Reviewerly/exHarmony huggingface dataset:
Experiments and Analyses of Anonymization Mechanisms for Trajectory Data Publishing

With the advancing of location-detection technologies and the increasing popularity of mobile phones and other location-aware devices, trajectory data is continuously growing. While large-scale trajectories provide opportunities for various applications, the locations in trajectories pose a threat to individual privacy. Recently, there has been an interesting debate on the reidentifiability of individuals in the Science magazine. The main finding of Sánchez et al. is exactly opposite to that of De Montjoye et al., which raises the first question: “what is the true situation of the privacy preservation for trajectories in terms of reidentification?” Furthermore, it is known that anonymization typically causes a decline of data utility, and anonymization mechanisms need to consider the trade-off between privacy and utility. This raises the second question: “what is the true situation of the utility of anonymized trajectories?” To answer these two questions, we conduct a systematic experimental study, using three real-life trajectory datasets, five existing anonymization mechanisms (i.e., identifier anonymization, grid-based anonymization, dummy trajectories, k-anonymity and ε-differential privacy), and two practical applications (i.e., travel time estimation and window range queries). Our findings reveal the true situation of the privacy preservation for trajectories in terms of reidentification and the true situation of the utility of anonymized trajectories, and essentially close the debate between De Montjoye et al. and Sánchez et al. To the best of our knowledge, this study is among the first systematic evaluation and analysis of anonymized trajectories on the individual privacy in terms of unicity and on the utility in terms of practical applications.
Interrogating paradata

The concept of paradata has received increasing attention in recent information scholarship. The literature however shows considerable disagreement about what paradata is and how it can be purposive. The aim of this paper is to facilitate the use of the resources and analytical impetuses offered by the concept of paradata by providing an explanation to what paradata is in operational terms.The examination of paradata is based on a scoping review of 53 scholarly texts that varyingly engage with the concept.The reviewed texts were analysed using a concept-analysis framework focusing principally on discerning the main descriptive features of paradata and prevalent instances of use and usefulness.The paper shows what the key elements, relationships, use-cases and uncertainties of paradata are.The paper offers a paradata proto-definition: paradata is information about processes that operate a network of means, mental and physical actions and intellectual regimen in the creation or shaping of a(n information) product. The paper also outlines interconnections between paradata, metadata and provenance data, and suggests why it might make analytical sense to continue exploring the concept of paradata in information research and what the objectives of such explorations might be.
Path Planning for 5-Axis CMM Inspection Considering Path Reuse

The 5-axis Coordinate Measuring Machine (CMM) is widely used for quality data collection of the machining parts, such as cylinder blocks and heads of the engines. High efficient inspection path planning for multiple feature groups from different stations is one of the key tasks for CMM application. In engineering practice, the inspection planning of diverse feature groups accounts for large labor cost and process development cycle. To improve the efficiency of path generation for the complex machining part, a five-axis CMM inspection path planning method considering path length, probe rotation and path reusability is proposed. Firstly, the measuring points (MPs) are classified based on feasible inspection direction cone and accessibility of the MPs to achieve the minimum times of probe rotation. Then, the rapidly exploring random trees with multi-root node (RRT-MRNC) algorithm is proposed to implement local path planning considering inspection path reuse. Furthermore, intra-group and inter-group path is generated simultaneously based on the proposed enhanced Genetic Algorithm (GA) algorithm. In order to evaluate the effectiveness of the proposed method, the cylinder block path planning case is used. Compared with the benchmark methods, the total planning time based on the proposed planning method for the dynamic tasks was reduced by 55.2% and 54.9% respectively.
Automatic Identification of Anomalous Driving Events from Trajectory Data

The high-resolution high-accuracy time-series trajectory data preserves rich motion status and characteristic information of vehicles in a microscopic manner. In order to analyze novel driving events or broadcast potentially hazardous situations to nearby road users, the initial step is how to identify them. Traditional approaches of manually checking trajectory datasets are extremely time-consuming and error-prone. Therefore, how to identify very few anomalous or atypical vehicle trajectories efficiently and effectively from a huge number of regular or typical trajectories is a problem that needs to be solved. This paper introduces an algorithm to automatically identify anomalous trajectories from recorded trajectory datasets in the order of trajectory clustering, template trajectory extraction, trajectory similarity comparison, and anomaly detection. A Dynamic Time Warping (DTW)-based hierarchical clustering model is leveraged to extract critical template trajectories from historical trajectories. By comparing the distance similarity with the templates, trajectories can be assigned a normal or anomalous label. A case study using a public vehicle trajectory dataset (inD dataset) demonstrates the effectiveness of the proposed method in anomaly detection at four urban intersections.
Analysis of the Effect of Audio Data Augmentation Techniques on Phone Digit Recognition For Algerian Arabic Dialect

In this study, we describe a solution for dealing with the problem of data scarcity in Speech Processing tasks involving low-resource languages, including Automatic Speech Recognition (ASR). This method is based on a set of Data Augmentation (DA) techniques that will be applied to the small corpus that was initially used. This corpus comprises the first 100 Arabic digits uttered by two native Algerians. We used a variety of DA techniques to increase the size of this corpus, including stretching the signal without changing the pitch, simulating an environment using white noise, and finally shifting the sound. Finally, a number of experiments were carried out on two alternative configurations to assess the influence of these strategies on ASR performance. Extensive tests are carried out to verify the impact of the augmented samples in the training set or the training and testing set. Experimental results show that data augmentation plays an important role in improving the accuracy of recognition models, in which the impacts of the data augmentation methods such as Noise, Time Stretch, and rotation are slightly obvious.
A Stochastic Approach for Blockchain Internet of Things Integration

The ease of adaptability of Internet of Things (IoT) enables multiple devices to communicate with the aim of shearing data and information resources. The IoT architecture of operation is by default centralized. However, the distributed and decentralized architecture of Blockchain enables efficient and scalable connectivity between conventional devices and IoT devices. A Blockchain is considered as a technology with a distributed ledger that enables data transactions with integrity. In this paper, an analysis of the benefits and drawbacks of integrating IoT and Blockchain technology is efficiently conducted. A problem is formulated to analyze the system model by considering both internal and external attacks on a network. Also, a security analysis is conducted by formulating equations. Use-case analysis is proposed to seamlessly locate an IoT device and ensure access control for inbound and outbound link throughout the IoT system. In our results, MATLAB2019b is used in presenting graphical results for compromised node, cooperative nodes, and total number of node count. This analysis is conducted using stochastic approach to determine successful defense for node attach in percentage counts.
Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications

Images and video frames captured by cameras placed throughout smart cities are often transmitted over the network to a server to be processed by deep neural networks for various tasks. Transmission of raw images, i.e., without any form of compression, requires high bandwidth and can lead to congestion issues and delays in transmission. The use of lossy image compression techniques can reduce the quality of the images, leading to accuracy degradation. In this paper, we analyze the effect of applying low-overhead lossy image compression methods on the accuracy of visual crowd counting, and measure the trade-off between bandwidth reduction and the obtained accuracy.
Authentication of IoT in Smart City Applications

The advancement of technology brings an extensive use of Internet of things (IoT) devices in our daily life. IoT primary used for data collection and provide real-time management information in the network paradigms such as Edge Computing and Fog Computing. However, assuring the reliability of data delivered via Edge Computing is a challenging task. Blockchain has been a technology that has gained traction in recent years. Using Blockchain with IoT allows Smart Cities (SC) applications to spread security information, which protects against Distributed Denial of Service (DDOS) attacks. IoT devices in a SC can have a lengthy life, increasing the possibility of security flaws caused by obsolete firmware. This paper will highlight the significance of the implementation considering the SC and demonstrate a testbed made up of Blockchain Ethereum and actual IoT devices. The effects of transmission time, memory, and CPU use on the sending also processing of these messages were demonstrated. The proposed Application Programming Interface (API) sign, identify, and validate the messages before collecting them for an IoT data management application.
Detecting Cheating in Large-Scale Assessment: The Transfer of Detectors to New Tests

Recent approaches to the detection of cheaters in tests employ detectors from the field of machine learning. Detectors based on supervised learning algorithms achieve high accuracy but require labeled data sets with identified cheaters for training. Labeled data sets are usually not available at an early stage of the assessment period. In this article, we discuss the approach of adapting a detector that was trained previously with a labeled training data set to a new unlabeled data set. The training and the new data set may contain data from different tests. The adaptation of detectors to new data or tasks is denominated as transfer learning in the field of machine learning. We first discuss the conditions under which a detector of cheating can be transferred. We then investigate whether the conditions are met in a real data set. We finally evaluate the benefits of transferring a detector of cheating. We find that a transferred detector has higher accuracy than an unsupervised detector of cheating. A naive transfer that consists of a simple reuse of the detector increases the accuracy considerably. A transfer via a self-labeling (SETRED) algorithm increases the accuracy slightly more than the naive transfer. The findings suggest that the detection of cheating might be improved by using existing detectors of cheating at an early stage of an assessment period.
Visibility enhancement of underwater images based on polarization common-mode rejection of a highly polarized target signal

Underwater active polarization imaging is promising due to its effect of significantly descattering. Polarization-difference is commonly used to filter out backscattered noise. However, the polarization common-mode rejection of target signal has rarely been utilized. In this paper, via taking full advantage of this feature of Stokes vectors S2 which ably avoids interference from target light, the spatial variation of the degree of polarization of backscattered light is accurately estimated, and the whole scene intensity distribution of background is reconstructed by Gaussian surface fitting based on least square. Meanwhile, the underwater image quality measure is applied as optimization feedback, through iterative computations, not only sufficiently suppresses backscattered noise but also better highlights the details of the target. Experimental results demonstrate the effectiveness of the proposed method for highly polarized target in strongly scattering water.
A Practical Light Field Representation and Coding Scheme with an Emphasis on Refocusing

Light field images have drawn a great deal of interest owing to their flexible post-processing and versatile functionalities. However, the tremendous data volume has imposed a practical limit in applications, which requires a better way to represent and compress the data. In this paper, we propose a practical representation and coding scheme for light field data with a special emphasis on retaining the refocusing functionality as much as possible. Under the proposed method, light field data are represented and compressed in the form of a rendered all-in-focus image and a depth map. Before encoding, the all-in-focus image is rendered accurately based on the depth map using the proposed difference focus measure. At the decoder, a focal stack comprising multiple images having different focus levels can be reconstructed by applying a defocusing function to the compressed all-in-focus image where the defocusing function parameter depends on the selected depth level of the compressed depth map. Compared with well-known state-of-the-art methods that compress the Fourier Disparity Layer or the focal stack, the proposed coding scheme shows much smaller loss in the refocusing capability (16.2% and 17.8% less than two state-of-the-art methods) and provides PSNR improvements of 1.60㏈ to 2.38㏈ at the same compression ratio.
LBENet: Lightweight boundary enhancement network for detecting salient objects in RGB-D images

Recent methods for RGB-D salient object detection are capable of achieving desirable performance by leveraging depth features extracted using convolutional neural networks (CNNs). However, the lack of pretrained networks currently available for extracting representative features from depth data, ambiguity in the fusion of depth and RGB features, and the contamination effect originating from unreliable depth data still hinder the further improvement of these methods. Recently, a boundary cue was embedded in a deep model to highlight the boundary region of a salient object and improve the performance of SOD. Furthermore, other methods use Backbone to extract depth map features, which increases the complexity of the model. Based on this, we propose a lightweight boundary enhancement network (LBENet) for RGB-D salient object detection. The proposed LBENet comprises two main modules: a normalized max-min filter (NMMF) and a boundary weight module (BWM). First, instead of directly using CNNs to extract features from the depth map, which may be suboptimal owing to the use of unreliable depth data, we used the NMMF to extract boundary information from the raw depth data that contain less noisy features and rich boundary information and used the BWM to adaptatively enhance the RGB boundary region features. A boundary loss function allows for the learning of precise boundaries of salient objects The proposed end-to-end method achieves state-of-the-art performance on two widely used benchmarks, while improving the number of FLOPs (44.271 G), number of parameters (15.96 M), model size (65.4 MB), and inference speed (29.41 frame/s).
The Performances of Gelman-Rubin and Geweke's Convergence Diagnostics of Monte Carlo Markov Chains in Bayesian Analysis

Bayesian statistics have been widely used given the development of Markov chain Monte Carlo sampling techniques and the growth of computational power. A major challenge of Bayesian methods that has not yet been fully addressed is how we can appropriately evaluate the convergence of the random samples to the target posterior distributions. In this paper, we focus on Gelman and Rubin's diagnostic (PSRF), Brooks and Gleman's diagnostic (MPSRF), and Geweke's diagnostics, and compare the Type I error rate and Type II error rate of seven convergence criteria: MPSRF&gt;1.1, any upper bound of PSRF is larger than 1.1, more than 5% of the upper bounds of PSRFs are larger than 1.1, any PSRF is larger than 1.1, more than 5% of PSRFs are larger than 1.1, any Geweke test statistic is larger than 1.96 or smaller than -1.96, and more than 5% of Geweke test statistics are larger than 1.96 or smaller than -1.96. Based on the simulation results, we recommend the upper bound of PSRF if we only can choose one diagnostic. When the number of estimated parameters is large, between the diagnostic per parameter (i.e., PSRF) or the multivariate diagnostic (i.e., MPSRF), we recommend the upper bound of PSRF over MPSRF. Additionally, we do not suggest claiming convergence at the analysis level while allowing a small proportion of the parameters to have significant convergence diagnosis results.
Polychronicity, job performance, and work engagement: The mediating role of supervisor’s organizational embodiment and moderation of psychological ownership

Despite its importance to the global economy and its role in generating foreign currency profits for many countries, the tourism industry is not without its share of problems. This calls for research on the causes of improved productivity in the workplace. Therefore, this study aimed to investigate the effect of polychronicity on job performance and work engagement of tourism employees. The supervisor’s organizational embodiment was analyzed as a mediator and psychological ownership as a moderator. The data were gathered from 733 workers in the private tourist facilities of the city of Petra, Jordan by using a structured questionnaire and data analysis was carried out using AMOS-24. The findings revealed that polychronicity can lead to better job performance and work engagement. Supervisor’s organizational embodiment positively mediates the relationship of polychronicity with job performance and work engagement. In addition, the psychological ownership strengthens the relationship of polychronicity with work engagement and job performance. The emphasis of this study is on the most important behavioral outcomes of employees (i.e. work engagement and job performance); therefore, the practitioners of the tourism industry and hospitality industry can use the findings of this study to manage engagement- and performance-related issues of employees. Similarly, the researchers interested in polychromic behavior in organizations can also consider this research as a guideline.
Stochastic versus Periodic Quantum Collision Models

Most literature on quantum collision models (CMs) usually considers periodic weak collisions featuring a fixed waiting time between two next collisions. Some works have yet addressed CMs with random waiting time and strong collisions (stochastic CMs). This short paper discusses how the open dynamics arising from these two types of models can be formally mapped with one another. This can be achieved for a given stochastic CM by constructing an associated periodic CM such that the waiting time randomness of the former is turned into the mixedness of the ancilla's initial state of the latter, introducing at the same time an additional state of the ancilla. Through this mapping, non-Markovian behaviour arising from a constrained number of stochatic collisions can be linked to initial ancilla-ancilla correlations of the associated periodic CM.
Problematics of Digital Literacy Implementation in Early Children at Nurul Aulia Kindergarten, Depok

The Covid 19 pandemic that occurred from the end of 2019 to 2021 has made all aspects of our lives undergo a complete change. Education is an important sector because of the change from the offline learning process (face-to-face) to bold learning (online). The problem is then due to the unpreparedness of the education stakeholders, schools, teachers, parents, and students. This condition is almost felt by all schools in Early Childhood Education (ECE). The purpose of the study is to determine how problematics and application of digital literacy are carried out in Nurul Aulia Kindergarten, Depok. This study employed a qualitative approach through phenomenological research design. The results showed that online learning for early childhood was felt to be less effective. This was due to the unpreparedness of the infrastructure and the superstructure in the Nurul Aulia Kindergarten, Depok.
The Relationship between Learner Autonomy and Students’ Writing Skills

Learner autonomy is considered one of the essential factors affecting students' success in language learning. Students who have autonomy can manage and apply learning strategies to advance their language skills. It means learner autotomy will also help students improve their writing skills. This study aims to examine the level of learner autonomy of higher education students and its relationship with their writing skills. This is a quantitative study that examines the correlation between the two variables. A learner autonomy questionnaire and a writing test were used to collect quantitative data, which were then analyzed descriptively using the SPSS program. The sample included 125 undergraduate students from the Teacher Training and Education Faculty in Indonesia. An analysis of the Pearson Product Moment correlation was conducted to determine whether learner autonomy and writing skills had a significant and positive correlation. The finding showed a significant and positive correlation between the degree of learner autonomy and students' writing skills. This indicates that students with greater autonomy benefit from developing good writing abilities. This study also discovered that learner autonomy is one of the characteristics examined to predict students' learning achievement. From the results of the study, some recommendations on how to promote learner autonomy in language classrooms are provided.
An RGB camera-based fall detection algorithm in complex home environments

Abstract Objectives: Accidental falls are a threat to the well-being of older people. This study aimed to develop a real-time human fall detection system to detect fall behaviors and provide timely medical treatment for older adults. Methods: An RGB camera-based fall detection system is designed and it can send alarm messages when a fall occurs. This fall detection system consists of two design aspects: a hardware and a software algorithm. The fall detection algorithm includes (1) algorithm initialization phase to obtain environmental parameters; (2) 2-dimensional pose detection to identify human targets and human joint locations; and (3) limb-length and multiframe fall judgment to confirm the occurrence of falls based on its practical features. Results: By combining fall detection algorithms with a hardware system, the test results in complex home environments showed that the system sensitivity was 94.2%, the specificity was 96%, and the accuracy was 94.5%. Conclusion: The proposed method is more robust compared with the algorithm based exclusively on action recognition. Using only a monocular camera is cost-friendly and can realize real-time fall detections, and help older people to get timely and effective care after a fall.