Reviewerly/exHarmony huggingface dataset:
A Machine Learning method for relabeling arbitrary DICOM structure sets to TG-263 defined labels

To present a Machine Learning pipeline for automatically relabeling anatomical structure sets in the Digital Imaging and Communications in Medicine (DICOM) format to a standard nomenclature that will enable data abstraction for research and quality improvement. DICOM structure sets from approximately 1200 lung and prostate cancer patients across 40 treatment centers were used to build predictive models to automate the relabeling of clinically specified structure labels to standardized labels as defined by the American Association of Physics in Medicine’s (AAPM) Task Group 263 (TG-263). Volumetric bitmaps were created based on the delineated volumes and were combined with associated bony anatomy data to build feature vectors. Feature reduction was performed with singular value decomposition and the resulting vectors were used for predicting the label of each structure using five different classifier algorithms on the Apache Spark platform with 5-fold cross-validation. Undersampling methods were used to deal with underlying class imbalance that hindered the performance of classifiers. Experiments were performed on both a curated version of the data, which included only annotated structures, and the non-curated data that included all structures from the original treatment plans. Random Forest provided the highest accuracies with F 1 scores of 98.77 for lung and 95.06 for prostate on the curated data sets. Scores were lower with 95.67 for lung and 90.22 for prostate on the non-curated data sets, highlighting some of the challenges of classifying real clinical data. Including bony anatomy data and pooling information from all structures for the same patient both increased accuracies. In some cases, undersampling with k -Means clustering for class balancing improved classifier accuracy but in all experiments it significantly reduced run time compared to random undersampling. This work shows that structure sets can be relabeled using our approach with accuracies over 95% for many structure types when presented with curated data. Although accuracies dropped when using the full non-curated data sets, some structure types were still correctly labeled over 90% of the time. With similar results obtained on an external test data set, we can infer that the proposed models are likely to work on other clinical data sets. • The best F1 scores were 98.77 for lung and 95.06 for prostate. • Bony anatomy and using patient dependence improved classifier accuracy. • k-Means undersampling can improve classifier predictive performance and run time. • A high percentage of structure labels were not common between training and test data. • Classifying real world clinical data sets remains a challenging problem.
Can We Predict the Quality of Spectrum-based Fault Localization?

Fault localization and repair are time-consuming and tedious. There is a significant and growing need for automated techniques to support such tasks. Despite significant progress in this area, existing fault localization techniques are not widely applied in practice yet and their effectiveness varies greatly from case to case. Existing work suggests new algorithms and ideas as well as adjustments to the test suites to improve the effectiveness of automated fault localization. However, important questions remain open: Why is the effectiveness of these techniques so unpredictable? What are the factors that influence the effectiveness of fault localization? Can we accurately predict fault localization effectiveness? In this paper, we try to answer these questions by collecting 70 static, dynamic, test suite, and fault-related metrics that we hypothesize are related to effectiveness. Our analysis shows that a combination of only a few static, dynamic, and test metrics enables the construction of a prediction model with excellent discrimination power between levels of effectiveness (eight metrics yielding an AUC of .86; fifteen metrics yielding an AUC of.88). The model hence yields a practically useful confidence factor that can be used to assess the potential effectiveness of fault localization. Given that the metrics are the most influential metrics explaining the effectiveness of fault localization, they can also be used as a guide for corrective actions on code and test suites leading to more effective fault localization.
Regulated Kalman filter based training of an interval type-2 fuzzy system and its evaluation

Most industrial systems are non-linear and prone to different sources of uncertainties. Accordingly, data generated by these systems for their model development can contain noise and outliers, which consecutively affect the approximation accuracy of their models. Hence, there is a need to use robust approaches, such as fuzzy systems. The type-2 fuzzy system is characterized by a foot print of uncertainty (FOU), which enables it to handle the impreciseness in non-linear systems. In this paper, a strategy for optimizing the membership function (MF) parameters of an interval type-2 fuzzy system using the Kalman gain and error covariance of its constant consequent parameter is presented. In the proposed strategy, the MF parameters are not a part of the input to the extended Kalman filter (EKF) algorithm, which reduces the computational cost of optimization. The proposed strategy was used to identify a robot hand path, Mackey–Glass Chaotic Series, dynamic system, and the inverse kinematics of a 6-PSS parallel robot. The proposed strategy was compared with an interval type-2 fuzzy system having only the consequent parameters being optimized by the extended Kalman filter (KFT2FS). The performance characteristic considered was the root mean square error between the desired output and the model output. The comparison results showed that the proposed model outperformed the KFT2FS, with an improved performance of 85%, 15.7%, 11.25%, and 3.6% for the robot hand path, Mackey–Glass Chaotic Series, dynamic system, and 6-PSS parallel robot respectively and also 7.3%, 17.5%, and 37.1% for 20 dB, 15 dB, and 10 dB corrupted 6-PSS parallel robot data respectively.
DNNARA: A Deep Neural Network Accelerator using Residue Arithmetic and Integrated Photonics

Deep Neural Networks (DNNs) are currently used in many fields, including critical real-time applications. Due to its compute-intensive nature, speeding up DNNs has become an important topic in current research. We propose a hybrid opto-electronic computing architecture targeting the acceleration of DNNs based on the residue number system (RNS). In this novel architecture, we combine the use of Wavelength Division Multiplexing (WDM) and RNS for efficient execution. WDM is used to enable a high level of parallelism while reducing the number of optical components needed to decrease the area of the accelerator. Moreover, RNS is used to generate optical components with short optical critical paths. In addition to speed, this has the advantage of lowering the optical losses and reducing the need for high laser power. Our RNS compute modules use one-hot encoding and thus enable fast switching between the electrical and optical domains.
A Robust Blind Oblivious Video Watermarking Scheme Using Undecimated Discrete Wavelet Transform

Intellectual property rights and claiming ownership are two prime requirements of the digital video watermarking. The issues which researchers are interested in digital video watermarking applications lie in the creation of new algorithms to cater four requirements of making oblivious, robust, high-capacity, and secured watermarking. This work presents an improved video watermarking scheme based on the undecimated discrete wavelet transform (UDWT). The frames of cover video are divided into $$\text {8} \times \text {8}$$ blocks. Two AC coefficients are selected in each $$\text {8} \times \text {8}$$ block to insert the watermark bit. The process is applied on four bands of UDWT and the redundancy in this transform allowed to produce a video watermarking with large capacity. Due to the masking properties of human visual system of UDWT, the watermarking scheme is made oblivious. The experimental results prove that proposed video watermarking scheme is providing all the four requirements of watermarking that is security, oblivious, robustness, and capacity.
An adaptive process of reverse engineering from point clouds to CAD models

In the field of industrial design, most manufactured objects are designed using 3D CAD (Computer-Aided Design) model. However, the original design may sometimes be lost or not available. A reverse engineering approach is then required to generate a geometric CAD model from 3D point clouds obtained by scanning objects. In this paper, an adaptive process for automatic reconstruction of CAD model from point clouds is proposed. First, the approach extracts primitive shapes from point clouds by RANSAC algorithm, then it analyses deviations of points from the fitted primitive shapes by histograms. For point cloud patches segmented unreasonable, the approach updates parameters of segmentation according to the Gaussian noise and repeats the primitive shape detection process. After certain rounds of iteration, the approach can detect reasonable primitive shapes from point clouds. Then a rule for obtaining CAD models by primitive shape alignment is introduced. The proposed approach is evaluated on datasets provided by the AIM@SHAPE Shape Repository.
Detecting Anomalies in Encrypted Traffic via Deep Dictionary Learning

The widely used encryption of network traffic poses a great challenge to anomaly detection. Currently, the supervised and semi-supervised solutions suffer from the problems of noisy label of data, non-stationary traffic distribution, and huge resource consumption for offline training. To address this, in this paper, we present an unsupervised, robust, and online anomaly detection method for encrypted traffic by using deep dictionary learning, called D2LAD. D2LAD offers the potential to extract sequential features from raw encrypted traffic data with the help of an LSTM-based autoencoder. Then, according to the sequential features, D2LAD is able to explore the hidden normal patterns by iterative deep dictionary learning. Eventually, D2LAD can obtain the anomaly score of raw data by calculating its relevance to the deep dictionary. We implement a prototype of D2LAD and evaluate its effectiveness and performance by experiments using realistic datasets. The experimental results show that our method achieves high accuracy and low resource usage, and outperforms the representative state-of-the-art methods in real-world settings.
Road Traffic Counting and Analysis Using Video Processing

The vehicle counting system can detect and count the number of vehicles that are moving on roads at any particular time period accurately using video processing. Highways, expressways, and roads are becoming overcrowded with the increase of a large number of vehicles. Many of the government operations to find a solution for the limitless traffic have been continuously working on this kind of data retrieval on traffic counting. The requirement to detect and count the moving vehicle is getting very high. Moving vehicle detection, tracking, and counting are very critical for traffic flow monitoring, planning, and controlling. The video-based solution is easy to install and use and does not disturb traffic flow. By analyzing the traffic sequence on roads at every instance of timing with video processing, the proposed method can detect and count moving vehicles accurately using video processing. As of now operations and research on how to find, the data for the further analysis of traffic counting are going on in the government sector to implement solutions for controlling the heavy traffic.
Probabilistic homogeneity for document image segmentation

In this paper we propose a novel probabilistic framework for document segmentation exploiting human perceptual recognition of text regions from complicated layouts. In particular, we conceptualize text homogeneity as the Gestalt pattern displayed in text regions, characterized by proximately and symmetrically arranged units with similar morphological and texture features. We model this pattern in the local region of a connected component (CC) using an hierarchical formulation, which simulates a random walk-and-check on a graph encoding the neighborhood of the CC. The proposed formulation allows an effective computation of what we call the probabilistic local text homogeneity (PLTH) using a weighted summation of the weights of the graph, which are derived from a probabilistic description of the homogeneity between neighboring CCs and computed through Bayesian cue integration. The proposed PLTH enables a multi-aspect analysis, where various primitives such as geometrical configuration, morphological features, texture characterization and location priors are integrated in one computational probabilistic model. This enables an effective text and non-text classification of CCs preceding any grouping process, which is currently absent in document segmentation. Experimental results show that our segmentation method based on the proposed PLTH model improves upon the state-of-the-art.
ROAchain: Securing Route Origin Authorization With Blockchain for Inter-Domain Routing

The inter-domain routing with BGP is highly vulnerable to malicious attacks, due to the lack of a secure means of verifying authenticity and legitimacy of inter-domain routes. Resource Public Key Infrastructure (RPKI) is a new security infrastructure to prevent the most devastating prefix hijacks in BGP by maintaining a Route Origin Authorization (ROA) repository. However, RPKI is a centralized hierarchical architecture that may empower the centralized authorities to unilaterally revoke or compromise any IP prefixes under their control. To eliminate the risks of RPKI, we present ROAchain, a novel BGP security infrastructure based on blockchain. Different from RPKI, ROAchain is a decentralized architecture, in which each AS maintains a globally consistent and tamper-proof ROA repository, authenticating the legitimacy of route origin and preventing BGP prefix hijacks. To ensure the strong consistency, scalability, and security of ROAchain, a novel consensus algorithm is proposed, in which the credence value, collective signing, sharding, and a penalty mechanism are introduced. Moreover, a compatibility design is proposed without changing the current BGP protocol. Finally, ROAchain is implemented in Golang and validated on the Google Cloud.
Informative scene decomposition for crowd analysis, comparison and simulation guidance

Crowd simulation is a central topic in several fields including graphics. To achieve high-fidelity simulations, data has been increasingly relied upon for analysis and simulation guidance. However, the information in real-world data is often noisy, mixed and unstructured, making it difficult for effective analysis, therefore has not been fully utilized. With the fast-growing volume of crowd data, such a bottleneck needs to be addressed. In this paper, we propose a new framework which comprehensively tackles this problem. It centers at an unsupervised method for analysis. The method takes as input raw and noisy data with highly mixed multi-dimensional (space, time and dynamics) information, and automatically structure it by learning the correlations among these dimensions. The dimensions together with their correlations fully describe the scene semantics which consists of recurring activity patterns in a scene, manifested as space flows with temporal and dynamics profiles. The effectiveness and robustness of the analysis have been tested on datasets with great variations in volume, duration, environment and crowd dynamics. Based on the analysis, new methods for data visualization, simulation evaluation and simulation guidance are also proposed. Together, our framework establishes a highly automated pipeline from raw data to crowd analysis, comparison and simulation guidance. Extensive experiments and evaluations have been conducted to show the flexibility, versatility and intuitiveness of our framework.
subs2vec: Word embeddings from subtitles in 55 languages

Abstract This paper introduces a novel collection of word embeddings, numerical representations of lexical semantics, in 55 languages, trained on a large corpus of pseudo-conversational speech transcriptions from television shows and movies. The embeddings were trained on the OpenSubtitles corpus using the fastText implementation of the skipgram algorithm. Performance comparable with (and in some cases exceeding) embeddings trained on non-conversational (Wikipedia) text is reported on standard benchmark evaluation datasets. A novel evaluation method of particular relevance to psycholinguists is also introduced: prediction of experimental lexical norms in multiple languages. The models, as well as code for reproducing the models and all analyses reported in this paper (implemented as a user-friendly Python package), are freely available at: https://github.com/jvparidon/subs2vec .
Integrating statistical learning into cognitive science

Over the last two decades statistical learning (SL) has evolved into a key explanatory mechanism underlying the incidental learning of regularities across different domains of cognition, such as language, visual and auditory perception, and memory. Yet SL has mainly been investigated as an independent research area, separated from the primary study of the relevant cognitive domains. The aim of this special issue is to foster a bilateral integration of SL research with cognitive science: not only should domain-relevant evidence about the complexity of real-world input become more tightly integrated into SL research, but non-SL studies should also carefully consider the nature and range of statistical regularities that may affect learning and processing in a given domain. Four papers on reading in this volume demonstrate that such integration can lead to a better understanding of reading, while also revealing the complexity and abundance of different statistical patterns present in printed text. Moving beyond disciplinary boundaries has the promise to broaden the focus of SL research beyond simple artificial patterns, to examine the rich and subtle intricacies of real-world cognition. A final paper on the neurobiological underpinnings of SL and the consolidation of learned statistical regularities further illustrates what might be gained from a better integration of SL and memory research. We conclude by discussing possible directions for taking an integrative approach to SL forward.
Projection-Based Classification of Chemical Groups for Provenance Analysis of Archaeological Materials

In provenance analysis, identifying the origin of the archaeological artifacts plays a significant role. Usually, this problem is addressed by discovering natural groups in data measured with spectroscopic techniques. Then, principal component and classical partitioning cluster analysis are employed to reveal the groups that supposedly define the origin of the investigated artefacts. However, this work shows that maximizing the variance and searching for specific cluster structures can be misleading because it fails to discriminate clearly the different archeological sources. In contrast, the new methodology reveals several acknowledged geological sources present in the materials through the exploitation of emergence and swarm intelligence without prior assumptions about the data structures. A combination of unsupervised and semi-supervised machine learning and chemometric is applied on samples of Mesoamerican geological sources and obsidian artefacts collected from the archaeological site of Xalasco in Mexico. The analysis of the artifacts showed a preference of Xalasco inhabitants to local obsidian deposits. The results show that this approach, in terms of robustness, is suitable for handling unbiased quantitative spectral analysis of archaeological materials revealing the natural groups of archeological data.
The redistribution of thorium, uranium, potassium by magmatic and hydrothermal processes versus surface processes in the Saraya Batholith (Eastern Senegal): Insights from airborne radiometrics data and topographic roughness

Potassium (K), thorium (Th), and uranium (U) are incompatible elements, with various degrees of mobility with respect to hydrothermal alteration or weathering and are taken as tracers for magmatic, fluid-rock interactions and mineralization. These elements may be mapped from airborne radiometric surveys revealing their spatial distribution at the surface. Magmatic processes (partial melting, migration and crystallization of silicate melts), hydrothermal alteration, and superficial processes (in situ weathering, erosion and transport) are responsible for the distribution of these elements in the rock, regolith and soil. However, these processes do not operate at the same scales, and we postulate that they produce distinct spatial distributions of K, Th and U concentrations. In this study, we combined geostatistical approaches, such as analyses of frequency distributions and variograms with roughness and topographic mapping to decipher the respective role of magmatic and hydrothermal processes versus surface processes on the spatial distribution of K, Th and U concentrations at the surface of the Saraya Batholith (Eastern Senegal). We found that the distributions of K and Th are anisotropic and correlate to the different magmatic sub-units of the Batholith in the northern part of the Saraya Batholith, which is currently incised by a network of riverbeds, as revealed by the roughness map. In contrast, the surface of the southern part of the Saraya Batholith is dominated by chemical weathering and is characterized by depletion and reduced anisotropy of K and enrichment in Th with respect to the northern part. The K, Th and U concentrations in the southern part are therefore controlled by surface processes masking the primary (magmatic) signatures. These results motivate similar analyses of spatial patterns of K, Th and U concentrations in other contexts in support for geological mapping and mineral exploration.
ICT officials' opinion on deploying Open Source Learning Management System for teaching and learning in universities in a developing society

Information and Communication Technology specialists, working within universities play important roles in the deployment of educational technologies for teaching and learning. Given the centrality of these specialists and the woeful dearth of empirics on this subject-matter in Sub-Saharan Africa, this paper interrogates the perspectives of ICT specialists working within universities in Ghana, on the deployment of Moodle®/Sakai® Learning Management System (LMS) by universities in the country for teaching and learning and the challenges involved. Data collection entailed semi-structured interviews with twenty informants. Thematic analysis was used for data analysis. It emerged that there has been impressive formulation of e-learning policy, construction of computer laboratories, Staff Resource Centre, Electronic Support Unit in the Balm Library, installation of Internet facility, Learning Management System, Enterprise Solution Software, Library Solution Software to promote ICT-mediated teaching and learning in the face of barriers. The paper adds knowledge to the extant literature in the field, impacts practice and policy along the pathway for ensuring sustainable deployment of LMS in universities in Sub-Saharan Africa.