Reviewerly/exHarmony huggingface dataset:
Defining Smart Contract Defects on Ethereum

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Smart contracts</i> are programs running on a blockchain. They are immutable to change, and hence can not be patched for bugs once deployed. Thus it is critical to ensure they are bug-free and well-designed before deployment. A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contract defect</i> is an error, flaw or fault in a smart contract that causes it to produce an incorrect or unexpected result, or to behave in unintended ways. The detection of contract defects is a method to avoid potential bugs and improve the design of existing code. Since smart contracts contain numerous distinctive features, such as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gas system. decentralized</i> , it is important to find smart contract specified defects. To fill this gap, we collected smart-contract-related posts from Ethereum StackExchange, as well as real-world smart contracts. We manually analyzed these posts and contracts; using them to define 20 kinds of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contract defects</i> . We categorized them into indicating potential security, availability, performance, maintainability and reusability problems. To validate if practitioners consider these contract as harmful, we created an online survey and received 138 responses from 32 different countries. Feedback showed these contract defects are harmful and removing them would improve the quality and robustness of smart contracts. We manually identified our defined contract defects in 587 real world smart contract and publicly released our dataset. Finally, we summarized 5 impacts caused by contract defects. These help developers better understand the symptoms of the defects and removal priority.
A minutely solar irradiance forecasting method based on real-time sky image-irradiance mapping model

Accurate minutely solar irradiance forecasting is the basis of minute-level photovoltaic (PV) power forecasting. In this paper, a minutely solar irradiance forecasting method based on real-time surface irradiance mapping model is proposed, which is beneficial to achieve higher accuracy in solar power forecasting. First, we extract the red–green–blue (RGB) values and position information of pixels in sky images after background elimination and distortion rectification, to explore the mapping relationship between sky image and solar irradiance. Then a real-time sky image-irradiance mapping model is built, trained, and updated according to real-time sky images and solar irradiance. Finally, the future solar irradiance within the time horizons varying from 1 min to 10 min ahead are capable to be forecasted by using the latest updated surface irradiance mapping model with extracted input from the current sky image. The average measures of proposed method by using MAPE, RMSE, MBE are 22.66%, 92.72, −1.26% for blocky clouds; 20.44%, 132.15, −1.06% for thin clouds and 18.82%, 120.78, −0.98% for thick clouds, thus deliver much higher forecasting accuracy than other benchmarks.
Contextual Hybrid Session-Based News Recommendation With Recurrent Neural Networks

Recommender systems help users deal with information overload by providing tailored item suggestions to them. The recommendation of news is often considered to be challenging, since the relevance of an article for a user can depend on a variety of factors, including the user's short-term reading interests, the reader's context, or the recency or popularity of an article. Previous work has shown that the use of Recurrent Neural Networks is promising for the next-in-session prediction task, but has certain limitations when only recorded item click sequences are used as input. In this work, we present a contextual hybrid, deep learning based approach for session-based news recommendation that is able to leverage a variety of information types. We evaluated our approach on two public datasets, using a temporal evaluation protocol that simulates the dynamics of a news portal in a realistic way. Our results confirm the benefits of considering additional types of information, including article popularity and recency, in the proposed way, resulting in significantly higher recommendation accuracy and catalog coverage than other session-based algorithms. Additional experiments show that the proposed parameterizable loss function used in our method also allows us to balance two usually conflicting quality factors, accuracy and novelty. Keywords: Artificial Neural Networks, Context-Aware Recommender Systems, Hybrid Recommender Systems, News Recommender Systems, Session-based Recommendation
Augmented reality tools for sports education and training

Augmented reality (AR) provides additional information to the reality of sportspeople, and might offer supplementary advantages compared to other technologies. The goals of this study were to characterize and understand the benefits of AR in sports education and training. We reviewed Pubmed, Scopus, Web of Science, and SportDiscus databases, and discussed the results according to their role in sport (practitioner, spectator, and customer). Our results showed that different AR approaches might be used for learning and providing feedback. New rules could be introduced for reducing the gap between players with different experience levels. Additional information could also be added to improve the audience experience. We also explored the limitations of current AR systems and their efficacy in training, and provided suggestions for designing training scenarios.
Ensuring survey research data integrity in the era of internet bots

We used an internet-based survey platform to conduct a cross-sectional survey regarding the impact of COVID-19 on the LGBTQ + population in the United States. While this method of data collection was quick and inexpensive, the data collected required extensive cleaning due to the infiltration of bots. Based on this experience, we provide recommendations for ensuring data integrity. Recruitment conducted between May 7 and 8, 2020 resulted in an initial sample of 1251 responses. The Qualtrics survey was disseminated via social media and professional association listservs. After noticing data discrepancies, research staff developed a rigorous data cleaning protocol. A second wave of recruitment was conducted on June 11–12, 2020 using the original recruitment methods. The five-step data cleaning protocol led to the removal of 773 (61.8%) surveys from the initial dataset, resulting in a sample of 478 participants in the first wave of data collection. The protocol led to the removal of 46 (31.9%) surveys from the second two-day wave of data collection, resulting in a sample of 98 participants in the second wave of data collection. After verifying the two-day pilot process was effective at screening for bots, the survey was reopened for a third wave of data collection resulting in a total of 709 responses, which were identified as an additional 514 (72.5%) valid participants and led to the removal of an additional 194 (27.4%) possible bots. The final analytic sample consists of 1090 participants. Although a useful and efficient research tool, especially among hard-to-reach populations, internet-based research is vulnerable to bots and mischievous responders, despite survey platforms’ built-in protections. Beyond the depletion of research funds, bot infiltration threatens data integrity and may disproportionately harm research with marginalized populations. Based on our experience, we recommend the use of strategies such as qualitative questions, duplicate demographic questions, and incentive raffles to reduce likelihood of mischievous respondents. These protections can be undertaken to ensure data integrity and facilitate research on vulnerable populations.
Swarm learning for decentralized artificial intelligence in cancer histopathology

Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL), in which partners jointly train AI models while avoiding data transfer and monopolistic data governance. Here, we demonstrate the successful use of SL in large, multicentric datasets of gigapixel histopathology images from over 5,000 patients. We show that AI models trained using SL can predict BRAF mutational status and microsatellite instability directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer. We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States, and validated the prediction performance in two independent datasets from the United Kingdom. Our data show that SL-trained AI models outperform most locally trained models, and perform on par with models that are trained on the merged datasets. In addition, we show that SL-based AI models are data efficient. In the future, SL can be used to train distributed AI models for any histopathology image analysis task, eliminating the need for data transfer.
Efficient and Secure Pairing-Free Certificateless Aggregate Signature Scheme for Healthcare Wireless Medical Sensor Networks

Recent advancements in wireless communication technologies have led to the development of practical applications, such as infrastructure monitoring, earthquake monitoring, environment monitoring, remote healthcare monitoring, etc. In these applications, we may integrate the wireless sensor networks (WSNs) with Internet of Things (IoT), where sensor nodes join the Internet dynamically and use it to collaborate and accomplish their tasks. In e-healthcare system, wireless medical sensor nodes continuously monitor and collect the physiological information and transmit the same to healthcare professionals through IoT terminals. To overcome the limitations in communication and computational capabilities in sensors and to attain privacy in healthcare wireless medical sensor networks (HWMSNs), few signature schemes were proposed in the literature. However, most of these schemes are insecure against various attacks. Hence to ensure privacy and security in medical data, in this paper, we proposed an efficient pairing-free aggregate signature scheme for HWMSNs in certificateless system. We exploit aggregation technique in certificateless system without using pairings to reduce computational complexity and transmission overhead in transferring the data. Our scheme achieves full aggregation to improve communicational efficiency and performance analysis shows that the proposed scheme is more efficient.
Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)

Sarcasm is often expressed through several verbal and non-verbal cues, e.g., a change of tone, overemphasis in a word, a drawn-out syllable, or a straight looking face.Most of the recent work in sarcasm detection has been carried out on textual data.In this paper, we argue that incorporating multimodal cues can improve the automatic classification of sarcasm.As a first step towards enabling the development of multimodal approaches for sarcasm detection, we propose a new sarcasm dataset, Multimodal Sarcasm Detection Dataset (MUS-tARD 1 ), compiled from popular TV shows.MUStARD consists of audiovisual utterances annotated with sarcasm labels.Each utterance is accompanied by its context of historical utterances in the dialogue, which provides additional information on the scenario where the utterance occurs.Our initial results show that the use of multimodal information can reduce the relative error rate of sarcasm detection by up to 12.9% in F-score when compared to the use of individual modalities.The full dataset is publicly available for use at https://github.com/soujanyaporia/MUStARD. * Equal contribution. 1 MUStARD is an abbreviation for MUltimodal SARcasm Dataset.Similar to how "mustard" adds spice to our food and meals, we believe sarcasm adds spice to our interactions and lives.
Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment

Load balancing of tasks on the cloud environment is an important aspect of distributing resources from a data centre. Due to the dynamic computing through the internet; cloud computing agonizes from overloading of requests. Load balancing has to be carried out in such a manner that all virtual machines (VM) should have balanced load to achieve optimal utilization of its capabilities. This paper proposes a novel methodology of dynamic balancing of load among the virtual machines using hybridization of modified Particle swarm optimization (MPSO) and improved Q-learning algorithm named as QMPSO. The hybridization process is carried out to adjust the velocity of the MPSO through the gbest and pbest based on the best action generated through the improved Q-learning. The aim of hybridization is to enhance the performance of the machine by balancing the load among the VMs, maximize the throughput of VMs and maintain the balance between priorities of tasks by optimizing the waiting time of tasks. The robustness of the algorithm has been validated by comparing the results of the QMPSO obtained from the simulation process with the existing load balancing and scheduling algorithm. The comparison of the simulation and real platform result shows our proposed algorithm is outperforming its competitor.
Carrying out consensual Group Decision Making processes under social networks using sentiment analysis over comparative expressions

Social networks are the most preferred mean for the people to communicate. Therefore, it is quite usual that experts use them to carry out Group Decision Making processes. One disadvantage that recent Group Decision Making methods have is that they do not allow the experts to use free text to express themselves. On the contrary, they force them to follow a specific user–computer communication structure. This is against social network nature where experts are free to express themselves using their preferred text structure. This paper presents a novel model for experts to carry out Group Decision Making processes using free text and alternatives pairwise comparisons. The main advantage of this method is that it is designed to work using social networks. Sentiment analysis procedures are used to analyze free texts and extract the preferences that the experts provide about the alternatives. Also, our method introduces two ways of applying consensus measures over the Group Decision Making process. They can be used to determine if the experts agree among them or if there are different postures. This way, it is possible to promote the debate in those cases where consensus is low.
Siamese attentional keypoint network for high performance visual tracking

In this paper, we investigate the impacts of three main aspects of visual tracking, i.e., the backbone network, the attentional mechanism, and the detection component, and propose a Siamese Attentional Keypoint Network, dubbed SATIN, for efficient tracking and accurate localization. Firstly, a new Siamese lightweight hourglass network is specially designed for visual tracking. It takes advantage of the benefits of the repeated bottom-up and top-down inference to capture more global and local contextual information at multiple scales. Secondly, a novel cross-attentional module is utilized to leverage both channel-wise and spatial intermediate attentional information, which can enhance both discriminative and localization capabilities of feature maps. Thirdly, a keypoints detection approach is invented to trace any target object by detecting the top-left corner point, the centroid point, and the bottom-right corner point of its bounding box. Therefore, our SATIN tracker not only has a strong capability to learn more effective object representations, but also is computational and memory storage efficiency, either during the training or testing stages. To the best of our knowledge, we are the first to propose this approach. Without bells and whistles, experimental results demonstrate that our approach achieves state-of-the-art performance on several recent benchmark datasets, at a speed far exceeding 27 frames per second.
Graphology based handwritten character analysis for human behaviour identification

Graphology-based handwriting analysis to identify human behavior, irrespective of applications, is interesting. Unlike existing methods that use characters, words and sentences for behavioural analysis with human intervention, we propose an automatic method by analysing a few handwritten English lowercase characters from a to z to identify person behaviours. The proposed method extracts structural features, such as loops, slants, cursive, straight lines, stroke thickness, contour shapes, aspect ratio and other geometrical properties, from different zones of isolated character images to derive the hypothesis based on a dictionary of Graphological rules. The derived hypothesis has the ability to categorise the personal, positive, and negative social aspects of an individual. To evaluate the proposed method, an automatic system is developed which accepts characters from a to z written by different individuals across different genders and age groups. This automatic privacy projected system is available on the website (http://subha.pythonanywhere.com). For quantitative evaluation of the proposed method, several people are requested to use the system to check their characteristics with the system automatic response based on his/her handwriting by choosing to agree or disagree options. The automatic system receives 5300 responses from the users, for which, the proposed method achieves 86.70% accuracy.
Deep learning for mass detection in Full Field Digital Mammograms

In recent years, the use of Convolutional Neural Networks (CNNs) in medical imaging has shown improved performance in terms of mass detection and classification compared to current state-of-the-art methods. This paper proposes a fully automated framework to detect masses in Full-Field Digital Mammograms (FFDM). This is based on the Faster Region-based Convolutional Neural Network (Faster-RCNN) model and is applied for detecting masses in the large-scale OPTIMAM Mammography Image Database (OMI-DB), which consists of ∼80,000 FFDMs mainly from Hologic and General Electric (GE) scanners. This research is the first to benchmark the performance of deep learning on OMI-DB. The proposed framework obtained a True Positive Rate (TPR) of 0.93 at 0.78 False Positive per Image (FPI) on FFDMs from the Hologic scanner. Transfer learning is then used in the Faster R-CNN model trained on Hologic images to detect masses in smaller databases containing FFDMs from the GE scanner and another public dataset INbreast (Siemens scanner). The detection framework obtained a TPR of 0.91±0.06 at 1.69 FPI for images from the GE scanner and also showed higher performance compared to state-of-the-art methods on the INbreast dataset, obtaining a TPR of 0.99±0.03 at 1.17 FPI for malignant and 0.85±0.08 at 1.0 FPI for benign masses, showing the potential to be used as part of an advanced CAD system for breast cancer screening.
Healthcare cyber-attacks and the COVID-19 pandemic: an urgent threat to global health

The Coronavirus Disease 2019 (COVID-19) pandemic has resulted in widespread disruption to the healthcare industry. Alongside complex issues relating to ensuring sufficient healthcare capacity and resourcing, healthcare organizations and universities are now also facing heightened cyber-security threats in the midst of the pandemic. Since the outbreak began, various healthcare providers and academic institutions across the world have been targeted in a variety of complex and coordinatized cyber-attacks. International and national regulatory bodies have stressed the urgent need for healthcare providers and universities to protect themselves against cyber-attacks during COVID-19, recognizing that a growing number of cyber-criminals are seeking to capitalize on the vulnerabilities of the healthcare sector during this period. This includes a desire to steal intellectual property such as data relating to COVID-19 vaccine development, modelling and experimental therapeutics. It is therefore essential that healthcare providers and universities ensure they are informed, protected and prepared to respond to any cyber-threat. This article outlines key COVID-19 cyber-security principles for both healthcare organizations and academic institutions.
Future directions for chatbot research: an interdisciplinary research agenda

Abstract Chatbots are increasingly becoming important gateways to digital services and information—taken up within domains such as customer service, health, education, and work support. However, there is only limited knowledge concerning the impact of chatbots at the individual, group, and societal level. Furthermore, a number of challenges remain to be resolved before the potential of chatbots can be fully realized. In response, chatbots have emerged as a substantial research area in recent years. To help advance knowledge in this emerging research area, we propose a research agenda in the form of future directions and challenges to be addressed by chatbot research. This proposal consolidates years of discussions at the CONVERSATIONS workshop series on chatbot research. Following a deliberative research analysis process among the workshop participants, we explore future directions within six topics of interest: (a) users and implications, (b) user experience and design, (c) frameworks and platforms, (d) chatbots for collaboration, (e) democratizing chatbots, and (f) ethics and privacy. For each of these topics, we provide a brief overview of the state of the art, discuss key research challenges, and suggest promising directions for future research. The six topics are detailed with a 5-year perspective in mind and are to be considered items of an interdisciplinary research agenda produced collaboratively by avid researchers in the field.
PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data

Prediction based on time series has a wide range of applications. Due to the complex nonlinear and random distribution of time series data, the performance of learning prediction models can be reduced by the modeling bias or overfitting. This paper proposes a novel planar flow-based variational auto-encoder prediction model (PFVAE), which uses the long- and short-term memory network (LSTM) as the auto-encoder and designs the variational auto-encoder (VAE) as a time series data predictor to overcome the noise effects. In addition, the internal structure of VAE is transformed using planar flow, which enables it to learn and fit the nonlinearity of time series data and improve the dynamic adaptability of the network. The prediction experiments verify that the proposed model is superior to other models regarding prediction accuracy and proves it is effective for predicting time series data.