Reviewerly/exHarmony huggingface dataset:
Comparing silent reading performance for single sentences and paragraphs: an eye movement-based analysis

Reading performance in clinical practice is commonly evaluated by reading 'aloud' and 'as fast as possible' single sentences. Assessing comprehensive silent reading performance using passages, composed of multiple sentences, is the preferred reading mode in real-life reading conditions.The purpose of this study was to compare eye movement-based silent reading performance for standardised short sentences and paragraphs.A group of 15 young volunteers (age range: 22-36 years) read silently and comprehensively in two sessions: (a) a paragraph with continuous text and (b) standardised short sentences. Text print size was 0.4 logMAR (1.0 M at 40 cm distance). Eye movements during reading were recorded using video oculography (EyeLink II, SR Research Ltd). Data analysis included computation of reading speed, fixation duration, the number of fixations, saccadic amplitude and percentage of regressions. Moreover, frequency distributions of fixation durations were analysed with ex-Gaussian fittings.Repeatability coefficient in silent reading speed was found better for the paragraph (66 wpm) than for short sentences (88 wpm). The superiority in repeatability coefficient for the corresponding eye movement parameters, i.e. fixation duration (35 vs 73 ms), regressions (10.1 vs. 22.3%) and fixations per word (0.21 vs. 0.37 fpw), was even more pronounced. In addition, a statistically significant improvement with the paragraph was found in average fixation duration (19 ± 26 ms, p = 0.02), regressions (4.2 ± 7.0%, p = 0.04) and ex-Gaussian fixation parameter, τ (82 vs. 111 ms). No statistically significant difference was found between average reading speed with the paragraph (220 ± 59 wpm) and the short sentences (206 ± 57 wpm) (p = 0.11).Due to their superior repeatability, paragraphs are preferable to short sentences when evaluating silent comprehensive reading. The concurrent recording of eye movement parameters in silent reading further improves variability and could offer an efficient measure of reading performance and a reliable biomarker of visuo-motor function.
A new modified bat algorithm for global optimization

Bat Algorithm, is an evolutionary computation technique based on the echolocation behaviour of microbats while looking for their prey. It is used to perform global optimization. It was developed by Xin-She Yang in 2010. Since then, it has extensively been applied in various optimization problems because of its simple structure and robust performance. Continuous, discrete, or binary, many variants were proposed over the last few years, with applications to solve real-world cases in different fields. Yet, it has one major drawback: its premature convergence due to a lack in its exploration ability. In this paper, we introduce a selection-based improvement and three other modifications to the standard version of this metaheuristic in order to enhance the diversification and intensification capabilities of the algorithm. The newly proposed method has been then tested on 20 standard benchmark functions and the CEC2005 benchmark suit. Some non-parametric statistical tests were also used to compare the New Bat algorithm with other algorithms, and results indicate that the new method is very competitive and outperforms some of the state-of-the-art algorithms.
MCL: Multimodal Contrastive Learning for Deepfake Detection

Advancements in computer vision and deep learning have led to difficulty in distinguishing Deepfake and real videos. In particular, forgery audios are also generated to accompany fake videos and make them more realistic, which makes Deepfake detection more difficult. Existing Deepfake detection methods that use multimodal information ignore the representation gap between different modalities, resulting in limited performance. To address this problem, in this paper, a novel Deepfake detection method utilizing multimodal contrastive learning (MCL) is proposed to better explore intra-modal and cross-modal forgery clues. To reduce the cross-modal gap and explore multimodal forgery artifacts, a cross-modal contrastive learning strategy is designed to learn a compositional embedding from multimodal information, which facilitates pulling together representations across uni-modalities and multi-modalities. Moreover, to supplement the intra-frame forgery clues mining ability of the video network, the frame knowledge is distilled to the video network without adding additional computation. Specifically, to mine intra-modal clues, three modality features are first extracted from audio, frame and video, respectively. Secondly, the audio and frame features are separately composed with the video feature to derive two cross-modal representations. Subsequently, these cross-modal features are contrastive with the intra-modal features to reduce cross-modal gap. By jointly pulling together the unimodal and multimodal features through MCL, a more effective representation that contains intra-modal and cross-modal forgery artifacts can be learned. Finally, a noise-based feature augmentation (NFA) module is proposed to adaptively perturb the audio-visual feature and further improve generalization performance. Extensive experiments demonstrate that the proposed framework outperforms SOTA methods.
Design of Convolutional Neural Networks Architecture for Non-Profiled Side-Channel Attack Detection

Deep learning (DL) is a new option that has just been made available for side-channel analysis. DL approaches for profiled side-channel attacks (SCA) have dominated research till now. In this attack, the attacker has complete control over the profiling device and can collect many traces for a range of critical parameters to characterise device leakage before the attack. In this study, we apply DL algorithms to non-profiled data. An attacker can only retrieve a limited number of side-channel traces from a closed device with an unknown key value in non-profiled mode. The authors conducted this research. Key estimations and deep learning measurements can reveal the secret key. We prove that this is doable. This technology is excellent for non-profits. DL and neural networks can benefit these organisations. Neural networks can provide a new technique to verify the safety of hardware cryptographic algorithms. It was recently suggested. This study creates a non-profiled SCA utilising convolutional neural networks (CNNs) on an AVR microcontroller with 8 bits of memory and the AES-128 cryptographic algorithm. We used aligned power traces with several samples to demonstrate how challenging CNN-based SCA is in practise. This will help us reach our goals. Here is another technique to create a solid CNN data set. In particular, CNN-based SCA experiment data and noise effects are examined. These experiments employ power traces with Gaussian noise. The CNN-based SCA works well with our data set for non-profiled attacks. Gaussian noise on power traces causes many more issues. These results show that our method can recover more bytes successfully from SCA compared to other methods in correlation power analysis (CPA) and DL-SCA without regularisation.
TCM Function Multi-classification Approach Using Deep Learning Models

Traditional Chinese Medicine prescriptions are regarded as an important resource that brings together the treatment experience and wisdom of doctors of all ages. Effectively sorting out and excavating them, especially combining with their functional indications and medication rules, can improve clinical efficacy and new drug research and development. Because the classification system of prescriptions is not completely unified, the data of Chinese patent medicines, national standard formulae and the seventh editions of Chinese formula textbooks are manually integrated, and a quadrat data set containing 21 efficacy classifications is constructed. Since on the text description of prescription information (prescription name, composition, indications and efficacy) in the data set, a variety of deep learning text classification models are used to automatically judge prescription classification, so as to establish an efficient and accurate classification model, and finally construct a prescription efficacy classification data set. The experimental results show that the pre-trained Bert-CNN model has the best effect, with the accuracy rate of 77.87%, and the weighted accuracy rate, weighted recall rate and weighted F1 value of 79.46%, 77.87% and 77.44%, respectively. This study provides a useful reference for further realizing the automatic information processing of ancient formulas.
Art of Latest Technologies: Problems, Prospects

The purpose or the study is to investigate permanent digitization, which creates a new platform for art with its virtual worlds and new ideas. Research methodology. To analyse new trends in the development of art in terms of technological development, theoretical and empirical analysis, synthesis, comparison, generalisation were used to study scientific sources, materials of theoretical research, modern artistic concepts. In addition, the study is based on the dialectical method necessary for art historical research, critical analysis of cultural and art historical sources; methods of deduction and induction; historical, logical methods, and method of interdisciplinary synthesis. Scientific novelty. The influence of the Internet on art today goes far beyond the Internet art community. Its diverse technological developments are shaping new generations of artists, while the culture and values that guided its creation continue to transform the ways we create and strengthen the relationship between artists, works, and the public. Technological innovations have influenced creativity and artistic expression. However, this relationship is not one-way, and it is in the description of this heterogeneity that the novelty of the work emerges. Conclusions. The development of technology is always accompanied by scientific advances. Art, on the other hand, conveys ideas that feed society with emotions. The technical framework in which we live shapes our creativity. However, this structure is changing, as ideas expressed through computer code take up more and more space in our everyday lives. And this, in turn, changes the concept of the artist in the age of networks. The development of the Internet and web technologies has had a profound impact on artistic creativity. The emergence of the Internet has given rise to networked art, a term that refers to works created on the Internet. Online artists use the web as a means of distribution, as a tool for artistic production, and as a living space for their work. They draw inspiration from its own characteristics to create new works, as well as to experiment with new creative processes. Researchers and engineers also turn to art when trying to solve scientific and technical problems. Contemporary artists are shaking up the way we think about creativity. Algorithms can now create new works of art, drawing inspiration from thousands of images and inventing new artistic styles. An overview of modern algorithms that are transforming the world of art is ready to challenge the conventional notion of creativity.&#x0D; Key words: Internet network, network art, artistic coding, computer culture, creativity.
JBLiMP: Japanese Benchmark of Linguistic Minimal Pairs

In this paper, we introduce JBLiMP (Japanese Benchmark of Linguistic Minimal Pairs), a novel dataset for targeted syntactic evaluations of language models in Japanese. JBLiMP consists of 331 minimal pairs, which are created based on acceptability judgments extracted from journal articles in theoretical linguistics. These minimal pairs are grouped into 11 categories, each covering a different linguistic phenomenon. JBLiMP is unique in that it successfully combines two important features independently observed in existing datasets: (i) coverage of complex linguistic phenomena (cf. CoLA) and (ii) presentation of sentences as minimal pairs (cf. BLiMP). In addition, JBLiMP is the first dataset for targeted syntactic evaluations of language models in Japanese, thus allowing the comparison of syntactic knowledge of language models across different languages. We then evaluate the syntactic knowledge of several language models on JBLiMP: GPT-2, LSTM, and n-gram language models. The results demonstrated that all the architectures achieved comparable overall accuracies around 75%. Error analyses by linguistic phenomenon further revealed that these language models successfully captured local dependencies like nominal structures, but not long-distance dependencies such as verbal agreement and binding.
Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing

We develop models to classify desirable evidence and desirable reasoning revisions in student argumentative writing. We explore two ways to improve classifier performance – using the essay context of the revision, and using the feedback students received before the revision. We perform both intrinsic and extrinsic evaluation for each of our models and report a qualitative analysis. Our results show that while a model using feedback information improves over a baseline model, models utilizing context - either alone or with feedback - are the most successful in identifying desirable revisions.
TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs

Aditya Sharma, Apoorv Saxena, Chitrank Gupta, Mehran Kazemi, Partha Talukdar, Soumen Chakrabarti. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
Parameter-efficient Modularised Bias Mitigation via AdapterFusion

Deepak Kumar, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickhoff, Markus Schedl, Navid Rekabsaz. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks

Dialogue systems capable of social influence such as persuasion, negotiation, and therapy, are essential for extending the use of technology to numerous realistic scenarios. However, existing research primarily focuses on either task-oriented or open-domain scenarios, a categorization that has been inadequate for capturing influence skills systematically. There exists no formal definition or category for dialogue systems with these skills and data-driven efforts in this direction are highly limited. In this work, we formally define and introduce the category of social influence dialogue systems that influence users’ cognitive and emotional responses, leading to changes in thoughts, opinions, and behaviors through natural conversations. We present a survey of various tasks, datasets, and methods, compiling the progress across seven diverse domains. We discuss the commonalities and differences between the examined systems, identify limitations, and recommend future directions. This study serves as a comprehensive reference for social influence dialogue systems to inspire more dedicated research and discussion in this emerging area.
ALANNO: An Active Learning Annotation System for Mortals

Supervised machine learning has become the cornerstone of today’s data-driven society, increasing the need for labeled data. However, the process of acquiring labels is often expensive and tedious. One possible remedy is to use active learning (AL) – a special family of machine learning algorithms designed to reduce labeling costs. Although AL has been successful in practice, a number of practical challenges hinder its effectiveness and are often overlooked in existing AL annotation tools. To address these challenges, we developed ALANNO, an open-source annotation system for NLP tasks equipped with features to make AL effective in real-world annotation projects. ALANNO facilitates annotation management in a multi-annotator setup and supports a variety of AL methods and underlying models, which are easily configurable and extensible.
TrelBERT: A pre-trained encoder for Polish Twitter

Pre-trained Transformer-based models have become immensely popular amongst NLP practitioners. We present TrelBERT – the first Polish language model suited for application in the social media domain. TrelBERT is based on an existing general-domain model and adapted to the language of social media by pre-training it further on a large collection of Twitter data. We demonstrate its usefulness by evaluating it in the downstream task of cyberbullying detection, in which it achieves state-of-the-art results, outperforming larger monolingual models trained on general-domain corpora, as well as multilingual in-domain models, by a large margin. We make the model publicly available. We also release a new dataset for the problem of harmful speech detection.
Comparing domain-specific and domain-general BERT variants for inferred real-world knowledge through rare grammatical features in Serbian

Transfer learning is one of the prevailing approaches towards training language-specific BERT models. However, some languages have uncommon features that may prove to be challenging to more domain-general models but not domain-specific models. Comparing the performance of BERTić, a Bosnian-Croatian-Montenegrin-Serbian model, and Multilingual BERT on a Named-Entity Recognition (NER) task and Masked Language Modelling (MLM) task based around a rare phenomenon of indeclinable female foreign names in Serbian reveals how the different training approaches impacts their performance. Multilingual BERT is shown to perform better than BERTić in the NER task, but BERTić greatly exceeds in the MLM task. Thus, there are applications both for domain-general training and domain-specific training depending on the tasks at hand.
Model-Free Visual Servo Swarming of Manned-Unmanned Surface Vehicles With Visibility Maintenance and Collision Avoidance

In this paper, aiming at a fleet of manned-unmanned surface vehicles (MUSVs), a novel visual servo swarming (VSS) mechanism is deliberately established by embodying visibility maintenance, swarm aggregation, collision avoidance and velocity matching. By making full use of line-of-sight ranges and angles between neighbors, a swarm of unmanned surface vehicles (USVs) with unknown inertia masses, internal dynamics and external disturbances can cooperate with a manned surface vehicle (MSV), thereby emerging flexibly collective behaviors in GPS-denied environments. To endow MUSVs with individually flexible behaviors, the VSS mechanism renders velocity matching of USVs with the MSV executing human-intelligence intention. Meanwhile, barrier Lyapunov functions are employed to reliably maintain visibility and avoid collisions among individuals, simultaneously. Distributed neural approximators using reduced-dimension inputs are devised to estimate unknown dynamics of USVs, while residual uncertainties are thoroughly suppressed by robust adaptations, thereby contributing to a model-free VSS (MVSS) scheme. Eventually, together with projection-based adaptive laws, the MVSS-based controller ensures uniform boundedness of estimation parameters and asymptotic convergence of regulation errors. Simulation results demonstrate remarkable efficacy in terms of collective performance.
Spatial-Temporal Graph Model Based on Attention Mechanism for Anomalous IoT Intrusion Detection

We propose an attention-weighted model for parallel extraction of spatial-temporal features to enhance the detection capabilities in the message queuing telemetry transport protocol, widely used in the Internet of Things. Our approach involves constructing perception node collection graphs based on packet header information, which capture transmission-dependent and context-sequence-dependent relationships in the data streams. We leverage a message-passing mechanism to aggregate adjacent nodes and update the weight matrix accordingly. Additionally, we employ a bidirectional long short-term memory model to capture long-distance dependencies in the sequence. The updated graph and the output of the time-series model are fused and processed by a self-attention mechanism, generating weights for classification. The classification results are obtained using a fully connected network. We evaluate our approach on four datasets (ToN-IoT, BoT-IoT, UNSW-NB15, and DoHBrw2020) and compare it against nine different algorithms. Experimental results demonstrate the effectiveness of our method, achieving high accuracy levels, such as 0.8874 on ToN-IoT, 0.9386 on BoT-IoT, 0.9390 on DoHBrw2020, and the best accuracy of 0.8659 on the unbalanced UNSW-NB15 dataset.
Overview of the CLEF 2023 SimpleText Lab: Automatic Simplification of Scientific Texts

There is universal consensus on the importance of objective scientific information, yet the general public tends to avoid scientific literature due to access restrictions, its complex language or their lack of prior background knowledge. Academic text simplification promises to remove some of these barriers, by improving the accessibility of scientific text and promoting science literacy. This paper presents an overview of the CLEF 2023 SimpleText track addressing the challenges of text simplification approaches in the context of promoting scientific information access, by providing appropriate data and benchmarks, and creating a community of IR and NLP researchers working together to resolve one of the greatest challenges of today. The track provides a corpus of scientific literature abstracts and popular science requests. It features three tasks. First, content selection (what is in, or out?) challenges systems to select passages to include in a simplified summary in response to a query. Second, complexity spotting (what is unclear?) given a passage and a query, aims to rank terms/concepts that are required to be explained for understanding this passage (definitions, context, applications). Third, text simplification (rewrite this!) given a query, asks to simplify passages from scientific abstracts while preserving the main content.
ELT Students' Perceptions Toward Mobile-Assisted Language Learning (MALL): Exploring its Effects on Motivation and Learner Autonomy

The current study investigated ELT students’ perceptions toward Mobile-Assisted Language Learning (MALL) and how MALL affects learners’ level of motivation and autonomy. 110 students who were enrolled in an English Language Teaching program at a university participated in the study. The study adopted a mixed-method design in which data were collected through an online questionnaire and written interview questions. Descriptive statistics were used to get the overall picture of the learners’ perceptions of MALL. ANOVA was conducted to see if there were any significant differences among the four grades of ELT students, and a chi-square test was implemented to compare the autonomy and motivation levels of students who claim to use mobile applications for language learning with those who claim they do not. The written interview questions were examined using content analysis method. Results of the study indicated that students generally had positive opinions about using mobile applications for language learning purposes. Another interesting finding of the study was that 1st-grade ELT students had more negative perceptions toward MALL compared to the perceptions of other grades. Finally, there were statistically no significant differences in terms of learner autonomy and motivation between the students who use mobile language learning applications and those who do not.