Reviewerly/exHarmony huggingface dataset:
Automated Quality Evaluation of Large-Scale Benchmark Datasets for Vision-Language Tasks

Authors:
raw_author_name: Zhiwei Xie
raw_affiliation_string: Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, P. R. China
raw_author_name: Philip L. H. Yu
raw_affiliation_string: Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, P. R. China

Abstract: Large-scale benchmark datasets are crucial in advancing research within the computer science communities. They enable the development of more sophisticated AI models and serve as “golden” benchmarks for evaluating their performance. Thus, ensuring the quality of these datasets is of utmost importance for academic research and the progress of AI systems. For the emerging vision-language tasks, some datasets have been created and frequently used, such as Flickr30k, COCO, and NoCaps, which typically contain a large number of images paired with their ground-truth textual descriptions. In this paper, an automatic method is proposed to assess the quality of large-scale benchmark datasets designed for vision-language tasks. In particular, a new cross-modal matching model is developed, which is capable of automatically scoring the textual descriptions of visual images. Subsequently, this model is employed to evaluate the quality of vision-language datasets by automatically assigning a score to each ‘ground-truth’ description for every image picture. With a good agreement between manual and automated scoring results on the datasets, our findings reveal significant disparities in the quality of the ground-truth descriptions included in the benchmark datasets. Even more surprising, it is evident that a small portion of the descriptions are unsuitable for serving as reliable ground-truth references. These discoveries emphasize the need for careful utilization of these publicly accessible benchmark databases.
High fidelity laser beam shaping using liquid crystal on silicon spatial light modulators as diffractive neural networks

Authors:
raw_author_name: Paul Buske
raw_author_name: Annika Völl
raw_author_name: Jochen Stollenwerk
raw_affiliation_string: Fraunhofer-Institute for Laser Technology
raw_author_name: Carlo Holly
raw_affiliation_string: Fraunhofer-Institute for Laser Technology

Abstract: Spatial light modulators (SLMs) based on liquid crystal on silicon (LCoS) are powerful tools for laser beam shaping as they can be used to dynamically create almost arbitrary intensity distributions. However, laser beam shaping with LCoS-SLMs often suffers from beam shaping artifacts in part caused by unconsidered properties of the LCoS devices: astigmatism that stems from the non-normal incidence of the laser beam on the SLM and the effect commonly referred to as the '0-th diffraction order' that is caused by both the crosstalk between neighboring pixels and the direct reflection at the cover glass of the SLM. We here present a method to consider and compensate for these inherent properties of LCoS devices by treating the SLM as a diffractive neural network.
Prior-Guided Attribution of Deep Neural Networks for Obstetrics and Gynecology

Authors:
raw_author_name: Jules Bonnard
raw_affiliation_string: CNRS, ISIR, Sorbonne Universit&#x00E9;, Paris, France
raw_author_name: Arnaud Dapogny
raw_affiliation_string: Datakalab, Paris, France
raw_author_name: Kevin Bailly
raw_affiliation_string: CNRS, ISIR, Sorbonne Universit&#x00E9;, Paris, France
raw_author_name: Ferdinand Dhombres
raw_affiliation_string: Paris Nord, INSERM, LIMICS, APHP, Service de m&#x00E9;decine f&#x0153;tale, GRC26, Sorbonne Universit&#x00E9;, Paris, France

Abstract: Obstetrics and gynecology (OB/GYN) are areas of medicine that specialize in the care of women during pregnancy and childbirth and in the diagnosis of diseases of the female reproductive system. Ultrasound scanning has become ubiquitous in these branches of medicine, as breast or fetal ultrasound images can lead the sonographer and guide him through his diagnosis. However, ultrasound scan images require a lot of resources to annotate and are often unavailable for training purposes because of confidentiality reasons, which explains why deep learning methods are still not as commonly used to solve OB/GYN tasks as in other computer vision tasks. In order to tackle this lack of data for training deep neural networks in this context, we propose Prior-Guided Attribution (PGA), a novel method that takes advantage of prior spatial information during training by guiding part of its attribution towards these salient areas. Furthermore, we introduce a novel prior allocation strategy method to take into account several spatial priors at the same time while providing the model enough degrees of liberty to learn relevant features by itself. The proposed method only uses the additional information during training, without needing it during inference. After validating the different elements of the method as well as its genericity on a facial analysis problem, we demonstrate that the proposed PGA method constantly outperforms existing baselines on two ultrasound imaging OB/GYN tasks: breast cancer detection and scan plane detection with segmentation prior maps.
Incomplete and possibly selective recording of signs, symptoms, and measurements in free text fields of primary care electronic health records of adults with lower respiratory tract infections

Authors:
raw_author_name: M. (Maarten) van Smeden
raw_affiliation_string: Department of Epidemiology & Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
raw_author_name: R.P. (Roderick) Venekamp
raw_affiliation_string: Department of General Practice & Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
raw_author_name: A.M. (Tuur) Leeuwenberg
raw_affiliation_string: Department of Epidemiology & Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands

Abstract: <h2>Abstract</h2><h3>Objectives</h3> To assess the completeness of recording of relevant signs, symptoms, and measurements in Dutch free text fields of primary care electronic health records (EHR) of adults with lower respiratory tract infections (LRTI). <h3>Study Design and Setting</h3> Retrospective cohort study embedded in a prediction modeling project using routine health care data of the Julius General Practitioners' Network of adult patients with LRTI. Free text fields of 1,000 primary care consultations of LRTI episodes between 2016 and 2019 were manually annotated to retrieve data on the recording of sixteen relevant signs, symptoms, and measurements. <h3>Results</h3> For 12/16 (75%) of the relevant signs, symptoms, and measurements, more than 50% of the values was not recorded. The patterns of recorded values indicated selective recording of positive or abnormal values. Recording rates varied across consultation type (physical consultation vs. home visit), diagnosis (acute bronchitis vs. pneumonia), antibiotic prescription issued (yes vs. no), and between practices. <h3>Conclusion</h3> In EHR of primary care LRTI patients, recording of signs, symptoms, and measurements in free text fields is incomplete and possibly selective. When using free text data in EHR-based research, careful consideration of its recording patterns and appropriate missing data handling techniques is therefore required.
Dataset construction to detect human behavior with the help of emotions, sentiments and mood for Roman Urdu

Authors:
raw_author_name: SYED ASIF ALI
raw_affiliation_string: Department of Computer Science, Sindh Madressatul Islam University, Hasrat Mohani Road, Karachi-74000, Pakistan

Abstract: Roman Urdu and English are often used together as a hybrid language for communication on social media. Because writers don't worry about spelling when utilising the English alphabet to write Urdu during texting, it becomes challenging to interpret mixed codes for emotions. There are over 14,000 emotion lexicons in this dataset, each of which lists nine different emotions and their polarities. The NRC emotion lexicons [8] provided in Urdu have been transliterated into Roman Urdu. To verify that the provided translation is accurate, we used three online dictionaries of Urdu. A Python script that transliterates words from Urdu to Roman Urdu has been used to develop Roman Urdu transliteration. Sentiment and mood, depending on the emotion lexicon, are also provided. The textual data has been annotated using the unigram feature and distance estimation among strings and lexicons. Approximately 10,000 sentences from the baseline sample have been automatically annotated.
An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people

Authors:
raw_author_name: Subrata Kumer Paul
raw_affiliation_string: Bangladesh Army University of Engineering and Technology
raw_author_name: Md. Abul Ala Walid
raw_affiliation_string: Khulna University of Engineering and Technology (KUET)
raw_author_name: Rakhi Rani Paul
raw_affiliation_string: Bangladesh Army University of Engineering and Technology
raw_author_name: Maloy Kumar Devnath
raw_affiliation_string: Bangabandhu Sheikh Mujibur Rahman Science and Technology University
raw_author_name: Md. Momenul Haque
raw_affiliation_string: Bangladesh Army University of Engineering and Technology

Abstract: Hand gestures and sign language are crucial modes of communication for deaf individuals. Since most people can't understand sign language, it's hard for a mute and an average person to talk to each other. Because of technological progress, computer vision and deep learning can now be used to count. This paper shows two ways to use deep knowledge to recognize sign language. These methods help regular people understand sign language and improve their communication. Based on American sign language (ASL), two separate datasets have been constructed; the first has 26 signs, and the other contains three significant symbols with the crucial sequence of frames or videos for regular communication. This study looks at three different models: the improved ResNet-based convolutional neural network (CNN), the long short-term memory (LSTM), and the gated recurrent unit (GRU). The first dataset is used to fit and assess the CNN model. With the adaptive moment estimation (Adam) optimizer, CNN obtains an accuracy of 89.07%. In contrast, the second dataset is given to LSTM and GRU and a comparison has been conducted. LSTM does better than GRU in all classes. LSTM has a 94.3% accuracy, while GRU only manages 79.3%. Our preliminary models' real-time performance is also highlighted.
A Bidirectional Feedforward Neural Network Architecture Using the Discretized Neural Memory Ordinary Differential Equation

Authors:
raw_author_name: Hao Niu
raw_affiliation_string: College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
raw_author_name: Zhang Yi
raw_affiliation_string: College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
raw_author_name: Tao He
raw_affiliation_string: College of Computer Science, Sichuan University, Chengdu 610065, P. R. China

Abstract: Deep Feedforward Neural Networks (FNNs) with skip connections have revolutionized various image recognition tasks. In this paper, we propose a novel architecture called bidirectional FNN (BiFNN), which utilizes skip connections to aggregate features between its forward and backward paths. The BiFNN accepts any FNN as a plugin that can incorporate any general FNN model into its forward path, introducing only a few additional parameters in the cross-path connections. The backward path is implemented as a nonparameter layer, utilizing a discretized form of the neural memory Ordinary Differential Equation (nmODE), which is named [Formula: see text]-net. We provide a proof of convergence for the [Formula: see text]-net and evaluate its initial value problem. Our proposed architecture is evaluated on diverse image recognition datasets, including Fashion-MNIST, SVHN, CIFAR-10, CIFAR-100, and Tiny-ImageNet. The results demonstrate that BiFNNs offer significant improvements compared to embedded models such as ConvMixer, ResNet, ResNeXt, and Vision Transformer. Furthermore, BiFNNs can be fine-tuned to achieve comparable performance with embedded models on Tiny-ImageNet and ImageNet-1K datasets by loading the same pretrained parameters.
Few-shot learning based oral cancer diagnosis using a dual feature extractor prototypical network

Authors:
raw_author_name: Zijun Guo
raw_affiliation_string: Department of Stomatology, Daping Hospital, Army Medical Center of PLA, Chongqing 400042, China
raw_author_name: Sha Ao
raw_affiliation_string: The People's Hospital of Rongchang District in Chongqing, Chongqing 402460, China

Abstract: A large global health issue is cancer, wherein early diagnosis and treatment have proven to be life-saving. This holds true for oral cancer, thus emphasizing the significance of timely intervention. Deep learning techniques have gained traction in early cancer detection, exhibiting promising outcomes in accurate diagnosis. However, collecting a substantial amount of training data poses a challenge for deep learning models in cancer diagnosis. To address this limitation, this study proposes an oral cancer diagnosis approach based on a few-shot learning framework that circumvents the need for extensive training data. Specifically, a prototypical network is employed to construct a diagnostic model, wherein two feature extractors are utilized to extract prototypical features and query features respectively, departing from the conventional use of a single feature extraction function in prototypical networks. Moreover, a customized loss function is designed for the proposed method. Rigorous experimentation using a histopathological image dataset demonstrates the superior performance of our proposed approach over comparison methods.
An empirical study of counterfactual visualization to support visual causal inference

Authors:
raw_author_name: Borland, David
raw_affiliation_string: RENCI, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
raw_author_name: Gotz, David
raw_affiliation_string: The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Abstract: Counterfactuals – expressing what might have been true under different circumstances – have been widely applied in statistics and machine learning to help understand causal relationships. More recently, counterfactuals have begun to emerge as a technique being applied within visualization research. However, it remains unclear to what extent counterfactuals can aid with visual data communication. In this paper, we primarily focus on assessing the quality of users’ understanding of data when provided with counterfactual visualizations. We propose a preliminary model of causality comprehension by connecting theories from causal inference and visual data communication. Leveraging this model, we conducted an empirical study to explore how counterfactuals can improve users’ understanding of data in static visualizations. Our results indicate that visualizing counterfactuals had a positive impact on participants’ interpretations of causal relations within datasets. These results motivate a discussion of how to more effectively incorporate counterfactuals into data visualizations.
A Parameter-independent Algorithm of Finding Maximum Clique with Seidel Continuous-Time Quantum Walks

Authors:
raw_author_name: Xiao Chen
raw_affiliation_string: School of Computer Science and Engineering, Southeast University, No.2, Sipailou District, Nanjing, Jiangsu 210096, China.
raw_author_name: Juan Xu
raw_affiliation_string: Collaborative Innovation Center of Novel Software Technology and Industrialization, No.29, Junjun Dadao, Jiangning District, Nanjing, Jiangsu 210023, China.; College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, No.29, Junjun Dadao, Jiangning District, Nanjing, Jiangsu 210016, China.
raw_author_name: Zhihao Liu
raw_affiliation_string: School of Computer Science and Engineering, Southeast University, No.2, Sipailou District, Nanjing, Jiangsu 210096, China.

Abstract: The maximum clique (MC) problem holds significance in network analysis. Quantum-based algorithms have recently emerged as promising approaches for this problem. However, these algorithms heavily depend on parameters of quantum system and vary significantly for different graphs. In order to tackle this issue, we initially demonstrate that continuous-time quantum walks (CTQW) driven by the Seidel matrix offer valuable insights into the clique structure of graphs, outperforming the CTQW driven by adjacency matrix. Specifically, we conduct an in-depth analysis for CTQW of 4 types of graphs, meticulously calculating the amplitudes associated with different vertices. Our findings consistently reveal that vertices belonging to MC exhibit the highest intensity at the largest frequency component of the probability amplitude for these types of graphs. Considering the varying intensities, we propose a parameter-independent algorithm for determining the MC. We compare our algorithm with a typical quantum-based algorithm, the results indicate that our algorithm exhibits greater stability.
Squeezing as a resource for time series processing in quantum reservoir computing

Authors:
raw_author_name: Jorge García-Beni
raw_affiliation_string: Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC)
raw_author_name: Gian Giorgi
raw_affiliation_string: Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC)
raw_author_name: Miguel Cornelles Soriano
raw_affiliation_string: Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC)
raw_author_name: Roberta Zambrini
raw_affiliation_string: Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC)

Abstract: Squeezing is known to be a quantum resource in many applications in metrology, cryptography, and computing, being related to entanglement in multimode settings. In this work, we address the effects of squeezing in neuromorphic machine learning for time-series processing. In particular, we consider a loop-based photonic architecture for reservoir computing and address the effect of squeezing in the reservoir, considering a Hamiltonian with both active and passive coupling terms. Interestingly, squeezing can be either detrimental or beneficial for quantum reservoir computing when moving from ideal to realistic models, accounting for experimental noise. We demonstrate that multimode squeezing enhances its accessible memory, which improves the performance in several benchmark temporal tasks. The origin of this improvement is traced back to the robustness of the reservoir to readout noise, which is increased with squeezing.
Construction and optimization of vending machine decision support system based on improved C4.5 decision tree

Authors:
raw_author_name: Xibei Huang
raw_affiliation_string: School of Information and Mechatronic Engineering, Hunan International Economics University, Changsha, 410205, China.
raw_author_name: Xiaojun Wen
raw_affiliation_string: School of Information and Mechatronic Engineering, Hunan International Economics University, Changsha, 410205, China.

Abstract: <h2>Abstract</h2> The intensification of market competition makes refined operation management become the focus of attention of major manufacturers. As an important branch of artificial intelligence (AI), machine learning (ML) plays a key role in it, and has its application prospect in various systems. Based on this situation, this paper takes vending machines as the research object. On the one hand, the product classification model of vending machine is constructed based on decision tree algorithm. On the other hand, based on neural network (NN), the sales forecast model of vending machines is built. Finally, based on the above research, the theoretical framework of decision support system (DSS) for vending machines is constructed. The research shows that: (1) The accuracy of C4.5 algorithm can reach 87 % at the highest and 68 % at the lowest. The accuracy of the improved C4.5 algorithm can reach 87 % at the highest and 67 % at the lowest, with little difference between them. (2) The maximum running time of the improved C4.5 algorithm is about 5500 ms, and the minimum is close to 1 ms. In addition, the running time of all seven datasets is better than that of the unmodified algorithm. (3) When the back propagation neural network (BPNN) is used to forecast the sales of vending machines, the curve of the predicted data basically coincides with the curve of the actual data, which shows that its accuracy is high. This paper aims to build a convenient and secure DSS by taking vending machines as an example. In addition, this paper also uses reinforcement learning to optimize the research methods of this paper. It can further optimize the performance and efficiency of vending machines and provide better service experience for customers. Meanwhile, the use of reinforcement learning can make the whole system more intelligent and adaptive to better cope with the changing market environment.
Leveraging Sampling Schemes on Skewed Class Distribution to Enhance Male Fertility Detection with Ensemble AI Learners

Authors:
raw_author_name: KC Santosh
raw_affiliation_string: Applied AI Research Lab, Department of Computer Science, University of South Dakota, Vermillion, SD 57069 USA

Abstract: Designing effective AI models becomes a challenge when dealing with imbalanced/skewed class distributions in datasets. Addressing this, re-sampling techniques often come into play as potential solutions. In this investigation, we delve into the male fertility dataset, exploring 14 re-sampling approaches to understand their impact on enhancing predictive model performance. The research employs conventional AI learners to gauge male fertility potential. Notably, five ensemble AI learners are studied, their performances are compared, and their results are evaluated using four measurement indices. Through comprehensive comparative analysis, we identify substantial enhancement in model effectiveness. Our findings showcase that the LightGBM model with SMOTE-ENN re-sampling stands out, achieving an efficacy of 96.66% and an F1-Score of 95.60% through 5-fold cross-validation. Interestingly, the CatBoost model, without re-sampling, exhibits strong performance, achieving an efficacy of 86.99% and an F1-Score of 93.02%. Furthermore, we benchmark our approach against state-of-the-art methods in male fertility prediction, particularly highlighting the use of re-sampling techniques like SMOTE and ESLSMOTE. Consequently, our proposed model emerges as a robust and efficient computational framework, promising accurate male fertility prediction.