Reviewerly/exHarmony huggingface dataset:
Survey in Image and Audio Steganography by using the Deep Learning Methods

Steganography is the practise of secretly encoding information into another medium (called a cover media) such that its existence cannot be identified. Steganography is the method that is utilised to successfully do this task. Through the use of digital image steganography, one is able to achieve the capacity of secure communication, which is vital in the bulk of the applications that are now being created. The use of steganography is applicable in a wide variety of advantageous settings. Its ascent to the forefront of current security systems may be attributed to a confluence of factors, including a phenomenal rise in processing power and a heightened awareness among individuals of the need of safeguarding their personal information. When it comes to the creation of a steganographic method, the most significant challenge that must be surmounted is finding the optimal balance between quality and quantity. The method is distinguished from linked systems like encryption and watermarking by its ability to embed information, as well as by the fact that it cannot be seen by the human eye. This article presents a thorough examination and analysis of some contemporary steganographic methods that are regarded as state-of-the-art approaches. In addition, we have included an in-depth discussion of the most important steganographic technologies now available. Recent steganographic techniques that are based on deep learning have been enhanced, which has enabled the resolution of issues that were previously present
Investigation of Camera-Free Eye-Tracking Glasses Compared to a Video-Based System

Technological advances in eye-tracking have resulted in lightweight, portable solutions that are capable of capturing eye movements beyond laboratory settings. Eye-tracking devices have typically relied on heavier, video-based systems to detect pupil and corneal reflections. Advances in mobile eye-tracking technology could facilitate research and its application in ecological settings; more traditional laboratory research methods are able to be modified and transferred to real-world scenarios. One recent technology, the AdHawk MindLink, introduced a novel camera-free system embedded in typical eyeglass frames. This paper evaluates the AdHawk MindLink by comparing the eye-tracking recordings with a research "gold standard", the EyeLink II. By concurrently capturing data from both eyes, we compare the capability of each eye tracker to quantify metrics from fixation, saccade, and smooth pursuit tasks-typical elements in eye movement research-across a sample of 13 adults. The MindLink system was capable of capturing fixation stability within a radius of less than 0.5∘, estimating horizontal saccade amplitudes with an accuracy of 0.04∘± 2.3∘, vertical saccade amplitudes with an accuracy of 0.32∘± 2.3∘, and smooth pursuit speeds with an accuracy of 0.5 to 3∘s, depending on the pursuit speed. While the performance of the MindLink system in measuring fixation stability, saccade amplitude, and smooth pursuit eye movements were slightly inferior to the video-based system, MindLink provides sufficient gaze-tracking capabilities for dynamic settings and experiments.
Harmonized Research Information for Classifying and Linking Research Data

A well-described, harmonized in standard formats and linked metadata accelerates and refines the search process. Hence, researchers can ensure that their data is properly organized, easily discoverable, and accessible to others. Machine-readable metadata is especially essential for automatic discovery of research datasets and outputs. It facilitates the search and retrieval of data with increased accuracy, which saves the user time in finding crucial information within their field of research. This requires rich and categorized metadata that allows computers to automatically retrieve and sort relevant search results. Well documented metadata which is harmonized, standardized and serialized in linked data formats increases not only the accuracy of the search results within a field of research, but can also provide relevant and helpful suggestions within disciplinary and interdisciplinary fields of research.
Labels for AI content expose gap between tech and laws

Headline INT: Labels for AI content expose lag on regulations
Improving English Vocabulary Mastery by Using the Alphabet Game for Class VIII at Junior High School Metro TMI RQ Metro

The students was the lack of enthusiasm for English learning. Students sometimes get bored in learning process. The purpose of this research was to improve how to get well students at SMP TMI RQ Metro in acquire English vocabulary material for class VIII A. The researchers used classroom action research, either in Cycle I and Cycle II. Data was collected through observation, interviews, tests and documentation. The result showed that 9 out of 20 students passed with a percentage of 45% in the observation cycle I. 11 students with a completeness level of 55% were students who did not complete. with an average value of 70.75. Hence, the classical completeness of the first cycle is 45%. It was observed that 80% of 20 students, 16 of which completed it during the second cycle. Four students with a 20% completion rate did not complete the course. with an average of 80 points. As a result, a typical cycle II is 80% complete. In other words, the results of the second cycle of observations succeeded in obtaining the KKM score that had been determined by the school and the completeness of the learning objectives for students.Keywords: Student Learning Outcomes, Vocabulary Material, Alphabet Games
Behavioral Authentication for Smartphones backed by Something you Process

Authentication of smartphone devices has been never so important nowadays. Machine learning techniques are not far behind to touch the new milestones of the latest and ever updating world. However, totally depending on machine learning will give you the scenarios of false user being accepted as true one and a true user being rejected as the false one, which can be devastating in some cases. Fifth factor of authentication “Something You Process” eradicates most of the cases of the false acceptance and false rejection, if used with the mentioned techniques. The novel approach applied here is the fifth factor combined with machine learning system and Behavioral authentication. The fifth factor is anti-shoulder surfing since the arithmetic operation is hidden by hand placed on the screen. After placing hand on the screen in such a way that it hides the code from others, the system shows the arithmetic operation and the processed calculation is performed in user’s mind. The pattern which is shown to the user is public, but machine learns the touch dynamics of the user along with his different postures including lying posture. The focus has been on the aspect of something that can be another layer or line of defense which can save the user’s authentication process. It results in decrement of false acceptance or false rejection upon unlocking of a smartphone device. This study deals with the postures of standing, sitting, and lying. The data is collected and the features are extracted in all of these positions.
Institutional Support for Academic Engagement in Online and Blended Learning Environments

In light of the disruptions caused by the COVID-19 pandemic, leaders of higher education institutions around the world have been contemplating ways to help their universities engage in a digital transformation that must have student engagement and learning as the foremost considerations. This study reports on the work conducted at a university in Colombia that created an evaluation instrument based on the Academic Communities of Engagement (ACE) framework (Borup et al., 2020) to examine how well the institution was supporting the affective, behavioral, and cognitive (ABC) dimensions of engagement in its online and blended learning course offerings. This survey, the ACE in Higher Education (ACE-HE), measures indicators of the ABC engagement dimensions as well as indicators of institutional support for those elements. The survey was completed by 1,295 university students representing a broad demographic profile. Structural equation modeling found good fit for both the model of ABC engagement dimensions and the model of institutional support for ABC engagement dimensions. Institutional support for affective engagement showed strong relationships to affective, behavioral, and cognitive indicators of engagement, while institutional support for behavioral and cognitive engagement did not have the same outcome. This research provides access to both English and Spanish versions of the ACE-HE instrument. It also highlights ideas for institutions that want to improve their support for student ABC engagement dimensions in online and blended environments. Finally, several implications for making updates to the ACE framework are shared.
Making Creative Videos as Learning Media

This article is the development of several field studies that researchers have summarized. This research was conducted for the process of making learning media into creative videos through the use of the Canva application. Learning media is needed by an educator to facilitate the teaching and learning process in schools through the material taught. Usually, in teaching material, the teacher will use many books according to the government-provided ones. Various methods will also be used to teach students in supporting the teaching and learning process. One application that is easy to use &amp; used in making videos as a learning medium is the Canva application. This application is online based, the contents of which provide exciting features, designs, templates and so on. By using the Canva application, teachers can develop skills materials for students so that the media used can be utilized in academic life.
Ease of life and sustainability (current events for the future life)

In today's world, the more we are trying to make life easier, the more we need to compromise with sustainability, that is why they are acting as resistance to each other, which are trying to exploit our resources along with this life is rapidly taking towards uncertainty, where there are all the complexities of human life, with accepting those challenges in the struggle to make this life more pleasant and balanced, a conceptualized concept of 15 Minute City has been created and all efforts are being made to maintain a balanced structure of human life in the coming time, which is collectively named as the 15-minute city. The name of this 15-minute city walk also describes its concept, on which today many countries of the world are coming forward to work in such projects according to their capabilities, whose main objective is to make human life balanced for a long time to maintain it. For this challenging task, an attempt has been made to write this paper on the basis of the ideas and studies of different researchers in the scientific context, so that the concept can be given a new direction.
LabIMotion Electronic Lab Notebook as Research Data Management Tool in Catalysis

In the field of heterogenous catalysis, Electronic Lab Notebooks (ELNs) are only rarely employed, due to complex data structure and different needs of the community with respect to the typical features provided by wide-spread ELNs. On the contrary, LabIMotion, an extension of the open-source ELN Chemotion, adapts to the characteristic complex workflows in heterogenous catalysis; these encompass catalyst synthesis, adaptation of devices and testing rigs, activity measurements, material characterization (possibly also in situ/operando, at large scale facilities) and are complemented by mathematical modelling and simulation. Direct links to metadata catalogues like SciCat (for synchrotron/neutron characterizations) and advanced research data management tools like Adacta (for improved traceability of catalytic data, experimental setups and related resources) are envisioned. The adaptability of LabIMotion in the catalysis field is presented via the topical examples of Cu-based catalysts for methanol synthesis and noble metal-based emission control catalysts.
Performances of Level 1 and Level 2 Students in Basic Math under New General Foundation Program’s Prospectus: A Comparative Study

With the implementation of New General Foundation Program’s (GFP) Prospectus, Basic Math is now being offered to Level 1 students, whereas foundation students study this course once they reach Level 2 only in the previous mechanism. A question comes in mind whether Level 1 students can cope up with the pressure of Basic Math course without the advantage of a semester-long study of an English language in an online mode of teaching. Query whether Level 2 students will be able to adjust quickly with the reduced absent percentage allowed invokes interest that requires factual basis. This study aims to compare the performances of Level 1 and Level 2 students in Basic Math on the initial semester of implementation of the GFP Prospectus.
Temporal Domain Adaptation for Historical Irish

The digitisation of historical texts has provided new horizons for NLP research, but such data also presents a set of challenges, including scarcity and inconsistency. The lack of editorial standard during digitisation exacerbates these difficulties.This study explores the potential for temporal domain adaptation in Early Modern Irish and pre-reform Modern Irish data. We describe two experiments carried out on the book subcorpus of the Historical Irish Corpus, which includes Early Modern Irish and pre-reform Modern Irish texts from 1581 to 1926. We also propose a simple orthographic normalisation method for historical Irish that reduces the type-token ratio by 21.43% on average in our data.The results demonstrate that the use of out-of-domain data significantly improves a language model’s performance. Providing a model with additional input from another historical stage of the language improves its quality by 12.49% on average on non-normalised texts and by 27.02% on average on normalised (demutated) texts. Most notably, using only out-of-domain data for both pre-training and training stages allowed for up to 86.81% of the baseline model quality on non-normalised texts and up to 95.68% on normalised texts without any target domain data. Additionally, we investigate the effect of temporal distance between the training and test data. The hypothesis that there is a positive correlation between performance and temporal proximity of training and test data has been validated, which manifests best in normalised data. Expanding this approach even further back, to Middle and Old Irish, and testing it on other languages is a further research direction.
Findings of the SIGTYP 2023 Shared task on Cognate and Derivative Detection For Low-Resourced Languages

Priya Rani, Koustava Goswami, Adrian Doyle, Theodorus Fransen, Bernardo Stearns, John P. McCrae. Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP. 2023.
The Role of Semantic Parsing in Understanding Procedural Text

In this paper, we investigate whether symbolic semantic representations, extracted from deep semantic parsers, can help reasoning over the states of involved entities in a procedural text. We consider a deep semantic parser (TRIPS) and semantic role labeling as two sources of semantic parsing knowledge. First, we propose PROPOLIS, a symbolic parsing-based procedural reasoning framework.Second, we integrate semantic parsing information into state-of-the-art neural models to conduct procedural reasoning.Our experiments indicate that explicitly incorporating such semantic knowledge improves procedural understanding. This paper presents new metrics for evaluating procedural reasoning tasks that clarify the challenges and identify differences among neural, symbolic, and integrated models.
Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models

Abteen Ebrahimi, Arya D. McCarthy, Arturo Oncevay, John E. Ortega, Luis Chiruzzo, Gustavo Giménez-Lugo, Rolando Coto-Solano, Katharina Kann. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
Understanding Ethics in NLP Authoring and Reviewing

With NLP research now quickly being transferred into real-world applications, it is important to be aware of and think through the consequences of our scientific investigation. Such ethical considerations are important in both authoring and reviewing. This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues and review common considerations that recur in NLP research. The methodology is interactive and participatory, including case studies and working in groups. Importantly, the participants will be co-building the tutorial outcomes and will be working to create further tutorial materials to share as public outcomes.
Do not Trust the Experts: How the Lack of Standard Complicates NLP for Historical Irish

In this paper, we describe how we unearthed some fundamental problems while building an analogy dataset modelled on BATS (Gladkova et al., 2016) to evaluate historical Irish embeddings on their ability to detect orthographic, morphological and semantic similarity.The performance of our models in the analogy task was extremely poor regardless of the architecture, hyperparameters and evaluation metrics, while the qualitative evaluation revealed positive tendencies. We argue that low agreement between field experts on fundamental lexical and orthographic issues, and the lack of a unified editorial standard in available resources make it impossible to build reliable evaluation datasets for computational models and obtain interpretable results. We emphasise the need for such a standard, particularly for NLP applications, and prompt Celticists and historical linguists to engage in further discussion. We would also like to draw NLP scholars’ attention to the role of data and its (extra)linguistic properties in testing new models, technologies and evaluation scenarios.
Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling

Dropout is a widely used regularization trick to resolve the overfitting issue in large feedforward neural networks trained on a small dataset, which performs poorly on the held-out test subset. Although the effectiveness of this regularization trick has been extensively studied for convolutional neural networks, there is a lack of analysis of it for unsupervised models and in particular, VAE-based neural topic models. In this paper, we have analyzed the consequences of dropout in the encoder as well as in the decoder of the VAE architecture in three widely used neural topic models, namely, contextualized topic model (CTM), ProdLDA, and embedded topic model (ETM) using four publicly available datasets. We characterize the dropout effect on these models in terms of the quality and predictive performance of the generated topics.
Memory-efficient Temporal Moment Localization in Long Videos

Cristian Rodriguez-Opazo, Edison Marrese-Taylor, Basura Fernando, Hiroya Takamura, Qi Wu. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
Investigating UD Treebanks via Dataset Difficulty Measures

Treebanks annotated with Universal Dependencies (UD) are currently available for over 100 languages and are widely utilized by the community. However, their inherent characteristics are hard to measure and are only partially reflected in parser evaluations via accuracy metrics like LAS. In this study, we analyze a large subset of the UD treebanks using three recently proposed accuracy-free dataset analysis methods: dataset cartography, 𝒱-information, and minimum description length. Each method provides insights about UD treebanks that would remain undetected if only LAS was considered. Specifically, we identify a number of treebanks that, despite yielding high LAS, contain very little information that is usable by a parser to surpass what can be achieved by simple heuristics. Furthermore, we make note of several treebanks that score consistently low across numerous metrics, indicating a high degree of noise or annotation inconsistency present therein.
OLEA: Tool and Infrastructure for Offensive Language Error Analysis in English

State-of-the-art models for identifying offensive language often fail to generalize over more nuanced or implicit cases of offensive and hateful language. Understanding model performance on complex cases is key for building robust models that are effective in real-world settings. To help researchers efficiently evaluate their models, we introduce OLEA, a diagnostic, open-source, extensible Python library that provides easy-to-use tools for error analysis in the context of detecting offensive language in English. OLEA packages analyses and datasets proposed by prior scholarship, empowering researchers to build effective, explainable and generalizable offensive language classifiers.
Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction

In this paper, we seek to improve the faithfulness of TempRel extraction models from two perspectives. The first perspective is to extract genuinely based on contextual description. To achieve this, we propose to conduct counterfactual analysis to attenuate the effects of two significant types of training biases: the event trigger bias and the frequent label bias. We also add tense information into event representations to explicitly place an emphasis on the contextual description. The second perspective is to provide proper uncertainty estimation and abstain from extraction when no relation is described in the text. By parameterization of Dirichlet Prior over the model-predicted categorical distribution, we improve the model estimates of the correctness likelihood and make TempRel predictions more selective. We also employ temperature scaling to recalibrate the model confidence measure after bias mitigation. Through experimental analysis on MATRES, MATRES-DS, and TDDiscourse, we demonstrate that our model extracts TempRel and timelines more faithfully compared to SOTA methods, especially under distribution shifts.
Towards Speech to Speech Machine Translation focusing on Indian Languages

We introduce an SSMT (Speech to Speech Machine Translation, aka Speech to Speech Video Translation) Pipeline(https://ssmt.iiit.ac.in/ssmtiiith), as web application for translating videos from one language to another by cascading multiple language modules. Our speech translation system combines highly accurate speech to text (ASR) for Indian English, pre-possessing modules to bridge ASR-MT gaps such as spoken disfluency and punctuation, robust machine translation (MT) systems for multiple language pairs, SRT module for translated text, text to speech (TTS) module and a module to render translated synthesized audio on the original video. It is user-friendly, flexible, and easily accessible system. We aim to provide a complete configurable speech translation experience to users and researchers with this system. It also supports human intervention where users can edit outputs of different modules and the edited output can then be used for subsequent processing to improve overall output quality. By adopting a human-in-the-loop approach, the aim is to configure technology in such a way where it can assist humans and help to reduce the involved human efforts in speech translation involving English and Indian languages. As per our understanding, this is the first fully integrated system for English to Indian languages (Hindi, Telugu, Gujarati, Marathi and Punjabi) video translation. Our evaluation shows that one can get 3.5+ MOS score using the developed pipeline with human intervention for English to Hindi. A short video demonstrating our system is available at https://youtu.be/MVftzoeRg48.