Reviewerly/exHarmony huggingface dataset:
A Photo Identification Framework to Prevent Copyright Infringement with Manipulations

In recent years, copyright infringement has been one of the most serious problems that hamper the development of the culture and arts industry. Due to the limitations of existing image search services, these infringements have not been properly identified and the number of infringements has been increasing continuously. To uncover these infringements and handle big data extracted from copyright photos, we propose a photo copyright identification framework to accurately handle manipulations of stolen photos. From a collage of cropped photos, regions of interest (RoIs) are detected to reduce the influence of cropping and identify each photo by Image RoI Detection. Binary descriptors for quick database search are generated from the RoIs by Image Hashing robustly to geometric and color manipulations. The matching results of Image Hashing are verified by measuring their similarity using the proposed Image Verification to reduce false positives. Experimental results demonstrate that the proposed framework outperforms other image retrieval methods in identification accuracy and significantly reduces the false positive rate by 2.8%. This framework is expected to identify copyright infringements in practical situations and have a positive effect on the copyright market.
Data Anonymization for Maintenance Knowledge Sharing

Formerly considered part of general enterprise costs, industrial maintenance has become critical for business continuity and a real source of data. Despite the heavy investments made by companies in smart manufacturing, traditional maintenance practices still dominate the industrial landscape. Maintenance knowledge sharing between industries can significantly optimize maintenance activity and improve process efficiency. Different international standards and initiatives are promoting such an approach. However, this trend failed to gain ground in the manufacturing industry. In this article, we present the results of our investigation about the real roadblocks that obstruct the progress of the maintenance knowledge sharing approach. We determined that knowledge graphs and, more importantly, automated data anonymization techniques can facilitate the development of general-purpose solutions to share maintenance knowledge among concerned actors.
Interactive Environment and Virtual Reality to Reconstruct Manufacturing Processes (Moscow Trekhgorny Brewery in the Late 19th - Early 20th Centuries).

The article studies the creation of virtual reality interactive environments as a form of presenting historical 3D-reconstruction results. This approach provides for restoring not only static objects such as architectural appearance and interiors but also dynamic ones. The latter are industrial processes of the past, for instance. The manufacturing building of Tryokhgorny brewery (the biggest in the Russian Empire) was an object for virtual 3D-reconstruction. The article describes the source base and the reconstruction of indoor spaces of the brewery building in the late 19th - early 20th centuries and reconstructs brewing processes as well. The result of the work done is the creation of a virtual reality application with historical sources verification and information tips systems included. The author tests technologies of mechanical animation and particle systems and forms interactive environment logic by means of basic software &amp;ldquo;Unreal Engine 4&amp;rdquo; through the visual programming interface &amp;ldquo;Blueprints&amp;rdquo;. The virtual environment created lets users take on the role of a brewery worker and make necessary brewing operations in virtual reality.
Influence of curved surface roughness on white light interferometer microscopy

We outline the basic theory behind white light interferometry and the workings of a typical light interferometer microscope. We study WLI images obtained for rough and smooth chrome steel spheres to illustrate the principle that curved rough surfaces can be imaged with such a device as long as the surface roughness is kept within certain limits.
Obstacle Avoidance Based on Component Separation-refusion Algorithm for Blind People

How to guarantee the basic rights and interests of blind people is an urgent problem that needs to be addressed. The visual assistance system for the blind people is a good choice. Obstacle detection in the visual assistance system for blind people is extremely important, however existing obstacle avoidance methods based on machine vision do not consider the impact of shadows in the environment. This paper proposes an obstacle avoidance method based on machine vision, includes an obstacle detection algorithm and a direction planning strategy, to assist blind people to move freely. Firstly, a component separation- refusion algorithm based on the HSV model is proposed to reduce the influence of shadows in the environment. Secondly, an obstacle avoidance method, includes an obstacle detection based on image blocks and a direction planning strategy, is developed to help blind people avoid obstacles both indoors and outsides. Finally, the proposed method is tested under various light intensities and venues, experiments show that this method can effectively help blind people avoid obstacles and improve mobility. Key Words: Obstacle avoidance, shadow removal, blind people, HSV, machine vision, path planning
Sustainable Research Quality Assessment Model (SRQAM) for Higher Education Using Regression

Although internal evaluation models at research institutions have been developed for comparative research at national and international levels, the quality of inter-institutional systematic research and trend assessment models have not previously been reported in the literature. In this research, a conceptual framework called the Sustainable Research Quality Assessment Model (SRQAM) was developed to evaluate research quality indicators such as the number of publications per year and citations per year in higher education institutions. Subsequently, RQAM was applied to six selected universities in Morocco in order to observe its applicability. SRQAM consists of five steps: Duration determination, selection of research quality indicators such as citations per year (CPY), and trend indicators such as the number of publications (NP), data extraction and statistical analyses. Although paper publication and citations play a vital role in higher education institutions, the literature on publication activities is relatively scarce. Several factors influence the publication and citation rate of the university. Therefore, this article focuses on research quality assessment and discusses publication activities by exploring factors that may increase or decrease the publication and citation rate of higher education institutions. The application of this research model in six institutions from 1996 to 2020 shows that during this period, the highest averages for academic staff (AS), citations per year (CPY) and number of publications (NP) ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{AS}=2004.6+317.3,\ \text{CPY}=5954.6\pm 2642.7,\ \text{NP}=749.1\pm 684.2$</tex> ) was at Mohammed V University, Rabat. SRQAM is a fundamental instrument for institutional research and assessment and can guide research policy.
Traceability and Management Method of Supply Chain Information Based on Wireless Sensor Network

Wireless sensor network is an emerging technology with broad application prospects and will play an increasingly important role in many aspects of people’s production practices. This article is aimed at conducting research on supply chain information traceability and management methods from the perspective of wireless sensor networks. This paper uses simulation methods and comparative experiment methods. From the perspective of Spanner algorithm research, the wireless sensor network technology is researched, the explanation and simplification of Spanner algorithm in supply chain information traceability are designed, and the parameters are optimized while the algorithm is optimized. Performance analysis introduced the research on supply chain disturbance management and conducted the reliability analysis of the supply chain trust scale. The experimental results show that among the three dimensions of supply chain information sharing, the reliability coefficients of the two dimensions of information sharing content and information sharing quality are 0.803 and 0.825, respectively, which are both greater than 0.8, indicating that the reliability is very good. Cronbach’s coefficient in the information sharing level dimension is equal to 0.665, which is in an acceptable range, the CITC value of this dimension is all higher than 0.4, and the value of the deleted item is lower than 0.665, so all the items of the information sharing level will also be retained. The research of supply chain information traceability and management methods based on wireless sensor network is well completed.
Automatic Detection of Grammatical Errors in English Verbs Based on RNN Algorithm: Auxiliary Objectives for Neural Error Detection Models

With the rapid development of neural network technology, we have widely used this technology in various fields. In the field of language translation, the research on automatic detection technology of English verb grammatical errors is in a hot stage. The traditional manual detection cannot be applied to the current environment. Therefore, this paper proposes an automatic detection technology of English verb grammatical errors based on recurrent neural network (RNN) algorithm to solve this problem. Firstly, the accuracy and feedback speed of traditional manual detection and recurrent neural network RNN algorithm are compared. Secondly, a detection model which can be calculated according to grammatical order combined with context is designed. Finally, when the output verb result is inconsistent with the original text, it can automatically mark the error detection effect. The experimental results show that the algorithm model studied in this paper can effectively improve the detection accuracy and feedback efficiency and is more applicable and effective than the traditional manual detection method.
On Uniform Consistency of Nonparametric Tests. I

For widespread nonparametric tests, we point out necessary and sufficient conditions of uniform consistency for nonparametric sets of alternatives. Nonparametric sets of alternatives can be defined both in terms of distribution functions and in terms of density. Such conditions are provided for χ2-tests with an increasing number of cells, for Cramér – von Mises tests, for tests generated by 𝕃2-norms of kernel estimators, and for tests generated by quadratic forms of estimators of Fourier coefficients. Necessary and sufficient conditions on sets of alternatives for the existence of uniformly consistent tests are treated as well.
Toward Node Liability in Federated Learning: Computational Cost and Network Overhead

Many machine learning (ML) techniques suffer from the drawback that their output (e.g., a classification decision) is not clearly and intuitively connected to their input (e.g., an image). To cope with this issue, several explainable ML techniques have been proposed to, for example, identify which pixels of an input image had the strongest influence on its classification. However, in distributed scenarios, it is often more important to connect decisions with the information used for the model training and the nodes supplying such information. To this end, in this article we focus on federated learning and present a new methodology, named node liability in federated learning (NL-FL), which permits identifying the source of the training information that most contributed to a given decision. After discussing NL-FL's cost in terms of extra computation, storage, and network latency, we demonstrate its usefulness in an edge-based scenario. We find that NL-FL is able to swiftly identify misbehaving nodes and to exclude them from the training process, thereby improving learning accuracy.
Validation of Remote Sensing Techniques in Greenfield Exploration Areas for Lithium (LI) in Central Portugal: A Study Case

Several algorithms were developed to map lithium (Li)-pegmatites in recent years. This preliminary study attempts to validate the same algorithms in the Trancoso region (Portugal), an area with unknown economic potential. The application of RGB combinations, Band Ratios (BR), and Selective Principal Component Analysis (PCA) to Sentinel-2 and Landsat-8 images allowed to identify four target areas for Li-exploration. Some targets correspond to an inert exploitation of the former mine's waste, confirming the potential of this methodology. Moreover, outcropping metric Li-dykes were missed with this approach, highlighting the need to refine the existing algorithms. Reflectance spectroscopy studies of Li-dykes and host rocks support these conclusions and will be crucial to improve the methodology. The collected spectra increased the knowledge on this greenfield area and its economic potential and provided information on new mineralization proxies to be used in future satellite detection studies.
Reservoir Computing Using Networks of CMOS Logic Gates

Reservoir computing is a brain-inspired architecture for machine learning, capable of rapid time series processing. A recurrent neural network called a reservoir is created, and a simple trained readout map is applied to the reservoir state. A real-world dynamical system at the edge of criticality can be used as a reservoir for information processing in place of a software model. In this paper we test the dynamics of a reservoir comprised of discrete digital logic chips on a printed circuit board. The logic gates run freely without a clock, exhibiting complex behavior that expands an input into a higher dimensional representation. By testing these circuits in a dataset-agnostic manner, we identify promising configurations for machine learning. We demonstrate that the reservoir circuit substantially improves the accuracy of a simple classifier on a noisy waveform classification machine learning task.
Reasoning Operational Decisions for Robots via Time Series Causal Inference

Justifying operational decisions for robots is a challenging task as the operator or the robot itself has to understand the underlying physical interaction between the robot and the environment to predict the potential outcome. It is desirable to understand how the decision influences the operational performance in the way of causal relationship for the purpose of explainable decision-making. Here we propose a novel causal inference framework for the discovery and inference on the reasoning of the operational decisions for robots. It unifies both domain knowledge integration and model-free causal inference, allowing a data-driven causal knowledge learning on time series data. The framework is evaluated in the experiments of an underwater robot with complex environmental interactions. The results show that the framework can learn the causal structure and inference model to accurately explain and predict the operation performance with integrated physics.
MeshChain: Secure 3D Model and Intellectual Property management Powered by Blockchain Technology

The intellectual value of digitized 3D properties in scientific, artistic, historical, and entertaining domains is increasing. However, there has been less attention on designing an immutable, secure database for their management. We propose a secure 3D property management platform powered by blockchain and decentralized storage. The platform connects various 3D modeling tools to a decentralized network-based database constructed on blockchain and decentralized storage technologies and provides the commit and checkout of the 3D model to that network. This structure provides 3D data protection from damages and attacks, intellectual property (IP) management, and data source authentication. We analyze its performance and show its applications to cooperative 3D modeling and IP management.
Weight Evolution: Improving Deep Neural Networks Training through Evolving Inferior Weight Values

To obtain good performance, convolutional neural networks are usually over-parameterized. This phenomenon has stimulated two interesting topics: pruning the unimportant weights for compression and reactivating the unimportant weights to make full use of network capability. However, current weight reactivation methods usually reactivate the entire filters, which may not be precise enough. Looking back in history, the prosperity of filter pruning is mainly due to its friendliness to hardware implementation, but pruning at a finer structure level, i.e., weight elements, usually leads to better network performance. We study the problem of weight element reactivation in this paper. Motivated by evolution, we select the unimportant filters and update their unimportant elements by combining them with the important elements of important filters, just like gene crossover to produce better offspring, and the proposed method is called weight evolution (WE). WE is mainly composed of four strategies. We propose a global selection strategy and a local selection strategy and combine them to locate the unimportant filters. A forward matching strategy is proposed to find the matched important filters and a crossover strategy is proposed to utilize the important elements of the important filters for updating unimportant filters. WE is plug-in to existing network architectures. Comprehensive experiments show that WE outperforms the other reactivation methods and plug-in training methods with typical convolutional neural networks, especially lightweight networks. Our code is available at https://github.com/BZQLin/Weight-evolution.
RICH: Strategy-proof and efficient coflow scheduling in non-cooperative environments

Coflow scheduling can effectively improve the application performance and has been studied a lot in cooperative environments (e.g., private datacenter networks), where fairness is not the primary concern. In non-cooperative environments (e.g., multi-tenant datacenter networks), coflow scheduling should be strategy-proof; otherwise, some tenants could unfairly acquire more resources by cheating the scheduler. As minimizing coflow completion time (CCT) must prioritize coflows based on some specific rules (e.g., shortest-coflow-first, smallest-effective-bottleneck-first), tenants can raise the priority of their coflows by lying about the coflow information. Thus, it is a common belief that optimizing coflow performance can inevitably violate strategy-proofness. In this paper, we argue that the average CCT can be reduced without violating strategy-proofness. Our key insight is that prioritization can inherently achieve better CCT even without those specific rules such as smallest-effective-bottleneck-first. We propose RICH, a coflow scheduler in non-cooperative environments. At its heart, RICH splits the time into multiple rounds. In each round, RICH ensures that the total data transmitted by each tenant can provide optimal isolation guarantee. Among different rounds, RICH prioritizes coflow transmission among tenants in a round-robin manner. In this way, all tenants are fairly prioritized, and tenants do not necessarily gain more bandwidth by cheating. Extensive simulations show that RICH outperforms other strategy-proof mechanisms by up to 39.3% in terms of average CCT.
Hyperspectral Image Denoising with Collaborative Total Variation and Low Rank Regularization

Variational regularization methods are the mainstream methods typically adopted for hyperspectral images (HSIs) denoising, which borrow architectures originally developed for RG-B images, exhibiting limitations when cope with HSI data cubes. To overcome this limitation, this paper proposes a new collaborative total variation and low-rank regularization model (LRCTV) to remove mixed noise from HSI data. Specifically, the proposed method unfolds the HSI cube into 2D extended spectral-matrix, then obtains the horizontal and vertical gradient matrices, and applies 2D collaborative norm to the gradient matrix to model the directional selective smoothness, while the matrix nuclear norm is used to model the low rank structure. Experimental results on both simulated and real HSI datasets validated that the proposed method outperformed several state-of-the-art methods.
How do Testers Feel it? An Experience Report on Evaluating TX in an Industrial Context

Background: Software development is almost always performed by teams. Consequently, several factors such as environment, human aspects, and technologies adopted are likely to affect individual and team performance. Method: Motivated by this context, we conducted a survey with 27 testers of the SIDIA Institute of Science and Technology. The goal was to understand how human and technological aspects can affect test effectiveness, by considering the practitioners’ perspective on their homologation testing review practices with respect three aspects: the testing infrastructure, how they perceive the value of their contribution, and how they feel about their work. Qualitative and quantitative analyses of testers’ perception using the Tester Experience (TX) approach was conducted. Results: Our results show that by using scenarios, the testers’ perceptions with respect to the value of their contribution and how they feel about their work were clearly the highest rated factors. Conclusion: This work can serve as a basis for other companies in their testing teams to evaluate how the use of testing artifacts and how testing activities can improve TX.
An Association Rules-Based Method for Outliers Cleaning of Measurement Data in the Distribution Network

For any power system, the reliability of measurement data is essential in operation, management and also in planning. However, it is inevitable that the measurement data are prone to outliers, which may impact the results of data-based applications. In order to improve the data quality, the outliers cleaning method for measurement data in the distribution network is studied in this paper. The method is based on a set of association rules (AR) that are automatically generated form historical measurement data. First, the association rules are mining in conjunction with the density-based spatial clustering of application with noise (DBSCAN), k-means and Apriori technique to detect outliers. Then, for the outliers repairing process after outliers detection, the proposed method uses a distance-based model to calculate the repairing cost of outliers, which describes the similarity between outlier and normal data. Besides, the Mahalanobis distance is employed in the repairing cost function to reduce the errors, which could implement precise outliers cleaning of measurement data in the distribution network. The test results for the simulated datasets with artificial errors verify that the superiority of the proposed outliers cleaning method for outliers detection and repairing.
Trustworthy Multimedia Analysis

This tutorial discusses the trustworthiness issue in multimedia analysis. Starting from introducing two types of spurious correlations learned from distilling human knowledge, we partition the (visual) feature space along two dimensions of task-relevance and semantic-orientation. Trustworthy multimedia analysis ideally relies on the task-relevant semantic features and consists of three modules as trainer, interpreter and tester. These three modules essentially form a closed loop, which respectively address goals of extracting task-relevant features, extracting task-relevant semantic features, and detecting spurious correlations to be corrected by the trainer and interpreter.