Reviewerly/exHarmony huggingface dataset:
PathosisGAN: Sick Face Image Synthesis with Generative Adversarial Network

research-article Open AccessPathosisGAN: Sick Face Image Synthesis with Generative Adversarial Network Share on Authors: Jinyu Hu Institute of Intelligent Machines, Chinese Academy of Sciences and University of Science and Technology of China, China Institute of Intelligent Machines, Chinese Academy of Sciences and University of Science and Technology of China, ChinaView Profile , Yuchen Ren Institute of Intelligent Machines, Chinese Academy of Sciences and University of Science and Technology of China, China Institute of Intelligent Machines, Chinese Academy of Sciences and University of Science and Technology of China, ChinaView Profile , Yuan Yuan Institute of Intelligent Machines, Chinese Academy of Sciences, China Institute of Intelligent Machines, Chinese Academy of Sciences, ChinaView Profile , Yin Li Institute of Software Application Technology, Chinese Academy of Sciences, China Institute of Software Application Technology, Chinese Academy of Sciences, ChinaView Profile , Lei Chen Institute of Intelligent Machines, Chinese Academy of Sciences, China Institute of Intelligent Machines, Chinese Academy of Sciences, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 258Pages 1–6https://doi.org/10.1145/3469213.3470691Online:18 August 2021Publication History 0citation22DownloadsMetricsTotal Citations0Total Downloads22Last 12 Months22Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Modified chemical reaction optimization and its application in engineering problems

Chemical Reaction Optimization (CRO) is a simple and efficient evolutionary optimization algorithm by simulating chemical reactions. As far as the current research is concerned, the algorithm has been successfully used for solving a number of real-world optimization tasks. In our paper, a new real encoded chemical reaction optimization algorithm is proposed to boost the efficiency of the optimization operations in standard chemical reactions optimization algorithm. Inspired by the evolutionary operation of the differential evolution algorithm, an improved search operation mechanism is proposed based on the underlying operation. It is modeled to further explore the search space of the algorithm under the best individuals. Afterwards, to control the perturbation frequency of the search strategy, the modification rate is increased to balance between the exploration ability and mining ability of the algorithm. Meanwhile, we also propose a new population initialization method that incorporates several models to produce high-quality initialized populations. To validate the effectiveness of the algorithm, nine unconstrained optimization algorithms are used as benchmark functions. As observed from the experimental results, it is evident that the proposed algorithm is significantly better than the standard chemical reaction algorithm and other evolutionary optimization algorithms. Then, we also apply the proposed model to address the synthesis problem of two antenna array synthesis. The results also reveal that the proposed algorithm is superior to other approaches from different perspectives.
Analisis Kelayakan Sistem Informasi Akademik Universitas Menggunakan PIECES dan TELOS

In line with the development of Information Technology (IT), it is appropriate for an educational institution to utilize Information Systems (IS) as an infrastructure that supports the academic service process. One of the universities in Central Java that has implemented IS, namely the Academic Information System. The Academic IS has been operating for 17 years since 2004. Therefore, the academic SI should be evaluated to determine its eligibility as an application. This study is a comparative analysis that has employed approaches of TELOS (Technical, Economic, Legal, Operations, Schedule) and PIECES (Performance, Information, Efficiency, Control and Security, Economics , Service), based on the WebQual dimensions. The aim is to assess the feasibility study of Academic IS, applying three approaches according to the user's perspective.
Pengembangan E-module berbasis digital flipbook pada mata pelajaran kearsipan untuk meningkatkan hasil belajar siswa

Abstract&#x0D; The Development of teaching material of E-Module in archival subject is needed because there are no modules intended for students so far. The development of this e-module is aimed at (1) Producing an electronic module (E-module) based on Digital Flipbook in Archival Subject (Basic Competency 3.7 Implementing alphabetical, chronological, geographical, number, and subject archive systems And Basic Competency 4.7 Carry out alphabetical, chronological, geographical, number, and subject archive systems) for class X OTKP’s students in SMK Negeri 1 Ngawi, (2) Knowing the feasibility of Digital Flipbook-based E-module in the subject of Archival based on expert validation and (3) Knowing the difference in learning outcomes of students in the experimental class using E-module with control class students who do not use E -module that has been made by researcher. This research uses Research and Development (R&amp;D) with Borg &amp; Gall research and development model, namely 1) Preliminary studies, 2) Initial data collection, 3) Initial product development, 4 ) Design Validation, 5) Design Revision, 6) Limited trials, 7) Initial product revisions, 8) field trials, 9) Final product revisions, 10) Final Products. The e-module validation is carried out by e-module experts, material experts and limited trials. E-module experts is 98%, material expert validation is 94.5%, the limited trial result is 89.6%. The combined validation average is 94.03% which shows that the e-module is very valid.&#x0D; Abstrak&#x0D; Pengembangan bahan ajar e-module kearsipan sangat dibutuhkan karena belum adanya modul yang diperuntukkan untuk siswa. Pengembangan e-module ini bertujuan untuk (1) Menghasilkan modul elektronik (E-module) berbasis Digital Flipbook pada Mata Pelajaran Kearsipan (KD 3.7 Menerapkan penyimpanan arsip sistem abjad, kronologis, geografi, nomor, dan subjek dan KD 4.7 Melakukan penyimpanan arsip sistem abjad, kronologis, geografi, nomor, dan subjek) untuk siswa kelas X OTKP di SMK Negeri 1 Ngawi, (2) Mengetahui kelayakan E-module berbasis Digital Flipbook pada Mata Pelajaran Kearsipan berdasarkan validasi para ahli dan (3) Mengetahui perbedaan hasil belajar siswa kelas eksperimen dengan siswa kelas kontrol. Penelitian ini menggunakan metode penelitian dan pengembangan (R&amp;D) dengan Model penelitian dan pengembangan Borg &amp; Gall yaitu 1) Studi pendahuluan, 2) Pengumpulan data awal, 3) Pengembangan produk awal, 4) Validasi Desain, 5) Revisi Desain, 6) Uji coba terbatas, 7) Revisi produk awal, 8) uji coba lapangan, 9) Revisi produk akhir, 10) Produk Akhir. Validasi e-module dilakukan oleh ahli e-module, ahli materi dan uji coba terbatas. Ahli e-module sebesar 98%, ahli materi sebesar 94,5%, dan uji coba terbatas sebesar 89,6%. Rata-rata gabungan validasi yaitu sebesar 94,03% yang menunjukkan e-module sangat valid
Continual Learning on Incremental Simulations for Real-World Robotic Manipulation Tasks

Current state-of-the-art approaches for transferring deep-learning models trained in simulation either rely on highly realistic simulations or employ randomization techniques to bridge the reality gap. However, such strategies do not scale well for complex robotic tasks; highly-realistic simulations are computationally expensive and hard to implement, while randomization techniques become sample-inefficient as the complexity of the task increases. In this paper, we propose a procedure for training on incremental simulations in a continual learning setup. We analyze whether such setup can help to reduce the training time for complex tasks and improve the sim2real transfer. For the experimental analysis, we develop a simulation platform that can serve as a training environment and as a benchmark for continual and reinforcement learning sim2real approaches
Fiber Recognition In Composite Materials

The accuracy of fiber recognition in cross-sectional scanned images is critical to material characterization for Fiber-Reinforced Polymer (FRP) composites. It is challenging to accurately detect and locate all fibers, especially broken fibers. We propose a two-step approach to automatically detect and categorize fibers. First we exploit distance-transform-based watershed segmentation to extract the boundaries of individual fiber cross-sections to handle cases where fibers contact each other, then use our proposed contour gradient charts to evaluate the breakage of each fiber cross-section and classify individual fiber boundaries as circles or ellipses. Our method accurately and robustly recognizes fibers in the cross-sectional images, whether they are aligned or misaligned, complete or broken.
MODIFIED WORD SENSE DISAMBIGUATION METHOD BASED ON DISTRIBUTED REPRESENTATION METHODS

In the text mining tasks, textual representation should be not only efficient but also interpretable,as this enables an understanding of the operational logic underlying the data miningmodels. This paper describes a modified Word Sense Disambiguation (WSD) method which extendstwo well-known variations of the Lesk WSD approach. Given a word and its context, Leskbases its calculations on the overlap between the context of a word and each definition of its senses(gloss) in order to select the proper meaning. The main contribution of the proposed method isthe adoption of the concept of “similarity” between definition and context instead of "overlap", inaddition to expanding the definition with examples provided by WordNet for each sense of thetarget word. The proposed method is also characterized by the use of text similarity measurementfunctions defined in a distributed semantic space. The proposed method has been tested on fivedifferent benchmark datasets for words sense disambiguation tasks and compared with severalbasic methods, including simple Lesk, extended Lesk, WordNet 1st sense, Babelfy and UKB. Theresults show that proposed method outperforms most basic methods with the exception of Babelfyand the WN 1st sense methods.
A Novel Pre-Processing Approach for the Denoising of Alzheimer Disease Image Dataset

Medical imaging doing an indispensable part in the area of medicine. Noise in the image is maddening as it worsens the quality of image. Thus, removal of noise is perpetually a problematic work in the images of all domain. Alzheimer’s disease, a neurological dysfunction in which destruction of cells in brain creates mental weakening and memory loss. The distinguished reason for Alzheimer’s disease is low brain activity and low blood flow. We proposed a framework for removal of noise in Alzheimer disease image using histogram equalization, thresholding, open morphological operation and a wavelet transform. This framework reduces the noise and significantly better than the existing methods used for Alzheimer disease images.
Deep Learning on Graphs for Natural Language Processing

There are a rich variety of NLP problems that can be best expressed with graph structures. Due to the great power in modeling non-Euclidean data like graphs, deep learning on graphs techniques (i.e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems, and have already achieved great success. Despite the success, deep learning on graphs for NLP (DLG4NLP) still faces many challenges (e.g., automatic graph construction, graph representation learning for complex graphs, learning mapping between complex data structures).
PENGARUH PENERAPAN IMOD TERHADAP KETERAMPILAN DEKLAMASI PUISI SISWA KELAS IV SDN RAMPAL CELAKET 2 MALANG

Literary learning is very important for children's education because it includes personal values and educational values. Based on observations in class IV SDN Rampal Celaket 2 Malang, learning Indonesian especially in poetry declamation material is still not optimal. Therefore it is necessary to have an action that can affect the poetry declamation skills of students. The purpose of this study was to determine the effect of Imod on poetry declamation skills in fourth grade students of SDN Rampal Celaket 2 Malang. This study uses a quantitative research approach and type, with quasi-experimental research designs. The form used is Nonequivalent Control Group Design. Based on testing the hypothesis with the Mann Whitney U test, the significance level of 0.000 ˂ 0.05 results, the null hypothesis (H0) is rejected and the initial hypothesis (Ha) is accepted. This means that the application of Imod significantly influences the poetry reclamation skills of fourth grade students of SDN Rampalcelaket 2 Malang. Keywords : Declamation Skill, Imod, Poetry
Implementation of the results of research work in the educational process

One of the most important tasks of higher education is to transform the research potential of higher educational institutions into a factor of economic development of Russia. According to the requirements of the Federal State Educational Standard for Higher Education and qualification requirements of the Unified Qualification Directory of positions of managers, specialists and civil servants, the organization, management, participation, and conduct of research work is one of the job responsibilities of lecturers, associate professors, and professors of the department, especially workers of science. Meanwhile, the results of research activities should be implemented in the educational process - only then the process of creating new knowledge and the process of its assimilation will be well-balanced, and the cycle of updating knowledge itself will be shorter. The article examines the experience of the Moscow University for the Humanities in the organization of work related to the implementation of the results of research activities in the educational process. Research work is considered in the article as a scientific activity that includes the formulation of a research task, scientific search, collection and analysis of information, its systematization, conducting research and experiments, identifying patterns that manifest themselves in nature and society, scientific generalizations, determination and justification of the scope of implementation of the obtained results. The final result of scientific activity should have practical significance and novelty. The article considers the experience of the Moscow University for the Humanities in terms of integrating research work into the educational process.
A Novel Hybrid Sampling Algorithm for Solving Class Imbalance Problem in Big Data

The uneven distribution of classes in any dataset poses a tendency of biasness toward the majority class when analyzed using any standard classifier. The instances of the significant class being deficient in numbers are generally ignored and their correct classification which is of paramount interest is often overlooked in calculating overall accuracy. Therefore, the conventional machine learning approaches are rigorously refined to address this class imbalance problem. This challenge of imbalanced classes is more prevalent in big data scenario due to its high volume. This study deals with acknowledging a sampling solution based on cluster computing in handling class imbalance problems in the case of big data. The newly proposed approach hybrid sampling algorithm (HSA) is assessed using three popular classification algorithms namely, support vector machine, decision tree and k-nearest neighbor based on balanced accuracy and elapsed time. The results obtained from the experiment are considered promising with an efficiency gain of 42% in comparison to the traditional sampling solution synthetic minority oversampling technique (SMOTE). This work proves the effectiveness of the distribution and clustering principle in imbalanced big data scenarios.
Visualization Technique for Comparison of Time-Based Large Data Sets

Comparison is one of the essential tasks that motivate a data set exploration, and it is particularly arduous when it involves large data sets. The objective of information visualization is to help gain insight into the data, and in this context, it should assist the user in the task of comparison. However, few visualization techniques have been specifically developed to support the comparison process, even less for large data sets. In this paper, we present a visualization technique, with its corresponding set of interactions, designed to assist in time-based large data sets comparison. The proposed solution is based on Dynamic Time Warping and presents a visual comparison of the misalignment between a set of time series. The proposal includes an overview of the misalignment between the data and a by-demand detailed view focusing on the comparison between any two of them. As a validation of the technique, we also introduce a case study on the historical meteorological data from Bahía Blanca city.
PRACTICAL-ORIENTED MODULE OF ROBOTICS IN VOCATIONAL TRAINING OF PHYSICS AND INFORMATICS TEACHERS

The article discusses the design and testing of a practice-oriented module on educational robotics in the vocational training of future teachers of physics and computer science at a pedagogical university. The module includes the development of the interdisciplinary content and conduct practical exercises with students. In the process of experimental work, it was shown that future teachers of physics and computer science can increase the level of special knowledge and skills in designing educational robots, if the form of training is an interdisciplinary module on educational robotics within disciplines "Electrodynamics" and "Computer architecture", and if one of the methods is working with schoolchildren on the assembly of robots under the supervision of university lecturers. The significance is the possibility of in-depth learning of junior courses students for future practical training using contemporary digital educational equipment. The practical tasks of the module contribute to the effective preparation of students if they are carried out in the process of working with students on educational robotic equipment, such as LEGO Mindstorms Education EV3 and Ultimate Robot Kit V2.0. The main issues of the educational robotics module–power supply of the robot, servomotors, sensors of robots, architecture of microcontrollers of robots–should be studied before completing tasks.
Hierarchical Domain-Consistent Network For Cross-Domain Object Detection

Cross-domain object detection is a very challenging task due to multi-level domain shift in an unseen domain. To address the problem, this paper proposes a hierarchical domain-consistent network (HDCN) for cross-domain object detection, which effectively suppresses pixel-level, image-level, as well as instance-level domain shift via jointly aligning three-level features. Firstly, at the pixel-level feature alignment stage, a pixel-level subnet with foreground-aware attention learning and pixel-level adversarial learning is proposed to focus on local foreground transferable information. Then, at the image-level feature alignment stage, global domain-invariant features are learned from the whole image through image-level adversarial learning. Finally, at the instance-level alignment stage, a prototype graph convolution network is conducted to guarantee distribution alignment of instances by minimizing the distance of prototypes with the same category but from different domains. Moreover, to avoid the non-convergence problem during multi-level feature alignment, a domain-consistent loss is proposed to harmonize the adaptation training process. Comprehensive results on various cross-domain detection tasks demonstrate the broad applicability and effectiveness of the proposed approach.
Disentangled Representation Learning and Its Application to Face Analytics

The goalRepresentation learning of every contemporary recognition approach is to learn robust and unambiguous object representations in feature space. These learned powerful disentangled representations make it possible to build effective classifiers and are an active research topic in many fields such as face analytics.
Formation Of Skills To Visualize Of Future Physics Teacher: Results Of The Pedagogical Experiment

It is relevant for teachers of science to get visualization skills for explaining abstract concepts, showing the logic of processes, and explaining the natural phenomena at the micro and macro levels. For research conducting, authors used theoretical and empirical methods such as analysis of specialized for physics visualization software; survey for determining the needs of physics teachers; analysis of the curricula content for the training of future physics teachers; performing of the pedagogical experiment to determine the effectiveness of the developed model, statistical evaluation is provided by using Student's criterion. The developed model of visualization skills formation of future physics teachers is based on cognitive-visual approaches and provides the modernization of the content of teachers’ training by including a special course. The analysis of software that supports the teaching of physics makes it possible to divide it into three classes: virtual and digital physics laboratories, mathematical and simulation software, office applications with Smart-Art-objects, and animation software. Analysis of the visualization studying material needs of physics teachers, based on a survey, showed the priority of skills in the office suite programs and skills of creating simulation models (static and dynamic). The introduction of a model of visualization skills formation of future physics teachers ensures the achievement of the goal. Further research is directed to the study of the problem of forming future science teachers' skills to use augmented reality in the educational process.
Hardware Context Switch-based Cryptographic Accelerator for Handling Multiple Streams

The confidentiality and integrity of a stream has become one of the biggest issues in telecommunication. The best available algorithm handling the confidentiality of a data stream is the symmetric key block cipher combined with a chaining mode of operation such as cipher block chaining (CBC) or counter mode (CTR). This scheme is difficult to accelerate using hardware when multiple streams coexist. This is caused by the computation time requirement and mainly by management of the streams. In most accelerators, computation is treated at the block-level rather than as a stream, making the management of multiple streams complex. This article presents a solution combining CBC and CTR modes of operation with a hardware context switching. The hardware context switching allows the accelerator to treat the data as a stream. Each stream can have different parameters: key, initialization value, state of counter. Stream switching was managed by the hardware context switching mechanism. A high-level synthesis tool was used to generate the context switching circuit. The scheme was tested on three cryptographic algorithms: AES, DES, and BC3. The hardware context switching allowed the software to manage multiple streams easily, efficiently, and rapidly. The software was freed of the task of managing the stream state. Compared to the original algorithm, about 18%–38% additional logic elements were required to implement the CBC or CTR mode and the additional circuits to support context switching. Using this method, the performance overhead when treating multiple streams was low, and the performance was comparable to that of existing hardware accelerators not supporting multiple streams.