Gathering Process Research Articles

TailorEd: Classroom Configuration and Activity Identifiers (CCID & CAID)

INTRODUCTION: The study of how classroom layout and activities affect learning outcomes of students with different demographics is difficult because it is hard to gather accurate information on the minute by minute progression of every class in a course. Furthermore, the process of data gathering must produce an abundance of data to work with and hence must be automated. OBJECTIVES: A machine learning model trained on images of a classroom and thus capable of accurately labeling the classroom layout and activity of many thousands of images much faster and cheaper than employing a human. METHODS: Transfer learning can allow for preexisting computer vision models to be retrained on a smaller, more specific dataset in order to still achieve a highly accurate result. RESULTS: In the case of the classroom layout, the final model achieved an accuracy of 97% on a four category classification. And for detecting the classroom activity, after experimentation with several different versions that could work on a very small sample sizes, the best model achieved an accuracy of 86.17%. CONCLUSION: In addition to showing that using computer vision to determine human activities is possible albeit more difficult than layouts of inanimate objects such as classroom desks, the study shows the differences between the use of self-supervised learning techniques and data augmentation techniques in order to overcome the problem of small training data-sets.

A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms.

Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region’s cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage.

A fast state-of-health estimation method using single linear feature for lithium-ion batteries

Data-driven methods are commonly used for state of health (SOH) estimation, which is essential to battery energy management. However, complex machine learning models, data gathering, and feature processing hinder its further implementation. A fast SOH estimation method based on linear properties of short-time charging is proposed to overcome these challenges. Only the exceptional single linear health factor (LHF) is required for effective SOH estimation. The LHF is chosen through correlation analysis from short-term feature derived from charging curves. The processing is straightforward. To define the relationship between LHF and SOH, a linear regression model is developed. For the simplicity and effectiveness of the method, it is suitable to be implemented in online applications with low hardware requirements. Finally, experiments show that the SOH estimation method has the highest accuracy of 0.54%, and the biggest estimation error is 2.20%. Furthermore, the data from first 20% cycles of the battery are used to build the model, ensuring that the SOH estimation accuracy is comparable. It is worth noting that the time cost of data acquisition does not exceed 30 s, which is important for fast estimation.

A Comparative Study on the English Proficiency of Students from Public and Private Schools

Previous studies showed that educational context factors like schools could influence the English proficiency of students. One of the greatest predictors of proficiency among language learners is the type of school they are attending – either public or private. Empirical data also suggested that English proficiency had an impact on students’ performance in science and math courses. Hence, this study examined which type of senior high school (SHS) – public or private school – could produce more proficient students in using the English language. The data gathering process focused on SHS students from both public and private schools, who were enrolled in the academic track of Science, Technology, Engineering, and Mathematics (STEM). The study investigated their English proficiency and looked into their profile variates; the difference in their English proficiency in terms of grammar, vocabulary, and reading comprehension, which turned out to be significant whether they were in a public school or private school; the relationship between their English Proficiency and the type of school they attended which was also significant; and the relationship between their English proficiency and their academic performance in English courses which was found to have nothing to do with the former.

Human Resource Management Planning at PT. Batang Hari Benulu Primary

The objective of this research is to describe the Human Resource Management Planning at PT. Batang Hari Bengkulu Pratama. This research is categorized as a qualitative descriptive data collection by means of interviews, observation and documentation. The selection of informants was done by purposive sampling of five people with theory. According to Schuler's opinion, (1987: 62-78) it can be concluded that there are four important stages in the HR planning process of (1) Gathering, Analizing, and Forecasting Supply and Demand Data. Collecting, Analyzing, Predicting Supply and Demand Data. Employees at the company are given special training, recruitment of new employees complete the requirements, placement of employees in accordance with their expertise and are professional but employees still get knowledge and technology. (2) Establishing Human Resource Objectives and Policies. Setting HR goals and policies. Not all employees get training and seminars, only a certain section on education, no help with student fees for formal education provided by the company. (3) Humand Resource Programming. There are incentives for employees who reach the target, pension funds and layoffs are given by the company in accordance with the work period of employees who are 55 years old, calculated as working years, while employees who are laid off are calculated as their years of service in the company. (4) Humand Resource Planning-Control and Evaluation. HR Planning and Evaluation Control. Supervision and evaluation of employee performance is carried out by the foreman to laboratory analysis of the company leadership, there is no specific space for counseling. There is a special forum formed by the company to select employees who excel to be promoted and occupy certain positions.

Failure behaviors of fractured granite during loading and unloading under high confining pressure based on acoustic emission multi-parameter analysis

To investigate failure and mechanical behaviors of fractured rocks during loading and unloading under conditions of high ground stress, a series of loading and unloading tests were conducted on granite containing prefabricated fractures under high confining pressure. Results indicate that the evolution of acoustic emission (AE) events of fractured granite during loading and unloading under high confining pressure is strongly correlated with the deformation and failure and the energy evolution process in the rock, the peak values of AE cumulative energy and AE cumulative count rate in the pre-peak deformation stage decrease first and then increase with the dip angle (the minimum value is reached at 60°), when the dip angle is 0° to 60°, the number of AE events increases abruptly due to closure of the prefabricated fracture in the early stage of loading(only about 30 % of the main fracture time). After the fractured granite enters the unstable crack propagation stage, the crack tips are subject to a dynamic cycle of stress concentration, crack propagation, and stress release, accompanied by significant fluctuations in the energy rate before and after the point of peak AE energy rate. Therefore, the cracks propagate in an intermittent or a stepped manner. Under the unloading path, the process of crack development, gathering, and coalescence in the granite happens earlier, and there are more high-energy AE events in the failure process. The value of the brittleness index Bd is larger under the unloading path under high confining pressure, which reflects more significant brittle failure characteristics. The research provides a basis for judging failure precursors of rocks using AE characteristics in the failure process.

Signs of Life

This essay advances an audible semiotics (hearing and listening) by challenging James Williams’ concept of signs in his book A Process Philosophy of Signs (2016). In it, Williams proposes that a sign exists due to, and as, its own intensive life. That is, he decouples consciousness from the semiotic relation in order to advance a materialist ontology. Through this thesis, Williams aims to move semiotic relations outside the limits of visual and linguistic thinking. In doing so, however, he has conflated two distinct activities—life and signs. This positions the sign on material grounds and thereby strips away the creative and interpretive element of ambiguity, which is vital to any theory of signs. This essay shares Williams’ interest in loosening semiotics from its adherence to language and visual thinking. But it challenges his arguments by addressing the topic from an audible rather than material standpoint. Without experience and interpretation—human, animal, plant or otherwise—a sound is only a sound, a process of life, and not a sign. An audible semiotics retains the sensory to seek out other avenues of discovery and thought beyond language and vision. In audibility, a sign returns to its relational element of sensation—its processes of gathering and interpretation. A process philosophy of audible signs thereby affirms two propositions: (1) that “life” and “sign” are not the same; and (2) that the value of semiotics is not a concern over what is, but about how any form of life responds to and interprets a situation. More than review or criticism, the essay advances a positive theory of audible signs. To this end, it narratively deconstructs a “scene” that Williams offers in his own account: a deer’s footsteps on a snowy hillside. By critiquing these accounts cinematically and narratively, we can see how an audible semiotics reinvigorates the vital necessity of ambiguity in hearing and listening, which also points toward the possibilities of audible design in filmmaking. The methodology is discursive and analytical but non-dialectical. That is, situating audibility in a narrative framework offers an other way into the value of signs and semiotic studies across a wide range of disciplines. The central thesis argued, contra Williams, is that signs are not reducible to “life” (and vice-versa), and that only an audible regard for signs can reveal this fact. To situate its arguments, this essay incorporates key aspects of semiotics, psychology, and epistemology. Thinkers engaged, other than Williams, include William James, Wittgenstein, Saussure, Peirce, Barthes and Deleuze. Philosophical in approach, this essay will have resonance as a starting point for considering the possibilities of creative hearing and listening in audiovisual arts and epistemology.

Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors.

The use of low-cost sensors in conjunction with high-precision instrumentation for air pollution monitoring has shown promising results in recent years. One of the main challenges for these sensors has been the quality of their data, which is why the main efforts have focused on calibrating the sensors using machine learning techniques to improve the data quality. However, there is one aspect that has been overlooked, that is, these sensors are mounted on nodes that may have energy consumption restrictions if they are battery-powered. In this paper, we show the usual sensor data gathering process and we study the existing trade-offs between the sampling of such sensors, the quality of the sensor calibration, and the power consumption involved. To this end, we conduct experiments on prototype nodes measuring tropospheric ozone, nitrogen dioxide, and nitrogen monoxide at high frequency. The results show that the sensor sampling strategy directly affects the quality of the air pollution estimation and that each type of sensor may require different sampling strategies. In addition, duty cycles of 0.1 can be achieved when the sensors have response times in the order of two minutes, and duty cycles between 0.01 and 0.02 can be achieved when the sensor response times are negligible, calibrating with hourly reference values and maintaining a quality of calibrated data similar to when the node is connected to an uninterruptible power supply.

Engaging students via online projects: cross-cultural inter-university teaching cooperation projects for business education

The article describes two initiatives between higher education institutions (HEIs) in Poland, Germany, and India, which have been carried out as a response to an increasing demand for pedagogical methodologies that enable to reach a deep level of student engagement. Active learning has been implemented in order to enrich students’ experience and equip them with skills that help to face contemporary labour market challenges. In the article, the case study methodology has been used. The subject of the analysis are two groups of students participating in initiatives presented by the article. The article takes an empirical approach; the evaluation surveys have been distributed and students evaluated the experience positively and would recommend participation to their peers. As benefits they emphasised mainly working in a culturally diverse environment, gaining useful skills, and carrying out interesting projects. The main challenges reported were remote and intercultural communication, time management and the data gathering process. The main limitation is the qualitative methodology – it does not allow the authors to form general conclusions.

Effects of foam on slug generation mechanism in a hilly terrain pipeline

Liquid accumulation is one of the most common challenges associated with natural gas gathering processes, particularly in pipelines where mechanical pigging is infeasible and there is a risk of terrain slugging. Aqueous foam drainage technology is a potential method to remove liquid accumulation owing to its low risk and cost, and lack of constraints. An experimental study of gas–liquid two-phase flow with foam was conducted to clarify the effects of foam on the slug generation mechanisms at the elbow of upward-inclined pipelines. The experiments were conducted in horizontal-upward pipelines at different inclination angles (5°, 10°, and 20°). The flow pattern was captured by a high-speed camera and the real-time differential pressures along the pipeline were recorded. The results indicate that the slug generation mechanisms at the elbow mainly consist of an interfacial wave growth mechanism at low gas velocities and an interfacial wave aggregation mechanism at high gas velocities. In the presence of aqueous foam, the interfacial wave growth mechanism still exists. In contrast, the interfacial wave aggregation mechanism disappears, and the flow pattern transforms into wavy foam flow under the same conditions. In addition, the increase in the inclination angles intensified the liquid backflow in the upward-inclined section and contributed to the liquid accumulation at the elbow. Furthermore, owing to the Bernoulli effect, the increase also enhanced the liquid-carrying capacity of the gas in the divergent section of the elbow, resulting in increased interfacial fluctuations and promoting the generation of liquid slugs. Meanwhile, the eigenvalues extracted from the fast Fourier transform (FFT) of the differential pressures correlate well with the flow characteristics at the elbow, and the average values at separated flow conditions are all below 0.05, which proves that the method enables an accurate prediction of whether the liquid slug is generated at the elbow of the upward-inclined pipe.

AFFNet: Attention Mechanism Network Based on Fusion Feature for Image Cloud Removal

Clouds frequently affect optical remote sensing pictures throughout the gathering process, resulting in low-resolution images that affect judgment and subsequent use of ground data. Because of the thick cloud cover, the ground surface information below is entirely incorrect. This kind of end-to-end image problem should not be dismissed as a simple task of image inpainting or image translation. Therefore, this paper proposes a multi-head self-attention module based on the encoding–decoding generative adversarial network, considering the redundant information of the deep network, furthermore this paper introduces Ghost convolution to effectively solve the influence of redundant feature maps in the network on the increase of time consumption and parameters. The method in this paper can solve the problem of cloud occlusion. By considering spatial information, it can better complete the prediction of cloud removal. It can reduce the amount of network calculations and parameters while maintaining the effect. In addition, Feature Fusion Module is proposed to integrate high-level features with low-level features, so that the network can extract enough feature information and better supplement the details to complete the cloud removal. The method in this paper has achieved excellent results on the RICE1 and RICE2 datasets.

CDS-MIP: CDS-based Multiple Itineraries Planning for mobile agents in wireless sensor network

using multi agents in the wireless sensor networks (WSNs) for aggregating data has gained significant attention. Planning the optimal itinerary of the mobile agent is an essential step before the process of data gathering. Many approaches have been proposed to solve the problem of planning MAs itineraries, but all of those approaches are assuming that the MAs visit all SNs and large number of intermediate nodes. This assumption imposed a burden; the size of agent increases with the increase in the visited SNs, therefore consume more energy and spend more time in its migration. None of those proposed approaches takes into account the significant role that the connected dominating nodes play as virtual infrastructure in such wireless sensor networks WSNs. This article introduces a novel energy-efficient itinerary planning algorithmic approach based on the minimum connected dominating sets (CDSs) for multi-agents dedicated in data gathering process. In our proposed approach, instead of planning the itineraries over all sensor nodes SNs, we plan the itineraries among subsets of the MCDS in each cluster. Thus, no need to move the agent in all the SNs, and the intermediate nodes (if any) in each itinerary will be few. Simulation results have demonstrated that our approach is more efficient than other approaches in terms of overall energy consumption and task execution time.

A Hybrid Algorithm for Reliable and Energy-efficient Data Gathering in Wireless Sensor Networks

Reliability and energy efficiency are two important requirements of the data gathering process in wireless sensor networks. Accordingly, we propose a novel data gathering algorithm which meets these requirements. The proposed scheme categorizes the sensed data into valuable and normal data and handles each type of data based on its demands. The main requirement of valuable data is reliability. Thus, the adopted strategy to gather this type of data is to send several copies of data packets toward the sink. The rise of energy exhaustion in this scheme is tolerable. This is due to that, the valuable data is generated at a low rate. On the other hand, our main concern in gathering normal data is energy efficiency. As most of the sensed data is normal, an energy-efficient approach to gather normal data results in considerable energy conserving. Thus, we exploit clustering technique for normal data gathering. We also propose a lightweight intrusion detection system to detect malicious nodes. Simulation results and theoretical analysis confirm that our proposed algorithm provides reliability and energy efficiency at an acceptable level.

Desire paths for workplace assessment in postgraduate anaesthesia training: analysing informal processes to inform assessment redesign

BackgroundIn postgraduate specialist training, workplace assessments are expected to provide the information required for decisions on trainee progression. Research suggests that meeting this expectation can be difficult in practice, which has led to the development of informal processes, or ‘shadow systems’ of assessment. Rather than rejecting these informal approaches to workplace assessment, we propose borrowing from sociology the concept of ‘desire paths’ to legitimise and strengthen these well-trodden approaches. We asked what information about trainees is currently used or desired by those charged with making decisions on trainee progression, and how is it obtained? MethodsWe undertook a qualitative study with thematic analysis of semi-structured interviews of supervisors of training across Australia and New Zealand. ResultsFrom 21 interviews, we identified four interrelated themes, the first being the local context of training sites. The other three themes represent dilemmas in the desire for authentic and representative information about the trainee: 1) how the process of gathering and documenting information can filter, transform, or limit the original message; 2) deciding when possible trainee deviation from performance norms warrants a closer look; and 3) how transparent vs covert information gathering affects the information supervisors will provide, and how control over assessment is distributed between trainee and supervisor. ConclusionFrom these themes, we propose a set of design principles for future workplace assessment. Understanding the reasons desire paths exist can inform future assessment redesign, and may address the current disjunct between the formal workplace assessment system and what happens in practice.

The effects of Cu2+ adsorption on surface dissolution of albite

Interactions between feldspar minerals and aqueous solution are essential processes of element migration and gathering near the earth’s surface. In this paper, coupling relationship between surface dissolution of albite and Cu2+ adsorption in various solutions is investigated. The release amounts of Si, Al and Na from albite are all lowest at pH 5 and increase with increasing or decreasing pH. The larger Al/Si ratios than 0.33 at pH 1 and 2 can be attributed to the formation of surface coatings that resemble a silica gel, also including the easier release of Al than Si in acidic solutions. According to XPS and EPR analysis, Cu2+ ions are chemically adsorbed on albite surface to form inner-sphere type Cu(II) surface complexes at pH 5. Significantly, the adsorption of Cu2+ ions could promote the releases of Si, Al and Na from the surface of albite to a certain extent. The promotion mechanism can be interpreted as the adsorbed Cu2+ ions bring more bound H2O molecules getting to albite surface, resulting in the easier hydrolysis of Si-O-Si and Si-O-Al bonds. While, the final destination of Cu2+ ions regulated by the dissolution of albite should be further investigated in next stage work.

The Natural Experiment Approach in Analysis of War Crowds vs. Agoral Gathering Impact on Macroeconomic Changes

In this study natural experiment approach will be employed in an analysis of two social forces: war crowds vs. agoral gatherings and their impact on macroeconomic changes. The paper presents empirical and historical evidence that the European countries which reached their state independence as a result of agoral gatherings (Poland, Hungary, Czech Republic, Slovakia, Bulgaria, East Germany, Romania, Lithuania, Latvia, Estonia, and Albania) obtained much higher indicators both in GDP and in GDP per capita in the decade 2009–2018 than the countries involved in the Yugoslavian war crowds (Croatia, Bosnia & Hercegovina, Serbia, North Macedonia, Kosovo, and Slovenia). For the purposes of our analysis, EUROSTAT data was used as containing macroeconomic indicators of the entire populations of the European countries, which are of interest to us in a distant perspective, at least eight years from the events that are the subject of our comparative analyses, as the primary independent variable. The results of a comparative analysis of these two indicators are presented and an attempt to interpret them is made from the point of view of behavioral economics. This interpretation takes into account the theories of crowd psychology and the theory of agoral gathering processes, as well as the psychosocial and economic importance of coupon privatization in the economic activation of citizens in the countries undergoing systemic transformation after the collapse of their totalitarian systems.

SEEDGT: Secure and energy efficient data gathering technique for IoT applications based WSNs

Internet of Things based Wireless Sensor Networks (IoT-WSNs) are widely employed in a variety of applications, including military, health-care, and industrial monitoring. Security parameters are essential to protect the network from various security threats and attacks because these applications handle sensitive information in potentially hostile and unattended environments. This paper aims to achieve the security parameters for IoT applications based WSNs by proposing a secure and energy aware data gathering technique, called SEEDGT, which integrates between trust, public key algorithm, and Compressive Sensing (CS) methods towards achieving security with fair energy load balance in IoT-WSN. The proposed SEEDGT technique has three phases, namely Cluster formation, network operation and reconfiguration phase. During the cluster formation phase, energy-aware and trust based methods are applied for the creation of clusters and cluster head selection. The network operation phase aims to achieve security by using the public key algorithm to encrypt network data during the data gathering process. Moreover, in this phase, CS strategy is employed to reduce the original data size, which leads to a reduction in energy usage. Finally, the changes that could occur during network operations is considered through the reconfiguration phase. The simulation studies illustrate that SEEDGT is effective in achieving better performance than other baseline approaches.

Sorting Insiders From Co-Workers: Remote Synchronous Computer-Mediated Triage for Investigating Insider Attacks.

Develop and investigate the potential of a remote, computer-mediated and synchronous text-based triage, which we refer to as InSort, for quickly highlighting persons of interest after an insider attack. Insiders maliciously exploit legitimate access to impair the confidentiality and integrity of organizations. The globalisation of organisations and advancement of information technology means employees are often dispersed across national and international sites, working around the clock, often remotely. Hence, investigating insider attacks is challenging. However, the cognitive demands associated with masking insider activity offer opportunities. Drawing on cognitive approaches to deception and understanding of deception-conveying features in textual responses, we developed InSort, a remote computer-mediated triage. During a 6-hour immersive simulation, participants worked in teams, examining password protected, security sensitive databases and exchanging information during an organized crime investigation. Twenty-five percent were covertly incentivized to act as an 'insider' by providing information to a provocateur. Responses to InSort questioning revealed insiders took longer to answer investigation relevant questions, provided impoverished responses, and their answers were less consistent with known evidence about their behaviours than co-workers. Findings demonstrate InSort has potential to expedite information gathering and investigative processes following an insider attack. InSort is appropriate for application by non-specialist investigators and can be quickly altered as a function of both environment and event. InSort offers a clearly defined, well specified, approach for use across insider incidents, and highlights the potential of technology for supporting complex time critical investigations.

Book Review

Book Review

An Efficient Method of Classification the Gestational Diabetes Using ID3 Classifier

Artificial intelligence algorithms have an important and effective role in the medical field, especially in the field of diagnosing diseases. This research focuses on predicting the diagnosis of gestational diabetes by using the Iterative Dichotomiser3 (ID3) classifier algorithm, which is utilized to identify gestational diabetes; it was one of the most significant algorithms employed in this study. Training and testing are two critical phases of the research study. This study employed the Pima Indians Diabetes dataset, which comprised 768 women aged 21 and abovewith the eight reported traits. A feature selection stage, a discretization step, and using the classifier model for producing decision rules are all part of the Pima Indians diabetes data gathering process (Diabetes Dataset). In this study, the decision tree is employed to develop the classifier model, which is based on Diabetes training. The Iterative Dichotomiser3 (ID3) technique may be used to run the decision tree classification process. Diabetes is tested using decision rules, and the classifier implementation confusion matrix was retrieved from the testing portion. The system delivered high-quality results with a 94 percent accuracy rate.