Reviewerly/exHarmony huggingface dataset:
Solving Fuzzy Job-Shop Scheduling Problem Using DE Algorithm Improved by a Selection Mechanism

The emergence of fuzzy sets makes job-shop scheduling problem (JSSP) become better aligned with the reality. This article addresses the JSSP with fuzzy execution time and fuzzy completion time (FJSSP). We choose the classic differential evolution (DE) algorithm as the basic optimization framework. The advantage of the DE algorithm is that it uses a special evolutionary strategy of difference vector sets to carry out mutation operation. However, DE is not very effective in solving some instances of FJSSP. Therefore, we propose a novel selection mechanism augmenting the generic DE algorithm (NSODE) to achieve better optimization results. The proposed selection operator adopted in this article aims at a temporary retention of all children generated by the parent generation, and then selecting N better solutions as the new individuals from N parents and N children. Various examples of fuzzy shop scheduling problems are experimented with to test the performance of the improved DE algorithm. The NSODE algorithm is compared with a variety of existing algorithms such as ant colony optimization, particle swarm optimization, and cuckoo search. Experimental results show that the NSODE can obtain superior feasible solutions compared with solutions produced by several algorithms reported in the literature.
Blockchain Governance—A New Way of Organizing Collaborations?

The recent emergence of blockchains may be considered a critical turning point in organizing collaborations. We outline the historical background and the fundamental features of blockchains and present an analysis with a focus on their role as governance mechanisms. Specifically, we argue that blockchains offer a way to enforce agreements and achieve cooperation and coordination that is distinct from both traditional contractual and relational governance as well as from other information technology solutions. We also examine the scope of blockchains as efficient governance mechanisms and highlight the tacitness of the transaction as a key boundary condition. We then discuss how blockchain governance interacts with traditional governance mechanisms in both substitutive and complementary ways. We pay particular attention to blockchains’ social implications as well as their inherent challenges and limitations. Our analysis culminates in a research agenda that explores how blockchains may change the way to organize collaborations, including issues of what different types of blockchains may emerge, who is involved and impacted by blockchain governance, why actors may want blockchains, when and where blockchains can be more (versus less) effective, and how blockchains influence a number of important organizational outcomes.
A Systematic Literature Review and Meta-Analysis on Cross Project Defect Prediction

Background: Cross project defect prediction (CPDP) recently gained considerable attention, yet there are no systematic efforts to analyse existing empirical evidence. Objective: To synthesise literature to understand the state-of-the-art in CPDP with respect to metrics, models, data approaches, datasets and associated performances. Further, we aim to assess the performance of CPDP versus within project DP models. Method: We conducted a systematic literature review. Results from primary studies are synthesised (thematic, meta-analysis) to answer research questions. Results: We identified 30 primary studies passing quality assessment. Performance measures, except precision, vary with the choice of metrics. Recall, precision, f-measure, and AUC are the most common measures. Models based on Nearest-Neighbour and Decision Tree tend to perform well in CPDP, whereas the popular naïve Bayes yields average performance. Performance of ensembles varies greatly across f-measure and AUC. Data approaches address CPDP challenges using row/column processing, which improve CPDP in terms of recall at the cost of precision. This is observed in multiple occasions including the meta-analysis of CPDP versus WPDP. NASA and Jureczko datasets seem to favour CPDP over WPDP more frequently. Conclusion: CPDP is still a challenge and requires more research before trustworthy applications can take place. We provide guidelines for further research.
Generalized Spatial Differentiation from the Spin Hall Effect of Light and Its Application in Image Processing of Edge Detection

Optics naturally provides us with some powerful mathematical operations. Here we experimentally demonstrate that during reflection or refraction at a single optical planar interface, the optical computing of spatial differentiation can be realized by analyzing specific orthogonal polarization states of light. We show that the spatial differentiation is intrinsically due to the spin Hall effect of light and generally accompanies light reflection and refraction at any planar interface, regardless of material composition or incident angles. The proposed spin-optical method takes advantages of a simple and common structure to enable vectorial-field computation and perform edge detection for ultra-fast and energy-efficient image processing.
Reading from paper compared to screens: A systematic review and meta‐analysis

Background Given the increasing popularity of reading from screens, it is not surprising that numerous studies have been conducted comparing reading from paper and electronic sources. The purpose of this systematic review and meta‐analysis is to consolidate the findings on reading performance, reading times and calibration of performance (metacognition) between reading text from paper compared to screens. Methods A systematic literature search of reports of studies comparing reading from paper and screens was conducted in seven databases. Additional studies were identified by contacting researchers who have published on the topic, by a backwards search of the references of found reports and by a snowball search of reports citing what was initially found. Only studies that were experiments with random assignment and with participants who had fundamental reading skills and disseminated between 2008 and 2018 were included. Twenty‐nine reports with 33 identified studies met inclusion criteria experimentally comparing reading performance ( k = 33; n = 2,799), reading time ( k = 14; n = 1,233) and/or calibration ( k = 11; n = 698) from paper and screens. Results Based on random effects models, reading from screens had a negative effect on reading performance relative to paper ( g = −.25). Based on moderator analyses, this may have been limited to expository texts ( g = −.32) as there was no difference with narrative texts ( g = −.04). The findings were similar when analysing literal and inferential reading performance separately ( g = −.33 and g = −.26, respectively). No reliable differences were found for reading time ( g = .08). Readers had better calibrated (more accurate) judgement of their performance from paper compared to screens ( g = .20). Conclusions Readers may be more efficient and aware of their performance when reading from paper compared to screens.
Blockchain Applications – Usage in Different Domains

Originally conceived as a mechanism to enable a trustless cryptocurrency-Bitcoin, blockchain has since unbound itself from its original purpose as an increasing number of industries and stakeholders' eye the technology as an attractive alternative to solve existing business solutions as well as disrupt mature industries. This paper presents a systematic literature review of the blockchain technology, tracking its increase in popularity in relation to similar technologies, such as cryptocurrencies and Bitcoin. The objective of this paper is to identify the current standing of the blockchain technology within the literature while also identifying the major fields of study and areas of application for which blockchain offers a valuable solution. This paper finds that unique features to the blockchain, such as privacy, security, anonymity, decentralization, and immutability, provide valuable benefits to various fields and subjects. This paper also finds that exploring the application of blockchain has only begun with some limited studies in areas, such as the Internet of Things, energy, finance, healthcare, and government, that also stand to benefit disproportionately from its implementation.
Analysing the impact of blockchain-technology for operations and supply chain management: An explanatory model drawn from multiple case studies

Blockchain technology is said to have a high disruptive potential and can do without an intermediary. Numerous contributions deal with its impact on and possibilities for logistics and supply chains. In this article, we use a multiple case analysis to develop an explanatory model for the interaction of actors in an operational supply chain involving blockchain technology. In addition, we show which intermediary tasks the blockchain could replace and what impact this would have on the industry logic. For this purpose, we analyze the status quo in practice based on a multiple case study with real use cases and find answers to our research questions. The findings of the paper include (1) insights into the impact of blockchain technology on the logistics industry, and (2) the implications and research questions related to blockchain technology and the impact of blockchain technology on business models.
Self-Training With Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identification

Person re-identification (Re-ID) has achieved great improvement with deep learning and a large amount of labelled training data. However, it remains a challenging task for adapting a model trained in a source domain of labelled data to a target domain of only unlabelled data available. In this work, we develop a self-training method with progressive augmentation framework (PAST) to promote the model performance progressively on the target dataset. Specially, our PAST framework consists of two stages, namely, conservative stage and promoting stage. The conservative stage captures the local structure of target-domain data points with triplet-based loss functions, leading to improved feature representations. The promoting stage continuously optimizes the network by appending a changeable classification layer to the last layer of the model, enabling the use of global information about the data distribution. Importantly, we propose a new self-training strategy that progressively augments the model capability by adopting conservative and promoting stages alternately. Furthermore, to improve the reliability of selected triplet samples, we introduce a ranking-based triplet loss in the conservative stage, which is a label-free objective function based on the similarities between data pairs. Experiments demonstrate that the proposed method achieves state-of-the-art person Re-ID performance under the unsupervised cross-domain setting.
Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks

Traditional image-based survival prediction models rely on discriminative patch labeling which make those methods not scalable to extend to large datasets. Recent studies have shown Multiple Instance Learning (MIL) framework is useful for histopathological images when no annotations are available in classification task. Different to the current image-based survival models that limit to key patches or clusters derived from Whole Slide Images (WSIs), we propose Deep Attention Multiple Instance Survival Learning (DeepAttnMISL) by introducing both siamese MI-FCN and attention-based MIL pooling to efficiently learn imaging features from the WSI and then aggregate WSI-level information to patient-level. Attention-based aggregation is more flexible and adaptive than aggregation techniques in recent survival models. We evaluated our methods on two large cancer whole slide images datasets and our results suggest that the proposed approach is more effective and suitable for large datasets and has better interpretability in locating important patterns and features that contribute to accurate cancer survival predictions. The proposed framework can also be used to assess individual patient’s risk and thus assisting in delivering personalized medicine.
3D Human Pose Estimation with Spatial and Temporal Transformers

Transformer architectures have become the model of choice in natural language processing and are now being introduced into computer vision tasks such as image classification, object detection, and semantic segmentation. However, in the field of human pose estimation, convolutional architectures still remain dominant. In this work, we present PoseFormer, a purely transformer-based approach for 3D human pose estimation in videos without convolutional architectures involved. Inspired by recent developments in vision transformers, we design a spatial-temporal transformer structure to comprehensively model the human joint relations within each frame as well as the temporal correlations across frames, then output an accurate 3D human pose of the center frame. We quantitatively and qualitatively evaluate our method on two popular and standard benchmark datasets: Human3.6M and MPI-INF-3DHP. Extensive experiments show that PoseFormer achieves state-of-the-art performance on both datasets. Code is available at https://github.com/zczcwh/PoseFormer
Image Super-Resolution Via Iterative Refinement

We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models (Ho et al. 2020), (Sohl-Dickstein et al. 2015) to image-to-image translation, and performs super-resolution through a stochastic iterative denoising process. Output images are initialized with pure Gaussian noise and iteratively refined using a U-Net architecture that is trained on denoising at various noise levels, conditioned on a low-resolution input image. SR3 exhibits strong performance on super-resolution tasks at different magnification factors, on faces and natural images. We conduct human evaluation on a standard 8× face super-resolution task on CelebA-HQ for which SR3 achieves a fool rate close to 50%, suggesting photo-realistic outputs, while GAN baselines do not exceed a fool rate of 34%. We evaluate SR3 on a 4× super-resolution task on ImageNet, where SR3 outperforms baselines in human evaluation and classification accuracy of a ResNet-50 classifier trained on high-resolution images. We further show the effectiveness of SR3 in cascaded image generation, where a generative model is chained with super-resolution models to synthesize high-resolution images with competitive FID scores on the class-conditional 256×256 ImageNet generation challenge.
Deep CNN-Based Blind Image Quality Predictor

Image recognition based on convolutional neural networks (CNNs) has recently been shown to deliver the state-of-the-art performance in various areas of computer vision and image processing. Nevertheless, applying a deep CNN to no-reference image quality assessment (NR-IQA) remains a challenging task due to critical obstacles, i.e., the lack of a training database. In this paper, we propose a CNN-based NR-IQA framework that can effectively solve this problem. The proposed method—deep image quality assessor (DIQA)—separates the training of NR-IQA into two stages: 1) an objective distortion part and 2) a human visual system-related part. In the first stage, the CNN learns to predict the objective error map, and then the model learns to predict subjective score in the second stage. To complement the inaccuracy of the objective error map prediction on the homogeneous region, we also propose a reliability map. Two simple handcrafted features were additionally employed to further enhance the accuracy. In addition, we propose a way to visualize perceptual error maps to analyze what was learned by the deep CNN model. In the experiments, the DIQA yielded the state-of-the-art accuracy on the various databases.
An intelligent system for spam detection and identification of the most relevant features based on evolutionary Random Weight Networks

With the incremental use of emails as an essential and popular communication mean over the Internet, there comes a serious threat that impacts the Internet and the society. This problem is known as spam. By receiving spam messages, Internet users are exposed to security issues, and minors are exposed to inappropriate contents. Moreover, spam messages waste resources in terms of storage, bandwidth, and productivity. What makes the problem worse is that spammers keep inventing new techniques to dodge spam filters. On the other side, the massive data flow of hundreds of millions of individuals, and the large number of attributes make the problem more cumbersome and complex. Therefore, proposing evolutionary and adaptable spam detection models becomes a necessity. In this paper, an intelligent detection system that is based on Genetic Algorithm (GA) and Random Weight Network (RWN) is proposed to deal with email spam detection tasks. In addition, an automatic identification capability is also embedded in the proposed system to detect the most relevant features during the detection process. The proposed system is intensively evaluated through a series of extensive experiments based on three email corpora. The experimental results confirm that the proposed system can achieve remarkable results in terms of accuracy, precision, and recall. Furthermore, the proposed detection system can automatically identify the most relevant features of the spam emails.
Feature selection with multi-view data: A survey

This survey aims at providing a state-of-the-art overview of feature selection and fusion strategies, which select and combine multi-view features effectively to accomplish associated tasks. The existing literatures on feature selection approaches are classified into three categories including filter method, wrapper method, and embedded method. Based on the feature selection methods mentioned above, feature-level fusion or known as low-level fusion methodology is further investigated from the perspective of the basic concept, procedure, and applications in analysis tasks as presented in the literatures. Moreover, several distinctive issues that influence the information fusion process such as the use of correlation, confidence level, synchronization, and the optimal features are also emphasized. Finally, we present the adaptive multi-view issues for further research in the area of feature selection and fusion by learning view-specific weights to each view data automatically.
A Comprehensive Survey of Blockchain: From Theory to IoT Applications and Beyond

As an innovated and revolutionized technology, blockchain has been applied in many fields, such as cryptocurrency, food traceability, identity management, or even market prediction. To discover its great potential, both industry and academia have paid great attention to it and numerous researches have been conducted. Based on the literature and industry whitepapers, in this survey, we unroll and structure the blockchain related discoveries and scientific results in many aspects. Particularly, we classify blockchain technologies into four layers and carry out a comprehensive study on the consensus strategies, the network, and the applications of blockchain. Different blockchain applications are put into the corresponding categories based on the fields, especially in Internet of Things (IoT). When introducing each layer, we not only organize and summarize the related works, but also discuss the fundamental issues and future research directions. We hope this survey could shed some light on the research of blockchain and serve as a guide for further studies.
Adaptive Transfer Network for Cross-Domain Person Re-Identification

Recent deep learning based person re-identification approaches have steadily improved the performance for benchmarks, however they often fail to generalize well from one domain to another. In this work, we propose a novel adaptive transfer network (ATNet) for effective cross-domain person re-identification. ATNet looks into the essential causes of domain gap and addresses it following the principle of "divide-and-conquer". It decomposes the complicated cross-domain transfer into a set of factor-wise sub-transfers, each of which concentrates on style transfer with respect to a certain imaging factor, e.g., illumination, resolution and camera view etc. An adaptive ensemble strategy is proposed to fuse factor-wise transfers by perceiving the affect magnitudes of various factors on images. Such "decomposition-and-ensemble" strategy gives ATNet the capability of precise style transfer at factor level and eventually effective transfer across domains. In particular, ATNet consists of a transfer network composed by multiple factor-wise CycleGANs and an ensemble CycleGAN as well as a selection network that infers the affects of different factors on transferring each image. Extensive experimental results on three widely-used datasets, i.e., Market-1501, DukeMTMC-reID and PRID2011 have demonstrated the effectiveness of the proposed ATNet with significant performance improvements over state-of-the-art methods.
USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since the frequency and severity of tumors differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net, i.e., one of the most effective CNNs in biomedical image segmentation. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study evaluates the generalization ability of CNN-based architectures on three T2-weighted MRI datasets, each one consisting of a different number of patients and heterogeneous image characteristics, collected by different institutions. The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations. USE-Net is compared against three state-of-the-art CNN-based architectures (i.e., U-Net, pix2pix, and Mixed-Scale Dense Network), along with a semi-automatic continuous max-flow model. The results show that training on the union of the datasets generally outperforms training on each dataset separately, allowing for both intra-/cross-dataset generalization. Enc USE-Net shows good overall generalization under any training condition, while Enc-Dec USE-Net remarkably outperforms the other methods when trained on all datasets. These findings reveal that the SE blocks’ adaptive feature recalibration provides excellent cross-dataset generalization when testing is performed on samples of the datasets used during training. Therefore, we should consider multi-dataset training and SE blocks together as mutually indispensable methods to draw out each other’s full potential. In conclusion, adaptive mechanisms (e.g., feature recalibration) may be a valuable solution in medical imaging applications involving multi-institutional settings.