Reviewerly/exHarmony huggingface dataset:
Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal Factors

Addressing inherent limitations in distinguishing metrics relying solely on Euclidean distance, especially within the context of geo-indistinguishability (Geo-I) as a protection mechanism for location-based service (LBS) privacy, this paper introduces an innovative and comprehensive metric. Our proposed metric not only incorporates geographical information but also integrates semantic, temporal, and query data, serving as a powerful tool to foster semantic diversity, ensure high servifice similarity, and promote spatial dispersion. We extensively evaluate our technique by constructing a comprehensive metric for Dongcheng District, Beijing, using road network data obtained through the OSMNX package and semantic and temporal information acquired through Gaode Map. This holistic approach proves highly effective in mitigating adversarial attacks based on background knowledge. Compared with existing methods, our proposed protection mechanism showcases a minimum 50% reduction in service quality and an increase of at least 0.3 times in adversarial attack error using a real-world dataset from Geolife. The simulation results underscore the efficacy of our protection mechanism in significantly enhancing user privacy compared to existing methodologies in the LBS location privacy-protection framework. This adjustment more fully reflects the authors' preference while maintaining clarity about the role of Geo-I as a protection mechanism within the broader framework of LBS location privacy protection.
Implementation of JPEG XS entropy encoding and decoding on FPGA

Abstract JPEG XS is the latest international standard for shallow compression fields launched by the International Organization for Standardization (ISO). The coding standard was officially released in 2019. The JPEG XS standard can be encoded and decoded on different devices, but there is no research on the implementation of JPEG XS entropy codec on FPGAs. This paper briefly introduces JPEG XS encoding, proposes a modular design scheme of encoder and decoder on FPGA for the entropy encoding and decoding part, and parallelizes the algorithm in JPEG XS coding standard according to the characteristics of FPGA parallelization processing, mainly including low-latency optimization design, storage space optimization design. The optimized scheme in this paper scheme enables encoding speeds of up to 4 coefficients/clock and decoding speeds of up to 2 coefficients/clock, with a 75% reduction in encoding and decoding time. The maximum clock frequency of the entropy encoder is about 222.6 MHz, and the maximum clock frequency of the entropy decoder is about 127 MHz. The design and implementation of the FPGA-based JPEG XS entropy encoding and decoding algorithm is of great significance and provides ideas for the subsequent implementation and optimization of the entire JPEG XS standard on FPGAs. This work is the first in the world to propose the design and implementation of an algorithm that can implement the JPEG XS entropy encoding and decoding process on FPGA. It creates the possibility for the effective application of JPEG XS standard in more media.
Global Digital Compact: A Mechanism for the Governance of Online Discriminatory and Misleading Content Generation

With the continuous development of artificial intelligence (AI), algorithmic discrimination and discriminatory and misleading content (DMC) generated by AI have given rise to many negative effects in cyberspace, such as racial and gender discrimination, misinformation, and so on. The growing concern in society over AI governance urgently necessitates the establishment of an effective mechanism to supervise and govern AI-generated DMC. In this article, the discriminatory and misleading contents of AIGC (Artificial Intelligence Generated Content) were extracted according to Text Classification Model and then classified by Naive Bayesian algorithm. The results showed that under the Global Digital Compact (GDC), countries differed in their degrees of discrimination related to race, gender, religion, and age. The racial discrimination accounted for the highest proportion in the United States, with a score of 0.15; that in Britain and France took up a share of 0.06 and 0.07, respectively; and merely 0.03 in Germany. Discriminatory content of racial discrimination (M1) and gender discrimination (M2) in science and technology industry was relatively low, accounting for 0.05 and 0.08, respectively. Analyzing data within the Global Digital Compact (GDC) illuminates the disparities and trends in DMC generation across various countries, cities, industries, and individual users. This analysis provides valuable references for subsequent research and problem-solving initiatives under the compact. Furthermore, GDC plays a pivotal role in addressing issues related to AI-generated DMC, contributing significantly to the creation of a secure, reliable, and equitable cyberspace.
Investigating the Effectiveness of Augmented Reality Technology in Science Education in Terms of Environmental Literacy, Self-Regulation, and Motivation to Learn Science

Augmented Reality (AR) is a technology that provides users with an engaging learning experience by placing interactive virtual 3D models in natural environments. It is known that the use of AR technology in science education has many educational benefits. This study investigated the impact of AR technology on the development of environmental literacy, self-regulation skills, and motivation for science learning, which are increasingly important skills. Acquiring these high-level skills is essential for supporting students’ development in preventing environmental problems, addressing the motivation decrease seen primarily due to distance learning, and acquiring the self-regulation skills that have gained importance due to distance learning. The study was conducted with a quasi-experimental design on fifth-grade students, with one experimental (34) and one control (36) group. The findings were analyzed through MANOVA and content analysis. The results showed that AR technology supports environmental sensitivity, positive behavior, and the development of environmental knowledge and skills. In addition, AR technology has significant contributions in terms of self-regulation. Finally, it is recommended to use AR technology in science education to develop these variables and positively affect participants’ motivation for science learning.
Feeling safe together vs. longing for touch. Affective, multisensorial contact in inclusive intra-active virtual/physical dance during the COVID-19 pandemic

This article explores how feelings of safety were experienced through inclusive virtual/physical dance in relation to experiences of touch during the COVID-19 pandemic in Finland. The following are the measures I took to achieve the aim of this study. First, I introduce the context through previous studies and an example from the ethnographic material I gained from the inclusive X-Dance festival organized in June 2021 in Finland. Second, I explore how inclusive virtual/physical dance might help us experience feelings of safety through multisensorial experiences of touch related to communality when responding to wellbeing challenges caused by isolation. Using Laura U. Mark's theory about haptic looking and Karen Barad's term intra-action, I discuss different possibilities to approach touch as a feeling sense, as affective multisensorial contact, and through relations between different materialities. Third, I contrast these ideas with sensations of longing for the physical touch that virtual/physical dancing evokes. These experiences of longing invite me to reflect on the ambivalence about feelings of safety related to intentions to restore experiences of touch and communality through digitality. I introduce two examples from my interviews with dancers during the pandemic to discuss these ambivalences. I reflect on these interviews through Magdalena Górska's theory about corpo-materiality and corpo-affectivity. I contribute to discussions on feelings of safety by showing that multi-sensorial experiences and anti-normative understandings of body and touch enabled by the non-verbal language of dance may help us to contribute toward more inclusiveness in society, allowing us to generate holistic experiences of safety, which is another critical aim for post-pandemic times.
Enhanced YOLOv5s + DeepSORT method for highway vehicle speed detection and multi-sensor verification

Addressing the need for vehicle speed measurement in traffic surveillance, this study introduces an enhanced scheme combining YOLOv5s detection with Deep SORT tracking. Tailored to the characteristics of highway traffic and vehicle features, the dataset data augmentation process was initially optimized. To improve the detector’s recognition capabilities, the Swin Transformer Block module was incorporated, enhancing the model’s ability to capture local regions of interest. <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mi>C</mml:mi><mml:mi>I</mml:mi><mml:mi>o</mml:mi><mml:mi>U</mml:mi></mml:mrow></mml:math> loss was employed as the loss function for the vehicle detection network, accelerating model convergence and achieving higher regression accuracy. The Mish activation function was utilized to reduce computational overhead and enhance convergence speed. The structure of the Deep SORT appearance feature extraction network was modified, and it was retrained on a vehicle re-identification dataset to mitigate identity switches due to obstructions. Subsequently, using known references in the image such as lane markers and contour labels, the transformation from image pixel coordinates to actual coordinates was accomplished. Finally, vehicle speed was measured by computing the average of instantaneous speeds across multiple frames. Through radar and video Multi-Sensor Verification, the experimental results show that the mean Average Precision (mAP) for target detection consistently exceeds 90%. The effective measurement distance for speed measurement is around 140 m, with the absolute speed error generally within 1–8 km/h, meeting the accuracy requirements for speed measurement. The proposed model is reliable and fully applicable to highway scenarios.
A closed-form analytical variational approach to thermally induced thin-film delamination

Thin polymeric films applied on stiff substrates are prone to delamination upon thermal loading. Different thermal expansion coefficients and different elastic properties can cause localized stress concentrations in the interface between applied layer and substrate. The present work proposes a variational approach to compute residual stresses and strains within the film. Using finite element calculations, it is shown that the presented closed-form analytical solution provides accurate interfacial stresses. Employing a critical distances failure criterion, these stresses are used to predict thermally induced crack nucleation. The proposed model is validated using experimental data of thermally loaded epoxy–glass specimens. It is in good agreement with the test data and provides predictions for thin-film delamination.
Smart hotels but not necessarily smart decisions: the smartness paradox

Purpose This paper aims to answer questions pertinent to whether or not services provided by smart hotels are really what customers are looking for, as well as to ascertain what are some unintended experiences guests may encounter. In essence, to the best of the authors’ knowledge, this research is the first in the field to acknowledge the paradox of smart service. Design/methodology/approach This inquiry adopts a qualitative approach with data-driven from online customer reviews and semistructured interviews. Thematic analysis was undertaken to interpret review comments. Findings Results point to a new phenomenon, which is coined as the smartness paradox. In particular, customers on one hand enjoy an array of smart-infused experiences that jointly offer patrons a sense of a futuristic lifestyle. On the other hand, smart devices superimpose a number of hindrances that bring guests dismay and annoyance. Research limitations/implications This investigation brings smart service failure to the fore to highlight several key failure themes that could jeopardize the entire operation with debased customers’ satisfaction and loyalty inclination. Originality/value The smartness-paradox framework used in the present inquiry entails both approach and avoidance consequences customers enact depending on their smart experiences.
Small Dataset, Big Gains: Enhancing Reinforcement Learning by Offline Pre-Training with Model-Based Augmentation

Offline reinforcement learning leverages pre-collected datasets of transitions to train policies. It can serve as an effective initialization for online algorithms, enhancing sample efficiency and speeding up convergence. However, when such datasets are limited in size and quality, offline pre-training can produce sub-optimal policies and lead to a degraded online reinforcement learning performance. In this paper, we propose a model-based data augmentation strategy to maximize the benefits of offline reinforcement learning pre-training and reduce the scale of data needed to be effective. Our approach leverages a world model of the environment trained on the offline dataset to augment states during offline pre-training. We evaluate our approach on a variety of MuJoCo robotic tasks, and our results show that it can jumpstart online fine-tuning and substantially reduce—in some cases by an order of magnitude—the required number of environment interactions.
An FPGA‐based tool for supporting the design, modeling, and evaluation of hybrid object recognition systems on computer engineering courses

Abstract The field of computer vision is characterized by computationally intensive algorithms and techniques with strict real‐time requirements. Field programmable gate arrays (FPGAs) are based on a concurrent paradigm which allows the design of efficient hardware architectures and has positioned FPGAs as an ideal device for implementing compute‐intensive applications. For this reason, FPGA technology has had a great impact in areas such as computer vision, where one of the main objectives for researchers working in this field is to create efficient automatic object recognition systems. Therefore, the need to provide undergraduates with the necessary skills to design FPGA‐based object recognition systems is evident. With this aim in mind, it is essential that specialization courses related to the design of these systems include the required resources for the student to apply the theoretical knowledge in solving practical problems. In this article, we present a development tool designed to help students, teachers, and researchers during the design‐modeling‐implementation process of object recognition systems based on FPGAs. The proposed tool operates under a modular approach as this facilitates the working on any of the phases of a recognition system and it is considered as a hybrid because the other phases can be developed using a software language. An empirical evaluation involving undergraduates enrolled in a Computer Engineering program was conducted to create a hardware architecture for the DAISY descriptor that uses the homogeneous features of objects immersed in images to produce an efficient representation. By considering similar descriptors such as Scale‐Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG), DAISY is computed by convolving orientation maps instead of using weighted sums of gradient norms, which offers the same kind of invariance at a lower computational cost for the dense case. The results obtained during such an evaluation indicated that students consider this FPGA‐based tool to be an alternative to receiving practical training on designing systems for solving problems related to the area of object recognition.
A real-time wearable AR system for egocentric vision on the edge

Abstract Real-time performance is critical for Augmented Reality (AR) systems as it directly affects responsiveness and enables the timely rendering of virtual content superimposed on real scenes. In this context, we present the DARLENE wearable AR system, analysing its specifications, overall architecture and core algorithmic components. DARLENE comprises AR glasses and a wearable computing node responsible for several time-critical computation tasks. These include computer vision modules developed for the real-time analysis of dynamic scenes supporting functionalities for instance segmentation, tracking and pose estimation. To meet real-time requirements in limited resources, concrete algorithmic adaptations and design choices are introduced. The proposed system further supports real-time video streaming and interconnection with external IoT nodes. To improve user experience, a novel approach is proposed for the adaptive rendering of AR content by considering the user’s stress level, the context of use and the environmental conditions for adjusting the level of presented information towards enhancing their situational awareness. Through extensive experiments, we evaluate the performance of individual components and end-to-end pipelines. As the proposed system targets time-critical security applications where it can be used to enhance police officers’ situational awareness, further experimental results involving end users are reported with respect to overall user experience, workload and evaluation of situational awareness.
Confidence partitioning sampling filtering

Abstract The confidence partitioning sampling filter (CPSF) method proposed in this paper is a novel approach for solving the generic nonlinear filtering problem. First, the confidence probability space (CPS) is defined, which restricts the state transition in a bounded and closed state space in the recursive Bayesian filtering. In the posterior CPS, the weighted grid samples, represented the posterior PDF, are obtained by using the partitioning sampling technique (PST). Each weighted grid sample is treated as an impulse function. The approximate expression of the posterior PDF, as key for the PST implementation, is obtained by using the properties of the impulse function in the integral operation. By executing the selection of the CPS and the PST step repeatedly, the CPSF framework is formed to achieve the approximation of the recursive Bayesian filtering. Second, the difficulty of the CPSF framework implementation lies in obtaining the real posterior CPS. Therefore, the space intersection (SI) method is suggested to obtain the approximate posterior CPS. On this basis, the SI_CPSF algorithm, as an executable algorithm, is formed to solve the generic nonlinear filtering problem. Third, the approximate error between the CPSF framework and the recursive Bayesian filter is analyzed theoretically. The consistency of the CPSF framework to the recursive Bayesian filter is proved. Finally, the performances of the SI_CPSF algorithm, including robustness, accuracy and efficiency, are evaluated using four representative simulation experiments. The simulation results showed that SI_CSPF requires far less samples than particle filter (PF) under the same accuracy. Its computation is on average one order of magnitude less than that of the PF. The robustness of the proposed algorithm is also evaluated in the simulations.
Program Behavior Dynamic Trust Measurement and Evaluation Based on Data Analysis

Industrial control terminals play an important role in industrial control scenarios. Due to the special nature of industrial control networks, industrial control terminal systems are vulnerable to malicious attacks, which can greatly threaten the stability and security of industrial production environments. Traditional security protection methods for industrial control terminals have coarse detection granularity, and are unable to effectively detect and prevent attacks, lacking real-time responsiveness to attack events. Therefore, this paper proposes a real-time dynamic credibility evaluation mechanism based on program behavior, which integrates the matching and symmetry ideas of credibility evaluation. By conducting a real-time dynamic credibility evaluation of function call sequences and system call sequences during program execution, the credibility of industrial control terminal application program behavior can be judged. To solve the problem that the system calls generated during program execution are unstable and difficult to measure, this paper proposes a partition-based dynamic credibility evaluation method, dividing program behavior during runtime into function call behavior and system call behavior within function intervals. For function call behavior, a sliding window-based function call sequence benchmark library construction method is proposed, which matches and evaluates real-time measurement results based on the benchmark library, thereby achieving symmetry between the benchmark library and the measured data. For system call behavior, a maximum entropy system call model is constructed, which is used to evaluate the credibility of system call sequences. Experiment results demonstrate that our method performs better in both detection success rate and detection speed compared to the existing methods.
Efficient Search and Detection of Faint Moving Objects in Image Data

Abstract The search and detection of faint moving objects in image data can enable discoveries of small solar system bodies. To detect objects fainter than the single-frame sensitivity limit, track-before-detect methods can improve the signal-to-noise ratio of the object of interest by incoherently adding the object’s signal across multiple frames. However, traditional track-before-detect techniques can become computationally intensive over large search volumes. In this work, we present a computational approach to significantly speed up the search process by applying dynamic-programming techniques to implement the discrete X-ray transform. In this approach, image frames are processed in stages, in each of which pairs of frames are shifted and added to generate short-track segments, which are combined in later stages to form longer tracks. The algorithm speedup comes from the fact that a single short track segment can be reused multiple times for different longer tracks without the need for recomputing. Benchmark testing with simulated data shows that the method presented in this paper results in a significant reduction in runtime in comparison to a traditional track-before-detect approach. As a proof of concept, we demonstrated the applicability of the technique in performing a blind search for faint asteroids in image data collected from the Transiting Exoplanet Survey Satellite, leading to the detection of more than a thousand asteroids below the single-frame detection limit with moderate computational resources. The approach presented in this work has the potential to enable efficient discovery of previously undetected faint solar system objects across multiple orbit classes.