Reviewerly/exHarmony huggingface dataset:
KPN-MFI: A Kernel Prediction Network with Multi-frame Interaction for Video Inverse Tone Mapping

Up to now, the image-based inverse tone mapping (iTM) models have been widely investigated, while there is little research on video-based iTM methods. It would be interesting to make use of these existing image-based models in the video iTM task. However, directly transferring the imagebased iTM models to video data without modeling spatial-temporal information remains nontrivial and challenging. Considering both the intra-frame quality and the inter-frame consistency of a video, this article presents a new video iTM method based on a kernel prediction network (KPN), which takes advantage of multi-frame interaction (MFI) module to capture temporal-spatial information for video data. Specifically, a basic encoder-decoder KPN, essentially designed for image iTM, is trained to guarantee the mapping quality within each frame. More importantly, the MFI module is incorporated to capture temporal-spatial context information and preserve the inter-frame consistency by exploiting the correction between adjacent frames. Notably, we can readily extend any existing image iTM models to video iTM ones by involving the proposed MFI module. Furthermore, we propose an inter-frame brightness consistency loss function based on the Gaussian pyramid to reduce the video temporal inconsistency. Extensive experiments demonstrate that our model outperforms state-ofthe-art image and video-based methods. The code is available at https://github.com/caogaofeng/KPNMFI.
A Theoretical Review of Mobile Robot Locomotion based on Mecanum Wheels

The motivation behind this paper is to introduce a broad survey identified with pragmatic applications for portable robot stages dependent on specific wheels.In today's technology of mobile robots, Omni-directional wheels are trending in almost every field such as flying robot, swarm robotics and humanoid applications etc.Among them, Omni-Directional Mobile Robot (OMR) with mecanum wheels are mostly preferred due to their versatility, limited space, low cost and endurance.They can undoubtedly navigate in a jam-packed environment with dynamic imperatives, or parochial regions.This paper describes the highlights of the practical application of the mecanum wheel in different fields with their classifications, methodologies and comparison with the conventional wheels.Their merits and demerits and future utilities in the application of mobile robot navigation are also proposed in this paper.
A New LBP Variant: Corner Rhombus Shape LBP (CRSLBP)

The local binary model is a straightforward, dependable, and effective method for extracting relevant local information from images. However, because it only uses sign information in the local region, the local binary pattern (LBP) is ineffective at capturing discriminating characteristics. Furthermore, most LBP variants select a region with one specific center pixel to fill all neighborhoods. In this paper, a new variant of a LBP is proposed for texture classification, known as corner rhombus-shape LBP (CRSLBP). In the CRSLBP approach, we first use three methods to threshold the pixel's neighbors and center to obtain four center pixels by using sign and magnitude information with respect to a chosen region of an even block. This helps determine not just the relationship between neighbors and the pixel center but also between the center and the neighbor pixels of neighborhood center pixels. We evaluated the performance of our descriptors using four challenging texture databases: Outex (TC10,TC12), Brodatz, KTH-TIPSb2, and UMD. Various extensive experiments were performed that demonstrated the effectiveness and robustness of our descriptor in comparison with the available state of the art (SOTA).
Trace Elements and Mineralogy of Upper Permian (Zechstein) Potash Deposits in Poland

Mineral composition and content analysis of selected trace elements (Ag, As, Ba, Be, Br, Cd, Ce, Co, Cr, Cs, Cu, Ga, I, La, Li, Mn, Mo, Ni, Pb, Rb, Sb, Se, Sn, Sr, Ti, Tl, U, V, and Zn; 308 rock samples) were studied in the Upper Permian (Zechstein) potash-bearing deposits in Poland. They represented K–Mg chlorides of PZ2 and PZ3 cyclothems from four salt domes and stratiform K–Mg sulphates of PZ1 cyclothem. The dominant mineral components of K–Mg sulphates (polyhalite) are anhydrite and polyhalite. The most common minerals of the K–Mg salts of PZ2 cyclothem are halite, sylvite, kieserite, and anhydrite, and the most common of PZ3 cyclothem are halite, carnallite, kieserite, and anhydrite. Most analysed trace elements in the Zechstein potash-bearing deposits show a low content (up to 26 mg/kg) that eliminates them as potential profitable source rocks of such required elements as Ce, Cs, La, Li, or Rb. Common elements, such as Br, Fe, and Sr, are more easily exploited from natural brines, sulphate, and ore deposits.
PerAnSel: A Novel Deep Neural Network-Based System for Persian Question Answering

Question answering (QA) systems have attracted considerable attention in recent years. They receive the user's questions in natural language and respond to them with precise answers. Most of the works on QA were initially proposed for the English language, but some research studies have recently been performed on non-English languages. Answer selection (AS) is a critical component in QA systems. To the best of our knowledge, there is no research on AS for the Persian language. Persian is a (1) free word order, (2) right-to-left, (3) morphologically rich, and (4) low-resource language. Deep learning (DL) techniques have shown promising accuracy in AS. Although DL performs very well on QA, it requires a considerable amount of annotated data for training. Many annotated datasets have been built for the AS task; most of them are exclusively in English. In order to address the need for a high-quality AS dataset in the Persian language, we present PASD; the first large-scale native AS dataset for the Persian language. To show the quality of PASD, we employed it to train state-of-the-art QA systems. We also present PerAnSel: a novel deep neural network-based system for Persian question answering. Since the Persian language is a free word-order language, in PerAnSel, we parallelize a sequential method and a transformer-based method to handle various orders in the Persian language. We then evaluate PerAnSel on three datasets: PASD, PerCQA, and WikiFA. The experimental results indicate strong performance on the Persian datasets beating state-of-the-art answer selection methods by 10.66% on PASD, 8.42% on PerCQA, and 3.08% on WikiFA datasets in terms of MRR.
CMA-ES and advanced adaptation mechanisms

tutorial Share on CMA-ES and advanced adaptation mechanisms Authors: Youhei Akimoto University of Tsukuba, Japan University of Tsukuba, JapanView Profile , Nikolaus Hansen Inria & Ecole Polytechnique, France Inria & Ecole Polytechnique, FranceView Profile Authors Info & Claims GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022 Pages 1243–1268https://doi.org/10.1145/3520304.3533648Published:19 July 2022Publication History 0citation24DownloadsMetricsTotal Citations0Total Downloads24Last 12 Months24Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Genetic improvement of shoreline evolution forecasting models

Coastal development and climate change are changing the geography of our coasts, while more and more people are moving towards the coasts. Recent advances in artificial intelligence allow for automatic analysis of observational data. Cartesian Genetic Programming (CGP) is a form of Genetic Programming (GP) that has been successfully used in a large variety of tasks including data-driven symbolic regression. We formulate the problem of shoreline evolution forecasting as a Genetic Improvement (GI) problem using CGP to encode and improve upon ShoreFor, an equilibrium shoreline prediction model, to study the effectiveness of CGP in GI in forecasting tasks. This work presents an empirical study of the sensitivity of CGP to a number of evolutionary configurations and constraints and compares the performances of the evolved models to the base ShoreFor model.
An Efficient Approach for Rational Next-Basket Recommendation

E-commerce is one of the most valuable and popular application scenarios of recommendation technology.In this context, making rational decisions is required due to the necessity of objectivity and decision rationale tracking.However, a significant number of irrational recommendations may be generated as a result of using a narrow range of interestingness criteria, considering inconsistent user preferences, overlooking the temporal weights of items, and using outlier choices in making a prediction.In this paper, we propose an efficient approach called MONBR (Multi-Objective Next-Basket Recommendation) to improve the quality of recommendations.An approach promotes rational recommendations by measuring the temporal importance of items to the user using several interestingness criteria, namely utility, popularity, stability, frequency, occupancy, and novelty.Moreover, combining the multi-criteria weights of items obtained from the MONBR approach with the item-based collaborative filtering technique results in more accurate and reasonable recommendations.Experiment results on real-world datasets demonstrate that the proposed approach improves the quality of recommendations.The average quality of recommendations derived from the proposed approach is up to 60.95% more accurate than state-of-the-art algorithms with a recommended basket size ranging from 10 to 100.Furthermore, the results highlight the importance of adapting objectivity and rationality in the recommendation process to meet the needs of as many users as possible.The average user-level performance is up to 10.21% higher on dense datasets.
Assessing Educators’ Readiness for Innovative Professional and Pedagogical Activities

The ongoing transformations in the modern world have a significant impact on the sustainable development of national economies, unify the requirements for human capital, and its educational level. Taking into account current trends, governments are taking significant measures to ensure and maintain the link between education, the economy and socio-economic development. These measures also change approaches to engineering training. At the same time, training, retraining, continuing training of educators are of great importance. Educators at present should follow trends of a dynamically changing reality. Thus, the purpose of this work is to assess the educators’ readiness for innovative professional and pedagogical activities (IPPA) in the process of their training on the module “Problembased, practice-oriented, project-based learning”, which is part of the continuing retraining program for engineering teachers iPET-3, developed within the framework of the international project ENTER (“EngineeriNg educaTors pEdagogical tRaining”), co-funded by the Erasmus + program of the European Union. The article proposes a model for the development of educators’ readiness for IPPA within the framework of professional retraining programs, a methodology for assessing the component composition of IPPA, and a three-level characteristic model of the component composition of IPPA. The use of the developed model during continuing retraining programs increases the level of teachers’ creative abilities, the amount of acquired material and their pedagogical skills.
EFL Students’ Attitudes towards Using Online Learning during Covid-19: Applying Technology Acceptance Model

This study examined the attitudes toward online learning of EFL students by applying technology acceptance model (TAM) constructs along with the characteristics of the online learning system and the experience of online learning as external independent variables. The study used a quantitative approach. The researchers collected data by administering a questionnaire using Microsoft’s online form to the tertiary students who were studying for their Bachelor of Arts degree (B.A.) in the English Language and Literature Department at Al-Balqa Applied University – Jordan. . One hundred eighty-five students out of 602 responded to the questionnaire. The researchers used SPSS and Structural Equational Modeling (SEM – Smart PLS) to analyze the data. Among the findings, the characteristics of an online learning system significantly affected perceived usefulness (PU), perceived ease of use (PEOU), attitudes, and the behavioral intentions (BIs) of EFL learners. In contrast, EFL learners’ PEOU negatively affected their attitudes towards online learning and BIs. Finally, the attitudes of students who studied online for four semesters or more were negative compared to those who took less than four semesters online. These findings could be attributed to the supposedly temporary and rapid shift to online learning due to Covid-19 lockdown measures. This study is significant because it develops an understanding of tertiary EFL attitudes towards online learning. It also reflects on their behavioral intentions to utilize online learning after the pandemic. The researchers believe that this is the first study to examine EFL learners’ attitudes and BIs on using online learning during/after the pandemic in Jordan using TAM.
Exploiting Cross-Modal Prediction and Relation Consistency for Semisupervised Image Captioning

The task of image captioning aims to generate captions directly from images via the automatically learned cross-modal generator. To build a well-performing generator, existing approaches usually need a large number of described images (i.e., supervised image-sentence pairs), requiring a huge effects on manual labeling. However, in real-world applications, a more general scenario is that we only have limited amount of described images and a large number of undescribed images. Therefore, a resulting challenge is how to effectively combine the undescribed images into the learning of cross-modal generator (i.e., semisupervised image captioning). To solve this problem, we propose a novel image captioning method by exploiting the cross-modal prediction and relation consistency (CPRC), which aims to utilize the raw image input to constrain the generated sentence in the semantic space. In detail, considering that the heterogeneous gap between modalities always leads to the supervision difficulty while using the global embedding directly, CPRC turns to transform both the raw image and corresponding generated sentence into the shared semantic space, and measure the generated sentence from two aspects: 1) prediction consistency: CPRC utilizes the prediction of raw image as soft label to distill useful supervision for the generated sentence, rather than employing the traditional pseudo labeling and 2) relation consistency: CPRC develops a novel relation consistency between augmented images and corresponding generated sentences to retain the important relational knowledge. In result, CPRC supervises the generated sentence from both the informativeness and representativeness perspectives, and can reasonably use the undescribed images to learn a more effective generator under the semisupervised scenario. The experiments show that our method outperforms state-of-the-art comparison methods on the MS-COCO "Karpathy" offline test split under complex nonparallel scenarios, for example, CPRC achieves at least 6 % improvements on the CIDEr-D score.
Search for new dimensions of the educational space: digital and non-digital vectors of development

Sociocultural, ecological, economic circumstances in which the constantly changing world finds itself persistently demand the emergence of new directions and subjects of research in the educational sphere. The purpose of the article is to identify promising vectors for studying the space of lifelong education. The following research methods were used: a) analysis of a combination of terms that combine philosophical, socio-cultural, economic and educational meanings; b) analysis of the author’s pedagogical experience in the formats of formal, non-formal and spontaneous education; c) an expert survey conducted online in October 2021; d) content analysis of detailed answers to the questions offered to respondents. As a result, the article outlines previously not obvious sources for creating an interdisciplinary methodological basis, the appearance of which can be predicted at the junctions of various fields of knowledge, in practice-oriented research attempts to implement the ideas of transdisciplinarity. The author’s research diagnoses fundamentally new methodological impulses that exist in science and modern philosophy when considering continuous knowledge as a vital attribute of a modern person. The possibilities that are hidden in paradoxical, at first glance, combinations and at the intersections of previously completely independent knowledge spheres are discussed. This is important for solving the problem of preserving anthropic qualities, which is especially prominent in the course of the chaotic development of learning systems that integrate human nature and artificial intelligence. The scientific novelty of the work is made up of conclusions about research methods that are most suitable for the formation of new psychological and pedagogical knowledge about permanent education.
An improved A star algorithm for wheeled robots path planning with jump points search and pruning method

Complex Engineering Systems are generally represented in terms of a set of interconnected systems that their collective/global behaviors/properties are somewhat difficult to be predicted or managed. The context of complex engineering systems is mainly concerned with developing multi-component engineering systems, designs, or algorithms to exploit those unpredictable collective/global behaviors/properties. Complexity in Engineering Systems is in general manifested in component, product, system, interconnections of interacting subsystems or multidisciplinary system designs. In a broad sense, complexity is related to the expected amount of information may need to describe a dynamical system.
Efficient Network Removing Feature Redundancy for Single Image Super-Resolution

The latest developments in single image super-resolution (SISR) combine the power of convolutional neural networks (CNN) to produce better performance. However, as the depth and width of the networks increase, CNN-based super-resolution methods have faced the challenges of computational complexity in practice. Feature redundancy in images is an essential characteristic of efficient CNNs, but it is rarely studied in the design of super-resolution methods. In this paper, we remove the redundancy of features in the space and channel dimensions, propose a fast and accurate SISR model called the spatial and channel feature distillation network (SCFDN). Specifically, SCFDN first compresses and splits the features along the space and channel dimensions to skip redundant calculations, then further processes the detailed features, and finally fuses all the features to obtain the HR image. We propose an inverse sub-pixel convolutional layer (ISPC) to map features from the spatial dimension to the channel dimension, so that the effect of feature distillation is improved. Our experimental results confirm that the proposed SCFDN has a better trade-off compared to the state-of-the-art methods in terms of performance and runtime. The source code is available at https://github.com/cyfwry/SCFDN-pytorch.
Understanding RowHammer Under Reduced Wordline Voltage: An Experimental Study Using Real DRAM Devices

RowHammer is a circuit-level DRAM vulnerability, where repeatedly activating and precharging a DRAM row, and thus alternating the voltage of a row’s wordline between low and high voltage levels, can cause bit flips in physically nearby rows. Recent DRAM chips are more vulnerable to RowHammer: with technology node scaling, the minimum number of activate-precharge cycles to induce a RowHammer bit flip reduces and the RowHammer bit error rate increases. Therefore, it is critical to develop effective and scalable approaches to protect modern DRAM systems against RowHammer. To enable such solutions, it is essential to develop a deeper understanding of the RowHammer vulnerability of modern DRAM chips. However, even though the voltage toggling on a wordline is a key determinant of RowHammer vulnerability, no prior work experimentally demonstrates the effect of wordline voltage (V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PP</inf> ) on the RowHammer vulnerability. Our work closes this gap in understanding.This is the first work to experimentally demonstrate on 272 real DRAM chips that lowering V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PP</inf> reduces a DRAM chip’s RowHammer vulnerability. We show that lowering V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PP</inf> 1) increases the number of activate-precharge cycles needed to induce a RowHammer bit flip by up to 85.8 % with an average of 7.4 % across all tested chips and 2) decreases the RowHammer bit error rate by up to 66.9 % with an average of 15.2 % across all tested chips. At the same time, reducing V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PP</inf> marginally worsens a DRAM cell’s access latency, charge restoration, and data retention time within the guardbands of system-level nominal timing parameters for 208 out of 272 tested chips. We conclude that reducing V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PP</inf> is a promising strategy for reducing a DRAM chip’s RowHammer vulnerability without requiring modifications to DRAM chips.
Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge

Open-domain question answering systems need to answer question of our interests with structured and unstructured information.However, existing approaches only select one source to generate answer or only conduct reasoning on structured information.In this paper, we propose a Document-Entity Heterogeneous Graph Network, referred to as DEHG, to effectively integrate different sources of information, and conduct reasoning on heterogeneous information.DEHG employs a graph constructor to integrate structured and unstructured information, a context encoder to represent nodes and question, a heterogeneous information reasoning layer to conduct multi-hop reasoning on both information sources, and an answer decoder to generate answers for the question.Experimental results on HybirdQA dataset show that DEHG outperforms the state-of-the-art methods.