Reviewerly/exHarmony huggingface dataset:
Understanding Out of Gas Exceptions on Ethereum

Ethereum is by far the most popular smart contract platform in the public blockchain category. In Ethereum, special programs named smart contracts codify the “self-governed accounts”. By design, users can send transactions to smart contracts, which will automatically lead to code execution and state modification. Unlike regular programs, smart contracts are restricted in execution by gas limit, i.e., a form of runtime resource. If a transaction uses up all available gas, an out of gas (OG) exception will trigger, reverting state until right before the transaction. In this work, we empirically studied the OG exceptions on Ethereum for the very first time. In particular, we collected exception transactions using an instrumented Ethereum client. By investigating OG exceptions, we found OG stand out in terms of both occurrences and damages. Moreover, we focused on individual contracts and transactions, aiming at discovering and identifying common causing factors triggering these exceptions. At last, we also investigate existing tools in preventing OG exceptions. The results call for further research and study in this direction.
Frequency analysis of light field sampling for texture information

Light field sampling (LFS) theory can properly reduce minimum sampling rate while ensuring that novel views are not distorted for image-based rendering (IBR). The minimum sampling rate is determined by spectral support of light field. The spectral support of light field has studied the influence of the following factors: the minimum depth and the maximum depth, non-Lambertian reflections, whether the scene surfaces are flat, maximum frequency of painted signals. In this paper, we further perfect the light field spectrum analysis from the quantitative description of scene texture information based on the existing spectrum analysis theory. The quantification of texture information can be interactively refined via detected regional entropy. Thus, we can derive a spectral analytical function of light field with respect to texture information. The new function allows the spectral support of light field to be analyzed and estimated for different texture information associated with scene objects. In this way, we limit the spectral analysis problems of light field to those of a simpler signal. We show that this spectral analysis approach can be easily extended to arbitrary scene complexity levels, as we simplify the LFS of complex scenes to a plane. Additionally, the spectral support of light field broadens as the plane texture information becomes more complex. We present experimental results to demonstrate the performance of LFS with texture information, verify our theoretical analysis, and extend our conclusions on the optimal minimum sampling rate.
Gaze Training by Modulated Dropout Improves Imitation Learning

Imitation learning by behavioral cloning is a prevalent method that has achieved some success in vision-based autonomous driving. The basic idea behind behavioral cloning is to have the neural network learn from observing a human expert's behavior. Typically, a convolutional neural network learns to predict the steering commands from raw driver-view images by mimicking the behaviors of human drivers. However, there are other cues, such as gaze behavior, available from human drivers that have yet to be exploited. Previous researches have shown that novice human learners can benefit from observing experts' gaze patterns. We present here that deep neural networks can also profit from this. We propose a method, gaze-modulated dropout, for integrating this gaze information into a deep driving network implicitly rather than as an additional input. Our experimental results demonstrate that gaze-modulated dropout enhances the generalization capability of the network to unseen scenes. Prediction error in steering commands is reduced by 23.5% compared to uniform dropout. Running closed loop in the simulator, the gaze-modulated dropout net increased the average distance travelled between infractions by 58.5%. Consistent with these results, the gazemodulated dropout net shows lower model uncertainty.
Insights of Trends and Developments in Cloud Computing

Under the competitive and technology dependent global environment, modern technologies like Cloud Computing are keep boosting.Undoubtedly, enhancements of Cloud Computing are advancing their levels of productivity, profits and gaining market shares.As Cloud Computing already become popular throughout the glove and various peoples from different sectors are using Cloud Computing for different reasons therefore, it is now required to discuss insights of technology trends and developments for Cloud Computing in general.This paper mainly focuses on fundamental understanding of the different aspects of Cloud Computing such as an insight of Cloud Computing, different definitions, characteristics, deployment models, service models, key drivers and key challenges of Cloud Computing.
Engineering Kernelization for Maximum Cut

Kernelization is a general theoretical framework for preprocessing instances of NP-hard problems into (generally smaller) instances with bounded size, via the repeated application of data reduction rules. For the fundamental Max Cut problem, kernelization algorithms are theoretically highly efficient for various parameterizations. However, the efficacy of these reduction rules in practice—to aid solving highly challenging benchmark instances to optimality—remains entirely unexplored.We engineer a new suite of efficient data reduction rules that subsume most of the previously published rules, and demonstrate their significant impact on benchmark data sets, including synthetic instances, and data sets from the VLSI and image segmentation application domains. Our experiments reveal that current state-of-the-art solvers can be sped up by up to multiple orders of magnitude when combined with our data reduction rules. On social and biological networks in particular, kernelization enables us to solve four instances that were previously unsolved in a ten-hour time limit with state-of-the-art solvers; three of these instances are now solved in less than two seconds.
Semantic Publication of Agricultural Scientific Literature Using Property Graphs

During the last decades, there have been significant changes in science that have provoked a big increase in the number of articles published every year. This increment implies a new difficulty for scientists, who have to do an extra effort for selecting literature relevant for their activity. In this work, we present a pipeline for the generation of scientific literature knowledge graphs in the agriculture domain. The pipeline combines Semantic Web and natural language processing technologies, which make data understandable by computer agents, empowering the development of final user applications for literature searches. This workflow consists of (1) RDF generation, including metadata and contents; (2) semantic annotation of the content; and (3) property graph population by adding domain knowledge from ontologies, in addition to the previously generated RDF data describing the articles. This pipeline was applied to a set of 127 agriculture articles, generating a knowledge graph implemented in Neo4j, publicly available on Docker. The potential of our model is illustrated through a series of queries and use cases, which not only include queries about authors or references but also deal with article similarity or clustering based on semantic annotation, which is facilitated by the inclusion of domain ontologies in the graph.
Children’s roles in Participatory Design processes: making the role of process designer ‘work’

Recent academic debates have urged us to rethink children’s roles in Participatory Design (PD). In this article, we feed this discussion by putting the role of process designer into practice, allowing children to define a PD process and methods. We report on a case study in which we involved children (aged 10 – 12) to generate ideas about the ‘Stiemerbeek’ valley (Genk, Belgium). Applying the concept of ‘Handlungsspielraum’as analytical lens, the role of process designer was put into practice in five ways: through creating a research plan, defining events, engaging in play, field explorations and taking design decisions. Whereas the first three ways departed from the children’s perspectives, the latter related to adults facilitating the role. These findings provide handles to adopt the role of process designer in real-life contexts and extend the debate on children’s roles in PD, e.g. by reflecting on adult roles.
Groupwise Ranking Loss for Multi-Label Learning

This work studies multi-label learning (MLL), where each instance is associated with a subset of positive labels. For each instance, a good multi-label predictor should encourage the predicted positive labels to be close to its ground-truth positive ones. In this work, we propose a new loss, named Groupwise Ranking LosS (GRLS) for multi-label learning. Minimizing GRLS encourages the predicted relevancy scores of the ground-truth positive labels to be higher than that of the negative ones. More importantly, its time complexity is linear with respect to the number of candidate labels, rather than square complexity for some pairwise ranking based methods. We further analyze GRLS in the perspective of label-wise margin and suggest that multi-label predictor is label-wise effective if and only if GRLS is optimal. We also analyze the relations between GRLS and some widely used loss functions for MLL. Finally, we apply GRLS to multi-label learning, and extensive experiments on several benchmark multi-label databases demonstrate the competitive performance of the proposed method to state-of-the-art methods.
Corners positioning for binocular ultra-wide angle long-wave infrared camera calibration

In this paper a approach of corners positioning for calibrating binocular ultra-wide angle long-wave infrared camera is presented. Firstly, the generalized camera model which suits for its imaging relationship is introduced. Secondly, a method for subpixel corners positioning is proposed, containing edge extraction, gray centroid and curve fitting. Thirdly, aiming at calibrating extrinsic parameters of the camera, we raise a method with least squares optimization. The calibration experiments are conducted on the basis of an active infrared radiation calibration checkerboard. The performance of the approach is discussed by using other data sets. The results reveal that: MRE (mean reprojection error) and RMS (root mean square) respectively reduce to 0.23pixels and 0.33pixels, and the error of the baseline length declines from 1.00 % to 0.23 % in comparison with common methods.
Research on the Realization Method of Augmented Reality based on Unity3D

A C TAiming at the problems of low stability and limited application conditions in traditional augmented reality, Unity 3D software is applied to realize augmented reality by combining with Vuforia SDK and EasyAR SDK (Software Development Kit).The developed results can be used on mobile terminal and augmented reality glasses-Hololens.At the same time, the 3D registration technique is studied for the augmented reality.Moreover, the principle and process to realize augmented reality in Unity3D and SDK are given.Finally, with the common image markers, the stability of the two SDKs was tested and analyzed with the angular variation experiment.Compared with the traditional system, the model stability is improved.
Regional geochemical zonation of cultivated floodplains–Application of multi-element associations for soil quality evaluation along the Ohře (Eger) River, Czech Republic

Floodplains are dynamic ecosystems wherein intensive farming can meet increased environmental concentrations and bioavailability of a number of trace elements. Therefore, the primary objective of this study was to investigate the partial digestion of selected trace elements in surficial soil samples (depth up to 40 cm) across a gradient of environmental conditions along the Ohře River and determining the elements of high priority, while also exploring their geographic variation. The established agrochemical testing of Czech soils provides a valuable resource to determine the geochemical differences of multi-element associations in cultivated areas (arable crops and hop fields) in periodically inundated areas in the catchment with a number of potential pollution sources. The topsoil samples were digested using the nitric acid (2 mol/L HNO3), analysed for trace elements (Be, Cd, Co, Cr, Cu, Ni, Pb, V, and Zn) using inductively coupled plasma mass spectrometry (ICP-MS) and atomic absorption spectroscopy (AAS), and for their total Hg content. Data (n = 130) were modelled against the sample location and cultivation using the log-ratio approach. The explanatory and inferential statistical tools (the Mantel's correlation test, PCA, MANOVA) adapted for compositions, combined with a simple spatial ranking and land-use information defined effective discriminating subcomposition (Cu–Hg–Ni) that were able to describe the changes of geochemical domains in floodplains and retained a decent representation (46%) of the entire compositional variability in our dataset. Mapping the Cu–Hg–Ni subcomposition using the RGB composite enabled to display the high-dimensional multivariate dataset with reduced information and enabled to discriminate spatial domains representing geochemical signatures related to changes in element inputs along the watercourse. The analysis of our results within the compositional framework showed a changing pattern of relative enrichment/depletion of the HNO3–extractable trace elements in which a regional contrast between groups of the chalcophile elements (Cu, Pb, Zn, and partly Cd), siderophile transition metals (Cr, Co, Ni, and V) and single elements (Hg, Be) was source-apportioned to the effect of point pollution sources (Hg, Ni, Cr, Co) as well as diffuse sources (Cu, Zn, Pb, Cd).
Biomedical Image Denoising Based on Hybrid Optimization Algorithm and Sequential Filters

Abstract Background: Nowadays, image de-noising plays a very important role in medical analysis applications and pre-processing step. Many filters were designed for image processing, assuming a specific noise distribution, so the images which are acquired by different medical imaging modalities must be out of the noise. Objectives: This study has focused on the sequence filters which are selected by a hybrid genetic algorithm and particle swarm optimization. Material and Methods: In this analytical study, we have applied the composite of different types of noise such as salt and pepper noise, speckle noise and Gaussian noise to images to make them noisy. The Median, Max and Min filters, Gaussian filter, Average filter, Unsharp filter, Wiener filter, Log filter and Sigma filter, are the nine filters that were used in this study for the denoising of medical images as digital imaging and communications in medicine (DICOM) format. Results: The model has been implemented on medical noisy images and the performances have been determined by the statistical analyses such as peak signal to noise ratio (PSNR), Root Mean Square error (RMSE) and Structural similarity (SSIM) index. The PSNR values were obtained between 59 to 63 and 63 to 65 for MRI and CT images. Also, the RMSE values were obtained between 36 to 47 and 12 to 20 for MRI and CT images. Conclusion: The proposed denoising algorithm showed the significantly increment of visual quality of the images and the statistical assessment.
IMPROVING ENGLISH VOCABULARY MASTERY THROUGH WORD GAME

Vocabulary is very important in learning English. To master four skills in English, we need mastery vocabulary first. So, the researchers use word game in the classroom to make students interest to learn vocabulary. The objective of this research is to know that the word game is effective to improve students’ vocabulary or not, through word game strategy among seventh-grade students of SMP Muslimin Cililin in the academic year of 2018/2019. The method used in this research is quantitative technique and the pre-experimental as a research design. This research was conducted in one class. The population in this research is seventh-grade students of SMP Muslimin Cililin with the total number of the population are 273 students consisting of 7 classes, and the sample is class VII.C that consist of 30 students. The data were collected with the pretest and posttest as an instrument. The researchers conducted the tests before (pre-test) and after (post-test). After that the researchers gave students treatments consists of five meetings. The result showed that Sig. 0.000 and it was lower than 0.005 so, the null hypothesis was rejected and the alternative hypothesis was accepted. It means that teaching English vocabulary using a word game was effective to improve students’ vocabulary at seventh-grade. Keywords: Word game, students’ vocabulary mastery.
Cryptanalysis of the Legendre PRF and Generalizations

The Legendre PRF relies on the conjectured pseudorandomness properties of the Legendre symbol with a hidden shift. Originally proposed as a PRG by Damgård at CRYPTO 1988, it was recently suggested as an efficient PRF for multiparty computation purposes by Grassi et al. at CCS 2016. Moreover, the Legendre PRF is being considered for usage in the Ethereum 2.0 blockchain.This paper improves previous attacks on the Legendre PRF and its higher-degree variant due to Khovratovich by reducing the time complexity from O(&lt; (p log p/M) to O(p log2 p/M2) Legendre symbol evaluations when M ≤ 4√ p log2 p queries are available. The practical relevance of our improved attack is demonstrated by breaking three concrete instances of the PRF proposed by the Ethereum foundation. Furthermore, we generalize our attack in a nontrivial way to the higher-degree variant of the Legendre PRF and we point out a large class of weak keys for this construction. Lastly, we provide the first security analysis of two additional generalizations of the Legendre PRF originally proposed by Damgård in the PRG setting, namely the Jacobi PRF and the power residue PRF.
Robust audio watermarking based on transform domain and SVD with compressive sampling framework

The growth of the internet and digital data has resulted forgery, modification and sharing of digital data without property rights. Audio watermarking is one of a solution to protect the copyright of an audio from copyright infringement. This paper proposes an audio watermarking method which is robust against attacks and high capacity. First, a synchronization bit is added to the audio host. After the audio host is decomposed by Lifting Wavelet Transform (LWT), then choose a subband from the output of LWT to be transformed by discrete cosine transform (DCT). Next, the matrix of the signal from DCT is selected for the singular value decomposition (SVD) process, so that is obtained U, S and V matrix. S matrix is embedded with the watermark. Before the embedding process, the watermark image is compressed by Compressive Sampling. The results show that the proposed watermarking system is highly robust against a kind attack of LPF, resampling, and linear speed change which is proven by its BER is zero.
Information Systems in a Transdisciplinary Perspective: Leaping to a Larger Stage

The article argues that a transdisciplinary approach can advance the field of Information Systems (IS).By taking a transdisciplinary approach, researchers reach beyond the bounds of their disciplinary ontology and epistemology in order to address complex phenomena.Such a high level of synthesis is not possible with interdisciplinary and multidisciplinary approaches.While these approaches have been deployed in the IS field, transdisciplinarity is still lacking.The article reviews the literature that indicates the relationship between transdisciplinarity and complexity, and posits that the former is a response to the latter.Following a conceptual research approach, it is demonstrated that mainstream IS research is interdisciplinary and multidisciplinary rather than transdisciplinary.Finally, it is argued that the IS field is a promising candidate for ascending up the transdisciplinary path that, in turn, can improve its development prospects.
Do You Need More Data? The DeepSignDB On-Line Handwritten Signature Biometric Database

Data have become one of the most valuable things in this new era where deep learning technology seems to overcome traditional approaches. However, in some tasks, such as the verification of handwritten signatures, the amount of publicly available data is scarce, what makes difficult to test the real limits of deep learning. In addition to the lack of public data, it is not easy to evaluate the improvements of novel approaches compared with the state of the art as different experimental protocols and conditions are usually considered for different signature databases. To tackle all these mentioned problems, the main contribution of this study is twofold: i) we present and describe the new DeepSignDB on-line handwritten signature biometric public database, and ii) we propose a standard experimental protocol and benchmark to be used for the research community in order to perform a fair comparison of novel approaches with the state of the art. The DeepSignDB database is obtained through the combination of some of the most popular on-line signature databases, and a novel dataset not presented yet. It comprises more than 70K signatures acquired using both stylus and finger inputs from a total of 1526 users. Two acquisition scenarios are considered, office and mobile, with a total of 8 different devices. Additionally, different types of impostors and number of acquisition sessions are considered along the database. The DeepSignDB and benchmark results are available in GitHub.
Novel Language Resources for Hindi: An Aesthetics Text Corpus and a Comprehensive Stop Lemma List

This paper is an effort to complement the contributions made by researchers working toward the inclusion of non-English languages in natural language processing studies.Two novel Hindi language resources have been created and released for public consumption.The first resource is a corpus consisting of nearly thousand pre-processed fictional and nonfictional texts spanning over hundred years.The second resource is an exhaustive list of stop lemmas created from 12 corpora across multiple domains, consisting of over 13 million words, from which more than 200,000 lemmas were generated, and 11 publicly available stop word lists comprising over 1000 words, from which nearly 400 unique lemmas were generated.This research lays emphasis on the use of stop lemmas instead of stop words owing to the presence of various, but not all morphological forms of a word in stop word lists, as opposed to the presence of only the root form of the word, from which variations could be derived if required.It was also observed that stop lemmas were more consistent across multiple sources as compared to stop words.In order to generate a stop lemma list, the parts of speech of the lemmas were investigated but rejected as it was found that there was no significant correlation between the rank of a word in the frequency list and its part of speech.The stop lemma list was assessed using a comparative method.A formal evaluation method is suggested as future work arising from this study.