Reviewerly/exHarmony huggingface dataset:
Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements?

This work investigates the effect of gender-stereotypical biases in the content of retrieved results on the relevance judgement of users/annotators. In particular, since relevance in information retrieval (IR) is a multi-dimensional concept, we study whether the value and quality of the retrieved documents for some bias-sensitive queries can be judged differently when the content of the documents represents different genders. To this aim, we conduct a set of experiments where the genders of the participants are known as well as experiments where the participants' genders are not specified. The set of experiments comprise of retrieval tasks, where participants perform a rated relevance judgement for different search query and search result document compilations. The shown documents contain different gender indications and are either relevant or non-relevant to the query. The results show the differences between the average judged relevance scores among documents with various gender contents. Our work initiates further research on the connection of the perception of gender stereotypes in users with their judgements and effects on IR systems, and aim to raise awareness about the possible biases in this domain.
MOBDrone: A Drone Video Dataset for Man OverBoard Rescue

Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an essential role in speeding up the identification and rescue of people who have fallen overboard, i.e., man overboard (MOB). To this end, Artificial Intelligence techniques can be leveraged for the automatic understanding of visual data acquired from drones. However, detecting people at sea in aerial imagery is challenging primarily due to the lack of specialized annotated datasets for training and testing detectors for this task. To fill this gap, we introduce and publicly release the MOBDrone benchmark, a collection of more than 125K drone-view images in a marine environment under several conditions, such as different altitudes, camera shooting angles, and illumination. We manually annotated more than 180K objects, of which about 113K man overboard, precisely localizing them with bounding boxes. Moreover, we conduct a thorough performance analysis of several state-of-the-art object detectors on the MOBDrone data, serving as baselines for further research.
The Effect of Social Media User Behaviors on Security and Privacy Threats

The number of online social network (OSN) users is increasing daily and attacks and threats against over the time spent on online networks has been increasing equally. Attacks against OSN users exploit not only system vulnerabilities but also user-induced vulnerabilities, which naturally affect the hacker&#x2019;s attack strategy as well. This study is designed to investigate the effect of social media user behaviors on their vulnerability level in terms of security and privacy. The study was conducted survey methods, which was applied to social media users in two countries -Turkey and Iraq. This study documents and analyzes the behaviors of 700 OSN users in two countries. This study examines the behaviors of social media users from two nationalities, investigating whether there is a relationship between social media users&#x2019; behaviors and security and privacy threats. Research findings demonstrate that there is a significant relationship between OSN users&#x2019; behaviors and their attitudes towards security and privacy. Additionally, Turkish social media users pay more attention to their behaviors in terms of privacy and security awareness than Iraq users.
Privacy-Preserving and Outsourced Multi-Party K-Means Clustering Based on Multi-Key Fully Homomorphic Encryption

The clustering algorithm is a useful tool for analyzing medical data. For instance, the k-means clustering can be used to study precipitating factors of a disease. In order to implement the clustering algorithm efficiently, data computation is outsourced to cloud servers, which may leak the private data. Encryption is a common method for solving this problem. But cloud servers are difficult to calculate ciphertexts from multiple parties. Hence, we choose multi-key fully homomorphic encryption (FHE), which supports computations on the ciphertexts that have different secret keys, to protect the private data. In this paper, based on Chen&#x0027;s multi-key FHE scheme, we first propose secure squared euclidean, comparison, minimum, and average protocols. Then, we design the basic and advanced schemes for implementing the secure multi-party k-means clustering algorithm. In the basic scheme, the implementation of homomorphic multiplication includes the process of transforming ciphertexts under different keys. In order to implement homomorphic multiplication efficiently, the advanced scheme uses an improved method to transform ciphertexts. Meanwhile, almost all computations are completely outsourced to cloud servers. We prove that the proposed protocols and schemes are secure and feasible. Simulation results also show that our improved method is helpful for improving the homomorphic multiplication of Chen&#x0027;s multi-key FHE scheme.
DP-CTGAN: Differentially Private Medical Data Generation Using CTGANs

Generative Adversarial Networks (GANs) are an important tool to generate synthetic medical data, in order to combat the limited and difficult access to the real data sets and accelerate the innovation in the healthcare domain. Despite their promising capability, they are vulnerable to various privacy attacks that might reveal information of individuals from the training data. Preserving privacy while keeping the quality of the generated data still remains a challenging problem. We propose DP-CTGAN, which incorporates differential privacy into a conditional tabular generative model. Our experiments demonstrate that our model outperforms existing state-of-the-art models under the same privacy budget on several benchmark data sets. In addition, we combine our method with federated learning, enabling a more secure way of synthetic data generation without the need of uploading locally collected data to a central repository.
Transformer With Bidirectional GRU for Nonintrusive, Sensor-Based Activity Recognition in a Multiresident Environment

Several techniques for human activity recognition (HAR) in a smart indoor environment have been developed and improved along with the rapid advancement of sensor technologies. However, recognizing multiple people’s activities is still challenging due to the complexity of their activities, such as parallel and collaborative activities. To address these challenges, we propose a transformer with a bidirectional gated recurrent unit (GRU) deep learning (DL) method, called TRANS-BiGRU, to efficiently learn and recognize different types of activities performed by multiple residents. We compare the proposed model with the state-of-the-art models and various DL models, such as Ensemble2LSTM (Ens2-LSTM), bidirectional GRUs (Bi-GRU), and traditional machine learning (ML) models, such as support vector machine (SVM). Our experimental results based on the center for advanced studies in adaptive system and ARAS public data sets show that our model significantly outperforms the existing models for complex activity recognition of multiple residents.
Knowledge Enhanced Reflection Generation for Counseling Dialogues

In this paper, we study the effect of commonsense and domain knowledge while generating responses in counseling conversations using retrieval and generative methods for knowledge integration.We propose a pipeline that collects domain knowledge through web mining, and show that retrieval from both domainspecific and commonsense knowledge bases improves the quality of generated responses.We also present a model that incorporates knowledge generated by COMET using soft positional encoding and masked self-attention.We show that both retrieved and COMETgenerated knowledge improve the system's performance as measured by automatic metrics and by human evaluation.Lastly, we present a comparative study on the types of knowledge encoded by our system, showing that causal and intentional relationships benefit the generation task more than other types of commonsense relations.
Query-Based Image Retrieval Using SVM

In today's world, images are used to extract information about objects in a variety of industries. To retrieve images, many traditional methods have been used. It determines the user's query interactively by asking the user whether the image is relevant or not. Graphics have become an important part of information processing in this day and age. The image is used in image registration processing to extract information about an item in a variety of fields such as tourism, medical and geological, and weather systems calling. There are numerous approaches that people use to recover images. It determines an individual's query interactively by asking users whether the image is relevant (similar) or not. In a content-based image retrieval (CBIR) system, effective management of this image database is used to improve the procedure's performance. The study of the content-based image retrieval (CBIR) technique has grown in importance. As individuals, we have studied and investigated various features in this manner or in combinations. We discovered that image Registration Processing (IRP) is a critical area in the aforementioned industries. Several research papers examining color feature and texture feature extraction concluded that point cloud data structure is best for image registration using the Iterative Closest Point (ICP) algorithm.
Interval Type-3 Fuzzy Aggregators for Ensembles of Neural Networks in Time Series Prediction

In this article we are presenting an approach for fuzzy aggregation in ensembles of neural networks for forecasting. The aggregator in an ensemble is used to combine the outputs of the networks forming the ensemble, in such a way that the total output is better than the outputs of the individual modules. In our approach a fuzzy system is used to estimate the weights that will be assigned to the outputs in the process of combining them in a weighted average calculation. The uncertainty in the process of aggregation is modeled with interval type-3 fuzzy, which in theory can outperform type-2 and type-1. Results of the Dow Jones time series show the potential of the approach to outperform other aggregators.
ELLE: Efficient Lifelong Pre-training for Emerging Data

Current pre-trained language models (PLM) are typically trained with static data, ignoring that in real-world scenarios, streaming data of various sources may continuously grow.This requires PLMs to integrate the information from all the sources in a lifelong manner.Although this goal could be achieved by exhaustive pretraining on all the existing data, such a process is known to be computationally expensive.To this end, we propose ELLE, aiming at efficient lifelong pre-training for emerging data.Specifically, ELLE consists of (1) function preserved model expansion, which flexibly expands an existing PLM's width and depth to improve the efficiency of knowledge acquisition; and (2) pre-trained domain prompts, which disentangle the versatile knowledge learned during pretraining and stimulate the proper knowledge for downstream tasks.We experiment ELLE with streaming data from 5 domains on BERT and GPT.The results show the superiority of ELLE over various lifelong learning baselines in both pre-training efficiency and downstream performances.
A Meta Survey of Quality Evaluation Criteria in Explanation Methods

The evaluation of explanation methods has become a significant issue in explainable artificial intelligence (XAI) due to the recent surge of opaque AI models in decision support systems (DSS). Explanations are essential for bias detection and control of uncertainty since most accurate AI models are opaque with low transparency and comprehensibility. There are numerous criteria to choose from when evaluating explanation method quality. However, since existing criteria focus on evaluating single explanation methods, it is not obvious how to compare the quality of different methods. In this paper, we have conducted a semi-systematic meta-survey over fifteen literature surveys covering the evaluation of explainability to identify existing criteria usable for comparative evaluations of explanation methods. The main contribution in the paper is the suggestion to use appropriate trust as a criterion to measure the outcome of the subjective evaluation criteria and consequently make comparative evaluations possible. We also present a model of explanation quality aspects. In the model, criteria with similar definitions are grouped and related to three identified aspects of quality; model, explanation, and user. We also notice four commonly accepted criteria (groups) in the literature, covering all aspects of explanation quality: Performance, appropriate trust, explanation satisfaction, and fidelity. We suggest the model be used as a chart for comparative evaluations to create more generalisable research in explanation quality.
A Systematic Literature Review on Automotive Digital Forensics: Challenges, Technical Solutions and Data Collection

A modern vehicle has a complex internal architecture and is wirelessly connected to the Internet, other vehicles, and the infrastructure. The risk of cyber attacks and other criminal incidents along with recent road accidents caused by autonomous vehicles calls for more research on automotive digital forensics. Failures in automated driving functions can be caused by hardware and software failures and cyber security issues. Thus, it is imperative to be able to determine and investigate the cause of these failures, something which requires trustable data. However, automotive digital forensics is a relatively new field for the automotive where most existing self-monitoring and diagnostic systems in vehicles only monitor safety-related events. To the best of our knowledge, our work is the first systematic literature review on the current research within this field. We identify and assess over 300 papers published between 2006–2021 and further map the relevant papers to different categories based on identified focus areas to give a comprehensive overview of the forensics field and the related research activities. Moreover, we identify forensically relevant data from the literature, link the data to categories, and further map them to required security properties and potential stakeholders. Our categorization makes it easy for practitioners and researchers to quickly find relevant work within a particular sub-field of digital forensics. We believe our contributions can guide digital forensic investigations in automotive and similar areas, such as cyber-physical systems and smart cities, facilitate further research, and serve as a guideline for engineers implementing forensics mechanisms.
Differentially Private <i>K</i>-Means Clustering Applied to Meter Data Analysis and Synthesis

The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called ‘15/15 rule’, to respond to the need. However, the current policies fail to adequately guarantee privacy. In this paper, we address the problem of allowing third parties to apply <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm’s utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset.
Receding-Horizon Trajectory Planning for Under-Actuated Autonomous Vehicles Based on Collaborative Neurodynamic Optimization

This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization. A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations. The feasibility of the formulated optimization problem is guaranteed under derived conditions. The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure. Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.
Privacy-Preserving Probabilistic Voltage Forecasting in Local Energy Communities

This paper presents a new privacy-preserving framework for the short-term (multi-horizon) probabilistic forecasting of nodal voltages in local energy communities. This task is indeed becoming increasingly important for cost-effectively managing network constraints in the context of the massive integration of distributed energy resources. However, traditional forecasting tasks are carried out centrally, by gathering raw data of end-users in a single database that exposes their private information. To avoid such privacy issues, this work relies on a distributed learning scheme, known as federated learning wherein individuals’ data are kept decentralized. The learning procedure is then augmented with differential privacy, which offers formal guarantees that the trained model cannot be reversed-engineered to infer sensitive local information. Moreover, the problem is framed using cross-series learning, which allows to smoothly integrate any new client joining the community (i.e., cold-start forecasting) without being plagued by data scarcity. Outcomes show that the proposed approach achieves improved performance compared to non-collaborative (locally trained) models, and is able to reach a trade-off between privacy and performance for different architectures of deep learning networks.
Membership Inference Attacks Against Machine Learning Models via Prediction Sensitivity

Machine learning (ML) has achieved huge success in recent years, but is also vulnerable to various attacks. In this article, we concentrate on membership inference attacks and propose Aster, which merely requires the target model&#x0027;s black-box API and a data sample to determine whether this sample was used to train the given ML model or not. The key idea of Aster is that the training data of a fully trained ML model usually has lower prediction sensitivities compared with that of the non-training data (i.e., testing data). Less sensitivity means that when perturbing a training sample&#x0027;s feature value in the corresponding feature space, the prediction of the perturbed sample obtained from the target model tends to be consistent with the original prediction. In this article, we quantify the prediction sensitivity with the Jacobian matrix which could reflect the relationship between each feature&#x0027;s perturbation and the corresponding prediction&#x0027;s change. Then we regard the samples with a lower as training data. Aster can breach the membership privacy of the target model&#x0027;s training data with no prior knowledge about the target model or its training data. The experiment results on four datasets show that our method outperforms three state-of-the-art inference attacks.
Procedures, Criteria, and Machine Learning Techniques for Network Traffic Classification: A Survey

Traffic classification is considered an important research area due to the increasing demand in network users. It not only effectively improve the network service identifications and security issues of the traffic network, but also provide robust accuracy and efficiency in different Internet application behaviors and patterns. Several traffic classification techniques have been proposed and applied successfully in recent years. However, the existing literature lack of comprehensive survey which could provide an overview and analysis towards the recent developments in network traffic classification. To this end, this survey presents a comprehensive investigation on traffic classification techniques by carefully reviewing existing methods from a new perspective. We comprehensively discuss the procedures and datasets for traffic classification. Additionally, traffic criteria are proposed, which could be beneficial to assess the effectiveness of the developed classification algorithm. Then, the traffic classification techniques are discussed in detail. Then, we thoroughly discussed the machine learning (ML) methods for traffic classification. For researcher&#x2019;s convenience, we present the traffic obfuscation techniques, which could be helpful for designing a better classifier. Finally, key findings and open research challenges for network traffic classification are identified along with recommendations for future research directions. In sum, this survey fills the gap of existing surveys and summarizes the latest research developments in traffic classification.
Instant_Anonymity: A Lightweight Semantic Privacy Guarantee for 5G-Enabled IIoT

Data publication and sharing are critical components of assessing network infrastructures in the Internet of Things for quality-of-service enhancement. Especially, the advancement in communication technology (e.g., 5G/6G) enables the improvement of the current bottlenecks in the Industrial Internet of Things. Recent approaches remove raw data and their source to achieve a privacy guarantee. However, the data are already anonymized; these still reveal the victim’s extra information using linkage attacks. When data are updated and combined or noise is introduced as part of conventional privacy protection approaches, such as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity, l-diversity, or differential privacy, the usefulness of the released data is diminished, however, posing data utility and computation constraints. In recent years, lightweight privacy-preservation techniques have been proposed for these reasons. However, most of the focus is on syntactic privacy instead of semantic privacy guarantee. Therefore, this article proposes a lightweight semantic privacy-preservation framework for maintaining privacy with high utility efficiency. The proposed paradigm ensures semantic privacy by combining probabilistic random sampling with Instant_Anonymity. Compared to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity, the suggested model demonstrates improved data utility with lower utility errors of 0.00036 and 0.41 for Kullback–Leibler divergence and query error, respectively. The classification accuracy is improved by 0.2%. In addition, the proposed approach is simpler to implement in computation time than the existing state-of-the-art lightweight privacy-preserving strategies.