Reviewerly/exHarmony huggingface dataset:
PDAN Light: An Improved Attention Network for Action Detection

Action detection in densely annotated, untrimmed videos is a challenging and important task, with important implications in practical applications. Not only the right actions must be discovered, but also their start and end times. Recent advances in deep neural networks have pushed forward the action detection capabilities, in particular the I3D network. This paper describes a network with attention, which is based on the I3D features and includes state-of-the-art blocks, namely: MLP-Mixer and Vision Permutator. A light version of the original network is proposed, called PDAN light, which has 22.5% fewer parameters than the original PDAN, while improving the accuracy a 1.98% on average; and the MLP-Mixer-based architecture which has 34.5% fewer parameters than the original PDAN, while improving the accuracy a 0.95% on average. All the code is available in https://github.com/dvidgar/PDAN_light .
Fine-Grained Sentiment Analysis of Cross-Domain Chinese E-Commerce Texts Based on SKEP_Gram-CDNN

This study aims to use pre-trained models and an improved DPCNN model to extract useful information for sentiment analysis in an e-commerce dataset by combining a general domain text dataset. However, owing to feature distribution differences between text data from different domains, the feature information obtained from a general domain text dataset may contain ambiguities and lead to a scarcity of target domain data, thereby increasing the training error and decreasing the model performance. To address these issues, this study proposes the "SKEP_Gram-CDNN" model for fine-grained sentiment analysis of cross-domain Chinese e-commerce comments. The model introduces the ERNIE_Gram+DPCNN_att model as a generator and the capsule network as a discriminator to construct a cross-domain Chinese e-commerce sentiment analysis model. The performance of the discriminator model was validated on the ASAP_ASPECT dataset released by Meituan to demonstrate its superiority. Moreover, experiments were conducted on the SE-ABSA16_PHNS and SE-ABSA16-CAME target datasets to compare the proposed model with the ERNIE_Gram-CDNN model, which not only proved the effectiveness of the proposed model but also provided favorable methods for cross-domain fine-grained sentiment analysis.
Developing Intelligent Education in China

Integrating intelligent technologies into education impacts educational practice and research.This article zooms in on the factors driving the development of intelligent education and provides suggestions for improving intelligent education.Multiple factors are mentioned, including the changes in talent training goals, the reform of the internal elements of the education system, the paradigm shift of educational science research, and the optimization of the existing educational discipline system.It is crucial to elucidate the discipline orientation, value collaborative innovation, and remold the talent training system.
Enhancing Security Monitoring with AI-Enabled Log Collection and NLP Modules on a Unified Open Source Platform

The number of computer attacks continues to increase daily, posing significant challenges to modern security administrators to provide security in their organizations.With the rise of sophisticated cyber threats, it is becoming increasingly difficult to detect and prevent attacks using traditional security measures.As a result, security monitoring solutions such as Security Information and Event Management (SIEM) have become a critical component of modern security infrastructures.However, these solutions still face limitations, and administrators are constantly seeking ways to enhance their capabilities to effectively protect their cyber units.This paper explores how advanced deep learning techniques can help boost security monitoring capabilities by utilizing them throughout all stages of log processing.The presented platform has the potential to fundamentally transform and bring about a significant change in the field of security monitoring with advanced AI capabilities.The study includes a detailed comparison of modern log collection platforms, with the goal of determining the most effective approach.The key benefits of the proposed solution are its scalability and multipurpose nature.The platform integrates an open source solution and allows the organization to connect any event log sources or the entire SIEM solution, normalize and filter data, and use this data to train and deploy different AI models to perform different security monitoring tasks more efficiently.
An Improved Educating Mode in Electrical Engineering Based on Washington Accord

To achieve the goal of developing new engineering disciplines and cultivating students with high moral standards and innovative skills, an improved educating mode in electrical engineering based on Washington Accord is proposed.Firstly, based on the requirements of the Washington Accord engineering certification, the teaching objectives for innovative electrical engineers under the new engineering discipline are formulated.Secondly, a five-stage blended teaching, group-based and layered teaching approach is adopted to promote the progress of all students.Finally, the five-element process indicator is used to evaluate the effectiveness.The students feedbacked that the improved teaching mode promoted their knowledge and skills, and enhanced their initiative and confidence in learning and practicing.
New Bounds on the Size of Binary Codes With Large Minimum Distance

Let A(n,d) denote the maximum size of a binary code of length n and minimum Hamming distance d. Studying A(n,d), including efforts to determine it as well to derive bounds on A(n,d) for large n’s, is one of the most fundamental subjects in coding theory. In this paper, we explore new lower and upper bounds on A(n,d) in the large-minimum distance regime, in particular, when d=n/2-Ω(n). We first provide a new construction of cyclic codes, by carefully selecting specific roots in the binary extension field for the check polynomial, with length n=2m-1, distance d≥n/2-2c-1n, and size nc+1/2, for any m≥4 and any integer c with 0≤c≤m/2-1. These code parameters are slightly worse than those of the Delsarte–Goethals (DG) codes that provide the previously known best lower bound in the large-minimum distance regime. However, using a similar and extended code construction technique we show a sequence of cyclic codes that improve upon DG codes and provide the best lower bound in a narrower range of the minimum distance d, in particular, when d=n/2-Ω(n2/3). Furthermore, by leveraging a Fourier-analytic view of Delsarte’s linear program, upper bounds on A(n,n/2-ρn ) with ρ(0.5,9.5) are obtained that scale polynomially in n. To the best of authors’ knowledge, the upper bound due to Barg and Nogin barg2006spectral is the only previously known upper bound that scale polynomially in n in this regime. We numerically demonstrate that our upper bound improves upon the Barg-Nogin upper bound in the specified high-minimum distance regime.
Criminalization of Copyright-Infringing Information Distribution Algorithms

With the rapid emergence of user-generated content, Internet service providers have frequently employed information distribution algorithms that recommend information to users based on use statistics.If the information it actively recommends infringes the copyright of others, it is unclear whether and to what degree the Internet service provider should face criminal charges.In answer to the aforementioned problems, this article examines the criminality of copyrightinfringing information distribution algorithms using the social harm theory and makes recommendations for improving Chinese criminal legislation.Based on the balance of interests between cyber-copyright owners and algorithm service providers, this article believes that information distributors should be considered unilateral accessory criminals to the uploaders of copyright infringement works, and relevant laws need to be revised urgently.
An Underwater Image Restoration Method Based on Adaptive Brightness Improvement and Local Image Descattering

This letter proposes an effective underwater image restoration method that consists of a local image descattering and an adaptive brightness improvement. First, we establish an adaptive objective function for improving the brightness of underwater image according to the best-preserved channel of an image, and an augmented Lagrange multiplier based alternating direction minimization algorithm is derived to solve the optimization problem. Second, we introduce a local transmission estimation method that takes into account the different attenuation of light on the red, green and blue channels, which overcomes the limitation that existing methods heavily depend on the global transmission over the entire image. Extensive experiments on real-world underwater images demonstrate the effectiveness of the proposed method in underwater image restoration. Moreover, our method shows good generalization capability for enhancing remote sensing and nighttime images.
Comparing Response Performances of Chatgpt-3.5, Chatgpt-4 and Bard to Health-Related Questions: Comprehensiveness, Accuracy and Being Up-to-Date

Assessing comparative performance of ChatGPT-3.5, ChatGPT-4 and Bard in answering health related questions for comprehensiveness, accuracy and actuality (being up to date) domains was the purpose of this study. Family Physicians scored the responses of three chatbots to five questions with a five-point scale. For comprehensiveness domain ChatGPT-4 had better mean score than Bard and Bard had better mean score than ChatGPT-3.5. For accuracy domain statistically significant difference was not observed between three chatbots. For actuality domain Bard had better mean score than ChatGPT-4 and ChatGPT-4 had better mean score than ChatGPT-3.5. Each of the chatbots were assessed as accurate, up-to-date and adequately comprehensive.
A Selective Supervised Latent Beta-Liouville Allocation for Document Classification

We propose a novel model, selective supervised Latent Beta-Liouville (ssLBLA), that improves the performance and generative process of supervised probabilistic topic models with a more flexible prior and simple framework. ssLBLA model utilizes the “bag-of-selective-words” instead of the “bag-of-words” in the topic modeling by using a Bernoulli distribution to identify the discrimination power of a word for its assigned topic. Indeed, ssLBLA improves and inherits the general framework of selective supervised Latent Dirichlet Allocation (ssLDA) and can predict many types of responses. This paper presents a simple framework that utilizes the collapsed Gibbs sampling inference technique coupled with the flexible Beta-Liouville (BL) distribution prior to achieve more accurate estimations. Experimental results in single-label document classification show the merits of our new approach.
Advanced Digitization Methods for the Protection and Dissemination of Cultural Heritage towards Digital transformation: The Archaeological Museum of Delphi

A new digital platform for museums, including 3D scanning of the physical space and of selected distinguished artifacts was developed for the Archaeological Museum of Delphi. Web technologies and QR codes for augmented information were applied, mobile apps were developed, and video audio and visual information input were added, conforming to Web Content Accessibility Guidelines (WCAG) 2.0, while it is accessible in Android and iOS platforms via Google Store and Play store free of charge. Connectivity with the social networks of the user is also available. The platform was implemented and demonstrated in an interactive virtual tour of the museum with added specifications for partially impaired visitors (partially blind and with restricted hearing), by the Ephorate of Antiquities of Phocis. The final aim is creation of a holistic interactive experience both for the physical and web visitors.
Integrated Sensing and Communication for Vehicular Networks

In this chapter, we study the employment of integrated sensing and communication (ISAC) technology in vehicular networks, where vehicle tracking and vehicular communications can be combined for improving the overall system throughput. In the context of ISAC system, we develop a novel predictive beamforming scheme. In particular, the road-side unit (RSU) estimates and predicts the motion parameters of vehicles based on the echoes of the ISAC signal, which addresses the limitations of the conventional feedback-based beam tracking approaches, such as the high signaling overhead and low accuracy of angle estimation. A novel Bayesian inference scheme is proposed based on the vehicle state evolution model. Considering that the point-target assumption is impractical, we introduce the extended target model. Then, the beamwidth is adjusted in real-time to cover the entire vehicle. In addition, to improve the tracking accuracy and communication quality of service (QoS), we model the complicated roadway geometry via curvilinear coordinate system (CCS) and develop an interacting multiple model extended Kalman filter (IMM-EKF) framework. An optimization problem is formulated to maximize the array gain through dynamically adjusting the array size and thereby controlling the beamwidth, which takes the performance loss caused by beam misalignment into account. Numerical results have demonstrated the effectiveness of our proposed ISAC techniques for supporting vehicular communications.
Recommendations Based on Reinforcement Learning and Knowledge Graph

Reinforcement learning is playing an increasingly important role in the field of recommender systems. In this paper, we enhance the performance of a reinforcement learning-based recommender system by incorporating a knowledge graph, which serves as the embedding method for translating entities and relationships into vectors. To recommend diverse items, we define an explorative reward function for the Markov decision process that determines the recommendation. We also describe an action space pruning strategy that narrows down the reasoning space, and present a target for policy gradient optimization. Our experimental results show that the proposed method improves the recommendation performance and provides rational explanations.
Multi-stream big data mining for industry 4.0 in machining: novel application of a Gated Recurrent Unit Network

In Industry 4.0, the availability of signals from multiple sensors stimulates the investigation of novel quality monitoring and prediction methods. This paper tackles the in-line machining process monitoring by exploiting big data in the shape of multi-stream complex signals, eventually containing degradation and tool wear signatures. The proposed novel solution is fed by real-time multichannel data to identify anomalous states in machining applications. We investigate the effectiveness of a category of ANNs specifically conceived to predict process patterns based on time series of sensor signals, i.e., the Gated-Recurrent-Unit-Network. A real case study shows the efficiency of the proposed solution in predicting wild, complex and drifting patterns, typical of real productions, highlighting its provided benefits for in-line big data mining in industrial applications.
User-Guided Machine Understanding of Legal Documents

We present a novel approach to gaining a machine understanding of a legal document and then modelling the logic of that document in an integrated process. This paper describes a smart editor that uses a declarative language to represent both the ontology and logic models of a legal document. A document is incrementally elaborated in a fixed sequence of steps beginning with an ontology discovery step that identifies the explicit and implicit artefacts and applicable constraints. This information is used to generate code representations paired with words and icons which provide the foundation required for modelling the legal logic. The pairing with words and icons achieves a formal correspondence that allows logic modelling via either a textual or a graphical means. Similarly, this mechanism also supports both verbal and visual user feedback, enhancing user understanding. The tree of rules produced during this process is embedded in the original legal document, which can then be used as a smart contract on a modified blockchain. The integrated use of a declarative language auto-generated from a smart user interface for modelling both the ontology and the logic of a legal document, provides a simplicity and agility that enables domain experts to create and test custom smart contracts.
Evangelization Through Education in Korea: From Simple Schooling to Universities

Since its establishment in 1784, the Catholic Church in Korea has strived to evangelize Korean society through education. Over the past 240 years, the Catholic Church has established and operated a range of educational institutions, from small schools within churches to elementary, middle, high schools and universities, as well as kindergartens and special schools for disabilities, in order to provide education tailored to the various ages and circumstances of students. However, this has not been without its challenges. Catholic schools have had to adapt in various ways due to the oppression of the Chosŏn Dynasty and the Japanese Empire, as well as the establishment and changes in the government of the Republic of Korea. In such circumstances, the Catholic Church has worked hard to carry out its mission of evangelization and to meet the demands of the times through its educational projects. These educational projects of the Korean Catholic Church have had an important influence on the history of Korean modern education. Although major variables such as national education policy and social change are still ahead, the efforts of the Korean Catholic Church for evangelization and a well-rounded education will continue.
Towards Extracting Reusable and Maintainable Code Snippets

Given the wide adoption of the agile software development paradigm, where efficient collaboration as well as effective maintenance are of utmost importance, and the (re)use of software residing in code hosting platforms, the need to produce qualitative code is evident. A condition for acceptable software reusability and maintainability is the use of idiomatic code, based on syntactic fragments that recur frequently across software projects and are characterized by high quality. In this work, we propose a methodology that can harness data from the most popular GitHub repositories in order to automatically identify reusable and maintainable code idioms, by grouping code blocks that have similar structural and semantic information. We also apply the same methodology on a single-project level, in an attempt to identify frequently recurring blocks of code across the files of a team. Preliminary evaluation of our methodology indicates that our approach can identify commonly used, reusable and maintainable code idioms and code blocks that can be effectively given as actionable recommendations to the developers.
Engineering Drawing Text Detection via Better Feature Fusion

In recent years, text detection technology has advanced significantly. However, research on text detection of engineering drawings is lacking. The challenges faced by engineering drawing text detection are the degradation of partial occlusion and adhesion within texts, as well as the complex background noise. To address this problem, we propose an end-to-end text detection framework for degraded drawings based on multiscale feature fusion and instance segmentation, which adopts pluggable and stackable multiscale feature fusion modules to enhance the accuracy of the degraded text. We conduct experiments on several benchmarks to demonstrate the effectiveness of the proposed method on degraded drawing text and natural scene text.
Postdigital Environmental Crises

Postdigital technologies can be a double-edged sword for environmental crises. Various postdigital technologies are being used to good effect to address environmental crises including the climate crisis, biodiversity loss, and the destruction of rainforests. For example, there is potential for postdigital technologies to prevent overfishing and to save the remaining rainforests. However, postdigital technologies can also add to environmental crises. Materials and energy are required for the production of postdigital technologies, and these technologies are not a silver bullet for addressing environmental problems. This entry considers the double-edged sword in using postdigital technologies for addressing environmental crises.
An Intelligent Wireless Signal Detection and Recognition Platform

The detection and recognition of wireless signals play an essential role in the communications security of the Internet of Things (IoT). In order to monitor wireless signals, a platform that can detect and recognize wireless signals in real time is needed. This paper describes an intelligent wireless signal detection and recognition platform. The platform can monitor the presence or absence of wireless signals in real time and perform parameter analysis and modulation classification of wireless signals. We design and implement an integrated detection and recognition scheme based on the You Only Look Once for Signal Detection (YOLO-SD) and the Convolutional Neural Networks (CNN). The YOLO-SD algorithm has a fast running speed, thus the platform can work in real-time. The platform is designed and implemented on Field Programmable Gate Array (FPGA) platform. The result shows that our proposed platform achieves a detection accuracy of 80% and recognition accuracy of 93%, thus demonstrating the significant potential for analyzing RF spectral activity with high accuracy.
On the Efficiency of Building Large Collections of Software: Modeling, Algorithms, and Experimental Results

Building software from source code requires a build environment that meets certain requirements, such as the presence of specific compilers, libraries, or other tools. Unfortunately, requirements for different packages can conflict with each other, so it is often impossible to use a single build environment when building a large collection of software. This paper develops techniques to minimize the number of distinct build environments required, and measures the practical impact of our techniques on build time. In particular, we introduce the notion of a “conflict graph,” and prove that the problem of minimizing the number of build environments is equivalent to the graph coloring problem on this graph. We explore several heuristic techniques to compute conflict graph colorings, finding solutions that result in surprisingly small sets of build environments. Using Ubuntu 20.04 as our primary experimental dataset, we computed just 4 different environments that were sufficient for building the “Top 500” most popular source packages, and 11 build environments were sufficient for building all 30,646 source packages included in Ubuntu 20.04. Finally, we experimentally evaluate the benefit of these environments by comparing the work required for building the “Top 500” with our environments to the work required using the traditional minimal environment build. We saw that the total work required for building these packages dropped from 139h36m (139 h and 36 min) to 54 h 18 m, a 61% reduction.