Reviewerly/exHarmony huggingface dataset:
Teachers' intention to adopt virtual reality technology in management education

The present study explores the role of teachers' personal dispositional factors in determining their willingness to adopt virtual reality (VR) applications in teaching. Premised in the Indian context, the study focuses on the perceptions of B-school teachers regarding the adoption of VR technology in the 'management' discipline of higher education. The proposed model was empirically tested using cross sectional design. The primary data was gathered through a survey of 508 teachers at 50 B-schools in the NCR of Delhi, India. Exploratory factor analysis and multiple regression analysis were used to statistically analyse the data. The findings of the study suggest that the technology-related personal characteristics of teachers such as, computer expertise, computer self-efficacy, and personal innovativeness; personality traits such as, extraversion, conscientiousness and openness to experience; and demographic characteristic such as age, significantly predict the intention of teachers regarding adoption of VR technology in management education.
Fast Image Encryption Algorithm for Logistics-Sine-Cosine Mapping

Because images are vulnerable to external attacks in the process of network transmission and traditional image encryption algorithms have limitations such as long encryption time, insufficient entropy or poor diffusion of cipher image information when encrypting color images, a fast image encryption algorithm based on logistics-sine-cosine mapping is proposed. The algorithm first generates five sets of encrypted sequences from the logistics-sine-cosine mapping, then uses the order of the encryption sequence to scramble the image pixels and designs a new pixel diffusion network to further improve the key sensitivity and plain-image sensitivity of the encryption algorithm. Finally, in a series of security analysis experiments, the experimental image Lena was tested 100 times, and the average encryption time was 0.479 s. The average value of the information entropy, pixel change rate and uniform average change intensity of the cipher image reached 7.9994, 99.62% and 33.48%, respectively. The experimental results show that the fast image encryption algorithm based on logistics-sine-cosine mapping takes less time to encrypt, and the cipher image has good information entropy and diffusivity. It is a safe and effective fast image encryption algorithm.
Causal Inference with Sample Balancing for Out-of-Distribution Detection in Visual Classification

Image classification algorithms are commonly based on the Independent and Identically Distribution (IID) assumption, but in practice, the Out-Of-Distribution (OOD) problem is widely existing, i.e., the contexts of images in the model predicting are usually unseen during training. In this case, existing models trained under the IID assumption are limiting generalization. Causal inference is an important method to enhance the out-of-distribution generalization of models by partitioning various contexts from data and leading models to learn context-invariant predictions in different situations. However, existing methods mostly have imbalance problems due to the lack of constraints when partitioning data, which weakens the improvement of generalization. Therefore, we propose a Balanced Partition Causal Inference (BP-Causal) method, which automatically generates fine-grained balanced data partitions in an unsupervised manner, thereby enhancing the generalization ability of models in different contexts. Experiments on the OOD datasets NICO and NICO++ demonstrate that BP-Causal achieves stable predictions on OOD data, and we also find that models using BP-Causal focus more accurately on the foreground of images compared with the existing causal inference method, which effectively improves the generalization ability.
DDistill-SR: Reparameterized Dynamic Distillation Network for Lightweight Image Super-Resolution

Recent research on deep convolutional neural networks (CNNs) has provided a significant performance boost on efficient super-resolution (SR) tasks by trading off the performance and applicability. However, most existing methods focus on subtracting feature processing consumption to reduce the parameters and calculations without refining the immediate features, which leads to inadequate information in the restoration. In this paper, we propose a lightweight network termed DDistill-SR, which significantly improves the SR quality by capturing and reusing more helpful information in a static-dynamic feature distillation manner. Specifically, we propose a plug-in reparameterized dynamic unit (RDU) to promote the performance and inference cost trade-off. During the training phase, the RDU learns to linearly combine multiple reparameterizable blocks by analyzing varied input statistics to enhance layer-level representation. In the inference phase, the RDU is equally converted to simple dynamic convolutions that explicitly capture robust dynamic and static feature maps. Then, the information distillation block is constructed by several RDUs to enforce hierarchical refinement and selective fusion of spatial context information. Furthermore, we propose a dynamic distillation fusion (DDF) module to enable dynamic signals aggregation and communication between hierarchical modules to further improve performance. Empirical results show that our DDistill-SR outperforms the baselines and achieves state-of-the-art results on most super-resolution domains with much fewer parameters and less computational overhead.
SLIC: Self-Supervised Learning with Iterative Clustering for Human Action Videos

Self-supervised methods have significantly closed the gap with end-to-end supervised learning for image classification [13], [24]. In the case of human action videos, however, where both appearance and motion are significant factors of variation, this gap remains significant [28], [58]. One of the key reasons for this is that sampling pairs of similar video clips, a required step for many self-supervised contrastive learning methods, is currently done conservatively to avoid false positives. A typical assumption is that similar clips only occur temporally close within a single video, leading to insufficient examples of motion similarity. To mitigate this, we propose SLIC, a clustering-based self-supervised contrastive learning method for human action videos. Our key contribution is that we improve upon the traditional intra-video positive sampling by using iterative clustering to group similar video instances. This enables our method to leverage pseudo-labels from the cluster assignments to sample harder positives and negatives. SLIC outperforms state-of-the-art video retrieval baselines by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$+15.4\%$</tex> on top-1 recall on UCF101 and by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$+5.7\%$</tex> when directly transferred to HMDB51. With end-to-end finetuning for action classi-fication, SLIC achieves 83.2% top-1 accuracy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(+0.8\%)$</tex> on UCF101 and 54.5% on HMDB51 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(+1.6\%$</tex> ,. SLIC is also competitive with the state-of-the-art in action classification after self-supervised pretraining on Kinetics400.
Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution

Deep learning-based super-resolution (SR) has gained tremendous popularity in recent years because of its high image quality performance and wide application scenarios. However, prior methods typically suffer from large amounts of computations and huge power consumption, causing difficulties for real-time inference, especially on resource-limited platforms such as mobile devices. To mitigate this, we propose a compiler-aware SR neural architecture search (NAS) framework that conducts depth search and per-layer width search with adaptive SR blocks. The inference speed is directly taken into the optimization along with the SR loss to derive SR models with high image quality while satisfying the real-time inference requirement. Instead of measuring the speed on mobile devices at each iteration during the search process, a speed model incorporated with compiler optimizations is leveraged to predict the inference latency of the SR block with various width configurations for faster convergence. With the proposed framework, we achieve real-time SR inference for implementing 720p resolution with competitive SR performance (in terms of PSNR and SSIM) on GPU/DSP of mobile platforms (Samsung Galaxy S21). Codes are available at link .
An Efficient Spatio-Temporal Pyramid Transformer for Action Detection

The task of action detection aims at deducing both the action category and localization of the start and end moment for each action instance in a long, untrimmed video. While vision Transformers have driven the recent advances in video understanding, it is non-trivial to design an efficient architecture for action detection due to the prohibitively expensive self-attentions over a long sequence of video clips. To this end, we present an efficient hierarchical Spatio-Temporal Pyramid Transformer (STPT) for action detection, building upon the fact that the early self-attention layers in Transformers still focus on local patterns. Specifically, we propose to use local window attention to encode rich local spatio-temporal representations in the early stages while applying global attention modules to capture long-term space-time dependencies in the later stages. In this way, our STPT can encode both locality and dependency with largely reduced redundancy, delivering a promising trade-off between accuracy and efficiency. For example, with only RGB input, the proposed STPT achieves 53.6% mAP on THUMOS14, surpassing I3D+AFSD RGB model by over 10% and performing favorably against state-of-the-art AFSD that uses additional flow features with 31% fewer GFLOPs, which serves as an effective and efficient end-to-end Transformer-based framework for action detection. Code is available at https://github.com/ziplab/STPT .
ActiveNeRF: Learning Where to See with Uncertainty Estimation

Recently, Neural Radiance Fields (NeRF) has shown promising performances on reconstructing 3D scenes and synthesizing novel views from a sparse set of 2D images. Albeit effective, the performance of NeRF is highly influenced by the quality of training samples. With limited posed images from the scene, NeRF fails to generalize well to novel views and may collapse to trivial solutions in unobserved regions. This makes NeRF impractical under resource-constrained scenarios. In this paper, we present a novel learning framework, ActiveNeRF, aiming to model a 3D scene with a constrained input budget. Specifically, we first incorporate uncertainty estimation into a NeRF model, which ensures robustness under few observations and provides an interpretation of how NeRF understands the scene. On this basis, we propose to supplement the existing training set with newly captured samples based on an active learning scheme. By evaluating the reduction of uncertainty given new inputs, we select the samples that bring the most information gain. In this way, the quality of novel view synthesis can be improved with minimal additional resources. Extensive experiments validate the performance of our model on both realistic and synthetic scenes, especially with scarcer training data.
Remember the Difference: Cross-Domain Few-Shot Semantic Segmentation via Meta-Memory Transfer

Few-shot semantic segmentation intends to predict pixel-level categories using only a few labeled samples. Existing few-shot methods focus primarily on the categories sampled from the same distribution. Nevertheless, this assumption cannot always be ensured. The actual domain shift problem significantly reduces the performance of few-shot learning. To remedy this problem, we propose an interesting and challenging cross-domain few-shot semantic segmentation task, where the training and test tasks perform on different domains. Specifically, we first propose a meta-memory bank to improve the generalization of the segmentation network by bridging the domain gap between source and target domains. The meta-memory stores the intra-domain style information from source domain instances and transfers it to target samples. Subsequently, we adopt a new contrastive learning strategy to explore the knowledge of different categories during the training stage. The negative and positive pairs are obtained from the proposed memory-based style augmentation. Comprehensive experiments demon-strate that our proposed method achieves promising results on cross-domain few-shot semantic segmentation tasks on COCO-20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> , PASCAL-S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> , FSS-1000, and SUIM datasets.
Performance Analysis of Image Caption Generation Using Deep Learning Techniques

In recent years, Artificial Intelligent (AI) is rapidly developed. The image caption is attracting the attention of many scientists. It is very interesting work. Automatically generating image caption into the natural language description according to the objects observed in the image. This is the part of scene understanding. Since, understanding is the combination of computer vision and natural language processing. The text is processed by using Recurrent Neural Network (RNN) with clubbed single dimensional feature obtained from Convolutional Neural Network (CNN) model. Given the data is given to trained model and last output is predicted. For the experiment of image captioning Flickr8k dataset is used.
How many mutually unbiased bases are needed to detect bound entangled states?

Abstract From a practical perspective it is advantageous to develop methods that verify entanglement in quantum states with as few measurements as possible. In this paper we investigate the minimal number of mutually unbiased bases (MUBs) needed to detect bound entanglement in bipartite <?CDATA $(d\times d)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo stretchy="false">(</mml:mo> <mml:mi>d</mml:mi> <mml:mo>×</mml:mo> <mml:mi>d</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:math> -dimensional states, i.e. entangled states that are positive under partial transposition. In particular, we show that a class of entanglement witnesses (EWs) composed of MUBs can detect bound entanglement if the number of measurements is greater than <?CDATA $d/2+1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>d</mml:mi> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:math> . This is a substantial improvement over other detection methods, requiring significantly fewer resources than either full quantum state tomography or measuring a complete set of d + 1 MUBs. Our approach is based on a partial characterisation of the (non-)decomposability of EWs. We show that non-decomposability is a universal property of MUBs, which holds regardless of the choice of complementary observables, and we find that both the number of measurements and the structure of the witness play an important role in the detection of bound entanglement.
Dynamic Edge Convolutional Neural Network for Skeleton-Based Human Action Recognition

To provide accessible, intelligent, and efficient remote access such as the internet of things, rehabilitation, autonomous driving, virtual games, and healthcare, human action recognition (HAR) has gained much attention among computer vision researchers. Several methods have already been addressed to ensure effective and efficient action recognition based on different perspectives including data modalities, feature design, network configuration, and application domains. In this article, we design a new deep learning model by integrating criss-cross attention and edge convolution to extract discriminative features from the skeleton sequence for action recognition. The attention mechanism is applied in spatial and temporal directions to pursue the intra- and inter-frame relationships. Then, several edge convolutional layers are conducted to explore the geometric relationships among the neighboring joints in the human body. The proposed model is dynamically updated after each layer by recomputing the graph on the basis of k-nearest joints for learning local and global information in action sequences. We used publicly available benchmark skeleton datasets such as UTD-MHAD (University of Texas at Dallas multimodal human action dataset) and MSR-Action3D (Microsoft action 3D) to evaluate the proposed method. We also investigated the proposed method with different configurations of network architectures to assure effectiveness and robustness. The proposed method achieved average accuracies of 99.53% and 95.64% on the UTD-MHAD and MSR-Action3D datasets, respectively, outperforming state-of-the-art methods.
FlashFill++: Scaling Programming by Example by Cutting to the Chase

Programming-by-Examples (PBE) involves synthesizing an "intended program" from a small set of user-provided input-output examples. A key PBE strategy has been to restrict the search to a carefully designed small domain-specific language (DSL) with "effectively-invertible" (EI) operators at the top and "effectively-enumerable" (EE) operators at the bottom. This facilitates an effective combination of top-down synthesis strategy (which backpropagates outputs over various paths in the DSL using inverse functions) with a bottom-up synthesis strategy (which propagates inputs over various paths in the DSL). We address the problem of scaling synthesis to large DSLs with several non-EI/EE operators. This is motivated by the need to support a richer class of transformations and the need for readable code generation. We propose a novel solution strategy that relies on propagating fewer values and over fewer paths. Our first key idea is that of "cut functions" that prune the set of values being propagated by using knowledge of the sub-DSL on the other side. Cuts can be designed to preserve completeness of synthesis; however, DSL designers may use incomplete cuts to have finer control over the kind of programs synthesized. In either case, cuts make search feasible for non-EI/EE operators and efficient for deep DSLs. Our second key idea is that of "guarded DSLs" that allow a precedence on DSL operators, which dynamically controls exploration of various paths in the DSL. This makes search efficient over grammars with large fanouts without losing recall. It also makes ranking simpler yet more effective in learning an intended program from very few examples. Both cuts and precedence provide a mechanism to the DSL designer to restrict search to a reasonable, and possibly incomplete, space of programs. Using cuts and gDSLs, we have built FlashFill++, an industrial-strength PBE engine for performing rich string transformations, including datetime and number manipulations. The FlashFill++ gDSL is designed to enable readable code generation in different target languages including Excel's formula language, PowerFx, and Python. We show FlashFill++ is more expressive, more performant, and generates better quality code than comparable existing PBE systems. FlashFill++ is being deployed in several mass-market products ranging from spreadsheet software to notebooks and business intelligence applications, each with millions of users.
Image and video-based crime prediction using object detection and deep learning

In recent years, the use of artificial intelligence (AI) for image and video-based crime detection has gained significant attention from law enforcement agencies and security experts. Indeed, deep learning (DL) models can learn complex patterns from data and help law enforcement agencies save time and resources by automatically identifying and tracking potential criminals. This contributes to make deep investigations and better steer their targets’ searches. Among others, handheld firearms and bladed weapons are the most frequent objects encountered at crime scenes. In this paper, we propose a DL-based surveillance system that can detect the presence of tracked objects, such as handheld firearms and bladed weapons, as well as may proceed to alert authorities regarding eventual threats before an incident occurs. After making a comparison of different DL-based object detection techniques, such as you only look once (YOLO), single shot multibox detector (SSD), or faster region-based convolutional neural networks (R-CNN), YOLO achieves the optimal balance of mean average precision (mAP) and inference speed for real-time prediction. Thus, we retain YOLOv5 for the implementation of our solution.
Non-fungible tokens (NFT): a safe and effective way to prevent plagiarism in scientific publishing

Purpose There is a need for technology that can protect scientific findings as a unique asset but is this possible? Non-fungible tokens (NFTs) are one possible solution. Design/methodology/approach Plagiarism is an unethical act and a violation of acceptable practices in the field of scholarly research and writing. Over the years, effective tools have been created to prevent and minimize plagiarism, especially through the integration of artificial intelligence technology to identify copied text. However, there is still scientific plagiarism of contents (such as articles, inventions or other digital objects). Findings NFTs are unique digital identifiers that use blockchain technology to ensure ownership rights of an object are maintained. NFT is a blockchain derivative that first entered the world of digital currencies and has now entered other areas, such as economy, fashion, music and computer games. Originality/value In this essay, how this technology may be useful to preventing acts of academic plagiarism is discussed.
HST: Hierarchical Swin Transformer for Compressed Image Super-Resolution

Compressed Image Super-resolution has achieved great attention in recent years, where images are degraded with compression artifacts and low-resolution artifacts. Since the complex hybrid distortions, it is hard to restore the distorted image with the simple cooperation of super-resolution and compression artifacts removing. In this paper, we take a step forward to propose the Hierarchical Swin Transformer (HST) network to restore the low-resolution compressed image, which jointly captures the hierarchical feature representations and enhances each-scale representation with Swin transformer, respectively. Moreover, we find that the pretraining with Super-resolution (SR) task is vital in compressed image super-resolution. To explore the effects of different SR pretraining, we take the commonly-used SR tasks (e.g., bicubic and different real super-resolution simulations) as our pretraining tasks, and reveal that SR plays an irreplaceable role in the compressed image super-resolution. With the cooperation of HST and pre-training, our HST achieves the fifth place in AIM 2022 challenge on the low-quality compressed image super-resolution track, with the PSNR of 23.51 dB. Extensive experiments and ablation studies have validated the effectiveness of our proposed methods.