Reviewerly/exHarmony huggingface dataset:
Use Cases are Essential

While the software industry is a fast-paced and exciting world in which new tools, technologies, and techniques are constantly being developed to serve business and society, it is also forgetful. In its haste for fast-forward motion, it is subject to the whims of fashion and can forget or ignore proven solutions to some of the eternal problems that it faces. Use cases, first introduced in 1986 and popularized later, are one of those proven solutions. Ivar Jacobson and Alistair Cockburn, the two primary actors in this domain, are writing this article to describe to a new generation what use cases are and how they serve.
ChatGPT in third-party logistics – The game-changer or a step into the unknown?

This study aims to examine the influence of ChatGPT on third-party logistics (3PL) operators, specifically in the context of enhancing the value of services they presently provide. This research employed a combination of survey analyses and a case study on an international logistics operator. Within this framework, specific services were identified and contrasted between the operator's current performance and a hypothetical scenario involving the implementation of ChatGPT. For the majority of the study, the open-source version of ChatGPT was utilized. However, for forecasting purposes, the proprietary GPT-3.5 version was employed. The intention behind this methodology was to highlight the capabilities of the basic ChatGPT, showcasing its applicability as a widely accessible tool, while recognizing the necessity of GPT-3.5 for more sophisticated numerical data analyses. The findings revealed that 3PL managers are informed about ChatGPT's capabilities and recognize its potential utility in logistics operations. The case study further underscored ChatGPT's competency in generating actionable insights which can guide managers in enhancing their value-added services. The primary constraint in this study was the limited number of expert participants. This limitation was due to stringent criteria set by the researchers in selecting experts. However, this also represents an avenue for more comprehensive research in the future. Subsequent studies might also explore other chatbot variations and delve deeper into the practical application of ChatGPT in automating interactions between 3PL entities, service beneficiaries, and the end customers of these services. This manuscript augments the current body of research concerning the application of ChatGPT in the realm of value-added services offered by logistics operators in the context of contract logistics. Moreover, it provides empirical evaluations of several use cases juxtaposed with existing solutions.
A study on unmanned combat vehicle path planning for collision avoidance with enemy forces in dynamic situations

Abstract This study focuses on the path planning problem for unmanned combat vehicles (UCVs), where the goal is to find a viable path from the starting point to the destination while avoiding collisions with moving obstacles, such as enemy forces. The objective is to minimize the overall cost, which encompasses factors like travel distance, geographical difficulty, and the risk posed by enemy forces. To address this challenge, we have proposed a heuristic algorithm based on D* Lite. This modified algorithm considers not only travel distance but also other military-relevant costs, such as travel difficulty and risk. It generates a path that navigates around both fixed unknown obstacles and dynamically moving obstacles (enemy forces) that change positions over time. To assess the effectiveness of our proposed algorithm, we conducted comprehensive experiments, comparing and analyzing its performance in terms of average pathfinding success rate, average number of turns, and average execution time. Notably, we examined how the algorithm performs under two UCV path search strategies and two obstacle movement strategies. Our findings shed light on the potential of our approach in real-world UCV path planning scenarios.
Power-law stratigraphy

Digital well-logs registering the mineral properties of a drilled section provide information directly referring to its layering. Returns analyses of such logs can be used to generate an inventory of the layer thicknesses defined by the sampled variable. This reveals that layer thickness relationships have fractal-like power-law attributes, with negative exponents around 1.7, independent of the scale, age, facies and rates of accumulation of the analysed sections. Further research may show this to be a universal property of the stratigraphic record. Also, global average rates of accumulation of a wide range of facies prove to decline, power-law fashion, as the time-span of the estimate increases. Hence the stratigraphic record's layer-limiting hiatuses appear fractal-like in their temporal scaling. These related lines of evidence combine to suggest that the record is neither naturally hierarchical, nor inherently cyclic; and the corollary of an established universal stratal power-law would be that accumulation rarely occurs other than in circumstances leading to this condition.
Book selection behaviors of emergent readers

Using an observational comparative case study, this study explored emergent readers’ book selection behaviors within their classrooms. Emergent literacy skills are the forefront of literacy development (Clay, 1982; Ferriero &amp; Teborsky, 1982; Teale, 1986; McNaughton, 1993; Chall, 1996; Neuman, 2000), yet reading motivation and engagement has largely focused on older students (Wigfield &amp; Guthrie, 1997; Gambrell et al. 2011; Marinak, 2013), and emergent readers’ motivation and book choice research has relied upon surveys (Saracho, 1986; Sperling, et al., 2013). This study of two pre-kindergarten and two kindergarten classrooms, along with their classroom teachers, utilized observations to examine emergent reader motivation and choice (Bronfenbrenner, 1979, 2005; Bronfenbrenner &amp; Morris, 2006; Clay, 1982, 1991, 2001; Rosenblatt, 1978, 1995). The observational data were analyzed for vertical and horizontal case analysis and findings suggest that emergent and early readers’ text selection behaviors rely on cover illustrations and familiarity with the text.
Hepatocellular Carcinoma Diagnosis Based on Ultrasound Images Using Feature Selection Techniques and K-nearest Neighbor Classifier

Background: Liver cancer is one of the most common types of cancer, in which early detection plays a significant role in preventing progression and reducing mortality. Ultrasound is one of the methods of liver examination recommended by guidelines due to its performance in detecting focal liver lesions. These small lesions may be missed in the early stages or diagnosed only when the prognosis is poor. Objectives: This study aimed to implement the best classification model for two liver stages by extracting optimal feature subsets to be used in computer-aided diagnosis systems (CAD). Methods: The model classifies the liver into two stages using B-mode ultrasound images of the liver. It involves extracting statistical texture features utilizing discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM). This study applied two feature selection methods: t-test and sequential forward floating selection (SFFS). The subset of selected features was presented to the k-nearest neighbor classifier for incorporation into a CAD system. Results: The accuracy, sensitivity, and specificity of the k-NN classifier were 98.75%, 98.82%, and 99.1%, respectively. Conclusions: Image analysis approaches were successfully performed to extract and select useful features. Therefore, this model is recommended for classifying two liver stages, normal and HCC.
Assessing the Strengths and Weaknesses of Large Language Models

Abstract The transformers that drive chatbots and other AI systems constitute large language models (LLMs). These are currently the focus of a lively discussion in both the scientific literature and the popular media. This discussion ranges from hyperbolic claims that attribute general intelligence and sentience to LLMs, to the skeptical view that these devices are no more than “stochastic parrots”. I present an overview of some of the weak arguments that have been presented against LLMs, and I consider several of the more compelling criticisms of these devices. The former significantly underestimate the capacity of transformers to achieve subtle inductive inferences required for high levels of performance on complex, cognitively significant tasks. In some instances, these arguments misconstrue the nature of deep learning. The latter criticisms identify significant limitations in the way in which transformers learn and represent patterns in data. They also point out important differences between the procedures through which deep neural networks and humans acquire knowledge of natural language. It is necessary to look carefully at both sets of arguments in order to achieve a balanced assessment of the potential and the limitations of LLMs.
Bayesian Multi-Target Tracking on Spatial Spectrum

In this paper, the multi-target tracking problem of passive monitoring system is studied, the target tracking model on the spatial spectrum is established, and the passive multi-target tracking method is proposed under the framework of Bayesian processing. In this method, the spectral peak corresponding to the spatial spectrum of the target is taken as contact and the Gaussian model is used for parametric characterization. On this basis, the observation and motion model of multi-target parameters can be established, and then the iterative estimation can be carried out by MAP method, and the tracking filtering can be carried out synchronously by Bayesian method. Simulation results show that the method can track multiple targets accurately and continuously under the complex condition of multiple targets overlapping each other.
MGAM: Mixed-Gaussian Attention Module for Convolutional Neural Networks

Abstract—Spatial attention holds great potential for enhancing the performance of convolutional neural networks (CNNs). This technique aims to select important spatial regions by generating attention masks across spatial domains. However, the majority of existing spatial attention works rely on convolutional blocks to compute attention weights, disregarding the modeling of spatial key positions. To address this limitation, this paper proposes a Mixed-Gaussian Attention Module (MGAM). The MGAM generates spatial attention masks that follow mixed Gaussian distribution, effectively highlighting the most discriminative semantic features and minimizing the influence of background noise while maintaining low model complexity. Our method based on ResNet50 achieves a 1.34% improvement on the CIFAR100 benchmark and a 1% improvement on the ImageNet-200 benchmark.
Dynamic Attention-Based Click-Through Rate Prediction Model

Many online firms rely significantly on marketing and subscription rankings, such as Tencent and TikTok. CTR prediction is Critical in this area, but existing methods frequently underestimate the impact of feature interactions. To overcome this issue, a unique CTR model based on attention processes is presented. The goal of this approach is to represent the dynamic interactions between characteristics and their contextual importance. It does this through the employment of a global perception module, which defines overarching features, and a local perception module, which concentrates on finer-grained component dynamics. These perceived characteristics are then blended. Experiments on two publicly available datasets reveal that this strategy beats benchmarks designs, with a 0.65% AUC increase on the Avazu database and a 0.25% AUC gain on the Criteo dataset, confirming its efficacy in CTR prediction.
Deepfake Detection Using Fusion Channel Information in a Multi-attentional Model

Abstract—In order to prevent the impact of difficult to recognize genuine and fake face changing images on people's lives and social stability, it is urgent to study efficient deepfake detection algorithms for such images. However, most existing deepfake detection algorithms require extracting a large number of video frames from dataset videos for training. To address this issue, this paper proposes a new deepfake detection algorithm based on a multi-attentional model. Firstly, this article adds and fuses channel information to multiple attention maps generated by multiple spatial attention heads, enabling the model to comprehensively utilize information from various different features and provide richer feature representations. Secondly, in the texture enhancement section, DenseBlock is used to expand the receptive field and enhance the ability to extract shallow features, enabling the model to capture more detailed information. This article uses single frame real faces extracted from the public dataset FaceForensics++, a few sample dataset created from forged faces tampered with by DeepFake, and a dataset created from forged faces tampered with by our own image editing software to verify the performance of the proposed method. The experimental results show that on both datasets, AUC and ACC have increased by 1.8%, 2.1%, and 1.7%, 3.9%, respectively, compared to the original model.
Adaptive Speech Recognition via Dual-Level Sequential Pseudo Labels

As a meaningful task of speech processing, automatic speech recognition (ASR) has attracted growing interest in recent years. However, traditional methods of ASR are mainly used to recognize speech information from the same data distribution. Due to the distribution shift, when the pre-trained model from one distribution is directly applied to another one, the performance of the model will usually deteriorate. Therefore, test-time adaptation (TTA) is proposed to improve the generalization ability of source pre-trained models on the target distribution by only using unlabeled target data. In this paper, we propose a novel method called Dual-level Sequential Pseudo labels (DSP) for ASR. Considering that the pseudo labels of vanilla TTA methods heavily rely on the source pre-trained model and neglect the dynamic knowledge during the adaptation stage, we utilize dual-level average predictions to enhance the accuracy of generated labels. Concretely, we adopt the teacher-student learning strategy combined with the weight-averaged and augmented-averaged predition to reduce the error accumulation. Further, since the text generation can be viewed as a sequential classification task, we propose a voting threshold mechanism for the augmentation process. We conduct extensive experiments on the standard ASR datasets, the experimental results reveal that DSP could achieves state-of-the-art performance for three mainstream ASR models under various domain shifts.
Frequency Domain Based Optimization for Image Demoireing

Image demoiréing is a corrective procedure aiming to eliminate the color-distorting moiré patterns when taking photos of digital screens. Currently, existing demoiréing methods typically rely on spatial domain loss optimization. However, moiré patterns at high frequencies cannot be effectively removed by spatial domain optimization. To further improve the restoration, we explore the potential impact of frequency domain optimization, which has not yet received extensive attention in this field. The frequency domain contains more detailed information about the image, particularly in higher frequencies. To this end, complementing the existing image-space loss functions, the implementation of frequency-space optimization enhances the performance of demoiréing networks by prioritizing frequency consistency. Our method is generic and can help classic image demoiréing models regain competitive performance. Extensive qualitative and quantitative experiments were conducted to rigorously evaluate the efficacy of our method. For instance, our method achieves an unprecedented performance of 45.3325 PSNR on LCD-Moiré datasets,which is an improvement of +1.6 percent points over the previous state of the art.
Dual-stream Assisted U-Net for Thyroid Nodule Segmentation

Abstract—Accurate and effective segmentation of thyroid nodules in ultrasound images is critical for computer-aided nodule diagnosis and treatment. However, due to the ill-defined margin of ultrasound images and the complexity of thyroid tissue structure, accurate segmentation of fine contours is still challenging. We propose a dual-stream network-assisted U-Net (DA-UNet) for these issues, which fully integrates features extracted by convolutional neural networks and vision transformer (ViT), applying them to the decoder and skip connection stages. The model is evaluated on the publicly available dataset TN3K, and the experimental results show that DA-UNet achieves 84.43% and 85.55% in Dice coefficient and F1-score respectively, with an average improvement of 3.75% and 3.12% compared to the current popular segmentation networks, reaching the optimal level.
Practical and Efficient Secure Aggregation for Privacy-Preserving Machine Learning

In recent years, Federal Learning has received much attention becourse it can train models by updating gradients without contacting users’ true data. However, adversaries also can track users’ privacy from the shared gradient. In this paper, we aim to solve three major issues during the process of federated learning: 1) how to protect users’ privacy during training; 2) How to verify the correctness of the aggregation results returned from the server; 3) How to reduce communication costs while ensuring training security. So we propose a verifiable aggregation scheme that can effectively verify the results of server aggregation. Specifically, we follow the classic double mask aggregation scheme, and use Paillier homomorphic encryption algorithm to implement the message authentication code with additive homomorphic property. Users can compare their local codes with server’s aggregation results to verify the correctness of the aggregation results and improve model’s accuracy. In our framework, we adopt a Top-k gradient selection scheme to reduce models’ communication and computing overhead. Experimental results indicate that our training framework is feasible and efficient.
Path-extrapolation in Evolutionary Algorithms

Evolutionary Algorithms are used to solve challenging optimization problems across a variety of domains. While simple and robust they often do not effectively exploit information generated during the search which in turn degrades their efficiency and often results in a slow convergence. As such this paper presents a new algorithm to accelerate the EA convergence by monitoring the path traversed by its population over several generations. This information is then used to construct interpolating polynomials which predict the position of the centroid in the next generation and is then used to shift the population along that direction to accelerate convergence. An extensive numerical performance analysis shows the effectiveness of the proposed approach.
Pixel Attention Feature Pyramid Network for Few-Shot Object Detection in Remote Sensing Images

Recently, few-shot object detection (FSOD) has attracted extensive attention in the field of computer vision. However, these FSOD methods for natural images have limited performance when applied to remote sensing images with arbitrary orientations, dense objects, and complex backgrounds. In this paper, we propose a novel few-shot object detection method to address the above challenges in remote sensing images. First, we design a non-freezing two-stage fine-tuning detector that is fully trained on a base class data to extract information in the first stage, and fine-tunes the entire network for better novel class detection in the second stage using balanced training data containing both base and novel class samples. Then, a pixel attention feature pyramid network (PA-FPN) is proposed to replace the traditional FPN, which adds a pixel attention-based feature selection module (PA-FSM) and a pixel attention-based sub-pixel convolution module(PA-SPCM). In PA-FSM, pixel attention is used to extract more discriminative channels, thereby highlighting key features while suppressing background noise. In PA-SPCM, the sub-pixel operation performs convolution and upsamples the low-resolution feature maps with the aim of obtaining a high-resolution feature maps with richer semantic information, while pixel attention is applied to the high-resolution feature maps to enhance the semantic information. Finally, we introduce a proposal rectification head to eliminate false positives with high scores and address missing samples with low scores, which can improve classification accuracy. Experiments conducted on two public remote sensing datasets demonstrate the effectiveness of our method.
An Enhanced Moth-Flame Optimization Algorithm for UAVs 3D Path Planning

The navigation of Unmanned Aerial Vehicles (UAVs) heavily rely on their path planning. For three-dimensional path planning of UAVs in complex environments, this paper proposes a method based on an Enhanced Moth-Flame Optimization algorithm for UAVs 3D path planning. This paper has two major contributions. Firstly, an evaluation function is designed considering various constraints during UAV flight. Secondly, adaptive flame adjustment strategy and evolutionary strategies for learning targets are proposed to enhance the original Moth-Flame Optimization algorithm. The enhanced algorithm is applied to the UAVs 3D path planning, and the proposed algorithm is evaluated in three simulated environments: mountains, forests, and buildings. The simulation results show that the proposed algorithm outperforms other compared algorithms in terms of global merit-seeking ability, with an average improvement of 4%. Furthermore, the algorithm shows faster convergence speed, evidenced by its quicker landing in early iterations, tendency to converge at middle iterations, and guaranteed convergence within 90 generations, which is very effective for 3D path planning in complex environments.
Enhancing Phishing URL Detection: A Comparative Study of Machine Learning Algorithms

Phishing constitutes a significant threat in the digital world, often exploiting human vulnerabilities to illicitly obtain sensitive data such as personal credentials, financial details, and private information. Misusing this information results in substantial financial loss and personal harm to victims. This study introduces an innovative approach to mitigate the risk of phishing attacks by employing machine learning algorithms to detect phishing URLs. The proposed method applies a suite of algorithms, including j48, Naïve Bayes, JRip, and Decision Table, to a robust dataset of 11,430 URLs, each with 87 extracted features. The results reveal the considerable potential of machine learning in identifying phishing threats. Furthermore, the study explores a novel server-side analysis concept where the server scrutinizes links transmitted via emails or social communication platforms such as WhatsApp, Messenger, and Instagram. The application of phishing detection algorithms filters and prevents the delivery of phishing links, thus reducing the potential harm to users. This research is poised to significantly contribute to cybersecurity by enhancing phishing detection mechanisms' accuracy and efficiency.