Reviewerly/exHarmony huggingface dataset:
Maintenance-related concerns for post-deployed Ethereum smart contract development: issues, techniques, and future challenges

Software development is a very broad activity that captures the entire life cycle of a software, which includes designing, programming, maintenance and so on. In this study, we focus on the maintenance-related concerns of the post-deployment of smart contracts. Smart contracts are self-executed programs that run on a blockchain. They cannot be modified once deployed and hence they bring unique maintenance challenges compared to conventional software. According to the definition of ISO/IEC 14764, there are four kinds of software maintenance, i.e., corrective, adaptive, perfective, and preventive maintenance. This study aims to answer (i) What kinds of issues will smart contract developers encounter for corrective, adaptive, perfective, and preventive maintenance after they are deployed to the Ethereum? (ii) What are the current maintenance-related methods used for smart contracts? To obtain the answers to these research questions, we first conducted a systematic literature review to analyze 131 smart contract related research papers published from 2014 to 2020. Since the Ethereum ecosystem is fast-growing, some results from previous publications might be out-of-date and there may be a gap between academia and industry. To address this, we performed an online survey of smart contract developers on Github to validate our findings and received 165 useful responses. Based on the survey feedback and literature review, we present the first empirical study on smart contract maintenance-related concerns. Our study can help smart contract developers better maintain their smart contract-based projects, and we highlight some key future research directions to improve the Ethereum ecosystem.
Cryptocurrencies and illicit practices: The role of governance

The paper aims to explore the role of national institutions in the adoption of cryptocurrencies. To achieve this, we apply PLS-SEM on a sample of 33 countries in order to test the potential causal effect of three governance constructs built on the basis of the World Bank’s Worldwide Governance Indicators. We find that the dimension that includes government efficiency and regulation quality acts as the main disincentive for citizens to use cryptocurrencies. Although the impact of the construct that comprises political stability and voice and accountability is negative too, its size is smaller. Those negative effects are partially offset by rule of law and control of corruption, since they encourage people to adopt cryptocurrencies. This reveals that a solid welfare state is the most effective factor in dissuading citizens from using those digital assets, while high-quality law enforcement and financial checks that usually apply to ordinary transactions encourage the adoption of cryptocurrencies.
Toward Real-World Super-Resolution via Adaptive Downsampling Models

Most image super-resolution (SR) methods are developed on synthetic low-resolution (LR) and high-resolution (HR) image pairs that are constructed by a predetermined operation, e.g., bicubic downsampling. As existing methods typically learn an inverse mapping of the specific function, they produce blurry results when applied to real-world images whose exact formulation is different and unknown. Therefore, several methods attempt to synthesize much more diverse LR samples or learn a realistic downsampling model. However, due to restrictive assumptions on the downsampling process, they are still biased and less generalizable. This study proposes a novel method to simulate an unknown downsampling process without imposing restrictive prior knowledge. We propose a generalizable low-frequency loss (LFL) in the adversarial training framework to imitate the distribution of target LR images without using any paired examples. Furthermore, we design an adaptive data loss (ADL) for the downsampler, which can be adaptively learned and updated from the data during the training loops. Extensive experiments validate that our downsampling model can facilitate existing SR methods to perform more accurate reconstructions on various synthetic and real-world examples than the conventional approaches.
Thirty Years of DNS Insecurity: Current Issues and Perspectives

When DNS was created, nobody expected that it would have become the base for the digital economy and a prime target for cybercriminals. And nobody expected that one main asset of the digital economy would have been users’ browsing habits, putting at risk their privacy. The DNS was designed and implemented according to speed, scalability, and reliability criteria, whereas security and privacy did not fit in the objectives. Although the first attacks were already conceived about thirty years ago, the DNS infrastructure - with a bunch of improvements but its original design - continues to play a pivotal role in enabling access to services, data and devices. And, despite the fairly widespread adoption of DNSSEC security extensions in recent years, DNS attacks are becoming more and more frequent, sophisticated and dangerous. They are global, varied, dynamic and can circumvent traditional security systems such as next-generation firewalls and data loss prevention systems. A revisitation of DNS assumptions has been proposed in very different ways, reflecting diverse point of views in terms of Internet governance and user freedom, and a great effort is in place by standardization bodies, industry consortia and academic research to converge toward an updated design and implementation. The present work overviews the most promising proposals, trying to shed some insight on the future of DNS.
ReGenMorph: Visibly Realistic GAN Generated Face Morphing Attacks by Attack Re-generation

Face morphing attacks aim at creating face images that are verifiable to be the face of multiple identities, which can lead to building faulty identity links in operations like border checks. While creating a morphed face detector (MFD), training on all possible attack types is essential to achieve good detection performance. Therefore, investigating new methods of creating morphing attacks drives the generalizability of MADs. Creating morphing attacks was performed on the image level, by landmark interpolation, or on the latent-space level, by manipulating latent vectors in a generative adversarial network. The earlier results in varying blending artifacts and the latter results in synthetic-like striping artifacts. This work presents the novel morphing pipeline, ReGenMorph, to eliminate the LMA blending artifacts by using a GAN-based generation, as well as, eliminate the manipulation in the latent space, resulting in visibly realistic morphed images compared to previous works. The generated ReGenMorph appearance is compared to recent morphing approaches and evaluated for face recognition vulnerability and attack detectability, whether as known or unknown attacks.
A comparative study between racking systems for photovoltaic power systems

We present a comparative study of the different racking systems used in photovoltaic power systems, with a new methodology for determining the total energy produced by each one under usual weather conditions (not clear-skies). In systems without solar tracker, the tilt angle is a major factor contributing to the energy production, and its optimization is essential. We study the effect of tilt update frequency (daily, monthly, or constant) on the total irradiation received by a plane surface, and present a method for computing the optimal tilt angle, which we validate using previous studies. This method is easily implemented, accurate, and valid for any location. We compare all the systems with the most energy-productive one, the dual-axis tracker, in two ways: with respect to energy production, and to levelized cost of energy, both in 39 cities around the World. The results provide a new insight on the relative and objective value of trackerless systems, and some remarkable properties arise, which may be relevant in budgetary consideration.
An AI-assisted chatbot for radiation safety education in radiotherapy

Abstract Purpose . We created a virtual assistant chatbot that will serve as a tool for radiation safety training for clinical staff, including radiation oncologist, radiotherapist and medical physicist, in cancer treatment. The Bot can also be used to test their knowledge on radiation safety. Methods . The Bot was constructed using IBM’s Watson Assistant functionalities on the IBM cloud. A layered structure approach was used in the workflow of the Bot to interact with the user. Through answering various questions concerning radiation safety in radiotherapy, the users can learn the essential information to gain knowledge, when working in a cancer centre/hospital. Results . The user interface of the Bot was a front-end window operating on Internet, which could easily be accessed by any Internet-of-things such as smartphone, tablet or laptop. The Bot could communicate with the user for radiation safety Q&amp;A. If the Bot could not identify what the user needed, the Bot would provide a list of options as a guidance. Using the natural language processing in communication, knowledge transfer from the Bot to user could be carried out. Conclusion . It is concluded that the radiation safety chatbot worked as intended, utilizing all the tools provided by the IBM Watson Assistant. The Bot could provide radiation safety information to the radiation staff effectively, and be used in staff training in radiotherapy.
Compositional and source patterns of potentially toxic elements (PTEs) in soils in southwestern Ghana using robust compositional contamination index (RCCI) and k-means cluster analysis

Soils are heavily impacted by geogenic and anthropogenic factors. They form an integral part in supporting agriculture, and the natural climate. This study investigated the soil chemistry in the Wassa and Prestea-Huni Valley areas in southwestern Ghana using the RCCI technique and multivariate statistics with utilizing a total of 300 soil samples. The results showed that 43%, 98%, 50%, and 100% of the samples exceeded the standards respectively for As, Ba, Co, and Cu. Meanwhile, for Cr, Ni, Mn, Fe, and Zn, 50%, 54%, 5%, 46%, and 71% were above their guideline values. The RCCI computation showed that 2%, 15%, 48%, 34%, and 1% of the samples were within lower, medium, moderate, high, and very high ranges respectively. The interpolation of the RCCI results revealed sparse but concentrated hotspot areas around mid-Wassa Amenfi Central, mid- towards the north of the Wassa Amenfi West and East Districts. Also, the K-means and ANOVA outputs showed that Cu and Ni contributed significantly to the chemistry of the soil. The EOF 1 showed a weighted sum of V, Fe, Cu, Ni, Zn, Ba, Cr, Ag, and Pb. Inferentially, these contained the highest signals of the data and predominantly influenced the quality and variability of the soils. The variations shown in the levels of the assessed elements across the areas suggest that processes impacting elemental accumulation and enrichment were influenced by different processes. These could also impact other environmental media including surface and groundwater quality, vegetation, and air quality. Residents within hotspot areas may be exposed to several deleterious health impacts through oral, nasal and dermal routes. This could further pose public health challenges down the line as these elements come into contact with people through the consumption of crops and groundwater obtained from the areas in future. Addressing such a challenge calls for interdisciplinary collaboration between environmental geologist/medical geology researchers, public health practitioners, epidemiologists, environmental toxicologists, nutritionists, dieticians, and agriculturists.
One thousand and one stories: a large-scale survey of software refactoring

Despite the availability of refactoring as a feature in popular IDEs, recent studies revealed that developers are reluctant to use them, and still prefer the manual refactoring of their code. At JetBrains, our goal is to fully support refactoring features in IntelliJ-based IDEs and improve their adoption in practice. Therefore, we start by raising the following main questions. How exactly do people refactor code? What refactorings are the most popular? Why do some developers tend not to use convenient IDE refactoring tools?
Comparative study on cryptocurrency transaction and banking transaction

Cryptocurrency is an advanced digital currency that is gotten by cryptography, numerous digital currencies are decentralized organizations dependent on blockchain innovation an appropriated record authorized by a different organization of computers. And Many present-day technologies are driving the transformative impact in the global financial system, in that impact cryptocurrency stands on first position in the list. Cryptocurrency offer several potential benefits, including better speed and efficiency in processing payments and transfers notably across borders and ultimately boosting financial inclusion. The intension of this paper is to summaries the difference between the normal or traditional method of currency transaction and crypto currency transaction. And why all are providing more interest towards crypto methods nowadays and what different they feel while choosing a crypto method over a normal or traditional currency methods. And how crypto currency is dragging current world attention towards its pocket and why many are developing interest towards following the crypto trend.
S-CUDA: Self-cleansing unsupervised domain adaptation for medical image segmentation

Medical image segmentation tasks hitherto have achieved excellent progresses with large-scale datasets, which empowers us to train potent deep convolutional neural networks (DCNNs). However, labeling such large-scale datasets is laborious and error-prone, which leads the noisy (or incorrect) labels to be an ubiquitous problem in the real-world scenarios. In addition, data collected from different sites usually exhibit significant data distribution shift (or domain shift). As a result, noisy label and domain shift become two common problems in medical imaging application scenarios, especially in medical image segmentation, which degrade the performance of deep learning models significantly. In this paper, we identify a novel problem hidden in medical image segmentation, which is unsupervised domain adaptation on noisy labeled data, and propose a novel algorithm named "Self-Cleansing Unsupervised Domain Adaptation" (S-CDUA) to address such issue. S-CUDA sets up a realistic scenario to solve the above problems simultaneously where training data (i.e., source domain) not only shows domain shift w.r.t. unsupervised test data (i.e., target domain) but also contains noisy labels. The key idea of S-CUDA is to learn noise-excluding and domain invariant knowledge from noisy supervised data, which will be applied on the highly corrupted data for label cleansing and further data-recycling, as well as on the test data with domain shift for supervised propagation. To this end, we propose a novel framework leveraging noisy-label learning and domain adaptation techniques to cleanse the noisy labels and learn from trustable clean samples, thus enabling robust adaptation and prediction on the target domain. Specifically, we train two peer adversarial networks to identify high-confidence clean data and exchange them in companions to eliminate the error accumulation problem and narrow the domain gap simultaneously. In the meantime, the high-confidence noisy data are detected and cleansed in order to reuse the contaminated training data. Therefore, our proposed method can not only cleanse the noisy labels in the training set but also take full advantage of the existing noisy data to update the parameters of the network. For evaluation, we conduct experiments on two popular datasets (REFUGE and Drishti-GS) for optic disc (OD) and optic cup (OC) segmentation, and on another public multi-vendor dataset for spinal cord gray matter (SCGM) segmentation. Experimental results show that our proposed method can cleanse noisy labels efficiently and obtain a model with better generalization performance at the same time, which outperforms previous state-of-the-art methods by large margin. Our code can be found at https://github.com/zzdxjtu/S-cuda.
Characterization of concept generation for engineering design through temporal brain network analysis

This research explores the effect of the structuredness of design concept generation techniques on temporal network neurocognition. Engineering graduate students (n = 30) completed three concept generation tasks using techniques with different levels of structuredness: brainstorming, morphological analysis, and TRIZ. Students' brain activation in their prefrontal cortex (PFC) was measured using functional near-infrared spectroscopy (fNIRS). The temporal dynamic of central regions in brain networks were compared between tasks. Central regions facilitate functional interaction and imply information flow through the brain. A consistent central region appears in the medial PFC. Consistent network connections occurred across both hemispheres suggesting a concurrent dual processing of divergent and convergent thinking. This study offers novel insights into the underlying neurophysiological mechanism when using these concept generation techniques.
Efficient Content-Adaptive Feature-Based Shot Detection for HTTP Adaptive Streaming

Video delivery over the Internet has been becoming a commodity in recent years, owing to the widespread use of Dynamic Adaptive Streaming over HTTP (DASH). The DASH specification defines a hierarchical data model for Media Presentation Descriptions (MPDs) in terms of segments. This paper focuses on segmenting video into multiple shots for encoding in Video on Demand (VoD) HTTP Adaptive Streaming (HAS) applications. Therefore, we propose a novel Discrete Cosine Transform (DCT) feature-based shot detection and successive elimination algorithm for shot detection and compare it against the default shot detection algorithm of the x265 implementation of the High Efficiency Video Coding (HEVC) standard. Our experimental results demonstrate that our proposed feature-based pre-processor has a recall rate of 25% and an F-measure of 20% greater than the benchmark algorithm for shot detection.
Improving evidence-based practice through preregistration of applied research: Barriers and recommendations

Preregistration is the practice of publicly publishing plans on central components of the research process before access to, or collection, of data. Within the context of the replication crisis, open science practices like preregistration have been pivotal in facilitating greater transparency in research. However, such practices have been applied nearly exclusively to basic academic research, with rare consideration of the relevance to applied and consultancy-based research. This is particularly problematic as such research is typically reported with very low levels of transparency and accountability despite being disseminated as influential gray literature to inform practice. Evidence-based practice is best served by an appreciation of multiple sources of quality evidence, thus the current review considers the potential of preregistration to improve both the accessibility and credibility of applied research toward more rigorous evidence-based practice. The current three-part review outlines, first, the opportunities of preregistration for applied research, and second, three barriers - practical challenges, stakeholder roles, and the suitability of preregistration. Last, this review makes four recommendations to overcome these barriers and maximize the opportunities of preregistration for academics, industry, and the structures they are held within - changes to preregistration templates, new types of templates, education and training, and recognition and structural changes.
MS MARCO Chameleons: Challenging the MS MARCO Leaderboard with Extremely Obstinate Queries

During the recent years and with the growing influence of neural architectures, tasks such as ad hoc retrieval have witnessed an impressive improvement in performance. For instance, the performance of rankers on the passage retrieval task on the MS MARCO dataset has improved by an order of magnitude in less than two years. In this paper, we go beyond the overall performance of the state of the art rankers and empirically study their performance from a finer-grained perspective. We find that while neural rankers have been able to consistently improve performance, this has been in part thanks to a specific set of queries from within the larger query set. We systematically show that there are subsets of queries that are difficult for each and every one of the neural rankers, which we refer to as obstinate queries. We show the obstinate queries are similar to easier queries in terms of their number of available relevant judgement documents and the length of the query itself but they are extremely more difficult to satisfy by existing rankers. Furthermore, we observe that query reformulation methods cannot help these queries. On this basis, we present three datasets derived from the MS MARCO Dev set, called the MS MARCO Chameleon datasets. We believe that the next breakthrough in performance would need to necessarily consider the queries in the MS MARCO Chameleons, as such, propose that a well-rounded evaluation strategy for any new ranker would need to include performance measures on both the overall MS MARCO dataset as well as the proposed MS MARCO Chameleon datasets.
Attentional matrix factorization with context and co-invocation for service recommendation

Easy accessibility of data and functions are the main advantages to develop mashups from abundant sources of Web APIs. However, it simultaneously brings difficulties to choose suitable APIs for a mashup. Existing probabilistic matrix factorization (PMF) recommender systems can effectively exploit the latent features of the invocations with the same weight but not all features are equally significant and predictive, and the useless features may bring noises to the model. In this work, we propose the Attentional PMF Model (AMF), which leverages a neural attentional network to learn the significance of latent features. We then inject the attentional scores and the mashup-API context similarity into the matrix factorization structure for training. Furthermore, our model exploits the relationship between APIs from both their context and co-invocation history as regularization terms to improve its prediction performance. Our experiments are evaluated on ProgrammableWeb. The results show that our model outperforms some state-of-art recommender systems in mashup service applications.
JaDe: Low Power Jamming Detection Using Machine Learning in Vehicular Networks

Vehicular networks, like any other wireless networks, are prone to jamming due to the inherent nature of the wireless environment. In this letter, we consider an attacker who jams the network whenever he observes that a packet is being transmitted, i.e., some communication happening in the network. Such a strategy leads to an increase in the packet drop rate and latency of the network. This causes serious disruption of delay-sensitive applications that can lead to life endangering situations and therefore such an attack needs to be addressed. In this letter, we propose a mechanism that uses a support vector machine to detect the presence of a jammer in the network. We obtain jointly sufficient statistics of packet drop probabilities and use them to generate the training data. The results demonstrate the effectiveness of the proposed detection system.