Reviewerly/exHarmony huggingface dataset:
Prediction of software defects by knowledge graph and genetic algorithm

Software defect detection is one of the biggest software development challenges and accounts for the largest budget in the software development process. One of the effective activities for software development and increasing its reliability is to predict software defects before reaching the test stage, which helps to save time in the production, maintenance and cost process. This research aims to present a software defect prediction method based on knowledge graphs and automated machine learning. We use knowledge acquisition, knowledge fusion, knowledge storage and knowledge calculation and other knowledge map construction technology research, to realize the knowledge map recommends high-quality software defect prediction models as the hot-start input conditions for automatic search. The empirical study uses NASA's open-source dataset experimental objects and six performance evaluation indicators include Precision, Recall, PRC (Precision Recall Characteristic), ROC (Receiver Operating Characteristic), F-Measure, MCC (Matthews Correlation Coefficient). The experimental results show that the proposed model performs better than the traditional classic software defect prediction model recommended by the knowledge map in terms of different datasets and evaluation indicators
Ethical Leadership and Auditor’s Underreporting of Audit Time: Mediating Role of Work Engagement

Considering the subsiding credibility of audit profession by recurring accounting scandals, this study investigates the impact of audit team leaders’ ethical leadership on staff auditors’ propensity to accept underreporting of audit time through mediating effect of staff auditors’ work engagement. This study collects primary data from 397 staff auditors working for the registered audit firms conducting audit of limited liability companies of Pakistan. The results provide that audit team leaders’ ethical leadership significantly reduces staff auditors’ engagement into underreporting of audit time through partial mediating role of staff auditors’ work engagement. Findings of current study are expected to uphold public confidence in the audit profession by indicating the mechanism of improving audit quality through decreasing dysfunctional audit behaviors.
Pojok Literasi Sebagai Upaya Peningkatan Minat Baca Siswa SDN Menampu 04 Kabupaten Jember

Interest in reading should be instilled from an early age, especially at school age. The fear of students coming to the library and the availability of books are internal and external factors. Both of these factors should be addressed properly. Thus, in this service, a literacy corner and book additions were held at SDN Menampu 04 Jember. There are several stages in the implementation of this service including 1) survey and observation, 2) planning, 3) implementation and 4) evaluation. A literacy corner is held in each class. In addition, 123 books, both fiction and non-fiction, were added. The books added also use Indonesian, English, and Regional languages. The publishers and authors of the added books also vary. So that it can increase reading interest from SDN Menampu 04 Jember. Procurement of literacy corners equipped with additional books can increase the reading interest of SDN Menampu 04 students. This is due to the existence of literacy corners in each class, as well as the many variations of books in SDN Menampu 04.&#x0D;
Joint Semantic Segmentation and Object Detection Based on Relational Mask R-CNN

As fundamental and important problems in computer vision field, semantic segmentation and object detection have made a series of breakthroughs in recent years. Although the existing semantic segmentation and object detection methods have achieved impressive performance in some detection benchmarks, they only focus on local information near the region of objects. However, an image usually contains rich semantic information, including scene context information and dependency information between objects. As a result, ignoring this semantic information will inevitably deteriorate their performance. In this paper, we propose a novel network named joint semantic segmentation and object detection based on relational Mask R-CNN (RM-RCNN) to solve above limitations. By designing the object dependence calculation module (DCM), we can model the relationship information between objects by their geometric and appearance features, so as to improve the accuracy of semantic segmentation and object detection. At the same time, we also design a cross-scale information transmission module (CSITM), which can make the features of different levels transmit information to each other. By using CSITM, our method can effectively retain the useful information and discard the useless information to further improve its performance. Experiments on two benchmark datasets demonstrate the effectiveness of our proposed network.
Arbitrary Voice Conversion via Adversarial Learning and Cycle Consistency Loss

Recently, Non-parallel voice conversion (VC) has attracted the attention of many researchers in the field of speech. However, such model suffers from the limitation that how to improve the generalization of the model to extract the speaker information from unseen speaker. In this paper, we proposed a novel zero-shot VC approach which performs VC only rely on the speaker belong to training set. To achieve this VC method, we disentangle the speaker and content representations with instance normalization, and then use the adversarial learning to encourage model to produce more similar converted result. The experiment results demonstrate that our approach can achieve arbitrary voice conversion without any supervision.
K-Nearest Neighbor Based Local Distribution Alignment

When massive labeled data are unavailable, domain adaptation can transfer knowledge from a different source domain. Many recent domain adaptation methods merely focus on extracting domain-invariant features via minimizing the global distribution divergence between domains while ignoring local distribution alignment. In order to solve the problem of incomplete distribution alignment, we propose a K-nearest neighbors based local distribution alignment method, where Maximum Mean Discrepancy (MMD) is adopted as the transfer loss function to reduce the global distribution discrepancy, and then a K-nearest neighbors based transfer loss function is also devised to minimize the local distribution difference for the complete alignment of source and target domain. The proposed method contributes to avoid the dilemma of incomplete alignment in MMD by local distribution alignment and improve its recognition accuracy. Experiments on multiple transfer learning datasets show that the proposed method performs comparatively well.
News Video Description Based on Template Generation and Entity Insertion

News video description aims to generate a knowledge-rich description for a news video with attached text. The difficulty of this task is how to mine events and named entities from attached text using video input. Existing approaches are all based on one-stage methods and do not filter redundant contextual sentences, resulting in inaccurate descriptions. This paper proposes a two-stage approach based on template generation and entity insertion, where the first stage focuses on the generation of events and the second stage focuses on the generation of named entities such as event participants. Specifically, we first design a sentence ranker based on pre-trained models to filter video-related sentences from the attached text, then use a multimodal encoder and transformer-based decoder to generate a description template, and finally do the entity insertion using the sorted sentences to get the final description. The results show that our method exhibits strong performance on the News Video Dataset.
A Classification Algorithm Based on Discriminative Transfer Feature Learning for Early Diagnosis of Alzheimer’s Disease

This paper proposes a discriminative transfer feature learning method for MCI conversion prediction using data from the target domain and the auxiliary domain. A transfer component analysis method based on the Maximum Mean Discrepancy (MMD) is proposed at first, which is used to weaken the difference of data distribution between the relevant domain and the target domain. Next, the discriminant optimization term is added to measure the correlation between the sample categories and the sample features of the auxiliary domain, and to improve the inter-class separability of the algorithm. Finally, the support vector machine (SVM) is used to classify MCI patients.
Measuring Shape and Reflectance of Real Objects Using a Handheld Camera

In this paper, we propose a system that can measure the shape and reflectance of real objects using a simple apparatus. Our system consists of a handheld camera with a single light source attached, a turntable, and a chessboard with markers. First, using the handheld camera, we acquire images of a target object from multiple viewpoints by capturing a video of the rotating target object placed on the turntable. The shape of the object is reconstructed using the silhouettes of the object seen from multiple viewpoints. For each vertex of the reconstructed three-dimensional shape, the parameters of a reflectance model are estimated using the brightness change in the multiple-viewpoint images. It was confirmed that our system was able to reproduce the appearance of real objects based on the reconstructed 3D shape with reflectance parameters without requiring a large apparatus.
Tunicate Swarm Algorithm Based Difference Variation Flower Pollination Algorithm

Flower pollination algorithm is a novel meta-heuristic swarm intelligence optimization algorithm, for its problems of insufficient solution accuracy, slow convergence speed and low stability, the Tunicate swarm algorithm based difference variation flower pollination algorithm (TSA-DVFPA) is proposed in this paper. The simplified Tunicate swarm algorithm and the random selection strategy were introduced into the process of cross-pollination. The differential variation strategy has been applied to the local search of the algorithm iteration to increase the diversity of the population. The improvement is validated by 16 benchmark functions. Compared with other similar algorithms, the results show that the proposed algorithm has a certain improvement in the convergence speed and the accuracy of optimization.
An Improved Block Truncation Coding Using Rectangular Non-symmetry and Anti-packing Model

Block truncated coding (BTC) is an image compression algorithm with a simple coding process and a high coding speed, which can be used in the field of military communications with high real-time requirements. As the price of pursuing simplicity and high speed, the compression ratio and the quality of the decoded image are sacrificed to some extent. Although some strategies have been proposed to improve the compression ratio and the quality of the decoded images, the effect is not obvious. Inspired by the quadtree-based block truncation coding (QEDBTC) and the non-symmetry and anti-packing model (NAM), in this paper, we propose a novel rectangular NAM-based block truncation algorithm (RNAMEDBTC), which uses rectangular NAM strategy to divide the initial blocks into rectangular homogeneous blocks. The spatial frequency measurement (SFM) is used as a measurement parameter to subdivide the initial blocks. For each homogeneous block, we replace the high and low quantization values and the binary bitmaps in the traditional block truncation coding with the average value of the pixels in the block, thereby a great improvement of the compression rate of the algorithm is achieved. In order to further improve the compression rate, we have increased the area of the smaller homogeneous blocks, and thus reducing the number of homogeneous blocks. These expanded blocks are called non-homogeneous blocks. For each non-homogeneous block, we need to do error diffusion block truncation coding (EDBTC) processing. The experimental results in this paper show that without degrading the quality of the decoded image, the proposed algorithm improves the compression rate significantly by 158.3% and 30.8% higher than the traditional BTC and QEDBTC algorithms, respectively.
Two-Channel VAE-GAN Based Image-To-Video Translation

We propose a VAE-GAN network with a two-channel decoder for addressing multiple image-to-video translation tasks, i.e., generating multiple videos of different categories by a single model. We consider this image-to-video translation as a video generation task rather than a video prediction that needs multiple frames as input. After training, the model only requires the first frame of the video and its corresponding attribute to generate the required video. The advantage of combining the Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) is to avoid the shortcomings of both: VAE components can give rise to blur, and unstable gradients caused by the GAN. Extensive qualitative and quantitative experiments are conducted on the MUG [1] dataset. We draw the following conclusions from this empirical study: compared with state-of-the-art approaches, our approach (VAE-GAN) exhibits significant improvements in generative capability.
Image Super-Resolution Reconstruction Based on MCA and ICA Denoising

Image super-resolution reconstruction is a high-resolution image that is reconstructed from a low-resolution image. The learning-based algorithm is one of the more effective algorithms for image super-resolution reconstruction, and the core idea of the algorithm is to use the sample library to train the information of the image in order to increase the high-frequency information of the test image and achieve the purpose of image super-resolution reconstruction. In this paper, we propose a new image super-resolution algorithm based on morphological component analysis and dictionary learning. Firstly we make independent component analysis for image denoising processing by the K-SVD method. And then, MCA algorithm is utilized to efficiently decompose low-resolution images into texture part and structure part. And the K-SVD method is used to make dictionary training of low-resolution images. The method not only improves the robustness of the images, but also adopts different reconstruction algorithms for the different characteristics of the texture and structure parts, which better retains the details of the images and improves the quality of the reconstructed images.
Research on Augmented Reality Assisted Material Delivery System in Digital Workshop

In the digital workshop with highly customized products, material delivery is the intermediate hub between warehouse management and production, which directly affects the products delivery speed and production efficiency. Due to the different kinds of materials, high flexibility of delivery plans and scattered workshop stations, the human-oriented material delivery mode still plays an important role. In this paper, augmented reality (AR) technology is introduced into the material delivery process to improve the level of system information visualization and human-machine interaction, reduce redundant labor. This paper also introduces a novel AR markerless 3D registration technology based on scene feature recognition and visual relocalization module. It helps to realize high-precision and robust 3D registration of virtual information in the whole workshop and basic material delivery path guidance function. This AR system has completed the transformation from image-level or object-level 3D registration to region-level 3D registration, which is helpful to broaden the scope of industrial application. Finally, the feasibility of the system is verified in a workshop production unit. All necessary information during material delivery process can be correctly and timely transmitted to workers with Microsoft HoloLens 2 (a high-performance AR headset). The AR assisted material delivery system effectively reduces the probability of missing delivery or wrong delivery and logistics cost.
An Improved NAMLab Algorithm Based on CIECDE2000 Color Difference Formula and Gabor Filter for Image Segmentation

Image representation is an important problem in the field of computer vision. The Non-symmetry and Anti-packing pattern representation Model (NAM) is an effective pattern representation model. In order to further improve the image segmentation quality and segmentation efficiency, in this paper, we improve the recently published NAMLab algorithm in two aspects. First, the CIEDE2000 color difference formula is used to replace the calculation formula of the color feature similarity in the original NAMLab algorithm. The formula is based on the human vision response to RGB and it is used to accurately represent the reception of the color. Secondly, the calculation formula of texture features in the original NAMLab algorithm is modified. The original NAMLab algorithm is based on the Weber Local Descriptor (WLD) texture descriptor to describe the feature texture of the image. In order to better meet the characteristics of human vision observation, we found that the Gabor wavelet is very similar to the stimulus response of simple cells in the human visual system, which is more in line with the characteristics of the human vision, so we choose Gabor filter as the feature texture description of the image. Finally, the improved algorithm is compared with the state-of-the-art algorithms in the field of image segmentation on six datasets, and it achieves better results in terms of visual presentation and the segmentation indicators.
Local Feature for Visible-Thermal PReID Based on Transformer

Person re-identification based on infrared image and RGB image is a cross-modality pedestrian recognition, which is a challenging task. The traditional goal of person re-identification is to find a given person's image from an image database, often from a single modality database. In real applications, there are often multiple modalities of data. Traditional single modality tasks have limitations. Cross-modality person re-identification needs to extract features from RGB and infrared images. In our work, we take advantage of both global and local features. First, we use a dual-path VIT structure to extract features from RGB images and infrared images, respectively. Secondly, we cut the local features in the spatial direction and input the shared VIT layer to learn the local features. The loss function consists of Identity loss, Triplet loss, and Center loss. The model can capture shared features between modality and improve cross-modality similarity. Finally, we performed experiments on two datasets, SYSU-MM01 and RegDB, and compared them with other methods in recent studies.
A 3D Medical Image Segmentation Framework Fusing Convolution and Transformer Features

Medical images can be accurately segmented to provide reliable basis for clinical diagnosis and pathology research, and assist doctors to make more accurate diagnosis, as well as deep learning technology can accelerate this process. Convolutional Neural Networks (CNNs) and Transformer have become two mainstream architectures of deep learning in medical image segmentation. However, the Transformer architecture has limited ability to obtain local inductive bias, and the Transformer architecture is at a disadvantage in a small sample data set. Many theories and experiments show that the above problems can be effectively solved by fusing Convolution and Transformer features. In this manuscript, a new U-shaped segmentation model based on Convolution and swin-transformer framework is proposed, which is called CST-UNET. In the encoder part, it combines the advantages of both dilated convolution and Transformer, which can make the model fully obtain semantic inductive bias information and long-term information. At the same time, it has the advantages of fewer parameters and lower Flops. Even if it is trained on a small sample data set, the framework still has strong generalization ability. In addition, on BraTS2021 dataset, the Dice coefficients of ET, TC and WT are 85.46%, 89.38%, 92.35% respectively, and the result of HD95 are 7.95, 5.06 and 4.07 respectively.
Handwritten Chemical Equations Recognition Based on Lightweight Networks

Handwritten chemical equations recognition is one of the important research directions of optical character recognition (OCR) and text recognition technology, which is widely used in life. Although the mainstream deep learning text recognition model can get good recognition results, the number of parameters of the model is too large to be carried on some portable devices. We develop a new lightweight network model (LCRNN) based on the CRNN model for handwritten chemical equations recognition. Firstly, in the convolutional layer of the LCRNN model, we propose a new MobileNetV3 (MobileNetV3M) to reduce number of the model parameters. The MobileNetV3M changed the original down-sampling method to max-pooling, so it can extract more critical information. Secondly, we use the BiGRU model in the recurrent layer. Finally, a new chemical equations encoding method is proposed, which can change the two-dimensional chemical equation into one-dimensional chemical equation encoding, so as to facilitate handwritten chemical equations recognition. The experiments demonstrate that the character precision of the LCRNN model is 2.1% lower than the CRNN model, but the number of parameters is significantly reduced.
Stitching High Resolution Notebook Keyboard Surface Based on Halcon Calibration

Aiming at the technical problems of poor stitching accuracy and difficulty in real-time stitching in the high resolution notebook keyboard surface image stitching algorithm, this paper proposes a high resolution notebook keyboard surface image stitching algorithm based on Halcon calibration. The method in this paper selects two cameras with different resolutions. First, the dot calibration board is used to calibrate the camera's internal parameters and 3D pose with Halcon; second, the 3D pose of the two cameras is mapped so that the two images are located in the same world coordinate system, and then the homography matrix is obtained; finally, the splicing fusion is performed. In addition, the method proposed in this paper can also be applied to image mosaic of more cameras. Extensive experiments demonstrate that compared with the widely-used SIFT, SURF and ORB-based methods, the proposed algorithm has several advantages: (1) compared with the algorithm with the smallest error, the error is reduced from 6.53 mm to 2.89 mm; (2) the homography matrix can be used directly, which greatly reduces the time of image stitching.
An Efficient Evaluation Mechanism for Evolutionary Reinforcement Learning

In recent years, many algorithms use Evolutionary Algorithms (EAs) to help Reinforcement Learning (RL) jump out of local optima. Evolutionary Reinforcement Learning (ERL) is a popular algorithm in this field. However, ERL evaluate the population in each loop of the algorithm, which is inefficient because of the uncertainties of the population's experience. In this paper, we propose a novel evaluation mechanism, which only evaluates the population when the RL agent has difficulty in studying further. This mechanism can improve the efficiency of the hybrid algorithms in most cases, and even in the worst scenario, it only reduces the performance marginally. We embed this mechanism into ERL, denoted as E-ERL, and compare it with original ERL and other state-of-the-art RL algorithms. Results on six continuous control problems validate the efficiency of our method.