Reviewerly/exHarmony huggingface dataset:
Leadership Styles of Meso-level Educational Leaders Perceived by Academic Members: An Exploratory Study among Chinese Universities

Purpose: The challenges faced by universities in China have presented opportunities to enhance the leadership of educational leaders in universities. Little has been written about the leadership styles of mid-level educational leaders in Chinese higher education (HE). The objective of this study is to provide an authentic understanding of 1) the leadership styles of mid-level educational leaders as experienced by academic members, 2) experienced contextual factors related to the leadership styles of mid-level educational leaders. &#x0D; Design: Qualitative research was conducted, and the perceptions of 13 academic members from nine universities in China were explored. &#x0D; Findings: This study revealed authoritarian leadership style was the most reported leadership style of the mid-level educational leaders, followed by transformational and transactional leadership styles. Participants suggested to minimise the adoption of authoritarian leadership style and to encourage the application of transformational leadership style. Regarding the factors that shape leadership style of mid-level educational leaders, this research supports the following factors: culture, organizational policies &amp; administration, discipline, and incentive mechanism. &#x0D; Originality: We gained a deep understanding of the leadership styles of mid-level educational leaders and the contextual factors that shape the leadership styles in this study. The results can be conducive to future leadership training and to the mid-level educational leaders to create environments that facilitate the application of educational leadership practices.
An Integrated Real-time Monocular Human Pose &amp; Shape Estimation Pipeline for Edge Devices

Human pose and shape estimation is crucial in perceiving gestures and actions intended by the human. It is necessary to perform this task in real-time so that consistency and safety can be ensured in practical settings. It is also important that this task is executable on practically affordable hardware. We thereby come up with an integrated pipeline for robustly perceiving human poses and shapes on an edge device. The pipeline is a combination of YOLOv8, AlphaPose, MotionBERT, and ExPose. Using these models, the pipeline performs bounding box detection, 2D skeleton estimation, 2D-to-3D pose lifting, and 3D mesh reconstruction, respectively. Results of the related experiments show that our pipeline can accurately and precisely process input images in real-time on the Jetson TX, a typical example of an edge device.
Optimal Trained Deep Learning Model for Breast Cancer Segmentation and Classification

Breast cancer is the most widespread cancer among women. Based on the International cancer research center analysis, the highest number of deaths among women is due to breast cancer. Hence, detecting breast cancer at the earliest may help the oncologist to make appropriate decisions. Due to variations in breast tissue density, there is still a challenge in precise diagnosis and classification. To overcome this challenge, a novel OTDEM-based breast cancer segmentation and classification is proposed with the following four stages: they are, preprocessing, segmentation, feature extraction and classification. The input image is passed to the initial stage using the CLAHE filter to enhance the image. Then the preprocessed image is given to the segmentation stage for the image sub-segments by correlation-based deep joint segmentation. Following that, the features such as statistical features, improved LGXP, texton features, and shape-based features are derived from the segmented image. Then the derived features are fed to the ensemble model that includes CNN, DBN, and BI-GRU classifier to finalize the classification outcome. Further, to enhance the performance of the ensemble model, the weight of BI-GRU is optimized via a new algorithm termed SIPOA. This ensures optimal training to make the model more appropriate in its classification process. Finally, the performance of the proposed work is validated over the traditional models concerning different performance measures.
MSF-NET: Foreground Objects Detection with Fusion of Motion and Semantic Features

Visual surveillance requires robust detection of foreground objects under challenging environments of abrupt lighting variation, stationary foreground objects, dynamic background objects, and severe weather conditions. Most classical algorithms leverage background model images produced by statistical modeling of the change of brightness values over time. Since they have difficulties using global features, many false detections occur at the stationary foreground regions and dynamic background objects. Recent deep learning-based methods can easily reflect global characteristics compared to classical methods. However, deep learning-based methods still need to be improved in utilizing spatiotemporal information. We propose an algorithm for efficiently using spatiotemporal information by adopting a split and merge framework. First, we split spatiotemporal information on successive multiple images into spatial and temporal parts using two sub-networks of semantic and motion networks. Finally, separated information is fused in a spatiotemporal fusion network. The proposed network consists of three sub-networks, which we note as MSF-NET (Motion and Semantic features Fusion NETwork). Also, we propose a method to train the proposed MSF-NET stably. Compared to the latest deep learning algorithms, the proposed MSF-NET gives 9% and 13% higher FM in the LASIESTA and SBI datasets. Also, we designed the proposed MSF-NET to be lightweight to run in real-time on a desktop GPU.
Improving Medical Speech-to-Text Accuracy using Vision-Language Pre-training Models

Automatic Speech Recognition (ASR) is a technology that converts spoken words into text, facilitating interaction between humans and machines. One of the most common applications of ASR is Speech-To-Text (STT) technology, which simplifies user workflows by transcribing spoken words into text. In the medical field, STT has the potential to significantly reduce the workload of clinicians who rely on typists to transcribe their voice recordings. However, developing an STT model for the medical domain is challenging due to the lack of sufficient speech and text datasets. To address this issue, we propose a medical-domain text correction method that modifies the output text of a general STT system using the Vision Language Pre-training (VLP) method. VLP combines textual and visual information to correct text based on image knowledge. Our extensive experiments demonstrate that the proposed method offers quantitatively and clinically significant improvements in STT performance in the medical field. We further show that multi-modal understanding of image and text information outperforms single-modal understanding using only text information.
Dynamic Analysis of a Four-wing Chaotic System and Application in Image Encryption Based on Compressive Sensing

Compared with the conventional multi-scroll attractor, the multi-wing butterfly chaotic attractors are easier to design and implement through analog circuitry, thus they has more potential applications. In this paper, a chaotic system with four-wing attractors is designed and the dynamic behaviors are analyzed in terms of phase diagram, bifurcation diagram, Lyapunov exponential spectrum and C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> structural complexity. It is found that each parameter has a large range of intervals that can keep the system in a chaotic state and the generated sequences have sufficient randomness to be well suited for applications in secure communications. Then, the circuit model of the constructed four-wing chaotic system is built with a basic operational amplifier circuit, and the accuracy of the circuit implementation is verified. Finally, a color image compression encryption scheme is designed based on the theory of compressive sensing and DNA dynamic coding. The algorithm is mainly composed of five parts: sparse, compression calculation, 3D projection scrambling, DNA diffusion and plaintext association confusion. The security test results show that the designed scheme not only has superior compression performance and high security, but also has no limitation on the size of the test image.
Improving Software Modularization Quality Through the Use of Multi-Pattern Modularity Clustering Algorithm

In contemporary software development processes, as the software development cycle undergoes continuous evolution, the subsequent maintenance phase often veers away from the original architectural plan. While clustering serves as an intuitive approach for implementing modularization, traditional clustering algorithms exhibit certain limitations, such as generating excessively large output modules. This paper introduces a novel multi-pattern modularity clustering algorithm (MPMC) aimed at enhancing the quality of software modularization. This algorithm hinges on two key components. Firstly, it employs a multi-pattern strategy to scrutinize the extent of dependencies between files. This involves distinct steps like tagging files, aggregating chain dependencies, and subsequently preprocessing and amalgamating the modules. Secondly, it incorporates a multi-level modularity clustering algorithm, which employs a graph partitioning technique to categorize graphs into clusters through processes like coarsening and multi-level refinement. To evaluate the performance of MPMC, five open-source and one closed-source programs, each varying in size and functionality, were employed for comparison against other conventional software grouping algorithms and existing software clustering methods. Experimental results reveal a substantial improvement in the performance of MPMC, approximately 2.71 times higher in terms of weighted modularity quality (MQ) criteria compared to alternative methods, and a 1.35 times improvement in the MoJoFM criteria. These findings establish the effectiveness of the MPMC algorithm as a valuable software clustering tool for developers, emphasizing its ability to yield superior module quality in comparison to other methods.
Horizon Profiling Methods for Photovoltaic Arrays

In this work, we introduce and compare the results of several methods for determining the horizon profile at a PV site, and compare their use cases and limitations. The methods in this paper include horizon detection from time-series irradiance or performance data, modeling from GIS topology data, manual theodolite measurements, and camera-based horizon detection. We compare various combinations of these methods using data from 4 Regional Test Center sites in the US, and 3 World Bank sites in Nepal. The results show many differences between these methods, and we recommend the most practical solutions for various use-cases.
Development and Evaluation of Typical Plane of Array Year (TPY) for Solar Energy Systems Over the Americas

A Typical Meteorological Year (TMY) dataset consists of a concatenation of 12 months of data extracted from a long-term time series of meteorological and radiation data, assuming a horizontal surface. However, the deployment of solar energy systems uses various orientations such as fixed, single-,or dual-axis tracking systems. This paper proposes a new method to develop and generate a typical dataset for the specific solar energy technologies (such as photovoltaics) that rely, for example, on fixed planar collectors. This new approach considers the plane-of-array (POA) irradiance as the main driving variable for the selection of 12 typical months that make up the new entity, referred to as Typical POA Year (TPY). The justification for this new method is that the current application of TMY datasets is prone to bias when converted to POA and can be significantly different from a TPY. This study uses the NSRDB dataset (1998-2018) version 3 to produce both TPYs and TMYs for solar energy and various other applications. Hypothetical solar systems information and POA irradiance data for both fixed-tilt and single-axis tracking are used to generate TPYs and associated generation capacity profiles for both TMYs and TPYs. A comparison between the new TPY and the existing TMY demonstrates that the latter mis-predicts the energy yield of typical PV systems by about ±5 % for the interquartile range as compared to the more appropriate TPY.
Tiny and Efficient Model for the Edge Detection Generalization

Most high-level computer vision tasks rely on low-level image operations as their initial processes. Operations such as edge detection, image enhancement, and super-resolution, provide the foundations for higher level image analysis. In this work we address the edge detection considering three main objectives: simplicity, efficiency, and generalization since current state-of-the-art (SOTA) edge detection models are increased in complexity for better accuracy. To achieve this, we present Tiny and Efficient Edge Detector (TEED), a light convolutional neural network with only 58K parameters, less than 0.2% of the state-of-the-art models. Training on the BIPED dataset takes less than 30 minutes, with each epoch requiring less than 5 minutes. Our proposed model is easy to train and it quickly converges within very first few epochs, while the predicted edge-maps are crisp and of high quality. Additionally, we propose a new dataset to test the generalization of edge detection, which comprises samples from popular images used in edge detection and image segmentation. The source code is available in https://github.com/xavysp/TEED.
Towards Vision-Language Mechanistic Interpretability: A Causal Tracing Tool for BLIP

Mechanistic interpretability seeks to understand the neural mechanisms that enable specific behaviors in Large Language Models (LLMs) by leveraging causality-based methods. While these approaches have identified neural circuits that copy spans of text, capture factual knowledge, and more, they remain unusable for multimodal models since adapting these tools to the vision-language domain requires considerable architectural changes. In this work, we adapt a unimodal causal tracing tool to BLIP to enable the study of the neural mechanisms underlying image-conditioned text generation. We demonstrate our approach on a visual question answering dataset, highlighting the causal relevance of later layer representations for all tokens. Furthermore, we release our BLIP causal tracing tool as open source to enable further experimentation in vision-language mechanistic interpretability by the community. Our code is available at this URL.
Implicit Neural Representation in Medical Imaging: A Comparative Survey

Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing neural networks to parameterize data through implicit continuous functions, INRs offer several benefits. Recognizing the potential of INRs beyond these domains, this survey aims to provide a comprehensive overview of INR models in the field of medical imaging. In medical settings, numerous challenging and ill-posed problems exist, making INRs an attractive solution. The survey explores the application of INRs in various medical imaging tasks, such as image reconstruction, segmentation, registration, novel view synthesis, and compression. It discusses the advantages and limitations of INRs, highlighting their resolution-agnostic nature, memory efficiency, ability to avoid locality biases, and differentiability, enabling adaptation to different tasks. Furthermore, the survey addresses the challenges and considerations specific to medical imaging data, such as data availability, computational complexity, and dynamic clinical scene analysis. It also identifies future research directions and opportunities, including integration with multi-modal imaging, real-time and interactive systems, and domain adaptation for clinical decision support. To facilitate further exploration and implementation of INRs in medical image analysis, we have provided a compilation of cited studies along with their available open-source implementations on ${\color{Magenta}GitHub}$. Finally, we aim to consistently incorporate the most recent and relevant papers regularly.
A Cross-Chain Transaction Model for Power Blockchain Based on Hash-Locking Mechanism

Blockchain is a closed island in the traditional power transaction platform and cannot freely trade with others, which limits the flexibility and autonomy of the distributed power trading market. Aiming at the problems of network isolation and interoperability among different power blockchains, we proposed a general and compatible power cross-chain trading protocol based on hash time locking technology, where the transaction power and price are locked by hash time to realize cross-chain exchanges of different assets. A flexible and specialized smart contract framework is designed, which can be easily adapted and extended according to specific needs, promoting the flexibility and freedom of the distributed power market. And the experimental shows that the transaction processing capability is significantly better than that of the relays technology when dealing with multiple concurrency problems.
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI

In this paper, we present a novel method for the automatic classification of medical images that learns and leverages weak causal signals in the image. Our framework consists of a convolutional neural network backbone and a causality-extractor module which extracts cause-effect relationships between feature maps that can inform the model on the appearance of a feature in one place of the image, given the presence of another feature within some other place of the image. To evaluate the effectiveness of our approach in low-data scenarios, we train our causality-driven architecture in a One-shot learning scheme where we propose a new meta-learning procedure which entails meta-training and meta-testing tasks that are designed using related classes but at different levels of granularity. We conduct binary and multi-class classification experiments on a publicly available dataset of prostate MRI images. To validate the effectiveness of the proposed causality-driven module, we perform an ablation study and conduct qualitative assessments using class activation maps to highlight regions strongly influencing the network’s decision-making process. Our findings show that causal relationships among features play a crucial role in enhancing the model’s ability to discern relevant information and yielding more reliable and interpretable predictions. This would make it a promising approach for medical image classification tasks.
Semantic Motif Segmentation of Archaeological Fresco Fragments

Archaeological fragment processing is crucial to support the analysis of pictorial contents of broken artifacts. In this paper, we focus on the unexplored task of semantic segmentation of fresco fragments. This task enables the extraction of semantic information from a fragment, facilitating subsequent tasks like fragment classification or reassembly. We introduce a semantic segmentation dataset of fresco fragments acquired at the Pompeii Archeological Site, accompanied by baseline models. Additionally, we introduce a supplementary task of fragment cleaning, providing a dataset with the detection of manual annotations of archaeological marks that require restoration before further analysis. Our experiments, using standard metrics and state-of-the-art baselines, demonstrate that semantic segmentation of fresco fragments is feasible, paving the way toward more complex activities that require a semantic understanding of fragmented artifacts. Dataset with annotations, and code will be released at https://repairproject.github.io/fragment-restoration/
What Does Really Count? Estimating Relevance of Corner Cases for Semantic Segmentation in Automated Driving

In safety-critical applications such as automated driving, perception errors may create an imminent risk to vulnerable road users (VRU). To mitigate the occurrence of unexpected and potentially dangerous situations, so-called corner cases, perception models are trained on a huge amount of data. However, the models are typically evaluated using task-agnostic metrics, which do not reflect the severity of safety-critical misdetections. Consequently, mis-detections with particular relevance for the safe driving task should entail a more severe penalty during evaluation to pinpoint corner cases in large-scale datasets. In this work, we propose a novel metric IoU <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</inf> that exploits relevance on the pixel level of the semantic segmentation output to extend the notion of the intersection over union (IoU) by emphasizing small areas of an image affected by corner cases. We (i) employ IoU <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</inf> to measure the effect of pre-defined relevance criteria on the segmentation evaluation, and (ii) use the relevance-adapted IoU <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</inf> to refine the identification of corner cases. In our experiments, we investigate vision-based relevance criteria and physical attributes as per-pixel criticality to factor in the imminent risk, showing that IoU <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</inf> precisely accentuates the relevance of corner cases.
On the Security of Secure Keyword Search and Data Sharing Mechanism for Cloud Computing

Nearly all of the previous attribute-based proxy re-encryption (ABPRE) schemes cannot support keyword search and keyword updating without the aid of private key generator (PKG) simultaneously. To resolve this problem, recently in IEEE Transactions on Dependable and Secure Computing (doi: 10.1109/TDSC.2020.2963978), Ge et al. proposed a ciphertext-policy ABPRE scheme with keyword search, dubbed CPAB-KSDS, which supports keyword updating without communicating with PKG. It also achieves indistinguishability against chosen-ciphertext attack (IND-CCA) security and indistinguishability against chosen-keyword attack (INDCKA) security in the random oracle model. In this paper, we carefully analyze the security of Ge et al.’s CPAB-KSDS scheme and find that they did not give a correct reduction from IND-CKA security of theirs to the underlying cryptographic assumption. Furthermore, we also give a concrete attack on IND-CKA security of the CPAB-KSDS scheme. Therefore, it fails to achieve IND-CKA security they claimed, which is an essential security requirement for the encryption scheme with keyword search.