Reviewerly/exHarmony huggingface dataset:
Clustering and pattern recognition in bioengineering and autonomous systems

Abstract Many artificial intelligence applications are dealing with large amount of data. The essence is to identify and recognize the structure in data and classify them according to similar attributes, features and patterns. Often, these data are fuzzy and do not belong to one cluster only. Presented is a method for optimum partitioning of fuzzy data which uses a concept of n-dimensional Euclidian space to determine the geometric closeness of data. Numeric examples, based on a prototype software, present clustering and pattern recognition algorithms including defuzzification founded on the maximum membership, cluster similarity analysis and classification metrics of the data sets decomposition.
Tracking with the CAD Model of Object for Visual Servoing

Tracking the 3D pose between the object and the camera is a very important research subject in position-based visual servoing (PBVS). The tracking requirements are extremely fast and accurate. In order to solve these problems, a real time and efficient tracking algorithm based on Computer Aided Design (CAD) model of object is proposed. This method needs only a monocular camera in PBVS system. It is robust to the calibration errors and the feature extraction errors, and can also be applied in the case where the part of target is not in the field of view. Tracking object is equivalent to calculating the pose of object in camera in real time. The priori CAD model of the object can be projected into the scene through the pose of the previous frame. The pose between the object and the camera is tracked and updated by virtual visual servoing (VVS) method. An edge tracking algorithm is used to track the edge of the object. In order to filter the noisy edge points, you only look once (YOLO) algorithm is used to get the range of the target position in the image plane. The use of M-estimator in control makes visual servoing more robust. The experimental results for six degrees-of-freedom visual servoing system demonstrate the effectiveness of the algorithm.
Exploration of information organization in language archives

ABSTRACT This submission reports preliminary results of the first stage of a research project that seeks to identify the information organization methods and techniques currently offered in the language data archives and the gaps between the tools and techniques available and the needs of actual and potential users of language data archives. We conducted an exploratory content analysis of the websites of the language archives (LAs) hosted by the institutions located in the United States and several other countries. The focus of our exploratory content analysis is on the information organization, including documentation on metadata standards, displaying of individual metadata records and availability of harvesting sets of metadata records, provision of advanced discovery and navigation options powered by metadata such as availability of adaptive and personalized search or social tagging functionality. Here, we report our preliminary findings and describe our plan for Stage 2 of the.
Static Video Summarization using Sparse Autoencoders

This paper presents a novel method for static video summarization. The video summarization methods generate an abstract representation of the input videos. The proposed method first extracts individual frames from the input video. Then, the redundant frames are eliminated based on motion vectors. The incorporation of redundancy elimination greatly reduces the complexity of the step in which the abstract representation is created. After the redundancy elimination, the high-level features are extracted from each frame. The feature extraction is done using Sparse Autoencoders (SAE). These high-level feature vectors from SAE are clustered using K-means algorithm. The frames closest to the centroid of each cluster is selected as the key frame of the input video. The method is tested on two benchmark datasets: VSUMM and OVP. The proposed approach attains better results compared to other state-of-the-art video summarization techniques. The efficiency of the proposed method is proved by comparing with other clustering-based approaches in video summarization.
Low Cost Gaze Estimation: Knowledge-Based Solutions

Eye tracking technology in low resolution scenarios is not a completely solved issue to date. The possibility of using eye tracking in a mobile gadget is a challenging objective that would permit to spread this technology to non-explored fields. In this paper, a knowledge based approach is presented to solve gaze estimation in low resolution settings. The understanding of the high resolution paradigm permits to propose alternative models to solve gaze estimation. In this manner, three models are presented: a geometrical model, an interpolation model and a compound model, as solutions for gaze estimation for remote low resolution systems. Since this work considers head position essential to improve gaze accuracy, a method for head pose estimation is also proposed. The methods are validated in an optimal framework, I2Head database, which combines head and gaze data. The experimental validation of the models demonstrates their sensitivity to image processing inaccuracies, critical in the case of the geometrical model. Static and extreme movement scenarios are analyzed showing the higher robustness of compound and geometrical models in the presence of user's displacement. Accuracy values of about 3° have been obtained, increasing to values close to 5° in extreme displacement settings, results fully comparable with the state-of-the-art.
Anti-forensics of median filtering and contrast enhancement

Digital images can be convincingly edited using image editing tools. In order to identify such image processing operations, various forensic techniques have been proposed. In response, anti-forensic operations designed as counter-measures have been devised. In this paper, we propose an anti-forensic technique to counter spatial domain forensic detectors and demonstrate its accuracy on popular image manipulation operations such as median filtering and contrast enhancement. The integrated anti-forensic attack is formulated as an optimization problem. The proposed optimization modifies the image so as to incorporate the median filtering or contrast enhancement operation while ensuring that its spatial characteristics do not change significantly. Through a series of experiments, we prove that the proposed algorithm can severely degrade the performance of median filtering and contrast enhancement detectors. The proposed algorithm also outperforms popular anti-forensic algorithms.
Gaze-based Product Filtering

User interfaces in self-order terminals aim to satisfy the need for information of a broad audience and thus get easily clut-tered. Online shops present personalized product recommen-dations based on previously gathered user data to channel the user's attention. In contrast, stateless point-of-sales machines generally have no access to the user's personal information nor previous purchase behavior. User preferences must therefore be determined during the interaction. We thus propose using gaze data to determine preferences in real-time. In this paper we present a system for dynamic gaze-based fltering.
DECIDE

DevOps represents a model for application development that enables close collaboration between software developers and IT operations with the objective of implementing continuous integration, continuous delivery and continuous development of software applications. This paper proposes an approach for extending the DevOps philosophy with the objective of supporting the development and operation of multi-cloud native applications deployed over heterogeneous cloud resources. The authors present the extended DECIDE DevOps framework and the supporting tool suite developed in the context of the DECIDE H2020 action
Computer Aided System for Automatic Detection and Marking Instance of Nuclei

Nuclei classification gained immense interest among researchers to build computer-assisted diagnosis (CAD) tools. With the advancement of image processing and pattern recognition techniques, CAD has been widely used to help medical professionals to interpret medical images. Digital pathology, as a vital aspect of CAD application, is gaining a lot of attention from both image analysis researchers and pathologists because of the advent of whole-slide imaging. The potential of digital pathology span over a wide range of applications such as segmentation of desired regions or objects, counting cancer cells, recognizing tissue structures, classifying cancer grades, a prognosis of cancers, etc. In this research study, we propose a deep learning-based approach in order to detect and segment different nuclei present in diverse cell images, followed by postprocessing method to identify the overlapping nuclei in the images. We have added preprocessing steps for processing the raw image which includes rescaling, resizing, converting to high intensity background, data augmentation and set of morphological operations. Preprocessed images are then used for pixel level classification and segmentation. We have implemented U-Net architecture, a Convolutional Neural Network (CNN) based neural network for semantic segmentation and added few steps that processes segmented image to identify overlapping nuclei and mark all nuclei. Our study shows that with the help of data preprocessing step, we were able to increase the efficiency of the model and post classification steps helps to identifying overlapping nuclei. The proposed methods have achieved around 0.96 of average dice coefficient as the accuracy of pixel level classification.
Toward Faster Reinforcement Learning for Robotics: Using Gaussian Processes

Standard robotic control works perfectly in case of ordinary conditions, but in the case of a change in the conditions (e.g. damaging of one of the motors), the robot won't achieve its task anymore. We need an algorithm that provide the robot with the ability of adaption to unforeseen situations. Reinforcement learning provide a framework corresponds with that requirements, but it needs big data sets to learn robotic tasks, which is impractical. We discuss using Gaussian processes to improve the efficiency of the Reinforcement learning, where a Gaussian Process will learn a state transition model using data from the robot (interaction) phase, and after that use the learned GP model to simulate trajectories and optimize the robot's controller in a (simulation) phase. PILCO algorithm considered as the most data efficient RL algorithm. It gives promising results in Cart-pole task, where a working controller was learned after seconds of (interaction) on the real robot, but the whole training time, considering the training in the (simulation) was longer. In this work, we will try to leverage the abilities of the computational graphs to produce a ROS friendly python implementation of PILCO, and discuss a case study of a real world robotic task.
Level set medical image segmentation aided by cooperative quantum particle optimization with Lévy flights

Image segmentation plays an important part of image processing, and is also the premise and basis of image analysis and image understanding and recognition. Among the level set based methods, the original Local Binary Fitting (LBF) algorithm is a successful deterministic algorithm that suffers from sensitization to size of the local minimum, image contours, shapes, and initial positions. Among them, Level Set method promotes the two-dimensional problem to the three-dimensional one and then solves it using implicit method to express closed curve of plane. In this article, a novel Level Set model aided by PSO was proposed to solve automated medical image segmentation. The experimental result of segmentations on the benchmark shows that our proposed method is effective to both simple and complex medical images.
Personalized Knowledge Graphs for the Pharmaceutical Domain

A considerable amount of scientific and technical content is still locked behind data formats which are not machine readable, especially PDF files - and this is particularly true in the healthcare domain. While the Semantic Web has nourished the shift to more accessible formats, in business scenarios it is critical to be able to tap into this type of content, both to extract as well as embed machine readable semantic information. We present our solution in the pharmaceutical domain and describe a fully functional pipeline to maintain up-to-date knowledge resources extracted from medication Package Inserts. We showcase how subject matter expert(s) can have their own view on the available documents, served by a personalized Knowledge Graph - or rather a view on the graph which is specific to them. We share lessons learned from our initial pilot study with a team of medical professionals. Our solution is fully integrated within the standard PDF data format and does not require the use of any external software - nor to be aware of the underlying graph.
Bulk microanalysis of assemblages of small fluid inclusions by LA-ICP-MS: Methodology and application to orogenic gold systems

Microanalysis of individual fluid inclusions by laser ablation inductively coupled mass spectrometry (LA-ICP-MS) is a powerful tool for reconstructing the composition of hydrothermal fluids, but it demands a sample quality that is unattainable in many cases. In orogenic gold deposits, the need for direct fluid microanalysis has been present for several decades, but due to the high fluid flux and prolonged hydrothermal and tectonic history that typifies these systems, most samples do not meet the criteria of fluid inclusion size and distribution that allow LA-ICP-MS analysis of individual isolated fluid inclusions. To overcome this difficulty, a method has been developed and tested, whereby areas of quartz densely populated with fluid inclusions (e.g., growth zones, secondary planes) are analyzed along a single continuous laser ablation profile; the generated signals are subsequently converted to time-slice datasets and plotted as element ratios in ternary diagrams to reconstruct specific major- and trace-element ratios. The estimated fluid compositions are in good agreement with previous analytical results of the same material (microthermometry, evaporate mound and conventional LA-ICP-MS) and are shown to be geologically viable on a boarder scale when compared to literature data from similar ore environments. The method has high spatial and chemical resolution, which allows the reconstruction of micro-scale fluid chemical changes, such as significant fluctuation in the relative element concentration (e.g., K, Rb, Ba, Sr and V versus Na, Li and As) during crystal growth, as observed in one of the test samples. The significance of this bulk LA protocol is that it allows the quick, easy and cost-effective microanalysis of samples that are typically inundated with fluid inclusions, such as those in orogenic and epithermal systems, which would otherwise not be amenable for further quantitative analysis to constrain fluid chemistry.
Ghanaian Sign Language Recognition Using Deep Learning

Sign Languages, unlike natural languages, involve the use of continuous gestures, body languages, facial expressions and hand movements to convey meaning and most importantly express a signer's thoughts more effectively. Ghanaian Sign Language is the standard sign language used by the deaf in Ghana with a substantial difference to other sign languages as well as cultural conditions that led to its emergence. In this paper, we proposed and implemented a novel yet deep convolutional neural network to classify and recognize Ghanaian Sign Language and attained an accuracy of 96.0%. Further, we leveraged transfer learning techniques by fine-tuning state-of-the-art network architectures pre-trained on the ImageNet database and improved the accuracy with a reported increase of 3.1%. There was no large publicly Ghanaian Sign Language dataset available, so we created our own dataset for evaluation of the proposed convolutional neural network architecture. Conclusively, we plan of extending the dataset with a view of releasing it in the future, subsequently, allowing researches to apply changes to the dataset using image processing and computer vision tools and techniques they consider can be applicable for their task at hand.
A Simplified Path Planning Algorithm for Surveillance Missions of Unmanned Aerial Vehicles

In this paper, a path planning algorithm is proposed for surveillance missions that does not only require visiting certain known specified regions, but also explores these regions. We set the motion model of the vehicle to a planar kinematic model with bounded steering rate, and we impose the region exploration as traversing an arc with a nonzero arc-length around a point. Using the above two assumptions, we define a time-optimal control problem to ensure completely visiting and exploring all of the sites. Similar to the literature, the problem is formulated as a Dubin Traveling Salesman Problem with Neighborhoods to derive a simplified algorithm, however, this paper builds on previous work done on the convex optimization solution to derive a geometrical representation of the local minima for path headings. Hence, solving for the optimal Dubin’s maneuver corresponding to the global shortest path.
A honeybees-inspired heuristic algorithm for numerical optimisation

Abstract Swarm intelligence is all about developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributors so that a complementary collective effort can be achieved to offer a useful solution. The main points in organising the harmony remain as managing the diversification and intensification actions appropriately, where the efficiency of collective behaviours depends on blending these two actions appropriately. In this paper, a hybrid bee algorithm is presented, which harmonises bee operators of two mainstream well-known swarm intelligence algorithms inspired of natural honeybee colonies. The parent algorithms have been overviewed with many respects, strengths and weaknesses are identified, first, and the hybrid version has been proposed, next. The efficiency of the hybrid algorithm is demonstrated in comparison with the parent algorithms in solving two types of numerical optimisation problems; (1) a set of well-known functional optimisation benchmark problems and (2) optimising the weights of a set of artificial neural network models trained for medical classification benchmark problems. The experimental results demonstrate the outperforming success of the proposed hybrid algorithm in comparison with two original/parent bee algorithms in solving both types of numerical optimisation benchmarks.
A Comprehensive Verification of Transformer in Text Classification

Recently, a self-attention based model, named Transformer, is proposed in Neural Machine Translation (NMT) domain, and outperforms the RNNs based seq 2seq model in most cases, hence it becomes the state-of-the-art model for NMT task. However, some studies find that the RNNs based model integrated with the Transformer structures could achieve almost the same experiment effect as the Transformer on the NMT task. In this paper, following the previous researches, we intend to further verify the performance of Transformer structures on the text classification task. Based on RNNs model, we gradually add each part of the Transformer block and evaluate their influence on the text classification task. We carry out the experiments on NLPCC2014 and dmsc_v2 datasets, and the experiment results show that multi-head attention mechanism and multiple attention layers could improve the performance of the model on the text classification task. Furthermore, the visualization of the attention weights also illustrates that multi-head attention outperforms the traditional attention mechanism.
Auto-scaling in the Cloud: Current Status and Perspectives

One of the main advantages of cloud computing is elasticity, which allows to rapidly expand or reduce the amount of leased resources in order to adapt to load variations, guaranteeing the desired quality of service. Auto-scaling is an extensively studied topic. Making optimal scaling choices is of paramount importance and can help reduce leasing costs, as well as power consumption. This paper analyzes the current status of auto-scaling in the cloud ecosystem, considering recent literature contributions as well as existing auto-scaling solutions. Then it discusses possible research directions in this field, fostering the development of a methodology that, on the basis of suitably-defined performance parameters, can produce an optimal auto-scaling policy to be implemented using existing auto-scaling services and tools.
PrioPrivacy: A Local Recoding K-Anonymity Tool for Prioritised Quasi-Identifiers

Extensive research in de-anonymisation has shown that in datasets not containing any personally identifying information (PII)—name, address, etc.—individuals can be identified through quasi-identifiers (QIs)—attributes whose combination serves as a unique identifier. In order to deal with this issue, necessary anonymisation measures need to be taken which, however, reduce the quality of a dataset by modifying its values. Data publishers may deem that some QIs are more important than others and, therefore, should be distorted as little as possible in the anonymisation process. Most existing tools do not take such weighting into consideration. In this demo, we present a tool addressing this issue by utilising a local recoding algorithm for k-anonymity, capable of outperforming the existing state-of-the-art tool ARX.
Object Detection for Autonomous Vehicle Using TensorFlow

The area of computer vision is emerging continually with the increasing interaction and development to provide a comfortable interaction between human and machines. One of the key aspects in the process of computer vision is object detection. Either objects can be identified partially or close to the original objects. The accuracy in detecting the objects can be improved by using state-of-the-art deep learning models like faster-Regional Convoluted Neural Network (faster-RCNN), You Only Look Once model (YOLO), Single Shot Detector (SSD) etc. Traditional algorithms can’t recognize objects as efficiently due to its limitations. Whereas the deep learning models require large amount of data for training the dataset, which has more resource and labour intensive in nature. The selection of algorithm determines its precision in object detection as well as its reliability. The recognition and classification of object begins with preparing dataset followed by splitting the dataset into training dataset and test dataset. The task of training the dataset can be assisted by both traditional as well as modern deep neural networks. The loss per step or epoch is calculated on the training dataset to signify the efficiency and accuracy of the model. In this model, the loss per step is 2.73. We have achieved a maximum accuracy of about 85.18% after training the dataset used.