Reviewerly/exHarmony huggingface dataset:
Efficient Shot Boundary Detection with Multiple Visual Representations

Due to the unlimited growth of video-capturing devices and media, searching and finding a particular video in this huge database becomes a laborious as well as expensive task. Information-rich shots are the inevitable factor of the content-based video processing (CBVP) system. Hence, shot boundary detection (SBD) becomes the basic step of all content-based video retrieval processes. The accuracy of the existing SBD methods highly suffers from false positives and false negatives due to the presence of multiple variants. An efficient SBD method with multiple invariant features is proposed in this paper. A right combination of invariant features such as edge change ratio (ECR), colour layout descriptor (CLD), and scale-invariant feature transform (SIFT) key point descriptors helped to improve the accuracy level of SBD. As the selected features are invariant to most of the variants in video frames, such as illuminance changes, motion, scaling, and rotation, a markable reduction in false detection is possible. Support vector machine (SVM) classifier is used for the classification of frames into transition frames and shot frames. This proposed method is experimented and analysed with the standard SBD dataset TRECVid 2007 videos. The experimental results are compared with some state-of-art methods, and our method shows better performance with a 97% of F1 score.
Efficient deep steering control method for self-driving cars through feature density metric

In this paper, a low cost method for input size reduction without sacrificing accuracy is proposed, which reduces required computation resources for both training and inference of deep convolutional neural network (DCNN) in the steering control of self-driving cars. Efficient processing of DCNNs is becoming prominent challenge due to its huge computation cost, number of parameters and also inadequate computation resources on power efficient hardware devices and the proposed method alleviates the problem comparing the state of the art. The proposed method introduces feature density metric (FDM) as criterion to mask and filter out regions of input image that do not contain adequate amount of features. This filtering method prevents DCNN from useless calculations belongs to feature-free regions. Compared to PilotNet, the proposed method accelerates overall training and inference phases of end-to-end (ETE) deep steering control of self-driving cars up to 1.3× and 2.0× respectively.
Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat?

Abstract The interpretability of deep neural networks has attracted increasing attention in recent years, and several methods have been created to interpret the “black box” model. Fundamental limitations remain, however, that impede the pace of understanding the networks, especially the extraction of understandable semantic space. In this work, the framework of semantic explainable artificial intelligence (S‐XAI) is introduced, which utilizes a sample compression method based on the distinctive row‐centered principal component analysis (PCA) that is different from the conventional column‐centered PCA to obtain common traits of samples from the convolutional neural network (CNN), and extracts understandable semantic spaces on the basis of discovered semantically sensitive neurons and visualization techniques. Statistical interpretation of the semantic space is also provided, and the concept of semantic probability is proposed. The experimental results demonstrate that S‐XAI is effective in providing a semantic interpretation for the CNN, and offers broad usage, including trustworthiness assessment and semantic sample searching.
Elliptical Multi-Orbit Circumnavigation Control of UAVS in Three-Dimensional Space Depending on Angle Information Only

In order to analyze the circumnavigation tracking problem in complex three-dimensional space, in this paper, we propose a UAV group circumnavigation control strategy, in which the UAV circumnavigation orbit is an ellipse whose size can be adjusted arbitrarily; at the same time, the UAV group can be assigned to multiple orbits for tracking. The UAVs only have the angle information of the target, and the position information of the target can be obtained by using the angle information and the proposed three-dimensional estimator, thereby establishing an ideal relative velocity equation. By constructing the error dynamic equation between the actual relative velocity and the ideal relative velocity, the circumnavigation problem in three-dimensional space is transformed into a velocity tracking problem. Since the UAVs are easily disturbed by external factors during flight, the sliding mode control is used to improve the robustness of the system. Finally, the effectiveness of the control law and its robustness to unexpected situations are verified by simulation.
Teachers' dilemmatic spaces connected to students' net-based out-of-school activities

Purpose The paper aims to investigate and describe the complex and dynamic dilemmas teachers are facing connected to students' net-based out-of-school activities. Design/methodology/approach The authors draw on the notion of dilemmatic spaces when thematically analyzing focus group interviews conducted with 41 teachers at three lower secondary schools in Sweden. Findings Two themes capture the teachers' dilemmas concerning their students´ net-based out-of-school activities: negotiations of content and negotiations of professional identity. When teachers take part in professional discussions where dilemmatic spaces are recognized, rather than focusing on either being for or against digitalization, they are enabled to express a multifaceted view of professional identity. Research limitations/implications This study is a starting point for further studies investigating how pedagogical and didactic decisions are made in a digital time. Practical implications The findings are expected to be helpful to policymakers in understanding teachers' work. Also, teachers can be empowered by taking the departure in the findings and discussing how to handle dilemmas fruitfully. Originality/value In a rapidly changing digital society, it is important to investigate what dilemmas teachers face in their work in order to learn from them. This study is a significant contribution.
An RRAM-based Neural Radiance Field Processor

Photorealistic rendering of virtual scenes is a crucial task in the applications of augmented reality (AR), virtual reality (VR) and metaverse. Traditional rasterization or raytracing-based approaches usually involve a lot of manual efforts, making them unsuitable for mass production of virtual contents. The recently proposed Neural Radiance Fields (NeRF)-based technique demonstrated state-of-the-art results, but it demands a huge amount of computation. In this paper, we propose a resistive random access memory (RRAM)-based NeRF rendering accelerator. In the proposed accelerator, dataflow is specifically designed according to the parallel nature of NeRF-based rendering to increase the utilization of the RRAM array. Moreover, positional encoding engines are included in the proposed accelerator to reduce the off-chip memory bandwidth. Simulation results show that, for the execution of NeRF-based rendering, the overall performance and energy efficiency of the proposed accelerator is much higher than the existing GPU and TPU implementations.
An Improved YOLOv5 Real-time Detection Method for Aircraft Target Detection

To address the lack of accuracy and speed of aircraft detection in complex background images, the YOLOv5 model is improved in this paper to meet the growing demand for aircraft detection. Firstly, Mosaic-9 data enhancement is performed on the dataset, and MobileNet V3 Small is used to replace ResNet feature extraction network to enrich small target samples and improve feature extraction speed. Secondly, over-fitting is prevented by Label Smoothing, and channel pruning is used to reduce the complexity and redundancy of the neural network. Finally, TensorRT is used to optimize the underlying hardware resources to improve the detection speed. The experiments use Military Aircraft Detection Dataset for model training and validation on NVIDIA Tesla P100 16GB GPU. The results show that our improvements maintain the accuracy of the model while effectively increasing the training speed and detection speed of the model. Our codes are available at https://github.com/imcyx/Military-Detect
Cryptanalysis of RSA with small difference of primes and two decryption exponents: Jochemsz and May approach

RSA is a well-known cryptosystem in Modern Cryptography and its efficiency is based on the hardness of the Integer Factorization problem. The algorithm is shown to be vulnerable to several attacks in a number of special scenarios with assumptions. In this paper, the strength of RSA is investigated if the primes in the modulus are close and the same modulus is used for two instances. The attack is highly efficient compared to other known attacks which are only concentrated on either closeness of the primes or the same modulus used for two or more instances. This attack examines the closeness of the primes chosen whenever the RSA system is used for two instances with the same modulus. The LLL algorithm is used to obtain the bound, and the bound is highly efficient compared to other known attacks.
SWAP test for an arbitrary number of quantum states

We develop a recursive algorithm to generalize the quantum SWAP test for an arbitrary number m of quantum states requiring O(m) controlled-swap (CSWAP) gates and $$O(\log m)$$ ancillary qubits. We construct a quantum circuit able to simultaneously measure overlaps $$|\langle \phi _i, \phi _j\rangle |^2$$ of m arbitrary pure states $${|{\phi _1\ldots \phi _m}\rangle }$$ . Our construction relies on a pairing unitary that generates a superposition state where every pair of input states is labeled by a basis state formed by the ancillaries. By implementing a simple genetic algorithm, we give numerical evidence indicating that our method of labeling each pair of inputs using CSWAP gates is optimal up to $$m=8$$ . Potential applications of the new circuits in the context of quantum machine learning are discussed.
Application of the Particle Swarm Algorithm to the Task of Image Segmentation for Remote Sensing of the Earth

The article proposes the application of the particle swarm algorithm to the task of image segmentation for remote sensing of the Earth. The well-known methods of image segmentation are analyzed. They are ineffective for segmentation of remote sensing of the Earth data due to the peculiarities of space images. The proposed approach to image segmentation uses the particle swarm algorithm in each channel of brightness; divides into segments in each channel of brightness based on the operation of the particle swarm algorithm; combines segmentation results in each channel; gets the overall result of image segmentation. Type I and type II segmentation errors are calculated for the proposed approach and the k-means method. The proposed approach to segmentation estimation reduces type I segmentation errors by about 11% and type II segmentation errors by about 8%.
Relevance of pre-service teacher training to use ICT for the actual use in classrooms – focus on German secondary schools

Integrating digital media into education and fostering school students’ competencies in computational thinking and media literacy requires comprehensive information and communication technology (ICT)-related pre-service teacher training. However, it remains unclear whether such training is associated with a more frequent use of ICT in classrooms and the fostering of students’ competencies. Furthermore, it is uncertain if the frequency of ICT use is related to pre-service teachers’ practical experience. These questions are examined based on a representative German secondary school teacher survey (N = 1218) using logistic regression analyses. Taking further variables on school level and teacher level into account, it could be shown that teachers’ self-assessed technological pedagogical and content knowledge to use ICT in instruction is the most relevant predictor of the frequency of use and the fostering of students’ computational thinking. Overall, there were hardly any differences between the more theoretical and the more practical phases of teacher education.
A Video Summarization Model Based on Deep Reinforcement Learning with Long-Term Dependency

Deep summarization models have succeeded in the video summarization field based on the development of gated recursive unit (GRU) and long and short-term memory (LSTM) technology. However, for some long videos, GRU and LSTM cannot effectively capture long-term dependencies. This paper proposes a deep summarization network with auxiliary summarization losses to address this problem. We introduce an unsupervised auxiliary summarization loss module with LSTM and a swish activation function to capture the long-term dependencies for video summarization, which can be easily integrated with various networks. The proposed model is an unsupervised framework for deep reinforcement learning that does not depend on any labels or user interactions. Additionally, we implement a reward function (R(S)) that jointly considers the consistency, diversity, and representativeness of generated summaries. Furthermore, the proposed model is lightweight and can be successfully deployed on mobile devices and enhance the experience of mobile users and reduce pressure on server operations. We conducted experiments on two benchmark datasets and the results demonstrate that our proposed unsupervised approach can obtain better summaries than existing video summarization methods. Furthermore, the proposed algorithm can generate higher F scores with a nearly 6.3% increase on the SumMe dataset and a 2.2% increase on the TVSum dataset compared to the DR-DSN model.
CUR Transformer: A Convolutional Unbiased Regional Transformer for Image Denoising

Image denoising is a fundamental problem in computer vision and multimedia computation. Non-local filters are effective for image denoising. But existing deep learning methods that use non-local computation structures are mostly designed for high-level tasks, and global self-attention is usually adopted. For the task of image denoising, they have high computational complexity and have a lot of redundant computation of uncorrelated pixels. To solve this problem and combine the marvelous advantages of non-local filter and deep learning, we propose a Convolutional Unbiased Regional (CUR) transformer. Based on the prior that, for each pixel, its similar pixels are usually spatially close, our insights are that (1) we partition the image into non-overlapped windows and perform regional self-attention to reduce the search range of each pixel, and (2) we encourage pixels across different windows to communicate with each other. Based on our insights, the CUR transformer is cascaded by a series of convolutional regional self-attention (CRSA) blocks with U-style short connections. In each CRSA block, we use convolutional layers to extract the query, key, and value features, namely Q , K , and V , of the input feature. Then, we partition the Q , K , and V features into local non-overlapped windows and perform regional self-attention within each window to obtain the output feature of this CRSA block. Among different CRSA blocks, we perform the unbiased window partition by changing the partition positions of the windows. Experimental results show that the CUR transformer outperforms the state-of-the-art methods significantly on four low-level vision tasks, including real and synthetic image denoising, JPEG compression artifact reduction, and low-light image enhancement.
Mining high utility itemsets using Genetic Algorithm Based-Particle Swarm Optimization (GA-PSO)

Mining high utility itemsets (HUIs) from transaction databases is one of the current research areas in the data mining field. HUI mining finds itemsets whose utility meets a predefined threshold. It enables users to quantify the usefulness or preferences of products by utilizing different values. Since utility mining approaches do not satisfy the downward closure property, the cost of candidate generation for HUI mining in terms of time and memory space is excessive. This paper presents Genetic Algorithm based Particle Swarm Optimization (GA-PSO), which can efficiently prune down the number of candidates and optimally acquire the complete set of high utility itemsets. The proposed algorithm’s performance is assessed using the synthetic dataset T20.I6.D100K and the real-time supermarket dataset, which comprises 38765 transactions and 167 unique products. It performs very effectively in terms of time and memory on large databases constituted of small transactions, which are challenging for existing high utility itemsets mining algorithms to manage. Experiments on real-world applications show the importance of high utility itemsets in business decisions, as well as the distinction between frequent and high utility itemsets.
Exploration and Reflection of Online Piano Teaching Practice in Normal Universities in China

At the beginning of 2020, due to the sudden outbreak of COVID-19, education in China was disrupted. The Chinese government has strongly supported online education, while the music education industry has also adopted a "Suspension of class but no suspension of learning" plan. Universities have also carried out online classroom practices, which has also opened a new door to online piano teaching in universities in China. The feasibility of online piano teaching in daily classrooms of universities in China is something worth researching.
Two-photon-absorption measurements in the presence of single-photon losses

We discuss how two-photon absorption (TPA) of squeezed and coherent states of light can be detected in measurements of the transmitted light fields. Such measurements typically suffer from competing loss mechanisms such as experimental imperfections (i.e., imperfect photodetectors) and other linear scattering losses inside the sample itself, which can lead to incorrect assessments of the two-photon-absorption cross section. We evaluate the sensitivity with which TPA can be detected and find that at sufficiently large photon numbers TPA sensitivity of squeezed vacua or squeezed coherent states can become independent of linear losses that occur after the TPA event has taken place. In particular, this happens for measurements of the photon number or of the antisqueezed field quadrature, where large fluctuations counteract and exactly cancel the degradation caused by single-photon losses.
Online eye-movement classification with temporal convolutional networks

The simultaneous classification of the three most basic eye-movement patterns is known as the ternary eye-movement classification problem (3EMCP). Dynamic, interactive real-time applications that must instantly adjust or respond to certain eye behaviors would highly benefit from accurate, robust, fast, and low-latency classification methods. Recent developments based on 1D-CNN-BiLSTM and TCN architectures have demonstrated to be more accurate and robust than previous solutions, but solely considering offline applications. In this paper, we propose a TCN classifier for the 3EMCP, adapted to online applications, that does not require look-ahead buffers. We introduce a new lightweight preprocessing technique that allows the TCN to make real-time predictions at about 500 Hz with low latency using commodity hardware. We evaluate the TCN performance against other two deep neural models: a CNN-LSTM and a CNN-BiLSTM, also adapted to online classification. Furthermore, we compare the performance of the deep neural models against a lightweight real-time Bayesian classifier (I-BDT). Our results, considering two publicly available datasets, show that the proposed TCN model consistently outperforms other methods for all classes. The results also show that, though it is possible to achieve reasonable accuracy levels with zero-length look ahead, the performance of all methods improve with the use of look-ahead information. The codebase, pre-trained models, and datasets are available at https://github.com/elmadjian/OEMC.
Ancient Tamil Character Recognition from Stone Inscriptions – A Theoretical Analysis

Character recognition on inscriptions is an area which explores our knowledge of an ancient language. Inscriptions were done in all kinds of environments. This work focuses on recognizing Tamil language characters on stone-based images. From the inscription images, we come to know about the importance of old century languages. Some of the general challenges researchers face in recognizing the characters in stone inscriptions are differentiating the foreground pixel from the background stone images, perspective distortion, different light illumination, the same kind of background/foreground, damaged stones, lack of shape and size of the text. Despite the different ways proposed by the researchers, obstacles and issues continue to exist. This survey attempts to give a detailed analysis of the recent research works on character recognition from the images of stone inscriptions with a special reference to Tamil. It details the methods applied for preprocessing, feature extraction and classification. It gives a road map for future researchers who wish to carry out research in this area.
Bi-directional LSTM Attention Mechanism for Sentiment Classification

Sentiment analysis enables firms to monitor the public perception of the products and services in real-time. Lexical sources are commonly used in the field of sentiment analysis because lexical resources constitute a source of sentiment knowledge that is explicitly encoded. Typically, sentiment lexica are employed to estimate polarity by matching words in a text to their lexicon-associated sentiment polarities. Such resources, however, have limits in terms of word coverage and domain applicability. We proposed an approach used to build bi-sense sentiment embedding for positive and negative texts to overcome this limitation. Also, we depict the attention mechanism to illustrate how the bi-sense sentiment embedding provides superior advice on the attention process, allowing for a more robust interpretation of the semantics and sentiments. We conduct an exhaustive evaluation to determine the proposed method effectiveness. Experiments demonstrate that the proposed strategy can significant improvement compared to baseline methods.