Reviewerly/exHarmony huggingface dataset:
Speech Emotion Recognition Using Deep Learning Transfer Models and Explainable Techniques

Authors:
raw_author_name: Tae-Wan Kim
raw_affiliation_string: Department of Electronics Engineering, Interdisciplinary Program in IT-Bio Convergence System, Chosun University, Gwangju 61452, Republic of Korea
raw_author_name: Keun-Chang Kwak
raw_affiliation_string: Department of Electronics Engineering, Interdisciplinary Program in IT-Bio Convergence System, Chosun University, Gwangju 61452, Republic of Korea

Abstract: This study aims to establish a greater reliability compared to conventional speech emotion recognition (SER) studies. This is achieved through preprocessing techniques that reduce uncertainty elements, models that combine the structural features of each model, and the application of various explanatory techniques. The ability to interpret can be made more accurate by reducing uncertain learning data, applying data in different environments, and applying techniques that explain the reasoning behind the results. We designed a generalized model using three different datasets, and each speech was converted into a spectrogram image through STFT preprocessing. The spectrogram was divided into the time domain with overlapping to match the input size of the model. Each divided section is expressed as a Gaussian distribution, and the quality of the data is investigated by the correlation coefficient between distributions. As a result, the scale of the data is reduced, and uncertainty is minimized. VGGish and YAMNet are the most representative pretrained deep learning networks frequently used in conjunction with speech processing. In dealing with speech signal processing, it is frequently advantageous to use these pretrained models synergistically rather than exclusively, resulting in the construction of ensemble deep networks. And finally, various explainable models (Grad CAM, LIME, occlusion sensitivity) are used in analyzing classified results. The model exhibits adaptability to voices in various environments, yielding a classification accuracy of 87%, surpassing that of individual models. Additionally, output results are confirmed by an explainable model to extract essential emotional areas, converted into audio files for auditory analysis using Grad CAM in the time domain. Through this study, we enhance the uncertainty of activation areas that are generated by Grad CAM. We achieve this by applying the interpretable ability from previous studies, along with effective preprocessing and fusion models. We can analyze it from a more diverse perspective through other explainable techniques.
Deep Reinforcement Learning for Autonomous Driving in Amazon Web Services DeepRacer

Authors:
raw_author_name: Armantas Ostreika
raw_affiliation_string: Department of Multimedia Engineering, Faculty of Informatics, Kaunas University of Technology, LT-51368 Kaunas, Lithuania

Abstract: The development of autonomous driving models through reinforcement learning has gained significant traction. However, developing obstacle avoidance systems remains a challenge. Specifically, optimising path completion times while navigating obstacles is an underexplored research area. Amazon Web Services (AWS) DeepRacer emerges as a powerful infrastructure for engineering and analysing autonomous models, providing a robust foundation for addressing these complexities. This research investigates the feasibility of training end-to-end self-driving models focused on obstacle avoidance using reinforcement learning on the AWS DeepRacer autonomous race car platform. A comprehensive literature review of autonomous driving methodologies and machine learning model architectures is conducted, with a particular focus on object avoidance, followed by hands-on experimentation and the analysis of training data. Furthermore, the impact of sensor choice, reward function, action spaces, and training time on the autonomous obstacle avoidance task are compared. The results of the best configuration experiment demonstrate a significant improvement in obstacle avoidance performance compared to the baseline configuration, with a 95.8% decrease in collision rate, while taking about 79% less time to complete the trial circuit.
Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction

Authors:
raw_author_name: Vishal Patel
raw_affiliation_string: Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand
raw_author_name: Alan Wang
raw_affiliation_string: Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand; Faculty of Medical and Health Sciences, The University of Auckland, Auckland 1010, New Zealand

Abstract: This study introduces a hybrid analytical super-resolution (SR) pipeline aimed at enhancing the resolution of medical magnetic resonance imaging (MRI) scans. The primary objective is to overcome the limitations of clinical MRI resolution without the need for additional expensive hardware. The proposed pipeline involves three key steps: pre-processing to re-slice and register the image stacks; SR reconstruction to combine information from three orthogonal image stacks to generate a high-resolution image stack; and post-processing using an artefact reduction convolutional neural network (ARCNN) to reduce the block artefacts introduced during SR reconstruction. The workflow was validated on a dataset of six knee MRIs obtained at high resolution using various sequences. Quantitative analysis of the method revealed promising results, showing an average mean error of 1.40 ± 2.22% in voxel intensities between the SR denoised images and the original high-resolution images. Qualitatively, the method improved out-of-plane resolution while preserving in-plane image quality. The hybrid SR pipeline also displayed robustness across different MRI sequences, demonstrating potential for clinical application in orthopaedics and beyond. Although computationally intensive, this method offers a viable alternative to costly hardware upgrades and holds promise for improving diagnostic accuracy and generating more anatomically accurate models of the human body.
Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction

Authors:
raw_author_name: Yusuf Brima
raw_affiliation_string: Comparative BioCognition, Institute of Cognitive Science, Osnabrueck University, 49074 Osnabrück, Germany; Computer Vision, Institute of Cognitive Science, Osnabrueck University, 49074 Osnabrück, Germany
raw_author_name: Ulf Krumnack
raw_affiliation_string: Computer Vision, Institute of Cognitive Science, Osnabrueck University, 49074 Osnabrück, Germany
raw_author_name: Simone Pika
raw_affiliation_string: Comparative BioCognition, Institute of Cognitive Science, Osnabrueck University, 49074 Osnabrück, Germany
raw_author_name: Gunther Heidemann
raw_affiliation_string: Computer Vision, Institute of Cognitive Science, Osnabrueck University, 49074 Osnabrück, Germany

Abstract: Self-supervised learning (SSL) has emerged as a promising paradigm for learning flexible speech representations from unlabeleddata. By designing pretext tasks that exploit statistical regularities, SSL models can capture useful representations that are transferable to downstream tasks. Barlow Twins (BTs) is an SSL technique inspired by theories of redundancy reduction in human perception. In downstream tasks, BTs representations accelerate learning and transfer this learning across applications. This study applies BTs to speech data and evaluates the obtained representations on several downstream tasks, showing the applicability of the approach. However, limitations exist in disentangling key explanatory factors, with redundancy reduction and invariance alone being insufficient for factorization of learned latents into modular, compact, and informative codes. Our ablation study isolated gains from invariance constraints, but the gains were context-dependent. Overall, this work substantiates the potential of Barlow Twins for sample-efficient speech encoding. However, challenges remain in achieving fully hierarchical representations. The analysis methodology and insights presented in this paper pave a path for extensions incorporating further inductive priors and perceptual principles to further enhance the BTs self-supervision framework.
Artificial intelligence (AI) cybersecurity dimensions: a comprehensive framework for understanding adversarial and offensive AI

Authors:
raw_author_name: Masike Malatji
raw_affiliation_string: Graduate School of Business Leadership (SBL), University of South Africa (UNISA), PO Box 392, Unisa, 0003, South Africa
raw_author_name: Alaa Tolah
raw_affiliation_string: College of Computing and Informatics, Saudi Electronic University (SEU), PO Box 93499, 11673, Riyadh, Saudi Arabia

Abstract: Abstract As Artificial Intelligence (AI) rapidly advances and integrates into various domains, cybersecurity emerges as a critical field grappling with both the benefits and pitfalls of AI technologies. This paper explores the multifaceted dimensions of AI-driven cyberattacks, offering insights into their implications, mitigation strategies, underlying motivations, and profound societal impacts. The research centres on developing and presenting the AI Cybersecurity Dimensions (AICD) Framework, a comprehensive, multidimensional schema designed to guide academics, policymakers, and industry professionals in understanding and combating the evolving challenges posed by AI-driven cyber threats. The research unveils the complex dynamics of offensive AI, stressing the need for adaptive defences and ethical considerations. Concurrently, the study highlights adversarial AI threats, calling for proactive measures to address their potential ramifications. Through rigorous textual analyses and extensive literature reviews, the paper underscores the urgency for interdisciplinary approaches to bridge the technology-humanity chasm traditionally observed in cybersecurity discussions. By synthesising these diverse elements, the AICD Framework emerges as an instrumental tool for holistic understanding and practical interventions in the AI-infused cybersecurity landscape. The paper concludes with an urgent call for collaborative efforts in research and practice to navigate the intricate challenges and capitalise on the opportunities borne from the convergence of AI and cybersecurity.
Deep Learning-Based Aspect Term Extraction for Sentiment Analysis in Hindi

Authors:
raw_author_name: Ashwani Gupta
raw_author_name: Utpal Sharma

Abstract: Objectives: Aspect terms play a vital role in finalizing the sentiment of a given review. This experimental study aims to improve the aspect term extraction mechanism for Hindi language reviews. Methods: We trained and evaluated a deep learning-based supervised model for aspect term extraction. All experiments are performed on a well-accepted Hindi dataset. A BiLSTM-based attention technique is employed to improve the extraction results. Findings: Our results show better F-score results than many existing supervised methods for aspect term extraction. Accuracy results are outstanding compared to other reported results. Results showed an outstanding 91.27% accuracy and an F–score of 43.16. Novelty: This proposed architecture and the achieved results are a foundational resource for future studies and endeavours in the field. Keywords: Sentiment analysis, Aspect based sentiment analysis, Aspect term extraction, Deep Learning, Bi LSTM, Indian language, Hindi
Deep Learning Video Prediction Based on Enhanced Skip Connection

Authors:
raw_author_name: Shefa A. Dawwd

Abstract: Video prediction theories have quickly progressed especially after a great revolution of deep learning methods. The prediction architectures based on pixel generation produced a blurry forecast, but it is preferred in many applications because this model is applied on frames only and does not need other support information like segmentation or flow mapping information making getting a suitable dataset very difficult. In this approach, we presented a novel end-to-end video forecasting framework to predict the dynamic relationship between pixels in time and space. The 3D CNN encoder is used for estimating the dynamic motion, while the decoder part is used to reconstruct the next frame based on adding 3DCNN CONVLSTM2D in skip connection. This novel representation of skip connection plays an important role in reducing the blur predicted and preserved the spatial and dynamic information. This leads to an increase in the accuracy of the whole model. The KITTI and Cityscapes are used in training and Caltech is applied in inference. The proposed framework has achieved a better quality in PSNR=33.14, MES=0.00101, SSIM=0.924, and a small number of parameters (2.3 M).
Entanglement Detection with Trace Polynomials

Authors:
raw_author_name: Felix Huber
raw_affiliation_string: Faculty of Physics, Astronomy and Applied Computer Science, Institute of Theoretical Physics, Jagiellonian University, 30-348 Kraków, Poland

Abstract: We provide a systematic method for nonlinear entanglement detection based on trace polynomial inequalities. In particular, this allows us to employ multipartite witnesses for the detection of bipartite states, and vice versa. We identify pairs of entangled states and witnesses for which linear detection fails, but for which nonlinear detection succeeds. With the trace polynomial formulation a great variety of witnesses arise from immanant inequalities, which can be implemented in the laboratory through the randomized measurements toolbox.
LeCaSiM: Learning Causal Structure via Inverse of M-Matrices with Adjustable Coefficients

Authors:
raw_author_name: Qingsong Cai
raw_affiliation_string: School of Computer Science and Engineering, Beijing Technology and Business University, No. 33, Fucheng Road, Haidian District, Beijing, 100048, China
raw_author_name: Yongchang Zhang
raw_affiliation_string: School of Computer Science and Engineering, Beijing Technology and Business University, No. 33, Fucheng Road, Haidian District, Beijing, 100048, China

Abstract: Abstract The objective of causal discovery is to uncover the causal relationships among natural phenomena or human behaviors, thus establishing the basis for subsequent prediction and inference. Traditional ways to reveal the causal structure between variables, such as interventions or random trials, are often deemed costly or impractical. With the diversity of means of data acquisition and abundant data, approaches that directly learn causal structure from observational data are attracting more interest in academia. Unfortunately, learning a directed acyclic graph (DAG) from samples of multiple variables has been proven to be a challenging problem. Recent proposals leverage the continuous optimization method to discover the structure of the target DAG from observational data using an acyclic constraint function. Although it has an elegant mathematical form, its optimization process is prone to numerical explosion or the disappearance of high-order gradients of constraint conditions, making the convergence conditions unstable and the result deviating from the true structure. To tackle these issues, we first combine the existing work to give a series of algebraic equivalent conditions for a DAG and its corresponding brief proofs, then leverage the properties of the M-matrix to derive a new acyclic constraint function. Based on the utilization of this function and linear structural equation models, we propose a novel causal discovery algorithm called LeCaSiM, which employs continuous optimization. Our algorithm exploits the spectral radius of the adjacency matrix to dynamically adjust the coefficients of the matrix polynomial, effectively solving the above problems. We conduct extensive experiments on both synthetic and real-world datasets, the results show our algorithm outperforms the existing algorithms on both computation time and accuracy under the same settings.
Spectro Temporal Fusion with CLSTM-Autoencoder based approach for Anomalous Sound Detection

Authors:
raw_author_name: S. Chandrakala
raw_affiliation_string: Department of Computer Science and Engineering, Shiv Nadar University, Chennai, Tamil Nadu, India

Abstract: Abstract Deep learning models are proved efficient for complex learning tasks. Anomalous sound detection is one such complex task for which self-supervised deep architectures are emerging in recent days. Self-supervised deep models efficiently capture the underlying structure of data. Self-supervised anomalous sound detection attempts to distinguish between normal sounds and unidentified anomalous sounds. With the use of appropriate autoencoders, reconstruction error based decision making is effective for anomaly detection in domains such as computer vision. Auditory image (Spectrogram) based representation of sound signals are commonly used in sound event detection. We propose convolutional long short-term memory (CLSTM) Auto Encoder based approach for anomalous sound detection. In this approach, we explore fusion of spectral and temporal features to model characteristics of normal sounds with noises. The proposed approach is evaluated using MIMII dataset and the DCASE Challenge (2020) Task 2—Anomalous sound detection dataset. Experiments on proposed approach reveal significant improvement over the state-of-the-art approaches.
Motion planner based on CNN with LSTM through mediated perception for obstacle avoidance

Authors:
raw_author_name: Satoshi Hoshino
raw_affiliation_string: Department of Mechanical and Intelligent Engineering, Graduate School of Engineering, Utsunomiya University, Utsunomiya, Japan
raw_author_name: Yusuke Yoshida
raw_affiliation_string: Hitachi Construction Machinery, Tokyo, Japan

Abstract: For autonomous navigation, a mobile robot is required to move toward a destination while avoiding obstacles. In this paper, we present a motion planner based on CNN. In terms of obstacle avoidance, since a position of a dynamic obstacle changes with time, it is important for the robot to plan avoidance motions in consideration of the time series variation in the images. For this purpose, an LSTM block is applied to the CNN. The policy of the motion planner represented by CNN with LSTM is trained through imitation learning. In this regard, however, it is difficult for the robot to recognize unknown objects as obstacles. For obstacle recognition, a perception process is further provided between the image inputs and CNN with LSTM in the motion planner. Moreover, the robot plans different avoidance motions depending on the velocity of the dynamic obstacle. For this purpose, an obstacle state classifier based on CNN is used ahead of the motion planner. A depth-difference image generated from two depth images is fed as the input to the classifier. A classified state that indicates the velocity of the obstacle is fed as the input to the following motion planner. In the navigation experiments, we show that the robot based on the proposed motion planner is able to move toward a destination autonomously while avoiding standing and walking persons, respectively. Furthermore, we show that the robot based on the motion planner with the obstacle state input is able to plan different avoidance motions for a person walking slowly or fast using the obstacle state classifier.
Improving Performance of Massive Text Real-Time Classification for Document Confidentiality Management

Authors:
raw_author_name: Lingling Tan
raw_affiliation_string: Institute of Automation, Beijing Information Science and Technology University, Beijing 100192, China
raw_author_name: Junkai Yi
raw_affiliation_string: Institute of Automation, Beijing Information Science and Technology University, Beijing 100192, China
raw_author_name: Fei Yang
raw_affiliation_string: Institute of Automation, Beijing Information Science and Technology University, Beijing 100192, China

Abstract: For classified and sensitive electronic documents within the scope of enterprises and organizations, in order to standardize and strengthen the confidentiality management of enterprises and meet the actual needs of secret text classification, a document automatic classification optimization method based on keyword retrieval and the kNN classification algorithm is proposed. The method supports keyword classification management, provides users with keywords of multiple risk levels, and then combines a matching scanning algorithm to label keywords of different levels. The text with labels is used as the training set of the kNN algorithm to classify the target text and realize the classification protection of text data. Aimed at solving the shortcomings of large feature vector dimension, low classification efficiency, and low accuracy in existing kNN text classification methods, an optimization method is proposed using a feature selection algorithm and a kNN algorithm based on an AVX instruction set to realize real-time classification of massive texts. By constructing a keyword dictionary and an optimized feature vector, parallel calculation of the feature vector weight and distance vector is realized, and the accuracy and efficiency of text classification are improved. The experimental results show that the multi-classification effect of the feature selection algorithm used in this paper, tf-DE, is better than that of the traditional tf-idf algorithm, and the classification effect of kNN is comparable to that of the support vector machine (SVM) algorithm. With the increase in feature vector dimensions, the classification effect of the text classification algorithm is improved and the classification time also increases linearly. The AVX-256 acceleration method takes about 55% of the time of the original version, thus verifying the effect of multi-classification of massive texts for document confidentiality management.
Dual view deep learning for enhanced breast cancer screening using mammography

Authors:
raw_author_name: Samuel Rahimeto Kebede
raw_affiliation_string: Research Development Cluster, Ethiopian Artificial Intelligence Institute, Addis Ababa, 40782, Ethiopia
raw_author_name: Fraol Gelana Waldamichael
raw_affiliation_string: Research Development Cluster, Ethiopian Artificial Intelligence Institute, Addis Ababa, 40782, Ethiopia
raw_author_name: Taye Girma Debelee
raw_affiliation_string: Research Development Cluster, Ethiopian Artificial Intelligence Institute, Addis Ababa, 40782, Ethiopia
raw_author_name: Muluberhan Aleme
raw_affiliation_string: Radiology, Pioneer Diagnostic Center, Addis Ababa, Ethiopia
raw_author_name: Wubalem Bedane
raw_affiliation_string: Radiology, St. Pauli Millenium Medical College, Addis Ababa, Ethiopia
raw_author_name: Bethelhem Mezgebu
raw_affiliation_string: Radiology, St. Pauli Millenium Medical College, Addis Ababa, Ethiopia
raw_author_name: Zelalem Chimdesa Merga
raw_affiliation_string: Department of Surgery, Zewditu Memorial Hospital, Addis Ababa, Ethiopia

Abstract: Abstract Breast cancer has the highest incidence rate among women in Ethiopia compared to other types of cancer. Unfortunately, many cases are detected at a stage where a cure is delayed or not possible. To address this issue, mammography-based screening is widely accepted as an effective technique for early detection. However, the interpretation of mammography images requires experienced radiologists in breast imaging, a resource that is limited in Ethiopia. In this research, we have developed a model to assist radiologists in mass screening for breast abnormalities and prioritizing patients. Our approach combines an ensemble of EfficientNet-based classifiers with YOLOv5, a suspicious mass detection method, to identify abnormalities. The inclusion of YOLOv5 detection is crucial in providing explanations for classifier predictions and improving sensitivity, particularly when the classifier fails to detect abnormalities. To further enhance the screening process, we have also incorporated an abnormality detection model. The classifier model achieves an F1-score of 0.87 and a sensitivity of 0.82. With the addition of suspicious mass detection, sensitivity increases to 0.89, albeit at the expense of a slightly lower F1-score of 0.79.