Reviewerly/exHarmony huggingface dataset:
Active Learning with an Adaptive Classifier for Inaccessible Big Data Analysis

Supervised machine learning (ML) approaches effectively derive valuable insights from big data. These approaches, on the other hand, require an extensive amount of high quality annotated data for training, created manually by domain experts through a costly and time-consuming process. To overcome this challenge, active learning (AL) is a promising approach, which can support a fast, cost-efficient and common strategy to deal with big data with limited labeling effort. Instead of annotating a large pool of unlabeled data, as in standard supervised learning, AL reduces the volume of data that requires manual annotation by effectively selecting subsets of highly informative samples for manual annotation within an iterative process. In this paper, we aim to present a robust approach utilizing AL to mitigate the aforementioned challenges and help the decision-makers. To be precise, we propose a framework involving a support vector machine (SVM) technique in AL for mining big data to manage inaccessible data situations. The proposed approach is tested on five different semi-supervised data sets. The performance of the proposed framework is evaluated using traditional ML classifiers such as Naïve Bayes (NB), Decision Tree (DT), Sequential Minimal Optimization (SMO), Random Forest (RF), Bagging and Adaboost. Among the reported classifiers, bagging achieves the best outcome, delivering 99.19% accuracy. According to the results of the experiment conducted we find that the proposed method increases the efficiency of the classifiers in AL with fewer training instances.
Illocutionary Performance and Objective Assessment in the Speech Act of Arguing

This paper endorses a view of argumentation and arguments that relates both to a special type of speech action, namely, the performance of speech acts of arguing. Its aim is to advance an analysis of those acts that takes into account two kinds of norms related to their correct performance, namely, felicity conditions and objective requirements related to the “correspondence with the facts.” It assumes that the requirement that certain objective conditions be satisfied is among the set of felicity conditions of speech acts of arguing. Taking this into account helps clarify the position and role of warrants in the performance and assessment of these acts.
Comparative Analysis of Hybrid Mobile App Development Frameworks

With the rise of mobile devices and their usage, a lot of development has been made in terms of the development of applications for mobile devices. Traditionally, app development was restricted to the particular operating system, and a separate codebase was required for applications to be developed for multiple operating systems. A new paradigm of development took place in recent years which was of Hybrid app development, leading to the development of multiple frameworks which allowed for a single codebase to be used for multiple operating systems. This paper explores the features and analysis of different hybrid app development frameworks available in the market. A comprehensive analysis has been made to compare the different frameworks which are cross-platform and support web, Android, and iOS platforms. The analysis shows that all the frameworks have their merits and usage of anyone framework over others can vary from case-to-case basis. The detailed analysis of the features will bring a general conclusion over the choice of framework.
Self-Gated Memory Recurrent Network for Efficient Scalable HDR Deghosting

We propose a novel recurrent network-based HDR deghosting method for fusing arbitrary length dynamic sequences. The proposed method uses convolutional and recurrent architectures to generate visually pleasing, ghosting-free HDR images. We introduce a new recurrent cell architecture, namely Self-Gated Memory (SGM) cell, that outperforms the standard LSTM cell while containing fewer parameters and having faster running times. In the SGM cell, the information flow through a gate is controlled by multiplying the gate's output by a function of itself. Additionally, we use two SGM cells in a bidirectional setting to improve output quality. The proposed approach achieves state-of-the-art performance compared to existing HDR deghosting methods quantitatively across three publicly available datasets while simultaneously achieving scalability to fuse variable-length input sequence without necessitating re-training. Through extensive ablations, we demonstrate the importance of individual components in our proposed approach. The code is available at https://val.cds.iisc.ac.in/HDR/HDRRNN/index.html.
Reference-Centric Models for Grounded Collaborative Dialogue

We present a grounded neural dialogue model that successfully collaborates with people in a partially-observable reference game.We focus on a setting where two agents each observe an overlapping part of a world context and need to identify and agree on some object they share.Therefore, the agents should pool their information and communicate pragmatically to solve the task.Our dialogue agent accurately grounds referents from the partner's utterances using a structured reference resolver, conditions on these referents using a recurrent memory, and uses a pragmatic generation procedure to ensure the partner can resolve the references the agent produces.We evaluate on the OneCommon spatial grounding dialogue task (Udagawa and Aizawa, 2019), involving a number of dots arranged on a board with continuously varying positions, sizes, and shades.Our agent substantially outperforms the previous state of the art for the task, obtaining a 20% relative improvement in successful task completion in self-play evaluations and a 50% relative improvement in success in human evaluations.
Virtual Dialogue Assistant for Remote Exams

A Virtual Dialogue Assistant (VDA) is an automated system intended to provide support for conducting tests and examinations in the context of distant education platforms. Online Distance Learning (ODL) has proven to be a critical part of education systems across the world, particularly during the COVID-19 pandemic. While the core components of ODL are sufficiently researched and developed to become mainstream, there is still a demand for various aspects of traditional classroom learning to be implemented or improved to match the expectations for modern ODL systems. In this work, we take a look at the evaluation of students’ performance. Various forms of testing are often present in ODL systems; however, modern Natural Language Processing (NLP) techniques provide new opportunities to improve this aspect of ODL. In this paper, we present an overview of VDA intended for integration with online education platforms to enhance the process of evaluation of students’ performance. We propose an architecture of such a system, review challenges and solutions for building it, and present examples of solutions for several NLP problems and ways to integrate them into the system. The principal challenge for ODL is accessibility; therefore, proposing an enhancement for ODL systems, we formulate the problem from the point of view of a user interacting with it. In conclusion, we affirm that relying on the advancements in NLP and Machine Learning, the approach we suggest can provide an enhanced experience of evaluation of students’ performance for modern ODL platforms.
Improved Preimage Attacks on 3-Round Keccak-224/256

In this paper, we provide an improved method on preimage attacks of standard 3-round Keccak-224/256. Our method is based on the work by Li and Sun. Their strategy is to find a 2-block preimage instead of a 1-block one by constructing the first and second message blocks in two stages. Under this strategy, they design a new linear structure for 2-round Keccak-224/256 with 194 degrees of freedom left, which is able to construct the second message block with a complexity of 231/262. However, the bottleneck of this strategy is that the first stage needs much more expense than the second one. Therefore, we improve the first stage by using two techniques. The first technique is constructing multi-block messages rather than one-block message in the first stage, which can reach a better inner state. The second technique is setting restricting equations more efficiently, which can work in 3-round Keccak-256. As a result, the complexity of finding a preimage for 3-round Keccak-224/256 can be decreased from 238/281 to 232/265.
Gyroscope-Based Video Stabilization for Electro-Optical Long-Range Surveillance Systems

Video stabilization is essential for long-range electro-optical systems, especially in situations when the field of view is narrow, since the system shake may produce highly deteriorating effects. It is important that the stabilization works for different camera types, i.e., different parts of the electromagnetic spectrum independently of the weather conditions and any form of image distortion. In this paper, we propose a method for real-time video stabilization that uses only gyroscope measurements, analyze its performance, and implement and validate it on a real-world professional electro-optical system developed at Vlatacom Institute. Camera movements are modeled with 3D rotations obtained by integration of MEMS gyroscope measurements. The 3D orientation estimation quality depends on the gyroscope characteristics; we provide a detailed discussion on the criteria for gyroscope selection in terms of the sensitivity, measurement noise, and drift stability. Furthermore, we propose a method for improving the unwanted motion estimation quality using interpolation in the quaternion domain. We also propose practical solutions for eliminating disturbances originating from gyro bias instability and noise. In order to evaluate the quality of our solution, we compared the performance of our implementation with two feature-based digital stabilization methods. The general advantage of the proposed methods is its drastically lower computational complexity; hence, it can be implemented for a low price independent of the used electro-optical sensor system.
Abrupt energy flipping in a continuously observed Rabi driven quantum bit

In the presence of a periodic external drive, the mean value of the energy of a two-level quantum system (or qubit) displays the well-known Rabi oscillatory dynamics that is usually described within the rotating wave approximation. In order to calculate its standard deviation, we get rid of this latter and obtain a huge ratio (of order unity) of standard deviation over mean value. This is due to the large-amplitude high-frequency oscillations of the internal as well as of the overall phase of the two-level system. In presence of continuous measurement of, say, the ground state energy that yields the quantum Zeno effect, the system remains frozen in its ground state as long as the dynamics of the driven system allows its energy to stay within its Rabi standard deviation. When reaching the boundary, the energy suddenly flips over to the excited level in order to stay further within its standard deviation. We model this energy flip by use of parametrized quantum transitions and show that it can be defined as the result of action quantization. We check our theory by recovering the so-called “quantum jumps” published by Minev et al. (2019).
Contribution of Artificial Intelligence and Virtual Worlds towards development of Super Intelligent AI Agents

Artificial Intelligence (AI) has always been a goal as well as a challenge in the scientific field with a whole new world being accessible to people by accomplishing various achievements in the past decades. However, in the last many years, tremendous popularity has been received by the interactive computer games involving multi-user virtual environment which has been the prime attraction for millions of users around the world. This paper focuses on AI agents along with exploring the progress towards super intelligent AI and the roles played by artificial intelligence and virtual worlds in its development. It follows a unique approach to give an unbiased view on the various problems and the relation between them. There are namely two benefits from this research: firstly, evolution in science and technology requires a profound understanding on the advanced AI agents in the virtual worlds, and secondly, the virtual worlds have always been a magnificent platform for research on the challenging problems faced in the domain of Artificial Intelligence.
NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender Neutral Alternatives

Recent years have seen an increasing need for gender-neutral and inclusive language.Within the field of NLP, there are various mono-and bilingual use cases where gender inclusive language is appropriate, if not preferred due to ambiguity or uncertainty in terms of the gender of referents.In this work, we present a rulebased and a neural approach to gender-neutral rewriting for English along with manually curated synthetic data (WinoBias+) and natural data (OpenSubtitles and Reddit) benchmarks.A detailed manual and automatic evaluation highlights how our NeuTral Rewriter, trained on data generated by the rule-based approach, obtains word error rates (WER) below 0.18% on synthetic, in-domain and out-domain test sets.
On the need for AI to triage encrypted data containers in U.S. law enforcement applications

This paper takes an analogical approach to define the parameters by which artificial intelligence (AI) can be utilized to facilitate warrantless searches at U.S. ports of entry. The authors tailor their discussion to the prevention of child pornography (also referred to as child abuse or exploitation materials in the academic literature), and the traffic thereof. By making the legal case to utilize AI, particularly eXplainable AI (XAI), to search encrypted devices for attributes indicative of child pornography, the authors hope to encourage research in this field and develop better technology to help catch criminals without relinquishing privacy rights.
Formalizing Geometric Algebra in Lean

Abstract This paper explores formalizing Geometric (or Clifford) algebras into the Lean 3 theorem prover, building upon the substantial body of work that is the Lean mathematics library, . As we use Lean source code to demonstrate many of our ideas, we include a brief introduction to the Lean language targeted at a reader with no prior experience with Lean or theorem provers in general. We formalize the multivectors as the quotient of the tensor algebra by a suitable relation, which provides the ring structure automatically, then go on to establish the universal property of the Clifford algebra. We show that this is quite different to the approach taken by existing formalizations of Geometric algebra in other theorem provers; most notably, our approach does not require a choice of basis. We go on to show how operations and structure such as involutions, versors, and the $$\mathbb {Z}_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>Z</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> -grading can be defined using the universal property alone, and how to recover an induction principle from the universal property suitable for proving statements about these definitions. We outline the steps needed to formalize the wedge product and $$\mathbb {N}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>N</mml:mi> </mml:math> -grading, and some of the gaps in that currently make this challenging.
EDUCATION 4.0 FRAMEWORK: ENRICHING ACTIVE LEARNING WITH VIRTUAL AND TECHNOLOGICAL TOOLS

At the beginning of 2020, many educators were still unclear what exactly it meant to design teaching-learning processes in the Fourth Industrial Revolution, “Education 4.0”. Despite this, and due to the Corona crisis, all institutions were dizzyingly pushed into partially known digital and technological environments, dragging teachers and students with them. In the field of higher education, the situation was doubly challenging: there were still deep misunderstandings about how to improve the cognitive abilities of Generation Z students, and serious confusion about the fact that technological tools -even advanced- can hardly replace an entire cognitive theory. The present work is a study on the effectiveness of the Active Learning approach in STEAM (Science, Technology, Engineering, Art and Mathematics); the didactic use of technological tools (including screencasting and podcasting) and, the design of innovative strategies to enhance the development of the skills declared in the Education 4.0 Framework: global citizenship, innovation and creativity, digital literacy and interpersonal awareness. The methodology used was quantitative-experimental with a 4-group Solomon design, involved more than 250 students and was developed over three years, including the two semesters of 2020 (fully online environments). The use of VALUE rubrics (Valid Assessment of Learning in Undergraduate Education) allowed the obtaining of conclusive results: (i) the importance of a correct diagnosis of the development of digital literacy skills to promote the formation of metacognitive awareness; (ii) the impact of the design and implementation of adequate cognitive tools in the quality of the learning outcomes; and (iii) the relevance of the dosed use of technological tools in each stage of a 2D learning taxonomy (cognitive process dimension / orthogonal knowledge dimension). Keywords: Critical Thinking, Creativity, Higher Education, Educational Innovation
Efficient nonlinear witnessing of non–absolutely separable states with lossy detectors

Entangled states are undoubtedly an integral part of various quantum information processing tasks. On the other hand, absolutely separable states which cannot be made entangled under any global unitary operations are useless from the resource theoretic perspective, and hence identifying non-absolutely separable states can be an important issue for designing quantum technologies. Here we report that nonlinear witness operators provide significant improvements in detecting non-absolutely separable states over their linear analogs, by invoking examples of states in various dimensions. We also address the problem of closing detection loophole and find critical efficiency of detectors above which no fake detection of non-absolutely separable (non-absolutely positive partial transposed) states is possible.
Designing Adaptive Extended Reality Training Systems Based on Expert Instructor Behaviors

Advances in training technologies using Extended Reality (XR) offer dramatic improvements in terms of time and associated costs over traditional training methods. Industries that are reliant on human labor are starting to embrace XR devices such as Virtual and Augmented Reality (VR/AR) Head-Mounted Displays (HMDs) to capitalize on these advantages. Additionally, developers now have the capacity to create customized user experiences and reduce instructor workload by employing adaptive automation in XR training simulations. Adaptive systems analyze the behavior of a learner for a trigger and then deploy an adaptation to alter the system state. While examples of adaptive XR training systems in current literature show that they are feasible to develop, further research must be done to quantify their efficacy. Furthermore, recommendations for how to design triggers and adaptations for XR training simulations are currently non-existent, resulting in negative impacts to training if inappropriate adaptations are implemented. This paper provides novel, evidence-based recommendations for the design of future adaptive XR training systems by learning from expert XR instructors. To this end, semi-structured interviews were conducted with 11 XR trainers. Participants were asked to discuss their experiences dealing with learners who exhibited confusion during XR training. Interviews were analyzed for existing and emerging themes. Finally, these themes were applied to existing trigger and adaptation models and synthesized into design recommendations for XR training developers. The outcomes of this work will inform the future development of adaptive XR training platforms.
DatalogMTL with Negation Under Stable Models Semantics

We introduce negation under stable models semantics in DatalogMTL—a temporal extension of Datalog with metric operators. As a result, we obtain a rule language which combines the power of answer set programming with the temporal dimension provided by metric operators. We show that, in this setting, reasoning becomes undecidable over the rationals and decidable in EXPSPACE in data complexity over the integers. We also show that, if we restrict our attention to forward-propagating programs (where rules propagate information in a single temporal direction), reasoning over integers becomes PSPACE-complete in data complexity and hence no harder than over positive programs; however, reasoning over the rationals in this fragment remains undecidable.
Fake science for sale? How Endocrine Connections is tackling paper mills

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Population Risk Improvement with Model Compression: An Information-Theoretic Approach

It has been reported in many recent works on deep model compression that the population risk of a compressed model can be even better than that of the original model. In this paper, an information-theoretic explanation for this population risk improvement phenomenon is provided by jointly studying the decrease in the generalization error and the increase in the empirical risk that results from model compression. It is first shown that model compression reduces an information-theoretic bound on the generalization error, which suggests that model compression can be interpreted as a regularization technique to avoid overfitting. The increase in empirical risk caused by model compression is then characterized using rate distortion theory. These results imply that the overall population risk could be improved by model compression if the decrease in generalization error exceeds the increase in empirical risk. A linear regression example is presented to demonstrate that such a decrease in population risk due to model compression is indeed possible. Our theoretical results further suggest a way to improve a widely used model compression algorithm, i.e., Hessian-weighted K-means clustering, by regularizing the distance between the clustering centers. Experiments with neural networks are provided to validate our theoretical assertions.
Virtual Reality Software as Preparation Tools for Oral Presentations: Perceptions From the Classroom

Although there is a wealth of studies on the role of virtual reality (VR) technology in language acquisition, little research has been done on the utilization of virtual reality mobile applications in English language acquisition, especially in developing oral presentation skills. The main purpose of this case study is to investigate the usefulness of VirtualSpeech, which is a virtual reality mobile application, as a preparation tool for oral presentations via the perceptions of international students and their instructor. The participants are five Academic English Program (AEP) students and their English language instructor at Saint Michael’s College. Qualitative analyses were performed on data collected from: (1) learning logs that were shared between the students and the researcher, (2) surveys that were sent to the students and their instructor, (3) one-to-one interviews with the students, and (4) the researcher’s fieldnotes of the students’ oral presentation performances. The results of the study show that all of the participants recommend using VirtualSpeech as a tool to prepare for oral presentations. Most of the students reported higher confidence levels while speaking English after their use of VirtualSpeech. Thus, this case study can be considered as a contribution to the research on VR mobile applications in English language acquisition.