Reviewerly/exHarmony huggingface dataset:
Insulator Detection in Aerial Images for Transmission Line Inspection Using Single Shot Multibox Detector

The detection of insulators with cluttered backgrounds in aerial images is a challenging task for an automatic transmission line inspection system. In this paper, we propose an effective and reliable insulator detection method based on a deep learning technique for aerial images. In the proposed deep detection approach, the single shot multibox detector (SSD), a powerful deep meta-architecture, is incorporated with a strategy of two-stage fine-tuning. The SSD-based model can realize automatic multi-level feature extractor from aerial images instead of manually extracting features. Inspired by transfer learning, a two-stage fine-tuning strategy is implemented using separate training sets. In the first stage, the basic insulator model is obtained by fine-tuning the COCO model with aerial images, including different types of insulators and various backgrounds. In the second stage, the basic model is fine-tuned by the training sets of the specific insulator types and specific situations to be detected. After the two-stage fine-tuning, the well-trained SSD model can directly and accurately identify the insulator by feeding the aerial images. The results show that both the porcelain insulator and composite insulator can be quickly and accurately identified in the aerial images with complex background. The proposed approach can enhance the accuracy, efficiency, and robustness significantly.
Unsupervised Person Re-Identification by Deep Asymmetric Metric Embedding

Person re-identification (Re-ID) aims to match identities across non-overlapping camera views. Researchers have proposed many supervised Re-ID models which require quantities of cross-view pairwise labelled data. This limits their scalabilities to many applications where a large amount of data from multiple disjoint camera views is available but unlabelled. Although some unsupervised Re-ID models have been proposed to address the scalability problem, they often suffer from the view-specific bias problem which is caused by dramatic variances across different camera views, e.g., different illumination, viewpoints and occlusion. The dramatic variances induce specific feature distortions in different camera views, which can be very disturbing in finding cross-view discriminative information for Re-ID in the unsupervised scenarios, since no label information is available to help alleviate the bias. We propose to explicitly address this problem by learning an unsupervised asymmetric distance metric based on cross-view clustering. The asymmetric distance metric allows specific feature transformations for each camera view to tackle the specific feature distortions. We then design a novel unsupervised loss function to embed the asymmetric metric into a deep neural network, and therefore develop a novel unsupervised deep framework named the DEep Clustering-based Asymmetric MEtric Learning (DECAMEL). In such a way, DECAMEL jointly learns the feature representation and the unsupervised asymmetric metric. DECAMEL learns a compact cross-view cluster structure of Re-ID data, and thus help alleviate the view-specific bias and facilitate mining the potential cross-view discriminative information for unsupervised Re-ID. Extensive experiments on seven benchmark datasets whose sizes span several orders show the effectiveness of our framework.
Channel Pruning via Automatic Structure Search

Channel pruning is among the predominant approaches to compress deep neural networks. To this end, most existing pruning methods focus on selecting channels (filters) by importance/optimization or regularization based on rule-of-thumb designs, which defects in sub-optimal pruning. In this paper, we propose a new channel pruning method based on artificial bee colony algorithm (ABC), dubbed as ABCPruner, which aims to efficiently find optimal pruned structure, i.e., channel number in each layer, rather than selecting "important" channels as previous works did. To solve the intractably huge combinations of pruned structure for deep networks, we first propose to shrink the combinations where the preserved channels are limited to a specific space, thus the combinations of pruned structure can be significantly reduced. And then, we formulate the search of optimal pruned structure as an optimization problem and integrate the ABC algorithm to solve it in an automatic manner to lessen human interference. ABCPruner has been demonstrated to be more effective, which also enables the fine-tuning to be conducted efficiently in an end-to-end manner. The source codes can be available at https: //github.com/lmbxmu/ABCPruner.
Blockchain Technology for Sustainable Supply Chain Management: A Systematic Literature Review and a Classification Framework

Through a systematic review of publications in reputed peer-reviewed journals, this paper investigates the role of blockchain technology in sustainable supply chain management. It uses the What, Who, Where, When, How, and Why (5W+1H) pattern to formulate research objectives and questions. The review considers publications since 2015, and it includes 187 papers published in 2017, 2018, 2019, and the early part of 2020, since no significant publications were found in the year 2015 or 2016 on this subject. It proposes a reusable classification framework—emerging technology literature classification level (ETLCL) framework—based on grounded theory and the technology readiness level for conducting literature reviews in various focus areas of an emerging technology. Subsequently, the study uses ETLCL to classify the literature on our focus area. The results show traceability and transparency as the key benefits of applying blockchain technology. They also indicate a heightened interest in blockchain-based information systems for sustainable supply chain management starting since 2017. This paper offers invaluable insights for managers and leaders who envision sustainability as an essential component of their business. The findings demonstrate the disruptive power and role of blockchain-based information systems. Given the relative novelty of the topic and its scattered literature, the paper helps practitioners examining its various aspects by directing them to the right information sources.
Template-Based Named Entity Recognition Using BART

There is a recent interest in investigating fewshot NER, where the low-resource target domain has different label sets compared with a resource-rich source domain.Existing methods use a similarity-based metric.However, they cannot make full use of knowledge transfer in NER model parameters.To address the issue, we propose a template-based method for NER, treating NER as a language model ranking problem in a sequence-to-sequence framework, where original sentences and statement templates filled by candidate named entity span are regarded as the source sequence and the target sequence, respectively.For inference, the model is required to classify each candidate span based on the corresponding template scores.Our experiments demonstrate that the proposed method achieves 92.55% F1 score on the CoNLL03 (rich-resource task), and significantly better than fine-tuning BERT 10.88%, 15.34%, and 11.73% F1 score on the MIT Movie, the MIT Restaurant, and the ATIS (low-resource task), respectively.
Investigating the impact of benefits and challenges of IOT adoption on supply chain performance and organizational performance: An empirical study in Malaysia

In Malaysia, manufacturing industry is a major contributor to the economic advancement. As a result, cutting-edge technology like the internet of things (IoT) is projected to have a significant impact on business operations and supply chain management (SCM). However, research into the influence of IoT deployment on supply chains and organizational performance is relatively sparse. Therefore, this study is to determine the relationship between benefits and challenges of IoT adoption and organizational performance. Furthermore, this study looks into the mediating role of supply chain performance in the relationship between IoT adoption benefits and challenges and organizational performance. The population of this study is comprised of 3019 manufacturing companies in Malaysia, while the minimum sample size needed is 43 manufacturing companies.1160 complete set of survey questionnaire were distributed through email and 63 responses received representing five per cent of response rate. Partial Least Square Structural Equation Modelling (PLS-SEM) is used to assess all of the study's hypotheses. The results of this paper support six out of the seven hypotheses tested. In conclusion, the manufacturing industry in Malaysia needs to be exposed more to the benefits of IoT rather than keep discussing its challenges. This study can be a guideline to the manufacturing companies in decision making for IoT adoption. The limitations and recommendation for future study is highlighted.
Distributional Footprints of Deceptive Product Reviews

This paper postulates that there are natural distributions of opinions in product reviews. In particular, we hypothesize that for a given domain, there is a set of representative distributions of review rating scores. A deceptive business entity that hires people to write fake reviews will necessarily distort its distribution of review scores, leaving distributional footprints behind. In order to validate this hypothesis, we introduce strategies to create dataset with pseudo-gold standard that is labeled automatically based on different types of distributional footprints. A range of experiments confirm the hypothesized connection between the distributional anomaly and deceptive reviews. This study also provides novel quantitative insights into the characteristics of natural distributions of opinions in the TripAdvisor hotel review and the Amazon product review domains.
Identification of deep network generated images using disparities in color components

With the powerful deep network architectures, such as generative adversarial networks, one can easily generate photorealistic images. Although the generated images are not dedicated for fooling human or deceiving biometric authentication systems, research communities and public media have shown great concerns on the security issues caused by these images. This paper addresses the problem of identifying deep network generated (DNG) images. Taking the differences between camera imaging and DNG image generation into considerations, we analyze the disparities between DNG images and real images in different color components. We observe that the DNG images are more distinguishable from real ones in the chrominance components, especially in the residual domain. Based on these observations, we propose a feature set to capture color image statistics for identifying DNG images. Additionally, we evaluate several detection situations, including the training-testing data are matched or mismatched in image sources or generative models and detection with only real images. Extensive experimental results show that the proposed method can accurately identify DNG images and outperforms existing methods when the training and testing data are mismatched. Moreover, when the GAN model is unknown, our methods also achieves good performance with one-class classification by using only real images for training.
Semantic Proposal for Activity Localization in Videos via Sentence Query

This paper presents an efficient algorithm to tackle temporal localization of activities in videos via sentence queries. The task differs from traditional action localization in three aspects: (1) Activities are combinations of various kinds of actions and may span a long period of time. (2) Sentence queries are not limited to a predefined list of classes. (3) The videos usually contain multiple different activity instances. Traditional proposal-based approaches for action localization that only consider the class-agnostic “actionness” of video snippets are insufficient to tackle this task. We propose a novel Semantic Activity Proposal (SAP) which integrates the semantic information of sentence queries into the proposal generation process to get discriminative activity proposals. Visual and semantic information are jointly utilized for proposal ranking and refinement. We evaluate our algorithm on the TACoS dataset and the Charades-STA dataset. Experimental results show that our algorithm outperforms existing methods on both datasets, and at the same time reduces the number of proposals by a factor of at least 10.
Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air-polluted regions

Increasing air pollutants significantly affect the proportion of diffuse (Rd) to global (Rs) solar radiation. This study proposed three new hybrid support vector machines (SVM) with particle swarm optimization algorithm (SVM-PSO), bat algorithm (SVM-BAT) and whale optimization algorithm (SVM-WOA) for predicting daily Rd in air-polluted regions. These models were further compared to standalone SVM, multivariate adaptive regression spline (MARS) and extreme gradient boosting (XGBoost) models. The results showed that models with suspended particulate matter with aerodynamic diameter smaller than 2.5 μm and 10 μm (PM2.5 and PM10) and ozone (O3) produced more accurate daily Rd estimates than those without air pollution parameters, with average relative decreases in root mean square deviation (RMSD) of 11.1%, 10.0% and 10.4% for sunshine duration-based, Rs-based and combined models, respectively. SVM showed better accuracy than XGBoost and MARS. However, compared to SVM, SVM-BAT further enhanced the prediction accuracy and convergence rate in daily Rd modeling, followed by SVM-WOA and SVM-PSO, with relative decreases in RMSD of 2.9%–5.6%, 1.9%–4.9% and 1.1%–3.3%, respectively. The results highlighted the significance of incorporating air pollutants for more accurate estimation of daily Rd in air-polluted regions. Heuristic algorithms, especially BAT, are highly recommended for improving performance of standalone machine learning models.
An Improved Faster R-CNN for Small Object Detection

With the increase of training data and the improvement of machine performance, the object detection method based on convolutional neural network (CNN) has become the mainstream algorithm in field of the current object detection. However, due to the complex background, occlusion and low resolution, there are still problems of small object detection. In this paper, we propose an improved algorithm based on faster region-based CNN (Faster R-CNN) for small object detection. Using the two-stage detection idea, in the positioning stage, we propose an improved loss function based on intersection over Union (IoU) for bounding box regression, and use bilinear interpolation to improve the regions of interest (RoI) pooling operation to solve the problem of positioning deviation, in the recognition stage, we use the multi-scale convolution feature fusion to make the feature map contain more information, and use the improved non-maximum suppression (NMS) algorithm to avoid loss of overlapping objects. The results show that the proposed algorithm has good performance on traffic signs whose resolution is in the range of (0, 32], the algorithm’s recall rate reaches 90%, and the accuracy rate reaches 87%. Detection performance is significantly better than Faster R- CNN. Therefore, our algorithm is an effective way to detect small objects.
End-to-End Neural Speaker Diarization with Permutation-Free Objectives

In this paper, we propose a novel end-to-end neural-network-based speaker diarization method. Unlike most existing methods, our proposed method does not have separate modules for extraction and clustering of speaker representations. Instead, our model has a single neural network that directly outputs speaker diarization results. To realize such a model, we formulate the speaker diarization problem as a multi-label classification problem, and introduces a permutation-free objective function to directly minimize diarization errors without being suffered from the speaker-label permutation problem. Besides its end-to-end simplicity, the proposed method also benefits from being able to explicitly handle overlapping speech during training and inference. Because of the benefit, our model can be easily trained/adapted with real-recorded multi-speaker conversations just by feeding the corresponding multi-speaker segment labels. We evaluated the proposed method on simulated speech mixtures. The proposed method achieved diarization error rate of 12.28%, while a conventional clustering-based system produced diarization error rate of 28.77%. Furthermore, the domain adaptation with real-recorded speech provided 25.6% relative improvement on the CALLHOME dataset. Our source code is available online at https://github.com/hitachi-speech/EEND.
FD-GAN: Generative Adversarial Networks with Fusion-Discriminator for Single Image Dehazing

Recently, convolutional neural networks (CNNs) have achieved great improvements in single image dehazing and attained much attention in research. Most existing learning-based dehazing methods are not fully end-to-end, which still follow the traditional dehazing procedure: first estimate the medium transmission and the atmospheric light, then recover the haze-free image based on the atmospheric scattering model. However, in practice, due to lack of priors and constraints, it is hard to precisely estimate these intermediate parameters. Inaccurate estimation further degrades the performance of dehazing, resulting in artifacts, color distortion and insufficient haze removal. To address this, we propose a fully end-to-end Generative Adversarial Networks with Fusion-discriminator (FD-GAN) for image dehazing. With the proposed Fusion-discriminator which takes frequency information as additional priors, our model can generator more natural and realistic dehazed images with less color distortion and fewer artifacts. Moreover, we synthesize a large-scale training dataset including various indoor and outdoor hazy images to boost the performance and we reveal that for learning-based dehazing methods, the performance is strictly influenced by the training data. Experiments have shown that our method reaches state-of-the-art performance on both public synthetic datasets and real-world images with more visually pleasing dehazed results.
Reminder of the First Paper on Transfer Learning in Neural Networks, 1976

This paper describes a work on transfer learning in neural networks carried out in 1970s and early 1980s, which produced its first publication in 1976. In the contemporary research on transfer learning there is a belief that pioneering work on transfer learning took place in early 1990s, and this paper updates that knowledge, pointing out that the transfer learning research started more than a decade earlier. This paper reviews that 1970s research and addresses important issues relevant for the current transfer learning research. It gives a mathematical model and geometric interpretation of transfer learning, and a measure of transfer learning indicating positive, negative, and no transfer learning. It presents experimental investigation in the mentioned types of transfer learning. And it gives an application of transfer learning in pattern recognition using datasets of images.
Topological and Subsystem Codes on Low-Degree Graphs with Flag Qubits

In this work we introduce two code families, which we call the heavy hexagon code and heavy square code. Both code families are implemented by assigning physical data and ancilla qubits to both vertices and edges of low degree graphs. Such a layout is particularly suitable for superconducting qubit architectures to minimize frequency collisions and crosstalk. In some cases, frequency collisions can be reduced by several orders of magnitude. The heavy hexagon code is a hybrid surface/Bacon-Shor code mapped onto a (heavy) hexagonal lattice whereas the heavy square code is the surface code mapped onto a (heavy) square lattice. In both cases, the lattice includes all the ancilla qubits required for fault-tolerant error-correction. Naively, the limited qubit connectivity might be thought to limit the error-correcting capability of the code to less than its full distance. Therefore, essential to our construction is the use of flag qubits. We modify minimum weight perfect matching decoding to efficiently and scalably incorporate information from measurements of the flag qubits and correct up to the full code distance while respecting the limited connectivity. Simulations show that high threshold values for both codes can be obtained using our decoding protocol. Further, our decoding scheme can be adapted to other topological code families.
Variational autoencoders for new physics mining at the Large Hadron Collider

A bstract Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoencoder training does not depend on any specific new physics signature, the proposed procedure doesn’t make specific assumptions on the nature of new physics. An event selection based on this algorithm would be complementary to classic LHC searches, typically based on model-dependent hypothesis testing. Such an algorithm would deliver a list of anomalous events, that the experimental collaborations could further scrutinize and even release as a catalog, similarly to what is typically done in other scientific domains. Event topologies repeating in this dataset could inspire new-physics model building and new experimental searches. Running in the trigger system of the LHC experiments, such an application could identify anomalous events that would be otherwise lost, extending the scientific reach of the LHC.
Towards Aircraft Maintenance Metaverse Using Speech Interactions with Virtual Objects in Mixed Reality

Metaverses embedded in our lives create virtual experiences inside of the physical world. Moving towards metaverses in aircraft maintenance, mixed reality (MR) creates enormous opportunities for the interaction with virtual airplanes (digital twin) that deliver a near-real experience, keeping physical distancing during pandemics. 3D twins of modern machines exported to MR can be easily manipulated, shared, and updated, which creates colossal benefits for aviation colleges who still exploit retired models for practicing. Therefore, we propose mixed reality education and training of aircraft maintenance for Boeing 737 in smart glasses, enhanced with a deep learning speech interaction module for trainee engineers to control virtual assets and workflow using speech commands, enabling them to operate with both hands. With the use of the convolutional neural network (CNN) architecture for audio features and learning and classification parts for commands and language identification, the speech module handles intermixed requests in English and Korean languages, giving corresponding feedback. Evaluation with test data showed high accuracy of prediction, having on average 95.7% and 99.6% on the F1-Score metric for command and language prediction, respectively. The proposed speech interaction module in the aircraft maintenance metaverse further improved education and training, giving intuitive and efficient control over the operation, enhancing interaction with virtual objects in mixed reality.