Reviewerly/exHarmony huggingface dataset:
Double Auction-Based Two-Level Resource Allocation Mechanism for Computation Offloading in Mobile Blockchain Application

It is increasingly popular that platforms integrate various services into mobile applications due to the high usage and convenience of mobile devices, many of which demand high computational capacities and energy, such as cryptocurrency services based on blockchain. However, it is hard for mobile devices to run these services due to the limited storage and computational capacity. In this paper, the problem of computation offloading that requires sufficient computing resources with high utilization in large-scale users and multiprovider MEC system was investigated. A mechanism based on the combinatorial double auction, G-TRAP, is proposed in this paper to solve the above problem. In the mechanism, resources are provided both in the cloud and at the edge of the network. Mobile users compete for these resources to offload computing tasks by the rule that the edge-level resources will be allocated at first while cloud-level resources could be the supplement for the edge level. Given that the proof-of-work (PoW), the core issue of blockchain application, is resource-expensive to implement in mobile devices, we provide resource allocation service to users of blockchain application as experimental subjects. Simulation results show that the proposed mechanism for serving large-scale users in a short execution time outperforms two existing algorithms in terms of social utility and resource utilization. Consequently, our proposed system can effectively solve the intensive computation offloading problem of mobile blockchain applications.
Using Process Models to Understand Security Standards

Many industrial software development processes today have to comply with security standards such as the IEC~62443-4-1. These standards, written in natural language, are ambiguous and complex to understand. This is especially true for non-security experts. Security practitioners thus invest much effort into comprehending standards and, later, into introducing them to development teams. However, our experience in the industry shows that development practitioners might very well also read such standards, but nevertheless end up inviting experts for interpretation (or confirmation). Such a scenario is not in tune with current trends and needs of increasing velocity in continuous software engineering. In this paper, we propose a tool-supported approach to make security standards more precise and easier to understand for both non-security as well as security experts by applying process models. This approach emerges from a large industrial company and encompasses so far the IEC62443-4-1 standard. We further present a case study with 16 industry practitioners showing how the approach improves communication between development and security compliance practitioners.
Characterizing Khetri copper mine environment using geospatial tools

Abstract Mines result in land use and land cover (LULC) change due to degradation of natural resources and establishment of new infrastructure for ore extraction and beneficiation. The present study was carried out to, with objectives, (1) characterize LULC change (from 1975 to 2017) in Khetri copper mine region, (2) spatial distribution of pollution indices and (3) spectral response of elemental concentration of soil and groundwater using Landstat and ASTER satellite data. The study was designed to fulfil the objectives and for the same NDVI values were calculated for LULC classification and generated maps were analyzed for landscape pattern. Spatial distribution of pollution indices calculated using geochemical data of soil and groundwater was plotted to understand the impact of contamination on landscape pattern. The correlation of spectral response of Landstat bands with heavy metals concentration was plotted to assess their possible use in quantification of heavy metals. Results show constant increase in settlements, mines and open area while vegetation cover has decreased. Landscape and class level metrics (number of patch, patch density, aggregation index and landscape shape index) indicate increase in the fragmentation of landscape in recent years. Shannon’s Evenness Index indicates increase in uniformity in landscape and it is attributed to loss of vegetation and agriculture patches. Pollution indices, Pollution Load Index for soil is high near the overburden materials and Index of Environmental Risk (I ER ) and Contamination Index for ground water is high near abandoned mines. Spectral bands 5 and 6 (SWIR 1) show significant negative correlation, and 9 (Cirrus) shows significant positive correlation with metal concentration in soil and water suggesting the possible use of remote sensing in assessment of metal concentration at ground level. Thus, it can be concluded that mines significantly influence the landscape pattern and remote sensing could be used for the assessment and predication of heavy metal contamination at broader scale in a cost-effective way.
Inequality and female labour force participation in West Africa

This study examined the impact of income inequality on female labour force participation in West Africa for the period 2004 to 2016. The study employed the Gini coefficient, the Atkinson index and the Palma ratio as measures of income inequality. For robustness, the study also utilises female employment and female unemployment as measures of female labour force participation. The study employed the instrumental variable fixed effects model with Driscoll and Kraay standard errors to account for simultaneity/reverse causality, serial correlation, groupwise heteroskedasticity and cross-sectional dependence. The empirical results reveal that the three measures of income inequality significantly reduce the participation of women in the labour force in West Africa. The study also revealed that domestic credit, remittances and female education are positively associated with female labour force participation in the sub-region. Further findings reveal that economic development reduces the participation of women in the labour force in West Africa with the U-shaped feminisation theory not valid for the West African region. The study, however, revealed an inverted U-shaped relationship between inequality and female unemployment. Policy recommendations based on these findings are discussed.
Fine-Grained Video Deblurring with Event Camera

Despite CNN-based deblurring models have shown their superiority on solving motion blurs, how to restore photorealistic images from severe motion blurs remains an ill-posed problem due to the loss of temporal information and textures. Video deblurring methods try to extract meaningful temporal information in multiple consecutive blurry frames during a long period of time. However, the information obtained in such a coarse period is overcomplicated, and all frames may suffer from severe motion blurs. Event cameras such as Dynamic and Active Pixel Vision Sensor (DAVIS) can simultaneously produce gray-scale Active Pixel Sensor (APS) frames and events, which capture motions as the events at very high temporal resolution, i.e., \(1\, \upmu \mathrm{s} \), and provide useful information for blurry APS frames. In this paper, we propose a deep fine-grained video deblurring pipeline consisting of a deblurring module and a recurrent module to address severe motion blurs. Concatenating the blurry image with event representations at a fine-grained temporal period, our proposed model achieves state-of-the-art performance on both popular GoPro and real blurry datasets captured by DAVIS, and is capable of generating high frame-rate video by applying a tiny shift to event representations in the recurrent module.
Sequential Workflow in Production Serverless FaaS Orchestration Platform

Today, serverless computing or Function-as-a-Service (FaaS) is an excellent way to exploit cloud computing benefits due to its simplicity, billing, and innate elasticity. FaaS providers own the responsibility of server provisioning, administration, management and patching thus making developers less burdened and more productive. Serverless applications based on complex workflows involve multiple functions which have data dependencies on each other. This brings the concept of function orchestration in picture and makes it important in implementing any serverless application. In this context Amazon Web Services (AWS Step Function), International Business Machines (IBM) Cloud Function Sequences and Azure Durable Functions are currently available in market. In this paper, different sequential composition workflows like Reflexive, Fusion, Chaining, and Client Scheduled are implemented in AWS serverless platform and compared their performance in terms of execution duration. Successively, sequential workflows in different language runtime environments in AWS Step function and IBM Cloud Function Sequences are also analyzed. Experiments are also performed to observe the cold start behavior in AWS Lambda and (IBM) Cloud functions.
Digital Mammograms with Image Enhancement Techniques for Breast Cancer Detection: A Systematic Review

Digital mammograms with appropriate image enhancement techniques will improve breast cancer detection, and thus increase the survival rates. The objectives of this study were to systematically review and compare various image enhancement techniques in digital mammograms for breast cancer detection.A literature search was conducted with the use of three online databases namely, Web of Science, Scopus, and ScienceDirect. Developed keywords strategy was used to include only the relevant articles. A Population Intervention Comparison Outcomes (PICO) strategy was used to develop the inclusion and exclusion criteria. Image quality was analyzed quantitatively based on peak signal-noise-ratio (PSNR), Mean Squared Error (MSE), Absolute Mean Brightness Error (AMBE), Entropy, and Contrast Improvement Index (CII) values.Nine studies with four types of image enhancement techniques were included in this study. Two studies used histogram-based, three studies used frequency-based, one study used fuzzy-based and three studies used filter-based techniques. All studies reported PSNR values whilst only four studies reported MSE, AMBE, Entropy, and CII values. Filter-based was the highest PSNR values of 78.93, among other types. For MSE, AMBE, Entropy, and CII values, the highest were frequency-based (7.79), fuzzy-based (93.76), filter-based (7.92), and frequency-based (6.54) respectively.In summary, image quality for each image enhancement technique is varied, especially for breast cancer detection. In this study, the frequency-based of Fast Discrete Curvelet Transform (FDCT) via the UnequiSpaced Fast Fourier Transform (USFFT) shows the most superior among other image enhancement techniques.
Analyzing free variation with harmony – A case study of verb-cluster serialization

Abstract In German, a verb selected by another verb normally precedes the selecting verb. Modal verbs in the perfect tense provide an exception to this generalization because they require the perfective auxiliary to occur in cluster-initial position according to prescriptive grammars. Bader and Schmid (2009b) have shown, however, that native speakers accept the auxiliary in all positions except the cluster-final one. Experimental results as well as corpus data indicate that verb cluster serialization is a case of free variation. I discuss how this variation can be accounted for, focusing on two mismatches between acceptability and frequency: First, slight acceptability advantages can turn into strong frequency advantages. Second, syntactic variants with basically zero frequency can still vary substantially in acceptability. These mismatches remain unaccounted for if acceptability is related to frequency on the level of whole sentence structures, as in Stochastic OT (Boersma and Hayes2001). However, when the acceptability-frequency relationship is modeled on the level of individual weighted constraints, using harmony as link (see Pater2009, for different harmony based frameworks), the two mismatches follow given appropriate linking assumptions.
DomainSenticNet: An Ontology and a Methodology Enabling Domain-Aware Sentic Computing

In recent years, SenticNet and OntoSenticNet have represented important developments in the novel interdisciplinary field of research known as sentic computing, enabling the development of a variety of Sentic applications. In this paper, we propose an extension of the OntoSenticNet ontology, named DomainSenticNet, and contribute an unsupervised methodology to support the development of domain-aware Sentic applications. We developed an unsupervised methodology that, for each concept in OntoSenticNet, mines semantically related concepts from WordNet and Probase knowledge bases and computes domain distributional information from the entire collection of Kickstarter domain-specific crowdfunding campaigns. Subsequently, we applied DomainSenticNet to a prototype tool for Kickstarter campaign authoring and success prediction, demonstrating an improvement in the interpretability of sentiment intensities. DomainSenticNet is an extension of the OntoSenticNet ontology that integrates each of the 100,000 concepts included in OntoSenticNet with a set of semantically related concepts and domain distributional information. The defined unsupervised methodology is highly replicable and can be easily adapted to build similar domain-aware resources from different domain corpora and external knowledge bases. Used in combination with OntoSenticNet, DomainSenticNet may favor the development of novel hybrid aspect-based sentiment analysis systems and support further research on sentic computing in domain-aware applications.
Solving Constrained Slot Placement Problems Using an Ising Machine and Its Evaluations

Ising machines have attracted attention, which is expected to obtain better solutions of various combinatorial optimization problems at high speed by mapping the problems to natural phenomena. A slot-placement problem is one of the combinatorial optimization problems, regarded as a quadratic assignment problem, which relates to the optimal logic-block placement in a digital circuit as well as optimal delivery planning. Here, we propose a mapping to the Ising model for solving a slot-placement problem with additional constraints, called a constrained slot-placement problem, where several item pairs must be placed within a given distance. Since the behavior of Ising machines is stochastic and we map the problem to the Ising model which uses the penalty method, the obtained solution does not always satisfy the slot-placement constraint, which is different from the conventional methods such as the conventional simulated annealing. To resolve the problem, we propose an interpretation method in which a feasible solution is generated by post-processing procedures. We measured the execution time of an Ising machine and compared the execution time of the simulated annealing in which solutions with almost the same accuracy are obtained. As a result, we found that the Ising machine is faster than the simulated annealing that we implemented.
Variance Loss in Variational Autoencoders

In this article, we highlight what appears to be major issue of Variational Autoencoders (VAEs), evinced from an extensive experimentation with different networks architectures and datasets: the variance of generated data is significantly lower than that of training data. Since generative models are usually evaluated with metrics such as the Fréchet Inception Distance (FID) that compare the distributions of (features of) real versus generated images, the variance loss typically results in degraded scores. This problem is particularly relevant in a two stage setting [8], where a second VAE is used to sample in the latent space of the first VAE. The minor variance creates a mismatch between the actual distribution of latent variables and those generated by the second VAE, that hinders the beneficial effects of the second stage. Renormalizing the output of the second VAE towards the expected normal spherical distribution, we obtain a sudden burst in the quality of generated samples, as also testified in terms of FID.
Designing Double-Click-Based Unlocking Mechanism on Smartphones

Smartphones have gained huge popularity and rapidly changed the way of people’s living habits and communication style. Despite the widespread adoption, there is a big challenge to protect data security on smartphones. This is because phone users often store their personal data (e.g., images) and proceed sensitive tasks (e.g., financial operations) on the device. To secure the device from unauthorized access, designing an appropriate unlocking mechanism is an expected solution. To complement existing schemes, we advocate that combining behavioral biometrics can enhance the security of unlocking mechanisms. In this work, we develop a double-click-based unlocking scheme (DCUS), which allows users to unlock the phone by double clicking on the pre-selected location on an image. For verification, DCUS has to check selected images, image location and behavioral features of double-click action. In the user study, we involve 40 participants and explore some common supervised algorithms. The results demonstrate that participants can achieve a good success rate (over 96%) with positive feedback.
A Chinese Conceptual Semantic Feature Dataset (CCFD)

Memory and language are important high-level cognitive functions of humans, and the study of conceptual representation of the human brain is a key approach to reveal the principles of cognition. However, this research is often constrained by the availability of stimulus materials. The research on concept representation often needs to be based on a standardized and large-scale database of conceptual semantic features. Although Western scholars have established a variety of English conceptual semantic feature datasets, there is still a lack of a comprehensive Chinese version. In the present study, a Chinese Conceptual semantic Feature Dataset (CCFD) was established with 1,410 concepts including their semantic features and the similarity between concepts. The concepts were grouped into 28 subordinate categories and seven superior categories artificially. The results showed that concepts within the same category were closer to each other, while concepts between categories were farther apart. The CCFD proposed in this study can provide stimulation materials and data support for related research fields. All the data and supplementary materials can be found at https://osf.io/ug5dt/ .
<tt>bib2gls</tt>: selection, cross-references and locations

The bib2gls command line application [17] combined with the glossaries-extra package [18] provides an alternative indexing method to those provided by the base glossaries package [19].In addition, since the terms are defined in .bibfiles, tools such as JabRef [3] can be used to manage large databases.
Fast Path Planning for Unmanned Aerial Vehicles by Self-Correction Based on Q-Learning

This paper addresses a path planning problem for unmanned aerial vehicles with correcting position errors through correction-point navigation, which requires a rapid response when determining the flight path. A two-layer nested iterative hybrid algorithm based on learning is proposed to achieve multiobjective optimization by minimizing path lengths and correction times while reducing the complexity of the algorithm and improving the efficiency of path planning. The lower-layer algorithm uses a -learning framework based on the experience playback mechanism and exploration/exploitation mechanism. The rewards and punishments for the -table values in the lower-layer algorithm are innovatively managed with the unit “path.” The upper-layer algorithm is based on the Pareto multiobjective algorithm. The Pareto frontier is continuously updated with the solutions from the lower-layer algorithm, which timely provides the lower-layer algorithm feedback. Finally, simulation experiments are conducted to evaluate the effectiveness of the algorithm, and the results show that the proposed algorithm outperforms particle swarm optimization.
WeNet: Production First and Production Ready End-to-End Speech Recognition Toolkit.

In this paper, we present a new open source, production first and production ready end-to-end (E2E) speech recognition toolkit named WeNet. The main motivation of WeNet is to close the gap between the research and the production of E2E speech recognition models. WeNet provides an efficient way to ship ASR applications in several real-world scenarios, which is the main difference and advantage to other open source E2E speech recognition toolkits. This paper introduces WeNet from three aspects, including model architecture, framework design and performance metrics. Our experiments on AISHELL-1 using WeNet, not only give a promising character error rate (CER) on a unified streaming and non-streaming two pass (U2) E2E model but also show reasonable RTF and latency, both of these aspects are favored for production adoption. The toolkit is publicly available at this https URL
Air temperature prediction using different machine learning models

Air temperature is an essential climatic component particularly in water resources management and other agro-hydrological/meteorological activities planning This paper examines the prediction capability of three machine learning models, least square support vector machine (LSSVM), group method and data handling neural network (GMDHNN) and classification and regression trees (CART) in air temperature forecasting using monthly temperature data of Astore and Gilgit climatic stations of Pakistan. The prediction capability of three machine learning models is evaluated using different time lags input combinations with help of root mean square error (RMSE), the mean absolute error (MAE) and coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;).statistical indicators. The obtained results indicated that the LSSVM model is more accurate in temperature forecasting than GMDHNN and CART models. LSSVM significantly decreases the mean RMSE of the GMHNN and CART models by 1.47-3.12% and 20.01-25.12% for the Chakdara and Kalam Stations, respectively.
Star-BiLSTM-LAN for Document-level Mutation-Disease Relation Extraction from Biomedical Literature

Relations between mutations and diseases hiding in biomedical literature are valuable for the analysis and interpretation of many complex diseases, which can help explore more effective treatment options for corresponding diseases. Most current document-level mutation-disease relation extraction methods are based on classification approaches and suffer the lack of ability to extract inter-sentential relations. To solve this problem, we regard extracting document-level mutation-disease relations as a sequence tagging task and propose a neural network-based method called Star-BiLSTM-LAN. By combining the star transformer and the Bi-directional Long Short-Term Memory network, this method achieves a strong ability to capture semantic and syntactic information at the document level from different aspects, and it can discover internal representations that prove useful for the task of interest. Star-BiLSTM-LAN is evaluated on EMU BCa and PCa datasets, and achieves the state-of-the-art F-scores of 89.20% and 90.43%, which are 4.70% and 2.43% higher than the baseline, respectively. Also, the proposed method achieves an F-score of 94.40% on BRONCO dataset.