Reviewerly/exHarmony huggingface dataset:
Ciphertext-Policy Attribute-Based Encryption for Cloud Storage: Toward Data Privacy and Authentication in AI-Enabled IoT System

People can store their data on servers in cloud computing and allow public users to access data via data centers. One of the most difficult tasks is to provide security for the access policy of data, which is also needed to be stored at cloud servers. The access structure (policy) itself may reveal partial information about what the ciphertext contains. To provide security for the access policy of data, a number of encryption schemes are available. Among these, CP-ABE (Ciphertext-Policy Attribute-Based Encryption) scheme is very significant because it helps to protect, broadcast, and control the access of information. The access policy that is sent as plaintext in the existing CP-ABE scheme along with a ciphertext may leak user privacy and data privacy. To resolve this problem, we hereby introduce a new technique, which hides the access policy using a hashing algorithm and provides security against insider attack using a signature verification scheme. The proposed system is compared with existing CP-ABE schemes in terms of computation and expressive policies. In addition, we can test the functioning of any access control that could be implemented in the Internet of Things (IoT). Additionally, security against indistinguishable adaptive chosen ciphertext attacks is also analyzed for the proposed work.
Evaluation of Virtual Reality Technology and Online Teaching System for Medical Students in Ukraine During COVID-19 Pandemic

A study has been conducted to evaluate the virtual reality technology and online teaching system among medical students of Bogomolets National Medical University, Ukraine during COVID-19 pandemic. The final questionnaire contained the 15 questions with 5 options to comprehensively evaluate the virtual reality technology and online teaching system. The feedback of the survey was analyzed to find effectiveness of virtual reality technology and online teaching in medical education in Ukraine. Data of survey reflected that mostly students adopted and agreed on virtual reality technology and online teaching and admitted that these technologies are best alternatives to physical learning with the 65.79% an agreement with the user-friendly interface for virtual reality and online teaching system, while, 64.03% showed an agreement that virtual reality and online teaching system compensated the suspension of face-to-face medical education during the COVID-19. During online learning, there was ample time for educational activities as depicted from their response of 36.84% as strongly agree, 35.96% as agree, 6.14% as disagree, 4.39% as strongly disagree and 16.67% as neither agree nor disagree. Tutors/teachers also enjoyed virtual reality and online learning through their experience and flexibility in time management. Likewise, the results of all questions showed positive reviews and encouraged virtual reality and online teaching in academic continuity and stability in medical education in Ukraine. It can be concluded that digital learning environments are extremely effective in terms of medical students and staff satisfaction, accomplishment, and technical learning skill growth.
Video as Conditional Graph Hierarchy for Multi-Granular Question Answering

Video question answering requires the models to understand and reason about both the complex video and language data to correctly derive the answers. Existing efforts have been focused on designing sophisticated cross-modal interactions to fuse the information from two modalities, while encoding the video and question holistically as frame and word sequences. Despite their success, these methods are essentially revolving around the sequential nature of video- and question-contents, providing little insight to the problem of question-answering and lacking interpretability as well. In this work, we argue that while video is presented in frame sequence, the visual elements (e.g., objects, actions, activities and events) are not sequential but rather hierarchical in semantic space. To align with the multi-granular essence of linguistic concepts in language queries, we propose to model video as a conditional graph hierarchy which weaves together visual facts of different granularity in a level-wise manner, with the guidance of corresponding textual cues. Despite the simplicity, our extensive experiments demonstrate the superiority of such conditional hierarchical graph architecture, with clear performance improvements over prior methods and also better generalization across different type of questions. Further analyses also demonstrate the model's reliability as it shows meaningful visual-textual evidences for the predicted answers.
Coverage Path Planning Methods Focusing on Energy Efficient and Cooperative Strategies for Unmanned Aerial Vehicles

The coverage path planning (CPP) algorithms aim to cover the total area of interest with minimum overlapping. The goal of the CPP algorithms is to minimize the total covering path and execution time. Significant research has been done in robotics, particularly for multi-unmanned unmanned aerial vehicles (UAVs) cooperation and energy efficiency in CPP problems. This paper presents a review of the early-stage CPP methods in the robotics field. Furthermore, we discuss multi-UAV CPP strategies and focus on energy-saving CPP algorithms. Likewise, we aim to present a comparison between energy efficient CPP algorithms and directions for future research.
Deep learning based face beauty prediction via dynamic robust losses and ensemble regression

In the last decade, several studies have shown that facial attractiveness can be learned by machines. In this paper, we address Facial Beauty Prediction from static images. The paper contains three main contributions. First, we propose a two-branch architecture (REX-INCEP) based on merging the architecture of two already trained networks to deal with the complicated high-level features associated with the FBP problem. Second, we introduce the use of a dynamic law to control the behaviour of the following robust loss functions during training: ParamSmoothL1, Huber and Tukey. Third, we propose an ensemble regression based on Convolutional Neural Networks (CNNs). In this ensemble, we use both the basic networks and our proposed network (REX-INCEP). The proposed individual CNN regressors are trained with different loss functions, namely MSE, dynamic ParamSmoothL1, dynamic Huber and dynamic Tukey. Our approach is evaluated on the SCUT-FBP5500 database using the two evaluation scenarios provided by the database creators: 60%–40% split and five-fold cross-validation. In both evaluation scenarios, our approach outperforms the state of the art on several metrics. These comparisons highlight the effectiveness of the proposed solutions for FBP. They also show that the proposed dynamic robust losses lead to more flexible and accurate estimators.
Efficient Non-local Contrastive Attention for Image Super-resolution

Non-Local Attention (NLA) brings significant improvement for Single Image Super-Resolution (SISR) by leveraging intrinsic feature correlation in natural images. However, NLA gives noisy information large weights and consumes quadratic computation resources with respect to the input size, limiting its performance and application. In this paper, we propose a novel Efficient Non-Local Contrastive Attention (ENLCA) to perform long-range visual modeling and leverage more relevant non-local features. Specifically, ENLCA consists of two parts, Efficient Non-Local Attention (ENLA) and Sparse Aggregation. ENLA adopts the kernel method to approximate exponential function and obtains linear computation complexity. For Sparse Aggregation, we multiply inputs by an amplification factor to focus on informative features, yet the variance of approximation increases exponentially. Therefore, contrastive learning is applied to further separate relevant and irrelevant features. To demonstrate the effectiveness of ENLCA, we build an architecture called Efficient Non-Local Contrastive Network (ENLCN) by adding a few of our modules in a simple backbone. Extensive experimental results show that ENLCN reaches superior performance over state-of-the-art approaches on both quantitative and qualitative evaluations.
Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints

Solar power has rapidly become an increasingly important energy source in many countries over recent years; however, the intermittent nature of photovoltaic (PV) power generation has a significant impact on existing power systems. To reduce this uncertainty and maintain system security, precise solar power forecasting methods are required. This study summarizes and compares various PV power forecasting approaches, including time-series statistical methods, physical methods, ensemble methods, and machine and deep learning methods, the last of which there is a particular focus. In addition, various optimization algorithms for model parameters are summarized, the crucial factors that influence PV power forecasts are investigated, and input selection for PV power generation forecasting models are discussed. Probabilistic forecasting is expected to play a key role in the PV power forecasting required to meet the challenges faced by modern grid systems, and so this study provides a comparative analysis of existing deterministic and probabilistic forecasting models. Additionally, the importance of data processing techniques that enhance forecasting performance are highlighted. In comparison with the extant literature, this paper addresses more of the issues concerning the application of deep and machine learning to PV power forecasting. Based on the survey results, a complete and comprehensive solar power forecasting process must include data processing and feature extraction capabilities, a powerful deep learning structure for training, and a method to evaluate the uncertainty in its predictions.
Frequency of De Quervain’s tenosynovitis and its association with SMS texting

Objective: to assess the frequency of De Quervain's tenosynovitis and its association with SMS texting.Method: a cross-sectional survey was conducted among undergraduate students studying in different physical therapy schools of Karachi belonging to both public and private sectors.Sample size was 300 students which were selected through convenience sampling.Data was collected through self-administered questionnaire and severity of the pain was assessed through Universal Pain Assessment Tool and De Quervain's tenosynovitis was diagnosed through Finkelstein test.Data entry and analysis were done using computer software SPSS version 20.Frequency and percentages were taken for categorical variable.Chi-square was applied to determine association between different variables and Finkelstein test.P value < 0.05 was considered significant.Results: male/female ratio was 1:4.Regarding the use of cell phones, majority 165 (55%) were using regular cell phones, another 89 (30%) were using touch screen and 38 (13%) were using QWERTY key pads.Almost half of the students were fre-quently using cell phones for texting, out of them 132 (44%) texted less than 50 messages per day.Another 96 (32%) did texting between 50-100 texts.Out of 300 students who filled the questionnaire 125 (42%) students were experiencing pain in the thumb/wrist.Finkelstein test when done on students almost half (n=149) showed positive results.It was noted that as frequency of mobile phone usage increased progressively more and more people showed positive Finkelstein Test (p value 0.000).Conclusion: the result of the study concluded that almost half of the students use their mobile phones for texting more than 50 S.M.S per day and because of their mobile key pads and high speed of texting they experienced pain and weakness over the base of the thumb/wrist which shows the De Quervain's positive in that students and there is a positive association between the thumb pain and frequent text messaging.
Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation

Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recommendations. Previous works have made many efforts to model item-item transitions over user interaction sequences, based on various architectures, e.g., recurrent neural networks and self-attention mechanism. Recently emerged graph neural networks also serve as useful backbone models to capture item dependencies in sequential recommendation scenarios. Despite their effectiveness, existing methods have far focused on item sequence representation with singular type of interactions, and thus are limited to capture dynamic heterogeneous relational structures between users and items (e.g., page view, add-to-favorite, purchase). To tackle this challenge, we design a Multi-Behavior Hypergraph-enhanced T ransformer framework (MBHT) to capture both short-term and long-term cross-type behavior dependencies. Specifically, a multi-scale Transformer is equipped with low-rank self-attention to jointly encode behavior-aware sequential patterns from fine-grained and coarse-grained levels. Additionally,we incorporate the global multi-behavior dependency into the hypergraph neural architecture to capture the hierarchical long-range item correlations in a customized manner. Experimental results demonstrate the superiority of our MBHT over various state-of- the-art recommendation solutions across different settings. Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework. Our implementation code is released at: https://github.com/yuh-yang/MBHT-KDD22.
Searching Twitter: Separating the Tweet from the Chaff

Within the millions of digital communications posted in online social networks, there is undoubtedly some valuable and useful information. Although a large portion of social media content is considered to be babble, research shows that people share useful links, provide recommendations to friends, answer questions, and solve problems. In this paper, we report on a qualitative investigation into the different factors that make tweets ‘useful’ and ‘not useful’ for a set of common search tasks. The investigation found 16 features that help make a tweet useful, noting that useful tweets often showed 2 or 3 of these features. ‘Not useful’ tweets, however, typically had only one of 17 clear and striking features. Further, we saw that these features can be weighted as according to different types of search tasks. Our results contribute a novel framework for extracting useful information from real-time streams of social-media content that will be used in the design of a future retrieval system.
Why We Filter Our Photos and How It Impacts Engagement

A variety of simple graphical filters are available to camera phone users to enhance their photos on the fly; these filters often stylize, saturate or age a photo. In this paper, we present a combination of large-scale data analysis and small scale in-depth interviews to understand filter-work. We look at producers’ practices of photo filtering and gain insights in the roles filters play in engaging photo consumers’ by driving their social interactions. We first interviewed 15 Flickr mobile app users (photo producers) to understand their use and perception of filters. Next, we analyzed how filters affect a photo’s engagement (consumers’ perspective) using a corpus of 7.6 million Flickr photos. We find two groups of serious and casual photographers among filter users. The serious see filters as correction tools and prefer milder effects. Casual photographers, by contrast, use filters to significantly transform their photos with bolder effects. We also find that filtered photos are 21% more likely to be viewed and 45% more likely to be commented on by consumers of photographs. Specifically, filters that increase warmth, exposure and contrast boost engagement the most. Towards the ongoing research in social engagement and photo-work, these findings suggest several practical implications such as designing filters for both serious and casual photographers or designing methods to prioritize and rank content in order to maximize engagement.
Large-Scale Cross-Game Player Behavior Analysis on Steam

Behavioral game analytics has predominantly been confined to work on single games, which means that the cross-game applicability of current knowledge remains largely unknown. Here four experiments are presented focusing on the relationship between game ownership, time invested in playing games, and the players themselves, across more than 3000 games distributed by the Steam platform and over 6 million players, covering a total playtime of over 5 billion hours. Experiments are targeted at uncovering high-level patterns in the behavior of players focusing on playtime, using frequent itemset mining on game ownership, cluster analysis to develop playtime-dependent player profiles, correlation between user game rankings and, review scores, playtime and game ownership, as well as cluster analysis on Steam games. Within the context of playtime, the analyses presented provide unique insights into the behavior of game players as they occur across games, for example in how players distribute their time across games.
Total roto-translational variation

We consider curvature depending variational models for image regularization, such as Euler’s elastica. These models are known to provide strong priors for the continuity of edges and hence have important applications in shape- and image processing. We consider a lifted convex representation of these models in the roto-translation space: in this space, curvature depending variational energies are represented by means of a convex functional defined on divergence free vector fields. The line energies are then easily extended to any scalar function. It yields a natural generalization of the total variation to curvature-dependent energies. As our main result, we show that the proposed convex representation is tight for characteristic functions of smooth shapes. We also discuss cases where this representation fails. For numerical solution, we propose a staggered grid discretization based on an averaged Raviart–Thomas finite elements approximation. This discretization is consistent, up to minor details, with the underlying continuous model. The resulting non-smooth convex optimization problem is solved using a first-order primal-dual algorithm. We illustrate the results of our numerical algorithm on various problems from shape- and image processing.
A polynomial Time Subsumption Algorithm for Nominal Safe<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si4.gif" overflow="scroll"><mml:mrow><mml:mi mathvariant="bold-script">E</mml:mi><mml:mi mathvariant="bold-script">L</mml:mi><mml:msub><mml:mi mathvariant="bold-script">O</mml:mi><mml:mi>⊥</mml:mi></mml:msub></mml:mrow></mml:math>under Rational Closure

Description Logics (DLs) under Rational Closure (RC) is a well-known framework for non-monotonic reasoning in DLs. In this paper, we address the concept subsumption decision problem under RC for nominal safe ELO⊥, a notable and practically important DL representative of the OWL 2 profile OWL 2 EL. Our contribution here is to define a polynomial time subsumption procedure for nominal safe ELO⊥ under RC that relies entirely on a series of classical, monotonic EL⊥ subsumption tests. Therefore, any existing classical monotonic EL⊥ reasoner can be used as a black box to implement our method. We then also adapt the method to one of the known extensions of RC for DLs, namely Defeasible Inheritance-based DLs without losing the computational tractability.
Scalable and robust sparse subspace clustering using randomized clustering and multilayer graphs

Sparse subspace clustering (SSC) is a state-of-the-art method for partitioning data points into the union of subspaces. However, it is not practical for large datasets as it requires solving a LASSO problem for each data point, where the number of variables in each LASSO problem is the number of data points. To improve the scalability of SSC, we propose to select a few sets of anchor points using a randomized hierarchical clustering method, and, for each set of anchor points, solve the LASSO problems for each data point allowing only anchor points to have a non-zero weight. This generates a multilayer graph where each layer corresponds to a set of anchor points. Using the Grassmann manifold of orthogonal matrices, the shared connectivity among the layers is summarized within a single subspace. Finally, we use k-means clustering within that subspace to cluster the data points, as done by SSC. We show on both synthetic and real-world datasets that the proposed method not only allows SSC to scale to large-scale datasets, but that it is also much more robust as it performs significantly better on noisy data and on data with close susbspaces and outliers, while it is not prone to oversegmentation.
Deep Learning Based Part-of-Speech Tagging for Malayalam Twitter Data (Special Issue: Deep Learning Techniques for Natural Language Processing)

Abstract The paper addresses the problem of part-of-speech (POS) tagging for Malayalam tweets. The conversational style of posts/tweets/text in social media data poses a challenge in using general POS tagset for tagging the text. For the current work, a tagset was designed that contains 17 coarse tags and 9915 tweets were tagged manually for experiment and evaluation. The tagged data were evaluated using sequential deep learning methods like recurrent neural network (RNN), gated recurrent units (GRU), long short-term memory (LSTM), and bidirectional LSTM (BLSTM). The training of the model was performed on the tagged tweets, at word level and character level. The experiments were evaluated using measures like precision, recall, f1-measure, and accuracy. During the experiment, it was found that the GRU-based deep learning sequential model at word level gave the highest f1-measure of 0.9254; at character-level, the BLSTM-based deep learning sequential model gave the highest f1-measure of 0.8739. To choose the suitable number of hidden states, we varied it as 4, 16, 32, and 64, and performed training for each. It was observed that the increase in hidden states improved the tagger model. This is an initial work to perform Malayalam Twitter data POS tagging using deep learning sequential models.
Semi-supervised domain adaptation via Fredholm integral based kernel methods

Along with the emergence of domain adaptation in semi-supervised setting, dealing with the noisy and complex data in classifier adaptation underscores its growing importance. We believe a large amount of unlabeled data in target domain, which are always only used in distribution alignment, are more of a great source of information for this challenge. In this paper, we propose a novel Transfer Fredholm Multiple Kernel Learning (TFMKL) framework to suppress the noise for complex data distributions. Firstly, with exploring unlabeled target data, TFMKL learns a cross-domain predictive model by developing a Fredholm integral based kernel prediction framework which is proven to be effective in noise suppression. Secondly, TFMKL explicitly extends the applied range of unlabeled target samples into adaptive classifier building and distribution alignment. Thirdly, multiple kernels are explored to induce an optimal learning space. Correspondingly, TFMKL is distinguished with allowing for noise resiliency, facilitating knowledge transfer and analyzing complex data characteristics at the same time. Furthermore, an effective optimization procedure is presented based on the reduced gradient, guaranteeing rapid convergence. We emphasize the adaptability of TFMKL to different domain adaptation tasks due to its extension to different predictive models. In particular, two models based on square loss and hinge loss respectively are proposed within the TFMKL framework. Comprehensive empirical studies on benchmark data sets verify the effectiveness and the noise resiliency of our proposed methods.