Reviewerly/exHarmony huggingface dataset:
Rapid automatic underwater image recovery method based on polarimetric imaging

Underwater imaging plays an important role in underwater scientific research, biological monitoring, underwater rescue, and military defense. However, the scattering and absorption of particles in the scattering medium lead to the distortion of the originally ordered wavefront phase and the image degradation. Polarimetric imaging technology has become an effective method for underwater image recovery based on its unique advantages. However, traditional polarimetric techniques require mechanically rotating the linear polarizer to find the two images that are brightest (the backscattered light is maximally through the polarizer) and darkest (the backscattered light is maximally blocked by the polarizer). Most polarimetric imaging techniques combined with conventional methods require the presence of a background region in the image to estimate the characteristics of backscattered light. There are also some polarimetric imaging methods that do not require a background area in the image, but most of them require prior knowledge and complex algorithms. All these greatly affect the real-time application of polarimetric imaging technology. In this paper, a rapid automatic underwater image recovery method based on polarimetric imaging is proposed. We optimize the traditional polarimetric imaging method based on the scattering imaging model and solve the problem that the traditional method requires an unsupervised image quality evaluation index to correct the intermediate parameters and selecting the background region, which realize the scattering suppression for the underwater image without background region. The experimental results show that our method can process a 320,000-pixel image in only 0.007 seconds. The proposed method can be applied to the current polarimetric imaging instrument system and has a good effect on underwater image recovery, and the recovery results are almost without distortion. It has strong robustness and is also applicable to images without background region, which provide a promising application prospect in the field of underwater imaging.
Multiple fluctuating targets track-before-detect using multi-Bernoulli filter in radar sensor

Abstract This paper addresses the detection and tracking of multiple fluctuating targets for a track-before-detect algorithm based on the Multi-Bernoulli (MB-TBD) filter in surveillance radar systems. MB-TBD usually considers target amplitude information and ignores the fact that radar measurements are complex-valued. In this paper, we first propose to utilize phase information to improve the discrimination of targets from noise. More precisely, complex likelihood ratios are used instead of squared modulus measurements likelihood ratios for fluctuations of types Swerling 0, 1, 3. Secondly, the traditional MB-TBD filter cannot solve the problem of coexistence between targets with stronger amplitude and weaker amplitude when multiple fluctuating targets are moving. To address this limitation, an adaptive birth distribution based on joint successive target cancellation and measurement likelihood ratio driven is proposed. Moreover, in order to reduce computational complexity, the Bernoulli components of the same targets are merged after the MB-TBD updating. Finally, the proposed algorithm is implemented using Sequential Monte Carlo technology. The simulation results show that in challenging scenarios, the performance of the improved algorithm is better than the traditional algorithm, and it has a good application prospect.
Hate speech and discrimination: an overview of the Portuguese situation

Fundamental values like human dignity, freedom, democracy, equality, and respect for human rights are essential values of a democratic, social and participatory society. Despite this, in the past few years, it’s possible to see an increasing of hate speech, racism and hate crimes in many countries of the EU. This paper aims to present the state-of-art of the Portuguese situation regarding to discrimination, hate speech and racism. It was made a literature review of national and international resources, and interviews with persons that have suffered from hate speech as well as experts of the field. Results shows a long way to do to mitigate these phenomena and it’s fundamental the intervention of all parts of society: government, organisations, civil society and citizens. The researchers hope that this paper may enrich the knowledge about these concepts and may be useful for the development of strategies to promote a society more equitable.
Evaluating research computing training and support as part of a broader digital research infrastructure needs assessment

Digital Research Infrastructure (DRI) refers to the suite of tools and services that enables the collection, processing, dissemination, and disposition of research data. This includes strategies for planning, organizing, storing, sharing, computing, and ultimately archiving or destroying one's research data. These services must be supported by highly qualified personnel with the appropriate expertise. From May 17 - June 12, 2021, the University of British Columbia (UBC) Advanced Research Computing (UBC ARC) and the UBC Library from both Vancouver and Okanagan Campuses launched the DRI Needs Assessment Survey to investigate UBC researchers’ needs in 25 distinct DRI tools and services. The survey received a total of 241 responses, and following the survey, three focus groups were conducted with survey respondents to gain additional insights.
Establishment and Optimization of Enterprise Financial Shared Center in the Era of Big Data–taking Haier as an Example

With the rapid development of Internet technology, the technological innovation based on "Internet+" has been applied in many industries and fields, which has played a vital role in promoting economic development. The development of big data has intensified economic globalization, the business scale of the group company continuously expands to all regions and the trend of extending from China to abroad is more obvious, increasing the difficulty in financial control of each branch (subsidiary) by the group. To solve the problem, the group enterprises are required to accelerate the promotion of financial transformation and upgrading, focus on enhancing financial value creation and business support capabilities and effectively control financial and operational risks. The research and practice show that the effect achieved by implementing financial sharing service is relatively remarkable. This paper takes Haier enterprise as a case for study and analysis, studies the problems in the application of enterprise Financial Shared Service Center under the big data environment, deeply analyzes the cause of the problem, and puts forward detailed solutions and suggestions for specific problems. With Haier's Financial Shared Service Center as the study object, this paper studies the advantage and application of financial sharing, the specific operation of each business process, the deficiencies of each financial management module and the process modules of the Financial Shared Service Center to be improved and optimized under the big data technology environment, and explores the theoretical methods and practical application value of enterprises optimizing the Financial Shared Center under the background of big data by deep analysis of Haier case.
Analysis on the Impact of Recommender Systems on Consumer Decision

With the popularization of online shopping platforms across China and in order to maximize profits, online shopping platforms developed various recommender systems (RS), which played an important role in consumers’ decision-making process. Therefore, the impact of RS on consumers’ decision-making process is worthy of studying. In this paper, the RS of online shopping platforms and two examples of them were introduced. This paper firstly analyzed the ethical challenges related to the RS, including algorithmic opacity, privacy concerns, and bias and behavior manipulation and provided solutions accordingly. Besides, the paper through a method of online questionnaire and data analysis, illustrated RS’ ability to influence consumers’ decision making by providing them with personalized recommendations during online shopping. In addition, with the help of an online questionnaire filled out by more than 128 Chinese consumers across different ages and occupations, the author also analyzed people's attitudes towards the RS. According to the research results, this overall finding implies that recommender systems in contemporary electronic commerce are not merely decision aids for lowering search costs, but may also play a substantial part in economic decision making. A randomised trial strategy was used in this work to support the idea that internet recommendations may influence willingness-to-pay judgements, even when the two are tested on distinct scales.
Satellite pose estimation using Earth radiation modeled by artificial neural networks

• An artificial neural network model is developed to predict the Earth albedo factor. • An artificial neural network model is developed to predict the Earth Infrared flux. • Simultaneous orbit and attitude estimation using temperature data is presented. • Observability of the temperature-based pose estimation is analyzed. The thermal energy received by each surface of an Earth-orbiting satellite strongly depends on its position and orientation. In this sense, simultaneous orbit and attitude estimation (SOAE) using the surface temperature data has been focused in the present study. The Earth infrared (IR) radiation and the Earth’s top-of-atmosphere (TOA) albedo are two key sources of radiation affecting the satellite surface temperature rate. The Earth’s radiation information has been monitored for the past two decades by the Clouds and the Earth’s Radiant Energy System (CERES) project, producing a comprehensive set of Earth radiation budget (ERB) data for climate, weather and applied science research. The current study utilizes the ERB data to forecast the IR radiation flux (IRF) and the TOA albedo factor (AF) via an artificial neural network (ANN). In this respect, the satellite longitude, latitude and time constitute the input vector of the learning set, while the AF and IRF are considered as the output. The ANN is then employed to model the satellite surface temperature rate in the radiation-based SOAE process. The feasibility of the proposed pose estimation approach has been addressed and verified via Monte Carlo simulation. Obtained results demonstrate suitability of the algorithm in sunlight intervals with good accuracy.
Classification of VTU Internal Questions Complexity Level using Cognitive Methods

The accurate estimation of the difficulty position of the questions posed to scholars is essential to help them to learn more effectively and efficiently. Still, its agreed that preceptors generally fail to identify the correct difficulty position of the questions, according to the answers and final scores attained by their scholars. Therefore, this process examines the capability of preceptors for grading questions by difficulty position, comparing it with the scholars perception and the measures attained by an expert system of question automatic bracket. The results show that scholars perceptive questions more delicate than preceptors, except for the harder ones. In addition, preceptors are only smoothly more accurate (near to the expert system), in malignancy of the general scholars tendency to overrate the difficulty position of less delicate questions. Although no general conclusions can be attained about behaviour and delicacy of preceptors and scholars when they dissect the difficulty of literacy material, the handed analysis could be veritably precious for preceptors in order to descry unclear problem statements and scholars misconceptions.
Towards interactive QoE assessment of robotic telepresence

This article provides insights towards the Telepresence Robot (TPR) QoE research agenda and summarizes the findings of a TPR QoE study as provided in H. Z. Jahromi, I. Bartolec, E. Gamboa, A. Hines and R. Schatz, "You Drive Me Crazy! Interactive QoE Assessment for Telepresence Robot Control," 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), Athlone, Ireland, 2020, pp. 1-6, doi: 10.1109/QoMEX48832.2020.9123117.
Relations in education system: management possibilities

The article shows that many of transformational processes taking place in the education system have spontaneous and hidden nature. This refers to the greatest extent to the relations between the participants in the educational process. The possibility of managing these relations is being considered.
An Ontological Model for Fire Evacuation Route Recommendation in Buildings

The study of the evacuation of buildings in emergency fire situations has deserved the attention of researchers for decades, particularly regarding the real-time guiding of occupants in their way to exit the building. However, finding solutions to guide the occupants evacuating a building requires a thorough knowledge of that domain. Using ontological models to model the knowledge of a domain allows the understanding of that domain to be shared. This paper presents an ontological model that pretends to reinforce and deepen knowledge of the domain under study and help develop solutions and systems capable of guiding the occupants during a building evacuation. The ontology was developed following the METHONTOLOGY methodology, and for implementation, the Protégé tool was used. The ontological model was successfully submitted to a thorough evaluation process and is publicly available on the Web.
Improving Model Accuracy by Means of Explanations

Recently, artificial intelligence (AI) methods are becoming popular due to their efficacy when applied to different fields; hence, there has been an increasing effort to provide meaningful explanations of the decision-making process of such methods. For computer vision methods, one of the most utilized is called layer-wise relevance propagation (LRP), which computes the relevance of each layer in the process of classification and generates a comprehensible relevance map image representing the importance of each part of the image during the classification process. In this work, we take one step toward the utilization of explainable artificial intelligence (XAI) methods, such as LRP, to improve the accuracy of well-established AI models. We propose a novel methodology that creates a meta-model that is able to learn the classification process of a model through observation of LRP relevance map images. This meta-model is able to improve the accuracy of the baseline model in up to 30% in the best case and 1% on average.
A Priori Study on Factors Affecting MapReduce Performance in Cloud-Based Environment

In the current era, global data have been rising at a very fast speed due to the excessive use of technologies including cloud and IoT. It leads to the development of big data that can handle and analyze a high volume of data regularly. Cloud computing provides a reliable, available, and scalable environment for the processing of this huge data. MapReduce has become an important computing model for processing and generating high-volume datasets on a cluster of machines. It not only allows distributed processing but also significant attributes like flexibility, versatility, load adjusting, and adaptation to internal failure. Despite these benefits, the performance of this framework gets affected by multiple factors, namely indexing, data skew, joining, caching, and load balancing. The main objective of this paper is to identify the factors to resolve its performance issues and investigate alternate strategies to improve the MapReduce query performance in the cloud-based environment.
Joint Deep Dictionary Learning

Inspired by the efficiency of dictionary learning and deep learning, we propose a novelty nonlinear feature representation method, namely Joint Deep Dictionary Learning (JDDL). JDDL embeds an ingenious design of network module which could achieve dictionary learning into common deep learning network structures. Specifically, JDDL learns the dictionary and sparse representation coefficients simultaneously in a low-dimensional latent space built upon deep auto-encoders. Hence, JDDL could produce compact and discriminative representation for the given data and even unseen data by incorporating dictionary learning and deep learning into a unified framework. Extensive experiments are conducted on four real-world data sets clustering to show that the proposed method could provide performance superior to many state of-the-art approaches.
Towards Scenario-Based and Question-Driven Explanations in Autonomous Vehicles

Benefit from the progress in the field of explainable artificial intelligence (XAI), explanations have been increasingly prospective in the autonomous vehicle (AV) context. Providing explanations has been proved to be vital for human-AV interaction, but what and how to explain are still to be addressed. This study seeks to bridge the areas of XAI and human-AV interaction by combining perspectives of both users and researchers. In this paper, a conceptual framework of explanation models was proposed to indicate what aspects to explain in human-AV interaction. Based on the framework, we introduced a scenario-based and question-driven method, i.e., the SQX-canvas, to guide the workflow of generating explanations from users’ demands in a certain AV scenario. To make an initial validation of the method, a co-design workshop involving researchers and users was conducted with four AV scenarios provided in forms of video clips. Participants produced explanation concepts and expressed their attitudes towards the AV scenarios following the “scenario, question and explanation” process. It was apparent that users’ demands of explanations varied across scenarios, and findings as well as limitations were discussed. This method could provide implications for research and practice on facilitating transparent human-AV interaction.
Unsupervised Ranking and Aggregation of Label Descriptions for Zero-Shot Classifiers

Zero-shot text classifiers based on label descriptions embed an input text and a set of labels into the same space: measures such as cosine similarity can then be used to select the most similar label description to the input text as the predicted label. In a true zero-shot setup, designing good label descriptions is challenging because no development set is available. Inspired by the literature on Learning with Disagreements, we look at how probabilistic models of repeated rating analysis can be used for selecting the best label descriptions in an unsupervised fashion. We evaluate our method on a set of diverse datasets and tasks (sentiment, topic and stance). Furthermore, we show that multiple, noisy label descriptions can be aggregated to boost the performance.
Transformer-Based Models for the Automatic Indexing of Scientific Documents in French

Automatic indexing is a challenging task in which computers must emulate the behaviour of professional indexers to assign to a document some keywords or keyphrases that represent concisely the content of the document. While most of the existing algorithms are based on a select-and-rank strategy, it has been shown that selecting only keywords from text is not ideal as human annotators tend to assign keywords that are not present in the source. This problem is more evident in scholarly literature. In this work we leverage a transformer-based language model to approach the automatic indexing task from a generative point of view. In this way we overcome the problem of keywords that are not in the original document, as the neural language models can rely on knowledge acquired during their training process. We apply our method to a French collection of annotated scientific articles.
Research on Online Education of English Listening and Speaking

With the continuous development of the Internet, the sharing and opening of highquality resources not only improves the level of educational resources serving teaching, but also bring opportunities and challenges to the reform of junior high school education and teaching. Under the background of a global COVID-19 outbreak, the whole-time and wholescale online education is a necessary teaching approach in the pandemic situation. Based on the inevitable advent of the era of online education, this paper takes the junior high school online English listening and speaking courses as its study object. From the perspective of studying online listening and speaking education and with relevant teaching theories, it tries to put forward solutions to the problems existing in the current online education of English listening and speaking through analyzing the problems in English teaching on the est100 platform. There is much research on the teaching of junior high school students' English listening and speaking ability, but little from the perspective of online education. On the basis of existing research results, this paper discusses the shortcomings of current online English listening and speaking education with cases, and puts forward suggestions for improvement, so as to provide reference for further optimization of online English listening and speaking education.
Best cloud node prediction and matchmaking using cloud resource prediction pattern

The cloud computing is the modern service for storing, computing and sharing service for the people with a help of resources. The principal topic of cloud computing is best Cloud node Prediction for assigned work. Based on the resource evaluation cloud resources are the ranked. With respect to the rank value resources are matched with jobs. The Quality of Service (QOS) parameters are helping to rank the resources and store it in the hash table. The resource ID is the index value in the hash table. The matchmaking process dependents on Evaluation Cum Classification (EC2). The EC2 calculation portrays three things, first one is cloud resource evaluation and ranking, second one is cloud evaluation framework by scheduler and third one is job assessment and grouping. In this research work, planned the new advancement calculation introduced is called as CRPP-PSO. The CRPP-PSO is integration of Particle Swarm optimization (PSO) calculation and Cloud Resource Prediction Pattern (CRPP). The CRPP-Pattern is configuration to locate the best cloud resource with task expectation component. The Cloud server is major role to take decision on best cloud node selection. The Cloudlets are serving as a computing resource for the give task. The array of Cloudlets is helping to form a cluster to perform optimal and powerful service to the give task. The trial results show the significant of proposed framework to discovering best cloud node.
Smart game development on removal of every K player and find the winner using linked list concepts

The main objective of the proposed research work is to create a smart game service using Linked List concepts. The problem statement of the smart game service is removal of every K player and find the winner of the game. The proposed work creates two different approaches for the given problem. Approach 1, considering the scenario to get the time taken by each player in each round and eliminate the player one who takes more time. Finally, the player with less time get announced as winner displaying his assigned number in the screen. Approach 2, the statement states to eliminate the K player with respect to the random position from the user in each round. Finally, the player remaining is announced as winner displaying his assigned Player ID in the screen. Approach 1 to solve this problem statement using Single linked list. Approach 2 to solve this problem statement using Circular linked list.