the receptive field through the aforementioned modifications, the model is able to integrate information from a larger context window, thus improving the robustness even when given inputs that comprise challenging image regions. We show empirically through extensive experiments the gain in accuracy through these modifications.
Entropy-based Stability-Plasticity for Lifelong Learning

The ability to continuously learn remains elusive for deep learning models.Unlike humans, models cannot accumulate knowledge in their weights when learning new tasks, mainly due to an excess of plasticity and the low incentive to reuse weights when training a new task.To address the stability-plasticity dilemma in neural networks, we propose a novel method called Entropy-based Stability-Plasticity (ESP).Our approach can decide dynamically how much each model layer should be modified via a plasticity factor.We incorporate branch layers and an entropy-based criterion into the model to find such factor.Our experiments in the domains of natural language and vision show the effectiveness of our approach in leveraging prior knowledge by reducing interference.Also, in some cases, it is possible to freeze layers during training leading to speed up in training.
Auto-DenseUNet: Searchable neural network architecture for mass segmentation in 3D automated breast ultrasound

Accurate segmentation of breast mass in 3D automated breast ultrasound (ABUS) plays an important role in breast cancer analysis. Deep convolutional networks have become a promising approach in segmenting ABUS images. However, designing an effective network architecture is time-consuming, and highly relies on specialist's experience and prior knowledge. To address this issue, we introduce a searchable segmentation network (denoted as Auto-DenseUNet) based on the neural architecture search (NAS) to search the optimal architecture automatically for the ABUS mass segmentation task. Concretely, a novel search space is designed based on a densely connected structure to enhance the gradient and information flows throughout the network. Then, to encourage multiscale information fusion, a set of searchable multiscale aggregation nodes between the down-sampling and up-sampling parts of the network are further designed. Thus, all the operators within the dense connection structure or between any two aggregation nodes can be searched to find the optimal structure. Finally, a novel decoupled search training strategy during architecture search is also introduced to alleviate the memory limitation caused by continuous relaxation in NAS. The proposed Auto-DenseUNet method has been evaluated on our ABUS dataset with 170 volumes (from 107 patients), including 120 training volumes and 50 testing volumes split at patient level. Experimental results on testing volumes show that our searched architecture performed better than several human-designed segmentation models on the 3D ABUS mass segmentation task, indicating the effectiveness of our proposed method.
PPT-LBS: Privacy-preserving top-k query scheme for outsourced data of location-based services

Location-based service (LBS) is enjoying a great popularity with the fast growth of mobile Internet. As the volume of data increases dramatically, an increasing number of location service providers (LSPs) are moving LBS data to cloud platforms for benefit of affordability and stability. However, while cloud server provides convenience and stability, it also leads to data security and user privacy leakage. Aiming at the problems of insufficient privacy protection and inefficient query in the existing LBS data outsourcing schemes, this paper presents a novel privacy-preserving top-k query for outsourcing situations. Firstly, to ensure data security of LSP and privacy of the user, the enhanced asymmetric scalar-product preserving encryption and public key searchable encryption have been adopted to encrypt outsourced data and LBS query, which can effectively lower the computational cost and realize the privacy protection search. Secondly, an efficient and secure index structure is constructed by using a coded quadtree and the bloom filter, so that the cloud server can quickly locate the user’s query region to improve retrieval efficiency. Finally, the formal security analysis is given under the random oracle model, and the performance is evaluated by experiments which demonstrates that our scheme is preferable to existing schemes.
Comparing BEMD and BEEMD for identifying geochemical anomalies associated with Cu polymetallic mineralization in the southwestern Fujian province (China)

In this paper, Bi-dimensional empirical mode decomposition (BEMD) and bi-dimensional ensemble empirical mode decomposition (BEEMD) are compared to identify geochemical anomalies using a case study from Cu polymetallic mineralization district in southwestern Fujian province (China). The results showed that (1) Both BEMD and BEEMD were useful for decomposing non-linear and non-stationary geochemical data into a set of simple Bi-intrinsic functions (BIMFs), however, there is a drawback of 2D mode mixing in BEMD, and the serious mode mixing can lead to incorrect geological interpretation; (2) BEEMD can give more robust and reliable results than BEMD, making the data easier to interpret; and (3) the BIMFs and the residue obtained by BEEMD can be combined into new components, which can represent geochemical anomalies, unwanted noise and the background of the study area.
Hybrid Recommendation of Movies Based on Deep Content Features

When a movie is uploaded to a movie Recommender System (e.g., YouTube), the system can exploit various forms of descriptive features (e.g., tags and genre) in order to generate personalized recommendation for users. However, there are situations where the descriptive features are missing or very limited and the system may fail to include such a movie in the recommendation list. This paper investigates hybrid recommendation based on a novel form of content features, extracted from movies, in order to generate recommendation for users. Such features represent the visual aspects of movies, based on Deep Learning models, and hence, do not require any human annotation when extracted. We have evaluated our proposed technique using a large dataset of movies and shown that automatically extracted visual features can mitigate the cold-start problem by generating recommendation with a superior quality compared to different baselines, including recommendation based on human-annotated features.
Thangka Image Segmentation Method Based on Enhanced Receptive Field

The portrait thangka image is a kind of religious scroll painting that expresses figures&#x2019; identity and duties through portraits, sitting platforms, and backlighting. The segmentation of significant semantic objects in the image is one of the essential ways for scholars to study and understand the image&#x2019;s content. To better understand this content, we elaborately collected a dataset of portrait-like thangkas, which consists of 4086 images covering four object categories. We provide rich annotation for this dataset. In addition, we propose an end-to-end deep learning method that effectively solves the problems of blurred target edges, segmentation errors, and missed segmentation in thangka image segmentation. First, regular convolution and atrous convolution of different sizes are concatenated after the high-level feature output. This method can effectively improve the receptive field of the model while obtaining more image feature information. Then, the attention module is introduced to fully utilize the spatial relationship between the image&#x2019;s semantic content and enhance the discriminative ability of the feature representation on the thangka image. Finally, cross-layer feature fusion is added to reduce the loss of edge details and improve the accuracy of target edge segmentation. The results show that compared to the base model, the mPA and mIoU indices of the model proposed in this paper reach 90.75% and 85.66%, respectively, which effectively improved the accuracy of the Tangka image segmentation.
Uniform Priors for Data-Efficient Learning

Few or zero-shot adaptation to novel tasks is important for the scalability and deployment of machine learning models. It is therefore crucial to find properties that encourage more transferable features in deep networks for generalization. In this paper, we show that models that learn uniformly distributed features from the training data, are able to perform better transfer learning at test-time. Motivated by this, we evaluate our method: uniformity regularization (UR) on its ability to facilitate adaptation to unseen tasks and data on six distinct domains: Few-Learning with Images, Few-shot Learning with Language, Deep Metric Learning, 0-Shot Domain Adaptation, Out-of-Distribution classification, and Neural Radiance Fields. Across all experiments, we show that using UR, we are able to learn robust vision systems which consistently offer benefits over baselines trained without uniformity regularization and are able to achieve state-of-the-art performance in Deep Metric Learning, Few-shot learning with images and language.
Generative Flows as a General Purpose Solution for Inverse Problems

Due to the success of generative flows to model data distributions, they have been explored in inverse problems. Given a pre-trained generative flow, previous work proposed to minimize the 2-norm of the latent variables as a regularization term. The intuition behind it was to ensure high likelihood latent variables that produce the closest restoration. However, high-likelihood latent variables may generate unrealistic samples as we show in our experiments. We therefore propose a solver to directly produce high-likelihood reconstructions. We hypothesize that our approach could make generative flows a general purpose solver for inverse problems. Furthermore, we propose 1 &#x00D7; 1 coupling functions to introduce permutations in a generative flow. It has the advantage that its inverse does not require to be calculated in the generation process. Finally, we evaluate our method for denoising, deblurring, inpainting, and colorization. We observe a compelling improvement of our method over prior works.
Continual Active Adaptation to Evolving Distributional Shifts

Building neural network models that are adaptable to evolving data distributions without suffering catastrophic forgetting is important for real-world deployment in many applications. In real-world setting, the observed data distribution changes over time due to non-stationary environment. In this paper, we consider the problem of evolving covariate shift and propose source-free active adaptation method to fine-tune the neural networks to continually evolving data without catastrophic forgetting. We evaluate the model performance with respect to adaptation as well as forgetting under sequential evolution of data based on fifteen different common corruptions and perturbations from CIFAR10-C related to shift in lighting, weather, noise etc. We demonstrate the proposed method improves model accuracy to the continually evolving data by 21.3% on an average over the different covariate shifts without catastrophic forgetting.