Reviewerly/exHarmony huggingface dataset:
Addition of Out-of-Population Search in JADE

JADE is an optimization algorithm that uses probability distributions to adaptively select parameters. However, it does not take into account the search for regions outside the solution population, so it can be improved by adding an efficient out-of-population search such as the Nelder-Mead method. In this study, a simple method with a small number of parameters to add out-of-population search was considered while keeping the search speed as high as possible, and its effectiveness was confirmed through numerical experiments.
Latent Feature Detection Using LDA in Interior Image

Interior image recommendations have been paid attention to with the development of image retrieval technology. Although a classification model is often used to model user-preferred interiors, it is not suitable for explaining why users prefer them. On the other hand, LDA, which analyzes sentence features based on latent features, can better understand sentences by estimating latent features in sentences. Therefore, we propose a latent feature estimation method in interior images by incorporating spatial features of images and LDA to understand the structure of preferred interior images by users. Specifically, instead of sentences, we use interior images to extract spatial features using BoVW based on SURF features and estimate topics based on the histograms by LDA. We compared the proposed method with subject experiments in interior images with several styles. We verified that the proposed method could estimate similar topics compared to the subject’s subjective view.
A Study on the Linguistic Explanation of Online Classification Mechanism Using Confidence-Weighted Learning Fuzzy Classifiers

Fuzzy classifiers can linguistically explain its mechanism while achieving high classification accuracy. In this paper, we aim to explain the classification mechanism in dynamic environments where the classification boundary changes over time. For this purpose, we propose an online updating of fuzzy classifiers by means of Confidence-Weighted Learning. We have confirmed that the model can linguistically explain the classification mechanism in dynamic environments by examining how the weights of the fuzzy If-Then rules change.
Learning Effect by Motor Imagery of Catching Motion Using Virtual Reality and Observation of Surface Cerebral Hemodynamics

[Purpose] The purpose of this study was to investigate the differences in effects of virtual reality (VR) and two-dimensional (2D) motion images on the immediate learning effect and surface cerebral blood hemodynamics. [Participants and Methods] Twenty-two subjects were randomly divided into two groups: a group that viewed a first-person motion image of the catching motion in VR (VR group) and a group that viewed the motion image in 2D (2D group). First, the catching motions of 10 balls of both groups were evaluated. Two weeks later, each group viewed motor images for 60 seconds, and their catching motions were evaluated again after 90 seconds. We evaluated the surface cerebral hemodynamics using a functional near-infrared spectroscopy system. [Results] The number of successful catches improved after viewing the motor imagery in both groups, and there was no significant difference between the groups. Surface cerebral blood flow activity showed in the right prefrontal cortex, left and right premotor cortex, and supplementary motor cortex in the VR group. [Conclusion] The results suggest the possibility of using VR motor imagery.
Federated learning by employing knowledge distillation on edge devices with limited hardware resources

This paper presents a federated learning approach based on utilizing computational resources of the IoT edge devices for training deep neural networks. In this approach, the edge devices and the cloud server collaborate in the training phase while preserving the privacy of the edge device data. Owing to the limited computational power and resources available to the edge devices, instead of the original neural network (NN), we suggest to use a smaller NN generated using a proposed heuristic method. In the proposed approach, the smaller model, which is trained on the edge device, is generated from the main NN model. By the exploiting Knowledge Distillation (KD) approach, the learned knowledge in the server and the edge devices can be exchanged, leading to lower required computation on the server and preserving data privacy of the edge devices. Also, to reduce the knowledge transfer overhead on the communication links between the server and the edge devices, a method for selecting the most valuable data to transfer the knowledge is introduced. The effectiveness of this method is assessed by comparing it to state-of-the-art methods. The results show that the proposed method lowers the communication traffic by up to 250 × and increases the learning accuracy by an average of 8.9 % in the cloud compared to the prior KD-based distributed training approaches in CIFAR-10 dataset.
Analysis of Embedded System's Functional Requirement using BERT-based Name Entity Recognition for Extracting IO Entities

The functional testing specification is usually designed based on the IO entities' recognition from the embedded system's functional requirement sentences. However, it is hard for the software testing engineers to ably recognize the appropriate IO entities from the functional requirement sentences without clearly indicated entities and much experience with the domain knowledge. The conventional rule-based methods of extracting IO entities are inapplicable when the requirement sentences drafted by humans become too semantically complex. Even though all the sentences keep aligned with the structure, it is still infeasible to manually hard-code each rule when those rules change from time to time without any explicit writing standard. With the successful application of artificial intelligent techniques in natural language processing (NLP), we propose a method that intelligently solves the issue of the entities recognition by using BERT (Bidirectional Encoder Representations from Transformers) based named entity recognition (NER) which is the technique of NLP to recognize the phrases having similar attributes in semantics. In this paper, we specifically focus on the issue of IO entities' recognition in the embedded systems that implement the inverter control function such as elevator and hybrid hydraulic excavator systems. Our evaluation result demonstrates that the best model variant fine-tuned on 829 sentences achieves more than 80% F-measure in recognizing the IO entities, and the model can provide applicable information for the improvement of industrial productivity in the target industries. Our contribution of this paper is to provide insight into the case whether the IO entities in the target system manages to be interpreted well by exploiting the BERT model with the sole reliance on the small size of exemplary IO entities data and three existing model variants pre-trained on large corpus open datasets with general language knowledge.
Estimating and Visualizing Persuasiveness of Participants in Group Discussions

In group discussions, each participant has his/her own opinion, and if necessary, tries to persuade other participants to reach an agreement. Therefore, persuasiveness is an important skill for communicating with others. Based on this motivation, this study aims to estimate the persuasiveness of group discussion participants. First, human annotators rated the level of persuasiveness of each participant in group discussions among four people. We then created multimodal and multiparty models for estimating persuasiveness of a participant using speech, language, and visual (head pose) features using gated recurrent unit (GRU)-based neural network. In our experiment, in estimating the highest persuasive participant among the group, the performance of the proposed method was 76% in accuracy. In the binary classification task for estimating high or low persuasiveness participants among the group, the performance of the best performing multimodal multiparty model achieved 80% in accuracy. The experimental results show that multimodal models are better than unimodal models, and multiparty features contribute to improving the model performance. As an application of the proposed method, we implemented a meeting browser with persuasiveness visualization. The level of persuasiveness is visually indicated using the background color of each participant's timeline. Finally, we conducted a user study for our meeting browser, and found that the persuasiveness visualization helps the subjects grasp the flow of the discussion in a shorter time compared to a browser without persuasiveness visualization.
Multiscale representation learning for biomedical analysis

Representation learning has gained prominence over last few years and has shown that for all underlying learning algorithm, results are vastly improved if feature representation of input is improved to capture the domain knowledge. Word embedding approaches like Word2Vec or subword information technique like fastText has shown to improve multiple NLP tasks in biomedical domain. These techniques mostly capture indirect relationships but often fail to capture deeper contextual relationships. This can be attributed to the fact that such techniques capture only short-range context defined via a co-occurrence window. In this chapter we describe recent advances in novel contextual embedding for a “wide sentential context.” These contextual embedding can generate composite word embedding achieving a multiscale word representation. We further illustrate that the composite embedding performs better than the present individual state-of-art techniques on both intrinsic and extrinsic evaluations.
Source distribution weighted multisource domain adaptation without access to source data

Unsupervised domain adaptation (UDA) aims to learn a predictive model for an unlabeled domain by transferring knowledge from a separate labeled source domain. Conventional UDA approaches make the strong assumption of having access to the source data during training. This may not be very practical due to privacy, security, and storage concerns. A recent line of work addressed this problem and proposed an algorithm that transfers knowledge to the unlabeled target domain from a single-source model without requiring access to the source data. However, for adaptation purposes, if there are multiple trained source models available to choose from, this method has to go through adapting each and every model individually, to check for the best source. A better question to ask is the following: can we find the optimal combination of source models, with no source data and without target labels, whose performance is no worse than the single best source? The answer to this is given by a recent efficient algorithm (Ahmed et al., 2021) which automatically combines the source models with suitable weights in such a way that it performs at least as good as the best source model. The work provided intuitive theoretical insights to justify their claim and extensive experiments were conducted on several benchmark datasets. In this chapter, we first review this work on multi-source source-free unsupervised domain adaptation followed by analysis of a new algorithm which we propose by relaxing some of the assumptions of this prior work. More specifically, instead of naively assuming source data distribution as uniform, we try to estimate it via a multilayer perceptron (MLP) in order to use this information for effective aggregation of source models.
Genuine Forgery Signature Detection using Radon Transform and K-Nearest Neighbour

Authentication is very much essential in managing security.In modern times, it is one in all priorities.With the advent of technology, dialogue with machines becomes automatic.As a result, the need for authentication for a variety of security purposes is rapidly increasing.For this reason, biometrics-based certification is gaining dramatic momentum.The proposed method describes an off-line Genuine/ Forgery signature classification system using radon transform and K-Nearest Neighbour classifier.Every signature features are extracted by radon transform and they are aligned to get the statistic information of his signature.To align the two signatures, the algorithm used is Extreme Points Warping.Many forged and genuine signatures are selected in K-Nearest Neighbour classifier training.By aligning the test signature with each and every reference signatures of the user, verification of test signature is done.Then the signature can be found whether it is genuine or forgery.A K-Nearest Neighbour is used for classification for the different datasets.The result determines how the proposed procedure is exceeds the current state-of-the-art technology.Approximately, the proposed system's performance is 90 % in signature verification system.
The implementation of digital applications in improving the quality of services in Pelindo III holding Badas port branch

Pelindo III Holding Badas Port Branch has now applied digitalization to improve ship and goods services to customers. Other Pelindo Holding should follow the digital innovation they made in any area in Indonesia. This study employs a qualitative descriptive method in which the data are obtained from observations, interviews at the location of Pelindo III Holding Badas Port Branch. The purpose of this research is for port managers to continue innovating by promoting digital service innovation for more effective and efficient services. The results obtained from this study are digital applications used by Pelindo III Holding Badas Port Branch, including the Ship Bridge Application used to make ship service applications, such as submitting ship docking applications, vessel mooring applications, and clean water, and other port service applications. The VASA application has been integrated with the System Application and Processing, or SAP reports can be generated quickly. One-day billing can be achieved and is connected to the Automatic Identification System or AIS so that we can detect ships in the port area, record planning, realization, and administration of ship services accurately and quickly following standard operating procedures in the working area of Pelindo III Holding (Persero) Badas Port Branch. The Gen-C application is utilized by employees of Pelindo III Holding Badas Port Branch to find out the number of goods that embark or leave each year and the monthly income and annual after loading and unloading activities at Badas Port. Spinerrb application is an application used by employees of Pelindo Holding to monitor, determine, realize receipts, and monitor the movement of activities and container status while at Badas Port.
Analysis of ISM code implementation on ship crew performance at Surf Marine Holding, Indonesia

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Uswatul Hasanah, Jumriani, Rosliawati; Analysis of ISM code implementation on ship crew performance at Surf Marine Holding, Indonesia. AIP Conf. Proc. 14 February 2023; 2675 (1): 030002. https://doi.org/10.1063/5.0118955 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
HAZMAT Vehicle Reidentification in Road Tunnels Based on the Fusion of Appearance and Spatiotemporal Information

Vehicles transporting hazardous material (HAZMAT) pose a severe threat to highway safety, especially in road tunnels. Vehicle reidentification is essential for identifying and warning abnormal states of HAZMAT vehicles in road tunnels. However, there is still no public dataset for benchmarking this task. To this end, this work releases a real-world tunnel HAZMAT vehicle reidentification dataset, VisInt-THV-ReID, including 10,048 images with 865 HAZMAT vehicles and their spatiotemporal information. A method based on multimodal information fusion is proposed to realize vehicle reidentification by fusing vehicle appearance and spatiotemporal information. We design a spatiotemporal similarity determination method for vehicles based on the spatiotemporal law of vehicles in tunnels. Compared with other reidentification methods based on multimodal information fusion, i.e., PROVID, Visual + ST, and Siamese-CNN, experimental results show that our approach significantly improves the vehicle reidentification recognition precision.
Optimal storage for solar energy self-sufficiency

We determine the energy storage needed to achieve self sufficiency to a given reliability as a function of excess capacity in a combined solar-energy generation and storage system. Based on 40 years of solar-energy data for the St. Louis region, we formulate a statistical model that we use to generate synthetic insolation data over millions of years. We use these data to monitor the energy depletion in the storage system near the winter solstice. From this information, we develop explicit formulas for the required storage and the nature of cost-optimized system configurations as a function of reliability and the excess generation capacity. Minimizing the cost of the combined generation and storage system gives the optimal mix of these two constituents. For an annual failure rate of less than 3%, it is sufficient to have a solar generation capacity that slightly exceeds the daily electrical load at the winter solstice, together with a few days of storage.
Forensic Entropy Assessment of Soil, Plant, Meat and Effluent: A Case Study of Obollo-Afor and Environs, Eastern Nigeria

Heavy metal concentrations were determined in four different locations: market, abattoir, mechanic village and farmland in Obollo–Afor and its environs. The samples were digested and subsequently analysed using Atomic Absorption Spectrophotometer (AAS). The mean concentration of metals in the soil samples were in the order Fe > Zn > Mn > Cr > Pb > Cu > Ni > Cd. The mean concentration of metals in the effluent samples were in the order Fe > Cr > Ni > Pb > Zn > Mn > Cd > Cd. The mean concentration of the metals in meat samples were in the order Fe > Zn > Cu > Ni > Mn > Cd > Cr > Pb. The mean concentration of metals in the plant samples were in the order Fe > Mn > Zn > Cu > Ni > Cr > Pb > Cd. Cadmium, zinc, chromium and lead were higher than the World Health Organization (WHO) standard for soil while cadmium, copper and zinc were higher than the WHO standard for plant. Iron, zinc, copper, nickel and chromium were higher than the WHO standard for meat while lead, nickel, iron, were higher than the WHO standard for water. Heavy metal pollution index (HPI) and heavy metal evaluation index (HEI) showed low risk for all locations while degree of contamination (Cdeg) revealed medium to high values for all locations except AB7. Consequently, the environment is not safe due to high concentration of these toxic metals. The multivariate analyses unraveled anthropogenic and geogenic sources of the contamination. With this trend of findings from the forensics entropy analysis, it can be said that Obollo Afor is the epicentre of these contaminants and attenuates towards the farmland area. The study therefore recommends effective and efficient environmental management strategy measures to curb this menace.
Neural network based multi-dimensional and nonlinear landscape design

In order to improve the effect of landscape design, based on the traditional multi-dimensional nonlinear landscape design and RBF neural network, this paper proposes and designs a multi-dimensional nonlinear landscape design method based on neural network. Firstly, the camera parameters are set, the landscape images are collected by UAV, and the collected landscape images are segmented. Landscape image features are extracted according to different classification criteria, and the feature information is used as training samples to train the neural network. Finally, the landscape design parameters are fitted and the results of the landscape design model are output. The experimental results show that the proposed method has better classification accuracy than the other two traditional landscape image classification algorithms. In different experiments, the landscape image classification accuracy of this method is kept above 85%, while the other two methods are lower. In addition, the regression analysis value and test value of this method also perform well. Finally, given a noisy image, it is found that the text method can effectively remove the noise in the landscape design image, making the image present a clearer landscape layout.
Fundamental Understanding of Network and Network Security

This paper encompasses the aspects of how website was created and operated in the 2008 to 2010 era. As current technologies has long surpassed the methods of the technologies just 12 years ago. I have made this paper for the people to understand the traditional and the fundamental way of hosting a website, and also the security aspects back then. This even includes an explanation of group policy management, a very overlooked concept in the field of IT, and for aspiring IT professionals as myself, this is a necessary skill to learn. Since current technologies require a variety of skills are required to present a fully working website (including programming languages such as python or C++, scripting languages such Java script, HTML, CSS and backend applications, understanding of API and etc..), it can be daunting for new aspiring developers, this paper creates the fundamental understanding of network and network security, in which these aspiring developers and take an understanding of the past technologies and move ahead with their careers. The working has all been done on windows 2008 r2 server using a virtual machine. The infrastructure required are Windows Desktop, Preferably with i5 Processor (Even i3 will be sufficient). RAM recommended 8 GB (Min 4 GB) HDD can be above 500 GB, Router: D-Link router with wi-fi support. Software are Operating System: Windows 10 (Prefer Windows 7 or Windows 8) Windows server 2008. Software: Virtual Box (VMware also acceptable) The first four topics are about creating website (Creation of forest, active domains, and DNS configurations), the next four topics are about group policy management, and the last topic is a simple router configuration.
Improving cybersecurity skill development through visual programming

Purpose Cybersecurity training plays a decisive role in overcoming the global shortage of cybersecurity experts and the risks this shortage poses to organizations' assets. Seeking to make the training of those experts as efficacious and efficient as possible, this study investigates the potential of visual programming languages (VPLs) for training in cyber ranges. For this matter, the VPL Blockly was integrated into an existing cyber range training to facilitate learning a code-based cybersecurity task, namely, creating code-based correlation rules for a security information and event management (SIEM) system. Design/methodology/approach To evaluate the VPL’s effect on the cyber range training, the authors conducted a user study as a randomized controlled trial with 30 participants. In this study, the authors compared skill development of participants creating SIEM rules using Blockly (experimental group) with participants using a textual programming approach (control group) to create the rules. Findings This study indicates that using a VPL in a cybersecurity training can improve the participants' perceived learning experience compared to the control group while providing equally good learning outcomes. Originality/value The originality of this work lies in studying the effect of using a VPL to learn a code-based cybersecurity task. Investigating this effect in comparison with the conventional textual syntax through a randomized controlled trial has not been investigated yet.