Reviewerly/exHarmony huggingface dataset:
BiodivAR: A Cartographic Authoring Tool for the Visualization of Geolocated Media in Augmented Reality

Location-based augmented reality technology for real-world, outdoor experiences is rapidly gaining in popularity in a variety of fields such as engineering, education, and gaming. By anchoring medias to geographic coordinates, it is possible to design immersive experiences remotely, without necessitating an in-depth knowledge of the context. However, the creation of such experiences typically requires complex programming tools that are beyond the reach of mainstream users. We introduce BiodivAR, a web cartographic tool for the authoring of location-based AR experiences. Developed using a user-centered design methodology and open-source interoperable web technologies, it is the second iteration of an effort that started in 2016. It is designed to meet needs defined through use cases co-designed with end users and enables the creation of custom geolocated points of interest. This approach enabled substantial progress over the previous iteration. Its reliance on geolocation data to anchor augmented objects relative to the user’s position poses a set of challenges: On mobile devices, GNSS accuracy typically lies between 1 m and 30 m. Due to its impact on the anchoring, this lack of accuracy can have deleterious effects on usability. We conducted a comparative user test using the application in combination with two different geolocation data types (GNSS versus RTK). While the test’s results are undergoing analysis, we hereby present a methodology for the assessment of our system’s usability based on the use of eye-tracking devices, geolocated traces and events, and usability questionnaires.
Weakly Supervised Semantic Segmentation in Aerial Imagery via Cross-Image Semantic Mining

Weakly Supervised Semantic Segmentation (WSSS) with only image-level labels reduces the annotation burden and has been rapidly developed in recent years. However, current mainstream methods only employ a single image’s information to localize the target and do not account for the relationships across images. When faced with Remote Sensing (RS) images, limited to complex backgrounds and multiple categories, it is challenging to locate and differentiate between the categories of targets. As opposed to previous methods that mostly focused on single-image information, we propose CISM, a novel cross-image semantic mining WSSS framework. CISM explores cross-image semantics in multi-category RS scenes for the first time with two novel loss functions: the Common Semantic Mining (CSM) loss and the Non-common Semantic Contrastive (NSC) loss. In particular, prototype vectors and the Prototype Interactive Enhancement (PIE) module were employed to capture semantic similarity and differences across images. To overcome category confusions and closely related background interferences, we integrated the Single-Label Secondary Classification (SLSC) task and the corresponding single-label loss into our framework. Furthermore, a Multi-Category Sample Generation (MCSG) strategy was devised to balance the distribution of samples among various categories and drastically increase the diversity of images. The above designs facilitated the generation of more accurate and higher-granularity Class Activation Maps (CAMs) for each category of targets. Our approach is superior to the RS dataset based on extensive experiments and is the first WSSS framework to explore cross-image semantics in multi-category RS scenes and obtain cutting-edge state-of-the-art results on the iSAID dataset by only using image-level labels. Experiments on the PASCAL VOC2012 dataset also demonstrated the effectiveness and competitiveness of the algorithm, which pushes the mean Intersection-Over-Union (mIoU) to 67.3% and 68.5% on the validation and test sets of PASCAL VOC2012, respectively.
Historical credibility for movie reviews and its application to weakly supervised classification

In this study, we deal with the problem of judging the credibility of movie reviews. The problem is challenging because even experts cannot clearly and efficiently judge the credibility of a movie review and the number of movie reviews is very large. To tackle this problem, we propose historical credibility that judges the credibility of reviews based on the historical ratings and textual reviews written by each reviewer. For this, we present three kinds of criteria that can clearly classify the reviews into trusted or distrusted ones. We validate the effectiveness of the proposed historical credibility through extensive analysis. Specifically, we show that characteristics between the trusted or distrusted reviews are quite distinguishable in terms of three viewpoints: 1) distribution, 2) statistics, and 3) correlation. Then, we present a multimodal-based classification model incorporating the historical credibility-based features with the textual review-based features in a weakly supervised manner to classify a given review as a trusted or distrusted one. Through experiments, we first show that our model is quite efficient because the entire data set is annotated according to the predefined criteria. Indeed, it can annotate 6,400 movie reviews only in 0.09 seconds, which occupies only 0.01% of the total learning time of the proposed classification model. Second, we show that our multimodal-based classification model outperforms the existing textual review-based classification model by 5.99%∼10.53% in terms of the average accuracy. In addition, we clearly confirm that our classification model shows higher accuracy as more data accumulates.
Enhancing discriminative appearance model for visual tracking

Most end-to-end discriminative trackers obtain the improvements by a large margin through mining more representative target features for localizing the target, yet it isn't trivial to get comprehensive features as a result of diverse challenges. Moreover, we note that prevalent discriminative trackers merely use a convolution block to process pre-trained residual features to acquire target classification features, which further is input into the appearance model to obtain a target classifier, aiming at obtaining a response map. In view of this problem, we propose a novel feature enhancement module to obtain a richer and comprehensive feature representation, which is end-to-end trainable. Furthermore, discriminative trackers equipped with an online update mechanism demand to refine the classification model with recent samples, which will more or less learn the inaccurate tracking results into the model, thus weakening its discriminative ability. To alleviate this issue, we use a metric learning method to devise a verifier, which is devoted to yielding a similarity score to make piecewise predictions. By comparing the similarity score and piecewise thresholds, this strategy can enhance the robustness of the tracker by adaptively regulating the update of the sample memory and the learning rate. It is worth mentioning that the verifier module has strong portability. We choose the recent SuperDiMP as a baseline and implement comprehensive experimental tests and analyses of our approach on six popular benchmarks, which confirm that the proposed methods perform favorably against state-of-the-art trackers.
Coordination of cybersecurity risk management in the U.K. insurance sector

The increasing threat of cyberattacks has resulted in increased efforts by both the U.K. government and regulatory authorities to coordinate efforts to influence cybersecurity risk management practices in the U.K. insurance sector, focusing on cyber risk underwriters. This paper provides an evaluation of these arrangements. It first provides a descriptive overview of the key U.K. regulatory authorities and the evolution of their efforts over the past decade, as well as the scope for broader collaborations with industry and member-based associations and international organisations. It then evaluates the effectiveness of these efforts by providing a multi-method study of the incidence, nature and evolution of cost of data breaches, investment in computer systems and software intangible assets at risk of cyberattack, and a content analysis of annual reports of both U.K. regulators and a sample of U.K. insurers. The findings suggest that while both the total costs of data breaches and the size of investment in computer systems and software intangibles at risk of cyberattack have gradually increased over time, the degree of engagement with cyber as a reporting issue by both cyber insurers and financial regulators has not. It is concluded that while these efforts have been apparently successful in avoiding a large-scale, systemic cyberattack on the U.K. insurance industry, there are significant gaps and overlaps in the system of cyber regulatory oversight.
Mobile Malaysian Sign Language Application

Due to the Covid-19 plague, children with hearing misfortune have experienced challenges in learning and practicing sign language due to need of educational services and learning assets. The next considerations for creating an educational mobile learning application to help deaf kids learn Malaysian Sign Language (MSL). The investigation was conducted through gatherings based on inquire about and interviews with subject matter experts. Instructors recommend that within the current circumstance, versatile learning application are the foremost reasonable stage for understudies to memorize or as an extra learning strategy in arrange to educate them well. This research was conducted through research-based and interview sessions with subject matter experts and followed the Multimedia Mobile Content Development (MMCD) methodology as it was shown to help accelerate development activities and ensure that the application would work and execute as planned. Once the application is developed, students from Sekolah Kebangsaan Silabukan in Lahad Datu, Sabah, will participate in beta testing. Based on the results of user acceptance tests, the application has received positive reviews from its target users. As such, the project's goals are said to have been achieved. At the conclusion of the study, the advantages, limitations and future work of Pocket Sign Language were also identified.
UAV Swarm Collaborative Path Planning Based on RB-ABC

As a major combat force in future warfare, the path planning problem of UAV swarms has attracted much attention. Based on the application of the traditional artificial bee colony algorithm in single-machine path planning, a non-deterministic search mechanism and a two-way planning method are introduced to make improvements to the way of food generation in the Artificial Bee Colony algorithm. An improved RB-ABC algorithm is proposed. Simulation experiments are conducted using a cooperative flight mode in which multiple UAVs take off from different starting points and reach the specified end point simultaneously or in a specified order. It is verified that RD-ABC algorithm can realize the path planning of UAVs and get a better path in a short time.
Capture My Head: A Convenient and Accessible Approach Combining 3D Shape Reconstruction and Size Measurement from 2D Images for Headwear Design

3D physical head capture has many applications for headwear and facewear design. However, the existing methods, usually using commercial scanners, are not cheap or convenient, thereby limiting their popularity and accessibility. Therefore, we propose a convenient and cheap approach to 3D head capture by combining shape reconstruction and size measurement from images, using only two printed templates and a mobile phone camera. For the shape reconstruction process, we presented a 3D morphable model (3DMM)-based method to reconstruct the 3D shape from an arbitrary number of face images. Using this method, two powerful 3DMMs of head shapes and textures are constructed, and a novel facial landmark-to-edge loss based on corrected landmarks is developed. For size measurement, we proposed a coarse-to-fine circular object diameter measurement method, used to measure a circular label or object (e.g., a coin) placed on the forehead. The experimental results confirm a cost-effective number and size of input face images for our method. Furthermore, the method comparisons demonstrate that our head reconstruction methods have superior accuracy to state-of-the-art methods. Our convenient approach can be applied to the ergonomic design field, as well as individual customizations, virtual try-ons, and size selection for head-related products.
Inception Convolution and Feature Fusion for Person Search

With the rapid advancement of deep learning theory and hardware device computing capacity, computer vision tasks, such as object detection and instance segmentation, have entered a revolutionary phase in recent years. As a result, extremely challenging integrated tasks, such as person search, might develop quickly. The majority of efficient network frameworks, such as Seq-Net, are based on Faster R-CNN. However, because of the parallel structure of Faster R-CNN, the performance of re-ID can be significantly impacted by the single-layer, low resolution, and occasionally overlooked check feature diagrams retrieved during pedestrian detection. To address these issues, this paper proposed a person search methodology based on an inception convolution and feature fusion module (IC-FFM) using Seq-Net (Sequential End-to-end Network) as the benchmark. First, we replaced the general convolution in ResNet-50 with the new inception convolution module (ICM), allowing the convolution operation to effectively and dynamically distribute various channels. Then, to improve the accuracy of information extraction, the feature fusion module (FFM) was created to combine multi-level information using various levels of convolution. Finally, Bounding Box regression was created using convolution and the double-head module (DHM), which considerably enhanced the accuracy of pedestrian retrieval by combining global and fine-grained information. Experiments on CHUK-SYSU and PRW datasets showed that our method has higher accuracy than Seq-Net. In addition, our method is simpler and can be easily integrated into existing two-stage frameworks.
Comparing the effectiveness of video and stereoscopic 360° virtual reality‐supported instruction in high school biology courses

Abstract The promise of using immersive technologies in learning has increasingly been attracting researchers' and practitioners' attention. However, relevant empirical works are usually conducted in fully controlled Virtual Reality (VR) laboratories, as opposed to conventional settings. This quasi‐experimental study compares the effectiveness of video learning resources to that of stereoscopic 360° VR, as supplements to the traditional instructional approach. The potential of such methods was examined in high school settings, in the context of the ‘Life and Evolution’ module, with participants ( n = 70) divided equally into control and experimental groups. As a point of reference (control condition), we considered the adoption of Video Learning Resources, as students are more acquainted with this instructional method. In the intervention approach (experimental condition), students adopted the use of low‐end mobile‐VR ( VeeR Mini VR Goggles ). The key findings indicate differences in the learning motivation, confidence and satisfaction, but no statistically significant difference was identified regarding the factual or conceptual knowledge gains. The study offers insights on the potential of the investigated technologies in the subject of secondary school Biology and further provides implications for theory and practice. Practitioner notes What is already known about this topic Researchers' interest over the potential of Virtual Reality on different STEM disciplines is increasing consistently. An increasing number of efforts can be identified discussing the integration of multimedia learning resources in the secondary school context. Empirical studies on the subject of Biology are focusing on students' academic performance and achievement but not on learning motivation and satisfaction. What this paper adds This quasi‐experimental study comparatively examines academic performance, with the focus being on learning motivation and satisfaction, across different modalities (stereoscopic 360° Virtual Reality applications‐VR, Video Learning Recourses‐VLR). The findings demonstrate that both instructional methods are sufficient in enhancing students' knowledge acquisition and academic performance. The adoption of stereoscopic 360° VR influences students' learning motivation and impacts long‐term memory retention. Implications for practice and policy Educators are advised to consider the systematic adoption of “immersive” multimedia tools to enhance the subject of Biology as they can greatly encourage scientific inquiry. Instructional designers are advised to adopt open educational resources aligned to the curriculum of the local context. Educational researchers are advised to integrate stereoscopic 360°‐VR solutions in the conventional classroom settings.
Multi‐agent reinforcement learning for process control: Exploring the intersection between fields of reinforcement learning, control theory, and game theory

Abstract The application of reinforcement learning (RL) in process control has garnered increasing research attention. However, much of the current literature is focused on training and deploying a single RL agent. The application of multi‐agent reinforcement learning (MARL) has not been fully explored in process control. This work aims to: (i) develop a unique RL agent configuration that is suitable in a MARL control system for multiloop control, (ii) demonstrate the efficacy of MARL systems in controlling multiloop process that even exhibit strong interactions, and (iii) conduct a comparative study of the performance of MARL systems trained with different game‐theoretic strategies. First, we propose a design of an RL agent configuration that combines the functionalities of a feedback controller and a decoupler in a control loop. Thereafter, we deploy two such agents to form a MARL system that learns how to control a two‐input, two‐output system that exhibits strong interactions. After training, the MARL system shows effective control performance on the process. With further simulations, we examine how the MARL control system performs with increasing levels of process interaction and when trained with reward function configurations based on different game‐theoretic strategies (i.e., pure cooperation and mixed strategies). The results show that the performance of the MARL system is weakly dependent on the reward function configuration for systems with weak to moderate loop interactions. The MARL system with mixed strategies appears to perform marginally better than MARL under pure cooperation in systems with very strong loop interactions.
Blockchain-Based Traceability of Counterfeited Drugs

In the healthcare industry, providing a vital backbone for services is critical. The supply chain is a complex network that crosses organizational and geographical borders. In the healthcare business, counterfeit pills are one of the primary reasons for the harmful impact on human health and financial loss. Thus, pharmaceutical supply chains and end-to-end tracking systems are the recent research in healthcare. In this paper, the authors propose blockchain-based traceability of counterfeited drugs (BBTCD) that implements tracking of counterfeited drugs using smart contracts on the Ethereum blockchain. They offer a solution to fully decentralize the tracking by storing BBTCD on IPFS (inter planetary file system) to provide transparency and cost-effectiveness.
Visual Navigation Based on Language Assistance and Memory

In order to solve outdoor mobile robots’ dependence on geographic information systems, and to realize automatic navigation in the face of complex and changeable scenes, we propose a method that selects landmark and adds prompt guidance so that the mobile robot can navigate relying on visual-language and memory. Visual-language can guide the direction of the mobile robot’s movement, obeying the annotation of people and according to its memory of the scene, which refers to the strategy of selecting passed-by landmarks for the route and remembering the scene features. When passing it, the agent can ascertain the position and match it to carry out the action. Experiments showed that our proposed method can achieve the purpose of independent navigation without GIS, and is superior to existing methods.
Membership Inference Attacks With Token-Level Deduplication on Korean Language Models

The confidentiality threat against training data has become a significant security problem in neural language models. Recent studies have shown that memorized training data can be extracted by injecting well-chosen prompts into generative language models. While these attacks have achieved remarkable success in the English-based Transformer architecture, it is unclear whether they are still effective in other language domains. This paper studies the effectiveness of attacks against Korean models and the potential for attack improvements that might be beneficial for future defense studies. The contribution of this study is two-fold. First, we perform a membership inference attack against the state-of-the-art Korean GPT model. We found approximate training data with 20% to 90% precision in the top-100 samples and confirmed that the proposed attack technique for naive GPT is valid across the language domains. Second, in this process, we observed that the redundancy of the selected sentences could hardly be detected with the existing attack method. Since the information appearing in a few documents is more likely to be meaningful, it is desirable to increase the uniqueness of the sentences to improve the effectiveness of the attack. Thus, we propose a deduplication strategy to replace the traditional word-level similarity metric with the BPE token level. Our proposed strategy reduces 6% to 22% of the underestimated samples among selected ones, thereby improving precision by up to 7%p. As a result, we show that considering both language- and model-specific characteristics is essential to improve the effectiveness of attack strategies. We also discuss possible mitigations against the MI attacks on the general language models.
EFID: Edge-Focused Image Denoising Using a Convolutional Neural Network

In this paper, we propose an edge-focused image denoising convolutional neural network for the restoration of noisy images corrupted with additive white Gaussian noise (AWGN). First, the edge information for an input image is obtained for each RGB channel using the simple Laplacian edge operator on a smoothened image using the smoothen mask. Second, residual convolutional blocks, where each block comprises two parallel depth layers (one for an image channel and the other for respective edge channels), perform image and edge processing. Finally, the processed image and edge features are mixed and mapped using a single convolutional layer into the restored image. Our proposed parallel image and edge processing blocks can recover edges and fine structures while smoothing out the noise. In addition, our proposed network is efficiently designed such that the total number of weight parameters can be considerably reduced compared with conventional methods. Experimental results show that the proposed model is more effective in preserving textures and edges while removing noise and achieves up to 0.8 dB higher PSNR and 0.05 higher SSIM on a real image dataset than conventional methods.