Reviewerly/exHarmony huggingface dataset:
Distributed Data Management Ecosystem for Intelligent Transportation Systems using Blockchain Technology

Blockchain systems are known for their trustless multi-party transaction systems. Possessing properties such as immutability, transparency, data consistency and consensus based updation, blockchain has become a very important decentralized tool for applications that are time sensitive and require security. The purpose of this paper is to explore how we can apply blockchain technology solutions to Intelligent transportation systems(ITS) with a data management perspective. We propose an ecosystem architecture for such ITS applications and dive deep into distributed data management concepts involved in each layer of the architecture. We examine the flow of data in blockchain based ITS applications of the proposed architecture. Finally we have also provided a use-case scenario.
Design of University Digital Party Building Cloud Platform based on HTML and Decentralized Storage

The construction of enterprise party construction and big data information platform of party construction need a large number of outstanding talents. In order to better realize the integration of big data technology and party building, enterprises must establish a professional working team to effectively improve team skills. Only by constantly strengthening the skills of professional teams can we effectively realize the integration of party building and big data technology. In this paper, a decentralized storage system based on distributed ledger is proposed, and the optimal file redundancy is given. This method can guarantee the storage performance and reduce the size of redundant data by several times.
Looking at Boundary: Siamese Densely Cooperative Fusion for Salient Object Detection

Though deep learning-based saliency detection methods have achieved gratifying performance recently, the predicted saliency maps still suffer from the boundary challenge. From the perspective of foreground-background separation, this article attempts to extract the edge information of objects by exploiting the difference between different color channels in the RGB color space and establishes a novel multicolor contrast extraction (MCE) mechanism to improve the learning ability of exquisite boundary information of the network. To make full use of the MCE outputs and RGB colors, and well depict and capture the complementary information between them, we devise a novel Siamese densely cooperative fusion (DCF) network (SDFNet) for saliency detection, which consists of two effective components: boundary-directed feature learning (BDFL) and DCF. The BDFL provides joint learning for both MCE and RGB modalities through a Siamese network, while the DCF module is devised for complementary feature discovery, in order to effectively combine the features learned from two modalities. Experiments on five well-known benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art approaches in terms of different evaluation metrics. We provide a detailed analysis of these results and indicate that our joint modeling of MCE and RGB colors helps to better capture the object details, especially in the object boundaries.
Processing multi-expert annotations in digital pathology: a study of the Gleason 2019 challenge

Deep learning algorithms rely on large amounts of annotations for learning and testing. In digital pathology, a ground truth is rarely available, and many tasks show large inter-expert disagreement. Using the Gleason2019 dataset, we analyse how the choices we make in getting the ground truth from multiple experts may affect the results and the conclusions we could make from challenges and benchmarks. We show that using undocumented consensus methods, as is often done, reduces our ability to properly analyse challenge results. We also show that taking into account each expert’s annotations enriches discussions on results and is more in line with the clinical reality and complexity of the application.
Semi-supervised Cloud Edge Collaborative Power Transmission Line Insulator Anomaly Detection Framework

The widely deployed power transmission line expedites developing the age of electricity. Thus, it is necessary to maintain a power system with a great quantity of manpower and material resources, especially for crucial equipment, such as insulator string. However, the current main inspection method relies on artificial with the problem of time-consuming and labor-intensive. There is a trend of utilizing deep learning techniques on unmanned aerial vehicles (UAVs) to accomplish the inspection task, but its development is restricted by the limitation of energy. In this paper, we propose a semi-supervised cloud edge collaborative insulator string anomaly detection framework. Specifically, an anchor-free object detector is deployed on the edge device for locating the insulator. On the cloud side, we propose a generative insulator defect detection model based on the autoencoder (AE) with a generator-discriminator pattern. Particularly, we introduce the variational memory encoder-decoder architecture to model defect-free insulator data distribution. Furthermore, the adversarial strategy is employed to regularize the generated data space with input data space. In the end, the anomaly can be detected if its data space is an outlier of training defect-free distribution. Comprehensive experiments demonstrate that our method can effectively reduce the computational load, meanwhile archiving superior performance, including accuracy (0.968) and recall (0.985), for defect recognition using a standard insulator data set.
PSF Estimation of Simple Lens Based on Circular Partition Strategy

Recently, single-lens computational imaging has gradually become a new research direction of computational photography, which combines the front-end simple optical imaging equipment with the late image restoration algorithm to obtain high-quality images. Single lens computational imaging is essentially a problem of image restoration. The estimation accuracy of point spread function (PSF) will directly affect the quality of image restoration. Existing spatially variant PSF estimation methods usually divide images into rectangular blocks. Considering the imaging characteristics of single lens, a PSF estimation method based on a circular partition strategy is proposed in this paper. Experimental results show that this segmented method can achieve better PSF estimation accuracy and improve image restoration quality.
User-location distribution serves as a useful feature in item-based collaborative filtering

Personalized recommender system is a powerful method to solve the problem of information overload, which has been widely applied in a variety of scenarios, such as e-commerce, video platforms and social networks, to help users find relevant items or friends of interest. Collaborative filtering is the most successful and widely used algorithm in the recommender systems as its powerful capability of generating recommendations by sharing collective experiences of users. In recent years, the use of mobile devices and the rapid development of internet infrastructures provide the possibility to analyze regional features of items based on user locations. Here we improve the performance of collaborative filtering by using user-location distribution to uncover the potential similarities between items. We find that the similarity of user-location distribution is one efficient measure for the item–item similarities in the framework of collaborative filtering to generate personalized recommendation for users. Furthermore, we have also mixed similarity measures of user-location distribution and the traditional method based on the number of common users linearly to optimize the performance of collaborative filtering. Based on the Movielens data set, we show that the performance of our methods could be improved in terms of the metrics of accuracy and diversity simultaneously. • A new measure of similarity for user-location distribution is proposed. • The proposed measure improves the quality of collaborative filtering greatly. • The proposed measure can be mixed with the traditional similarity measure. • The hybrid method improves the algorithmic accuracy and diversity simultaneously.
Image Hashing Algorithm Based on Mirror Flipping and a Three-Dimensional Space Angle

In order to make full use of image flipping information to get comprehensive image features and improve the distinguishing performance of hash algorithm, this paper proposes a new image hashing algorithm based on mirror flipping and a three-dimensional space angle. Firstly, the original image is preprocessed and then combined with mirror flipping image to obtain the new luminance component and opposite color components. Then, we combine new luminance component with the different sizes of structural elements to construct morphological features. The new opposite color components are used to construct a three-dimensional space. The angle between vectors formed by the pixels in the three-dimensional space is computed to construct the space angle features. Finally, the morphological features and space angle features are combined and disturbed to form the final hash sequence. Simulation results show that the algorithm has good security and tamper image recognition accuracy. Compared with some existing algorithms, it has better image classification performance and shorter computation time.
MSDTRON: A High-Capability Multi-Speaker Speech Synthesis System for Diverse Data Using Characteristic Information

In multi-speaker speech synthesis, data from a number of speakers usually tend to have great diversity due to the fact that the speakers may differ largely in ages, speaking styles, emotions, and so on. It is important but challenging to improve the modeling capabilities for multi-speaker speech synthesis. To address the issue, this paper proposes a high-capability speech synthesis system, called Msdtron, in which 1) a representation of the harmonic structure of speech, called excitation spectrogram, is designed to directly guide the learning of harmonics in mel-spectrogram. 2) conditional gated LSTM (CGLSTM) is proposed to control the flow of text content information through the network by re-weighting the gates of LSTM using speaker information. The experiments show a significant reduction in reconstruction error of mel-spectrogram in the training of the multi-speaker model, and a great improvement is observed in the subjective evaluation of speaker adapted model.
Light-SAR-ShipNet (LSSNet): A Lightweight SAR Image Ship Detection System with Enhanced Deep Supervision

Abstract Applying deep learning methods to achieve synthetic aperture radar (SAR) image ship target detection has become a current hot research topic. However, most of the existing detection networks are designed based on optical images, so when they are directly applied to SAR image target detection, there will be the following two problems: 1) There are big differences between SAR image and optical image in imaging mechanism, geometric characteristics and radiation characteristics, etc, which makes the detection network have more redundant imformation; 2) Real-time performance and accuracy are both very important in SAR image target detection task, and applying a network designed based on optical image cannot balance the performance of the two well. In response to the above problems, this paper proposes a lightweight network LSSNet specially designed for SAR image ship detection task. In earlier layers, we use depthwise separable convolution instead of conventional convolution to design dense block; in deeper layers, stacked dense blocks with shortcuts are used to enhance deep supervision, and finally achieve high-speed and high-accuracy detection. This paper uses SSDD dataset as baseline for expeiment and results show that LSSNet have higher accurracy and detecition speed. Its lightweight structure will also help transplant to hardware devices in the future.
EyeKnowYou: A DIY Toolkit to Support Monitoring Cognitive Load and Actual Screen Time using a Head-Mounted Webcam

Studies show that frequent screen exposure and increased cognitive load can cause mental-health issues. Although expensive systems capable of detecting cognitive load and timers counting on-screen time exist, literature has yet to demonstrate measuring both factors across devices. To address this, we propose an inexpensive DIY-approach using a single head-mounted webcam capturing the user’s eye. By classifying camera feed using a 3D Convolutional Neural Network, we can determine increased cognitive load and actual screen time. This works because the camera feed contains corneal surface reflection, as well as physiological parameters that contain information on cognitive load. Even with a small data set, we were able to develop generalised models showing 70% accuracy. To increase the models’ accuracy, we seek the community’s help by contributing more raw data. Therefore, we provide an opensource software and a DIY-guide to make our toolkit accessible to human factors researchers without an engineering background.
Quantum computation using action variables

In 2014, Lloyd and Montangero have made a brief research proposal on universal quantum computation in integrable systems. The main idea is to encode qubits into quantum action variables and build up quantum gates by the method of resonant control. We study this proposal to argue quantum computation using action variables as fault-tolerant quantum computation, whose fault tolerance is guaranteed by the quantum KAM theorem. Besides, we view the Birkhoff norm form as a mathematical framework of the extended harmonic oscillator quantum computation.
Why Some Bug-bounty Vulnerability Reports are Invalid?

Background: Despite the increasing popularity of bug-bounty platforms in industry, little empirical evidence exists to identify the nature of invalid vulnerability reports. Mitigation of invalid reports is a serious concern of organisations running or using bug-bounty platforms as well as security researchers. Aims: In this work we aim to identify: (i) why some reports are considered as invalid? (ii) what are the characteristics of reports considered as invalid due to being out-of-scope? Method: We conducted an empirical study on disclosed invalid reports in HackerOne to examine the reasons these reports are marked as invalid and we found that out-of-scope is the leading reason. Since all out-of-scope reports were rejected according to the programs policy page we studied all programs policy pages in two major bug-bounty platforms to understand the characteristics of an out-of-scope report. We developed a generalised out-of-scope taxonomy model and we used our model to further analyse HackerOne out-of-scope reports to find the leading attributes of this model that contributes to the fate of these reports. Results: We identified out-of-scope followed by false-positive as two main reasons for a report to be deemed invalid. We found that the attribute of vulnerability type in our taxonomy model is the leading characteristic of out-of-scope reports. We also identified the top 9 out-of-interest vulnerability types according to policy pages. Conclusions: Our study can help bug-bounty platforms and researchers to better understand the nature of invalid reports. Our finding about the importance of vulnerability type in validating reports can be used to justify future works to develop automated classification techniques based on vulnerability types to better triage invalid reports. Our top 9 out-of-interest vulnerability types can be used as a blacklist to automatically classify possibly an out-of-scope report. Finally our generalised out-of-scope taxonomy model can guide organisations as a base model to create their policy page and tailor it as they need.
Semantic Segmentation of Aerial Images Using Binary Space Partitioning

The semantic segmentation of aerial images enables many useful applications such as tracking city growth, tracking deforestation, or automatically creating and updating maps. However, gathering enough training data to train a proper model for the automated analysis of aerial images is usually too labor-intensive and thus too expensive in most cases. Therefore, domain adaptation techniques are often necessary to be able to adapt existing models or to transfer knowledge from existing datasets to new unlabeled aerial images. Modern adaptation approaches make use of complex architectures involving many model components, losses and loss weights. These approaches are hard to apply in practice since their hyperparameters are hard to optimize for a given adaptation problem. This complexity is the result of trying to separate domain-invariant elements, e.g., structures and shapes, from domain-specific elements, e.g., textures. In this paper, we present a novel model for semantic segmentation, which not only achieves state-of-the-art performance on aerial images, but also inherently learns separate feature representations for shapes and textures. Our goal is to provide a model which can serve as the basis for future domain adaptation approaches which are simpler but still effective. Through end-to-end training our deep learning model learns to map aerial images to feature representations which can be decoded into binary space partitioning trees, a resolution-independent representation of the semantic segmentation, which can then be rendered into a pixelwise semantic segmentation in a differentiable way.
Requirement Barriers to Implement the Software Projects in Agile Development

The success of an organization is to deliver the good-quality products as per the client's needs. But few organizations are unable to deliver the successful product due to number of software barriers. The research is based on the study and analysis of different requirement barriers, which causes problem in agile implementation. Several authors identified the barriers for successful implementation of software, but none have found the barriers at the initial stage of requirements. The motivation behind this work is to classify the main requirement barriers to the effective implementation of software projects in agile development. For the study, interviews were conducted with developers and testers. The results recognized the key barriers and it will deliver the roadmap to managers to take suitable steps to overwhelm the major barriers to effective software implementation.
CrowdSLR: a tool to support the use of crowdsourcing in systematic literature reviews

Systematic Literature Reviews (SLR) have been used by Software Engineering (SE) community to produce reliable scientific evidence. An SLR process can be exhaustive and time-consuming, therefore, many approaches have been proposed to reduce time and efforts during the SLR conduction process. Although the SLR process is amenable to automation, nowadays full automation is not yet possible. An alternative to reduce time and efforts in SLR conduction is the use of crowdsourcing. However, there is no crowdsourcing tool to support a crowd-based SLR process. In this context, we present CrowdSLR, a tool to support the application of crowdsourcing in SLR during the selection of primary studies. Furthermore, we present its main features, potential users, and the architecture that was implemented to allow researchers to adopt this tool. The results of the CrowdSLR application indicate that the tool is able to provide the use crowdsourcing during the SLR selection process. The results that the proposed tool, indeed, show a significant improvement in the crowdsourcing approach in terms of time and effort to facilitate the SLR selection activity.
Comparative study of pedestrian detection techniques for driver assistance system

Pedestrian detection is one of the major concerns in the field of intelligent transportation systems. Recently, the deep learning based algorithms has greatly improved the accuracy of pedestrian detection. The major challenge in pedestrian detection is varying body shape, posture and different color of clothes. This article compares the performance of YOLO v3 and HOG SVM model on INRIA person dataset containing pedestrians in different pose, shape and lighting conditions. Bounding boxes are generated to indicate the location of the pedestrian in an input image. The experimentation results obtained are presented.
Traffic Signs Detection and Segmentation Based on the Improved Mask R-CNN

Traffic signs detection and segmentation is one of the important parts of advanced driving assistance system. But there are predictable difficulties in detecting traffic signs from images or videos from car cameras owing to the next reasons: traffic signs are usually small-sized or medium-sized objects, and there is quantity imbalance between different traffic signs in the existed public data sets. Therefore, two main developments have been proposed in this paper. Firstly, an improved TT-100K-HHU traffic sign data set based on TT-100K is constructed. New images are collected from the Tencent Street View and labeled by Labelme software. Secondly, an improved Mask R-CNN is presented by revising the structure. More specific, feature pyramid network (FPN) is introduced into the backbone network of Mask R-CNN to achieve the fusion of feature maps at multiple scales, which can improve the representation abilities of network for objects with small or medium size. And in the prediction network, multiple cascaded Box Heads are applied to acquire more accurate location predictions and segmentation results. Experimental results show that the performance of the improved Mask R-CNN network is better than the existing algorithms.
Adaptive Neuro Fuzzy Approach for Assessment of Learner’s Domain Knowledge

E-learning lends a significant contribution to the education system. Learner’s interests, skills, domain knowledge, learning behavior, and learning styles are essential for developing a personalized e-learning system. Student centric learning process improves learners’ satisfaction, leading to improving the education system’s outcome. Due to the advancement of ICT technologies variety of e-study material is available through various online sources. It is a challenge for today’s e-learners to select the best e-learning material which fulfills their learning needs. This paper explores an intelligent, personalized learning approach to assess learner’s domain knowledge. The result of the said assessment helps to recommend a learning path for the learner.
Gender Recognition in Informal and Formal Language Scenarios via Transfer Learning

The interest in demographic information retrieval based on text data has increased in the research community because applications have shown success in different sectors such as security, marketing, heath-care, and others. Recognition and identification of demographic traits such as gender, age, location, or personality based on text data can help to improve different marketing strategies. For instance it makes it possible to segment and to personalize offers, thus products and services are exposed to the group of greatest interest. This type of technology has been discussed widely in documents from social media. However, the methods have been poorly studied in data with a more formal structure, where there is no access to emoticons, mentions, and other linguistic phenomena that are only present in social media. This paper proposes the use of recurrent and convolutional neural networks, and a transfer learning strategy for gender recognition in documents that are written in informal and formal languages. Models are tested in two different databases consisting of Tweets and call-center conversations. Accuracies of up to 75% are achieved for both databases. The results also indicate that it is possible to transfer the knowledge from a system trained on a specific type of expressions or idioms such as those typically used in social media into a more formal type of text data, where the amount of data is more scarce and its structure is completely different.