Reviewerly/exHarmony huggingface dataset:
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon

Estimating the risk level of adversarial examples is essential for safely deploying machine learning models in the real world. One popular approach for physical-world attacks is to adopt the “sticker-pasting” strategy, which however suffers from some limitations, including difficulties in access to the target or printing by valid colors. A new type of non-invasive attacks emerged recently, which attempt to cast perturbation onto the target by optics based tools, such as laser beam and projector. However, the added optical patterns are artificial but not natural. Thus, they are still conspicuous and attention-grabbed, and can be easily noticed by humans. In this paper, we study a new type of optical adversarial examples, in which the perturbations are generated by a very common natural phenomenon, shadow, to achieve naturalistic and stealthy physical-world adversarial attack under the black-box setting. We extensively evaluate the effectiveness of this new attack on both simulated and real-world environments. Experimental results on traffic sign recognition demonstrate that our algorithm can generate adversarial examples effectively, reaching 98.23% and 90.47% success rates on LISA and GTSRB test sets respectively, while continuously misleading a moving camera over 95% of the time in real-world scenarios. We also offer discussions about the limitations and the defense mechanism of this attack <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Our code is available at https://github.com/hncszyq/ShadowAttack.
LiDAR Snowfall Simulation for Robust 3D Object Detection

3D object detection is a central task for applications such as autonomous driving, in which the system needs to localize and classify surrounding traffic agents, even in the presence of adverse weather.In this paper, we address the problem of LiDAR-based 3D object detection under snowfall.Due to the difficulty of collecting and annotating training data in this setting, we propose a physically based method to simulate the effect of snowfall on real clearweather LiDAR point clouds.Our method samples snow particles in 2D space for each LiDAR line and uses the induced geometry to modify the measurement for each LiDAR beam accordingly.Moreover, as snowfall often causes wetness on the ground, we also simulate ground wetness on LiDAR point clouds.We use our simulation to generate partially synthetic snowy LiDAR data and leverage these data for training 3D object detection models that are robust to snowfall.We conduct an extensive evaluation using several state-of-the-art 3D object detection methods and show that our simulation consistently yields significant performance gains on the real snowy STF dataset compared to clearweather baselines and competing simulation approaches, while not sacrificing performance in clear weather.Our code is available at github.com/SysCV/LiDARsnow sim.
Image Encryption Scheme Based on Newly Designed Chaotic Map and Parallel DNA Coding

In this paper, a new one-dimensional fractional chaotic map is proposed and an image encryption scheme based on parallel DNA coding is designed by using the chaotic map. The mathematical model of the new chaotic system combines a sine map and a fraction operation. Compared with some traditional one-dimensional chaotic systems, the new chaotic system has a larger range of chaotic parameters and better chaotic characteristics, which makes it more suitable for applications in information encryption. In addition, an image encryption algorithm based on parallel DNA coding is proposed, which overcomes the shortcoming of common DNA coding-based image encryption algorithms. Parallel computing significantly increases the speed of encryption and decryption algorithms. The initial key of the cryptosystem is designed to be related to the SHA-3 hash value of the plaintext image so that the algorithm can resist a chosen-plaintext attack. Simulation experiments and security analysis results show that the proposed image encryption scheme has good encryption performance and less time overhead, and has strong robustness to noise and data loss attacks, which indicates that the proposed image encryption scheme has good application potential in secure communication applications.
Leveraging Blockchain Technology for Ensuring Security and Privacy Aspects in Internet of Things: A Systematic Literature Review

As the Internet of Things (IoT) concept materialized worldwide in complex ecosystems, the related data security and privacy issues became apparent. While the system elements and their communication paths could be protected individually, generic, ecosystem-wide approaches were sought after as well. On a parallel timeline to IoT, the concept of distributed ledgers and blockchains came into the technological limelight. Blockchains offer many advantageous features in relation to enhanced security, anonymity, increased capacity, and peer-to-peer capabilities. Although blockchain technology can provide IoT with effective and efficient solutions, there are many challenges related to various aspects of integrating these technologies. While security, anonymity/data privacy, and smart contract-related features are apparently advantageous for blockchain technologies (BCT), there are challenges in relation to storage capacity/scalability, resource utilization, transaction rate scalability, predictability, and legal issues. This paper provides a systematic review on state-of-the-art approaches of BCT and IoT integration, specifically in order to solve certain security- and privacy-related issues. The paper first provides a brief overview of BCT and IoT's basic principles, including their architecture, protocols and consensus algorithms, characteristics, and the challenges of integrating them. Afterwards, it describes the survey methodology, including the search strategy, eligibility criteria, selection results, and characteristics of the included articles. Later, we highlight the findings of this study which illustrates different works that addressed the integration of blockchain technology and IoT to tackle various aspects of privacy and security, which are followed by a categorization of applications that have been investigated with different characteristics, such as their primary information, objective, development level, target application, type of blockchain and platform, consensus algorithm, evaluation environment and metrics, future works or open issues (if any), and further notes for consideration. Furthermore, a detailed discussion of all articles is included from an architectural and operational perspective. Finally, we cover major gaps and future considerations that can be taken into account when integrating blockchain technology with IoT.
Toward the third generation artificial intelligence

There have been two competing paradigms in artificial intelligence (AI) development ever since its birth in 1956, i.e., symbolism and connectionism (or sub-symbolism). While symbolism dominated AI research by the end of 1980s, connectionism gained momentum in the 1990s and is gradually displacing symbolism. This paper considers symbolism as the first generation of AI and connectionism as the second generation. However, each of these two paradigms simulates the human mind from only one perspective. AI cannot achieve true human behaviors by relying on only one paradigm. In order to develop novel AI technologies that are safe, reliable, and extensible, it is necessary to establish a new explainable and robust AI theory. To this end, this paper looks toward developing a third generation artificial intelligence by combining the current paradigms.
Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation

Large-scale training data with high-quality annotations is critical for training semantic and instance segmentation models. Unfortunately, pixel-wise annotation is labor-intensive and costly, raising the demand for more efficient labeling strategies. In this work, we present a novel 3D-to-2D label transfer method, Panoptic NeRF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , which aims for obtaining per-pixel 2D semantic and instance labels from easy-to-obtain coarse 3D bounding primitives. Our method utilizes NeRF as a differentiable tool to unify coarse 3D annotations and 2D semantic cues transferred from existing datasets. We demonstrate that this combination allows for improved geometry guided by semantic information, enabling rendering of accurate semantic maps across multiple views. Furthermore, this fusion process resolves label ambiguity of the coarse 3D annotations and filters noise in the 2D predictions. By inferring in 3D space and rendering to 2D labels, our 2D semantic and instance labels are multiview consistent by design. Experimental results show that Panoptic NeRF outperforms existing label transfer methods in terms of accuracy and multi-view consistency on challenging urban scenes of the KITTI-360 dataset.
HoLoCo: Holistic and local contrastive learning network for multi-exposure image fusion

Multi-exposure image fusion (MEF) targets to integrate multiple shots with different exposures and generates a single higher dynamic image than each. Existing deep learning-based MEF approaches only adopt reference high dynamic images (HDR) as positive samples to guide the training of fusion networks. However, simply relying on these positive samples are difficult to find the optimal parameters for the network as a whole. Thus, the structure or texture information is blurred or missed in the generated HDR results. Moreover, few approaches attempted to prevent illumination degeneration during the fusion process, resulting in poor color saturation on their fused results. To address such limitations, in this paper, we introduce a novel holistic and local constraint built upon contrastive learning, namely HoLoCo, to discover the intrinsic information of both source LDR images and the reference HDR one. In this manner, the generated fused images are pulled to the HDR image and pushed away from the LDR source images in both image-based and path-based latent feature spaces. Besides, inspired by Retinex theory, we propose a color correction module (CCM) to refine illumination features. CCM involves dual streams that can collaborate to ensure the nature of color information and details consistency. Extensive experiments on two datasets show that our HoLoCo can continuously generate visual-appealing HDR results with precise detail and vivid color rendition, performing favorably against the state-of-the-art MEF approaches. Source code is available in github https://github.com/JinyuanLiu-CV/HoLoCo.
Coronavirus Mask Protection Algorithm: A New Bio-inspired Optimization Algorithm and Its Applications

Nowadays, meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization problems. In this paper, a COVID-19 prevention-inspired bionic optimization algorithm, named Coronavirus Mask Protection Algorithm (CMPA), is proposed based on the virus transmission of COVID-19. The main inspiration for the CMPA originated from human self-protection behavior against COVID-19. In CMPA, the process of infection and immunity consists of three phases, including the infection stage, diffusion stage, and immune stage. Notably, wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves, which are similar to the exploration and exploitation in optimization algorithms. This study simulates the self-protection behavior mathematically and offers an optimization algorithm. The performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions, CEC2020 suite problems, and three truss design problems. The statistical results demonstrate that the CMPA is more competitive among these state-of-the-art algorithms. Further, the CMPA is performed to identify the parameters of the main girder of a gantry crane. Results show that the mass and deflection of the main girder can be improved by 16.44% and 7.49%, respectively.
Underwater Image Enhancement via Piecewise Color Correction and Dual Prior Optimized Contrast Enhancement

Due to the absorption and scattering of light, underwater captured images often face serious quality degradation issues. In this letter, we propose to cope with the aforementioned issues via piecewise color correction and dual prior optimized contrast enhancement. Specifically, we first present the piecewise color correction method using the maximum mean and two gain factors to correct the color cast of each color channel. Then, we propose a dual prior optimized contrast enhancement method, which relies on the spatial and texture priors to decompose the base layer and detail layer of the V channel in HSV color space. Meanwhile, we employ different enhancement strategies in different layers to enhance the contrast and texture detail of underwater images. Our extensive experiments on several benchmark datasets show that our method outperforms eleven compared state-of-the-art methods. Moreover, our method has good generalization capability for fog and low-light images. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Li-Chongyi/PCDE</uri> .
Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?

Systematic reviews are comprehensive literature reviews for a highly focused research question. These reviews are considered the highest form of evidence in medicine. Complex Boolean queries are developed as part of the systematic review creation process to retrieve literature, as they permit reproducibility and understandability. However, it is difficult and time-consuming to develop high-quality Boolean queries, often requiring the expertise of expert searchers like librarians. Recent advances in transformer-based generative models have shown their ability to effectively follow user instructions and generate answers based on these instructions. In this paper, we investigate ChatGPT as a means for automatically formulating and refining complex Boolean queries for systematic review literature search. Overall, our research finds that ChatGPT has the potential to generate effective Boolean queries. The ability of ChatGPT to follow complex instructions and generate highly precise queries makes it a tool of potential value for researchers conducting systematic reviews, particularly for rapid reviews where time is a constraint and where one can trade off higher precision for lower recall. We also identify several caveats in using ChatGPT for this task, highlighting that this technology needs further validation before it is suitable for widespread uptake.
Blind Stereoscopic Image Quality Assessment Based on Hierarchical Learning

We proposed a blind image quality assessment model which used classification and prediction for three-dimensional (3D) image quality assessment (denoted as CAP-3DIQA) that can automatically evaluate the quality of stereoscopic images. First, in the classification stage, the model separated the distorted images into several subsets according to the types of image distortions. This process will assign the images with the same distortion type to the same group. After the classification stage, the classified distorted image set is fed into the image quality predictor that contains five different perceptual channels which predict the image quality score individually. Finally, we used the regression module of the support vector machine to evaluate the final image quality score, where the input of the regression model is the combination of five channel’s outputs. The model, we proposed is tested on three public and popular databases, which are LIVE 3D Image Quality Database Phase I, LIVE 3D Image Quality Database Phase II, and MCL 3D Image Quality Database. The experimental results show that our proposed model leads to significant performance improvement on quality prediction for stereoscopic images compared with other existing state-of-the-art quality metrics.
Institutional complementarity for social and economic development

oday the prevention of global challenges (from global security to the problems of poverty) relates to the institutional quality. Nowadays, the social standards or other “social rules” make the part of the market system, since they are built into the country’s institutional structure. Neither social nor economic reforms can be implemented without the support through institutional mechanisms.&#x0D; The purpose of this paper is to explore the relationship between social sector institutions and basic institutions, taking into account the economic development of countries and the way in which they are formed.&#x0D; A number of empiric studies confirmed significant role of institutions to provide conditions for economic development. In order to define and assess the link between the basic institutions and the social sector institutions, we formed panel data that includes 20 countries for the period from 2007-2014. We assessed quality of the basic institutions using The Worldwide Governance Indicators (WGI). WGI methodology provides an evaluation of six dimensions of the institutional quality that enables to define the connection and the impact of every dimension on the institutional quality of the social sector.&#x0D; The model additionally evaluates the impact of the incomes distribution inequality, general economic welfare on the institutional quality of the social sector. Among all dimensions of governance “Rule of Law” and “Regulatory Quality” the statistically significant direct impact on the institutional quality of the social sector has been revealed. It confirms the complementarity of basic institutions and institutions of the social sector.
The Introduction of a Novel Virtual Reality Training System for Gynecology Learning and Its User Experience Research

The researchers of this study designed a new virtual reality (VR)-assisted training system, IFOREAL, for gynecology students at their university and introduced it to potential trainees. Two versions of IFOREAL, each employing different devices, were developed. The consumer version uses a traditional LCD display and computer mouse, whereas the professional version utilizes a head-mounted display (HMD) and joystick controller for the virtual learning. Trainees watched simulated videos and interacted with the system to accomplish tasks. IFOREAL consists of several major learning modules of gynecology. This study used the normal spontaneous delivery module to research user experience and perceptions of the IFOREAL VR training system. The results suggested that most of the trainees' user experiences and perceptions of IFOREAL, using both the types of VR-assisted technology, were positively reported. Trainees who used the consumer version of IFOREAL perceived a stronger internal control, whereas trainees using the professional version perceived a better sense of virtual presence. Overall, trainees perceived the usefulness of the IFOREAL system which predicted their future intention to use the system and other similar VR-assisted training systems for learning. Furthermore, whether the virtual content of IFOREAL grabbed the trainees' attention predicted their future intenon to use professional devices of HMD/joysck for learning other subjects. The gender difference was also explored in the study. Generally speaking, female trainees gave better evaluations of IFOREAL compared to their male counterparts. They perceived a better internal control while using the consumer version than the professional version. Male trainees believed that the gadgets used in the professional version provided a better virtual presence and met their expectation of a virtual experience better. This study suggested that different versions of IFOREAL could serve trainees' different needs. Technology developers and trainers should tailor the application of different VR devices to assist with the training/learning in accordance with different conditions/trainees.
A Hybrid Technique for Day-Ahead PV Generation Forecasting Using Clear-Sky Models or Ensemble of Artificial Neural Networks According to a Decision Tree Approach

PhotoVoltaic (PV) plants can provide important economic and environmental benefits to electric systems. On the other hand, the variability of the solar source leads to technical challenges in grid management as PV penetration rates increase continuously. For this reason, PV power forecasting represents a crucial tool for uncertainty management to ensure system stability. In this paper, a novel hybrid methodology for the PV forecasting is presented. The proposed approach can exploit clear-sky models or an ensemble of artificial neural networks, according to day-ahead weather forecast. In particular, the selection among these techniques is performed through a decision tree approach, which is designed to choose the best method among those aforementioned. The presented methodology has been validated on a real PV plant with very promising results.
Whale optimization algorithm-based email spam feature selection method using rotation forest algorithm for classification

Email has continued to be an integral part of our lives and as a means for successful communication on the internet. The problem of spam mails occupying a huge amount of space and bandwidth, and the weaknesses of spam filtering techniques which includes misclassification of genuine emails as spam (false positives) are a growing challenge to the internet world. This research work proposed the use of a metaheuristic optimization algorithm, the whale optimization algorithm (WOA), for the selection of salient features in the email corpus and rotation forest algorithm for classifying emails as spam and non-spam. The entire datasets were used, and the evaluation of the rotation forest algorithm was done before and after feature selection with WOA. The results obtained showed that the rotation forest algorithm after feature selection with WOA was able to classify the emails into spam and non-spam with a performance accuracy of 99.9% and a low FP rate of 0.0019. This shows that the proposed method had produced a remarkable improvement as compared with some previous methods.
Digital Literacy, Digital Competence and Research Productivity of Educators

This study determined the relationship between digital literacy, digital competence and research productivity of educators.This research employed the descriptive-correlational research design.The respondents of the study were the permanent faculty members of the university.The research instruments utilized were the standardized scales of Being Digital: Digital Literacy Checklist of The Open University © 2012 United Kingdom, measured the digital literacy of the respondents.And the digital competence was assessed through the Self-Assessment Tool of the European Digital Competence Framework for Educators (DigComEdu).The statistical tools used were the weighted mean, Standard Deviation and Chi-Square.The findings revealed that there is a strong and significant relationship between faculty members' digital literacy and research productivity.This means that the increase in understanding, finding, using, and creating information using digital technologies is positively related to faculty members' ability to conduct, complete, present and publish a research article.Likewise, faculty member's digital competence is strong and significantly correlated to their research productivity, which clearly indicates that as their knowledge, skills and attitudes for working, living and learning in the knowledge society increases, there is also a significant increase in their ability to produce publishable research outputs.