Reviewerly/exHarmony huggingface dataset:
Interactive 3D Marathi Language Alphabets: AR-Based Mobile App in Context of Rural India

India is a developing country having a rural population of near about 65.5%, and the literacy rate in rural areas is 61.14%. So, there is a need to improve the education system for rural people in India. It is evident from many researches that traditional teaching from books is failing to engage children. Also, unable to create their interest and motivation. To bring in active participation of children, the education process needs to be creative and interactive. Currently in rural schools of India, many information and communication technologies (ICT) like computers, laptops, tablets, projectors, and Internet facilities have been introduced to support the existing teaching–learning methods. Augmented Reality (AR) is one such growing technology that can be used in educational settings to enhance traditional learning methods. Therefore, we have proposed an AR-based mobile application, named as ‘ॲनिलर्न’, (Anilearn) which means ‘anyone can learn’ for rural pre-primary school children. We have prepared 3D alphabets in Marathi language using Blender software. These 3D alphabets are incorporated in the proposed AR-based application which is developed using Unity with Vuforia software development kit (SDK). The 3D alphabets include the 3D letter and 3D interactive objects associated with each letter. In Marathi language, there are fifty-one alphabet including fifteen swar (vowels) and thirty-six vyanjan (consonants). ‘ॲनिलर्न’ (Anilearn) displays 3D alphabet along with corresponding 3D object. These alphabets have audio support (pronunciation) and also children can interact with 3D objects. This form of 3D interaction can create interest and motivation among pre-school children. This AR-based application would help to improve the education system of India especially in the rural areas. In future, we are planning to incorporate computer vision so that the static markups become dynamic.
Exploring Out-of-Distribution in Image Classification for Neural Networks Via Concepts

The currently dominating artificial intelligence and machine learning technology, neural networks, builds on inductive statistical learning processes. Being void of knowledge that can be used deductively, these systems cannot distinguish exemplars part of the target domain from those not part of it. This ability is critical when the aim is to build human trust in real-world settings and essential to avoid usage in domains wherein a system cannot be trusted. In the work presented here, we conduct two qualitative contextual user studies and one controlled experiment to uncover research paths and design openings for the sought distinction. Through our experiments, we find a need to refocus from average case metrics and benchmarking datasets towards systems that can be falsified. The work uncovers and lays bare the need to incorporate and internalise a domain ontology in the systems and/or present evidence for a decision in a fashion that allows a human to use our unique knowledge and reasoning capability. Additional material and code to reproduce our experiments can be found at https://github.com/k3larra/ood.
A Hybrid Federated Learning-Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN

The significant advancements in computational power create a vast opportunity for using artificial intelligence in different applications of healthcare and medical science. A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging a combination of SWIN transformer and CNN is the combination of cutting-edge technology of AI and federated learning. Since, medical specialists and hospitals will have shared data space, based on that data, with the help of artificial intelligence and integration of federated learning, we can introduce a secure and distributed system for medical data processing and create an efficient and reliable system. The proposed hybrid model enables the detection of COVID-19 and pneumonia based on X-ray reports. We will use advanced and the latest available technology offered by TensorFlow and Keras along with Microsoft-developed vision transformer that can help to fight against the pandemic that the world has to fight together as a united. We focused on using the latest available CNN models (DenseNet201, InceptionV3, VGG19) and the transformer model SWIN transformer in order to prepare our hybrid model that can provide a reliable solution as a helping hand for the physician in the medical field. In this research, we will discuss how the federated learning-based hybrid AI model can improve the accuracy of disease diagnosis and severity prediction of a patient using the real-time continual learning approach and how the integration of federated learning can ensure hybrid model security and keep the authenticity of the information.
Neurophysiological UX Evaluation of Industry 4.0 Technologies: E-commerce, e-learning Websites, and VR Simulation

Industry 4.0 technologies are a product of human computer interaction (HCI). Users may engage with online instructors and virtual chatbots in e-commerce, e-learning, and virtual reality (VR) interactions, depending on the epistemology behind the design of Industry 4.0 technologies. E-commerce, e-learning, and virtual reality are one of the powerful forces advancing the industrial world. Rapid technological improvements and the way people do business through e-commerce, e-learning, and VR-based applications can only add to the reality that Industry 4.0 is an intrinsic part of this process. Numerous designers invest a substantial amount of time and energy in designing adaptive technology training and user-friendly interfaces to optimize the value of digital interactions. Regardless of the strategy used, a number of questions always arise: How efficiently do Industry 4.0 technologies interact? Is the interaction consistent with the UX expectations of the customers? How may they be enhanced further? Traditional approaches include UX evaluation, interaction design, and interface analysis for answering these problems. The UX evaluation and usability analysis of Industry 4.0 technologies have been hampered by the limited quantity of data from busy users unwilling to share at the conclusion of a digital interaction, the limited quality of self-reported and retrospective data, and a significant lag between the reported events and the implementation of an intervention. Therefore, researchers performed three distinct studies on e-commerce, e-learning, and virtual reality to assess the user experience (UX) provided by neurophysiological devices such as EEG and GSR. The proposed neurophysiological measure of UX of Industry 4.0 technology investigates the numerous methods by which we may quantify varying degrees of successful engagement and improve UX in any digital transition.
Comparing Mixed Reality Hand Gestures to Artificial Instruction Means for Small Target Objects

Hand gestures are a valuable means for the instruction of complex handling processes. They are used and perceived in an intuitive way and outperform artificial representations such as arrows or symbols. On the other hand, referring finger gestures require a certain object size to avoid ambiguities, and often they are replaced by artificial means. However, this comes to the cost of reduced intuition due to the change of a hand gesture to an artificial gesture, which consequently makes it more difficult to learn long instruction sequences and keep them in mind. This paper thus introduces study results showing that hand pointing gestures perform well even for small objects, so that unnecessary switches to artificial representations can be avoided in the future.
I Am Bot the “Fish Finder”: Detecting Malware that Targets Online Gaming Platform

Malware in the gaming industry presents many forms of risk for users, such as phishing, trojans, malware, and network attacks. Few studies have been published on gaming industry malware. The ones identified were found to be primarily focused mainly on in-game cheat detection, such as cheat clients and aimbots. This paper leverages related research drawn from the broader field of cybersecurity, including, URL-phishing detection and crytpojacking. To detect URL phishing attacks data, we used eight filter, wrapper, and embedded-based feature selection and five machine learning techniques, i.e., AdaBoost, extra trees (ET), random forest (RF), decision tree (DT), and gradient boosting (GB) where highest accuracy, precision, recall, and F1_score are achieved with RF. We further scrutinize the feature selection and classifiers based on threshold that will help to provide an aggregate simplified recommendation to the user and alerting about the malicious URL. The outcome of whether the URL is benign or malicious can easily be seen on developed bot application named “Fish Finder.” For allowing Discord users to protect themselves from future phishing attacks, we have shared the built application on github: https://github.com/Dinnerspy/Discord-Bot-Phishing-Detection.
Dynamic Parameterization of Metaheuristics Using a Multi-agent System for the Optimization of Electricity Market Participation

Metaheuristic optimization algorithms are increasingly used to reach near-optimal solutions for complex and large-scale problems that cannot be solved in due time by exact methods. Metaheuristics’ performance is, however, deeply dependent on their effective configuration and fine-tuning to align the algorithm’s search process with the specific characteristics of the problem that is being solved. Although the literature already offers some solutions for automatic algorithm configuration, these are usually either algorithm-specific or problem-specific, thus lacking the capability of being used for diverse metaheuristic models or diverse optimization problems. This work proposes a new approach for the automatic optimization of metaheuristic algorithms’ parameters based on a multi-agent system approach. The proposed model includes an automated fine-tuning process, which is used to optimize a given function in an algorithm- and problem-agnostic manner. Results show that the proposed model is able to achieve better optimization results than standard metaheuristic algorithms, with a negligible increase in the required execution time.
Towards an Adversary-Aware ML-Based Detector of Spam on Twitter Hashtags

After analysing messages posted by health-related spam campaigns in Twitter Arabic hashtags, we found that these campaigns use unique hijacked accounts (we call them adversarial hijacked accounts) as adversarial examples to fool deployed ML-based spam detectors. Existing ML-based models build a behaviour profile for each user to detect hijacked accounts. This approach is not applicable for detecting spam in Twitter hashtags since they are computationally expensive. Hence, we propose an adversary-aware ML-based detector, which includes a new designed feature (avg_posts) to improve the detection of spam tweets posted by the adversarial hijacked accounts at a tweet-level in trending hashtags. The proposed detector was designed considering three key points: robustness, adaptability, and interpretability. The new feature leverages accounts’ temporal patterns (i.e., account age and number of posts). It is faster to compute compared to features discussed in the literature, and improves the accuracy of detecting the identified hijacked accounts by 73%.
Measuring the Performance of An Object-Based Multi-cloud Data Lake

As the amount of data generated by society continues to become less structured and larger in size, more and more organizations are implementing data lakes in the public cloud to store, process, and analyze this data. However, concerns over the availability of this data as well as the potential of vendor lock-in lead more users to adopt the multi-cloud approach. This study investigates the viability of this approach in data lake use cases. Results that a multi-cloud data lake can potentially be implemented with less than 1% performance impact to query run times at the cost of a 300% increase in one-time loading. This opens the door for future work on more algorithms and implementations that leverage multi-cloud deployments to enhance availability, scalability, and cost optimization.
Formal Stability Analysis of Two-Dimensional Digital Image Processing Filters

There are several image processing applications that require to partition frequency components of images. This requirement is usually fulfilled by using digital image processing filters. Most of this processing is done in two-dimensions given the two-dimensions of the regular images. It is very important to ascertain that these filters provide a stable output for a bounded input, and this requirement is usually termed as stability. The stability analysis of these filters is usually conducted analytically, on a piece of paper, or by simulations. However, these techniques provide approximate or inaccurate results as paper-based analysis can have human error and simulations suffer from computer arithmetic related roundoff limitations. We advocate formally analyzing the stability of digital filters for two-dimensional (2D) images using interactive theorem proving. In this regard, we present a formal dynamical model and a formal notion of stability of 2D digital image processing filters in HOL Light. The proposed formal model is used to perform the stability analysis of a real-world 2nd-order filter in HOL Light.
Variational Autoencoders Versus Recurrent Neural Network for Detection of Anomalous Trajectories

Anomalous trajectory detection has a prominent place in many real-world applications, as for example taxi fraud detection. Several approaches have been proposed over time, but in this paper the goal is to analyze and compare two novel models (GM-VSAE and ATD-RNN) which have addressed the problem differently. However, both of them have already overcome traditional anomalous trajectory detection methods. For this purpose, we have worked on a real-world taxi trajectory dataset, in which we introduced some anomalies. First, we conducted an explanatory analysis of this dataset. Then, we explained the principles of the two models, highlighting their differences and finally, we evaluated their performances on the considered dataset. Results show that GM-VSAE is more efficient even if both models have shown their relevance in detecting anomalous trajectories.
EDT0L grammars with only one variable have tractable generating functions

We introduce new counting techniques for computing the growth of EDT0L languages based on generalized Parikh vectors. The structure generating function S ( z ) for any language formed from an unambiguous EDT0L system with one variable and m tables is tractable in the sense that its Taylor polynomial S N ( z ) can be constructed in time O ( N 2+log 2 m ). We illustrate by example that this result is not necessarily true for systems with several variables. The dichotomy between one variable and several variables is partially explained using matrix algebra.
The Role of Organizational Citizenship Behavior (OCB) as a Mediating Variable on the Influence Between Motivation and Employee Performance

Organizational Citizenship Behavior (OCB) can be interpreted as behavior that exceeds the scope of one's work.This behavior is characterized by the actions of members of the organization that go beyond the work and tasks that have been formally defined and regulated in carrying out their roles and functions in the organization.This study aims to examine the existence of OCB as a mediating variable between motivation and employee performance..This study used 329 respondents who came from the academic staff of Universitas Brawijaya.The data collection method used is a survey method using a questionnaire as the instrument.While the data analysis consists of descriptive analysis and inferential analysis using Smart PLS.The results showed that there was a positive and significant effect between employee motivation and performance, which was indicated by a P value of 0.033 < 0.05 and a path coefficient value of 0.126.The role of OCB in mediating the influence of motivation on performance has been proven to be significantly positive with a weighted value of 0.144, which is greater than the direct effect of 0.126.Thus, it can be concluded that OCB is a pure mediation.
Parsing IsiZulu Text Using Grammatical Framework

While Grammatical Framework (GF) resource grammars are primarily intended as linguistic software libraries, they can also be employed to enable syntax parsing via the GF runtime system. The isiZulu resource grammar (ZRG) in conjunction with the GF runtime system constitutes the first openly available parser for isiZulu. A key feature of the implementation of the ZRG is to model aspects of morphology as equivalent to syntax. In the absence of a baseline, we evaluate the parser at the token level against a morphological analyser, before characterising its phrase level analysis in order to establish a baseline for parsing of isiZulu text.
Effectiveness of Flood Management Through Pump Houses Based on Geographic Information Systems in the City of Surabaya

Floods or puddles hit several points in the city of Surabaya.The main factor that often becomes the problem of flooding is the infrastructure and environmental carrying capacity no longer following its designation.Floods cause ecological damage and have socio-economic impacts.Another solution in flood control is developing and placing pump house points based on geographic information systems (GIS).This study uses a qualitative descriptive approach.This paper aims to study how the construction of pump houses in Surabaya can solve the flood problem.Create a web-based GIS application for the development and placement of pump houses based on the parameters of the inundation area, length of inundation time, and average inundation height.The results of this study will determine the mapping of the distribution of pump points and detailed information on pump points to be used as a basis for policy-making by the Department of Water Resources and Highways in the development of new pump houses and the placement of pump house constructions.The policy results of this research are expected to be harmonious coordination between agencies.Performance achievements will continue to be improved to have flood prevention regulations.
Synectics Multiliteracy Learning Model in Writing Short Stories

Learning to write short stories has not gone well, so it is necessary to increase creativity through the use of multiliteracy synectic learning models.This study aims to implement a multiliteracy synectic learning model in junior high schools in Ponorogo, East Java, Indonesia.The method used is classroom action research with the stages of research, namely planning, implementation, observation, and reflection.The results of the study obtained observational data, namely learning to write poetry with this multiliteracysynectic learning model was successful and the class was very active.Based on interview data or comments submitted by the teacher positively, it can be analyzed that learning to write short stories with this multiliteracysynectic learning model is successful.Based on the questionnaire data of students who have positive opinions, it can be analyzed that learning to write short stories with the multiliteracysynectic learning model is successful.The results of the class average score in learning to write short stories using the multiliteracysynectic learning model is 80.So it can be concluded that the learning outcomes of the Indonesian language study field of basic competence in writing short stories with the multiliteracysynectic learning model are considered successful according to the description of the modified Likert scale because the average grade value lies on an interval scale of 61-80.
Evaluating Transformers’ Sensitivity to Syntactic Embedding Depth

Various probing studies have investigated the ability of neural language models trained only on word prediction over large corpora to process hierarchical structures, a hallmark of human linguistic abilities. For instance, it has been shown that Long Short-Term Memory (LSTM), a type of Recurrent Neural Networks (RNNs), can capture long-distance subject-verb agreement patterns and attraction effects found in human sentence processing. However, although it’s found in human experiments that attractors that are syntactically closer to but linearly farther from the verb elicit greater attraction effects than those that are linearly closer but syntactically farther, LSTM shows the opposite pattern, suggesting that it lacks knowledge regarding syntactic distance and is more sensitive to local information. The current article investigates whether state-of-the-art Generative Pre-trained Transformers (GPTs) can capture the prominence of syntactic distance. We experimented with various versions of GPT-2 and GPT-3 and found that all of them succeeded at the task. We conclude that GPT may be able to better model human linguistic cognition than LSTM, corroborating previous research, and that further investigating what mechanisms enable GPT to do so may inform research on human syntactic processing.
The Needs Analysis of Online Learning

Online learning is a trend during the COVID-19 pandemic, which requires teachers to apply it in their learning.This study tries to analyze the needs and provide recommendations for the fulfillment of the efficiency and effectiveness of the implementation of online learning.The research method used is descriptive qualitative by using questionnaires and observation sheets.Needs assessment technique by assessing actual conditions and evaluating according to the ideal standard criteria that have been prepared.The results of the study show that aspects of online learning including pedagogic models, learning strategies, and learning technology have been fulfilled.The use of diverse learning resources and technology testing should be carried out before the learning program is implemented.
The Use of Modification Audio Media in Recognition of Braille Letters Symbol on Multiple Disabilities with Visual Impairment (MDVI) Students

This study aims to determine the use of modified audio media in the introduction of braille symbols to students with Multiple Disabilities With Visual Impairment (MDVI) in class VI at Special Elementary School (SLB) Negeri Branjangan Jember.This study uses the Single Subject Research (SSR) method A-B design.The subjects of this study were students with visual impairment (blindness) accompanied by cognitive impairment with the initial "Rx".This research was conducted in 10 sessions consisting of two phases, the Baseline phase (A) and the Intervention phase (B) where each phase consisted of 5 sessions.The percentage results from the baseline phase (A) are 31%, 25%, 25%, 25%, and 25%.While the percentage results of the intervention phase (B) are 50%, 69%, 69%, 75%, and 75%.The data were then analyzed using analysis in conditions and analysis between conditions.The Mran level obtained in the baseline phase (A) is 26.2 while in the intervention phase (B) is 67.6.The difference in the level of change in the baseline and intervention phases is 25-50 = (+25) which shows an increase and the percentage of overlap is 0%.Therefore, it can be seen that the use of modified audio media in the introduction of braille symbols in students with Multiple Disabilities With Visual Impairment (MDVI) is increase.