Reviewerly/exHarmony huggingface dataset:
Dawn of the transformer era in speech emotion recognition: closing the valence gap

Recent advances in transformer-based architectures which are pre-trained in self-supervised manner have shown great promise in several machine learning tasks. In the audio domain, such architectures have also been successfully utilised in the field of speech emotion recognition (SER). However, existing works have not evaluated the influence of model size and pre-training data on downstream performance, and have shown limited attention to generalisation, robustness, fairness, and efficiency. The present contribution conducts a thorough analysis of these aspects on several pre-trained variants of wav2vec 2.0 and HuBERT that we fine-tuned on the dimensions arousal, dominance, and valence of MSP-Podcast, while additionally using IEMOCAP and MOSI to test cross-corpus generalisation. To the best of our knowledge, we obtain the top performance for valence prediction without use of explicit linguistic information, with a concordance correlation coefficient (CCC) of .638 on MSP-Podcast. Furthermore, our investigations reveal that transformer-based architectures are more robust to small perturbations compared to a CNN-based baseline and fair with respect to biological sex groups, but not towards individual speakers. Finally, we are the first to show that their extraordinary success on valence is based on implicit linguistic information learnt during fine-tuning of the transformer layers, which explains why they perform on-par with recent multimodal approaches that explicitly utilise textual information. Our findings collectively paint the following picture: transformer-based architectures constitute the new state-of-the-art in SER, but further advances are needed to mitigate remaining robustness and individual speaker issues. To make our findings reproducible, we release the best performing model to the community.
A Hybrid Deep Residual Network for Efficient Transitional Activity Recognition Based on Wearable Sensors

Numerous learning-based techniques for effective human behavior identification have emerged in recent years. These techniques focus only on fundamental human activities, excluding transitional activities due to their infrequent occurrence and short period. Nevertheless, postural transitions play a critical role in implementing a system for recognizing human activity and cannot be ignored. This study aims to present a hybrid deep residual model for transitional activity recognition utilizing signal data from wearable sensors. The developed model enhances the ResNet model with hybrid Squeeze-and-Excitation (SE) residual blocks combining a Bidirectional Gated Recurrent Unit (BiGRU) to extract deep spatio-temporal features hierarchically, and to distinguish transitional activities efficiently. To evaluate recognition performance, the experiments are conducted on two public benchmark datasets (HAPT and MobiAct v2.0). The proposed hybrid approach achieved classification accuracies of 98.03% and 98.92% for the HAPT and MobiAct v2.0 datasets, respectively. Moreover, the outcomes show that the proposed method is superior to the state-of-the-art methods in terms of overall accuracy. To analyze the improvement, we have investigated the effects of combining SE modules and BiGRUs into the deep residual network. The findings indicates that the SE module is efficient in improving transitional activity recognition.
A new optimization algorithm based on mimicking the voting process for leader selection

Stochastic-based optimization algorithms are effective approaches to addressing optimization challenges. In this article, a new optimization algorithm called the Election-Based Optimization Algorithm (EBOA) was developed that mimics the voting process to select the leader. The fundamental inspiration of EBOA was the voting process, the selection of the leader, and the impact of the public awareness level on the selection of the leader. The EBOA population is guided by the search space under the guidance of the elected leader. EBOA’s process is mathematically modeled in two phases: exploration and exploitation. The efficiency of EBOA has been investigated in solving thirty-three objective functions of a variety of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and CEC 2019 types. The implementation results of the EBOA on the objective functions show its high exploration ability in global search, its exploitation ability in local search, as well as the ability to strike the proper balance between global search and local search, which has led to the effective efficiency of the proposed EBOA approach in optimizing and providing appropriate solutions. Our analysis shows that EBOA provides an appropriate balance between exploration and exploitation and, therefore, has better and more competitive performance than the ten other algorithms to which it was compared.
Using Chatbots as AI Conversational Partners in Language Learning

Recent advances in Artificial Intelligence (AI) and machine learning have paved the way for the increasing adoption of chatbots in language learning. Research published to date has mostly focused on chatbot accuracy and chatbot–human communication from students’ or in-service teachers’ perspectives. This study aims to examine the knowledge, level of satisfaction and perceptions concerning the integration of conversational AI in language learning among future educators. In this mixed method research based on convenience sampling, 176 undergraduates from two educational settings, Spain (n = 115) and Poland (n = 61), interacted autonomously with three conversational agents (Replika, Kuki, Wysa) over a four-week period. A learning module about Artificial Intelligence and language learning was specifically designed for this research, including an ad hoc model named the Chatbot–Human Interaction Satisfaction Model (CHISM), which was used by teacher candidates to evaluate different linguistic and technological features of the three conversational agents. Quantitative and qualitative data were gathered through a pre-post-survey based on the CHISM and the TAM2 (technology acceptance) models and a template analysis (TA), and analyzed through IBM SPSS 22 and QDA Miner software. The analysis yielded positive results regarding perceptions concerning the integration of conversational agents in language learning, particularly in relation to perceived ease of use (PeU) and attitudes (AT), but the scores for behavioral intention (BI) were more moderate. The findings also unveiled some gender-related differences regarding participants’ satisfaction with chatbot design and topics of interaction.
Deep Gradient Learning for Efficient Camouflaged Object Detection

Abstract This paper introduces deep gradient network (DGNet), a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branches, i.e., a context and a texture encoder. The essential connection is the gradient-induced transition, representing a soft grouping between context and texture features. Benefiting from the simple but efficient framework, DGNet outperforms existing state-of-the-art COD models by a large margin. Notably, our efficient version, DGNet-S, runs in real-time (80 fps) and achieves comparable results to the cutting-edge model JCSOD-CVPR21 with only 6.82% parameters. The application results also show that the proposed DGNet performs well in the polyp segmentation, defect detection, and transparent object segmentation tasks. The code will be made available at https://github.com/GewelsJI/DGNet .
Analysing the Role of ChatGPT in Improving Student Productivity in Higher Education

Student productivity in higher education is still a problem. In the digital era, technology is increasingly developing and provides convenience for doing various things, including in terms of learning. The purpose of this study will be an analysis of the role of ChatGPT in helping to improve the quality of student productivity. This research is qualitative in nature. Data collection techniques include listening and recording important information to conduct data analysis through data reduction, data display, and conclusion drawing. This study concludes that ChatGPT can make a significant contribution in improving the quality of student productivity. This language model can help students in various ways, such as providing useful information and resources, helping to improve language skills, facilitating collaboration, increasing time efficiency and effectiveness, and providing support and motivation.
Nerfstudio: A Modular Framework for Neural Radiance Field Development

Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging applications in computer vision, graphics, robotics, and more. In order to streamline the development and deployment of NeRF research, we propose a modular PyTorch framework, Nerfstudio. Our framework includes plug-and-play components for implementing NeRF-based methods, which make it easy for researchers and practitioners to incorporate NeRF into their projects. Additionally, the modular design enables support for extensive real-time visualization tools, streamlined pipelines for importing captured in-the-wild data, and tools for exporting to video, point cloud and mesh representations. The modularity of Nerfstudio enables the development of Nerfacto, our method that combines components from recent papers to achieve a balance between speed and quality, while also remaining flexible to future modifications. To promote community-driven development, all associated code and data are made publicly available with open-source licensing.
Visual Servoing of Wheeled Mobile Robots Without Desired Images

This paper proposes a novel monocular visual servoing strategy, which can drive a wheeled mobile robot to the desired pose without a prerecorded desired image. Compared with existing methods that adopt the teaching pattern for visual regulation, this scheme can still work well in the situation that the desired image has not been previously acquired. Thus, with the aid of this method, it is more convenient for mobile robots to execute visual servoing tasks. Specifically, to deal with nonexistence of the desired image, the reference frame is craftily defined by taking advantage of visual targets and the planar motion constraint, and the pose estimation algorithm is designed for the mobile robot with respect to the reference frame. Then, an adaptive visual regulation controller is developed to drive the mobile robot to the intermediate frame, where the parameter updating law is constructed for the unknown feature height based on the concurrent learning framework. Stability analysis shows that regulation errors and height identification error can converge simultaneously. Afterwards, the mobile robot is driven to the metric desired pose with the identified feature height. Both simulation and experimental results are provided to validate the performance of this strategy.
Blockchain Governance Game

This paper deals with the design of the secure blockchain network framework to prevent damages from an attacker. The decentralized network design called the Blockchain Governance Game is a new hybrid theoretical model and provides a new stochastic game framework to find best strategies for preventing a network malfunction by attackers. Analytically tractable results are obtained by using the fluctuation theory and the mixed strategy game theory. These results enable to predict the moment of operations and deliver the optimal portion of backup nodes to protect the blockchain network. This research helps for whom considers the initial coin offering or launching new blockchain based services with enhancing the security features.
Cognitive shifts within leader and follower teams: Where consensus develops in mental models during an organizational crisis

This empirical study investigates cognitive shifts in both leader and follower teams when developing consensus or agreement in how to resolve a slowly emerging organizational crisis over time. The cognitive maps of leaders and followers are analyzed in team settings to explain where consensus is formed. The findings indicate that consensus, or the agreement on the causal beliefs held to be critical to organizational adaptation and success, builds over time within both leader and follower teams. However, when comparing the development of consensus longitudinally, the findings confirm that the mental models of leadership teams converge towards follower teams, and not the other way around, during the crisis. The study provides new insights into the importance of the causal beliefs of follower teams when developing a vision to coordinate action to resolve a crisis.
A Path Planning Method for Robotic Wire and Arc Additive Manufacturing of Thin-Walled Structures with Varying Thickness

Wire and arc additive manufacturing (WAAM) is a potential technology for manufacturing components with a shorter period of time and by a lower cost. Path planning is a key research interest in WAAM. This paper presents an adaptive path planning method for manufacturing thin-walled structures with varying thickness. Firstly, the straight skeleton, which represents the skeleton of a given geometry, is extracted to express the geometry. Then the deposition with weaving technology is applied to obtain required varied width of the geometry. The resulting adaptive paths are able to fill thin-walled structures with varying thickness less than twice of the single-bead width.
Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones

Measuring and monitoring YouTube Quality of Experience is a challenging task, especially when dealing with cellular networks and smartphone users. Using a large-scale database of crowdsourced YouTube-QoE measurements in smartphones, we conceive multiple machine-learning models to infer different YouTube-QoE-relevant metrics and userbehavior- related metrics from network-level measurements, without requiring root access to the smartphone, video-player embedding, or any other reverse-engineering-like approaches. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our preliminary results suggest that QoE-based monitoring of YouTube mobile can be realized through machine learning models with high accuracy, relying only on network-related features and without accessing any higher-layer metric to perform the estimations.
The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy

Privacy-preserving data analysis is a rising challenge in contemporary statistics, as the privacy guarantees of statistical methods are often achieved at the expense of accuracy. In this paper, we investigate the tradeoff between statistical accuracy and privacy in mean estimation and linear regression, under both the classical low-dimensional and modern high-dimensional settings. A primary focus is to establish minimax optimality for statistical estimation with the $(\varepsilon,\delta)$-differential privacy constraint. To this end, we find that classical lower bound arguments fail to yield sharp results, and new technical tools are called for. By refining the "tracing adversary" technique for lower bounds in the theoretical computer science literature, we formulate a general lower bound argument for minimax risks with differential privacy constraints, and apply this argument to high-dimensional mean estimation and linear regression problems. We also design computationally efficient algorithms that attain the minimax lower bounds up to a logarithmic factor. In particular, for the high-dimensional linear regression, a novel private iterative hard thresholding pursuit algorithm is proposed, based on a privately truncated version of stochastic gradient descent. The numerical performance of these algorithms is demonstrated by simulation studies and applications to real data containing sensitive information, for which privacy-preserving statistical methods are necessary.
Commonsense Reasoning for Natural Language Understanding: A Survey of Benchmarks, Resources, and Approaches

Commonsense knowledge and commonsense reasoning are some of the mainbottlenecks in machine intelligence. In the NLP community, many benchmarkdatasets and tasks have been created to address commonsense reasoning forlanguage understanding. These tasks are designed to assess machines' ability toacquire and learn commonsense knowledge in order to reason and understandnatural language text. As these tasks become instrumental and a driving forcefor commonsense research, this paper aims to provide an overview of existingtasks and benchmarks, knowledge resources, and learning and inferenceapproaches toward commonsense reasoning for natural language understanding.Through this, our goal is to support a better understanding of the state of theart, its limitations, and future challenges.
Addiction beyond pharmacological effects: The role of environment complexity and bounded rationality

Several decision-making vulnerabilities have been identified as underlying causes for addictive behaviours, or the repeated execution of stereotyped actions despite their adverse consequences. These vulnerabilities are mostly associated with brain alterations caused by the consumption of substances of abuse. However, addiction can also happen in the absence of a pharmacological component, such as seen in pathological gambling and videogaming. We use a new reinforcement learning model to highlight a previously neglected vulnerability that we suggest interacts with those already identified, whilst playing a prominent role in non-pharmacological forms of addiction. Specifically, we show that a dual-learning system (i.e. combining model-based and model-free) can be vulnerable to highly rewarding, but suboptimal actions, that are followed by a complex ramification of stochastic adverse effects. This phenomenon is caused by the overload of the capabilities of an agent, as time and cognitive resources required for exploration, deliberation, situation recognition, and habit formation, all increase as a function of the depth and richness of detail of an environment. Furthermore, the cognitive overload can be aggravated due to alterations (e.g. caused by stress) in the bounded rationality, i.e. the limited amount of resources available for the model-based component, in turn increasing the agent’s chances to develop or maintain addictive behaviours. Our study demonstrates that, independent of drug consumption, addictive behaviours can arise in the interaction between the environmental complexity and the biologically finite resources available to explore and represent it.
DeepLight: light source estimation for augmented reality using deep learning

This paper presents a novel method for illumination estimation from RGB-D images. The main focus of the proposed method is to enhance visual coherence in augmented reality applications by providing accurate and temporally coherent estimates of real illumination. For this purpose, we designed and trained a deep neural network which calculates a dominant light direction from a single RGB-D image. Additionally, we propose a novel method for real-time outlier detection to achieve temporally coherent estimates. Our method for light source estimation in augmented reality was evaluated on the set of real scenes. Our results demonstrate that the neural network can successfully estimate light sources even in scenes which were not seen by the network during training. Moreover, we compared our results with illumination estimates calculated by the state-of-the-art method for illumination estimation. Finally, we demonstrate the applicability of our method on numerous augmented reality scenes.
Cross reality to enhance worker cognition in industrial assembly operations

In this paper, we discuss how Cross Reality (XR) and the Industrial Internet of Things (IIoT) can support assembly tasks in hybrid human-machine manufacturing lines. We describe a Cross Reality system, designed to improve efficiency and ergonomics in industrial environments that require manual assembly operations. Our objective is to reduce the high costs of authoring assembly manuals and to improve the process of skills transfer, in particular, in assembly tasks that include workers with disabilities. The automation of short-lived assembly tasks, i.e., manufacturing of limited batches of customized products, does not yield significant returns considering the automation effort necessary and the production time frame. In the design of our XR system, we discuss how aspects of content creation can be automated for short-lived tasks and how seamless interoperability between devices facilitates skills transfer in human-machine hybrid environments.
Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings

Text classification aims at mapping documents into a set of predefined categories.Supervised machine learning models have shown great success in this area but they require a large number of labeled documents to reach adequate accuracy.This is particularly true when the number of target categories is in the tens or the hundreds.In this work, we explore an unsupervised approach to classify documents into categories simply described by a label.The proposed method is inspired by the way a human proceeds in this situation: It draws on textual similarity between the most relevant words in each document and a dictionary of keywords for each category reflecting its semantics and lexical field.The novelty of our method hinges on the enrichment of the category labels through a combination of human expertise and language models, both generic and domain specific.Our experiments on 5 standard corpora show that the proposed method increases F1-score over relying solely on human expertise and can also be on par with simple supervised approaches.It thus provides a practical alternative to situations where lowcost text categorization is needed, as we illustrate with our application to operational risk incidents classification.
A Large-Scale Comparison of Historical Text Normalization Systems

There is no consensus on the state-of-theart approach to historical text normalization.Many techniques have been proposed, including rule-based methods, distance metrics, character-based statistical machine translation, and neural encoder-decoder models, but studies have used different datasets, different evaluation methods, and have come to different conclusions.This paper presents the largest study of historical text normalization done so far.We critically survey the existing literature and report experiments on eight languages, comparing systems spanning all categories of proposed normalization techniques, analysing the effect of training data quantity, and using different evaluation methods.The datasets and scripts are made publicly available.
An Empirical Study of Span Representations in Argumentation Structure Parsing

Tatsuki Kuribayashi, Hiroki Ouchi, Naoya Inoue, Paul Reisert, Toshinori Miyoshi, Jun Suzuki, Kentaro Inui. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.