Reviewerly/exHarmony huggingface dataset:
The effect of peripheral cues on motion sickness mitigationwhen using a VR HMD in a car

Virtual Reality (VR) is a promising candidate for an in-vehicle personal viewing system. However, motion sickness caused by VR in vehicles is a major obstacle to its use. In this study, we propose mitigating motion sickness by presenting peripheral cues that are integral to a VR scene. The cues react to the vehicle’s rotation with a three-dimensional opposing rotation. An on-road experiment was conducted to evaluate whether the peripheral cues mitigate motion sickness. Outcomes were assessed by both subjective motion sickness ratings and physiological responses. There are two conditions: watching a video with the cues and without the cues. Results indicate that motion sickness gradually increased with exposure time, and the mean levels of motion sickness were lower when the cues were presented. We discuss how these results are related to the cue design and suggest directions for future work.
Path planning algorithm for sweeping robot full traversal cleaning area

Path planning is an important part of the research and development of sweeping robots, and algorithm research is the core content of path planning. This paper proposes a path planning algorithm for sweeping robot sweeping areas in complex environments, which can be applied to both static and dynamic environments. Simulation experiments show that the algorithm proposed in this paper can cover 100% of the cleaning area, and the path repetition rate is about 5%; the cleaning efficiency is significantly better than other reference algorithms. The full traversal path planning algorithm proposed in this paper can provide new ideas for the development of sweeping robots and promote the construction of smart home services.
1-WL Expressiveness Is (Almost) All You Need

It has been shown that a message passing neural networks (MPNNs), a popular family of neural networks for graph-structured data, are at most as expressive as the first-order Weisfeiler-Leman (1-WL) graph isomorphism test, which has motivated the development of more expressive architectures. In this work, we analyze if the limited expressiveness is actually a limiting factor for MPNNs and other WL-based models in standard graph datasets. Interestingly, we find that the expressiveness of WL is sufficient to identify almost all graphs in most datasets. Moreover, we find that the classification accuracy upper bounds are often close to 100%. Furthermore, we find that simple WL-based neural networks and several MPNNs can be fitted to several datasets. In sum, we conclude that the performance of WL/MPNNs is not limited by their expressiveness in practice.
Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms

Accurate breast cancer diagnosis through mammography has the potential to save millions of lives around the world. Deep learning (DL) methods have shown to be very effective for mass detection in mammograms Additional improvements of current DL models will further improve the effectiveness of these methods. A critical issue in this context is how to pick the right hyperparameters for DL models. In this paper, we present GA-E2E, a new approach for tuning the hyperparameters of DL models for breast cancer detection using Genetic Algorithms (GAs). Our findings reveal that differences in parameter values can considerably alter the area under the curve (AUC), which is used to determine a classifier’s performance.
Approaches to developing creative thinking skills in students at an engineering university

Development of creative abilities of students of different majors during their training is implemented in various universities. The analysis of universities’ process of future engineers training allowed us to see what we can use in our own classes. The implementation of innovative methodology took place at the Faculty of “Oil and Petrochemistry” of Kazan National Research Technological University with students from the first to fourth years. At the first stage (first and second courses), students studied some TRIZ methods, such as brainstorming, analogy method, intellectual warm-up, method "365" and used them at seminars. In the third year of student training, we used Mind mapping method at the discipline “Processes and devices of chemical technology”. During their fourth year students of the “Faculty of Oil and Petrochemistry” defend their course project in the form of a business game. Business game is designed with the use of TRIZ methodology and using the experience of Moscow State Industrial University in developing creativity of future engineers. Business game was based on working with technological schemes of the absorption process at creative level. Pedagogical observations of this stage show that the model for the development of future engineers’ creative abilities developed and tested by us at present, makes it possible to activate their learning process, increase students’ academic performance and acquisition of the studied disciplines.
Communities of Making - A Visual Exploration of Rural Makerspaces in India

This pictorial seeks to capture both the tangible and intangible aspects of site visits to Makerspaces situated in rural India. While over twenty-five Makerspaces across four regions were documented through conversational interviews, photography, sketching and reflective writing for the project, only a small selection of this wealth of visual and factual information, with a particular focus on rural locations, has been compiled in this pictorial. It provides a rich tapestry of Indian maker cultures, their contribution to local communities, and how these spaces interpret their role as facilitators of creation within the complex global narratives of sustainability.
The Shared Memory Based Cryptographic Card Virtualization

Driven by cloud computing technology, traditional cryptography is transforming into cloud cryptographic service. Cryptographic cards must be virtualized if they are to be used in cloud. Hardware virtualization is the most commonly used cryptographic card virtualization solution, however, all existing hardware solutions rely on high-performance cryptographic cards that support Single Root I/O Virtualization (SR-IOV). Such cryptographic cards are expensive, providing a limited number of virtual cryptographic cards, making it challenging to support large-scale computing in cloud. Furthermore, existing software-based virtualization solutions perform poorly and do not support operations such as virtual machine(VM) migration and replication. This paper proposes a shared memory based cryptographic card software virtualization solution, which virtualizes a single PCIe cryptographic card into multiple virtual cryptographic cards, and encapsulates it as a virtual cipher machine (VCM) for users. This solution enables multiple VCMs to share hardware cryptographic resources effectively and reduce the hardware requirements for cloud servers to use cryptographic resources dramatically, realizing elastic expansion and an on-demand supply of cryptographic resources. Experimental results demonstrate that the performance of the solution proposed in this paper is better than the existing software virtualization solutions and can meet the requirements of high availability and high concurrency of cryptographic applications.
Evaluating Text Augmentation for Boosting the Automatic Mapping of Vulnerability Information to Adversary Techniques

MITRE ATT&CK is a well known framework which provides knowledge about adversary techniques' lifecycle and the targeted platforms. This knowledge is acquired by manually mapping vulnerability information to adversary techniques. However, the amount of published vulnerabilities makes it tedious and impractical for the expert. To this end, a model is developed to automate this mapping by solving a multi-label text classification problem. That is, to assign multiple adversary techniques, i.e., labels, to a vulnerability text description. In this paper, state-of-the-art models based on neural networks are utilized to solve the mapping problem. A common issue in multi-label classification is the existence of underrepresented classes. Here, text augmentation techniques are leveraged to help the developed models confront this by increasing, explicitly or implicitly, the input information. It is experimentally demonstrated that the proposed models surpass previous state-of-the-art. Additionally, when the proposed text augmentation techniques are used performance is boosted across all metrics providing a more accurate mapping.
GNN-based Method for Predicting Access Control Relationships for Big Data

Access control is one of the important technical means to achieve secure and controllable big data, but the lack of relational data inevitably affects the completeness of relational-based access control rules. Although existing link prediction techniques can be used for the prediction of entity relationships, they still face problems such as incomplete information elements and undifferentiated entity weights. To address these problems, this paper proposes a GNN-based link prediction model LPMG. The model proposes a node embedding feature learning method based on directed neighbor subgraphs, incorporates elements such as attention coefficients and relationship types, improves the representation capability of node embedding, and achieves link prediction of entity relationship graphs. The experiment results show that the model effectively improves the effect of link prediction and can meet the demand of big data access control relationship prediction.
A Hybrid Model for Detecting Satellite Telemetry Data Anomalies

Anomaly detection of satellite telemetry data is of great significance for on-orbit satellite health monitoring. However, owing to the complex inherent characteristics, satellite telemetry data anomaly detection may be very difficult. To improve the accuracy of detecting satellite telemetry data anomalies, a novel hybrid model, called EEMD-SE-GWO-SVM, is proposed in this paper. In this model, ensemble empirical mode decomposition (EEMD) and sample entropy (SE) theory are firstly cooperated to extract critical features embedded in the raw telemetry data. Support vector machine (SVM) optimized by grey wolf optimizer (GWO) is then served as the predictive technique for the prediction of the reconstructed critical features. The anomaly can be finally detected by observing whether the residual error between the actual and predicted values exceeds a certain threshold. Experiment with the telemetry data of a real-world satellite is carried out and the results demonstrate that the proposed hybrid model outperforms comparison models in terms of anomaly detection accuracy.
Focus and Adjust: Progressive Refinement Network for Human Object Interaction Detection

Many recent works model the Human Object Interaction detection process as a set prediction problem with the help of transformer architecture while demonstrating promising performance. However, previous transformer-based detectors suffer from the variable scale of instances involved in HOI. Moreover, the naive transformer provides a global receptive field for the query to search for contextual information, which also introduces potential redundant information for the corresponding human-object pair. In this paper, we propose PR-Net, a Progressive Refinement Network equipped with two designed refinement modules to tackle the problems above, which provides a coarse-to-fine framework for Human Object Interaction detection. In addition, the refinement modules are organized in a localization-interaction mutual-guided manner to exploit the benefits of the co-optimization between the instance localization and interaction classification and promote HOI detection performance. Our proposed method achieves competitive performance in two HOI detection benchmarks and extensive experiments demonstrate its effectiveness.
Fraud Detection in Supply Chain with Machine Learning

A variety of fraud in Supply Chains may be detected either in physical parts or in cyber data. We use supervised machine learning to detect various fraud and misinformation in supply chains. The study is based on a car manufacturer concerned with increasing fraud, ranging from fraudulent invoices to inflated prices. Big data is provided for pattern recognition. A macro-level code is presented with actual algorithms developed in Python. The research is continuing, while the current work is presented with promising results.
Image Retrieval and Pattern Spotting on Historical Documents with Binary Descriptors

This paper describes a method to perform the tasks of Image Retrieval and Pattern Spotting in a collection of historical documents, in a zero-shot manner, i.e. without prior knowledge or any training on the pattern to be searched. The proposed method measures the similarity between images using representation schemes based on feature maps provided by intermediate layers of a CNN. Moreover, to improve the time response and reduce the storage need, we propose to binarize these image representations. Experimental results obtained on the DocExplore dataset show that the proposed method improves the mAP on this dataset by 38.46% for Image Retrieval and 134.6% for Pattern Spotting compared to the state-of-the-art methods. The proposed binarization strategy provides a reduction of memory usage by a factor of 16 with a slight decrease of less than one percentage point in the mAP for both tasks and a reduction of 13.3% in search time when using the binary representation.
Research on position and attitude control method of planar motion of 5-DOF hybrid robot

In the agriculture industry, scholars are increasingly paying attention to the advantages of hybrid robots and applying them in agricultural production. Agricultural products such as tree trunks or fruit stems is formed by growth. When the hybrid robot is working, It is easier to obtain the ideal end pose of the hybrid robot by describing the pose relationship of the robot end relative to the work object than the robot end relative to the world coordinate system. In order to realize the continuous motion control of the hybrid robot with a specified relative attitude relative to a certain plane in space, a method to realized planar motion based on 2SPU+U+RRR type five-degree-of-freedom hybrid robot position and attitude control is proposed. First, the trajectory of the task is discretized, according to the required posture, and the position and attitude matrix of each position on the trajectory is derived. Then through the inverse solution of the kinematic model, the five-degree-of-freedom hybrid robot is completely decoupled. The pose of the robot end is calculated to realize the plane motion control of the robot with a specified pose in Cartesian space according to the kinematic model of the hybrid robot based on the mechanism equivalent principle. Finally, the pose control method of the robot is tested. The experimental results show that the control method can realize the accurate planar motion control of the specified pose in Cartesian space during the repeated planar motion of the 2SPU+U+RRR type 5-DOF hybrid robot. This paper provides a five-degree-of-freedom hybrid robot theoretical model for the position and attitude synchronous motion control of arbitrary plane in Cartesian space.
Construction of Winter Wheat Optimal Irrigation Yield Prediction Model Based on DM Algorithm

The practice shows that there is a close and nonlinear correlation between the greenness and yield of winter wheat. As we all know, the agricultural production system is a complex system with a high degree of uncertainty, including soil fertility grade, climate, domain management and many other factors, which brings difficulties to the prediction of winter wheat yield. This paper studies the construction of winter wheat optimal irrigation yield prediction model based on DM algorithm. In this article, ANN technique is used to fit and describe the relationship between green degree and yield of winter wheat, and a yield model is established. The algorithm used in this paper is BP neural network, which uses physical devices to simulate some organizations and functions of biological neural network, that is, many neurons with simple functions are interconnected to form a network system that can simulate human learning, decision-making, recognition and other functions. The steepest descent means is used to continuously adjust the weight and threshold of the network through back propagation to minimize the sum of squares of errors of the network. Through the research of this paper, the algorithm in this paper is effective and suitable for use.
Independent events and their complements. Part II

In a previous classroom note (Crispim et al., 2021), we addressed the proving (and the teaching) of a former and famous probability fact: if n random events are independent, then changing one, some, or all of them by the corresponding complements ends up again with independent events. Now, we explore the consequences. The first, for example, is the extension to when the number of events are infinite – which, indeed and perhaps surprisingly, stands as a corollary of the finite case. Also, and beyond covering only two events, we deal with the logical and intuitive consistency between independence and conditional probability. The theoretical content concludes with a proof that two seemingly different definitions of independent events found in the literature actually compare. Then, we finish the paper by discussing some classroom possibilities.
Educating Students at Teacher Education Faculties in Art Fields (Visual Arts and Music) in the Online Environment

During the crisis caused by the COVID-19 pandemic, the instruction at faculties in Croatia was realised mostly online. Online teaching exclusively has proven inadequate due to the specificities of realising art courses regarding their practical part normally implemented in specialised art studios and music cabinets, that provide special equipment, tools and means, and due to the importance of monitoring and supporting students by their mentors in the creative learning process itself. Students’ didactic-methodical practice with children in preschools and schools can be realized in a quality manner only through direct work in their natural surroundings where, besides teaching the subject area, social contact and the preschool teacher/child and teacher/student interactions play an important role. This research was carried out at faculties of teacher education in the academic year 2020/2021. The research goal was to determine the satisfaction with online instruction in art courses (visual arts and music) and technical readiness of students at teacher education faculties. The participants evaluated the quality of studying and support in the online environment in the art fields. Research results have shown that students of teacher education are satisfied with the implementation of art practice in online teaching as well as with their own work results. As was manifested, they are not satisfied with didactic-methodical practice nor regard themselves competent enough to perform art activities with early and preschool age children independently. The participants have expressed their satisfaction with the feedback from teachers, which positively influenced their motivation and encouraged them to work further.
Human Saliency Based Object Detection from Natural Images

In natural images, there are a few regions and objects, which catch the attention of human beings and are called “saliency”. The saliency algorithm is used to identify fixation points from the natural images. Saliency detection method has various applications, namely object segmentation and object recognition. In this paper, the task of object detection is performed through saliency detection algorithms. Lastly, the implemented models are tested and compared using iCoSeg data.
ARawNet: A Lightweight Solution for Leveraging Raw Waveforms in Spoof Speech Detection

An emerging trend in audio processing is capturing low-level speech representations from raw waveforms. These representations have shown promising results on a variety of tasks, such as speech recognition and speech separation. Compared to handcrafted features, learning speech features via backpropagation can potentially provide the model greater flexibility in how it represents data for different tasks. However, results from empirical studies show that, in some tasks, such as spoof speech detection, handcrafted features still currently outperform learned features. Instead of evaluating handcrafted features and raw waveforms independently, this paper proposes an Auxiliary Rawnet model to complement handcrafted features with features learned from raw waveforms for spoof speech detection. A key benefit of the approach is that it can improve accuracy at a relatively low computational cost. The proposed Auxiliary Rawnet model is tested using the ASVspoof 2019 dataset and pooled EER and min-tDCF are 1.11% and 0.03645 respectively. Results from this dataset indicate that a lightweight waveform encoder can boost the performance of handcrafted-features-based encoders for 10 types of spoof attacks, including 3 challenging attacks, in exchange for a small amount of additional computational work.
SecureLD: Secure And Accessible Learning for Students with Disabilities

COVID-19 has impacted various sectors of everyday living, including the education sector. Due to the pandemic, the educational institutions everywhere were forced to switch to the online platform as per CDC guidelines. However, the transition and adaptation to the online learning format have had varied effects on different populations. This study looks at 62 students with disabilities and their experience with online learning. The technology and tools used in online learning have many vulnerabilities related to privacy and security; thus, we aim to understand students' perceptions of security and privacy in an online learning platform. We have found that, although students with learning disabilities like the option of online learning, they want and require more guidance and coordination in learning to use online learning platforms. We also see that neurodiverse students with learning disabilities recognize the need for a secure and privacy-preserving online learning environment.
Development of Web-Based E-Library with Open Source System for Elementary School

Along with the development of learning technology, the existence of school libraries is now also increasingly optimized in the e-library. The results of the needs analysis show that there is a need for a special e-library design that can be used by schools efficiently to overcome the limitations of special library management personnel in schools. Based on this condition, this research is aimed at developing an e-library designed to be web-based with an open-source system. The research procedure is carried out referring to the ADDIE model with its data collection through questionnaires and documentation. The data from this study were processed with descriptive qualitative to describe the implementation of the developed system and descriptive quantitative to describe the results of expert validation of the feasibility of the e-library. The results showed that a web-based e-library with an open-source system was successfully developed and was suitable for use referring to the criteria of initiative, metadata, objects, and collections applied. The results of this development show that web-based e-libraries with open source systems can be used more practically and efficiently referring to administrative management features, book isolation, and visitor access for the elementary school level.