Reviewerly/exHarmony huggingface dataset:
MC-MLP: A Multiple Coordinate Frames MLP-Like Architecture for Vision

In deep learning, Multi-Layer Perceptrons (MLPs) have once again garnered attention from researchers. This paper introduces MC-MLP, a general MLP-like backbone for computer vision that is composed of a series of fully-connected (FC) layers. In MC-MLP, we propose that the same semantic information has varying levels of difficulty in learning, depending on the coordinate frame of features. To address this, we perform an orthogonal transform on the feature information, equivalent to changing the coordinate frame of features. Through this design, MC-MLP is equipped with multi-coordinate frame receptive fields and the ability to learn information across different coordinate frames. Experiments demonstrate that MC-MLP outperforms most MLPs in image classification tasks, achieving better performance at the same parameter level. The code will be available at: https://github.com/ZZM11/MC-MLP .
Medical Image Segmentation and Saliency Detection Through a Novel Color Contextual Extractor

Image segmentation is a critical step in computer-aided system diagnosis. However, many existing segmentation methods are designed for single-task driven segmentation, ignoring the potential benefits of incorporating multi-task methods, such as salient object detection (SOD) and image segmentation. In this paper, we propose a novel dual-task framework for the detection and segmentation of white blood cells and skin lesions. Our method comprises three main components: hair removal preprocessing for skin lesion images, a novel color contextual extractor (CCE) module for the SOD task, and an improved adaptive threshold (AT) paradigm for the image segmentation task. We evaluate the effectiveness of our proposed method on three medical image datasets, demonstrating superior performance compared to representative approaches.
Improving Limited Resource Speech Recognition Performance with Latent Regression Bayesian Network

In limited resource speech recognition scenarios, the limited data may result in overfitting and decreased recognition rates when the traditional acoustic features are employed. To enhance speech recognition performance, it is essential to extract representative and robust features from speech signals. This paper explores the latent regression Bayesian network (LRBN) to derive more efficient speech representation from traditional acoustic features to train end-to-end speech recognition models. The LRBN is an effective generative model that captures the inherent dependencies from the original data. To evaluate the effectiveness of LRBN for speech representation, we compare traditional acoustic features and bottleneck features with the hidden features extracted by LRBN. Our experimental results demonstrate that LRBN improves the accuracy of speech recognition on five speech datasets.
Interpretable Image Recognition by Screening Class-Specific and Class-Shared Prototypes

Convolutional neural networks (CNNs) have shown impressive performance in various domains, but their lack of interpretability remains an important issue. Prototype-based methods have been proposed to address this problem. Prototype-based methods assign a fixed number of prototypes to categories. But prototype networks are limited by the non-learnable relationship between prototypes and categories, which restricts each prototype to only one category. Furthermore, the large number of prototypes used in these methods often leads to poor prototype distribution. To address these limitations, we propose a deep learning approach with an active learning concept inspired by the associative function of the human brain. We introduce the Prototype Screening Matrix (PSM). We optimize PSM by label smoothing to describe the relationship between categories and prototypes, so that it can dynamically filter prototypes and retain prototypes that are more suitable for concept learning. PSM enables similar prototypes to be shared among different classes, which significantly reduces the number of prototypes and leads to a more rational distribution of prototypes. We experimentally validate the effectiveness of our proposed method on the CUB-200-2011 and Stanford Cars datasets and show that it achieves higher accuracy than existing methods. Our method is more interpretable, uses fewer prototypes, and has a simpler structure, advancing the state-of-the-art in interpretable and efficient prototype-based image classification methods. The code is available at https://github.com/Lixiaomemg/PSMnet .
Adaptive Training Strategies for Small Object Detection Using Anchor-Based Detectors

Small object detection is a crucial task in computer vision due to its wide range of real-world applications. Detecting small objects accurately and efficiently remains a challenging task due to the reduced size of the objects, low contrast to their surroundings, and potential occlusions. To tackle this issue, we proposed a method for detecting small objects in object detection tasks, including a new strategy for balancing positive and negative samples, a loss function that adapts the weight of detection losses according to object size, and an anchor mechanism that accommodates objects with diverse sizes and aspect ratios. The experimental data substantiates that our method has achieved a 12.9% increase in average accuracy for small objects on the COCO dataset, compared to the baseline.
Opinions cum sentiments of the textual content through ML methods: a smart city-based approach in 21st century using NLP

The ambition of this research is to advance a functional classifier for automatic and error-free sentiment classification of an unknown tweet stream. In the past decades, new shape of communication, such as text messaging and microblogging have come up and become universal. Beside there is no range of information to be transmit by text and tweets, often these message are used to give opinions and sentiment that people have around what is going around on the world. The researcher team also worked on the following task. The task was to give a message, so as to categorise whether the message is of positive, negative or neutral sentiment. The corpus-base method was used to find semantic orientation of dictionary-based method and adjectives to find the semantic orientation of adverb and verbs. Algorithms used in this research are Naive Bayes classifier, decision tree, AdaBoost classifier, random forest, support vector machine, KNN classifier, 7XGBOOST and logistic regression. The work in this paper gives a novel approach for sentiment analysis on data. The initial result shows it is a motivating technique.
Category Enhanced Dual View Contrastive Learning for Session-Based Recommendation

Session-based recommendation aims to predict the next item based on users’ behavior sequence within a short time. Traditional session-based recommendation models usually assume that there exists only one type of interaction between users and items and fails to consider the impact of multiple types of behaviors. Although some recent studies have proposed to utilize different types of behaviors, they still have some challenges. First, they do not consider the impact of category information on user preferences. Secondly, they do not leverage the complementary information between multiple behaviors. To overcome the above challenges, we propose a novel Category Enhanced Dual View Contrastive Learning (CaDVCL) model, which explores the influence of item categories and multiple interaction behaviors on user interests. The model combines category sequence information and item sequence information to learn session representations through an attention mechanism and captures the correlation between different behaviors by maximizing the mutual information of session representations obtained from different behavioral views through contrastive learning. Extensive experiments on two public datasets show that CaDVCL can outperform the state-of-the-art models.
Distinguishing the Correctness of Knowledge Makes Knowledge Transfer Better

Using the Large Language Model(LLM) for in-context learning to solve Natural Language Processing(NLP) tasks has become one of the most popular and effective methods. There has been a sea of works to elicit knowledge from LLM to perform commonsense reasoning, but those methods consume a lot of time and space. Our work considers how to transfer the generated knowledge from LLM to a small model for knowledge generation. We find that the incorporation of wrong knowledge is important in knowledge transfer, which has been neglected by previous work. We propose different filter methods to different generated knowledge to distinguish the correctness of knowledge, and use both correct and wrong knowledge in Contrastive-Learning for knowledge transfer to improve the ability of small models to generate knowledge. In this paper, we first figure out what kind of prompts in in-context learning can better motivate LLM to generate knowledge that has higher generalization and is more helpful in answering questions. Then, we compare various filtering methods for knowledge correctness determination. At last, we use Contrastive-Learning based knowledge generation for transferring knowledge from LLM to the small model. In this way, the knowledge generated by the small model are not only richer but also more correct, which boost reasoning tasks with performance improved up to 1.7% on the CommonsenseQA and 3.2% on the OpnebookQA comparing the knowledge generated by simply fine-tuned on all knowledge.
rOpenSci Software Peer Review: Still Improving

rOpenSci’s suite of packages is comprised of contributions from staff engineers and the wider R community, bringing considerable diversity of skills, expertise and experience to bear on the suite. How do we ensure that every package is held to a high standard?
Power Prediction of Parabolic Trough Power System Based on BP Neural Network

The Parabolic Trough Power System (PTPS) is a renewable energy source that utilizes solar power for electricity generation, playing a significant role in the optimization of the global energy structure and environmental protection. However, the power output of PTPS is influenced by various factors including environmental conditions, system parameters, and operational parameters, making accurate power output prediction a challenging task. The objective of this paper is to develop a PTPS power prediction model based on Back Propagation (BP) neural network. The improvement and optimization strategies of BP neural networks are proposed. The Mean Absolute Percentage Error is 0.4137, and the Root Mean Square Error is 42.36. Finally, power station output simulation verification is conducted using simulation software. By analyzing and comparing the actual values and predicted values of the model, the applicability and reliability of the prediction method are demonstrated.
Automated Requirements Engineering in Agile Development: A Practitioners Survey

Requirements Engineering (RE) in the context of agile development is the process of discovering, analysing, validating, and managing software requirements through engaging with the stakeholders. RE activities are central to software development and its success. Advances in automation have been explored in requirements engineering activities and processes in the industry. However, the application of automated support is still limited in practice. In this study, we examine automated RE support in agile development through a qualitative analysis using industry experts. We have found that the application of automated RE support tools is primarily in the experimental stage, largely due to the limited practical implementation in industry settings. The current focus has been on research experiments, with relatively fewer instances of real-world industry application. Although experts are optimistic about its huge potential to reduce development efforts and costs if automation is harnessed. Therefore, we anticipate that these findings will be beneficial for practitioners responsible for developing RE tools for agile development, as well as for the researchers.
Differential Privacy Federated Learning based Privacy-Preserving Transmission Scheme of Distributed PV Stations

As a renewable and sustainable energy source, solar energy is attracting more and more attention, where photovoltaics being one of the primary ways. However, the distributed nature of the PV stations raises serious privacy issues, as the data for each PV station is owned by different utilities that are unwilling to share their data. In order to effectively ensure data security and privacy, this paper proposes a novel federated learning (FL)-based distributed PV station transmission framework. Since the private information can still be leaked by analyzing the uploaded parameters of PV stations. Differential privacy (DP) algorithm is added to the proposed FL-based PV station transmission framework by inserting artificial noise before the aggregation process of FL, so that the attacker cannot tell whether a particular customer is included in the dataset or not. The simulation results evaluate the performance of the proposed algorithm with different data distribution, privacy leakage and DP mechanisms.
BLTESTI: Benchmarking Lightweight TinyJAMBU on Embedded Systems for Trusted IoT

Embedded systems face an increasing number of attacks, necessitating improved security measures. However, the physical constraints of these devices often limit the use of traditional cryptographic techniques. To address this challenge, NIST organized a competition focused on lightweight cryptographic algorithms (LWC) tailored for embedded systems, aiming to minimize resource usage in low-power devices. This paper benchmarks the Rust, C, and Golang programming languages, considering their advantages in memory safety, portability, and concurrency, to implement the LWC algorithm finalist named TinyJAMBU. TinyJAMBU utilizes the non-linear feedback shift register (NLFSR) model as a core security element. The evaluation is conducted on embedded systems and FPGAs to determine the optimal strategy for mapping LWC cryptographic algorithms onto resource-constrained devices. Among the three languages, the C-based implementation achieves the highest runtime efficiency, while TinyGo demonstrates the lowest power consumption. Rust falls between the two, indicating trade-offs associated with each language. Despite its larger memory footprint of 73kB, the C-based implementation of TinyJAMBU outperforms other language implementations, achieving a faster runtime of ≈ 81 seconds for the same algorithm and 4-byte message size. Golang exhibits the smallest memory footprint of 19kB, while Rust’s memory footprint falls between the other two at 66kB. Furthermore, implementing TinyJAMBU on an FPGA using software-hardware partitioning with MicroBlaze takes ≈ 6.30 seconds to complete 10,000 encryption and decryption cycles, consuming around 1.09W of power. Although employing a proposed NLFSR co-processor with MicroBlaze for the 128-bit version of TinyJAMBU may not be the fastest approach, the reconfigurable nature of FPGAs allows for replicating the co-processor multiple times, offering significant advantages over the current state-of-the-art architecture of embedded systems.
Hardware Specification Aware Timing Side Channel Security Analysis

Most software programs in modern Internet-of-Things (IoT) systems in diverse domains such as healthcare, smart cities, banking, and industrial automation deal with secret data/assets. Timing side-channel analysis is a potent attack vector against such modern systems and it involves measuring the execution time of a program towards leaking secret assets, stealing intellectual property, and identifying other vulnerabilities in the system. Timing side-channel analysis typically targets branches and program behaviours that are dependent on the value of a secret variable or asset. The traditional approach towards mitigating this security concern involves performing a software-level analysis followed by branch balancing and/or constant time programming. However, a pure software-level analysis and repair might not be ideal at the current time due to the tighter coupling of hardware-software components in IoT systems. Hence, we believe, a more hardware-aware software timing analysis and repair might be necessary towards tackling this evolving area. In this article, we highlight how a purely software-level analysis and repair might be problematic, analyze the current research trend in timing side-channel analysis, and propose a potential pathway for future research.
Go Science Drone Med Box – Medical Emergency Drone for Emergency Healthcare Management

The Medical Emergency Drone Box stands as a transformative development in healthcare technology, designed to address a spectrum of emergency medical needs. This paper delineates the comprehensive capabilities of the device, which ranges from its potential role in rural healthcare support to its utilization in critical emergencies, road accidents, and even organ transportation. The device integrates advanced sensors, such as body temperature and pulse detection units, to facilitate real-time monitoring and data-driven decision-making during medical interventions. It also incorporates a WiFi module and microcontrollers for efficient data transmission and seamless connectivity. To ensure the maintenance of required medical standards, the drone box includes a heating and cooling system for preserving critical medical supplies. The cloud-based storage of sensor data allows healthcare professionals to remotely access vital information in real-time. Moreover, the device is built to operate effectively even in regions with limited electrical access, courtesy of its robust battery backup system. Additional features include charging units and an assortment of essential medical equipment like portable defibrillators and drilling machines. Solar Panels are also added as a redundant power support. This paper aims to showcase the multifaceted features, data-centric functionalities, and the enormous potential the Medical Emergency Drone Box holds in revolutionizing emergency healthcare delivery across diverse settings. Developed by Go Science Technical Team. IndexTerms – Go Science, Medical Emergency Drone Box, Healthcare Innovation, Critical medical Interventions, Sensor Technology, Real-Time Data Monitoring, Data Transmission, Battery Back-up, Rural healthcare, Road Accidents, Organ Transportation, Cloud Data Storage, Portable Medical Equipment.
Variation in metadiscourse verb patterns in English academic papers from intra- and interdisciplinary analysis

Abstract The study of metadiscourse word resources is an important way to reveal academic knowledge constructs and genre features in English papers. This paper selects intra- and interdisciplinary English academic papers as data samples, and the text representation is based on the vector space model. The text similarity is calculated using the word movement distance model to complete the clustering based on the density peak, and then the semantic annotation of metadiscourse verb patterns is performed using the conditional random field. Then, the intra- and interdisciplinary metadiscourse annotations are compared to analyze the variation of verb patterns. The intra-disciplinary variation of metadiscourse verb patterns was mainly found using attitude markers, where the log-likelihood ratios of 48.56, 30.25, and 35.64 were obtained for affirmative, critical, and critical declarative attitudes, respectively. In interdisciplinary studies, basic disciplines preferred to use verb patterns as transitions, while applied disciplines preferred to express the author’s point of view. The study of metadiscourse verb patterns effectively enriches the linguistic resources in academic English teaching and deepens the understanding of the characteristics of disciplinary essay genres.
Intelligent recommendation method for offline course resources tax law based on chaos particle swarm optimization algorithm

In view of the individual differences in learners’ abilities, learning objectives, and learning time, an intelligent recommendation method for offline course resources of tax law based on the chaos particle swarm optimization algorithm is proposed to provide personalized digital courses for each learner. The concept map and knowledge structure theory are comprehended to create the network structure map of understanding points of tax law offline courses and determine the learning objectives of learners; the project response theory is used to analyze the ability of different learners; According to the learners’ learning objectives and ability level, the intelligent recommendation model of offline course resources of tax law is established with the minimum concept difference, minimum ability difference, minimum time difference, and minimum learning concept imbalance as the objective functions; Through the cultural framework, the chaotic particle swarm optimization algorithm based on the cultural framework is obtained by combining the particle swarm optimization algorithm and the chaotic mapping algorithm; The algorithm is used to solve the intelligent recommendation model, and the intelligent recommendation results of offline course resources in tax law are obtained. The experiential outcomes indicate that the process has a smaller inverse generation distance, larger super-volume, and smaller distribution performance index when solving the model; that is, the convergence performance and distribution performance of the model is better; This method can effectively recommend offline course resources of tax law for learners intelligently, and the minimum normalized cumulative loss gain is about 0.75, which is significantly higher than other methods, that is, the effect of intelligent recommendation is better.
Visualization design of talent training paths in university education based on pattern knowledge

Abstract In the background of big data informatization, social and economic development needs talent support, and colleges and universities are the main positions of talent training. Using the features of self-organization, distributed multi-intelligence and positive feedback of the ant colony algorithm, a personalized learning path recommendation algorithm supporting pattern knowledge is proposed by combining the above learning feature model. The subject starts an independent solution search simultaneously at multiple points in the problem space, which increases the algorithm’s reliability and gives the algorithm a strong global search capability. When performing talent development path recommendation, the collaborative mechanism of distributed multi-subjects of the ant colony algorithm makes it possible to find an acceptable path quickly. Combined with the new knowledge representation model, the matching degree of students and learning objects is calculated from three dimensions, the heuristic information is obtained accordingly, and the Euclidean distance is used for matching degree calculation. Using the ant colony optimization algorithm to analyze the paths for cultivating talents in college education, it is concluded that in terms of the number of teachers: the total number of teachers with positive senior titles in five colleges and universities is 238, accounting for 8.88%, and the number of full-time teachers with senior associate titles is 618, accounting for 23.07%, all of which are lower than the average level in Fujian Province. This study has some reference significance to the cultivation of applied talents in local colleges and universities and has some reference value to the government in formulating the policy of cultivating talents in colleges and universities.