Reviewerly/exHarmony huggingface dataset:
Analysis of path planning of UAV in short-distance logistics application

China's e-commerce sector has grown quickly in recent years, and the logistics sector has grown to be a significant sector of the national economy. Meanwhile, the business volume of the logistics industry has also increased continuously. The characteristics of intelligence and informatization of short-distance logistics UAVs, advantages such as high efficiency and not being constrained by road grids and related technical applications can greatly save manpower and time in logistics and transportation, so that the goods can be transported to the receiving place faster. Therefore, UAVs are an important part of the logistics field, among them, the path planning of UAVs is an important technical component to ensure the transportation of goods. This article first introduces the importance of UAV path planning technology in the field of short-distance logistics. Secondly, the path planning of UAVs in short-distance logistics applications is divided into two categories. The overview and analysis are carried out from the two aspects of traditional mathematical algorithm and bionic algorithm. Meanwhile, based on the available information, the benefits and drawbacks of the available methods are examined. Then, based on MATLAB, two classical methods are selected from the traditional mathematical algorithm and the bionic algorithm to carry out path planning simulation experiments in scenarios of different complexity, and the results are analyzed. Finally, the advantages and disadvantages of the above two algorithms are summarized and analyzed, and the prospect of UAV citation in short-distance logistics is prospected.
Effectiveness of Blind Face Restoration to Boost Face Recognition Performance at Low-Resolution Images

This paper studies the effectiveness of Blind Face Restoration methods to boost the performance of face recognition systems on low-resolution images. We investigate the use of three blind face restoration techniques, which have demonstrated impressive results in generating realistic high-resolution face images. Three state-of-the-art face recognition methods were selected to assess the impact of using the generated high-resolution images on their performance. Our analysis includes both, synthesized and native low-resolution images. The conducted experimental evaluation show that this is still an open research problem.
Exploration of Computer Teaching Reform in Colleges and Universities under the Background of Informatization

Abstract Computer teaching in colleges and universities under the background of informationization is developing rapidly, but the teaching mode still has more serious problems, and computer teaching reform needs to be emphasized. This paper first proposes solutions to the problems of computer teaching in colleges and universities. Secondly, it uses hierarchical analysis to construct a computer teaching evaluation system model, then carries out educational information data mining through the DMOTE method, and uses C4.5 to optimize the model of DMOTE. Setting up a controlled experiment to analyze the students’ performance after receiving teaching reform, most of the students in the experimental group scored in the two segments of 80-89 and 90 or more, accounting for 60%, and the number of students scoring less than 60 points was less, only 6%. After the reform of the computer model, the students’ performance improvement is more obvious, and the reform effect is good.
Analysis of Teachers’ Discourse in the Digital Platform Mode of College English Teaching Based on the Information Diffusion Model

Abstract This paper utilizes the information diffusion mechanism of the information extension model, which can act on the influence of students in the process of university English teaching to the information extension model to the diffusion ability that English information has. It examines the level of student activity and English information coverage in university English teaching and discusses the use of the information extension model in English teaching. According to the objectives of university English teaching, the functional requirements of the university English teaching platform are analyzed, the university English teaching platform based on the information diffusion model is designed through big data technology, and the English teaching platform combined with the information extension model is analyzed in an example study. The results show that on the model simulation analysis, the performance of this paper’s model is better than the User Rank model and UI-LR model when mining less than 30 information extensions. In the analysis of teachers’ discourse, the speech rate of teachers in the university English teaching platform is mainly concentrated in the range of 140-149 syllables/minute and 150-159 syllables/minute, and only one teacher’s speech rate exceeds 170-179 syllables/minute. This study has the potential to enhance students’ understanding of teachers’ discourse and establish a solid platform for communication between teachers and learners.
Keyword-Aware Relative Spatio-Temporal Graph Networks for Video Question Answering

The main challenge in video question answering (VideoQA) is to capture and understand the complex spatial and temporal relations between objects based on given questions. Existing graph-based methods for VideoQA usually ignore keywords in questions and employ a simple graph to aggregate features without considering relative relations between objects, which may lead to inferior performance. In this paper, we propose a Keyword-aware Relative Spatio-Temporal (KRST) graph network for VideoQA. First, to make question features aware of keywords, we employ an attention mechanism to assign high weights to keywords during question encoding. The keyword-aware question features are then used to guide video graph construction. Second, because relations are relative, we integrate the relative relation modeling to better capture the spatio-temporal dynamics among object nodes. Moreover, we disentangle the spatio-temporal reasoning into an object-level spatial graph and a frame-level temporal graph, which reduces the impact of spatial and temporal relation reasoning on each other. Extensive experiments on the TGIF-QA, MSVD-QA and MSRVTT-QA datasets demonstrate the superiority of our KRST over multiple state-of-the-art methods.
On the Impact of Language Selection for Training and Evaluating Programming Language Models

The recent advancements in Transformer-based Language Models have demonstrated significant potential in enhancing the multilingual capabilities of these models. The remarkable progress made in this domain not only applies to natural language tasks but also extends to the domain of programming languages. Despite the ability of these models to learn from multiple languages, evaluations typically focus on particular combinations of the same languages. In this study, we evaluate the similarity of programming languages by analyzing their representations using a CodeBERT-based model. Our experiments reveal that token representation in languages such as C++, Python, and Java exhibit proximity to one another, whereas the same tokens in languages such as Mathematica and R display significant dissimilarity. Our findings suggest that this phenomenon can potentially result in performance challenges when dealing with diverse languages. Thus, we recommend using our similarity measure to select a diverse set of programming languages when training and evaluating future models.
A Two-Phase ResNet for Object Detection in Aerial Images

Unmanned Aerial Vehicles (UAVs) have experienced great growth in real-world applications (surveillance, smart farming, etc.). One of the most popular features of UAVs in combination with optical sensors is real-time object detection. In this regard, Deep Learning algorithms have been proven to be the best solution to such kinds of problems. In this work, a modified Retina Network (ResNet) is proposed in which a larger number of Convolutional Blocks is introduced and features are combined from different levels of the Neural Network. The extra convolution block is a mirror of the FPN network; therefore, the new model is called Two-Phase ResNet. Since the goal is also to provide a sensitivity study, the classical ResNet and the Two-Phase ResNet are trained and tested using the Stanford Drone Dataset. The Two-Phase ResNet model achieves an accuracy score 6% higher than the baseline model (ResNet), and it seems to outperform the original model for each metric (Accuracy, Precision, Recall, and F1 score). Finally, both the original and the modified ResNet, are compared with other well-known object detectors. The proposed architecture seems to achieve similar or better performance compared to almost each object detector from the literature in terms of mean Average Precision. The Two-Phase ResNet can potentially be used to detect tiny objects in applications where accuracy is a critical factor.
Federated Learning Aggregation based on Weight Distribution Analysis

Federated learning has recently been proposed as a solution to the problem of using private or sensitive data for training a central deep model, without exchanging the local data. In federated learning, local models are trained on the client side using the available data, while a server is responsible for aggregating the weights of these models into a global model. However, the traditional weight averaging approach does not take into consideration the importance of the different weights for the performance of a model. To this end, this work proposes a novel federated learning weight aggregation method that estimates the statistical distance of each client’s parameters from the Gaussianity, and weighs the contribution of each client to the global model accordingly so that the most significant information is retained and enhanced. To create an accurate global model, a complex weighted averaging of the parameters of clients’ models at the layer level is performed, considering as low quality the parameters following the Gaussian distribution. The proposed method can be employed to both convolutional and linear layers and it is based on the notion that parameters following a Gaussian distribution do not significantly affect the output of a model. Experiments with different network architectures and a comparison with a plethora of state-of-the-art approaches on three well-known image classification datasets demonstrate the superiority of the proposed method for federated learning weight aggregation.
Comparing YOLO-based Detectors for Pedestrian and Car Detection in Aerial Static Video: An Evaluation of Generalization Capacity and Performance

Over the last decades, the famous deep-learning framework of ‘You Only Look Once’ – YOLO for object detection has gasped important interest due to its vital performance for object detection. In this paper, we compare three deep learning architectures of YOLO family for the task of pedestrian and car detection from static video frames. We have chosen YOLOv5s, YOLOv5su, and YOLOv8s architectures as they can perform real-time detection, which is the focus of our study. Our training and evaluation were conducted on a dataset from Stanford University, which includes a combination of eight birds-eye view scenes. The dataset was separated into train, validation, and test sets, and the discrepancies between validation and test results were used to evaluate the generalization capacity of each architecture. For the purpose of evaluating the generalization capacity of the model, we isolated one scene as the test set, ensuring it was not seen during the training phase. Our experimental results show that the recent YOLOv5su and YOLOv8s do not outperform YOLOv5s in terms of accuracy and generalization despite their anchorless architecture. The challenges faced by all the architectures in terms of generalization can be ascribed to a combination of factors, namely the model's capability to differentiate the background and the static characteristics of the dataset frames.
Bitcoin volatility in bull <i>vs</i>. bear market-insights from analyzing on-chain metrics and Twitter posts

Cryptocurrencies have emerged as a popular investment vehicle, prompting numerous efforts to predict market trends and identify metrics that signal periods of volatility. One promising approach involves leveraging on-chain data, which is unique to cryptocurrencies. On-chain data, extracted directly from the blockchain, provides valuable information, such as the hash rate, total transactions, or the total number of addresses that hold a specified amount of cryptocurrency. Some studies have also explored the relationship between social media sentiment and Bitcoin, using data from platforms such as Twitter and Google Trends. However, the quality of Twitter sentiment analysis has been lackluster due to suboptimal extraction techniques. This research proposes a novel approach that combines a superior sentiment analysis technique with various on-chain metrics to improve predictions using a deep learning architecture based on long-short term memory (LSTM). The proposed model predicts outcomes for multiple time horizons, ranging from one day to 14 days, and outperforms the Martingale (random walk) approach by over 9%, as measured by the mean absolute percentage error metric, as well as recent results reported in literature. To the best of our knowledge, this study may be among the first to employ this combination of techniques to improve cryptocurrency market prediction.
Trustworthy Artificial Intelligence: Comprehensible, Transparent and Correctable

Abstract With the digital transformation, artificial intelligence (AI) applications are also finding their way into more and more areas of work and life. In particular, models learned from data are being used, which are mostly opaque black boxes. The fact that people can understand why an AI system behaves the way it does is necessary for various reasons: The model developers themselves must be able to assess properties of the learned models—in particular, possible biases due to overfitting to the data used for learning. For safety-critical applications, aspects of certification and testing are also becoming increasingly relevant. Domain experts—for example, in medical diagnostics or quality control in industrial production—must be able to comprehend, verify and, if necessary, correct system decisions. Consumers should understand why a system—a smart home control, a driving assistance—behaves in a certain way and why they are recommended certain products, offered certain tariffs or denied certain offers. After a brief introduction to the topic of AI, the chapter gives an overview of methods of the so-called third wave of AI. Central to this are approaches of explainable AI (XAI), which are intended to make the decisions of AI systems comprehensible. The main approaches are characterized and shown for which objectives and applications they are suitable in each case. It is shown that in addition to the highly regarded methods for visualization, methods that allow system decisions to be described in a differentiated manner are also particularly important. It is also argued that, in addition to comprehensibility, interactivity and correctability of AI systems are necessary so that AI systems do not restrict human competences but support them in partnership.
Cost-benefit analysis of solar energy integration in buildings: a case study of affordable housing in Brazil

Introduction: The construction sector plays a pivotal role in global natural resource consumption, underscoring the urgency of promoting energy efficiency in buildings. With the escalating demand for renewable energy, solar power has gained significant traction. This study focuses on conducting a comprehensive cost-benefit analysis of solar energy integration in residential buildings. Methods: The approach involves a novel comparison between photovoltaic panels and Solar Heating Systems (SHS) based on both environmental and financial considerations. To evaluate the practical implications, a case study was undertaken on an affordable housing complex in Brazil. Three distinct models were simulated for analysis: Model 1, featuring a grid-connected photovoltaic project with zero energy balance; Model 2, incorporating a grid-connected photovoltaic project with two solar panels generating 340 W each; and Model 3, integrating an SHS. Results: The findings reveal the technical and economic feasibility of all proposed models. Model 1 stands out with superior performance in terms of estimated energy generation, energy savings, and annual reduction of CO 2 emissions. On the other hand, Model 3 excels in the financial analysis, indicating its viability from a cost perspective. Discussion: This research contributes to informed decision-making processes regarding the utilization of photovoltaic panels and SHS, thereby fostering energy efficiency and sustainability in buildings. The nuanced comparison of environmental and financial aspects provides valuable insights for stakeholders in the construction and renewable energy sectors. The identified strengths and trade-offs of each model enable a more holistic understanding of the implications of solar energy integration in residential buildings.
Transfer learning for the generalization of artificial intelligence in breast cancer detection: a case-control study

Background Some researchers have questioned whether artificial intelligence (AI) systems maintain their performance when used for women from populations not considered during the development of the system. Purpose To evaluate the impact of transfer learning as a way of improving the generalization of AI systems in the detection of breast cancer. Material and Methods This retrospective case-control Finnish study involved 191 women diagnosed with breast cancer and 191 matched healthy controls. We selected a state-of-the-art AI system for breast cancer detection trained using a large US dataset. The selected baseline system was evaluated in two experimental settings. First, we examined our private Finnish sample as an independent test set that had not been considered in the development of the system (unseen population). Second, the baseline system was retrained to attempt to improve its performance in the unseen population by means of transfer learning. To analyze performance, we used areas under the receiver operating characteristic curve (AUCs) with DeLong's test. Results Two versions of the baseline system were considered: ImageOnly and Heatmaps. The ImageOnly and Heatmaps versions yielded mean AUC values of 0.82±0.008 and 0.88±0.003 in the US dataset and 0.56 (95% CI=0.50–0.62) and 0.72 (95% CI=0.67–0.77) when evaluated in the unseen population, respectively. The retrained systems achieved AUC values of 0.61 (95% CI=0.55–0.66) and 0.69 (95% CI=0.64–0.75), respectively. There was no statistical difference between the baseline system and the retrained system. Conclusion Transfer learning with a small study sample did not yield a significant improvement in the generalization of the system.
A Short Introduction to Artificial Intelligence: Methods, Success Stories, and Current Limitations

Abstract This chapter gives an overview of the most important methods in artificial intelligence (AI). The methods of symbolic AI are rooted in logic, and finding possible solutions by search is a central aspect. The main challenge is the combinatorial explosion in search, but the focus on the satisfiability problem of propositional logic (SAT) since the 1990s and the accompanying algorithmic improvements have made it possible to solve problems on the scale needed in industrial applications. In machine learning (ML), self-learning algorithms extract information from data and represent the solutions in convenient forms. ML broadly consists of supervised learning, unsupervised learning, and reinforcement learning. Successes in the 2010s and early 2020s such as solving Go, chess, and many computer games as well as large language models such as ChatGPT are due to huge computational resources and algorithmic advances in ML. Finally, we reflect on current developments and draw conclusions.
INTERACTION METHOD BETWEEN WEBVIEW OBJECTS IN HYBRID JAVA APPLICATIONS

The article deals with method of interaction between JavaScript objects of different web pages in hybrid java applications. To solve this problem, the WebView component is used, its functionality to provide an interface for JavaScript objects, as well as the SharedPreferences global settings class, with its ability to store data in local storage. The software implementation is considered and the results of the practical use of the developed method of interaction between JavaScript WebView objects in the form of a hybrid Java application embedded in the electronic university system – the Timetable SNU electronic timetable module are presented.
A Distributed Internet of Vehicles Data Privacy Protection Method Based on Zero-knowledge Proof and Blockchain

Aiming at the existing problems of insufficient security and poor scalability of existing blockchain data management solutions for the Internet of Vehicles, we use IPFS distributed technology to design a data privacy protection method for the Internet of Vehicles based on zero-knowledge proof. First, we use the Schnorr identity authentication protocol, a classic non-interactive zero-knowledge proof scheme, to complete the authentication and authorization of vehicles by RSU under the premise of protecting vehicle privacy. Secondly, we store encrypted data on IPFS-based edge distributed servers, and use Hyperledger Fabric to store authentication records and data indexes, which solves the problem of insufficient blockchain scalability. Finally, in order to improve the controllability and security of the data, we use the proxy re-encryption mechanism to manage the Symmetric-key of the data, and let RSU act as a proxy to perform re-encryption. Compared with the existing schemes, the proposed scheme reduces the communication overhead of the vehicle identity authentication phase, which is only 1728 bits. And while improving the scalability of the blockchain, it also enhances the privacy protection of the data of the Internet of Vehicles.