Reviewerly/exHarmony huggingface dataset:
Understanding the motivations, challenges and needs of Blockchain software developers: a survey

The blockchain technology has potential applications in various areas such as smart-contracts, Internet of Things (IoT), land registry, supply chain management, storing medical data, and identity management. Although GitHub currently hosts more than six thousand active Blockchain software (BCS) projects, few software engineering researchers have investigated these projects and their contributors. Although the number of BCS projects is growing rapidly, the motivations, challenges, and needs of BCS developers remain a puzzle. Therefore, the primary objective of this study is to understand the motivations, challenges, and needs of BCS developers and analyze the differences between BCS and non-BCS development. On this goal, we sent an online survey to 1,604 active BCS developers identified by mining the GitHub repositories of 145 popular BCS projects. The survey received 156 responses that met our criteria for analysis. The results suggest that the majority of the BCS developers are experienced in non-BCS development and are primarily motivated by the ideology of creating a decentralized financial system. Although most of the BCS projects are Open Source Software (OSS) projects by nature, more than 93% of our respondents found BCS development somewhat different from a non-BCS development as BCS projects have higher emphasis on security and reliability than most of the non-BCS projects. Other differences include: higher costs of defects, decentralized and hostile environment, technological complexity, and difficulty in upgrading the software after release. These differences were also the primary sources of challenges to them. Software development tools that are tuned for non-BCS development are inadequate for BCS and the ecosystem needs an array of new or improved tools, such as: customized IDE for BCS development tasks, debuggers for smart-contracts, testing support, easily deployable simulators, and BCS domain specific design notations.
A dynamic information platform for underground coal mine safety based on internet of things

Mechanical and intelligent mining technology reduces labor intensity, accidents and casualties while improving mining efficiency in underground coal mines. Such advanced mining methods are gradually realized in China with increasing application of an Internet of Things (IoT) technique. In this study, a dynamic information platform is established for underground coal mine based on IoT technology. The platform aims to satisfy various objectives of potential users through combining four subsystems, including data acquisition, transmission, analyzation and application systems. To achieve such functions, six functional layers are composed of the platform, including the supporting layer, perception layer, transmission layer, service layer, data extraction layer and application layer. The platform can monitor and record working conditions data of the coal mine production systems as well as location information of underground equipment and miners. Based on cloud computing techniques, big data related to underground coal mining can be analyzed quickly, and critical data associated with user requirements can be extracted precisely. The platform provides a 3D virtual mine system, safety diagnosis system, safety inspection system and emergency rescue system for coal mines. With the established platform, the danger-spotting and subsequent decision-making capabilities of users can be greatly enhanced to ensure underground mining safety.
Problems of encoder-decoder frameworks for high-resolution remote sensing image segmentation: Structural stereotype and insufficient learning

This work explores the use of deep convolutional neural networks for high resolution remote sensing imagery segmentation. Encoder-decoder frameworks are popular in semantic image segmentation. However, encoder-decoder models face two main problems. The one is structural stereotype which is receptive fields imbalance rooted in this kind of frameworks. The other is insufficient learning that deeper neural networks tend to encounter the notorious problem of vanishing gradients. Structural stereotype leads to unfair learning and inhomogeneous reasoning. We are the first to reveal the problem and propose ensemble training and inference strategies to suppress the adverse consequences of structural stereotype as far as possible. To alleviate the problem of insufficient learning, we propose a novel residual architecture for encoder-decoder models. The proposed method yields state-of-the-art performances on the ISPRS 2D semantic labeling contest benchmark.
A Novel Semisupervised Deep Learning Method for Human Activity Recognition

Human activity recognition (HAR) based on inertial sensors has been investigated for many industrial informatics applications, such as healthcare and ubiquitous computing. Existing methods mainly rely on supervised learning schemes, which require large labeled training data. However, labeled data are sometimes difficult to acquire, while unlabeled data are readily available. Thus, we intend to make use of both labeled and unlabeled data with semisupervised learning for accurate HAR. In this paper, we propose a semisupervised deep learning approach, using temporal ensembling of deep long short-term memory, to recognize human activities with smartphone inertial sensors. With the deep neural network processing, features are extracted for local dependencies in the recurrent framework. Besides, with an ensemble approach based on both labeled and unlabeled data, we can combine together the supervised and unsupervised losses, so as to make good use of unlabeled data that the supervised learning method cannot leverage. Experimental results indicate the effectiveness of our proposed semisupervised learning scheme, when compared to several state-of-the-art semisupervised learning approaches.
A survey on image and video stitching

Image/video stitching is a technology for solving the field of view (FOV) limitation of images/ videos. It stitches multiple overlapping images/videos to generate a wide-FOV image/video, and has been used in various fields such as sports broadcasting, video surveillance, street view, and entertainment. This survey reviews image/video stitching algorithms, with a particular focus on those developed in recent years. Image stitching first calculates the corresponding relationships between multiple overlapping images, deforms and aligns the matched images, and then blends the aligned images to generate a wide-FOV image. A seamless method is always adopted to eliminate such potential flaws as ghosting and blurring caused by parallax or objects moving across the overlapping regions. Video stitching is the further extension of image stitching. It usually stitches selected frames of original videos to generate a stitching template by performing image stitching algorithms, and the subsequent frames can then be stitched according to the template. Video stitching is more complicated with moving objects or violent camera movement, because these factors introduce jitter, shakiness, ghosting, and blurring. Foreground detection technique is usually combined into stitching to eliminate ghosting and blurring, while video stabilization algorithms are adopted to solve the jitter and shakiness. This paper further discusses panoramic stitching as a special-extension of image / video stitching. Panoramic stitching is currently the most widely used application in stitching. This survey reviews the latest image/video stitching methods, and introduces the fundamental principles/advantages/weaknesses of image/video stitching algorithms. Image/video stitching faces long-term challenges such as wide baseline, large parallax, and low-texture problem in the overlapping region. New technologies may present new opportunities to address these issues, such as deep learning-based semantic correspondence, and 3D image stitching. Finally, this survey discusses the challenges of image/video stitching and proposes potential solutions.
Morphological Convolutional Neural Network Architecture for Digit Recognition

Deep neural networks have proved promising results in many applications and fields, but they are still assimilated to a black box. Thus, it is very useful to introduce interpretability aspects to prevent the blind application of deep networks. This paper proposed an interpretable morphological convolutional neural network called Morph-CNN for pattern recognition, where morphological operations were incorporated using counter-harmonic mean into the convolutional layer in order to generate enhanced feature maps. Morph-CNN was extensively evaluated on MNIST and SVHN benchmarks for digit recognition. The different tested configurations showed that Morph-CNN outperforms the existing methods.
Improving Opportunities in Healthcare Supply Chain Processes via the Internet of Things and Blockchain Technology

Despite key advances in healthcare informatics and management, little progress to address supply chain process-related problems has been made to date. Specifically, key healthcare supply chain processes include product recalls, monitoring of product supply shortages, expiration, and counterfeits. Implementing and executing these processes in a trusted, secure, efficient, globally accessible and traceable manner is challenging due to the fragmented nature of the healthcare supply chain, which is prone to systemic errors and redundant efforts that may compromise patient safety and impact health outcomes adversely. Blockchain, combined with the Internet of things (IoT), is an emerging technology that can offer a practical solution to these challenges. Accordingly, IoT blockchain offers a superior way to track and trace products via a peer-to-peer distributed, secure, and shared ledger of the blockchain network. This article highlights key challenges related to healthcare supply chains, and illustrates how IoT blockchain technologies can play a role in overcoming these challenges now and in the near future.
Blockchain and smart contracts for higher education registry in Brazil

Summary The validation of academic credits and issuance of academic degree certificates in the Brazilian education system currently occurs in a semi or completely noncomputerized way. The actual digitization of this system could make it more secure and decrease bureaucracy in terms of document validation, saving in storage and labor. Due to the recent increase in forgeries and loss of records, it is paramount that this process becomes more transparent and reliable for all involved parties. This article presents a proposal and an implementation for the digitization of degree certificates and academic credits for higher education in the Brazilian education system. A transparent model based on blockchain is proposed, in which higher education institutions register students and their academic credits in a chain of records using the Brazilian Public Key Infrastructure for identity management. This information, associated with smart contracts, enables the reliable and decentralized issuance of degree certificates through the validation of a historical database and the triggering of transactions using smart contracts. The focus of this article is the demonstration of an experiment that validates this proposal.
Visual Attention-Aware Omnidirectional Video Streaming Using Optimal Tiles for Virtual Reality

Owing to its interactive look around nature and very large resolution requirement, providing immersive omnidirectional video (ODV) streaming experiences in virtual reality applications demands cost-effective solutions to meet both the content delivery network and device constraints. In this paper, we introduce an adaptive ODV streaming pipeline that optimizes dynamic adaptive streaming over HTTP representations of ODV content considering their visual attention (VA) maps. The main contribution of this paper is the use of VA maps: 1) to compute a novel objective quality metric that captures the fact that not all of the ODV is actually watched by users: the VA spherical weightedbased objective quality measurement; 2) to define optimal tile representations of the ODV frames, namely, tiling schemes, which are composed of variable-sized and non-overlapping tiles; and 3) to efficiently distribute a given bitrate budget among the set of tiles within a tiling scheme for an ODV. We evaluate the proposed system performance with varying bandwidth conditions and the tracked head orientations from user experiments. Results show that the proposed system significantly outperforms the existing non-tiled and tile-based streaming solutions.
Online Multi-Object Tracking Using Joint Domain Information in Traffic Scenarios

Visual tracking of multiple objects is an essential component for a perception system in autonomous driving vehicles. One of the favorable approaches is the tracking-by-detection paradigm, which links current detection hypotheses to previously estimated object trajectories (also known as tracks) by searching appearance or motion similarities between them. As this search operation is usually based on a very limited spatial or temporal locality, the association can fail in cases of motion noise or long-term occlusion. In this paper, we propose a novel tracking method that solves this problem by putting together information from both enlarged structural and temporal domain. For efficiency without loss of optimality, this approach is decomposed in to three stages, with each dealing with only one constrained association task, and thus, it follows the alternating optimization fashion. In our approach, detections are first assembled into small tracklets based on meta-measurements of object affinity. The association task for tracklets-to-tracks is solved by structural information based on a motion pattern between them. Here, we propose new rules to decouple the processing time from the tracklet length. Furthermore, constraints from temporal domain are introduced to recover objects, which are long-time disappearing due to failed detection or long-term occlusion. By putting together the heterogeneous domain information, our approach exhibits an improved state-of-the-art performance on standard benchmarks. With relatively little processing time, an online and real-time tracking is also permitted in our approach.
Exploring eWOM in online customer reviews: Sentiment analysis at a fine-grained level

Customer reviews in social media and electronic commerce Web sites contain valuable electronic word-of-mouth (eWOM) information of products, which facilitates firms’ business strategy and individual consumers’ comparison shopping. Exploring eWOM of products embedded in customer reviews has attracted interest from researchers in various fields. Coarse-grained and context-free sentiment analysis approaches have been used in existing researches, which however often fail to satisfy the firms’ demands of fine-grained extraction of market intelligence from social media. In this study, we propose an original method to explore eWOM of products based on sentiment analysis at fine-grained level from a large volume of online customer reviews. We illustrate a feature-based and context-sensitive sentiment analysis mechanism that can leverage the sheer volume of customer reviews in social media sites. A novel semi-supervised fuzzy product ontology mining algorithm is proposed to extract semantic knowledge from online customer reviews with positive or negative labels. Based on real-world online customer review data set, the proposed method shows remarkable performance improvement over baseline methods at exploring eWOM of product a fine-grained level. With the novel eWOM exploring method, firms can improve their product design and marketing strategies, and potential consumers can make better online purchase decisions.
SVD-based robust image steganographic scheme using RIWT and DCT for secure transmission of medical images

The advances in computer technologies and the Internet have made rapid strides and breakthroughs in the field of data communication, which nowadays, is easily accessed. Unfortunately, this easy access offers almost endless opportunities for pirating copyrighted and confidential medical imagery. Many methods are proposed in the literature, however, most of them lack in robustness and perceptibility, and are prone to attacks. Hence, this study proposes a robust image steganographic approach that combines Redundant Integer Wavelet Transform (RIWT), Discrete Wavelet Transforms (DCT) and Singular Value Decomposition (SVD) and the logistic chaotic map. RIWT being a shift invariant, reversibility and robustness were achieved in this proposed technique. Better level of imperceptibility was achieved using SVD and DCT, with embedding carried out on singular values. Extra security was provided using the logistic chaotic map for encryption of secret medical images, which also enhanced the robustness of the technique. The effectiveness of our proposed scheme was compared with similar schemes available in the literature using common parameters such as imperceptibility, robustness and resistance to several geometric transformation attacks. This technique proved superior to other existing methods. The UCID benchmarking database was used during validation.
Hybrid approach for detection of malicious profiles in twitter

Emergence of social media invokes social actors to share their information digitally. Moreover, it enables them to maintain their links with other people worldwide. Due to its massive popularity, it has become a fascinating test bed to initiate various attacks. Attackers form Sybil nodes to disseminate malicious content in order to infect legitimate users aiming to steal sensitive information, such as user's credentials. Therefore, the focus of this paper is to detect malicious profiles, specifically on the Twitter microblogging platform. We propose a hybrid approach which leverages the capabilities of machine learning techniques to identify malicious profiles. Initially, Petri net structure analyzes the user's profile and various features, and these features are then used to train classifier. This is achieved to identify malicious profiles from legitimate users. Finally, to prove the efficiency, a comparative analysis of our approach is conducted with existing state-of-the-art techniques. The experimental results reveal that our approach achieves a high detection rate of 99.16% which is better than other techniques.
Does government information release really matter in regulating contagion-evolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics

The breeding and spreading of negative emotion in public emergencies posed severe challenges to social governance. The traditional government information release strategies ignored the negative emotion evolution mechanism. Focusing on the information release policies from the perspectives of the government during public emergency events, by using cognitive big data analytics, our research applies deep learning method into news framing framework construction process, and tries to explore the influencing mechanism of government information release strategy on contagion-evolution of negative emotion. In particular, this paper first uses Word2Vec, cosine word vector similarity calculation and SO-PMI algorithms to build a public emergencies-oriented emotional lexicon; then, it proposes a emotion computing method based on dependency parsing, designs an emotion binary tree and dependency-based emotion calculation rules; and at last, through an experiment, it shows that the emotional lexicon proposed in this paper has a wider coverage and higher accuracy than the existing ones, and it also performs a emotion evolution analysis on an actual public event based on the emotional lexicon, using the emotion computing method proposed. And the empirical results show that the algorithm is feasible and effective. The experimental results showed that this model could effectively conduct fine-grained emotion computing, improve the accuracy and computational efficiency of sentiment classification. The final empirical analysis found that due to such defects as slow speed, non transparent content, poor penitence and weak department coordination, the existing government information release strategies had a significant negative impact on the contagion-evolution of anxiety and disgust emotion, could not regulate negative emotions effectively. These research results will provide theoretical implications and technical supports for the social governance. And it could also help to establish negative emotion management mode, and construct a new pattern of the public opinion guidance.
Mapping Twitter hate speech towards social and sexual minorities: a lexicon-based approach to semantic content analysis

Though there are currently no statistics offering a global overview of online hate speech, both social networking platforms and organisations that combat hate speech have recognised that prevention strategies are needed to address this negative online phenomenon. While most cases of online hate speech target individuals on the basis of ethnicity and nationality, incitements to hatred on the basis of religion, class, gender and sexual orientation are increasing. This paper reports the findings of the 'Italian Hate Map' project, which used a lexicon-based method of semantic content analysis to extract 2,659,879 Tweets (from 879,428 Twitter profiles) over a period of 7 months; 412,716 of these Tweets contained negative terms directed at one of the six target groups. In the geolocalized Tweets, women were the most insulted group, having received 71,006 hateful Tweets (60.4% of the negative geolocalized tweets), followed by immigrants (12,281 tweets, 10.4%), gay and lesbian persons (12,140 tweets, 10.3%), Muslims (7,465 tweets, 6.4%), Jews (7,465 tweets, 6.4%) and disabled persons (7,230 tweets, 6.1%). The findings provide a real-time snapshot of community behaviours and attitudes against social, ethnic, sexual and gender minority groups that can be used to inform intolerance prevention campaigns on both local and national levels.
RotoSwype

We propose RotoSwype, a technique for word-gesture typing using the orientation of a ring worn on the index finger. RotoSwype enables one-handed text-input without encumbering the hand with a device, a desirable quality in many scenarios, including virtual or augmented reality. The method is evaluated using two arm positions: with the hand raised up with the palm parallel to the ground; and with the hand resting at the side with the palm facing the body. A five-day study finds both hand positions achieved speeds of at least 14 words-per-minute (WPM) with uncorrected error rates near 1%, outperforming previous comparable techniques.