Reviewerly/exHarmony huggingface dataset:
Student Abnormal Behavior Recognition in Classroom Video Based on Deep Learning

For the complication and low speed of traditional human behavior recognition process, a method of student abnormal behavior recognition in classroom video based on deep learning was proposed. First of all, for the poor effect of small target identification with the original network, our method introduces an cascading improved RFB module by adding a branch to the RFB to increase the reference to the peripheral visual field. This network was called Rs-YOLOv3, and it can enhance the feature extraction capability of the original network and make full use of the shallow information to improve the identification effect of small targets. Secondly, for the character occlusion due to classroom structure and student density, the resn module of Darknet-53 in YOLOv3 was replaced with SE-Res2net module. The feeling field of each layer of the network can be increased to represent feature information in more fine-grained and realize multi-layer feature multiplexing. Finally, the border regression calculation was performed by changing the border loss function to DIoU_Loss. The experimental results show that the improved network SE-Res2Net-DIoU achieves 80.1% in the accuracy of student abnormal behavior recognition, a 5.8% improvement compared with the traditional YOLOv3, and reduces the missed recognition rate.
A Development of Robotic Scrub Nurse System - Detection for Surgical Instruments Using Faster Region-Based Convolutional Neural Network –

There is presently a shortage of nurses in Japan, with a further shortage of 3,000–130,000 nurses expected. There is also shortage of scrub nurses. Scrub nurses are nurses who work in the operating room. The main job of scrub nurses is to assist surgeons. Scrub nurses are a high turnover rate, because it is a difficult job. Therefore, system for assisting scrub nurses are needed. The purpose of this study was to develop a robotic scrub nurse. As a first step, a detection system for surgical instruments was developed using the “Faster Region-Based Convolutional Neural Network” (Faster R-CNN). In experiments, computer graphics (CG) model images and 3D-printed model images were evaluated, and the system showed high accuracy. Consequently, the Faster R-CNN system can be considered as suitable for detecting surgical instruments.
Plagiarism Detection Tool for Enhanced Entity-Relationship Diagrams

Plagiarism is presenting someone else’s work as one’s own work without giving credit to the original owner. Recently, plagiarism has become a serious issue in the fields of Education and Technology. To address this issue, many systems have been implemented to detect plagiarism. However, most of them are designed to deal with plagiarism of text content. Detecting plagiarism in figures and diagrams is equally important. Although there is research done on detecting plagiarism in images and flow charts, there is no research done on detecting plagiarism in more complex diagrams such as Enhanced Entity-Relationship (EER) diagrams. This paper presents a methodology to detect plagiarism in EER diagrams using Deep Neural Networks (DNN), image processing techniques, Optical Character Recognition (OCR) techniques, and text similarity detection algorithms. Since the students are aware of the existence of a plagiarism detecting tool, it will encourage the students to do work on their own and it will reduce exam offenses. The similarity report can be presented as proof to the offenders who are not accepting that they have plagiarized others' work. Using the proposed system, the EER diagram plagiarism can be detected much faster and accurately. Therefore, the efficiency of marking examinations will be increased. The final outcome of the system will be a similarity report including the plagiarized content in the compared EER diagrams.
Multisensor Fusion and Explicit Semantic Preserving-Based Deep Hashing for Cross-Modal Remote Sensing Image Retrieval

Cross-modal hashing is an important tool for retrieving useful information from very-high-resolution (VHR) optical images and synthetic aperture radar (SAR) images. Dealing with the intermodal discrepancies, including both spatial–spectral and visual semantic aspects, between VHR and SAR images is extremely vital to generate high-quality common hash codes in the Hamming space. However, existing cross-modal hashing methods ignore the spatial–spectral discrepancy when representing VHR and SAR images. Moreover, existing methods employ derived supervised signals, such as pairwise training images, to implicitly guide hashing learning, which fails to effectively deal with the visual semantic discrepancy, i.e., cannot adequately preserve the intraclass similarity and interclass discrimination between VHR and SAR images. To address these drawbacks, this article proposes a multisensor fusion and explicit semantic preserving-based deep Hashing method, termed as MsEspH, which can effectively deal with the discrepancies. Specifically, we design a novel cross-modal hashing network to eliminate the spatial–spectral discrepancies by fusing extra multispectral images (MSIs), which are generated in real time by a generative adversarial network. Then, we propose an explicit semantic preserving-based objective function by analyzing the connection between classification and hash learning. The objective function can preserve the intraclass similarity and interclass discrimination with class labels directly. Moreover, we theoretically verify that hash learning and classification can be unified into a learning framework under certain conditions. To evaluate our method, we construct and release a large-scale VHR-SAR image dataset. Extensive experiments on the dataset demonstrate that our method outperforms various state-of-the-art cross-modal hashing methods.
Media Production Using Cloud and Edge Computing: Recent Progress and NBMP-Based Implementation

Media production is facing tremendous challenges during processing and transmission, due to large data volume, high computational complexity and real-time transmission requirement. Hence, more and more media servers tend to spread media content in a distributed manner, over e.g., cloud platform or edge computing nodes, which are closer to client side and/or have adequate computing capacity. Benefiting from cloud or edge computing, users can receive various media service with high quality and low latency. Network-based Media Processing (NBMP) established by International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) standard organization-Moving Picture Experts Group (MPEG), is one of these representative distributed architectures, which has attracted a wide range of attention. This paper focuses on emerging cloud and edge computation for media production to highlight the necessity of adopting a distributed computing framework. We take NBMP architecture as an example, and introduce the basic components and workflow in distributed processing frameworks. Moreover, we also provide related application instances based on NBMP with the hope of promoting future development of cloud and edge computation.
The Ethics of Virtual Reality Interactive Digital Narratives in Cultural Heritage

As IDNs are used to represent complex phenomena, we are bound to assess the ethical dimension of these representations in order to help IDN mature as a practice and a discipline. In this paper, we consider ethical aspects arising from applications of IDN in VR for Cultural Heritage experiences. Using a discussion of ethical aspects of cultural heritage and virtual reality as a foundation, and considering a range of IDN VR cultural heritage experiences, we derive a set of ethical questions for IDN design in general and for cultural heritage specifically as the basis for the development of standard ethics guidelines and help start a conversation on the topic in the community.
Multi-View Collaborative Learning for Semi-Supervised Domain Adaptation

Recently, Semi-supervised Domain Adaptation (SSDA) has become more practical because a small number of labeled target samples can significantly boost the empirical target performance when using SSDA. Several current methods focus on prototype-based alignment to achieve cross-domain invariance in which the labeled samples from the source and target domains are concatenated to estimate the prototypes. The model is then trained to assign the unlabeled target data to the prototype within the same class. However, such methods fail to exploit the advantage of using few labeled target data because the labeled source data dominate the prototypes in the supervision process. Moreover, a recent method [1] showed that concatenating source and target samples for training can damage the semantic information of representations, which degrades the trained model’s ability to generate discriminative features. To solve these problems, in this paper, we divide labeled source and target samples into two subgroups for training. One group includes a large number of labeled source samples, and the other obtains a few labeled target samples. Then, we propose a novel SSDA framework that consists of two models. A model trained on the group that has the labeled source samples to provide an “inter-view” on the unlabeled target data is called the inter-view model. A model trained on a few labeled target samples that provides an “intra-view” of the unlabeled target data is called the intra-view model. Finally, both of these models collaborate to fully exploit information on the unlabeled target data. To the best of our knowledge, our proposed method achieves the state-of-the-art classification performance of SSDA in extensive experiments conducted on several visual benchmark domain adaptation datasets that utilize the advantages of multiple views and collaborative training.
Toward Piano Teaching Evaluation Based on Neural Network

With the rise of piano teaching in recent years, many people participated in the team of learning steel playing. However, expensive piano teaching fees and its unique one-to-one teaching model have caused piano education resources to be very short, so learning piano performance has become a very extravagant event. The factors affecting music performance are varying, and there are many types of their evaluation such as rhythm, expressiveness, music, and style grasp. The computer is used to simulate this evaluation process to essentially identify the mathematical relationship between factors affecting music performance and evaluation indicators. The use of computer multimedia software for piano teaching has become a feasible way to alleviate the contradiction. This paper discusses the implementation method of piano teaching software, the issues of computer piano teaching, the computer teaching as one-way knowledge, and the lack of interaction. The neural network (NN) model is used to evaluate the piano performance and simulate teachers to guide students through their exercise. The performance of the proposed system is tested for the piano music of “Ode to Joy,” which is different from the collection of NN training samples, and is delivered ten times by another piano teacher, student A (piano level 6), and student B (piano level 5).
An Anti-Steganalysis HEVC Video Steganography With High Performance Based on CNN and PU Partition Modes

The steganography research of videos leads to excellent communication methods for transmitting secret message, and high efficiency video coding(HEVC) video is one popular steganographic carrier. This article proposes a prediction unit(PU) based wide residual-net steganography(PWRN) for HEVC videos. The visual quality distortion of modifying PUs is theoretically analyzed, which illustrates that modifying PUs only has a little negative effect on visual quality. Therefore, the data hiding method in this article allows to modify all types of PUs except for <inline-formula><tex-math notation="LaTeX">$2N\times 2N$</tex-math></inline-formula> to each other according to the secret data. In this way, high embedding efficiency is achieved, and the PU distributions in stego-videos can be kept similar to those of cover-videos, which is essential for resisting steganalysis. Meanwhile, a super-resolution convolutional neural network(CNN) with wide residual-net filter(WRNF) is proposed to replace the in-loop filter in HEVC for reconstructing I-pictures, which results in more precisely predicted P-pictures, and it further leads to less bitrate cost and better visual quality of stego-videos. The experimental results show that the proposed PWRN successfully resists the latest PU-targeted steganalysis algorithms, and compared with the state-of-the-art work, PWRN has achieved the lowest bitrate cost and the highest visual quality under the same capacity.
An On-Chain Analysis-Based Approach to Predict Ethereum Prices

The Ethereum blockchain generates a significant amount of data due to its intrinsic transparency and decentralized nature. It is also referred to as on-chain data and is openly accessible to the world. Moreover, the on-chain data is timestamped, integrated, and validated into an open ledger. This important blockchain feature enables us to assess the network’s health and usage. It serves as a massive data warehouse for complex prediction algorithms that can effectively detect systemic trends and forecast future behavior. We adopt a quantitative approach using a subset of these metrics to determine the network’s true monetary value by developing a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) with the metrics most closely associated with the price as inputs. Since several hyperparameters regulate the learning process in an RNN, they are highly sensitive to their values. It is thus critical, to select optimal hyperparameters so that the training is quick and effective. Determining the optimal parameters of an RNN model is a tedious and complex process. Hence, previous studies have developed several self-adaptive approaches to determine the optimal values for various parameters effectively. However, none of the prior studies explore self-adaptive algorithms in deep learning models in conjunction with on-chain data to predict cryptocurrency prices. In this paper, we propose three self-adaptive techniques, each of which converges on a set of optimal parameters to predict the price of Ethereum accurately. We compare our results to a traditional LSTM model. Our approach exhibits 86.94% accuracy while maintaining a minimum error rate.
Visibility graph entropy based radiometric feature for physical layer identification

Emerging physical layer identification methods have demonstrated the capability to complement and enhance the device authentication of Internet of Things networks by exploiting the uncontrollable, unclonable, and unforgeable radiometric features resulted from randomly generated hardware imperfection in wireless devices. Multiple feature-based identification has proven an efficient and feasible approach to improving identification performance. At the same time, the lack of radiometric features effective for device identification is a major problem. Most of the existing features are derived from the view of the time, frequency, or phase domain. In this study, we explore the graph domain of wireless frame’s preambles and propose a new radiometric feature called normalized horizontal visibility graph Shannon entropy (HVGE). At first, we introduce a preprocessing consisting of sample truncation and downsampling to enable the adjustment between the computational time of visibility graph (VG) conversion and the identification performance. Secondly, we propose the calculation method of the new HVGE feature from the VG representation. Finally, an experimental study using 50 off-the-shelf wireless devices was conducted to investigate the impact of the preprocessing parameters and the effect of noise and feature combinations on the identification performance gain.
Unraveling the origin of higher success probabilities in quantum annealing versus semi-classical annealing

Abstract Quantum annealing aims at finding optimal solutions to complex optimization problems using a suitable quantum many body Hamiltonian encoding the solution in its ground state. To find the solution one typically evolves the ground state of a soluble, simple initial Hamiltonian adiabatically to the ground state of the designated final Hamiltonian. Here we explore whether and when a full quantum representation of the dynamics leads to higher probability to end up in the desired ground when compared to a classical mean field approximation. As simple but nontrivial example we target the ground state of interacting bosons trapped in a tight binding lattice with small local defect by turning on long range interactions. Already two atoms in four sites interacting via two cavity modes prove complex enough to exhibit significant differences between the full quantum model and a mean field approximation for the cavity fields mediating the interactions. We find a large parameter region of highly successful quantum annealing, where the semi-classical approach largely fails. Here we see strong evidence for the importance of entanglement to end close to the optimal solution. The quantum model also reduces the minimal time for a high target occupation probability. Surprisingly, in contrast to naive expectations that enlarging the Hilbert space is beneficial, different numerical cut-offs of the Hilbert space reveal an improved performance for lower cut-offs, i.e. an nonphysical reduced Hilbert space, for short simulation times. Hence a less faithful representation of the full quantum dynamics sometimes creates a higher numerical success probability in even shorter time. However, a sufficiently high cut-off proves relevant to obtain near perfect fidelity for long simulations times in a single run. Overall our results exhibit a clear improvement to find the optimal solution based on a quantum model versus simulations based on a classical field approximation.
Color Random Valued Impulse Noise Removal Based on Quaternion Convolutional Attention Denoising Network

A new quaternion convolutional attention denoising network known as DeQCANet for color random-valued impulse noise removal is proposed in this paper. First, a structural information extraction block based on a dilated convolution operation is designed to extract the structure and detail feature map. Subsequently, a quaternion convolutional neural network with a new quaternion map construction strategy is implemented to gain the color features across channels further. Finally, to integrate the global and local features, a feature enhancement block based on the attention mechanism is proposed to guide the network for impulse noise denoising. The experimental results demonstrate that the proposed denoising technique exhibits competitive performance compared to other well-known color image denoising methods.
Wearable Sensor Gait Analysis of Fall Detection using Attention Network

The statistical data from the National Council on Aging indicates that a senior adult dies in the US from a fall every 19 minutes. The care of elderly people can be improved by enabling the detection of falling events, especially if it triggers the pneumatic actuation of a protective airbag. This work focuses on detecting impending fall risk of senior subjects within the geriatric population, towards a planned approach to mitigating fall injuries through pneumatic airbag deployment. With the widespread adoption of wearable sensors, there is an increased emphasis on fall prediction models that effectively cope with accelerometry signal data. Fall detection and gait classification are challenging tasks, especially in differentiating falls from near falls. We propose to apply attention to the deep neural network (DNN) analysis of acceleration data where a fall is known to have occurred. We take the maximum value of the sensor signals to define the observation window of the detector. Powered by a transformer DNN with word embedding, attention networks have achieved a state-of-the-art in natural language processing (NLP) tasks. Besides the success of the transformer for efficiently processing long sequences, it supports parallel computing with fast computation. In this paper, we propose a novel transformer attention network for gait analysis of fall detection modeling with Time2Vec positional encoding- founded on a Masked Transformer Network. Using our dataset, we demonstrate that the proposed approach achieves better specificity and sensitivity than the present models.
Face Recognition using Convolutional Neural Network in Machine Learning

Our sense of ourselves is inextricably linked to our looks. It’s required for everyday interactions, communication, and other routine duties. Face recognition algorithms that are both durable and perfect are required to construct fully automated systems that analyse the data contained in face photographs, and a variety of methodologies are currently being used. Partial facial occlusion is one of the most difficult challenges in face recognition. In real-world applications, face recognition algorithms can recognize faces hidden under masks, scarves, or sunglasses, hands on the face, things carried by a person, or external sources. The outcome, when compared to other existing algorithms, produces the best results. When utilising the suggested dataset, they provide high accuracy and a low loss function. With both trainable and non-trainable parameters, the suggested model performs admirably. The above-average accuracy of 80% indicates a strong performance in facial recognition. Face recognition from video and photos is extremely important.
Soft focal loss: Evaluating sample quality for dense object detection

Classic detectors divide the candidate boxes into positive and negative groups based on their intersection-over-union (IoU) with matched objects. Such a sharp label assignment method does not directly consider the distance between the centers of the two boxes. Consequently, some positive samples are ambiguous, which limits the detection performance. In this paper, we propose a new label assignment method by considering two different perspectives. The first perspective is IoU to indicate the degree of overlap. The other one is the defined variable to characterize the center-distance between the candidate box and its matched ground truth. In addition, the classification is usually optimized by Focal Loss for paying more attention to hard examples, but it affects the training of high-quality samples. Therefore, we define the Soft Focal Loss (SFL) and the quality factor that reflects the quality of the samples. Embedding the quality factor into SFL makes the network focus on learning high-quality rather than hard examples. Furthermore, the quality factor is utilized to re-weight the classification and regression losses to enhance the correlation between these two tasks. Experiments on COCO show that the proposed approach can improve RetinaNet by 1.3% and 1.2% AP with backbone ResNet-50 and ResNet-101 in 1x training schedule, without incurring any additional overhead.