Reviewerly/exHarmony huggingface dataset:
Trainable Model Based on New Uniform LBP Feature to Identify the Risk of the Breast Cancer

In developing countries breast cancer has been found to be one of the diseases that threatens the lives of women, and that is why finding ways of detecting efficiently is of great importance. The detection of breast cancer at an early stage through self-examination is very difficult. In this study, we proposed a new descriptor that can help to identify the abnormality of the breast by enhancing the features of LBP texture and enhance the LPB descriptor by using a new threshold that can help to identify the important information for the detection of abnormal cases. In the next stage, the significant features are extracted from the breast tumours images that have been segmented. Such features could be found in frequency or spatial domain. The extracted features for diagnosing tumour automatically, are additional and different from those features which the radiologist extracts manually. The proposed method demonstrates the possibility of using the LBP based texture feature with the new proposed method for categorising ultrasound images, which registered a high accuracy of 96%, the sensitivity of 94%, specificity of 97%.
A Survey on Evaluation Methods for Chatbots

Nowadays chatbots have been widely adopted in many industries to automatically answer users' questions and requests via chat interfaces. While it has become much easier to develop a chatbot system, the system itself is a complex system in nature. It is a challenge to evaluate and compare various chatbot systems in terms of effectiveness, efficiency, goal achievability, and the ability to satisfy users. This paper presents a survey, starting from literature review, chatbot architecture, evaluation methods/criteria, and comparison of evaluation methods. Focused on the three subprocesses in the chatbot architecture: text processing, semantic understanding, and response generation. Moreover, the survey is conducted with classification of chatbot evaluation methods and their analysis according to chatbot types and three main evaluation schemes; content evaluation, user satisfaction, and chat function.
Sequential Anomaly Detection using Inverse Reinforcement Learning

One of the most interesting application scenarios in anomaly detection is when sequential data are targeted. For example, in a safety-critical environment, it is crucial to have an automatic detection system to screen the streaming data gathered by monitoring sensors and to report abnormal observations if detected in real-time. Oftentimes, stakes are much higher when these potential anomalies are intentional or goal-oriented. We propose an end-to-end framework for sequential anomaly detection using inverse reinforcement learning (IRL), whose objective is to determine the decision-making agent's underlying function which triggers his/her behavior. The proposed method takes the sequence of actions of a target agent (and possibly other meta information) as input. The agent's normal behavior is then understood by the reward function which is inferred via IRL.
Sharing and re-using open data: A case study of motivations in astrophysics

Open data sharing and re-use is currently more common in some academic disciplines than others. Although each discipline has unique challenges and characteristics which can influence data sharing and re-use behavior, it may be possible to gain transferable insight from disciplines where these practices are more common. Several studies of the motivations underlying data sharing and re-use have been conducted, however these studies often remain at a high level of abstraction rather than providing in-depth insight about discipline-specific challenges and opportunities. This study sought to provide in-depth insight about the complex interaction of factors influencing motivations for sharing and re-using open research data within a single discipline, namely astrophysics. We focused on this discipline due to its well-developed tradition of free and open access to research data. Eight factors were found to influence researchers’ motivations for sharing data openly, including the researcher’s background, personal drivers, experience, legislation, regulation and policy, data characteristics, performance expectancy, usability, and collaboration. We identified six factors that influence researchers’ motivations to re-use open research data, including the researcher’s background, facilitating conditions, expected performance, social and affiliation factors, effort and experience. Finally, we discuss how data sharing and re-use can be encouraged within the context of astrophysics research, and we discuss how these insights may be transferred to disciplines with low rates of data sharing and re-use.
V3C1 Dataset

In this work we analyze content statistics of the V3C1 dataset, which is the first partition of theVimeo Creative Commons Collection (V3C). The dataset has been designed to represent true web videos in the wild, with good visual quality and diverse content characteristics, and will serve as evaluation basis for the Video Browser Showdown 2019-2021 and TREC Video Retrieval (TRECVID) Ad-Hoc Video Search tasks 2019-2021. The dataset comes with a shot segmentation (around 1 million shots) for which we analyze content specifics and statistics. Our research shows that the content of V3C1 is very diverse, has no predominant characteristics and provides a low self-similarity. Thus it is very well suited for video retrieval evaluations as well as for participants of TRECVID AVS or the VBS.
Reconstruct and Represent Video Contents for Captioning via Reinforcement Learning

In this paper, the problem of describing visual contents of a video sequence with natural language is addressed. Unlike previous video captioning work mainly exploiting the cues of video contents to make a language description, we propose a reconstruction network (RecNet) in a novel encoder-decoder-reconstructor architecture, which leverages both forward (video to sentence) and backward (sentence to video) flows for video captioning. Specifically, the encoder-decoder component makes use of the forward flow to produce a sentence based on the encoded video semantic features. Two types of reconstructors are subsequently proposed to employ the backward flow and reproduce the video features from local and global perspectives, respectively, capitalizing on the hidden state sequence generated by the decoder. Moreover, in order to make a comprehensive reconstruction of the video features, we propose to fuse the two types of reconstructors together. The generation loss yielded by the encoder-decoder component and the reconstruction loss introduced by the reconstructor are jointly cast into training the proposed RecNet in an end-to-end fashion. Furthermore, the RecNet is fine-tuned by CIDEr optimization via reinforcement learning, which significantly boosts the captioning performance. Experimental results on benchmark datasets demonstrate that the proposed reconstructor can boost the performance of video captioning consistently.
Radiance–Reflectance Combined Optimization and Structure-Guided $\ell _0$-Norm for Single Image Dehazing

Outdoor images are subject to degradation regarding contrast and color because atmospheric particles scatter incoming light to a camera. Existing haze models that employ model-based dehazing methods cannot avoid the dehazing artifacts. These artifacts include color distortion and overenhancement around object boundaries because of the incorrect transmission estimation from a depth error in the skyline and the wrong haze information, especially in bright objects. To overcome this problem, we present a novel optimization-based dehazing algorithm that combines radiance and reflectance components with an additional refinement using a structure-guided ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm filter. More specifically, we first estimate a weak reflectance map and optimize the transmission map based on the estimated reflectance map. Next, we estimate the structure-guided ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> transmission map to remove the dehazing artifacts. The experimental results show that the proposed method outperforms state-of-the-art algorithms in terms of qualitative and quantitative measures compared with simulated image pairs. In addition, the real-world enhancement results demonstrate that the proposed method can provide a high-quality image without undesired artifacts. Furthermore, the guided ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm filter can remove textures while preserving edges for general image enhancement algorithms.
A fast color image encryption technique based on three dimensional chaotic map

In this paper, a fast color image encryption method based on the suggested three dimensional chaotic map is presented. Generally, the suggested map generates three numbers and then these numbers are Bitxor-ed with RGB channels of image. Process is continued with dividing an image into 4 × 4 parts and changing these parts locations. Proposed technique divides each part into 16 × 16 blocks and then the blocks are permuted with different keys of presented map. The encrypted image is generated after all blocks of parts permuted. This process guarantees the sensitivity of proposed approach in changing one bit of image and refers to the diffusion. Furthermore, proposed scheme saves the latest generated numbers of permuting process and uses them to permute next block. This process is very sensitive to any changes in keys and refers to the confusion. The evaluation results of standard tests on some images confirm that the proposed method has good key space, security, fast speed and strong robustness against most well-known attacks.
A Perceptual Prediction Framework for Self Supervised Event Segmentation

Temporal segmentation of long videos is an important problem, that has largely been tackled through supervised learning, often requiring large amounts of annotated training data. In this paper, we tackle the problem of self-supervised temporal segmentation that alleviates the need for any supervision in the form of labels (full supervision) or temporal ordering (weak supervision). We introduce a self-supervised, predictive learning framework that draws inspiration from cognitive psychology to segment long, visually complex videos into constituent events. Learning involves only a single pass through the training data. We also introduce a new adaptive learning paradigm that helps reduce the effect of catastrophic forgetting in recurrent neural networks. Extensive experiments on three publicly available datasets - Breakfast Actions, 50 Salads, and INRIA Instructional Videos datasets show the efficacy of the proposed approach. We show that the proposed approach outperforms weakly-supervised and unsupervised baselines by up to 24% and achieves competitive segmentation results compared to fully supervised baselines with only a single pass through the training data. Finally, we show that the proposed self-supervised learning paradigm learns highly discriminating features to improve action recognition.
Analysis of Identity Management Systems Using Blockchain Technology

The emergence of Blockchain technology as the biggest innovations of the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century, has given rise to new concepts of Identity Management to deal with the privacy and security challenges on the one hand, and to enhance the decentralization and user control in transactions on Blockchain infrastructures on the other hand. This paper investigates and gives analysis of the most popular Identity Management Systems using Blockchain: uPort, Sovrin, and ShoCard. It then evaluates them under a set of features of digital identity that characterizes the successful of an Identity Management solution. The result of the comparative analysis is presented in a concise way to allow readers to find out easily which systems satisfy what requirements in order to select the appropriate one to fit into a specific scenario.
Redundancy and Attention in Convolutional LSTM for Gesture Recognition

Convolutional long short-term memory (ConvLSTM) networks have been widely used for action/gesture recognition, and different attention mechanisms have also been embedded into ConvLSTM networks. This paper explores the redundancy of spatial convolutions and the effects of the attention mechanism in ConvLSTM, based on our previous gesture recognition architectures that combine the 3-D convolutional neural network (CNN) and ConvLSTM. Depthwise separable, group, and shuffle convolutions are used to replace the convolutional structures in ConvLSTM for the redundancy analysis. In addition, four ConvLSTM variants are derived for attention analysis: 1) by removing the convolutional structures of the three gates in ConvLSTM; 2) by applying the attention mechanism on the ConvLSTM input; and 3) by reconstructing the input and 4) output gates with the modified channelwise attention mechanism. Evaluation results demonstrate that the spatial convolutions in the three gates scarcely contribute to the spatiotemporal feature fusion and that the attention mechanisms embedded into the input and output gates cannot improve the feature fusion. In other words, ConvLSTM mainly contributes to the temporal fusion along with the recurrent steps to learn long-term spatiotemporal features when taking spatial or spatiotemporal features as input. A new LSTM variant is derived on this basis in which the convolutional structures are embedded only into the input-to-state transition of LSTM. The code of the LSTM variants is publicly available. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> https://github.com/GuangmingZhu/ConvLSTMForGR.
Multi-Situated App Studies: Methods and Propositions

This article discusses methodological approaches to app studies, focusing on their embeddedness and situatedness within multiple infrastructural settings. Our approach involves close attention to the multivalent affordances of apps as software packages, particularly their capacity to enter into diverse groupings and relations depending on different infrastructural situations. The changing situations they evoke and participate in, accordingly, make apps visible and accountable in a variety of unique ways. Therefore, engaging with and even staging these situations allows for political-economic, social, and cultural dynamics associated with apps and their infrastructures to be investigated through a style of research we describe as multi-situated app studies. This article offers an overview of four different entry points of enquiry that are exemplary of this multi-situated approach, focusing on app stores, app interfaces, app packages, and app connections. We conclude with nine propositions that develop out of these studies as prompts for further research.
The Software Heritage Graph Dataset: Public Software Development Under One Roof

Software Heritage is the largest existing public archive of software source code and accompanying development history: it currently spans more than five billion unique source code files and one billion unique commits, coming from more than 80 million software projects. This paper introduces the Software Heritage graph dataset: a fully-deduplicated Merkle DAG representation of the Software Heritage archive. The dataset links together file content identifiers, source code directories, Version Control System (VCS) commits tracking evolution over time, up to the full states of VCS repositories as observed by Software Heritage during periodic crawls. The dataset's contents come from major development forges (including GitHub and GitLab), FOSS distributions (e.g., Debian), and language-specific package managers (e.g., PyPI). Crawling information is also included, providing timestamps about when and where all archived source code artifacts have been observed in the wild. The Software Heritage graph dataset is available in multiple formats, including downloadable CSV dumps and Apache Parquet files for local use, as well as a public instance on Amazon Athena interactive query service for ready-to-use powerful analytical processing. Source code file contents are cross-referenced at the graph leaves, and can be retrieved through individual requests using the Software Heritage archive API.
RingCT 3.0 for Blockchain Confidential Transaction: Shorter Size and Stronger Security

In this paper, we propose the most efficient blockchain ring confidential transaction protocol (RingCT3.0) for protecting the privacy of the sender’s identity, the recipient’s identity and the confidentiality of the transaction amount. For a typical 2-input transaction with a ring size of 1024, the ring signature size of our RingCT3.0 protocol is 98% less than the ring signature size of the original RingCT1.0 protocol used in Monero. Taking the advantage of our compact RingCT3.0 transcript size, privacy-preserving cryptocurrencies can enjoy a much lower transaction fee which will have a significant impact on the crypto-economy. In addition to the significant improvement in terms of efficiency, our scheme is proven secure in a stronger security model. We remove the trusted setup assumption used in RingCT2.0. Our scheme is anonymous against non-signing users who are included in the ring, while we show that the RingCT1.0 is not secure in this improved model. Our implementation result shows that our protocol outperforms existing solutions, in terms of efficiency and security.
Communicability of traditional interfaces VS chatbots in healthcare and smart home domains

This paper presents a study about communicability of conversational interfaces (namely chatbots) under a semiotic perspective. A chatbot is a software system that allows you to simulate real conversations between devices and users by means of a conversational interface (CI). After introducing the chatbot concept, focusing on its advantages and issues, we will present two domains of use in which chatbot interfaces can be effective: healthcare and smart home. For carrying out simple tasks such as finding information or triggering operations, users need an easy-to-use and to an easy-to-learn system to communicate with. To face this, conversational interfaces represent the latest trend in the field of digital design. For studying the communicability aspects of a CI, we carried out a user test to compare traditional and chatbot interfaces. This paper aims at evaluating the benefits at the communicability level of a chatbot in comparison to traditional GUI for incrementing the effectiveness and efficacy of communication between users and the system specifically for users with poor attitude in using technologies. In details, we evaluated the communicability of two prototypes that can be used to solve simple tasks in order to favour user inclusion, including everyone with very little exposure to technologies.
Emerging technologies towards enhancing privacy in genomic data sharing

As the scale of genomic and health-related data explodes and our understanding of these data matures, the privacy of the individuals behind the data is increasingly at stake. Traditional approaches to protect privacy have fundamental limitations. Here we discuss emerging privacy-enhancing technologies that can enable broader data sharing and collaboration in genomics research.
Improved adaptive genetic algorithm for the vehicle Insurance Fraud Identification Model based on a BP Neural Network

With the development of the insurance industry, insurance fraud is increasing rapidly. The existence of insurance fraud considerably hinders the development of the insurance industry. Fraud identification has become the most important part of insurance fraud research. In this paper, an improved adaptive genetic algorithm (NAGA) combined with a BP neural network (BP neural network) is proposed to optimize the initial weight of BP neural networks to overcome their shortcomings, such as ease of falling into local minima, slow convergence rates and sample dependence. Finally, the historical automobile insurance claim data of an insurance company are taken as a sample. The NAGA-BP neural network model was used for simulation and prediction. The empirical results show that the improved genetic algorithm is more advanced than the traditional genetic algorithm in terms of convergence speed and prediction accuracy.
Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks

Learning-to-Rank is a branch of supervised machine learning that seeks to produce an ordering of a list of items such that the utility of the ranked list is maximized. Unlike most machine learning techniques, however, the objective cannot be directly optimized using gradient descent methods as it is either discontinuous or flat everywhere. As such, learning-to-rank methods often optimize a loss function that either is loosely related to or upper-bounds a ranking utility instead. A notable exception is the approximation framework originally proposed by Qin et al. that facilitates a more direct approach to ranking metric optimization. We revisit that framework almost a decade later in light of recent advances in neural networks and demonstrate its superiority empirically. Through this study, we hope to show that the ideas from that work are more relevant than ever and can lay the foundation of learning-to-rank research in the age of deep neural networks.
Learning to Calibrate Straight Lines for Fisheye Image Rectification

This paper presents a new deep-learning based method to simultaneously calibrate the intrinsic parameters of fisheye lens and rectify the distorted images. Assuming that the distorted lines generated by fisheye projection should be straight after rectification, we propose a novel deep neural network to impose explicit geometry constraints onto processes of the fisheye lens calibration and the distorted image rectification. In addition, considering the nonlinearity of distortion distribution in fisheye images, the proposed network fully exploits multi-scale perception to equalize the rectification effects on the whole image. To train and evaluate the proposed model, we also create a new large-scale dataset labeled with corresponding distortion parameters and well-annotated distorted lines. Compared with the state-of-the-art methods, our model achieves the best published rectification quality and the most accurate estimation of distortion parameters on a large set of synthetic and real fisheye images.