Reviewerly/exHarmony huggingface dataset:
Adaptive attention augmentor for weakly supervised object localization

Weakly Supervised Object Localization (WSOL) is a technique for obtaining the object location from attention maps of the classification network, without using bounding box annotations. Existing WSOL approaches lack the modeling of the correlations between different regions of the target object. Hence they can only locate some discriminative attentions, which are small and sparse. Besides, they introduce too many background attentions when mining more object parts. In this paper, we propose a novel Adaptive Attention Augmentor (A 3 ) to adaptively augment the target object attentions on class attention maps. It can supplement object attentions by discovering the semantic correspondence between different regions and dynamically suppress background attentions through the proposed Focal Dice loss. Extensive experiments demonstrate the effectiveness of our approach. On the ILSVRC dataset, A 3 achieves a new state-of-the-art localization performance. On the fine-grained datasets including CUB-200–2011 and Cars-196, it also achieves very competitive results.
A Study of Multi-viewpoint Risk Assessment of Internet of Things (IoT)

As global businesses now extensively depend on data, Internet of Things (IoT) sensors have becomes the primary source of real-time data that enable the digital transformation. According to Bain’s Insight, the IoT market will grow to more than $520 billion by 2021. The technology has already been adopted for a wide array of use-cases, but due to the ever- expanding threat landscape, many customers have indicated that security remains the primary barrier when it comes to their acceptance of IoT. The current security risk management methodologies focus mostly on the cyber view. In this work, we identify 28 risk factors extracted using the risk breakdown structure method and expand this traditional view to include others (physical, psychological) that are critical to business operations. We also propose and quantitatively evaluate countermeasures for all the extracted risks. Our findings, help clarify IoT security and its relation to non-cyber risks for proper implementation of IoT systems.
Progressive Voice Trigger Detection: Accuracy vs Latency

We present an architecture for voice trigger detection for virtual assistants. The main idea in this work is to exploit information in words that immediately follow the trigger phrase. We first demonstrate that by including more audio context after a detected trigger phrase, we can indeed get a more accurate decision. However, waiting to listen to more audio each time incurs a latency increase. Progressive Voice Trigger Detection allows us to trade-off latency and accuracy by accepting clear trigger candidates quickly, but waiting for more context to decide whether to accept more marginal examples. Using a two-stage architecture, we show that by delaying the decision for just 3% of detected true triggers in the test set, we are able to obtain a relative improvement of 66% in false rejection rate, while incurring only a negligible increase in latency.
Research on Data Mining of Physical Examination for Risk Factors of Chronic Diseases Based on Classification Decision Tree

This paper analyzes the concept of classification analysis and the properties of decision tree, and gives the implementation process of ID3 algorithm. The medical examination data of medical examination information management system of Xi'an Shiyou University Hospital from 2013 to 2020 are selected as the training sample set and discretized, and a direct data model suitable for classification analysis is designed. ID3 algorithm is employed to classify and analyze the sampled data set, and the classification rules are extracted. Using the prediction conclusions of these classification rules, physical examination doctors can quickly and scientifically predict the possibility of chronic diseases of each university teacher. It can provide information technology support for the screening and prediction of chronic diseases and personalized intervention of chronic diseases.
NeuralFP: Out-of-distribution Detection using Fingerprints of Neural Networks

Edge devices use neural network models learnt on cloud to predict labels of its data records, which may lead to incorrect predictions especially for records that are different from the data involved in the training process, i.e., out-of-distribution (OOD) records. However, recent efforts in OOD detection either require the retraining of the model or assume the existence of a certain amount of OOD records, thus limiting their application in practice. In this work, we propose a novel OOD detection method (named as NeuralFP) without requiring any access to OOD records, which constructs non-linear fingerprints of neural network models memorizing the information of data observed during training. The key idea of NeuralFP is to exploit the difference in how the neural network model responds to data records in its training set versus data records that are anomalous. Specifically, NeuralFP builds autoencoders for each layer of the neural network model and then carefully analyzes the error distribution of the autocoders in reconstructing the training set to identify OOD records. Through extensive experiments on multiple real-world datasets, we show the effectiveness of NeuralFP in detecting OOD records as well as its advantages over previous approaches. Furthermore, we provide useful guidelines for parameter selection in the practical adoption of NeuralFP.
Which Airline is This? Airline Logo Detection in Real-World Weather Conditions

The detection of logos in images, for instance, logos of airlines on airplane tails, is a difficult task in real-world weather conditions. Most systems used for logo detection are very good at detecting logos in clean images. However, they exhibit problems when images are degraded by effects of adverse weather conditions as they frequently occur in real-world scenarios. For investigating this problem on airline logo detection as a subproblem of logo detection, we first present a new dataset for airline logo detection on airplane tails containing a test split with images degraded by adverse weather effects. Second, to handle the detection of airline logos effectively, a new two-stage airline logo detection system based on a state-of-the-art object proposal generation system and a specifically tailored classifier is proposed. Finally, improving the results on images degraded by adverse weather effects, we introduce a learning-free application-agnostic data augmentation strategy simulating effects like rain and fog. The results show the superior performance of our airline logo detection system compared to state-of-the-art. Furthermore, applying our data augmentation approach to a variety of systems, reduces the significant drop in performance on degraded images.
Preventing Early Endpointing for Online Automatic Speech Recognition

With the recent development of end-to-end models in speech recognition, there have been more interests in adapting these models for online speech recognition. However, using end-to-end models for online speech recognition is known to suffer from an early endpointing problem, which brings in many deletion errors. In this paper, we propose to address the early endpointing problem from the gradient perspective. Specifically, we leverage on the recently proposed ScaleGrad technique, which was proposed to mitigate the text degeneration issue. Different from ScaleGrad, we adapt it to discourage the early generation of the end-of-sentence (<eos>) token. A scaling term is added to directly maneuver the gradient of the training loss to encourage the model to learn to keep generating non-<eos> tokens. Compared with previous approaches such as voice-activity-detection and end-of-query detection, the proposed method does not rely on various types of silence, and it also saves the trouble from obtaining the ground truth endpoint with forced alignment. Nevertheless, it can be jointly applied with other techniques. Experiments on AISHELL-1 dataset show that our model brings relative 5.4%-10.1% CER reductions over the baseline, and surpasses the unlikelihood training method which directly reduces the generation probability of <eos> token.
Learning with the heart or with the mind: using virtual reality to bring historical experiences to life and arouse empathy

Virtual reality (VR) technology can increase prosocial behaviour toward a target person or group by enhancing their empathic response for the subject, but such technology has not always improved learning outcomes. This interdisciplinary study compared the potential advantages of delivering the same learning material about daily life in an ancient Greek household via two modes of delivery: VR technology and classroom lecture. The VR group explored a Greek villa containing historical artefacts and virtual characters with whom they were able to interact through set dialogues. The dialogues illustrated social hierarchies, gender relations, the situation of slaves, cult practice, and religious beliefs. The classroom group received the same information in a classroom environment. Both randomly-assigned groups answered a multiple-choice quiz to evaluate the knowledge gained. They also responded to open-text questions designed to test the degree of empathy that was aroused. We found that classroom lecture delivery was significantly superior in terms of the acquisition of factual knowledge, consistent with cognitive learning theory. We identified this as learning with the mind. The immersive VR environment, however, imparted a level of empathic response to the lived experiences of people in ancient Greece; in that sense it allowed learning with the heart.
A Plug and Play Fast Intersection Over Union Loss for Boundary Box Regression

Bounding box regression is a very effective method to improve the localization accuracy of object detection. Recently, the IoU-based regression losses have been widely used in object detection algorithms. However, we observe that they degenerate seriously in the late training period, leading to slow convergence and inaccurate localization. In this paper, we design a Fast Intersection over Union (FIoU) loss, which can not only keep the advantages but also solve the weakness of IoU-based losses. Furthermore, FIoU can be directly applied to Non-Maximum Suppression (NMS) as a criterion to improve the localization performance. Numerous experiments on two popular benchmark datasets show that our method is superior to other the-state-of-art methods.
Design of 2D LiDAR and camera fusion system improved by differential evolutionary PID with nonlinear tracking compensator

An improved 2D LiDAR and camera fusion system is proposed for the 3D reconstruction of unknown environments. It combines the advantages of dense 2D point cloud and rich color image, adopting a differential evolutionary nonlinear tracking PID to control the pitching motion of LiDAR and camera accurately. The quadratic polynomial transition function is used to optimize the pitching trajectory. The environment was scanned by the system and converted into a 3D colored point cloud by the data fusion algorithm. The experimental results show: the proposed PID control algorithm can accurately control the pitching motion with a small average error (0.0267°) and significantly reduce the point cloud inhomogeneity (0.00698); the processing time for converting each 2D point cloud into the 3D point cloud is about 0.6 ms; combined with the data fusion algorithm, the system can obtain the dense colored 3D point cloud; compared with binocular camera, depth camera and 3D LiDAR under the condition of strong light interference, the fusion system outperforms, with the reconstruction object errors of distance, length and width of 0.23%, 0.17% and 0.46% respectively. In conclusion, the system can obtain homogeneous, and dense colored 3D point cloud in real time while ensuring stable refresh frame rate.
HOCA: Higher-Order Channel Attention for Single Image Super-Resolution

Convolutional neural networks (CNNs) have obtained great success in single image super-resolution (SR). More recent works (e.g., RCAN and SAN) have obtained remarkable performance with channel attention based on first- or second-order statistics of features. However, these methods neglect the rich feature statistics higher than second-order, thus hindering the representation ability of CNNs. To address this issue, we propose a higher-order channel attention (HOCA) module to enhance the representation ability of CNNs. In our HOCA module, to capture different types of semantic information, we first compute k-order of feature statistics, followed by channel attention to learn the feature interdependencies. Considering the diversity of input contents, we design a gate mechanism to adaptively select a specific k-order channel attention. Besides, our HOCA module serves as a plug-and-play module and can be easily plugged into existing state-of-art CNN-based SR methods. Extensive experiments on public benchmarks show that our HOCA module effectively improves the performance of various CNN-based SR methods.
Method for building an information model specification based on a sensemaking approach to user involvement in the development process

The ongoing problem of inconsistency in the stages of system development encourages the development and elaboration of approaches and methods aimed at eliminating the causes of their occurrence. According to the research community, one of the most promising approaches is to involve users in the development process. Six directions in the implementation of the approach to user involvement in the system development process were formed and analyzed in the article. The analysis of the applicability of such approaches and methods to the task of involving the user in the process of developing an information data model is carried out. The problems of this direction were highlighted. To solve these problems, the sensemaking approach principles which is a development of the methodology for sensemaking proposed by Brenda Dervin, are proposed to use. On the basis of the proposed sensemaking approach, the implementation of the proposed approach is shown with the help of the development of the method, a method for constructing the specification of the information model was developed. The steps of the method are represented by methods that implement the idea of involving the user in the development process by analyzing his work with elements of documentation support.
<scp>DAE‐GAN</scp>: An <scp>autoencoder</scp> based adversarial network for Gaussian denoising

Abstract Image denoising is one of the most classic problems in computer vision for restoring corrupted images. It has been approached by using various traditional state of the art architectures in convolutional neural network (CNN), which has demonstrated considerably better results than the prior methods. There has been recent advancements in approaching the problem using generative adversarial networks (GAN), which has shown considerable promise. In this paper, we propose a novel denoising adversarial architecture to generate denoised image samples from a noisy distribution. A denoising autoencoder has been employed as the Generator to learn image distributions and generate denoised images while the discriminator penalizes the generated output. We employ an additive loss comprising of root mean square and mean absolute error for the Generator function. The model is trained adversarially followed by extensive experiments. We achieved PSNR and SSIM values comparable to the state‐of‐the‐art for a range of blind and non‐blind Gaussian noise.
Lane Detection Algorithm Based on Haar Feature Based Coupled Cascade Classifier

To improve the accuracy of lane detection in complex environment, The lane detection algorithm based on Haar feature coupled cascade classifier was proposed. The input image was scaled, and the Regions of Interest (ROI) was extracted according to the position of the vanishing line. The Haar feature of the lane was extracted from the ROI, and a cascade Lane classifier was introduced to roughly detect the lane in the ROI. The line segment detector (LSD) method was used to fit roughly detected lanes. The growth strategy and geometric checking were combined to optimize fitting results and complete target detection. The tests were conducted on multiple datasets, and results show compared with current lane detection methods, the proposed algorithm has higher robustness and accuracy, which was up to 96.5%.
B-Small: A Bayesian Neural Network Approach to Sparse Model-Agnostic Meta-Learning

There is a growing interest in the learning-to-learn paradigm, also known as meta-learning, where models infer on new tasks using a few training examples. Recently, meta-learning based methods have been widely used in few-shot classification, regression, reinforcement learning, and domain adaptation. The model-agnostic meta-learning (MAML) algorithm is a well-known algorithm that obtains model parameter initialization at meta-training phase. In the meta-test phase, this initialization is rapidly adapted to new tasks by using gradient descent. However, meta-learning models are prone to over-fitting since there are insufficient training tasks resulting in over-parameterized models with poor generalization performance for unseen tasks. In this paper, we propose a Bayesian neural network based MAML algorithm, which we refer to as the B-SMALLalgorithm. The proposed framework incorporates a sparse variational loss term alongside the loss function of MAML, which uses a sparsifying approximated KL divergence as a regularizer. We demonstrate the performance of B-MAMLusing classification and regression tasks, and high-light that training a sparsifying BNN using MAML indeed improves the parameter footprint of the model while performing at par or even outperforming the MAML approach. We also illustrate applicability of our approach in distributed sensor networks, where sparsity and meta-learning can be beneficial.
Toward more efficient locality‐sensitive hashing via constructing novel hash function cluster

Abstract Locality‐sensitive hashing (LSH) is widely used in the context of nearest neighbor search of large‐scale high‐dimensions. However, there are serious imbalance problems between the efficiency of data index structure construction and the query accuracy of LSH methods. In this article, a novel higher‐entropy‐hyperplane clusters LSH (HEHC‐LSH) algorithm is proposed, which we improve vector quantization to preprocess the data and greatly shortens the preprocessing time; We innovatively integrate the maximum entropy principle into the distribution estimation algorithm to construct a novel hash function cluster method, also incorporate bootstrap aggregating of ensemble learning, and adopt the parallel index dictionary to improve the generalization performance of the index structure. And in the query stage, we realize the comprehensive filtering of index set using integrated learning idea, which not only avoids a lot of distance calculation, but also improves the quality of query results. We also analyze the rationality and effectiveness of the proposed method. Finally, extensive experiment results show that HEHC‐LSH can achieve more higher precision and efficiency simultaneously comparing to current methods, and reflect the strong robustness on different datasets.
Face Super-Resolution Network with Incremental Enhancement of Facial Parsing Information

Recently, facial priors based face super-resolution (SR) methods have obtained significant performance gains in dealing with extremely degraded facial images, and facial priors have also been proved useful in facilitating the inference of face images. Based on this, how to efficiently fuse facial priors into deep features to improve face SR performance has attracted a major attention. In this paper, we propose a learning-based face SR approach with Incremental boosting Parsing information (dubbed IPFSR) for high magnification factor. The proposed IPFSR method consists of three main parts: i) a multi-stage parsing map embedded features upsampling network, in which image recovery and prior estimation processes are performed simultaneously and progressively to improve image resolution; ii) a progressive training method and joint facial attention loss and heatmap loss to obtain better facial attributes; iii) the channel attention strategy is introduced into residual dense blocks to adaptively learn facial features. Extensive experimental results show that compared with the state-of-the-art methods in terms of quantitative and qualitative metrics, our approach can achieve an outstanding balance between SR image quality and low network complexity.
A Novel Matrix Completion Model Based on the Multi-Layer Perceptron Integrating Kernel Regularization

Matrix completion has been widely used in image recovery and recommendation. The conventional matrix completion models based on multi-layer perceptron (MLP) only has local constraints on the observation data so that the completed matrix contains a lot of noise. Therefore, this paper proposes a novel matrix completion model based on the MLP integrating kernel regularization (MCKR). The proposed model uses the MLP to extract feature, which can automatically extract interaction feature of missing data and observational data. Moreover, through a non-linear mapping of low-dimensional latent subspace and schatten p-norm, a novel kernel regularization is derived to provide a global constraint for reducing the noise of the completed matrix. Finally, the validity and practicability of the proposed model is verified on three publicly available datasets. Compared with baselines including matrix factorization (MF), Logistic matrix factorization (LMF), expertise matrix factorization (EMF) and multi-layer perceptron (MLP), the proposed model achieves state-of-the-art result.
Mobility-Aware Prefetching and Replacement Scheme for Location-Based Services

Location-based services (LBS) are gaining prominence in today's environment. When a mobile user submits a location-based query in LBS, an adversary may infer the locations or other related sensitive information. Thus, an efficient location privacy preservation model (LPPM) with minimal overhead needs to be built by considering contextual understanding and analytical ability. With consideration of service efficiency and privacy, a location privacy preservation policy, namely mobility-aware prefetching and replacement (MOPAR) policy, has been proposed by the cloaking area formulation through user location, cache contribution rate, and data freshness in LBS. An incorporation of prefetching and replacement to anonymizer and consumer cache with formulation of cloak area is being deployed to protect customer sensitive information. The Markov mobility model-based next-position prediction procedure is used in this chapter for caching and formulation of cloaking area. The results of the simulation show significant enhancement in the efficiency of the location-privacy preservation model.