Reviewerly/exHarmony huggingface dataset:
On Mapping the Interconnections in Today’s Internet

Internet interconnections are the means by which networks exchange traffic between one another. These interconnections are typically established in facilities that have known geographic locations, and are owned and operated by so-called colocation and interconnection services providers (e.g., Equinix, CoreSite, and EdgeConneX). These previously under-studied colocation facilities and the critical role they play in solving the notoriously difficult problem of obtaining a comprehensive view of the structure and evolution of the interconnections in today's Internet are the focus of this paper. We present mi <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , a new approach for mapping Internet interconnections inside a given colocation facility. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> We infer the existence of interconnections from localized traceroutes and use the Belief Propagation algorithm on a specially defined Markov Random Field graphical model to geolocate them to a target colocation facility. We evaluate mi <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> by applying it initially to a small set of US-based colocation facilities. In the process, we compare our results against those obtained by two recently developed related techniques and discuss observed discrepancies that derive from how the different techniques determine the ownership of border routers. As part of our validation approach, we also identify drastic changes in today's Internet interconnection ecosystem (e.g., new infrastructures in the form of “cloud exchanges” that offer new types of interconnections called “virtual private interconnections”), and discuss their wide-ranging implications for obtaining an accurate and comprehensive map of the Internet's interconnection fabric.
Path planning for UAV tracking target based on improved A-star algorithm

Regarding the problem of path planning for UAV tracking target, the traditional method is to use a yaw control law to control the UAV to track the known targets. The path obtained by this method has the disadvantages that the time taken to reach the desired target is long and the tracking stability is poor. In order to make up for these shortcomings, this paper proposes a path planning strategy based on improved A-star algorithm. Through experimental simulation, the algorithm proposed in this paper can converge to the desired distance faster than the traditional algorithm, and the oscillation is smaller and the tracking effect is better.
pylustrator: code generation for reproducible figures for publication

One major challenge in science is to make all results potentially reproducible. Thus, along with the raw data, every step from basic processing of the data, evaluation, to the generation of the figures, has to be documented as clearly as possible. While there are many programming libraries that cover the basic processing and plotting steps (e.g. Matplotlib in Python), no library yet addresses the reproducible composing of single plots into meaningful figures for publication. Thus, up to now it is still state-of-the-art to generate publishable figures using image-processing or vector-drawing software leading to unwanted alterations of the presented data in the worst case and to figure quality reduction in the best case. Pylustrator a open source library based on the Matplotlib aims to fill this gap and provides a tool to easily generate the code necessary to compose publication figures from single plots. It provides a graphical user interface where the user can interactively compose the figures. All changes are tracked and converted to code that is automatically integrated into the calling script file. Thus, this software provides the missing link from raw data to the complete plot published in scientific journals and thus contributes to the transparency of the complete evaluation procedure.
An Intelligent Control of an Inverted Pendulum Based on an Adaptive Interval Type-2 Fuzzy Inference System

Interval Type-2 fuzzy controllers have become increasingly popular, and have been applied in many engineering applications over the past few decades. In this paper, a knowledge-based interval Type-2 fuzzy controller is proposed to control an inverted pendulum on a cart system in the presence of disturbance, random noise and parameter variations. The proposed controller utilizes the Takagi-Sugeno fuzzy inference system, supported by the Nie-Tan (NT) type-reduction method for the input-output mapping. The adaptation laws for the Type-2 fuzzy consequent parameters are derived based on the sliding mode control (SMC) theory. A comparison study of the proposed interval Type-2 fuzzy controller with a conventional PID controller is investigated in the presence of disturbance, external noise and parameter variations. Simulation results show the efficacy of the proposed controller with respect to a conventional PID controller as indicated by lower RMSE values.
A Soft STAPLE Algorithm Combined with Anatomical Knowledge

Supervised machine learning algorithms, especially in the medical domain, are affected by considerable ambiguity in expert markings. In this study we address the case where the experts' opinion is obtained as a distribution over the possible values. We propose a soft version of the STAPLE algorithm for experts' markings fusion that can handle soft values. The algorithm was applied to obtain consensus from soft Multiple Sclerosis (MS) segmentation masks. Soft MS segmentations are constructed from manual binary delineations by including lesion surrounding voxels in the segmentation mask with a reduced confidence weight. We suggest that these voxels contain additional anatomical information about the lesion structure. The fused masks are utilized as ground truth mask to train a Fully Convolutional Neural Network (FCNN). The proposed method was evaluated on the MICCAI 2016 challenge dataset, and yields improved precision-recall tradeoff and a higher average Dice similarity coefficient.
ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection in Multiplex Immunohistochemistry Images

In digital pathology, cell detection and classification are often prerequisites to quantify cell abundance and explore tissue spatial heterogeneity. However, these tasks are particularly challenging for multiplex immunohistochemistry (mIHC) images due to high levels of variability in staining, expression intensity, and inherent noise as a result of preprocessing artefacts. We proposed a deep learning method to detect and classify cells in mIHC whole-tumor slide images of breast cancer. Inspired by inception-v3, we developed Cell COunt RegularizeD Convolutional neural Network (ConCORDe-Net) which integrates conventional dice overlap and a new cell count loss function for optimizing cell detection, followed by a multi-stage convolutional neural network for cell classification. In total, 20447 cells, belonging to five cell classes were annotated by experts from 175 patches extracted from 6 whole-tumor mIHC images. These patches were randomly split into training, validation and testing sets. Using ConCORDe-Net, we obtained a cell detection F1 score of 0.873, which is the best score compared to three state of the art methods. In particular, ConCORDe-Net excels at detecting closely located and weakly stained cells compared to other methods. Incorporating cell count loss in the objective function regularizes the network to learn weak gradient boundaries and separate weakly stained cells from background artefacts. Moreover, cell classification accuracy of $$96.5\%$$ was achieved. These results support that incorporating problem specific knowledge such as cell count into deep learning based cell detection architectures improves robustness of the algorithm.
A Swarm Intelligence Approach to Avoid Local Optima in Fuzzy C-Means Clustering

Clustering analysis is an important computational task that has applications in many domains. One of the most popular algorithms to solve the clustering problem is fuzzy c-means, which exploits notions from fuzzy logic to provide a smooth partitioning of the data into classes, allowing the possibility of multiple membership for each data sample. The fuzzy c-means algorithm is based on the optimization of a partitioning function, which minimizes inter-cluster similarity. This optimization problem is known to be NP-hard and it is generally tackled using a hill climbing method, a local optimizer that provides acceptable but sub-optimal solutions, since it is sensitive to initialization and tends to get stuck in local optima. In this work we propose an alternative approach based on the swarm intelligence global optimization method Fuzzy Self-Tuning Particle Swarm Optimization (FST-PSO). We solve the fuzzy clustering task by optimizing fuzzy c-means' partitioning function using FST-PSO. We show that this population-based metaheuristics is more effective than hill climbing, providing high quality solutions with the cost of an additional computational complexity. It is noteworthy that, since this particle swarm optimization algorithm is self-tuning, the user does not have to specify additional hyperparameters for the optimization process.
3D Motion Decomposition for RGBD Future Dynamic Scene Synthesis

A future video is the 2D projection of a 3D scene with predicted camera and object motion. Accurate future video prediction inherently requires understanding of 3D motion and geometry of a scene. In this paper, we propose a RGBD scene forecasting model with 3D motion decomposition. We predict ego-motion and foreground motion that are combined to generate a future 3D dynamic scene, which is then projected into a 2D image plane to synthesize future motion, RGB images and depth maps. Optional semantic maps can be integrated. Experimental results on KITTI and Driving datasets show that our model outperforms other state-of-the- arts in forecasting future RGBD dynamic scenes.
Detection of Spatial Outlier by Using Improved Z-Score Test

Outlier detection is a prominent technique in data mining since it has various vital applications. Outlier detection can be used to remove noise or study the specific observations in data that are dissimilar to those in its neighborhood. The detection of outliers are being used to show out hidden and valuable information in many areas of applications. [1]. The z-score test has long been used to detect outliers in data. In this work, a modified version of the z-score test is proposed which can lead to a reduction of the time-complexity of the traditional z-score test by a value of n each iteration, where n donates the number of spatial points in the data set which have not been discovered as outliers. Using the outlier threshold value to calculate the value of the modified z-score function rather than calculating the z-score value for each observation in the dataset makes this possible.
Hybrid Pre-processing Technique for Handling Imbalanced Data and Detecting Outliers for KNN Classifier

Data mining is a technique of examining huge quanta of pre-existing data in order to discover new patterns and relationships among them, which will help to make better decisions. Classification is a data mining technique which organises data into categories. In this paper, in order to enhance the performance of k nearest neighbour (kNN) classifier-a kind of classification technique that is among the most widely used-a new data pre-processing technique has been proposed, which can handle some classification issues such as imbalanced data and outliers. In an imbalanced dataset, the classification categories are not equally distributed. Imbalanced dataset have an inherent issue when it comes to using classifiers on them that have been developed using machine learning algorithms. The basic nature of these algorithms is to reduce errors without relying on balance of classes. Another issue addressed in this paper is the matter of outliers or extreme values. Outlier or extreme values are those values that are outside the expected range of values. The quality of Classification modelling can be greatly enhanced by identifying and excision of these values. In this proposed technique two data pre-processing techniques have been combined to form a hybrid pre-processing technique. The two data pre-processing techniques are resample technique and inter quartile range technique (IQR). Some Imbalanced dataset with outliers that can be considered as yardsticks were taken for this study. It was observed that the classification results obtained were far superior to the classification done without the pre-processing technique.
Prediction of Basketball Free Throw Shooting by OpenPose

OpenPose, which is developed by Carnegie Mellon University (CMU) presented in CVPR 2017, takes in real-time motion images via a simple web camera and is capable of recognizing skeletons of multiple persons in these images. It also generates recognized skeleton point coordinates to files. OpenPose is featured by CMU's original top-down method for real-time recognition and it is open online especially for research purposes. Thus we aimed to build a posture analysis model using OpenPose skeletal recognition data and verifying the practicality of OpenPose by verifying the accuracy of the model. As a posture analysis model, we adopted a logistic regression model that predicts the shooting probability of the basketball free throw with skeleton posture data as explanatory variables and the fact whether the ball enters the basket or not as a binary target variable. As the result, sufficiently significant prediction accuracy was obtained. Therefore, posture analysis using OpenPose has been verified to be practical with our model. We consider that with many skeleton data which are easily provided by a simple web camera, OpenPose makes statistical diagnostic approach possible. We also consider it could lower costs (in both financial and time-wise) of such an analysis which has previously required more equipments and more time for preparation regarding motion capture analysis systems.
Encrypted Traffic Classification Based ML for Identifying Different Social Media Applications

increasing the deployment of encryption in network protocols and applications poses a challenge for traditional traffic classification approaches. Social media applications such as Skype, WhatsApp, Facebook, YouTube etc. as popular representatives of encrypted traffics have attracted big attention to communication and entertainment. Therefore, the accurate identification of them within encrypted traffic has become a big issue and a hot topic to explore them in detail. In this context, Machine Learning (ML) approaches have shown promise in this area especially for detecting and classifying the encrypted traffic data. Therefore, this work is concentrated on the challenges and has explored the ability to use ML algorithms for social media classification from traffic traces and provides a developed solution, which is able to identify the social media sub-class.
Synergetic information bottleneck for joint multi-view and ensemble clustering

Multi-view and ensemble clustering methods have been receiving considerable attention in exploiting multiple features of data. However, both of these methods have their own set of limitations. Specifically, the performance of multi-view clustering may degrade due to the conflict between heterogeneous features, while ensemble clustering relies heavily on the quality of basic clusterings since it discovers the final clustering partition without considering the original feature structures of the source data. In this study, we propose a novel clustering scheme called synergetic information bottleneck (SIB) for joint multi-view and ensemble clustering. First, the proposed SIB utilizes multiple original features to characterize data information from different views while exploiting the basic clusterings to relieve the conflict of heterogeneous features. Second, the SIB generally formulates the problem of joint multi-view and ensemble clustering as a function of mutual information maximization, in which the relatedness between the original features and auxiliary basic clusterings is maximally preserved with respect to the final clustering partition. Finally, to optimize the objective function of SIB, a novel “draw-and-merge” optimization method is proposed. In addition, we prove that this novel optimization method can ensure that the objective function of SIB converges to a stable optimal in a finite number of iterations. Extensive experiments conducted on several practical tasks demonstrate that the SIB outperforms the state-of-the-art multi-view and ensemble clustering methods.
Open Data Chatbot

Recently, chatbots received an increased attention from industry and diverse research communities as a dialogue-based interface providing advanced human-computer interactions. On the other hand, Open Data continues to be an important trend and a potential enabler for government transparency and citizen participation. This paper shows how these two paradigms can be combined to help non-expert users find and discover open government datasets through dialogue.
Transforming Device Fingerprinting for Wireless Security via Online Multitask Metric Learning

Device fingerprinting is a crucial part in the Internet of Things applications. Existing device-fingerprinting solutions either ignore the influence of the type of network traffic or separately learn a fingerprinting model for each traffic type. This often leads to suboptimal solutions, especially when training data are limited. Considering that the data distributions of different traffic types may be different but related, we propose a novel multitask learning method to learn the fingerprinting models for several traffic types simultaneously. Specifically, we first design a system for device fingerprinting using the popular k-nearest neighbor (KNN) approach. Then, a novel distance metric learning (DML) algorithm termed online multitask metric learning (OMTML) is developed to improve the distance estimation in our system. OMTML enables the models describing different traffic types to help each other during the metric learning procedure, and thus improving their respective accuracies. OMTML can also be updated adaptively, and the updating process is efficient. The experimental results show that the proposed KNN-based system outperforms the artificial neural network (ANN)-based counterpart significantly. Besides, the comparisons of our OMTML and other representative online and multitask DML approaches demonstrate both effectiveness and efficiency of the proposed metric learning method.
Blockchain Technology for Intelligent Environments

In the last few years, we have been witnessing the convergence of the physical with the digital world. The Internet of Things (IoT) is progressing at a fast pace, and IoT devices are becoming pervasive in our physical environments, bringing the vision of Intelligent Environments closer to reality. At the same time, the newly-introduced blockchain technology is offering for the first time ever cryptographically proven trust based on a set of mutually untrusted nodes. Blockchain technology thus has the potential to become a key component of many IoT systems, offering them an unprecedented level of accountability, transparency, and reliability. This paper first lays out the principles on which blockchain systems are operating, along with descriptions of the most noteworthy blockchain implementations. It then presents a number of systems through which blockchains may interact with external systems and third-party data sources. Finally, it provides a survey of the state-of-the-art blockchain-based systems targeting IoT applications.
An efficient JPEG image compression based on Haar wavelet transform, discrete cosine transform, and run length encoding techniques for advanced manufacturing processes

Image compression plays a key role in the transmission of an image and storage capacity. Image compression aims to reduce the size of the image with no loss of significant information and no loss of quality in the image. To reduce the storage capacity of the image, the image compression is proposed in order to offer a compact illustration of the information included in the image. Image compression exists in the form of lossy or lossless. Even though image compression mechanism has a prominent role for compressing images, certain conflicts still exist in the available techniques. This paper presents an approach of Haar wavelet transform, discrete cosine transforms, and run length encoding techniques for advanced manufacturing processes with high image compression rates. These techniques work by converting an image (signal) into half of its length which is known as “detail levels”; then, the compression process is done. For simulation purposes of the proposed research, the images are segmented into 8 × 8 blocks and then inversed (decoded) operation is performed on the processed 8 × 8 block to reconstruct the original image. The same experiments were done on two other algorithms, that is, discrete cosine transform and run length encoding schemes. The proposed system is tested by comparing the results of all the three algorithms based on different images. The comparison among these techniques is drawn on the basis of peak signal to noise ratio and compression ratio. The results obtained from the experiments show that the Haar wavelet transform outperforms very well with an accuracy of 97.8% and speeds up the compression and decompression process of the image with no loss of information and quality of image. The proposed study can easily be implemented in industries for the compression of images. These compressed images are suggested for multiple purposes like image compression for metrology as measurement materials in advanced manufacturing processes, low storage and bandwidth requirements, and compressing multimedia data like audio and video formats.
Super-resolved thermal imagery for high-accuracy facial areas detection and analysis

In this study, we evaluate various Convolutional Neural Networks based Super-Resolution (SR) models to improve facial areas detection in thermal images. In particular, we analyze the influence of selected spatiotemporal properties of thermal image sequences on detection accuracy. For this purpose, a thermal face database was acquired for 40 volunteers. Contrary to most of existing thermal databases of faces, we publish our dataset in a raw, original format (14-bit depth) to preserve all important details. In our experiments, we utilize two metrics usually used for image enhancement evaluation: Peak-Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM). In addition, we present how to design a SR network with a widened receptive field to mitigate the problem of contextual information being spread over larger image regions due to the heat flow in thermal images. Finally, we determine whether there is a relation between achieved PSNR and accuracy of facial areas detection that can be analyzed for vital signs extraction (e.g. nostril region). The performed evaluation showed that PSNR can be improved even by 60% if full bit depth resolution data is used instead of 8 bits. Also, we showed that the application of image enhancement solution is necessary for low resolution images to achieve a satisfactory accuracy of object detection.