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Spiritual/Religious Perfectionism Scale throughout Iran: Psychometric Examination.
The online server for SemanticCS is freely available at http//qianglab.scst.suda.edu.cn/semanticCS/.Deficits in interpersonal communication along with difficulty in putting oneself into the shoes of others characterizes individuals with Autism Spectrum Disorder (ASD). Additionally, they exhibit atypical looking pattern causing them to miss aspects related to understanding other's preference for a context that is crucial for effective social communication. Prior research studies show the use of multiplayer platforms can improve interaction among these individuals. However, these multiplayer platforms do not demand players to understand each other's preference, important for effective social interaction. In this work, we have developed a multiplayer interaction platform using virtual reality augmented with eye-tracking technology. Thirty-six participants comprising of individuals with ASD (n = 18; GroupASD) and typically developing (TD) individuals (n = 18; GroupTD) interacted in pairs within each participant group using our platform. Results indicate that both GroupASD and GroupTD showed improvement in performance across the tasks with the GroupTD performing better than the GroupASD. Additionally, the eye-gaze data indicated an underlying relationship between one's looking pattern and task performance that was differentiated between the GroupASD and GroupTD. ML-SI3 order The current results indicate a potential of our multiplayer interaction platform to serve as a complementary tool in the hands of the interventionist promoting social reciprocity and interaction among individuals with ASD.Spatial presence encompasses the user's ability to experience a sense of "being there". While particular attention was given to assess spatial presence in real and virtual environments, few have been interested in measuring it in telepresence situations. To bridge this gap, the present work introduces a study that compares the execution of a task in three conditions a real physical environment, a remote environment via a telepresence system, and a virtual simulation of the real environment. Following a within-subject design, 27 participants performed a navigation task consisting in following a route while avoiding obstacles. Spatial presence and five related factors (affordance, enjoyment, attention allocation, reality, and cybersickness) were evaluated using a presence questionnaire. In addition, performance measures were gathered regarding environment recollection and task execution. The evaluation also included a behavioral metric measured by obstacle avoidance distance extracted from participants' traject physical existence of the space in which participants operate can influence their performance and behavior.Synthetic 3D object models have been proven crucial in object pose estimation, as they are utilized to generate a huge number of accurately annotated data. The object pose estimation problem is usually solved for images originating from the real data domain by employing synthetic images for training data enrichment, without fully exploiting the fact that synthetic and real images may have different data distributions. In this work, we argue that 3D object pose estimation problem is easier to solve for images originating from the synthetic domain, rather than the real data domain. To this end, we propose a 3D object pose estimation framework consisting of a two-step process, where a novel pose-oriented image-to-image translation step is first employed to translate noisy real images to clean synthetic ones and then, a 3D object pose estimation method is applied on the translated synthetic images to finally predict the 3D object poses. A novel pose-oriented objective function is employed for training the image-to-image translation network, which enforces that pose-related object image characteristics are preserved in the translated images. As a result, the pose estimation network does not require real data for training purposes. Experimental evaluation has shown that the proposed framework greatly improves the 3D object pose estimation performance, when compared to state-of-the-art methods.Despite the thrilling success achieved by existing binary descriptors, most of them are still in the mire of three limitations 1) vulnerable to the geometric transformations; 2) incapable of preserving the manifold structure when learning binary codes; 3) NO guarantee to find the true match if multiple candidates happen to have the same Hamming distance to a given query. All these together make the binary descriptor less effective, given large-scale visual recognition tasks. In this paper, we propose a novel learning-based feature descriptor, namely Unsupervised Deep Binary Descriptor (UDBD), which learns transformation invariant binary descriptors via projecting the original data and their transformed sets into a joint binary space. Moreover, we involve a ℓ2,1-norm loss term in the binary embedding process to gain simultaneously the robustness against data noises and less probability of mistakenly flipping bits of the binary descriptor, on top of it, a graph constraint is used to preserve the original manifold structure in the binary space. Furthermore, a weak bit mechanism is adopted to find the real match from candidates sharing the same minimum Hamming distance, thus enhancing matching performance. Extensive experimental results on public datasets show the superiority of UDBD in terms of matching and retrieval accuracy over state-of-the-arts.The field of computer vision has witnessed phenomenal progress in recent years partially due to the development of deep convolutional neural networks. However, deep learning models are notoriously sensitive to adversarial examples which are synthesized by adding quasi-perceptible noises on real images. Some existing defense methods require to re-train attacked target networks and augment the train set via known adversarial attacks, which is inefficient and might be unpromising with unknown attack types. To overcome the above issues, we propose a portable defense method, online alternate generator, which does not need to access or modify the parameters of the target networks. The proposed method works by online synthesizing another image from scratch for an input image, instead of removing or destroying adversarial noises. To avoid pretrained parameters exploited by attackers, we alternately update the generator and the synthesized image at the inference stage. Experimental results demonstrate that the proposed defensive scheme and method outperforms a series of state-of-the-art defending models against gray-box adversarial attacks.
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