Reviewerly/exHarmony huggingface dataset:
The State of Agile Software Development Teams During the Covid-19 Pandemic

The Covid-19 pandemic in 2020 forced Agile Software Development Teams (ASDT) to rapidly transition to remote work and adapt to new business circumstances. The focus of this research was to investigate the impact of the Covid-19 pandemic on ASDT work and what tools and metrics are used by ASDT. A global survey was performed with 120 answers from different software engineering teams. The results of the research indicate that the work of ASDT wasn't significantly impacted. Most of the ASDT had experience with working in a distributed or remote environment. Therefore, most of the ASDT were able to transitioned to full remote work. Results indicate the Covid-19 pandemic didn't have much impact on Product Backlog and Vision. Moreover, most ASDT didn't change their Definition of Done and release frequency, indicating that the pace and quality of work wasn't disturbed during the Covid-19 pandemic. The few ASDT that changed their work organization did it together with changes to Product Backlog and Vision. Results indicate that the prevalence of distributed teams and remote work among ASDT helped with the transition to fully remote work during the Covid-19 pandemic. Additionally, this article presents gathered data of popularity of different online cooperation tools and metrics used by ASDT.
End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks

Current GAN-based art generation methods produce unoriginal artwork due to their dependence on conditional input. Here, we propose Sketch-And-Paint GAN (SAPGAN), the first model which generates Chinese landscape paintings from end to end, without conditional input. SAPGAN is composed of two GANs: SketchGAN for generation of edge maps, and PaintGAN for subsequent edge-to-painting translation. Our model is trained on a new dataset of traditional Chinese landscape paintings never before used for generative research. A 242-person Visual Turing Test study reveals that SAPGAN paintings are mistaken as human artwork with 55% frequency, significantly outperforming paintings from baseline GANs. Our work lays a groundwork for truly machine-original art generation.
Differential cryptanalysis of image cipher using block-based scrambling and image filtering

Recently, an image encryption algorithm using block-based scrambling and image filtering was proposed by Hua et al. The main idea of the encryption algorithm is to utilize image filtering to permutate and diffuse plain-images. In this paper, we analyze the security problems of the encryption algorithm in detail and break the encryption using a codebook attack. A linear relation between plain-images and cipher-images is disclosed by differential cryptanalysis. Using the linear relation, we build a codebook containing (M×N+1) pairs of cipher-images and the corresponding plain-images, where M×N is the size of plain-images. The proposed codebook attack indicates that the encryption algorithm is insecure. To defend the codebook attack, an improved algorithm is proposed. Experimental results show that the improved algorithm not only inherits the merits of the original algorithm, but also improves security robustness against the proposed differential cryptanalysis.
A method for mixed data classification base on RBF-ELM network

The classification tasks for numerical or categorical data have been well developed. However, the data collected in the real world are frequently the mixed type containing numerical and categorical values, and how to classify the mixed data quickly and efficiently is a critical yet challenging task. Existing classification models for mixed data usually treat the mixed data processing and subsequent classification as two independent phases, without considering their compatibility. By fusing the mixed data processing into a classification algorithm, this paper proposes an extended version of RBF-ELM (Radial Basis Function-Extreme Learning Machine), a Mixed Data RBF-ELM method (MD-RBF-ELM for short), which can achieve direct, fast, and efficient classification for mixed data. Specifically, a distance metric method for mixed data is firstly designed to calculate the distances between the input data and the RBF centers, and then these distances are used to train the network structure and weights of MD-RBF-ELM, thereby realizing the fusion of data processing with model learning. In addition, to alleviate the problem of MD-RBF-ELM’s unstable performance caused by randomly selecting the RBF centers, we propose an improved density peak clustering algorithm and use it to select the optimal RBF centers automatically and adaptively. Extensive experimental results on 34 data sets demonstrate that MD-RBF-ELM significantly enhances the classification performance (increasing 2.37% for F1-score, up to 14/34 for the number of best results, and reaching 2.4/8 for the averaged ranks), compared with seven state-of-the-art competitors.
Dual-modality hard mining triplet-center loss for visible infrared person re-identification

Visible infrared person re-identification (VI-reid) has gradually increased in popularity as an crucial branch of person re-identification (reid). It not only has intra-class variations caused by different viewpoint, pedestrian posture changes, complex backgrounds, resolution, occlusions existing in traditional visible visible person re-identification (VV-reid), but also has been subjected to enormous cross-modality discrepancies resulted from the difference in the reflection spectrum of visible light and infrared cameras. In this paper, we put forward a novel loss function, called dual-modality hard mining triplet-center loss (DTCL), to optimize intra-class and inter-class distance and to supervise the network to learn discriminative feature representations. The DTCL learns a visible modality center and an infrared modality center separately for each class and selects online novel cross-modality triplets and intra-modality triplets for each sample respectively. In particular, it makes the samples from the same class closer to the class center and pushes them away from the centers of the different classes. Besides, we also propose a dual-path part-based feature learning network (DPFLN) framework, to learn the local features and solve the problem of cross-modality discrepancies. We conduct experiments on two cross-modality reid datasets, and obtain promising results.
RETRACTED: Detection of attacks in IoT sensors networks using machine learning algorithm

Assault and peculiar location on the Internet of Things (IoT) framework is an increasing worry in the IoT region. By the expanded IoT foundation utilization in every area, assaults, and dangers in these frameworks are likewise developing proportionately. Malicious control, Spying, Forswearing of Service, Scan, Data Type Probing, Wrong setup, and malicious operation are such assaults and irregularities that may source an IOT framework disappointment. This project proposes a few Machine learning (ML) module that is contrasted with foresee assault and abnormalities on the IoT frameworks precisely. The ML algorithms that have been utilized here are Artificial Neural Network (ANN), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT). The assessment measurements utilized in the examination of presentation are f1 score, exactness, area, recollect, and precision under the ROC Curve. Even though these strategies have similar accuracy, different measurements demonstrate that RF executes relatively preferable.
Data-driven fault diagnosis for residential variable refrigerant flow system on imbalanced data environments

Data-driven approach has emerged as a popular research method for fault detection and diagnosis (FDD) of variable refrigerant flow system. However, one serious issue has been raised in recent studies, which shows that training classifiers with datasets which suffer of imbalanced class distributions can significantly influence the final classification accuracy. So, aiming at the imbalanced dataset environment problem of variable refrigerant flow (VRF) system based on data-driven fault detect and diagnosis, we conducted a study. First of all, through comparative research, it is concluded that the geometric mean accuracy of the fault detection in the VRF system has reduced an average of 42.49% due to the imbalanced data environment. Then we proposed a Principal Component Analysis-Synthetic Minority Oversampling Technique (PCA-SMOTE) method to save this problem, and verified the reliability and versatility of the proposed method with three types of modeling algorithms commonly used in VRF systems. The result shows that this method not only has obvious improvement effect, but also has strong versatility. In the end, we gradually reduce the number of minority from 50 to 10 to explore minimum fault data requirements of this method. The final result shows that this method can still ensure that common machine learning algorithms can effectively detect and diagnosis the VRF system fault when only 10 fault data are included.
Inverted LSB image steganography using adaptive pattern to improve imperceptibility

Various image steganography methods have been developed to improve the stego image quality, i.e. imperceptibility, capacity, and security. This research proposes an adaptive method that can select the most optimal pattern to minimize the error ratio due to message embedding. This adaptive pattern can optimize the performance of the inverted LSB substitution method, based on the two-bit + least-significant-bit (LSB) pattern in the container image. Before embedding, the message and container image bits are tested and the error ratio is calculated using the inverted LSB substitution method for various possible patterns. The pattern that has the least error rate is selected to embed the message. The use of this adaptive pattern in inverted LSB image steganography significantly increases imperceptibility. Based on the test results and by comparing it to previous studies, it is found increased results, the PSNR value ranges from 52.49 to 57.45, and the SSIM ranges from 0.9991 to 0.9999 with 1 BPP capacity.
Cascade saccade machine learning network with hierarchical classes for traffic sign detection

Traffic signs detection is one of the significant tasks for autonomous driving. It conveys notable traffic information timely to road users and maintains traffic safety in smart grid of cities. However, the size of most traffic signs is less than 0.5% of the image of traffic scene, and the uneven distribution of training samples limits the accuracy of the model. Moreover, the appearance of traffic signs within the same category of meaning always varies in shapes, colors, from one country to another. Few works have provided robust solutions to these problems simultaneously. In this paper, motivated from the property of saccade in human vision, we developed a novel architecture cascade saccade network with class hierarchy structure for traffic sign detection and domain shift problem. Experiments on Chinese traffic sign benchmarks (TT100K) demonstrated that the proposed detector achieves comparable performance with the state-of-the-art methods with an average performance improvement of 6% in precision and 14% in recall for small size, and detection time with a GPU is 0.08 s per 2048 × 2048 sized image, which can satisfy the real time requirements of driving safety applications. Moreover, proposed model can be easily extended to solve the cross-domain detection of traffic signs.
Learning from Noisy Data with Robust Representation Learning

Learning from noisy data has attracted much attention, where most methods focus on label noise. In this work, we propose a new learning framework which simultaneously addresses three types of noise commonly seen in real-world data: label noise, out-of-distribution input, and input corruption. In contrast to most existing methods, we combat noise by learning robust representation. Specifically, we embed images into a low-dimensional subspace, and regularize the geometric structure of the subspace with robust contrastive learning, which includes an unsupervised consistency loss and a supervised mixup prototypical loss. We also propose a new noise cleaning method which leverages the learned representation to enforce a smoothness constraint on neighboring samples. Experiments on multiple benchmarks demonstrate state-of-the-art performance of our method and robustness of the learned representation. Code is available at https://github.com/salesforce/RRL/.
Effects of a virtual reality-based pottery making approach on junior high school students’ creativity and learning engagement

Pottery making in technology education aims to foster students’ practical and creative ability. In a typical pottery making class, students receive the teacher’s instruction on the fundamental techniques used in pottery making and have opportunities to practice. It is meaningful to construct an observation-doing-reflection process for students in a pottery making class to promote their learning experience and performance. Therefore, in this study, a Virtual Reality (VR)-based pottery making approach was applied to technology education in a junior high school to examine its effects on students’ creativity and learning engagement. A total of 97 seventh graders from three classes participated in pottery making in class. One class of 34 students was assigned to be the experimental group learning with the VR-based approach, while the other two classes were assigned to be the control groups, with 30 students learning with the paper-and-pencil approach and 33 students with the clay-based approach. The results showed that the students learning with the VR-based approach were not only more creative in terms of their products, but also had higher cognitive engagement than those in the control groups. Moreover, the VR-based approach improved the students’ behavioral, emotional and social engagement compared to the paper-and-pencil approach.
A survey of deep learning models in medical therapeutic areas

Artificial intelligence is a broad field that comprises a wide range of techniques, where deep learning is presently the one with the most impact. Moreover, the medical field is an area where data both complex and massive and the importance of the decisions made by doctors make it one of the fields in which deep learning techniques can have the greatest impact. A systematic review following the Cochrane recommendations with a multidisciplinary team comprised of physicians, research methodologists and computer scientists has been conducted. This survey aims to identify the main therapeutic areas and the deep learning models used for diagnosis and treatment tasks. The most relevant databases included were MedLine, Embase, Cochrane Central, Astrophysics Data System, Europe PubMed Central, Web of Science and Science Direct. An inclusion and exclusion criteria were defined and applied in the first and second peer review screening. A set of quality criteria was developed to select the papers obtained after the second screening. Finally, 126 studies from the initial 3493 papers were selected and 64 were described. Results show that the number of publications on deep learning in medicine is increasing every year. Also, convolutional neural networks are the most widely used models and the most developed area is oncology where they are used mainly for image analysis.
3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstruction

The use of MRI for prostate cancer diagnosis and treatment is increasing rapidly. However, identifying the presence and extent of cancer on MRI remains challenging, leading to high variability in detection even among expert radiologists. Improvement in cancer detection on MRI is essential to reducing this variability and maximizing the clinical utility of MRI. To date, such improvement has been limited by the lack of accurately labeled MRI datasets. Data from patients who underwent radical prostatectomy enables the spatial alignment of digitized histopathology images of the resected prostate with corresponding pre-surgical MRI. This alignment facilitates the delineation of detailed cancer labels on MRI via the projection of cancer from histopathology images onto MRI. We introduce a framework that performs 3D registration of whole-mount histopathology images to pre-surgical MRI in three steps. First, we developed a novel multi-image super-resolution generative adversarial network (miSRGAN), which learns information useful for 3D registration by producing a reconstructed 3D MRI. Second, we trained the network to learn information between histopathology slices to facilitate the application of 3D registration methods. Third, we registered the reconstructed 3D histopathology volumes to the reconstructed 3D MRI, mapping the extent of cancer from histopathology images onto MRI without the need for slice-to-slice correspondence. When compared to interpolation methods, our super-resolution reconstruction resulted in the highest PSNR relative to clinical 3D MRI (32.15 dB vs 30.16 dB for BSpline interpolation). Moreover, the registration of 3D volumes reconstructed via super-resolution for both MRI and histopathology images showed the best alignment of cancer regions when compared to (1) the state-of-the-art RAPSODI approach, (2) volumes that were not reconstructed, or (3) volumes that were reconstructed using nearest neighbor, linear, or BSpline interpolations. The improved 3D alignment of histopathology images and MRI facilitates the projection of accurate cancer labels on MRI, allowing for the development of improved MRI interpretation schemes and machine learning models to automatically detect cancer on MRI.
Multiple-boundary clustering and prioritization to promote neural network retraining

With the increasing application of deep learning (DL) models in many safety-critical scenarios, effective and efficient DL testing techniques are much in demand to improve the quality of DL models. One of the major challenges is the data gap between the training data to construct the models and the testing data to evaluate them. To bridge the gap, testers aim to collect an effective subset of inputs from the testing contexts, with limited labeling effort, for retraining DL models.
Human acceptance evaluation of AR-assisted assembly scenarios

The contribution examines the acceptance of Augmented Reality (AR) in assembly scenarios by a model-based approach for acceptance evaluation. After a critical literature research and analysis, a proprietary model for acceptance measurement is developed, which includes and synthesizes previous models and simplifies them considerably for the purpose of industrial assembly. Consequently, a structural model is derived which is based on the basic concepts of the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technology Acceptance Model (TAM). This is built up as a path model to explain user acceptance. Following, a laboratory experiment is set up to collect data. At the final assembly workstation of the FHWS c-Factory the study participants assemble a toy truck once without and once with AR support. The c-Factory is a concept factory for smart production in an IoT environment. Afterwards, relevant data are collected by means of a survey. The evaluation shows that the acceptance of AR is given and the model is resilient. The results show also, that AR is accepted by the participants supporting their work.
ASK: Adaptively Selecting Key Local Features for RGB-D Scene Recognition

Indoor scene images usually contain scattered objects and various scene layouts, which make RGB-D scene classification a challenging task. Existing methods still have limitations for classifying scene images with great spatial variability. Thus, how to extract local patch-level features effectively using only image label is still an open problem for RGB-D scene recognition. In this article, we propose an efficient framework for RGB-D scene recognition, which adaptively selects important local features to capture the great spatial variability of scene images. Specifically, we design a differentiable local feature selection (DLFS) module, which can extract the appropriate number of key local scene-related features. Discriminative local theme-level and object-level representations can be selected with DLFS module from the spatially-correlated multi-modal RGB-D features. We take advantage of the correlation between RGB and depth modalities to provide more cues for selecting local features. To ensure that discriminative local features are selected, the variational mutual information maximization loss is proposed. Additionally, the DLFS module can be easily extended to select local features of different scales. By concatenating the local-orderless and global-structured multi-modal features, the proposed framework can achieve state-of-the-art performance on public RGB-D scene recognition datasets.
MMMNet: An End-to-End Multi-Task Deep Convolution Neural Network With Multi-Scale and Multi-Hierarchy Fusion for Blind Image Quality Assessment

As the evaluation of image quality depends on the human visual system (HVS), many existing image quality assessment (IQA) methods focus on modeling the HVS to account for subjective perception. The visual attention of the HVS makes humans more sensitive to distortion on the attended regions than on regions which are not the focus of attention. Therefore, we propose an end-to-end multi-task deep convolution neural network with multi-scale and multi-hierarchy fusion (MMMNet), in which the IQA and saliency subtasks are jointly optimized to improve saliency-guided IQA performance. Particularly, the incorporation of saliency information is achieved by fusing saliency features with IQA features hierarchically to progressively improve the IQA features over network depth. A multi-scale feature extraction module (MSFE) is proposed to provide effective saliency features for the IQA network. Based on the saliency fusion, MMMNet introduces an auxiliary saliency task, achieving the multi-task learning to improve the generalization of the IQA task. Experimental results show that MMMNet achieves state-of-the-art performance and strong generalization ability on IQA databases.
Enabling Intelligent IoCV Services at the Edge for 5G Networks and Beyond

The Fifth Generation (5G) communication technology has paved the way for intelligent and diversified Internet of Connected Vehicles (IoCV) services that meet stringent Quality of Service (QoS) requirements. Both Artificial Intelligence (AI) and Blockchain are playing and will continue to play an imperative role in providing secure and decentralized resource sharing to solve complex and time-sensitive problems at the edge. The integration of both those techniques will enhance the performance of smart vehicular services, especially in beyond 5G (B5G) networks. Ensuring secure transactions in complex autonomous network architectures is an immense challenge. This article addresses computational, storage, connectivity and intelligence concerns using a collaborative approach to engage multiple Internet of Things (IoT) nodes such as connected- vehicles, drones and mobile devices for the provisioning of QoS-optimal complex service compositions in autonomous mobile networks. Continuous and fast compositions emerge using decentralized decisions and interactions with diversified neighboring nodes with the aid of reinforcement learning. Blockchain is used to ensure that nodes interact with each other verifiably and record transactions without the need for trusted intermediaries. We assess whether having an AI-enabled blockchain collaborative composition solution improves service availability and delivery of smart city vehicular services.