Reviewerly/exHarmony huggingface dataset:
Communicative Visualizations as a Learning Problem

Significant research has provided robust task and evaluation languages for the analysis of exploratory visualizations. Unfortunately, these taxonomies fail when applied to communicative visualizations. Instead, designers often resort to evaluating communicative visualizations from the cognitive efficiency perspective: “can the recipient accurately decode my message/insight?” However, designers are unlikely to be satisfied if the message went `in one ear and out the other.' The consequence of this inconsistency is that it is difficult to design or select between competing options in a principled way. The problem we address is the fundamental mismatch between how designers want to describe their intent, and the language they have. We argue that visualization designers can address this limitation through a learning lens: that the recipient is a student and the designer a teacher. By using learning objectives, designers can better define, assess, and compare communicative visualizations. We illustrate how the learning-based approach provides a framework for understanding a wide array of communicative goals. To understand how the framework can be applied (and its limitations), we surveyed and interviewed members of the Data Visualization Society using their own visualizations as a probe. Through this study we identified the broad range of objectives in communicative visualizations and the prevalence of certain objective types.
Feature grouping and selection: A graph-based approach

Most current feature selection techniques are focused on the incremental inclusion or exclusion of single individual features with respect to the candidate feature subset(s). The use of such approaches, where only the individual inclusion/exclusion of features is considered, means that information such as the collaborative contribution or correlation between features may be lost. The result is that the final selected feature subset may contain high levels of inter-feature redundancy, assuming that the key information embedded in the original feature set can still be retained. To address this problem, a general framework based on graph processing and three-way mutual information metrics is proposed in this paper that works by clustering similar features into groups, from which representative features are then drawn. Two different feature selection techniques based on this framework are presented: one by straightforward selection of representative features from the resulting feature groups and the other via a music-inspired metaheuristic search. Comparative experimental evaluation against traditional feature selection techniques over a diverse range of 20 benchmark datasets demonstrates the efficacy of the proposed approach. With these implementations, significant performance gains can be made in terms of classification accuracy in general and dimensionality reduction in particular while retaining feature semantics and considerably lessening the redundancy in the returned feature subsets.
The Impact of the Behavioral Factors on Investment Decision-Making: A Systemic Review on Financial Institutions

The purpose of the study is to identify the effects of behavioral factors (cognitive biases) on financial decision-making. A systematic review method was implemented and selected 29 research published studies between the years 2010-2020 and were critically reviewed. The main findings of the study indicate that the most common factors appear in papers were overconfidence (18), anchoring bias (11), herding effect (10) and loss aversion (9), which has a significant impact on the financial decision making process. Moreover, almost half of the articles were survey-based (questionnaire), quantitative method and the rest of the articles were the qualitative and mixed-methods. The study concluded that the overall impact of behavioral/psychological factors highly influence on financial decision-making. However, the time and search of the key terms in the papers’ title were considered as the key limitations, which prevent in-depth investigation of the study. For future research, those most repetitive cognitive bias should be measured during COVID-19 pandemic uncertain situation.
A novel integrated approach for ranking solar energy location planning: a case study

Purpose Every day, the sun provides by far more energy than the amount necessary to meet the whole world’s energy demand. Solar energy, unlike fossil fuels, does not suffer from depleting resource and also releases no greenhouse gas emissions when being used. Hence, using solar irradiance to produce electricity via photovoltaic (PV) systems has significant benefits which can lead to a sustainable and clean future. In this regard, the purpose of this study is first to assess the technical and economic viability of solar power generation sites in the capitals of the states of Canada. Then, a novel integrated technique is developed to prioritize all the alternatives. Design/methodology/approach In this study, ten provinces in Canada are evaluated for the construction of solar power plants. The new hybrid approach composed of data envelopment analysis (DEA), balanced scorecard (BSC) and game theory (GT) is implemented to rank the nominated locations from techno-economic-environmental efficiency aspects. The input data are obtained using HOMER software. Findings Applying the proposed hybrid approach, the order of high to low efficiency locations was found as Winnipeg, Victoria, Edmonton, Quebec, Halifax, St John’s, Ottawa, Regina, Charlottetown and Toronto. Construction of ten solar plants in the ten studied locations was assessed and it was ascertained that usage of solar energy in Winnipeg, Victoria and Edmonton would be economically and environmentally justified. Originality/value As to novelty, it should be clarified that the authors propose an effective hybrid method combining DEA, BSC and GT for prioritizing all available scenarios concerned with the construction of a solar power plant.
Modeling of time series using random forests: Theoretical developments

In this paper we study asymptotic properties of random forests within the framework of nonlinear time series modeling.While random forests have been successfully applied in various fields, the theoretical justification has not been considered for their use in a time series setting.Under mild conditions, we prove a uniform concentration inequality for regression trees built on nonlinear autoregressive processes and, subsequently, we use this result to prove consistency for a large class of random forests.The results are supported by various simulations.
Neural news recommendation with negative feedback

News recommendation is important for online news services. Precise user interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually rely on the implicit feedback of users like news clicks to model user interest. However, news click may not necessarily reflect user interests because users may click a news due to the attraction of its title but feel disappointed at its content. The dwell time of news reading is an important clue for user interest modeling, since short reading dwell time usually indicates low and even negative interest. Thus, incorporating the negative feedback inferred from the dwell time of news reading can improve the quality of user modeling. In this paper, we propose a neural news recommendation approach which can incorporate the implicit negative user feedback. We propose to distinguish positive and negative news clicks according to their reading dwell time, and respectively learn user representations from positive and negative news clicks via a combination of Transformer and additive attention network. In addition, we propose to compute a positive click score and a negative click score based on the relevance between candidate news representations and the user representations learned from the positive and negative news clicks. The final click score is a combination of positive and negative click scores. Besides, we propose an interactive news modeling method to consider the relatedness between title and body in news modeling. Extensive experiments on real-world dataset validate that our approach can achieve more accurate user interest modeling for news recommendation.
A novel improved crow‐search algorithm to classify the severity in digital mammograms

Abstract The survival rates of breast cancer are going up due to the emerging increase in its screening and diagnosis methods. However, breast cancer is yet the most intrusive disease found in women. Many techniques are emerging during recent years for the investigation of breast cancer using imaging modalities. The paper intends to categorize the severity present in the digital mammography images as either benign (B) or malignant (M) using an improved crow‐search optimization algorithm (ImCSOA). In the literature, the CSOA is generally used for solving several feature selection and numerical optimization problems. The objective is to utilize this popular optimization algorithm for the problem of biomedical image classification. However, if this algorithm is applied directly to classification problems, then it will result in poor classification of data. Hence, the original CSO (OCSO) algorithm undergoes suitable enhancements using a novel controlled parameter tuning, control operator and chaotic‐maps‐based controlled randomness. Four distinct chaotic maps are used for controlling the randomness in the OCSO algorithm. The mammogram images are obtained from the Mammographic Image Analysis Society and Digital Database for Screening Mammography data sets for the evaluation. The classification is accomplished through discrete wavelet transform‐based statistical features that are extracted at two levels [level 4 (L4) and level 6 (L6)] of decomposition. For both data sets, the ImCSOA with L4 and L6 decomposed bior4.4 wavelet features provides the maximum accuracy of around 85% to 86%, which is approximately 62% to 88% better than the OCSO algorithm with L4 and L6 decomposed bior4.4 wavelet features.
Scoping review of the potentials of fuzzy cognitive maps as a modeling approach for integrated environmental assessment and management

Fuzzy Cognitive Maps were missing in the latest guidance by Kelly et al. (2013), which reviewed five common integrated modeling approaches. Through a scoping review, the paper shows how Fuzzy Cognitive Maps satisfy key criteria for suitability in integrated environmental assessment, such as model purpose, types of data available to build a model, requirements in terms of spatial and temporal scales, stakeholder involvement, and treatment of uncertainty. The paper consequently updates the guiding framework that can be used by modelers to select the most appropriate modeling approach by including Fuzzy Cognitive Map techniques. We argue that the method can be the starting point of any integrated environmental assessment research to map out perspectives and potential system sensitivities. They constitute a great addition to the integrated assessment modeling toolbox as they can complement quantitative model approaches that are invariably constructed around a single perspective that relates to available data.
A reconstruction of multipreference closure

The paper describes a preferential approach for dealing with exceptions in KLM preferential logics, based on the rational closure. It is well known that rational closure does not allow an independent handling of inheritance of different defeasible properties of concepts. In this work, we consider an alternative closure construction, called Multi Preference closure (MP-closure), which has been first considered for reasoning with exceptions in DLs. We reconstruct the notion of MP-closure in the propositional case and show that it is a natural (weaker) variant of Lehmann's lexicographic closure, which appears to be too bold in some cases. The MP-closure defines a preferential consequence relation that, although weaker than lexicographic closure, is stronger than Relevant Closure.
The impact of inclusive leadership behaviors on innovative workplace behavior with an emphasis on the mediating role of work engagement

Work creativity manifested in innovative workplace behavior (IWB) and employee work engagement is fundamental to maintaining firm’s sustainability and competitiveness. In this regard, this study investigates the supporting effect of innovative leadership on IWB and employee engagement by maximizing employee vigor, dedication, and absorption. The study data were collected using a questionnaire-based technique, and the questionnaires were administered to 150 respondents working in mobile phone companies in Southern and Central Iraq. The results were analyzed using the Statistical Package for the Social Sciences (SPSS) and SmartPLS software. The study results revealed that inclusive leadership and work engagement were significantly related to IWB, and work engagement played a mediating role between inclusive leadership and IWB. The findings further indicated that inclusive leadership behaviors such as openness, accessibility, and availability motivated the subordinates to be engaged in IWB. To promote IWB, company leaders need to effectively engage their followers by taking pride and satisfaction in employee output, which might help employee work engagement and IWB.
Social Botnet Community Detection: A Novel Approach based on Behavioral Similarity in Twitter Network using Deep Learning

Detecting social bots and identifying social botnet communities are extremely important in online social networks (OSNs). In this paper, we first construct a weighted signed Twitter network graph based on the behavioral similarity and trust values between the participants (i.e., OSN accounts) as weighted edges. The behavioral similarity is analyzed from the viewpoints of tweet-content similarity, shared URL similarity, interest similarity, and social interaction similarity for identifying similar types of behavior (malicious or not) among the participants in the Twitter network; whereas the participant's trust value is determined by a random walk model. Next, we design two algorithms - Social Botnet Community Detection (SBCD) and Deep Autoencoder based SBCD (called DA-SBCD) - where the former detects social botnet communities of social bots with malicious behavioral similarity, while the latter reconstructs and detects social botnet communities more accurately in presence of different types of malicious activities. Finally, we evaluate the performance of proposed algorithms with the help of two Twitter datasets. Experimental results demonstrate the efficacy of our algorithms with better performance than existing schemes in terms of normalized mutual information (NMI), precision, recall and F-measure. More precisely, the DA-SBCD algorithm achieves about 90% precision and exhibits up to 8% improvement on NMI.
Segmentation of coronary arteries images using global feature embedded network with active contour loss

Coronary heart disease (CHD) is a serious disease that endangers human health and life. In recent years, the morbidity and mortality of CHD are increasing significantly. Because of the particularity and complexity of medical image, it is challenging to segment coronary artery accurately and efficiently. This paper proposes a novel global feature embedded network for better coronary arteries segmentation in 3D coronary computed tomography angiography (CTA) data. The global feature combines multi-level layers from various stages of the network, which contains semantic information and detailed features, aiming to accurately segment target with precise boundary. In addition, we integrate a group of improved noisy activating functions with parameters into our network to eliminate the impact of noise in CTA data. And we improve the learning active contour model, which obtains a refined segmentation result with smooth boundary based on the high-quality score map produced by the networks. The experimental results show that the proposed framework achieved the state-of-the-art performance intuitively and quantitively.
Blockchain-enabled secure communication for drone delivery

COVID-19 made the world stop, with people trapped inside their homes and governments trying to restrict the public movement. However, to accomplish this, one big problem that emerged and outscored everything else was catering to the day to day necessary items of the people without human involvement. In this regard, we propose a blockchain-enabled secure communication framework for delivering the goods in COVID-19 like scenarios by leveraging the drones that are available with commercial retail providers. The blockchain scheme is used to create smart contracts to build the trust of buyers and sellers on the framework as the payments are made through the smart contract executions. The blockchain based order processing ensures the integrity and authenticity of the information. Moreover, a communication model is presented along with the order, delivery and payment phases. The results prove the effectiveness of the proposed scheme by evaluating it based on gas price, transaction time, and mining time.
Automatic paper writing based on a RNN and the TextRank algorithm

Academic research is crucial to the development of science and technology and is an important factor that affects national strength. When writing an academic research paper, a rhetorical structure is typically used to present the paper’s ideas, but this task is quite difficult for junior researchers. To solve this problem, some studies have adopted text mining to assist with the writing, but the existing methods still require human intervention to generate sentences. Recently, due to the increasing maturity of deep learning technology and the ability to address the problem of automatic text generation, progress has been made in this area. The highly complex deep learning operations can correctly generate sequences and find correlations between sequences. When a user provides a few keywords and key sentences, the proposed algorithm can generate an introduction section for the user. The results show that the generated introduction is more coherent, clearer, and more fluent than existing summarization methods. In addition, the method proposed in this study improves the accuracy compared with traditional text extraction methods. The manuscript produced by this study has been evaluated to show that the study can produce a comprehensive introduction compared with previous studies.
Towards Viewport-dependent 6DoF 360 Video Tiled Streaming for Virtual Reality Systems

Previous studies of 360-degree video streaming with regard to virtual reality allowed users to move their head freely, while their position is fixed according to the camera's location in virtual reality. One of the approaches to overcome the problem is transmitting multiview video to provide six degrees of freedom (6DoF). However, 6DoF streaming system implementation is challenging because multiple high-quality video streaming requires several decoders and a high bandwidth. Therefore, this paper proposes a viewport-dependent high-efficiency video coding (HEVC)-compliant tiled streaming system on test model for immersive video (TMIV), MPEG-Immersive multiview compression reference software. This paper proposes a 6DoF viewport tile selector (VTS) for multiple 360-degree video tiled streaming. Furthermore, this paper introduces a viewport-dependent multiple-tile extractor. The proposed system detects the user's head movement, selects the tile sets that correspond to the user's viewport, extracts tile bitstreams, and generates single bitstream. The extracted bitstream is transmitted and decoded to render the user's viewport The proposed viewport-dependent streaming method can reduce the decoding time as well as the bandwidth. Experimental results demonstrated 12.04% bjontegaard delta rate (BD-rate) saving for the luma peak signal-to-noise ratio (PSNR) compared to those obtained via the TMIV anchor without tiled encoding and a 55.51% decoding time saving compared to those obtained via the TMIV anchor with the existing tiled streaming method.
ALANET

Existing works address the problem of generating high frame-rate sharp videos by separately learning the frame deblurring and frame interpolation modules. Most of these approaches have a strong prior assumption that all the input frames are blurry whereas in a real-world setting, the quality of frames varies. Moreover, such approaches are trained to perform either of the two tasks - deblurring or interpolation - in isolation, while many practical situations call for both. Different from these works, we address a more realistic problem of high frame-rate sharp video synthesis with no prior assumption that input is always blurry. We introduce a novel architecture, Adaptive Latent Attention Network (ALANET), which synthesizes sharp high frame-rate videos with no prior knowledge of input frames being blurry or not, thereby performing the task of both deblurring and interpolation. We hypothesize that information from the latent representation of the consecutive frames can be utilized to generate optimized representations for both frame deblurring and frame interpolation. Specifically, we employ combination of self-attention and cross-attention module between consecutive frames in the latent space to generate optimized representation for each frame. The optimized representation learnt using these attention modules help the model to generate and interpolate sharp frames. Extensive experiments on standard datasets demonstrate that our method performs favorably against various state-of-the-art approaches, even though we tackle a much more difficult problem. The project page is available at https://agupt013.github.io/ALANET.html.
Textual Dependency Embedding for Person Search by Language

Person search by language aims to associate the pedestrian images with free-form natural language descriptions. Although great efforts have been made to align images with sentences, most researchers neglect the difficulty of long-distance dependency modeling in textual encoding, which is very important for solving this problem because the description sentences are always long and have complex structures for distinguishing different pedestrians. In this work, we focus on the long-distance dependencies in a sentence for better textual encoding, and accordingly propose the Textual Dependency Embedding (TDE) method. We first employ the sentence analysis tools to figure out the long-distance syntactic dependencies from a dependent to its governor in a sentence. Then we embed the dependent representations to their governor adaptively in our Governor-guided Dependent Attention Module (GDAM) to model these long-distance relations. After that, we further consider the dependency types, which also tell the importance of different dependents semantically, and embed them together with the dependents' features to clarify their inequivalent contributions to their governor. Extensive experiments and analysis on person search by language and image-text matching have validated the effectiveness of our method, and we have obtained the state-of-the-art performance on the CUHK-PEDES and Flickr30K datasets.