Reviewerly/exHarmony huggingface dataset:
Videolytics

We present Videolytics, a web-based system for advanced analytics over recorded video streams. Video cameras have become widely used for indoor and outdoor surveillance. Covering even more public space in cities, the cameras serve various purposes ranging from security to traffic monitoring, urban life, and marketing. The goal is to obtain effective and efficient models to process the video data automatically and produce the desired features for data analytics. Videolytics combines the best of deep learning and hand-designed analytical models to create a solution applicable in real-life situations. The architecture of the Videolytics framework is centered around a database of video features and detected objects, where new higher-level objects result from fusion of (lower-level) objects and features already stored in the database. The system provides a number of visualization options, an SQL-based analytics module as well as a real-time surveillance mode.
Automatic Recognition of Financial Instruments Based on Anisotropic Partial Differential Equations

In this paper, anisotropic partial differential equations are used to conduct an indepth study and analysis of automatic recognition of financial bills. Firstly, it obtains the invoices of the group enterprise, uses scanning technology and related image recognition technology to capture, process, compress and slice the paper bill content, and then carries out data identification and verification of the image. It classifies the obtained electronic data information into bills, converts it into electronic information related to bills according to the corresponding categories of the bill template, and stores it in the bill table of the database to achieve the management operation of formatted electronic files. After categorizing the bills according to the electronic information of bills to match the business scenarios, financial journal vouchers can be generated according to the preconfigured voucher templates of the corresponding business scenarios, and the financial journal vouchers are converted into voucher messages using XML technology. Finally, we use agent technology to design middleware for heterogeneous financial systems to realize the function of communicating voucher messages to each other in different business systems. The system automatically extracts the key information of invoices through OCR technology and performs real-time verification and cyclical feedback to the verification results to the suppliers. The system has realized the intelligent management of the power company’s VAT invoices, thus greatly enhancing the efficiency of VAT invoice verification and settlement. The automatic tax invoice recognition system adopts a network structured tax invoice recognition model, which eliminates the cumbersome steps of character decomposition and character classification in traditional OCR character recognition. After several trials, it has obtained better experimental results in terms of recognition accuracy, with an accuracy rate of over 93% in the recognition of tax invoice data set.
An Overview of Face Deep Forgery

With the rise of deep learning and the improvement of computer computing power, many interesting topics have emerged in the field of DeepFake in recent years. Deep forgery includes, but is not limited to, the forgery of human faces, but also the forgery of scenes. Among them, deep forgery of human face has received greater attention due to its importance and sensitivity. At present, various cutting-edge deep forgery methods can easily forge fake faces that are difficult for ordinary people to distinguish, which has already posed a threat to information security and social trust. This paper focuses on face deep forgery, will introduce different types of face deep forgery and its technologies. And we found that deep forgery’ s real-time and robustness are crutial drawbakcs. Finally, the future development trend of deep forgery of human face are proposed.
Application of Smart Wearable Devices in Elderly Care

This article uses technical case analysis and field survey data to study the application of smart wearable devices in elderly care. Firstly, the research background is introduced, that is, with the huge number of elderly people and the increasingly serious aging trend, the physical and mental health of the elderly need to be paid attention to; secondly, the technical characteristics of smart wearable devices and the special needs of the elderly for smart wearable devices are analyzed. Further focused analysis on the health monitoring function of existing smart wearable devices, and preliminary prediction of the development direction of smart wearable devices are given as well; thirdly, through the author's field research data, it is found that the elderly hope that smart wearable devices can give them more help in entertainment and health. Finally, the author concludes that the development trend of smart wearable devices in the field of elderly care will be reflected in entertainment support and health management.
DP-Nets: Dynamic programming assisted quantization schemes for DNN compression and acceleration

In this work, we present effective quantization schemes called DP-Nets for the compression and acceleration of deep neural networks (DNNs). A key ingredient is a novel dynamic programming (DP) based algorithm to obtain the optimal solution of scalar K-means clustering. Based on the approaches with regularization and quantization function, two weight quantization approaches called DPR and DPQ for compressing normal DNNs are proposed respectively. Accordingly, a technique based on DP-Nets for inference acceleration is presented. Experiments show that DP-Nets produce models with higher inference accuracy than recently proposed counterparts while achieving same or larger compression. They are also extended for compressing robust DNNs, and the relevant experiments show 16X compression of the robust ResNet-18 model with less than 3% accuracy drop on both natural and adversarial examples. The experiments with FPGA demonstrate that the technique for inference acceleration brings over 5X speedup on matrix–vector multiplication.
PS-GAN: a single image snow removal framework using pseudo-Siamese GANs

In this article, we innovatively use Pearson correlation coefficient, etc. to analyze the components of the snowy image (the snow-free image and the mask image), and then create an effective and accurate snow-free model using the relationship between the components of the snowy image. For the snow-free model, we innovatively consider the similarity in the generation of the snow-free image and the mask image; we also consider this relationship in our neural network framework. We set the generator model in the generative adversarial network as the pseudo-siamese network with the same structure but the different parameters. Each branch of the pseudo-siamese network adopts the autoencoder and the multi-scale perception structure. The former can guarantee the acquisition of high-resolution images, and the latter can perceive context information at different scales. We restore the mask image first and then restore the snow-free image because the mask image has a simpler background than the snow-free image and the mask image is easier to recover than the snow-free image. The results show that our (PS-GAN) pseudo-siamese generative adversarial network not only has better performance on the data set, but also has good results in the real world which greatly improves the effect of using the yolov5 to detect.
Efficient enhancement of underwater images using color correction and fusion

This paper proposes an enhancement technique for underwater images that are degraded due to scattering and absorption. The method comprises of channel compensation and white balancing steps, and the output is given in parallel to the gamma correction and sharpening to adjust the contrast and remove high frequency irregularities. Using suitable fusion technique, the images are combined together, after which the quality metrics are evaluated. Measures are adopted to reduce the computational complexity of the method and to automate the execution. This gives user the flexibility to test the images and collect results with almost 50% less computation time. The method does not require any prior knowledge/training or channel model. Such features can be very crucial in time constrained or power limited applications.
From Project to Product

Abstract As technical advances have enabled organizations to deliver software to the market faster, in turn shortening the feedback loop for new ideas and spurring innovation, legacy organizations need to update their mindset from a project-driven to a product-driven approach or risk being displaced by product-native organizations. This poster shows the high-level principles that represent our experience guiding organizations with a project-to-product approach.
Exploring the impact of classification probabilities on users' trust in ambiguous instances

The large-scale adoption of systems that automate classifications using Machine Learning (ML) algorithms raises pressing challenges as they support or make decisions with profound consequences for human beings. It is important to understand how users' trust is affected by ML models' suggestions, even when those models are wrong. Many research efforts have focused on the user's ability to interpret what a model has learned. In this paper, we seek to understand another aspect of ML interpretability: how the presence of classification probabilities affects users' trust in the model outcomes, especially in ambiguous instances. To this end, we conducted an online survey in which we asked participants to evaluate their agreement with an automatic classification made by an ML model before and after presenting them the model classification probabilities. Surprisingly, we found that, in ambiguous instances, respondents agreed more with incorrect model outcomes than with correct ones, requiring further analyses.
Embedding an interactive art installation into a building for enhancing citizen's awareness on urban environmental conditions

The paper presents an artwork, embedded in a public-use building complex, comprising four interactive, sight specific installations. The artwork employs ubiquitous computing technologies and a variety of other components (projector, LED matrix displays, physical and custom-made objects) to sense, collect and translate urban, environmental data and human input into evolving multisensory representations, affording hybrid spatial experiences. The artwork aims at highlighting the impact of human activity upon the environment and the nonhuman entities that inhabit it, shifting the focus to a post-anthropocentric view of the world.
PicToText: Text Recognition using Firebase Machine Learning Kit

Back in the days, documents, payment receipts, and books are being published in printed format. To make a digital format, readers need to manually type them into a computer. However, things have change rapidly especially in the information technology (IT) industry. Optical character recognition (OCR) has been introduced to simplify the digitalization process for users. However, OCR not able to translate scanned text images into different human readable languages. Therefore, this paper proposed an Android-based application programming interface (API) developed using Firebase as an improvement text recognition program that able to translate the scanned text images into language of user preference. This proposed API processes text images, detects text from the scanned images, and translate the text into user-preferred languages. The used data in this paper is the research papers available in the university library.
From Qgis to Qfield and Vice Versa: How the New Android Application Is Facilitating the Work of the Archaeologist in the Field

The aim of this paper is to highlight the main benefits of using the Qfield app in archaeological jobs. In particular this article provides examples to use Qfield in open area excavation, Archaeological survey and impact assessment (HIA) projects.
Application features‐based virtual machine deployment strategy in cloud environment

Abstract Traditionally virtual machine (VM) deployment is based on one or two resources on the hosts and rarely considers other resources as well as the resource preferences of different applications. This leads to the imbalance and inefficient utilization of the resources on the physical hosts which further results in the waste of the cloud resources. To solve this problem, a novel VM deployment strategy is proposed. In consideration of the features of applications, the strategy deploys VMs based on the resource preferences of applications as well as the remained resources of the hosts. Utilizing the resource preferences the weights of various resources are determined, and the weight tables for VMs are obtained. Combining the resources weights of VMs and the resource configurations of physical hosts the values whether the resources are used in balance are calculated. To verify the correctness and effectiveness of the proposed strategy, extensive experiments were carried out on Cloudsim. Experimental results show that the proposed strategy is correct and effective. It outperforms other algorithms in VM deployment and ensures that the cloud resources are more efficiently utilized.
Relational Attention with Textual Enhanced Transformer for Image Captioning

Image captioning has attracted extensive research interests in recent years, which aims to generate a natural language description of an image. However, many approaches focus only on individual target object information without exploring the relationship between objects and the surrounding. It will greatly affect the performance of captioning models. In order to solve the above issue, we propose a relation model to incorporate relational information between objects from different levels into the captioning model, including low-level box proposals and high-level region features. Moreover, Transformer-based architectures have shown great success in image captioning, where image regions are encoded and then attended into attention vectors to guide the caption generation. However, the attention vectors only contain image-level information without considering the textual information, which fails to expand the capability of captioning in both visual and textual domains. In this paper, we introduce a Textual Enhanced Transformer (TET) to enable addition of textual information into Transformer. There are two modules in TET: text-guided Transformer and self-attention Transformer. The two modules perform semantic and visual attention to guide the decoder to generate high-quality captions. We extensively evaluate model on MS COCO dataset and it achieves 128.7 CIDEr-D score on Karpathy split and 126.3 CIDEr-D (c40) score on official online evaluation server.
Optimal Speed Controller Design with Interval Type-2 Fuzzy Sets

Interval type-2 fuzzy sets are often used in the designing of fuzzy systems associated with the speed control under uncertainty. In this paper, we consider the possibilities of using interval type-2 fuzzy sets to describing the uncertainties that arise when defining input variables. These uncertainties can be presented as the different meanings on the same dynamic system or changes in the technical parameters that occur due to external factors. We also present complex interval type-2 fuzzy sets and give the examples of their use to reduce the number of fuzzy sets and rules in a fuzzy knowledge base. The necessary conditions for the construction of complex interval type-2 fuzzy sets and methods for determining lower and upper membership functions in them are also given. The footprint of uncertainty for the complex interval type-2 fuzzy set is presented with upper and lower membership functions of different fuzzy sets. All methods which are described in this work can be used in decision support systems design, to select the optimal parameters of a nonlinear system, or in fuzzy speed controller design.
Age-Style and Alignment Augmentation for Facial Age Estimation

Facial age is an important soft biometric trait for better identification of a human subject. The development of a facial age estimation system requires a large collection of age-labeled data. However, the imbalanced data distribution across age poses a major challenge to making a decent model to describe the variation of facial appearance caused by age. The cross-age data imbalance can be observed in common facial age datasets, for example, the MORPH [], FG-NET [] and the MIVIA dataset [] considered in the GTA Contest. It can be often seen that insufficient data are provided for younger ages and senior ages, and the insufficiency becomes worsened as the age moves close to both ends. To deal with the data imbalance issues, many approaches implement various data augmentation schemes. In our approach, we propose a data augmentation scheme built upon the Age-Style GAN (ASGAN), which we propose for facial age regression and progression. In addition to the ASGAN-based data augmentation, we leverage the mean-variance loss to improve the age classification accuracy, and exploit face alignment as an auxiliary scheme to augment the whole dataset with an aligned subset. We conducted extensive experiments on the MIVIA dataset for verifying the performance of our approach.
Performance Comparison of Two Statistical Parametric Methods for Outlier Detection and Correction

Outlier detection and correction referred to as data preprocessing, is crucial in time series analysis and modeling. It has been a challenge to preprocess a volatile time series data possessing intricate trend characteristics. Two well-established statistical parametric methods, such as improved sliding window prediction and portrait dataset-based, perform adequate data preprocessing. While the former is equipped with an optimal window width selection approach, the latter, on the other hand, is based on a data visualization approach named portrait. This paper compares both methods' preprocessing performance when applied to seasonal time series data with varying time resolutions and complex trend patterns for different content of outliers through detailed result analyses. Further, a new metric to measure outlier correction capability is suggested.
When Anchoring Fails: Interactive Alignment of Large Virtual Objects in Occasionally Failing AR Systems

Augmented reality systems show virtual object models overlaid over real ones, which is helpful in many contexts, e.g., during maintenance. Assuming all geometry is known, misalignments in 3D poses will still occur without perfectly robust viewer and object 3D tracking. Such misalignments can impact the user experience and reduce the potential benefits associated with AR systems. In this paper, we implemented several interaction algorithms to make manual virtual object alignment easier, based on previously presented methods, such as HoverCam, SHOCam, and a Signed Distance Field. Our approach also simplifies the user interface for manual 3D pose alignment in 2D input systems. The results of our work indicate that our approach can reduce the time needed for interactive 3D pose alignment, which improves the user experience.
System-level Anomaly Detection for Nuclear Power Plants Using Variational Graph Auto-encoders

In order to efficiently identify the system-level anomalies of the nuclear power plants (NPP), a Variational Graph Auto-encoder (VGAE) anomaly detection method with coarse-grained feature input is proposed to solve the large-scale unlabeled and multi-source coupled operational data of NPP. First, detrended cross-correlation analysis (DCCA) was used to quantitatively evaluate the correlation between variables, the multivariate coupling networks were constructed, and the weakly correlated edges were removed. Second, the first-order difference sequences of variables were symbolized. The symbolic values represent the fluctuation characteristics of the variables at the corresponding time. Based on the above input, a semi-supervised learning VGAE model was established, the reconstruction loss of real-time operating data is considered as the anomaly detection indicator. Finally, it was proved by a case of an actual NPP circulating water system. The results show that comprehensive consideration of the fluctuation characteristics and correlation characteristics of the operating data can effectively improve the accuracy of the anomaly detection model. Compared with traditional network evaluation indicators, such as network structure entropy, the proposed reconstruction loss has higher sensitivity and accuracy, and can realize early anomaly detection.
An Improved Unsupervised White Blood Cell Classification via Contrastive Learning

The classification and counting of white blood cells (WBCs, leukocytes) in blood smears are of great significance for clinicopathological diagnosis. Therefore, the classification of WBCs in the images is a basic task. Most of the existing WBCs classification methods are based on supervised learning, which highly depends on a large number of image labels. To cope with the challenge of image annotation, in this paper, we propose an unsupervised WBCs classification method, which combines the advantages of contrastive learning and a deep clustering algorithm. Specifically, the proposed method firstly employs contrastive learning to pre-train the feature encoder, which is able to improve the similarity of feature coding among the same kind of cell categories. Then, the classical clustering algorithm is used based on the pre-trained image features for unsupervised classification of WBCs. Finally, the high confidence clustering results are fed back to the pre-trained feature encoder, which can be used as the pseudo labels to form a closed loop. Experimental results on a dataset containing 574 leukocyte images demonstrate the effectiveness of the proposed method.
ANF: Attention-Based Noise Filtering Strategy for Unsupervised Few-Shot Classification

How to learn concepts from few-shot samples remains an open challenge in the deep learning era. The previous meta-learning methods require a large number of annotated samples in the training phase, which still contributes to high manual-labeling costs. In this paper, we propose a unsupervised few-shot learning framework and pointed out that negative queue constructed via randomly sampling contains many false-negative samples (noise), which has negative impacts on the model’s generalized performance especially when only few samples are available. Specially, we propose an Attention-based Noise Filtering (ANF) strategy to make momentum contrastive loss more applicable to few-shot learning scenario. In addition, we also propose a dynamic momentum update method, which can greatly improve the classification accuracy. Our evaluations demonstrate state-of-the-art unsupervised few-shot learning performance, which is comparable to supervised baseline models.
SPARQL-vision

The wide adaptation of the Semantic Web and the Resource Description Framework (RDF) has made available many important datasets. The SPARQL query language facilitates the exploration of this information, which is available in a semi-structured way that does not comply with relational data models, deviating from exploration techniques that most researchers are familiar with. Usually, only people with training and extensive knowledge of the RDF model can explore and understand it in depth. We present here a platform that supports the users with querying, exploring and visualizing information available in SPARQL endpoints. A dedicated visualization module, built upon a knowledge database, allows us to provide case-specific visualization solutions for SPARQL query results. The selection is based exclusively on features extracted from the result, without any knowledge about the structure, content and characteristics of the underlying dataset.