Reviewerly/exHarmony huggingface dataset:
Surgical Navigation System for Low-Dose-Rate Brachytherapy Based on Mixed Reality

This article presents a personalized mixed reality (MR) surgical assistance system for brachytherapy. Using a novel, modified multi-information fusion method, the fusion of virtual organs and a preoperative plan for an actual patient and the real-time tracking of surgical tools were achieved. Using the quaternion-based iterative closest point (QICP) algorithm and a hand-eye calibration method, the preoperative plan can be fused into individual patients. Using the electromagnetic (EM) tracker, users can track the surgery tools in real time, without multiple CT scans, and doctors can immediately perform the surgery. We performed a series of experiments, including phantom and animal experiments, to test the accuracy and efficiency of the system. In the phantom experiment, the average needle location error was 0.957 mm. Based on the results of animal experiments, the needle insertion error was 2.416 mm. All experimental results indicated that the procedure could be applied in further clinical studies.
A survey on semanticized and personalized health recommender systems

Abstract Health 3.0 is a health‐related extension of the Web 3.0 concept. It is based on the semantic Web which provides for semantically organizing electronic health records of individuals. Health 3.0 is rapidly gaining ground as a new research topic in many academic and industrial disciplines. Due to the recent rapid spread of wearable sensors and smart devices with access to social media, migrating health services from the traditional centre‐based health system to personal health care is inevitable. In this current era of greater personalization, treating patients' health problems according to their profile and medical data gathered is possible using the latest information technologies. Consequently, personalized health recommender systems have gained importance. Empowering the utility of advanced Web technology in personalized health systems is still challenging due to pressing issues, such as lack of low cost and accurate smart medical sensors and wearable devices, existing investment in legacy Web system architecture in health sector, heterogeneity of medical data gathered by myriad health care institutions and isolated health services, and interoperability issues as well as multi‐dimensionality of medical data. By tracing recent developments, this paper offers a systematic review through recent research on semantic Web‐enabled personalized health systems, namely, semanticized personalized health recommender systems with the key enabling technologies, major applications, and successful case studies. Critical questions derived from the research studies were discussed, and main directions of open issues were identified leading to recommendations for future study in the field of personalized health recommender systems.
Highly Parallel Seedless Random Number Generation from Arbitrary Thread Schedule Reconstruction

Security is a universal concern across a multitude of sectors involved in the transfer and storage of computerized data. In the realm of cryptography, random number generators (RNGs) are integral to the creation of encryption keys that protect private data, and the production of uniform probability outcomes is a revenue source for certain enterprises (most notably the casino industry). Arbitrary thread schedule reconstruction of compare-and-swap operations is used to generate input traces for the Blum-Elias algorithm as a method for constructing random sequences, provided the compare-and-swap operations avoid cache locality. Threads accessing shared memory at the memory controller is a true random source which can be polled indirectly through our algorithm with unlimited parallelism. A theoretical and experimental analysis of the observation and reconstruction algorithm are considered. The quality of the random number generator is experimentally analyzed using two standard test suites, DieHarder and ENT, on three data sets.
Privacy Preservation in Distributed Optimization via Dual Decomposition and ADMM

In this work, we explore distributed optimization problems, as they are often stated in energy and resource optimization. More precisely, we consider systems consisting of a number of subsystems that are solely connected through linear constraints on the optimized solutions. The focus is put on two approaches; namely dual decomposition and alternating direction method of multipliers (ADMM), and we are interested in the case where it is desired to keep information about subsystems secret. To this end, we propose a privacy preserving algorithm based on secure multiparty computation (SMPC) and secret sharing that ensures privacy of the subsystems while converging to the optimal solution. To gain efficiency in our method, we modify the traditional ADMM algorithm.
Study on the Satellite Telemetry Data Classification Based on Self-Learning

Since great redundancy of telemetry data of spacecraft, telemetry data compression is a good solution for the limited bandwidth and contact wireless links. It is important to obtain accurate data characteristic firstly. State-of-the-art machine learning methods work well on data mining and pattern recognition under conditions of the given test data set, which could be used as the available tools for post-event data processing and analysis, such as trend forecasting and outlier detection, but they have not provided the proper solution from the source on-board. In this paper, four base classes of the telemetry data are suggested and studied through the time series feature and information entropy analysis, then a new on-board lightweight self-learning algorithm named Classification Probability calculation - Window Step optimization (CP-WS) is proposed to obtain the class features and make the decision of each single parameter from the continuous discrete telemetry time series. Simulation results show that, our algorithm correctly classifies the simulation and real mission data into the appropriate base class with advantages of high classification accuracy as 100% and adaptive computational complexity from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(L^{2})$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(L)$ </tex-math></inline-formula> , which could be used in satellite on-board data compression for space-to-ground transmission, especially for the deep space explorers to save important status with less on-board storage space.
FFD: Figure and Formula Detection from Document Images

In this work, we present a novel and generic approach, Figure and Formula Detector (FFD) to detect the formulas and figures from document images. Our proposed method employs traditional computer vision approaches in addition to deep models. We transform input images by applying connected component analysis (CC), distance transform, and colour transform, which are stacked together to generate an input image for the network. The best results produced by FFD for figure and formula detection are with F1-score of 0.906 and 0.905, respectively. We also propose a new dataset for figures and formulas detection to aid future research in this direction. The obtained results advocate that enhancing the input representation can simplify the subsequent optimization problem resulting in significant gains over their conventional counterparts.
Foundations of Data Quality Assurance for IoT-based Smart Applications

Most current scientific and industrial efforts in IoT are geared towards building integrated platforms to finally realize its potential in commercial scale applications. The IoT and Big Data contemporary context brings a number of challenges, such as providing quality assurance (defined by availability and veracity) for sensor data. Traditional signal processing approaches are no longer sufficient, requiring combined approaches in both architectural and analytical layers. This paper proposes a discussion on the adequate foundations of a new general approach aimed at increasing robustness and antifragility of IoT-based smart applications. In addition, it shows results of preliminary experiments with real data in the context of precision irrigation using multivariate methods to identify relevant situations, such as sensor failures and the mismatch of contextual sensor information due to different spatial granularities capture. Our results provide initial indications of the adequacy of the proposed framework.
Analysis of a Multichannel Lightweight Identity Authentication Method

In view of the limited computing resources of the current Internet of things terminal equipment and the security requirements of identity authentication, this paper figures out a method to replace the original ID single-path authentication mode with ID multichannel authentication, and to replace the conventional cryptographic algorithm with lightweight cryptographic algorithm to ensure the security of identity authentication. This method can improve the accuracy and success rate of identification and reduce the encryption strength of each sub-ID by exchanging the transmission resources for computing resources. Also, this paper demonstrates the algorithm between the multiple of cryptographic strength reduction, the success rate of identity authentication and the way of ID grouping, which has great practical significance and value.
Assessing semantic similarity between concepts: A weighted‐feature‐based approach

Summary Traditional feature‐based semantic similarity (SS) approaches exploit the Wikipedia features in term of sets. They evaluate the similarity of concepts based on the commonalities among their feature sets. However, these feature‐based approaches treat all the features equally in similarity evaluation. Therefore, they ignore the underlying statistics of the features and consequently lose the essential semantic details about them. One solution is that each feature can be assigned a specific weight using its statistics. This weight will reflect the relative importance of a feature in similarity evaluation. Therefore, in this paper, based on two statistical models, ie, information content and T F I D F , we propose some hybrid semantic similarity measurement methods. Firstly, we propose some new methods called weighting functions to compute the weights of the features and feature sets in Wikipedia. Secondly, based on the weighting functions, we propose some new weighted feature‐based SS approaches for Wikipedia concepts. Thirdly, we evaluate the proposed methods on well‐known benchmarks for English, German, French, and Spanish languages. Finally, we compare the performance of our methods with the traditional feature‐based and some state‐of‐the‐art SS approaches. The experimental evaluation shows that our weighted methods perform better than the traditional feature‐based and some state‐of‐the‐art approaches in similarity evaluation.
Rapid perceptual processing in two- and three-dimensional prostate images

Radiologists can identify whether a radiograph is abnormal or normal at above chance levels in breast and lung images presented for half a second or less. This early perceptual processing has only been demonstrated in static two-dimensional images (e.g., mammograms). Can radiologists rapidly extract the “gestalt” from more complex imaging modalities? For example, prostate multiparametric magnetic resonance imaging (mpMRI) displays a series of images as a virtual stack and comprises multiple imaging sequences: anatomical information from the T2-weighted (T2W) sequence, functional information from diffusion-weighted imaging, and apparent diffusion coefficient sequences. We first tested rapid perceptual processing in static T2W images then among the two functional sequences. Finally, we examined whether this rapid radiological perception could be observed using T2W multislice imaging. Readers with experience in prostate mpMRI could detect and localize lesions in all sequences after viewing a 500-ms static image. Experienced prostate readers could also detect and localize lesions when viewing multislice image stacks presented as brief movies, with image slices presented at either 48, 96, or 144 ms. The ability to quickly extract the perceptual gestalt may be a general property of expert perception, even in complex imaging modalities.
Machine Learning-Based Classification of the Health State of Mice Colon in Cancer Study from Confocal Laser Endomicroscopy

Abstract In this article, we address the problem of the classification of the health state of the colon’s wall of mice, possibly injured by cancer with machine learning approaches. This problem is essential for translational research on cancer and is a priori challenging since the amount of data is usually limited in all preclinical studies for practical and ethical reasons. Three states considered including cancer, health, and inflammatory on tissues. Fully automated machine learning-based methods are proposed, including deep learning, transfer learning, and shallow learning with SVM. These methods addressed different training strategies corresponding to clinical questions such as the automatic clinical state prediction on unseen data using a pre-trained model, or in an alternative setting, real-time estimation of the clinical state of individual tissue samples during the examination. Experimental results show the best performance of 99.93% correct recognition rate obtained for the second strategy as well as the performance of 98.49% which were achieved for the more difficult first case.
Automatic Classifying Self-Admitted Technical Debt Using N-Gram IDF

Technical Debt (TD) introduces a quality problem and increases maintenance cost since it may require improvements in the future. Several studies show that it is possible to automatically detect TD from source code comments that developers intentionally created, so-called self-admitted technical debt (SATD). Those studies proposed to use binary classification technique to predict whether a comment shows SATD. However, SATD has different types (e.g. design SATD and requirement SATD). In this paper, we therefore propose an approach using N-gram Inverse Document Frequency (IDF) and employ a multi-class classification technique to build a model that can identify different types of SATD. From the empirical evaluation on 10 open-source projects, our approach outperforms alternative methods (e.g. using BOW and TF-IDF). Our approach also improves the prediction performance over the baseline benchmark by 33%.
Using Fuzzy Cognitive Maps in Analyzing and Studying International Economic and Political Stability

Challenging Issues of International Economic and Political Stability is considered. Various models describing them are briefly presented and their strong and weak points are considered. Today's world problem cannot be analysed and understood with present mathematical models. The approach of Fuzzy Cognitive Maps (FCM) is used to model the economic behaviour of a country. A new model is proposed for international affairs using FCM. Two cases are considered, and simulations are conducted. Obtained results are presented and discussed. Future research directions are presented.
Plausible deniability as a notion of privacy

This work is motivated by privacy concerns as a result of the growing rate of information exchange among components of complex cyber-physical systems, agents in a network, or actuators/sensors of a process. We propose a deterministic notion of privacy for a dynamical system, and completely characterize it for linear time-invariant dynamics. The proposed notion relies on a "plausible deniability" principle, which implies that a curious party will always be in doubt about the actual value of private variables of the system. In case privacy is guaranteed, we propose analytical metrics to assess the degree of privacy or privacy margin of the system. The size of the latter depends on the amount and structure of the information on the system which can be accessed by a curious party. We study the proposed notions and metrics for a class of distributed averaging algorithms.
Towards Auto-labelling Issue Reports for Pull-Based Software Development using Text Mining Approach

In recent years continuous integration has become an important practice for pull-based software development. It helps developers make contributions flexibly to an isolated copy of the project’s repository, create a pull request that represent such changes, and submit it to the project for evaluation. Despite its test centric approach, the ability of teams to scale up distributed development is limited by the manually demanding task of adding and updating labels to an issue report. Automating this step enables a project to produce quality results. We categorized issue reports to derive a specific classification of issue reports of pull-based development projects. Given that manual classification of issues is a tedious task and needs an expert domain knowledge, auto-classification is extremely needed. We considered different strategies and developed diverse text analytics techniques for automatic labelling issue reports of a GitHub’s project. We applied J48, SMO, Naïve Byes, and LibSVM algorithms so that different classification models are produced. Our results showed that SMO is able to label the issue reports with accuracy of 87.15 and F-measure of 0.869, when both the title and the body description of issues are considered in a balanced binary classification model.
Efficient Computation and Visualization of Multiple Density-Based Clustering Hierarchies

HDBSCAN*, a state-of-the-art density-based hierarchical clustering method, produces a hierarchical organization of clusters in a dataset w.r.t. a parameter mpts.While a small change in mpts typically leads to a small change in the clustering structure, choosing a "good" mpts value can be challenging: depending on the data distribution, a high or low mpts value may be more appropriate, and certain clusters may reveal themselves at different values.To explore results for a range of mpts values, one has to run HDBSCAN* for each value independently, which can be computationally impractical.In this paper, we propose an approach to efficiently compute all HDBSCAN* hierarchies for a range of mpts values by building upon results from computational geometry to replace HDBSCAN*'s complete graph with a smaller equivalent graph.An experimental evaluation shows that our approach can obtain over one hundred hierarchies for the computational cost equivalent to running HDBSCAN* about twice, which corresponds to a speedup of more than 60 times, compared to running HDBSCAN* independently that many times.We also propose a series of visualizations that allow users to analyze a collection of hierarchies for a range of mpts values, along with case studies that illustrate how these analyses are performed.
Review and Design of GPS-RFID Localization for Autonomous Vehicle Navigation

This paper discusses an alternative solution to the problem of autonomous vehicle navigation systems on the choice of technology in positioning the vehicle at a low cost but carries a high level of risk. The idea of using sonar and GPS in the navigation system can be an option, but the use of sonar is constrained by crosstalk problems between one sonar sensor and other sonar sensors. The use of Light Detection and Ranging (LIDARs) is also constrained by the high costs and stereo vision in LIDARs requiring powerful specific hardware to process camera information. Research studies on navigation systems using GPS-RFID based localization for visually impaired people become an inspiration for the idea of developing autonomous vehicle navigation using RFID-GPS fusion technology. The idea frame is to install RFID tags on the roadside that contain information on the location of routes to be taken by autonomous vehicles, reading RFID tags by Reader RFID with combined GPS module reading data will result in a more accurate location and is believed to be a reference for autonomous vehicle navigation systems. The initial results of several research studies on RFID testing for localization combined with GPS modules indicate that this idea is feasible.
Exposing Speech Resampling Manipulation by Local Texture Analysis on Spectrogram Images

Speech tampering may be aided by the resampling operation. It is significant for speech forensics to effectively detect the resampling; however, there are few studies on speech resampling detection. The purpose of this paper was therefore to provide a new training ideal to detect speech resampling. After resampling, the speech signal changes regularly in the time–frequency domain. In this paper, we theoretically analyzed the corresponding relationship between time domain and frequency domain of the resampled speech. Compared with the original speech, the bandwidth of resampled speech was stretched or compressed. First, the spectrogram was generated by short-time Fourier transform (STFT) from the speech. Then, the local binary pattern (LBP) operator was applied to model the statistical changes in the spectrogram and the LBP histogram was calculated as discriminative features. Finally, a support vector machine (SVM) was applied to classify the developed features to identify whether the speech had undergone the resampling operation. The experimental results show that the proposed method has superior detection performance in different resampling scenarios than some existing methods, and the proposed features are very robust against the commonly used compression post-processing operation. This highlights the promising potential of the proposed method as a speech resampling detection tool in practical forensics applications.
Trace Clustering Exploration for Detecting Sudden Drift: A Case Study in Logistic Process

Handling concept drift in process mining is one of the challenges tasks to construct the process model. Process model discovery, as the crucial perspective of process mining, should consider concept drift to discover the changes of a business process over time from execution trace. Many previous works have been dedicated to detect the drift using approaches from process discovery. As a matter of fact, there are many statistical parameters involving in the existing approaches that could be a barrier to construct the representative model. Unsupervised learning (i.e., trace clustering in process mining) could be the option to understand the changes of a process through learning the sequential patterns. However, there was a limited study on using trace clustering to detect the concept drift. This study attempts to explore the use of trace clustering techniques (i.e., profiles) to deal with change process discovery as the one of categorization of concept drift in process mining. The results of various trace clustering approaches were compared to the ground truth determined by both domain experts and existing concept drift approach. To verify the results, a dataset from logistics process was used. The case study in logistics process shows that partition-based clustering could be used to understand the concept drift.