Reviewerly/exHarmony huggingface dataset:
A Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter for Spacecraft-Relative Navigation

This study is focused on addressing the problem of delayed measurements and contaminated Gaussian distributions in navigation systems, which both have a tremendous deleterious effect on the performance of the traditional Kalman filtering. We propose a non-linear, multiple-step, randomly delayed, robust filter, referred to as the multiple-step, randomly delayed, dynamic-covariance-scaling cubature Kalman filter (MRD-DCSCKF). First, Bernoulli random variables are adopted to describe the measurement system in the presence of multiple-step random delays. Then, the MRD-DCSCKF uses the framework of the multiple-step randomly delayed filter, based on a state-augmentation approach, to address the problem of delayed measurements. Meanwhile, it depends on a dynamic-covariance-scaling (DCS) robust kernel to reject the outliers in the measurements. Consequently, the proposed filter can simultaneously address the problem of delayed measurements and inherit the virtue of robustness of the DCS kernel function. The MRD-DCSCKF has been applied to vision-based spacecraft-relative navigation simulations, where quaternions are adopted to represent spacecraft’s attitude kinematics, and the attitude update is completed with quaternions and generalized Rodrigues parameters. Monte Carlo simulations have illustrated that MRD-DCSCKF is superior to other well-known algorithms by providing high-accuracy position and attitude estimations in an environment with different delay probabilities and/or different outlier-contamination probabilities. Therefore, the proposed filter is robust to delayed measurements and can suppress outliers.
Steane code analysis by randomized benchmarking

Quantum error correction codes (QECC) play a fundamental role in protecting the information processed in today’s noisy quantum computers. To build good error correction schemes, it is essential to understand how noise affects the behavior of these codes. In this research paper, we analyze Steane code, a 7-qubit QECC, using a randomized benchmarking (RB) protocol. With RB protocols, we can partially characterize the quality of implementation of a set of quantum gates. We show a scenario where Steane code with one logical qubit is advantageous compared to the situation with no quantum code. We obtained our results using a quantum simulator with custom noise models considering different numbers of noisy qubits.
Time-Aware Data Partition Optimization and Heterogeneous Task Scheduling Strategies in Spark Clusters

Abstract The Spark computing framework provides an efficient solution to address the major requirements of big data processing, but data partitioning and job scheduling in the Spark framework are the two major bottlenecks that limit Spark’s performance. In the Spark Shuffle phase, the data skewing problem caused by unbalanced data partitioning leads to the problem of increased job completion time. In response to the above problems, a balanced partitioning strategy for intermediate data is proposed in this article, which considers the characteristics of intermediate data, establishes a data skewing model and proposes a dynamic partitioning algorithm. In Spark heterogeneous clusters, because of the differences in node performance and task requirements, the default task scheduling algorithm cannot complete scheduling efficiently, which leads to low system task processing efficiency. In order to deal with the above problems, an efficient job scheduling strategy is proposed in this article, which integrates node performance and task requirements, and proposes a task scheduling algorithm using greedy strategy. The experimental results prove that the dynamic partitioning algorithm for intermediate data proposed in this article effectively alleviates the problem that data skew leads to the decrease of system task processing efficiency and shortens the overall task completion time. The efficient job scheduling strategy proposed in this article can efficiently complete the job scheduling tasks under heterogeneous clusters, allocate jobs to nodes in a balanced manner, decrease the overall job completion time and increase the system resource utilization.
Role of Machine Learning on Key Extraction for Data Privacy Preservation of Health Care Sectors in IoT Environment

Abstract Privacy and security in the medical field are major aspects to consider in the current era. This is due to the huge necessity for data among providers, payers and patients, respectively. Recently, more researchers are making their contributions in this field under different aspects. But, there need more enhancements concerning security. In this circumstance, this paper intends to propose a new IoT-dependent health care privacy preservation model with the impact of the machine learning algorithm. Here, the information or data from IoT devices is subjected to the proposed sanitization process via generating the optimal key. In this work, the utility of the machine learning model is the greatest gateway to generating optimal keys as it is already trained with the neural network. Moreover, identifying the optimal key is the greatest crisis, which is done by a new Improved Dragonfly Algorithm algorithm. Thereby, the sanitization process works, and finally, the sanitized data are uploaded to IoT. The data restoration is the inverse process of sanitization, which gives the original data. Finally, the performance of the proposed work is validated over state-of-the-art models in terms of sanitization and restoration analysis.
Higher Education in the Pacific Alliance: Descriptive and Exploratory Analysis of the Didactic Potential of Virtual Reality

In this paper, we conducted descriptive quantitative research on the assessment of virtual reality (VR) technologies in higher education in the countries of the Pacific Alliance (PA). Specifically, differences between PA countries in terms of the above perceptions were identified and the behavior of the gender and knowledge area gaps in each of them was analyzed. A validated quantitative questionnaire was used for this purpose. As a result, we found that PA professors express high ratings of VR but point out strong disadvantages regarding its use in lectures; in addition, they have low self-concept of their digital competence. In this regard, it was identified that there are notable differences among the PA countries. Mexico is the country with the most marked gender gaps, while Chile has strong gaps by areas of knowledge. We give some recommendations towards favoring a homogeneous process of integration of VR in higher education in the PA countries.
Promoting favorable routes through visual communication: a design study for creating ‘Social’ route maps for the case of air pollution

This paper presents an approach for promoting routes that reduce exposure of road users to areas that should be temporarily avoided due to traffic related or environmental reasons. At the same time, routes that are already heavily polluted should be avoided, in order to reduce the pollution and distribute it more equally in the environment. In this research, we present a design study for creating route maps that intend to visually communicate favorability of route options to the traveler, in the case of increased air pollution. Our proposed method recommends routes that are calculated as the shortest path while minimizing the current concentrations of particulate matter (PM2.5 and PM10) along the route. Based on a dynamic distribution of traffic flows, our system recommends routes that are not necessarily the shortest or fastest (i.e. individually efficient), but rather the options that avoid areas with particularly high air pollution, while prioritizing other, not so polluted areas; and thus are beneficial to individual and public health (socially favorable). We propose seven different visualization variants for representing line and areal objects in a route map that visualize route options based on pollution levels. A user survey showed that while for most of the variants the symbology has been rated as intuitive, visual attractiveness and suitability for communicating pollution information seems to be limited to less complex visualizations that primarily use variations in color. The focus of the paper is to develop design options to optimally communicate favorable routes by designing different cartographic visualization techniques.
The analysis of data metamodels’ extensional layer via extended generalized graph

Abstract There are several limitations known in data modeling discipline, which are related directly to the traditionally used data modeling languages expressiveness. The strong limitations of the expressiveness of the existing well known data modelling languages combined with the lack of a very general universal data modeling language have negative impact to modelling naturalness. As the result of mentioned limits the reality must be transformed to avoid (workaround) the limits introduced by the modelling language. In turn, the transformation process requires extra effort. The problem is strengthened by the lack of mechanisms, which can be used to measure the expressiveness of a particular data modeling language. Some limitations of the existing data modeling languages result from both their metamodel (abstract syntax) and model (metamodel instance) graph-like structure constraints. This kind of limits also has negative impact to a domain-specific modeling naturalness. The paper addresses all problems mentioned above. The problems can be solved with the help of the EGG data modeling language introduced in the paper. First, a universal and customizable EGG data modeling language together with the customization mechanisms (extensions and generalizations) is introduced. According to the first usage scenario the EGG may be applied for domain-specific data modelling tasks in place of other data modeling languages. Second, the paper proposes and applies (for some data modeling languages: RDF, XML, RDBM, UML and AOM) a novel concept of measuring and comparing data modelling languages via mapping their metamodels to the EGG metamodel. So, according to the second usage scenario the EGG metamodel can be used as a reference metamodel for the data modeling language expressiveness comparative studies. It may also support the decision process when a data modeling language must be chosen for a particular domain-specific data modeling task. Third, the EGG introduced in the paper helps to avoid transforming reality to the needs resulting from the data modeling language as the EGG is general enough for the domain data modeling task. Complete abstract syntax of the Extended Generalized Graph is introduced and is expressed through its implementations in terms of the Association-Oriented Metamodel and the Unified Modeling Language. Semantics of each syntactical category of abstract syntax is described. Two complete concrete syntaxes for the Extended Generalized Graph are also introduced in the paper. The case studies related to both social network and knowledge modeling illustrate the applicability and usefulness of the EGG. Abstract syntax is compared to several other metamodels. The comparative study of the case study models created first in different metamodels and then expressed in the Extended Generalized Graph metamodel is summarized quantitatively in the form of a proposed measure.
Agent-Based Virtual Machine Migration for Load Balancing and Co-Resident Attack in Cloud Computing

The majority of cloud computing consists of servers with different configurations which host several virtual machines (VMs) with changing resource demands. Additionally, co-located VMs are vulnerable to co-resident attacks (CRA) in a networked environment. These two issues may cause uneven resource usage within the server and attacks on the service, leading to performance and security degradation. This paper presents an Agent-based VM migration solution that can balance the burden on commercially diverse servers and avoid potential co-resident attacks by utilizing VM live migrations. The Agent’s policies include the following: (i) a heuristic migration optimization policy to select the VMs to be migrated and the matching hosts; (ii) a migration trigger policy to determine whether the host needs to relocate the VMs; (iii) an acceptance policy to decide if the migration request should be accepted; and (iv) a balancer heuristic policy to make the initial VM allocation. The experiments and analyses demonstrate that the Agents can mitigate CRA in a distributed way to mitigate the associated risks while achieving acceptable load balancing performance.
PrivacyGLUE: A Benchmark Dataset for General Language Understanding in Privacy Policies

Benchmarks for general language understanding have been rapidly developing in recent years of NLP research, particularly because of their utility in choosing strong-performing models for practical downstream applications. While benchmarks have been proposed in the legal language domain, virtually no such benchmarks exist for privacy policies despite their increasing importance in modern digital life. This could be explained by privacy policies falling under the legal language domain, but we find evidence to the contrary that motivates a separate benchmark for privacy policies. Consequently, we propose PrivacyGLUE as the first comprehensive benchmark of relevant and high-quality privacy tasks for measuring general language understanding in the privacy language domain. Furthermore, we release performances from multiple transformer language models and perform model–pair agreement analysis to detect tasks where models benefited from domain specialization. Our findings show the importance of in-domain pretraining for privacy policies. We believe PrivacyGLUE can accelerate NLP research and improve general language understanding for humans and AI algorithms in the privacy language domain, thus supporting the adoption and acceptance rates of solutions based on it.
PACR: Pixel Attention in Classification and Regression for Visual Object Tracking

Anchor-free-based trackers have achieved remarkable performance in single visual object tracking in recent years. Most anchor-free trackers consider the rectangular fields close to the target center as the positive sample used in the training phase, while they always use the maximum of the corresponding map to determine the location of the target in the tracking phase. Thus, this will make the tracker inconsistent between the training and tracking phase. To solve this problem, we propose a pixel-attention module (PAM), which ensures the consistency of the training and tracking phase through a self-attention module. Moreover, we put forward a new refined branch named Acc branch to inherit the benefit of the PAM. The score of Acc branch can tune the classification and the regression of the tracking target more precisely. We conduct extensive experiments on challenging benchmarks such as VOT2020, UAV123, DTB70, OTB100, and a large-scale benchmark LaSOT. Compared with other anchor-free trackers, our tracker gains excellent performance in small-scale datasets. In UAV benchmarks such as UAV123 and DTB70, the precision of our tracker increases 4.3% and 1.8%, respectively, compared with the SOTA in anchor-free trackers.
Centralized Mission Planning for Multiple Robots Minimizing Total Mission Completion Time

Most mission planning algorithms solve multi-robot-multi-mission problems based on mixed integer linear programming. In these algorithms, the rewards (or costs) of missions for each robot are calculated according to the purpose of the user. Then, the (robot-mission) pair that has maximum rewards (or minimum costs) is found in the rewards (or costs) table and the mission is allocated to the robot. However, it is hard to design the reward for minimizing total mission completion time because not only a robot, but also the whole robots’ mission plans must be considered to achieve the purpose. In this paper, we propose centralized mission planning for multi-robot-multi-mission problems, minimizing total mission completion time. First, mission planning for single-robot-multi-mission problems is proposed because it is easy to solve. Then, this method is applied for multi-robot-multi-mission problems, adding a mission-plan-adjustment step. To show the excellent performance of the suggested algorithm in diverse situations, we demonstrate simulations for 3 representative cases: a simple case, which is composed of 3 robots and 8 missions, a medium case, which is composed of 4 robots and 30 missions, and a huge case, which is composed of 6 robots and 50 missions. The total mission completion time of the proposed algorithm for each case is lower than the results of the existing algorithm.
Systematic Approach for Measuring Semantic Relatedness between Ontologies

Measuring ontology matching is a critical issue in knowledge engineering and supports knowledge sharing and knowledge evolution. Recently, linguistic scientists have defined semantic relatedness as being more significant than semantic similarities in measuring ontology matching. Semantic relatedness is measured using synonyms and hypernym–hyponym relationships. In this paper, a systematic approach for measuring ontology semantic relatedness is proposed. The proposed approach is developed with a clear and fully described methodology, with illustrative examples used to demonstrate the proposed approach. The relatedness between ontologies has been measured based on class level by using lexical features, defining semantic similarity of concepts based on hypernym–hyponym relationships. For evaluating our proposed approach against similar works, benchmarks are generated using five properties: related meaning features, lexical features, providing technical descriptions, proving applicability, and accuracy. Technical implementation is carried out in order to demonstrate the applicability of our approach. The results demonstrate an achieved accuracy of 99%. The contributions are further highlighted by benchmarking against recent related works.
A Multi-Hop Graph Neural Network for Event Detection via a Stacked Module and a Feedback Network

Event detection is an important subtask of information extraction, aiming to identify triggers and recognize event types in text. Previous state-of-the-art studies using graph neural networks (GNNs) are mainly applied to obtain long distance features of text and have achieved impressive performance. However, these methods face the issues of over-smoothing and semantic feature destruction, when containing multiple GNN layers. For the reasons, this paper proposes an improved GNN model for event detection. The model first proposes a stacked module to enrich node representation to alleviate the over-smoothing. The module aggregates multi-hop neighbors with different weights by stacking different GNNs in each hidden layer, so that the representation of nodes no longer tends to be similar. Then, a feedback network is designed with a gating mechanism to retain effective semantic information in the propagation process of the model. Finally, experimental results demonstrate that our model achieves competitive results in many indicators compared with state-of-the-art methods.
Online Harassment in Sri Lanka: A Thematic Analysis

Online harassment has become a growing menace worldwide for which every nation is trying to find a solution. Existing literature demonstrates that online harassment is widespread in diverse forms and so is its impact on the victims. Despite the lack of any comprehensive studies in this area, there is sufficient evidence to demonstrate the prevalence of online harassment in Sri Lanka and the need to find effective solutions to it. In such circumstances, this study aims to understand the nature of online harassment in the Sri Lankan context using qualitative research methodology. To that end, the study uses thematic analysis for analysing the data collected through semi-structured interviews. The thematic analysis was employed for the study as it assists in classifying complex qualitative data into different and simplified themes for better understanding and interpretation of such data. Among other findings, the analysis revealed three global themes, namely the motives of the perpetrators, the impact on victims, and remedies. Under each global theme, the study revealed various organising and basic themes, also indicating that the motives of the perpetrators and the impact on the victims are closely connected. It further reveals that impacts could be extremely serious, ranging from helplessness to suicidal thoughts, humiliation to broken relationships, and even having adverse impacts on the careers of victims. The study also found that the available remedies are not systematic or strong enough to meet the expectations of the victims. Thus, we conclude that the threat from online harassment in Sri Lanka is similar to that in any other country, requiring immediate and well-planned legal and policy responses, as exposed by the key themes identified in the study.
Understanding table content for mineral exploration reports using deep learning and natural language processing

The geological reports contain various tables, and can offer mineral element content data and stratum detailed information. Geological tabular information extraction and its semantic fusion with text is of great significance in converting and fusing geological unstructured data into structured knowledge to guide cognitive intelligence analysis in the geoscience domain. While the performance of general tools and existing table structure analysis methods is limited due to the various merged cells and diagonally split table headers. To address this issue, we propose a novel approach based on the improved Mask R-CNN model to identify and parse the forms. The geological table parsing network constructed in this paper consists of two key steps: (1) A cell feature augmentation (CFA) module to learn the contextual features for identifying cells of different sizes. (2) A table parsing method (GTab) to parse the table header cells with split lines. We compare the proposed method with commonly used table parsing methods on our constructed geological table dataset. Our models are easily integrated into a prototype system to provide joint information processing and analysis.
A novel smart photoelectric lock system: Speech transmitted by laser and speech to text

We propose a circuit that modulates a speech signal to a laser, using which the speech signal can be transmitted using the laser. Also, it shows the use of a platform based on embedded ARM (Advanced RISC Machine), running a small deep learning model based on TDNN (Time delay neural network) and LSTM (Long short-term memory), and converting speech to text, and use the text cipher for unlocking. This research implements a smart lock system that can set a pre-record speech cipher and verify the similarity through a laser transmission speech cipher to unlock it. In our experiment result, the English speech of laser transmission can reach a WER (Word error rate) of 14.06% through the deep learning model to recognize the content of the speech cipher. We also design a similarity comparison algorithm based on LCS (Longest common subsequence) to compare the character set of the laser transmission speech compare and the prerecord speech cipher to calculate the similarity rate. Through the similarity comparison algorithm, when the WER is 27.27%, the male speech samples used in this study still have a 95% unlocking success rate, while the female speech samples have a 100% unlocking success rate. Compared with only using automatic speech recognition (ASR) to unlock, the method we propose is to compare the similarity of the content of speech cipher. The method significantly improves the unlocking fault tolerance of using lasers to transmit audio. Therefore, by using the laser to transmit the speech cipher, the usability of the photoelectric smart lock system has been significantly improved. At the same time, the characteristics of the laser are not easy to eavesdrop on the cipher, which can also improve security.