Reviewerly/exHarmony huggingface dataset:
The Pylos Tablets Digital Project: Prehistoric Scripts in the 21st Century

This paper describes new approaches to the documentation and publication of texts in the Greek Bronze Age through discussion of the authors’ ongoing project to study and publish the Linear B documents from the ‘Palace of Nestor’ at Pylos in southwestern Greece. Recent developments move in two distinct directions. The first, digitisation, involves the production of digital models to represent the texts, using both computational photography and three-dimensional modelling. The second, the study of the texts as material culture, involves examining the physical properties of the texts and associated uninscribed documents. Traditional print-based editions are limited in the amount of data they can present, and the use of static images and text requires the user to rely on the authority of the editor, the illustrator, and even the photographer. Digital models, on the other hand, provide a dynamic and interactive environment in which users can work with the texts at a level of resolution that approximates autopsy. That is, they place less emphasis on editorial interpretations and interventions and put more primary data directly into the hands of users. The digital models thus decentre editorial authority by easing scholarly access to material.
Digital Image Tamperin Gdetection using sift Key Point

Copy-move imitation is a widespread and generally utilized operation to corrupt digital image. It is considered as the most effective research areas in the domain of blind digital image forensics area. Keypoint based totally identification techniques have been regarded to be very environment-friendly in exposing copy-move proof because of their steadiness against a number of attacks, as like large-scale geometric movements. Conversely, these techniques don’t have the capabilities to cope with the instances if copy-move forgeries only engage in minor or clean areas, the place the quantity of keypoints is more restricted. To affirm the originality of image, detection of digital image tempering is required. To manage this difficulty, a quick and efficient copy-move imitation detection process is promoted by using the skill of hierarchical function point matching. It is viable to produce an adequate quantity of key points that are present in small or easy areas with the aid of reducing the brightness threshold and resizing the enter digital image. After that, construct a novel hierarchical equivalent technique to remedy the key point equivalent issues over a huge quantity of the key points. To decrease the false alarm charge and exactly localize the affected areas, we similarly advise an innovative iterative localization approach by way of using the steady elements (which comprises of the overriding orientation and the scale data) and the color data of all key point. The proposed technique validates the highest quality overall functioning of the suggested approach in terms of efficiency and precision.
Image Copy–Move Forgery Detection Using Combination of Scale-Invariant Feature Transform and Local Binary Pattern Features

Today, manipulating, storing, and sending digital images are simple and easy because of the development of digital imaging devices from hardware and software points of view. Digital images are used in different contexts of people’s lives such as news, forensics, and so on. Therefore, the reliability of received images is a question that often occupies the viewer’s mind and the authenticity of digital images is increasingly important. Detecting a forged image as a genuine one as well as detecting a genuine image as a forged one can sometimes have irreparable consequences. For example, an image that is available from the scene of a crime can lead to a wrong decision if it is detected incorrectly. In this paper, we propose a combination method to improve the accuracy of copy–move forgery detection (CMFD) reducing the false positive rate (FPR) based on texture attributes. The proposed method uses a combination of the scale-invariant feature transform (SIFT) and local binary pattern (LBP). Consideration of texture features around the keypoints detected by the SIFT algorithm can be effective to reduce the incorrect matches and improve the accuracy of CMFD. In addition, to find more and better keypoints some pre-processing methods have been proposed. This study was evaluated on the COVERAGE, GRIP, and MICC-F220 databases. Experimental results show that the proposed method without clustering or segmentation and only with simple matching operations, has been able to earn the true positive rates of 98.75%, 95.45%, and 87% on the GRIP, MICC-F220, and COVERAGE datasets, respectively. Also, the proposed method, with FPRs from 17.75% to 3.75% on the GRIP dataset, has been able to achieve the best results.
Learners' Autonomy Development in LSP Teaching

The article considers the problem of learners' autonomy development in LSP learning while modernizing higher education in Russia. The authors investigate the opportunities for learners' autonomy development, underline the effectiveness of information-and-communication technologies and the importance of the teacher's leading role in motivating students to develop their autonomy.
Layout Optimization of PV Arrays on Building Rooftop: Influence of Geographical Location and Weather Conditions

The paper describes a methodology to determine whether or not a tilted PV array works better than a horizontal one on top of a building roof. Vertical and horizontal positioning of the PV panels for tilted PV array has been considered. The method is based on the maximum number of PV panels that can be installed on specific building rooftop surface and calculates the energy generated by the PV array,comparing the different configurations and taking into consideration the effects of the angle of incidence of the solar radiation. The method takes into account not only the geographical position of the location but also the effects of rainfall on the PV panels and the attenuation of solar radiation through the water layer deposited on the PV panel surface. The paper predicts that the horizontal configuration of the PV array works better for low rainfall areas or non-polluted areas. On the contrary, for very rainfall zones or polluted areas like cities or industrial conglomerates, the tilted PV array generates more energy and works more efficiently.
Central Jakarta Rainfall Intensity Forecast using Single Exponential Smoothing

Rainfall is the precipitation amount that is falling from clouds. In extreme conditions, rainfall could arise many problems. It is the leading cause of landslides and flood disasters. In D.K.I. Jakarta, the capital city of Indonesia, rainfall intensity plays a very vital role since it could easily be puddled and caused floods in many areas. Therefore, in this study, we try to make a rainfall intensity prediction in Central Jakarta using a very popular forecasting method, i.e., the Single Exponential Smoothing (SES). Based on the experiments conducted using Phatsa, it can be concluded that the SES method has been successfully used to predict rainfall intensity. However, it cannot give a very good prediction result due to its high forecast error values.
Human Motion Detection Based Upon CNN with Pruning and Edge Detection

Human activity detection is basic requirement especially within smart environments like smart homes. The primary requirement of smart environment is energy conservation. To achieve this, first target is to detect the human activities accurately using neural network-based approach. This paper presents a unique combination of CNN with pruning and edge detection mechanism to accurately detect human activities. The entire process of human activity detection is portioned into set of phases. In the first phase, data acquisition is performed. The CNN with pruning and edge detection utilised KTH dataset derived from Kaggle. In the second phase, pre-processing to eliminate the noise from the image frames. In the third phase, edge detection and feature extraction were ensured. In the last phase classification is performed. The result of the human activity detection mechanism is expressed in the form of classification accuracy. High classification accuracy of over 95% is observed.
Cloud Computing in Teaching Conference Interpreting

Global COVID-19 crisis requires a modern approach to teaching. One way to modernize the work of a teacher and to respond to new challenges is to use cloud services. This article deals using cloud services for teaching conference interpreting.Cloud technologies include various hardware, software, methodologies and tools like Internet services provided to the user for fulfiling certain tasks and projects. Cloud computing is a distributed data processing technology in which computer resources and capacities are provided to the user as an Internet service, i.e. the user is provided with a platform on the Internet that is physically located on a remote server.Using cloud technologies for teaching has a number of advantages. They include:1) accessibility from any device with Internet access.2) scalability of the system at the request of the user.3) flexibility of settings and services.4) integration of various services on one platform.5) security ( mandatory encryption and data backup algorithms).6) cost-effectivenessEffective training of conference interpreting skills implies combining a number of cloud services.First, it is the webinar platform. There is a number of cloud webinar platforms that provide a great number of options necessary for teaching conference interpreting. It is worth saying that most modern webinar platforms have the same number of options we need, for example, webinars, chat, video and audio calling, screen sharing. Nevertheless, the webinar platform cannot provide us with all the tools we need for teaching conference interpreting, that is why we need additional services.Second, the additional services we need for teaching, include: cloud data storage, online video hosting, online voice recorder.A typical conference interpreting lesson implies unseen translation, special exercises for improving the basic skills necessary for interpreting ( active listening, analysing the message and reproducing it in another language) and oral translation of audiovisual texts. Besides, cloud technology allows role-playing (one of the students gives a speech on the topic of the lesson while the rest of the group translate it either simultaneously or consecutively).All in all, cloud services allow us to face the global crisis and keep on teaching even so complex disciplines as conference interpreting, online.
FaPN: Feature-aligned Pyramid Network for Dense Image Prediction

Recent advancements in deep neural networks have made remarkableleap-forwards in dense image prediction. However, the issue of featurealignment remains as neglected by most existing approaches for simplicity.Direct pixel addition between upsampled and local features leads to featuremaps with misaligned contexts that, in turn, translate to mis-classificationsin prediction, especially on object boundaries. In this paper, we propose afeature alignment module that learns transformation offsets of pixels tocontextually align upsampled higher-level features; and another featureselection module to emphasize the lower-level features with rich spatialdetails. We then integrate these two modules in a top-down pyramidalarchitecture and present the Feature-aligned Pyramid Network (FaPN). Extensiveexperimental evaluations on four dense prediction tasks and four datasets havedemonstrated the efficacy of FaPN, yielding an overall improvement of 1.2 - 2.6points in AP / mIoU over FPN when paired with Faster / Mask R-CNN. Inparticular, our FaPN achieves the state-of-the-art of 56.7% mIoU on ADE20K whenintegrated within Mask-Former. The code is available fromhttps://github.com/EMI-Group/FaPN.
Trainable fourth-order partial differential equations for image noise removal

Image processing by partial differential equations (PDEs) has been an active topic in the area of image denoising, which is an important task in computer vision. In PDE-based methods for unprocessed image process ing, the original image is considered as the initial value for the PDE and the solution of the equation is the outcome of the model. Despite the advan tages of using PDEs in image processing, designing and modeling different equations for various types of applications have always been a challenging and interesting problem. In this article, we aim to tackle this problem by introducing a fourth-order equation with flexible and trainable coefficients, and with the help of an optimal control problem, the coefficients are determined; therefore the proposed model adapts itself to each particular application. At the final stage, the image enhancement is performed on the noisy test image and the performance of our proposed method is compared to other PDE-based models.
Penerapan Latent Class Regression Analysis dalam Segmentasi Pasar

Cluster analysis is a method of grouping observation objects into several classes. One method of mixed-scale data grouping is Latent Class Regression Analysis (LCRA). The purpose of this research is to classify the opinion of Wardah's product consumers on marketing strategies (product aspects, price aspects, location aspects, and promotion aspects) PT. Paragon Technology and Innovation Regional Samarinda in 2017 with covariate variables arelength of subscription, type of work and age of consumers. Estimation of LCRA using the Expectation Maximization (EM) method, solved by the Newton-Raphson method. The result of LCRA analysis that based on consumer opinion on market segmentation, consumers are grouped into two classes.The first class is 31 consumers  that strongly agrees the aspects of product, price, promotion and position are appropriate market segmentation, and the second class is 69  that quite agrees product aspects, prices, promotion and position is the appropriate market segmentation.
Emotion recognition in talking-face videos using persistent entropy and neural networks

&lt;abstract&gt;&lt;p&gt;The automatic recognition of a person's emotional state has become a very active research field that involves scientists specialized in different areas such as artificial intelligence, computer vision, or psychology, among others. Our main objective in this work is to develop a novel approach, using persistent entropy and neural networks as main tools, to recognise and classify emotions from talking-face videos. Specifically, we combine audio-signal and image-sequence information to compute a &lt;italic&gt;topology signature&lt;/italic&gt; (a 9-dimensional vector) for each video. We prove that small changes in the video produce small changes in the signature, ensuring the stability of the method. These topological signatures are used to feed a neural network to distinguish between the following emotions: calm, happy, sad, angry, fearful, disgust, and surprised. The results reached are promising and competitive, beating the performances achieved in other state-of-the-art works found in the literature.&lt;/p&gt;&lt;/abstract&gt;
Extraction Algorithm of Opinion Information Based on Double Embedding CMLA

With the popularity and development of the Internet, people have recorded a large number of comment texts on government service hotlines, social media and other network platforms to express their opinions. For the task of viewpoint information extraction, this paper proposes a joint viewpoint information extraction algorithm based on dual-embedding and coupled multi-layer attention mechanism. Combining dual-embedding word representation and coupled multi-layer attention mechanism improves the extraction of aspect words and opinion words, and achieves joint extraction of their correspondence.
A structural point feature based Neural Network Approach for Handwritten word Recognition

Character recognition is one of common but major application of image processing. It is used to convert published and handwritten documents in digital form. The character recognition has various complexities in terms of different fonts, alignment, strokes. With the inclusion of words and lines the complexities also increase.  In this paper, the structural point information is extracted from hand written words. A layer based work model is designed in this research. As the work is applied on real-time characters, in the first layer the nosie is removed by using Gaussian filter. In the second stage, the structural points of image are extracted by using mathematical filter. In the final layer, the neural network is applied for classifying and recognizing the words. The results identified that the proposed model achieved the effective accuracy.
Methodological aspects of the development of the future choirmasters’ emotional competence

The article reveals the essence and features related to the development of the future choirmasters’ emotional competence in the process of profession-oriented training. The purpose of the article is to determine the methodological approaches and pedagogical principles of forming the emotional competence of future choirmasters in the process of profession-oriented training. The competence-based paradigm of art-centred education is defined as the one that is aimed at gaining the experience necessary for making constructive decisions in a nonlinear, variable and unpredictable process of performing and art-pedagogical practice. Emotional competence is interpreted as an integral quality, a set of knowledge, skills, abilities and practical experience of the conscious handling of emotional information and volitional regulation of emotional behaviour in the process of working with the choir aimed at optimizing the emotional aspect of artistic communication through reflexivity, empathy and appropriate conducting expression. The component structure of the future choirmasters’ emotional competence has been determined; it includes person-oriented cognitive, analytical-interpretive, sensory-expressive and reflexive-regulatory components. The theoretical and methodological basis for the development of the future choirmasters’ emotional competence is considered. The acmeological approach is aimed at the maximum disclosure of the student’s internal potentials, which is possible through designing individual performing and pedagogical trajectory. The hermeneutic approach helps to master new knowledge related to one's own inner world, emotional manifestations of other knowledge and artistic emotions that are transmitted in the musical text. The reflexive approach enables a substantive vision of the emotional component of profession-related activities as a result of productive introspection. The pedagogical principles facilitating the development of the future choirmasters’ emotional competence have been distinguished: the principle of subjective activity, the principle of emotional dominance and the principle of systematization of emotional experience.
TWO-DIMENSIONAL MODEL OF QUARTZ RESONATOR WITH SURFACE MASS LAYER ON BASIS OF MATRIX-OPERATOR METHOD

The paper investigates a mathematical model of thickness-shear (TSh) oscillations of piezoelectric plates of limited dimensions of an inverted Y-cut of quartz with a symmetric mass load, which is based on solving the equations of state of an electric-elastic field by matrix-operator method. Analytical equations are obtained for derivation of resonant oscillation frequency of the piezoelectric element and electrical conductivity, which take into account the dependence on spatial coordinates (thickness and width of the piezoelectric element) and the symmetric relative mass load. The expression for electrical conductivity can be used to determine equivalent substitution scheme of a quartz resonator. In addition, analytical equations for determining the mechanical bias, electric potential and mechanical stress are obtained. The results of numerical analysis of the proposed mathematical model for the case of AT-cut of quartz are presented: the dependences of relative change of the oscillation frequency on relative mass load for different oscillation modes are constructed. The dependences of the mechanical shear on two spatial coordinates , are constructed for several fixed values of the relative mass load. It is shown that the shape of the mechanical displacement is distorted as it moves away from the median plane , this fact should be taken into account when designing piezo-resonant devices. The proposed mathematical model of piezoelectric plate oscillations can be used in the design of new resonators, filters, in information and communication devices and systems
Unsupervised Metric Relocalization Using Transform Consistency Loss

Training networks to perform metric relocalization traditionally requires accurate image correspondences. In practice, these are obtained by restricting domain coverage, employing additional sensors, or capturing large multi-view datasets. We instead propose a self-supervised solution, which exploits a key insight: localizing a query image within a map should yield the same absolute pose, regardless of the reference image used for registration. Guided by this intuition, we derive a novel transform consistency loss. Using this loss function, we train a deep neural network to infer dense feature and saliency maps to perform robust metric relocalization in dynamic environments. We evaluate our framework on synthetic and real-world data, showing our approach outperforms other supervised methods when a limited amount of ground-truth information is available.
Branch Prediction as a Reinforcement Learning Problem: Why, How and Case Studies

Recent years have seen stagnating improvements to branch predictor (BP) efficacy and a dearth of fresh ideas in branch predictor design, calling for fresh thinking in this area. This paper argues that looking at BP from the viewpoint of Reinforcement Learning (RL) facilitates systematic reasoning about, and exploration of, BP designs. We describe how to apply the RL formulation to branch predictors, show that existing predictors can be succinctly expressed in this formulation, and study two RL-based variants of conventional BPs.
Relative Role of Border Restrictions, Case Finding and Contact Tracing in Controlling SARS-CoV-2 in the Presence of Undetected Transmission

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Induction Learning Based on Specific Objects

This paper proposes a new induction learning method based on the specific object, which allows the learning of the specific object in answer set programming (ASP), which is helpful to the research of inductive logic learning. The framework is composed of background knowledge , learning object (OBJECT), hypothesis space (Sm) and hypothesis (H). This kind of programming method which obtains hypothesis through background knowledge learning is called inductive logic programming.
Language MOOD system in the old Javanese “Asta Kosali” text: a study of functional systemic linguistics

This study aims to analyze the MOOD system, clause structures, as well as the polarity and modality of the Old Javanese language in an Asta Kosali Text (AKT). The data in this study were taken from the Asta Kosali text stored at the Cultural Office of Bali Province. The theory employed in this study is the theory of functional systemic linguistics, especially in the sub-theory of clause as an exchange of meaning, put forward by Halliday, Halliday and Matthiessen. The results of the study showed that the types of MOOD used in the AKT were the indicative-declarative and the imperative ones. In the indicative-declarative clause structure, the elements that must be present are Subj^Finite, or Subj^Polar/Predicator, while in the imperative clause structure, the element that must be present is the Predicator. The polarity of the Old Javanese language in AKT can be identified by affixing the words tan, nora, and haywa.