Reviewerly/exHarmony huggingface dataset:
Learning to Explore Saliency for Stereoscopic Videos Via Component-Based Interaction

In this paper, we devise a saliency prediction model for stereoscopic videos that learns to explore saliency inspired by the component-based interactions including spatial, temporal, as well as depth cues. The model first takes advantage of specific structure of 3D residual network (3D-ResNet) to model the saliency driven by spatio-temporal coherence from consecutive frames. Subsequently, the saliency inferred by implicit-depth is automatically derived based on the displacement correlation between left and right views by leveraging a deep convolutional network (ConvNet). Finally, a component-wise refinement network is devised to produce final saliency maps over time by aggregating saliency distributions obtained from multiple components. In order to further facilitate research towards stereoscopic video saliency, we create a new dataset including 175 stereoscopic video sequences with diverse content, as well as their dense eye fixation annotations. Extensive experiments support that our proposed model can achieve superior performance compared to the state-of-the-art methods on all publicly available eye fixation datasets.
On q-ary Codes with Two Distances d and d + 1

We consider q-ary block codes with exactly two distances: d and d + 1. Several constructions of such codes are given. In the linear case, we show that all codes can be obtained by a simple modification of linear equidistant codes. Upper bounds for the maximum cardinality of such codes are derived. Tables of lower and upper bounds for small q and n are presented.
Construction and Optimization of Fuzzy Rule-Based Classifier with a Swarm Intelligent Algorithm

In this paper, we propose a particle swarm optimization method incorporating quantum qubit operation to construct and optimize fuzzy rule-based classifiers. The proposed algorithm, denoted as QiQPSO, is inspired by the quantum computing principles. It employs quantum rotation gates to update the probability of each qubit with the corresponding quantum angle updating according to the update equation of the quantum-behaved particle swarm optimization (QPSO). After description of the principle of QiQPSO, we show how to apply QiQPSO to establish a fuzzy classifier through two procedures. The QiQPSO algorithm is first used to construct the initial fuzzy classification system based on the sample data and the grid method of partitioning the feature space, and then the fuzzy rule base of the initial fuzzy classifier is optimized further by QiQPSO in order to reduce the number of the fuzzy rules and thus improve its interpretability. In order to verify the effectiveness of the proposed method, QiQPSO is tested on various real-world classification problems. The experimental results show that the QiQPSO is able to effectively select feature variables and fuzzy rules of the fuzzy classifiers with high classification accuracies. The performance comparison with other methods also shows that the fuzzy classifier optimized by QiQPSO has higher interpretability as well as comparable or even better classification accuracies.
Lower Density Selection Schemes via Small Universal Hitting Sets with Short Remaining Path Length

Universal hitting sets (UHS) are sets of words that are unavoidable: every long enough sequence is hit by the set (i.e., it contains a word from the set). There is a tight relationship between UHS and minimizer schemes, where minimizer schemes with low density (i.e., efficient schemes) correspond to UHS of small size. Local schemes are a generalization of minimizer schemes that can be used as replacement for minimizer scheme with the possibility of being much more efficient. We establish the link between efficient local schemes and the minimum length of a string that must be hit by a UHS. We give bounds for the remaining path length of the Mykkeltveit UHS. In addition, we create a local scheme with the lowest known density that is only a log factor away from the theoretical lower bound.
Towards Reliable ARDS Clinical Decision Support: ARDS Patient Analytics with Free-text and Structured EMR Data.

In this work, we utilize a combination of free-text and structured data to build Acute Respiratory Distress Syndrome(ARDS) prediction models and ARDS phenotype clusters. We derived 'Patient Context Vectors' representing patientspecific contextual ARDS risk factors, utilizing deep-learning techniques on ICD and free-text clinical notes data. The Patient Context Vectors were combined with structured data from the first 24 hours of admission, such as vital signs and lab results, to build an ARDS patient prediction model and an ARDS patient mortality prediction model achieving AUC of 90.16 and 81.01 respectively. The ability of Patient Context Vectors to summarize patients' medical history and current conditions is also demonstrated by the automatic clustering of ARDS patients into clinically meaningful phenotypes based on comorbidities, patient history, and presenting conditions. To our knowledge, this is the first study to successfully combine free-text and structured data, without any manual patient risk factor curation, to build real-time ARDS prediction models.
Witness Indistinguishability for Any Single-Round Argument with Applications to Access Control

Consider an access policy for some resource which only allows access to users of the system who own a certain set of attributes. Specifically, we consider the case where such an access structure is defined by some monotone function $$f:\{0,1\}^N\rightarrow \{0,1\}$$ , belonging to some class of function $$F$$ (e.g. conjunctions, space bounded computation), where N is the number of possible attributes. In this work we show that any succinct single-round delegation scheme for the function class $$F$$ can be converted into a succinct single-round private access control protocol. That is, a verifier can be convinced that an approved user (i.e. one which holds an approved set of attributes) is accessing the system, without learning any additional information about the user or the set of attributes. As a main tool of independent interest, we show that assuming a quasi-polynomially secure two-message oblivious transfer scheme with statistical sender privacy (which can be based on quasi-polynomial hardness of the DDH, QR, DCR or LWE assumptions), we can convert any single-round protocol into a witness indistinguishable one, with similar communication complexity.
Beyond top‐<i>N</i> accuracy indicator: a comprehensive evaluation indicator of CNN models in image classification

Nowadays, a large number of deep convolutional neural network (CNN) models are applied to image classification tasks. However, the authors find that the most widely used evaluation indicator, the Top‐ N Accuracy indicator, cannot discriminate these models effectively. In this study, they propose a new indicator called Maximum‐Spanning‐Confusion‐Tree indicator to solve this problem. The Maximum‐Spanning‐Confusion‐Tree indicator is computed based on the hierarchical structure of the Maximum Spanning Confusion Tree of the deep CNN model on the dataset and reflect the ability of deep CNN models to discriminate confused categories in the dataset. The hierarchical structure of the Maximum Spanning Confusion Tree can reveal the confused category set of one selected category in the dataset efficiently and flexibly. Experiments show that they can discriminate ten different deep CNN models more accurately with the Maximum Spanning Confusion Tree indicator than the Top‐ N Accuracy indicator and the Maximum Spanning Confusion Tree intuitively shows the distribution of confused category sets in the dataset so they can find out the weakness of deep CNN models effectively.
Digitalization of Educational Processes in Universities: Achievements and Problems

Digitalization of the global economic processes has had a great impact on the digitalization of higher education.The idea of developing this area of educational activity is supported by many researchers who speak about the possibilities of expanding 24/7 learning technologies for the digital generation, thereby increasing the competitiveness of the university.The purpose of this article is to analyze the pros and cons of organizing the higher education process using digital technologies.The methodological basis of the research were the theories of management, consumer behavior and sociology.Based on structural and logical analysis and deduction, the positive and problematic aspects of implementing distance learning in universities were identified.The article presents a critical review of the literature that reveals the essence and directions of digital technologies management and the basics of consumer behavior of digital users.Based on desk research, the analysis of university students ' feedback on the use of distance learning technologies was carried out.The attitude of students to the online educational process was determined.The positive sides and the problems of using digital technologies in the university were identified.Recommendations were made on improving distance education.
IMU sensor-based data glove for finger joint measurement

&lt;p&gt;The methods used to quantify finger range of motion significantly influence how hand disability is reported. To date, the accuracy of sensors being utilized in data gloves from the literature has been ascertained yet need further analysis. This paper presents an inertial measurement unit sensor-based data glove for finger joint measurement developed for collecting a range of motion data of distal interphalangeal, proximal interphalangeal and metacarpophalangeal finger joints of an index finger. In this study, three inertial measurement sensors, MPU-6050 and two flexible bend sensors which are capable to detect angle displacement were attached to the distal interphalangeal, proximal interphalangeal and metacarpophalangeal finger joint points on the glove. The data taken from inertial measurement unit sensors and flexible bend sensors were acquired using Arduino and MATLAB software interface. The data obtained were compared with the reference data measured from goniometer to allow for accurate comparative measurement. The percentage of error resulted from MPU-6050 sensor unit were ranged from 0.81 % to 5.41 % were very low which indicates high accuracy when compared with the measurements obtained using goniometer. On the other hand, flexible bend sensor shows low accuracy (11.11 % to 19.35 % error). In conclusion, the inertial measurement unit sensor-based data glove using MPU-6050 sensors can be a reliable solution for tracking the progress of finger rehabilitation exercises. In order to motivate patients to adhere to the therapy exercises, interactive rehabilitation game will be developed in the future incorporating MPU-6050 sensors on all five fingers.&lt;/p&gt;
An improved simplified rules Fuzzy Logic Speed Controller method applied for induction motor drive

Fuzzy Logic Speed Controller (FLSC) has been widely used for motor drive due to its robustness and its non-reliance to real plant parameters. However, it is computationally expensive to be implemented in real-time and prone to the fuzzy rules’ selection error which results in the failure of the drive’s system. This paper proposes an improved simplified rules method for Fuzzy Logic Speed Controller (FLSC) based on the significant crisp output calculations to address these issues. A systematic procedure for the fuzzy rules reduction process is first described. Then, a comprehensive evaluation of the activated crisp output data is presented to determine the fuzzy dominant rules. Based on the proposed method, the number of rules was significantly reduced by 72%. The simplified FLSC rule is tested on the Induction Motor (IM) drives system in which the real-time implementation was carried out in the dSPACE DS1103 controller environment. The simulation and experimental results based on the proposed FLSC have proved the workability of the simplified rules without degrading the motor performance.
Claims-Based VM Authorization on OpenStack Private Cloud using Blockchain

The complexity of providing secure access, protecting critical data and end-user privacy in cloud data centre is leading to a demand for a new approach in network and data security. Recently, blockchain technology is being used in claims management to provide a decentralized and secure solution. The issue of security is crucial in the Virtual machine authorization in the cloud data centers. In the traditional approach of VM authorization, SSH key and IP address is given to the user to log into virtual machines. This opens much vulnerability as it might get spoofed or sniffed from the network, leading to accesses of private data to intruder. In this work, we propose a method which intends to aid in the security of the VM authorization using claims-based authorization system in conjunction with Blockchain based decentralized storage. Furthermore, the proposed system automates the process of launching a VM in OpenStack orchestration software.
Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning

We propose a Semi-supervIsed GeNerative Active Learning (SIGNAL) model to address the imbalance, efficiency, and text camouflage problems of Chinese text spam detection task.A "self-diversity" criterion is proposed for measuring the "worthiness" of a candidate for annotation.A semi-supervised variational autoencoder with masked attention learning approach and a character variation graph-enhanced augmentation procedure are proposed for data augmentation.The preliminary experiment demonstrates the proposed SIGNAL model is not only sensitive to spam sample selection, but also can improve the performance of a series of conventional active learning models for Chinese spam detection task.To the best of our knowledge, this is the first work to integrate active learning and semisupervised generative learning for text spam detection.
Smart City Moving Target Tracking Algorithm Based on Quantum Genetic and Particle Filter

In the application of moving target tracking in smart city, particle filter technology has the advantages of dealing with nonlinear and non-Gaussian problems, but when the standard particle filter uses resampling method to solve the degradation phenomenon, simply copying the particles will cause local optimization difficulties, resulting in unstable filtering accuracy. In this paper, a particle filter algorithm combined with quantum genetic algorithm (QGA) is proposed to solve the above problems. Aiming at the problem of particle exhaustion in particle filter, the algorithm adopts the method of combining evolutionary algorithm. Each particle in particle filter is regarded as a chromosome in genetic algorithm, and the fitness of each chromosome corresponds to the weight of particle. For each particle state with weight, the particle is first binary coded with qubit and quantum superposition state, and then quantum rotation gate is used for selection, crossing, mutation, and other operations, after a set number of iterations, the final particle set with accuracy and better diversity. In this paper, the filter state estimation and RMSF of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>N</mml:mi><mml:mo>=</mml:mo><mml:mn>50</mml:mn></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>N</mml:mi><mml:mo>=</mml:mo><mml:mn>100</mml:mn></mml:math> for nonlinear target tracking and the comparison of real state and state estimation trajectory in time-constant model under nonlinear target tracking are given. It can be seen that in nonlinear state, the quantum genetic and particle filter (QGPF) algorithm can achieve a higher accuracy of state estimation, and the filtering error of QGPF algorithm at each time is relatively uniform, which shows that the algorithm in this paper has better algorithm stability. Under the time-constant model, the algorithm fits the real state and realizes stable and accurate tracking.
Just-in-Time Learning and QR Codes: A Must-Have Tool for Nursing Professional Development Specialists

Technology can and will transform staff development. Tools such as quick response (QR) codes enable immediate and just-in-time access when staff need information or training material. Understanding the how and why of QR code use is the first step. Application of this tool will be a game changer. [ J Contin Educ Nurs . 2020; 51(7):302–303.]
Neural Replicator Dynamics: Multiagent Learning via Hedging Policy Gradients

Policy gradient and actor-critic algorithms form the basis of many commonly used training techniques in deep reinforcement learning. Using these algorithms in multiagent environments poses problems such as nonstationarity and instability. In this paper, we first demonstrate that standard softmax-based policy gradient can be prone to poor performance in the presence of even the most benign nonstationarity. By contrast, it is known that the replicator dynamics, a well-studied model from evolutionary game theory, eliminates dominated strategies and exhibits convergence of the time-averaged trajectories to interior Nash equilibria in zero-sum games. Thus, using the replicator dynamics as a foundation, we derive an elegant one-line change to policy gradient methods that simply bypasses the gradient step through the softmax, yielding a new algorithm titled Neural Replicator Dynamics (NeuRD). NeuRD reduces to the exponential weights/Hedge algorithm in the single-state all-actions case. Additionally, NeuRD has formal equivalence to softmax counterfactual regret minimization, which guarantees convergence in the sequential tabular case. Importantly, our algorithm provides a straightforward way of extending the replicator dynamics to the function approximation setting. Empirical results show that NeuRD quickly adapts to nonstationarities, outperforming policy gradient significantly in both tabular and function approximation settings, when evaluated on the standard imperfect information benchmarks of Kuhn Poker, Leduc Poker, and Goofspiel.
Improved fast partitional clustering algorithm for text clustering

Document clustering has become an important task for processing the big amount of textual information available on the Internet. On the other hand, k-means is the most widely used algorithm for clustering, mainly due to its simplicity and effectiveness. However, k-means becomes slow for large and high dimensional datasets, such as document collections. Recently the FPAC algorithm was proposed to mitigate this problem, but the improvement in the speed was reached at the cost of reducing the quality of the clustering results. For this reason, in this paper, we introduce an improved FPAC algorithm, which, according our experiments on different document collections, allows obtaining better clustering results than FPAC, without highly increasing the runtime.
UTILIZATION OF ARTIFICIAL INTELLIGENCE TO IMPROVE FLOOD DISASTER MITIGATION

Flood disaster is one of predominant disaster event in Indonesia. The frequency and intensity of this disaster tend to increase from year to year as well as the losses caused thereby. To reduce the risks and losses due to flood disasters, innovation in disaster mitigation is needed. Artificial intelligence and machine learning are technological innovations that have been widely applied in various fields of life and can also be used to improve flood disaster mitigation. A literature study conducted in this research shows that the use of artificial intelligence and machine learning has proven to be able, and succeed to fastly and accurately perform flood prediction, flood risk mapping, flood emergency response and, flood damage mapping. ANNs, SVM, SVR, ANFIS, WNN and DTs are popular methods used for flood mitigation in the pre-disaster phase and it is recommended to use a combination or hybrid of these methods. During the flood disaster response phase, the application of artificial intelligence and machine learning are still not much has been done and need to be developed. Examples of the application are the use of big data from social media Twitter and machine learning both supervised learning with Random Forest and unsupervised learning with CNN which have shown good results and have a good prospect to be applied. For the use of artificial intelligence in post-disaster flood phase, are still also rare, because it requires actual data from the field. However, in the future, it will become a promising program for the assessment and application of artificial intelligence in the flood disaster mitigation.
Evaluation of Group Modelling Strategy in Model-Based Collaborative Filtering Recommendation

Recommender systems for groups are becoming increasingly popular since many information needs instigate from group and social activities, such as listening to music, watching movies, and traveling.One of the important aspects in group recommendation is group modelling aggregation strategy which is a process to generate the overall ratings of the group.Such ratings are considered as representations of the groups.There are few group aggregations approaches.In this paper we evaluated two group aggregation approaches which are the Most Pleasure and Average strategy group modelling.We implemented both approaches on the model-based collaborative filtering technique using the single value decomposition and average least square prediction algorithms.The experimental results show that the Average strategy outperformed the Most Pleasure strategy for both prediction algorithms in terms of MAE, RMSE, and precision and recall metrics.
A novel fully convolutional network for visual saliency prediction

A human Visual System (HVS) has the ability to pay visual attention, which is one of the many functions of the HVS. Despite the many advancements being made in visual saliency prediction, there continues to be room for improvement. Deep learning has recently been used to deal with this task. This study proposes a novel deep learning model based on a Fully Convolutional Network (FCN) architecture. The proposed model is trained in an end-to-end style and designed to predict visual saliency. The entire proposed model is fully training style from scratch to extract distinguishing features. The proposed model is evaluated using several benchmark datasets, such as MIT300, MIT1003, TORONTO, and DUT-OMRON. The quantitative and qualitative experiment analyses demonstrate that the proposed model achieves superior performance for predicting visual saliency.
How spatial frequencies and color drive object search in real-world scenes: A new eye-movement corpus

When studying how people search for objects in scenes, the inhomogeneity of the visual field is often ignored. Due to physiological limitations peripheral vision is blurred and mainly uses coarse-grained information (i.e., low spatial frequencies) for selecting saccade targets, whereas high-acuity central vision uses fine-grained information (i.e., high spatial frequencies) for analysis of details. Here we investigated how spatial frequencies and color affect object search in real-world scenes. Using gaze-contingent filters we attenuated high or low frequencies in central or peripheral vision while viewers searched color or grayscale scenes. Results showed that peripheral filters and central high-pass filters hardly affected search accuracy, whereas accuracy dropped drastically with central low-pass filters. Peripheral filtering increased the time to localize the target by decreasing saccade amplitudes and increasing number and duration of fixations. The use of coarse-grained information in the periphery was limited to color scenes. Central filtering increased the time to verify target identity instead, especially with low-pass filters. We conclude that peripheral vision is critical for object localization and central vision is critical for object identification. Visual guidance during peripheral object localization is dominated by low-frequency color information, whereas high-frequency information, relatively independent of color, is most important for object identification in central vision.