Reviewerly/exHarmony huggingface dataset:
The Development of Integrated Medical Expert System for Diabetes Mellitus Patient

Diabetes Mellitus (DM) is a degenerative disease that has many causative factors. DM patients cannot recover completely but they can maintain the stability of their glucose level by following a healthy lifestyle. The goal of this research is to make an integrated system for people to promote a healthy lifestyle. This system also allows people to monitor the changes in their daily health conditions regularly. Main features in this system diagnose, treatment, guidelines for a healthy lifestyle, including a feature for counting and saving daily calorie consumption and physical activity. The system using the backward inference method to get the conclusion from a set of rules in the knowledge base. The verification using a query, validation with using recall and precision, and usability testing using a questionnaire, was conducted to test the performance of the system. The result of the testing shows that the system gives a good response to all queries given and the user also satisfied with the system. The recall value is 1 with the precision is 0.658. This precision value also promotes the inference process used in the system, which every user will suspect has DM when they start to use the diagnose feature.
AI pedigree verification platform using blockchain

In order for AI to be accepted by society and become a social infrastructure technology, the ethical aspect of AI is also important in addition to the technical aspect of AI. This paper proposes “AI pedigree verification platform” to verify “what kind of data is used to train the AI model at hand”. Building such the system using an existing database system is problematic in terms of system availability and integrity of the registration content. Therefore, we constructed the proposed system using Hyperledger Fabric (HF), which is a blockchain infrastructure. In this way, the proposed system can ensure the availability of the system and the integrity of the registration content. To ensure the reproducibility of the AI model, the data items in the system were created based on the “Reproducibility list” proposed by authoritative society for AI. Since the integrity and reproducibility of the data in the blockchain can be ensured, this system enables automatic AI model generation using open data and automatic trading of AI models.
Marketplace Sentiment Analysis Using Naive Bayes And Support Vector Machine

Abstract&#x0D; &#x0D; Technology implementation in the marketplace world has attracted the attention of researchers to analyze the reviews from customers. The Klik Indomaret application page on GooglePlay is one application that can be used to get information on review data collection. However, getting information on consumer’s opinion or review is not an easy task and need a specific method in categorizing or grouping these reviews into certain groups, i.e. positive or negative reviews. The sentiment analysis study of a review application in GooglePlay is still rare. Therefore, this paper analysis the customer’s sentiment from klikindomaret app using Naive Bayes Classifier (NB) algorithm that is compared to Support Vector Machine (SVM) as well as optimizing the Feature Selection (FS) using the Particle Swarm Optimization method. The results for NB without using FS optimization were 69.74% for accuracy and 0.518 for Area Under Curve (AUC) and for SVM without using FS optimization were 81.21% for accuracy and 0.896 for AUC. While the results of cross-validation NB with FS are 75.21% for accuracy and 0.598 for AUC and cross-validation of SVM with FS is 81.84% for accuracy and 0.898 for AUC, while there is an increase when using the Feature Selection (FS) Particle Swarm Optimization and also the modeling algorithm SVM has a higher value compared to NB for the dataset used in this study.&#x0D; &#x0D; Keywords: Naive Bayes, Particle Swarm Optimization, Support Vector Machine, Feature Selection, Consumer Review.
Time-Optimal Trajectory Planning Based on Dynamics for Industrial Robot

Given the trajectory of the industrial robot in Cartesian space, the existing methods only consider the kinematic constraints when optimizing the time trajectory planning, and ignore the maximum driving ability of the motor, which is likely to cause an overrun alarm and greatly reduce the trajectory accuracy. Therefore, this paper studies the time-optimal trajectory planning algorithm based on dynamics. Firstly, the algorithm discretizes the path, calculates the Cartesian coordinates of each discrete point and the differential about the path length. Then, this algorithm considers the constraints of dynamics and kinematics, uses the principle of linear programming, recursively calculates the upper limit value of speed and the maximum acceleration value at each discrete point, and calculates the speed value at each discrete point and keeps it within its allowable ranges. Finally, this algorithm uses the speed to calculate the corresponding time of each discrete point, and according to the interpolation period, outputs the path position of each period, the position and orientation in the Cartesian space and joint parameters. The MATLAB program is written to verify the feasibility and efficiency of the algorithm.
QMUL-SDS @ SardiStance: Leveraging Network Interactions to Boost Performance on Stance Detection using Knowledge Graphs

This paper presents our submission to the SardiStance 2020 shared task, describing the architecture used for Task A and Task B. While our submission for Task A did not exceed the baseline, retraining our model using all the training tweets, showed promising results leading to (f-avg 0.601) using bidirectional LSTM with BERT multilingual embedding for Task A. For our submission for Task B, we ranked 6th (f-avg 0.709). With further investigation, our best experimented settings increased performance from (f-avg 0.573) to (f-avg 0.733) with same architecture and parameter settings and after only incorporating social interaction features- highlighting the impact of social interaction on the model's performance.
An Effective Image Encryption Method Based on Space Filling Curve and Plaintext-Related Josephus Traversal

To improve the effectiveness and security of image encryption, this article proposes an image encryption scheme based on a Y-index Space Filling Curve (SFC) and variable step Josephus traversal. The scheme adopts a confusion-diffusion structure. First, a novel Y-index SFC is designed. SFC is a method of continuously traversing each pixel of an image to encrypt the image, and images are confused by the Y-index SFC. Second, the random sequences generated by the chaotic map are taken as the starting point and step length of the Josephus traversal, and the pixels of the image are scrambled at a bit-level to change the values of the pixels. By changing the step length, the diversity of the Josephus traversal is increased. Finally, ciphertext feedback and chaotic sequence operations are used to further enhance the confusion and diffusion characteristics of the algorithm. By analyzing the experimental results and comparing them with the results of other image encryption algorithms, we show that this algorithm performs better and achieves higher security than other algorithms.
Class Experiences with Inquiry Learning Spaces in Go-Lab in African Secondary Schools

Inquiry Based Learning (IBL) is a form of active learning, often used in STEM education to promote conceptual learning and to acquire scientific investigation skills. This paper reports on a study in which teachers in Kenya, Nigeria and the Republic of Benin implemented IBL embedded in online and offline Inquiry Learning Spaces (ILS) in their classes using the Go-Lab platform (https://www.golabs.eu). After a brief description of the IBL methodology, of lab work and in particular virtual labs for STEM education, of the process of preparing teachers to use IBL in class, and of the context of this study, we highlight the methodology used, and finally report our results. These show that the introduction and class enactment of a digital inquiry based learning platform such as Go-Lab in Africa (i) is possible, although challenging, (ii) does lead to student learning, (iii) for this to take place teacher training is necessary, (iv) the digital infrastructure is present in the schools though minimal and fragile, and (v) a local partner needs to provide assistance when required.
The use of remote sensing in mining prospecting in Situbondo, East Java, Indonesia

Mineral resources of a region are development assets and are one of the essential capital of development as well as the source or potential of wealth in the area. To make the right use for the welfare of the community, there is a need for proper identification of types, positions and reserves, excavation, and management. Therefore, to manage the mine deposit, especially industrial minerals, an area needs to be mapped to reveal mining resources concerning the type, size, and position of deposits in a field. The purpose of the study was to determine the potential for the dissemination of mining excavations in Situbondo Regency. The research method used remote sensing with guided techniques based on data in the field and geological maps. Based on remote sensing results, it was found that there were 5 minerals in Situbondo Regency. The excavated materials are sand, limestone, trass, mountain rock, and phosphate.
The Prototype of Marine Data Integration System Based on J2EE

Gao, X., 2020. The prototype of marine data integration system based on J2EE. In: Li, L. and Huang, X. (eds.), Sustainable Development in Coastal Regions: A Perspective of Environment, Economy, and Technology. Journal of Coastal Research, Special Issue No. 112, pp. 414-417. Coconut Creek (Florida), ISSN 0749-0208.Aiming at the heterogeneity of data sources in marine disciplines, this article uses XML technology for integration to achieve a unified representation of marine data. At the same time, it is supported by J2EE technology to propose and implement a multi-layer B/S architecture for marine XML data integration. The system prototype uses the scalable vector graphics (SVG) technology, an emerging XML technology, to realize the interaction of ocean data based on XML and provide support for ocean informatization.
Virtual UV Fluorescence Microscopy from Hematoxylin and Eosin Staining of Liver Images Using Deep Learning Convolutional Neural Network

The use of UV (ultraviolet fluorescence) light in microscopy allows improving the quality of images and observation of structures that are not visible in visible spectrum. The disadvantage of this method is the degradation of microstructures in the slide due to exposure to UV light. The article examines the possibility of using a convolutional neural network to perform this type of conversion without damaging the slides. Using eosin hematoxylin stained slides, a database of image pairs was created for visible light (halogen lamp) and UV light. This database was used to train a multi–layer unidirectional convolutional neural network. The results of the study were subjectively and objectively assessed using the SSIM (Structural Similarity Index Measure) and SSIM (structure only) image quality measures. The results show that it is possible to perform this type of conversion (the studies used liver slides for 100× magnification), and in some cases there was an additional improvement in image quality.
Visual Data Justice? Datafication of Urban Informality in South Africa Using 360° Imaging Technologies

Uneven economic development is closely associated with the proliferation of informal settlements in the global South, a process generally understood to harbour injustices according to a resource access view of society. An understanding of social justice in terms of distribution provides a powerful framework for challenging uneven development, however, a key contention of this paper is that further forms of injustice accrue to the residents of these places over and above issues of distribution. With its focus on sociocultural representations, recognition provides a basis for ‘making visible’ further injustices that threaten dignity and self-identification, which may also act as a preliminary step towards advancing socioeconomic justice for informal settlements.This paper draws on ongoing research into the datafication of informal settlements in South Africa, including interviews with data activists and experiences of conducting a 360° geovisual imagery pilot study in Kya Sands informal settlement in Johannesburg. In first drawing attention to the limitations of quantitative data for representing injustice, and the problematic effects of conventional framed photographs of informality from aerial and ground-level perspectives, this paper explores the possibilities of ground-level 360° imagery as a realist, non-reductionist form of representation for informal settlements. Three 360° street views were produced to illustrate key possibilities and limitations for advancing recognition, based on the notion that visibility is essential for recognition. Further, the piece reflexively critiques how – through new advancements in image analytics – informal settlement visibility projects risk further engendering injustices of misrecognition for these highly marginalized urban spaces.As such, this paper provides a basis for further inquiry into the relationship between visual data and the social justice principle of recognition, towards an emerging research agenda on visual data justice. The paper concludes with recommendations for practitioners and key topics for research. Important items on the agenda include: determining the forms and examples of injustice that can be remedied through datafication, the relationship between misrecognition and maldistribution in the context of datafication, and the acceleration of new injustices due to advances in computer vision applied to ground-level imagery.
Rosella: A Self-Driving Distributed Scheduler for Heterogeneous Clusters

Large-scale interactive web services and advanced AI applications make sophisticated decisions in real-time, based on executing a massive amount of computation tasks on thousands of servers. Task schedulers, which often operate in heterogeneous and volatile environments, require high throughput, i.e., scheduling millions of tasks per second, and low latency, i.e., incurring minimal scheduling delays for millisecond-level tasks. Scheduling is further complicated by other users’ workloads in a shared system, other background activities, and the diverse hardware configurations inside datacenters.We present Rosella, a new self-driving, distributed approach for task scheduling in heterogeneous clusters. Rosella automatically learns the compute environment and adjusts its scheduling policy in real-time. The solution provides high throughput and low latency simultaneously because it runs in parallel on multiple machines with minimum coordination and only performs simple operations for each scheduling decision. Our learning module monitors total system load and uses the information to dynamically determine optimal estimation strategy for the backends’ compute-power. Rosella generalizes power-of-two-choice algorithms to handle heterogeneous workers, reducing the max queue length of O(logn) obtained by prior algorithms to O(logn). We evaluate Rosella with a variety of workloads on a 32-node AWS cluster. Experimental results show that Rosella significantly reduces task response time, and adapts to environment changes quickly.
User Profile Linkage Across Multiple Social Platforms

Linking user profiles belonging to the same people across multiple social networks underlines a wide range of applications, such as cross-platform prediction, cross-platform recommendation, and advertisement. Most of existing approaches focus on pairwise user profile linkage between two platforms, which can not effectively piece up information from three or more social platforms. Different from the previous work, we investigate the user profile linkage across multiple social platforms by proposing an effective and efficient model called MCULK. The model contains two key components: 1) Generating a similarity graph based on user profile matching candidates. To speed up the generation, we employ the locality sensitive hashing (LSH) to block user profiles and only measure the similarity for the ones within the same bucket. 2) Linking user profiles based on similarity graph. Extensive experiments are conducted on two real-world datasets, and the results demonstrate the superiority of our proposed model MCULK compared with the state-of-art methods.
A Classification Javanese Letters Model using a Convolutional Neural Network with KERAS Framework

One of the essential things in research engaged in the field of Computer Vision is image classification, wherein previous studies models were used to classify an image.Javanese Letters, in this case, is a basis of a sentence that uses the Javanese language.The problem is that Javanese sentences are often found in Yogyakarta, especially the use of name tourist attractions, making it difficult for tourists to translate these Javanese sentences.Therefore, in this study, we try to create a Javanese character classification model hoping that this model will later be used as a basis for developing research into the next stage.One of the most popular methods lately for dealing with image classification problems is to use Deep Learning techniques, namely using the Convolutional Neural Network (CNN) method using the KERAS framework.The simplicity of the training model and dataset used in this work brings the advantage of computation weight and time.The model has an accuracy of 86.68% using 1000 datasets and conducted for 50 epochs based on the results.The average inference time with the same specification mentioned above is 0.57 seconds, and again the fast inference time is because of the simplicity of the model and dataset toolbar.This model's advantages with fast and light computation time bring the possibility to use this model on devices with limited computation resources such as mobile devices, familiar web server interface, and internet-of-things devices.
Formant Tracking Using Dilated Convolutional Networks Through Dense Connection with Gating Mechanism

Formant tracking is one of the most fundamental problems in speech processing. Traditionally, formants are estimated using signal processing methods. Recent studies showed that generic convolutional architectures can outperform recurrent networks on temporal tasks such as speech synthesis and machine translation. In this paper, we explored the use of Temporal Convolutional Network (TCN) for formant tracking. In addition to the conventional implementation, we modified the architecture from three aspects. First, we turned off the causal mode of dilated convolution, making the dilated convolution see the future speech frames. Second, each hidden layer reused the output information from all the previous layers through dense connection. Third, we also adopted a gating mechanism to alleviate the problem of gradient disappearance by selectively forgetting unimportant information. The model was validated on the open access formant database VTR. The experiment showed that our proposed model was easy to converge and achieved an overall mean absolute percent error (MAPE) of 8.2% on speech-labeled frames, compared to three competitive baselines of 9.4% (LSTM), 9.1% (Bi-LSTM) and 8.9% (TCN).
Effective Survey on Handwriting Character Recognition

Researchers have found out the many technologies to recognize hand written character into text. Handwriting recognition is the capability of a computer to read handwriting as actual text. To convert handwriting to text, this is irrefutably the best program which can be used to overcome the many problems which are present in recognition. The paper mainly focuses on basic problem faced in recognition of stroke edges in the handwritten character with various pattern recognition techniques. Based on the survey taken for this problem, say that segmentation of the character is low and prevents high popularity accuracy of unconstrained handwriting.
Short Text Processing for Analyzing User Portraits: A Dynamic Combination

The rich digital footprint left by users on the Internet has led to extensive researches on all aspects of Internet users. Among them, topic modeling is used to analyze text information posted by users on websites to generate user portraits. For dealing with the serious sparsity problems when extracting topics from short texts by traditional text modeling methods such as Latent Dirichlet Allocation (LDA), researchers usually aggregate all the texts published by each user into a pseudo-document. However, such pseudo-documents contain a lot of irrelevant topics, which is not consistent with the documents published by people in reality. To that end, this paper introduces the LDA-RCC model for dynamic text modeling based on the actual text, which is used to analyze the interests of forum users and build user portraits. Specifically, this combined model can effectively process short texts through the iterative combination of text modeling method LDA and robust continuous clustering method (RCC). Meanwhile, this model can automatically extract the number of topics based on the user's data. In this way, by processing the clustering results, we can obtain the preferences of each user for deep user analysis. A large number of experimental results show that the LDA-RCC model can obtain good results and is superior to both traditional text modeling methods and short text clustering benchmark methods.
Automatic Misogyny Detection with Linguistic and Morphological Features in Spanish

Social media allows many people to keep in touch and get updated of their surroundings, but these positive features do not bury the fact that another large portion of the users employ this tool with the intention of harassing people under anonymity. Due to the amount of content created on these platforms, spotting hate comments individually becomes an impossible task, and automatic hate speech detectors take place on those tasks. Furthermore, identifying hate speech towards women is specially complex due to the cultural background and the subtlety that characterizes it, which makes it interesting to consider it as a phenomenon by itself, differentiating it from traditional hate speech. Our contribution to automatic misogyny identification entails the creation of a machine-learning model based on linguistic and morphological features, applied to the training set of corpus developed at the Automatic Misogyny Evaluation task which contains 3307 tweets written in Spanish. We evaluate our proposal with different machine-learning classifiers achieving the best accuracy of 77% applying Support Vector Machines.
Construction of Industry-University-Research-Application System under the Background of Mass Entrepreneurship and Innovation Education

In recent years, entrepreneurship and innovation education has become an important theme in the reform and deepening of China's education system.Based on the analysis of the current situation of China's economic reform and social development, this paper fully analyzes and verifies the practical significance of entrepreneurship and innovation education, as well as the curriculum content, education system, application system and other practical problems existing in the existing development.Combined with the policy guidance and the main resource advantages of all parties, this paper puts forward the optimization countermeasures and suggestions on the construction of the production, teaching, research and application system of the entrepreneurship and innovation education and the application level of the educational achievements, so as to provide some reference for the development and deepening of China's entrepreneurship and innovation education.