Reviewerly/exHarmony huggingface dataset:
Comparison of Middle School Students" Perception on Offline and Online Physical Education Classes

This study is intended to compare middle school student’s perception on online and offline physical education classes. A survey was conducted targeting a total of 625 students―310 male students and 315 female students―in a middle school located in Gyeongsangnam-do.BR Collected data from the questionnaire was analyzed according to the aim of this study with the use of SPSS/PC Window V.23.0. The results are as followsBR In terms of the importance, preference, participation, and satisfaction of physical education classes perceived by middle school students, offline physical education classes scored statistically higher than online physical education classes. Also, male students scored statistically higher than female students. Between different grades in school, offline physical education classes had statistically significant difference. for importance, preference, participation, and satisfaction, while online physical education classes had no statistically significant difference.
Enlightenment and challenge of the concept of metaverse to film and television creation

&lt;p&gt;Starting from the enlightenment of the concept of metaverse to film and television creation, this paper puts forward that metaverse will be possible to reconstruct digital virtual space-time, open a new context of interactive narration and replace real actors with virtual personification. The role of film and television audience will also experience the transformation from viewer to experiencer, from audience to player, and from audience to creator. In addition, the virtual world constructed by the metaverse may cause ethical risks due to the unlimited creative freedom, domesticate the film and television audience into a new “container”, encourage users to form a value orientation from reality to emptiness, and also pose new challenges to the film and television content audit mechanism and management in the digital virtual space.&lt;/p&gt;
Two-Client Inner-Product Functional Encryption with an Application to Money-Laundering Detection

In this paper, we extend Inner-Product Functional Encryption (IPFE), where there is just a vector in the key and a vector in the single sender's ciphertext, to two-client ciphertexts. More precisely, in our two-client functional encryption scheme, there are two Data Providers who can independently encrypt vectors x and y for a data consumer who can, from a functional decryption key associated to a vector α, compute ∑αi xiyi = x ⋅ Diag(α) ⋅ yT. Ciphertexts are linear in the dimension of the vectors, whereas the functional decryption keys are of constant size. We study two interesting particular cases:
Direct measurement for general quantum states using parametric quantum circuits

Abstract In the field of quantum information, the acquisition of information for unknown quantum states is very important. When we only need to obtain specific elements of a state density matrix, the traditional quantum state tomography will become very complicated, because it requires a global quantum state reconstruction. Direct measurement of the quantum state allows us to obtain arbitrary specific matrix elements of the quantum state without state reconstruction, so direct measurement schemes have obtained extensive attention. Recently, some direct measurement schemes based on weak values have been proposed, but extra auxiliary states in these schemes are necessary and it will increase the complexity of the practical experiment. Meanwhile, the post-selection process in the scheme will reduce the utilization of resources. In order to avoid these disadvantages, a direct measurement scheme without auxiliary states is proposed in this paper. In this scheme, we achieve the direct measurement of quantum states by using quantum circuits, then we extend it to the measurement of general multi-particle states and complete the error analysis. Finally, when we take into account the dephasing of the quantum states, we modify the circuits and the modified circuits still work for the dephasing case.
Security and Privacy Vulnerabilities in Human Activity Recognition systems

Human activity recognition systems (HARS) should allow the secure and trustworthy exchange of sensitive data between several kinds of participating parties with different aims and claims, regarding security, data protection, and trust issues. Initially in this work, a security flaw has been identified in a complete medical IoT application using wearable devices and smart sensors. Then, we list the security vulnerabilities and attempt to make suggestions on the prevention of security flaws that may appear during the implementation of HARS and we analyze a specific attack, the Man in the Middle attack, where a third malicious entity interferes with communication between two entities and is associated with key exchange protocols. Moreover, we discuss various design considerations for protecting the data that is transmitted and stored from different sources like smart wearables, mobile phones, and cloud applications by using cryptographic and privacy-preserving techniques. Finally, we show how the use of the OAuth2.0 protocol can ensure that only authenticated users interact with the HARS.
Cooperative Video Quality Adaptation for Delay-Sensitive Dynamic Streaming using Adaptive Super-Resolution

This paper proposes a cooperative and dynamic quality adaptation scheme for delay-sensitive video streaming between the transmitter and the receiver. We present a novel adaptive super-resolution (SR) technique that adaptively controls the quality enhancement rate and computation time. Due to the capability of enhancing the quality of video chunks at the user device side, the transmitter can aggressively transcode video chunks to reduce the delivery latency and power consumption. Also, adaptive SR can control the tradeoff between playback stall rate and CPU consumption of the user device. Simulation results verify the performance of adaptive SR and show that the proposed video delivery scheme is very good to balance tradeoff among the following performance metrics for online video services: 1) playback stall rate, 2) average quality measure, 3) transmission power, and 4) CPU consumption of the user device.
Developing a news classifier for Greek using BERT

Text categorization is a significant task in the re-search field of text mining, which has recently benefited from deep neural network algorithms and advanced learning techniques that extract language models from large textual corpora. These Pre-Trained Language Models are the main components of state-of-the-art solutions in many natural language processing and text-mining tasks can be very generic, trained in generic text corpora, or domain-specific when they employ large corpora from specific application domains (e.g. social media, news, sciences, etc.). When only generic language models are available the overall performance in the task can be improved by adapting or fine-tuning the model used for the task, e.g. the classifier. Although multilingual language models are reported in the literature, such models are usually language-specific. This work presents a news article classifier, which has been trained on a small corpus and employs a Greek version of BERT language model. Comparison with existing machine learning-based classifiers shows that the proposed method outperforms well-known methods in text classification. In addition, the proposed approach allows the continuous training of the classifier through user-provided feedback on falsely classified articles.
Optimal Camera Placement Leaning of Multiple Cameras for 3D Environment Reconstruction

Recently, research and development on immersive virtual reality(VR) technology to provide a realistic experience is being widely conducted. To provide realistic experience in immersive virtual reality for VR participants, virtual environments should consist of high-realistic environments using 3D reconstruction. In this paper, to acquire 3D information in real space using multiple cameras in the reconstruction process, we propose a novel method of optimal camera placement for accurate reconstruction to minimize distortion of 3D information. Through our approach in this paper, real 3D information can obtain with minimized errors during environment reconstruction, and it is possible to provide a more immersive experience with the created virtual environment.
Artistic Text Style Transfer: An overview of state-of-the-art methods and datasets [SP Forum]

Word art, which is text rendered with properly designed appealing artistic effects, has been a popular form of art throughout human history. Artistic text effects are of great aesthetic value and symbolic significance. Decorating with appropriate effects not only makes text more attractive but also significantly enhances the atmosphere of a scene. Thus, artistic text effects are widely used in publicity and advertising. Some text effects are simple, such as colors and shadows, while some can be complex, such as the burning flames in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1</xref> and exquisite decorations in <xref ref-type="fig" rid="fig2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 2</xref> . Manually creating vivid text effects requires lots of time and a series of complicated operations: observing the target glyphs, designing appropriate artistic effects, warping the texture to match the character shapes, and so on. It consumes hours of time even for well-trained designers. To produce word art more conveniently and efficiently, artistic text style transfer has been proposed recently to automatically render text with given artistic effects.
Accelerator Design and Exploration for Deformable Convolution Networks

Deformable Convolution Network (DCN) is a special type of CNN that achieves superior detection accuracy. DCN has input-dependent dynamic data access patterns. In this work, we propose a hybrid DCN accelerator design. It exploits both hardware re-use and hardware pipelining on an FPGA. Hardware pipelining overlaps data communication with computation. Hardware re-use ensures scalability of the design to very deep networks. We adopt channel-major order data layout to reduce the access time to DRAM; This hides the data access overheads in DCNs. We propose an efficient design space exploration (DSE) heuristic to generate optimized hybrid accelerators on a given device. We implement our design targeting Deformable ResNet-50 and ResNet-101. Our design achieves up to 24.7 (2.4) times higher throughput compared with the CPU (GPU) baselines, and up to 4.5 times improvement in effective resource utilization compared with state-of-the-art FPGA accelerators for DCN.
Towards Designing and Development of an Artifact Storage and Trading Framework utilizing the Ethereum Blockchain

Historic landmarks, antiquities, and museums showcase the area’s culture and history [4]. Archaeologists suffer from stolen items sold illegally, and many museums reject pieces from unknown sources and locations due to laws and ethics; as a result, many artifacts are destroyed and their identity is lost [4, 5]. Cutting-edge technology is one way to preserve our cultural heritage for future generations. The study’s findings improve the use of blockchain technology for cultural preservation, benefiting both public museum collections and private collections.Collectors can register their objects with unique serial numbers to trace them in museums and using smart contracts. Since its inception in October 2008 as part of an initiative called Bitcoin to create peer-to-peer money independent of lenders, Bitcoin has introduced strategies to help solve the problem of human trust in aging, core (blockchain) technology enables us to trust the system’s outputs without the ratification of a particular actor inside, and the information is permanently secured. To avoid these concerns, we proposed a blockchain-based artifacts storage and trade system.
An Efficient Authentication Technique using Convolution Chebyshev Chaotic Maps for TMIS

Authentication mechanisms for Telecare Medicine Information Systems (TMIS) have been extensively researched to safeguard the privacy and integrity of sensitive user data, such as telephone numbers, health information, medical records, etc. This paper presents a new efficient authentication technique for TMIS leveraging convolution Chebyshev chaotic maps. Based on the findings of this investigation, the suggested technique is secure against password guessing, privileged insider, replay, stolen verifier, and man-in-the-middle attacks. Generally, the computational costs of the proposed authentication technique outperform other competing techniques in the existing literature.
On the Security of Content key Distribution Framework for DRM systems

After the advancement of digital and Internet technologies, digital content mugging has become a major concern for the multimedia business to deal with. To combat piracy, digital rights management (DRM) systems are implemented. In DRM systems, protocols are being developed to provide secure communication. In this paper, we calibrate a recently proposed authentication protocol for DRM systems to understand security and privacy challenges. The objective of this cryptanalysis is to get a broad perspective of authentication schemes, which would help us to design secure and efficient authentication schemes for DRM systems. In this present study, we perform cryptanalysis of authentication protocol for DRM systems using biometrics by Khan et al.’s. Their scheme supports efficient communication and gives a better solution for the digital right management system. However, we identified that their scheme has some serious security flaws. Based on our analysis, we present a comparative study in terms of execution cost and security attributes to establish a tradeoff between efficiency and security.
A Semantic-Guided LiDAR-Vision Fusion Approach for Moving Objects Segmentation and State Estimation

Moving Objects Segmentation (MOS) is critical and indispensable for secure intelligent vehicle operation in the dynamic environment.For the state estimation task which is based on the assumption of static surroundings, to identify and filter out the moving objects plays an important role in robust ego-motion estimation.In this paper, a LiDAR-Vision fusion approach is developed to segment moving objects in the scene, which utilizes the LiDAR-based semantic segmentation as a prior and vision-based geometric information for validation.The effectiveness of our approach to segment moving objects is highlighted by the comparison with the traditional robust kernel-based outlier rejection methods.Our approach is benchmarked with three city category sequences in the KITTI dataset, which outperforms the kernel-based methods and achieves the leading results of 77.9% average fitness and 7.65 cm RMSE respectively.
Improving the Hand Pose Estimation from Egocentric Vision via HOPE-Net and Mask R-CNN

Hand pose estimation is the task of predicting the position and orientation of the hand and fingers relative to some coordinate system. It is an important task or input for applications in robotics, medical or human-computer interaction. In recent years, the success of deep convolutional neural networks and the popularity of low-cost consumer wearable cameras have made hand pose estimation on egocentric images using deep neural networks a hot topic in the computer vision field. This paper proposes a novel deep model for accurate 2D hand pose estimation that combines HOPE-Net, which estimates hand pose, and Mask R-CNN, which provides hand detection and segmentation to localize the hand in the image. First, HOPENet is used to predict the initial 2D hand pose, and the hand features are extracted from an image with a hand in the center, which is cropped from the original image based on Mask RCNN’s output. Then, we combine the initial 2D hand pose and the hand features into a fully connected layer to predict the 2D hand pose correctly. Our experiments show that the proposed model outperforms the original HOPE-Net in 2D hand pose estimation. The proposed method’s mean endpoint error (mEPE) is 48.82 pixels, while the mEPE of the 2D HOPE-Net predictor is 86.30 pixels on the First-Person Hand Action dataset.
S<sup>2</sup>-Labeling: Shot-By-Shot Microscopic Badminton Singles Tactical Dataset

In this study, an affordable data collection solution is proposed for the tactical and strategic analysis of badminton. To make mass technical data collection possible, broadcast video is considered as the data source, and an intuitive and efficient video labeling tool is designed and implemented. The contribution of this work includes three folds. First, in response to the needs of the next generation of badminton technical and tactical analysis, the concept of Shot-By-Shot Labeling is proposed and a prototype data representation is formulated. Second, UI designs and computer vision techniques are utilized to develop an efficient computer-aided labeling tool for shot-by-shot technical data collection from match videos. Third, a Shot-By-Shot Labeling dataset composed of six badminton matches is shared with the community to accelerate related research. The dataset is available at https://drive.google.com/drive/folders/1eweBFa_M_CnYcqkQ5IHJ-Y8ag3TqRwUh.
Machine-learning-assisted Traffic Classification of User Activities at Programmable Data Plane

The increasing diversity of network terminals not only brings the exponential growth of network traffic, but also increases the complexity of user activities analysis. Packet encryption and dynamic port techniques also bring new challenges to traffic classification. Machine Learning (ML) techniques have been deployed at the control plane of programmable networks with better accuracy and expensive communication overhead. This paper proposes a traffic classification framework with ML classifier offloaded to the programmable data plane to decrease communication overhead. Considering the limitation of memory space in programmable switches, we design a storage algorithm combining hash tables and Count-Min sketch to store traffic features to be used in the ML classifier. We first use K-Means to classify flows into different categories, then we use decision tree to obtain the activity labels. Experimental results show that the proposed traffic classification framework can achieve high classification accuracy (above 93.7%) with minor performance degradation.
Quality of Service Evaluation over a 496 km Quantum Key Distribution Network

Quantum key distribution (QKD) technology has remarkably developed, and some countries are in the stage of preparing quantum cryptography communication services beyond the PoC. To ensure the stability of QKD network operation telecommunication service providers and engineers need to verify the quality of service (QoS). To do this, we built a quantum cryptography communication network testbed for 496km distance with nine trusted relay nodes in Korea, to evaluate the QoS of the quantum cryptography communication network. Also, we apply four QoS parameter values defined which are quantum key response delay, quantum key response delay variation, quantum key loss rate, and availability. By measuring each parameter in a real quantum cryptography communication network, we show that this method is valid to evaluate QoS, and it is expected to contribute to the realization of quantum cryptography communication services.
SIF-NPU: A 28nm 3.48 TOPS/W 0.25 TOPS/mm<sup>2</sup> CNN Accelerator with Spatially Independent Fusion for Real-Time UHD Super-Resolution

This paper proposes a convolutional neural network (CNN)-based super-resolution accelerator for up-scaling to ultra-HD (UHD) resolution in real-time in edge devices. A novel error-compensated bit quantization is adopted to reduce bit depth in the SR task. Spatially independent layer fusion is exploited to satisfy high throughput requirements at UHD resolution by increasing parallelism. Burst operation with write mask in the dual-port SRAM increases the process element utilization by allowing the concurrent multi-access without exploiting additional memory. The accelerator is implemented in the 28nm technology and shows at least 4.3 times higher <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{FoM}(\text{TOPS}/\text{mm}^{2}\times \text{TOPS/W)}$</tex> of 0.87 than the state-of-art CNN accelerators. The implemented accelerator supports up-scaling up to 96 frames-per-seconds in UHD resolution.
Whale Optimization Algorithm for Color Image Segmentation using Supra-Extensive Entropy

Image segmentation plays an important role for image analysis. Image thresholding technique is one of the most effective segmentation techniques. Although, bi-level thresholding is widely applied to segment non-complex color images, however, bi-level thresholding is not suitable in case of color complex images. In case of color complex images which contain multiple objects, only multi-level thresholding works efficiently. The conventional thresholding approaches give efficient results for bi-level thresholding, but the time complexity of the conventional approaches may be excessively high for color image multilevel thresholding due to search multiple threshold values for three (red-green-blue, RGB) components. Thus, color image multilevel thresholding segmentation can be considered as NP-hard combinatorial optimization problem because the time complexity of the searching procedure increases exponentially as levels of thresholding increase. Here, the major objective is to search optimal threshold values for segmenting the color image into appropriate segments. In this paper, Supra-Extensive entropy based new objective function is designed to find optimal threshold values for segmenting the color image into multiple segments. For optimizing the proposed objective function, two well-established population based optimization approaches Whale Optimization Algorithm (WOA) is explored. Such approach is called WOA-based SEEMT. The proposed approach is compared with Grey Wolf Optimizer (GWO) called GWO-based SEEMT algorithm. Experiments are performed on six color benchmark images in terms of optimal threshold values, peak signal to noise ratio (PSNR), uniformity, structure similarity (SSIM) index, mean structure similarity (MSSIM) index, number of iterations and CPU time. The experimental results show that the there is no significant difference between the performance of WOA-SEEMT and GWO-SEEMT algorithms in terms quality parameters PSNR, uniformity, SSIM index and MSSIM index while WOA-SEEMT algorithm is much faster than GWO-SEEMT from computational point of view.
Parameter Estimation of Photovoltaic System Using Marine Predators Optimization Algorithm-Based Multilayer Perceptron

Solar irradiation data is an essential input for solar-thermal and photovoltaic systems. In this case, the coherence parameter estimation is a fundamental level in solar energy applications. For this purpose, a PV system has been modeled by the parametric solver of ANSYS-Electronics software and a data set has been created for the output voltage value depending on different input variables. In this paper, marine predators’ algorithm is integrated to the multilayer perceptron algorithm in order to estimate the parameter. In the estimation level, air temperature, thermal coefficient and diffuse horizontal solar radiation parameters are evaluated in 2-tupled and 3-tupled input systems. In addition, the accuracy of the hybrid forecasting model developed is also tested on the basis of the hyperbolic tangent, sinus and sigmoid activation functions employed in the multilayer perceptron algorithm. Estimation results show that marine predators-based multilayer perceptron model is appropriate to parameter estimation, efficiently.