Reviewerly/exHarmony huggingface dataset:
Epistemology and ontology in Science: the challenge of Artificial Intelligence

The gap between predictability and comprehensibility threatens the entire scientific project because mathematical models of processes, fed by enormous amounts of data of very diverse origin, provide exceptionally precise results but, at the same time, hide the explanation of the processes. The knowledge of “what we know” of ontology is as relevant in science as that of “how we know” and “how much we know” of epistemology. Artificial intelligence (AI) involves the scientific understanding of the mechanisms underlying intelligent thought and behavior, as well as their embodiment in machines trained by their creators to reason in a conventional sense. Its “weak” formulation refers to the use of complex computer programs, designed with the purpose of complementing or assisting human reasoning to solve or complete complex problems of calculation, system maintenance, recognition of all types of images, design, analysis of data patterns, etc., many of which would be practically unapproachable using conventional procedures; but all this without including human sentient or ethical capabilities, which would be the subject of a – at the moment – non-existent “strong” AI, that would equal or even exceed human sentient intelligence. The popularization of “generative” AI, developed to create content – text, images, music or videos, among many other areas – from previous information, is helping to popularly consolidate the erroneous idea that current AI exceeds reasoning human level and exacerbates the risk of transmitting false information and negative stereotypes to people. The language models of artificial intelligence do not work by emulating a biological brain but are based on the search for logical patterns from large databases from diverse sources, which are not always updated or purged of falsehoods, errors or errors. conceptual or factual biases, both involuntary and self-serving. And the AI used in science is no stranger to these limitations and biases. A particularly sensitive issue is the possibility of using generative AI to write or even invent scientific articles that go unnoticed by the peer reviewers of the most prestigious scientific journals in the world, pointing to an even deeper problem: peer reviewers. Reviewers often do not have the time to review manuscripts thoroughly for red flags and, in many cases, they also lack adequate computing resources and specialized training.
UNSUPERVISED NEURAL NETWORK APPROACH FOR IDENTIFYING ANOMALOUS EVENTS IN NUCLEAR POWER PLANTS

Machine learning is a branch of engineering that focuses on designing algorithms that can analyze and make predictions from data. It is a subfield of artificial intelligence that utilizes mathematical optimization methods for specific tasks.
Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks

While rich, open-domain textual data are generally available and may include interesting phenomena (humor, sarcasm, empathy, etc.) most are designed for language processing tasks, and are usually in a non-conversational format. In this work, we take a step towards automatically generating conversational data using Generative Conversational Networks, aiming to benefit from the breadth of available language and knowledge data, and train open domain social conversational agents. We evaluate our approach on conversations with and without knowledge on the Topical Chat dataset using automatic metrics and human evaluators. Our results show that for conversations without knowledge grounding, GCN can generalize from the seed data, producing novel conversations that are less relevant but more engaging and for knowledge-grounded conversations, it can produce more knowledge-focused, fluent, and engaging conversations. Specifically, we show that for open-domain conversations with 10% of seed data, our approach performs close to the baseline that uses 100% of the data, while for knowledge-grounded conversations, it achieves the same using only 1% of the data, on human ratings of engagingness, fluency, and relevance.
Dialog Acts for Task Driven Embodied Agents

Embodied agents need to be able to interact in natural language – understanding task descriptions and asking appropriate follow up questions to obtain necessary information to be effective at successfully accomplishing tasks for a wide range of users. In this work, we propose a set of dialog acts for modelling such dialogs and annotate the TEACh dataset that includes over 3,000 situated, task oriented conversations (consisting of 39.5k utterances in total) with dialog acts. To our knowledge,TEACh-DA is the first large scale dataset of dialog act annotations for embodied task completion. Furthermore, we demonstrate the use of this annotated dataset in training models for tagging the dialog acts of a given utterance, predicting the dialog act of the next response given a dialog history, and use the dialog acts to guide agent’s non-dialog behaviour. In particular, our experiments on the TEACh Execution from Dialog History task where the model predicts the sequence of low level actions to be executed in the environment for embodied task completion, demonstrate that dialog acts can improve end performance by up to 2 points compared to the system without dialog acts.
Reducing Model Churn: Stable Re-training of Conversational Agents

Retraining modern deep learning systems can lead to variations in model performance even when trained using the same data and hyper-parameters by simply using different random seeds. This phenomenon is known as model churn or model jitter. This issue is often exacerbated in real world settings, where noise may be introduced in the data collection process. In this work we tackle the problem of stable retraining with a novel focus on structured prediction for conversational semantic parsing. We first quantify the model churn by introducing metrics for agreement between predictions across multiple retrainings. Next, we devise realistic scenarios for noise injection and demonstrate the effectiveness of various churn reduction techniques such as ensembling and distillation. Lastly, we discuss practical trade-offs between such techniques and show that co-distillation provides a sweet spot in terms of churn reduction with only a modest increase in resource usage.
Towards Socially Intelligent Agents with Mental State Transition and Human Value

Building a socially intelligent agent involves many challenges. One of which is to track the agent’s mental state transition and teach the agent to make decisions guided by its value like a human. Towards this end, we propose to incorporate mental state simulation and value modeling into dialogue agents. First, we build a hybrid mental state parser that extracts information from both the dialogue and event observations and maintains a graphical representation of the agent’s mind; Meanwhile, the transformer-based value model learns human preferences from the human value dataset, ValueNet. Empirical results show that the proposed model attains state-of-the-art performance on the dialogue/action/emotion prediction task in the fantasy text-adventure game dataset, LIGHT. We also show example cases to demonstrate: (i) how the proposed mental state parser can assist the agent’s decision by grounding on the context like locations and objects, and (ii) how the value model can help the agent make decisions based on its personal priorities.
The Open-domain Paradox for Chatbots: Common Ground as the Basis for Human-like Dialogue

There is a surge in interest in the development of open-domain chatbots, driven by the recent advancements of large language models. The “openness” of the dialogue is expected to be maximized by providing minimal information to the users about the common ground they can expect, including the presumed joint activity. However, evidence suggests that the effect is the opposite. Asking users to “just chat about anything” results in a very narrow form of dialogue, which we refer to as the “open-domain paradox”. In this position paper, we explain this paradox through the theory of common ground as the basis for human-like communication. Furthermore, we question the assumptions behind open-domain chatbots and identify paths forward for enabling common ground in human-computer dialogue.
Dynamic and Real-Time Object Detection Based on Deep Learning for Home Service Robots

Home service robots operating indoors, such as inside houses and offices, require the real-time and accurate identification and location of target objects to perform service tasks efficiently. However, images captured by visual sensors while in motion states usually contain varying degrees of blurriness, presenting a significant challenge for object detection. In particular, daily life scenes contain small objects like fruits and tableware, which are often occluded, further complicating object recognition and positioning. A dynamic and real-time object detection algorithm is proposed for home service robots. This is composed of an image deblurring algorithm and an object detection algorithm. To improve the clarity of motion-blurred images, the DA-Multi-DCGAN algorithm is proposed. It comprises an embedded dynamic adjustment mechanism and a multimodal multiscale fusion structure based on robot motion and surrounding environmental information, enabling the deblurring processing of images that are captured under different motion states. Compared with DeblurGAN, DA-Multi-DCGAN had a 5.07 improvement in Peak Signal-to-Noise Ratio (PSNR) and a 0.022 improvement in Structural Similarity (SSIM). An AT-LI-YOLO method is proposed for small and occluded object detection. Based on depthwise separable convolution, this method highlights key areas and integrates salient features by embedding the attention module in the AT-Resblock to improve the sensitivity and detection precision of small objects and partially occluded objects. It also employs a lightweight network unit Lightblock to reduce the network’s parameters and computational complexity, which improves its computational efficiency. Compared with YOLOv3, the mean average precision (mAP) of AT-LI-YOLO increased by 3.19%, and the detection precision of small objects, such as apples and oranges and partially occluded objects, increased by 19.12% and 29.52%, respectively. Moreover, the model inference efficiency had a 7 ms reduction in processing time. Based on the typical home activities of older people and children, the dataset Grasp-17 was established for the training and testing of the proposed method. Using the TensorRT neural network inference engine of the developed service robot prototype, the proposed dynamic and real-time object detection algorithm required 29 ms, which meets the real-time requirement of smooth vision.
Reinforcement Learning for Mobile Robot’s Environment Exploration

Abstract Mobile robots are being are being applied in various industries to perform repetitive or dangerous tasks for humans to carry out. Autonomous mobile robots are more capable than automated guided vehicles (AGV) due to their ability to be adaptable to their environment which is important for exploration of unknown environments. It is difficult to program autonomous mobile robots to adapt to various situations it may face, thus machine learning can be applied to allow a mobile robot to learn how to navigate through environments by itself. Reinforcement learning is applied in this project so that a differential drive mobile robot can learn how to navigate through its environment while avoiding collision with surrounding walls and obstacles. The reinforcement learning process is simulated by using the Robot Operating System (ROS) and its simulator Gazebo. Controlled simulation environments are created using Gazebo for the purposes of training and performance testing. Simultaneous Localization and Mapping (SLAM) will be applied to generate a map of the environment. At the end of this project, the Turtlebot3 is able to map smaller controlled environments ranging between 18m 2 to 27m 2 without colliding with the surrounding walls.
Development and Pilot Testing of a Comprehensive Mobile Application to Assist Cell Count Determination During Peripheral Smear and Bone Marrow Examination

Background: In the modern era of complete blood count analysis, manual differential count is performed whenever 'flags' are generated by an automated hematology analyzer. Traditionally, tally counters with five or eight keys are used for manual differential count. A few mobile applications are available to perform this task; however, the application features and cell representation are limited. Objectives: The primary objective of our study was to develop an indigenous, comprehensive mobile application to assist with manual blood cell differential count. The secondary objective was to measure the usability of a newly developed application among undergraduate medical students. Materials and methods: A new mobile application was developed using a Java development kit, Version 11.0.13 (Oracle Corporation, Austin, USA) in Android Studio Dolphin (2021.3.1) (Google, California, USA). The application content was validated by three pathologists with more than five years of experience. The app's usability was tested among 60 participants using a validated mHealth App Usability Questionnaire (MAUQ). The questionnaire had 18 items covering three domains: ease of use, interface &amp; satisfaction, and usefulness. Results: The newly developed application supports peripheral smear WBC differential count, platelet count, reticulocyte count, malaria parasite quantification, and bone marrow differential count. During usability testing, the app was easy to use in 95% (57/60) of participants, time-efficient in 91.7% (55/60), and helpful for healthcare practice learning in 96.7% (58/60). The total mean score was 6.11, indicating high usability. Conclusion: A comprehensive mobile application to assist manual differential count with adequate cell representation was developed. The mobile application was easy to use, time-efficient, and valuable among the study participants.
Introducing Open Peer Review at<i>JNeurosci</i>

Scientific publishing has dramatically changed in recent years, and, as a result, there have been a number of exciting and very useful improvements to the peer review process. This process focuses on the evaluation of scientific work as a method of scholarly quality assurance in terms of scientific
adaptMLLM: Fine-Tuning Multilingual Language Models on Low-Resource Languages with Integrated LLM Playgrounds

The advent of Multilingual Language Models (MLLMs) and Large Language Models (LLMs) has spawned innovation in many areas of natural language processing. Despite the exciting potential of this technology, its impact on developing high-quality Machine Translation (MT) outputs for low-resource languages remains relatively under-explored. Furthermore, an open-source application, dedicated to both fine-tuning MLLMs and managing the complete MT workflow for low-resources languages, remains unavailable. We aim to address these imbalances through the development of adaptMLLM, which streamlines all processes involved in the fine-tuning of MLLMs for MT. This open-source application is tailored for developers, translators, and users who are engaged in MT. It is particularly useful for newcomers to the field, as it significantly streamlines the configuration of the development environment. An intuitive interface allows for easy customisation of hyperparameters, and the application offers a range of metrics for model evaluation and the capability to deploy models as a translation service directly within the application. As a multilingual tool, we used adaptMLLM to fine-tune models for two low-resource language pairs: English to Irish (EN↔ GA) and English to Marathi (EN↔MR). Compared with baselines from the LoResMT2021 Shared Task, the adaptMLLM system demonstrated significant improvements. In the EN→GA direction, an improvement of 5.2 BLEU points was observed and an increase of 40.5 BLEU points was recorded in the GA→EN direction representing relative improvements of 14% and 117%, respectively. Significant improvements in the translation performance of the EN↔MR pair were also observed notably in the MR→EN direction with an increase of 21.3 BLEU points which corresponds to a relative improvement of 68%. Finally, a fine-grained human evaluation of the MLLM output on the EN→GA pair was conducted using the Multidimensional Quality Metrics and Scalar Quality Metrics error taxonomies. The application and models are freely available.
The generation of genuine quadripartite Einstein–Podolsky–Rosen steering in an optical superlattice

Einstein-Podolsky-Rosen (EPR) steering is a quantum effect based on quantum entanglement and it is the key resource for building quantum networks because of its useful properties. Based on the criterion for genuine multipartite EPR steering, the genuine quadripartite EPR steering is confirmed and it can be generated by a spontaneous parametric down-conversion cascaded process with two sum-frequency generations in an optical superlattice. This occurs either below the oscillation threshold and without oscillation threshold. The influence of the parameters of cascaded nonlinear process on the quadripartite EPR steering among signal, idler, and two sum-frequency beams are also discussed. Choosing appropriate nonlinear parameters can achieve good quadripartite quantum steering. This scheme of the generation of genuine quadripartite EPR steering has potential applications in quantum communication and computing.
A Machine Anomalous Sound Detection Method Using the lMS Spectrogram and ES-MobileNetV3 Network

Unsupervised anomalous sound detection by machines holds significant importance within the realm of industrial automation. Currently, the task of machine-based anomalous sound detection in complex industrial settings is faced with issues such as the challenge of extracting acoustic feature information and an insufficient feature extraction capability within the detection network. To address these challenges, this study proposes a machine anomalous sound detection method using the lMS spectrogram and ES-MobileNetV3 network. Firstly, the log-Mel spectrogram feature and the SincNet spectrogram feature are extracted from the raw wave, and the new lMS spectrogram is formed after fusion, serving as network input features. Subsequently, based on the MobileNetV3 network, an improved detection network, ES-MobileNetV3, is proposed in this paper. This network incorporates the Efficient Channel Attention module and the SoftPool method, which collectively reduces the loss of feature information and enhances the feature extraction capability of the detection network. Finally, experiments are conducted on the dataset provided by DCASE 2020 Task 2. Our proposed method attained an averaged area under the receiver operating characteristic curve (AUC) of 96.67% and an averaged partial AUC (pAUC) of 92.38%, demonstrating superior detection performance compared to other advanced methods.
Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving

Deep learning networks have demonstrated outstanding performance in 2D and 3D vision tasks. However, recent research demonstrated that these networks result in failures when imperceptible perturbations are added to the input known as adversarial attacks. This phenomenon has recently received increased interest in the field of autonomous vehicles and has been extensively researched on 2D image-based perception tasks and 3D object detection. However, the adversarial robustness of 3D LiDAR semantic segmentation in autonomous vehicles is a relatively unexplored topic. This study expands the adversarial examples to LiDAR-based 3D semantic segmentation. We developed and analyzed three LiDAR point-based adversarial attack methods on different networks developed on the SemanticKITTI dataset. The findings illustrate that the Cylinder3D network has the highest adversarial susceptibility to the analyzed attacks. We investigated how the class-wise point distribution influences the adversarial robustness of each class in the SemanticKITTI dataset and discovered that ground-level points are extremely vulnerable to point perturbation attacks. Further, the transferability of each attack strategy was assessed, and we found that networks relying on point data representation demonstrate a notable level of resistance. Our findings will enable future research in developing more complex and specific adversarial attacks against LiDAR segmentation and countermeasures against adversarial attacks.
A First Look at Toxicity Injection Attacks on Open-domain Chatbots

Chatbot systems have improved significantly because of the advances made in language modeling. These machine learning systems follow an end-to-end data-driven learning paradigm and are trained on large conversational datasets. Imperfections or harmful biases in the training datasets can cause the models to learn toxic behavior, and thereby expose their users to harmful responses. Prior work has focused on measuring the inherent toxicity of such chatbots, by devising queries that are more likely to produce toxic responses. In this work, we ask the question: How easy or hard is it to inject toxicity into a chatbot after deployment? We study this in a practical scenario known as Dialog-based Learning (DBL), where a chatbot is periodically trained on recent conversations with its users after deployment. A DBL setting can be exploited to poison the training dataset for each training cycle. Our attacks would allow an adversary to manipulate the degree of toxicity in a model and also enable control over what type of queries can trigger a toxic response. Our fully automated attacks only require LLM-based software agents masquerading as (malicious) users to inject high levels of toxicity. We systematically explore the vulnerability of popular chatbot pipelines to this threat. Lastly, we show that several existing toxicity mitigation strategies (designed for chatbots) can be significantly weakened by adaptive attackers.
Breast Cancer Mammography Classification Using Convolutional Neural Networks and WOA-MPA Optimization

Breast cancer analysis is crucial for female well-being and mortality statistics. Digital mammograms improve accuracy and survival rates. This study introduces a deep-learning algorithm-based training approach for automated breast cancer identification at early stages, enhancing edge detail and reducing false positives.The proposed methodology involves the integration of a Convolutional Neural Network (CNN) with an optimization strategy to establish a classification model for the diagnosis of breast cancer. The current study utilized a hybrid approach involving the Marine Predators Algorithm (MPA) and the Whale Optimization Algorithm (WOA) to determine the well-suitable hyperparameter values for the CNN framework. The proposed approach employs Inception v3, a previously trained convolutional neural network model, and DenseNet. MPA-WOA with Inception v3 and MPA-WOA with DenseNet are two instances of architectu res that integrate this paradigm with the MPA-WOA algorithm. By conducting a comparative evaluation of the attainment of two hybrid models, this study has established that the MPA-WOA with DenseNet has demonstrated an exactness rate of 94% and 95% for the CBIS-DDSM and MIAS datasets, respectively.