Reviewerly/exHarmony huggingface dataset:
Virtual mono-layered continuous containers for vehicle detection applications in intelligent transportation systems

The increase in vehicular traffic demands an effective and well-organized solution to manage and regulate traffic. For recent advances in Intelligent Transportation Systems (ITS), the video-based traffic monitoring system is the most popular and practical solution. The major component of such systems is the vehicle detection process. The accuracy of the existing detection approaches depends on the effectiveness of the image processing technique used, but it should be noted that it depends primarily on section of the scene in the video being processed. This prerequisite task of selecting the significant area of video is ignored which can elevate the accuracy of detection to a different level. This paper proposes a detection framework specific to vehicle detection systems known as Virtual Mono-Layered Continuous Containers (VMC2), which simplifies the entire detection process. The performance of the proposed framework is analyzed using existing popular methods of detection based on background subtraction such as running average; Gaussian mixture (MOG) and K-Nearest Neighbor (KNN). The results in terms of computational complexity and storage space are presented. From the results it is seen that the proposed framework significantly reduces the false positives leading to a highly accurate vehicle detection system.
Pre-Processing Algorithms on Digital X-ray Mammograms

Breast cancer is one of the most typical sorts of cancer worldwide, and the most frequent cancer in women. Mammography remains the most effective tool for the early detection of breast cancer, as well as the systems of computer-aided diagnosis (CAD) is typically used as a second opinion by the radiologists. These CAD systems based on mammography images for automatic diagnosis breast cancer. Generally, the mammography images contain some noises and additional unnecessary objects, Such as: artifacts, identification labels, etc. These objects can disrupt the diagnostic process partially or completely. Therefore, a technique of preparing these images before using it is mandatory. So, in this article, we propose a method for automatic preprocessing image. Our method consists of five steps. Firstly suppressed the additional unnecessary objects, next by removing the additional black background, third find the orientation of mammograms, fourth segmented the pectoral muscle, and we end by contrast enhancement. The proposed method is tested on DDSM (Digital database for Screening Mammography) database, consisting of 10480 mammograms, and Mini-MIAS (Mammogram Image Analysis Society, UK) database, consisting of 322 mammograms. The obtained results confirm the efficacy of the proposed method.
Gender and Age Classification Based on Human Features to Detect Illicit Activity in Suspicious Sites

We developed a method to recognize gender and age groups using facial features and upper body proportions. We used scraping images from sites that were classified as suspicious to perform illicit activities with people under the consent age. In these places, the facial features were often blurred, and the classification had to be made with only information of upper body shape and proportions. The proposed method uses Haar-like features for the extraction of characteristics. We obtained a shape description for the face and upper body, and then we extract features and evaluate them according to the spatial relationships and angles between these characteristics. Lastly, we developed SVM algorithms and apply them to each feature to classify classes: male or female and to distinguish people within the group of 14 years old or less. For comparison purposes, we also use Convolutional Neural Networks to categorize the mentioned classes. The attained results were satisfactory enough after five iterations. However, CNN had a much larger execution time than the SVM algorithm. Also, CNN did not perform as well when facial features were not available. Therefore, we considered CNN not suitable for monitoring a large number of sites in the context of our research. Thus, we suggest the use of the procedure with Haar-like features and SVM classifiers.
Is your document novel? Let attention guide you. An attention-based model for document-level novelty detection

Abstract Detecting, whether a document contains sufficient new information to be deemed as novel , is of immense significance in this age of data duplication. Existing techniques for document-level novelty detection mostly perform at the lexical level and are unable to address the semantic-level redundancy. These techniques usually rely on handcrafted features extracted from the documents in a rule-based or traditional feature-based machine learning setup. Here, we present an effective approach based on neural attention mechanism to detect document-level novelty without any manual feature engineering. We contend that the simple alignment of texts between the source and target document(s) could identify the state of novelty of a target document. Our deep neural architecture elicits inference knowledge from a large-scale natural language inference dataset, which proves crucial to the novelty detection task. Our approach is effective and outperforms the standard baselines and recent work on document-level novelty detection by a margin of $\sim$ 3% in terms of accuracy.
Aircraft parameter estimation using ELM network

Purpose The purpose of this paper is to investigate the estimation methodology with a highly generalized cost-effective single hidden layer neural network. Design/methodology/approach The aerodynamic parameter estimation is a challenging research area of aircraft system identification, which finds various applications such as flight control law design and flight simulators. With the availability of the large database, the data-driven methods have gained attention, which is primarily based on the nonlinear function approximation using artificial neural networks. A novel single hidden layer feed-forward neural network (FFNN) known as extreme learning machine (ELM), which overcomes the issues such as learning rate, number of epochs, local minima, generalization performance and computational cost, as encountered in the conventional gradient learning-based FFNN has been used for the nonlinear modeling of the aerodynamic forces and moments. A mathematical formulation based on the partial differentiation is proposed to estimate the aerodynamic parameters. Findings The real flight data of longitudinal and lateral-directional motion have been considered to estimate their respective aerodynamic parameters using the proposed methodology. The efficacy of the estimates is verified with the results obtained through the conventional parameter estimation methods such as the equation-error method and filter-error method. Originality/value The present study is an outcome of the research conducted on ELM for the estimation of aerodynamic parameters from the real flight data. The proposed method is capable to estimate the parameters in the presence of noise.
Self-attention-based Group Recommendation

With the advent of the era of big data, the traditional recommendation system mainly for a single user, but with the society and the rapid development of e-commerce, more and more people participate in activities together, in the form of multiple users and groups of recommendation system recommended service object by a single user extensions for group members, has become a hot topic in the current society. In view of the low accuracy rate of group recommendation and the inconsistent fusion strategy among group members, the traditional solution method is matrix decomposition. MF USES a simple and fixed inner product to estimate the complex user-project interaction in low-dimensional potential space, which will cause the problem of limitation. Therefore, a group recommendation algorithm combining self-attention and NCF to solve the problem of group preference fusion is proposed. We use neural network to learn the group recommendation of fusion strategy through self-attention, and further integrate the user-project interaction improvement group recommendation through NCF model. The self-attention mechanism provided in this paper was verified on qunar and CAMRA2011 data sets. Compared with other common fusion strategies, the overall average performance of the proposed mechanism in NDCG and HR was improved.
PEARL: Probabilistic Exact Adaptive Random Forest with Lossy Counting for Data Streams

In order to adapt random forests to the dynamic nature of data streams, the state-of-the-art technique discards trained trees and grows new trees when concept drifts are detected. This is particularly wasteful when recurrent patterns exist. In this work, we introduce a novel framework called PEARL, which uses both an exact technique and a probabilistic graphical model with Lossy Counting, to replace drifted trees with relevant trees built from the past. The exact technique utilizes pattern matching to find the set of drifted trees, that co-occurred in predictions in the past. Meanwhile, a probabilistic graphical model is being built to capture the tree replacements among recurrent concept drifts. Once the graphical model becomes stable, it replaces the exact technique and finds relevant trees in a probabilistic fashion. Further, Lossy Counting is applied to the graphical model which brings an added theoretical guarantee for both error rate and space complexity. We empirically show our technique outperforms baselines in terms of cumulative accuracy on both synthetic and real-world datasets.
Removal of Cu (II) from Industrial Wastewater Using Mechanically Activated Serpentinite

We investigate with this study the effectiveness of mechanically activated serpentinite in capturing Cu (II) from the multi-constituent acidic wastewater of the pit lakes of the Agios Philippos mine (Greece), proposing specific areas with serpentinites suitable for such environmental applications. For this purpose ultramafic rock samples that are characterized by variable degrees of serpentinization from ophiolitic outcrops exposed in the regions of Veria-Naousa and Edessa have been examined regarding their capacity to remove the toxic load of Cu (II) from wastewater after having been mechanically activated through a Los Angeles (LA) machine (500, 1000 and 1500 revolutions). The more serpentinized and mechanically activated samples, as they have been characterized after a combination of various mineralogical, petrographic, geochemical analyses as well as after different stresses of abrasion and attrition, seem to be more effective in Cu removal than the less serpentinized ones. Selective removal of Cu (II) in the wroewolfeite phase was obtained by using the mechanically activated highly serpentinized ultramafic rocks. Furthermore, areas with highly serpentinized ultramafic rocks defined after petrographic mapping, using GIS method, which can potentially be used as filters for the effective Cu (II) removal from industrial wastewater are suggested.
Using Complexity-Identical Human- and Machine-Directed Utterances to Investigate Addressee Detection for Spoken Dialogue Systems

Human-machine addressee detection (H-M AD) is a modern paralinguistics and dialogue challenge that arises in multiparty conversations between several people and a spoken dialogue system (SDS) since the users may also talk to each other and even to themselves while interacting with the system. The SDS is supposed to determine whether it is being addressed or not. All existing studies on acoustic H-M AD were conducted on corpora designed in such a way that a human addressee and a machine played different dialogue roles. This peculiarity influences speakers’ behaviour and increases vocal differences between human- and machine-directed utterances. In the present study, we consider the Restaurant Booking Corpus (RBC) that consists of complexity-identical human- and machine-directed phone calls and allows us to eliminate most of the factors influencing speakers’ behaviour implicitly. The only remaining factor is the speakers’ explicit awareness of their interlocutor (technical system or human being). Although complexity-identical H-M AD is essentially more challenging than the classical one, we managed to achieve significant improvements using data augmentation (unweighted average recall (UAR) = 0.628) over native listeners (UAR = 0.596) and a baseline classifier presented by the RBC developers (UAR = 0.539).
First application of the super-resolution imaging technique using a Compton camera

In medical imaging, precise and reliable images are very important. However, the quality of medical images is sometimes limited by low-event statistics owing to the low sensitivity of the detectors commonly used in radiology. On the other hand, long exposure to radiation and long inspection duration can become a burden for patients. In this paper, we propose a method for generating high-quality images of gamma ray sources from low statistic data by using machine learning methods based on dictionary learning and sparse coding. As the first application, we generated a high-quality image of 137Cs, which emits 662-keV gamma rays, from low-event statistics measured using a Compton camera. We simulated with Geant4 various geometries of the gamma-ray source (137Cs; 662 keV) as measured with a Compton camera by Geant4. Then, complete sets of low-resolution and high-resolution dictionaries were prepared. We generated super-resolution images from low-resolution test images obtained from actual measurements. The convergence of the gamma-ray images was similar for both the ground truth and predicted images, as supported by the improvements in the structural similarity (SSIM), peak signal-to-noise (PSNR) ratio, and root mean square error (RMSE) in the corresponding images. We also discuss future plans to use the super-resolution technique for visualizing radium chloride (223RaCl2) in the patient’s body, which will make it possible to achieve in-vivo imaging of alpha-particle internal therapy for the first time.
Vanishing point detection using the teaching learning‐based optimisation algorithm

Within the computer vision field, estimating image vanishing points has many applications regarding robotic navigation, camera calibration, image understanding, visual measurement, 3D reconstruction, among others. Different methods for detecting vanishing points relies on accumulator space techniques, while others employ a heuristic approach such as RANSAC. Nevertheless, these types of methods suffer from low accuracy or high computational cost. To explore a different technique, this paper focuses on improving the efficiency of the metaheuristic search for vanishing points by using a recently proposed population-based method: The Teaching Learning Based Optimisation algorithm (TLBO). The TLBO algorithm is a metaheuristic technique inspired by the teaching–learning process. In our method, the TLBO algorithm is used after a line segment detection, to cluster line segments according to their more optimal vanishing point. Thus, our algorithm detects both orthogonal and nonorthogonal vanishing points in real images. To corroborate the performance of our proposed algorithm, different comparison and tests with other approaches were carried out. The results validate the accuracy and efficiency of our proposed method. Our approach had an average computational time of1.42 seconds and obtained a cumulative focal length error of 1 pixel, and cumulative angular error of 0.1°.
Robotic Surveillance Based on the Meeting Time of Random Walks

This article analyzes the meeting time between a pair of pursuer and evader performing random walks on digraphs. The existing bounds on the meeting time usually work only for certain classes of walks and cannot be used to formulate optimization problems and design robotic strategies. First, by analyzing multiple random walks on a common graph as a single random walk on the Kronecker product graph, we provide the first closed-form expression for the expected meeting time in terms of the transition matrices of the moving agents. This novel expression leads to necessary and sufficient conditions for the meeting time to be finite and to insightful graph-theoretic interpretations. Second, based on the closed-form expression, we set up and study the minimization problem for the expected capture time for a pursuer/evader pair. We report theoretical and numerical results on basic case studies to show the effectiveness of the design.
Blockchain and the Future of Open Innovation Intermediaries: The Case of Crowdsourcing Platforms

There is now a consensus on the role of open innovation intermediaries, especially in relation to the transaction costs associated with open and collaborative innovation. On the other hand, since its “invention” in 2008, blockchain has been given considerable attention across various fields. A common view is that by drastically reducing transaction costs, blockchain could suppress the need of middlemen and intermediaries. With this in mind, the aim of this paper is to assess the potential impact of blockchain on the role of open innovation intermediaries, with a particular focus on crowdsourcing platforms. Following Schenk et al. (2018), the role of crowdsourcing platforms is considered along three complementary streams of literature: transaction costs theory, resource-based theory, and the theory of two-sided markets. Through its registry and smart-contract applications (which are yet to be characterized precisely), we argue that if vastly implemented, blockchain will significantly reduce the possibility of opportunistic behavior, as well as the associated transaction costs. This could in turn lead to a reconsideration of the role of open innovation intermediaries and platforms. But the role of these platforms also includes the matchmaking between seekers and solvers (which involves network effects) and the provision of resources and competences required along the open innovation process. The discussion proposed in this chapter suggests that these roles should not be altered by blockchain (at least in a foreseeable future).
A Holistic Approach for Automatic Deep Understanding and Protection of Technical Documents

A Technical Document (TD) is mainly composed by a set of modalities appropriately structured and associated. These modalities could be NL-text, block diagrams, formulas, tables, graphics, pictures etc. A deep understanding of a TD will be based on the synergistic understanding and associations of these modalities. This paper offers a novel methodology for the implementation of a holistic approach for deep understanding of technical documents by understanding and associating these modalities. This approach is based on the homogeneous expression (mapping) of the technical document modalities into the same medium, which in this case is the Stochastic Petri-nets (SPN). Then, these modalities are associated to each other generating new knowledge about the technical document topic and a SPN simulator is created to offer additional information about the functional behavior of the system described in the document. Some results from our studies are provided to prove the overall concept.
Comparing Deep Learning Models for Multi-cell Classification in Liquid- based Cervical Cytology Image.

Liquid-based cytology (LBC) is a reliable automated technique for the screening of Papanicolaou (Pap) smear data. It is an effective technique for collecting a majority of the cervical cells and aiding cytopathologists in locating abnormal cells. Most methods published in the research literature rely on accurate cell segmentation as a prior, which remains challenging due to a variety of factors, e.g., stain consistency, presence of clustered cells, etc. We propose a method for automatic classification of cervical slide images through generation of labeled cervical patch data and extracting deep hierarchical features by fine-tuning convolution neural networks, as well as a novel graph-based cell detection approach for cellular level evaluation. The results show that the proposed pipeline can classify images of both single cell and overlapping cells. The VGG-19 model is found to be the best at classifying the cervical cytology patch data with 95 % accuracy under precision-recall curve.
Trends and best practices in health care cybersecurity insurance policy

Abstract Health care organizations are a major target for cyberattacks. This is primarily due to their peculiar vulnerabilities and attractiveness to nefarious cyber actors. Data breaches from these attacks present a significant threat to the viability of health care organizations, ranging from financial losses to compromised patient safety. Cybersecurity insurance has become an essential tool for mitigating financial liabilities that may arise from breaches for many organizations. This paper reviews the current state of cybersecurity insurance adoption in the health care sector. It highlights best practices in cybersecurity insurance policy for health care organizations and recommends future directions to strengthen cybersecurity and improve cybersecurity insurance.
Security Aware Data Transaction Using Optimized Blowfish Algorithm in Cloud Environment

Cloud computing has become a growing technique due to its on-demand service and scalable features. Most of the applications of the cloud today are in data storage. Data protection and privacy have thus become a major concern. There are huge risks of storing transaction data on third-party hosts. Service providers need to make right investments in proving and ensuring appropriate security over time. Malicious users can exploit weak spots in the data protection model to increase unauthorized access to data. To overcome the problem, in this paper, security-aware data transaction on the cloud using the blowfish algorithm (BA) is proposed. To improve the security of the system, initially, the proposed system verifies the authentication of the user. After the authentication process, the uploaded data are initially split using a pattern-matching algorithm. Then, the separated data are encrypted with the help of BA. Finally, the encrypted data are stored in the cloud at an optimal location. This method is more secure as the data are column-separated and optimized in the cloud, so it is difficult to attack. This method is highly guaranteed because the user cannot recover the document without authentication checking. The performance of the proposed approach is analyzed in terms of different metrics, namely, memory, encryption and decryption time. The experimental results clearly say that the proposed method took minimum encryption time compared with the existing approach.