Reviewerly/exHarmony huggingface dataset:
Unified Coarse-to-Fine Alignment for Video-Text Retrieval

The canonical approach to video-text retrieval leverages a coarse-grained or fine-grained alignment between visual and textual information. However, retrieving the correct video according to the text query is often challenging as it requires the ability to reason about both high-level (scene) and low-level (object) visual clues and how they relate to the text query. To this end, we propose a Unified Coarse-to-fine Alignment model, dubbed UCoFiA. Specifically, our model captures the cross-modal similarity information at different granularity levels. To alleviate the effect of irrelevant visual clues, we also apply an Interactive Similarity Aggregation module (ISA) to consider the importance of different visual features while aggregating the cross-modal similarity to obtain a similarity score for each granularity. Finally, we apply the Sinkhorn-Knopp algorithm to normalize the similarities of each level before summing them, alleviating over- and under-representation issues at different levels. By jointly considering the cross-modal similarity of different granularity, UCoFiA allows the effective unification of multi-grained alignments. Empirically, UCoFiA outperforms previous state-of-the-art CLIP-based methods on multiple video-text retrieval benchmarks, achieving 2.4%, 1.4% and 1.3% improvements in text-to-video retrieval R@1 on MSR-VTT, Activity-Net, and DiDeMo, respectively. Our code is publicly available at https://github.com/Ziyang412/UCoFiA.
Prototypical Mixing and Retrieval-based Refinement for Label Noise-resistant Image Retrieval

Label noise is pervasive in real-world applications, which influences the optimization of neural network models. This paper investigates a realistic but understudied problem of image retrieval under label noise, which could lead to severe overfitting or memorization of noisy samples during optimization. Moreover, identifying noisy samples correctly is still a challenging problem for retrieval models. In this paper, we propose a novel approach called Prototypical Mixing and Retrieval-based Refinement (TITAN) for label noise-resistant image retrieval, which corrects label noise and mitigates the effects of the memorization simultaneously. Specifically, we first characterize numerous prototypes with Gaussian distributions in the hidden space, which would direct the Mixing procedure in providing synthesized samples. These samples are fed into a similarity learning framework with varying emphasis based on the prototypical structure to learn semantics with reduced overfitting. In addition, we retrieve comparable samples for each prototype from simple to complex, which refine noisy samples in an accurate and class-balanced manner. Comprehensive experiments on five benchmark datasets demonstrate the superiority of our proposed TITAN compared with various competing baselines.
Long-Term Photometric Consistent Novel View Synthesis with Diffusion Models

Novel view synthesis from a single input image is a challenging task, where the goal is to generate a new view of a scene from a desired camera pose that may be separated by a large motion. The highly uncertain nature of this synthesis task due to unobserved elements within the scene (i.e. occlusion) and outside the field-of-view makes the use of generative models appealing to capture the variety of possible outputs. In this paper, we propose a novel generative model capable of producing a sequence of photorealistic images consistent with a specified camera trajectory, and a single starting image. Our approach is centred on an autoregressive conditional diffusion-based model capable of interpolating visible scene elements, and extrapolating unobserved regions in a view, in a geometrically consistent manner. Conditioning is limited to an image capturing a single camera view and the (relative) pose of the new camera view. To measure the consistency over a sequence of generated views, we introduce a new metric, the thresholded symmetric epipolar distance (TSED), to measure the number of consistent frame pairs in a sequence. While previous methods have been shown to produce high quality images and consistent semantics across pairs of views, we show empirically with our metric that they are often inconsistent with the desired camera poses. In contrast, we demonstrate that our method produces both photorealistic and view-consistent imagery. Additional material is available on our project page: https://yorkucvil.github.io/Photoconsistent-NVS/.
Multi-point Mosaic Fusion Authentication: A Strategy for Luxury Goods Authentication

Currently, research on the authentication of luxury goods based on deep learning is relatively limited, and the authentication accuracy often depends on the quality and quantity of the dataset. The constant changes in forgery designs pose a challenge for authentication models, as the detection performance for new date may significantly decrease. Therefore, further research is necessary to improve the robustness of identification results. This paper proposes a scheme based on the mosaic enhancement algorithm, which integrates features from multiple identification points for detection. Our method solves the issue of performance decline when detecting mismatched data source. Our method performs well in luxury good authentication, with accuracy surpassing other sample fusion schemes such as CutMix, MixUp, as well as ensemble method. Our method can be applied to detecting and combatting various counterfeit products.
Learning Adaptive Neighborhoods for Graph Neural Networks

Graph convolutional networks (GCNs) enable end-to-end learning on graph structured data. However, many works assume a given graph structure. When the input graph is noisy or unavailable, one approach is to construct or learn a latent graph structure. These methods typically fix the choice of node degree for the entire graph, which is suboptimal. Instead, we propose a novel end-to-end differentiable graph generator which builds graph topologies where each node selects both its neighborhood and its size. Our module can be readily integrated into existing pipelines involving graph convolution operations, replacing the predetermined or existing adjacency matrix with one that is learned, and optimized, as part of the general objective. As such it is applicable to any GCN. We integrate our module into trajectory prediction, point cloud classification and node classification pipelines resulting in improved accuracy over other structure-learning methods across a wide range of datasets and GCN backbones. We will release the code.
Open Source Intelligence Driven Moving Target Defense for Secure Software-defined WAN: a Game Theoretic Approach

The recent devastating cyberattacks targeting enterprise networks have raised many concerns regarding their reliability, resilience, and ability to adapt to changes. The heterogeneous WAN interfaces, protocols, and switching mechanisms make exploitation easy for attackers and management and control a challenging task for system administrators.Software-defined Wide Area Networks (SD-WAN) were introduced to help address such challenges. However, the software-based foundation of SD-WANs opens the door to new attack vectors that target software vulnerabilities, a threat that was not an issue in traditional networks. Furthermore, given the scale of such networks and the large number of services they offer, SD-WANs do not limit the attacker’s access to publicly accessible information that can lead to successful coordinated attacks.In this white paper, we introduce a game-based theoretic approach that operates on open-source intelligence-based information gathering to model potential attack scenarios and drive a proactive moving target defense technique to prevent potential exploitation of SD-WANs. Initial qualitative investigations showed that the proposed approach can help reduce the potential attack success rates.
Tiny Machine Learning Business Intelligence in the Semiconductor Industry: A Case Study

This paper sets its primary objective to understand the transformative potential of Tiny Machine Learning (Tiny ML) and Artificial Intelligence (AI) in enhancing industrial efficiency. Using a research design that juxtaposes the theoretical understanding of these technologies with real-world applications, the methodology adopted emphasizes three pivotal use cases, substantiated with tangible examples. The main outcomes show the influential role of STMicroelectronics, a leading semiconductor entity, in bridging the gap between Tiny ML, AI, and industrial applications. Results from in-depth examinations highlight the value of Predictive Maintenance as evidenced by offshore wind farms, the importance of Gesture Recognition in Human-Machine Interaction with a focus on autonomous vehicles, and the efficient integration of Tiny ML into IoT Sensor Networks, notably seismic monitoring. In conclusion, this manuscript underscores the impending future where Tiny ML and AI synergize, particularly hinting at breakthroughs in humanoid robotics. Such advancements are anticipated to redefine the contours of human-technology interaction impacting everyone in everyday life.
A Deep Learning-based Approach for an Automated Vulnerability Detection in Source Code

With the advancements and significant increase in today’s software projects, software vulnerabilities have become a crucial and serious concern to the software industry. These software defects pose familiar cybersecurity threats and identifying potential vulnerable code is difficult because of various elements related to volume, diversity, and complexity. Traditionally, vulnerabilities in source code have been detected either statically by relying on expert knowledge, which is time consuming, or through dynamic analysis tools. However, lacking robust investigation of source code resulted in decreased efficacy of the existing solutions. In this respect, as an alternative to traditional source code detection methods, deep learning seems to have become an attractive solution. Thus, in this research work, we propose a deep learning-based neural networks approach to construct a function-level vulnerability analysis for an automated identification of vulnerabilities in source code. The proposed solution can handle source code vulnerability detection on function level without expert knowledge by utilizing deep learning convolutional neural networks (CNNs) and artificial neural networks (ANNs). To generate meaningful numerical feature representation of input source code functions, we have generated abstract syntax tree (AST). Moreover, we enhanced the deep learning neural network models through the selection of optimal parameter values and employing a validation methodology. To evaluate the performance of the proposed models we used a public dataset with verified vulnerability labels using various performance metrics such as Precision, Accuracy, F1-score, Recall and ROC-AUC. After conducting multiple experiments, the best result (Accuracy = 84.22%) was achieved using our ANN model.
Research on business English accent evaluation based on self-supervised speech recognition model

In order to further improve the oral learning effect of business foreign language learners, this paper proposes a pronunciation error detection and evaluation model based on self-supervision. Among them, the convolutional neural network and the long short-term memory network (LSTM) are combined as the basic pronunciation error detection method, and the pronunciation error detection accuracy is further improved by introducing the attention mechanism and self-supervised pre-training. The experimental results show that compared with the pronunciation error detection models trained by CNN-RNN-CTC model and other pre-training models, the constructed pronunciation error detection and evaluation model based on self-supervision has higher pronunciation error detection accuracy, and it can evaluate learners&apos; oral pronunciation more accurately. To sum up, the constructed pronunciation error detection and evaluation model based on self-supervision has good performance and high detection accuracy. The model can be applied to actual business English oral training, and it can help learners improve their business English oral level, which is highly feasible.
Research on tax knowledge automatic retrieval based on ASDPQ image retrieval

In order to further improve the effect of tax knowledge learning, a tax knowledge image retrieval method based on improved fast product quantization is proposed. Among them, the basic search method is the fast product quantization algorithm, which is further improved to improve the image search effect. The simulation results show that compared with the standard fast product quantization method, the improved fast product quantization method has higher search accuracy, and the search speed is significantly improved when the subspace reaches a certain dimension. When the constructed tax knowledge picture is applied to the image retrieval test, the accuracy is high and it can meet the real demand. In summary, the designed tax knowledge image retrieval method has good performance, can be applied to the image search of actual tax knowledge learning scenarios, and helps users improve the learning effect, which has high feasibility.
Ethical Leadership in Information Technology Decision Making and its Impact on Risk Management

In the rapidly growing digital era, information technology has become an integral part of almost all aspects of our lives, including in the world of business and management. Appropriate and effective use of information technology is the key to achieving competitive advantage in today's competitive business environment. However, making decisions related to information technology is not an easy task, as information technology is often complex and high-risk. The purpose of this study is to analyse ethical leadership in information technology-related decision making and its impact on risk management. The method used is a qualitative literature review that focuses on an in-depth understanding of the topic in the time span from 2006 to 2023. The main objective of this method is to identify, analyse, and synthesise relevant scientific literature that has been published in various journals, conference papers, and other academic sources accessible through Google Scholar. The study results show that ethical leadership plays a central role in ensuring that IT-related decisions are taken with ethical principles in mind and with the aim of minimising risk. Ethical leadership contributes to the avoidance of data misuse, creating a culture of transparency and accountability, ensuring fair use of technology, and being mindful of the impact of IT decisions on society and the environment.
Educational Management Strategies to Address Information Security Challenges in the Use of Social Media in Learning Environments

With the advancement of information technology, social media has become an integral part of daily life, including in the field of education. However, the use of social media in the learning environment is not exempt from information security challenges that can impact the effectiveness and sustainability of the learning process. This research aims to examine educational management strategies to address information security challenges in the use of social media in learning environments. The study employed an extensive review of existing literature through qualitative analysis to gain a comprehensive insight into the subject, covering the period from 2017 to 2023. The study's findings indicate that, in addressing the complexity of information security challenges related to the use of social media in learning environments, a holistic educational management approach is necessary. Awareness of risks, the development of clear policies, the selection of secure technologies, external collaboration, and continuous monitoring are key factors in creating a safe and innovative learning environment.
Optimizing Economic Development Through the Synergistic Influence of Information Technology on Educational Access and Human Capital Quality Enhancement

This research aims to provide a deeper insight into the potential synergy between information technology, educational access, and the enhancement of human capital quality in the context of economic development. The research employs a comprehensive examination of available literature using qualitative analysis to gain a thorough understanding of the topic, encompassing the timeframe from 2015 to 2023. The study's findings indicate that optimizing economic development through the synergy of information technology, educational access, and the enhancement of human capital quality is a fundamental and relevant paradigm in addressing the challenges and opportunities of the globalization era. The integration of information technology into the education sector provides broader and more equitable access, while the improvement of human capital quality through technology establishes a strong foundation for sustainable economic growth.
A study about how to describe the work of teachers: craft workers, executive technicians or professional technicians

The definition of teacher identity is significant for the professional development and teaching methods used by teachers. In reality, teachers most of the time play more than one role. This study analyzes the three roles of teachers as craft workers, executive technicians and professional technicians. The result shows that the combination of executive technicians and professional technicians is currently the most suitable model in China. More efforts should be put on how to more effectively promote the teaching of teachers and improve the quality of education, which is the point that all teachers in any category should consider.
The application and effects of educational technology in English education

In the current information age, people interact with various technologies daily, and education, as a social activity for imparting knowledge and nurturing talents, naturally relies on technological support. The integration of information technology into curriculum and the application of modern educational technology in teaching are gradually changing people's perception and understanding of education. Based on this, the paper examines the application performance, effects, and drawbacks of educational technology in English education. It proposes targeted optimization suggestions with the aim of leveraging the advantages of educational technology to promote reforms in English education and enhance the quality of English education.
Research on English teaching practice for college students in the context of intelligent classroom

Because of the rapid development of modern information technology, big data, multimedia, cloud computing, and others play a vital role in people's daily life. Especially in the field of education, the establishment of an intelligent classroom is a reform of English education ideas, methods, and models, and also brings convenience to the teaching of English teachers in universities. Students can also learn more English knowledge in classroom learning. However, for a long time, English teachers have encountered many difficulties in utilizing and reforming smart classrooms due to the limitations of English education resources. In order to improve the current situation of English teaching in universities and enhance the effectiveness of smart classroom teaching, this article will conduct in-depth exploration of English teaching practices for college students in the context of intelligent classrooms, hoping to provide reference for future related research.
Optimization strategies for informatization teaching of higher vocational English

This article aims to explore how to improve English informatization teaching in higher vocational education. Firstly, the current status of the use of information-based teaching tools, the degree of integration of information technology and education, and the challenges faced were analyzed. Then, a series of optimizations were proposed for vocational English education in the context of informatization. By updating the concept of informatization teaching, constructing an information-based course resource system, and innovating informatization teaching models, we are committed to improving the quality of vocational English teaching and promoting the comprehensive development of students.
Gesture-Based Control of Multimedia Player Using Python and OpenCV

The increasing significance of computers in our daily lives, coupled with the rise of ubiquitous computing, has necessitated effective human-computer interaction. Hand gesture recognition systems have emerged as a real-time video-based solution for detecting and interpreting hand gestures, offering intelligent and natural human-computer interaction (HCI) methods. This project focuses on leveraging human hands as input devices for computer operation. Developed using Python and the OpenCV library, the program utilizes a computer webcam to capture and analyze hand shapes and patterns. The program provides real-time feedback by displaying recognized hand gestures on the live video stream. The ultimate outcome of this project is an application that enhances user experiences in contactless systems. The project recognizes and detects human hand motions using the Python computer language through a process flow that includes background subtraction, hand ROI segmentation, contour detection, and finger recognition. Techniques for processing images are used, including hand gesture detection, pattern recognition, thresholding, and contour detection. The processing of incoming photos and the creation of related keystrokes are made possible by OpenCV, a rich set of image processing tools
Digital Literacy and Numeracy Education to Enhance Students' Interest in Madrasah Ibtidaiyah

"Digital literacy involves the knowledge and skills of users in utilizing digital media, such as communication tools, internet networks, and so on. Digital numeracy emphasizes the ability to apply basic mathematical concepts and symbols to solve everyday problems in a digital context. The limited use of digital technology for accessing digital literacy and numeracy education resources is a concern for educators aiming to prepare high-quality students globally at the Ihyaul Islam Madrasah Ibtidaiyah Educational Institution. Researchers employ videos in the learning process to demonstrate that the use of video media in education can enhance students' learning interest and knowledge of digital literacy and numeracy. It is hoped that this program will help increase interest in digital literacy and numeracy learning for students. This research aims to provide an understanding of the importance of enhancing digital literacy and numeracy, especially in the development of engaging, innovative, efficient, and effective learning media suitable for children's early development, to educators and parents"