Reviewerly/exHarmony huggingface dataset:
The Inaugural Address: Fostering Objects of Agreement

The Inaugural Address is a genre that seeks agreement not expediently, to win the presidential race, but because its very institutional purpose is the ‘restoration of ideological normality’ after the bitter divisions of the electoral strife. The Chapter provides an analysis of the Inaugural Address divided in two main parts. In the first, the focus is on the cognitive moves of the genre, highlighting recursive structural and ideological elements which are functional to the construction of agreement. In the second part, building on the findings of the first, attention is turned to the eminently argumentative component of the IA. The scope is limited to the Inaugurals of presidents at their first mandate, from Eisenhower to Obama, coming after a predecessor of the opposite party, as these contextual factors are predictive of greater argumentativeness. This notwithstanding, the genre comes through as having a mainly epideictic function, with three out of four main moves bent on fostering the agreement with the audience. For the remaining really argumentative move, a comparison is carried out of how topoi, as intended in the AMT model, are used by the different presidents. The argumentation here is meant to state the main challenge for the years ahead and to present the president’s approach as the best suited to the task of facing it. In doing so, the arguer tries to win the audience agreement to the proposed reading of the historical context and of the role of the president in that given contingency.
Short-cuts on quantum network simulators

An important idea in quantum networks is that of employing virtual edges or “short-cuts” between quantum nodes. These edges, which are shared entangled pairs between two nodes that are not directly connected “physically”, are expected to reduce the average latency of entanglement distribution in quantum network routing. In this work, we show how to create such edges on an existing discrete event simulator for quantum networks called SeQUeNCe, and study the impact that these edges have on latency. Additionally, we implement a distributed routing algorithm on the simulator and demonstrate its use in conjunction with the virtual edges. We observe a reduction in latency when short-cuts are used, as is expected. Thus, with these modifications, we believe that SeQUeNCe (and other similar simulators) can be used for experimenting with advanced quantum networking protocols.
Freedom and Restraint in Dark Forest: A Peek at the Metaverse Through a Blockchain Game

Dark forest (DF) is the first successful decentralized real-time strategy (RTS) game. Based on smart contracts, the game explores the application possibilities of new technologies, and prefigures the operation of the social system in the metaverse era. In this study, we collect on-chain data from DF, map player behavior, and discuss players’ game asset management, combat strategies in extreme mode, and the game’s exit mechanism. We believe that blockchain games, such as DF, can be viewed as a new technological space with metaverse characteristics, in which individuals art free to challenge centralized social structures using decentralized technology while subjected to more secret postmodern social surveillance.
AR Mobile Application Evaluation for Learning History: High School Students’ Perspective

Current technology trend in providing a creative and exciting learning assistance range from augmented reality, virtual reality as well as mixed reality. Students demand innovative application to assist or complement their current learning resources. To date, students are still having problems when they learn history. Among the complaints included students easily feel bored and could not relate what they have learned from the textbook. SejarAR is a mobile application developed to solve high school students’ history learning experience. Before we can recommend this application to the students, several evaluations must be conducted to make sure the mobile application is usable, and its AR features does make an improvement to their learning experience. The evaluations which comprised of both formative and summative had been applied to SejarAR mobile application. The participants involved in both evaluations were the high school students and teachers. We allowed them to use SejarAR mobile application then interviewed as well as gave them the System Usability Scale (SUS) to evaluate SejarAR. As anticipated, it has scored 74 when used System Usability Scale (SUS), which means the SejarAR mobile application is deemed acceptable by high school students. We also received many positive feedbacks from qualitative evaluation approach that we conducted. To sum up, SejarAR mobile application is usable as well as enjoyable application for high school students to use as a tool to assist them in learning history subject.
Static test flakiness prediction

The problem of flakiness occurs when a test case is non-deterministic and exhibits both a passing and failing behavior when run against the same code. Over the last years, the software engineering research community has been working toward defining approaches for detecting and addressing test flakiness, but most of these approaches suffer from scalability issues. Recently, this limitation has been targeted through machine learning solutions that could predict flaky tests using various features, both static and dynamic. Unfortunately, the proposed solutions involve features that could be costly to compute. In this paper, I perform a step forward and predict test flakiness only using statically computable metrics. I conducted an experiment on 18 Java projects coming from the FlakeFlagger dataset. First, I statistically assess the differences between flaky and non-flaky tests in terms of 25 static metrics in an individual and combined way. Then, I experimented with a machine learning approach that predicts flakiness based on the previously evaluated factors. The results show that static features can be used to characterize flaky tests: this is especially true for metrics and smells connected to source code complexity. In addition, this new static approach has performance comparable to the machine learning models already in the literature in terms of F-Measure.
Detection of Forged Handwriting Through Analyzation of Handwritten Characters Using Support Vector Machine

People often use a keyboard to input data in digital form. However, there are still some cases where handwriting is still used and often in significant scenarios such as cheques. The current study focuses mainly on detecting forgery in a person's signature or cases where original handwriting was altered or additional characters were added. Thus, the study proposed a handwriting forgery detection system that utilizes image processing and Support Vector Machine (SVM), a linear classification model. The system will take the original handwriting of a person as its training data to create a model that would evaluate whether the presented handwriting is original or forged. In addition, SVM will also be used for text recognition of handwritten letters. The models are then evaluated using a confusion matrix and F1 score. The evaluated result for the text recognition model achieved an F1 score of 0.9052. On the other hand, the forgery detection model had an F1 score of 0.6013.
Development of teachers' meta-competencies using pedagogical facilitation

The meta-competencies in professional activity are as important as their development ways in the system of general and vocational education. An integrated approach is necessary in the formation of meta-competencies as universal flexible structures in the basis for professional competencies both in the process of vocational education and in obtaining direct work experience. They begin to form spontaneously mainly during the period of secondary general education, therefore it is advisable to make this process more manageable. Suggested pedagogical facilitation is a suitable tool for this. However, the organization and implementation of the facilitation session requires the teacher to have experience as a participant. We describe the methodology of organizing the practical part of the advanced training course for teachers of specialized classes of Derzhavin Tambov State University based on the principles of pedagogical facilitation. The purpose of the practical part of the course is to create educational technology as an author's collective product. The use of pedagogical facilitation in this case allows teachers to gain positive experience of participation in the facilitated process, creation of a group creative product, new application of their personal pedagogical experience, development of their own meta-competencies. In general, the results recorded in the form of created collective author's educational technologies and feedback from the course participants confirm the effectiveness of our approach to organizing a refresher course for teachers. It is concluded, that teachers have a unique professional experience, the best of which can be transformed into an effective author's educational technology if they are given a new form (structure of educational technology) and combine their efforts in a pedagogical facilitation session.
Synchronisms Using Reinforcement Learning as an Heuristic

The concept of Synchronism describes circumstances that appear meaningfully related yet nevertheless lack a casual connection. Every day we make use of this concept, from forming new relationships to grooming a new set of skills. Therefore one day in the future we will be able to make use of them for a specific goal. Reinforcement Learning (RL) is a type of learning framework where the solution is presented as a Markov Decision Process (MDP) where the goal is to learn by trial and error, emulating how the human begins to learn. With this type of framework, we can solve very complex non-linear and not deterministic problems. In this novel method, we train the model using a non-deterministic RL and try to find the causal link between those circumstances by observation. This is known as the “Keen Eye” where each circumstance finds a consensus link affecting the outcome using the knowledge from each circumstance involved. We trained agents in two experiments with different domains, the first experiment tries to solve a linear non-deterministic problem, while the second is a non-linear and non-deterministic simulation. With this type of framework, the most common metric is to gather the reward value obtained from the outcome of the experiments, therefore we use this value to measure how performance behaves. Finally, with the results of experiments we discovered that compared with traditional RL, this method can train RL models faster, by obtaining better rewards earlier and makes the RL less susceptible to fall into a local minimum.
A Recommendation Method for Recipes Containing Unskillful Elements Using Naïve Bayes Classifier to Improve Cooking Skills

We cannot acquire cooking skills overnight; although our skills can be improved through repeated practice. This study proposes a method to recommend recipes that amateurs should attempt to turn failures into successes, based on their logs of cooking failures and successes. Initially, the user classifies past recipes and labels them as failures and successes. Next, we use a naive Bayes classifier to find the factors of failures and successes from the recipe’s ingredients and cooking actions. By recommending recipes with multiple success factors and some failure factors, we aim to lower the psychological hurdle for attempting recipes with difficult factors that have resulted in failure earlier.
String Matching Based Framework for Online Hindi Question Answering System

With the expansion of digital communication on the web, the necessity to process specific details has grown enormously in the last few years. However, communication is not restricted to one language and the WWW is loaded with multilingual data. Nevertheless, the national Indian languages, i.e., Hindi is under-represented and overlooked on the web. Therefore, in this paper a hindi language specific question answering framework is developed. This work is mainly focused on questions with single line or one word answers. The work employs an information retrieval based approach to process the questions and answers. The performance of the proposed model is evaluated with respect to the three types of queries and the results show 93.33% of accuracy.
Improving Weakly Supervised Scene Graph Parsing through Object Grounding

Weakly supervised scene graph parsing, which learns structured image representations without annotated correspondences between graph nodes and visual objects, has been prevalent in recent computer vision research. Existing methods mainly focus on designing task-specific loss functions, model architectures, or optimization algorithms. We argue that correspondences between objects and graph nodes are crucial for the weakly supervised scene graph parsing task and are worth learning explicitly. Thus we propose GroParser, a framework that improves weakly supervised scene graph parsing models by grounding visual objects. The proposed weakly supervised grounding method learns a metric among visual objects and scene graph nodes by incorporating information from both object features and relational features. Specifically, we apply multi-instance learning to learn the object category information and exploit a two-stream graph neural network to model the relational similarity metric. Extensive experiments on the scene graph parsing task verify the grounding found by our model can reinforce the performance of the existing weakly supervised scene graph parsing methods, including the current state-of-the-art. Further experiments on Visual Genome (VG) and Visual Relation Detection (VRD) datasets verify that our model brings an improvement on scene graph grounding task over existing approaches.
Pluralistic Face Inpainting With Transformation of Attribute Information

Most face-inpainting methods perform well in face repair. However, these methods can only complete a single face image per input. Although existing various image-inpainting methods can achieve pluralistic image inpainting, they typically produce faces with distorted structures or the same texture. To resolve these shortcomings and achieve high-quality diverse face inpainting, we propose PFTANet, a two-stage pluralistic face-inpainting network that transforms attribute information. In the first stage, the face-parsing network is fine-tuned to obtain semantic facial region information. In the second stage, a generator consisting of SNBlock, CF_ShiftBlocks, and CF_MergeBlock, which ensures that high-quality pluralistic face results are generated, is used. Specifically, CF_ShiftBlocks completes pluralistic face generation by transforming the attribute information from the conditional face extracted by the attribute extractor and ensuring the consistency of the attribute information between the conditional and generated faces. CF_MergeBlock ensures structural consistency between the masked and background regions of the generated face using facial region semantic information. A multi-patch discriminator is used to enhance facial detail generation. Experimental results for the CelebA and CelebA-HQ datasets indicated that PFTANet achieved pluralistic and visually realistic face inpainting.
Mutual Learning Inspired Prediction Network for Video Anomaly Detection

Video anomaly detection has made great achievements in security work. A basic assumption is that the abnormal is the outlier of the normal. However, most existing methods only focus on minimizing the reconstruction or prediction error of normal samples while ignoring to maximize that of abnormal samples. The completeness of the training data and the similarity between certain normal and abnormal samples can cause the network overfitting to normal samples and generalizing to abnormal samples. To address the two problems, we propose Mutual Learning Inspired Prediction Network. Specifically, it consists of two student generators and one discriminator to predict the future frame, together with our proposed Boundary Perception-Based Mimicry Loss and Self-Supervised Weighted Loss. The proposed Boundary Perception-Based Mimicry Loss guides the generators to learn the predicted frame from each, which can help to increase the diversity of training data and prevent interference at the same time. The proposed Self-Supervised Weighted Loss constraints the confusion samples in training data with a small weight, which can clarify the modeling goal of the network and enlarge the distance between normal and abnormal samples. Experiments on four mainstream datasets demonstrate the effectiveness of our proposed method.
On the Complexity of Object Detection on Real-world Public Transportation Images for Social Distancing Measurement

Social distancing in public spaces has become an essential aspect in helping to reduce the impact of the COVID-19 pandemic. Exploiting recent advances in machine learning, there have been many studies in the literature implementing social distancing via object detection through the use of surveillance cameras in public spaces. However, there has been no study of social distance measurement on public transport to date. The public transport setting has some unique challenges, including low-resolution images and physical camera locations that can lead to the partial occlusion of passengers, making it challenging to perform accurate detection. Thus, this paper investigates the challenges of performing accurate social distance measurements on public transportation. We benchmark several state-of-the-art object detection algorithms using real-world footage taken from the London Underground and bus network. The work highlights the complexity of performing social distancing measurements on images from current public transportation onboard cameras. Further, exploiting domain knowledge of expected passenger behaviour, we attempt to improve the quality of the detections using various strategies and show improvement over using vanilla object detection alone.
Learning management systems in 2020-2021: Problems, challenges and prospects

The article deals with the analysis of expertise of a number of countries that were actively engaged in the process of transitioning their learning environment from campuses with their in-person classes to an online learning environment using various learning management systems, or online educational platforms, each of which possesses a number of tools. The term ‘remote learning’ is used together with more conventional terms of ‘distance learning’ or ‘distance education’. The key purposes of the current research are to systematically describe the factual situation in the system of higher education in different world regions - from North-America to Asia – during the Covid-19 Pandemic spread in the year 2020 and to compare the online platforms used in the mentioned regions in the course of digital and distance learning in higher educational establishments. The findings of this multi-faceted research are compared and contrasted in the Discussions section which is aimed at summarizing the technological and methodological solutions of the leading learning management systems (LMS) that could be used in the process of developing a locally targeted educational platform.
Neural Networks

Rule-based systems and Bayesian networks cannot effectively solve problems such as image or speech recognition. Artificial neural networks (ANNs), or simply neural networks, are effective in solving complex problems, i.e., in modeling complex nonlinear functions. ANNs model the functioning of the brain’s neurons; ANN can be trained to “learn” how to recognize patterns and classify data [1].
Role Of Blockchain in Securing Patient Data for Health Care Application

The purpose of blockchain technology is to make it feasible to store and transfer digital data without the danger of it being changed in transit. Immutable ledgers or transaction records that cannot be edited, erased, or destroyed are created based on a blockchain. DLT is another term for blockchain (DLT). Hashing is a complicated method that cannot be changed or reversed. When it comes to data security, it's all about preserving your information from unauthorized access and corruption at every step of its existence. Encryption, hashing, tokenization, and key management mechanisms are all part of the data security process. The term “identity and access management” encompasses all of the techniques and technologies that an organization uses to identify, authenticate, and authorize someone to access services or systems inside that organization or those of its affiliates. Due to the immutability of a distributed ledger, no personal data should be kept on it. This is why employing a distributed ledger is necessary, when using blockchain for identity management for healthcare. Patient identity has been secured in such a system. In the simulation, the comparative analysis of performance has been made where conventional processing time is compared to proposed processing time, error rate, and immunity toward external attacks. It has been observed that the proposed work is consuming less time and yields less error rate. More the impact of the external attack is less in the case of the proposed work as compared to conventional.
A Two-layer MPC Approach for Consistent Planning and Control of Autonomous Vehicles

The widely-used algorithms for motion planning and control of autonomous vehicles usually adopt a layered structure. However, consistency between the two levels is critical to obtain high control performance and maneuvering ability. Aiming at this issue, we propose a two-layer model predictive control (MPC) approach for consistent planning and control of autonomous vehicles. In the higher layer, we propose a finite-horizon convex optimization algorithm for time-optimal motion planning using obstacle convexification of non-convex obstacles. In the proposed planner, the vehicle model uncertainty was learned by a radial-basis-function-based (RBF) neural network whose weight is optimized using pre-collected motion datasets. The surrounding obstacles were detected by a radar, and a clustering algorithm was utilized to process the radar point clouds to isolate polygonal obstacles. Also, the obstacle avoidance constraints were convexified with polyhedrons. As such, the planning problem at the higher layer is transformed into a convex optimization problem to be solved. In the lower layer, a model predictive controller is designed to follow the planned trajectory using the learned dynamic model and a similar performance index to the higher layer. Consequently, the inconsistency between the higher layer and the lower layer can be highly reduced and the control performance can be improved compared with the planning scenario with a nominal model. Extensive simulated and experimental results on planning tasks of unstructured environments have been performed to verify the effectiveness of the proposed approach.