Reviewerly/exHarmony huggingface dataset:
Validity Under Assumptions and Modus Ponens

Slightly altering and extending McGee’s semantics for conditionals, we define a ternary notion of validity for natural language arguments, which can be regarded as a unification of two kinds of validity in the literature. By the new notion of validity, an inference is not just valid or invalid, but valid or invalid under a set of assumptions. Based on this notion, we formulate the validity and invalidity of modus ponens.
Generalized Additive Modeling for Learning Trajectories in E-Learning Environments

Adaptive E-learning is growing in popularity as it personalizes recommendations in response to learners’ learning needs. An a priori expectation of the learning environment is that the learners’ performance levels may change in real time as they complete a sequence of items and receive feedback. Also, the learners’ learning (performance) trajectories may be irregularly shaped over time. Therefore, a modeling approach that flexibly explores the learner’s learning change is desirable. In this study, we demonstrate the applicability of a semi-parametric modeling approach that can estimate learners’ unique learning trajectories in the E-learning environment. We use a generalized additive mixed model that integrates properties of generalized linear mixed models with those of additive models, in which the linear predictor is given by a sum of smooth functions of the covariates as well as a parametric component of the linear predictor. The model we consider explores the effect of time that the learners spend inside and outside the learning environment. We demonstrate its applicability to log data generated by a real-life E-learning environment.
Cardinal rank metric codes over Galois rings

In 1985, Gabidulin introduced the rank metric in coding theory over finite fields, and used this kind of codes in a McEliece cryptosystem, six years later. In this paper, we consider rank metric codes over Galois rings. We propose a suitable metric for codes over such rings, and show its main properties. With this metric, we define Gabidulin codes over Galois rings, propose an efficient decoding algorithm for them, and hint their cryptographic application.
See an Issue? Broken URL? Report It Here!

In the summer of 2020, Austin Peay State University’s Woodward Library added a report feature to all catalog records, allowing library users to alert technical services personnel of any errors discovered while searching, including broken links or incorrect information. With the implementation of the report feature, spot checking efforts were crowdsourced to library users, and data were collected on the types of errors appearing most often in catalog records.
Locate and Combine: A Two-Stage Framework for Aspect-Category Sentiment Analysis

Aspect category sentiment classification aims at predicting the sentiment polarity of the given aspect category. Since the aspect category may not occur in the sentence, it is hard for the model to directly find the appropriate sentiment words for the aspect category and disregard unrelated ones. To address it, previous works have explored leveraging implicitly the information of the aspect term in the sentence and demonstrated the effectiveness of such information. Inspired by this conclusion, we propose a two-stage strategy named Locate-Combine(LC) to utilize the aspect term in a more straightforward way, which first locates the aspect term and then takes it as the bridge to find the related sentiment words. Specifically, in the "Locate" stage, we locate the aspect term corresponding to the given aspect category in the sentence, which can crystallize the target and further enable our model to focus on the target-related words. In the "Combine" stage, we first apply the graph convolutional network (GCN) over the dependency tree of the sentence to combine the information of the aspect term and related sentiment words and then take the output representation corresponding to the located aspect term to predict the sentiment polarity. The experimental results on the public datasets show that the proposed two-stage strategy is effective, which achieves state-of-the-art performance. Furthermore, our model can output explainable intermediate results for model analysis. (Code can be found at https://github.com/SCIR-MSA-Team/LC-ACSA)
The Influence of Code Retrieval from the Web on Programmer’s Skills, Meth-odologies, and Coding Behaviors

The development of software projects consists of several stages, such as analysis and design. It also requires a set of skills that the software developer can use to work on the project, such as specifying the requirements and writing code. Developers usually search for source code on the internet for remix and reuse in software production. This paper aims to investigate the influence and effect of code retrieved from the web on programmers’ views, decisions, and skills. A questionnaire instrument was designed and distributed to programmers for their feedback. As a result, we were able to address some points and achieved a better understanding of the interaction between programmers and the code from the web, especially the code from programming forums such as Stack Over Flow.
THAI SIGN LANGUAGE TO ENGLISH TRANSLATION USING VARIABLE HIDDEN NEURON ANN - A CASE STUDY

Sign language translation has been a major challenge in all walks of life. The current society has been more accepting of the specially abled and the government has been actively making policy changes to accommodate and assimilate the specially abled into the society. Every country has made a conscious effort to develop its own syllable set in its native language even though globally used language is American Sign Language (ASL). In this paper a method proposed by the authors for ASL is applied on Thai Sign Language and the working of the ANN model is explored.
Monitoring Software for Project Based Learning Tasks

The complex task of control of multiple configurations of documents, diagrams and code in the student projects in e-learning process requires a project management software tools. This paper presents the developed technology for managing course design, based on a sequential workflow and registration of the states of its stages. The design of a software tool that implements the proposed approach, data model and technical solutions used in the implementation is described. The flexibility of the developed tool is achieved by specifying a list of project stages and states in a local database. Change management is supported by the formation of a chain of messages, version control is based on sequential numbering and is fully automated.
An Exploratory Case Study for Turkish Sentiment Classification Using Graph Convolutional Neural Networks

Graph Convolutional Neural Networks (GCNs) are highly popular in recent years. It gives very successful results for various natural language processing (NLP) tasks such as sentiment classification. It has recently been shown to be effective and successful models to solve sentiment classification problem of texts. However, there is no research demonstrating the performance of this model on Turkish texts. In this study, we observe performance of the GCN model on the sentiment classification problem of Turkish texts as first research. Since the structure of Turkish language is agglutinative, different preprocessing approaches are presented and performance results on three real-world Turkish sentiment datasets are shown. It is observed that the TripAdv dataset, which was used in this study, yielded a 0.76 F-measure value. This can be considered a reasonable success for a sentiment classification with three sentiment classes. On the other hand, this study is presented as an exploratory case study in preparation for more detailed and extensive research in the future.
Ensemble Bagging Script Identification of Handwritten South Asian Documents

The world population is 7.7 billion and the largest and most continent is Asia where 59.66% population consists of the entire world. Southern Asia accounts for 39.49% of the total Asian population. This region hosts a variety of languages, playing a critical role in the polygraphia formation, sharing of one script by several languages which have applications in multilingual access to patents, business regulatory information for independently evaluating all regional market requirements. Ideographic languages in Southeast Asian scripts from left-to-right or vertically from top-to-bottom shows more flexibility in their direction of writing. This paper presents the challenges involved in analyzing handwritten documents of popular scripts namely Chinese, Hiragana, Hangul, Khmer, Latin, Thai, Sinhala, Arabic and Devanagari. The proposed technique for script identification is based on the methods of mathematical features, Gabor filter and wavelet moments feature extraction classifying the scripts using Ensemble Bagging Algorithm achieving an accuracy of 88.4%.
Machine Learning Approaches for Patient State Prediction in Pediatric ICUs

We consider the problem of characterizing and predicting the condition of pediatric patients in intensive care units (ICUs). This population is often typified by rapid changes in patient conditions which necessitate predictions that can capture transition in patient states. While the assessment of patient’s condition is currently usually done using domain based scoring systems, we employ machine learning models for predicting the state of the pediatric patient. Additionally, we explore how model explainability could affect the usage of predictive models in a real world settings.
LSTM and CNN Based IMU Sensor Fusion Approach for Human Pose Identification in Manual Handling Activities

In recent years, human pose estimation has become a very important research topic in the context of control engines, and exoskeletons. In this paper, we propose a Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) based Hybrid Deep Neural network, aimed to estimate human pose while handling of loads. The proposed model is capable to identify three such activities, i.e. load lifting from the ground, load shifting, and uplifting of the load. For this purpose, a inertial sensor unit (IMU)-based system was developed to collect the raw data. Next, to obtain more robust and accurate results, Kalman filtering has been used as a fusion technique. Rigorous fine-tuning and simulations show that the model obtained from the Kalman filtering achieves better results as compared to the raw data. Our proposed model can classify the target activities with a test accuracy of 86%.
UPLIGHT: A Novel Portable Game Device with Omnidirectional Projection Display

We hypothesized that the act of actively moving one’s body to see the hidden parts of a sphere, cube, or any other structure with height and sides would be entertaining. In this paper, we propose a novel portable game device with omnidirectional display called “UPLIGHT,” which was created by combining the element of entertainment with the play style of a portable game device. We also describe the design of a prototype and a playable game application that we developed to achieve this interaction.
The Impact of Social Engineer Attack Phases on Improved Security Countermeasures

The objective of this paper is to examine a model to identify Social Engineer Attack Phases to improve the security countermeasures by Social-Engineer Involvement. A questionnaire was developed and distributed to a sample of 243 respondents who were actively engaged in 3 Jordanian telecommunication companies. All hypotheses were tested using (PLS-SEM). The results of the study indicate that Social Engineer Attack Phases (Identification the potential target, Target Recognition, Decision approach, and Execution) have a partially mediate and significant impact on improving the security countermeasures by Social-Engineer Involvement. On the other hand, the Social Engineer Attack Phases (Information Aggregations, Analysis and Interpretation, Armament, and Influencing) have a fully mediate and significant impact on improving the security countermeasures by Social-Engineer Involvement. The findings of this study help to provide deep insight to help security professionals prepare better and implement the right and appropriate countermeasures, whether technical or soft measures.
MoatNet: Registration for Multi-Temporal Optical Remote Sensing Images Using Deep Convolutional Features

Image registration is an important technique that has been widely used in many areas. It is an indispensable premise for remote sensing image tasks like change detection and image fusion. In this paper, we propose a deep learning framework to generate descriptors for key points and then combine the descriptors constructed with FAST key points for accurate image registration. During the process of training, we adopt a novel loss function named Moat Loss (ML) to train our model, which is accordingly called MoatNet. Experiments show that our method is more robust than traditional algorithms like SIFT and is more accurate than the end-to-end deep learning methods in much more complex cases.
Dynamical Analysis of Quantum Annealing

Abstract Quantum annealing aims to provide a faster method than classical computing for finding the minima of complicated functions, and it has created increasing interest in the relaxation dynamics of quantum spin systems. Moreover, problems in quantum annealing caused by first-order phase transitions can be reduced via appropriate temporal adjustment of control parameters, and in order to do this optimally, it is helpful to predict the evolution of the system at the level of macroscopic observables. Solving the dynamics of quantum ensembles is nontrivial, requiring modeling of both the quantum spin system and its interaction with the environment with which it exchanges energy. An alternative approach to the dynamics of quantum spin systems was proposed about a decade ago. It involves creating stochastic proxy dynamics via the Suzuki-Trotter mapping of the quantum ensemble to a classical one (the quantum Monte Carlo method), and deriving from this new dynamics closed macroscopic equations for macroscopic observables using the dynamical replica method. In this chapter, we give an introduction to this approach, focusing on the ideas and assumptions behind the derivations, and on its potential and limitations.
Towards a Sound and Complete Dialogue System for Handling Enthymemes

A common assumption for argumentation-based dialogues is that any argument exchanged is complete, in the sense that its premises entail its claim. However, in real world dialogues, agents commonly exchange enthymemes—arguments with incomplete logical structure. This paper formalises the dialogical exchange of enthymemes that are missing some constituent elements, such that it is not possible to directly entail the claim of the intended argument from the premises of the enthymeme exchanged. This can lead to misunderstandings between agents; we provide a rich set of locutions for identifying and resolving such misunderstandings, and a protocol that governs the use of these. We show that, under certain conditions, the status of moves made during a dialogue conforming to our system corresponds with the status of arguments in the Dung argument framework instantiated by the contents of the moves made at that stage in the dialogue. This is significant since it ensures that the use of enthyememes does not prevent the agents from reaching the appropriate decision according to the information they have shared.
Text Similarity Identification Based on CNN and CNN-LSTM Model

Natural Language Processing is an active area of research where new challenges and issues such as text similarity, semantic analysis, paraphrase identification, citation matching, and language translation, etc. foster the need to further exploit this area. Among the above issues, text similarity is one of the key operative challenges which is being explored by many researchers. In text similarity, extracting the meaningful word, phrase, and sentence patterns from texts requires an effective way that can be evaluated based on various standardized metrics. Therefore, we propose a model based on Convolutional Neural Networks (CNN) and Convolutional Neural Networks- Long Short Term Memory (CNN-LSTM) to find the text similarity as image recognition. Our results show that we achieved better classification results with the precision of 61.3% and recall of 57.9% with MatchGridTower as compared to the earlier MatchPyramid model having a precision of 54.0% and recall of 53.1%.
The Utilization of G-Suite Features Combination on Developing Small Size of Android Application

In this era, the digitalisation process increases to replace manual processes in daily activities. The need to fulfil the demand of applications as quick and efficient as possible is the target of developers. Many services are available, but it requires a careful selection to consider and sometimes it is costly. The objective of this study is to highlight the benefits of combining Google Apps with small to medium size application development; hence could be a substitute for cloud storage on the application, especially mobile app. This study utilises a combination of free services provided by Google to form an EInvitation mobile app in Android. E-Invitation using QR Code is an android-based application which can be alternative for a traditional way of inviting people. It is made to accommodate participant registration to the distribution of certificates at the end of the event. This application combines some services that have been provided by Google such as Gmail, GForm, Spreadsheet, and App Script. Thus, users could find it easier to use because the features are popular. This application is an implementation to find out more about the function and capabilities of Google Suite services. After the application is successfully implemented according to the requirements, it is known that these Google features have beneficial functions though is rarely chosen as an alternative for app developers.
Study on the Effectiveness of VR-based Curriculum in Improving the Counseling Ability of Pre-service Training for Gifted Educators

This two-year research intends to use the virtual reality curriculum to enhance the counseling ability of pre-service training for gifted educators. Combining with the viewpoints of differentiated teaching and functional analysis, it tries to observe and interview with gifted teachers, and develop the indicator-style scale for gifted students’ emotional needs, in order to improve pre-service teachers' awareness and appropriate counseling strategy. First, the study tries to establish the virtual reality classroom for pre-service teacher's training, and test the user's feelings. The VR-based classroom consists of a six-seater resource rooms, 6 gifted students, 6 sets of 10-15 minutes VR-based counseling training. It also compares the difference between VR-based training and general teaching to provide the preventive counseling mechanism for gifted students. It tries to analyze “the Observation Scale for Classroom Behavior Performance and Affective Needs of Gifted Students ", and provide the training to pre-service gifted educators. The study also analyzes the teacher effectiveness and conducted in-service teachers for the control group. According to the results, the level of simulation and comfort of VR videos is acceptable or even very acceptable. VR videos have a better effect on improving pre-service teachers to counsel for gifted students than those who use general text responses (p<.05), which helps improve their ability to judge gifted students' affective counseling needs and use related strategies. In addition, there are still differences how to guide gifted students between the on-site teachers in the gifted class and the pre-service students. However, through VR-based training, their ability of perceiving the affective needs of gifted students and applying counseling strategies can be enhanced, and this training can strengthen training of gifted teacher. It is suggested that the development direction of the follow-up is the development of more refined VR videos, and quantification of the scale and appropriate cut-off standards of “the Observation Scale for Classroom Behavior Performance and Affective Needs of Gifted Students ".
The Smart Sailing Robot for N avigational I nvestigation i s Used to Explore a ll the Details on the Z one of the W ater Pura

Ocean Exploration and Navigational Research is driving undertakings by supporting undertakings with PC vision frameworks have shown potential for Sailboat robots made to make assessments at the surface. The marine environment presents an in each commonsense sense, ideal showing ground for the assessment and improvement of robotized progressions. Robot cruising is a tricky task in both turn of events and controlling the boat consequently it joins a wide degree of orders. The cruising robot researches in comprehension of video film, the ID of cruising features, human-robot correspondence, vehicle control, position assessment and mechanical course of action. Key applications for this vessel are the appraisal of marine living spaces and complex moves. An idea presented has been with a Robotic vehicle what starts normally and truly control the moving thing in the water the robot will get and sends the information to the PC which uses advanced picture managing improvement and investigates appropriate pictures by seeing cut down features which will follow the article present in the outside of ocean. The DC motors are used to turn the arms of the robot to get living spaces.
Sample-Based Motion Planning of Mobile Robot in Dynamic Environment

Given poor path quality, long replanning time, and slow convergence of traditional sampling planning algorithms in dynamic environments, an optimal dynamic rapidly exploring random tree*(D-RRT*) algorithm with global planning combined with local replanning is proposed. First, for the blind search problem inherent in the traditional RRT algorithm, a goal-biased strategy is introduced to reduce the redundant search and accelerate the convergence of the algorithm. Secondly, a triangular inequality-based inverse optimization strategy is introduced to optimize the path. Then, global planning combined with local replanning is used to improve the real-time performance of the algorithm for dynamic environments. Finally, D-RRT* is simulated and compared with the traditional sampling algorithm for analysis, and the experimental results verify the efficiency and stability of the D-RRT* algorithm in the dynamic environment.
Learning a Geometric Representation for Data-Efficient Depth Estimation via Gradient Field and Contrastive Loss

Estimating a depth map from a single RGB image has been investigated widely for localization, mapping, and 3- dimensional object detection. Recent studies on a single-view depth estimation are mostly based on deep Convolutional neural Networks (ConvNets) which require a large amount of training data paired with densely annotated labels. Depth annotation tasks are both expensive and inefficient, so it is inevitable to leverage RGB images which can be collected very easily to boost the performance of ConvNets without depth labels. However, most self-supervised learning algorithms are focused on capturing the semantic information of images to improve the performance in classification or object detection, not in depth estimation. In this paper, we show that existing self- supervised methods do not perform well on depth estimation and propose a gradient-based self-supervised learning algorithm with momentum contrastive loss to help ConvNets extract the geometric information with unlabeled images. As a result, the network can estimate the depth map accurately with a relatively small amount of annotated data. To show that our method is independent of the model structure, we evaluate our method with two different monocular depth estimation algorithms. Our method outperforms the previous state-of-the- art self-supervised learning algorithms and shows the efficiency of labeled data in triple compared to random initialization on the NYU Depth v2 dataset.