Reviewerly/exHarmony huggingface dataset:
Searching for Class Models

Models in model-based development play a major role and serve as the main design artifacts, in particular class models. As there are difficulties in developing high-quality models, different repositories of models are established to address that challenge, so developers would have a reference model. Following the existence of such repositories, there is a need for tools that can retrieve similar high-quality models. To search for models in these repositories, we propose a greedy algorithm that matches the developer’s intention by considering semantic similarity, structure similarity, and type similarity. The initial evaluation indicates that the algorithm achieved high performance in finding the relevant class model fragments. Though additional examination is required, the sought algorithm can be easily adapted to other modeling languages for searching models and their encapsulated knowledge.
In-order sliding-window aggregation in worst-case constant time

Sliding-window aggregation is a widely-used approach for extracting insights from the most recent portion of a data stream. While aggregations of interest can usually be expressed as binary operators that are associative, they are not necessarily commutative nor invertible. Non-invertible operators, however, are difficult to support efficiently. DABA is the first algorithm for sliding-window aggregation with worst-case constant time. Prior to DABA, the best published algorithms would require $$O(\log n)$$ aggregation steps per window operation for a window of size n—and while for strictly in-order streams, this bound could be improved to O(1) aggregation steps in the amortized sense, it was not known how to achieve an O(1) bound in the worst case, which is critical for latency-sensitive applications. In this article, besides describing DABA in more detail, we introduce a new variant, DABA Lite, which achieves the same time bounds in less memory. Whereas DABA requires space for storing 2n partial aggregates, DABA Lite only requires space for $$n+2$$ partial aggregates. Our experiments on synthetic and real data support the theoretical findings.
The Interplay between the Digital Services Act and Sector Regulation: How Special is Copyright?

On 15 December 2020, the European Commission published its proposal for a Regulation on a Single Market for Digital Services (Digital Services Act). It carries out a regulatory overhaul of the 21-year- old horizontal rules on intermediary liability in the Directive and introduces new due diligence obligations for intermediary services.Our analysis illuminates an important point that has so far received little attention: how would the Digital Services Act’s rules interact with existing sector-specific lex specialis rules? In this paper, we look specifically at the intersection of the Digital Services Act with the regime for online content sharing service providers (OCSSPs) set forth in art. 17 of Directive (EU) 2019/790 on copyright in the Digital Single Market (CDSM Directive). At first glance, these regimes do not appear to overlap as the rules on copyright are lex specialis to the Digital Services Act. A closer look shows a more complex and nuanced picture. Our analysis concludes that the DSA will apply to OCSSPs insofar as it contains rules that regulate matters not covered by art. 17 CDSM Directive, as well as specific rules on matters where art. 17 leaves margin of discretion to Member States. This includes, to varying degrees, rules in the DSA relating to the liability of intermediary providers and to due diligence obligations for online platforms of different sizes. Importantly, we consider that such rules apply even where art. 17 CDSM Directive contains specific (but less precise) regulation on the matter.From a normative perspective, this might be a desirable outcome, to the extent that the DSA aims to establish “uniform rules for a safe, predictable and trusted online environment, where fundamental rights enshrined in the Charter are effectively protected”. Based on our analysis, we suggest a number of clarifications that might be help achieve that goal.
Application of the C-N fractal model, factor analysis and geochemical mineralization probability index (GMPI) for delineating geochemical anomalies related to a Mn-Fe deposit and associated Cu mineralization in west-central Sinai, Egypt

The west-central Sinai area includes one of the most productive manganese deposits in Egypt (Um Bogma Mn-Fe ore). The explored area is covered by a Carboniferous and Cambro-Ordovician sedimentary succession overlying Precambrian basement rocks. The purpose of this study is to delineate geochemical anomalies of ore and related elements and track their dispersion trains, which may lead to the discovery of unknown ore deposits. For this purpose, 143 stream sediment samples were analysed and the data were interpreted using the concentration–number (C-N) fractal model, factor analysis and the geochemical mineralization probability index (GMPI). Geochemical thresholds obtained from the C-N fractal model, factor scores and GMPI were used for constructing geochemical anomaly maps and delineating probable anomalous sites. The spatial distribution of Mn, Cu, Co, Pb and Zn anomalies was correlated to Mn and Cu mineralization sites whereas those of Fe and Cr were consistent with mafic rock distributions. Factor analysis revealed significant element associations for mineralization (Cu, Co, Mn, Zn, Pb), country rock composition (Fe, Cr) and element mobility (Cd, Zn). The spatial distribution of ore elements (Mn, Cu) delineated by the factor score and GMPI distribution maps was confirmed and more accurately interpreted using geochemical anomaly maps constructed based on the fractal-derived thresholds. Based on the current study, recent Cu and Mn mineralization sites may be suggested. Cobalt, Zn, Pb and Cd are suggested as efficient pathfinder elements for marine Mn deposits. A dispersion sequence of Cd&gt;Mn&gt;Co&gt;Pb&gt;Zn&gt;Cu&gt;Cr&gt;Fe was proposed based on the threshold distribution patterns of these elements. Supplementary material: Tables S1–S3 and Figures S1 and S2 are available at https://doi.org/10.6084/m9.figshare.c.5463511
HierClasSArt: Knowledge-Aware Hierarchical Classification of Scholarly Articles

A huge number of scholarly articles published every day in different domains makes it hard for the experts to organize and stay updated with the new research in a particular domain. This study gives an overview of a new approach, HierClasSArt, for knowledge aware hierarchical classification of the scholarly articles for mathematics into a predefined taxonomy. The method uses combination of neural networks and Knowledge Graphs for better document representation along with the meta-data information. This position paper further discusses the open problems about incorporation of new articles and evolving hierarchies in the pipeline. Mathematics domain has been used as a use-case.
Linkages Between Globalisation, Carbon Dioxide Emissions and Governance in Sub-Saharan Africa

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Handwritten Devanagari Character Recognition Using CNN with Transfer Learning

In this paper, we review the use of CNNs, along with the transfer learning for handwritten Devanagari character recognition. We compare the performance of VGG16 and DenseNet121 with transfer learning for recognition of handwritten Devanagari character dataset. The results of models trained in different conditions and compared with other methods are presented. Our study shows that DenseNet121 with deep fine-tuning method outperformed the other pre-trained models and other supplemental learning strategies. The learning accuracy further improved with some tweaking of hyperparameters like batch size, learning rate, etc.
Microsoft's Your Phone environment from a digital forensic perspective

Your Phone is a Microsoft dual mobile/desktop application that links a Windows 10 environment to a smartphone. The Android version provides the smartphone's user with the ability to control the mobile device from Windows 10, allowing to place/receive calls, send/receive text messages such as SMS, MMS and RCS, access up to the last 2000 photos/screenshots of the device and to receive notifications from applications, all through the Windows 10 Your Phone application and, if configured to do so, within Windows 10 notification center. This work analyzes the Your Phone environment , that is, Your Phone Companion for Android and Your Phone for Windows 10. The paper studies the digital forensic artifacts that can be found in a post mortem analysis, focusing on the SQLite3 databases used by both the Android and Windows 10 applications. We also compare the examined version with a previous version of Your Phone, showing that Your Phone newest functionalities bring new valuable artifacts for forensic examiners. The study shows that Your Phone data left on a Windows 10 device can be useful to access a copy of messages, photos, and document interactions, especially when the Android device is inaccessible or even physically unavailable. To ease the task for digital forensic examiners, we have updated our open-source YPA software that collects and analyzes Your Phone data from a Windows 10 system. YPA runs as a module within the digital forensic Autopsy software. • Thorough study of Windows 10's Your Phone environment, analyzing Your Phone's digital artifacts left in the computer. • Study of Android's Your Phone Companion, covering the digital forensic artifacts of the app. • Presentation of the renewed version of Your Phone Analyzer, our open-source module to process Your Phone forensic artifacts.
A Method of Stable Long-Term Single Object Tracking

We propose a stable long-term tracking method to deal with visual tracking in multiple complex scenarios to solve the problem of frequent disappearance and reappearance of targets in long-term tracking. In our method, we do not blindly start the global tracker once the target disappears, but use it only when necessary and in reasonable scope with the assistance of localization module. In addition, we designed an FP-verifier based on feature pools to reevaluate the candidate bounding boxes given by our local tracker and global tracker to ensure that online learning local tracker can be updated stably. Our method outperforms the state-of-the-art results on the VOT2020LT challenge. In addition, the control experiments show that our FP-verifier is more effective than the RT-MDNet verifier used by the top three winners of VOT2020LT challenge.
Chaotic Signal Denoising Algorithm Based on Self‐Similarity

Inspired by the self-similar fractal properties of chaotic attractors and the heuristics of similarity filtering of images, a novel chaotic signal denoising algorithm is proposed. By grouping the chaotic signal with similar segments, the denoising of one-dimensional input is transformed into a two-dimensional joint filtering problem. Singular value decomposition is performed on the grouped signal segments and the transform coefficients are processed by thresholding to attenuate noise and finally undergo inverse transformation to recover the signal. Because the similar segments in the grouping have good correlation, the two-dimensional transformation of the grouping can obtain a more sparse representation of the original signal compared with the threshold value denoising in the direct one-dimensional transform domain, thereby having better noise suppression effect. Simulation results show that the algorithm can improve the reconstruction accuracy and has better signal-to-noise ratio than existing chaotic signal denoising algorithms.
CTG:A Controllable Text Generation Method based on the Joint Work of Language Model and Text Classifier

Text generation is an important research field in natural language processing. Today pre-trained models can generate text with high readability and fluency. However, the generated text content is usually not restricted, and the generated content cannot be controlled. Therefore, there is an urgent need for a controllable text generation method. Traditional controllable text generation methods usually have the disadvantages of high training cost and difficulty in adjustment. We propose a lightweight and easy-to-adjust method to update the language model through the discrimination results of the text classification model on the language model, thereby completing controllable text generation. And we have solved the problem of imbalance and quality degradation in text generation. Experiments show that the quality of the text generated by this method can also reach a higher level with strong subjectivity, can achieves the purpose of controlling text generation. And this method is easy to adjust, suitable for various scenarios.
Automated Extreme Learning Machine to Forecast the Monthly Flows: A Case Study at Zambezi River

This paper proposes an approach to predicting the monthly natural streamflow at the Cahora Bassa dam, Mozambique, combining a Genetic Algorithm and an Extreme Learning Machine. The model uses measurements of the past monthly river flow, rainfall, temperature, and evaporation from the lake upstream of the dam to forecast stream flows three months ahead. A Genetic Algorithm automates the choice of the internal parameters of the Extreme Learning Machine. We analyzed the model’s forecasting ability using the past two, three, six, and nine months as input data. The efficiency of the proposed approach was assessed using four metrics. The results show that ELM produces precise estimates and outperforms other machine learning methods.
EFRNet: A Lightweight Network with Efficient Feature Fusion and Refinement for Real-Time Semantic Segmentation

To pursue high accuracy, most image semantic segmentation methods are computationally costly and thus not suitable to real-time applications. Existing lightweight methods either adopt a single branch without feature fusion, which dam-ages accuracy, or introduce extra branches for feature fusion, which harms efficiency. In this paper, we propose a lightweight network named EFRNet, with feature fusion and refinement in a single branch to achieve better balance between accuracy and efficiency in real-time semantic segmentation. Specifically, in EFRNet, we design a novel Feature Fusion Module to fuse multi-stage features in a single CNN efficiently, and we propose a lightweight Channel Attention Refinement Module to refine features with few extra parameters. Extensive experiments show that our EFRNet achieves decent accuracy with an extremely small model size and high inference speed. It achieves the best accuracy of 70.02% mIoU compared with state-of-the-art lightweight methods on CamVid with only 0.48M parameters.
Time-aware cloud manufacturing service selection using unknown QoS prediction and uncertain user preferences

Selecting the most efficient from several functionally equivalent services remains an ongoing challenge. Most manufacturing service selection methods regard static quality of service (QoS) as a major competitiveness factor. However, adaptations are difficult to achieve when variable network environment has significant impact on QoS performance stabilization in complex task processes. Therefore, dynamic temporal QoS values rather than fixed values are gaining ground for service evaluation. User preferences play an important role when service demanders select personalized services, and this aspect has been poorly investigated for temporal QoS-aware cloud manufacturing (CMfg) service selection methods. Furthermore, it is impractical to acquire all temporal QoS values, which affects evaluation validity. Therefore, this paper proposes a time-aware CMfg service selection approach to address these issues. The proposed approach first develops an unknown-QoS prediction model by utilizing similarity features from temporal QoS values. The model considers QoS attributes and service candidates integrally, helping to predict multidimensional QoS values accurately and easily. Overall QoS is then evaluated using a proposed temporal QoS measuring algorithm which can self-adapt to user preferences. Specifically, we employ the temporal QoS conflict feature to overcome one-sided user preferences, which has been largely overlooked previously. Experimental results confirmed that the proposed approach outperformed classical time series prediction methods, and can also find better service by reducing user preference misjudgments.
Vision threads: a novel approach to generic camera calibration

Optical metrology techniques such as fringe projection technology and phase-measuring deflectometry, which use cameras as image sensors for 3D coordinate measurement and inspection, are becoming increasingly demanding. The necessary precise and at the same time robust calibration of image sensors of these systems is offered by so called vision ray calibration. This generic approach is a model-free description of a vision ray and are determined for each imaging sensor pixel. Numerous images of the sinusoidal fringe images displayed on the active calibration target (LCD) are captured in different poses. This generates a large amount of data and consequently leads to time-consuming evaluation for the camera calibration. In this paper, we propose a new optimization method that uses “vision threads instead of vision rays to enhance the computation efficiency associated with conventional vision ray calibration. We present MATLAB simulation results in to validate our novel approach.
Multi‐camera traffic scene mosaic based on camera calibration

Recently, the traffic application based on vision in the single traffic-monitoring scene has been widely studied and developed. However, cross-regional research is still in its infancy. In order to help solve the application of cross-regional traffic surveillance scenarios, this paper proposes a more reliable and accurate road scene mosaic method under multi-camera surveillance. The mosaic road panorama contains physical information, which can be used to achieve a cross-regional measurement. It also lays the foundation for vehicle spatial location, vehicle speed and traffic incident detection across regions. First, the mapping relationship between the three-dimensional sub-world coordinates and their corresponding two-dimensional image coordinates is established by camera calibration. Second, the projection transformation relationship between two cameras is established by two sub-world coordinate systems and their common information. Finally, we use the proposed inverse projection idea and translation vector relationship to complete the mosaic of two traffic-monitoring road scenes. The experimental results show that the camera calibration accuracy can reach more than 97% in a single scene. The measurement accuracy of the mosaic block is over 95%. The experimental results show that the proposed method has a higher accuracy, which has great value in related theoretical research and practical applications.
Comparative study of different denoising techniques on Brain NCCT images

Medical images often get affected by noise and artifacts while acquisition techniques or patient's movement. Some time radiologists unable to make useful and accurate conclusion from noisy images. The main aim of this study is to conduct a comparative measure of various denoising techniques such as Gaussian filter, Median filter, Bilateral filter, Non-Local Mean filter(NLM), Total Variation (TV) Anisotropic Diffusion (AD), BM3D method, on brain NCCT(Non-Contrast computed tomography) images. The prime focus of this research is to make comparative analysis of these techniques not merely to remove but also to preserve edges of brain NCCT images. The experimental results present that BM3D shows the best performance in terms of PSNR value, followed by total variation method and anisotropic diffusion method in terms of removal of noise. However marginal loss of edges and other fine details are there.
Multi-Dimensional Attentive Hierarchical Graph Pooling Network for Video-Text Retrieval

Video-text retrieval task has raised increasing attention due to the rapid growth of videos on the Internet. Existing works adopt various networks to encode videos and texts into a common latent space and calculate their similarities. However, most works ignore mining significant frames of videos and the difference among different dimensions in word representations, leading to unsatisfactory retrieval results. In this paper, we propose a Multi-Dimensional Attentive Hierarchical Graph Pooling Network (MAGP) to learn improved representations for video-text retrieval. Specifically, we design a novel hierarchical graph pooling method to extract significant frames in videos and discard unrelated frames, hence the model can learn hierarchical and discriminative video representations. Moreover, a multi-dimensional attention mechanism is utilized in text encoder to strengthen representation ability by dimension-level attention. Experimental results on three video-text datasets demonstrate our MAGP model out-performs the state-of-the-art models.
Modeling Complex Hydropower Systems for Research and Training

This paper presents the software package that implements the ability to study large-scale hydroelectric power systems with complex hydraulic and electrical connections, planning long-term regime of hydro power plant with the requirements of water users on the example of the Votkinsk hydroelectric plants, and training for the calculation of the water-energy regime of hydroelectric power. The complex is developed in the high-level Python programming language based on the current requirements specified in the rules for the use of water resources of the Votkinsk reservoir. The calculation in the software package is performed by the direct method. Given that the software package is suitable for both research and training. To improve the quality of the calculation, we consider method of swarm optimization. The complex is planned to be used in the course of laboratory work on the planning of the HPP operation mode, and for checking course papers.
APPLICATION AND ANALYSIS OF PSYCHOLOGICAL SCALE BASED ON GENETIC ALGORITHM IN SPORTS PSYCHOLOGY

ABSTRACT With the rapid development and application of computer technology, the study of sports psychology is one of the major research tasks in current universities. The psychological scales need to be converted according to current computer technologies. Because the old-fashioned methods cannot meet the increasing demand, application of psychometric scale based on genetic algorithm in sports psychology is applied to construct the research model. Analytic hierarchy process is used to compare the weights of indicators. In the research mode of sports psychology, the auxiliary ability of college psychological scale is applied to improve the application of sports psychology ability.
A semantic metric for concepts similarity in knowledge graphs

Semantic similarity between concepts concerns expressing the degree of similarity in meaning between two concepts in a computational model. This problem has recently attracted considerable attention from researchers in attempting to automate the understanding of word meanings to expedite the classification of users’ opinions and attitudes embedded in text. In this article, a semantic similarity metric is presented. The proposed metric, namely, weighted information-content ( wic), exploits the information content of the least common subsumer of two compared concepts and the depth information in knowledge graphs such as DBPedia and YAGO. The two similarity components were combined using calibrated cooperative contributions from both similarity components. A statistical test using the Spearman correlations on well-known human judgement word-similarity data sets showed that the wic metric produced more highly correlated similarities compared with state-of-the-art metrics. In addition, a real-world aspect category classification was evaluated, which exhibited further increased accuracy and recall.