Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Segmentation of Leukocytes Images using Particle Swarm

Blood smear image analysis is an essential task for many health related issues. Among the many blood structures present in these images, leukocytes play an important role in the detection of many diseases (such as leukemias), which can be detected by the amount, or abnormal aspect, of the leukocytes. To address this problem, this paper presents an unsupervised segmentation method for the nuclear structures in leukocytes. Our method uses color deconvolution to separate the dyes in different channels and a PSO algorithm to estimate an optimal kernel filter to combine local features in different stain channels to emphasize the leukocytes structures so that simple thresholding techniques are able to perform image segmentation. We also used a postprocessing approach based on morphological operators to refine the border of detected structures, thus improving our performance. We performed a comparison with different approaches found in literature using 367 images containing leukocytes and other blood structures and results demonstrated the superiority of our approach in terms of Jaccard index.
A Summarized Semantic Structure to Represent Manipulation Actions

: To represent human manipulation actions in a simple and understandable way, we had proposed a framework called enriched semantic event chains (eSEC) which creates a temporal sequence of static and dynamic spatial relations between objects in a manipulation. The eSEC framework has so far only been used in manipulation actions consisting of one hand. As the eSECs descriptors are in the form of huge matrices, we need to have a concise version of them. Here, we want to extend this framework to interactions which involve more hands. Therefore, we applied statistical and semantic analyses to summarize the current eSEC while preserving its important features and introducing an enhanced eSEC (e 2 SEC). This summarization is done by reducing the number of rows in an eSEC matrix and merging semantic spatial relations between manipulated objects. Eventually, we presented the new e 2 SEC framework which has 20% fewer rows, 16.7% less static spatial and 11.1% less dynamic spatial relations while still maintaining the eSEC efﬁciency in recognition and differentiation of manipulation actions. This simpliﬁcation paves the way for a simpler recognition and predicting complex actions and interactions in a shorter time and is beneﬁcial in real time applications such as human-robot interactions.
Categorizing Quantities using an Interactive Fuzzy Membership Function

: In this paper, we investigate how an interactive version of the membership function of the Fuzzy Logic Theory can be used to categorize quantitative data. This function is simple and similar to a line chart and provides an explicit mapping of the categorization process. We ﬁrst review the requirements for such quantitative values partitioning process and provide the mathematical foundations in Fuzzy Logic related to the membership function. We then report on the implementation of the interactive function for several quantitative datasets case studies (e.g., age, temperature, speed). We expect this interactive function to provide more control over the categorization process, as well as a way to make the categorization process more explicit.
Reduction in Communication via Image Selection for Homomorphic Encryption-based Privacy-protected Person Re-identification

We propose a method for reducing the amount of communication by selecting pedestrian images for privacyprotected person re-identification. Recently, it has become necessary to pay attention to how features corresponding to personal information can be protected. Owing to homomorphic encryption, which enables a distance between features to be computed without decryption, our method can use a cloud server on a public network while protecting personal information. However, we must consider the problem of the large amount of communication that occurs between camera clients and the server when homomorphic encryption is used. Our method aims to reduce the amount of this communication by selecting appropriate pedestrian images using reference leg postures. In our experiment, we confirmed that the amount of communication dynamically reduces without significant degradation in the accuracy of privacy-protected person re-identification with homomorphic encryption.
Modeling a priori Unknown Environments: Place Recognition with Optical Flow Fingerprints

In this research we present a novel method for place recognition that relies on optical flow fingerprints of features. We make no assumptions about the properties of features or the environment such as color, shape, and size, as we approach the problem parsimoniously with a single camera mounted on a robot. In the training phase of our algorithm an accurate camera model is utilized to model and simulate the optical flow vector magnitudes with respect to velocity and distance to features. A lognormal distribution function, that is the result of this observation, is used as an input during the testing phase that is taking place with real sensors and features extracted using Lucas-Kanade optical flow algorithm. With this approach we have managed to bridge the gap between simulation and real-world environments by transferring the output of simulated training data sets to real testing environments. In addition, our method is highly adaptable to different types of sensors and environments. Our algorithm is evaluated both in indoor and outdoor environments where a robot revisits places from different poses and velocities demonstrating that modeling an unknown environment using optical flow properties is feasible yet efficient.
Automatic Real-time Beat-to-beat Detection of Arrhythmia Conditions

With the spread of Internet of Medical Things (IoMT) systems, the scientific community has dedicated a lot of effort in the definition of approaches for supporting specialized staff in the early diagnosis of pathological conditions and diseases. Several approaches have been defined for the identification of arrhythmia, a pathological condition that can be detected from an electrocardiogram (ECG) trace. There exist many types of arrhythmia and some of them present a great impact on the patients in terms of worsening of physical conditions or even mortality. In this work we present NEAPOLIS, a novel approach for the accurate detection of arrhythmia conditions. NEAPOLIS takes as input a heartbeat signal, extracted from an ECG trace, and provides as output a 5-class classification of the beat, namely normal sinus rhythm and four main types of arrhythmia conditions. NEAPOLIS is based on ECG characteristics that do not need a long-term observation of an ECG for the classification of the beat. This choice makes NEAPOLIS a (near) real-time detector of arrhythmia because it allows the detection within few seconds of ECG observation. The accuracy of NEAPOLIS has been compared to one of the best and most recent work from the literature. The achieved results show that NEAPOLIS provides a more accurate detection of arrhythmia conditions.
Cloud Cost City: A Visualization of Cloud Costs using the City Metaphor

: Many companies are in the process of migrating their entire IT infrastructure into the cloud in order to beneﬁt from its high elasticity and scalability. While cloud providers offer basic cost visualizations such as line, bar or pie charts, companies lose track of which part of their infrastructure causes which costs. We adapt the city metaphor to visualize both the architecture and the costs. We offer a ﬂexible framework to structure and tailor the visualization as desired. Using the Goal-Question-Metric approach, we identify cost savings potential and demonstrate for an example cloud infrastructure that the deﬁned metrics are easier to grasp in our visualization compared to a standard cost dashboard.
3D Reconstruction of Deformable Objects from RGB-D Cameras: An Omnidirectional Inward-facing Multi-camera System

This is a paper describing a system made up of several inward-facing cameras able to perform reconstruction of deformable objects through synchronous acquisition of RGBD data. The configuration of the camera system allows the acquisition of 3D omnidirectional images of the objects. The paper describes the structure of the system as well as an approach for the extrinsic calibration, which allows the estimation of the coordinate transformations between the cameras. Reconstruction results are also presented.
Replacing Probability Distributions in Security Games via Hellinger Distance

Security of cryptographic primitives is usually proved by assuming “ideal” probability distributions. We need to replace them with approximated “real” distributions in the real-world systems without losing the security level. We demonstrate that the Hellinger distance is useful for this problem, while the statistical distance is mainly used in the cryptographic literature. First, we show that for preserving λ -bit security of a given security game, the closeness of 2 − λ/ 2 to the ideal distribution is suﬃcient for the Hellinger distance, whereas 2 − λ is generally required for the statistical distance. The result can be applied to both search and decision primitives through the bit security framework of Micciancio and Walter (Eurocrypt 2018). We also show that the Hellinger distance gives a tighter evaluation of closeness than the max-log distance when the distance is small. Finally, we show that the leftover hash lemma can be strengthened to the Hellinger distance. Namely, a universal family of hash functions gives a strong randomness extractor with optimal entropy loss for the Hellinger distance. Based on the results, a λ -bit entropy loss in randomness extractors is suﬃcient for preserving λ -bit security. The current understanding based on the statistical distance is that a 2 λ -bit entropy loss is necessary.
Upsampling Attention Network for Single Image Super-resolution

Recently, convolutional neural network (CNN) has been widely used in single image super-resolution (SISR) and made significant advances. However, most of the existing CNN-based SISR models ignore fully utilization of the extracted features during upsampling, causing information bottlenecks, hence hindering the expressive ability of networks. To resolve these problems, we propose an upsampling attention network (UAN) for richer feature extraction and reconstruction. Specifically, we present a residual attention groups (RAGs) based structure to extract structural and frequency information, which is composed of several residual feature attention blocks (RFABs) with a non-local skip connection. Each RFAB adaptively rescales spatialand channel-wise features by paying attention to correlations among them. Furthermore, we propose an upsampling attention block (UAB), which not only applies parallel upsampling processes to obtain richer feature representations, but also combines them to obtain better reconstruction results. Experiments on standard benchmarks show the advantage of our UAN over state-of-the-art methods both in objective metrics and visual qualities.
Real-time Multispectral Image Processing and Registration on 3D Point Cloud for Vineyard Analysis

Nowadays, precision agriculture and precision viticulture are under strong development. In order to accomplish effective actions, robots require robust perception of the culture and the surrounding environment. Computer vision systems have to identify plant parts (branches, stems, leaves, flowers, fruits, vegetables, etc.) and their respective health status. Moreover, they must merge various plant information, to measure agronomic indices, to classify them and finally to extract data to enable the agriculturist or expert to make a relevant decision. We propose a real-time method to acquire, process and register multispectral images fused to 3D. The sensors system, consisting of a multispectral camera and a RGB-D sensor, can be embedded on a ground robot or other terrestrial vehicles. Experiments conducted in the vineyard field demonstrate that agronomic analyses are allowed.
What do You Mean, Doctor? A Knowledge-based Approach for Word Sense Disambiguation of Medical Terminology

Word Sense Disambiguation (WSD) is an essential step for any NLP system; it can improve the performance of a more complex task, like information extraction, named entity linking, among others. Consequently, any error, while disambiguating a term, spreads to later stages with a snowball effect. Knowledge-based strategies for WSD offer the advantage of wider coverage of medical terminology than supervised algorithms. In this research, we present a knowledge-based approach for word sense disambiguation that can use different semantic similarity measures to determine the correct sense of a term in a given context. Our experiments show that when our approach used WordNet-based similarity measures, it achieved a very close performance when using the semantic measures based on word embeddings. We also constructed a small dataset from real-world data, where the feedback received from the annotators made us distinguish between true ambiguous terms and vague terms. This distinction needs to be considered for future research for WSD algorithms and dataset construction. Finally, we analyzed a state-of-the-art dataset with linguistic variables that helped to explain our approach’s performance. Our analysis revealed that texts containing a high score of lexical richness and a high ratio of nouns and adjectives lead to better WSD performance.
A Multimodal Workflow for Modeling Personality and Emotions to Enable User Profiling and Personalisation

The Personality Emotion Model (PEM) is a workflow for generating quantifiable and bi-directional mappings between 15 personality traits and the basic emotions. PEM utilises Affective computing methodology to map this relationship across the modalities of self-report, facial expressions, semantic analysis, and affective prosody. The workflow is an end-to-end solution integrating data collection, feature extraction, data analysis, and result generation. PEM results in a real-time model that provides a high-resolution correlated mapping between personality traits and the basic emotions. The robustness of PEM’s model is supported by the workflow’s ability to conduct meta-analytical and multimodal analysis; each state-to-trait mapping is dynamically updated in terms of its magnitude, direction, and statistical significance as data is processed. PEM provides a methodology that can contribute to long-standing research questions in the fields of Psychology and Affective computing. These research questions include (i) quantifying the emotive nature of personality, (ii) minimising the effects of context variance in basic emotions research, and (iii) investigating the role of emotion sequencing effects in relation to individual differences. PEM’s methodology enables direct applications in any domain that requires the provision of individualised and personalised services (e.g. advertising, clinical care, research).
Handling Comparison between a Human and a Patient Simulator for Nursing Care Related Physical Human-robot Interaction

The occurrence of musculoskeletal diseases among nursing staff leads to an early withdrawal from the profession, which reinforces the already existing lack of caregivers. To counteract this problem, we would like to provide physical relief through robotic assistance at the bedside. However, the problem arises that for safety reasons robotic assistance concepts should not be tested on humans at first. In this case, patient simulators with an average person’s weight can function as a substitute. For the best results, both cases should behave very similarly when evaluating robot assistance concepts so that the transfer from patient simulator to human is small and therefore no major adjustments need to be made. To measure this potential difference, we have compared the handling properties of both cases in this paper. We examined force measuring platform data while a nurse mobilized an 80 kg human and a patient simulator from the back to the side. The experimental results show that moving a patient simulator is more physically demanding compared to moving a human with similar weight and that conventional collaborative lightweight robots are able to push and move a patient simulator that is weighing far higher than the robot’s actual payload suggests.
U-Net based Zero-hour Defect Inspection of Electronic Components and Semiconductors

Automated visual inspection is a popular way of detecting many kind of defects at PCBs and electronic components without intervening in the manufacturing process. In this work, we present a novel approach for anomaly detection of PCBs where a U-Net architecture performs binary anomalous region segmentation and DBSCAN algorithm detects and localizes individual defects. At training time, reference images are needed to create annotations of anomalous regions, whereas at test time references images are not needed anymore. The proposed approach is validated on DeepPCB dataset and our internal chip defect dataset. We have achieved 0.80 and 0.75 mean Intersection of Union (mIoU) scores on DeepPCB and chip defect datasets, respectively, which demonstrates the effectiveness of the proposed approach. Moreover, for optimized and reduced models with computational costs lower than one giga FLOP, mIoU scores of 0.65 and above are achieved justifying the suitability of the proposed approach for embedded and potentially real-time applications.
An Effective 3D ResNet Architecture for Stereo Image Retrieval

While recent stereo images retrieval techniques have been developed based mainly on statistical approaches, this work aims to investigate deep learning ones. More precisely, our contribution consists in designing a twobranch neural networks to extract deep features from the stereo pair. In this respect, a 3D residual network architecture is first employed to exploit the high correlation existing in the stereo pair. This 3D model is then combined with a 2D one applied to the disparity maps, resulting in deep feature representations of the texture information as well as the depth one. Our experiments, carried out on a large scale stereo image dataset, have shown the good performance of the proposed approach compared to the state-of-the-art methods.
Disambiguating Clinical Abbreviations using Pre-trained Word Embeddings

Abbreviations are broadly used in clinical texts and most of them have more than one meaning which makes them highly ambiguous. Determining the right sense of an abbreviation is considered a Word Sense Disambiguation (WSD) task in clinical natural language processing (NLP). Many approaches are applied to disambiguate abbreviations in clinical narrative. However, supervised machine learning approaches are studied in this field extensively and have proven a good performance at tackling this problem. We have investigated four strategies that integrate pre-trained word embedding as features to train two supervised machine learning models: Support Vector Machines (SVM) and Naive Bayes (NB). Our training features include information of the context of target abbreviation, which is applied on 500 sentences for each of the 13 abbreviations that have been extracted from public clinical notes data sets from the University of Minnesota-affiliated (UMN) Fairview Health Services in the Twin Cities. Our results showed that SVM performs better than NB in all four strategies; the highest accuracy being 97.08% using a pre-trained model trained from Wikipedia, PubMed and PMC (PubMedCentral) texts.
Exploring Media Portrayals of People with Mental Disorders using NLP

Media plays an important role in creating an impact in society. Several studies show that news media and entertainment channels, at times may create overwhelming images of the mental illness that emphasize criminality and dangerousness. The consequences of such negative impact may impact the audience with stigma and on the other hand, they impair the self-esteem and help-seeking behavior of the people with mental disorders. This is the first study to examine the Singapore media’s portrayal of persons with mental disorders (MDs) using text analytics and natural language processing. To date, most studies on media portrayal of people with MDs have been conducted in developed Western countries. This study found that media articles on MDs in Singapore were largely negative in sentiment; even quotes from experts contain aspects of stigma. In addition, crime-related articles on MDs accounted for a significant portion of the corpus. Our model is also extended to detect positive health articles that discuss recovery and motivation. We further developed a stigma classifier based on the machine learning algorithms and text mining techniques. The classifier based on the XGBoosts performed best with an F1-score around 76%.
On the Optimal Strategy for Tackling Head Motion in fMRI Data

Head motion critically hampers the quality of functional magnetic resonance imaging (fMRI) data, with several methods for its correction being already available in the literature. Head shifts are usually corrected by realigning all functional volumes with relation to a reference volume using affine transformations, from which the estimated motion parameters (MPs) can be additionally regressed out from fMRI data. However, a consensus regarding the number of MPs to regress has not been achieved yet. More critically, abrupt head motion induces the so-called motion outliers in the data, which cannot be accounted for by affine transformations. Two common approaches are widely used to tackle this type of motion, namely modelling strategies such as censoring, and volume interpolation. However, a direct comparison between strategies to tackle motion outliers has not been performed so far. Importantly, to our knowledge no study has focused on determining the extent at which the effects of different head motion correction methods differ between groups in clinical studies. This is particularly relevant in task-related functional connectivity fMRI studies, which are rapidly increasing in clinical research. In this study, we started by determining the optimal number of MPs (between 6 and 24) to be regressed out from fMRI data collected from 8 participants (4 patients with Multiple Sclerosis and 4 healthy controls) performing a perceptual decision-making task. Then we tested motion censoring and volume interpolation for correcting motion outliers, using FD and DVARS metrics to detect the outlier volumes. We found that task-specific activated brain regions were detected with higher sensitivity when using 6 MPs relatively to using 24 MPs. As for the correction of motion outliers, our results suggest that volume interpolation is the best method to use, however more data and external validation is needed to achieve a definite conclusion. Importantly, the performance of motion correction algorithms was irrespective of the subject group (patients and healthy participants). Our results pave the way towards finding an optimal motion correction strategy, which is required to improve the accuracy of fMRI analyses in healthy and patient populations and are an encouragement to test comprehensively different approaches.