Reviewerly/exHarmony huggingface dataset:
A Data-Driven Method for Metric Extraction to Detect Faults in Robot Swarms

Robot swarms are increasingly deployed in real-world applications. Making swarms safe will be critical to improve adoption and trust. Fault detection is a useful component in systems which require a level of safety: a key element of which are metrics that allow us to differentiate between faulty and normal (non-faulty) robots - metrics which are measurable on-board the individual robots for self-detection of faults. In this paper, we develop a method for identifying and evaluating such metrics and discuss how these metrics may be used in building a model for fault detection. We demonstrate this method for real-time error detection in a realistic use-case: intralogistics using swarms. We show that we are able to identify metrics of large effect size for various faults, demonstrating the potency of metrics selected in this way with a simple fault detection model.
PRIOR

Video service providers have deployed dynamic video bitrate adaptation services to fulfill user demands for higher video quality. However, fluctuations and instability of network conditions inhibit the performance promotion of adaptive bitrate (ABR) algorithms. Existing rule-based approaches fail to guarantee accurate throughput estimates, and learning-based algorithms are considerably sensitive to the variability of network. Therefore, how to gain effective and stable throughput estimates has become one of the critical challenges to further enhancing ABR methods. To eliminate this concern, we propose PRIOR, an ABR algorithm that fuses an effective throughput prediction module and a state-of-the-art multi-agent reinforcement learning method to provide a high quality of experience (QoE). PRIOR aims to maximize the QoE metric by straightforwardly utilizing accurate throughput estimates rather than past throughput measurements. Specifically, PRIOR employs a light-weighted prediction module with attention mechanism to obtain effective future throughput. Considering the excellent features introduced by the HTTP/3 protocol, we apply PRIOR to trace-driven simulations and real-world scenarios over HTTP/1.1 and HTTP/3. Trace-driven emulation illustrates that PRIOR outperforms existing ABR schemes over HTTP/1.1 and HTTP/3, and our prediction module can also reinforce the performance of other ABR algorithms. Extensive results on real-world evaluation demonstrate the superiority of PRIOR over existing state-of-the-art ABR schemes.
Re-Visiting the Quantification of Hematite by Diffuse Reflectance Spectroscopy

Hematite concentration is an important climatic proxy for environmental (climatic) studies of soils and sediments. However, the accurate quantification of naturally occurring hematite has always been a difficult question, especially for those areas with lower hematite concentrations. Diffuse reflectance spectroscopy (DRS) is an effective method for hematite identification and quantification with lower detection limits. In this study, we synthesized a set of samples with well-determined concentrations to explore the exact detectable range of hematite and propose the most effective transfer function between the DRS proxy and hematite concentration. In addition, natural sediments from Inland Asia and the Western Pacific Ocean were used to further test the feasibility of the new transfer function. Results show that the lowest DRS detection limit for hematite could reach ~0.00078%, but is affected by the natural matrix. We also find that the second derivative of the Kubelka–Munk (K–M) function is monotonically correlated with the hematite concentration (0.00078%–100%), but ambiguities exist for the first derivative. Therefore, the second derivative of the K–M function is highly suggested for the hematite quantification, especially when concentration exhibits a wide range of variations. This study provides important references for the application of hematite proxy and promotes the popularization and development of the DRS method.
Defining “digitally competent teacher”: An examination of pre-service teachers’ metaphor

This study aims to determine the perceptions of pre-service teachers regarding the concept of a “digitally competent teacher” through metaphors. The participants consist of 510 pre-service teachers. According to the findings, metaphors for a digitally competent teacher were classified under three themes: digital competence, pedagogical competence, and personal characteristics. Pre-service teachers describe a digitally competent teacher as an individual who has digital competences such as information and data literacy, communication, safety, problem-solving, and productivity; have pedagogical competences such as empowering learners, guiding their students, being the source and transmitter of information, and facilitating learning; have personal characteristics such as being open to change and development, equipped, versatility, valuable, using technology effectively, creative, innovative, and querent. It is predicted that the use of metaphors in the process of training a digitally competent teacher may be effective in correcting the misconceptions of pre-service teachers regarding the concept of a digitally competent teacher and determining the roadmap for their personal and professional development.
Data format standards in analytical chemistry

Abstract Research data is an essential part of research and almost every publication in chemistry. The data itself can be valuable for reuse if sustainably deposited, annotated and archived. Thus, it is important to publish data following the FAIR principles, to make it findable, accessible, interoperable and reusable not only for humans but also in machine-readable form. This also improves transparency and reproducibility of research findings and fosters analytical work with scientific data to generate new insights, being only accessible with manifold and diverse datasets. Research data requires complete and informative metadata and use of open data formats to obtain interoperable data. Generic data formats like AnIML and JCAMP-DX have been used for many applications. Special formats for some analytical methods are already accepted, like mzML for mass spectrometry or nmrML and NMReDATA for NMR spectroscopy data. Other methods still lack common standards for data. Only a joint effort of chemists, instrument and software vendors, publishers and infrastructure maintainers can make sure that the analytical data will be of value in the future. In this review, we describe existing data formats in analytical chemistry and introduce guidelines for the development and use of standardized and open data formats.
Approximate Task Tree Retrieval in a Knowledge Network for Robotic Cooking

Flexible task planning continues to pose a difficult challenge for robots, where a robot is unable to creatively adapt their task plans to new or unseen problems, which is mainly due to the limited knowledge it has about its actions and world. Motivated by a human's ability to adapt, we explore how task plans from a knowledge graph, known as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Functional Object-Oriented Network</i> (FOON), can be generated for novel problems requiring concepts that are not readily available to the robot in its knowledge base. Knowledge from 140 cooking recipes are structured in a FOON knowledge graph, which is used for acquiring task plan sequences known as task trees. Task trees can be modified to replicate recipes in a FOON knowledge graph format, which can be useful for enriching FOON with new recipes containing unknown object and state combinations, by relying upon semantic similarity. We demonstrate the power of task tree generation to create task trees with never-before-seen ingredient and state combinations as seen in recipes from the Recipe1M+ dataset, with which we evaluate the quality of the trees based on how accurately they depict newly added ingredients. Our experimental results show that our system is able to provide task sequences with 76% correctness.
Drone Footage Wind Turbine Surface Damage Detection

In this work a new publicly available dataset of wind turbine surface damage images is presented. Moreover, a comparison between ResNet-101 Faster R-CNN and YOLOv5 for Wind Turbine Surface Damage Detection is analysed and performance of these models on drone footage with active turbines is also discussed. Results show that YOLOv5 outperforms ResNet-101 Faster R-CNN in predicting the bounding box coordinates of the damaged surfaces of the wind turbines. However, unlike YOLOv5, ResNet-101 Faster R-CNN estimates an entire instance of damage as a single prediction.
Federated Generative Adversarial Networks based Channel Estimation

With the rapid growth of connectivity and demands on reliable communications, channel estimation becomes a critical problem. Recently, the advancement of deep learning enables real-time data-driven channel estimation. Particularly, this paper proposes a generative adversarial network (GAN) based method for channel estimation in a distributed approach. We leverage GAN to accurately estimate the orthogonal frequency division multiplexing channels with a shorter length of pilots. Considering the low complexity of the least square channel estimator, we use it as the input of GAN and train the neural network to learn the distribution of channels. To guarantee an efficient learning process, federated learning is adopted in this paper. Meanwhile, we develop dynamic regularizers to improve the federated learning performance. Simulation results demonstrate that the proposed federated GAN model provides better channel estimation performance with lower pilot overhead, compared to the conventional channel estimation methods.
Comparing design review outcomes in immersive and non-immersive collaborative virtual environments

Collaborative virtual environments (CVEs) such as virtual reality (VR) technologies have great potential to support design reviews. They enable users to view a design (solution) under review and simultaneously represent team members with social cues such as avatars. However, while CVEs are promising for design practice and education, it is still unclear how they affect review outcomes. This study presents an experiment that compares 10 three-member teams during design review sessions conducted within two different CVEs. The first CVE is non-immersive, that is, a collaborative computer-aided design (CAD) tool used on a conventional desktop interface, while the second CVE is immersive and employs three head-mounted display VR headsets. The results show that teams in immersive CVE identify fewer issues than the teams in non-immersive CVE. Furthermore, although the reviewed designs exhibit a similar level of complexity, the results show that the distribution of issues in immersive CVE was slightly different than the distribution in non-immersive CVE. These results suggest that teams might perform better when using collaborative CAD than collaborative VR when the review goal is to identify as many issues as possible. However, the decision on which CVE to employ should depend on the design under review, as the two technologies might support reviews of different types of designs.
Depth-first random forests with improved Grassberger entropy for small object detection

This paper proposes a random forests-based method appropriate for detecting small objects such as Unmanned Aerial Vehicles (UAVs) and aircrafts when these objects occupy a small portion within an image filmed by an autonomously moving camera. Random forests classifiers are machine learning methods that manage an accurate prediction ability and are computationally efficient both during training and testing. In the random forests classifier model, the split node data is divided into left and right child node data based on the optimal split parameter determined by the node having the maximum information gain, which is calculated based on information entropy. It is well known that the information entropy estimation procedure is biased, and therefore we replace it with an improved Grassberger entropy scheme that achieves a better random forests classifier. Grassberger entropy is improved in terms of its representation and digamma function properties, and we adequately justify its validity. Although a breadth-first training scheme is a natural choice, it uses excessive memory when the tree grows to deeper layers. To compensate, a depth-first recursive training random forests classifier is used, where only one node is split in each recursive process. A depth-first recursive training random forests classifier uses a constant amount of memory to deal with underfitting. The performance of the proposed method is evaluated on classification and object detection datasets. The experimental results demonstrate that the improved Grassberger entropy estimation improves predictive performance, and the tree generated by the depth-first method suppresses underfitting. We believe that employing a depth-first random forests classifier with an improved Grassberger entropy is appealing and effective for real-world applications.
CSFPA: An intelligent hybrid workflow scheduling algorithm based upon global and local optimization approach in cloud

Summary In cloud, the most prominent area is workflow scheduling due to its widespread application in different domains. It comes under the NP‐complete problem, henceforth researchers have suggested the nature‐inspired heuristics and metaheuristic algorithms but still, the results of these heuristics are not optimal. These algorithms are not competitive but complementary to each other, so in that case, hybridization may yield better results. Since then, researchers have started to combine different heuristics for better results. But in this process of hybridization, the two most important factors have not been considered yet, namely global and local optimization. The proposed work has considered the two most popular nature‐inspired workflow scheduling Cuckoo Search (CS) algorithm and Flower Pollination Algorithm (FPA) for hybridization. CS works best for global optimization, and FPA provides good results in the case of local optimization. The proposed work leveraged the benefits of both optimizations and proposed a novel hybrid algorithm entitled Cuckoo Search Flower Pollination Algorithm (CSFPA). The proposed hybrid algorithm uses multi‐objective functions to minimize cost and time and thus enhance the utilization of resources. The CSFPA algorithm has been compared with both single nature aspired algorithms and hybrid algorithm for performance evaluation.
Path Planning for Multiple Unmanned Vehicles (MUVs) Formation Shape Generation Based on Dual RRT Optimization

In this paper, dual RRT optimization is proposed to solve the formation shape generation problem for a large number of MUVs. Since large numbers of MUVs are prone to collision during formation shape generation, this paper considers the use of path planning algorithms to solve the collision avoidance problem. Additionally, RRT as a commonly used path planning algorithm has non-optimal solutions and strong randomness. Therefore, this paper proposes a dual RRT optimization to improve the drawbacks of RRT, which is applicable to the formation shape generation of MUVs. First, an initial global path can be obtained quickly by taking advantage of RRT-connect. After that, RRT* is used to optimize the initial global path locally. After finding the section that needs to be optimized, RRT* performs a new path search on the section and replaces the original path. Due to its asymptotic optimality, the path obtained by RRT* is shorter and smoother than the initial path. Finally, the algorithm can further optimize the path results by introducing a path evaluation function to determine the results of multiple runs. The experimental results show that the dual RRT operation optimization can greatly reduce the running time while avoiding obstacles and obtaining better path results than the RRT* algorithm. Moreover, multiple runs still ensure stable path results. The formation shape generation of MUVs can be completed in the shortest time using dual RRT optimization.
A Comprehensive Assessment of Human Factors in Cyber Security Compliance toward Enhancing the Security Practice of Healthcare Staff in Paperless Hospitals

Recent reports indicate that over 85% of data breaches are still caused by a human element, of which healthcare is one of the organizations that cyber criminals target. As healthcare IT infrastructure is characterized by a human element, this study comprehensively examined the effect of psycho-socio-cultural and work factors on security behavior in a typical hospital. A quantitative approach was adopted where we collected responses from 212 healthcare staff through an online questionnaire survey. A broad range of constructs was selected from psychological, social, cultural perception, and work factors based on earlier review work. These were related with some security practices to assess the information security (IS) knowledge, attitude and behavior gaps among healthcare staff in a comprehensive way. The study revealed that work emergency (WE) has a positive correlation with IS conscious care behavior (ISCCB) risk. Conscientiousness also had a positive correlation with ISCCB risk, but agreeableness was negatively correlated with information security knowledge (ISK) risk and information security attitude (ISA) risk. Based on these findings, intrinsic and extrinsic motivation methods combined with cutting-edge technologies can be explored to discourage IS risks behaviors while enhancing conscious care security practice.
Agnostic Explanation of Model Change based on Feature Importance

Abstract Explainable Artificial Intelligence (XAI) has mainly focused on static learning tasks so far. In this paper, we consider XAI in the context of online learning in dynamic environments, such as learning from real-time data streams, where models are learned incrementally and continuously adapted over the course of time. More specifically, we motivate the problem of explaining model change , i.e. explaining the difference between models before and after adaptation, instead of the models themselves. In this regard, we provide the first efficient model-agnostic approach to dynamically detecting, quantifying, and explaining significant model changes. Our approach is based on an adaptation of the well-known Permutation Feature Importance (PFI) measure. It includes two hyperparameters that control the sensitivity and directly influence explanation frequency, so that a human user can adjust the method to individual requirements and application needs. We assess and validate our method’s efficacy on illustrative synthetic data streams with three popular model classes.
Transformer Feature Enhancement Network with Template Update for Object Tracking

This paper proposes a tracking method combining feature enhancement and template update, aiming to solve the problems of existing trackers lacking global information attention, weak feature characterization ability, and not being well adapted to the changing appearance of the target. Pre-extracted features are enhanced in context and on channels through a feature enhancement network consisting of channel attention and transformer architectures. The enhanced feature information is input into classification and regression networks to achieve the final target state estimation. At the same time, the template update strategy is introduced to update the sample template judiciously. Experimental results show that the proposed tracking method exhibits good tracking performance on the OTB100, LaSOT, and GOT-10k benchmark datasets.
A Novel Fuzzy-Logic-Based Multi-Criteria Metric for Performance Evaluation of Spam Email Detection Algorithms

The increasing volume of unsolicited bulk emails has become a major threat to global security. While a significant amount of research has been carried out in terms of proposing new and better algorithms for email spam detection, relatively less attention has been given to evaluation metrics. Some widely used metrics include accuracy, recall, precision, and F-score. This paper proposes a new evaluation metric based on the concepts of fuzzy logic. The proposed metric, termed μO, combines accuracy, recall, and precision into a multi-criteria fuzzy function. Several possible evaluation rules are proposed. As proof of concept, a preliminary empirical analysis of the proposed scheme is carried out using two models, namely BERT (Bidirectional Encoder Representations from Transformers) and LSTM (Long short-term memory) from the domain of deep learning, while utilizing three benchmark datasets. Results indicate that for the Enron and PU datasets, LSTM produces better results of μO, with the values in the range of 0.88 to 0.96, whereas BERT generates better values of μO in the range of 0.94 to 0.96 for Lingspam dataset. Furthermore, extrinsic evaluation confirms the effectiveness of the proposed fuzzy logic metric.
Sensitivity Optimization and Experimental Study of the Long-Range Metal Detector Based on Chaotic Duffing Oscillator

Sensors based on chaotic oscillators have a simple design, combined with high sensitivity and energy efficiency. Among many developed schemes of such sensors, the promising one is based on the Duffing oscillator, which possesses a remarkable property of demonstrating chaotic oscillations only in the presence of a weak sine wave at the input. The main goal of this research was to evaluate the maximal sensitivity of a practically implemented metal detector based on the Duffing oscillator and compare its sensitivity with conventional sensors. To achieve high efficiency of the Duffing-based design, we proposed an algorithm which performs a bifurcation analysis of any chaotic system, classifies the oscillation modes and determines the system sensitivity to a change in different parameters. We apply the developed algorithm to improve the sensitivity of the electronic circuit implementing the Duffing oscillator, serving as a key part of a three-coil metal detector. We show that the developed design allows detecting the presence of metal objects near the coils more reliably than the conventional signal analysis techniques, and the developed detector is capable of sensing a large metal plate at distances up to 2.8 of the coil diameter, which can be considered a state-of-the-art result.
The use of video essays and podcasts to enhance creativity and critical thinking in engineering

Abstract The current demands of the labor market demand a new compendium of skills from engineering graduates. To develop skills at a more complex level, this study analyzed the use of second-generation Video Essays and Podcasts to improve soft skills. The characteristics of students belonging to Generation Z as digital natives were considered and digital platforms were adapted for interaction in social networks to enhance critical thinking and creativity (Criticality). Active learning experiences in different engineering programs were analyzed using the 4-group Solomon methodology with a quantitative design and different assessment instruments were used for Pre-Tests and Post-Tests, including various fluency and originality tests, as well as answering articulation ability tests. and modified VALUE rubrics from the Association of American Colleges and Universities (AAC&amp;U). Our results clearly suggest that the use of these two tools substantially improves understanding of scientific concepts in engineering subjects, a greater ability to develop increasingly in-demand skill sets, and a greater awareness of creative thinking competence.