Reviewerly/exHarmony huggingface dataset:
Precise Learning of Source Code Contextual Semantics via Hierarchical Dependence Structure and Graph Attention Networks

Deep learning is being used extensively in a variety of software engineering tasks, e.g., program classification and defect prediction. Although the technique eliminates the required process of feature engineering, the construction of source code model significantly affects the performance on those tasks. Most recent works was mainly focused on complementing AST-based source code models by introducing contextual dependencies extracted from CFG. However, all of them pay little attention to the representation of basic blocks, which are the basis of contextual dependencies. In this paper, we integrated AST and CFG and proposed a novel source code model embedded with hierarchical dependencies. Based on that, we also designed a neural network that depends on the graph attention mechanism.Specifically, we introduced the syntactic structural of the basic block, i.e., its corresponding AST, in source code model to provide sufficient information and fill the gap. We have evaluated this model on three practical software engineering tasks and compared it with other state-of-the-art methods. The results show that our model can significantly improve the performance. For example, compared to the best performing baseline, our model reduces the scale of parameters by 50\% and achieves 4\% improvement on accuracy on program classification task.
Spin squeezing from bilinear spin-spin interactions: Two simple theorems

We prove two simple theorems about squeezing induced by bilinear spin-spin interactions that conserve spin parity---including a vast majority of quantum spin models implemented by state-of-the-art quantum simulators. In particular we show that squeezing captures the first form of quantum correlations which are produced (1) at equilibrium, by adiabatically turning on the spin-spin interactions starting from a factorized state aligned with an external, arbitrary field; and (2) away from equilibrium, by evolving unitarily the same state with the interacting Hamiltonian.
Real World Anomalous Scene Detection and Classification using Multilayer Deep Neural Networks

Surveillance videos record malicious events in a locality utilizing various machine learning algorithms for detection.Deep-learning algorithms being the most prominent AI algorithms are data-hungry as well as computationally expensive.These algorithms perform better when trained over a diverse and huge set of examples.These modern AI methods have a dire need of utilizing human intelligence to pamper the problem in such a way as to reduce the ultimate effort in terms of computational cost.In this research work, a novel methodology termed Bag of Focus (BoF) based training methodology has been proposed.BoF is based on the concept of selecting motionintensive blocks in a long video, for training different deep neural networks (DNN's).The methodology reduced the computational overhead by 90% (ten times) in comparison to when full-length videos are entertained.It has been observed that training networks using BoF are equally effective in terms of performance for the same network trained over the full-length dataset.In this research work, firstly, a fine-grained annotated dataset including instance and activity information has been developed for real-world volume crimes.Secondly, a BoFbased methodology has been introduced for effective training of the state-of-the-art 3D, and 2D Convolutional Neural Networks (CNNs).Lastly, a comparison between the state-of-the-art networks have been presented for malicious event recognition in videos.It has been observed that 2D CNN even with lesser parameters achieved a promising classification accuracy of 98.7% and Area under the curve (AUC) of 99.7%.
On the nature of the sustainability of the process in time

Introduction. Why do the ideas about time in ‘The Iliad’ and in the conditions of modern transmission speeds of large flows of information unexpectedly converge, and the key point is the study of the forms of stability of the process in time? What do we really mean by ingrained thinking and sustainable development, and how do they relate to the evolutionary nature of sustainable processes? The purpose of this paper is to present the form of a sustainable process, its constructive deployment in time by means of a natural science analysis. Materials and Methods. Based on the materials and methods of measuring time-varying quantities, as well as set-theoretic prerequisites for branching the process, it is proposed to look for natural scientific grounds for analyzing the sustainable development of trends in culture in general, in information processes and the education system, in particular. Here we will also try to take a well-known point of view (K. Lorentz, I. Prigozhin), when time, irreversibility, randomness in some constructive process relate to inanimate matter, to life, and to human. Results. Based on the analysis carried out by the author, the answer to the question of whether there are natural conditions for the sustainability of the object-process, in what aspect we can discuss the constructiveness of time, the evolutionary rooting of sustainable processes, branching points and the ‘stretching’ of a sustainable educational process into the future is presented. Conclusions. Our ideas about the processes in time and the conditions of their sustainability in the broadest cultural aspect, including the education system, need to be clarified. The constructive deployment of the process in time has its own structure that ensures the evolutionary rooting of the process and, in particular, the educational process.
Addressing out-of-distribution label noise in webly-labelled data

A recurring focus of the deep learning community is towards reducing the labeling effort. Data gathering and annotation using a search engine is a simple alternative to generating a fully human-annotated and human-gathered dataset. Although web crawling is very time efficient, some of the retrieved images are unavoidably noisy, i.e. incorrectly labeled. Designing robust algorithms for training on noisy data gathered from the web is an important research perspective that would render the building of datasets easier. In this paper we conduct a study to understand the type of label noise to expect when building a dataset using a search engine. We review the current limitations of state-of-the-art methods for dealing with noisy labels for image classification tasks in the case of web noise distribution. We propose a simple solution to bridge the gap with a fully clean dataset using Dynamic Softening of Out-of-distribution Samples (DSOS), which we design on corrupted versions of the CIFAR-100 dataset, and compare against state-of-the-art algorithms on the web noise perturbated MiniImageNet and Stanford datasets and on real label noise datasets: WebVision 1.0 and Clothing1M. Our work is fully reproducible https://git.io/JKGcj.
A Zeroth-Order Adaptive Learning Rate Method to Reduce Cost of Hyperparameter Tuning for Deep Learning

Due to powerful data representation ability, deep learning has dramatically improved the state-of-the-art in many practical applications. However, the utility highly depends on fine-tuning of hyper-parameters, including learning rate, batch size, and network initialization. Although many first-order adaptive methods (e.g., Adam, Adagrad) have been proposed to adjust learning rate based on gradients, they are susceptible to the initial learning rate and network architecture. Therefore, the main challenge of using deep learning in practice is how to reduce the cost of tuning hyper-parameters. To address this, we propose a heuristic zeroth-order learning rate method, Adacomp, which adaptively adjusts the learning rate based only on values of the loss function. The main idea is that Adacomp penalizes large learning rates to ensure the convergence and compensates small learning rates to accelerate the training process. Therefore, Adacomp is robust to the initial learning rate. Extensive experiments, including comparison to six typically adaptive methods (Momentum, Adagrad, RMSprop, Adadelta, Adam, and Adamax) on several benchmark datasets for image classification tasks (MNIST, KMNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100), were conducted. Experimental results show that Adacomp is not only robust to the initial learning rate but also to the network architecture, network initialization, and batch size.
Practical quantum access network over a 10 Gbit/s Ethernet passive optical network

Quantum key distribution (QKD) provides an information-theoretically secure method to share keys between legitimate users. To achieve large-scale deployment of QKD, it should be easily scalable and cost-effective. The infrastructure construction of quantum access network (QAN) expands network capacity and the integration between QKD and classical optical communications reduces the cost of channel. Here, we present a practical downstream QAN over a 10 Gbit/s Ethernet passive optical network (10G-EPON), which can support up to 64 users. In the full coexistence scheme using the single feeder fiber structure, the co-propagation of QAN and 10G-EPON signals with 9 dB attenuation is achieved over 21 km fiber, and the secure key rate for each of 16 users reaches 1.5 kbps. In the partial coexistence scheme using the dual feeder fiber structure, the combination of QAN and full-power 10G-EPON signals is achieved over 11 km with a network capacity of 64-user. The practical QAN over the 10G-EPON in our work implements an important step towards the achievement of large-scale QKD infrastructure.
Application of Thermal Remote Sensing Technique for Mapping ofUltramafic, Carbonate and Siliceous Rocks using ASTER Data in Southern Rajasthan, India

In the present study, thermal remote sensing technique and ASTER data have been used to delineate ultramafic, carbonate and siliceous rocks.The study gains importance as mineralized carbonate and ultramafic rocks are present in the southern region of Rajasthan, India between Udaipur and Dungarpur districts.The rocks in the study area include phyllites, mica schist, chlorite schist, quartzite, dolomite, granite, granitoids, gneiss and intrusive serpentinite.ASTER thermal bands were used to map ultramafics, siliceous and carbonate rocks on a scale of 1 : 380,000.Delineation of ultramafics was done using MRI-AV and MI-N indices, however the former provided a more informative map compared to the latter.QRI-AV, QI-N and QI-RH indices were used for mapping siliceous rock.QI-RH provided a more informative map compared to QRI-AV and QI-N.The index used for carbonate rocks was CI-N, but this did not provide a satisfactory map.
Exploiting Knowledge Graph for Multi-faceted Conceptual Modelling using GCN

The relevant information obtained from multiple sources usually contributes to one intricate phenomenon in the industrial processes. Data fusion of different sources usually leads to more expressive and informative information than that of each single data source. Integrated information has been widely used to model a multi-faceted conceptual phenomenon, which provides a comprehensive and versatile view of understanding of the process. However, the conventional approaches concatenate feature vectors to integrate different facets, not considering the semantic gaps between them. Meanwhile, knowledge graph (KG) receives considerable attention in recent years as it comprises rich relational information among elements. Thus, KG provides a promising way to fuse multiple data sources by bridging the semantic gaps, which can be exploited in the modelling of a multi-faceted phenomenon. Inspired by the advancement of KG, we proposed an approach based on KG and a machine learning algorithm for multi-faceted modelling. Firstly, a domain-specified ontology was built to eliminate the varying distance metrics across facet boundaries, and KGs were generated by populating the data surrounding a multi-faceted phenomenon into this ontology. Secondly, the KGs were fed into a graph convolutional neural network (GCN) to learn the node features and the graph structure for graph embedding simultaneously with the shared parameters. Lastly, with the aim of multi-faceted conceptual modelling, the features obtained from the GCN model were used as inputs for machine learning algorithms to learn the hidden patterns of KGs. An experimental study using real-world data from the cold rolling process was conducted to demonstrate the feasibility of the proposed model.
Examining gender differences in reading strategies, reading skills, and English proficiency of EFL University students

Many previous studies have examined metacognitive strategy use among ESL students in offline reading settings, but very few have linked them to a gender context, especially among EFL students when reading online English texts. This research, therefore, explored gender differences in metacognitive strategy usage among Indonesian EFL students in relation to online reading abilities and English proficiency. It enlisted the participation of 426 female and 176 male students from the University of Borneo Tarakan. The data was collected using the OSORS (Online Survey of Reading Strategies), a self-rated online reading ability question, and an English proficiency test. An independent sample t-test, Pearson correlation, and simple and multiple linear regression were used to analyze the data. Significant gender differences were discovered in overall, problem-solving, and support strategy usage, despite the small effect size values, with the female students scoring better on average than the males. For self-assessed online reading ability and online English proficiency, no gender difference was identified. Among the less skilled readers, significant gender differences were identified in overall and support strategies, whereas among the skilled readers, no significant gender differences were observed in both overall and category strategies. There were no gender differences in online English proficiency among the less skilled and skilled readers. Overall strategy use among the females predicted online reading abilities and online English proficiency, while among the males only predicted online reading abilities. These findings add to the growing body of research demonstrating female dominance in EFL online reading programs and have pedagogical implications.
Measurement Method and Application of a Deep Learning Digital Economy Scale Based on a Big Data Cloud Platform

In recent years, with the acceleration of the process of economic globalization and the deepening of my country's financial liberalization, the scale of international short-term capital flows has been extremely rapid. This article mainly studies the deep learning digital economy scale measurement method and its application based on the big data cloud platform. This article uses the indirect method to estimate the stock of renminbi circulating abroad. The results show that the application of big data cloud platforms can increase the development share of digital media and digital transactions in the digital economy, and optimize the structure of China's digital economy.
Distributional semantics insights on agentive suffix rivalry in French

The formation of French agent nouns (ANs) involves a large variety of morphological constructions, and particularly of suffixes. In this study, we focus on the semantic counterpart of agentive suffix diversity and investigate whether the morphological variety of ANs correlates with different agentive subtypes. We adopt a distributional semantics approach and combine manual, computational and statistical analyses applied to French ANs ending in -aire, -ant, -eur, -ien, -ier and -iste. Our methodology allows for a large-scale study of ANs and involves both top-down and bottom-up procedures. We first characterize agentive suffixes with respect to their morphosemantic and distributional properties, outlining their specificities and similarities. Then we automatically cluster ANs into distributionally relevant subsets and examine their properties. Based on quantitative analysis, our study provides a new perspective on agentive suffix rivalry in French that both confirms existing claims and sheds light on previously unseen phenomena.
Is Bitcoin Still a King? Relationships between Prices, Volatility and Liquidity of Cryptocurrencies during the Pandemic

We try to establish the commonalities and leadership in the cryptocurrency markets by examining the mutual information and lead-lag relationships between Bitcoin and other cryptocurrencies from January 2019 to June 2021. We examine the transfer entropy between volatility and liquidity of seven highly capitalized cryptocurrencies in order to determine the potential direction of information flow. We find that cryptocurrencies are strongly interrelated in returns and volatility but less in liquidity. We show that smaller and younger cryptocurrencies (such as Ripple's XRP or Litecoin) have started to affect the returns of Bitcoin since the beginning of the pandemic. Regarding liquidity, the results of the dynamic time warping algorithm also suggest that the position of Monero has increased. Those outcomes suggest the gradual increase in the role of privacy-oriented cryptocurrencies.
Spatial Presence in Mixed Realities–Considerations About the Concept, Measures, Design, and Experiments

Plenty of theories, models, measures, and investigations target the understanding of virtual presence, i.e., the sense of presence in immersive Virtual Reality (VR). Other varieties of the so-called eXtended Realities (XR), e.g., Augmented and Mixed Reality (AR and MR) incorporate immersive features to a lesser degree and continuously combine spatial cues from the real physical space and the simulated virtual space. This blurred separation questions the applicability of the accumulated knowledge about the similarities of virtual presence and presence occurring in other varieties of XR, and corresponding outcomes. The present work bridges this gap by analyzing the construct of presence in mixed realities (MR). To achieve this, the following presents (1) a short review of definitions, dimensions, and measurements of presence in VR, and (2) the state of the art views on MR. Additionally, we (3) derived a working definition of MR, extending the Milgram continuum. This definition is based on entities reaching from real to virtual manifestations at one time point. Entities possess different degrees of referential power, determining the selection of the frame of reference. Furthermore, we (4) identified three research desiderata, including research questions about the frame of reference, the corresponding dimension of transportation, and the dimension of realism in MR. Mainly the relationship between the main aspects of virtual presence of immersive VR, i.e., the place-illusion , and the plausibility-illusion , and of the referential power of MR entities are discussed regarding the concept, measures, and design of presence in MR. Finally, (5) we suggested an experimental setup to reveal the research heuristic behind experiments investigating presence in MR. The present work contributes to the theories and the meaning of and approaches to simulate and measure presence in MR. We hypothesize that research about essential underlying factors determining user experience (UX) in MR simulations and experiences is still in its infancy and hopes this article provides an encouraging starting point to tackle related questions.
ATTITUDES OF INDIVIDUALS ABOUT USING CRYPTOCURRENCIES: EVIDENCE FROM TURKEY

Purpose: The study aimed to determine why Turkish participants are voluntary or hesitant to use cryptocurrencies and to assess which factors affected cryptocurrencies usage.&#x0D; Method: Technology acceptance model (TAM) was used in the study to analyze Turkish individuals’ cryptocurrency usage behavior. Structural equation (regression) modeling (SEM) was used to test the research model. AMOS and SPSS programs were used for analysis.&#x0D; Findings: The study model showed that cryptocurrency usage was exposed to the most effect by perceived benefit. Contrary to expectations, perceived risk did not make any significant effect on usage behavior. A causality relation between perceived benefit and decentralization factor was found more than other benefit sub-factors. Perceived ease of use had no positive direct relation with usage behavior in the study. Perceived ease of use had a positive relation with perceived benefit in the study.
Not Just Classification: Recognizing Implicit Discourse Relation on Joint Modeling of Classification and Generation

Implicit discourse relation recognition (IDRR) is a critical task in discourse analysis.Previous studies only regard it as a classification task and lack an in-depth understanding of the semantics of different relations.Therefore, we first view IDRR as a generation task and further propose a method joint modeling of the classification and generation.Specifically, we propose a joint model, CG-T5, to recognize the relation label and generate the target sentence containing the meaning of relations simultaneously.Furthermore, we design three target sentence forms, including the question form, for the generation model to incorporate prior knowledge.To address the issue that large discourse units are hardly embedded into the target sentence, we also propose a target sentence construction mechanism that automatically extracts core sentences from those large discourse units.Experimental results both on Chinese MCDTB and English PDTB datasets show that our model CG-T5 achieves the best performance against several state-of-the-art systems.
Study on Feasibility of Photovoltaic Power to Grid Parity in China Based on LCOE

Today, photovoltaic (PV) power generation accounts for a relatively small proportion of total power generation in China. If photovoltaic power can achieve grid parity, it can replace the original traditional thermal power generation, which has positive significance on the environment. The Levelized Cost of Energy (LCOE) is the main general economic indicator for the evaluation of power generation technology in the world. Based on the traditional LCOE evaluation model and considering the shortcomings of the previous LCOE evaluation models, the cost of photovoltaic power generation is refined in this paper. The cost of the taxation with calculating the depreciation, the loss, and the income of the Clean Development Mechanism (CDM) are taken into consideration. Hence, a more accurate and more comprehensive LCOE model is presented. Moreover, combined with other investment indicators, a photovoltaic power generation benefit model corresponding to the current development status is proposed. Taking a 3-MW distributed photovoltaic power station project in Nanjing as a case study, the sensitivity analysis of factors that affect the benefit of photovoltaic power generation is carried out to further explore the feasibility of photovoltaic power to grid parity.