Reviewerly/exHarmony huggingface dataset:
Deep attention super-resolution of brain magnetic resonance images acquired under clinical protocols

Vast quantities of Magnetic Resonance Images (MRI) are routinely acquired in clinical practice but, to speed up acquisition, these scans are typically of a quality that is sufficient for clinical diagnosis but sub-optimal for large-scale precision medicine, computational diagnostics, and large-scale neuroimaging collaborative research. Here, we present a critic-guided framework to upsample low-resolution (often 2D) MRI full scans to help overcome these limitations. We incorporate feature-importance and self-attention methods into our model to improve the interpretability of this study. We evaluate our framework on paired low- and high-resolution brain MRI structural full scans (i.e., T1-, T2-weighted, and FLAIR sequences are simultaneously input) obtained in clinical and research settings from scanners manufactured by Siemens, Phillips, and GE. We show that the upsampled MRIs are qualitatively faithful to the ground-truth high-quality scans (PSNR = 35.39; MAE = 3.78E-3; NMSE = 4.32E-10; SSIM = 0.9852; mean normal-appearing gray/white matter ratio intensity differences ranging from 0.0363 to 0.0784 for FLAIR, from 0.0010 to 0.0138 for T1-weighted and from 0.0156 to 0.074 for T2-weighted sequences). The automatic raw segmentation of tissues and lesions using the super-resolved images has fewer false positives and higher accuracy than those obtained from interpolated images in protocols represented with more than three sets in the training sample, making our approach a strong candidate for practical application in clinical and collaborative research.
Research on Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

In this paper, we address the problem that in path planning research, the traditional ant colony algorithm suffers from slow convergence and is easily trapped in local optimal solutions. Therefore, this paper proposes an improved ant colony algorithm that can find the optimal path with fewer iterations. The pheromone initial distribution strategy based on the idea of quadrant discrimination is proposed to make the pheromone differentially distributed, thus improving the orientation of the pheromone at the early stage of the algorithm search. The heuristic function is improved by introducing the distance between the next node to the target node and the number of grids that the path passes through to increase the influence of local paths. The pheromone concentration adaptive update strategy is proposed, and the dynamic pheromone volatility factor is introduced to make the pheromone concentration vary in a controlled range. Experiments demonstrate that, under the same environment, the improved algorithm in this paper reduces the path length by 11.80% and 7.15%, and the number of convergence iterations by 55.86% and 38.82%, respectively, compared with the traditional ant colony algorithm and the algorithm in literature [6]. The improved algorithm is significantly better than the other two algorithms in terms of convergence speed and path length, which effectively shortens the optimal path length and the number of iterations of the robot, and has certain feasibility.
AM-YOLO: Improved YOLOV4 based on attention mechanism and multi-feature fusion

To address the problem that target detection models such as FASTER RCNN, YOLO and SSD focus too much on the depth of the network and neglect to make full use of the deep semantic feature information of the image, this paper proposes a new network: AM-YOLO. The network makes full use of contextual relationship between shallow and deep layers to achieve multi-feature fusion of the target. In AM-YOLO, SE blocks are firstly added in the backbone network to differentiate the channel importance of feature maps. Then a new path aggregation network is proposed to achieve the full fusion of shallow and deep features. This paper uses YOLOV4 as the baseline, PASCAL VOC07+12 for dataset and the experimental results show that on the 3060 GPU, the map of AM-YOLO is improved by 2.86&#x0025; compared with YOLOV4 model, which validates the comprehensive performance of AM-YOLO.
Clinically Applicable Pathological Diagnosis System for Cell Clumps in Endometrial Cancer Screening via Deep Convolutional Neural Networks

The soaring demand for endometrial cancer screening has exposed a huge shortage of cytopathologists worldwide. To address this problem, our study set out to establish an artificial intelligence system that automatically recognizes and diagnoses pathological images of endometrial cell clumps (ECCs).We used Li Brush to acquire endometrial cells from patients. Liquid-based cytology technology was used to provide slides. The slides were scanned and divided into malignant and benign groups. We proposed two (a U-net segmentation and a DenseNet classification) networks to identify images. Another four classification networks were used for comparison tests.A total of 113 (42 malignant and 71 benign) endometrial samples were collected, and a dataset containing 15,913 images was constructed. A total of 39,000 ECCs patches were obtained by the segmentation network. Then, 26,880 and 11,520 patches were used for training and testing, respectively. On the premise that the training set reached 100%, the testing set gained 93.5% accuracy, 92.2% specificity, and 92.0% sensitivity. The remaining 600 malignant patches were used for verification.An artificial intelligence system was successfully built to classify malignant and benign ECCs.
Improved YOLOv5 Based on Hybrid Domain Attention for Small Object Detection in Optical Remote Sensing Images

The object detection technology of optical remote sensing images has been widely applied in military investigation, traffic planning, and environmental monitoring, among others. In this paper, a method is proposed for solving the problem of small object detection in optical remote sensing images. In the proposed method, the hybrid domain attention units (HDAUs) of channel and spatial attention mechanisms are combined and employed to improve the feature extraction capability and suppress background noise. In addition, we designed a multiscale dynamic weighted feature fusion network (MDW-Net) to improve adaptive optimization and deep fusion of shallow and deep feature layers. The model is trained and tested on the DIOR dataset, and some ablation and comparative experiments are carried out. The experimental results show that the mAP of the proposed model surpasses that of YOLOv5 by a large margin of +2.3 and has obvious advantages regarding the detection performance for small object categories, such as airplane, ship, and vehicle, which support its application for small target detection in optical remote sensing images.
Understanding the social aspects of earthquake early warning: A literature review

Earthquake early warning (EEW) systems aim to warn end-users of incoming ground shaking from earthquakes that have ruptured further afield, potentially reducing risks to lives and properties. EEW is a socio-technical system involving technical and social processes. This paper contributes to advancing EEW research by conducting a literature review investigating the social science knowledge gap in EEW systems. The review of 70 manuscripts found that EEW systems could benefit society, and the benefits may go beyond its direct function for immediate earthquake response. The findings also show that there are social processes involved in designing, developing, and implementing people-centered EEW systems. Therefore, social science research should not just be concerned with the end-user response but also investigate various stakeholders' involvement throughout the development process of EEW systems. Additionally, EEW is a rapidly evolving field of study, and social science research must take a proactive role as EEW technological capacities improve further and becomes more accessible to the public. To improve EEW effectiveness, further research is needed, including (1) advancing our understanding of why people take protective action or not, and ways to encourage appropriate action when alerted; (2) enhancing public understanding, investigating best practices for communicating, educating, and engaging with the public about EEW and overall earthquake resilience; and (3) keeping up with technological advances and societal changes and investigating how these changes impact communities' interactions with EEW from various standpoints including legal perspectives.
Saliency-Enabled Coding Unit Partitioning and Quantization Control for Versatile Video Coding

The latest video coding standard, versatile video coding (VVC), has greatly improved coding efficiency over its predecessor standard high efficiency video coding (HEVC), but at the expense of sharply increased complexity. In the context of perceptual video coding (PVC), the visual saliency model that utilizes the characteristics of the human visual system to improve coding efficiency has become a reliable method due to advances in computer performance and visual algorithms. In this paper, a novel VVC optimization scheme compliant PVC framework is proposed, which consists of fast coding unit (CU) partition algorithm and quantization control algorithm. Firstly, based on the visual saliency model, we proposed a fast CU division scheme, including the redetermination of the CU division depth by calculating Scharr operator and variance, as well as the executive decision for intra sub-partitions (ISP), to reduce the coding complexity. Secondly, a quantization control algorithm is proposed by adjusting the quantization parameter based on multi-level classification of saliency values at the CU level to reduce the bitrate. In comparison with the reference model, experimental results indicate that the proposed method can reduce about 47.19% computational complexity and achieve a bitrate saving of 3.68% on average. Meanwhile, the proposed algorithm has reasonable peak signal-to-noise ratio losses and nearly the same subjective perceptual quality.
Identifying and Monitoring Students’ Classroom Learning Behavior Based on Multisource Information

Understanding human activity and behavior, particularly real-time understanding in video feeds, is one of the most active areas of research in Computer Vision (CV) and Artificial Intelligence (AI) nowadays. To advance the topic of integrating learning engagement research with university teaching practice, accurate and efficient assessment, and analysis of students’ classroom learning behavior engagement is very important. The recently proposed classroom behavior recognition algorithms have some limitations, such as the inability to quickly and accurately identify students’ classroom behaviors because they do not consider the motion information of students between consecutive frames. In recent years, action recognition algorithms based on Convolutional Neural Networks (CNN) have improved significantly. To address the limitations of existing algorithms, in this study, a 3D-CNN is selected as a network model for classroom student behavior recognition, which increases information multisourcing and classroom student localization with high accuracy and robustness. For better analysis of human behavior in videos, the 3D convolution extends the 2D convolution to the spatial–temporal domain. In the proposed system, first of all, a real-time picture stream of each student is obtained by combining real-time target detection and tracking. Then, a deep spatiotemporal residual CNN is used to learn the spatiotemporal features of each student’s behavior, so, as to achieve real-time recognition of classroom behaviors for multistudent targets in classroom teaching scenarios. To verify the effectiveness of the proposed model, different experiments are conducted using the labeled classroom behavior dataset. The experimental results demonstrate that the proposed model exhibits better performance in classroom behavior recognition. The accurate recognition of classroom behaviors can assist the teachers and students to understand the classroom learning situation and help to promote the development of smart classroom.
UNISOLAR: An Open Dataset of Photovoltaic Solar Energy Generation in a Large Multi-Campus University Setting

We introduce an open dataset of high-granularity Photovoltaic (PV) solar energy generation, solar irradiance, and weather data from 42 PV sites deployed across five campuses at La Trobe University, Victoria, Australia. The dataset includes approximately two years of PV solar energy generation data collected at 15-minute intervals. Geographical placement and engineering specifications for each of the sites are also provided to aid researchers in modelling solar energy generation. Weather data is available at 1-minute intervals and is provided by the Australian Bureau of Meteorology (BOM). Apparent temperature, air temperature, dew point temperature, relative humidity, wind speed, and wind direction were provided under the weather data. The paper describes the data collection methods, cleaning, and merging with weather data. This dataset can be used to forecast, benchmark, and enhance operational outcomes in solar sites.
Effective Throughput Analysis of Different Task Execution Strategies for Mid-Air Fitts' Tasks in Virtual Reality

Fitts' law and throughput based on effective measures are two mathematical models frequently used to analyze human motor performance in a standardized pointing task, e.g., to compare the performance of input and output devices. Even though pointing has been deeply studied in 2D, it is not well understood how different task execution strategies affect throughput in pointing in 3D virtual environments. In this work, we examine the effective throughput measure, claimed to be invariant to task execution strategies, in Virtual Reality (VR) systems with three such strategies, “as fast, as precise, and as fast and as precise as possible” for ray casting and virtual hand interaction, by re-analyzing data from a 3D pointing ISO 9241-411 study. Results show that effective throughput is not invariant for different task execution strategies in VR, which also matches a more recent 2D result. Normalized speed vs. accuracy curves also did not fit the data. We thus suggest that practitioners, developers, and researchers who use MacKenzie's effective throughput formulation should consider our findings when analyzing 3D user pointing performance in VR systems.
Verified symbolic execution with Kripke specification monads (and no meta-programming)

Verifying soundness of symbolic execution-based program verifiers is a significant challenge. This is especially true if the resulting tool needs to be usable outside of the proof assistant, in which case we cannot rely on shallowly embedded assertion logics and meta-programming. The tool needs to manipulate deeply embedded assertions, and it is crucial for efficiency to eagerly prune unreachable paths and simplify intermediate assertions in a way that can be justified towards the soundness proof. Only a few such tools exist in the literature, and their soundness proofs are intricate and hard to generalize or reuse. We contribute a novel, systematic approach for the construction and soundness proof of such a symbolic execution-based verifier. We first implement a shallow verification condition generator as an object language interpreter in a specification monad, using an abstract interface featuring angelic and demonic nondeterminism. Next, we build a symbolic executor by implementing a similar interpreter, in a symbolic specification monad. This symbolic monad lives in a universe that is Kripke-indexed by variables in scope and a path condition. Finally, we reduce the soundness of the symbolic execution to the soundness of the shallow execution by relating both executors using a Kripke logical relation. We report on the practical application of these techniques in Katamaran, a tool for verifying security guarantees offered by instruction set architectures (ISAs). The tool is fully verified by combining our symbolic execution machinery with a soundness proof of the shallow verification conditions against an axiomatized separation logic, and an Iris-based implementation of the axioms, proven sound against the operational semantics. Based on our experience with Katamaran, we can report good results on practicality and efficiency of the tool, demonstrating practical viability of our symbolic execution approach.
An Empirical Study on Discovering Software Bugs Using Machine Learning Techniques

Bug is a defect in software which needs to be identified early so as to avoid unnecessary burden caused by it later. Bug discovery from software modules has been around. However, of late, machine learning (ML) became a useful and appropriate solution to many real-world problems. In this context, usage of machine learning has become an important step forward in improving state of the art in bug detection. It is an artificial intelligence-based (AI) approach that makes it more effective due to the bulk of software modules. Many existing methods strived to incorporate ML for bug discovery. However, there is need for improvement with appropriate methodology. In this paper, we proposed a methodology that exploits two ML techniques known as decision tree (DT) and random forest (RF) for efficient means of discovering bugs from software modules. An empirical study is made using Python data science platform. Experimental results showed that RF performs better than DT in terms of accuracy of bug prediction.
ARSD: An Adaptive Region Selection Object Detection Framework for UAV Images

Due to the rapid development of deep learning, the performance of object detection has greatly improved. However, object detection in high-resolution Unmanned Aerial Vehicles images remains a challenging problem for three main reasons: (1) the objects in aerial images have different scales and are usually small; (2) the images are high-resolution but state-of-the-art object detection networks are of a fixed size; (3) the objects are not evenly distributed in aerial images. To this end, we propose a two-stage Adaptive Region Selection Detection framework in this paper. An Overall Region Detection Network is first applied to coarsely localize the object. A fixed points density-based targets clustering algorithm and an adaptive selection algorithm are then designed to select object-dense sub-regions. The object-dense sub-regions are sent to a Key Regions Detection Network where results are fused with the results at the first stage. Extensive experiments and comprehensive evaluations on the VisDrone2021-DET benchmark datasets demonstrate the effectiveness and adaptiveness of the proposed framework. Experimental results show that the proposed framework outperforms, in terms of mean average precision (mAP), the existing baseline methods by 2.1% without additional time consumption.
Students’ adaptive deep learning path and teaching strategy of contemporary ceramic art under the background of Internet +

With the rapid expansion of Internet technology, this research aims to explore the teaching strategies of ceramic art for contemporary students. Based on deep learning (DL), an automatic question answering (QA) system is established, new teaching strategies are analyzed, and the Internet is combined with the automatic QA system to help students solve problems encountered in the process of learning. Firstly, the related theories of DL and personalized learning are analyzed. Among DL-related theories, Back Propagation Neural Network (BPNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) are compared to implement a single model and a mixed model. Secondly, the collected student questions are selected and processed, and experimental parameters in different models are set for comparative experiments. Experiments reveal that the average accuracy and Mean Reciprocal Rank (MRR) of traditional retrieval methods can only reach about 0.5. In the basic neural network, the average accuracy of LSTM and GRU structural models is about 0.81, which can achieve better results. Finally, the accuracy of the hybrid model can reach about 0.82, and the accuracy and MRR of the Bidirectional Gated Recurrent Unit Network-Attention (BiGRU-Attention) model are 0.87 and 0.89, respectively, achieving the best results. The established DL model meets the requirements of the online automatic QA system, improves the teaching system, and helps students better understand and solve problems in the ceramic art courses.
MEST: An Action Recognition Network with Motion Encoder and Spatio-Temporal Module

As a sub-field of video content analysis, action recognition has received extensive attention in recent years, which aims to recognize human actions in videos. Compared with a single image, video has a temporal dimension. Therefore, it is of great significance to extract the spatio-temporal information from videos for action recognition. In this paper, an efficient network to extract spatio-temporal information with relatively low computational load (dubbed MEST) is proposed. Firstly, a motion encoder to capture short-term motion cues between consecutive frames is developed, followed by a channel-wise spatio-temporal module to model long-term feature information. Moreover, the weight standardization method is applied to the convolution layers followed by batch normalization layers to expedite the training process and facilitate convergence. Experiments are conducted on five public datasets of action recognition, Something-Something-V1 and -V2, Jester, UCF101 and HMDB51, where MEST exhibits competitive performance compared to other popular methods. The results demonstrate the effectiveness of our network in terms of accuracy, computational cost and network scales.
Post-Quantum Authenticated Encryption against Chosen-Ciphertext Side-Channel Attacks

Over the last years, the side-channel analysis of Post-Quantum Cryptography (PQC) candidates in the NIST standardization initiative has received increased attention. In particular, it has been shown that some post-quantum Key Encapsulation Mechanisms (KEMs) are vulnerable to Chosen-Ciphertext Side-Channel Attacks (CC-SCA). These powerful attacks target the re-encryption step in the Fujisaki-Okamoto (FO) transform, which is commonly used to achieve CCA security in such schemes. To sufficiently protect PQC KEMs on embedded devices against such a powerful CC-SCA, masking at increasingly higher order is required, which induces a considerable overhead. In this work, we propose to use a conceptually simple construction, the ΕtS KEM, that alleviates the impact of CC-SCA. It uses the Encrypt-then-Sign (EtS) paradigm introduced by Zheng at ISW ’97 and further analyzed by An, Dodis and Rabin at EUROCRYPT ’02, and instantiates a postquantum authenticated KEM in the outsider-security model. While the construction is generic, we apply it to the CRYSTALS-Kyber KEM, relying on the CRYSTALSDilithium and Falcon signature schemes. We show that a CC-SCA-protected EtS KEM version of CRYSTALS-Kyber requires less than 10% of the cycles required for the CC-SCA-protected FO-based KEM, at the cost of additional data/communication overhead. We additionally show that the cost of protecting the EtS KEM against fault injection attacks, necessarily due to the added signature verification, remains negligible compared to the large cost of masking the FO transform at higher orders. Lastly, we discuss relevant embedded use cases for our EtS KEM construction.