Location Of Person Research Articles

User profile visualisation for privacy awareness on Geo-Social Networks

ABSTRACT Geo-Social Networks (GeoSNs) enable user interaction by sharing personal location and contextual information, leading to the accumulation of large databases of users’ physical presence and experiences in geographic places. These data form the basis of building (geo)graphic profiles for users on GeoSNs. Users’ awareness of the data they share is limited. Without active reminders of the contents of their geoprofiles, users may fail to recognise possible privacy risks associated with their location sharing behaviour. In this work, we argue that users’ awareness on GeoSNs should consider the spatial, temporal, and social dimensions of the data, as well as their combinations. We propose a basic strategy for representing and visualizing geoprofiles that aggregates the interaction of users in space over time to summarise the degree of relatedness of users to place and to other users. We conducted a user study with 26 participants to assess the perception of their geoprofiles on a typical GeoSN against the proposed approach. Results demonstrate the potential value of the proposal for improving user awareness of their data and privacy implications, compared to basic search and retrieval functions offered by typical GeoSNs. This work highlights the need for considering and improving user awareness on these platforms.

Research on Network Security and Privacy Protection Technology of Autonomous Vehicle

With the rapid development of information technology and the increasing maturity of intelligent transportation systems, autonomous vehicles, as an important carrier of future travel, are gradually moving from concept to commercial application. However, the highly connected and intelligent nature of autonomous vehicles also makes them face unprecedented cybersecurity and privacy protection challenges. This paper deeply discusses the security threats and privacy disclosure risks of autonomous vehicles in network architecture, data transmission, data processing and other links, and systematically reviews the current technical means and research progress to address these challenges. This paper Outlines the data collection and transmission mechanism of autonomous vehicles, including the collection and processing of sensor data as well as the communication process between vehicles and the cloud, other vehicles and transportation infrastructure, and analyzes in detail the network security threats faced by autonomous vehicles, including remote control attacks, malware attacks, communication hijacking, etc. These threats can lead to serious consequences such as vehicle failure, data breach, or system crash. At the same time, the paper also points out the challenges faced by autonomous vehicles in terms of privacy protection, such as the risk of leakage of sensitive data such as personal location information and driving habits, as well as the risk of data abuse and sharing. In order to address the above challenges, this paper focuses on the technical research progress of cybersecurity and privacy protection of autonomous vehicles. In the aspect of network security, this paper discusses the key technical means such as data encryption, access control, intrusion detection and response system, and emphasizes the importance of security hardware and software in improving the overall security of the system. In terms of privacy protection, this paper proposes strategies such as anonymization and desensitization technology, privacy protection protocols and standards to protect users' privacy from invasion.

Secure IoT Assistant-Based System For Alzheimer’s Disease

Alzheimer's disease is a neurological condition that gradually impairs a person's capacity for thought and memory. Gradually, it gets worse until it becomes a major hindrance to the way the person with the illness lives and functions in the world. The purpose of this research is to create a prototype for mild to moderate AD that provides safe information transmission and support services, allowing a family member to investigate it and safeguard the AD patient. The wearable prototype can distinguish between family and non-family members in recognized images and give a vocal communication with the individual's name. Additionally, the prototype makes it possible to track the AD person's location. The prototype makes their daily lives easier. Thus far, the prototype follows the individual with AD around and notifies caregivers of their whereabouts so that, if the patient leaves the house, they can securely return them home. Additionally, by detecting dampness, this prototype informs the patient's caregiver of their excrement. So, the prototype will help the patient in their daily life very efficiently.

Exploring named‐entity recognition techniques for academic books

Recent advances in the natural language processing (NLP) field have achieved impressive results in various tasks. However, NLP techniques are underrepresented in the analysis of Humanities and Social Science texts and in languages other than English. In particular, academic books are a highly valuable source of information that has not been exploited by these techniques at all. The recognition of named entities (person names, organizations or locations) and their semantic annotation over books could enrich the visibility and discoverability of the information by users. This is an opportunity for academia and the academic publishing industry in which semantic search is a central task and now books can be queried by named entities of interest that are in their content. This work proposes a methodology to apply named‐entity recognition to publish the results into an ontological semantic‐web format. The work has been performed over a corpus of academic books provided by UNE (Unión de Editoriales Universitarias Españolas, Union of Spanish University Presses). Results show an enrichment of the information extracted over the books and of the possibilities of querying them at the individual level but also within the whole set of books, increasing the possibilities for books to be discovered or retrieved beyond metadata.

Finding Missing Person Using Face Recognition

Abstract: This project presents a novel approach to enhance the efficiency and effectiveness of locating missing persons through the utilization of face recognition technology The main objective is to develop a robust system that can extract facial features, match them with a database, and notify the missing person's location. The system uses a special type of artificial intelligence called CNN to compare faces with the database and find the best matches. The face recognition system uses the CNN (Convolutional Neural Network) algorithm to compare the extracted faces with the database and find the best matches. The alert module sends notifications to the family members of the missing person with the location information if a match is found. The system also updates the database with the latest information about the missing person. The project leverages state-of-the-art machine learning algorithms to extract and compare facial features, it reduces the effort and time required for manual process. This project aims to find missing people faster and better by using facial recognition technology. The main goal is to build a strong system that can study facial details, match them with a database of known people, and help to find missing people quickly and correctly. By harnessing the power of artificial intelligence, this system offers a promising solution to the pressing issue of finding missing individuals, potentially helping to reunite families and improve public safety.

Syndromic Gastrointestinal Panel Diagnostic Tests Have Changed our Understanding of the Epidemiology of Yersiniosis-Foodborne Diseases Active Surveillance Network, 2010-2021.

In the US, yersinosis was understood to predominantly occur in winter and among Black or African American infants and Asian children. Increased use of culture-independent diagnostic tests (CIDTs) has led to marked increases in yersinosis diagnoses. We describe differences in the epidemiology of yersiniosis diagnosed by CIDT versus culture in 10 US sites, and identify determinants of health associated with diagnostic method. Annual reported incidence increased from 0.3/100 000 in 2010 to 1.3/100 000 in 2021, particularly among adults ≥18 years, regardless of race and ethnicity, and during summer months. The proportion of CIDT-diagnosed infections increased from 3% in 2012 to 89% in 2021. An ill person's demographic characteristics and location of residence had a significant impact on their odds of being diagnosed by CIDT. Improved detection due to increased CIDT use has altered our understanding of yersinosis epidemiology, however differential access to CIDTs may still affect our understanding of yersinosis.

Tourists’ Adoption of Context-Aware Applications: A Case Study, Using Personal Locator Beacons at the Mole National Park, Ghana

The use of technology to provide contextual information for decision-making has been a long-standing practice. The study aims to identify the antecedents influencing tourists’ use of context-aware applications. It employs the Unified Model of Electronic Government Adoption (UMEGA) and the Expectation Confirmation Model (ECM) to investigate tourists’ behaviors in the use of Personal Locator Beacons (PLBs) during the pre-adoption and post-adoption stages at the Mole National Park in Ghana. Data was analyzed using Smart PLS 3.0 software. The findings suggest that pre-adoption performance expectancy, motivational needs, and personal situation significantly affect tourists’ pre-adoption intentions to continue using context-aware applications. In addition, information recommendation, service quality, confirmation expectation, and satisfaction influenced continued use and recommendation. However, social influence and enjoyment experience did not significantly impact tourists’ behavioral intentions. The findings contribute to theory by integrating components of the two models (UMEGA & ECM) and delving into the antecedents of users’ decision-making processes.

Where you go is who you are: a study on machine learning based semantic privacy attacks

Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data. Location data is particularly sensitive since they allow us to infer activity patterns and interests of users, e.g., by categorizing visited locations based on nearby points of interest (POI). On top of that, machine learning methods provide new powerful tools to interpret big data. In light of these considerations, we raise the following question: What is the actual risk that realistic, machine learning based privacy attacks can obtain meaningful semantic information from raw location data, subject to inaccuracies in the data? In response, we present a systematic analysis of two attack scenarios, namely location categorization and user profiling. Experiments on the Foursquare dataset and tracking data demonstrate the potential for abuse of high-quality spatial information, leading to a significant privacy loss even with location inaccuracy of up to 200 m. With location obfuscation of more than 1 km, spatial information hardly adds any value, but a high privacy risk solely from temporal information remains. The availability of public context data such as POIs plays a key role in inference based on spatial information. Our findings point out the risks of ever-growing databases of tracking data and spatial context data, which policymakers should consider for privacy regulations, and which could guide individuals in their personal location protection measures.

A review on positioning techniques of mobile robots

In this article, we have reviewed the available positioning, localization and navigation techniques for mobile robots. Different localization techniques based on diverse technologies are compared with one another, along with diverse algorithms and techniques for analyzing this information. The article highlights algorithms based on odometry, triangulation, visual analysis, and marker detection. The analysis included global, local, and personal location. One acquires knowledge on which method is suitable for indoor use and which for outdoor use, as well as the appropriate environmental conditions for each. The accuracy of the individual methods was compared with that of integrated systems consisting of several methods. For practical knowledge, it is possible to determine whether a particular method is cost-effective for a particular solution and to compare the expenses involved.

Innovative Blind Stick for The Visually Impaired

Visual impairment is an appellation that alludes to the state of being visionless. Individuals with optical blockage are likely to have limitations in their independence and movement, as well as a higher risk of falling, fractures, injuries, poor mental health, and social isolation. Thus, we propose the design of a blind stick to help blind people and assist them in familiarizing themselves with a clear path. In our proposed prototype, multiple sensors are attached to the individual's stick. The sensor detects an obstruction, the receiver's output activates, and the microcontroller detects this change since the receiver's outputs are inputs to the microcontroller. This stick detects the impediment in front and reacts by vibrating or mandating the user. Furthermore, a smoke sensor, fall detection sensor, heart rate, SPO2 level sensor, and Temperature sensor are utilized to detect multiple parameters and not only indicate the individual using the blind stick but to the caregiver as well through an application connected to the blind stick. Moreover, GPS tracking is also present which helps the individual with the stick and also informs the caregiver about the location of the blind person. The results of our proposed prototype demonstrate that this electronic device is a sufficient solution for overcoming the difficulties faced by visually impaired people and their caregivers and allowing them to live their lives independently. In conclusion, our innovative blind stick offers a transformative solution for visually impaired individuals, enhancing their mobility and independence. We invite readers to delve into the full paper to discover the full extent of its capabilities and its potential to make a meaningful difference in the lives of millions worldwide.

University Work Experiences in International Development: Expanding Locations, Spaces and Pathways

How can university work experiences contribute to reframing International Development from expert saviourism rooted in colonial legacies into a project of social justice and global solidarity? In this article, I propose the design of work-experiential pedagogies that integrate practical work experiences with critical theoretical teaching and an emphasis on students’ experiential knowledge. Such pedagogies call for an expansion of the forms of university work experiences, challenging students to reflect on their manifold personal and institutional locations, to explore the diverse spaces in which work experiences can take place and to experiment with multiple pathways for change. My proposal draws on teaching examples and student journeys at my home university in the UK.

Predicting the locations of missing persons in China by using NGO data and deep learning techniques

ABSTRACT Missing person crimes can seriously affect the well-being of Chinese families, and missing person destination prediction can help to solve this problem. Using nongovernmental organization (NGO) data to predict the locations of missing persons by random forest (RF) model has made progress. However, studies using these data have ignored the mass of oral information. Recent studies have demonstrated the effectiveness of oral information in detecting missing persons, but the impact on destination prediction remains unexplored. Therefore, this study proposes a missing person prediction (MP-Net) framework to incorporate oral information into missing person destination prediction and quantitatively describe the effect of different word properties on the prediction. The results show that compared to the baseline RF model, the proposed framework achieves a higher recall rate (87.18%) in the location prediction of missing persons. According to a quantitative word analysis, verbs and nouns in oral information significantly contributed to location prediction. After adjectives that might cause adverse effects were removed, the stability of the model was improved considerably. Overall, the findings of the proposed model and quantitative word analysis can help police or NGOs collect descriptive information in a targeted manner and make more accurate predictions about the whereabouts of missing persons.

EuroCity Persons 2.0: A Large and Diverse Dataset of Persons in Traffic.

We present the EuroCity Persons (ECP) 2.0 dataset, a novel image dataset for person detection, tracking and prediction in traffic. The dataset was collected on-board a vehicle driving through 29 cities in 11 European countries. It contains more than 250K unique person trajectories, in more than 2.0M images and comes with a size of 11 TB. ECP2.0 is about one order of magnitude larger than previous state-of-the-art person datasets in automotive context. It offers remarkable diversity in terms of geographical coverage, time of day, weather and seasons. We discuss the novel semi-supervised approach that was used to generate the temporally dense pseudo ground-truth (i.e. 2D bounding boxes, 3D person locations) from sparse, manual annotations at keyframes. Our approach leverages auxiliary LiDAR data for 3D uplifting and vehicle inertial sensing for ego-motion compensation. It incorporates keyframe information in a threestage approach (tracklet generation, tracklet merging into tracks, track smoothing) for obtaining accurate person trajectories. We validate our pseudo ground-truth generation approach in ablation studies, and show that it significantly outperforms existing methods. Furthermore, we demonstrate its benefits for training and testing of state-of-the-art tracking methods. Our approach provides a speed-up factor of about 34 compared to frame-wise manual annotation. The ECP2.0 dataset is made freely available for non-commercial research use.

Stick and Smart Wallet

The research aims to equip the blind person with a cane and smart wallet equipped with front and side sensors. These sensors will provide the blind person information in the form of audio signals or vibrations when he gets within two meters of an obstacle. The vibration signals will intensify as the person gets closer to the obstacle, alerting him. The stick was chosen because it can protect him in the event of a hole in the road or climbing stairs. While the rest of the sensors warn him of high barriers whose height is less than the height of the blind person, sensing devices have been added to sense and determine the location of the blind person if he is lost in any place so that his location is determined. The purpose of the research is to design and implement a smart stick and then develop it into an electronic (smart) wallet to guide the visually impaired, which works to guide the blind as he passes on the road without fear and without any help from others.

Impact of IoT on Security and Data Protection

The Internet of Things (IoT) is revolutionizing the way we live and work, but it also brings new challenges to security and data protection. The vast number of interconnected devices, the diversity of their software and hardware, and the sensitivity of the data they collect and transmit create a complex security landscape.
 The IoT has the potential to bring about significant benefits, but it is important to address the security and data protection challenges before it can be fully realized. By implementing strong security measures, educating users, and developing data protection regulations, we can ensure that the IoT is a safe and secure environment for everyone.
 IoT devices collect and transmit vast amounts of data, including personal information, location data, and usage patterns. This data can be used for legitimate purposes, such as improving the performance of IoT devices or providing personalized services. However, it can also be misused for malicious purposes, such as identity theft, stalking, or discrimination. While IoT offers immense benefits in terms of efficiency, convenience, and automation, it also raises concerns about data security and privacy.

The Development of User Requirements as a Framework for the Design and Evaluation of a Fit-for-Purpose Missing Person Locator System for Underground Mines

Entrapments are a huge risk in underground mines. This happens when miners (or any other persons who have entered underground) survive the initial event of an accident, become trapped or lost in unknown and life-threatening locations after evacuation, and remain unaccounted for. At this point, every second counts, and emergency response is highly desired for any chances of saving survivors. The survival chances of the missing persons depend on how quickly they can be located. Rescue teams are often deployed to search for the missing persons underground. However, the rescue teams are forced to search in random and presumed areas of the mine. This is due to the unavailability of information pertaining to the whereabouts of the missing persons. Not only the provision of accurate locations, but various other types of information such as post-accident two-way communication can enhance the survival chances of the missing persons. This information can be provided by a fit-for-purpose missing person locator system. A wide variety of missing person locator systems are already commercially available, some are in testing phases and others in prototype and development phases. This includes tracking, tracing, location detectors and post-accident two-way communication systems. These systems have unique functionalities, capabilities and limitations; making it difficult for mines to select the most effective, applicable and suitable system specific to their mine. In this study, the user requirements for a fit-for-purpose missing person locator system were developed. The purpose of the user requirements was to provide a guideline to facilitate the system selection process. The user requirements can also be used as a tool to measure and evaluate the performance of the selected missing person locator system. The user requirements were also used to predict the success of the selected system under different missing person incident scenarios. The development of the user requirements showed a potential for many other benefits for missing person locator systems.

Mathematics Education Amidst the Pandemic: The Experiences of Freshmen University Students Challenged at the Margin

This phenomenography describes and analyses the experiences of geographically challenged students in learning mathematics during the pandemic. The sample includes six geographically challenged first-year Bachelor of Secondary Education University students in a public State University in the Philippines. Data is collected using a semi-structured interview guide. Data was analysed using phenomenographic data analysis. Results show that the experiences of these geographically challenged students fall within three qualitatively different categories of description. These are new modalities and new mathematics learning experiences, mathematics difficulty vis-a-vis teaching styles and struggling, coping, and persisting. Challenges like personal, financial, technological, instructional, socio-economic, and geographic location persist as they pursue education in the new normal. These students attribute course difficulty in mathematics to pedagogical aspects, teaching approaches of instructors, personally challenging situations, and the inability to blend with the trend. Hence, school administrators may strengthen the quality of mathematics instruction in the university by considering the voices of the marginalised and not just offering a one-size-fits-all policy to make the mathematics curriculum flexible, inclusive, and equitable. This will address the demands and needs of the learners, especially the geographically challenged ones.

Specific associations of passively sensed smartphone data with future symptoms of avoidance, fear, and physiological distress in social anxiety

BackgroundPrior literature links passively sensed information about a person's location, movement, and communication with social anxiety. These findings hold promise for identifying novel treatment targets, informing clinical care, and personalizing digital mental health interventions. However, social anxiety symptoms are heterogeneous; to identify more precise targets and tailor treatments, there is a need for personal sensing studies aimed at understanding differential predictors of the distinct subdomains of social anxiety. Our objective was to conduct a large-scale smartphone-based sensing study of fear, avoidance, and physiological symptoms in the context of trait social anxiety over time. MethodsParticipants (n = 1013; 74.6 % female; M age = 40.9) downloaded the LifeSense app, which collected continuous passive data (e.g., GPS, communication, app and device use) over 16 weeks. We tested a series of multilevel linear regression models to understand within- and between-person associations of 2-week windows of passively sensed smartphone data with fear, avoidance, and physiological distress on the self-reported Social Phobia Inventory (SPIN). A shifting sensor lag was applied to examine how smartphone features related to SPIN subdomains 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction). ResultsA decrease in time visiting novel places was a strong between-person predictor of social avoidance over time (distal β = −0.886, p = .002; medial β = −0.647, p = .029; proximal β = −0.818, p = .007). Reductions in call- and text-based communications were associated with social avoidance at both the between- (distal β = −0.882, p = .002; medial β = −0.932, p = .001; proximal β = −0.918, p = .001) and within- (distal β = −0.191, p = .046; medial β = −0.213, p = .028) person levels, as well as between-person fear of social situations (distal β = −0.860, p < .001; medial β = −0.892, p < .001; proximal β = −0.886, p < .001) over time. There were fewer significant associations of sensed data with physiological distress. Across the three subscales, smartphone data explained 9–12 % of the variance in social anxiety. ConclusionFindings have implications for understanding how social anxiety manifests in daily life, and for personalizing treatments. For example, a signal that someone is likely to begin avoiding social situations may suggest a need for alternative types of exposure-based interventions compared to a signal that someone is likely to begin experiencing increased physiological distress. Our results suggest that as a prophylactic means of targeting social avoidance, it may be helpful to deploy interventions involving social exposures in response to decreases in time spent visiting novel places.

PPVC: Towards a Personalized Local Differential Privacy-Preserving Scheme for V2G Charging Networks

The rapid development of electric vehicles provides users with convenience of life. When users enjoy the V2G charging service, privacy leakage of their charging location is a crucial security issue. Existing privacy-preserving algorithms for EV access to charging locations suffer from the problem of nondefendable background knowledge attacks and privacy attacks by untrustworthy third parties. We propose a personalized location privacy protection scheme (PPVC) based on differential privacy to meet users’ personalized EV charging requirements while protecting their privacy. First, by constructing a decision matrix, PPVC describes recommended routes’ utility and privacy effects. Then, a utility model is constructed based on the multiattribute theory. The user’s privacy preferences are integrated into the model to provide the route with the best utility. Finally, considering the privacy preference needs of users, the Euclidean distance share is used to assign appropriate privacy budgets to users and determine the generation range of false locations to generate the service request location with the highest utility. The experimental results show that the proposed personalized location privacy protection scheme can meet the service demands of users while reasonably protecting their privacy to provide higher service quality. Compared with existing solutions, PPVC improves the charging efficiency by up to 25%, and 8% at the same privacy protection level.

Scorpion-inspired bionic gait activity location and recognition smart home system

Vibration-based contactless technologies are arising for indoor person location and gait activity recognition applications, which can overcome issues such as privacy breaches and wearing discomfort with traditional methods to satisfy the critical need for health and safety monitoring in smart home. However, simultaneous location and recognition of occupant gait activity using information-starved vibration signals remains challenging due to its less characterization of event details. In nature, scorpions use a 3/1 neuron coding pattern to enrich vibration information by enhancing sensory differences from eight vibration sensilla embedded in their metatarsal ends of each walking leg, thus exhibiting high accuracy in locating moving prey. Inspired by this, we exploit a coding pattern to make vibration signal features richer for discrimination, while designing a bionic gait activity location and recognition system in which the data acquisition device is built by imitating scorpion vibration sensilla array distribution. To verify the feasibility of the system, we collect vibration signals produced by different people with varying gait activities (walking, jogging, running, and stepping) in an actual scene, and a trained hidden Markov model is employed to identify the activity of the current location target. The average estimated error and the relative estimated error of position estimation are calculated to be 0.3517 m and 8.79 %, respectively. The average recall and average precision of gait activity recognition are 95.83 % and 95.84 %, respectively. Our system is proven to simultaneously locate and recognize occupant gait activities, which can meet the needs including smart home, and health care monitoring.