Reviewerly/exHarmony huggingface dataset:
Evaluation of Concentration Levels in Learning Using Zoom Meeting by Applying the Histogram of Oriented Gradients (HOG) Method and Support Vector Machine (SVM) Classification

The online learning system is one of the implementations of distance education through a learning system, an application that is widely used during distance learning is Zoom Meeting. This research was conducted to find out how effective the use of the Zoom Meeting application is for online learning. This study focuses on detecting eyes to determine a person’s focus during online learning using zoom meetings, analyzing a person’s blink of an eye, and then determining the size of the ratio in the eyes. It has been established that the level of attention of a normal person can be determined by the degree of a blink of an eye. If a person feels tired or out of focus, it can be seen in the blink of his eyes. In this study, a method will be developed that is used to detect eyes using a Histogram of Oriented Gradients (HOG) as the extraction of its characteristics and a Support Vector Machine (SVM) as its classification. Base on data analysis proved that the ability of the process to identify a person’s focus level is 99.59% accuracy, 99.49% accuracy, and 98.84% recall. The system’s skills to detect blinking eyes with PERCLOS is excellent with an average 98.60%.
Application of Virtual Reality technology for fall protection in the AEC industry

The scope of this paper is to present the first results of an ongoing research and application development project investigating the potential applications of Virtual Reality in the field of fall protection focusing on the AEC industry.
Enhanced Performance of Pre-Trained Networks by Matched Augmentation Distributions

There exists a distribution discrepancy between training and testing, in the way images are fed to modern CNNs. Recent work tried to bridge this gap either by fine-tuning or re-training the network at different resolutions. However retraining a network is rarely cheap and not always viable. To this end, we propose a simple solution to address the train-test distributional shift and enhance the performance of pretrained models - which commonly ship as a package with deep learning platforms e.g., PyTorch. Specifically, we demonstrate that running inference on the center crop of an image is not always the best as important discriminatory information may be cropped-off. Instead we propose to combine results for multiple random crops for a test image. This not only matches the train time augmentation but also provides the full coverage of the input image. We explore combining representation of random crops through averaging at different levels i.e., deep feature level, logit level, and softmax level. We demonstrate that, for various families of modern deep networks, such averaging results in better validation accuracy compared to using a single central crop per image. The softmax averaging results in the best performance for various pre-trained networks without requiring any re-training or fine-tuning whatsoever. On modern GPUs with batch processing, the paper's approach to inference of pre-trained networks, is essentially free as all images in a batch can all be processed at once. Our code is available at: https://github.com/TouqeerAhmad/MID
GTTM and TPS

Schenkerian analysisSchenkerian analysis claimedGenerative Theory of Tonal Music (GTTM) that there existed UrsatzUrsatz (a fundamental structureFundamental structure) in each music piece, which was obtained by the process called reductionReduction.
Exploring Elderly and Young Users’ Perceptions of Video Exercise on Mobile Fitness Applications

Researchers suggested that people have physical activity at home during COVID-19, and mobile applications as easily accessible devices replace face-to-face exercise programs. However, despite a growing number of research focusing on the user experience of mobile applications, we lack evaluating exercise experience with video on mobile fitness applications. This paper explores how different cognitive abilities of elderly and young users manifest during the video exercise on applications. We experimented with 30 participants (15 elderly participants and 15 young participants) from Beijing, China. In addition, we had semi-structured interviews to evaluate their perceptions, resulting in four themes: activity presentation, interface design challenges, sensory preferences, and integration into existing practices. From the findings, recommendations for the design of video exercise on mobile fitness applications were derived. We contribute a better understanding of elderly and young users' perceptions in the context of interactive multimedia exercise training.
The Impact of Artificial Intelligence on Graphic Design: Exploring the Challenges and Possibilities of AI-Driven Autonomous Branding

Artificial intelligence (AI)’s potential impact on graphic design has stimulated a range of questions and concerns from both design practitioners and academics about the future of AI-driven designs. For instance, how will AI tackle issues associated with ethics, cultural acceptance, and creativity, and what are the possibilities of having autonomous AI-driven brands? This study investigates the potential impact of AI on graphic designers, including an assessment of how to use AI as a self-governed system in branding rather than an application tool exploring new opportunities associated with data and algorithms. Speculative co-design methodology was the main approach to initiating provocative discussions and debates through semi-structured interviews and co-design workshop. The study was conducted in Saudi Arabia with participants from academia and the industry. The findings suggest alternating human-machine entanglements around self-driven AI brands, which will enable designers and researchers to explore alternative futures in this field.
NL-FCOS: Improving FCOS through Non-Local Modules for Object Detection

During the last years, we have seen significant advances in the object detection task, mainly due to the outperforming results of convolutional neural networks. In this vein, anchor-based models have achieved the best results. However, these models require prior information about the aspect and scales of target objects, needing more hyperparameters to fit. In addition, using anchors to fit bounding boxes seems far from how our visual system does the same visual task. Instead, our visual system uses the interactions of different scene parts to semantically identify objects, called perceptual grouping. An object detection methodology closer to the natural model is anchor-free detection, where models like FCOS or Centernet have shown competitive results, but these have not yet exploited the concept of perceptual grouping. Therefore, to increase the effectiveness of anchor-free models keeping the inference time low, we propose to add non-local attention (NL modules) modules to boost the feature map of the underlying backbone. NL modules implement the perceptual grouping mechanism, allowing receptive fields to cooperate in visual representation learning. We show that non-local modules combined with an FCOS head (NL-FCOS) are practical and efficient. Thus, we establish state-of-the-art performance in clothing detection and handwritten amount recognition problems.
Lightweight RadioYOLO for Radio Signal Detection

The detection of radio signals using object detection algorithms is one of the applications of deep learning in the realm of communication. However, due to the vast size of deep neural networks, inference takes longer and is more challenging to install on mobile terminals. In this paper, we present a lightweight radio detector (L-RadioYOLO), which requires fewer parameters for faster and more accurate radio signal location. Our model leverages the lightweight network GhostNet as its backbone to reduce parameters. Furthermore, YOLOV4 was used as a teacher detector to transfer knowledge to our model throughout the knowledge distillation process. We run experiments on several detectors with varying backbones, and the results show that our model achieves an Average Precision (AP) of 96.01% which is just 0.8% lower than baseline (YOLOV4), while the scale of parameters is reduced to 1/6 of baseline.
StrokeNet: Harmonizing Stoke Domains Between Sketches and Natural Images for Sketch-based Image Retrieval

Sketch-based image retrieval (SBIR) is a growing research field due to its potential application in both commercial value and academic value. Using sketches to retrieve images is an ideal substitute to text retrieve in capturing the fine-grained visual information. However, allowing to retrieve natural images from ambiguous and abstract sketch remains a challenging problem in computer vision and image processing. The main challenge of this task is not only the domain difference between free-hand sketch and natural image but also the abstraction of sketch. In this paper, in order to tackle this challenging task, we introduce a novel strategy named stroke harmonization, which is aim at effectively narrow the sketch-image domain gap by explicitly aligning strokes of sketches and natural images to the same stroke style domain. Based on the stroke harmonization, we propose a novel network, named StrokeNet, which consists of generative model and feature extraction model, to harmonizing free-hand sketch and natural image to the same intermediate domain, i.e., stroke domain, and then perform sketch-based image retrieval in this domain. The proposed method is comprehensively evaluated on two representative fine-grained SBIR datasets, QMUL-ShoeV2 and QMUL-ChairV2. The experiments consistently indicate that our method gains the superior accuracies over recent state-of-the-art methods without relying on complex architectures.
Lightweight and accurate system for entity extraction and linking

Entity extraction and linking components in dialogue assistants should meet the requirements of low resource consumption and high accuracy. In this paper we present lightweight system which extracts entity mentions from the text and finds corresponding Wikidata ids and Wikipedia pages links. Entity extraction and linking is performed into the following steps: extraction of entity substrings from the text, retrieval of candidate entities from Wikidata knowledge base and entity disambiguation. Entity extraction is based on RoBERTa-tiny model for token classification. Extracted substrings are classified into 42 fine-grained tags for filtering of candidate entities. Candidate entities are ranked by number of connections of candidate entities in the text in Wikidata knowledge graph. The proposed system outperforms on WNED-WIKI other lightweight solutions, such as REL and OpenTapioca. The system supports easy adding new Wikidata entities to the database and using other knowledge bases for entity linking.
Analysis of approaches to evaluate the quality of distant educational services

Due to the process of digitalization modern economy of Russia has faced the dramatic increase of service sector and the active participation of a consumer in the formation of quality criteria. The requirements for skills of workers have changed significantly; the importance of multi-disciplinary knowledge and digital tools is increasing. The trends mentioned above have formed the base for the creation and fast development of distant educational services which currently have no criteria and instrumentation of quality evaluation. The key approaches of services quality appraisal implemented nowadays and the possibilities of using them in education sector have been analyzed in this research work. The authors revealed the main advantages and drawbacks of each approach on a practical level, the grade and the methods of state support were assessed, and the estimation of using these tools by consumers in effect was carried out.
The Incorporation of Digital Technology into Education from the Point of View of School Principals and ICT Coordinators

Technological advancement can be seen in all areas of life. The technological revolution affects everyone, the old and the young, and every sector, both the corporate and the private. Employers expect their employees to possess life and working skills that allow them to grow both personally and professionally in today's digital world. The digital world offers tremendous benefits to us all. In education, it allows users to work with various platforms that enable interconnectedness and collaboration, learning about new topics and innovation in education. Therefore, one of education's main goals is to teach learners how to use digital technology and to help them develop their digital literacy. The paper outlines the current state, tried and tested methods and problems related to digital technology-based instruction from the perspective of Czech elementary and high school principals and ICT coordinators.
Study on the Influence of Upper Limb Representations and Haptic Feedback in Virtual Reality

Visual representations are used in various digitization processes to display objects in immersive environments. In industrial environments, the self-representation can be very helpful when reviewing 3D models to conform realistic proportions, particularly in combi-nation with haptic feedback that goes beyond vibrating controllers. For instance, haptic feedback in combination with a virtual representation supports working on use cases where collision is an important part to maintain data quality. To understand the dependency of both, we conducted a pilot study with 15 users from the automotive sector to examine the influence of upper limb representations on haptic feedback in Virtual Reality. Each participant was assigned with one of three upper limb representations which was used in different scenarios using haptic feedback devices. Overall, we found that the realistic arm representation was rated highest in terms of perceived realism and achieved the best task performance.
Hierarchical Graph Attention Network with Heterogeneous Tripartite Graph for Numerical Reasoning over Text

Numerical reasoning machine reading comprehension (MRC) is a challenging natural language understanding task, which requires machines to have the numerical reasoning ability to add, subtract, sort and count over text to predict answers. Previous state-of-the-art models typically use graph neural networks (GNNs) to perform reasoning over heterogeneous graphs containing numbers and entities, to enhance their numerical reasoning ability. However, their constructed graphs contain only number nodes or only consider related entities in the same sentence, ignoring the relationship between entities. To alleviate the issue, in this paper, we propose a hierarchical graph attention network with heterogeneous tripartite graph (HGAT-HTG) for the numerical reasoning MRC task. It introduces additional sentence nodes as the intermediary between number nodes and entity nodes to construct the heterogeneous tripartite graph, then its hierarchical graph attention network (GAT) reasoning module first performs coarse-grained reasoning on the whole graph, then performs iterative fine-grained reasoning on the number-sentence subgraph and entity-sentence subgraph respectively, to enhance its numerical reasoning ability for the task. Experimental results indicate that HGAT-HTG model offers significant and consistent improvements, outperforming previous state-of-the-art QDGAT on DROP dataset.
Regression Analysis of Predictions and Forecasts of Cloud Data Center KPIs Using the Boosted Decision Tree Algorithm

Cloud data centers seek to optimize their provision of pooled CPU, bandwidth and storage resources. While over-provision is wasteful, under-provision may lead to violating Service Level Agreements (SLAs) with their consumers; yet the relationship between low-level Key Performance Indicators (KPIs) and SLA violations is not well understood. State-of-the art monitoring systems typically react to service failures after the fact, partly due to unexpected nonlinearities in the aggregated performance data. We seek to provide better modelling of KPIs using predictive algorithms that could be used for the proactive monitoring and adaptation of cloud services. In this paper, we investigate the Boosted Decision Tree (BDT) regression algorithm. We tested the BDT algorithm in a real monitoring framework deployed on a novel Azure cloud test-bed distributed over multiple geolocations, using thousands of robot-user requests to produce huge volumes of KPI data. The BDT algorithm achieved an R-Squared score of 0.9991 at the 0.2 learning rate. This closely predicted the KPI data and outperformed other approaches, such as Ordinary Least Squares and Stochastic Gradient Descent; and is a promising candidate for making short- and long-term predictions for cloud resource allocation.
Content Extraction of Chinese Archive Images via Synthetic and Real Data

With the development of the information age, the awareness of archives informatization of governments, organizations and groups at all levels is constantly increasing. The key content extraction of scanned archive images has been widely concerned by researchers. Based on the deep learning method, we extract text and key elements (ID photo, seal, date of birth) by analyzing the archive images. Based on the real archive images data set and synthetic archive images data set, we use the PP-OCR [1] model to finish the text structured extraction and YOLOv5 [2] to train a target detection model of ID photos, seals, and dates of birth to achieve the extraction of key elements. Since the real data set cannot be made public, we use ordinary archive data for explanation.
Software identifies a child’s talent interests with certainty factor methods

Parents tend to impose their will when choosing formal or non-formal schools for their children. Due to not knowing the interests and talents of the child. This makes it difficult for children to develop. Finding a child psychologist to consult is also one of the challenges for parents today. In dealing with problems, answers are often found that do not have certainty. The high and low level of uncertainty in the identification results is influenced by uncertain rules and user answers. The Certainty Factor method is a mathematical theory to accommodate the uncertainty of an expert’s thinking, in order to describe the level of expert confidence in the problem at hand. This research produces a counseling software model in obtaining important information about children’s career development and helps facilitate the individual development of children through the form of services so that they are able to plan their careers based on majors, interests, talents, knowledge, personality, competencies and other factors that support their progress in determining choices and decisions that are in accordance with the work world of children’s choices.
Experiencing Age-Related Movement Impairment Through Visual Delegation in VR Can Substitute Haptic Impairments of an Age Simulation Suit

To investigate limitations of older adults in everyday situations, age simulation suits are used to reflect on the experience of age-related impairments in body movement. However, age simulation suits have drawbacks in accessibility. As an alternative to a physical arm movement impairment induced by an age simulation suit, we designed a virtual arm movement impairment inside a virtual reality environment by using a visual tracking offset and a transparency effect. We compared our virtual movement impairment with the physical movement impairment of the “adit” age simulation suit and to a baseline without impairment in a between-groups user study (N=30). We investigated perceived arm movement impairment, task performance, presence, usability, and simulator sickness. Results show that the virtual impairment we designed can elicit comparable experiences as an age simulation suit while offering more flexibility and adaptability with users. With our paper we want to contribute to designing more effective visual pseudo-haptic effects for virtual reality age simulations.
Analysis of Campus Network Security

Campus network provides a critical stage to student service and campus administration, which assumes a paramount part in the strategy of 'Rejuvenating the Country through Science and Education' and 'Revitalizing China through Talented Persons'.However, with the rapid development and continuous expansion of campus network, network security needs to be an essential issue that could not be overlooked in campus network construction.In order to ensure the normal operation of various functions of the campus network, the security risk level of the campus network is supposed to be controlled within a reasonable range at any moment.Through literature research, theory analysis and other methods, this paper systematically combs the research on campus network security at home and abroad, analyzing and researching the campus network security issues from a theoretical perspective.A series of efficient solutions accordingly were also put forward.
A Format Compliant Encryption Method for 3D Objects Allowing Hierarchical Decryption

With the increasing popularity of 3D objects in industry and everyday life, 3D object security has become essential. While there exists methods for 3D selective encryption, where a clear 3D object is encrypted so that the result has the desired level of visual security, to our knowledge, no method exists for decrypting encrypted 3D objects hierarchically. In this paper, we are the first to propose propose a method which allows us to hierarchically decrypt an encrypted 3D object according to a generated ring of keys. This ring consists of a set of keys that allow a stronger or weaker decryption of the encrypted 3D object. Each hierarchically decrypted 3D object has a different visual security level, where the 3D object is more or less visually accessible. Based on a master key, these hierarchical keys are generated using our method during the encryption process. Our method is essential when it comes to preventing trade secrets from being leaked from within a company or by exterior attackers. It is also ecologically friendly and more secure than traditional selective encryption methods.
Uncertainty Aware Model Integration on Reinforcement Learning

Model-based reinforcement learning is an effective approach to reducing sample complexity by adding more data from the model. Dyna is a well-known architecture that contains model-based reinforcement learning and integrates learning from interactions with an environment and a model of the environment. Although the model can greatly help to speed up the agent's learning, acquiring an accurate model is a hard problem in spite of the recent great success of function approximation using neural networks. A wrong model causes degradation of the agent's performance and raises another question: to which extent should an agent rely on the model to update its policy? In this paper, we propose to use the confidence of the model simulations to the integrated learning process so that the agent avoids updating its policy based on uncertain simulations by the model. To obtain confidence, we apply the Monte Carlo dropout technique to the state transition model. We show that this approach contributes to improving early-stage training, thus helping speed up the agent to reach reasonable performance. We conduct experiments on simulated robotic locomotion tasks to demonstrate the effectiveness of our approach.