Reviewerly/exHarmony huggingface dataset:
JAUNet: A U-Shape Network with Jump Attention for Semantic Segmentation of Road Scenes

The task of complex scene semantic segmentation is to classify and label the scene image pixel by pixel. For the complex image information in autonomous driving scenes, its characteristics such as many kinds of targets and various scene changes make the segmentation task more difficult, making various kinds of FCN-based networks unable to restore the image information well. In contrast, the encoder–decoder network structure represented by SegNet and UNet uses jump connections and other methods to restore image information. Still, its extraction of shallow details is simple and unfocused. In this paper, we propose a U-shaped convolutional neural network with a jump attention mechanism, which is an improved encoder plus decoder structure to achieve semantic segmentation by four times of convolutional downsampling and four transposed convolutional upsamplings while adding a jump attention module in the upsampling process to realize selective extraction of contextual information from high-dimensional features to guide low-dimensional features, improve the fusion of deep and shallow features, and ensure the consistency of the same type of pixel prediction. The CamVid and Cityscapes datasets are sampled for the experiments, and the model ground mIoU evaluation metrics can reach 66.3% and 69.1%. Compared with other mainstream semantic segmentation algorithms, this method is competitive in terms of segmentation performance and model size.
Crime Scene Testimony in Virtual Reality Applicability Assessment

3D reconstruction represents sophisticated approach to crime scene documentation and farther forensic analysis; however, quality of results is strictly depended on source data. This article identifies feasibility limits and defines critical requirements for crime scene 3D reconstruction. An evaluation triangle is proposed to realize method applicability assessment. Moreover, individual factors influencing the feasibility were described in detail and their mutual relationship has been formulated. A crime scene testimony in virtual reality workflow was provided to visualize individual process relation.
Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy.Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc.The CNN models are still sensitive to noise and attack.The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model.This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks.The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images.Secondly, an alpha fusion attack is generated to fool the classification model.The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models.The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack.The highest F1 score before the attack was achieved is 97.45And after the attack lowest F1 score recorded is 22%.Results elucidate the performance in terms of accuracy, precision, F1 score and Recall.
Creating and detecting specious randomness

Abstract We present a new test of non-randomness that tests both the lower and the upper critical limit of a $\chi ^{2}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>χ</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> -statistic. While checking the upper critical value has been employed by other tests, we argue that also the lower critical value should be examined for non-randomness. To this end, we prepare a binary sequence where all possible bit strings of a certain length occurs the same number of times and demonstrate that such sequences pass a well-known suite of tests for non-randomness. We show that such sequences can be compressed, and therefore are somewhat predictable and thus not fully random. The presented test can detect such non-randomness, and its novelty rests on analysing fixed-length bit string frequencies that lie closer to the a priori probabilities than could be expected by chance alone.
An Experimental Investigation on Unsupervised Text Summarization for Customer Reviews

People generally turn to websites such as Yelp to find reviews for the food/restaurant before trying it first-hand. However, some reviews are so long and ambiguous that users get confused about whether the review appreciates or disparages the food. Here, websites might want to employ summarization techniques so that the crux of the study is well understood in just a sentence. The process of compressing the information/data while preserving its idea is text summarization. The present work's main objective is to empirically investigate the suitable algorithm for summarizing reviews so that users do not need to spend time reading through the entire study. The performance of the conducted investigations is analyzed with the metrics such as cosine similarity, the score of the bilingual evaluation understudy (precision), and recall-oriented understudy for gisting evaluation analysis. The experiments show that the text summarization technique, LexRank outperforms other methods with the precision and recall values of 0.586 and 0.346, respectively.
An Analysis of University Students’ Needs for Learning Support Functions of Learning Management System Augmented with Artificial Intelligence Technology

The aim of this study is to identify intelligent learning support functions in Learning Management System (LMS) to support university student learning activities during the transition from face-to-face classes to online learning.To accomplish this, we investigated the perceptions of students on the levels of importance and urgency toward learning support functions of LMS powered with Artificial Intelligent (AI) technology and analyzed the differences in perception according to student characteristics.As a result of this study, the function that students considered to be the most important and felt an urgent need to adopt was to give automated grading and feedback for their writing assignments.The functions with the next highest score in importance and urgency were related to receiving customized feedback and help on task performance processed as well as results in the learning progress.In addition, students view a function to receive customized feedback according to their own learning plan and progress and to receive suggestions for improvement by diagnosing their strengths and weaknesses to be both vitally important and urgently needed.On the other hand, the learning support function of LMS, which was ranked as low importance and urgency, was a function that analyzed the interaction between professors and students and between fellow students.It is expected that the results of this student needs analysis will be helpful in deriving the contents of learning support functions that should be developed as well as providing basic information for prioritizing when applying AI technology to implement learner-centered LMS in the future.
Implementation of melodic morphing based on generative theory of tonal music

This paper proposes a new morphing method to generate a melody, given two melodies represented by time-span trees obtained from the generative theory of tonal music (GTTM). In this paper, we define a feature structure based on a time-span tree and the distance between two structures. Next, we construct a lattice of subsumption relations among these structures and define three algebraic operations: reduction, meet, and join. First, we conduct these operations in the pure algebraic domain and a new melody is as an interpolation in the lattice; thereafter, we render the obtained tree into a music melody, assigning a priority order among candidate branches. We confirmed that our morphing algorithm generated new variations with satisfactory quality.
Multi-Party Sequential Data Publishing Under Differential Privacy

Given a set of local sequential datasets held by multiple parties, we study the problem of publishing a synthetic dataset that preserves approximate sequentiality information of the integrated dataset while satisfying differential privacy for each local dataset. The existing solutions for publishing differentially private sequential data in the centralized setting mostly adopt tree-based approaches. Such approaches rely on different tree structures that encode sequential data's statistical information. The construction of a tree structure is normally done by recursively splitting nodes whose noisy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scores</i> (e.g., entropy or count) are larger than a given threshold. However, extending similar ideas to the multi-party setting is challenging. First, the comparison between noisy scores and a given threshold needs to be done in a distributed manner without letting the parties know the noisy scores, while satisfying differential privacy for each local dataset. Second, in the multi-party setting the large number of node splitting decisions incurs prohibitive computation costs. In addressing the above challenges, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DPST</i> , a distributed prediction suffix tree construction solution. In DPST, we first introduce a novel node splitting decision method that calculates the comparison result under encryption with substantially improved efficiency. Then we present a novel batch-based tree construction approach to reduce computation costs. In order to achieve high parallel performance without incurring any extra communication cost, we introduce the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">conjunction</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">slide</i> methods to ensure that each batch contains a stable number of carefully arranged <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decision tasks</i> . To further reduce communication and computation costs, we propose a prefix-based pre-pruning method to reduce the number of nodes that need to be judged whether to split by an interactive protocol. Extensive experiments on real datasets demonstrate that our DPST solution offers desirable data utility with low computation and communication costs.
Retracted: The use of the intelligent Bayesian network method combined with blockchain technology in the optimisation of tunnel construction quality control

Abstract Retraction: [Liping Cai, Qiaona Gong, Feng Jiang, Mingzhan Yuan, Zhiyong Xiao, Shuai Zhang, Chengcheng Zheng, Yue Wu, The use of the intelligent Bayesian network method combined with blockchain technology in the optimisation of tunnel construction quality control, IET Software 2023 ( https://doi.org/10.1049/sfw2.12109 )]. The above article from IET Software , published online on 2 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
Token Pooling in Vision Transformers for Image Classification

Pooling is commonly used to improve the computation-accuracy trade-off of convolutional networks. By aggregating neighboring feature values on the image grid, pooling layers downsample feature maps while maintaining accuracy. In standard vision transformers, however, tokens are processed individually and do not necessarily lie on regular grids. Utilizing pooling methods designed for image grids (e.g., average pooling) thus can be sub-optimal for transformers, as shown by our experiments. In this paper, we propose Token Pooling to downsample token sets in vision transformers. We take a new perspective — instead of assuming tokens form a regular grid, we treat them as discrete (and irregular) samples of an implicit continuous signal. Given a target number of tokens, Token Pooling finds the set of tokens that best approximates the underlying continuous signal. We rigorously evaluate the proposed method on the standard transformer architecture (ViT/DeiT) and on the image classification problem using ImageNet-1k. Our experiments show that Token Pooling significantly improves the computation-accuracy trade-off without any further modifications to the architecture. Token Pooling enables DeiT-Ti to achieve the same top-1 accuracy while using 42% fewer computations.
Recipe2Video: Synthesizing Personalized Videos from Recipe Texts

Procedural texts are a special type of documents that contain complex textual descriptions for carrying out a sequence of instructions. Due to the lack of visual cues, it often becomes difficult to consume the textual information effectively. In this paper, we focus on recipes - a particular type of procedural document and introduce a novel deep-learning driven system - Recipe2Video that automatically converts a recipe document into a multimodal illustrative video. Our method employs novel retrieval and re-ranking methods to select the best set of images and videos that can provide the desired illustration. We formulate a Viterbi-based optimization algorithm to stitch together a coherent video that combines the visual cues, text and voice-over to present an enhanced mode of consumption. We design automated metrics and compare performance across several baselines on two recipe datasets (RecipeQA, Tasty Videos). Our results on downstream tasks and human studies indicate that Recipe2Video captures the semantic and sequential information of the input in the generated video.
ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy

This paper analyzes the effects of dynamically varying video contents and detection latency on the real-time detection accuracy of a detector and proposes a new run-time accuracy variation model, ROMA, based on the findings from the analysis. ROMA is designed to select an optimal detector out of a set of detectors in real time without label information to maximize real-time object detection accuracy. ROMA utilizing four YOLOv4 detectors on an NVIDIA Jetson Nano shows real-time accuracy improvements by 4 to 37% for a scenario of dynamically varying video con-tents and detection latency consisting of MOT17Det and MOT20Det datasets, compared to individual YOLOv4 detectors and two state-of-the-art runtime techniques.
Face Forgery Detection Based on Facial Region Displacement Trajectory Series

Deep-learning-based technologies such as deepfakes ones have been attracting widespread attention in both society and academia, particularly ones used to synthesize forged face images. These automatic and professional-skill-free face manipulation technologies can be used to replace the face in an original image or video with any target object while maintaining the expression and demeanor. Since human faces are closely related to identity characteristics, maliciously disseminated identity manipulated videos could trigger a crisis of public trust in the media and could even have serious political, social, and legal implications. To effectively detect manipulated videos, we focus on the position offset in the face blending process, resulting from the forced affine transformation of the normalized forged face. We introduce a method for detecting manipulated videos that is based on the trajectory of the facial region displacement. Specifically, we develop a virtual-anchor-based method for extracting the facial trajectory, which can robustly represent displacement information. This information was used to construct a network for exposing multidimensional artifacts in the trajectory sequences of manipulated videos that is based on dual-stream spatial-temporal graph attention and a gated recurrent unit backbone. Testing of our method on various manipulation datasets demonstrated that its accuracy and generalization ability is competitive with that of the leading detection methods.
TK-BERT: Effective Model of Language Representation using Topic-based Knowledge Graphs

Recently, the K-BERT model was proposed to add knowledge for language representation in specialized fields. The K-BERT model uses a knowledge graph to perform transfer learning on the pre-trained BERT model. However, the K-BERT model adds the knowledge that exists in the knowledge graph rather than the data relevant to the topic of the input data when using the knowledge graph of the corresponding field. Hence, the K-BERT model can cause confusion in the training. To solve this problem, this study proposes a topic-based knowledge graph BERT (TK-BERT) model, which uses the topic modeling technique. The TK-BERT model divides the knowledge graph by topic using the knowledge graph&#x0027;s topic model and infers the topic for the input sentence, adding only knowledge relevant to the topic. Therefore, the TK-BERT model does not add unnecessary knowledge to the knowledge graph. Moreover, the proposed TK-BERT model outperforms the K-BERT model.
Visual Interaction Perceptual Network for Blind Image Quality Assessment

In observing images, the perception of the human visual system (HVS) is affected by both image contents and distortions. Obviously, the visual quality of the same image varies under different distortion types and intensities. Furthermore, the visual masking effects reveal that image content and distortion have a visual interaction, where the HVS presents different visibility of the identical distortion for different image contents. Based upon this, we propose a visual interaction perceptual network that can perceive both content and distortion of an image. The proposed model consists of three sub-modules: content perception module (CPM), distortion perception module (DPM), and visual interaction module (VIM). However, the subjective quality score cannot guide the model to explicitly learn the feature representations of image content and distortion. Thus, we perform a two-stage training procedure. In the first stage, we obtain CPM and DPM, where semantic features are extracted to recognize the image content in CPM, and distortion features are extracted to capture the image distortion type and intensity in DPM. In the second stage, the VIM is applied to model the interaction between semantic and distortion features, and the final predicted quality score is given by a fully connected layer. Experimental results demonstrate that the proposed method can achieve state-of-the-art performance on multiple benchmark databases, e.g., CSIQ, TID2013, KADID-10K, and KonIQ-10K.
Multi-Resolution Stacked 1D-CNN for Small-Footprint keyword Spotting with Two-Stage Detection

Keyword spotting (KWS) is an important technique to free users’ hands in man-machine communication. It is quite challenging to build a system with both low False Reject Ratio (FRR) and low False Alarm Ratio (FAR) for real scenarios, especially when computational resources are limited. In this paper, we propose a two-stage KWS system to obtain the trade-off between low computation and high performance. To meet the low-computation requirement, we propose an acoustic model based on multi-resolution GLU stacked 1D convolutional neural network (MRG-SID). The second requirement is achieved by a second stage classification strategy, in which the neural network features are selected as classifier input for final wakeup word detection. Without increasing the relative FRR, it can reduce the FAR by introducing a few network parameters only. Experiments on a 10K hours Mandarin dataset show that the proposed model can achieve a 39.8% relative FRR reduction compared to the traditional Stacked 1D-CNN. With the second stage classifier, we are further able to reduce the FAR relatively by about 70%. In total, our proposed system significantly leads to a 62.1% relative FRR reduction at 0.1 false alarm per hour.
Voice orientation of conversational interfaces in vehicles

Connected vehicles have become a promising platform for conversational agents. However, drivers might struggle to control systems that feature multiple AI agents. To improve the usability of increasingly complex systems and the user’s interaction experience, the integration of various conversational agents needs to be carefully considered. This study aims to investigate the following: What constitutes an efficient, user-friendly arrangement of the tasks performed by a home and a car artificial intelligence (AI) agent? What is the optimal method to trigger the use of such agents? A between-subjects factorial experiment was conducted with three types of AI agent setting (home AI generalist vs. vehicle AI generalist vs. home and vehicle AI specialists) and two activation methods (voice activation vs. push-to-talk activation). The results indicate that interacting with specialist AI agents enhances the perceived ease of use and the credibility of these agents. Furthermore, voice activation improves the social presence and attractiveness of an AI agent. The findings suggest that an AI agent that offers role specialization and a natural interaction method will improve drivers’ interaction experience.
Applications of Deep Learning in Object Detection

Computer vision includes many branches, among which image classification, object detection, image segmentation, and object tracking are the most important research topics in the field of computer vision. Object detection is the foundation for tackling more difficult and higher-level visual tasks, including segmentation, scene understanding, object tracking, image description, event detection, and activity recognition, since it is the baseline of image understanding and computer vision. In the past decade, deep learning object detection algorithms based on CNN have developed rapidly, and many detection algorithms have emerged with better performance than traditional or earlier algorithms. This paper summarizes the representative object detection algorithms proposed in recent years and introduces them in two parts: region proposal-based methods and regression/classification-based methods. By introducing the idea in detail and the main features of each algorithm and the network structure, it helps one understand the two methods and their representative algorithms and the existing problems in the object detection field.
Analysis on the Steps of Physical Education Teaching Based on Deep Learning

The rapid progress of the internet of things and artificial intelligence has brought new opportunities for the construction and development of intelligent sports. This paper designs an analysis and evaluation system of physical education teaching steps based on deep learning technology. The intelligent wearable devices are used to conduct real-time dynamic monitoring of students' exercise steps and heart rate in class so as to build a sports teaching activity data set. The authors analyze the time step sequence based on transformer deep model to realize the estimation of motion effect. In addition, they propose a hierarchical fusion model based on transformer, which makes full use of the steps and heart rate information to predict the abnormal situation in physical education. The experimental results show the effectiveness of the system.