Reviewerly/exHarmony huggingface dataset:
Costs and benefits of business information security

The article considers the process of information security from the position of cost-benefit assessment that allows to understand economic aspects of feasibility study of measures in the field of business information protection. In the practice of Russian business, the issues of information security are focused on compliance with regulatory requirements of the legislation. This leads to the fact that business entities neglect the procedure of comprehensive assessment of measures effectiveness that should consider the costs and benefits of legal, organizational, and technical information protection implementation. At the same time, in order to meet the new conditions of technological development of society, it is necessary to revise the concept of information protection of business entities and to fix in the policy of information security of activities the approaches to risk assessment based on assets value. The study contains a description of the cost/ benefit approach to the constituent components of ensuring information security of business process. To understand the economic aspects of information security, the research focuses on business entities of the market type of economic activity (the enterprise makes profit in its activity). The composition of components of the ensuring information security process has been defined. On the example of risk management costs and benefits of information security have been considered in detail.
An overview of differentiable particle filters for data-adaptive sequential Bayesian inference

By approximating posterior distributions with weighted samples, particle filters (PFs) provide an efficient mechanism for solving non-linear sequential state estimation problems. While the effectiveness of particle filters has been recognised in various applications, their performance relies on the knowledge of dynamic models and measurement models, as well as the construction of effective proposal distributions. An emerging trend involves constructing components of particle filters using neural networks and optimising them by gradient descent, and such data-adaptive particle filtering approaches are often called differentiable particle filters. Due to the expressiveness of neural networks, differentiable particle filters are a promising computational tool for performing inference on sequential data in complex, high-dimensional tasks, such as vision-based robot localisation. In this paper, we review recent advances in differentiable particle filters and their applications. We place special emphasis on different design choices for key components of differentiable particle filters, including dynamic models, measurement models, proposal distributions, optimisation objectives, and differentiable resampling techniques.
Metaverse Risk Perceptions of Gifted Students

This research aims to examine the views of gifted secondary school students about the metaverse and their risk perceptions toward the digital environment. In this context, the study group consisting of 55 gifted students was determined through purposive sampling. A survey was used to collect participants' views on the metaverse, and the "Virtual World Risk Perception Scale" was used to measure the risks they felt in digital environments. A mixed method was used in this research. Content analysis was used in the qualitative data analysis, and the ANOVA test and t-test were used in the quantitative data analysis. The findings obtained in the analysis showed that while students defined the metaverse as a virtual universe, virtual reality, commerce, and socializing place. It was seen that the place where they met this concept was social media. While the students stated that the use of metaverse in education could have advantages, such as motivation, fun learning experience, and contribution to personal development, they mentioned disadvantages, such as health problems, addiction, safety, ethics, and disconnection from real life. Virtual world risk perceptions did not differ according to gender and class level. Students who did not have metaverse experience stated that the digital environment had a risk regarding corruption compared to those with experience.
Adaptive K-means clustering based under-sampling methods to solve the class imbalance problem

In the field of machine learning, the issue of class imbalance is a common problem. It refers to an imbalance in the quantity of data collected, where one class has a significantly larger number of data compared to another class, which can negatively affect the classification efficiency of algorithms. Under-sampling methods address class imbalance by reducing the quantity of data in the majority class, thereby achieving a balanced dataset and mitigating the class imbalance problem. Traditional under-sampling methods based on k-means clustering either set the unified value of k (number of clusters) or determine it directly based on the quantity of data in the minority or majority class. This paper proposes an adaptive k-means clustering under-sampling algorithm that calculates an appropriate k for each dataset. After clustering the majority class dataset into k clusters, our algorithm calculates the distances between the data within each cluster and the cluster centroids from two perspectives and selects data based on these distances. Subsequently, the subset of the majority class dataset are combined with the minority class dataset to generate a new balanced dataset, which is then used for classification algorithms. The performance of our algorithm is evaluated on 45 datasets. Experimental results demonstrate that our algorithm can dynamically determine appropriate k for different datasets and output a balanced dataset, thus enhancing the classification efficiency of machine learning algorithms. This work can provide new algorithmic ensemble strategies for addressing class imbalance problem.
Energy and Orientation Maps for Interactive Visualization and Retrieval of Ancient Coins

We propose a model to represent quasi-flat objects, such as ancient coins. These objects are flat surfaces, meaning their length and their width largely exceed their height, and feature a distinctive relief. This relief characterizes the object and its perception is directly influenced by the position of the object, the light direction and the viewer’s direction. Our model is a single (non classic) image representation containing the underlying structural variations of the object. This model, that we call “Multi-Light Energy Map”, is constructed out of several classic images taken with several illumination directions without computing the object’s surface normals. Together with this information about the magnitude of the object variations, it is possible to compute the information about the angular part, that we call “Multi-Light Orientation Map”. Using these two maps, it is possible to render an image of the object at any light azimuth, enabling the user to move the light sources during the visualization of the object. Moreover, these maps can be useful for the retrieval of ancient coins, either by robust image registration, or by object recognition using either contours or textures.
Multi-Agent Planning and Diagnosis with Commonsense Reasoning

In multi-agent systems, multi-agent planning and diagnosis are two key subfields – multi-agent planning approaches identify plans for the agents to execute in order to reach their goals, and multi-agent diagnosis approaches identify root causes for faults when they occur, typically by using information from the multi-agent planning model as well as the resulting multi-agent plan. However, when a plan fails during execution, the cause can often be related to some commonsense information that is neither explicitly encoded in the planning nor diagnosis problems. As such existing diagnosis approaches fail to accurately identify the root causes in such situations.
Cultural Heritage Enhancement through Digital Storytelling and Context-Aware Recommender System

The employment of Information and Communication Technologies helps Cultural Heritage enhancement and allows users’ cultural experience improvement. In particular, multimedia content employment enables better involvement through Storytelling techniques aimed at capturing the users’ attention. Moreover, to guarantee the personalization of the cultural experience, Digital Storytelling requires integrating tools for identifying the users’ preferences in the specific environment in which the cultural experience evolves. Therefore, this paper aims to introduce an architecture in which a Context-Aware Recommender System is able to provide personalized paths in which each Point of Interest is presented according to Digital Storytelling techniques. The proposed architecture is validated via two experimental phases: the first aimed to evaluate the accuracy of the recommender system, and the second based on user experience. Obtained results are promising.
Learning to Coordinate with Anyone

In open multi-agent environments, the agents may encounter unexpected teammates. Classical multi-agent learning approaches train agents that can only coordinate with seen teammates. Recent studies attempted to generate diverse teammates in order to enhance the generalizable coordination ability, but were restricted by pre-defined teammates. In this work, our aim is to train agents with strong coordination ability by generating teammates that fully cover the teammate policy space, so that agents can coordinate with any teammates. Since the teammate policy space is too huge to be enumerated, we find only dissimilar teammates that are incompatible with controllable agents, which highly reduces the number of teammates that needed to be trained with. However, it is hard to determine the number of such incompatible teammates beforehand. We therefore introduce a continual multi-agent learning process, in which the agent learns to coordinate with different teammates until no more incompatible teammates can be found. The above idea is implemented in the proposed Macop (Multi-agent compatible policy learning) algorithm. We conduct experiments in 8 scenarios from 4 environments that have distinct coordination patterns. Experiments show that Macop generates training teammates with much lower compatibility than previous methods. As a result, in all scenarios Macop achieves the best overall coordination ability while never significantly worse than the baselines, showing strong generalization ability.
Towards Face De-identification for Wearable Cameras

Wearable cameras provide valuable new sources of data for health and wellness monitoring, however, such visual data brings privacy concerns. This paper proposes a prototype egocentric face de-identification system for wearable camera images by swapping the original faces with synthetic faces. The motivation of this paper is to: (1) de-identify faces in egocentric images and (2) preserve the existence of each identity in images where the source identity is altered. The system incorporates our proposed method, which promises a privacy-aware and cost-effective approach. We evaluated the system on the Ego4D audio-visual PoV diarization training set by analysing six activities where faces are visible in wearable camera data. The results show promising de-identification on the source faces while most existences remain.
New Stage Aesthetics in the Digital Age

The new style Peking opera, The Imperial Concubine of the Tang Dynasty, staged by the Shanghai Peking Opera Theatre and the dance drama, The Never Vanishing Radio Wave, staged by Shanghai Song and Dance Ensemble, are the most acclaimed theatrical performances in 2019 in Shanghai. Apart from the charm of the performers, the two plays' appropriate application of digital multimedia and the virtual audio and video designed in line with the plot and the acting also greatly added to the power impacting the audience and enriched the language for stage performance. One example is replacing the traditional physical scenes and property on the stage with high-tech dynamic pictures on large screens, which resulted in giving the new Peking opera The Imperial Concubine of the Tang Dynasty more flexibility for stage performance, and giving a movie-like quality to the dance drama The Never Vanishing Radio Wave, which depicts the espionage war between the Communists (CCP) and the Kuomintang (KMT) in the late 1940s. The use of digital multimedia has broken through the traditional concept of time and space and promoted the development of 3D presentation on the stage. It is the result of merging various artistic forms and technologies in creative experimentation with new modes of performance and has successfully brought to the audience a new aesthetic experience in the 21st century theatre.
Adapting Video Instance Segmentation for Instance Search

Modern applications of instance search face many challenges. Real-life applications are often complex, containing a large variety of object classes. Furthermore, the same object can be subject to large appearance variations due to viewpoint changes, partial occlusion, and different geometric transformations. Much of the research up to now has focused on narrow instance search applications for rigid and near-planar objects with a small number of queries, such as searching for landmarks or brand logos. In this paper, we adapt YouTube-VIS, a large dataset for video instance segmentation, for the instance search task. The dataset contains 8,430 unique queries and 40 different object classes, where many objects inhibit viewpoint variations and occlusion. We present a two-stage architecture to account for the difference between the two tasks of instance search and video instance segmentation. The first stage performs instance segmentation to separate the instances from the background, while the second stage encodes the isolated instances using a fine-tuned CLIP image encoder. Despite our relatively simple architecture, we achieve promising results without performing any post-processing steps such as query expansion or re-ranking.
On the stability, correctness and plausibility of visual explanation methods based on feature importance

In the field of Explainable AI, multiples evaluation metrics have been proposed in order to assess the quality of explanation methods w.r.t. a set of desired properties. In this work, we study the articulation between the stability, correctness and plausibility of explanations based on feature importance for image classifiers. We show that the existing metrics for evaluating these properties do not always agree, raising the issue of what constitutes a good evaluation metric for explanations. Finally, in the particular case of stability and correctness, we show the possible limitations of some evaluation metrics and propose new ones that take into account the local behaviour of the model under test.
SACAT: Student-Adaptive Computerized Adaptive Testing

Computerized Adaptive Testing refers to an online student ability testing system. It consists of Selection Algorithm and Cognitive Diagnosis Module (CDM). The Selection Algorithm aims to carefully select a small set of questions for the newly arrived student to answer, and the CDM aims to infer the ability of the newly arrived student based on his answers to the selected questions. The CDM faces the challenge of inferring student ability based on the short question-answer sequence. The state-of-the-art approach employs a meta-learning framework to solve this challenge. Specifically, in the outer-layer of the meta-learning framework, it learns an average student ability from all students as the initial ability for the newly arrived student, while in the inner-layer of the meta-learning framework, it infers the newly arrived student’s ability from the short sequence. However, this approach suffers from two major limitations: Firstly, collaborative information between the newly arrived student and all students was not utilized. Secondly, the meta-learning framework has a slow convergence. This paper introduces Student-Adaptive Computerized Adaptive Testing (SACAT), which addresses these issues by: Firstly, use the short sequence of the newly arrived student to retrieve similar students from all students. Secondly, design a fast adaptive function that can adaptively generate an ability representation suitable for the newly arrived student from abilities of similar students. Through extensive experiments on three real-world student response datasets, we validate that SACAT can outperform existing CAT methods. Furthermore, through the fast adaptation, we show that SACAT achieves fast model convergence.
Assessing the difficulty of predicting media memorability

Memorability is a critical aspect of human cognition that has been studied extensively in various fields, including psychology, education, and computer vision. The ability to remember information and experiences over time is essential for learning, decision-making, and creating lasting impressions. While the number of computer vision works that attempt to predict the memorability score of videos has recently seen a significant boost, thanks to several benchmarking tasks and datasets, some questions related to the performance of automated systems on certain types of videos are still largely unexplored. Given this, we are interested in discerning what makes a video sample easy or hard to classify or predict from a memorability standpoint. In this paper, we use a large set of runs, created and submitted by the participants to the MediaEval Predicting Video Memorability task, and, using their results and a set of visual, object, and annotator-based features and analyses, we attempt to find and define common traits that make the memorability scores of videos hard or easy to predict.
Auditing for Federated Learning: A Model Elicitation Approach

Federated learning provides a promising paradigm for collecting machine learning models from distributed data sources without compromising users’ data privacy. The success of a credible federated learning system builds on the assumption that the decentralized and self-interested users will be willing to participate to share their local models in a trustworthy way. However, without proper incentives, users might simply opt out of the contribution cycle or will be mis-incentivized to contribute spam/false information. This paper introduces solutions to audit the truthful reporting of a local, user-side machine learning model for federated learning. Our results build on the literature of information elicitation but focus on the questions of verifying machine learning models (rather than human predictions). We provide a scoring rule-based framework that verifies and audits reporting of local models such that truthfully reporting is a Bayesian Nash Equilibrium. We study the market implementation, accuracy as well as robustness properties of our proposed solution. We verify the effectiveness of our methods using MNIST and CIFAR-10 datasets. Particularly we show that by reporting low-quality hypotheses, users will receive decreasing scores (rewards, payment, or higher punishment).
Action Text Diffusion Prior Network for Action Segmentation

Action segmentation is a challenging task that requires accurate parsing and labeling of each action. There are two types of methods for action segmentation. The first type primarily focuses on extracting high-quality features from videos, while the second type focuses on combining textual and perceptual features through multimodal fusion. However, both types of methods have their limitations. The first type is limited to a single modality and does not leverage multimodal information, while the second type, although promising, is restricted by the language used to describe the actions in the texts. To solve these problems, we propose in this paper, an Action Text Diffusion Prior Network (ATDPN) which simultaneously improves the quality of the extracted visual features (by introducing a Video-level Diffusion Prior Sampling) and integrates the textual information to fullest extent. This leads to superior action segmentation results. Our experiments performed on GTEA dataset demonstrate the effective feature extraction ability of ATDPN.
Retrieved Generative Captioning for Medical Images

Understanding the content of medical images and mapping it into text is a very trending topic in intersection of two main domains; computer vision and natural language processing. This is known as medical image captioning, which plays a vital role in developing automatic systems for diagnosis purposes. Recent research in the medical field provided promising results for both deep-learning based and retrieval-based models for image captioning. However, each one of them has its own drawbacks, that can be overcome if combined. In addition, existing diagnosis systems are still not able to provide enough explanation about the findings, which might be similar to what a physician can deliver. In this regard, we present in this paper a combination of a generative deep-learning based method and a retrieval-based model for medical image captioning. First, we train an attention-based encoder-decoder model to generate new captions for given medical images. Then, we fit the generated caption from the generative model to the retrieval-based model, which retrieves the most similar caption from the training database. This multi-stage approach allows us to generate most important words of the caption (with the generative model) and then search for the most close caption that includes such words (with the retrieval-based model). Another way of combining both models is by selecting at each time the caption with highest score among generated and retrieved captions. We evaluate our proposed model on the medical ROCO dataset for which we achieved a BLEU-4 score of 07.89 for the radiology class and 03.19 for the out-of-class data, for the multi-stage model. Similarly, best results were achieved for the fused model (predicted caption is the best among generated and retrieved) where we obtain a BLEU-4 values of 18.61 for the radiology class and 13.28 for the out-of-class data. Even though our results seem to be low, they outperformed the state-of-the-art results on the same dataset and could be further improved.
Explainable face verification for video archive documentation

Film and video archives hold vast collections of historically relevant content, and the (co-)appearance of specific persons is often one of the most interesting properties. However, while archive documentation does not always contain metadata which person is depicted in the content (and if so, where), it is often desired to obtain a list of persons related to the entire broadcast. We propose to obtain a set of sample images from web searches, and then perform a face verification task using an AI-based method to determine whether (and where) a person appears in the content. We analyse the influence of different annotation conditions and types of errors in automated face sample retrieval on the end-to-end performance of the face verification process. To give the user insights into the success or failure of the verification, we apply a recent explainable face verification method. Thereby we investigate how the explainability performs on a completely different dataset.
Wasserstein loss for Semantic Editing in the Latent Space of GANs

The latent space of GANs contains rich semantics reflecting the training data. Different methods propose to learn edits in latent space corresponding to semantic attributes, thus allowing to modify generated images. Most supervised methods rely on the guidance of classifiers to produce such edits. However, classifiers can lead to out-of-distribution regions and be fooled by adversarial samples. We propose an alternative formulation based on the Wasserstein loss that avoids such problems, while maintaining performance on-par with classifier-based approaches. We demonstrate the effectiveness of our method on two datasets (digits and faces) using StyleGAN2. Code is available at: https://github.com/perladoubinsky/latent-wasserstein