Reviewerly/exHarmony huggingface dataset:
PPDA-FAF: Maintaining Data Security and Privacy in Green IoT-Based Agriculture

Nowadays, Green IoT-Based Agriculture plays an essential role in farming to improve the yield. Here, IoT devices are embedded in the farming equipment, which helps to enhance the irrigation and yield with minimum cost-cutting. Data security and privacy are major challenges in green IoT-related agriculture. Therefore, a secured system should create to maintain data confidentiality, authentication, integrity, availability, and privacy. This system uses the privacy-preserving data aggregation (PPDA) with a Fair access framework (FAF) that manages the data security. The data aggregation concept is used to protect the green IoT data from false data injection. The FAF utilizes the blockchain technique to grant, get, revoke and delegate access to the user. The developed security system can adapt the green IoT-based agriculture and provide confidentiality, which is done with the help of an enhanced ciphertext access control mechanism. This system resolves the security and privacy issues involved in the Green IoT-based agriculture, and the effectiveness of the system is evaluated using implementation results.
Infrastructure as Code as a Foundational Technique for Increasing the DevOps Maturity Level: Two Case Studies

We present two cases about projects in different maturity levels regarding infrastructure as code. The intent is to expose experiences and problems that may arise on new or already existing projects. Each case is exposed in a separate session.
Global Information-Assisted Fine-Grained Visual Categorization in Internet of Things

In fine-grained visual categorization (FGVC), most part-based frameworks do not work effectively in some extremely challenging scenarios such as partial occlusion. This limitation is due to the heavy disorder of local features extracted from such occluded targets. To address this issue, we propose a global information-assisted network (GIAN), where auxiliary global information can search the useful elements of local information and integrate with them for an efficient unified feature representation. In particular, in order to acquire the global information, we design a global attention-concentrated convolutional neural network (GAC-CNN) by extending a convolutional neural network with a nonlocal GCN module. Then, the unified feature representation is produced by two strategies. On the one hand, a global–local aggregation strategy is developed to selectively integrate global features with local features through consistency evaluation and reweighting method. On the other hand, an alternative knowledge distillation strategy is developed to help generate more powerful global and local features. Two strategies collaboratively make the unified features more robust and more discriminative than traditional part-based features. Experimental results show that the proposed GIAN can achieve accuracies of 92.8%, 93.8%, and 95.7% on CUB-200-2011, FGVC Aircraft, and Stanford Cars, respectively.
Research on text fault recognition for on-board equipment of a C3 train control system based on an integrated XGBoost algorithm

Abstract The robust guarantee of train control on-board equipment is inextricably linked to the safe functioning of a high-speed train. A fault diagnostic model of on-board equipment is built utilizing the integrated learning XGBoost (eXtreme Gradient Boosting) algorithm to help technicians assess the malfunction category of high-speed train control on-board equipment accurately and rapidly. The XGBoost algorithm iterates multiple decision tree models to improve the accuracy of fault diagnosis by lifting the predicted residual and adding regular terms. To begin, the text features were extracted using the improved TF-IDF (Term Frequency–Inverse Document Frequency) approach, and 24 fault feature words were chosen and converted into weight word vectors. Secondly, considering the imbalanced fault categories in the data set, the ADASYN (Adaptive Synthetic sampling) adaptive synthetically oversampling technique was used to synthesize a few category fault samples. Finally, the data samples were split into training and test sets based on the fault text data of CTCS-3 train control on-board equipment recorded by Guangzhou Railway Group maintenance personnel. The XGBoost model was utilized to realize the automatic fault location of the test set after optimized parameter tuning through grid search. Compared with other methods, the evaluation index of the XGBoost model was significantly improved. The diagnostic accuracy reached 95.43%, which verifies the effectiveness of the method in text fault diagnosis.
Salt Commodity Data Clustering Using Fuzzy C-Means

Abstract Indonesia has a sea area of about 96,079.15 km 2 rich in natural resources. The advantage is in the form of abundant natural resources in the sea including fish and salt. Several regions such as Cirebon, Indramayu, Rembang, and Madura contribute to the salt pond commodity which is extremely valuable for Indonesia. These resources need to be monitored and inventoried appropriately. Local people are difficult and increasingly cornered to compete in the salt trade. For the strategic commodity group, by maintaining the stability of the salt commodity in the community with its irreplaceable function, the salt trade system is regulated. The purpose of this research is to use the Fuzzy C-Means Clustering method for grouping national salt commodities. This study used a test with 10 clusters for the distribution of training and testing data purposes. The research trials were carried out using the mean imputation method and grouping using the Fuzzy C-Means Clustering method. The test results of the Fuzzy C-Means Clustering method using the Silhouette Coefficient method show that the Fuzzy C-Means Clustering method with the calculation of the closest distance using Manhattan Distance is the best choice in research with the results obtained in the Silhouette Coefficient assessment is at a value of 0.274880 with a value of k = 2.
Could the digital literacy of preservice physics teachers be improved by Learning Management System Supported Smartphone (LMS3) application in a physics online lecture?

Abstract Digital literacy is a vital competency needed by physics teachers, so it should be trained through lectures using technology, one of which is LMS3. The study aims to describe the LMS3 implementation in school physics lectures to train digital literacy and elucidate an enhancement of prospective physics teachers’ digital literacy in school physics lectures using LMS3. One-group pre-test–post-test design was conducted on 38 prospective physics teachers who enrolled in school physics lectures. The instruments consisted of digital literacy tests, lecture implementation forms, and interview guides. Besides, LMS3 is an application designed independently using the ADDIE (analysis, design, development, implementation, and evaluation) development model. The learning process using LMS3 occurred synchronously after the lecturer activated the learning process menu and adopted problem-based learning syntax. In general, the enhancement of digital literacy of prospective physics teachers after attending school physics lectures using LMS3 was the high category (N-gain = 0.72). In conclusion, school physics lectures using LMS3 had a significant impact on enhancing digital literacy. This study suggests developing lectures using various technologies that support the improvement of teacher competence in facing challenges in education.
The Study of Biopore Infiltration Holes (BIH) Implementation to Reduce Waterlogging in President University Campus Area

Abstract The continuous infrastructure development results in the lack of green open space as water catchment areas into the ground. The reduced water infiltration zona will cause runoff. The soil on President University’s campus is fine-grained, with high plasticity due to clay mineral particles. One factor that affects rainwater utilization is the soil type. Clay types have slower permeability, resulting in reduced rainwater infiltration areas. One of the technologies to reduce water runoff is the Biopore Infiltration Hole (BIH). To find out how much water can infiltrate into the soil to reduce waterlogging and to plan the total amount of Biopore Infiltration Hole (BIH) that can be applied at President University. This study uses quantitative analysis methods to identify the ideal Biopore Infiltration Hole (BIH) requirement on President University’s campus and determine the BIH. The findings of this study indicate that the total planned implementation of BIH that can be used on the campus of President University is 1711.8 pcs in an open space zona of 13,600 m 2 and, based on the annual infiltration calculation, is 0.97 m 3 /min that significant enough to reduce waterlogging in the President University Campus Area.
Implementation of IMU sensor for Fall Detection in Dementia Syndrome Patients with Location Notification System

Abstract Monitoring tool for patients at risk of dementia has been made using an IMU sensor with Location Notification System. The aim of this research is to design and manufacture a tool using the IMU sensor to identify and determine the characteristics of a person’s condition in falling for patients at risk of dementia. The research method starts from making prototypes, and measuring system performance. The results of the measurement of system performance are shown by the IMU sensor readings with an average x angle value of 175.74, angular velocity x, and angular acceleration x an average of 175.70 and 0.052 for forward fall activities. For activities falling backwards the average x angle value is 27.97, the angular velocity x, and the angular acceleration x averages 28.12 and -0.25. For activities falling to the right the average x angle value is 85.07, the angular velocity x, and the angular acceleration x averages 37.31 and 79.59. As for the activity falling to the left, the average x angle value is 32.91, the angular velocity x, and the angular acceleration x averages 32.54 and 0.62. Overall the tool can function well. Suggestions for further research, methods can be added to predict the position of the nearest hospital and police station based on the Internet of Things.
Simultaneous measurement of fine pattern shape and overall shape by a nano-profiler

Improving the optical performance and functions of mirrors and lenses requires manufacturing pattern shapes such as those of microlens arrays. In this context, various measuring devices have been developed to accurately measure the depth of shapes with a microscopic interferometer. However, with an interferometer, the number of pixels of a light-receiving element, such as a charge-coupled device, is limited; furthermore, when measuring a pattern shape, the measurement field of view becomes narrow. Thus, measuring a wide range of shapes is not possible. By contrast, if the device can arbitrarily increase the number of measurement points, such as in the case of a three-dimensional measuring machine, in principle, a pattern shape can be measured, while ensuring the measurement capability for a wide range of shapes; however, in this case, the measurement time increases. A nano-profiler previously developed by the authors can measure the shape and slope angle distribution of a surface with a laser beam. In this study, the measurement program was changed to realize continuous high-speed scanning at 1 kHz, and the number of measurement points was increased to verify the compatibility between fine pattern shape measurements and diverse shape measurements. The results measured by the nano-profiler were compared with those obtained using microscopic and Fizeau-type interferometers, which can measure the pattern and overall shapes, respectively. In comparison, the difference in pattern depth and overall shape was 2.1 and 2.9 nm PV, respectively.
DOOSRA—Distributed Object‐Oriented Software Restructuring Approach using DIM‐K‐means and MAD‐based ENRNN classifier

There exists a need to generate well-designed software systems because of the extensive adoption of object-oriented programming in software growth. Thus, the total software maintenance cost is decreased and the component's reusability is augmented. However, the software system's internal structure worsens owing to extended maintenance activities. For enhancing the system's overall internal structure without varying its external behaviour, restructuring is an extensively utilised solution in this circumstance. Thus, utilising the Deterministic Initialisation Method based K-Means (DIM-K-Means) and Median Absolute Deviation-based Elastic Net Regulariser Neural Network (MAD-ENRNN) classifier, a framework called Distributed Object-Oriented (DOO) software restructuring model is created by the study. Five steps are undertaken by the developed framework. Centred on source code along with change history, the interactions amongst the classes are initially pre-processed where the dependencies of disparate classes are detected and formulated into a graphical structure. After that, from the graph, the extraction of significant features is done. Utilising a multi variant objective-based Aquila optimiser, the most pertinent features are selected as of the extracted features. Next, for minimising the complexity, the selected features are created into clusters. Then, the formed clusters are offered to the classifier named MAD-ENRNN. The DOO software is effectively restructured by MAD-ENRNN. The proposed methodology's performance is contrasted with the prevailing systems in an experimental evaluation. The outcomes displayed that the proposed framework is capable of restructuring the DOO software with improved accuracy of 9.94% when analogised to the top-notch methods.
Attributed network embedding with dual fusion strategies

Attributed network embedding (ANE) maps nodes in a network into a low-dimensional space while preserving the intrinsic essence of node attribute and network topology. Incorporating node attribute and network topology with more deeply and more harmoniously is a critical and challenging issue in the ANE, because node attribute and network topology are two kinds of heterogeneous information. Existing approaches fuse two kinds of heterogeneous information at different stages: i.e. before, during or after the learning process. In fact, fusions at different stages have their own advantages and disadvantages. To maximise the profit of utilising the attributed and networked information in ANE, we propose an Attributed Network Embedding model with Dual Fusion strategies (abbr. ANEDF), which consists of both mutually beneficial components: early fusion component for capturing the latent complementarity and late fusion component for extracting the unique and distinctive information from node attribute and network topology. The two components are co-trained during the learning process, which promotes information interaction and captures the consensus of heterogeneous information. Extensive experiments with the tasks of node classification, node clustering, link prediction and visualisation on eight publicly available networks have been conducted to evaluate the effectiveness and rationality of the proposed model. The experimental results demonstrate that ANEDF obtains the best classification, clustering and link prediction performance on 6–7 of 8 datasets, respectively.
Supplemental Material for Improving Content Validity Evaluation of Assessment Instruments Through Formal Content Validity Analysis

a URL, doi, or other permanent path for accessing the specified information in a public, openaccess repository, and the specified information must be provided in a format that is time-stamped, immutable, and permanent.Qualifying public, open-access repositories are committed to preserving data, materials, and/or registered analysis plans and keeping them publicly accessible via the web in perpetuity.Examples include the Open Science Framework (OSF) and the various Dataverse networks.Hundreds of other qualifying data/materials repositories are listed at http://re3data.org/.Preregistration of an analysis plan must take place via a publicly accessible registry system (e.g., OSF, ClinicalTrials.govor other trial registries in the WHO Registry Network, institutional registration systems).Personal websites and most departmental websites do not qualify as repositories.
KLAUS-Tr: Knowledge &amp; learning-based unit focused arithmetic word problem solver for transfer cases

Abstract Solving the Arithmetic Word Problems (AWPs) using AI techniques has attracted much attention in recent years. We feel that the current AWP solvers are under-utilizing the relevant domain knowledge. We present a knowledge- and learning-based system that effectively solves AWPs of a specific type—those that involve transfer of objects from one agent to another (Transfer Cases (TC)). We represent the knowledge relevant to these problems as TC Ontology. The sentences in TC-AWPs contain information of essentially four types: before-transfer, transfer, after-transfer, and query. Our system (KLAUS-Tr) uses statistical classifier to recognize the types of sentences. The sentence types guide the information extraction process used to identify the agents, quantities, units, types of objects, and the direction of transfer from the AWP text. The extracted information is represented as an RDF graph that utilizes the TC Ontology terminology. To solve the given AWP, we utilize semantic web rule language (SWRL) rules that capture the knowledge about how object transfer affects the RDF graph of the AWP. Using the TC ontology, we also analyze if the given problem is consistent or otherwise. The different ways in which TC-AWPs can be inconsistent are encoded as SWRL rules. Thus, KLAUS-Tr can identify if the given AWP is invalid and accordingly notify the user. Since the existing datasets do not have inconsistent AWPs, we create AWPs of this type and augment the datasets. We have implemented KLAUS-Tr and tested it on TC-type AWPs drawn from the All-Arith and other datasets. We find that TC-AWPs constitute about 40% of the AWPs in a typical dataset like All-Arith. Our system achieves an impressive accuracy of 92%, thus improving the state-of-the-art significantly. We plan to extend the system to handle AWPs that contain multiple transfers of objects and also offer explanations of the solutions.
SEN: A subword-based ensemble network for Chinese historical entity extraction

Abstract Understanding various historical entity information (e.g., persons, locations, and time) plays a very important role in reasoning about the developments of historical events. With the increasing concern about the fields of digital humanities and natural language processing, named entity recognition (NER) provides a feasible solution for automatically extracting these entities from historical texts, especially in Chinese historical research. However, previous approaches are domain-specific, ineffective with relatively low accuracy, and non-interpretable, which hinders the development of NER in Chinese history. In this paper, we propose a new hybrid deep learning model called “subword-based ensemble network” (SEN), by incorporating subword information and a novel attention fusion mechanism. The experiments on a massive self-built Chinese historical corpus CMAG show that SEN has achieved the best with 93.87% for F1-micro and 89.70% for F1-macro, compared with other advanced models. Further investigation reveals that SEN has a strong generalization ability of NER on Chinese historical texts, which is not only relatively insensitive to the categories with fewer annotation labels (e.g., OFI) but can also accurately capture diverse local and global semantic relations. Our research demonstrates the effectiveness of the integration of subword information and attention fusion, which provides an inspiring solution for the practical use of entity extraction in the Chinese historical domain.
Development of a website for wound assessment

The assessment and treatment of wounds are nurses' and their teams' responsibilities, as it is up to the nurses to outline a therapeutic plan for tissue repair. For the evaluation process, the nurse must be scientifically trained and use reliable instruments.Website development for wound assessment.This is a methodological study that developed a website to evaluate wounds based on an assessment questionnaire called Expected Results of the Evaluation of Chronic Wound Healing (RESVECH 2.0), which consists of an adapted and validated instrument.The website construction followed the basic flowchart of elaboration. To use it, the professionals create their login and subsequently register their patients. Then, they answer six questionnaires that form the evaluation process according to RESVECH 2.0. The website allows nurses to monitor the patient's evolution through graphs and previous assessments that are filed in a database. For the evaluation process, the professional needs to have a technological internet-accessed device, such as a tablet or cell phone, in order to make wound care assistance more practical and efficient.the findings demonstrate the importance of adding technology to assistance in the treatment of wounds and may provide more qualified service and more resolutive treatment.
Random projection tree similarity metric for SpectralNet

SpectralNet is a graph clustering method that uses neural network to find an embedding that separates the data. So far it was only used with $k$-nn graphs, which are usually constructed using a distance metric (e.g., Euclidean distance). $k$-nn graphs restrict the points to have a fixed number of neighbors regardless of the local statistics around them. We proposed a new SpectralNet similarity metric based on random projection trees (rpTrees). Our experiments revealed that SpectralNet produces better clustering accuracy using rpTree similarity metric compared to $k$-nn graph with a distance metric. Also, we found out that rpTree parameters do not affect the clustering accuracy. These parameters include the leaf size and the selection of projection direction. It is computationally efficient to keep the leaf size in order of $\log(n)$, and project the points onto a random direction instead of trying to find the direction with the maximum dispersion.
Method for Recognizing Pressing Position and Shear Force Using Active Acoustic Sensing on Gel Plates

A touch interface is an important technology used in many devices, including touch panels in smartphones. Many touch panels only detect the contact position. If devices can detect shear force in addition to the contact position, various touch interactions are possible. We propose a two-step recognition method for recognizing the pressing position and shear force using active acoustic sensing, which transmits acoustic signals to an object and recognizes the state of the object by analyzing its response. Specifically, we attach a contact speaker transmitting an ultrasonic sweep signal and a contact microphone receiving ultrasonic waves to a plate of gel. The propagation characteristics of ultrasonic waves differ due to changes in the shape of the gel caused by the user's actions on the gel. This system recognizes the pressing position and shear force on the basis of the difference in acoustic characteristics. An evaluation of our method involving a user-independent model confirmed that four pressing positions were recognized with an F1 score of 85.4%, and four shear-force directions were recognized with an F1 score of 69.4%.