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This work presents a unique approach to the design, fabrication, and characterization of paper-based origami robotic systems consisting of stackable pneumatic actuators. These paper-based actuators (PBAs) use materials with high elastic modulus-to-mass ratios, accordion-like structures, and direct coupling with pneumatic pressure for extension and bending. The study contributes to the scientific and engineering understanding of foldable components under applied pneumatic pressure by constructing stretchable and flexible structures with intrinsically nonstretchable materials. Experiments showed that a PBA possesses a power-to-mass ratio greater than 80 W/kg, which is more than four times that of human muscle. This work also illustrates the stackability and functionality of PBAs by two prototypes: a parallel manipulator and a legged locomotor. The manipulator consisting of an array of PBAs can bend in a specific direction with the corresponding actuator inflated. In addition, the stacked actuators in the manipulator can rotate in opposite directions to compensate for relative rotation at the ends of each actuator to work in parallel and manipulate the platform. The locomotor rotates the PBAs to apply and release contact between the feet and the ground. Furthermore, a numerical model developed in this work predicts the mechanical performance of these inflatable actuators as a function of dimensional specifications and folding patterns. Overall, we use stacked origami actuators to implement functionalities of manipulation, gripping, and locomotion as conventional robotic systems. Future origami robots made of paper-like materials may be suitable for single use in contaminated or unstructured environments or low-cost educational materials.

The International Conference on Cognitive Modeling brings together researchers from around the world whose main goal is to build computational systems that reflect the internal processes of the mind. In this issue, we present the five best representative papers on this work from our 19th meeting, ICCM 2021, which was held virtually from July 3 to July 9, 2021. Three of these papers provide new techniques for refining computational models, giving better methods for taking empirical data and producing accurate computational models of the cognitive systems that produce them. The other two papers focus on explanation: using models to elucidate the underlying processes affecting cognition in such diverse domains as logical reasoning and the effects of caffeine.

Objective The purpose of this study was to develop a natural language processing (NLP) pipeline to identify incidental thyroid nodules (ITNs) meeting criteria for sonographic follow-up and to assess both adherence rates to white paper recommendations and downstream outcomes related to these incidental findings. Methods 21583 non-contrast chest CT reports from 2017 and 2018 were retrospectively evaluated to identify reports which included either an explicit recommendation for thyroid ultrasound, a description of a nodule ≥ 1.5 cm, or description of a nodule with suspicious features. Reports from 2018 were used to train an NLP algorithm called fastText for automated identification of such reports. Algorithm performance was then evaluated on the 2017 reports. Next, any patient from 2017 with a report meeting criteria for ultrasound follow-up was further evaluated with manual chart review to determine follow-up adherence rates and nodule-related outcomes. Results NLP identified reports with ITNs meeting criteria for sonographic follow-up with an accuracy of 96.5% (95% CI 96.2-96.7) and sensitivity of 92.1% (95% CI 89.8-94.3). In 10006 chest CTs from 2017, ITN follow-up ultrasound was indicated according to white paper criteria in 81 patients (0.8%), explicitly recommended in 46.9% (38/81) of patients, and obtained in less than half of patients in which it was appropriately recommended (17/35, 48.6%). Discussion NLP accurately identified chest CT reports meeting criteria for ITN ultrasound follow-up. Radiologist adherence to white paper guidelines and subsequent referrer adherence to radiologist recommendations showed room for improvement.

Artificial Intelligence research is presenting phenomenal progress in two directions: (i) new theories and methodologies, and (ii) applications that expand traditional domains with innovative interventions. As indicated by recent reports, this progress has created a disequilibrium, where demand for scientists with skills in Artificial Intelligence is not fulfilled, a trend that will intensify further in the years to come. A potential solution to this shortage of specialised workforce may come from encouraging more women to get educated and follow a career in one of the Artificial Intelligence areas. This special collection of invited papers is dedicated to all women researchers and practitioners in Artificial Intelligence and coincides with the March 8, 2023 International Women’s Day. Moreover, it has two specific goals: (i) to inspire more women to study and practice Artificial Intelligence through presentation of recognized women researchers who can act as role models, and (ii) to highlight some streamlined research areas of Artificial Intelligence.

The Artificial Intelligence Recommender System has emerged as a significant research interest. It aims at helping users find things online by offering recommendations that closely fit their interests. Recommenders for research papers have appeared over the last decade to make it easier to find publications associated with the field of researchers’ interests. However, due to several issues, such as copyright constraints, these methodologies assume that the recommended articles’ contents are entirely openly accessible, which is not necessarily the case. This work demonstrates an efficient model, known as RPRSCA: Research Paper Recommendation System Using Effective Collaborative Approach, to address these uncertain systems for the recommendation of quality research papers. We make use of contextual metadata that are publicly available to gather hidden relationships between research papers in order to personalize recommendations by exploiting the advantages of collaborative filtering. The proposed system, RPRSCA, is unique and gives personalized recommendations irrespective of the research subject. Thus, a novel collaborative approach is proposed that provides better performance. Using a publicly available dataset, we found that our proposed method outperformed previous uncertain methods in terms of overall performance and the capacity to return relevant, valuable, and quality publications at the top of the recommendation list. Furthermore, our proposed strategy includes personalized suggestions and customer expertise, in addition to addressing multi-disciplinary concerns.

We are pleased to announce that the JACIII Awards of 2022 have been decided by the JACIII editorial boards. This year, the award winning papers were severely and fairly selected among 321 papers published in JACIII Vols. 23 (2019) to 25 (2021) and there was no entries that deserved the Best Review Paper award. The award ceremony was held online in order to prevent spreading of COVID-19. JACIII BEST PAPER AWARD 2022 Katsuhiro Honda, Issei Hayashi, Seiki Ubukata, and Akira Notsu Three-Mode Fuzzy Co-Clustering Based on Probabilistic Conceptand Comparison with FCM-Type Algorithms JACIII Vol.25 No.4, pp. 478-488, 2021. JACIII YOUNG RESEARCHER AWARD 2022 Bijun Tang Path Planning Based on Improved Hybrid A* Algorithm By Bijun Tang, Kaoru Hirota, Xiangdong Wu, Yaping Dai, and Zhiyang Jia JACIII Vol.25 No.1, pp. 64-72, 2021. JACIII YOUNG RESEARCHER AWARD 2022 Takato Sasagawa Recommendation System Based on Generative Adversarial Network with Graph Convolutional Layers By Takato Sasagawa, Shin Kawai, and Hajime Nobuhara JACIII Vol.25 No.4, pp. 389-396, 2021.

An electrocardiogram (ECG) uses electrodes to monitor the heart rhythm and identify minute electrical changes that occur with each beat. It is employed to investigate particular varieties of aberrant heart activity, such as arrhythmias and conduction problems. One of the most essential tools for detecting heart problems is the electrocardiogram (ECG). The majority of ECG records are still on paper. Manual ECG paper record analysis can be difficult and time-consuming. It is possible to digitally digitize these paper ECG recordings for automated analysis and diagnosis. In this paper, we proposed a system to digitize the ECG paper, automatically detecting R peaks, calculating the average heart rate, and sending SMS to the doctor via cloud in the event of detection of abnormality. The method of the system is uploading an ECG image, then dimensionality reduction, feature extraction in the form of digital signals, and saving it in a CSV file format using the MATLAB programming language. After that, the system retrieves the signals for further processing of the raw signals. We used the fast Fourier transform (FFT) algorithm to calculate R peaks and calculate the heart rate. If the heart rate is abnormal, the system sends SMS messages to doctors via a technology platform (Twilio) using the Python programming language.

The number of scientific papers has been increasing ever more rapidly. Researchers have to spend a lot of time classifying papers relevant to their study, especially into fine-grained subfields. However, almost all existing paper classification models are coarse-grained, which can not meet the needs of researchers. Observing this, we propose a lightweight fine-grained classification model for scientific paper. Dynamic weighting coefficients on feature words are incorporated into the model to improve the classification accuracy. The feature word weight is optimized by the Mean Decrease Accuracy (MDA) algorithm. Considering applicability, the lightweight processing is conducted through algorithm pruning and training sample pruning. Comparison with mainstream models shows simultaneous improvement in accuracy and time efficiency by our model.

This study supports the labour-friendly nature of product innovation among developers of artificial intelligence (AI) technologies. GMM-SYS estimates on a worldwide longitudinal dataset covering 3,500 companies that patented inventions related to AI technologies over the period 2000-2016 show a positive and significant impact of AI patent families on employment. The effect is small in magnitude and limited to service sectors and younger firms, which are front-runners of the AI revolution. We also detect some evidence of increasing returns suggesting that innovative companies more focused on AI technologies are those obtaining larger impacts in terms of job creation. ispartof: FEB Research Report MSI_2306 pages:1-36 status: published

In this modern age, Internet of Things (IoT) and Wireless Sensor Network (WSN) as its derivatives have become one of the most popular and important technological advancements. In IoT, all things and services in the real world are digitalized and it continues to grow exponentially every year. This growth in number of IoT device in the end has created a tremendous amount of data and new data services such as big data systems. These new technologies can be managed to produce additional value to the existing business model. It also can provide a forecasting service and is capable to produce decision-making support using computational intelligence methods. In this survey paper, we provide detailed research activities concerning Computational Intelligence methods application in IoT WSN. To build a good understanding, in this paper we also present various challenges and issues for Computational Intelligence in IoT WSN. In the last presentation, we discuss the future direction of Computational Intelligence applications in IoT WSN such as Self-Organizing Network (dynamic network) concept.

The clinical importance of blood group (BG) antigens is related to their ability to induce immune antibodies that can cause hemolysis. Yet, ABO and D (Rh) are still considered to be the key antigens for healthy blood transfusion and secondary antigens are the next priority. Serological typing is the most widely used typing method. Rapid and accurate blood grouping plays an important role in some clinical conditions, rather than conventional techniques. Hence, developing a simple and economical model for rapid blood grouping would facilitate these tests. In recent decades, paper-based microfluidics such as μPADs has gained much interest in wide application areas such as point-of-care diagnostic. In this study, we evaluated μPADs that are performed for blood grouping and its recent progress. A comprehensive literature search was performed using databases including PUBMED, SCOPUS, Web of Science and Google Scholar. Keywords were blood grouping or typing, paper analytical device, rapid test, etc. After investigation of search results, 16 papers from 2010 to 2020 were included. Further information in detail was classified in Table 1. Generally, two principles for blood typing μPADs are introduced. The lateral chromatographic flow method and the vertical flow-through method that detects BG in a visual-based manner. To detect results with acceptable clarity many factors and challenges like paper, blood sample, buffer, Ab and RBC interaction and also μPADs stability need to be considered, which are discussed. In conclusion, the simplicity, stability, cheapness, portability and biocompatibility of μPADs for blood grouping confirming its utility and also they have the capability to robust, universal blood-grouping platform. Table 1 Summary of blood grouping tests using paper-based analytical devices Antigens Type of diagnosis Validation method Sample No Accuracy Action time Paper type Stability Sample dilution Buffer Ref A, B, Rh Forward volunteers records 5 - - Whatman No. 4 - 1/2 PBS* (Khan et al. 2010) A, B, Rh Forward gel assay test and conventional slide test 100 100% 1 min Whatman No. 4 and Kleeenex paper towel 7 Days in 4 °C 1/1 NSS (Al-Tamimi et al. 2012) A, B, Rh Forward gel card assay 99 100% 20 Sec + Washing Kleeenex paper towel - 1/1 NSS (Li et al. 2012) A, B, Rh Forward - - - - Kleeenex paper towel - 45/100 PSS (Li et al. 2013) A, B, Rh Forward gel card assay 98 100% 1.5 min Kleeenex paper towel - 85/100 PBS (Guan et al. 2014b) C, E, c, e, K, Jka, Jkb, M, N, S, P1, and Lea Forward gel card assay 266 100% - Kleeenex paper towel - 1/1 NSS (Li et al. 2014b) A, B, Rh Forward and Reverse conventional slide test 96 ≈ 91% 10 min Whatman No. 1 21 Days in 4 °C 1/2 NSS (Noiphung et al. 2015) C, c, E, e, K, k, Fya, Fyb, Jka, Jkb, M, N, S and s, P1, Lea and Leb Forward - 478 - - Kleeenex paper towel - 1/1 NSS, PBS (Then et al. 2015) A, B Forward and Reverse conventional slide test 76 100% 5-8 min Whatman No. 4 38 Days in 4 °C 1/4, 1/1 NSS (Songjaroen and Laiwattanapaisal 2016) D, K Forward volunteers records 210 - 7.5 min Kleenex paper towel - 1/1 NSS (Yeow et al. 2016) A, B, c, e, D, C, E, M, N, S, s, P1, Jka, Jkb, Lea, Leb, Fya, and Fyb Forward and Reverse gel card assay 3550 ≈100% 30 s Fiber glass and cotton linter 180 Days in 25 °C 45/100, 1/1 PBS (Zhang et al. 2017) A, B Forward conventional slide test 598 100% 3 min Whatman No. 113 14 Day in 4 °C 1/1 NSS (Songjaroen et al. 2018) A, B, Rh Forward conventional slide test - - 30 Sec + Washing Unrefined sisal paper - 1/2 NSS (Casals-Terré et al. 2019) A, B, Rh Forward - - - - Whatman No.1 - 1/1 NSS (Ansari et al. 2020) ABORh Forward and Reverse conventional slide test - 100% Unrefined Eucalyptus papers - 1/2 NSS, PBS (Casals-Terré et al. 2020) A, B, Rh Forward - - - 30 Sec + Washing Whatman No. 4 modified with chitosan ≥ 100 days in 25 °C 1/1 NSS (Parween et al. 2020)

Visual individual recognition requires animals to distinguish among conspecifics based on appearance. Though visual individual recognition has been reported in a range of taxa, the features that animals rely on to discriminate between individuals are often not well understood. Northern paper wasp females, Polistes fuscatus, possess individually distinctive color patterns on their faces, which mediate individual recognition. It is currently unclear what facial features P. fuscatus use to distinguish individuals. The anterior optic tubercle, a chromatic processing brain region, is especially sensitive to social experience during development, suggesting that color may be important for recognition in this species. We sought to test the roles of color in wasp facial recognition. Color may be important simply because it creates a pattern. If this is the case, then wasps should perform similarly when discriminating color or grayscale images of the same faces. Alternatively, color itself may be important for recognition, which would predict poorer performance on grayscale image discrimination relative to color images. We found wasps trained on grayscale faces, unlike those trained on color images, did not perform better than chance. Suggesting that color is necessary for the recognition of an image as a face by the wasp visual system.

Due to the increasing use of the Internet and other online resources, there is tremendous growth in the data of text documents. It is not possible to manage this huge data manually. This has led to the growth of fields like text mining and text summarization. This paper presents a title prediction model for research papers using Recursive Recurrent Neural Network (Recursive RNN) and evaluates its performance by comparing it with sequence-to-sequence models. Research papers published between 2018 and 2020 were obtained from a standard repository, viz. Kaggle, to train the title prediction model. The performance of different versions of Recursive RNN and Seq2Seq was evaluated in terms of training and hold-out loss. The experimental results show that Recursive RNN models perform significantly better than the other models.

For the detection of product color uniformity and seam texture defects in the production process of test paper, detection methods are proposed respectively. For the detection of the color uniformity, a uniformity detection method based on dynamic light correction is proposed. The illumination correction image is used for color dynamic compensation of the test paper image to solve the influence of uneven illumination on the test paper. For the detection of texture defects such as seams, a texture defect detection method based on gray gradient aggregation intensity is proposed. This method uses one-way Gaussian convolution to strengthen features, and then calculates the gradient, divides the region and calculates the gradient extremum aggregation strength to judge texture defects, which can quickly and efficiently find seam defects. The experimental results show that the above methods have obvious effects on the color uniformity and seam detection of the test paper. The color uniformity accuracy rate is 96%, the seam detection accuracy rate is 97%. The total detection time of a single test paper is about 90ms. The algorithm is real-time and accurate, and can be used in actual production detection.

The articles in this special section are based on selected papers presented at the 2021 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS 2021), a premier single-track conference that promotes development of fundamental principles that underpin the integration of cyber and physical elements, as well as the development of technologies, tools, architectures, and infrastructure for the design and implementation of CPS. ICCPS 2021 focused on contributions related to smart and connected cities, autonomous CPS, verification and control, security and privacy, and human health and biomedical CPS.

Performance/security trade-off is widely noticed in CFI research, however, we observe that not every CFI scheme is subject to the trade-off. Motivated by the key observation, we ask three questions: ➊ does trade-off really exist in different CFI schemes? ➋ if trade-off do exist, how do previous works comply with it? ➌ how can it inspire future research? Although the three questions probably cannot be directly answered, they are inspiring. We find that a deeper understanding of the nature of the trade-off will help answer the three questions. Accordingly, we proposed theGPTconjecture to pinpoint the trade-off in designing CFI schemes, which says that at most two out of three properties (fine granularity, acceptable performance, and preventive protection) could be achieved.

AimTo discuss ethical issues related to a complex study (PROFID) involving the development of a new, partly artificial intelligence-based, prediction model to enable personalised decision-making about the implantation of an implantable cardioverter defibrillator (ICD) in postmyocardial infarction patients, and a parallel non-inferiority and superiority trial to test decision-making informed by that model.MethodThe position expressed in this paper is based on an analysis of the PROFID trials using concepts from high-profile publications in the ethical literature.ResultsWe identify ethical issues related to the testing of the model in the treatment setting, and to both the superiority and the non-inferiority trial. We underline the need for ethical-empirical studies about these issues, also among patients, as a parallel to the actual trials. The number of ethics committees involved is an organisational, but also an ethical challenge.ConclusionThe PROFID trials, and probably other studies of similar scale and complexity, raise questions that deserve dedicated parallel ethics and social science research, but do not constitute a generic obstacle. A harmonisation procedure, comparable to the Voluntary Harmonization Procedure (VHP) for medication trials, could be needed for this type of trials.

The International Conference on Cognitive Modeling brings together researchers from around the world whose main goal is to build computational systems that reflect the internal processes of the mind. In this issue, we present the four best representative papers on this work from our 18th meeting, ICCM 2020, which was also the first meeting to be held virtually. Two of these papers develop novel techniques for building larger and more complex models using Reinforcement Learning and Learning By Instruction, respectively. The other two show how cognitive models connect to neuroscience, drawing on details of the hippocampus and cerebellum to constrain and explain the cognitive processes involved in memory and conditioning.

The folder contains the data and codes used in the analysis described in the paper: Parteka A., Kordalska A. (2023) Artificial intelligence and productivity: global evidence from AI patent and bibliometric data. Technovation, Volume 125, July 2023, 102764

In this presidential address, I would like to start with a personal reflection on the field and then share with you the research directions I am pursuing and my excitement about the future of AI. In my personal research to advance AI while advancing scientific discoveries, one question that I have been pondering for some years now is whether AI will write scientific papers in the future. I want to reflect on this question, and look back at the many accomplishments in our field that can make us very hopeful that the answer will be yes, and that it may happen sooner than we might expect.

In the current era of information technology Big Data has gained significant importance in almost all the industries throughout the world. Big Data is now renowned for having the capability of effective decision making. Now companies around the globe are using Big Data for market analysis, customer analysis, however, the utilization of Big Data is much higher in the financial sector, yet publications on Big Data and finance are limited because of having significant challenges. Even though utilization of Big Data is highest in the financial sector and its importance cannot be ignored, the studies and analysis are inadequate. Considering the importance of Big Data in the financial sector this paper is an attempt to conduct a comprehensive literature review in the field of Big Data and finance. Thus, the study will contribute to the body of knowledge by providing horizons for empirical research in the field of Big Data and finance.

Blockchain can be thought of as a distributed database allowing tracing of the origin of data, and who has manipulated a given data set in the past. Medical applications of blockchain technology are emerging. Blockchain has many potential applications in medical imaging, typically making use of the tracking of radiological or clinical data. Clinical applications of blockchain technology include the documentation of the contribution of different “authors” including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic medical records, and a better control of users over their personal health records. Applications of blockchain in research include a better traceability of image data within clinical trials, a better traceability of the contributions of image and annotation data for the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Blockchain also allows for dynamic consenting and has the potential to empower patients and giving them a better control who has accessed their health data. There are also many potential applications of blockchain technology for administrative purposes, like keeping track of learning achievements or the surveillance of medical devices. This article gives a brief introduction in the basic technology and terminology of blockchain technology and concentrates on the potential applications of blockchain in medical imaging.

The color gamut of imaging media is significant for the reproduction of color images because its magnitude directly affects the degree to which colors change during the printing process. Over the last few years, digital impression technology has started to play a substantial role in the printing industry due to the quest for short runs and variable information printing. The color gamut of electrophotographic digital printing depends on various parameters including the printer and toner, but especially the properties (whiteness, roughness, and gloss) of the paper, which influence the final printed color gamut and replication quality. Artificial intelligence approaches are applied herein for the first time to choose and predict the performance of a paper with appropriate properties to achieve the maximum color gamut. A genetic algorithm-based computer code is developed to optimize the architecture of an artificial neural network, thereby yielding an accurate model to predict the color gamut achievable in electrophotographic color printing. The gamut volume was generated using an Eye-One spectrophotometer, ProfileMaker, and ColorThink software. The properties of 11 dissimilar types of paper were assessed by atomic force microscopy, spectrophotometer, and goniophotometer. The results indicate that the reproducibility depended considerably on the features of the paper. Although high whiteness and gloss increased the color gamut volume, and high roughness decreased the reproducibility of the printing machine, the artificial intelligence approach provided the opportunity to achieve a high gamut volume with low gloss and high roughness.

Headline generation is a challenging subtask of abstractive text summarization, which its output should be a summary, shorter than one sentence. It would be precious to develop a dataset for the evaluation of abstractive summarization methods on this task in the Persian language. There are several datasets for headline generation in Persian, most of which are not large enough to be used by more sophisticated methods of text summarization, such as deep learning models. Moreover, all of these datasets are focused on daily news and there is no dataset for summarizing scientific Persian papers. In this article, we present “ElmNet,” a headline generation dataset of about 400,000 abstract/headline pairs of scientific papers, gathered from six major publishers for scientific articles in Persian. We, moreover, evaluate the performance of the most important deep learning-based headline generation methods, on the proposed dataset. The results prove the comparability of the performance of the state-of-the-art methods on this task, to their results on the existing English datasets.

Cardiac computed tomography (CT) was initially developed as a non-invasive diagnostic tool to detect and quantify coronary stenosis. Thanks to the rapid technological development, cardiac CT has become a comprehensive imaging modality which offers anatomical and functional information to guide patient management. This is the second of two complementary documents endorsed by the European Association of Cardiovascular Imaging aiming to give updated indications on the appropriate use of cardiac CT in different clinical scenarios. In this article, emerging CT technologies and biomarkers, such as CT-derived fractional flow reserve, perfusion imaging, and pericoronary adipose tissue attenuation, are described. In addition, the role of cardiac CT in the evaluation of atherosclerotic plaque, cardiomyopathies, structural heart disease, and congenital heart disease is revised.

For product packaging, the visual elements in it can further enhance the appeal of the package to customers. This article briefly introduces visual elements and packaging design and made an example analysis with the gift packaging design of Squirrel Design Studio. In the case study, the packaging design of the studio’s mirror, storage bag, and puzzle was rated by hierarchical analysis and questionnaires, and the packaging design was analyzed based on the rating results. A convolutional neural network (CNN) was also used to evaluate packages in batches. The results showed that the CNN could make a batch evaluation of gift packaging design accurately; the three gift packaging designs were based on the studio’s logo, making the ratings similar; in addition, the packaging design patterns were composed of different geometric shapes to show the studio’s innovative design theme, and the squirrel silhouette and text description were used to strengthen the impression of the studio among customers.

Text summarization is one problem in natural language processing that generates a brief version of the original document. This research took attention for some researchers in this last decade and growing fast, including Indonesia language. This paper aims to recap summarization text research especially in Indonesia language. As usual, this paper discusses two summarization approaches, extractive and abstractive. In fact, the number of research of extractive is more than abstractive. This paper investigates some methods such as Statistical Based Approach, Graph Based Approach, Machine Learning Approach, Fuzzy Logic Approach, Algebraic Approach, and Hybrid Approach. This paper shows some methods details and summarize the results. Keywords— Text summarization, extractive summary, abstractive summary, natural language processing

The Artificial Intelligence (AI) Pet Robot is a combination of various fields of computer science. This paper showcases the various functionalities of our AI Pet. Most of the functionalities showcased use the immage processing modules made available through OpenCV. The pet robot has various features such as emotion recognition, follow routine, mini-game etc. This paper discusses the mini-game aspect of the robot. The game has been developed by using VGG16 convolutional network for identification of the action performed by the user. To improve the accuracy we have made use of background subtraction which gives removes all the unwanted objects from the background and gives a simple cutout of the users hand.

Predicting the impact of academic papers can help scholars quickly identify the high-quality papers in the field. How to develop efficient predictive model for evaluating potential papers has attracted increasing attention in academia. Many studies have shown that early citations contribute to improving the performance of predicting the long-term impact of a paper. Besides early citations, some bibliometric features and altmetric features have also been explored for predicting the impact of academic papers. Furthermore, paper metadata text such as title, abstract and keyword contains valuable information which has effect on its citation count. However, present studies ignore the semantic information contained in the metadata text. In this paper, we propose a novel citation prediction model based on paper metadata text to predict the long-term citation count, and the core of our model is to obtain the semantic information from the metadata text. We use deep learning techniques to encode the metadata text, and then further extract high-level semantic features for learning the citation prediction task. We also integrate early citations for improving the prediction performance of the model. We show that our proposed model outperforms the state-of-the-art models in predicting the long-term citation count of the papers, and metadata semantic features are effective for improving the accuracy of the citation prediction models.