Reviewerly/exHarmony huggingface dataset:
SENTIMENT ANALYSIS FROM SOCIAL MEDIA COMMENTS

Nowadays, many firms and companies are curious about what people think and want and they are working in this direction. For this reason, it is tried to learn the ideas and emotions of people in various ways. However, as it is impossible to process and analyze a large number of emotions and thoughts with human hands, emotion analysis gain more importance. The emotions and thoughts of the people are analyzed and acted according to these requests through the emotion analysis which is quite functional in social networks. The aim of this study is to realize the learning with the data sets obtained from the interpretations made to the social platforms of the determined brands and to transfer the subject of the emotion analysis to the researchers in the best way. The range of accuracy rates reached is wide because of the disadvantages such as not paying attention to the rules of writing on social media or other digital platforms. In our study, a accuracy rate of 70% was achieved. This demonstrates the usefulness of machine learning in interpretation classification and emotion analysis.
Multiple Graph Matching and Clustering via Decayed Pairwise Matching Composition

Jointly matching of multiple graphs is challenging and recently has been an active topic in machine learning and computer vision. State-of-the-art methods have been devised, however, to our best knowledge there is no effective mechanism that can explicitly deal with the matching of a mixture of graphs belonging to multiple clusters, e.g., a collection of bikes and bottles. Seeing its practical importance, we propose a novel approach for multiple graph matching and clustering. Firstly, for the traditional multi-graph matching setting, we devise a composition scheme based on a tree structure, which can be seen as in the between of two strong multi-graph matching solvers, i.e., MatchOpt (Yan et al. 2015a) and CAO (Yan et al. 2016a). In particular, it can be more robust than MatchOpt against a set of diverse graphs and more efficient than CAO. Then we further extend the algorithm to the multiple graph matching and clustering setting, by adopting a decaying technique along the composition path, to discount the meaningless matching between graphs in different clusters. Experimental results show the proposed methods achieve excellent trade-off on the traditional multi-graph matching case, and outperform in both matching and clustering accuracy, as well as time efficiency.
Evaluating Neural Morphological Taggers for Sanskrit

Neural sequence labelling approaches have achieved state of the art results in morphological tagging.We evaluate the efficacy of four standard sequence labelling models on Sanskrit, a morphologically rich, fusional Indian language.As its label space can theoretically contain more than 40,000 labels, systems that explicitly model the internal structure of a label are more suited for the task, because of their ability to generalise to labels not seen during training.We find that although some neural models perform better than others, one of the common causes for error for all of these models is mispredictions due to syncretism. 1
Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods
Weaving Healthy Behaviors into New Technology Routines

Sitting in front of computers has become a major part of our workaday routines, challenging us in maintaining active and healthy lifestyles. This challenge becomes even more salient during this worldwide work-from-home period due to COVID-19. While a wide variety of existing interactive systems have been developed to facilitate health tracking and healthy exercises, relatively little research concerns incorporating healthy behaviors as HCI elements. To maximize pervasive health benefits in users' technology routines, this workshop sets out to explore a design paradigm that enables users to use lightweight, healthy behaviors to perform daily interactions with computing systems. To navigate this new design space, this workshop calls for interdisciplinary endeavors, synergizing expertise from HCI design, health informatics, persuasive technology, exertion game, and psychology.
A Leakage-Resilient Certificateless Authenticated Key Exchange Protocol Withstanding Side-Channel Attacks

Certificateless public-key cryptography has conquered both the certificate management problem in the traditional public-key cryptography and the key escrow problem in the ID-based public-key cryptography. Certificateless authenticated key exchange (CLAKE) protocol is an important primitive of the certificateless public-key cryptography. A CLAKE protocol is employed to provide both mutual authentication and establishing a session key between two participators. Indeed, all conventional public-key cryptographies have encountered a new kind of attack, named “side-channel attacks”. Fortunately, leakage-resilient cryptography is a flexible approach to withstand such attacks. However, the design of leakage-resilient CLAKE (LR-CLAKE) protocols is not studied. In the article, by extending the well-known extended-Canetti-Krawczyk (eCK) model, we present the security notions (adversary model) of LR-CLAKE protocols, called continual-leakage-resilient eCK (CLReCK) model. The first LR-CLAKE protocol withstanding side-channel attacks is proposed. By employing the proof technique of the generic bilinear group (GBG) model, we formally prove the security of our protocol in the CLReCK model.
Hybrid Whale Optimization Algorithm with Modified Conjugate Gradient Method to Solve Global Optimization Problems

Whale Optimization Algorithm (WOA) is a meta-heuristic algorithm.It is a new algorithm, it simulates the behavior of Humpback Whales in their search for food and migration.In this paper, a modified conjugate gradient algorithm is proposed by deriving new conjugate coefficient.The sufficient descent and the global convergence properties for the proposed algorithm proved.Novel hybrid algorithm of the Whale Optimization Algorithm (WOA) proposed with modified conjugate gradient Algorithm develops the elementary society that randomly generated as the primary society for the Whales optimization algorithm using the characteristics of the modified conjugate gradient algorithm.The efficiency of the hybrid algorithm measured by applying it to (10) of the optimization functions of high measurement with different dimensions and the results of the hybrid algorithm were very good in comparison with the original algorithm.
Coherent detection schemes for subcarrier wave continuous variable quantum key distribution

We examine different methods to implement coherent detection in subcarrier wave quantum key distribution (SCW QKD) systems. For classical wave fields, we present the models describing homodyne-type and heterodyne-type coherent detection schemes needed to extract information from the quadrature phase-coded multimode signals used in SCW QKD. Practical feasibility of the proposed schemes is corroborated by experiments.
A Mobile Application of Augmented Reality for Aircraft Maintenance of Fan Cowl Door Opening

Maintenance error such as failure of fan cowl door opening is one of the causes of aircraft accidents.In this research, we aim to develop an augmented reality application that allows the animation of each maintenance procedure of fan cowl door opening to be superimposed on the fan cowl door of aircraft.The marker detectability of the augmented reality application is checked based on different camera angles and distances.Hence, by using the developed application, the aircraft maintenance technician can interpret the information of the fan cowl door opening procedure in the form of texts, threedimensional models and animations.
Efektifitas Penggunaan Media Edutainment Di Tengah Pandemi Covid-19

Pada bulan Maret 2020 Presiden Indonesia dan berdasar Surat Edaran Kemendikbud Nomor 4 Tahun 2020 memandatkan untuk siswa belajar dari rumah akibat dari pandemi Covid-19. Adapun studi ini bertujuan untuk menguji keefektifan penggunaan media edutainment saat belajar dari rumah mulai diberlakukan akibat dari Covid-19. Data dianalisis menggunakan metode kuantitatif dan kualitatif. Adapaun data didapatkan menggunakan suatu instrumen tes dan non tes yang dapat diakses secara online. Sebanyak 2 jenis angket yang digunakan pada penelitian ini, yakni angket guru dan angket siswa. Sampel penelitian terdiri dari 232 siswa (M = 15 tahun; SD = 0,5 tahun) serta 32 guru matematika (M = 34 tahun; SD = 0,4 tahun). Hasil studi menunjukkan bahwa mayoritas siswa dan guru memiliki persepsi yang positif untuk menggunakan media edutainment (pada fase kuesioner). Selain itu melalui media edutainment pada pembelajaran via daring, pencapaian aspek kognitif siswa (pada fase tes) juga memperoleh hasil yang positif. Sehingga hasil secara keseluruhan menunjukkan bahwa media edutainment efektif dalam menemani siswa belajar dari rumah. Abstract In March 2020, President of Indonesia and based on the Ministry of Education and Culture Circular Letter 4 of 2020 mandated for students to learn from home as a result of the Covid-19 pandemic . The study aims to test the effectiveness of the use of media edutainment while learning from home began to apply as a result of Covid-19. Data is analyzed using quantitative and qualitative methods. The data is obtained using a test and non-test instrument that can be accessed online. A total of 2 types of questionnaires were used in this study, namely the teacher questionnaire and student questionnaire. The research sample consisted of 232 students (M = 15 years; SD = 0.5 years) and 32 mathematics teachers (M = 34 years; SD = 0.4 years). The results of the study show that the majority of students and teachers have a positive perception in using edutainment media (questionnaire phase). In addition, through the media edutainment,, students of achievement (in the test phase) has also gained positive results. So the overall results show that the media edutainment is effective in accompanying students learning from home.
Multi-Agents Indexing System (MAIS) for Plagiarism Detection

With the extensive availability of technological systems all over the world and the increasing diffusion of information and documents by users, especially for the Arabic population, the development of semantic plagiarism detection systems has become essential due to its importance for protecting the rights of authors. Developing such a system that can covers the content of all Arabic documents using ontology as a semantic resource would be a complex and time-consuming task and would require intelligent natural language-processing capabilities. In the context of Arabic plagiarism detection systems using an Arabic ontology and permitting these systems to support semantic representation to more efficiently verify the originality of the research and meet the needs of researchers, this paper presents a novel approach for addressing plagiarism named Multi-agents Indexing System. The proposed system is composed of three phases: (1) natural language processing phase, (2) indexing phase and (3) evaluation phase. Our experimentations are based on the training dataset released for the AraPlagDet curpus. The obtained results indicated that the proposed system has improved the performance of plagiarism detection in Arabic documents with semantic indexing and mutli-agents system.
Content-Based Image Retrieval Using Color Layout Descriptor, Gray-Level Co-Occurrence Matrix and K-Nearest Neighbors

Content-based image retrieval (CBIR) is the process of retrieving similar images of a query image from a source of images based on the image contents.In this paper, color and texture features are used to represent image contents.Color layout descriptor (CLD) and graylevel co-occurrence matrix (GLCM) are used as color and texture features respectively.CLD and GLCM are efficient for representing images with local dominant regions.For retrieving similar images of a query image, the features of the query image is matched with that of the images of the source.We use cityblock distance for this feature matching purpose.K-nearest images using cityblock distance are the similar images of a query image.Our CBIR approach is scale invariant as CLD is scale invariant.Another set of features, GLCM defines color patterns.It makes the system efficient for retrieving similar images based on spatial relationships between colors.We also measure the efficiency of our approach using k-nearest neighbors algorithm.Performance of our proposed method, in terms of precision and recall, is promising and better, compared to some recent related works.
Secure Data Transmission of Video Steganography Using Arnold Scrambling and DWT

For the sensitive information, the best privacy demand must be supported in commercial, technical and legal regulations.In this system we used combination of steganography and cryptography techniques in order to improve imperceptibility, robustness, payload capacity and to transmit data securely.As the preprocessing step, Arnold scrambling and discrete wavelet transform (DWT) techniques are used over the secret image.Then the referable values are calculated from the values of transformed secret image with the use of a secret key and embed these referable values in the video file by least significant bit (LSB) technique.Moreover, the secret key is encrypted with a pre-shared key by a new stream cipher Twisted Exchange Algorithm.And the result encrypted message is embedded behind the audio file by Parity coding technique.As the experimental results, performance of the system is tested with different real time images and various video quality files and measured by different parameters (PSNR, MSE), then it is also analyzed with attacks.
Grounding Language to Non-Markovian Tasks with No Supervision of Task Specifications

Natural language instructions often exhibit sequential constraints rather than being simply goal-oriented, for example "go around the lake and then travel north until the intersection".Existing approaches map these kinds of natural language expressions to Linear Temporal Logic expressions but require an expensive dataset of LTL expressions paired with English sentences.We introduce an approach that can learn to map from English to LTL expressions given only pairs of English sentences and trajectories, enabling a robot to understand commands with sequential constraints.We use formal methods of LTL progression to reward the produced logical forms by progressing each LTL logical form against the groundtruth trajectory, represented as a sequence of states, so that no LTL expressions are needed during training.We evaluate in two ways: on the SAIL dataset, a benchmark artificial environment of 3,266 trajectories and language commands as well as on 10 newly-collected real-world environments of roughly the same size.We show that our model correctly interprets natural language commands with 76.9% accuracy on average.We demonstrate the end-to-end process in real-time in simulation, starting with only a natural language instruction and an initial robot state, producing a logical form from the model trained with trajectories, and finding a trajectory that satisfies sequential constraints with an LTL planner in the environment.
Impacts of Consensus Protocols and Trade Network Topologies on Blockchain System Performance

Blockchain can be viewed as a public ledger maintained collectively by a large number of participators based on consensus protocol. We are interested in how difference consensus protocols and trade network topologies affect the performance of a blockchain system, which has not been studied in the literature yet. In this paper, we proposed an agent-based model consisting of multiple trader and miner agents, and one system agent. We investigated three consensus protocols, namely proof-of-work (PoW), proof-of-stake (PoS), and delegated proof-of-stake (DPoS). We also examined three common trade network topologies: random, small-world, and scale-free. We find that both consensus protocol and trade network topology can impact the performance of blockchain system. PoS and DPoS are generally better than PoW in terms of increasing trade efficiency and equalizing wealth. Besides, scale-free trade network is not favorable because its trade efficiency is quite low, which moderates the price fluctuation and wealth inequality. Since connectivity inequality determines wealth inequality, it is crucial to increase the connectivity among participants when designing a sustainable blockchain system. We suggest that our findings could be useful to the designers, practitioner and researchers of blockchain system and token economy.
Communication Accommodation Theory: A Brief Review of the Literature

Studies on Communication Accommodation Theory has advanced rapidly in recent years.Communication is an important part of human life and from the beginning of human history, people have always tried to communicate together and to understand the others and make themselves understood.This paper aims to have a brief review on the notion "Communication Accommodation Theory" (CAT), which is an important aspect of different sciences (e.g., Linguistics, sociology, sociolinguistics, and psychology).The concept of Communication Accommodation is used in all people"s social life, for instance, between mother and child, teacher and student, reporter and listener, doctor and patient, and immigrants (newcomers) and citizens, however, this review helps us to have a better understanding through CAT.
ACE: ARIA-CTR Encryption for Low-End Embedded Processors

In this paper, we present the first optimized implementation of ARIA block cipher on low-end 8-bit Alf and Vegard’s RISC processor (AVR) microcontrollers. To achieve high-speed implementation, primitive operations, including rotation operation, a substitute layer, and a diffusion layer, are carefully optimized for the target low-end embedded processor. The proposed ARIA implementation supports the electronic codebook (ECB) and the counter (CTR) modes of operation. In particular, the CTR mode of operation is further optimized with the pre-computed table of two add-round-key, one substitute layer, and one diffusion layer operations. Finally, the proposed ARIA-CTR implementations on 8-bit AVR microcontrollers achieved 187.1, 216.8, and 246.6 clock cycles per byte for 128-bit, 192-bit, and 256-bit security levels, respectively. Compared with previous reference implementations, the execution timing is improved by 69.8%, 69.6%, and 69.5% for 128-bit, 192-bit, and 256-bit security levels, respectively.
Learning Task-Driven Control Policies via Information Bottlenecks

This paper presents a reinforcement learning approach to synthesizing task-driven control policies for robotic systems equipped with rich sensory modalities (e.g., vision or depth).Standard reinforcement learning algorithms typically produce policies that tightly couple control actions to the entirety of the system's state and rich sensor observations.As a consequence, the resulting policies can often be sensitive to changes in taskirrelevant portions of the state or observations (e.g., changing background colors).In contrast, the approach we present here learns to create a task-driven representation that is used to compute control actions.Formally, this is achieved by deriving a policy gradient-style algorithm that creates an information bottleneck between the states and the task-driven representation; this constrains actions to only depend on task-relevant information.We demonstrate our approach in a thorough set of simulation results on multiple examples including a grasping task that utilizes depth images and a ball-catching task that utilizes RGB images.Comparisons with a standard policy gradient approach demonstrate that the task-driven policies produced by our algorithm are often significantly more robust to sensor noise and task-irrelevant changes in the environment.
Similarity Search for Efficient Active Learning and Search of Rare Concepts

Many active learning and search approaches are intractable for large-scale industrial settings with billions of unlabeled examples. Existing approaches search globally for the optimal examples to label, scaling linearly or even quadratically with the unlabeled data. In this paper, we improve the computational efficiency of active learning and search methods by restricting the candidate pool for labeling to the nearest neighbors of the currently labeled set instead of scanning over all of the unlabeled data. We evaluate several selection strategies in this setting on three large-scale computer vision datasets: ImageNet, OpenImages, and a de-identified and aggregated dataset of 10 billion publicly shared images provided by a large internet company. Our approach achieved similar mAP and recall as the traditional global approach while reducing the computational cost of selection by up to three orders of magnitude, enabling web-scale active learning.
The end of Enlaces: 25 years of an ICT education policy in Chile

This article analyzes the reasons behind the end of the ICT in education policy (Enlaces) driven by the Chilean Ministry of Education for 25 years. The argument is that the institution that sought to give sustainability to this policy over time, ended up losing its ability to lead relevant responses to the educational challenges posed by the growing digitalization of the Chilean society. To develop this argument, first we describe the main characteristics, history and results of Enlaces until 2018, and second, we analyze the institutional development of Enlaces in light of the challenges posed to ICT in education policies in the world.
Performance Evaluation of a Multi-User Virtual Reality Platform

Virtual Reality (VR) popularity is increasing as it is becoming more affordable for end users.Available VR hardware includes low-end inexpensive devices like Google Cardboard and high-end ones like HTC Vive or Oculus Rift, which are more expensive headsets.Using VR as a platform for content delivery allows better user engagement than other traditional methods, as VR headsets remove external distractions.Multiuser VR applications provide shared experiences where users can communicate and interact in the same virtual space.This shared environment, however, introduces challenges regarding network performance, quality of service (QoS) and sessions privacy.This paper presents a multi-user VR application and aims to evaluate network behaviour in a number of scenarios, including real VR headsets (i.e.Oculus Rift), as well as simulated ones.This QoS analysis is important for the understanding of how many VR users can be simultaneously connected with high image quality.