Miracle-dz/UniFAR-FTUs huggingface dataset:
Chloroplast: The Emerging Battlefield in Plant–Microbe Interactions

Higher plants and some algae convert the absorbed light into chemical energy through one of the most important organelles, chloroplast, for photosynthesis and store it in the form of organic compounds to supply their life activities. However, more and more studies have shown that the role of chloroplasts is more than a factory for photosynthesis. In the process of light conversion to chemical energy, any damage to the components of chloroplast may affect the photosynthesis efficiency and promote the production of by-products, reactive oxygen species, that are mainly produced in the chloroplasts. Substantial evidence show that chloroplasts are also involved in the battle of plants and microbes. Chloroplasts are important in integrating a variety of external environmental stimuli and regulate plant immune responses by transmitting signals to the nucleus and other cell compartments through retrograde signaling pathways. Besides, chloroplasts can also regulate the biosynthesis and signal transduction of phytohormones, including salicylic acid and jasmonic acid, to affect the interaction between the plants and microbes. Since chloroplasts play such an important role in plant immunity, correspondingly, chloroplasts have become the target of pathogens. Different microbial pathogens target the chloroplast and affect its functions to promote their colonization in the host plants.
CryptoKitties Transaction Network Analysis: The Rise and Fall of the First Blockchain Game Mania

CryptoKitties was the first widely recognized blockchain game. Players could own, breed, and trade kitties, which are the only prop in the game. The game gained explosive growth upon its release but quickly collapsed in a short time. This study analyzes its entire player activity history for the first time in literature and tries to find the reasons for the rise and fall of this first blockchain game mania. First, we extracted the five million transaction records among 100 thousand addresses involved in CryptoKitties in the past three years. Based on the numbers of addresses involved in the game each day, we divide the game progress into four stages: the primer, the rise, the fall, and the serenity. We construct a temporal kitty ownership transfer network and analyze the varying network parameters in the four stages. We find that a large number of players poured in during the 10th and 18th days since the game release and quickly exited in the following month. Since then, a few big players have gradually dominated the game, concentrating the game resources. Through further analysis, we find that the main reason for the rapid increase in the game popularity was the increase of public attention by media outlets, while the reasons for the rapid decline in the game popularity include the oversupply of kitties, the decreasing of player income, a widening gap between the rich and poor players, and the limitations of blockchain systems. Based on these observations, we advise on the further blockchain game design: (1) to finely control the production of props and avoid an oversupply, (2) to balance the gaming cost and revenue and protect the enjoyment of players, (3) to narrow down the gap between rich and poor and create an equal gaming community, (4) to consider the limitations of blockchain systems in their game designs.
Inspection of Industrial Coatings based on Multispectral BTF

In this paper, we propose a method to inspect coatings of industrial products in a factory automation system. The coating of industrial products is important because the coating directly affects the impression of the product, and a large amount of cost is spent on its inspection. Because lots of colors are used in the coating of industrial products, as well as there are various surface treatments such as matte and mirror finishes, the appearance of these products varies hugely. Therefore, it is difficult to obtain the properties of the surfaces by ordinary camera systems, and thus, they are inspected manually in the current system in most cases. In this paper, we present a method of representing surface properties of them, called multispectral BTF, by taking products under narrow-band light from various directions. We also show a method for inspection using a oneclass discriminator based on Deep Neural Network using the multispectral BTF. Several experimental results show that our proposed BTF and one-class classifier can inspect various kinds of coating.
Development of Wearable Devices for Measurement of Multiple Physiological Variables and Evaluation of Emotions by Fingerprints and Population Hypotheses

We live in an age in which technology provides us with constant access to virtual online services. Consumption and production of an immense amount of instant data, which is becoming the basic raw material autonomously processed by artificial intelligence algorithms, open up brave new possibilities and levels of research and its application in many traditional scientific disciplines. Biometrics is one of the disciplines experiencing an unexpected renaissance, owing to the wide availability of cheap sensory technologies connected to the network. We find great untapped potential, especially in devices that allow measuring the body’s physiological responses to emotional stimuli, such as heart rate (HR) and electrodermal activity (EDA), also known as galvanic skin response (GSR). Many readily available and professional wearable devices provide digital recordings of these variables. However, each of these technologies suffers from multiple shortcomings. These shortcomings stand in the way of the mass popularization of the technology, which enables, among other things, real-time monitoring and digital recording of the body’s physiological reactions to emotional stimuli. In other words, creating big data that can be used for digital, automated reconstruction of certain aspects of emotionality. In our research, we have identified three main social areas where these technologies are of interest: laboratories, professionals working with the human psyche-bodyemotionality, and regular users of biofeedback devices such as wearable devices (WD). Each of these groups has specific requirements in terms of the hardware implementation of the technology, and software and measurement methodology open to users. In our emotion laboratory, we have developed a series of comprehensive solutions, Sensetio, based on a thorough analysis of the needs of all three groups of users of biofeedback technologies. We intend to obtain standardized big data sets for further thorough scientific
A Study about Discovery of Critical Food Consumption Patterns Linked with Lifestyle Diseases for Swiss Population using Data Mining Methods

Background: This article demonstrates that using data mining methods such as association analysis on an integrated Swiss database derived from a Swiss national dietary survey (menuCH) and Swiss demographical and health data is a powerful way to determine whether a specific population subgroup is at particular risk for developing a lifestyle disease based on its food consumption patterns. Objective: The objective of the study was to use an integrated database of dietary and health data from a large group of Swiss population to discover critical food consumption patterns linked with lifestyle diseases known to be strongly tied with food consumption. Design: Food consumption databases from a Swiss national survey menuCH were gathered along with corresponding large survey of demographics and health data from Swiss population conducted by Swiss Federal Office of Public Health (FOPH). These databases were integrated and reported in a previous study as a single integrated database. A data mining method such as A-priori association analysis was applied to this integrated database. Results: Association mining analysis was used to incorporate rules about food consumption and lifestyle diseases. A set of promising preliminary rules and their corresponding interpretation was generated, which is reported in this paper. As an example, the found rules of the sample show that smoking is relatively irrelevant to the high blood pressure and Diabetes, whereas consuming vegetables at regular basis reduces the risk of high Cholesterol. Conclusions: Association rule mining was successfully used to describe and predict rules linking food consumption patterns with lifestyle diseases. The gained association rules reveal that the appearance of the mutually independent nutritional characteristics in the rules are equally distributed. Furthermore, most of the sample show no chronical diseases as they smoke little and exercise regularly, which can be interpreted that sport is a strong preventive factor for chronical/lifestyle diseases. Nevertheless, a small percentage of the sample shows chronic illnesses due to unhealthy eating. Further research should consider the weighting of chronic diseases’ characteristics for them not to be pruned out early by data mining computation.
Multi-layer Feature Fusion and Selection from Convolutional Neural Networks for Texture Classification

: Deep feature representation in Convolutional Neural Networks (CNN) can act as a set of feature extractors. However, since CNN architectures embed different representations at different abstraction levels, it is not trivial to choose the most relevant layers for a given classiﬁcation task. For instance, for texture classiﬁcation, low-level patterns and ﬁne details from intermediate layers could be more relevant than high-level semantic information from top layers (commonly used for generic classiﬁcation). In this paper, we address this problem by aggregating CNN activations from different convolutional layers and encoding them into a single feature vector after applying a pooling operation. The proposed approach also involves a feature selection step. This process is favorable for the classiﬁcation accuracy since the inﬂuence of irrelevant features is minimized and the ﬁnal dimension is reduced. The extracted and selected features from multiple layers can be further manageable by a classiﬁer. The proposed approach is evaluated on three challenging datasets, and the results demonstrate the effectiveness of selecting and fusing multi-layer features for texture classiﬁcation problem. Furthermore, by means of comparisons to other existing methods, we demonstrate that the proposed approach outperforms the state-of-the-art methods with a signiﬁcant margin.
Automatic Emotion Recognition from DEMoS Corpus by Machine Learning Analysis of Selected Vocal Features

Although Speech Emotion Recognition (SER) has become a major area of research in affective computing, the automatic identification of emotions in some specific languages, such as Italian, is still under-investigated. In this regard, we assess how different machine learning methods for SER can be applied in the identification of emotions in Italian language. In agreement with studies that criticize the use of acted emotions in SER, we considered DEMoS, a new database in Italian built through mood induction procedures. The corpus consists of 9365 spoken utterances produced by 68 Italian native speakers (23 females, 45 males) in a variety of emotional states. Experiments were carried out for female and male separately, considering for each a specific feature set. The two feature sets were selected by applying Correlation-based Feature Selection from the INTERSPEECH 2013 ComParE Challenge feature set. For the classification process, we used Support Vector Machine. Confirming previous work, our research outcomes show that the basic emotions anger and sadness are the best identified, while others more ambiguous, such as surprise, are worse. Our work shows that traditional machine learning methods for SER can be also applied in the recognition of an under-investigating language, such as Italian, obtaining competitive results.
Towards Visual Sociolinguistic Network Analysis

: Investigation of social networks formed by individuals in various contexts provides numerous interesting and important challenges for researchers and practitioners in multiple disciplines. Within the ﬁeld of variationist sociolinguistics, social networks are analyzed in order to reveal the patterns of language variation and change while taking the social, cultural, and geographical aspects into account. In this ﬁeld, traditional approaches usually focusing on small, manually collected data sets can be complemented with computational methods and large digital data sets extracted from online social network and social media sources. However, increasing data size does not immediately lead to the qualitative improvement in the understanding of such data. In this position paper, we propose to address this issue by a joint effort combining variationist sociolinguistics and computational network analyses with information visualization and visual analytics. In order to lay the foundation for this interdisciplinary collaboration, we analyse the previous relevant work and discuss the challenges related to operationalization, processing, and exploration of such social networks and associated data. As the result, we propose a roadmap towards realization of visual sociolinguistic network analysis.
Terminology Expansion with Prototype Embeddings: Extracting Symptoms of Urinary Tract Infection from Clinical Text

Many natural language processing applications rely on the availability of domain-specific terminologies containing synonyms. To that end, semi-automatic methods for extracting additional synonyms of a given concept from corpora are useful, especially in low-resource domains and noisy genres such as clinical text, where nonstandard language use and misspellings are prevalent. In this study, prototype embeddings based on seed words were used to create representations for (i) specific urinary tract infection (UTI) symptoms and (ii) UTI symptoms in general. Four word embedding methods and two phrase detection methods were evaluated using clinical data from Karolinska University Hospital. It is shown that prototype embeddings can effectively capture semantic information related to UTI symptoms. Using prototype embeddings for specific UTI symptoms led to the extraction of more symptom terms compared to using prototype embeddings for UTI symptoms in general. Overall, 142 additional UTI symptom terms were identified, yielding a more than 100% increment compared to the initial seed set. The mean average precision across all UTI symptoms was 0.51, and as high as 0.86 for one specific UTI symptom. This study provides an effective and cost-effective solution to terminology expansion with small amounts of labeled data.
Fitting Personalized Mechanistic Mathematical Models of Acute Myeloid Leukaemia to Clinical Patient Data

In this position paper, we discussed the potential to fit mechanistic mathematical models of acute myeloid leukaemia to patient data. The overarching aim was to estimate personalized models. We briefly introduced one selected mechanistic ODE model to illustrate the approach. The usually available outcome measures, e.g. in clinical datasets, were aligned with the model’s prediction capabilities. Among the most relevant outcomes (blast load, complete remission, and survival), only blast load turned out to be well suited to be used in the model fitting process. We formulated an optimization problem that, finally, resulted in personalized model parameters. The degree of personalization could be chosen by selecting only a subset of parameters within the optimization problem. To illustrate the fitness landscape for individual patients we performed a grid search and calculated the fitness values for each grid point. The grid search revealed that an optimum exists, but that the fitness landscape can be very noisy. In these cases, gradient-based solvers will perform poorly and other algorithms needs to be chosen. Finally, we belief that personalized model fitting will be a promising approach to integrate mechanistic mathematical models into clinical research.
Tragus based Vagus Nerve Stimulation for Stress Reduction

Non-invasive vagus nerve stimulation is fast becoming a popular alternative treatment method for various health disorders. The authors investigated the effects of auricular vagus nerve stimulation at tragus for activating the parasympathetic nervous system to reduce stress, in light of mixed results from other studies. Stimulation frequency of 25 Hz with a pulse-width of 200 μs was administered at tragus with ECG data recorded during preand post-stimulation trials to investigate changes in the low-frequency (LF) and high-frequency (HF) parameters of heart rate variability (HRV). The results from five subjects demonstrate an increase in the HF component and a decrease in LF when comparing preand poststimulation values, denoting that VNS stimulated more of the parasympathetic activity. The LF/HF ratio was reduced for all participants after stimulation, with an average reduction of 64.5% observed. Overall, this study has indicated the feasibility of using tragus as a stimulation site to stimulate the vagus nerve; tragus being easier to administrate than many alternative sites while still being effective for stress reduction.
Managing Mutual Occlusions between Real and Virtual Entities in Virtual Reality

: This paper describes a mixed interactive system managing mutual occlusions between real and virtual objects displayed by virtual reality display wall environments. These displays are physically unable to manage mutual occlusions between real and virtual objects. A real occluder located between the user’s eyes and the wall hides virtual objects regardless of their depth. This problem confuses the user’s stereopsis of the virtual environment, harming its user experience. For this reason, we present a mixed interactive system combining a stereoscopic optical see-through head-mounted display with a static stereoscopic display in order to manage mutual occlusions and enhance direct user interactions with virtual content. We illustrate our solution with a use case and an experiment proposal.
GeST: A New Image Segmentation Technique based on Graph Embedding

We propose a new framework to develop image segmentation algorithms using graph embedding, a wellstudied tool from complex network analysis. So-called embeddings are low-dimensional representations of nodes of the graph that encompass several structural properties such as neighborhoods and community structure. The main idea of our framework is to first consider an image as a set of superpixels, and then compute embeddings for the corresponding undirected weighted Region Adjacency Graph. The resulting segmentation is then obtained by clustering embeddings. To the best of our knowledge, known complex network-based segmentation techniques rely on community detection algorithms. By introducing graph embedding for image segmentation, we combine two nice properties of aforementioned segmentation techniques, namely working on small graphs with low-dimensional representations. To illustrate the relevance of our approach, we propose GeST, an implementation of this framework using node2vec and agglomerative clustering. We experiment our algorithm on a publicly available dataset and show that it produces qualitative results compared to state-ofthe-art segmentation techniques while requiring low computational complexity and memory.
Assessing Parkinson's Disease Speech Signal Generalization of Clustering Results across Three Countries: Findings in the Parkinson's Voice Initiative Study

Progress in exploring speech and Parkinson’s Disease (PD) has been hindered due to the use of different protocols across research labs/countries, single-site studies with relatively small numbers, and no external validation. We had recently reported on the Parkinson’s Voice Initiative (PVI), a large study where we collected 19,000+ sustained vowel phonations (control and PD groups) across seven countries, under acoustically non-controlled conditions. In this study, we explored how well findings generalize in the three English-speaking PVI cohorts (data collected in Boston, Oxford, and Toronto). We acoustically characterized each sustained vowel /a/ phonation using 307 dysphonia measures which had previously been successfully employed in speech-PD applications. We used the previously identified feature subset from the Boston cohort and explored hierarchical clustering with Ward’s linkage combined with 2D-data projections using tdistributed stochastic neighbor embedding to facilitate visual exploration of PD subgroups. Furthermore, we computed feature weights using LOGO to assess feature selection consistency towards differentiating PD from controls. Overall, findings are very consistent across the three cohorts, strongly suggesting the presence of four main PD clusters, and consistent identification of key contributing features. Collectively, these findings support the generalization of sustained vowels and robustness of the presented methodology across the English-speaking PVI cohorts.
CUPR: Contrastive Unsupervised Learning for Person Re-identification

Most of the current person re-identification (Re-ID) algorithms require a large labeled training dataset to obtain better results. For example, domain adaptation-based approaches rely heavily on limited real-world data to alleviate the problem of domain shift. However, such assumptions are impractical and rarely hold, since the data is not freely accessible and require expensive annotation. To address this problem, we propose a novel pure unsupervised learning approach using contrastive learning (CUPR). Our framework is a simple iterative approach that learns strong high-level features from raw pixels using contrastive learning and then performs clustering to generate pseudo-labels. We demonstrate that CUPR outperforms the unsupervised and semi-supervised state-of-the-art methods on Market-1501 and DukeMTMC-reID datasets.
Single-image Background Removal with Entropy Filtering

Background removal is often used for segmentation of the main subject from a photograph. This paper proposes a new method of background removal for a single image. The proposed method uses Shannon entropy to quantify the texture complexity of background and foreground areas. A normalized entropy filter is applied to compute the entropy of each pixel. The pixels can be classified effectively if the entropy distributions of the background and foreground can be distinguished. To optimize performance, the proposed method constructs an image pyramid such that most background pixels can be labeled in a low-resolution image; thus, the computational cost of entropy calculation can be reduced in the image with the original resolution. Connected component labeling is also adopted for denoising to retain the main subject area completely.
A Layered Software City for Dependency Visualization

A Software City is a an established way to visualize metrics such as the test coverage or complexity. As current layouting algorithms are mainly based on the static code structure (e.g., classes and packages), dependencies that are orthogonal to this structure often clutter the visualization and are hard to grasp. This paper applies layered graph drawing to layout a Software City in 3D. The proposed layout takes both the dependencies and the static code structure into account. This minimizes the number of explicitly displayed dependencies. The resulting lower cognitive load makes the software architecture easier to understand. We evaluate the advantages of our layout over a classic layouting algorithm in a controlled study on a real world project.