Reviewerly/exHarmony huggingface dataset:
Part Recognition and Pose Estimation Based on Convolutional Neural Network

Automated sorting of parts is the basis and premise of an automated production system. In order to accurately and quickly solve the problem of part type recognition and pose estimation, this paper proposes a method of part recognition and pose estimation based on convolutional neural network. By adapting and optimizing the application of the existing mature network model, the training can be used for the type recognition of mechanical parts and the posture estimation of the network model, which provides an accurate information foundation for industrial automation sorting and assembly. At the same time, a virtual data set generation method based on OSG is proposed. A large number of training data sets are generated in the virtual scene to ensure the accuracy of model training.
Smart Intelligent Troubleshooter to Solve Windows Operating System Specific Issues

While working on computers, people frequently confront with various kinds of problems, those beyond their extensive expertise. Microsoft Windows is the widely used Operating System running on numerous personal computers and the reason which gives more irritating problems that require to be addressed. Currently, troubleshooting is considered as a costly and time-consuming approach. The SAITA is an Artificial Intelligent Troubleshooting Agent that utilizes natural language generation, machine learning, and dependency resolving and ontology-based methodologies for solving most common Windows-specific issues within a short period of time than the traditional approach. The assistant learns from the accessible data and accomplishes the task for users as performed by human experts. The main objective of this exploration venture is to distinguish the constraints of existing troubleshooting software and create an AI troubleshooting assistant to provide solutions to fix the identified user issues. The use of this assistant would be economical as an IT help desk alternative in the industry. SAITA is developed to serve as a representative troubleshooter for fundamental user issues, service issues, application issues, and perform environment setup by analyzing software. This system will be able to solve the common Windows user's issues as same as a human with less time.
Blockchain Technology

Currently, the financial transactions between individuals, organizations, and companies are taking place with the help of third-party approval of intermediaries such as banks, financial institutions, standardizing bodies, or credit card providers. These transactions involve multilevel approvals, costs, and inefficient processes in some cases, which also lead to waste of time and resources. To resolve these issues, blockchain technology has appeared as a new financial digital innovative solution. Here, financial transactions are online, open, and transparent. In this chapter, the authors present systematic literature of relevant research on blockchain technology. The objective is to understand the historical evolutions, current ongoing research, base technologies, and applications. The authors have extracted research articles from scientific databases including EBSCO, Scopus, Web of Science, and Google Scholar. The online blogs, wikis, media articles, YouTube videos, and companies' white papers on blockchain technology are also used for content analysis.
Towards human‐oriented solutions for deep semantic data analysis

Summary This paper presents a new data analysis technology based on human‐oriented analysis. This analysis covers semantic methods of data description and interpretation referring to marketing preferences of system users. The proposed methods of cognitive marketing – in order to interpret fully all possible preferences which can occur – are a subject to an analysis focusing on their meaning and usefulness at the stage of product evaluation, promotion, but also regarding product distribution and price. All these processes will constitute marketing analysis due to the possibilities to assess some preferences of the analyzed data. The essence of this paper is a possibility to present the methodology of cognitive marketing based on the application of the meaning analysis which reaches the human brain and the attention attractors registered by the brain, which give rise to some interest or, quite the contrary, which remain unnoticed. A new solution is dedicated to deep semantic analysis based on the registration, processing, and analysis of attention attractors and their perception by individual person. Such attractors can be detected on the basis of observation of how attention is focused on some specific features of the examined information groups. The variety of the examined information and data can enable a wide‐ranging analysis of the issue discussed here; as a result, can assess the degree to which human attention focuses on the process of meaning interpretation of various cognitive features and observations.
Technical Assessment for Solar Powered Pumps in IRAQ

Abstract IRAQ has been described as having high solar powered irrigation resources. The seasonal changes in climate have a direct effect on the determination of crop water requirements, irrigation schedules and potential of solar energy generation to operate the water pumping. In this study, solar pumping and pressurized drip irrigation systems were theoretically evaluated to estimate the required water requirement (one hectare) for potato planting in the ALKhlis district using calculated climatic data from the Al-Khalis Agrometeorological weather station for a period (2013-2017).CROPWAT 8.0 Software was adopted in simulation of crop water requirement. A mathematical modeling of irrigation scheduling processes was carried by using Microsoft Excel worksheets. The pumping system was simulated by using web based Grundfos Product Center tool. The highest average value of 7.6 kWh / m2 / month of solar radiation in all monitoring data years in July, while the minimum average value of 2.6 kWh / m2 / month in December when the sun is at the lowest point in the atmosphere. The average of peak sun hours corresponding to the time of the pumping system and the highest water intake was observed at eight months, from March to October, is 7.2 hours / day and 5.2 hours/day for January, February, November and December
Evaluating the Jordanian Experience in the Transition to a Knowledge Economy

Abstract In 2002, Jordan sought to shift towards a knowledge economy through the adoption of several policies supporting the transition towards a knowledge economy. The current study aims to evaluate the transition process as Jordan moves towards knowledge economy. Majority of the indicators presented in this study showed regression, despite some important steps taken by Jordan. The results showed a noticeable decline in the knowledge economy indicators in Jordan and also that Jordan is going through difficult economic conditions on account of minimal global support. The pace of the transition towards the knowledge economy has been greatly affected by deteriorated economic situations of Jordan. However, Jordan has been able to obtain some positive indicators with respect to human capital and creativity, and these endorse the significance of investing in human capital, reviewing the law and legal system, and education policies in Jordan. These results provide a guideline to Jordanian authorities so that they can take necessary measures to meaningfully invest in knowledge economy.
Temperature &amp; Humidity-Weighted-Modified GRNN Based Prediction Model of Photovoltaic Power Generation

Weather factors directly affect photovoltaic (abbr PV) power generation, in order to improve the prediction accuracy of PV power generation, this paper proposed a modified prediction GRNN for which with temperature and humidity weighted. The prediction model was built according to different type of weather separately, first order function and exponential function for temperature and humidity are weighted to modify the input neurons of GRNN model, and grey correlation degree function (abbr GCDF) is used to select single-day and multi-day training data sets for point prediction and hour prediction respectively. To improve the stability of the prediction model, a coefficient optimization method based on relative standard deviation is proposed. The experimental results showed that: (1) The prediction accuracy of the modified GRNN model weighted by temperature and humidity is significantly higher than that of the unmodified GRNN model, and all of the root mean square error of point prediction corresponding to the optimal GRNN model for four kinds of weather is less than 1.2238%; (2) The GRNN with exponential function for temperature and humidity has the best fitness for point prediction, the GRNN with first order function for temperature and humidity fit best instead for the hour prediction.
Automatic Speaker-level Pronunciation Assessment of L2 Speech Using Posterior Probabilities from Multiple Utterances

Evaluation of the level of accentedness is important for second language education, both in qualifying language teachers and in offering advice and feedback to the learners. Previous methods evaluated accentedness of a speaker based on a limited number of utterance(s) from the speaker in focus, which leads to biased/unstable results since sparse data cannot fully cover speaker-specific pronunciation errors. To enhance stability in evaluation, we investigate the use of speaker-level features and speaker-level neural networks trained on multiple utterances. Experimental results demonstrate that using speaker-level features and speaker-level models provide high accent classification accuracy comparable with human annotations. The proposed approach also enhances the stability of the evaluation results.
<i>SGXTuner</i>: Performance Enhancement of Intel SGX Applications Via Stochastic Optimization

Intel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SGX</i> has started to be widely adopted. Cloud providers (Microsoft Azure, IBM Cloud, Alibaba Cloud) are offering new solutions, implementing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data-in-use</i> protection via SGX. A major challenge faced by both academia and industry is providing transparent SGX support to legacy applications. The approach with the highest consensus is linking the target software with SGX-extended <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">libc</i> libraries. Unfortunately, the increased security entails a dramatic performance penalty, which is mainly due to the intrinsic overhead of context switches, and the limited size of protected memory. Performance optimization is non-trivial since it depends on key parameters whose manual tuning is a very long process. We present the architecture of an automated tool, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SGXTuner</i> , which is able to find the best setting of SGX-extended <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">libc</i> library parameters, by iteratively adjusting such parameters based on continuous monitoring of performance data. The tool is — to a large extent — algorithm agnostic. We decided to base the current implementation on a particular type of stochastic optimization algorithm, specifically <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Simulated Annealing</i> . A massive experimental campaign was conducted on a relevant case study. Three client-server applications — <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Memcached</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Redis</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Apache</i> — were compiled with SCONE's <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sgx-musl</i> and tuned for best performance. Results demonstrate the effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SGXTuner</i> .
Histopathology Image Classification for Soft Tissue Sarcoma in Limbs using Artificial Neural Networks

Clinical imaging techniques have been widely used in the classification of cancer biopsy specimen histopathology images of limb soft tissue sarcoma (STS). Here, by automatically differentiating cell patterns in malignant and non-malignant tumors, an efficient classifier based on both accuracy and time requirements is significantly improved, which further reduces intra-inter-observer variations. Color normalization is carried out using a linear transformation into a grayscale image and the region of interest (ROI) of the image is selected by the pathologist. The wavelet transform has been used to extract statistical texture features (SFT) from the grayscale image of this ROI, and neural correlates with extracted features networks were trained For the purpose test, the features of a new limb STS tissue sample image are extracted and these extracted values are presented to the already trained networks for classification. In this case, the proposed research uses an artificial neural network (ANN), which leads to prominence by improving the classification methods based on accuracy, sensitivity and specificity. Here, two different types of ANN classifiers are discussed with back propagation neural network (BPNN) and radial basis function network (RBFN) classifiers. Furthermore, here the most significant difference between BPNN and RBFN is analyzed using the receiver operating characteristics (ROC) area under the curve. The performance accuracy of these two classification methods reaches 96.36% and 90.91 % for RBFN and BPNN, respectively. Based on these accuracy values, RBFN is found to be more efficient than BPNN classifiers. Finally the cancer cell classification accuracy is increased, decision- making time is reduced, and the initial treatment plan for chronic disease of the limbs tumor has been achieved.
The Role of a Social Robot in Behavior Change Coaching

This experimental study evaluates the effects of coaching people into behavior change with a simulation of the social robot Haru. In order to support participants in their attempts to change their behavior and to create a new habit, a coaching session was created based on the 'Tiny Habits' method developed by BJ Fogg [1]. This coaching session was presented to altogether 41 participants in three conditions. In Condition 1, the dialogue between the participant and the simulated robot was interspersed with emotional expressions and behaviors such as dancing, bowing and vocalizing. Condition 2 used the same set-up with the robot simulator and provided participants with the same guidance, using the same synthesized voice from Condition 1, but without any emotional elements. The third condition was created to evaluate the effect of using a robot as a session coach by comparing the two conditions with Haru to a condition in which the same content was presented, just without a robot. The same script as in the two robot conditions was presented as a text on a website, divided into sections reflecting the human-robot dialogue in the two robot conditions. Data from a post-session questionnaire were supplemented by another questionnaire which was administered 10 days later and focuses on habit retention. Participants from the session with the robot that uses emotional behaviors felt significantly more confident that they will incorporate their behavior change in their lives and thought differently about behavior change. People participating in the session with a robot simulation also had a significantly higher retention rate of their behavior change, thus revealing a positive effect of the social robot.
Research on Hybrid Index Method of Double-Layer B+ Tree for Power Big Data Considering Knowledge Graph

Abstract Efficient power data access can not only ensure the normal operation of power system, but also be one of the key supporting technologies to improve the operational efficiency of power grid enterprises. Aiming at the problem that traditional B+ tree does not take into account the relevance of power information fragments and cannot be indexed flexibly, a hybrid index structure and index method of power big data with knowledge graph is proposed, that is, the first layer uses B+ tree structure to store attributes, and the second layer uses knowledge graph to store attribute value, thus realizing the relevance index of power data. The simulation results show that the hybrid index structure can achieve the index of power big data with relevance information without affecting the index efficiency, which provides technical reference for efficient retrieval of multi-modal data in the future.
Power Forecasting with Minimal Loss using LSTM and PV Model

This paper presents temperature, irradiance and PV power output prediction with the help of time series predictor Long-Short Term Memory (LSTM), a special type of Recurrent Neural Network (RNN) model. By using this model with changing hyper-parameters predicted data are calculated and with the help of forecasted data output power of photovoltaic (PV) model is obtained. This compared result shows us how much efficient is our forecasting method, which is having very small loss in neural network. Fixed hysteresis controlled type of PV model is considered here for better and steady output. Activation functions like tanh, Sigmoid and optimizers like stochastic gradient descent, Adam, Adagrad and Adadelta are used. In the last part of Simulation results, it shows the next 30-days hourly basis future forecasted temperature data of a particular place with better output.
Two-Way Perceived Color Difference Saliency Algorithm for Image Segmentation of Port Wine Stains

The image segmentation of port wine stains (PWS) lesions is of great significance to assess PDT treatment outcomes. However, it mainly depends on the manual division of doctors at present, which is time-consuming and laborious. Therefore, it is urgent and necessary to explore an efficient and accurate automatic extraction method for PWS lesion images. A two-way perceived color difference saliency algorithm (TPCS) for PWS lesion extraction is proposed to improve the efficiency and accuracy, and is compared with other image segmentation algorithms. The proposed algorithm shows the best performance with 88.91% accuracy and 96.36% sensitivity over 34 test images of PWS lesions.
Remote sensing abnormal extraction of hydroxyl alteration based on PCA method

Abstract Anomalous geological events often occur during the formation and evolution of mineral deposits. The use of remote sensing technology to extract anomalies is one of the important contents of remote sensing prospecting. The principle is to use different minerals to show differences in the responses of different bands of remote sensing images. Landsat satellite has accumulated massive amounts of remote sensing image data since its launch, providing a research foundation for geological remote sensing. This paper takes the eastern part of Tuoli County, Tacheng District, Xinjiang Uygur Autonomous Region as the research area, and selects ENVI software for remote sensing image preprocessing and remote sensing anomaly extraction. Four bands including TM1, TM4, TM5, and TM7 of Landsat5 TM image are used as the basic bands for extracting hydroxyl remote sensing anomaly information. After preprocessing, the influence of vegetation and water bodies on remote sensing prospecting is suppressed by masking method, and then the Corsta method is used to delimit the threshold value to judge the degree of alteration. The PCA method is generally used to extract the remote sensing anomalies of hydroxyl alteration in the study area, and finally the thematic map visually displays the results of the remote sensing anomaly extraction, which proves that the PCA method based on Landsat5 TM images can provide a certain reference for geological work such as prospecting.
Early Diagnosis of Alzheimer's Disease Using Hybrid Word Embedding and Linguistic Characteristics

Early detection of Alzheimer's Disease (AD) is of great importance to the benefits of AD patients, including lessening symptoms and alleviating the financial burden of health care. As one of the leading signs of AD, changes of language capability can potentially be used for early diagnosis of AD. In this paper, I develop an automatic and accurate diagnostic model by using the linguistic characteristics of the subjects and hybrid word embedding. I detected linguistic features such as pauses, unintelligible words, repetitions, etc. from transcripts of interviews. Then I create a text embedding by combining word vectors from Doc2vec and ELMo. Moreover, by tuning hyperparameters of the machine learning pipeline (e.g., model regularization parameter, learning rate and vector size of Doc2vec, and vector size of ELMo), I achieve 91% classification accuracy and an Area Under the Curve (AUC) of 97% for distinguishing early AD from healthy subjects. Compared with the method which only uses word count, I improved the absolute detection accuracy by 10%, and the absolute AUC by 9%. Moreover, I study the stability of the model by repeating experiment and find out that the model is stable even though my training data is split randomly. My algorithms have high detection accuracy and are stable. This model could be used as a large-scale screening method for AD, as well as a complement to doctors’ detection of AD.
Persistent Visual Coverage Control Ensuring Field of Views' Overlap with Information Reliability and Energy Management

In this paper, we propose a control algorithm with information reliability and energy management for persistent visual coverage ensuring field of views’ overlap. We first introduce a visual coverage control with the control barrier function which ensures no coverage holes in-between sensing areas. In order to persistify this visual monitoring, we utilize a control barrier function for energy management of robots, and propose a persistification method using information reliability over monitoring area with an integrated persistent visual coverage algorithm. Finally, we demonstrate the validity of the proposed algorithm through a simulation and experiment.
Co-Kriging method performance in estimating number of COVID-19 positive confirmed cases in East Java Province

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 Siti A. D. Safitri, Fajrina A. Putri, Belindha A. Ardhani, Nur Chamidah; Co-Kriging method performance in estimating number of COVID-19 positive confirmed cases in East Java Province. AIP Conf. Proc. 26 February 2021; 2329 (1): 060034. https://doi.org/10.1063/5.0042286 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
Fuzzy Clustering of Paper Mill Data

In the present work, the 48 input-output data points of the paper mill process have been considered with the desired machine speed as the input and required total head as the output. The data has been taken from the literature. Two clustering techniques are applied on this data using MATLAB. One is the FCM (fuzzy C-means clustering), another is the subtractive clustering. In present work, initial FIS (fuzzy inference system) developed by subtractive clustering is further optimized to improve its performance, and finally their responses are compared.