Reviewerly/exHarmony huggingface dataset:
Characterizing Cyber Attacks against Space Systems with Missing Data: Framework and Case Study

Cybersecurity of space systems is an emerging topic, but there is no single dataset that documents cyber attacks against space systems that have occurred in the past. These incidents are often scattered in media reports while missing many details, which we dub the missing-data problem. Nevertheless, even “low-quality” datasets containing such reports would be extremely valuable because of the dearth of space cybersecurity data and the sensitivity of space systems which are often restricted from disclosure by governments. This prompts a research question: How can we characterize real-world cyber attacks against space systems? In this paper, we address the problem by proposing a framework, including metrics, while also addressing the missing-data problem, by extrapolating” the missing data in a principled fashion. To show the usefulness of the framework, we extract data for 72 cyber attacks against space systems and show how to extrapolate this “low-quality” dataset to derive 4,076 attack technique kill chains. Our findings include: cyber attacks against space systems are getting increasingly sophisticated; and, successful protection against on-path and social engineering attacks could have prevented 80% of the attacks.
Cross-modality Representation Interactive Learning for Multimodal Sentiment Analysis

Effective alignment and fusion of multimodal features remain a significant challenge for multimodal sentiment analysis. In various multimodal applications, the text modal exhibits a significant advantage of compact yet expressive representation ability. In this paper, we propose a Cross-modality Representation Interactive Learning (CRIL) approach, which adopts the text modality to guide other modalities for learning representative feature tokens, contributing to effective multimodal fusion in multimodal sentiment analysis. We propose a semantic representation interactive learning module to learn concise semantic representation tokens for audio and video modalities under the guidance of the text modality, ensuring semantic alignment of representations among multiple modalities. Furthermore, we design a semantic relationship interactive learning module, which calculates a self-attention matrix for each modality and controls their consistency to enable the semantic relationship alignment for multiple modalities. Finally, we present a two-stage interactive fusion solution to bridge the modality gap for multimodal fusion and sentiment analysis. Extensive experiments are performed on the CMU-MOSEI, CMU-MOSI, and UR-FUNNY datasets, and experiment results demonstrate the effectiveness of our proposed approach.
SecureImgStego: A Keyed Shuffling-based Deep Learning Model for Secure Image Steganography

Steganography ensures secure transmission of digital messages, including image steganography where a secret image is hidden within a non-secret cover image. Deep learning-based methods in image steganography have recently gained popularity but are vulnerable to various attacks. An adversary with varying levels of access to the vanilla deep steganography model can train a surrogate model using another dataset and retrieve hidden images. Moreover, even when uncertain about the presence of hidden information, the adversary with access to the surrogate model can distinguish the carrier image from the unperturbed one. Our paper includes such attack demonstrations that confirm the inherent vulnerabilities present in deep learning-based steganography. Deep learning-based steganography lacks lossless transmission assurance, rendering sophisticated image encryption techniques unsuitable. Furthermore, key concatenation-based techniques for text data steganography fall short in the case of image data. In this paper, we introduce a simple yet effective keyed shuffling approach for encrypting secret images. We employ keyed pixel shuffling, multi-level block shuffling, and a combination of key concatenation and block shuffling, embedded within the model architecture. Our findings demonstrate that the block shuffling-based deep image steganography has negligible error overhead compared to conventional methods while providing effective security against adversaries with different levels of access to the model. We extensively evaluate our approach and compare it with existing methods in terms of human perceptibility, key sensitivity, adaptivity, cover image availability, keyspace, and robustness against steganalysis
Empirical Analysis of Software Vulnerabilities Causing Timing Side Channels

Timing attacks are considered one of the most damaging side-channel attacks. These attacks exploit timing fluctuations caused by certain operations to disclose confidential information to an attacker. For instance, in asymmetric encryption, operations such as multiplication and division can cause time-varying execution times that can be m-treated to obtain an encryption key. Whilst several efforts have been devoted to exploring the various aspects of timing attacks, particularly in cryptography, little attention has been paid to empirically studying the timing attack-related vulnerabilities in non-cryptographic software. By inspecting these software vulnerabilities, this study aims to gain an evidence-based understanding of weaknesses in non-cryptographic software that may help timing attacks succeed. We used qualitative and quantitative research approaches to systematically study the timing attack-related vulnerabilities reported in the National Vulnerability Database (NVD) from March 2003 to December 2022. Our analysis was focused on the modifications made to the code for patching the identified vulnerabilities. We found that a majority of the timing attack-related vulnerabilities Were introduced due to not following known secure coding practices. The findings of this study are expected to help the software security community gain evidence-based information about the nature and causes of the vulnerabilities related to timing attacks.
The Tables Have Turned: GPT-3 Distinguishing Passwords from Honeywords

In the field of information security, there has been a noteworthy trend toward leveraging machine learning models to develop and exploit security solutions. The emergence of Generative Pre-trained Transformer: version 3 (GPT-3), a pre-trained language model developed by OpenAI, has generated considerable excitement due to its unprecedented ability to generate different solutions. In the realm of timely detecting threats on a password-file, the generation of realistic yet fictitious passwords or honeywords has long been recognized as a crucial aspect of security solutions. However, meeting this requirement has proven to be a persistent challenge. In the face of this crisis, researchers have recently proposed employing GPT-3 as a means to surpass this barrier. This paper presents an analysis of how GPT-3 can potentially undermine the effectiveness of this security solution by accurately distinguishing genuine passwords from a set of honeywords it generates. The experiments conducted for this study reveal that GPT-3 can accurately guess a significant percentage of actual passwords, reaching as high as 53.45% with just three attempts. Though we emphasize the careful use of GPT-3 for generating honeywords, one of the primary findings in this study strongly indicates that GPT-3 can effectively be transformed into an attack mechanism, thus altering the dynamics of the present notion.
Prediction of Heart Attack and Death: Comparison Between 1 DCNN and Conventional ML Approaches

Since heart diseases influence human life significantly, so rigorous investigations and experiments are needed for the treatment of patients in the early stage. Physicians could treat after identifying the heart diseases. After getting related information about the human body, immediate prediction of heart stock is awakened by the physicians for appropriate treatment and reduce the chances of a heart attack. Again, if the probability of death due to a heart attack is detected, then the treatment may help to recover the patients. To detect heart diseases, machine learning and deep learning approaches can be coded. In this study, two datasets are experimented with conventional machine learning algorithms and deep learning algorithms. One dataset is related to the prediction of chances of heart attack and another dataset is related to the prediction of death due to heart stock. Among all approaches, the one-dimensional convolutional neural network has been shown 99.9% accuracy of prediction. More experiments on heart diseases using different machine learning approaches can conclude the appropriate model for detecting heart diseases, whereas, in this study, the one-dimensional convolutional neural network has given the best performance.
Harvesting Event Schemas from Large Language Models

Event schema provides a conceptual, structural and formal language to represent events and model the world event knowledge. Unfortunately, it is challenging to automatically induce high-quality and high-coverage event schemas due to the open nature of real-world events, the diversity of event expressions, and the sparsity of event knowledge. In this paper, we propose a new paradigm for event schema induction – knowledge harvesting from large-scale pre-trained language models, which can effectively resolve the above challenges by discovering, conceptualizing and structuralizing event schemas from PLMs. And an Event Schema Harvester (ESHer) is designed to automatically induce high-quality event schemas via in-context generation-based conceptualization, confidence-aware schema structuralization and graph-based schema aggregation. Empirical results show that ESHer can induce high-quality and high-coverage event schemas on varying domains.
Feature Enhanced Structured Reasoning for Question Answering

Answering complex natural language questions requires comprehensive reasoning about the question context and related knowledge. There are two main problems with the existing LM (language model) +KG (knowledge graph) methods. Firstly, they ignore the impact of negative words on Q &A inference. Secondly, they do not consider the effects of contextual entities on relation weight. Taking into account the above issues, we propose a method for Feature Enhanced Structured Reasoning (FESR) that exploits a two-branch graph neural network to improve the structured reasoning ability of question answering. Specifically, FESR first sets feature constraints and changes the attention scores between nodes, thereby strengthening the processing of negative-type question answering, and then optimizes the relation weights to enhance the effect of relations on question-answering inference by introducing contextual entities. We evaluate our model on three datasets in the fields of commonsense reasoning and medical question answering, and the experimental results indicate the effectiveness of our method.
Fall Detection Using HOG Feature Extraction and Adaptive Boosting Technique

This research holds significant importance as it focuses on the development of a reliable and accurate fall detection system, addressing a critical need for the elderly and individuals with disabilities who are more vulnerable to fall-related incidents. The objective of this study is to utilize the Fall Detection Dataset from Kaggle to create an effective fall detection system using the Histogram of Oriented Gradients (HOG) method for feature extraction from accelerometer and gyroscope measurements. To enhance accuracy, various classification algorithms, such as LogisticRegression, K-nearest neighbors (KNN), and decision tree (DT), are explored. Additionally, ensemble learning AdaBoosting along with cross-validation techniques are employed to further improve the model's performance. By overcoming the limitations of existing fall detection systems, this research aims to provide valuable insights and contribute to the development of trustworthy fall detection systems that can offer timely assistance and enhance the safety and well-being of vulnerable individuals.
USE OF VIDEO STEGANOGRAPHY FOR REALIZATION OF CONFIDENTIAL INFORMATION TRANSMISSION CHANNELS

Today Internet and digital media are very popular, so to prevent secret information from unwanted people, there is requirement to transmit a data more securely. Video steganogra-phy is to hide the existence of the message from unauthorized party using video as cover file and hiding data in video. Data hiding is the process of embedding information in a video without changing its perceptual quality and also keep away from knowledge of existence of message. The article investigates creation and transformation of video steganographic channels via social network – Facebook, Youtube, cloud storage – Google Photos, Google Drive, WhatsApp, Telegram, Viber and Line. Keywords: video steganography, digital steganography, confidential information.
Utilization of Virtual Reality for Docker Learning

Technological developments in the last few decades have increased rapidly and have affected the education sector. Currently, learning generally uses conventional methods such as using books or monitor screens to study learning material. Virtual reality technology opens up opportunities for more interactive learning so as to increase learning effectiveness. Docker learning materials have a lot of architecture that needs to be understood so that virtual reality can make learning more interactive. The purpose of this research is to find a way to build virtual reality applications to help docker learning and find out the effectiveness of this application. The virtual reality application was developed using the ADDIE method and at the development stage, the Luther-Sutopo media development model was used. Virtual reality applications are built with Unity and use the XR Interaction Toolkit package as a package for virtual reality. The tests that have been carried out resulted in 60 percent of the respondents feeling that the application helps in understanding docker learning and the remaining 40 percent feel that the application is very helpful in understanding docker learning. 90 percent of respondents who have tested the application stated that this application has great potential in the world of education and can be developed further. Based on the input from the test results, the application can be further developed for future research. The results indicate that the application of Docker-based learning using virtual reality can enhance understanding of Docker-related topics. Furthermore, this research underscores the potential of integrating virtual reality within the education sector to enhance learning materials, particularly those demanding advanced visualization techniques.
Bioinspired Encoder–Decoder Recurrent Neural Network with Attention for Hydroprocessing Unit Modeling

The hydroprocessing technique is used to refine crude oil and produce lighter, valuable products. Developing models of these units is crucial for predicting the process dynamics and facilitating optimization and control. In this research, we develop attention-based encoder–decoder recurrent neural network (A-ED-RNN) models, employing various RNN cells such as bioinspired neural circuit policies (NCPs), gated recurrent unit (GRU), and long short-term memory (LSTM), to predict diesel and jet production rates within an industrial hydroprocessing unit. A key innovation is integrating the NCP into the A-ED-RNN models, harnessing its advanced computational power to attain enhanced performance with a smaller model size compared to that of GRU and LSTM cells. The developed RNN models effectively capture the dynamics of diesel and jet production, surpassing the traditional data-driven models. Notably, the NCP-based A-ED-RNN model demonstrates superior memory efficiency and predictive ability, standing out among all of the developed RNN models, underscoring its potential for modeling complex processes.
MistNet: A superior edge-cloud privacy-preserving training framework with one-shot communication

Classical federated learning methods aggregate decentralized data from different devices into a central location for efficient training. However, these approaches raise significant concerns regarding data privacy and requires extensive communication between edge devices and the cloud, leading to high communication costs. To conquer these challenges, we propose an edge-cloud privacy-preserving model training system that enables the cloud and edge devices to collaboratively perform deep neural network(DNN) training, namely MistNet, which has the following advantages. (1) Different from previous works that required frequent communication between the edge and cloud, MistNet requires only one-shot communication. (2) MistNet introduces the rigorous local differential privacy (LDP) technique to the intermediate feature maps, guaranteeing that the user’s local data and model parameters remain undisclosed. (3) In MistNet, the feature extractor is transferred from pre-trained models that are designed for similar application domains. This feature extractor remains fixed during training, thereby eliminating the requirement to synchronize feature extractors across different devices. Furthermore, we first design an object detection algorithm based on Yolov5 with the MistNet framework. Finally, the extensive experiments results on multiple models and datasets demonstrate that by choosing an appropriate partition layer and privacy budget, MistNet achieves lower communication and faster convergence, as well as acceptable model utility while greatly reducing privacy leakage from the released intermediate features. Our framework is primarily designed for image data and has already been applied to Plato. You can find the source code on GitHub page at https://github.com/TL-System/plato.
ImEW: A Framework for Editing Image in the Wild

The ability to edit images in a realistic and visually appealing manner is a fundamental requirement in various computer vision applications. In this paper, we present ImEW, a unified framework designed for solving image editing tasks. ImEW utilizes off-the-shelf foundation models to address four essential editing tasks: object removal, object translation, object replacement, and generative fill beyond the image frame. These tasks are accomplished by leveraging the capabilities of state-of-the-art foundation models, namely the Segment Anything Model, Grounding DINO, LaMa, and Stable Diffusion. These models have undergone extensive training on large-scale datasets and have exhibited exceptional performance in understanding image context, object manipulation, and texture synthesis. Through extensive experimentation, we demonstrate the effectiveness and versatility of ImEW in accomplishing image editing tasks across a wide range of real-world scenarios. The proposed framework opens up new possibilities for realistic and visually appealing image editing and enables diverse applications requiring sophisticated image modifications. Additionally, we discuss the limitations and outline potential directions for future research in the field of image editing using off-the-shelf foundation models, enabling continued advancements in this domain.
NFT-enabled organization design: prospects, promises and hurdles

Abstract Non-fungible tokens (NFTs) are digital assets based on blockchain technology that are increasingly being used for various applications in organizations. Given NFTs’ unique technological features, we posit that traditional, centralized organizations can adopt them to introduce novel solutions to the fundamental problems of organizing, namely, division of labor and integration of efforts. We examine the prospects and promises of NFT-enabled organization design and suggest how organizations can navigate its potential hurdles. We discuss critical boundary conditions for the deployment of NFTs in organization design and conclude by articulating how our Point of View article contributes to scholarship on blockchain technology and decentralized autonomous organizations (DAOs).
A Multiple Object Tracker with Spatiotemporal Memory Network

Target tracking is an important application of unmanned aerial vehicles (UAVs). The template is the identity of the target and has a great impact on the performance of target tracking. Most methods only keep the latest template of the target, which is intuitive and convenient but has poor ability to resist the change of target appearance, especially to reidentify a target that has disappeared for a long time. In this paper, we propose a practical multiobject tracking (MOT) method, which uses historical information of targets for better adapting to appearance variations during tracking. To preserve the spatial-temporal information of the target, we introduce a memory pool to store masked feature maps at different moments, and precise masks are generated by a segmentation network. Meanwhile, we fuse the feature maps at different moments by calculating the pixel-level similarity between the current feature map and the masked historical feature maps. Benefiting from the powerful segmentation features and the utilization of historical information, our method can generate more accurate bounding boxes of the targets. Extensive experiments and comparisons with many trackers on MOTS, MOT17, and MOT20 demonstrate that our method is competitive. The ablation study showed that the introduction of memory improves the multiobject tracking accuracy (MOTA) by 2.1.
Explicit State Representation Guided Video-based Pedestrian Attribute Recognition

The pedestrian attribute recognition aims to generate a structured description of pedestrians, which serves an important role in surveillance. Current works usually assume that the images and the specific pedestrian states, including pedestrian occlusion and pedestrian orientation, are given. However, we argue that the current works ignore the guidance of the pedestrian state and cannot achieve the appropriate performance since the appearance feature will become unreliable due to the variance of the pedestrian state, which is common in practice. Therefore, this paper proposes the Explicit State Representation (ExSR) Guided Pedestrian Attribute Recognition to improve the accuracy through state learning and attribute fusion among frames. Firstly, the pedestrian state is explicitly represented by concatenating the pedestrian orientation and occlusion, which can be accurately determined via analyzing the pose. Secondly, the state-aware pedestrian attribute fusion method is proposed and divided into two cases, namely the inter-state case and the intra-state case. In the intra-state case, the appearance feature will remain stable and the attribute relations are propagated to refine. The method of exploiting attribute relations within a single frame is the Graph Neural Network. In the inter-state case, the state changes, the attribute relationship propagation is prevented, and the advantages of attribute recognition in each frame are complemented to make a reliable judgment on the invisible region. The experimental results demonstrate that the ExSR outperforms the state-of-the-art methods on two public databases, benefiting from the explicit introduction of the state into the attribute recognition.