Reviewerly/exHarmony huggingface dataset:
From Low to Super Resolution and Beyond

The tutorial starts with an introduction of digital image interpolation, and single image super-resolution. It continues with the definition of various image interpolation performance measurement indices, including both objective and subjective indices. The core of this tutorial is the application of covariance based interpolation to achieve high visual quality image interpolation and single image super-resolution results. Layer on layer, the covariance based edge-directed image interpolation techniques that makes use of stochastic image model without explicit edge map, to iterative covariance correction based image interpolation. The edge based interpolation incorporated human visual system to achieve visually pleasant high resolution interpolation results. On each layer, the pros and cons of each image model and interpolation technique, solutions to alleviate the interpolation visual artifacts of each techniques, and innovative modification to overcome limitations of traditional edge-directed image interpolation techniques are presented in this tutorial, which includes: spatial adaptive pixel intensity estimation, pixel intensity correction, error propagation mitigation, covariance windows adaptation, and iterative covariance correction. The tutorial will extend from theoretical and analytical discussions to detail implementation using MATLAB. The audience shall be able to bring home with implementation details, as well as the performance and complexity of the interpolation algorithms discussed in this tutorial.
A Diachronic Italian Corpus based on “L’Unità”

In this paper, we describe the creation of a diachronic corpus for Italian by exploiting the digital archive of the newspaper “L’Unit`a”. We automatically clean and annotate the corpus with PoStags, lemmas, named entities and syntactic dependencies. Moreover, we compute frequency-based time series for tokens, lemmas and entities. We show some interesting corpus statistics taking into account the temporal dimension and describe some examples of usage of time series.
Patent Protection of Inventions Involving Artificial Intelligence

The Fourth Industrial Revolution has created a variety of new technologies. It seems to be undisputed that they will not only be an important economic driver but also have a great impact in basically every field of technology, on the economy and society at large, and on the relevant underlying laws.2 The development and control of these technologies is thus directly linked to future technological and military supremacy, and is the main reason for the trade dispute between the US and China.
Lower Bounds for Testing Complete Positivity and Quantum Separability

In this work we are interested in the problem of testing quantum entanglement. More specifically, we study the separability problem in quantum property testing, where one is given n copies of an unknown mixed quantum state $$\varrho $$ on $$\mathbb {C}^d \otimes \mathbb {C}^d$$ , and one wants to test whether $$\varrho $$ is separable or $$\epsilon $$ -far from all separable states in trace distance. We prove that $$n = \varOmega (d^2/\epsilon ^2)$$ copies are necessary to test separability, assuming $$\epsilon $$ is not too small, viz. $$\epsilon = \varOmega (1/\sqrt{d})$$ . We also study completely positive distributions on the grid $$[d] \times [d]$$ , as a classical analogue of separable states. We analogously prove that $$\varOmega (d/\epsilon ^2)$$ samples from an unknown distribution p are necessary to decide whether p is completely positive or $$\epsilon $$ -far from all completely positive distributions in total variation distance.
UnihanLM: Coarse-to-Fine Chinese-Japanese Language Model Pretraining with the Unihan Database

Chinese and Japanese share many characters with similar surface morphology. To better utilize the shared knowledge across the languages, we propose UnihanLM, a self-supervised Chinese-Japanese pretrained masked language model (MLM) with a novel two-stage coarse-to-fine training approach. We exploit Unihan, a ready-made database constructed by linguistic experts to first merge morphologically similar characters into clusters. The resulting clusters are used to replace the original characters in sentences for the coarse-grained pretraining of the MLM. Then, we restore the clusters back to the original characters in sentences for the fine-grained pretraining to learn the representation of the specific characters. We conduct extensive experiments on a variety of Chinese and Japanese NLP benchmarks, showing that our proposed UnihanLM is effective on both mono- and cross-lingual Chinese and Japanese tasks, shedding light on a new path to exploit the homology of languages.
A Targeted Topic Model based Multi-Label Deep Learning Classification Framework for Aspect-based Opinion Mining

Recently, deep Convolutional Neural Network (CNN) model has achieved remarkable results in Natural Language Processing (NLP) tasks, such as information retrieval, relation classification, semantic parsing, sentence modeling and other traditional NLP tasks, etc. On the other hand, topic modeling method has been proved to be effective by exploiting hidden knowledge in a corpus of documents. Motivated from these successes, we propose a framework that takes the advantages of closure domain measure to get enriched knowledge from close domains to the training dataset to improve the CNN model, and apply a Targeted Topic Model to take more detailed exploration on each labeled aspect of an opinion. Experimental results on different scenarios show the effectiveness of the proposed framework for multi-label classification task in comparison to other related models on the same Hotel review dataset.
Exploration and Practice of Cultivating Civil Engineering Talents under the Lead of Digital Construction

Combined with the background of the transformation, upgrading and transformation of the construction industry, this paper analyzes the problems existing in the construction of the civil engineering professional training model, curriculum system, teaching team and practical platform based on BIM technology under the guidance of digital construction, points out the ways and methods of multi-disciplinary integration and transformation of traditional engineering majors, and introduces the research and practical results of Kunming University of Science and Technology in the cultivation of BIM technical talents.
Analysis of the Tsunami Evacuation Route in Kunjir Village and Way Muli Village, South Lampung Regency

This research was conducted to analyze the effectiveness of existing evacuation routes in Kunjir Village and Way Muli Village by calculating the level of safety of the population when evacuating in the event of a tsunami disaster. The data used are direct field observations, topographic maps derived from aerial photo mapping results, existing conditions of evacuation routes, gathering conditions, evacuation direction signs, and data on tsunami wave heights that may occur in the study area. We analyze the tsunami disaster evacuation route using Network Analyst in ArcGIS. Furthermore, the evacuation route is determined which has a road width of more than 2 m so that not all roads in both villages are used as evacuation routes. In determining the evacuation point modeling tsunami inundation is used with a wave height of 4 m. Later the route and direction of the nearest evacuation route will be known to the safe evacuation point. Based on the results of the study, the number of evacuation routes in Way Muli Village amounted to 5 with 4 site points while in Kunjir Village there were 7 with 4 site points that could be used as evacuation sites. The farthest distance from the evacuation route to the evacuation site in Way Muli Village is 441.5 m with a travel time of 4 minutes and for Kunjir Village is 704.3 m with a travel time of 6.38 minutes.
Open Access (OA) Folklor Lisan di Perpustakaan Khusus

Open Access (OA) is a digital library service concept that distributes a whole library collection. A special library of oral folklore can provide oral folklore collections through OA services to the community or society. The purpose of this research is to know how the application of oral folklore OA in special libraries. The research method uses descriptive qualitative through literature studies. The author collects, sorts, and analyzes references according to oral folklore studies and OA that come from books, journals, and the internet. The results showed that librarians in special libraries can look for oral folklore in the community or society through cultural documentation and research activities. Cultural documentation is the activity of searching, collecting data, recording, and storing in various media, while research is a structured observation process in oral folklore. The results of cultural documentation and oral folklore then become a special collection of special libraries that can be shared openly to the community or society. So, librarians design Library Management Systems (LMS) and formulate policies in implementing OA. In addition, librarians also organize activities to share information on oral folklore through social media settings, the internet, library websites, and organize Search Engine Optimization (SEO). The conclusion of this research is that OA services can be implemented in special libraries through good cooperation between librarians and the community or society.
Efficient Design of a Reversible Sorting Circuit

Reversible logic is a very significant exposure for low-power consumption and faster computation in recent research areas. The aim of designing a reversible sorting circuit is to reduce power consumption and reliable communication. Two efficient sorting circuits are presented in this paper which can sort binary numbers in descending order. The proposed circuits includes 3 or 4 inputs where each input consists of 3 bits. Fredkin and Toffoli gates have been used to design the comparator. Fredkin gate has also been used for the proposed switch which generates result bit according to the comparator’s input. The proposed circuit needs 102 primary reversible gates for sorting 3 inputs and 170 gates for 4 inputs, 195 garbage outputs for 3 inputs and 325 garbage outputs for 4 inputs, 510 quantum cost for 3 inputs and 850 quantum cost for 4 inputs and (150α + 246β + 96γ) hardware complexity for 3 inputs and (250 + 410β + 160γ) hardware complexity for 4 inputs. In this modern era, sorting is used in many fields like pulse generation, counting and searching operations. Many research have been done regarding reversible sequential and combinational circuits but reversible sorting circuit is still an unexplored area, so this may be one of the pioneering works in this field.
PEDAGOGÍA DE LA ESPERANZA DE PAULO FREIRE FRENTE A LOS ESPACIOS VIRTUALES DE APRENDIZAJE

The book entitled “A Pedagogy of Hope”, by Paulo Freire, summarizes his pedagogical viewpoints and coincides with the uprising of pedagogical proposals which have supported the use of technology in education through virtualization as part of globalization. In his work, Freire proposes several concerns dealing with the role of technology in education. Thus, it is vital to briefly explore the technology-based educational profile and, particularly, the virtual contexts of learning so as to establish the contrast between both proposals. Both trends are not likely to be regarded as complementary, as they both differ especially in terms of their objectives. Indeed, while Freire regards education as a tool for transforming people´s lives, globalizations views education as a vehicle to enhance production models.
THE ACCURACY PREDICTION OF TIME-SERIES DATA ON THE OCCURRENCE OF POLLUTANTS FROM SMOKING USING MACHINE LEARNING

Recently, indoor activities have increased due to the harmful effect of the fine dust and Covid-19, accordingly, interest in creating a safe indoor environment is emerging Even though there are various causes of indoor air contamination, and indoor smoking, which is both illegal and violent to health, and it needs to be regulated In this paper, we use IoT sensors for detecting indoor smoking and predict it in the time series method IoT sensors feature a large number of devices and a large amount of data Therefore, the time series method is suitable even though it has limitations in predicting and analyzing data on a short-term basis The aim of our paper is to predict the accuracy of data through a time series method As a result, considering the characteristics of cigarettes was found to be the most effective with Long-Short Term Memory (LSTM) Models © 2020 Pushpa Publishing House, Prayagraj, India
Understanding brain agents and academy

The brain architecture is an important aspect of the ML Agents Toolkit. In the previous chapter, we installed the ML Agents Toolkit and also learned briefly about this architecture. Internally the ML Agents Toolkit uses three different kinds of brains, with the addition of a player brain that is controlled by the user. We concern ourselves with understanding the inner workings of certain scripts in the ML Agents package, which uses the neural networks trained in Tensorflow in Unity Agents. Since we have tried to get a glimpse of deep Q-learning as the only deep RL algorithm as of now, we can also use this algorithm to train the brain of the agent. Whereas in Unity ML Agents, the default algorithm for the internal brain is proximal policy operation (PPO), which is robust and has a comfortable balance between ease of implementation, sample tuning, and complexity, we will explore different algorithms that will be used as the brain for the agent. In this section, we will have a deep insight into the brain architecture and all the associated C# scripts associated with it, including the different aspects of model training and hyperparameter tuning. We will be building games using ML Agents in Unity.
Fine and Ultra-Fine Entity Type Embeddings for Question Answering

We describe our system for the SeMantic AnsweR (SMART) Type prediction task 2020 for both the DBpedia and Wikidata Question Answer Type datasets. The SMART task challenge introduced fine-grained and ultra-fine entity typing to question answering by releasing two datasets for question classification using DBpedia and Wikidata classes. We propose a flexible framework for both entity types using paragraph vectors and word embeddings to obtain high quality contextualized question representations. We augment the document similarity provided by paragraph vectors with semantic modeling and sentence alignment using word embeddings. For the answer category prediction, we achieved a maximum accuracy score of 85% for Wikidata and 88% for DBpedia. For the answer types prediction, we achieved a maximum MRR of 40% for Wikidata and a maximum nDCG@5 of 54% for DBpedia datasets.
Stereo Image Matching Algorithm Based on Texture Segmentation and Color Segmentation

The traditional binocular vision stereo matching replaces and responds to the maximum point as the feature point, which is prone to uneven distribution of feature points, especially when the distribution of image texture strength is large. The Euclidean distance needs to be calculated when the feature points are matched, and the constraints considered are basically position relationships, ignoring the color block constraints. Based on the image texture and color distribution characteristics, the texture strength area and RGB color block are respectively calculated, and the image is segmented. The multi threshold feature points detection is used, and the matching points are calculated according to the color segmentation template. The experimental results show that compared with the traditional ORB algorithm, the proposed algorithm improves the uniformity of feature points and the matching accuracy.
A Frame Rates Stabilization Scheme for Cloud Distributed Live Video Processing Systems

One of the main problems for cloud distributed live video processing systems is unstable frame rates. It is difficult to stabilize the frame rates in the systems because the communication times and the processing times are unpredictable. In this paper, to solve this fundamental problem, we propose a scheme that dynamically changes the video processes to stabilize the frame rates and evaluate its effectiveness.
Using Interval Type-2 Recurrent Fuzzy Cerebellar Model Articulation Controller Based on Improved Differential Evolution for Cooperative Carrying Controller of Mobile Robots

Mobile robot is widely utilized in various fields such as navigation control, obstacle avoidance and object carrying. For keeping away from obstacles to avoid collision and preventing object carrying from dropping down, we propose a state manager (SM) designed to assist the mobile robots so that they can switch operation between wall-following carrying (WFC) and toward goal carrying (TGC) by different external condition. In this controlling model, interval type-2 recurrent fuzzy cerebellar model articulation controller (IT2RFCMAC), embedded with a modified evolutionary optimization and dynamic grouping differential evolution (DGDE), is implemented for WFC and TGC. By adopting reinforcement learning strategy, mobile robots equip with adaptively wall-following control to make cooperative carrying control in real.
Intelligent lighting system using single shot multibox detector

Single Shot MultiBox Detector is a single shot detector (uses a single shot) for multiple categories, which is faster than the latest generation such as YOLO (You only look once) and significantly more accurate, in fact just as accurate. As well as the slower techniques, which make explicit region proposals and collection (including faster R-CNN). SSD predicts category scores and box offsets for a fixed set of default bounding boxes, using small convolutional filters applied on feature maps. Recent researches in object detection are driven by the success of convolutional neural networks (CNN). They are able to learn rich features outperforming hand-crafted features. To obtain high detection accuracy, we produce predictions of different scales from feature maps of different scales, and explicit predictions based on the aspect ratio. These design features lead to simple end-to-end training and high accuracy, even on low resolution input images, further improving speed and accuracy with precision [2]. The SSD only needs an input image and truth boxes for each object during training. Convolutely, we evaluate a small set of boxes implicit by different aspect ratios at each location, in several feature maps with different [3]. For each implicit box, we preach both shape offsets and confidences for all object categories. During training, for the first time these default boxes match the truth boxes. The SSD approach is based on a convolutional feed-forward network that produces a fixed size collection of bounding boxes and scores for the presence of object class instances in those boxes, followed by a non-maximal suppression step to produce the final detections.
Investigation of the Secondary School Teachers' Opinion about Mobile Learning

The paper examines the opinion of 43 secondary school teachers from different schools in Bulgaria about mobile learning. For the purposes of the investigation, a questionnaire was created and used. It includes questions about the respondents (gender, age, owned mobile devices and computers), as well as questions related to e-learning and mobile learning. An analysis of respondents' answers and relevant conclusions are presented.
A Study on Yeoulwoon Zhang Ji-young’s Chinese Textbook Manchuria Language Lecture

This study is aimed at introducing the Chinese textbook Manchuria Language Lecture of Zhang Ji-young, who is in the library of Yonsei University, to academia and analyzing and examining the bibliographical features and contents of Manchuria Language Lecture.BR Manchuria Language Lecture is a textbook used for radio Chinese education conducted for the first time in 1933 at Kyungsung Broadcasting Station 2nd Radio Broadcasting. Both 1933 and 1934 were broadcast, and the textbooks which publish are housed in Yeoulwoon library in the Yonsei University Library. Since it was the textbook used for the first radio education at the time and is not yet known to academia, this manuscript looked at the composition and system comparison of these two materials.
How to Overcoming Cyber Security Challenges Using Data Science

Data science, in its utmost essential form, is wholly about realizing. It encompasses reviewing, Processing, and Extracting valued identifications from a set of information. However, the word and procedure have-been about for quite a few time spans, it was principally a subsection of computer science. Currently, it has established into a self-determining field. One of the modern application of data-science includes cyber security. Cyber Security is an emerged and a saturated field which is omnipresent. It’s sound strange to study Data-Science with the expectations of refining cybersecurity, but in realism, it makes a lot of intellect. This research paper evaluates the progresses in using Data science for cyber security, the categories of cybersecurity threats and challenges posed. The authors have analyzed how data science concepts are being used to solve these challenges and detect and prevent attacks real-time.
Web-based Live Chat Application uses Advanced Encryption Standard Methods and Rivest Shamir Adleman

Abstract The development of technology, especially in the data security system in maintaining the security of information data has been growing rapidly. In maintaining the security of information data there are branches of science in its development such as cryptography and steganography. In its application is not only done on one security technique, but can be done in combination with information security data. This study aims to create a data security system by implementing cryptography on messages in the live chat application. The message security in live chat applies a combination of AES and RSA algorithms. The AES (Advanced Encryption Standard) method is a cryptographic algorithm that can be used to secure data, the AES algorithm is a symmetric chipertext block that can encrypt and decrypt information, and the RSA Method is the RSA Algorithm is a block cipher algorithm (an algorithm that works per block of data) that groups plaintext into blocks first before being encrypted to become ciphertext. This application aims to secure messages sent by the sender to stay safe when received by the recipient. The system will be based on a website using the PHP programming language, using a codeigniter framework and a MySQL database. This information system is in the form of live chat which will be encrypted by 2 methods. The sender will send a message through the live chat website then the message is encrypted so that its security is guaranteed, the message stored in the database will be automatically encrypted by the two methods applied then after arriving at the recipient the message is automatically described. The message sent is in the form of text and png files.
Græcissāre: Ancient Greek Loanwords in the LiLa Knowledge Base of Linguistic Resources for Latin

This paper describes the addition of an index of 1,763 Ancient Greek loanwords to the collection of Latin lemmas of the LiLa: Linking Latin Knowledge Base of interoperable linguistic resources. This lexical resource increases LiLa’s lemma count and tunes its underlying data model to etymological borrowing.