Reviewerly/exHarmony huggingface dataset:
Document-Image Related Visual Sensors and Machine Learning Techniques

Document imaging/scanning approaches are essential techniques for digitalizing documents in various real-world contexts, e.g., libraries, office communication, managementof workflows, and electronic archiving [...]
Ensembling Shallow Siamese Neural Network Architectures for Printed Documents Verification in Data-Scarcity Scenarios

The popularity of printing devices has multiplied the diffusion of printed documents, raising concerns regarding the security and integrity of their content. The same device that prints reliable contracts, newspapers, and others, can also be used for malicious purposes, such as printing fake money, forging fake contracts, and produce illegal packaging, thus calling for the development of image forensics techniques to pinpoint criminal printed materials and trace back to their origin. In this work, we address the source linking problem of printed color documents by treating it as a verification problem. Specifically, we aim at deciding if two documents have been printed by the same printer or not. To achieve this goal, and to cope with the data scarcity deriving from the difficulty of gathering massive amounts of printed and scanned documents, we propose to use an ensemble of Siamese Neural Networks, with unique architectures expressly designed to work with a small training dataset. As a further unique feature, the proposed approach is suited to work in an open set scenario, where the printers used to produce the documents analyzed at the test time are not included in the training set. Results obtained under both open and closed set conditions, with a thorough comparison with available baseline methods, highlight the validity of the proposed solution and its capability to work in real-world settings.
Chomsky's Transformational Linguistic Theory in Writing Skill at Islamic Senior High School: Transforming Language Learning Pedagogy

This research aimed to determine the implementation's effectiveness as supporting and inhibiting factors of Chomsky's Transformational Linguistic Theory in Writing Skill implementation. The data were collected through interviews, observations, and tests. The results of this study are: 1) the researchers obtained the difference in scores between the pretest and posttest of the control class and the experimental class. 2) there were five supporting factors and three inhibiting factors in learning maharah kitabah at MA Muallimat Malang Indonesia. The supporting factors are: 1) the transformation of sentences is easy to understand, 2) students have the ability of basic Arabic rules, 3) students can write Arabic, 4) enthusiasm in participating in learning activities, 5) availability of sufficient media for learning. Meanwhile, the inhibiting factors are: 1) the difficulty of students distinguishing mudzakkar and muannats,2) only a few materials limits transformation, 3) some students do not like to learn Arabic.
A Novel Neurofuzzy Approach for Semantic Similarity Measurement

The problem of identifying the degree of semantic similarity between two textual statements automatically has grown in importance in recent times. Its impact on various computer-related domains and recent breakthroughs in neural computation has increased the opportunities for better solutions to be developed. This research takes the research efforts a step further by designing and developing a novel neurofuzzy approach for semantic textual similarity that uses neural networks and fuzzy logics. The fundamental notion is to combine the remarkable capabilities of the current neural models for working with text with the possibilities that fuzzy logic provides for aggregating numerical information in a tailored manner. The results of our experiments suggest that this approach is capable of accurately determining semantic textual similarity.
The Effect of E-Learning as One of the Information Technology-Based Learning Media on Student Learning Motivation

The teaching and learning process is a process that involves participants and educators. The teaching and learning process will be successful if there is interaction between the two parties. Along with the development of information technology, especially the internet in information systems, the teaching and learning process has utilized information technology both from elementary, secondary and higher education levels. Especially for higher education which requires students to be more independent and active in the learning process, information technology is considered very helpful for students. As one of the information technology-based universities, STIKOM Bali utilizes e-learning to support the learning process. Lecturers and students are required to use and utilize e-learning as a means to share information related to lecture materials, assignments, quizzes, discussion forums and others. One of the goals of implementing e-learning in lectures is to increase student learning motivation so that learning achievement will be better. It is also hoped that students will be more interested in the lecture process. One of the things that affect student learning motivation is learning media. The learning media referred to in this study is e-learning learning media. The long and stable application of e-learning at STIKOM Bali is thought to be one of the drivers of student learning motivation. The research method used in this research is problem definition, data collection, data analysis and conclusion drawing. Based on the results of hypothesis testing with H1 hypothesis regression with a significance value of 0.000 and a t-count of 4.015 proven, so that it is concluded that according to STIKOM Bali students e-learning affects student learning motivation.
Application of Image Reconstruction Based on Inverse Radon Transform in CT System Parameter Calibration and Imaging

According to the requirements of CT system parameter calibration and imaging, using OpenCV and MATLAB software, the reverse Radon transform and the Canny edge detection algorithm in the projection edge methods can determine the position and geometry of two different media in the square tray based on given data. Besides, it can apply and analyze the shape and the absorption rate of the specified point, the accuracy, and stability of the template calibration parameters by this way, which enables designing a new template and calibrating the new parameters.
Networked library information interaction, the case of the central library of Pushchino Scientific Center of the Russian Academy of Sciences

The issues of networked library interaction is examined and exemplified by the experience of the Pushchino Scientific Center Central Library (division of the Library for Natural Sciences of the Russian Academy of Sciences, LNS RAS). Ready-made software solutions designed by LNS RAS specialists enabled the participating libraries to implement computer-aided collection development system, union reference aids, online ordering, lending and refusal registration, digital resources access system. The authors focus, in particular, on specific networked interactions between the scientific library and users of its information services. Within the framework of the Library-Research Institute of Pushchino Center cooperation, the main efforts of the Library are focused on implementing modern network technologies, providing access to information resources, research information support, developing union Internet-based catalogs within the online order system, designing problem-oriented databases and maintaining current list of networked resources in physico-chemical biology. New library services are described, e.g. bibliometric and patent research, design and maintenance of the library website. Approaches to building network interaction with libraries of various types, institutions of memory (archives, museums, etc.), publishers’, and mass media, are discussed. The authors come to the conclusion that networked cooperation is efficient organizational and technological instrument to provide equal use opportunities for every member of library information network.
The Teaching Strategies for Metaphorical Competence of Chinese College English Learners in Internet Era

Internet+ new era brings both opportunities and new requirements for College English Teaching in China.According to the latest college English Teaching Guide (2020 Edition), much importance should be attached to cultivating students' English application ability and enhancing their cross-cultural communication ability.In order to achieve this goal, the cultivation of foreign language metaphor ability is indispensable.Based on the conceptual metaphor theory and the definition of metaphorical competence in the field of cognitive linguistics, and the current level of College Students' English Metaphorical Competence in China, this paper puts forward the top-level design concept and the explicit and implicit metaphorical competence teaching strategies, so as to improve the level of college students' foreign language metaphorical competence in China.
Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience

Pretrained transformer-based models such as BERT have demonstrated state-of-the-art predictive performance when adapted into a range of natural language processing tasks.An open problem is how to improve the faithfulness of explanations (rationales) for the predictions of these models.In this paper, we hypothesize that salient information extracted a priori from the training data can complement the task-specific information learned by the model during fine-tuning on a downstream task.In this way, we aim to help BERT not to forget assigning importance to informative input tokens when making predictions by proposing SALOSS; an auxiliary loss function for guiding the multi-head attention mechanism during training to be close to salient information extracted a priori using TextRank.Experiments for explanation faithfulness across five datasets, show that models trained with SA-LOSS consistently provide more faithful explanations across four different feature attribution methods compared to vanilla BERT.Using the rationales extracted from vanilla BERT and SALOSS models to train inherently faithful classifiers, we further show that the latter result in higher predictive performance in downstream tasks. 1
BERT-BU12 Hate Speech Detection Using Bidirectional Encoder-Decoder

In the recent times transfer learning models have known to exhibited good results in the area of text classification for question-answering, summarization, next word prediction but these learning models have not been extensively used for the problem of hate speech detection yet. We anticipate that these networks may give better results in another task of text classification i.e. hate speech detection. This paper introduces a novel method of hate speech detection based on the concept of attention networks using the BERT attention model. We have conducted exhaustive experiments and evaluation over publicly available datasets using various evaluation metrics (precision, recall and F1 score). We show that our model outperforms all the state-of-the-art methods by almost 4%. We have also discussed in detail the technical challenges faced during the implementation of the proposed model.
A New Method for Head Direction Estimation based on Dlib Face Detection Method and Implementation of Sine Invers Function

The detection and tracking of head movements have been such an active area of research during the past years.This area contributes highly to computer vision and has many applications of computer vision.Thus, several methods and algorithms of face detection have been proposed because they are required in most modern applications, in which they act as the cornerstone in many interactive projects.Implementation of the detected angles of the head direction is very useful in many fields, such as disabled people assistance, criminal behavior tracking, and other medical applications.In this paper, a new method is proposed to estimate the angles of head direction based on Dlib face detection algorithm that predicts 68 landmarks in the human face.The calculations are mainly based on the predicated landmarks to estimate three types of angles Yaw, Pitch and Roll.A python program has been designed to perform face detection and its direction.To ensure accurate estimation, the particular landmarks were selected, such that, they are not affected by the movement of the head, so, the calculated angles are approximately accurate.The experimental results showed high accuracy measures for the entire three angles according to real and predicted measures.The sample standard deviation results for each real and calculated angle were Yaw (0.0046), Pitch (0.0077), and Roll (0.0021), which confirm the accuracy of the proposed method compared with other studies.Moreover, the method performs faster which promotes accurate online tracking.
Postquantum Cut-and-Choose Oblivious Transfer Protocol Based on LWE

We propose postquantum universal composable (UC) cut-and-choose oblivious transfer (CCOT) protocol under the malicious adversary model. In secure two-party computation, we construct <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>s</mi> </math> copies’ garbled circuits, including half check circuit and half evaluation circuit. The sender can transfer the key to the receiver by CCOT protocol. Compared to PVW-OT [6] framework, we invoke WQ-OT [35] framework with reusability of common random string ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mtext>crs</mtext> </math> ) and better security. Relying on LWE’s assumption and the property of the Rounding function, we construct an UC-CCOT protocol, which can resist quantum attack in secure two-party computation.
Scenario model of intelligent decision support based user digital life models

Introduction. In the decision support domain, the practice of using information from user digital traces has not been widespread so far. Earlier, the authors of this paper developed a conceptual framework of intelligent decision support based on user digital life models that was aimed at recommending decisions using information from the user digital traces. The present research is aiming at the development of a scenario model that implements this framework. Purpose: the development of a scenario model of intelligent decision support based on user digital life models and an approach to grouping users with similar preferences and decision-making behaviours. Results: A scenario model of intelligent decision support based on user digital life models has been developed. The model is intended to recommend to the user decisions based on the knowledge about the user decision-maker type, decision support problem, and problem domain. The scenario model enables to process incompletely formulated problems due to taking into account the preferences of users who have preferences and decision-making behaviour similar to the active user. An approach to grouping users with similar preferences and decision-making behaviours has been proposed. The approach enables to group users with similar preferences and decision-making behaviours based on the information about user behavioural segments that exist in various domains, behavioural segmentation rules, and user actions represented in their digital life models. Practical relevance: the research results are beneficial for the development of advanced recommendation systems expected to tracking digital traces.
“It doesn’t look good for a date”: Transforming Critiques into Preferences for Conversational Recommendation Systems

Conversations aimed at determining good recommendations are iterative in nature.People often express their preferences in terms of a critique of the current recommendation (e.g., "It doesn't look good for a date"), requiring some degree of common sense for a preference to be inferred.In this work, we present a method for transforming a user critique into a positive preference (e.g., "I prefer more romantic") in order to retrieve reviews pertaining to potentially better recommendations (e.g., "Perfect for a romantic dinner").We leverage a large neural language model (LM) in a fewshot setting to perform critique-to-preference transformation, and we test two methods for retrieving recommendations: one that matches embeddings, and another that fine-tunes an LM for the task.We instantiate this approach in the restaurant domain and evaluate it using a new dataset of restaurant critiques.In an ablation study, we show that utilizing critiqueto-preference transformation improves recommendations, and that there are at least three general cases that explain this improved performance.
A novel factor graph-based optimization technique for stereo correspondence estimation

Dense disparities among multiple views are essential for estimating the 3D architecture of a scene based on the geometrical relationship between the scene and the views or cameras. Scenes with larger extents of homogeneous textures, differing scene illumination among the multiple views and with occluding objects affect the accuracy of the estimated disparities. Markov random fields based methods for disparity estimation address these limitations using spatial dependencies among the observations and among the disparity estimates. These methods, however, are limited by spatially fixed and smaller neighborhood systems or cliques. Recent learning-based methods generate rich set of stereo features for generating cost volume and estimating disparity. In this work, we present a new factor graph-based probabilistic graphical model for disparity estimation that allows a larger and a spatially variable neighborhood structure determined based on the local scene characteristics. Our algorithm improves the accuracy of disparity estimates in stereo image pairs with varying texture and illumination characteristics by enforcing spatial dependencies among scene characteristics as well as among disparity estimates. We evaluated our method using the Middlebury benchmark stereo datasets and the Middlebury evaluation dataset version 3.0 and compared its performance with recent state-of-the-art disparity estimation algorithms. Our factor graph-based algorithm provided disparity estimates with higher accuracy when compared to the recent non-learning- and learning-based disparity estimation algorithms. The factor graph formulation can be used for obtaining maximum a posteriori estimates from models or optimization problems with complex dependency structure among hidden variables. The strategies of using a priori distributions with shorter support and spatial dependencies were useful for reducing the computational cost and improving message convergence in the model. The factor-graph algorithm is also useful for other dense estimation problems such as optical flow estimation.
Promoting Students’ Perceived Self-Efficacy towards 21st Century Skills through Everyday Life-Related Scenarios

Interdisciplinary science learning can play a central role in promoting students’ 21st century skills. However, students tend to have low perceived self-efficacy towards 21st century skills, thus limiting the application of their actual scientific competence. This study seeks, based on a 1.5-year intervention study, to promote students’ perceived self-efficacy towards 21st century skills through science learning compared to a non-experimental group. During the intervention, everyday life-related scenarios were utilized, with students guided to create core idea maps. Data on students’ perceived self-efficacy were collected before and after the intervention. Results showed that students’ perceived self-efficacy towards 21st century skills changed in a significantly positively way after the intervention.
Reservoir Pattern Sampling in Data Streams

Many applications generate data streams where online analysis needs are essential. In this context, pattern mining is a complex task because it requires access to all data observations. To overcome this problem, the state-of-the-art methods maintain a data sample or a compact data structure retaining only recent information on the main patterns. This paper addresses online pattern discovery in data streams based on pattern sampling techniques. Benefiting from reservoir sampling, we propose a generic algorithm, named Open image in new window , that uses a limited memory space and that integrates a wide spectrum of temporal biases simulating landmark window, sliding window or exponential damped window. For these three window models, we provide fast damping optimizations and we study their temporal complexity. Experiments show that the performance of Open image in new window algorithms is particularly good. Finally, we illustrate the interest of our approach with online outlier detection in data streams.
Modeling and Fabrication of Smart Robotic Wheelchair Instructed by Head Gesture

The confronting problem faced by the handicapped, paralyzed, disabled, and quadriplegic people is their independent mobility. They need external assistance to perform their daily life activities. This paper aims to solve that problem by smart designing and deployment of the robotic wheelchair for those who cannot perform their voluntary activities and movements. The proposed automated wheelchair comprises two parts; the first part is the user's helmet that works as a master device, and the second part is a slave device, a smart wheelchair. The master device consists of an accelerometer, microcontroller, and wireless transmitter, in which the Accelerometer recognizes the movements of the user's head and transmits the signal according to the tiltation of the user's head. Besides this, the slave device consists of a wireless receiver, microcontroller, Gyroscope, power MOSFETs, and DC geared motors mounted on a smart wheelchair, which response as per the instructions of the master device. Furthermore, the paper also provides a brief construction of this mechatronic and amphibian system using static and dynamic equations.
Introducing Symmetries to Black Box Meta Reinforcement Learning

Meta reinforcement learning (RL) attempts to discover new RL algorithms automatically from environment interaction. In so-called black-box approaches, the policy and the learning algorithm are jointly represented by a single neural network. These methods are very flexible, but they tend to underperform compared to human-engineered RL algorithms in terms of generalisation to new, unseen environments. In this paper, we explore the role of symmetries in meta-generalisation. We show that a recent successful meta RL approach that meta-learns an objective for backpropagation-based learning exhibits certain symmetries (specifically the reuse of the learning rule, and invariance to input and output permutations) that are not present in typical black-box meta RL systems. We hypothesise that these symmetries can play an important role in meta-generalisation. Building off recent work in black-box supervised meta learning, we develop a black-box meta RL system that exhibits these same symmetries. We show through careful experimentation that incorporating these symmetries can lead to algorithms with a greater ability to generalise to unseen action &amp; observation spaces, tasks, and environments.