Reviewerly/exHarmony huggingface dataset:
PIMoG: An Effective Screen-shooting Noise-Layer Simulation for Deep-Learning-Based Watermarking Network

With the omnipresence of camera phone and digital display, capturing digitally displayed image with camera phone are getting widely practiced. In the context of watermarking, this brings forth the issue of screen-shooting robustness. The key to acquiring screen-shooting robustness is designing a good noise layer that could represent screen-shooting distortions in a deep-learning-based watermarking framework. However, it is very difficult to quantitatively formulate the screen-shooting distortion since the screen-shooting process is too complex. In order to design an effective noise layer for screen-shooting robustness, we propose new insight in this paper, that is, it is not necessary to quantitatively simulate the overall procedure in the screen-shooting noise layer, only including the most influenced distortions is enough to generate an effective noise layer with strong robustness. To verify this insight, we propose a screen-shooting noise layer dubbed PIMoG. Specifically, we summarize the most influenced distortions of screen-shooting process into three parts (p erspective distortion, i llumination distortion and mo iré distortion) and further simulate them in a differentiable way. For the rest distortion, we utilize the G aussian noise to approximate the main part of them. As a result, the whole network can be trained end-to-end with such noise layer. Extensive experiments illustrate the superior performance of the proposed PIMoG noise layer. In addition to the noise layer design, we also propose a gradient mask-guided image loss and an edge mask-guided image loss to further improve the robustness and invisibility of the whole network respectively. Based on the proposed loss and PIMoG noise layer, the whole framework outperforms the SOTA watermarking method with at least 5% in extraction accuracy and achieves more than 97% accuracy in different screen-shooting conditions.
Feelings of presence and perceived social support in social virtual reality platforms

Social virtual reality (social VR) platforms are gaining popularity among users. Previous qualitative research suggests that feelings of presence can make these platforms an attractive environment to obtain social support from others. Building on these exploratory insights, we carried out a quantitative study to illuminate how different types of presence in social VR platforms facilitate social support. The results of a large survey conducted among users (N = 1231) show that feelings of social presence and self-presence are predictors of perceived social support and that this perception of social support is positively associated with users' subjective well-being. Perceived social support is greater for women than for men, and it differs across platforms, although with small effect sizes. These results underline the role of presence in the perception of computer-mediated social support, suggesting that the affordances of social VR make it a particularly well-suited medium for facilitating beneficial interactions among users.
Swin transformer based vehicle detection in undisciplined traffic environment

Intelligent vehicle detection (IVD) plays a prominent role in evolving an intelligent traffic management system (ITMS). It can help to decrease the average waiting time at the traffic post, save fuel consumption, control traffic congestion, decrease accident rates, and build up human safety. Recent developments in the artificial intelligence (AI) domain have increased the demand for IVD in the undisciplined traffic environment, which is a usual condition in developing countries. IVD is a difficult task in an undisciplined traffic environment because different vehicle categories travel very close to each other on the roads and do not follow traffic rules. Previously, several convolutional neural network (CNN) based deep learning (DL), and visual transformer-based techniques for vehicle and object detection have been presented. They are complex and do not accurately extract multi-scale features due to the involvement of existing CNN feature extraction backbones. Also, most techniques failed to account for an undisciplined traffic environment due to the unavailability of labeled vehicle datasets. Therefore, this paper proposes a swin transformer-based vehicle detection (STVD) framework in an undisciplined traffic environment. Swin transformer (ST) wholly exchanges information within and between image patches and provides hierarchical feature maps, effectively alleviating the multi-scale feature extraction problem. A bi-directional feature pyramid network (BIFPN) is presented, which combines low-resolution features with high-resolution features in a bidirectional way and provides robust multi-scale features with different scales and resolutions. A fully connected vehicle detection head (FCVDH) is applied to improve the matching relationship between vehicle sizes and the BIFPN hierarchy. FCVDH predicts the locations and categories of vehicles in the input image. STVD is analyzed, experimented, and measured over realistic traffic data. Also, it is compared with the existing state-of-the-art vehicle detection methods. It achieves 91.32% detection accuracy on diverse traffic labeled dataset (DTLD), 87.4% on IITM-hetra, and 88.45% on KITTI datasets.
Deep multi-view multiclass twin support vector machines

Multi-view learning (MVL) is a rapidly evolving direction in the field of machine learning. Despite the positive results, most algorithms that combine multi-view learning with twin support vector machines (TSVM) focus on the traditional machine learning domain. No method has been accomplished for combining MVL, TSVM, and deep learning. In this paper, we propose two novel multi-view deep models to solve the multiclass classification problem, namely deep multi-view twin support vector machines (DMvTSVM) based on deep neural network (DNN) and auto-encoder (AE) network. They find two non-parallel hyperplanes such that each hyperplane is as close to its own class as possible while being as far away from the other class as possible. Meanwhile, we apply similarity regularization to the output of the Deep TSVM classifier for each view to learn consensus information between views, and use this to refine the joint weights of the deep model and TSVM. Finally, the novel models employ the one−vs−rest strategy to allow the DMvTSVM classifier to solve the multiclass classification problems. In the experiments, the proposed methods are compared with existing state-of-the-art algorithms to prove their effectiveness.
Memory-Augmented Deep Unfolding Network for Guided Image Super-resolution

Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of a HR image. However, previous model-based methods mainly take the entire image as a whole, and assume the prior distribution between the HR target image and the HR guidance image, simply ignoring many non-local common characteristics between them. To alleviate this issue, we firstly propose a maximum a posteriori (MAP) estimation model for GISR with two types of priors on the HR target image, i.e., local implicit prior and global implicit prior. The local implicit prior aims to model the complex relationship between the HR target image and the HR guidance image from a local perspective, and the global implicit prior considers the non-local auto-regression property between the two images from a global perspective. Secondly, we design a novel alternating optimization algorithm to solve this model for GISR. The algorithm is in a concise framework that facilitates to be replicated into commonly used deep network structures. Thirdly, to reduce the information loss across iterative stages, the persistent memory mechanism is introduced to augment the information representation by exploiting the Long short-term memory unit (LSTM) in the image and feature spaces. In this way, a deep network with certain interpretation and high representation ability is built. Extensive experimental results validate the superiority of our method on a variety of GISR tasks, including Pan-sharpening, depth image super-resolution, and MR image super-resolution. Code will be released at https://github.com/manman1995/pansharpening .
PLAID

Pre-trained language models are increasingly important components across multiple information retrieval (IR) paradigms. Late interaction, introduced with the ColBERT model and recently refined in ColBERTv2, is a popular paradigm that holds state-of-the-art status across many benchmarks. To dramatically speed up the search latency of late interaction, we introduce the Performance-optimized Late Interaction Driver (PLAID) engine. Without impacting quality, PLAID swiftly eliminates low-scoring passages using a novel centroid interaction mechanism that treats every passage as a lightweight bag of centroids. PLAID uses centroid interaction as well as centroid pruning, a mechanism for sparsifying the bag of centroids, within a highly-optimized engine to reduce late interaction search latency by up to 7x on a GPU and 45x on a CPU against vanilla ColBERTv2, while continuing to deliver state-of-the-art retrieval quality. This allows the PLAID engine with ColBERTv2 to achieve latency of tens of milliseconds on a GPU and tens or just few hundreds of milliseconds on a CPU at large scale, even at the largest scales we evaluate with 140M passages.
Salient Object Detection via Dynamic Scale Routing

Recent research advances in salient object detection (SOD) could largely be attributed to ever-stronger multi-scale feature representation empowered by the deep learning technologies. The existing SOD deep models extract multi-scale features via the off-the-shelf encoders and combine them smartly via various delicate decoders. However, the kernel sizes in this commonly-used thread are usually "fixed". In our new experiments, we have observed that kernels of small size are preferable in scenarios containing tiny salient objects. In contrast, large kernel sizes could perform better for images with large salient objects. Inspired by this observation, we advocate the "dynamic" scale routing (as a brand-new idea) in this paper. It will result in a generic plug-in that could directly fit the existing feature backbone. This paper's key technical innovations are two-fold. First, instead of using the vanilla convolution with fixed kernel sizes for the encoder design, we propose the dynamic pyramid convolution (DPConv), which dynamically selects the best-suited kernel sizes w.r.t. the given input. Second, we provide a self-adaptive bidirectional decoder design to accommodate the DPConv-based encoder best. The most significant highlight is its capability of routing between feature scales and their dynamic collection, making the inference process scale-aware. As a result, this paper continues to enhance the current SOTA performance. Both the code and dataset are publicly available at https://github.com/wuzhenyubuaa/DPNet.
Assessment of 3D Models Placement Methods in Augmented Reality

Augmented reality (AR) is a relevant technology, which has demonstrated to be efficient for several applications, especially in the architecture, engineering, construction and operation (AECO) domain, where the integration of building information modeling (BIM) and AR has proved to be optimal in handling construction projects. However, the main challenge when integrating a virtual 3D model in an AR environment is the lack of precision and accuracy of placement that can occur between the real and the virtual environments. Although methods for placement via AR have been reported in the literature, there is a lack of investigations addressing their evaluation. Therefore, this paper proposes a methodology to perform a quantitative and qualitative assessment of several AR placement methods and a discussion about their usability in the specific context of AECO. We adopt root mean square error (RMSE) to quantify the placement accuracy of a 3D model and standard deviation to examine its stability (jittering). The results revealed that the AR placement error range is extremely wide (from a few centimeters up to meters). In marker-based methods, the results showed centimeter-range in both indoor and outdoor environments, compared to other methods (Inertial, Marker-less, etc.), while marker-less methods have widely varying error range from centimeters to a few meters. Other commercial solutions based on placement-sensors (GNSS and IMU), such as Trimble SiteVision, have proven placement performance in manual mode with centimeter order, while for the automatic mode, the order of placement and stability is metric, due to the low coverage of RTX (real time extended) in the study area.
The economics of cryptocurrency: Bitcoin and beyond

Abstract How well can a cryptocurrency serve as a means of payment? Cryptocurrencies need to overcome double‐spending by costly mining and by delaying settlement. We formalize this insight through an incentive constraint that rules out double‐spending and pins down the welfare costs of a cryptocurrency. We find that it is optimal to use seignorage rather than transaction fees to finance costly mining. In supplementary material, we study an extension with endogenous transaction fees and show quantitatively that the prime cost of Bitcoin arises from mining, but can be reduced substantially by optimally designing the reward system.
Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis

Social media platforms such as (Twitter, Facebook, and Weibo) are being increasingly embraced by individuals, groups, and organizations as a valuable source of information. This social media generated information comes in the form of tweets or posts, and normally characterized as short text, huge, sparse, and low density. Since many real-world applications need semantic interpretation of such short texts, research in Short Text Topic Modeling (STTM) has recently gained a lot of interest to reveal unique and cohesive latent topics. This article examines the current state of the art in STTM algorithms. It presents a comprehensive survey and taxonomy of STTM algorithms for short text topic modelling. The article also includes a qualitative and quantitative study of the STTM algorithms, as well as analyses of the various strengths and drawbacks of STTM techniques. Moreover, a comparative analysis of the topic quality and performance of representative STTM models is presented. The performance evaluation is conducted on two real-world Twitter datasets: the Real-World Pandemic Twitter (RW-Pand-Twitter) dataset and Real-world Cyberbullying Twitter (RW-CB-Twitter) dataset in terms of several metrics such as topic coherence, purity, NMI, and accuracy. Finally, the open challenges and future research directions in this promising field are discussed to highlight the trends of research in STTM. The work presented in this paper is useful for researchers interested in learning state-of-the-art short text topic modelling and researchers focusing on developing new algorithms for short text topic modelling.
AIE-YOLO: Auxiliary Information Enhanced YOLO for Small Object Detection

Small object detection is one of the key challenges in the current computer vision field due to the low amount of information carried and the information loss caused by feature extraction. You Only Look Once v5 (YOLOv5) adopts the Path Aggregation Network to alleviate the problem of information loss, but it cannot restore the information that has been lost. To this end, an auxiliary information-enhanced YOLO is proposed to improve the sensitivity and detection performance of YOLOv5 to small objects. Firstly, a context enhancement module containing a receptive field size of 21×21 is proposed, which captures the global and local information of the image by fusing multi-scale receptive fields, and introduces an attention branch to enhance the expressive ability of key features and suppress background noise. To further enhance the feature expression ability of small objects, we introduce the high- and low-frequency information decomposed by wavelet transform into PANet to participate in multi-scale feature fusion, so as to solve the problem that the features of small objects gradually disappear after multiple downsampling and pooling operations. Experiments on the challenging dataset Tsinghua-Tencent 100 K show that the mean average precision of the proposed model is 9.5% higher than that of the original YOLOv5 while maintaining the real-time speed, which is better than the mainstream object detection models.
Women’s perceptions and attitudes to the use of AI in breast cancer screening: a survey in a cancer referral centre

Although breast cancer screening can benefit from Artificial Intelligence (AI), it is still unknown whether, to which extent or under which conditions, the use of AI is going to be accepted by the general population. The aim of our study is to evaluate what the females who are eligible for breast cancer screening know about AI and how they perceive such innovation.We used a prospective survey consisting of a 11-multiple-choice questionnaire evaluating statistical associations with Chi-Square-test or Fisher-exact-test. Multinomial-logistic-regression was performed on items with more than two response categories. Odds ratio (OR) with 95% CI were computed to estimate the probability of a specific response according to patient's characteristics.In the 800 analysed questionnaires, 51% of respondents confirmed to have knowledge of AI. Of these, 88% expressed a positive opinion about its use in medicine. Non-Italian respondents were associated with the belief of having a deep awareness about AI more often than Italian respondents (OR = 1.91;95% CI[1.10-3.33]). Higher education level was associated with better opinions on the use of AI in medicine (OR = 4.69;95% CI[1.36-16.12]). According to 94% of respondents, the radiologists should always produce their own report on mammograms, whilst 77% agreed that AI should be used as a second reader. Most respondents (52%) considered that both the software developer and the radiologist should be held accountable for AI errors.Most of the females undergoing screening in our Institute approve the introduction of AI, although only as a support to radiologist, and not in substitution thereof. Yet, accountability in case of AI errors is still unsolved. advances in knowledge:This survey may be considered as a pilot-study for the development of large-scale studies to understand females's demands and concerns about AI applications in breast cancer screening.
Post-COVID-19 strategies for higher education institutions in dealing with unknown and uncertainties

The COVID-19 global pandemic outbreak has presented higher education institutions with the impediment of transforming to a new curriculum, pedagogy, and educational management. Inevitable transformation in higher education triggered by COVID-19 is still ongoing, albeit most countries are now at the endemic stage. However, transformation should not just be about simply changing instructional delivery. The COVID-19 pandemic is a unique opportunity for educators and policymakers to rethink education systems and reimagine what is important, necessary, and desirable for future generations. Hence, this study focuses on identifying strategies for higher education institutions to deal with unknown uncertainties during and after the pandemic. Expert Opinion Method was conducted involving five professors who are senior management in their respective universities and have been directly involved in formulating strategies and policies during the pandemic. Four major dimensions were developed from the findings: (1) Resilience and Change Management, (2) Digital Transformation and Online Learning, (3) Curriculum Change, and (4) Sustainability. Flexibility is also the most common issue discussed by experts. Based on thematic analysis, post COVID-19 strategic framework for higher education institution sustainability is proposed. In addition, this study can be a roadmap for educators, policymakers, and all relevant stakeholders to prepare for future disruptions in the education sector.
Automation Is Coming! Exploring Future(s)-Oriented Methods in Education

Abstract Emerging automated-decision making (ADM) technologies invite scholars to engage with future points in time and contexts that have not yet arisen. This particular state of not knowing yet implies the methodological challenge of examining images of the future and how such images will materialize in practice. In this respect, we ask the following: what are appropriate research methods for studying emerging ADM technologies in education? How do researchers explore sociotechnical practices that are in the making? Guided by these questions, we investigate the increasing adoption of ADM in teachers’ assessment practices. This constitutes a case in point for reflecting on the research methods applied to address the future of assessment in education. In this context, we distinguish between representational methods oriented to recounting past experiences and future(s) methods oriented to making futures. Studying the literature on speculative methods in digital education, we illustrate four categories of future(s)-oriented methods and reflect on their characteristics through a backcasting workshop conducted with teachers. We conclude by discussing the need to reconsider the methodological choices made for studying emerging technologies in critical assessment practices and generate new knowledge on methods able to contribute to alternative imaginaries of automation in education.
A short review on quantum identity authentication protocols: how would Bob know that he is talking with Alice?

Secure communication has achieved a new dimension with the advent of the schemes of quantum key distribution (QKD) as in contrast with classical cryptography, quantum cryptography can provide unconditional security. However, a successful implementation of a scheme for QKD requires identity authentication as a prerequisite. A security loophole in the identity authentication scheme may lead to the vulnerability of the entire secure communication scheme. Consequently, identity authentication is extremely important, and in the last three decades several schemes for identity authentication using quantum resources have been proposed. The chronological development of these protocols, which are now referred to as quantum identity authentication (QIA) protocols, is briefly reviewed here with specific attention to the causal connection involved in their development. The existing protocols are classified on the basis of the required quantum resources, and their relative merits and demerits are analyzed. Further, in the process of the classification of the protocols for QIA, it is observed that the existing protocols can also be classified in a few groups based on the inherent computational tasks used to design the protocols. Realization of these symmetries has led to the possibility of designing a set of new protocols for quantum identity authentication, which are based on the existing schemes for the secure computational and communication tasks. The security of such protocols is also critically analyzed.