Reviewerly/exHarmony huggingface dataset:
K-Anonymity-Based Privacy-Preserving and Efficient Location-Based Services for Internet of Vehicles Withstand Viterbi Attack

With the population of Internet of Vehicles (IoV), location-based services (LBSs) for vehicles have drawn great attention. Meanwhile, users’ location privacy is also placed at high risk. Using dummy locations to hide the real locations of vehicles in LBSs is a popular method to protect users’ privacy in literature. Whereas, traditional techniques in achieving this goal rely heavily on a centralized location anonymizer. Caching-based privacy-preserving schemes have been proposed to achieve decentralized privacy-preserving LBSs in IoV. Recently, Shaham et al. point out that an adversary is able to inference the trajectory of vehicles with some side-channel information. Such an attack is named Viterbi attack. In this work, an entropy-based anonymous optimization method is proposed to withstand such attacks. The proposed method takes vehicle trajectory privacy as an indicator of location privacy at a given moment and hence reduce the success probability of Viterbi attacks. Simulation results show that the proposed scheme can reducing the probability of revealing vehicles’ actual locations with Viterbi attack, and reduce the communications overhead between the vehicle and the LBS server.
AMIR

Activity recognition using video data is widely adopted for elder care, monitoring for safety and security, and home automation. Unfortunately, using video data as the basis for activity recognition can be brittle, since models trained on video are often not robust to certain environmental changes, such as camera angle and lighting changes. There has been a proliferation of network-connected devices in home environments. Interactions with these smart devices are associated with network activity, making network data a potential source for recognizing these device interactions. This paper advocates for the synthesis of video and network data for robust interaction recognition in connected environments. We consider machine learning-based approaches for activity recognition, where each labeled activity is associated with both a video capture and an accompanying network traffic trace. We develop a simple but effective framework AMIR (Active Multimodal Interaction Recognition)1 that trains independent models for video and network activity recognition respectively, and subsequently combines the predictions from these models using a meta-learning framework. Whether in lab or at home, this approach reduces the amount of "paired" demonstrations needed to perform accurate activity recognition, where both network and video data are collected simultaneously. Specifically, the method we have developed requires up to 70.83% fewer samples to achieve 85% F1 score than random data collection, and improves accuracy by 17.76% given the same number of samples.
“EFL teachers” TPACK self-efficacy beliefs, web 2.0 competence and perceptions towards web 2.0 tools

The recent advancement of digital technologies has triggered a prominent shift within the field of language education. Considering the teachers' vital roles in adopting modern instructional technologies, this study investigates in-service EFL teachers' TPACK self-efficacy levels, explores the relationship between their TPACK self-efficacy levels and their demographic features and perceptions towards Web 2.0 technologies. Employing a mixed method design, the study collected quantitative and qualitative data in two phases. The quantitative data was collected through an online survey with 227 EFL teachers from various schools. Following the first phase, seven voluntary teachers were interviewed. The findings suggested that participants had a high level of content knowledge, pedagogical content knowledge and pedagogical knowledge, but they reported a relatively low level of self-esteem when the knowledge of technology was included. Furthermore, statistically significant differences were found between the teachers' TPACK self-efficacy levels and their educational level, school type, perceived digital literacy levels and previous INSET experience on Web 2.0 technologies. A positive and high level of correlation between TPACK self-efficacy of EFL teachers and their Web 2.0 selfperceptions was also reported. Semi-structured interviews demonstrated that despite their favorable perceptions towards Web 2.0 technologies, EFL teachers do not amply utilize Web 2.0 tools in teaching mainly due to their inadequate technological and technological pedagogical knowledge.
Frustration and deprivation in ecology and in people’s life

The article analyses the significance of human frustration and deprivation caused by life conditions or ecological frustration/deprivation. Ecological frustration means deprivation of access to some objects of the environment, more precisely, human environment. Ecological deprivation means formation of a psychological view of an individual (social group) on unfair deprivation of benefits due to it, those connected with the use of environmental resources. The article presents a classification of types of ecological frustration and deprivation. The structure of any frustration and deprivation is based on subjects and objects thereof, and its characteristics are intensity, localisation and spread. The subject of frustration (deprivation) is a person or a group of persons – bearers of the given psychological phenomenon (individual, group or mass mentality); the object thereof is an element of the environment which, in case of its inaccessibility, causes frustration or deprivation. The intensity of these phenomena is measured as an indicator of integral manifestation of their subjects’ response. The localisation of both frustration and deprivation is determined by the composition of the affected societal groups, and their spread – by the proportion of these people in such group or in the society as a whole. Frustration and deprivation of life conditions represent critical factors in shaping environmental attitudes. For most people, poor environment is not a result of analysis of a set of physico-chemical and biological indicators. It is a perception of environmental reality formed under the influence of various frustration and deprivation feelings. Monitoring the processes of formation and spread of such phenomena in the society and analysing the results thereof can prevent numerous socio-ecological conflicts. Frustration and deprivation of life conditions that are prevalent in the society should be taken into account when developing environmental education and training programmes.
Blockchain: The Future of Smart City Development

As cities around the world are growing at an unprecedented rate, there is a pressing need for smarter, more sustainable, and efficient urban infrastructure. In recent years, blockchain technology has emerged as a potential solution for various challenges faced by smart cities. Blockchain's unique features such as decentralization, immutability, and transparency offer new opportunities for secure and efficient management of city systems. This paper explores the potential of blockchain technology in smart city development, highlighting its applications in areas such as energy management, transportation, governance, and public services. By analyzing various use cases and examples, the paper demonstrates how blockchain can enhance the reliability, efficiency, and transparency of smart city infrastructure. Additionally, the paper discusses the challenges and limitations of blockchain adoption in smart cities and suggests potential solutions for overcoming these obstacles. Overall, this paper argues that blockchain technology has the potential to transform the future of smart city development, enabling cities to become more resilient, sustainable, and citizen-centric
Multi-label Feature Selection Based on Fuzzy Neighborhood Mutual Discrimination Index

Multi-label feature selection advances the performance of multi-label classification models by eliminating irrelevant and redundant features. Most multi-label feature selection algorithms assume that the training set containing logical labels, which means that labels are equally important for instances. However, in practical applications, there are different importances of labels. To solve the problem, we proposes a multi-label feature selection method based on fuzzy neighborhood mutual discriminant index. Firstly, logical labels are transformed into label distribution by label enhancement. Secondly, the information entropy is referenced to the label distribution learning, the label neighborhood similarity matrix is constructed to describe the ambiguity between labels. Finally, linking the fuzzy neighborhood discrimination index under the selected feature subset with label neighborhood similarity matrix is proposed, which is used for judging the distinguishing ability of the feature subset to label. Comprehensive experiments on 6 multi-label datasets show that the algorithm outperforms other algorithms in classification performance.
Automatic assessment of mammographic density using a deep transfer learning method

PurposeMammographic breast density is one of the strongest risk factors for cancer. Density assessed by radiologists using visual analogue scales has been shown to provide better risk predictions than other methods. Our purpose is to build automated models using deep learning and train on radiologist scores to make accurate and consistent predictions.ApproachWe used a dataset of almost 160,000 mammograms, each with two independent density scores made by expert medical practitioners. We used two pretrained deep networks and adapted them to produce feature vectors, which were then used for both linear and nonlinear regression to make density predictions. We also simulated an “optimal method,” which allowed us to compare the quality of our results with a simulated upper bound on performance.ResultsOur deep learning method produced estimates with a root mean squared error (RMSE) of 8.79 ± 0.21. The model estimates of cancer risk perform at a similar level to human experts, within uncertainty bounds. We made comparisons between different model variants and demonstrated the high level of consistency of the model predictions. Our modeled “optimal method” produced image predictions with a RMSE of between 7.98 and 8.90 for cranial caudal images.ConclusionWe demonstrated a deep learning framework based upon a transfer learning approach to make density estimates based on radiologists’ visual scores. Our approach requires modest computational resources and has the potential to be trained with limited quantities of data.
A Joint Denoising Technique for Mixed Gaussian-Impulse Noise Removal in HSI

Hyperspectral imaging (HSI) is the procedure of acquiring a scene over a wide range of electromagnetic spectrum for the purpose of detailed analysis and prediction. The occurrence of noise during the acquisition procedure, however, poses a limitation on this imaging system. Noise in HSI is classified as a mixture of Gaussian and impulse noise statistics, and noise removal or denoising forms an integral part of this imaging system. In this paper, we consider the problem of removing this mixed Gaussian-impulse noise from HSI data-sets by formulating a joint optimization problem based on the maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> (MAP) estimates for Gaussian and impulse noise distributions. The proposed method is then solved using an efficient minimization strategy realizied through half-quadratic split. Extensive experimentation on synthetic and real HSI data-sets corroborate the effectiveness of the proposed denoising technique.
Accelerated Weak Signal Search Using Mode Entanglement and State Swapping

A quantum-enhanced sensing technique based on two cavities coupled via a Josephson ring modulator is developed to substantially accelerate a detection scan rate, ultimately boosting significantly the search of axions.
Recursive relations and quantum eigensolver algorithms within modified Schrieffer-Wolff transformations for the Hubbard dimer

We derive recursive relations for the Schrieffer--Wolff (SW) transformation applied to the half-filled Hubbard dimer. While the standard SW transformation is set to block-diagonalize the transformed Hamiltonian solely at the first order of perturbation, we infer from recursive relations two types of modifications, variational or iterative, that approach, or even enforce for the homogeneous case, the desired block-diagonalization at infinite order of perturbation. The modified SW unitary transformations are then used to design an test quantum algorithms adapted to the noisy and fault-tolerant era. This work paves the way toward the design of alternative quantum algorithms for the general Hubbard Hamiltonian.
Uncertainty-Aware Gaze Tracking for Assisted Living Environments

Effective assisted living environments must be able to infer how their occupants interact in a variety of scenarios. Gaze direction provides strong indications of how a person engages with the environment and its occupants. In this paper, we investigate the problem of gaze tracking in multi-camera assisted living environments. We propose a gaze tracking method based on predictions generated by a neural network regressor that relies only on the relative positions of facial keypoints to estimate gaze. For each gaze prediction, our regressor also provides an estimate of its own uncertainty, which is used to weigh the contribution of previously estimated gazes within a tracking framework based on an angular Kalman filter. Our gaze estimation neural network uses confidence gated units to alleviate keypoint prediction uncertainties in scenarios involving partial occlusions or unfavorable views of the subjects. We evaluate our method using videos from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MoDiPro</i> dataset, which we acquired in a real assisted living facility, and on the publicly available GazeFollow and Gaze360 datasets. Experimental results show that our gaze estimation network outperforms sophisticated state-of-the-art methods, while additionally providing uncertainty predictions that are highly correlated with the actual angular error of the corresponding estimates. Finally, an analysis of the temporal integration performance of our method demonstrates that it generates accurate and temporally stable gaze predictions.
An Unsupervised Machine-Learning Workflow for Outlier Detection and Log Editing With Prediction Uncertainty

Recent advances in data science and machine learning (ML) have brought the benefits of these technologies closer to the mainstream of petrophysics. ML systems, where decisions and self-checks are made by carefully designed algorithms, in addition to executing typical tasks such as classification and regression, offer efficient and liberating solutions to the modern petrophysicist. The outline of such a system and its application in the form of a multilevel workflow to a 59-well multifield study are presented in this paper. The main objective of the workflow is to identify outliers in bulk density and compressional slowness logs and to reconstruct them using data-driven predictive models. A secondary objective of the project is to predict shear slowness in zones where such data do not exist. The system is fully automated, designed to optimize the use of all available data, and provide uncertainty estimates. It integrates modern concepts for outlier detection, predictive classification, and regression, as well as multidimensional scaling based on inter-well similarity. Benchmarking of ML results against those created by experienced petrophysicists shows that the ML workflow can provide high-quality answers that compare favorably to those produced by human experts. A second validation exercise, that compares acoustic impedance logs computed from ML answers to actual seismic data, provides further evidence for the accuracy of the ML-generated results. The ML system supports the petrophysicist by easing the burden on repetitive and burdensome quality control tasks. The efficiency gains and time savings created can be used for enhanced effective cross-discipline integration, collaboration, and further innovation.
Locking Down Big Data: A Comprehensive Survey of Data Encryption Methods

With the increasing volume of data being generated every day, the need for data security has become more crucial than ever. Encryption is one of the most effective techniques for protecting sensitive data from unauthorized access or theft. This comparative survey aims to provide a comprehensive analysis of different data encryption methods in the context of big data. The survey covers both traditional encryption techniques and newer, more advanced methods such as homomorphic encryption and quantum cryptography. The effectiveness, advantages, and limitations of each technique are examined, and a comparison of their performance in terms of speed, scalability, and security is presented. The study also explores the challenges and issues associated with implementing data encryption in big data environments. The findings of this survey will be useful for organizations seeking to secure their big data assets and for researchers interested in the latest developments in data encryption techniques.
Optimal Task and Motion Planning and Execution for Multiagent Systems in Dynamic Environments

Combining symbolic and geometric reasoning in multiagent systems is a challenging task that involves planning, scheduling, and synchronization problems. Existing works overlooked the variability of task duration and geometric feasibility intrinsic to these systems because of the interaction between agents and the environment. We propose a combined task and motion planning approach to optimize the sequencing, assignment, and execution of tasks under temporal and spatial variability. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. At the task level, timeline-based planning deals with temporal constraints, duration variability, and synergic assignment of tasks. At the action level, online motion planning plans for the actual movements dealing with environmental changes. We demonstrate the approach’s effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic in the shortest time possible. Compared with existing works, our approach applies to a broader range of applications and reduces the execution time of the process.
Beyond computationality: Radical play and aesthetics in future compasses

The subreddit r/FutureCompasses is an online community where users play with the notion of a computationality engineered futurity. Future compasses are intricate 4 × 4 grids that imagine different futures. Each grid offers sixteen different futuristic scenarios thematically structured around concepts such as ecology or technology. These compasses combine computationality with experimental aesthetics through play. As per Roger Callois, play here crucially involves the creation of a new universal reality The authors of these compasses participate in the making of new social reality by applying a computational lens to the future and offering speculative scenarios about what the future could be. However, these scenarios do not reflect a realistic future for humanity. Instead, they subvert the idea of being able to calculate the future. Through creative play and experimental aesthetics, users who produce future compasses contrast computationality with human creativity. In this way, future compasses take their place in the artistic tradition of the New Aesthetic as defined by James Bridle. This article argues that future compasses contrast what can be technologically calculated and what should be creatively imagined.
Predicting relations between SOAP note sections: The value of incorporating a clinical information model

Physician progress notes are frequently organized into Subjective, Objective, Assessment, and Plan (SOAP) sections. The Assessment section synthesizes information recorded in the Subjective and Objective sections, and the Plan section documents tests and treatments to narrow the differential diagnosis and manage symptoms. Classifying the relationship between the Assessment and Plan sections has been suggested to provide valuable insight into clinical reasoning. In this work, we use a novel human-in-the-loop pipeline to classify the relationships between the Assessment and Plan sections of SOAP notes as a part of the n2c2 2022 Track 3 Challenge. In particular, we use a clinical information model constructed from both the entailment logic expected from the aforementioned Challenge and the problem-oriented medical record. This information model is used to label named entities as primary and secondary problems/symptoms, events and complications in all four SOAP sections. We iteratively train separate Named Entity Recognition models and use them to annotate entities in all notes/sections. We fine-tune a downstream RoBERTa-large model to classify the Assessment-Plan relationship. We evaluate multiple language model architectures, preprocessing parameters, and methods of knowledge integration, achieving a maximum macro-F1 score of 82.31%. Our initial model achieves top-2 performance during the challenge (macro-F1: 81.52%, competitors' macro-F1 range: 74.54%–82.12%). We improved our model by incorporating post-challenge annotations (S&O sections), outperforming the top model from the Challenge. We also used Shapley additive explanations to investigate the extent of language model clinical logic, under the lens of our clinical information model. We find that the model often uses shallow heuristics and nonspecific attention when making predictions, suggesting language model knowledge integration requires further research.
NA-OR: A path optimization method for manipulators via node attraction and obstacle repulsion

This paper is concerned with the issue of path optimization for manipulators in multi-obstacle environments. Aimed at overcoming the deficiencies of the sampling-based path planning algorithm with high path curvature and low safety margin, a path optimization method, named NA-OR, is proposed for manipulators, where the NA (node attraction) and OR (obstacle repulsion) functions are developed to refine the path by iterations. In the iterations of path optimization, the node attraction function is designed to pull the path nodes toward the center of their neighbor nodes, thereby reducing the path curvature and improving the smoothness. Also, the obstacle repulsion function is developed to push the path nodes out of the potentially unsafe region by generating a repulsive torque on the path nodes, thus improving the safety margin of the motion. By introducing the effect of NA-OR, the optimized path has a significant improvement in path curvature and safety margin compared with the initial path planned by Bi-RRT, which meaningfully enhances the operation ability of manipulators for the applications that give a strong emphasis on security. Experimental results on a 6-DOF manipulator in 4 scenarios demonstrate the effectiveness and superiority of the proposed method in terms of the path cost, safety margin, and path smoothness.
Representation of hypersonic glide vehicles as fluctuating radar targets

The monostatic RCS of a generic Hypersonic Glide Vehicle (HGV) is presented at S-band and is derived using a Physical Optics (PO) based simulation approach. Target RCS histograms are then produced and used to generate a custom statistical distribution which is subsequently compared against the conventional Swerling models associated with representation of target fluctuation when using the Radar Range Equation (RRE). The accuracy of the Swerling models for this particular target type and frequency band is subsequently discussed.