Miracle-dz/UniFAR-FTUs huggingface dataset:
Lidar-based detection of airborne particles for robust robot perception

Airborne particles such as dust, smoke and fog have a significant detrimental impact on Lidar- based robotic perception systems. Lidar rays can reflect on these particles, leading modern perception methods to erroneous results, such as false obstacles or misclassified elements. We propose a method to detect airborne particles in 3D Lidar point clouds using classification from geometric features and Lidar intensity re- turns. We compare three different classifiers and evaluate our approach on dust and fog data collected in outdoor scenarios. We achieve an accuracy of up to 95% in detecting airborne particles in Lidar point clouds, making our pro- posed method a promising solution for appli- cations such as obstacle detection and object recognition in outdoor environments. We make available the code and data for this work at https://leo-stan.github. io/particles detection.
Trust in sharing encounters among millennials

The sharing economy is a defining feature of the millennial generation. We report on a study of 1047 millennials and find that trust has more of an impact on their willingness to engage in some sharing encounters than in others. In particular, trust matters more in high‐dimensionality sharing encounters—those encounters that have a longer duration, require a greater financial investment, involve more social interaction, etc. Further, it is important to note that all sharing encounters take place through two‐sided intermediary platforms, and we find that the role of trust among the different types of sharing partners is not symmetrical across the two sides. Trust in the intermediary platform matters more to customers than to service providers in their willingness to engage in service encounters. Overall, this research contributes to existing literature by developing the notion of a service encounter and associated dimensionality and by demonstrating that service encounter dimensionality affects the importance of trust in the sharing economy. Also, we show how trust matters differently for different sides of two‐sided sharing platforms. Finally, this research focuses squarely on understanding the behaviours of millennials—the generation important to the sharing economy.
A Unified View of Local Learning : Theory and Algorithms for Enhancing Linear Models. (Une Vue Unifiée de l'Apprentissage Local : Théorie et Algorithmes pour l'Amélioration de Modèles Linéaires)

In Machine Learning field, data characteristics usually vary over the space: the overall distribution might be multi-modal and contain non-linearities.In order to achieve good performance, the learning algorithm should then be able to capture and adapt to these changes. Even though linear models fail to describe complex distributions, they are renowned for their scalability, at training and at testing, to datasets big in terms of number of examples and of number of features. Several methods have been proposed to take advantage of the scalability and the simplicity of linear hypotheses to build models with great discriminatory capabilities. These methods empower linear models, in the sense that they enhance their expressive power through different techniques. This dissertation focuses on enhancing local learning approaches, a family of techniques that infers models by capturing the local characteristics of the space in which the observations are embedded. The founding assumption of these techniques is that the learned model should behave consistently on examples that are close, implying that its results should also change smoothly over the space. The locality can be defined on spatial criteria (e.g. closeness according to a selected metric) or other provided relations, such as the association to the same category of examples or a shared attribute. Local learning approaches are known to be effective in capturing complex distributions of the data, avoiding to resort to selecting a model specific for the task. However, state of the art techniques suffer from three major drawbacks: they easily memorize the training set, resulting in poor performance on unseen data; their predictions lack of smoothness in particular locations of the space;they scale poorly with the size of the datasets. The contributions of this dissertation investigate the aforementioned pitfalls in two directions: we propose to introduce side information in the problem formulation to enforce smoothness in prediction and attenuate the memorization phenomenon; we provide a new representation for the dataset which takes into account its local specificities and improves scalability. Thorough studies are conducted to highlight the effectiveness of the said contributions which confirmed the soundness of their intuitions. We empirically study the performance of the proposed methods both on toy and real tasks, in terms of accuracy and execution time, and compare it to state of the art results. We also analyze our approaches from a theoretical standpoint, by studying their computational and memory complexities and by deriving tight generalization bounds.
Deep Learning-based Method for Classifying and Localizing Potato Blemishes

In this paper we address the problem of potato blemish classification and localization. A large database with multiple varieties was created containing 6 classes, i.e., healthy, damaged, greening, black dot, common scab and black scurf. A Convolutional Neural Network was trained to classify face potato images and was also used as a filter to select faces where more analysis was required. Then, a combination of autoencoder and SVMs was applied on the selected images to detect damaged and greening defects in a patch-wise manner. The localization results were used to classify the potato according to the severity of the blemish. A final global evaluation of the potato was done where four face images per potato were considered to characterize the entire tuber. Experimental results show a face-wise average precision of 95% and average recall of 93%. For damaged and greening patch-wise localization, we achieve a False Positive Rate of 4.2% and 5.5% and a False Negative Rate of 14.2% and 28.1% respectively. Concerning the final potato-wise classification, we achieved in a test dataset an average precision of 92% and average recall of 91%.
Temporal Quilting for Survival Analysis

The importance of survival analysis in many disciplines (especially in medicine) has led to the development of a variety of approaches to modeling the survival function. Models constructed via various approaches o er di erent strengths and weaknesses in terms of discriminative performance and calibration, but no one model is best across all datasets or even across all time horizons within a single dataset. Because we require both good calibration and good discriminative performance over di erent time horizons, conventional model selection and ensemble approaches are not applicable. This paper develops a novel approach that combines the collective intelligence of different underlying survival models to produce a valid survival function that is well-calibrated and o ers superior discriminative performance at di erent time horizons. Empirical results show that our approach provides signi cant gains over the benchmarks on a variety of real-world datasets.
Plant Diseases Recognition from Digital Images using Multichannel Convolutional Neural Networks

Plant diseases are considered one of the main factors influencing food production and to minimize losses in production, it is essential that crop diseases have a fast detection and recognition. Nowadays, recent studies use deep learning techniques to diagnose plant diseases in an attempt to solve the main problem: a fast, low-cost and efficient methodology to diagnose plant diseases. In this work, we propose the use of classical convolutional neural network (CNN) models trained from scratch and a Multichannel CNN (M-CNN) approach to train and evaluate the PlantVillage dataset, containing several plant diseases and more than 54,000 images (divided into 38 diseases classes with 14 plant species). In both proposed approaches, our results achieved better accuracies than the state-of-the-art, with faster convergence and without the use of transfer learning techniques. Our multichannel approach also demonstrates that the three versions of the dataset (colored, grayscaled and segmented) can contribute to improve accuracy, adding relevant information to the proposed artificial neural network.
Evaluation of the Sample Clustering Process on Graphs

An increasing number of networks are becoming large-scale and continuously growing in nature, such that clustering on them in their entirety could be intractable. A feasible way to overcome this problem is to sample a representative subgraph and exploit its clustering structure (namely, sample clustering process). However, there are two issues that we should address in current studies. One underlying question is how to evaluate the clustering quality of the entire sample clustering process. Another non-trivial issue is that multiple ground-truths exist in networks, thus evaluating the clustering results in such scenario is also a challenging task. In this paper, first we utilize the set-matching methodology to quantitatively evaluate how differently the clusters of the sampled counterpart correspond to the ground-truth(s) in the original graph, and propose several new quality metrics to capture the differences of clustering structure in various aspects. Second, we put forward an evaluation framework for the general problems of evaluating the clustering quality on graph samples. Extensive experiments on various synthetic and real-world graphs demonstrate that our new quality metrics are more accurate and insightful for the sample clustering evaluation than conventional metrics (e.g., NMI). Thus the evaluation framework is effective and practical to assess the clustering quality of the sample clustering process on massive graphs.
Improving the discoverability and web impact of open repositories: techniques and evaluation

In this contribution we experiment with a suite of repository adjustments and improvements performed on Strathprints, the University of Strathclyde, Glasgow, institutional repository powered by EPrints 3.3. 13. These adjustments were designed to support improved repository web visibility and user engagement, thereby improving usage. Although the experiments were performed on EPrints it is thought that most of the adopted improvements are equally applicable to any other repository platform. Following preliminary results reported elsewhere, and using Strathprints as a case study, this paper outlines the approaches implemented, reports on comparative search traffic data and usage metrics, and delivers conclusions on the efficacy of the techniques implemented. The evaluation provides persuasive evidence that specific enhancements to technical aspects of a repository can result in significant improvements to repository visibility, resulting in a greater web impact and consequent increases in content usage. COUNTER usage grew by 33% and traffic to Strathprints from Google and Google Scholar was found to increase by 63% and 99% respectively. Other insights from the evaluation are also explored. The results are likely to positively inform the work of repository practitioners and open scientists.
FREQUENCY AND SEVERITY OF ANEMIA IN CHILDREN LESS THAN 15 YEARS OF AGE AT GWADAR DEVELOPMENT AUTHORITY HOSPITAL, GWADAR, BALUCHISTAN

Objective: To determine the frequency and severity of anemia in children <15 years of age and find association ofanemia with age and gender at Gwadar Development Authority (GDA) Hospital, Gwadar, Baluchistan.Study Design: Cross sectional study.Place and Duration of Study: GDA Hospital Gwadar, from June 2016 to June 2017.Material and Methods: Through consecutive sampling, 483 children reporting to the pathology department forhemoglobin estimation were sampled. The samples were taken through antecubital veins using aseptic techniqueand the hemoglobin was analyzed using Medonic M-series M32 Hematology Analyzer. The sample was dividedinto three groups based on age i.e. group-1 (age 6-59 months), group-2 (age: 5-11 years), and group-3 (age: 11-14years). We used definitions of anemia given by the World Health Organization. The data were analyzed usingSPSS version 20.Results: The mean age was 7.3 ± 4.3 years (range: 1-14 years). The frequency of anemia was 64.6% (n=312). Considering gender, the percentage of anemic children among male gender was 62.9% (n=132) and amongfemales was 65.9% (n=180). The percentage of anemic children was highest (74.5%) in group-1 (p<0.001). Thegender could not find an association with anemia (p=0.483). Conclusion: Anemia had a frequency of 64.6% in children under 15 years of age at GDA Hospital, Gwadar. Agegroupof 1-59 months had the highest percentage of anemic children. The gender did not have any associationwith anemia.
Privacy-Preserving Data Certification in the Internet of Things: Leveraging Blockchain Technology to Protect Sensor Data

A constantly growing pool of smart, connected Internet of Things (IoT) devices poses completely new challenges for business regarding security and privacy. In fact, the widespread adoption of smart products might depend on the ability of organizations to offer systems that ensure adequate sensor data integrity while guaranteeing sufficient user privacy. In light of these challenges, previous research indicates that blockchain technology may be a promising means to mitigate issues of data security arising in the IoT. Building upon the existing body of knowledge, we propose a design theory, including requirements, design principles, and features, for a blockchain-based sensor data protection system (SDPS) that leverages data certification. We then design and develop an instantiation of an SDPS (CertifiCar) in three iterative cycles that prevents the fraudulent manipulation of car mileage data. Furthermore, we provide an ex-post evaluation of our design theory considering CertifiCar and two additional use cases in the realm of pharmaceutical supply chains and energy microgrids. The evaluation results suggest that the proposed design ensures the tamper-resistant gathering, processing, and exchange of IoT sensor data in a privacy-preserving, scalable, and efficient manner.
Performance Enhancement in Learning-to-Rank Approach for Software Defect Prediction

Software Defect Prediction is an important part before start the testing phase of any software. It is used to find out the defected modules from the software. This research paper shows the enhanced Learning-to-Rank approach used for find out the defects from the test cases. In this paper Boltzmann learning can be used for enhancing the learning to rank approach. By using Boltzmann learning approach the rate of defect prediction will be increase. This research paper mainly focus to increase the rate of defect prediction. This implementation can be performing by using test cases. This paper is implemented on 50 test cases. The algorithm of learning-to -rank approach is also enhanced by using Boltzmann learning approach. It also shows the comparison results of learning-to –rank approach or enhanced learning to rank approach. Enhanced learning to rank approach is take less time to execute test cases and find out the defect prediction rate.
Preference Classification Method Using EEG Analysis Based on Gray Theory and Personality Analysis

This paper introduces a method to classify the preference patterns of sounds on the basis of an electroencephalogram (EEG) analysis and a personality analysis. We analyze the EEG of the left prefrontal cortex by single-point sensing. For EEG recording, a dry-type sensor and few electrodes were used. The proposed feature extraction method employs gray relational grade detection on the frequency bands of EEG and egogram. The gray relational grade is used for extracting the EEG feature. The egogram is extracted for quantifying the subject’s personality. The preference patterns generated when the subject is hearing a sound are classified using the nearest neighbor method. To show the effectiveness of the proposed method, we conduct experiments using real EEG data. These results show that the accuracy rate of the preference classification using the proposed method is better than that using the method that does not to consider the subject’s personality.
A Review of Judgment Analysis Algorithms for Crowdsourced Opinions

The crowd-powered systems have been shown to be highly successful in the current decade to manage collective contribution of online workers for solving different complex tasks. It can also be used for soliciting opinions from a large set of people working in a distributed manner. Unfortunately, the online community of crowd workers might involve non-experts as opinion providers. As a result, such approaches may give rise to noise making it hard to predict the appropriate (gold) judgment. Judgment analysis is in general a way of learning about human decision from multiple opinions. A spectrum of algorithms has been proposed in the last few decades to address this problem. They are broadly made up of supervised or unsupervised types. However, they have been readdressed in recent years having focus on different strategies for obtaining the gold judgment from crowdsourced opinions, viz., estimating the accuracy of opinions, difficulties of the problem, spammer identification, handling noise, etc. Besides this, investigation of various types of crowdsourced opinions to solve complex real-life problems provide new insights in this domain. In this survey, we provide a comprehensive overview of the judgment analysis problem and some of its novel variants, addressed with different approaches, where the opinions are crowdsourced.
Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0)

Abstract. In this paper I present new methods for bias adjustment and statistical downscaling that are tailored to the requirements of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). In comparison to their predecessors, the new methods allow for a more robust bias adjustment of extreme values, preserve trends more accurately across quantiles, and facilitate a clearer separation of bias adjustment and statistical downscaling. The new statistical downscaling method is stochastic and better at adjusting spatial variability than the old interpolation method. Improvements in bias adjustment and trend preservation are demonstrated in a cross-validation framework.
Robust Person Identification based on DTW Distance of Multiple-Joint Gait Pattern

Gait information can be used to identify and track persons. This work proposes a new gait identification method aggregating multiple features observed by a motion capture sensor and evaluates the robustness against obstacles in walking. The simplest gait identification is to use gait statistics, but these are not a significant feature with regard to identifying people accurately. Hence, in this work, we use the dynamic time warping (DTW) algorithm to calculate distances of gait sequences. DTW is a pattern-matching algorithm mainly used in speech recognition. It can compare two sets of time series data, even when they have different lengths. We also propose an optimal feature integration method for DTW distances. For evaluating the proposed method, we developed a prototype system and calculated the equal error rate (EER) using 31 subjects. As a result, we clarified that the EER of the proposed method is 0.036 for normal walking, and that it is robust to some obstacles in walking.
Nickel Based RTD Fabricated via Additive Screen Printing Process for Flexible Electronics

A novel nickel (Ni)-based resistance temperature detector (RTD) was successfully developed for temperature monitoring applications. The RTD was fabricated by depositing Ni ink on a flexible polyimide substrate using the screen printing process. Thermogravimetric analysis was performed to study the thermal behavior of the Ni ink, and it was observed that the Ni ink can withstand up to 200 °C before the decomposition of the binder in the ink system. Scanning electron microscopy and white light interferometry were used to analyze the surface morphology of the printed Ni. X-ray diffractometry was used to obtain structural information, phase, and crystallite size of the deposited Ni nanoparticles. Energy dispersive X-ray spectroscopy was used to obtain semi-quantitative information of the elements present in the fabricated RTD. The capability of the RTD to detect temperatures varying from −60 °C to 180 °C, in steps of 20 °C, was investigated at a constant relative humidity of 20%RH. The results of the RTD demonstrated a linear response with resistive changes as high as 113% at 180 °C when compared with its base resistance at −60 °C. An average TCR of 0.44%/°C was calculated for the printed RTD with a response time of <10 s. The obtained results demonstrated the feasibility of employing Ni on flexible substrates for the development of flexible temperature sensors.
Dynamic Reconfiguration of Intelligence for High Behaviour Adaptability of Autonomous Distributed Discrete-Event Systems

This paper deals with the intelligence adaptation of distributed real-time embedded control systems when scenarios of reconfiguration happen in their hardware or software level. The reconfiguration process is a composition of controllers reconfiguration by adding/deleting or updating tasks and intelligence reconfiguration by adding/deleting and updating the rule base. The system architecture is composed of an application layer implemented as the real-time periodic tasks and an intelligence layer for autonomous and adaptive control behavior. In this research work, a rule-based system is used as the artificial intelligence component, where we propose to optimize the inference process by splitting the rule base into two sub-bases; the effective one and the general meta base. With this facility, the coordination in decision making for the distributed platform and system Quality of Service (QoS) in complex and robust system implementation should be faced by new strategies and correct policies. In this sense, we propose a new protocol for the coordination in the two levels of the reconfiguration process to get correctness in the system results. Dealing with performance, we propose to supervise the intelligence QoS of the whole distributed system. Also, we present the correctness of the coordination between the different decisions by the field of the coordination factor. An implementation of the paper contribution with Drools as an integrated rule-based system framework to RTDroid and a discussion of the inferring time and the memory consumption are presented in this paper.
De-crypto-ing Signals in Initial Coin Offerings: Evidence of Rational Token Retention

Using the market for initial coin offerings (ICOs) as a laboratory, we provide evidence that entrepreneurs use retention to alleviate information asymmetry. The underlying technology and the absence of regulation make the ICO market well suited to study this question empirically. Using a hand-collected data set, we show that ICO issuers that retain a larger fraction of their tokens are more successful in their funding efforts and are more likely to develop a working product. Moreover, we find that retention is a stronger signal when markets are crowded, and investors do not have as much time to conduct due diligence. This paper was accepted by Will Cong, Special Issue of Management Science: Blockchains and crypto economics. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022. 4631 .