Reviewerly/exHarmony huggingface dataset:
Interculture-Based Language Learning Model to Improve Prospective English Teachers’ Speaking Skills Viewed From Linguistic Awareness

This study aimed to know the use of the interculture-based language learning (IBLL) model to improve learners’ speaking skills integrated with linguistic awareness and their responses to learning experiences. A mixed-method study was employed, employing the concurrent embedded design, to investigate the learning processes associated with the IBLL model in enhancing speaking skills. The data consisted of qualitative and quantitative data. The qualitative data were collected using observation sheets and interviews, while the qualitative data were attained using tests. The finding showed that the IBLL model facilitates learners to improve grammar competence, vocabulary acquisition, fluency, pronunciation, and description detail. The speaking improvement of learners was affected with learners’ linguistic awareness. Based on this, learners have positive responses to the use of the IBLL model in the speaking class. This study can be concluded that the use of the IBLL model was able to improve speaking skills viewed from linguistic awareness. The implication of the current study acknowledges that language learning goes beyond mere linguistic competence and it entails an exploration of cultural nuances, social contexts, and the interconnectedness between language and societal practices.
Neural Network Classification with Pandas, Scikit-Learn, and PySpark

In this chapter, we explore the world of Multilayer Perceptron (MLP) classifiers, a powerful approach to supervised machine learning. MLPs have several advantages, making them versatile for a wide range of tasks, from regression to classification spanning across various domains such as image recognition, natural language processing, and speech recognition, to name a few.
3D LiDAR Multi-Object Tracking with Short-Term and Long-Term Multi-Level Associations

LiDAR-based Multi-Object Tracking (MOT) is a critical technology employed in various autonomous systems, including self-driving vehicles and autonomous delivery robots. In this paper, a novel LiDAR-based 3D MOT approach is introduced. The proposed method was built upon the Tracking-by-Detection (TbD) paradigm and incorporated multi-level associations that exploit an object’s short-term and long-term relationships with the existing tracks. Specifically, the short-term association leverages the fact that objects do not move much between consecutive frames. In contrast, the long-term association assesses the degree to which a long-term trajectory aligns with current detections. The evaluation of the matching between the current detection and the maintained trajectory was performed using a Graph Convolutional Network (GCN). Furthermore, an inactive track was maintained to address the issue of incorrect ID switching for objects that have been occluded for an extended period. The proposed method was evaluated on the KITTI benchmark MOT tracking dataset and achieved a Higher-Order Tracking Accuracy (HOTA) of 75.65%, marking a 5.66% improvement over the benchmark method AB3DMOT, while also accomplishing the number of ID switches of 39, 74 less than AB3DMOT. These results confirmed the effectiveness of the proposed approach in diverse road environments.
Application of Smart Insoles for Recognition of Activities of Daily Living: A Systematic Review

Recent years have witnessed the increasing literature on using smart insoles in health and well-being, and yet, their capability of daily living activity recognition has not been reviewed. This paper addressed this need and provided a systematic review of smart insole-based systems in the recognition of Activities of Daily Living (ADLs). The review followed the PRISMA guidelines, assessing the sensing elements used, the participants involved, the activities recognised, and the algorithms employed. The findings demonstrate the feasibility of using smart insoles for recognising ADLs, showing their high performance in recognising ambulation and physical activities involving the lower body, ranging from 70% to 99.8% of Accuracy, with 13 studies over 95%. The preferred solutions have been those including machine learning. A lack of existing publicly available datasets has been identified, and the majority of the studies were conducted in controlled environments. Furthermore, no studies assessed the impact of different sampling frequencies during data collection, and a trade-off between comfort and performance has been identified between the solutions. In conclusion, real-life applications were investigated showing the benefits of smart insoles over other solutions and placing more emphasis on the capabilities of smart insoles.
Investigating the Effectiveness of Edmodo on EFL Learners’ Motivation in Writing Class

This study aims at scrutinizing the effectiveness of the utilization web-based learning platform, Edmodo in teaching academic writing to EFL learners. This study employed a mixed method approach on the fifth-semester students and confirmed that 10 students take part in this study. A mixed method approach was utilized in this study which applied questionnaires to gain quantitative data. Meanwhile, the qualitative data were attained through semi-structured interviews to measure the effectiveness of the Edmodo application in writing class. The findings revealed that 88% of students prefer to learn writing skills using the Edmodo learning platform through collaborative tasks. The research findings illustrated that there was an improvement and students’ interest in writing by applying the Edmodo learning platform. Besides, students find Edmodo as a user-friendly media, and easy to use. Edmodo has helped and developed their writing creativity and their cognitive skills and social interaction with peers are also positively developed. This confirmed that there were enough shreds of evidence of how Edmodo was said to be effective in fostering EFL learners' motivation in writing.
XR for First Responders: Concepts, Challenges and Future Potential of Immersive Training

This paper highlights the significance of Extended Reality (XR) technology as a crucial component of future first responder (FR) training to address the challenges faced by Europe in maintaining public safety and peace. XR, which includes virtual reality (VR), augmented reality (AR), and mixed reality (MR), offers unique opportunities to enhance FR training. We present a mapping of specific training goals to different XR solutions, categorizing them based on immersion level and extent of world knowledge. We discuss the benefits of XR, while also acknowledging the challenges and ethical considerations. Lastly, we discuss the main future developments we deem to be crucial for the field. With this, we hope to foster a common understanding of terminology, needs and future directions between end-users, researchers and tech-providers.
Support Vector Machine Method for Predicting Non-Linear Data

The aim of this research is to evaluate the performance of a classification model on nonlinear data. The study utilizes accuracy, precision, sensitivity, and specificity metrics based on data from e-commerce X sellers. The classification model is developed using the Support Vector Machine (SVM) approach, employing different kernel functions such as linear, polynomial, and Radial Basis Function (RBF). By comparing the performance scores of each model, the best model is determined. The results indicate that the SVM model with a linear kernel outperforms the others, demonstrating the highest performance scores. This approach is applied to predict the status of sellers on e-commerce X.
An ensemble-based serial cascaded attention network and improved variational auto encoder for breast cancer prognosis prediction using data

AbstractBreast cancer is one of the most common types of cancer in women and it produces a huge amount of death rate in the world. Early recognition is lessening its impact. The early recognition of breast cancer could convince patients to receive surgical therapy, which will significantly improve the chance of restoration. This information is used by the machine learning technique to find links between them and appraise our forecasts of fresh occurrences. Later recognition of breast cancer can lead to death. An accurate prescient framework for breast cancer prediction is urgently needed in the current era. In order to accomplish the objective, an adaptive ensemble model is proposed for breast cancer prognosis prediction using data. At the initial stage, the raw data are fetched from benchmark datasets. It is then followed by data cleaning and preprocessing. Subsequently, the pre-processed data is fed into the Improved Variational Autoencoder (IVAE), where the deep features are extracted. Finally, the resultant features are given as input to the Ensemble-based Serial Cascaded Attention Network (ESCANet), which is built with Deep Temporal Convolution Network (DTCN), Bi-directional Long Short-Term Memory (BiLSTM), and Recurrent Neural Network (RNN). The effectiveness of the model is validated and compared with conventional methodologies. Therefore, the results elucidate that the proposed methodology achieves extensive results; thus, it increases the system’s efficiency.Keywords: Breast cancer prognosis predictionImproved Variational Autoencoderensemble-based serial cascaded attention networkdeep temporal convolution networkrecurrent neural network Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.
Neural Graph Revealers

Sparse graph recovery methods work well where the data follows their assumptions, however, they are not always designed for doing downstream probabilistic queries. This limits their adoption to only identifying connections among domain variables. On the other hand, Probabilistic Graphical Models (PGMs) learn an underlying base graph together with a distribution over the variables (nodes). PGM design choices are carefully made such that the inference and sampling algorithms are efficient. This results in certain restrictions and simplifying assumptions. In this work, we propose Neural Graph Revealers (NGRs) which attempt to efficiently merge the sparse graph recovery methods with PGMs into a single flow. The task is to recover a sparse graph showing connections between the features and learn a probability distribution over them at the same time. NGRs use a neural network as a multitask learning framework. We introduce graph-constrained path norm that NGRs leverage to learn a graphical model that captures complex non-linear functional dependencies between features in the form of an undirected sparse graph. NGRs can handle multimodal inputs like images, text, categorical data, embeddings etc. which are not straightforward to incorporate in the existing methods. We show experimental results on data from Gaussian graphical models and a multimodal infant mortality dataset by CDC (Software: https://github.com/harshs27/neural-graph-revealers ).
SAGE: Generating Symbolic Goals for Myopic Models in Deep Reinforcement Learning

Model-based reinforcement learning algorithms are typically more sample efficient than their model-free counterparts, especially in sparse reward problems. Unfortunately, many interesting domains are too complex to specify complete models, and learning a model takes a large number of environment samples. If we could specify an incomplete model and allow the agent to learn how best to use it, we could take advantage of our partial understanding of many domains. In this work we propose SAGE, an algorithm combining learning and planning to exploit a previously unusable class of incomplete models. This combines the strengths of symbolic planning and neural learning approaches in a novel way that outperforms competing methods on variations of taxi world and Minecraft.
Simultaneous Job Interview System Using Multiple Semi-autonomous Agents

In recent years, spoken dialogue systems have been applied to job interviews where an applicant talks to a system that asks pre-defined questions, called on-demand and self-paced job interviews. We propose a simultaneous job interview system, where one interviewer can conduct one-on-one interviews with multiple applicants simultaneously by cooperating with the multiple autonomous job interview dialogue systems. However, it is challenging for interviewers to monitor and understand all the parallel interviews done by the autonomous system at the same time. As a solution to this issue, we implemented two automatic dialogue understanding functions: (1) response evaluation of each applicant’s responses and (2) keyword extraction as a summary of the responses. It is expected that interviewers, as needed, can intervene in one dialogue and smoothly ask a proper question that elaborates the interview. We report a pilot experiment where an interviewer conducted simultaneous job interviews with three candidates.
Comparison of Lexical Alignment with a Teachable Robot in Human-Robot and Human-Human-Robot Interactions

Yuya Asano, Diane Litman, Mingzhi Yu, Nikki Lobczowski, Timothy Nokes-Malach, Adriana Kovashka, Erin Walker. Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2022.
WeaSuL: Weakly Supervised Dialogue Policy Learning: Reward Estimation for Multi-turn Dialogue

An intelligent dialogue system in a multi-turn setting should not only generate the responses which are of good quality, but it should also generate the responses which can lead to long-term success of the dialogue. Although, the current approaches improved the response quality, but they over-look the training signals present in the dialogue data. We can leverage these signals to generate the weakly supervised training data for learning dialog policy and reward estimator, and make the policy take actions (generates responses) which can foresee the future direction for a successful (rewarding) conversation. We simulate the dialogue between an agent and a user (modelled similar to an agent with supervised learning objective) to interact with each other. The agent uses dynamic blocking to generate ranked diverse responses and exploration-exploitation to select among the Top-K responses. Each simulated state-action pair is evaluated (works as a weak annotation) with three quality modules: Semantic Relevant, Semantic Coherence and Consistent Flow. Empirical studies with two benchmarks indicate that our model can significantly out-perform the response quality and lead to a successful conversation on both automatic evaluation and human judgment.
How Much Does Prosody Help Turn-taking? Investigations using Voice Activity Projection Models

Turn-taking is a fundamental aspect of human communication and can be described as the ability to take turns, project upcoming turn shifts, and supply backchannels at appropriate locations throughout a conversation. In this work, we investigate the role of prosody in turn-taking using the recently proposed Voice Activity Projection model, which incrementally models the upcoming speech activity of the interlocutors in a self-supervised manner, without relying on explicit annotation of turn-taking events, or the explicit modeling of prosodic features. Through manipulation of the speech signal, we investigate how these models implicitly utilize prosodic information. We show that these systems learn to utilize various prosodic aspects of speech both on aggregate quantitative metrics of long-form conversations and on single utterances specifically designed to depend on prosody.
HL Dataset: Visually-grounded Description of Scenes, Actions and Rationales

Current captioning datasets focus on object-centric captions, describing the visible objects in the image, often ending up stating the obvious (for humans), e.g. “people eating food in a park”. Although these datasets are useful to evaluate the ability of Vision & Language models to recognize and describe visual content, they do not support controlled experiments involving model testing or fine-tuning, with more high-level captions, which humans find easy and natural to produce. For example, people often describe images based on the type of scene they depict (“people at a holiday resort”) and the actions they perform (“people having a picnic”). Such concepts are based on personal experience and contribute to forming common sense assumptions. We present the High-Level Dataset, a dataset extending 14997 images from the COCO dataset, aligned with a new set of 134,973 human-annotated (high-level) captions collected along three axes: scenes, actions and rationales. We further extend this dataset with confidence scores collected from an independent set of readers, as well as a set of narrative captions generated synthetically, by combining each of the three axes. We describe this dataset and analyse it extensively. We also present baseline results for the High-Level Captioning task.
This is not correct! Negation-aware Evaluation of Language Generation Systems

Large language models underestimate the impact of negations on how much they change the meaning of a sentence. Therefore, learned evaluation metrics based on these models are insensitive to negations. In this paper, we propose NegBLEURT, a negation-aware version of the BLEURT evaluation metric. For that, we designed a rule-based sentence negation tool and used it to create the CANNOT negation evaluation dataset. Based on this dataset, we fine-tuned a sentence transformer and an evaluation metric to improve their negation sensitivity. Evaluating these models on existing benchmarks shows that our fine-tuned models outperform existing metrics on the negated sentences by far while preserving their base models’ performances on other perturbations.
When can I Speak? Predicting initiation points for spoken dialogue agents

Current spoken dialogue systems initiate their turns after a long period of silence (700-1000ms), which leads to little real-time feedback, sluggish responses, and an overall stilted conversational flow. Humans typically respond within 200ms and successfully predicting initiation points in advance would allow spoken dialogue agents to do the same. In this work, we predict the lead-time to initiation using prosodic features from a pre-trained speech representation model (wav2vec 1.0) operating on user audio and word features from a pre-trained language model (GPT-2) operating on incremental transcriptions. To evaluate errors, we propose two metrics w.r.t. predicted and true lead times. We train and evaluate the models on the Switchboard Corpus and find that our method outperforms features from prior work on both metrics and vastly outperforms the common approach of waiting for 700ms of silence.
Are Interaction Patterns Helpful for Task-Agnostic Dementia Detection? An Empirical Exploration

Dementia often manifests in dialog through specific behaviors such as requesting clarification, communicating repetitive ideas, and stalling, prompting conversational partners to probe or otherwise attempt to elicit information. Dialog act (DA) sequences can have predictive power for dementia detection through their potential to capture these meaningful interaction patterns. However, most existing work in this space relies on content-dependent features, raising questions about their generalizability beyond small reference sets or across different cognitive tasks. In this paper, we adapt an existing DA annotation scheme for two different cognitive tasks present in a popular dementia detection dataset. We show that a DA tagging model leveraging neural sentence embeddings and other information from previous utterances and speaker tags achieves strong performance for both tasks. We also propose content-free interaction features and show that they yield high utility in distinguishing dementia and control subjects across different tasks. Our study provides a step toward better understanding how interaction patterns in spontaneous dialog affect cognitive modeling across different tasks, which carries implications for the design of non-invasive and low-cost cognitive health monitoring tools for use at scale.
Dial-M: A Masking-based Framework for Dialogue Evaluation

In dialogue systems, automatically evaluating machine-generated responses is critical and challenging. Despite the tremendous progress in dialogue generation research, its evaluation heavily depends on human judgments. The standard word-overlapping based evaluation metrics are ineffective for dialogues. As a result, most of the recently proposed metrics are model-based and reference-free, which learn to score different aspects of a conversation. However, understanding each aspect requires a separate model, which makes them computationally expensive. To this end, we propose Dial-M, a Masking-based reference-free framework for Dialogue evaluation. The main idea is to mask the keywords of the current utterance and predict them, given the dialogue history and various conditions (like knowledge, persona, etc.), thereby making the evaluation framework simple and easily extensible for multiple datasets. Regardless of its simplicity, Dial-M achieves comparable performance to state-of-the-art metrics on several dialogue evaluation datasets. We also discuss the interpretability of our proposed metric along with error analysis.
Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation

Recent approaches to empathetic response generation try to incorporate commonsense knowledge or reasoning about the causes of emotions to better understand the user’s experiences and feelings. However, these approaches mainly focus on understanding the causalities of context from the user’s perspective, ignoring the system’s perspective. In this paper, we propose a commonsense-based causality explanation approach for diverse empathetic response generation that considers both the user’s perspective (user’s desires and reactions) and the system’s perspective (system’s intentions and reactions). We enhance ChatGPT’s ability to reason for the system’s perspective by integrating in-context learning with commonsense knowledge. Then, we integrate the commonsense-based causality explanation with both ChatGPT and a T5-based model. Experimental evaluations demonstrate that our method outperforms other comparable methods on both automatic and human evaluations.
DORAM Revisited: Maliciously Secure RAM-MPC with Logarithmic Overhead

Distributed Oblivious Random Access Memory (DORAM) is a secure multiparty protocol that allows a group of participants holding a secret-shared array to read and write to secret-shared locations within the array. The efficiency of a DORAM protocol is measured by the amount of communication required per read/write query into the array. DORAM protocols are a necessary ingredient for executing Secure Multiparty Computation (MPC) in the RAM model. Although DORAM has been widely studied, all existing DORAM protocols have focused on the setting where the DORAM servers are semi-honest. Generic techniques for upgrading a semi-honest DORAM protocol to the malicious model typically increase the asymptotic communication complexity of the DORAM scheme. In this work, we present a 3-party DORAM protocol which requires $$O((\kappa + D)\log N)$$ communication per query, for a database of size N with D-bit values, where $$\kappa $$ is the security parameter. Our hidden constants in the big-O nation are small. We show that our protocol is UC-secure in the presence of a malicious, static adversary. This matches the communication complexity of the best semi-honest DORAM protocols, and is the first malicious DORAM protocol with this complexity.