Reviewerly/exHarmony huggingface dataset:
VIRTUAL BRAINSTORMING AND CREATIVITY: AN ANALYSIS OF MEASURES, AVATARS, ENVIRONMENTS, INTERFACES, AND APPLICATIONS

Abstract How to enhance creativity, especially by applying new technologies to creativity methods, is a question posed continuously by researchers. One reason for this is that creativity is an important part of people's daily lives and an essential component of society. Therefore, many methods to enhance creativity have been created, especially brainstorming, which is one of the most popular and effective tools that inspire individuals to generate ideas, hence enhancing creativity. Moreover, technologies, such as virtual reality (VR), provide an opportunity for individuals and groups to be creative. In response, recent studies have adopted VR in brainstorming to enhance creativity. However, there is a lack of systematic analysis of experimental approaches and creativity measures employed in this context. Addressing this question, this study categorized existing articles on the topic related to categories of avatars, environments, interfaces, or applications. The findings elaborate on trends, measures employed to evaluate creativity and idea generation, identified categories, and results of these studies.
Reasoning like Humans: On Dynamic Attention Prior in Image Captioning

Attention-based models have been widely used in image captioning. Nevertheless, most conventional deep attention models perform attention operations for each block/step independently, which neglects prior knowledge obtained by previous steps. In this paper, we propose a novel method — DYnamic Attention PRior (DY-APR), which combines both attention distribution prior and local linguistic context for caption generation. Like human beings, DY-APR can gradually shift its attention from a multitude of objects to the one of keen interest when coping with an image of a complex scene. DY-APR first captures rough information and then explicitly updates attention weights step by step. Besides, DY-APR fully leverages local linguistic context from the previous tokens, that is, capitalizes on local information when performing global attention — which we refer to as “local–global attention”. We show that the prior knowledge from previous steps provides meaningful semantic information, serving as guidance to build more accurate attention for the latter layers. Experiments on the MS-COCO dataset demonstrate the effectiveness of DY-APR, leading to CIDEr-D improvement by 2.32% with less than 0.2% additional FLOPs and parameters.
Cloud-based privacy- and integrity-protecting density peaks clustering

Artificial intelligence (AI) has been extensively used in today's society. The full utilization of AI requires massive amounts of data and strong computing power. Cloud provides such a platform for AI with large storage and powerful computation capabilities. However, the cloud also poses significant security issues since it is not fully trustworthy. Especially, third-party clouds are generally profit-driven, and thus they have strong financial incentives to execute less computation to save costs, thereby not ensuring the integrity of their returned results. In this work, we design a privacy- and integrity-protecting density peaks clustering algorithm in the ciphertext domain (PIDPC). Particularly, we develop efficient strategies guaranteeing the integrity of outsourced DPC results by leveraging game theory. It uses economic approaches to create incentives for the single cloud to compute honestly. Also, we design three optimization techniques to improve the efficiency of PIDPC. Besides, extensive experiments demonstrate that PIDPC guarantees clustering accuracy while ensuring the privacy of training data and the integrity of returned results.
Scalable Privacy-preserving Geo-distance Evaluation for Precision Agriculture IoT Systems

Precision agriculture has become a promising paradigm to transform modern agriculture. The recent revolution in big data and Internet-of-Things (IoT) provides unprecedented benefits including optimizing yield, minimizing environmental impact, and reducing cost. However, the mass collection of farm data in IoT applications raises serious concerns about potential privacy leakage that may harm the farmers’ welfare. In this work, we propose a novel scalable and private geo-distance evaluation system, called SPRIDE, to allow application servers to provide geographic-based services by computing the distances among sensors and farms privately. The servers determine the distances without learning any additional information about their locations. The key idea of SPRIDE is to perform efficient distance measurement and distance comparison on encrypted locations over a sphere by leveraging a homomorphic cryptosystem. To serve a large user base, we further propose SPRIDE+ with novel and practical performance enhancements based on pre-computation of cryptographic elements. Through extensive experiments using real-world datasets, we show SPRIDE+ achieves private distance evaluation on a large network of farms, attaining 3+ times runtime performance improvement over existing techniques. We further show SPRIDE+ can run on resource-constrained mobile devices, which offers a practical solution for privacy-preserving precision agriculture IoT applications.
Convex Q-Learning

It is well known that the extension of Watkins' algorithm to general function approximation settings is challenging: does the “projected Bellman equation” have a solution? If so, is the solution useful in the sense of generating a good policy? And, if the preceding questions are answered in the affirmative, is the algorithm consistent? These questions are unanswered even in the special case of Q-function approximations that are linear in the parameter. The challenge seems paradoxical, given the long history of convex analytic approaches to dynamic programming. Our main contributions are summarized as follows: (i)A new class of convex Q-learning algorithms is introduced based on a convex relaxation of the Bellman equation. Convergence is established under general conditions for linear function approximation. (ii)A batch implementation appears similar to LSPI and DQN algorithms, but the difference is substantial: while convex Q-learning solves a convex program that approximates the Bellman equation, theory for DQN is no stronger than for Watkins algorithm with function approximation. These results are obtained for deterministic nonlinear systems with total cost criterion. Extensions are proposed.
Investigating Effects of Interactive Virtual Reality Games and Gender on Immersion, Empathy and Behavior Into Environmental Education

Environmental concerns are obstacles that all humans should confront. Accordingly, Taiwan has incorporated environmental education into its curriculum guidelines; however, difficulties have been encountered in its implementation because Taiwanese society clings to the concept of credentialism, students cannot focus in class, and course content is too abstract. Virtual reality (VR) techniques have been incorporated into the field of education for years; because they can increase the interestingness of learning and also concretely present matters through gamification, the present study applied VR techniques to resolve the aforementioned difficulties. VR can build environments and situations that would be impossible to visit in the real world, e.g., travel inside a human body, or the physically impossible—the surface of Mars. The aim was to increase students’ immersion in class would generate empathy toward the natural environment and perform actual behaviors to protect it. In terms of the experimental design, gender and teaching methods were defined as independent variables, and observation and questionnaires were conducted and applied to examine students’ immersion, empathy, and behaviors after learning. Participants in the experimental group watched a 3D video through a semi-immersive VR device, whereas those in the control group watched an ordinary video accompanied by an explanation from the teacher. The numbers of male and female students were equally allocated, and the learning outcomes of the two groups were further examined. A two-way multivariate analysis of variance test was conducted to examine the influences of 2(genders) × 2(teaching methods) on students’ immersion, empathy, and actual behaviors and also mainly to inspect whether the differences between genders and teaching methods resulted in a direct influence or interaction. The experimental results revealed that the effectiveness of the application of VR techniques is affected by gender; female students presented more favorable performance in both empathy and actual behaviors. In brief, VR techniques can generally enhance students’ learning outcomes; however, limitations such as the cost of VR devices and material designs should also be considered.
Developing a smart multifunctional outdoor jacket with wearable sensing technology for user health and safety

Abstract Over the decades, there has been a sustained effort to use fashion as a medium for delivering digital functionality. The goal is to integrate information technology (IT) into clothing to provide users with functions to assist them in their tasks. Regarding the direction of previous efforts, this study developed a multifunctional smart outdoor jacket prototype that senses, recognizes, responds, and manages various safety risks and potentially hazardous situations and identifies environmental factors that are difficult to predict. The prototype’s research and development (R &amp; D) was carried out through the following steps. First, to determine functions that can practically assist users in outdoor environments and help ensure their health and safety, a user requirement survey subject to expert evaluation was conducted. Six functions were selected: (1) Bluetooth hands-free calling and audio streaming, (2) heart rate monitoring for self-health care, (3) emergency calls to request assistance, (4) temperature-reactive heating to retain body heat for survival, (5) fall detection and automatic emergency calls, and (6) ultraviolet monitoring for self-health care. Next, a wearable system and its garment platform were developed, containing detachable device modules for washability and ease of maintenance. Lastly, a dedicated smartphone application was developed for extended functionality. By exploring the use of clothing in diversifying wearable health care and HAR systems, the study could be used to diversify wearable healthcare and safety platforms.
Monocular 3-D Vehicle Detection Using a Cascade Network for Autonomous Driving

Three-dimensional object detection plays an important role in autonomous driving because it provides the 3-D locations of objects for subsequent use in decision-making modules. A novel method is proposed using a monocular image and cascade geometric constraints to achieve robust 3-D vehicle detection. The framework is divided into two stages. In the first stage, the monocular image input is processed using a heatmap-based detection network with five branches to regress the orientation, dimension, center projection of the bottom face, viewpoint classification, and 2-D bounding box. In the second stage, the intersection-over-union threshold is increased to filter out imprecise 2-D bounding boxes. Thereafter, cascade geometric constraints are used to obtain the final 3-D box output, which improves the detection performance under truncation and occlusion conditions. The proposed method is tested on the KITTI-3-D benchmark and is shown to be effective and efficient. The proposed framework does not depend on external sources or subnetworks and can be trained in an end-to-end manner.
Introduction to the special issue on time and HCI

This special issue focuses on new perspectives of time within HCI research. We begin by explicating how HCI’s views of time have broadened over the years, going beyond the traditional subjective-objective dichotomy. We describe how the HCI community was originally anchored to clock time, but more recently the field has begun to incorporate the existential, social, and cultural dimensions of time. We introduce the papers in this special issue by contextualizing them according to different research areas. We conclude by identifying open issues and opportunities that should be explored in future HCI research and practice.
Basketball shooting angle calculation and analysis by deeply-learned vision model

To solve the problem that standard image decomposition cannot conduct image decomposition of basketball shooting angle according to the change of basketball projection Angle, an intelligent visual image decomposition method of basketball projection angle using odd-difference value decomposition was proposed. By analyzing the change of basketball shooting angle under different shooting conditions, the basketball shooting angle is determined. Based on this, singular value decomposition is introduced to judge the image decomposition of intelligent vision. Observations have shown that the intelligent vision image decomposition judgment method can improve the image contrast of shooting motion decomposition. In the shortest time, according to the change of basketball projection angle, the shooting motion image decomposition is fulfilled. In order to improve the accuracy of human motion recognition in videos and computation of large-scale data sets, a motion recognition technique by leveraging double-core extreme learning machine and deep learning technology are proposed. In the first layer of the double-core extreme learning component, the linear kernel extreme learning machine is used to predict the dense trajectory characteristics and deep learning characteristics. In the second layer, dense trajectory features are combined with deep learning features. In deep features, deep video features are fused with video RGB trichromatic features as deep features. Simulation experiments are conducted on a massive-scale and real-world data sets and a small basketball gesture data set. The experimental results have shown that our method exhibits a high recognition accuracy for both data sets. The analysis results have shown that the improved model has higher recognition rate and better action recognition effect than its competitors.
Distributed Interoperable Records: The Key to Better Supply Chain Management

The global supply chain is a network of interconnected processes that create, use, and exchange records, but which were not designed to interact with one another. As such, the key to unlocking the full potential of supply chain management (SCM) technologies is achieving interoperability across participating records systems and networks. We review existing research and solutions using distributed ledger technology (DLT) and provide a survey of its current state of practice. We additionally propose a holistic solution: a DLT-based interoperable future state that could enable the interoperable, efficient, reliable, and secure exchange of records with integrity. Finally, we provide a gap analysis between our proposed future state and the current state, which also serves as a gap analysis for many fractional DLT-based SCM solutions and research.
Anomalous Human Action Detection Using a Cascade of Deep Learning Models

Human actions that do not conform to usual behavior are considered as anomalous and such actors are called anomalous entities. Detection of anomalous actions using visual data is a challenging problem in computer vision. This paper presents a new approach to detect anomalous actions in complex situations of examination halls. The proposed method uses a cascade of deep convolutional neural network models. In the first stage, we apply a pretrained model of human pose estimation on frames of videos to extract key feature points of body. Patches extracted from each key point are utilized in the second stage to build a densely connected deep convolutional neural network model for detecting anomalous actions. For experiments we collect a video database of students undertaking examination in a hall. Our results show that the proposed method can detect anomalous actions and warrant unusual behavior with high accuracy.
Medical image editing in the latent space of Generative Adversarial Networks

We consider a set of arithmetic operations in the latent space of Generative Adversarial Networks (GANs) to edit histopathological images. We analyze thousands of image patches from whole-slide images of breast cancer metastases in histological lymph node sections. Image files were downloaded from the pathology contests CAMELYON 16 and 17. We show that widely known architectures, such as: Deep Convolutional Generative Adversarial Networks (DCGAN) and Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN), allow image editing using semantic concepts that represent underlying visual patterns in histopathological images, expanding GAN's well-known capabilities in medical image editing. We computed the Grad-cam heatmap of real positive images and of generated positive images, validating that the highlighted features both in the real and synthetic images match. We also show that GANs can be used to generate quality images, making GANs a valuable resource for augmenting small medical imaging datasets.
NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration

With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed. Prior methods towards this goal, including model compression and network architecture search (NAS), are largely performed independently, and do not fully consider compiler-level optimizations which is a must-do for mobile acceleration. In this work, we first propose (i) a general category of fine-grained structured pruning applicable to various DNN layers, and (ii) a comprehensive, compiler automatic code generation framework supporting different DNNs and different pruning schemes, which bridge the gap of model compression and NAS. We further propose NPAS, a compiler-aware unified network pruning and architecture search. To deal with large search space, we propose a meta-modeling procedure based on reinforcement learning with fast evaluation and Bayesian optimization, ensuring the total number of training epochs comparable with representative NAS frameworks. Our framework achieves 6.7ms, 5.9ms, and 3.9ms ImageNet inference times with 78.2%, 75% (MobileNet-V3 level), and 71% (MobileNet-V2 level) Top-1 accuracy respectively on an off-the-shelf mobile phone, consistently outperforming prior work.
Image Segmentation Approach Based on Hybridization Between K-Means and Mask R-CNN

In this article, we will introduce a hybrid method based on the combination of two image segmentation techniques. The first method adopted is the k-means algorithm which is an unsupervised machine learning technique used to group data points, the second is the Mask R-CNN which is a neural network architecture which combines two sub-problems: object detection and semantic segmentation. The main objective of this study is to approve the segmentation of the image using k-means. The first step is to apply Mask R-CNN on our original image to detect the objects that are present in the image, then, we will apply k-means clustering to have better segmentation. For our approach we used a set of metrics to evaluate our proposed approach such as the mean square error (MSE); the peak signal to noise ratio (PSNR) and many other measures such as the difference between two images. These measurements have shown satisfactory results and a performance of the proposed method.
Spatial-Frequency dual-branch attention model for determining KRAS mutation status in colorectal cancer with T2-weighted MRI

Background and Objective: Identifying the KRAS mutation status accurately in medical images is very important for the diagnosis and treatment of colorectal cancer. Despite the substantial progress achieved by existing methods, it remains challenging due to limited annotated dataset, large intra-class variances, and a high degree of inter-class similarities. Methods: To tackle these challenges, we propose a spatial-frequency dual-branch attention model (SF-DBAM) to determine the KRAS mutation status of colorectal cancer patients using a limited T2-weighted MRI dataset. The dataset contains 169 wild-type patients (2151 images) and 137 mutation-type patients (1666 images). The first branch utilizes part of the pre-trained Xception model to capture spatial-domain information and alleviate the small-scale dataset problem. The second branch builds frequency-domain information into cube columns using block-based discrete cosine transform and channel rearrangement. Then the cube columns are fed into convolutional long short-term memory (convLSTM) to explore the effective information between the reconstructed frequency-domain channels. Also, we design a channel enhanced attention module (CEAM) at the end of each branch to make them focus on the lesion areas. Finally, we concatenate the two branches and output the classified results through fully connected layers. Results: The proposed method achieves 88.03% overall accuracy with AUC of 94.27% and specificity of 90.75% in 10-fold cross-validation, which is better than the current non-invasive methods for determining KRAS mutation status. Conclusions: We believe that the proposed method can assist physicians to diagnose the KRAS mutation status in patients with colorectal cancer, and other medical problems can benefit from the spatial and frequency domains information.
Persian Language Model based on BiLSTM Model on COVID-19 Corpus

Coronavirus disease 2019 (COVID-2019) appeared in China, in 2019. COVID-2019 expanded all over the world quickly and caused many deaths. COVID-19 has become one of the hottest research areas recently. In this paper, we create a language model (LM) to determine the probability of a given sequence of words occurring in a sentence. Some of the LM applications include machine translation, question answering, and spell checking. In this study, long short-term memory is applied to language modeling of Persian. To do this, we use unidirectional and bidirectional Long Short-Term Memory (LSTM) Models to give contextual informaton. We compared their results together. Our experiments demonstrate how different LSTM language models operate. BiLSTM with two layers is the best language model for Persian COIVD-2019 news. The corpus contains 10,000 pieces of news about COVID-2019 and more than 2,100,000 words, which were provided by the Lobkalam system.
6D-Chaotic System and 2D Fractional Discrete Cosine Transform Based Encryption of Biometric Templates

A new algorithm for biometric templates using a 6D-chaotic system, and 2D fractional discrete cosine transform (FrDCT) is proposed in this paper. In this technique, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> biometric templates are represented into three groups. After representation, these three groups are converted into row vectors and scrambled by using keys generated by the 6D-chaotic system and after that, these row vectors are combined into three matrices. The three matrices are then mixed horizontally and divided into two halves, with the left half serving as the real part and the right half serving as the imaginary part of a complex-valued matrix (CVM). This CVM is further subjected to 2D FrDCT. The output of 2D FrDCT is separated into three parts. The robustness of the technique is further enhanced by substitution operation using keys generated by the 6D-chaotic system. Thus, the final encrypted template is obtained. The analysis like security, statistical, and attacks are given to authenticate the reliability of the proposed technique. The experimental values also show that the proposed technique is resistant to brute force attacks.