Location Information Research Articles

You Don’t Have to Say Where to Edit! jLED – Joint Learning to Localize and Edit Source Code

Learning to edit code automatically is becoming more and more feasible. Thanks to recent advances in Neural Machine Translation (NMT), various case studies are being investigated where patches are automatically produced and assessed either automatically (using test suites) or by developers themselves. An appealing setting remains when the developer must provide a natural language input of the requirement for the code change. A recent proof of concept in the literature showed that it is indeed feasible to translate these natural language requirements into code changes. A recent advancement, MODIT [8], has shown promising results in code editing by leveraging natural language, code context, and location information as input. However, it struggles when location information is unavailable. While several studies [29, 81] have demonstrated the ability to edit source code without explicitly specifying the edit location, they still tend to generate edits with less accuracy at the line level. In this work, we address the challenge of generating code edits without precise location information, a scenario we consider crucial for the practical adoption of NMT in code development. To that end, we develop a novel joint training approach for both localization and source code editions. Building a benchmark based on over 70k commits (patches and messages), we demonstrate that our jLED ( j oint L ocalize and ED it) approach is effective. An ablation study further demonstrates the importance of our design choice in joint training.

Bird Species Detection Net: Bird Species Detection Based on the Extraction of Local Details and Global Information Using a Dual-Feature Mixer.

Bird species detection is critical for applications such as the analysis of bird population dynamics and species diversity. However, this task remains challenging due to local structural similarities and class imbalances among bird species. Currently, most deep learning algorithms focus on designing local feature extraction modules while ignoring the importance of global information. However, this global information is essential for accurate bird species detection. To address this limitation, we propose BSD-Net, a bird species detection network. BSD-Net efficiently learns local and global information in pixels to accurately detect bird species. BSD-Net consists of two main components: a dual-branch feature mixer (DBFM) and a prediction balancing module (PBM). The dual-branch feature mixer extracts features from dichotomous feature segments using global attention and deep convolution, expanding the network's receptive field and achieving a strong inductive bias, allowing the network to distinguish between similar local details. The prediction balance module balances the difference in feature space based on the pixel values of each category, thereby resolving category imbalances and improving the network's detection accuracy. The experimental results using two public benchmarks and a self-constructed Poyang Lake Bird dataset demonstrate that BSD-Net outperforms existing methods, achieving 45.71% and 80.00% mAP50 with the CUB-200-2011 and Poyang Lake Bird datasets, respectively, and 66.03% AP with FBD-SV-2024, allowing for more accurate location and species information for bird detection tasks in video surveillance.

Prediction of GNSS Velocity Accuracies Using Machine Learning Algorithms for Active Fault Slip Rate Determination and Earthquake Hazard Assessment

GNSS technology utilizes satellite signals to determine the position of a point on Earth. Using this location information, the GNSS velocities of the points can be calculated. GNSS velocity accuracies are crucial for studies requiring high precision, as fault slip rates typically range within a few millimeters per year. This study employs machine learning (ML) algorithms to predict GNSS velocity accuracies for fault slip rate estimation and earthquake hazard analysis. GNSS data from four CORS stations collected over 1-, 2-, and 3-year intervals with observation durations of 2, 4, 6, 8, and 12 h, were analyzed to generate velocity estimates. Position accuracies, observation intervals, and corresponding velocity accuracies formed two datasets for the East and North components. ML models, including Support Vector Machine, Random Forest, K-Nearest Neighbors, and Multiple Linear Regression, were used to model the relationship between position and velocity accuracies. The findings reveal that the Random Forest, which makes more accurate and reliable predictions by evaluating many decision trees together, achieved over 90% accuracy for both components. Velocity accuracies of ±1.3 mm/year were obtained for 1-year interval data, while accuracies of ±0.6 mm/year were achieved for the 2- and 3-year intervals. Three campaigns were deemed sufficient for Holocene faults with higher slip rates. However, for Quaternary faults with lower slip rates, longer observation periods or additional campaigns are necessary to ensure reliable velocity estimates. This highlights the need for GNSS observation planning based on fault activity.

Validation of spatial accuracy with high-speed imaging to support in situ flaw detection for laser directed energy deposition processes

The application of in situ monitoring systems for part quality verification or process qualification of the laser based directed energy deposition process will require the ability to confidently detect (or estimate) the type, size, and location of defects in an additive manufactured component. These developments are requested by the industry to supplement the expensive and time-consuming ex situ inspections for part certification to further support the number of viable business cases. This confidence level of the in situ monitoring solutions should be comparable to that of the ex situ inspection techniques (such as microcomputed tomography). These developments will also require very large experimental data sets with spatially resolved in situ monitoring signals which must be correlated to ex situ inspection data. A crucial element in this correlation is the spatial absolute accuracy of these signals from both in situ and ex situ solutions. The in situ solutions should, preferably, at least match with ex situ accuracy. Currently, there is no consensus framework for validation of the spatial accuracy of the in situ monitoring signals. Therefore, a method will be presented in this paper to evaluate the spatial accuracy obtainable with the typical available location information on ethernet field bus implemented in the laser based directed energy deposition process. The accuracy is verified by the implementation of a high-speed imaging setup that additionally enables to verify the synchronization accuracy of the imaging solutions. Cameras act often as a source of data for the in situ monitoring solutions of the melt pool.

Sybil Attack-Resistant Blockchain-Based Proof-of-Location Mechanism with Privacy Protection in VANET.

In this paper, we propose a Proof-of-Location (PoL)-based location verification scheme for mitigating Sybil attacks in vehicular ad hoc networks (VANETs). For this purpose, we employ smart contracts for storing the location information of the vehicles. This smart contract is maintained by Road Side Units (RSUs) and acts as a ground truth for verifying the position information of the neighboring vehicles. To avoid the storage of fake location information inside the smart contract, vehicles need to solve unique computational puzzles generated by the neighboring RSUs in a limited time frame whenever they need to report their location information. Assuming a vehicle has a single Central Processing Unit (CPU) and parallel processing is not allowed, it can solve a single computational puzzle in a given time period. With this approach, the vehicles with multiple fake identities are prevented from solving multiple puzzles at a time. In this way, we can mitigate a Sybil attack and avoid the storage of fake location information in a smart contract table. Furthermore, the RSUs maintain a dedicated blockchain for storing the location information of neighboring vehicles. They take part in mining for the purpose of storing the smart contract table in the blockchain. This scheme guarantees the privacy of the vehicles, which is achieved with the help of a PoL privacy preservation mechanism. The verifier can verify the locations of the vehicles without revealing their privacy. Experimental results show that the proposed mechanism is effective in mitigating Sybil attacks in VANET. According to the experiment results, our proposed scheme provides a lower fake location registration probability, i.e., lower than 10%, compared to other existing approaches.

Identifying cell-type-specific spatially variable genes with ctSVG.

Spatially variable genes (SVGs) reveal the molecular and functional heterogeneity of cells across different spatial regions of a tissue. We found that sample-wide SVGs, identified by previous methods across the whole sample, largely overlap with cell-type marker genes derived from single-cell gene expression, leaving the spatial location information largely underutilized. We developed ctSVG, a computational method specifically tailored for Visium HD spatial transcriptomics at single-cell resolution. ctSVG accurately assigns Visium squares to cells and identifies cell-type-specific SVGs. We show that cell-type-specific SVGs identified by ctSVG include many new genes that do not overlap with sample-wide SVGs or cell-type marker genes, and that these genes reveal important biological functions in real spatial datasets.

Spatial variation in housing construction material in low- and middle-income countries: A Bayesian spatial prediction model of a key infectious diseases risk factor and social determinant of health.

Housing infrastructure and quality is a major determinant of infectious disease risk and other health outcomes in regions where vector borne, waterborne and neglected tropical diseases are endemic. It is important to quantify the geographical distribution of improvements to dwelling components to identify and target resources towards populations at risk. This study aimed to model the sub-national spatial variation in housing materials using covariates with quasi-global coverage and use the resulting estimates to map predicted coverage across the world's low- and middle-income countries. Data on materials used in dwelling construction were sourced from nationally representative household surveys conducted since 2005. Materials used for construction of flooring, walls, and roofs were reclassified as improved or unimproved. Households lacking location information were georeferenced using a novel methodology. Environmental and demographic spatial covariates were extracted at those locations for use as model predictors. Integrated nested Laplace approximation models were fitted to obtain, and map predicted probabilities for each dwelling component. The dataset compiled included information from households in 283,000 clusters from 350 surveys. Low coverage of improved housing was predicted across the Sahel and southern Sahara regions of Africa, much of inland Amazonia, and areas of the Tibetan plateau. Coverage of improved roofs and walls was high in the Central Asia, East Asia and Pacific and Latin America and the Caribbean regions. Improvements in all three components, but most notably floors, was low in Sub-Saharan Africa. The strongest determinants of dwelling component quality related to urbanization and economic development, suggesting that programs should focus on supply-side interventions, providing resources for housing improvements directly to the populations that need them. These findings are made available to researchers as files that can be imported into a GIS for integration into relevant analyses to derive improved estimates of preventable health burdens attributed to housing.

Analysis of Time–Frequency Characteristics and Influencing Factors of Air Quality Based on Functional Data in Fujian

Increased air pollution is driven by anthropogenic pollution emissions and climate change, which pose great challenges to environmental governance. Strengthening the monitoring of regional air quality levels and analyzing the causes of regional pollution is conducive to the management and sustainable development of the regional atmosphere. Functional data obtained on a wavelet basis were used in the fitting of air quality data of Fujian Province, and wavelet decomposition was performed to obtain low-frequency and high-frequency information. While the Fourier basis cannot adaptively adjust the time–frequency window, resulting in the loss of location information of special frequencies, the wavelet basis solves this problem. Functional analysis of variance was utilized for analyzing spatial differences in air pollution characteristics. Furthermore, the study established a multivariate functional linear regression model to explore the impact of meteorological factors and ozone precursor factors. The results indicated that the overall air quality was gradually improving in Fujian Province, but the concentration of ozone was progressively increasing. Air pollution in coastal areas was higher than that in inland areas. The p-values of the functional analysis of variance for energy values and crest values were less than 0.05. Moreover, the energy entropy and kurtosis values were greater than 0.05. There were significant differences of AQI in the fluctuation amplitude and variation characteristics of different cities. The total squared multiple correlation of regression model was above 50% on average. Ozone is currently the most serious pollution factor, mainly affected by wind speed, temperature, NO2, and CO. In summer, it was principally influenced by VOCs. The findings of this study could act as a reference in exploring the time–frequency characteristics of air quality data and support of air pollution control.

Adaptive Recommendation of Teaching Content in Higher Education Using Mobile Interaction Technology

With the rapid advancement of mobile interaction technology, teaching methodologies in higher education are increasingly moving toward personalization and intelligence. The use of mobile interaction technology for adaptive recommendation of teaching content has become a critical topic for enhancing educational effectiveness. Existing research in content recommendation, primarily based on collaborative filtering algorithms, often relies on single-dimensional data applications and lacks comprehensive consideration of both location information and temporal effects. Consequently, these approaches fall short in addressing the complex requirements of dynamic learning environments. This study proposes a multi-dimensional dynamic adaptive recommendation system for teaching content based on mobile interaction technology to address the limitations of existing methods. The research encompasses location-based collaborative filtering for teaching content, time-effect-based collaborative filtering, and an integrated multi-dimensional dynamic recommendation model that considers both location and temporal factors. This study is expected to provide a more precise and dynamically adaptive solution for personalized teaching in higher education.

All Means All: Correlates for Success on a Social Studies Licensure Exam

The social studies subtest on the TExES 391 teacher licensure exam consistently causes failure among preservice teachers from a regional university. Failing one section of the exam is enough to be a barrier to achieving teacher certification. To understand potential influences on success, eighty-five exam scores were examined in correlation to academic history, program location, and basic demographic information. The results point to variables that may be useful to identify students for potential intervention. They also highlight the need for more in-depth exploration of student struggles to facilitate the intentional construction of effective supports.

Direct Prosthesis Force Control with Tactile Feedback May Connect with the Internal Model.

Dynamic modulation of grip occurs mainly within the major structures of the brain stem, in parallel with cortical control. This basic, but fundamental level of the brain, is robust to ill-formed feedback and to be useful, it may not require all the perceptual information of feedback we are consciously aware. This makes it viable candidate for using peripheral nerve stimulation (PNS), a form of tactile feedback that conveys intensity and location information of touch well but does not currently reproduce other qualities of natural touch. Previous studies indicate that PNS can integrate with the basic levels of the motor system at a pre-perceptual level and can be processed optimally in multisensory integration, but there is little evidence if PNS is used effectively for motor corrections. We performed a study with an individual with a mid-radial upper limb difference who has cuff electrodes on his peripheral nerves to give him the sense of touch to perform an object movement over a barrier task. During this task we measured how the participant moved the object with a prosthetic hand in space, how they varied their grip force on the object, and how their muscle signals varied as force changed. We tested this with four different conditions: with and without stimulated tactile sensation combined with the user having control over force on an object or velocity of hand movement. Given direct control of force, the participant's output force significantly correlated with the puck's displacement up to the apex of the movement, but did not correlate afterwards. This indicated a trend of increasing force when lifting the puck, but no decrease when lowering it. In comparison, when the participant moved the puck with the intact hand, they had a small but significant increase in force when lifting the puck in half the cases, but always had a significantly decrease in force when lowering the puck. When the participant used a force controller with stimulation, the puck slipped or dropped significantly more times (p < 0.05) compared to the velocity controller with stimulated feedback. This result implied that when the participant intended to loosen their grip, the prosthesis opened instead, which would explain the lack of force reduction in the initial results. The analysis of intent decoded from EMG during use of the force controller shows that the participant intended to lower their grip force with or without stimulation when using a high shatter threshold, but when using a lower threshold, the stimulation gave the participant a better sense of where the shatter threshold was. With a low shatter force, the participant tended to modulate their muscle contractions to a constant level if they were given stimulation (no significant correlation with movement) or they generally increased their intended force towards the shatter force threshold without stimulated feedback. With a moderate shatter force, the participant kept a relatively constant contractile force with or without stimulation. In contrast the EMG analysis with the velocity controller has a mixed trend of increasing and decreasing muscle indicating no global desire to change their grip force in one direction or the other. Finally, analysis of the puck movement showed that the participants moved the puck higher above the barrier with the force controller compared to movements with the velocity controller (p < 0.001), but the addition of stimulation with either controller lowered the participant's movements significantly closer to the barrier (p < 0.001). Stimulation may cause an instantaneous increase in confidence with a controller or create better positional awareness with either controller. While the participant of this study did not show any significant output grip force changes during the object movement tasks, their decoded intent combined with the higher number of loosening events when using the force controller and with stimulation indicates they may have been trying to reduce their grip force during the task. This behavior matches with the force output of the participant's intact hand. In order convert the participant's intent into the correct output force, there needs to be changes to the overall design of modern prosthetic devices to allow for smaller grip force changes and changes to force within a static grip. Furthermore, improvements to the stimulation that amplify small changes in force and estimate the any slip forces on the fingertips will provide more useful signals to the participant.

Cellular Phone Number Tracking System Based on ArcGIS and Google Maps

Over 4 billion individuals are projected to own a mobile device, with smartphone use continuing to rise around the world. Associated with this trend is the collection and usage of location information provided by these devices. The procedure for tracking and evaluating a mobile phone number is crucial for crime and terrorist attacks preventing concerns, especially in countries with deteriorated security conditions. The locational tracking information can enable real-time operational awareness and can save people's lives. In this aspect, the Geographic Information System (GIS) system is frequently used to provide intensive spatial information along the tracking process. This research aims to examine, follow up on, and trace a certain person's phone number inside a certain area using a combination of ArcGIS and Google Maps application software they are programs used to track and determine locations through the use of various types of coordinates, including latitude and longitude. In the ArcGIS program, there are several methods and algorithms used in various fields, including (locating people, determining tourist places, determining water and sewage networks, forecasting in the event of storms or floods, and many other areas), an example of these methods in the program is (Kriging method, Idw method, Spline method) and many other methods, each method is used for a specific purpose and field. In this research, the kriging method was used, which is a design algorithm that is used in many analysis and tracking processes and gives clear and accurate results. The process of determining locations in the maps used in the search depends on taking the longitude and latitude coordinates and downloading them to those maps. The maps that have been worked on in the research provide a set of basic concepts adopted in the process of tracking a particular person’s phone number, such as (the person’s location, the numbers connected to him, and the duration of a call to numbers in his contact list). The tracking system used is tested by creating a virtual contact list for a specific person’s phone number and making use of all the data in the contact list and applying it in the ArcGIS program, knowing that the duration of the contact list used is 9 months (from January to October 2021). The obtained results are analyzed and sorted in different ways to provide a wide model of the locational communication and movement activity of the tracked person. They showed that the system is efficient and can be applied to enhance the security in Iraq for tracking suspected people in crimes like terrorism, kidnapping, drugs, extortion … etc.

Fingerprint Augmentation Based on Location Information Consistency in Dynamic Environment

Fingerprint Augmentation Based on Location Information Consistency in Dynamic Environment

Digital Twin System Framework and Implementation for Grid-Integrated Electric Vehicles

Research on digital twins (DTs) in the power system field has mainly focused on implementing DTs for specific resources, while few studies on electric vehicle (EV)-based DT implementation have considered integration and interoperability between systems. This study introduces a DT-based EV system operation framework to address the aforementioned research gap. The framework implements individual EVs, charging stations, and charging station operators (CPOs) as DTs, enabling integrated operation with the power grid. The DT-based EV agent supports independent decision-making on power service participation by considering location information, distance, charging amount, spare time, and incentives. In addition, the CPO can establish an optimal incentive strategy to induce EV users to participate in grid power services. The proposed DT systems map information between EVs, charging stations, and the grid, enabling analysis and verification of the impact of participants on charging station operation, grid stability, and economic efficiency in an independent environment. The effectiveness and usability of the proposed framework were verified through a case study on an incentive-based demand response program.

Multipopulation Whale Optimization-Based Feature Selection Algorithm and Its Application in Human Fall Detection Using Inertial Measurement Unit Sensors.

Feature selection (FS) is a key process in many pattern-recognition tasks, which reduces dimensionality by eliminating redundant or irrelevant features. However, for complex high-dimensional issues, traditional FS methods cannot find the ideal feature combination. To overcome this disadvantage, this paper presents a multispiral whale optimization algorithm (MSWOA) for feature selection. First, an Adaptive Multipopulation merging Strategy (AMS) is presented, which uses exponential variation and individual location information to divide the population, thus avoiding the premature aggregation of subpopulations and increasing candidate feature subsets. Second, a Double Spiral updating Strategy (DSS) is devised to break out of search stagnations by discovering new individual positions continuously. Last, to facilitate the convergence speed, a Baleen neighborhood Exploitation Strategy (BES) which mimics the behavior of whale tentacles is proposed. The presented algorithm is thoroughly compared with six state-of-the-art meta-heuristic methods and six promising WOA-based algorithms on 20 UCI datasets. Experimental results indicate that the proposed method is superior to other well-known competitors in most cases. In addition, the proposed method is utilized to perform feature selection in human fall-detection tasks, and extensive real experimental results further illustrate the superior ability of the proposed method in addressing practical problems.

A Multi-Attribute Decision-Making Approach for Critical Node Identification in Complex Networks.

Correctly identifying influential nodes in a complex network and implementing targeted protection measures can significantly enhance the overall security of the network. Currently, indicators such as degree centrality, closeness centrality, betweenness centrality, H-index, and K-shell are commonly used to measure node influence. Although these indicators can identify critical nodes to some extent, they often consider node attributes from a narrow perspective and have certain limitations. Therefore, evaluating the importance of nodes using most existing indicators remains incomplete. In this paper, we propose the multi-attribute CRITIC-TOPSIS network decision indicator, or MCTNDI, which integrates closeness centrality, betweenness centrality, H-index, and network constraint coefficients to identify critical nodes in a network. This indicator combines information from multiple perspectives, including local neighborhood importance, network topological location, path centrality, and node mutual information, thereby solving the issue of the one-sided perspective of single indicators and providing a more comprehensive measure of node importance. Additionally, MCTNDI is validated through the analysis of several real-world networks, including the Contiguous USA network, Dolphins network, USAir97 network, and Tech-routers-rf network. The validation is conducted from four aspects: the results of simulated network attacks, the distribution of node importance, the monotonicity of rankings, and the similarity of indicators, illustrating MCTNDI's effectiveness in real networks.

Entire-barn dairy cow tracking framework for multi-camera systems

The early detection of abnormalities, rapid diagnosis and prevention of diseases, and maintenance of desirable pregnancy schedules are crucial to ensure stable milk production. Non-intrusive and non-contact health management techniques based on image analysis have thus attracted much attention. The health status of dairy cows can be estimated by maintaining individual tracking records, such as the level of walking and the number of visits to feeding and drinking stations. In this paper, we propose a location-based tracking method for multiple dairy cows that can, for the first time, use multi-camera systems to consistently track dairy cows throughout the entire barn. Unlike existing methods that rely on image features, the proposed method uses only location information, making it robust to cow appearance features and the view warping caused by camera lens distortion. Our experiments on video datasets of moving and close packed cows show that our proposed method achieves an accuracy of about 90% for MOTA and about 80% for IDF1. The experiments demonstrate that our method consistently outperforms existing image feature-based methods.

Feeling Presence in the Dark

In this paper, we show that there is a distinctive mode of spatial awareness in blind individuals, which we call sense of volume, that is not to be confused with echolocation based on self-generated sounds. It is based on the analysis of variations in the ambient sound field and it provides locational information about objects in one’s surroundings. We propose that the sense of volume offers a primitive contact with the outside world. It does not give access to perceptual objects as such, but it should rather be understood as a rudimentary sense of presence that expresses that there is something out there at a certain distance and in a certain direction. We conclude by highlighting its tactile quality. More specifically, we argue that the sense of volume consists in a form of tactile anticipation: when one experiences something as being out there, one tactually expects that one could come in contact with it.

Improving university students awareness of cookies used by web technologies

Web technologies are developing rapidly and although the advantages provided by these technologies are quite numerous, they also bring with them problems related to the security of personal data. Especially, the fact that personal data is obtained by using cookies on websites requires web users to be made aware of this issue. While cookies have important advantages in terms of providing fast and easy access to websites, they also have the ability to record and process what web users read, location information, IP addresses, user device information, e-mail addresses, usernames and passwords. It is not legal to record this data without the explicit consent of the person. However, this situation is generally unknown to users or users who have incomplete information. The aim of this study is to raise awareness of university students who frequently use web technologies about cookies so that they can ensure their personal data security with their own efforts. In line with this purpose, a mixed method study was implemented. 25 university students participated in this study. Participants were determined using the convenience sampling method. Online interactive training consisting of 12 lesson hours was organized for the participants. Since quantitative and qualitative data from the participants were collected simultaneously and support each other, a concurrent mixed method model was applied. Quantitative data were collected with awareness pre-test and post-test, and qualitative data were collected with the interview form. Test scores obtained by a single group at different times were analyzed with the Paired Samples t-Test. Qualitative data were also analyzed by content analysis method. As a result of the study, it was revealed that the participants' awareness levels about cookies increased significantly. In addition, the participants' knowledge about risks, advantages and working principles of cookies increased.

Localizing From Classification: Self-Directed Weakly Supervised Object Localization for Remote Sensing Images.

In recent years, object localization and detection methods in remote sensing images (RSIs) have received increasing attention due to their broad applications. However, most previous fully supervised methods require a large number of time-consuming and labor-intensive instance-level annotations. Compared with those fully supervised methods, weakly supervised object localization (WSOL) aims to recognize object instances using only image-level labels, which greatly saves the labeling costs of RSIs. In this article, we propose a self-directed weakly supervised strategy (SD-WSS) to perform WSOL in RSIs. To specify, we fully exploit and enhance the spatial feature extraction capability of the RSIs' classification model to accurately localize the objects of interest. To alleviate the serious discriminative region problem exhibited by previous WSOL methods, the spatial location information implicit in the classification model is carefully extracted by GradCAM ++ to guide the learning procedure. Furthermore, to eliminate the interference from complex backgrounds of RSIs, we design a novel self-directed loss to make the model optimize itself and explicitly tell it where to look. Finally, we review and annotate the existing remote sensing scene classification dataset and create two new WSOL benchmarks in RSIs, named C45V2 and PN2. We conduct extensive experiments to evaluate the proposed method and six mainstream WSOL methods with three backbones on C45V2 and PN2. The results demonstrate that our proposed method achieves better performance when compared with state-of-the-arts.