Miracle-dz/UniFAR-FTUs huggingface dataset:
Sclera Segmentation using Spatial Kernel Fuzzy Clustering Methods

: Biometrics is one of the domain that is gaining lot of importance in the present digital industry. Biometrics are getting integrated in different devices and reaching the end users at a very affordable cost. Among various biometric traits, Sclera is one such trait that is getting popular in the research community for its distinct nature of authenticating and identification of individuals. The recognition system using sclera trait purely depends on efficient segmentation of sclera image. Segmentation process is considered to be significant in image processing system because of better visualization. The segmentation can be done using region based, edge based, threshold based and also clustering based techniques. This paper concentrates on clustering based technique by proposing a variant of conventional Fuzzy C Means (FCM) algorithm. Though the Fuzzy C Means presents outstanding results in many applications, unfortunately it is sensitive to noise and ignore neighbourhood information. Thus to alleviate these limitations this paper presents Generalized Spatial Kernel Fuzzy C Means (GSK-FCM) clustering algorithms for sclera segmentation. To evaluate the proposed methods, experimentation are conducted on Sclera Segmentation and Recognition Benchmarking Competition (SSRBC 2015) dataset. The result of the experiments reveals that the proposed methods outperform the other variants of FCM.
File Name Classification Approach to Identify Child Sexual Abuse

When Law Enforcement Agencies seize a computer machine from a potential producer or consumer of Child Sexual Exploitation Material (CSEM), they need accurate and time-efficient tools to analyze its files. However, classifying and detecting CSEM by manual inspection is a high time-consuming task, and most of the time, it is unfeasible in the amount of time available for Spanish police using a search warrant. An option for identifying CSEM is to analyze the names of the files stored in the hard disk of the suspect person, looking in the text for patterns related to CSEM. However, due to the particularity of this file names, mainly its length and the use of obfuscated words, current file name classification methods suffer from a low recall rate, which is essential in the context of this problem. This paper presents our ongoing research to identify CSEM through their file names. We evaluate two approaches of short text classification: a proposal based on machine learning classifiers exploring the use of Logistic Regression and Support Vector Machine and an approach using deep learning by adapting two popular Convolutional Neural Network (CNN) models that work on character-level. The presented CNN achieved an average class recall of 0.86 and a recall rate of 0.78 for the CSEM class. The CNN based classifier could be integrated into forensic tools and services that might support Law Enforcement Agencies to identify CSEM without the need to access systematically to the visual content of every file.
A Manifold Learning Framework for the Detection of Cardiac Disorders in Acoustic Signals

: Cardiac disorders are clinical situations in which the heart does not function properly. These disorders may be fatal to patients if they are not detected. Detecting such disorders often involves special and in some cases very expensive medical devices such as Computer Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound imaging or Electrocardiograms. Acoustic detection of these disorders by simply listening to the heart using a stethoscope - although being the cheapest detection method - requires a highly skilled doctor. We propose a method that detects cardiac disorders from simple acoustic recordings of the heart. Acquiring such recording is in most cases cheaper than the above mentioned devices. The proposed algorithm is composed of two steps: an ofﬂine training step which constructs a classiﬁer based on labeled recordings; and an online classiﬁcation step which detects cardiac disorders given a recording of the heart. Given the online nature of the algorithm, the proposed algorithm can be implemented as a smartphone application. One of the key elements of oth the training and detection steps is the concise and informative representation of the acoustic signal. This representation is obtained using the application of the spline wavelet packet transform followed by the application of the Diffusion Maps (DM) dimensionality reduction algorithm. The proposed approach is generic and can be applied to various signal types for solving different classiﬁcation problems.
The Bi-objective Minimum Latency Problem with Profit Collection and Uncertain Travel Times

: This paper introduces a new bi-objective minimum latency problem with proﬁt collection, where routes must be constructed in order to maximize the collected proﬁt and to minimize the total latency. These objectives are usually conﬂicting. Thus, considering some important features, as the segmentation of the customers into two classes, mandatory and optional, and the presence of uncertain travel times, we follow a bi-objective approach, aiming to compute a set of Pareto-optimal alternatives with different trade-offs for a decision-maker to choose from. In order to address this computationally challenging problem, we propose a Multi-Objective Iterated Local Search. Computational results conﬁrm the practicality of the algorithm, in terms of the quality of the solutions, and its computational efﬁciency in terms of time spent. We conclude that the algorithm ﬁnds good-quality solutions for small and medium-size instances.
The unedited public sphere

The health of democratic public spheres is challenged by the circulation of falsehoods. These epistemic problems are connected to social media and they raise a classic problem of how to understand the role of technology in political developments. We discuss three sets of technological affordances of social media that facilitate the spread of false beliefs: obscuring the provenance of information, facilitating deception about authorship, and providing for manipulation of social signals. We argue that these do not make social media a “cause” of problems with falsehoods, but explanations of epistemic problems should account for social media to understand the timing and widespread occurrence of epistemic problems. We argue that “the marketplace of ideas” cannot be adequate as a remedy for these problems, which require epistemic editing by the press.
Learning Question Similarity in CQA from References and Query-logs

: Community question answering (CQA) sites are quickly becoming an invaluable source of information in many domains. Since CQA forums are based on the contributions of many authors, the problem of ﬁnding similar or even duplicate questions is essential. In the absence of supervised data for this problem, we propose a novel approach to generate weak labels based on easily obtainable data that exist in most CQAs, e.g., query logs and references in the answers. These labels accommodate training of auxiliary supervised text classiﬁcation models. The internal states of these models serve as meaningful question representations and are used for semantic similarity. We demonstrate that these methods are superior to state of the art text embedding methods for the question similarity task.
Reinforcement Learning of Robot Behavior based on a Digital Twin

: A reinforcement learning approach using a physical robot is cumbersome and expensive. Repetitive execution of actions in order to learn from success and failure requires time and money. In addition, misbehaviour of the robot may also damage or destroy the test bed. Therefore, a digital twin of a physical robot has been used in our research work to train a model within the simulation environment Unity. Later on, the trained model has been transferred to a real-world scenario and used to control a physical agent.
Simultaneous Flexible Keyword Detection and Text-dependent Speaker Recognition for Low-resource Devices

: This paper proposes a new method for simultaneous ﬂexible keyword detection and text-dependent speaker identiﬁcation using a recognized keyword. The purpose is to identify a speaker from among a set of pre-registered speakers on the basis of a short-command utterance in an ofﬁce or home on low-resource chip devices. The ﬁrst contribution is to construct the process that includes a neural network (NN) and a customized Viterbi-based algorithm for ﬂexible keyword detection, and Gaussian mixture models (GMMs) for speaker identiﬁcation. Outputs of a middle layer in the NN and alignment information for keyword detection are also used for creating feature vectors for speaker GMMs. The second contribution is to apply DropConnect in speaker-modeling uncertainties of the Bayesian NN that is used for speaker reacognition. It results in robust speaker models when enrollment utterances are few. Evaluation was conducted using 39 Japanese keywords by 100 speakers. Recognition performance was measured on the basis of false acceptances and false rejects using keyword utterances. Speaker identiﬁcation for 100 pre-registered speakers for recognized keywords was simultaneously evaluated. The identiﬁcation rate when using a conventional i-vector method was 71.22%. By contrast, the identiﬁcation rate of the proposed method was 89.29% while using low-cost resources.
Detection of Privacy Disclosure in the Medical Domain: A Survey

When it comes to increased digitization in the health care domain, privacy is a relevant topic nowadays. This relates to patient data, electronic health records or physician reviews published online, for instance. There exist different approaches to the protection of individuals’ privacy, which focus on the anonymization and masking of personal information subsequent to their mining. In the medical domain in particular, measures to protect the privacy of patients are of high importance due to the amount of sensitive data that is involved (e.g. age, gender, illnesses, medication). While privacy breaches in structured data can be detected more easily, disclosure in written texts is more difficult to find automatically due to the unstructured nature of natural language. Therefore, we take a detailed look at existing research on areas related to privacy protection. Likewise, we review approaches to the automatic detection of privacy disclosure in different types of medical data. We provide a survey of several studies concerned with privacy breaches in the medical domain with a focus on Physician Review Websites (PRWs). Finally, we briefly develop implications and directions for further research.
Detecting Geckler Classification from Gram Stained Smears Images for Sputum

: A Geckler classiﬁcation is a criterion how the smear image is quality based on the number of buccal squamous epithelial (BSE) cells and leukocytes in the Gram stained smears images per 100 × ﬁeld for sputum. The Geckler classiﬁcation then determines which of images is valuable to microscope testing for the Gram stained smears images per 1 , 000 × ﬁeld for sputum. In this paper, we develop the system to detect the Geckler classiﬁcation from Gram stained smears images per 100 × ﬁeld for sputum. In this system, ﬁrst we detect the regions of BSE cells and leukocytes and then construct the classiﬁer of the BSE cells and leukocytes by SVM and DNN. Then, we detect the Geckler class of every test image by detecting the candidate regions and by applying the classiﬁer.
Segmentation of Moving Objects in Traffic Video Datasets

: In this paper, we aim to automate segmentation of multiple moving objects in video datasets speciﬁc to trafﬁc use case. This automation is achieved in two steps. First, we generate bounding boxes using our proposed multi-object tracking algorithm based on convolutional neural network (CNN) model which is capable of re-identiﬁcation. Second, we convert the various tracked objects into pixel masks using an instance segmentation algorithm. The proposed method of tracking has shown promising results with high precision and success rate in trafﬁc video datasets speciﬁcally when there is severe object occlusion and frequent camera motion present in the video. Generating instance aware pixel masks for multiple object instances of a video dataset for ground truth is a tedious task. The proposed method offers interactive corrections with human-in-the-loop to improve the bounding boxes and the pixel masks as the video sequence proceeds. It exhibits powerful generalization capabilities and hence the proposed tracker and segmentation network was applied as a part of an annotation tool to reduce human effort and time.
Variational Inference of Dirichlet Process Mixture using Stochastic Gradient Ascent

: The variational inference of Bayesian mixture models such as the Dirichlet process mixture is not scalable to very large datasets, since the learning is based on computing the entire dataset each iteration. Recently, scalable version notably the stochastic variational inference, addresses this issue by performing local learning from randomly sampled batches of the full dataset or minibatch each iteration. The main problem with stochastic variational inference is that it still relies on the closed form update in variational inference to work. Stochastic gradient ascent is a modern approach to machine learning and it is widely deployed in the training of deep neural networks. It has two interesting properties. Firstly it runs on minibatch and secondly, it does not rely on closed form update to work. In this work, we explore using stochastic gradient ascent as a baseline for learning Bayesian mixture models such as Dirichlet process mixture. However, stochastic gradient ascent alone is not optimal for learning in terms of convergence. Instead, we turn our focus to stochastic gradient ascent techniques that use decaying step-size to optimize the convergence. We consider two methods here. The commonly known momentum approach and the natural gradient approach which uses an adaptive step-size through computing Fisher information. We also show that our new stochastic gradient ascent approach for training Dirichlet process mixture is compatible with deep ConvNet features and applicable to large scale datasets such as the Caltech256 and SUN397. Lastly, we justify our claims when comparing our method to an existing closed form learner for Dirichlet process mixture on these datasets.
Data-driven Algorithm for Scheduling with Total Tardiness

In this paper, we investigate the use of deep learning for solving a classical NP-Hard single machine scheduling problem where the criterion is to minimize the total tardiness. Instead of designing an end-to-end machine learning model, we utilize well known decomposition of the problem and we enhance it with a data-driven approach. We have designed a regressor containing a deep neural network that learns and predicts the criterion of a given set of jobs. The network acts as a polynomial-time estimator of the criterion that is used in a single-pass scheduling algorithm based on Lawler's decomposition theorem. Essentially, the regressor guides the algorithm to select the best position for each job. The experimental results show that our data-driven approach can efficiently generalize information from the training phase to significantly larger instances (up to 350 jobs) where it achieves an optimality gap of about 0.5%, which is four times less than the gap of the state-of-the-art NBR heuristic.
Subject-independent Pain Recognition using Physiological Signals and Para-linguistic Vocalizations

: Pain is the result of a complex interaction among the various parts of the human nervous system. It plays an important role in the diagnosis and treatment of patients. The standard method for pain recognition is self-report; however, not all patients can communicate pain effectively. In this work, the task of automated pain recognition is addressed using para-linguistic and physiological data. Hand-crafted and automatically generated features are extracted and evaluated independently. Several state-of-the-art machine learning algorithms are applied to perform subject-independent binary classiﬁcation. The SenseEmotion dataset is used for evaluation and comparison. Random forests trained on hand-crafted features from the physiological modalities achieved an accuracy of 82.61%, while support vector machines trained on hand-crafted features from the para-linguistic data achieved an accuracy of 63.86%. Hand-crafted features outperformed automatically generated features.
A Neural Information Retrieval Approach for Résumé Searching in a Recruitment Agency

: Finding r´esum´es that match a job description can be a daunting task for a recruitment agency, due to the fact that these agencies are dealing with hundreds of job descriptions and tens of thousands of r´esum´es simultaneously. In this paper we explain a search method devised for a recruitment agency by measuring similarity be-tween r´esum´e documents and job description documents. Document vectors are obtained via TF-IDF weights from word embeddings arising from a neural language model with a skip-gram loss function. We show that, with this approach, successful searches can be achieved, and that the number of skips assumed in the skip gram loss function determines how successful it can be for different job descriptions.
On the Exploitation of Textual Descriptions for a Better-informed Task Assignment Process

: Human resources always play a crucial role on firms’ profitability and sustainability. For a long time already, the identification of the appropriate personnel possessing the skills and expertise required to undertake a task is mainly performed through simple keyword searches exploiting structured data. In this paper, we build on Natural Language Processing techniques to take this identification to a higher level of detail, granularity and accuracy. Our approach elaborates unstructured data such as descriptions and titles of tasks to extract valuable information about the employees’ capability of successfully engaging with a particular task. Text classifiers such as naive Bayes, logistic regression, support vector machines, k-nearest neighbors and neural networks are being tested and comparably assessed.
On the Similarity between Hidden Layers of Pruned and Unpruned Convolutional Neural Networks

: During the last few decades, artiﬁcial neural networks (ANN) have achieved an enormous success in regression and classiﬁcation tasks. The empirical success has not been matched with an equally strong theoretical understanding of such models, as some of their working principles (training dynamics, generalization properties, and the structure of inner representations) still remain largely unknown. It is, for example, particularly difﬁcult to reconcile the well known fact that ANNs achieve remarkable levels of generalization also in conditions of severe over-parametrization. In our work, we explore a recent network compression technique, called Iterative Magnitude Pruning (IMP), and apply it to convolutional neural networks (CNN). The pruned and unpruned models are compared layer-wise with Canonical Correlation Analysis (CCA). Our results show a high similarity between layers of pruned and unpruned CNNs in the ﬁrst convolutional layers and in the fully-connected layer, while for the intermediate convolutional layers the similarity is signiﬁcantly lower. This suggests that, although in intermediate layers representation in pruned and unpruned networks is markedly different, in the last part the fully-connected layers act as pivots , producing not only similar performances but also similar representations of the data, despite the large difference in the number of parameters involved.
New Commercial Representation for Cattle Information Gathering

As the development of Wireless Sensor Networks improves, new applications of Internet of Things are emerging in sectors as diverse as military, environmental, health or food. In many of these applications, the autonomy of the devices is an essential element in order to make reasonable use of them. For the cattle domain, there is a need for an efficient use of energy by sending few messages that accumulate as much information as possible. This paper proposes a new strategy for sending summarized information from devices that are commercially used in cattle to analyze animal behavior. Experiments using 120 different daily time series related to animal behavior have been performed. The obtained results show that the proposed strategy highly improves the current operation mode of the equipment.
Twitter Topic Progress Visualization using Micro-clustering

: This paper proposes a method for visualizing the progress of a bursty topic on Twitter using a previously-proposed micro-clustering technique, which reveals the cause and the progress of a burst. Micro-clustering can efﬁciently represent sub-topics of a bursty topic, which allows visualizing transitions between these sub-topics over time. This process allows for a Twitter user to see the origin of a bursty topic more easily. To show the method’s effectiveness, we conducted an experiment on a real bursty topic, a controversy over childcare leave in Japan. When we extract sub-topics using micro-clustering, and analyze micro-clusters over time, we can understand the progress of the target topic and discover the micro-clusters that caused the burst.
Comparison of Algorithms for Tree-top Detection in Drone Image Mosaics of Japanese Mixed Forests

Counting trees is a common problem in forest applications often solved by performing field studies that are exceedingly cost-intensive in time and manpower. Consequently, many researchers have used computer vision techniques to automatically detect trees by finding tree tops. The success of these algorithms is highly dependent on the data that they are used on. We present a study using data acquired by ourselves in a natural mixed forest using an Unmanned Aerial Vehicle (UAV). Given the particularly challenging nature of our data, we developed a pre-processing step aimed at preparing the data so that it could be used with six common clustering algorithms to detect tree tops. Extensive experiments using data covering over 40 ha is presented and tree detection accuracy, tree counting metrics and computation and use time considerations are taken into account. Our algorithms detect over 80% with high location accuracy and up to 90% with lower accuracy. Tree counting errors range from 8% to 14% for most methods. Data Acquisition and runtime considerations show how this techniques are ready to have an immediate impact in the processing of real forest data.
Metaphoricity detection in adjective-noun pairs

In this paper we propose a neural network approach to detect the metaphoricity of Adjective-Noun pairs using pre-trained word embeddings and word similarity using dot product. We found that metaphorical word pairs tend to have a lower dot product score while literal pairs a higher score. On this basis, we compared seven optimizers and two activation functions, from which the best performing pairs obtained an accuracy score of 97.69% and 97.74%, which represents an improvement of 6% over other current approaches.