Reviewerly/exHarmony huggingface dataset:
Conceptions of the metaverse in higher education: A draw-a-picture analysis and surveys to investigate the perceptions of students with different motivation levels

In recent years, the application of the metaverse in higher education has attracted the attention of numerous educators and researchers. It allows students to engage in immersive experiential learning and to experience a variety of real human interaction modes. Researchers have pointed out that although the introduction of emerging technologies may bring a sense of novelty, it cannot sustain student learning for a long time. Learners' learning performance still depends on their learning motivation. The present study aimed to explore the conceptions and perceptions (i.e., growth mindset, learning attitudes, and self-efficacy) of students with different motivation levels regarding the metaverse in higher education. A draw-a-picture analysis and surveys were conducted. The results indicated that most students believed that with the metaverse, they could learn the knowledge of unspecified subject domains in any location, realizing the idea of studying for practical application. In the drawings of students with low motivation levels, they still conducted static learning activities (i.e., reading/studying, and searching for sources) in the metaverse learning environment. On the other hand, students with high motivation levels focused on experiential learning, operation, or practice (i.e., hands-on practicing/performance and observing/experiencing). Moreover, the students with high motivation levels generally show better growth mindsets, learning attitudes, and self-efficacy than those with low motivation levels. This implies that learning motivation could be an important factor affecting students’ growth mindsets, learning attitudes, and self-efficacy of learning with the metaverse. The findings of this study serve as a foundation for future research, and provide preliminary evidence for understanding the conceptions of students with different motivation levels regarding the metaverse in higher education. Lastly, based on the analytic results, this study puts forward suggestions for the learning approaches of students in the metaverse as a reference for future researchers and teachers in higher education to design related activities.
Cyber Sentinel Chronicles: Navigating Ethical Hacking's Role in Fortifying Digital Security

It is impossible to stress how vitally important cybersecurity is in our day and age, characterized by the predominance of digital technologies. The concept of ethical hacking, sometimes known as "white hat" hacking, has recently emerged as an essential technique for protecting digital assets. Ethical hackers are at the forefront of cybersecurity because they use their skills to expose security flaws, put defenses to the test, and safeguard networks from harmful cyberattacks. Their operations are carried out honestly and within the law, primarily emphasizing anticipatorily locating and addressing vulnerabilities in the security system. We delve into the ever-evolving world of ethical hacking and shed light on the crucial part cyber sentinels play in preserving the digital landscape. It demonstrates ethical hackers' methods to find hazards and take preventative measures against them. This article discusses the importance of ethical hacking in strengthening cybersecurity, enhancing privacy, and maintaining the integrity of digital systems and digs deeply into ethical hackers' processes. Uncovering the practices and principles of these cyber sentinels highlights the indispensable role they play in ensuring a secure and resilient online environment.
A Sequential Decision Approach to Ordinal Preferences in Recommender Systems

We propose a novel sequential decision approach to modeling ordinal ratings in collaborative filtering problems. The rating process is assumed to start from the lowest level, evaluates against the latent utility at the corresponding level and moves up until a suitable ordinal level is found. Crucial to this generative process is the underlying utility random variables that govern the generation of ratings and their modelling choices. To this end, we make a novel use of the generalised extreme value distributions, which is found to be particularly suitable for our modeling tasks and at the same time, facilitate our inference and learning procedure. The proposed approach is flexible to incorporate features from both the user and the item. We evaluate the proposed framework on three well-known datasets: MovieLens, Dating Agency and Netflix. In all cases, it is demonstrated that the proposed work is competitive against state-of-the-art collaborative filtering methods.
Web and Social Media Image Forensics for News Professionals

User-generated content -commonly referred to as "eyewitness media"- has become an essential component in journalism and news reporting. Increasingly more news providers, such as news agencies, broadcasters and Web-only players have set up teams of dedicated investigators or are in the process of training parts of their journalistic workforce to gather and evaluate material from social networks and the Web. If verified, such content can be invaluable in delivering a news story. However, while source checking and verification is as old as journalism itself, the verification of digital material is a relatively young field, with protocols and assisting tools still being developed. In this work, we present our efforts towards a Web-based image verification platform. The platform, currently in its alpha stage, features image tampering detection using a number of state-of-the-art algorithms and image metadata visualization. We discuss the current strengths and limitations of the platform and the implemented state-of-the-art with respect to the specific requirements of the task, resulting from its Web-based nature and its intended use by news investigators with limited expertise in the domain of image forensics.
An active and incremental learning framework for the online prediction of link quality in robot networks

This paper presents a comprehensive framework for the active and incremental learning of link quality (LQ) in robot networks. Mobile robots need foresight into the quality of their wireless links in order to proactively optimize routing, plan mobility routes, avoid disconnects, and make other network optimizations. However, the task of predicting LQ is nontrivial. Many environmental factors that influence the quality of radio wave propagation are dynamic, and thus, robots must continually learn and update their prediction models while they operate online. Prior work in the field uses online learning algorithms for predicting LQ, but this approach is costly in terms of energy and network capacity because of the need for a consistent stream of LQ labels to be transmitted from the receiver to the transmitter. Hence, this paper introduces a framework to reduce these overhead expenses by incorporating active learning to selectively label only a portion of the samples from the data stream. The framework also uses incremental training batches to conserve labeling resources, and updates the batches using change detection and forgetting mechanisms to mitigate concept drift. Experimental results reveal that the framework reduces label queries by up to 21.5% and prediction error by up to 9% after periods of concept drift.
A regularization parameter selection model for total variation based image noise removal

• The consistency principle is proposed to estimate the model parameter on image noise removal problem. • A dual method is developed simultaneously to solve the optimization problem and to find the regularization parameter . • The gain can be as great as 2.23 dB in PSNR by the proposed method. Total variation regularized model is a powerful tool in image noise removal due to its edge-preserving property of an image. One important procedure in the model is to determine the regularization parameter which has an important role in balancing the data-fidelity and the regularity of the denoised image . Discrepancy principle is a classical method for selecting the regularization parameter, which provides an upper bound for the value of the data-fitting term. For the regularization parameter, it is easier to estimate its upper bound as the statistical property of the noise is known. The contribution of this paper is twofold. First, we propose an iterative algorithm to estimate an optimal upper bound by applying the consistency between the value of data-fitting term and the upper bound. Second, we develop a dual-based method to solve the constrained problem which can avoid the computation of the Lagrangian multiplier associated with the constraint. The new algorithm can simultaneously solve the solution of the constrained problem and the estimate of the regularization parameter. Numerical results are given to show that the proposed algorithm is better than some state-of-the-art methods in both efficiency and accuracy.
Image distortion of working digital camera induced by environmental temperature and camera self-heating

Abstract We investigated image distortion induced by the coupling effect between the self-heating of a digital camera and environmental temperature, and then proposed a model to explain the relationship between environmental temperature and thermal-induced image distortion. First, we analyzed the heat transfer between the (self-heating) camera and the environment to elucidate the relationship between the environmental and camera-component temperatures. Next, we analyzed the thermal-induced change of imaging optical path to derive the relationship between the camera-component temperatures and image distortion. Finally, we established and verified a new model for thermal-induced image distortion. Our results indicate that camera-self-heating-induced image distortion forms a special case described by our model.
Safety and efficiency management in LGV operated warehouses

In the industrial world, physical borders between men and machines are vanishing because of the collaborative tasks imposed by the new productive methods. Clearly, this poses safety issues which were not present in classical plants. For example, safety problems naturally arise in the case of autonomously guided industrial vehicles because, for obvious service needs, they share their workspace with human coworkers: only limited physical protections can be adopted. In this context, a speed planner has been recently proposed for the management of the laser guided vehicles operating in automatic warehouses. Such planner does not only generate efficient trajectories, but it also ensures that they are safe. This work describes, after a brief survey on the safety management in automatic warehouses, the tests which were carried out for the experimental validation of the planner. Tests were first executed in a demonstration plant appositely designed and, later, in a real warehouse by considering actual operating conditions. The validation campaign in the production plant lasted several months, thus allowing the acquisition of a consistent amount of statistical data.
Progressively diffused networks for semantic visual parsing

Recent deep models advance the task of semantic visual parsing by increasing the depth of networks and the resolution (size) of the predicted labelmaps. However, the contextual information within each layer and between layers is not fully explored. Long Short Term Memory Networks(LSTM) that learn to propagate information is well-suited to model pixels dependencies with respect to spacial locations within layers and depths across layers. Unlike previous LSTM-based methods that tend to enhance representation of each pixel only by involving the information from adjacent area. This work proposes Progressively Diffused Networks (PDNs) to deal with complex semantic parsing tasks. It can explore spatial dependencies in a larger field that represents the rich contextual information among pixels. The proposed model has three appealing properties. First, it enables information to be progressively broadcast across feature maps by stacking multiple diffusion layers. Second, in each layer, multiple convolutional LSTMs are adopted to generate a series of feature maps with different ranges of contexts. Third, in each LSTM unit, a special type of atrous filters are designed to capture the short range and long range dependencies from various neighbors. Extensive experiments demonstrate the effectiveness of PDNs to substantially improve the performances of existing LSTM-based models.
A High-Computational Efficiency Human Detection and Flow Estimation Method Based on TOF Measurements

State-of-the-art human detection methods focus on deep network architectures to achieve higher recognition performance, at the expense of huge computation. However, computational efficiency and real-time performance are also important evaluation indicators. This paper presents a fast real-time human detection and flow estimation method using depth images captured by a top-view TOF camera. The proposed algorithm mainly consists of head detection based on local pooling and searching, classification refinement based on human morphological features, and tracking assignment filter based on dynamic multi-dimensional feature. A depth image dataset record with more than 10k entries and departure events with detailed human location annotations is established. Taking full advantage of the distance information implied in the depth image, we achieve high-accuracy human detection and people counting with accuracy of 97.73% and significantly reduce the running time. Experiments demonstrate that our algorithm can run at 23.10 ms per frame on a CPU platform. In addition, the proposed robust approach is effective in complex situations such as fast walking, occlusion, crowded scenes, etc.
Mobile Robot Path Planning Based on Optimized Fuzzy Logic Controllers

The path planning task is defined as the process to compute the motion sequence allowing the robot to move from the start position to the final destination autonomously without human actions. The path planning is one of the popular tasks encountered by imprecision and uncertainties and it has been studied using fuzzy logic systems (FLS). The construction of a well performing fuzzy controller is not always easy. The problem of finding appropriate membership functions and fuzzy rules is a difficult task. However, the design of fuzzy rules is often reliant on heuristic experience and it lacks systematic methodology, therefore these rules might not be correct and consistent. The design can prove to be long and delicate due to the important number of parameters to determine, and can lead then to a solution with poor performance. To cope with this difficulty, many researchers have been working to find and apply learning algorithms for fuzzy controller design. These automatic methods enable to extract information when the knowledge is not available. The most popular approach to optimize fuzzy logic controllers may be a kind of supervised learning where the training data is available. However, in real applications, extraction of training data is not always easy and become impossible when the cost to obtain training data is expensive. For these problems, reinforcement learning (RL) is more suitable than supervised learning. A control strategy with a learning capacity can be carried out by using Q-learning for tuning fuzzy logic controllers; which the robot receives only a scalar signal likes a feedback. This information makes to adjust the robot behavior in order to improve their performances. The basic idea in Q-learning algorithm of RL is to maximize the received rewards after each interaction with the environment. In this chapter, Q-learning algorithm is used to optimize Takagi-Sugeno fuzzy logic controllers for autonomous path planning of a mobile robot. These optimized fuzzy controllers are used for the different robot tasks: goal seeking, obstacle avoidance and wall-following. The obtained results of this optimization method present significant improvements of the robot behaviors.
Survey on biometry for cognitive automotive systems

A survey on biometry for cognitive automotive systems is presented in this paper, specially those biometric systems used for high tech security access, law enforcement and/or commercial transactions. In general, biometric systems can be expensive due to the amount of sensors and processing resources involved. Efforts have been made to integrate these systems to vehicles mostly for security purposes and user authentication. Until now, most of the systems do not go beyond using facial and fingerprint data to start the engine or access the car; however, new generations demand more personalization plus the vehicle making decisions based on their physiological characteristics. Although this kind of technology is considered a luxury feature in general, actually it could help users and save their lives. Indeed, biometry is the way to make the human-vehicle relationship happen, whether the biometric devices are embedded inside the vehicle, used as an accessory or wearables. By using as input not only the sensors inside the vehicle but also data from outside, the vehicle could adapt and/or learn new information to make the best possible decision.
Partially Exchangeable Networks and architectures for learning summary statistics in Approximate Bayesian Computation

We present a novel family of deep neural architectures, named partially exchangeable networks (PENs) that leverage probabilistic symmetries. By design, PENs are invariant to block-switch transformations, which characterize the partial exchangeability properties of conditionally Markovian processes. Moreover, we show that any block-switch invariant function has a PEN-like representation. The DeepSets architecture is a special case of PEN and we can therefore also target fully exchangeable data. We employ PENs to learn summary statistics in approximate Bayesian computation (ABC). When comparing PENs to previous deep learning methods for learning summary statistics, our results are highly competitive, both considering time series and static models. Indeed, PENs provide more reliable posterior samples even when using less training data. (Less)
Dimension-by-dimension enhanced cuckoo search algorithm for global optimization

Cuckoo search (CS) algorithm is an efficient meta-heuristic algorithm that has been successfully applied in many fields. However, the algorithm uses the whole updating and evaluating strategy on solutions. For solving multi-dimensional optimization problems, solutions with partial dimension evolution may be discarded due to mutual interference among dimensions. Therefore, this strategy may deteriorate the quality solution and convergence rate of algorithm. To overcome this defect and enhance the algorithm performance, a dimension-by-dimension enhanced CS algorithm is proposed. In the global explorative random walk, the improved algorithm uses the dimension-by-dimension updating and evaluating strategy on solutions. This strategy combines the updated values of each dimension with the values of other dimensions into a new solution. In addition, a greedy strategy is adopted to accept new solution and the search center is set as the current optimal solution. The proposed algorithm was tested on fourteen well-known benchmark functions. The numerical results show that the improved algorithm can effectively enhance the quality solution and convergence rate for the global optimization problems.
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Collecting a sufficient amount of 3D training data for autonomous vehicles to handle rare, but critical, traffic events (e.g., collisions) may take decades of deployment. Abundant video data of such events from municipal traffic cameras and video sharing sites (e.g., YouTube) could provide a potential alternative, but generating realistic training data in the form of 3D video reconstructions is a challenging task beyond the current capabilities of computer vision. Crowdsourcing the annotation of necessary information could bridge this gap, but the level of accuracy required to obtain usable reconstructions makes this task nearly impossible for non-experts. In this paper, we propose a novel hybrid intelligence method that combines annotations from workers viewing different instances (video frames) of the same target (3D object), and uses particle filtering to aggregate responses. Our approach can leveraging temporal dependencies between video frames, enabling higher quality through more aggressive filtering. The proposed method results in a 33% reduction in the relative error of position estimation compared to a state-of-the-art baseline. Moreover, our method enables skipping (self-filtering) challenging annotations, reducing the total annotation time for hard-to-annotate frames by 16%. Our approach provides a generalizable means of aggregating more accurate crowd responses in settings where annotation is especially challenging or error-prone.
Publisher Correction: ComPath: an ecosystem for exploring, analyzing, and curating mappings across pathway databases

[This corrects the article DOI: 10.1038/s41540-018-0078-8.].
SSL: A Novel Image Hashing Technique Using SIFT Keypoints with Saliency Detection and LBP Feature Extraction against Combinatorial Manipulations

Image hashing schemes have been widely used in content authentication, image retrieval, and digital forensic. In this paper, a novel image hashing algorithm (SSL) by incorporating the most stable keypoints and local region features is proposed, which is robust against various content-preserving manipulations, even multiple combinatorial manipulations. The proposed algorithm combines <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:munder accentunder="false"><mml:mrow><mml:mi mathvariant="normal">S</mml:mi></mml:mrow><mml:mo>_</mml:mo></mml:munder></mml:mrow></mml:math>cale invariant feature transform (SIFT) with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:munder accentunder="false"><mml:mrow><mml:mi mathvariant="normal">S</mml:mi></mml:mrow><mml:mo>_</mml:mo></mml:munder></mml:mrow></mml:math>aliency detection to extract the most stable keypoints. Then, the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:munder accentunder="false"><mml:mrow><mml:mi mathvariant="normal">L</mml:mi></mml:mrow><mml:mo>_</mml:mo></mml:munder></mml:mrow></mml:math>ocal binary pattern (LBP) feature extraction method is exploited to generate local region features based on these keypoints. After that, the information of keypoints and local region features are merged into a hash vector. Finally, a secret key is used to randomize the hash vector, which can prevent attackers from forging the image and the hash value. Experimental results demonstrate that the proposed hashing algorithm can identify visually similar images which are under both single and combinatorial content-preserving manipulations, even multiple combinations of manipulations. It can also identify maliciously forged images which are under various content-changing manipulations. The collision probability between hashes of different images is nearly zero. Besides, the evaluation of key-dependent security shows that the proposed scheme is secure that an attacker cannot forge or estimate the correct hash value without the knowledge of the secret key.