Reviewerly/exHarmony huggingface dataset:
Research on Automatic Recognition of Pointer Meter Reading Based on Deep Learning Algorithm

Abstract With the rapid development of computer technology and intelligent high-performance hardware, deep learning algorithm plays an increasingly important role in society. The development of automatic identification method of instrument and instrument based on deep learning can effectively improve the working efficiency of the basic power grid operation and maintenance personnel, avoid personnel approaching the high-risk operation area, and ensure the continuous monitoring of the readings of key instruments all day. In this paper, a recognition algorithm of pointer types circular instrument based on deep learning algorithm is proposed. Gaussian filter and binary method are used to achieve better recognition effect, which lays a good theoretical foundation for further practical application.
Next-Generation EEW Empowered by NDSHA: From Concept to Implementation

In this paper, we discuss a possible combination of Earthquake Early Warning (EEW) and Neo-deterministic Seismic Hazard Assessment (NDSHA), and propose a new warning model, EEW2.0. The aim is to provide a differentiated warning alert to various end-users based on the results of seismic hazard assessment evaluation. The implementation of such a system contains three basic steps: (a) classification of “potential to cause hazard” in terms of magnitude; (b) determination of the source areas and building a hazard database in terms of Modified Mercalli Intensity (MMI) maps, considering all possible earthquake scenarios in the source area, for the whole protected area; (3) equipping unique decision framework for specific end-users. When a damaging earthquake (M ≥ 5.0) is detected, EEW2.0 quickly matches the prepared MMI map by estimated magnitude and epicenter, then directly extracts the MMI value and issues an early warning to the public. With the great attention and resources put into the reduction in seismic and its secondary risk in the 21st century, the proposed EEW2.0 will likely play an active role in protecting lives and reducing economic losses.
Weakly-Supervised Video Object Grounding via Causal Intervention

We target at the task of weakly-supervised video object grounding (WSVOG), where only video-sentence annotations are available during model learning. It aims to localize objects described in the sentence to visual regions in the video, which is a fundamental capability needed in pattern analysis and machine learning. Despite the recent progress, existing methods all suffer from the severe problem of spurious association, which will harm the grounding performance. In this paper, we start from the definition of WSVOG and pinpoint the spurious association from two aspects: (1) the association itself is not object-relevant but extremely ambiguous due to weak supervision; and (2) the association is unavoidably confounded by the observational bias when taking the statistics-based matching strategy in existing methods. With this in mind, we design a unified causal framework to learn the deconfounded object-relevant association for more accurate and robust video object grounding. Specifically, we learn the object-relevant association by causal intervention from the perspective of video data generation process. To overcome the problems of lacking fine-grained supervision in terms of intervention, we propose a novel spatial-temporal adversarial contrastive learning paradigm. To further remove the accompanying confounding effect within the object-relevant association, we pursue the true causality by conducting causal intervention via backdoor adjustment. Finally, the deconfounded object-relevant association is learned and optimized under a unified causal framework in an end-to-end manner. Extensive experiments on both IID and OOD testing sets of three benchmarks demonstrate its accurate and robust grounding performance against state-of-the-arts.
Losing and regaining the thread: learning from the experience of moving from in-person to Zoom observations

The authors of this paper describe and share their experiences of moving from in-person infant observation to observing remotely on Zoom during the Covid-19 Pandemic. The story is told chronologically, firstly reflecting on their own response to the change, and then, through vignettes, looking at detailed aspects of the move for the babies and their families, as well as tracking the emergence of distinct and meaningful online observations. This is followed by a discussion of a particular feature of remote observations that came to be seen as a significant dimension of the material in both observations: that of the family’s agency in positioning the device, and thus the observer, during observations. The authors were members of the same Infant Observation seminar and were both in the second year of two-year observations when the UK government imposed a national lockdown.
Credentials as a Service Providing Self Sovereign Identity as a Cloud Service Using Trusted Execution Environments

With increasing digitization, more and more people use their identification credentials for accessing online services; which increases concern for data privacy. To ensure user's privacy, alternate credential management schemes must be adopted. Self-Sovereign Identity (SSI) is a form of credential management where users are in charge of their credentials. Privacy-critical data is stored at the user's end and they can choose to do selective disclosure of minimal required information to access services. Currently, SSI solutions are not being widely adopted by service providers and the ecosystem is fragmented. One of the reasons for the lack of adoption is the need for maintaining private infrastructure for credential issuance, as critical user information is to be handled during credential issuance. To cater to this, we present a solution that enables the service providers to run their credential issuers on public cloud - a so-called Credentials as a Service (CaaS). CaaS issuers run inside Trusted Execution Environments (TEE) enabling credential issuers to ensure user's privacy while enjoying the flexibility of the pay-per-use cloud model. CaaS can pave the way for making SSI credentials ubiquitous in identity management solutions.
Efficient infrared imaging–blur kernel estimation with multi-scale feature learning for online learning video source

To enhance the infrared images resolution, a novel infrared imaging–blur kernel estimation model is proposed, which is based on multiscale deconvolution and total variation regularization. The blur kernel can be refined by the deconvolution algorithm with increasing scale level. Furthermore, the split Bregman iteration algorithm is used to optimize the proposed cost functions. The major novelty of the study is that the proposed model can recover the high-resolution image and an accurate blur kernel from a degraded image simultaneously. Qualitative and quantitative results on simulated and actual images demonstrate that the proposed method can estimate the infrared blur function accurately and produce high-resolution image. The enhanced infrared images can be applied to the application for the learning video quality improvement.
Classification method of PV production patterns for energy flow analysis in design phase of energy-sharing community

To build an energy-sharing community, the energy flow, while considering the changes in the data within the community, must be identified in the planning and design phases. In this study, a procedure was suggested to assume PV production in a specific district and plan PV systems for the energy-sharing community using a method to identify and quantify daily patterns of PV production based on Discrete Fourier Transform (DFT). To verify its validity, the measured energy production of five PV systems and solar radiation within a district in Jincheon, South Korea, in 2019 were used. The proposed procedure based on the result of the study was as follows: First, apply DFT for daily PV production and solar radiation to convert continuously changing data with respect to time to simple expressions with amplitudes in frequency domain. Second, identify similar patterns in the data with different amplitudes by calculating the distribution of DFT results. Third, compare the DFT distribution of PV production and solar radiation for the same date and defined the relationship between them. Fourth, classify daily solar radiation types with respect to DFT distribution and define the characteristics of solar radiation based on the DFT distribution. Finally, assume annual PV production for a specific district from the past annual solar radiation. The proposed method is expected to accurately predict energy for a district via a simple calculation using past solar radiation data without complex simulation in the design phase of an energy-sharing community. Furthermore, this method can be used to estimate the efficiency of annual PV production for each district. Therefore, this can aid in the decision making for installing PV systems for specific districts.
Characterizing Scattered Occlusions for Effective Dense-Mode Crowd Counting

We propose a novel deep learning approach for effective dense crowd counting by characterizing scattered occlusions, named CSONet. CSONet recognizes the implications of event-induced, scene-embedded, and multitudinous obstacles such as umbrellas and picket signs to achieve an accurate crowd analysis result. CSONet is the first deep learning model for characterizing scattered occlusions of effective dense-mode crowd counting to the best of our knowledge. We have collected and annotated two new scattered occlusion object datasets, which contain crowd images occluded with umbrellas (csoumbrellas dataset) and picket signs (cso-pickets dataset). We have designed and implemented a new crowd overfit reduction network by adding both spatial pyramid pooling and dilated convolution layers over modified VGG16 for capturing high-level features of extended receptive fields. CSONet was trained on the two new scattered occlusion datasets and the ShanghaiTech A and B datasets. We also have built an algorithm that merges scattered object maps and density heatmaps of visible humans to generate a more accurate crowd density heatmap output. Through extensive evaluations, we demonstrate that the accuracy of CSONet with scattered occlusion images outperforms over the state-of-art existing crowd counting approaches by 30% to 100% in both mean absolute error and mean square error.
Hardware Implementation for Fending off Side-Channel Attacks

Side-channel attacks (SCAs) are to obtain physical information (e.g., time information, power consumption, electromagnetic leakage) from the physical implementation of a cryptosystem for further cracking the cryptosystem. In this paper, we study the protection to SCAs for the Advanced Encryption Standard (AES) cryptosystem. We add extra circuits (components) to the AES circuit to produce almost equal power consumption (for different key values) without affecting the encryption results. Experimental results show that the approach works well in practice.
Networked Professional Learning in the Postdigital Age: Asking Critical Questions of Postgraduate Education

Taking the notion of the postdigital as its starting point, this chapter begins with my experience teaching in postgraduate teacher education courses. In postgraduate teacher education, professional learning is understood as being a ceaseless process. Mirroring the way, the concept of the postdigital illuminates our almost seamless relationship with digital technologies in education, the concept of lifelong learning highlights that education is now conceptualized as a continuous process of upskilling. This chapter explores the public pedagogical implications that occur with the use of social media sites for networked professional learning. Drawing on feminist theorizing and postdigital conceptualizations of education, this chapter asks critical questions about the promotion of social networking sites and reflects on the implications of those questions for postgraduate education. This postdigital analysis of Twitter demonstrates the utility of postdigital theory as a reflective tool when applied to postgraduate education.
Programming language implementations for context-oriented self-adaptive systems

The context-oriented programming paradigm is designed to enable self-adaptation, or dynamic behavior modification of software systems, in response to changes in their surrounding environment. Contextoriented programming offers an adaptation model, from a programming language perspective, that maintains a clean modularisation between the application and adaptation logic, as well as between the components providing adaptations. We use three implementation techniques for context-oriented programming languages to assess their appropriateness to foster self-adaptive systems. These approaches take advantage of the capabilities offered by the host programming language to realize self-adaptation as proposed by context-oriented languages. We evaluate each of these approaches by assessing their modularity and complexity when defining adaptations, and by comparing their run-time performance on a simple benchmark. Our results show a higher modularity than that for common architecture based self-adaptive systems, while maintaining comparable performance. We conclude that context-oriented programming is an appropriate paradigm to realize self-adaptation.
3DFP-FCGAN: Face completion generative adversarial network with 3D facial prior

Face completion is a domain-specific image inpainting problem. Most existing face completion methods fail to synthesize fine-grained facial structures due to the undifferentiated treatment of face images and other scene images. To handle this problem, we propose an end-to-end deep generative model based approach which makes full use of the facial prior knowledge, including 2D facial geometry priors from facial parsing maps and landmarks, as well as the 3D depth prior. We adopt a coarse-to-fine inpainting framework where the 2D facial geometry priors based on coarse faces are extracted to guide the refinement network for better planar facial textures and structures. Moreover, a novel 3D regularized reconstruction loss is proposed for the enhancement of the stereo perception of generated faces. Experimental results on two large-scale benchmarks CelebA and CelebA-HQ show that our method significantly outperforms the state-of-the-arts in generating more visually realistic and pleasing faces. Code is available at .
Named entity recognition in the food field based on BERT and Adversarial training

Aiming at extracting effective entity information from unstructured corpus in the food field, a named entity recognition (NER) method based on BERT (Bidirectional Encoder Representations from Transformers) and Adversarial training is proposed. The task of NER requires both the identification of entity boundaries and entity types. In order to improve the precision of identifying entity boundaries, we use the BERT word embedding method to enhance the feature extraction ability of input information. To optimize the NER task, adversarial training is introduced, which not only use the shared information obtained from task training of Chinese word segmentation (CWS) and NER, but also prevent the private information of CWS task from generating noise. The experiment is based on the corpus of two categories which are Chinese food safety cases and People's Daily news, respectively. Among them, the Chinese food safety cases data set is used to train the NER task, and People's Daily news data set is used to train the CWS task. We use adversarial training to improve the precision of the NER task for entity recognition (including person, location, organization, food and toxic substance). The Precision rate, Recall rate and F1 score are 95.46%, 89.50% and 92.38% respectively. Experimental results show that this method has a high precision rate for Chinese NER task where the boundary of a specific domain is indistinct.
Examining the sustainable development approach of migrants' remittances and financial development in sub‐Saharan African countries

Abstract In achieving a desirable sustainable economic growth in developing countries, the role of financial development and international migrant remittances cannot be underplayed. This study attempts to investigate the dynamic interactions between the migrant remittances and the financial sector development from a multidimensional perspective in 22 sub‐Sahara African countries using a panel vector autoregression model over the period 2004–2017. Specifically, the study focuses on the multidimensions of financial development including financial depth, financial access, and financial efficiency in financial institutions (FI) and their relationship with the remittances. The findings suggest that: First, the migrant's remittances are detrimental for the overall FI while the FI are found to positively influence the remittances inflows. Second, the relationship between the remittances and the FI varies with the dimensions of FI. There is a positive dynamic interaction between the remittances, the financial efficiency, and financial access, except for the financial depth. The analysis in this investigation offers relevant policy directives especially on the role of remittances in building stable FI of the sub‐Saharan African economies and other developing states.
Analysis of Electromagnetic Information Leakage from Overdesigned Power Delivery Network of Cryptographic Devices

In this paper, an analysis of electromagnetic (EM) information leakage from an overdesigned power delivery network (PDN) of cryptographic devices is presented. In the target hierarchical PDN, sufficient decoupling capacitors are mounted in both on-chip and printed circuit board (PCB) PDNs which maintained the hierarchical PDN impedance below a target impedance from 1 MHz to 1 GHz. Correlation electromagnetic analysis (CEMA) and secret key extraction are conducted based on measured electric and magnetic field radiations from various locations in the hierarchical PDN such as above chip, PCB PDN, and decoupling capacitors. For the first time, it is verified that a cryptographic device complying with the target impedance specification with a sufficient impedance margin can leak full-byte secret key information via EM field radiation. Based on the analysis result, the PDN design methodology for the cryptographic device is presented.
Street-Level Image Localization Based on Building-Aware Features via Patch-Region Retrieval under Metropolitan-Scale

The aim of image-based localization (IBL) is to localize the real location of query image by matching reference image in database with GNSS-tags. Popular methods related to IBL commonly use street-level images, which have high value in practical application. Using street-level image to tackle IBL task has the primary challenges: existing works have not made targeted optimization for urban IBL tasks. Besides, the matching result is over-reliant on the quality of image features. Methods should address their practicality and robustness in engineering application, under metropolitan-scale. In response to these, this paper made following contributions: firstly, given the critical of buildings in distinguishing urban scenes, we contribute a feature called Building-Aware Feature (BAF). Secondly, in view of negative influence of complex urban scenes in retrieval process, we propose a retrieval method called Patch-Region Retrieval (PRR). To prove the effectiveness of BAF and PRR, we established an image-based localization experimental framework. Experiments prove that BAF can retain the feature points that fall on the building, and selectively lessen the feature points that fall on other things. While this effectively compresses the storage amount of feature index, we can also improve recall of localization results; implemented in the stage of geometric verification, PRR compares matching results of regional features and selects the best ranking as final result. PRR can enhance effectiveness of patch-regional feature. In addition, we fully confirmed the superiority of our proposed methods through a metropolitan-scale street-level image dataset.
A Federated Recommendation System Based on Local Differential Privacy Clustering

Traditional recommendation systems (RS) play an important role in applications such as electricity trade, e-commerce etc. However, there is a serious risk of data privacy leakage in traditional recommendation system (RS). To overcome the issues, Federated Learning (FL) and RS are employed for distributed training in recommendation system, which focuses on improving the accuracy to achieve similar performance as centralized training. However, it also causes bad training efficiency and privacy leakage. In this paper, we present a secure federated RS scheme based on local differential privacy and security aggregation to achieve centralized training-like performance while balancing data privacy protection and training efficiency. Finally, we demonstrate the performance of our scheme.
Exploring Simple and Transferable Recognition-Aware Image Processing

Recent progress in image recognition has stimulated the deployment of vision systems at an unprecedented scale. As a result, visual data are now often consumed not only by humans but also by machines. Existing image processing methods only optimize for better human perception, yet the resulting images may not be accurately recognized by machines. This can be undesirable, e.g., the images can be improperly handled by search engines or recommendation systems. In this work, we examine simple approaches to improve machine recognition of processed images: optimizing the recognition loss directly on the image processing network or through an intermediate input transformation model. Interestingly, the processing model's ability to enhance recognition quality can transfer when evaluated on models of different architectures, recognized categories, tasks, and training datasets. This makes the methods applicable even when we do not have the knowledge of future recognition models, e.g., when uploading processed images to the Internet. We conduct experiments on multiple image processing tasks paired with ImageNet classification and PASCAL VOC detection as recognition tasks. With these simple yet effective methods, substantial accuracy gain can be achieved with strong transferability and minimal image quality loss. Through a user study we further show that the accuracy gain can transfer to a black-box cloud model. Finally, we try to explain this transferability phenomenon by demonstrating the similarities of different models' decision boundaries. Code is available at https://github.com/liuzhuang13/Transferable_RA.