Reviewerly/exHarmony huggingface dataset:
Exploring the performance of automatic speaker recognition using twin speech and deep learning-based artificial neural networks

Authors:
raw_author_name: Plinio A. Barbosa
raw_affiliation_string: Department of Linguistics, Institute of Language Studies, University of Campinas, Campinas, Brazil

Abstract: This study assessed the influence of speaker similarity and sample length on the performance of an automatic speaker recognition (ASR) system utilizing the SpeechBrain toolkit. The dataset comprised recordings from 20 male identical twin speakers engaged in spontaneous dialogues and interviews. Performance evaluations involved comparing identical twins, all speakers in the dataset (including twin pairs), and all speakers excluding twin pairs. Speech samples, ranging from 5 to 30 s, underwent assessment based on equal error rates (EER) and Log cost-likelihood ratios (Cllr). Results highlight the substantial challenge posed by identical twins to the ASR system, leading to a decrease in overall speaker recognition accuracy. Furthermore, analyses based on longer speech samples outperformed those using shorter samples. As sample size increased, standard deviation values for both intra and inter-speaker similarity scores decreased, indicating reduced variability in estimating speaker similarity/dissimilarity levels in longer speech stretches compared to shorter ones. The study also uncovered varying degrees of likeness among identical twins, with certain pairs presenting a greater challenge for ASR systems. These outcomes align with prior research and are discussed within the context of relevant literature.
An approach based on maximal cliques and multi-density clustering for regional co-location pattern mining

Authors:
raw_author_name: Dongsheng Wang
raw_affiliation_string: School of Information Science and Engineering, Yunnan University, Kunming, 650091, China
raw_author_name: Lizhen Wang
raw_affiliation_string: Dianchi College, Kunming 650228, China; School of Information Science and Engineering, Yunnan University, Kunming, 650091, China
raw_author_name: Xiaoxu Wang
raw_affiliation_string: Dianchi College, Kunming 650228, China
raw_author_name: Vanha Tran
raw_affiliation_string: FPT University, Hanoi 155514, Vietnam

Abstract: Spatial co-location pattern (SCP) mining aims to mine the implicit relationships between different spatial features. These features often have certain connections and co-occur in close geographical proximity. Regional co-location pattern (RCP) mining is a branch of SCP mining, which is usually used to discover some sets of spatial features that do not often co-occur in large spatial scales but co-occur in local regions. Discovering RCPs is still very challenging, because different RCPs will be obtained under different region partitions. However, existing region division methods still suffer from ignoring the influence of the density of individual feature instances, low recognition rate of regions with low density distribution but containing RCPs, and lack of semantic information in the delineated regions. To this end, first, we propose a novel multi-density clustering method based on maximal cliques (MCs) during the partitioning phase of RCP mining. Second, we design a two-stage mining algorithm based on MCs in the mining phase, which fully exploits the advantages of the MC to improve the mining efficiency, and the algorithm can quickly obtain new mining results when changing the prevalence threshold. Third, regional similarity is defined based on RCPs over regions to merge similar sub-regions. Finally, the proposed method is compared with a state-of-the-art method on both synthetic and real datasets. The experimental results show that the proposed method can not only effectively solve the issues of existing methods to make the divided sub-regions more closely matched with the real spatial distribution, but also quickly obtain new mining results when changing the prevalence threshold.
Re-scoring using image-language similarity for few-shot object detection

Authors:
raw_author_name: Joohee Kim
raw_affiliation_string: AI Lab, Infiniq, Seoul, 06232, Republic of Korea

Abstract: Few-shot object detection, which focuses on detecting novel objects with few labels, is an emerging challenge in the community. Recent studies show that adapting a pre-trained model or modified loss function can improve performance. In this paper, we explore leveraging the power of Contrastive Language-Image Pre-training (CLIP) and hard negative classification loss in low data setting. Specifically, we propose Re-scoring using Image-language Similarity for Few-shot object detection (RISF) which extends Faster R-CNN by introducing Calibration Module using CLIP (CM-CLIP) and Background Negative Re-scale Loss (BNRL). The former adapts CLIP, which performs zero-shot classification, to re-score the classification scores of a detector using image-class similarities, the latter is modified classification loss considering the punishment for fake backgrounds as well as confusing categories on a generalized few-shot object detection dataset. Extensive experiments on MS-COCO and PASCAL VOC show that the proposed RISF substantially outperforms the state-of-the-art approaches. The code will be available.
Practitioners' expectations on automated release note generation techniques

Authors:
raw_author_name: Sristy Sumana Nath
raw_affiliation_string: Department of Computer Science University of Saskatchewan Saskatoon Saskatchewan Canada
raw_author_name: Banani Roy
raw_affiliation_string: Department of Computer Science University of Saskatchewan Saskatoon Saskatchewan Canada

Abstract: Abstract The software development life cycle relies heavily on the software release note, a crucial document. Various practitioners, including project managers and clients, benefit from release notes as they provide an overview of the latest software release. However, the manual generation of release notes is a time‐consuming and stressful task. Researchers have recently proposed automated techniques to generate release notes, saving developers' time and enhancing their understanding of software projects. Unfortunately, the adoption of these tools in practice remains limited. To address this gap, we have taken steps to understand the expectations and requirements of practitioners regarding release note generation techniques before implementing new automated approaches. Consequently, our approach involves two main stages: First, we conduct a comprehensive review of the relevant literature and analyze existing release notes from GitHub repositories to gain insights into the current practices. Second, we conduct an online survey study to gather input from practitioners and understand their expectations regarding release notes. We have reviewed 16 papers related to release notes and explored 3347 release notes from 21 GitHub repositories. Our analysis revealed key artifacts present in release note contents, including issues (29%), pull requests (32%), commits (19%), and common vulnerabilities and exposures (CVE) issues (6%). Additionally, we conducted a survey study involving 32 professionals to understand the essential information that should be included in release notes based on users' roles. For instance, project managers were more interested in learning about new features rather than less critical bug fixes. Furthermore, we identified gaps in existing systems and essential factors to consider when implementing release notes techniques in software engineering. The insights gained from our study can guide future research directions and assist practitioners in generating release notes with relevant content, thus improving the overall quality of documentation in software development.
Orientation-aware leg movement learning for action-driven human motion prediction

Authors:
raw_author_name: Chunzhi Gu
raw_affiliation_string: Toyohashi University of Technology, Toyohashi-shi, Aichi 4418122, Japan
raw_author_name: Chao Zhang
raw_affiliation_string: University of Fukui, Fukui-shi, Fukui 9108507, Japan
raw_author_name: Shigeru Kuriyama
raw_affiliation_string: Toyohashi University of Technology, Toyohashi-shi, Aichi 4418122, Japan

Abstract: The task of action-driven human motion prediction aims to forecast future human motion based on the observed sequence while respecting the given action label. It requires modeling not only the stochasticity within human motion but the smooth yet realistic transition between multiple action labels. However, the fact that most datasets do not contain such transition data complicates this task. Existing work tackles this issue by learning a smoothness prior to simply promote smooth transitions, yet doing so can result in unnatural transitions especially when the history and predicted motions differ significantly in orientations. In this paper, we argue that valid human motion transitions should incorporate realistic leg movements to handle orientation changes, and cast it as an action-conditioned in-betweening (ACB) learning task to encourage transition naturalness. Because modeling all possible transitions is virtually unreasonable, our ACB is only performed on very few selected action classes with active gait motions, such as “Walk” or “Run”. Specifically, we follow a two-stage forecasting strategy by first employing the motion diffusion model to generate the target motion with a specified future action, and then producing the in-betweening to smoothly connect the observation and prediction to eventually address motion prediction. Our method is completely free from the labeled motion transition data during training. To show the robustness of our approach, we generalize our trained in-betweening learning model on one dataset to two unseen large-scale motion datasets to produce natural transitions. Extensive experimental evaluations on three benchmark datasets demonstrate that our method yields the state-of-the-art performance in terms of visual quality, prediction accuracy, and action faithfulness.
GRRLN: Gated Recurrent Residual Learning Networks for code clone detection

Authors:
raw_author_name: Xiangping Zhang
raw_affiliation_string: Hunan Automotive Engineering Vocational College Zhuzhou Hunan China; School of Computer Science and Technology Hunan University of Science and Technology Xiangtan Hunan China
raw_author_name: Jianxun Liu
raw_affiliation_string: School of Computer Science and Technology Hunan University of Science and Technology Xiangtan Hunan China
raw_author_name: Min Shi
raw_affiliation_string: Harvard Ophthalmology AI Lab Harvard Medical School Boston Massachusetts USA

Abstract: Abstract Code clone detection is a critical problem in software development and maintenance domains. It aims to identify functionally identical or similar code fragments within an application. Existing works formulate the code clone detection task as a binary classification problem which predicts a code pair as a clone or not based on a pre‐defined threshold. In reality, there are various types of code clone subject to the degree of how a pair of code fragments are similar to each other. To investigate the effect of different code clone detection manners on the clone detection result, we propose Gated Recurrent Residual Learning Networks (GRRLN), a novel neural network model for code clone detection. To train GRRLN, we first represent each code fragment as a statement‐level tree sequence derived from the whole abstract syntax tree (AST). Then, a gated recurrent neural network with residual connections is adopted to fully extract the semantics of all individual statement trees together with their dependency relationships across the input statement sequence. Finally, the output representations of code fragments by GRRLN are used for similarity calculation and clone detection. We evaluate GRRLN using two real‐world datasets for code clone detection and clone type classification. Experiments show that GRRLN achieves promising and compelling results and meanwhile needs significantly less time and memory consumption compared with the state‐of‐the‐art methods.
Expanding information behaviour boundaries: A study with religious leaders

Authors:
raw_author_name: Evandro Ribeiro Rodrigues
raw_affiliation_string: Faculty of Arts and Humanities, University of Coimbra, Portugal
raw_author_name: Ana Lúcia Terra
raw_affiliation_string: Faculty of Arts and Humanities, University of Coimbra, Portugal

Abstract: Information behaviour is a very productive research area in information science, although some study contexts are still little explored, such as religious and spiritual practices, particularly related to minority religions. Thus, this study investigated the information behaviour of religious leaders of Santo Daime, a religion originating in the Brazilian Amazon. Exploratory research with a qualitative approach and semi-structured interviews with four leaders of the doctrine were carried out. The most used sources of information were documents about the Daime religion, personal experience, contact with other leaders and the institution. Information was sought to guide the participants in their rituals, solve administrative problems, improve their knowledge and strengthen their faith. Used in a critical, reflective and creative way, it seeks coherence with the Daimist principles. The findings may contribute to the understanding of the information behaviour of this little-studied social group and the appreciation of cultural and religious diversity in society.
From NeurODEs to AutoencODEs: A mean-field control framework for width-varying neural networks

Authors:
raw_author_name: Massimo Fornasier
raw_affiliation_string: Munich Center for Machine Learning (MCML), Munich, Germany; Munich Data Science Institute (MDSI), Munich, Germany; School of Computation, Information and Technology, Technical University Munich, Munich, Germany

Abstract: Abstract The connection between Residual Neural Networks (ResNets) and continuous-time control systems (known as NeurODEs) has led to a mathematical analysis of neural networks, which has provided interesting results of both theoretical and practical significance. However, by construction, NeurODEs have been limited to describing constant-width layers, making them unsuitable for modelling deep learning architectures with layers of variable width. In this paper, we propose a continuous-time Autoencoder, which we call AutoencODE, based on a modification of the controlled field that drives the dynamics. This adaptation enables the extension of the mean-field control framework originally devised for conventional NeurODEs. In this setting, we tackle the case of low Tikhonov regularisation, resulting in potentially non-convex cost landscapes. While the global results obtained for high Tikhonov regularisation may not hold globally, we show that many of them can be recovered in regions where the loss function is locally convex. Inspired by our theoretical findings, we develop a training method tailored to this specific type of Autoencoders with residual connections, and we validate our approach through numerical experiments conducted on various examples.
Modification of the wave algorithm for Euclidean distances calculation in transport applications

Authors:
raw_author_name: A. L. Kuznetsov
raw_affiliation_string: Admiral Makarov State University of Maritime and Inland Shipping

Abstract: Many engineering problems could be either reduced to or directly involve the mathematical problem of finding the shortest path in some confined space with obstacles. To solve this problem, graph theory and dynamic programming offers quite a lot of exact and heuristic algorithms that work with directed and undirected, weighted and unweighted, connected and disconnected graphs. All these methods are based on their own techniques of calculating the distance between the vertices, the choice of which is dictated by the convenience of implementation and methodological considerations. As a result, the resulted paths found by the algorithms usually are characterized by a certain abstract length, which is often difficult to convert into Euclidean one. Nevertheless, spatial problems in engineering practice often require an answer in terms of real physical distances. Specifically, such a task fully arises in engineering applications related to the design of seaports, namely, in the laying of routes of intra-terminal transport, power supply networks, in the design of approach channels. A modification of the most common method for finding the shortest paths, the wave orthogonal-diagonal algorithm, is described in the paper. It allows you to include the geometric characteristics of its individual sections in the calculation of the length of the found route, which makes it possible to accurately estimate the length in the sense of the Euclidean distance.
The ecology of online hate speech: Mapping expert perspectives on the drivers for online hate perpetration with the Delphi method

Authors:
raw_author_name: Ina Weber
raw_affiliation_string: Department of Communication Studies University of Antwerp Antwerp Belgium
raw_author_name: Heidi Vandebosch
raw_affiliation_string: Department of Communication Studies University of Antwerp Antwerp Belgium
raw_author_name: Karolien Poels
raw_affiliation_string: Department of Communication Studies University of Antwerp Antwerp Belgium
raw_author_name: Sara Pabian
raw_affiliation_string: Department of Communication Studies University of Antwerp Antwerp Belgium; Department of Communication and Cognition Tilburg University Tilburg The Netherlands

Abstract: Abstract Online hate speech (OHS) is a prevalent issue on social media with harmful consequences for individuals and society. Counteracting this issue requires a better understanding of the factors that drive behaviors such as posting hate speech or making hateful comments. The present study contributes to this need by investigating the personal, social, and digital determinants for OHS perpetration. By conducting a two‐round Delphi study, data was collected from an interdisciplinary sample of experts. Practitioners were included in this sample to bridge gaps between research and fieldwork on OHS. The first survey collected information through open‐ended questions, which was then organized in different themes and lists of determinants in a thematic analysis. These determinants were rated for relevance in a follow‐up survey. Data analysis of the second survey used nonparametric statistics. From a total list of 129 determinants, the participants reached a consensus on 27 determinants as most relevant (median &gt; 4) for facilitating the production and dissemination of OHS. An ecological perspective was applied to interpret these findings. As a result, we propose an ecological model of personal, social, and digital determinants for online hate perpetration to illustrate their influence on individual behavior as well as their possible connections. The model provides a comprehensive overview of factors facilitating OHS perpetration, which can help to identify points for intervention.
One-step Bayesian example-dependent cost classification: The OsC-MLP method

Authors:
raw_author_name: Javier Mediavilla-Relaño
raw_affiliation_string: Signal Theory and Communications Department, Universidad Carlos III de Madrid, Avda. de la Universidad, No. 30, 28911, Leganés, Madrid, Spain
raw_author_name: Marcelino Lázaro
raw_affiliation_string: Signal Theory and Communications Department, Universidad Carlos III de Madrid, Avda. de la Universidad, No. 30, 28911, Leganés, Madrid, Spain

Abstract: Example-dependent cost classification problems are those where the decision costs depend not only on the true and the attributed classes but also on the sample features. Discriminative algorithms that carry out such classification tasks must take this dependence into account. In some applications, the decision costs are known for the training set but not in production, which complicates the problem. In this paper, we introduce a new one-step Bayesian formulation to train Neural Networks and solve the above limitation for binary cases with one-step Learning Machines, avoiding the drawbacks that unknown analytical forms of the example-dependent costs create. The formulation is based on defining an artificial likelihood ratio by using the available training classification costs in its definition, and proposes a test that does not require the values of the costs for unseen samples. Furthermore, it also includes Bayesian rebalancing mechanisms to combat the negative effects of class imbalance. Experimental results support the consistency and effectiveness of the corresponding algorithms.
Fast simulation for multi-photon, atomic-ensemble quantum model of linear optical systems addressing the curse of dimensionality

Authors:
raw_author_name: Seiji Kajita
raw_affiliation_string: Toyota Central R&D Labs., Inc., 41-1 Yokomichi, Nagakute, Aichi, 480-1192, Japan
raw_author_name: Akihito Soeda
raw_affiliation_string: Principles of Informatics Research Division, National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan

Abstract: Abstract Photons are elementary particles of light in quantum mechanics, whose dynamics can be difficult to gain detailed insights, especially in complex systems. Simulation is a promising tool to resolve this issue, but it must address the curse of dimensionality, namely, that the number of bases increases exponentially in the number of photons. Here we mitigate this dimensionality scaling by focusing on optical systems composed of linear optical objects, modeled as an ensemble of two-level atoms. We decompose the time evolutionary operator on multiple photons into a group of time evolution operators acting on a single photon. Since the dimension of a single-photon time evolution operator is exponentially smaller than that of a multi-photon one in the number of photons, the decomposition enables the multi-photon simulations to be performed at a much lower computational cost. We apply this method to basic single- and multi-photon phenomena, such as Hong–Ou–Mandel interference and violation of the Bell-CHSH inequality, and confirm that the calculated properties are quantitatively comparable to the experimental results. Furthermore, our method visualizes the spatial propagation of photons hence provides insights that aid experiment designs for quantum-enabled technologies.
Online and Mobile-Assisted Language Learning

Authors:
raw_author_name: Omnia Ibrahim Mohamed

Abstract: Online language classes and mobile-assisted language learning applications (Apps) are non-traditional methods of education that differ from traditional classes, which involve face-to-face instruction and teacher-led lessons. Web or mobile-assisted language learning applications cater to the changing world, making them convenient and affordable for those with limited time. These applications can be used at the learners’ convenience, making them a qualitative leap in the field of language learning. There is a world full of language applications some of which are dedicated to the education of the English Language as the Global language and others have many languages to learn. In the explorations of language applications, the Google Bundle is one of the best for online classes as it is the most comprehensive bundle and Duolingo is the best language application, if we considered the increasing number of its downloads and its continuously increasing revenue. Most of the applications have free and Pro or Premium features, and some only offer a free trial of one lesson or a seven-day trial. Each application has different teaching methods; some use gamification, interactive exercises, communicative approach, spaced repetition, contextual learning, audio-visual learning, and personalization. Moreover, most of the applications employ Artificial intelligence (AI) for immediate feedback.