Reviewerly/exHarmony huggingface dataset:
Dual Video Summarization: From Frames to Captions

Video summarization and video captioning both condense the video content from the perspective of visual and text modes, i.e. the keyframe selection and language description generation. Existing video-and-language learning models commonly sample multiple frames for training instead of observing all. These sampled deputies greatly improve computational efficiency, but do they represent the original video content enough with no more redundancy? In this work, we propose a dual video summarization framework and verify it in the context of video captioning. Given the video frames, we firstly extract the visual representation based on the ViT model fine-tuned on the video-text domain. Then we summarize the keyframes according to the frame-lever score. To compress the number of keyframes as much as possible while ensuring the quality of captioning, we learn a cross-modal video summarizer to select the most semantically consistent frames according to the pseudo score label. Top K frames ( K is no more than 3% of the entire video.) are chosen to form the video representation. Moreover, to evaluate the static appearance and temporal information of video, we design the ranking scheme of video representation from two aspects: feature-oriented and sequence-oriented. Finally, we generate the descriptions with a lightweight LSTM decoder. The experiment results on the MSR-VTT and MSVD dataset reveal that, for the generative task as video captioning, a small number of keyframes can convey the same semantic information to perform well on captioning, or even better than the original sampling.
Keep Skills in Mind: Understanding and Implementing Skills in Commonsense Question Answering

Commonsense Question Answering (CQA) aims to answer questions that require human commonsense. Closed-book CQA, as one of the subtasks, requires the model to answer questions without retrieving external knowledge, which emphasizes the importance of the model's problem-solving ability. Most previous methods relied on large-scale pre-trained models to generate question-related knowledge while ignoring the crucial role of skills in the process of answering commonsense questions. Generally, skills refer to the learned ability in performing a specific task or activity, which are derived from knowledge and experience. In this paper, we introduce a new approach named Dynamic Skill-aware Commonsense Question Answering (DSCQA), which transcends the limitations of traditional methods by informing the model about the need for each skill in questions and utilizes skills as a critical driver in CQA process. To be specific, DSCQA first employs commonsense skill extraction module to generate various skill representations. Then, DSCQA utilizes dynamic skill module to generate dynamic skill representations. Finally, in perception and emphasis module, various skills and dynamic skill representations are used to help question-answering process. Experimental results on two publicly available CQA datasets show the effectiveness of our proposed model and the considerable impact of introducing skills.
ScriptWorld: Text Based Environment for Learning Procedural Knowledge

Text-based games provide a framework for developing natural language understanding and commonsense knowledge about the world in reinforcement learning based agents. Existing text-based environments often rely on fictional situations and characters to create a gaming framework and are far from real-world scenarios. In this paper, we introduce ScriptWorld: a text-based environment for teaching agents about real-world daily chores and hence imparting commonsense knowledge. To the best of our knowledge, it is the first interactive text-based gaming framework that consists of daily real-world human activities designed using scripts dataset. We provide gaming environments for 10 daily activities and perform a detailed analysis of the proposed environment. We develop RL-based baseline models/agents to play the games in ScriptWorld. To understand the role of language models in such environments, we leverage features obtained from pre-trained language models in the RL agents. Our experiments show that prior knowledge obtained from a pre-trained language model helps to solve real-world text-based gaming environments.
22 Pandas: groupby

No AccessNumerisches PythonAug 202322 Pandas: groupbyhttps://doi.org/10.3139/9783446473669.022SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2023Pages: 272-284Print ISBN: 978-3-446-47170-2eISBN: 978-3-446-47366-9 PDF downloadLoading ...
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification

Recently, Zero-Shot Node Classification (ZNC) has been an emerging and crucial task in graph data analysis. This task aims to predict nodes from unseen classes which are unobserved in the training process. Existing work mainly utilizes Graph Neural Networks (GNNs) to associate features' prototypes and labels' semantics thus enabling knowledge transfer from seen to unseen classes. However, the multi-faceted semantic orientation in the feature-semantic alignment has been neglected by previous work, i.e. the content of a node usually covers diverse topics that are relevant to the semantics of multiple labels. It's necessary to separate and judge the semantic factors that tremendously affect the cognitive ability to improve the generality of models. To this end, we propose a Knowledge-Aware Multi-Faceted framework (KMF) that enhances the richness of label semantics via the extracted KG (Knowledge Graph)-based topics. And then the content of each node is reconstructed to a topic-level representation that offers multi-faceted and fine-grained semantic relevancy to different labels. Due to the particularity of the graph's instance (i.e., node) representation, a novel geometric constraint is developed to alleviate the problem of prototype drift caused by node information aggregation. Finally, we conduct extensive experiments on several public graph datasets and design an application of zero-shot cross-domain recommendation. The quantitative results demonstrate both the effectiveness and generalization of KMF with the comparison of state-of-the-art baselines.
Unbiased Gradient Boosting Decision Tree with Unbiased Feature Importance

Gradient Boosting Decision Tree (GBDT) has achieved remarkable success in a wide variety of applications. The split finding algorithm, which determines the tree construction process, is one of the most crucial components of GBDT. However, the split finding algorithm has long been criticized for its bias towards features with a large number of potential splits. This bias introduces severe interpretability and overfitting issues in GBDT. To this end, we provide a fine-grained analysis of bias in GBDT and demonstrate that the bias originates from 1) the systematic bias in the gain estimation of each split and 2) the bias in the split finding algorithm resulting from the use of the same data to evaluate the split improvement and determine the best split. Based on the analysis, we propose unbiased gain, a new unbiased measurement of gain importance using out-of-bag samples. Moreover, we incorporate the unbiased property into the split finding algorithm and develop UnbiasedGBM to solve the overfitting issue of GBDT. We assess the performance of UnbiasedGBM and unbiased gain in a large-scale empirical study comprising 60 datasets and show that: 1) UnbiasedGBM exhibits better performance than popular GBDT implementations such as LightGBM, XGBoost, and Catboost on average on the 60 datasets and 2) unbiased gain achieves better average performance in feature selection than popular feature importance methods.
Hierarchical Semantic Contrast for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation (WSSS) with image-level annotations has achieved great processes through class activation map (CAM). Since vanilla CAMs are hardly served as guidance to bridge the gap between full and weak supervision, recent studies explore semantic representations to make CAM fit for WSSS and demonstrate encouraging results. However, they generally exploit single-level semantics, which may hamper the model to learn a comprehensive semantic structure. Motivated by the prior that each image has multiple levels of semantics, we propose hierarchical semantic contrast (HSC) to ameliorate the above problem. It conducts semantic contrast from coarse-grained to fine-grained perspective, including ROI level, class level, and pixel level, making the model learn a better object pattern understanding. To further improve CAM quality, building upon HSC, we explore consistency regularization of cross supervision and develop momentum prototype learning to utilize abundant semantics across different images. Extensive studies manifest that our plug-and-play learning paradigm, HSC, can significantly boost CAM quality on both non-saliency-guided and saliency-guided baselines, and establish new state-of-the-art WSSS performance on PASCAL VOC 2012 dataset. Code is available at https://github.com/Wu0409/HSC_WSSS.
PPAT: Progressive Graph Pairwise Attention Network for Event Causality Identification

Event Causality Identification (ECI) aims to identify the causality between a pair of event mentions in a document, which is composed of sentence-level ECI (SECI) and document-level ECI (DECI). Previous work applies various reasoning models to identify the implicit event causality. However, they indiscriminately reason all event causality in the same way, ignoring that most inter-sentence event causality depends on intra-sentence event causality to infer. In this paper, we propose a progressive graph pairwise attention network (PPAT) to consider the above dependence. PPAT applies a progressive reasoning strategy, as it first predicts the intra-sentence event causality, and then infers the more implicit inter-sentence event causality based on the SECI result. We construct a sentence boundary event relational graph, and PPAT leverages a simple pairwise attention mechanism, which attends to different reasoning chains on the graph. In addition, we propose a causality-guided training strategy for assisting PPAT in learning causality-related representations on every layer. Extensive experiments show that our model achieves state-of-the-art performance on three benchmark datasets (5.5%, 2.2% and 4.5% F1 gains on EventStoryLine, MAVEN-ERE and Causal-TimeBank). Code is available at https://github.com/HITsz-TMG/PPAT.
Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors

Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model prediction. Gradient estimation is a critical step in black-box adversarial attacks, as it will directly affect the query efficiency. Recent works have attempted to utilize gradient priors to facilitate score-based methods to obtain better results. However, these gradient priors still suffer from the edge gradient discrepancy issue and the successive iteration gradient direction issue, thus are difficult to simply extend to decision-based methods. In this paper, we propose a novel Decision-based Black-box Attack framework with Gradient Priors (DBA-GP), which seamlessly integrates the data-dependent gradient prior and time-dependent prior into the gradient estimation procedure. First, by leveraging the joint bilateral filter to deal with each random perturbation, DBA-GP can guarantee that the generated perturbations in edge locations are hardly smoothed, i.e., alleviating the edge gradient discrepancy, thus remaining the characteristics of the original image as much as possible. Second, by utilizing a new gradient updating strategy to automatically adjust the successive iteration gradient direction, DBA-GP can accelerate the convergence speed, thus improving the query efficiency. Extensive experiments have demonstrated that the proposed method outperforms other strong baselines significantly.
Cyber Security Audit using CIS CSC, NIST CSF and COBIT 2019 Framework

Tingginya penggunaan teknologi dan informasi saat ini mengakibatkan peningkatan risiko dan ancaman keamanan data dan informasi. Dinas Komunikasi dan Informatika Kota Pontianak, dinas pemerintahan yang memanfaatkan dan menggunakan banyak teknologi informasi. Untuk mengetahui sejauh mana kemampuan Dinas Komunikasi dan Informatika Kota Pontianak dalam mengelola keamanan siber, maka diperlukan audit keamanan siber. Audit dapat dilakukan dengan menggabungkan framework CIS CSC (Center for Internet Security Critical Security Controls) untuk membatasi focus area keamanan siber aset TI serta menggunakan NIST CSF (National Institute of Standards and Technology Cybersecurity Framework) dan COBIT 2019 (Control Objective for Information Technologies) untuk melakukan perhitungan level kapabilitas. Perhitungan level kapabilitas menggunakan metode CPM (COBIT Performance Model). Hasil perhitungan level kapabilitas keamanan siber Dinas Komunikasi dan Informatika Kota Pontianak pada Identify (ID) mencapai level 3.9, Protect (PR) mencapai level 3.4, Detect (DE) mencapai level 2.5, dan Respond (RS) mencapai level 4. Terdapat 19 rekomendasi aktivitas untuk dilakukan agar mencapai level keamanan siber yang diinginkan, kemudian dilakukan pemetaan aktivitas rekomendasi ke dalam action priority matrix, 10 aktivitas masuk ke dalam kuadran Quick Wins, dan 9 aktivitas yang masuk ke dalam kuadran Major Projects. The frequent use of technology and information today impacts the increased risk and threats to data and information security. Department of Information and Communications of Pontianak is the department that utilizes and uses a lot of information technology. To find out how far the Pontianak City Communication and Informatics Office is capable of managing cyber security, a cyber security audit is needed. Audits can be conducted by combining the CIS CSC (Center for Internet Security Critical Security Controls) framework to define the cybersecurity focus areas of IT assets and using the NIST CSF (National Institute of Standards and Technology Cybersecurity Framework) and COBIT 2019 (Control Objective for Information Technologies) to calculate the capability level. Capability level calculation uses the CPM (COBIT Performance Model) method. The results of calculating the level of cyber security capability of the Pontianak City Communication and Informatics Service for Identification (ID) reaches level 3.9, Protect (PR) reaches level 3.4, Detect (DE) reaches level 2.5, and Respond (RS) reaches level 4. There are 19 activity recommendations to be carried out in order to achieve the desired level of cybersecurity, then capture recommendation activities into the action priority matrix, 10 activities included in the Quick Wins quadrant, and 9 activities entered into the Major Projects quadrant.
Analysis of vulnerabilities and security problems of web applications

The article provides a comprehensive analysis of vulnerabilities, methods, tools and problems faced by web application security testing. The analysis of scientific research in the field of web application security testing revealed a significant interest of scientists in finding effective ways to minimize site security risks and vulnerabilities. It was found out that the list of the most common web application vulnerabilities includes: broken access control, cryptographic failures, misconfiguration of security, SQL and other injections, insecure design, identification and authentication errors, etc. Specific features of the security vulnerabilities of web applications are highlighted. The problems faced by automated tools for web security testing are separately considered, namely the development of automated tools for web security testing, the use of RIA (Rich Internet Application) web applications, and the use of insecure cryptographic storage. Web application security risks can be associated with the design phase, the development phase, the deployment phase, and the maintenance phase. It is security testing that is used to identify these risks of the web application, to investigate the vulnerabilities and weak points of the web application. The conducted analysis of security vulnerabilities, methods and problems of testing web applications revealed the presence of different approaches to protect software products. A combination of manual and automated web application security testing techniques is advisable, starting with automated security testing and complementing it with manual penetration testing. A comprehensive approach should integrate testing into all stages of the software development life cycle. Such approach helps to use the most appropriate and effective available methods for the current phase of software product development.
Improving the interference resistance of multi-color barcode images

A method of improving the interference resistance of multi-color barcodes is pro-posed, which is based on the application of two-level control of errors that occur when scanning a multi-color barcode image, using two multi-valued correcting codes: the BCH code - at the level of barcode patterns (lower level), and the Reed-Solomon code - at barcode symbol level (top level). The proposed method makes it possible to create multi-color barcodes with im-proved immunity characteristics, which ensure reliable reproduction of data during scanning of large-capacity multi-color barcode images in automatic identification sys-tems.
Introduction

This chapter provides definitions of basic concepts that are essential to understand the field of virtual reality. By the end of the chapter, you should have a good understanding of the fundamental concepts of virtual reality, be familiar with the history of the field, and be aware of the multiple disciplines involved in the development of virtual reality applications. You should also understand the evolution of the metaverse and its future.
Other VR Applications

This last chapter presents a few different VR applications in vehicle simulation, manufacturing, and entertainment.
Data Mining as an essential Tool for Data Driven Vehicle Development

In an environment of steadily increasing competitiveness, data driven product development offers a great opportunity for automotive manufacturers. Vehicle usage data can be used, for example, to optimize technical components for their real-life stress or to develop new customer functions based on real user behavior. With the increasing interconnectedness of modern vehicles, usage data in the form of vehicle bus signals can be used for data-driven development. However, the use of this data often poses a challenge because it was designed for internal communication of the vehicle rather than for later analysis. Accordingly, this paper presents how data mining (DM) methods can be used to extract customer behavior from this data. The central idea of the presented methods is the derivation and enrichment of the vehicle bus signals with metadata of the vehicle usage, for example but not exclusively by applying statistical methods like machine learning. The method of metadata enrichment is embedded in an adapted version of the Cross Industry Standard Process for Data Mining (CRISP-DM). The developed methods are presented based on concrete application examples and finally a general recommendation for action for data-driven product development in the automotive sector is derived
Ability of ordinal spline logistic regression model in the classification of nutritional status data

In this study, an ordinal spline logistic regression model was developed and used to classify data on the nutritional status of children under five in the Gowa district, Indonesia. The nutritional status of toddlers consists of 3 categories: malnutrition, good nutrition, and excess nutrition. So nutritional status data for toddlers can be modeled by ordinal spline logistic regression. The results of this study indicate that the data on the nutritional status of children is optimal in the ordinal spline logistic regression model using 2-knot points with a GCV value of 0.2158. The estimation results of the ordinal spline logistic regression model show that toddlers aged 18 months and 24 months tend to have a good chance of getting good nutrition. In comparison, toddlers aged 18 to 24 months tend to have a minimal chance of getting good nutrition, and the accuracy of the classification model of the nutritional status of toddlers uses the ordinal spline logistic regression of 92.25%.
EFL students' views, experiences, and expectations toward plagiarism practices in Indonesian higher education

This research investigates the English as a Foreign Language (EFL) students' views, experiences, and expectations toward plagiarism practices in Indonesian higher education. The researchers employed a qualitative method with a case study approach. Ten EFL students conveniently participated in this study. The data were obtained through semi-structured interviews. The data was analyzed using thematic analysis. This study showed that EFL students view plagiarism as an act of taking someone's idea, a bad habit, intentional and unintentional. EFL students' experience with plagiarism revealed that they committed plagiarism due to a lack of understanding and unfamiliarity with the plagiarism concept. Besides, the students reported being too lazy and less likely to read. EFL students expect to reduce plagiarism by being more active in reading and providing free workshop seminars, and lecturers must strictly apply the rules. Moreover, instilling self-motivation, receiving advice and feedback from the lecturer, understanding the definition of plagiarism, having the desire to read, enhancing personal awareness, doing Turnitin checker, using an application, and self-confidence were their expectations of reducing plagiarism practices.
KDRM: Kernel Data Relocation Mechanism to Mitigate Privilege Escalation Attack

A privilege escalation attack by memory corruption based on kernel vulnerability has been reported as a security threat to operating systems. Kernel address layout randomization (KASLR) randomizes kernel code and data placement on the kernel memory section for attack mitigation. However, a privilege escalation attack will succeed because the kernel data of privilege information is identified during a user process execution in a running kernel. In this paper, we propose a kernel data relocation mechanism (KDRM) that dynamically relocates privilege information in the running kernel to mitigate privilege escalation attacks using memory corruption. The KDRM provides multiple relocation-only pages in the kernel. The KDRM selects one of the relocation-only pages and moves the privilege information to the relocation-only pages when the system call is invoked. This allows the virtual address of the privilege information to change by dynamically relocating for a user process. The evaluation results confirmed that privilege escalation attacks by user processes on Linux could be prevented with KDRM. As a performance evaluation, we showed that the overhead of issuing a system call was up to 149.67%, and the impact on the kernel performance score was 2.50%, indicating that the impact on the running kernel can be negligible.