Reviewerly/exHarmony huggingface dataset:
On rational entailment for Propositional Typicality Logic

Propositional Typicality Logic (PTL) is a recently proposed logic, obtained by enriching classical propositional logic with a typicality operator capturing the most typical (alias normal or conventional) situations in which a given sentence holds. The semantics of PTL is in terms of ranked models as studied in the well-known KLM approach to preferential reasoning and therefore KLM-style rational consequence relations can be embedded in PTL. In spite of the non-monotonic features introduced by the semantics adopted for the typicality operator, the obvious Tarskian definition of entailment for PTL remains monotonic and is therefore not appropriate in many contexts. Our first important result is an impossibility theorem showing that a set of proposed postulates that at first all seem appropriate for a notion of entailment with regard to typicality cannot be satisfied simultaneously. Closer inspection reveals that this result is best interpreted as an argument for advocating the development of more than one type of PTL entailment. In the spirit of this interpretation, we investigate three different (semantic) versions of entailment for PTL, each one based on the definition of rational closure as introduced by Lehmann and Magidor for KLM-style conditionals, and constructed using different notions of minimality.
A Block-Based Quantum Image Scrambling for GNEQR

Image scrambling on classical computers has been widely studied, however, the corresponding quantum algorithms are rare. This paper proposes a block-based image scrambling scheme for the generalized model of novel enhanced quantum representation (GNEQR). It is not only suit for grayscale images and color images, but also can be used in 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> × 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> images (i.e., rectangular images). To realize the image scrambling method, we use geometric transformations and an operation of bitplane scrambling to perform position and pixel scrambling, respectively. Furthermore, the complexity of the implementation circuit of the proposed image scrambling is only O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) for a 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> × 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> image. Therefore, it shows our proposed quantum image scrambling is efficient. Then, the result analysis of simulation experiments demonstrates that our proposed image scrambling is effective. In addition, security analysis shows that the proposed scrambling algorithm is secure.
Using 360 VR video to improve the learning experience in veterinary medicine university degree

We present the results of the Quality of Experience (QoE) evaluation of 360 degree immersive video in university education.Fourth-year Veterinary Medicine students virtually attended some practical lessons which had been recorded in immersive 360 video format, covering topics of Surgical Pathology and Surgery related to horses.One hundred students participated in the experience.They evaluated it through an extensive questionnaire covering several QoE factors, including presence, audiovisual quality, satisfaction or cybersickness: 79% evaluated the experience as excellent or good, and they acknowledged an improvement of the learning process by the implementation of VR as didactic tool, and 91% reported that they would recommend it to other students.Female students consistently gave slightly better average scores than their male counterparts, although mostly within condence intervals.Strongest inter-gender differences appeared in active social presence dimensions, according to the Temple Presence Inventory.The study also evaluates the suitability of synthetic measurement protocols, such as the Distributed Reality Experience Questionnaire (DREQ) and Net Promoter Score (NPS).We show that NPS is a valid tool for QoE analysis, but that its clustering boundary values must be adapted to the speci cities of the experiment population.
Fast string matching for DNA sequences

In this paper we propose the Maximal Average Shift (MAS) algorithm that finds a pattern scan order that maximizes the average shift length. We also present two extensions of MAS: one improves the scan speed of MAS by using the scan result of the previous window, and the other improves the running time of MAS by using q-grams. These algorithms show better average performances in scan speed than previous string matching algorithms for DNA sequences.
Dynamic Recommendation of POI Sequence Responding to Historical Trajectory

Point-of-Interest (POI) recommendation is attracting the increasing attention of researchers because of the rapid development of Location-based Social Networks (LBSNs) in recent years. Differing from other recommenders, who only recommend the next POI, this research focuses on the successive POI sequence recommendation. A novel POI sequence recommendation framework, named Dynamic Recommendation of POI Sequence (DRPS), is proposed, which models the POI sequence recommendation as a Sequence-to-Sequence (Seq2Seq) learning task, that is, the input sequence is a historical trajectory, and the output sequence is exactly the POI sequence to be recommended. To solve this Seq2Seq problem, an effective architecture is designed based on the Deep Neural Network (DNN). Owing to the end-to-end workflow, DRPS can easily make dynamic POI sequence recommendations by allowing the input to change over time. In addition, two new metrics named Aligned Precision (AP) and Order-aware Sequence Precision (OSP) are proposed to evaluate the recommendation accuracy of a POI sequence, which considers not only the POI identity but also the visiting order. The experimental results show that the proposed method is effective for POI sequence recommendation tasks, and it significantly outperforms the baseline approaches like Additive Markov Chain, LORE and LSTM-Seq2Seq.
Multi-Sessions Mechanism for Decentralized Cash on Delivery System

To date, cash on delivery (COD) is one of the most popular payment methods in developing countries thanks to the blossom of customer-to-customer e-commerce.With the widespread of a very small business model and the Internet, online shopping has become part of people's daily activity.People browse for desirable products at the comfort of their homes and ask the online vendor that a shipper can deliver the merchandise at their doorstep.Then, COD allows customers to pay in cash when the product is delivered to their desired location.Since customers receive goods before making a payment, COD is, therefore, considered as a payment system.However, the crucial issue that previous research has not yet addressed is that their models only support single delivering session at a time.More precisely, if the current buyer is not available to receive the goods, the shipper has to wastefully wait for the complete payment and he/she cannot start shipping another merchandise.The tracking system seems to poorly handle this issue.In particular, we propose a multi-session mechanism, which consists of blockchain technology, smart contracts and hyperledger fabric platform to achieve distributed and transparent across delivering sessions in the decentralized markets.Our proposed mechanism ensure the efficiency of delivering process.The authors release our sources codes for further reproducibility and development.We conclude that the integration of multi-session mechanism and blockchain technology will cause significant efficiency across several disciplines.
One-Stage Shape Instantiation from a Single 2D Image to 3D Point Cloud

Shape instantiation which predicts the 3D shape of a dynamic target from one or more 2D images is important for real-time intra-operative navigation. Previously, a general shape instantiation framework was proposed with manual image segmentation to generate a 2D Statistical Shape Model (SSM) and with Kernel Partial Least Square Regression (KPLSR) to learn the relationship between the 2D and 3D SSM for 3D shape prediction. In this paper, the two-stage shape instantiation is improved to be one-stage. PointOutNet with 19 convolutional layers and three fully-connected layers is used as the network structure and Chamfer distance is used as the loss function to predict the 3D target point cloud from a single 2D image. With the proposed one-stage shape instantiation algorithm, a spontaneous image-to-point cloud training and inference can be achieved. A dataset from 27 Right Ventricle (RV) subjects, indicating 609 experiments, were used to validate the proposed one-stage shape instantiation algorithm. An average point cloud-to-point cloud (PC-to-PC) error of 1.72 mm has been achieved, which is comparable to the PLSR-based (1.42 mm) and KPLSR-based (1.31 mm) two-stage shape instantiation algorithm.
Transmission Line Abnormal Target Detection Based on Machine Learning Yolo V3

The power video surveillance system must be able to quickly detect abnormal targets of power lines, detect and alarm suspicious external intrusions, focusing on the detection of off-line operations of large construction vehicles such as cranes and excavators, so as to prevent the damage or failure of power lines caused by external intrusions of power lines. Based on machine learning Yolo V3 algorithm, this paper trains and learns the routine intrusion targets of power lines, realizes the detection of intrusion targets of power lines in real time, discovers abnormalities in time and protects the safety and normal operation of power lines.
Measurement and application of patient similarity in personalized predictive modeling based on electronic medical records

Abstract Background Conventional risk prediction techniques may not be the most suitable approach for personalized prediction for individual patients. Therefore, individualized predictive modeling based on similar patients has emerged. This study aimed to propose a comprehensive measurement of patient similarity using real-world electronic medical records data, and evaluate the effectiveness of the individualized prediction of a patient’s diabetes status based on the patient similarity. Results When using no more than 30% of the whole training sample, the personalized predictive models outperformed corresponding traditional models built on randomly selected training samples of the same size as the personalized models ( P &lt; 0.001 for all). With only the top 1000 (10%), 700 (7%) and 1400 (14%) similar samples, personalized random forest, k -nearest neighbor and logistic regression models reached the globally optimal performance with the area under the receiver-operating characteristic (ROC) curve of 0.90, 0.82 and 0.89, respectively. Conclusions The proposed patient similarity measurement was effective when developing personalized predictive models. The successful application of patient similarity in predicting a patient’s diabetes status provided useful references for diagnostic decision-making support by investigating the evidence on similar patients.
Data Summarization Using Clustering and Classification: Spectral Clustering Combined with k-Means Using NFPH

Clustering has been very helpful in knowledge discovery. Data miners are focused in creating quality clusters with reduced time complexity to get the most significant information. This paper aims to analyse existing techniques used in data mining for clustering and find ways to maximize accuracy of clustering. The purpose of our paper is to improve an existing clustering algorithm. This paper will introduce a novel algorithm by combining Spectral clustering with k-means with NFPH. The proposed system replaces the initialization method for cluster centroids in classical k-means algorithms which should solve some of the limitations of the k-means algorithm. We aim to select the most appropriate first centroid rather than selecting randomly. Test data sets from the medical domain which are available for research purposes will be used to train the model and an open source data mining application called WEKA is used for testing. From tests carried out on 10 different UCI data sets using the proposed solution, we found that the clustering error was reduced up to 2 percent while the processing time increased from 4~5 seconds. The increase in processing time is caused by the replacement of the initialization method of k-means. The proposed system reduced the clustering error of the spectral clustering algorithm. This system improved levels of accuracy but the processing time increased to 4 seconds.
A Heterogeneous Cloud Storage Platform With Uniform Data Distribution by Software-Defined Storage Technologies

Recently, the variety of cloud technologies and applications has been increasing significantly. With the popularity of cloud applications, the number of business and personal needs is also increasing rapidly. Meanwhile, the industrial development process with cloud technology costs so much that performance will be essential in employing the cloud infrastructure in a large-scale environment. In this paper, OpenStack is first implemented as a heterogeneous cloud storage environment to increase the availability and efficiency of the cloud system. Secondly, Ceph, HDFS, and Swift as a storage service are integrated into the environment based on a proportion-based file distribution mechanism. In this case, the sub-files are distributed to different storage services. Additionally, a user-friendly web interface is developed for the administrator and regular users. Finally, the performance analysis is presented to evaluate the proposed system.
Abnormal Situations Interpretation in Industry 4.0 using Stream Reasoning

With the coming era of Industry 4.0, more assets and machines in plants are equipped with sensors which collect big amount of data for effective on-line equipment condition monitoring. Monitoring equipment conditions can not only reduce unplanned downtime by early detection of relevant situations like anomalies but also avoid unnecessary routine maintenance. For the detection of these situations it is necessary to integrate distributed, heterogeneous data sources and data streams. In this context, semantic web technologies are increasingly considered as key technologies to improve data integration. However, they are mainly used for data that is assumed not to change very often in time. In order to tackle this issue, stream reasoning combines reasoning and stream processing methods. Such a combination enables the processing of dynamic and heterogeneous data continuously produced from a large number of sources and implementing real-time services. This paper presents an approach that uses stream reasoning to identify in real time certain situations that lead to potential failures. Early detection enables to choose the most appropriate decision to avoid the interruption of manufacturing processes. In order to achieve this, data collected from sensors are enriched with contextual information. The use of stream reasoning allows the integration of data from different data sources, with different underlying meanings, different temporal resolutions as well as the processing of these data in real time.
An Improved Artificial Bee Colony Algorithm based on Beetle Antennae Search

The use of the Artificial Bee Colony algorithm (ABC) for optimization has significantly grown in the last few years. This paper presents an improved ABC by combining the Beetle Antennae Search Algorithm (BAS). This algorithm(BAS-ABC) concentrates on selecting the direction of movement of an employed bee in the process of finding the food source. It can avoid the randomness of searching to develop the performance of original ABC. Inspired by the method of BAS algorithm, we imitate the searching and detecting behaviors of beetles to determine the direction of the solutions' evolution. The proposed algorithm can converge to the best solution faster than the original ABC. Several standard functions are tested and compared with original ABC and Particle Swarm optimization algorithm(PSO). Numerical results validate the better accuracy of our proposed algorithm.
Image identification method on high speed railway contact network based on YOLO v3 and SENet

In order to timely discover the missing spare nut on the U-shaped hoop of the high-speed railway contact network insulator area and add new nut, reduce the risk of failure of insulator connection, research on U-shaped hoop missing spare nut image recognition methods on high speed railway contact network based on YOLO v3 and SENet was carried out. The identification method is divided into two stages: the first stage detects and locates the U-shaped hoop of the insulator area in the high-speed railway contact network image through YOLO v3, and then the positioned U-shaped hoop is taken out from the original image and scaled to a size of 224×224 In the second stage, the scaled U-shaped hoop image is classified and identified by SENet, and the missing spare nut image of the U-shaped hoop is obtained. To enhance the visualization of the image, class activation map is used to locate the U-shaped hoop image. The method solves the problem of high resource occupancy rate for small object recognition on high-resolution images. The recognition result based on the U-shaped hoop missing spare nut scene of Xinjiang high speed railway contact network showed that the accuracy of the identification method reached 88.24%, which could be applied to U-shaped hoop missing spare nut identification on high speed railway contact network.
ML4IoT: A Framework to Orchestrate Machine Learning Workflows on Internet of Things Data

Internet of Things (IoT) applications generate vast amounts of real-time data.Temporal analysis of these data series to discover behavioural patterns may lead to qualified knowledge affecting a broad range of industries.Hence, the use of machine learning (ML) algorithms over IoT data has the potential to improve safety, economy, and performance in critical processes.However, creating ML workflows at scale is a challenging task that depends upon both production and specialized skills.Such tasks require investigation, understanding, selection, and implementation of specific ML workflows, which often lead to bottlenecks, production issues, and code management complexity and even then may not have a final desirable outcome.This paper proposes the Machine Learning Framework for IoT data (ML4IoT), which is designed to orchestrate ML workflows, particularly on large volumes of data series.The ML4IoT framework enables the implementation of several types of ML models, each one with a different workflow.These models can be easily configured and used through a simple pipeline.ML4IoT has been designed to use container-based components to enable training and deployment of various ML models in parallel.The results obtained suggest that the proposed framework can manage real-world IoT heterogeneous data by providing elasticity, robustness, and performance.
Radiomic Features for Medical Images Tamper Detection by Equivalence Checking

Digital medical images are very easy to be modified for illegal purposes. An attacker may perform this act in order to stop a political candidate, sabotage research, commit insurance fraud, perform an act of terrorism, or even commit murder. Between the machine that performs medical scans and the radiologist monitor, medical images pass through different devices: in this chain an attacker can perform its malicious action. In this paper we propose a method aimed to avoid medical images modifications by means of equivalence checking. Magnetic images are represented as finite state automata and equivalence checking is exploited to check whether the medical resource have been subject to illegal modifications.
thurstonianIRT: Thurstonian IRT Models in R

In the human sciences, we often aim to measure certain person characteristics that are latent, that is, not directly observable.Examples for these latents characteristics are personality traits such as extraversion or emotional stability as well as performance related traits such as intelligence or creativity.When measuring personality traits, we mostly rely on self-reported measures based on rating scales where people answer how much they agree on an item.This format is easily manipulatable, for example, if participants know which answers are desirable.Thus its application in high stakes situations (e.g., in personnel selection) is problematic, as participants may be motivated to answer dishonestly (Brown & Maydeu-Olivares, 2011).
Stereoscopic Rendering via Goggles Elicits Higher Functional Connectivity During Virtual Reality Gaming

Virtual reality (VR) simulates real-world scenarios by creating a sense of presence in its users. Such immersive scenarios lead to behavior that is more similar to that displayed in real world settings, which may facilitate the transfer of knowledge and skills acquired in VR to similar real world situations. VR has already been used in education, psychotherapy, rehabilitation and it comes as an appealing choice for training intervention purposes. The aim of the present study was to investigate to what extent VR technology for games presented via goggles can be used in a magnetic resonance imaging scanner (MRI), addressing the question of whether brain connectivity differs between VR stimulation via goggles and a presentation from a screen via mirror projection. Moreover, we wanted to investigate whether stereoscopic goggle stimulation, where both eyes receive different visual input, would elicit stronger brain connectivity than a stimulation in which both eyes receive the same visual input (monoscopic). To our knowledge, there is no previous research using games and functional connectivity (FC) in MRI to address this question. Multiple analyses approaches were taken so that different aspects of brain connectivity could be covered: fractional low-frequency fluctuation, independent component analysis (ICA), seed-based FC (SeedFC) and graph analysis. In goggle presentation (mono and stereoscopic) as contrasted to screen, we found differences in brain activation in left cerebellum and postcentral gyrus as well as differences in connectivity in the visual cortex and frontal inferior cortex [when focusing on the visual and default mode network (DMN)]. When considering connectivity in specific areas of interest, we found higher connectivity between bilateral superior frontal cortex and the temporal lobe, as well as bilateral inferior parietal cortex with right calcarine and right lingual cortex. Furthermore, we found superior frontal cortex and insula/putamen to be more strongly connected in goggle stereoscopic vs. goggle monoscopic, in line with our hypothesis. We assume that the condition that elicits higher brain connectivity values should be most suited for long-term brain training interventions given that, extended training under these conditions could permanently improve brain connectivity on a functional as well as on a structural level.
A Deep Neural Network Model for Joint Entity and Relation Extraction

Joint extraction of entities and their relations from the text is an essential issue in automatic knowledge graph construction, which is also known as the joint extraction of relational triplets. The relational triplets in sentence are complicated, multiple and different relational triplets may have overlaps, which is commonly seen in reality. However, multiple pairs of triplets cannot be efficiently extracted in most of the previous works. To mitigate this problem, we propose a deep neural network model based on the sequence-to-sequence learning, namely, the hybrid dual pointer networks (HDP), which extracts multiple pairs of triplets from the given sentence by generating the hybrid dual pointer sequence. In experiments, we tested our model using the New York Times (NYT) public dataset. The experimental results demonstrated that our model outperformed the state-of-the-art work, and achieved a 17.1% improvement on the F1 values.