Reviewerly/exHarmony huggingface dataset:
Construction of curriculum system of building environment and energy application engineering specialty based on information technology requirements

In the large data of today’s rapid development, HVAC engineering design, construction and debugging of cannot leave the use of information technology, through the investigation of hvac enterprises, such as information technology for hvac engineering and technical personnel of engineering practice ability demand, analysis of hvac enterprise development demand and the gap between applied talents in colleges and universities, This paper analyzes the problems existing in the curriculum system setting of architectural and energy application engineering in application-oriented universities, and then constructs the curriculum system of architectural environment and energy application engineering oriented by the demand of enterprise engineering practice ability, integrates theoretical knowledge into engineering practice, and improves students' engineering practice ability such as information technology application. Suggestions can be provided for the training of engineering and technical personnel in enterprises and the improvement of the curriculum system of building environment and energy application engineering in applied universities.
HANA: A handwritten name database for offline handwritten text recognition

Methods for linking individuals across historical data sets, typically in combination with AI based transcription models, are developing rapidly. Perhaps the single most important identifier for linking is personal names. However, personal names are prone to enumeration and transcription errors and although modern linking methods are designed to handle such challenges, these sources of errors are critical and should be minimized. For this purpose, improved transcription methods and large-scale databases are crucial components. This paper describes and provides documentation for HANA, a newly constructed large-scale database which consists of more than 3.3 million names. The database contains more than 105 thousand unique names with a total of more than 1.1 million images of personal names, which proves useful for transfer learning to other settings. We provide three examples hereof, obtaining significantly improved transcription accuracy on both Danish and US census data. In addition, we present benchmark results for deep learning models automatically transcribing the personal names from the scanned documents. Through making more challenging large-scale databases publicly available we hope to foster more sophisticated, accurate, and robust models for handwritten text recognition.
Ensemble Learning using Error Correcting Output Codes: New Classification Error Bounds

New bounds on classification error rates for the error-correcting output code (ECOC) approach in machine learning are presented. These bounds have exponential decay complexity with respect to codeword length and theoretically validate the effectiveness of the ECOC approach. Bounds are derived for two different models: the first assumes that all base classifiers are independent and the second assumes that all base classifiers are mutually correlated up to first-order. Moreover, we perform ECOC classification on six datasets and compare their error rates with our bounds to experimentally validate our work and show the effect of correlation on classification accuracy.
DESA: Disparity Estimation With Surface Awareness

Depth map plays an important role in our daily life including automatic driving, intelligent robots, and commercial manufacture. However, existing depth estimation methods are still facing some difficulties in real-life applications. Firstly, using merely point-wise metrics during training usually causes surface distortion due to the lack of geometric restrictions. To tackle this problem, we introduce a normal restriction module to the network to improve the method performance with regards to surface integrity. Secondly, although 3D methods usually achieve higher accuracy by directly concatenating features as cost aggregation, they sacrifice the time and memory efficiency. To make the disparity estimation network light enough for adding the normal restriction, we propose a 2D aggregation method to replace the 3D ones. Experiments prove the effectiveness of our method and we show that our results are competitive to 3D ethods under popular benchmarks including KITTI, SceneFlow and Midddlebury.
Generative Adversarial Nets for Multiple Text Corpora

Generative adversarial nets (GANs) have been successfully applied to the artificial generation of image data. In terms of text data, much has been done on the artificial generation of natural language from a single corpus. We consider multiple text corpora as the input data, for which there can be two applications of GANs: (1) the creation of consistent cross-corpus word embeddings given different word embeddings per corpus; (2) the generation of robust bag-of-words document embeddings for each corpora. We demonstrate our GAN models on real-world text data sets from different corpora, and show that embeddings from both models (weGAN and deGAN) lead to improvements in supervised learning problems.
Artificial Neural Network Applied to Soccer Field Reconstruction

The 3D reconstruction and automatic statistical analysis of sporting events is an area of increasing demand and innovation. In this work, it is proposed the use of two different CNNs (AlexNet and LeNet) with different activation functions for the classification of soccer field view. The images will be classified as viewing the right goal, the center field, and the left goal. The classification of the soccer field view facilitates the creation of an automated algorithm for creating correspondences of relevant points. This activity is the most difficult to be automated. As it is very difficult to obtain real images for training the CNNs, it is proposed a method for creating synthetic soccer field views. Three CNNs presented good results and high accuracy.
A Lightweight Metric Defence Strategy for Graph Neural Networks Against Poisoning Attacks

Graph neural networks (GNN) are a specialized type of deep neural networks on graph structured data by aggregating the learned representations of node neighborhood, which has been widely applied in a variety of domains. However, recent studies demonstrate that using unnoticeable, artificially-crafted perturbations on graph structure can drastically damage the performance of GNNs. Hence, developing robust algorithms to defend poisoning attacks is of great significance. A natural idea to defend them is to delete perturbed edges from a poisoned graph and several efforts have been taken. However, current works either adopt specific threshold as a criteria to filter out poisoned effect for particular graphs or design quite complicated framework with higher time consumption, which can’t well be scablable in practice. Thus in this work, we first investigate the distinction of perturbed edges and normal edges behaved on metric space, then design a defense strategy, called MD-GNN based on Jaccard similarity. Its core principle is to discern the perturbed edges via deleting those edges with lower metric values. Besides, to preserve the valuable information of graph structure and avoid the appearance of the single node during deleting process, MD-GNN deploy the minimum connectivity principle as the terminated condition. Extensive experiments on three real-world datasets show that MD-GNN can effectively preserve state-of-the-art performance of GNNs in the face of poisoning attacks with less time consumption.
Attention-Based Bi-LSTM for Anomaly Detection on Time-Series Data

Anomaly detection in time-series data is a significant research problem that has applications in multiple areas. Unsupervised anomaly detection is a fundamental aspect of developing intelligent automated systems. Existing work in this field has primarily focused on developing intelligent systems that use dimensionality reduction or regression-based approaches to annotate data based on a certain static threshold. Researchers in fields such as Natural Language Processing (NLP) and Computer Vision (CV) have realized considerable improvement by incorporating attention in prediction-related tasks. In this work, we propose an attention-based bi-directional long short term memory (Attention-Bi-LSTM) networks for anomaly detection on time-series data. It helps in assigning optimal weights to instances in sequential data. We evaluate the proposed approach on the entirety of the popularly used Numenta Anomaly Benchmark (NAB). Additionally, we also contribute by creating new baselines on the NAB with recent models such as REBM, DAGMM, LSTM-ED, and Donut, which have not been previously used on the NAB.
Applying QR Code in Mobile Banking Use

The QR technology has changed the payment industry especially in retail market. More and more companies introduce their easy payment through QR payment. The fast penetration in the market has made QR payment become necessary payment for major SME industries to survive in the market. QR payment has become essential substitution to current cash or debit card payment. Although QR payment has convenience experience, it poses to security threats associated with its system design. Many mobile applications still have many difficulties to balance to adopt its benefits and managing threats. The article takes case study of a well-known banking mobile banking application in applying QR system. The outcome of the article is expected to provide reference for mobile application to apply QR system.
Learning To Rank Relevant Documents for Information Retrieval in Bioengineering Text Corpora

In this paper, we present a Learning To Rank-based approach that helps EXPLORE and understand Relevant documents for bioengineering text corpora, called LTREXPLORER. Based on the likelihood of being the most relevance to a search query, the ranking model sorts documents according to their degrees of relevance, preference, or importance with various domain-specific features. The evaluation results demonstrated that our approach has the potential to effectively provide the retrieval scoring functions to researchers, who focus on the most relevant documents in bioengineering information retrieval.
COMIRE: A Consistence-Based Mislabeled Instances Removal Method

Training neural network classifiers (NNCs) usually requires all instances to be correctly labeled, which is difficult and/or expensive to satisfy in some practical applications. When label noise is present, mislabeled data will severely mislead the training of NNCs, resulting in poor generalization performance. In this work, we address the label noise issue by removing mislabeled instances from the training data. A COnsistence-based Mislabeled Instances REmoval (COMIRE) method is proposed. The main idea is based on the observation that during the training of the NNC, the training loss and the model's prediction uncertainty of correctly labeled instances show similar trends, while those of mislabeled instances have quite different trends. Thus, the consistency between the two trends can be used to distinguish correctly labeled instances from mislabeled ones. On this basis, an iteration scheme is introduced to further increase the separability between the two types of data. Experimental results show that COMIRE can effectively identify the mislabeled instances. Moreover, the classification performance is significantly improved after removing the identified instances from the noisy training data.
Fraudulent E-Commerce Websites Detection Through Machine Learning

With the emergence of e-commerce, many users are exposed to fraudulent websites, where attackers sell counterfeit products or goods that never arrive. These websites take money from users, but also they can stole their identity or credit card information. Current applications for user protection are based on blacklists and rules that turn out into a high false-positive rate and need a continuously updating. In this work, we built and make publicly available a suspicious of being fraudulent website dataset based on distinctive features, including seven novel features, to identify these domains based on recently published approaches and current web page properties. Our model obtained up to \(75\%\) F1-Score using Random Forest algorithm and 11 hand-crafted features, on a 282 samples dataset.
Image Inpainting Using Multi-Scale Neural Network and Shift-Net

Image Inpainting is to repair the images with given mask or missing area and produce a realistic fake image similar to the original image. Methods with Generative Adversarial Networks(GANs) and deep convolutional neural network(CNNs) have achieved great performance on generating visual plausible results. However, those results still have blurry issues on restored region and unpleasant boundaries. Based on these problems, a two-phase neural network method is proposed to produce better results on image inpainting in this paper. The proposed method adopts the Shift-Net [1] as the first phase to generate primary images, which are then fed into refinement network (the second phase) for further detailed texture restoring. This model adopts improved Wasserstein GAN(WGAN-GP) to ensure the training stability and performance on generating high-resolution images. Experiments on shows the proposed model can produce clear and realistic plausible face images. Through the comparison with state-of-the-art methods on both PSNR and SSIM metrics, the proposed method has a better performance on recovering the feature information on missing region.
CVFuzz: Detecting complexity vulnerabilities in OpenCL kernels via automated pathological input generation

OpenCL programs typically employ complex storage models and diverse data types as well as manifest various memory access patterns, which make it challenging to detect the performance problems effectively. However, few research efforts have been dedicated to cope with this challenge so far. In this paper, we introduce CVFuzz, a domain-independent tool that can effectively detect and locate algorithmic complexity vulnerabilities in OpenCL kernels. The key enabling idea is leveraging automatically generated pathological inputs to trigger the worst-case behavior during the execution of OpenCL kernels. Our approach takes advantage of the metrics such as code coverage and run time to guide the generation of inputs that can slow down the execution of a given OpenCL kernel. We evaluate CVFuzz on more than 250 real-world OpenCL kernels. The evaluation results demonstrate that the inputs generated by CVFuzz are effective in detecting the worst-case time algorithmic complexity and optimization vulnerabilities.
Comparative Study of Low-Cost Tools to Create Effective Educational Infographics Content

In recent years, infographics have become increasingly popular as a means of visually presenting data. It is a form of visualization that seeks to present any material with a visual composition for the target audience, incorporating elements such as forms, icons, graphics, photos, diagrams, and texts. Simple tools such as graphic paper, pencils, markers, and rulers can create infographics by hand. People nowadays tend to use computer software because it is quicker and easier. A significant number of experienced designers use Adobe’s advanced paid software, which is Adobe Illustrator and Adobe Photoshop. These tools are ideal for designing graphic designs because they are simple to use and offer a wide range of choices. However, there are several low-cost and free resources available online, especially for creating infographics, including Canva, Piktochart, Microsoft Powerpoint, Mind the Graph, and Adobe Spark. This study aims to analyze these several low-cost tools that can produce effective infographics in education content as well as conduct a comparative semiotic analysis on the elements design of infographic. Canva is an effective and low-cost solution for users seeking to produce professional-looking infographic material for marketing and services. Users can easily customize templates, graphics, and text. Canva also introduces new features and strengthens its overall capabilities to assist consumers in producing visually pleasing templates that cater to their target market.
3D Liver and Tumor Segmentation in CT Scans with Robust Self-Paced Learning

Liver segmentation is a challenging problem where fully supervised deep learning models require large amounts of voxel-wise labels, which are usually laborious, expensive, and time consuming to obtain. However, massive non-annotated 3D CT volumes are usually easily accessible. Most deep learning based works to address semi-supervised CT medical image segmentation problems have mostly been graph-based or adversarial-based. In sharp contrast, this work investigates the potential of a semi-supervised learning method; novel self-training framework utilizing self-paced learning strategy to generate and select pseudo-labels from easy-to-transfer(well represented classes) to hard-to-transfer(less represented classes) samples. The proposed scheme introduces an effective pseudo-label generation and selection algorithm to generate highly confident pseudo-labels, jointly learn and optimize a model which can be solved by alternatively generating pseudo labels on unlabeled target data and re-training the model with the generated labels. Further, we also propose a novel class balancing framework to avoid the gradual eminence of easy-to-transfer discriminative features on pseudo-label generation utilizing class-wise normalization of confidence scores. Comprehensive experiments conducted on publicly available datasets show that the proposed method outperformed other state-of-the-art semi-supervised and some fully supervised methods on the segmentation results of tumors and achieved very competitive performance for liver segmentation.
ADKGN: An Attentive Dynamic Knowledge Graph Network for Sequential Recommendation

Sequential recommendation system's goal is to predict users' next actions based on their historical behavior sequences. As we know, more recent items have a larger impact than the previous ones. Meanwhile, modeling users' current interests are challenging. Knowledge graph (KG) contains a vast of information, which can help us capture users' interests by propagating their interactions. In this paper, we propose an Attentive Dynamic Knowledge Graph Networks (ADKGN) for sequential recommendation, which includes an embedding module, an attentive dynamic knowledge graph module, a sequential process module and a prediction module. Specifically, embedding module learns initial item embedding vectors by combing latent features and sequential features. For users' recent interacted items, attentive dynamic knowledge graph module learns dynamic weights for each pretrained KG embedding, and then utilizes top-k layer and parallel-based aggregation layer to effectively aggregate useful information from multi-hop neighbors. Sequential process module combines a user's history interactions and processed current interactions, and employs sequential models over them to get final user representations. Prediction module predicts the clicking probability using final user representation and target item representation. We conduct experiments on a public dataset, finding that ADKGN significantly outperforms state-of-the-art solutions.
A Light Ranker for Open-Domain Question Answering

Open-domain question answering aims to extract answers from some unlabeled texts. Existing approaches usually follow the retrieve-then-read pipeline, which depends heavily on the quality of the retriever. Therefore, a ranker is significant to denoise those irrelevant paragraphs by giving each candidate paragraph a confidence score. In this paper, we design a light paragraph ranker, which utilizes an iterative approach to combine the paragraph-paragraph relevance and the paragraph-question relevance. Our ranker can fully mine global paragraph information and is lighter than that in most other Open-domain QA models. Furthermore, we explore two types of readers based on whether they sample candidate paragraphs or retain all. Experiments on public datasets show that our model achieves improvements compared to baselines.
Application of small angle approximation in circumsolar irradiance modelling

Circumsolar irradiance is the part of diffuse or scattered irradiance within a small angle (2.5° for typical pyrheliometers) around the sun which is measured, together with direct normal irradiance, by pyrheliometers and reported as part of the direct normal irradiance. This could create an inconsistency as much as 300 W/m2 between radiative transfer modelling and pyrheliometer measurements, having further ramifications for the validation of surface solar irradiance data derived from various sources such as sky imaging camera, satellite and numerical weather prediction models. In addition, accurate modelling of the angular pattern of solar irradiance within a very small angle from the centre of the sun is important for solar energy generation using concentrating (thermal or photovoltaic) technologies. This article presents an analytical model for the calculation of circumsolar radiation, based on the ‘small angle approximation’. Validation against full radiative transfer calculation shows that the approximate method is accurate for common pyrheliometers used operationally. The error is less than 0.5 W/m2 for opening half angle of 2.5°, and less than 2.0 W/m2 for opening half angle of 5°, for typical cloud and aerosol types and the full range of optical depths. This small angle approximation method is relatively quick to calculate (when compared to full radiative transfer modelling calculations times) and can underpin enhanced modelling of solar radiation for satellite validation, solar energy generation and numerous other applications across the globe.
Towards Compressing Efficient Generative Adversarial Networks for Image Translation via Pruning and Distilling

Deploying GANs (Generative Adversarial Networks) for Image Translation tasks on edge devices is plagued with the constraints of storage and computation. Compared to some methods like neural architecture search (NAS), filter pruning is an effective DNN (Deep Neural Network) compressing method. It can compressing DNNs in a short time. The filter importance is measured by the filter norm, the filters with low norm are pruned. As for image classification, the filter with larger norm has larger influence on the final classification scores. However, as illustrated in Fig. 4, the filter with large norm don’t always have a big impact on the quality of generated images for GANs. Based on the observation that the filter close to the filters’ center in the same convolution layer can be represented by others in [], we develop a distance-based pruning criterion. We prune the filters which are close to the filters’ center in a convolution layer. KD (Knowledge distillation) trains the compressed model and improves its performance. The most common KD method ignores the transformation information across the feature maps, which is important for GANs. We take them as additional knowledge and transfer it from the uncompressed GAN to the pruned GAN. Our experiments on CycleGan, Pix2pix, and GauGan achieved excellent performance. Without losing image quality, we obtain 51.68 \(\times \) and 36.20 \(\times \) compression on parameters and MACs (Multiply-Accumulate Operations) respectively on CycleGan. Our code (We will open source within one week after the paper being received) will be made available at github.