Reviewerly/exHarmony huggingface dataset:
Story-based authentication for mobile devices using semantically-linked images

We introduce SemanticLock, a simple, fast, and memorable single-factor graphical authentication approach for mobile devices. SemanticLock uses a set of graphical images as password tokens that allow the construction of a semantically memorable story representing the user’s password. Passwords are entered via the familiar and quick action of dragging and positioning user-defined images on the touchscreen. It is well known that for (un)locking mechanisms such as PIN or PATTERN, users tend to pick memorable passwords such as dates or simple (often regular) patterns. This practice by users significantly reduces the effective password space for these mechanisms. The authentication strength of SemanticLock is based on the large number of possible semantic constructs derived from the positioning of the image tokens and the type of images selected. While graphical passwords have been shown in some cases to have lower entropy than other password types, we avoid this problem by (1) performing a series of experiments and analyses to understand which images and image pairs users prefer and then (2) selecting images that avoid any type of explicit or implicit bias, resulting in an effective password space that is essentially the same as the total password space. Results of our study comparing SemanticLock against other authentication systems show that SemanticLock performs similarly to PIN and PATTERN in usability while having significantly increased memorability and security.
Graph-based association rule learning for context-based health monitoring to enable user-centered assistance

In response to the demographic change and the accompanying challenges for effective healthcare, approaches to enable using advancements of digitalization and IoT infrastructures as well as AI methods to deliver results in the field of personalized health assistance are necessary. In our research, we aim at enabling user-centered assistance with the help of networked sensors and Health Assistance Systems as well as learning methods based on connected graph data that model the shared system, user, and environmental context. In particular, this paper demonstrates a graph-based dynamic context model for a medication assistance system and presents an association rule learning method using Apriori algorithm to learn correlations between user vitals, activities as well as medication intake behavior. An application scenario for context-based heart rate monitoring is consequently presented as proof of concept, where associated contextual elements from the modeled context relating surges in monitored heart rate to environmental and user activity are shown.
Data clustering using leaders and followers optimization and differential evolution

Data clustering is an important research topic in data mining. Although cluster analysis based on optimization algorithms has attracted great attention, optimization-based techniques face difficulties due to the non-linear objective function and complicated search space. Leaders and followers optimization (LaF) introduced in the 2015 IEEE Congress on Evolutionary Computation, and differential evolution algorithm (DE) are two efficient evolutionary computation methods, and they own some special advantages. The key power of LaF is the exploration in multi-modal search spaces, but it has a poor performance in the exploitation. On the other hand, DE based on the DE/best/1 mutation operator significantly promotes the exploitation process. In this study, the strong properties of LaF and DE are combined to balance exploration and exploitation in the search space to discover the cluster centroids. Besides, the proposed clustering method, i.e., LaF-DE, does not need parameter settings, unlike the existing optimization-based partitional clustering methods. Hence, this study proposes a straightforward, parameter-free, and efficient novel hybrid algorithm for the optimization-based partitional data clustering problem. Many experiments on the functions from CEC2017 test suite show that LaF-DE has better optimization performance and higher stability than the state-of-the-art metaheuristic algorithms. LaF-DE has been compared with well-known clustering techniques on the UCI and Shape datasets. The experimental results and statistical tests indicate that LaF-DE outperforms the well-known partitional clustering methods on 8 of 12 datasets in terms of internal performance metrics. Besides, LaF-DE performs better than density peaks clustering on 9 of 12 datasets in terms of external performance metrics.
General parsing with regular expression matching

The context-free grammars extended with regular expressions (RE), known as ECF or EBNF grammars, are commonly used as they often allow for terser language definitions. Yet for such grammars the notion of syntax tree, and consequently of ambiguity, lacks an agreed definition. The simplified tree structures returned by the existing parsing algorithms do not faithfully represent all the ways a sentence is derivable by means of the REs present in the grammar rules. We contribute a precise definition of the regular parts in the structures of the EBNF syntax trees, which is aligned with the tree representations that have been adopted by the recent algorithms for RE matching. For an EBNF rule, the finest representation shows all the RE operators as nodes in the sub-tree, while the flat representation simply appends the string generated by the RE to the nonterminal node. A consequent notion of representation-dependent ambiguity follows. The above representations are incorporated into a Tomita-style parsing algorithm, i.e., a GLR(1) parser. To construct such a parser, we follow the positional method of the Berry–Sethi parsers for regular languages. Given an EBNF grammar, our parser-generator produces a parser compliant with the choices expressed by the user about the representation of the syntax trees. We also report the parsing performance, over a few representative sets of languages (benchmarks) for programming and data representation, in comparison with existing parsers for EBNF grammars.
On the learning of vague languages for syntactic pattern recognition

Abstract The method of the learning of vague languages which represent distorted/ambiguous patterns is proposed in the paper. The goal of the method is to infer the quasi-context-sensitive string grammar which is used in our model as the generator of patterns. The method is an important component of the multi-derivational model of the parsing of vague languages used for syntactic pattern recognition.
Real-Time Attribute Based Deep Learning Network for Traffic Sign Detection

Traffic sign detection is one of the key components of the Advanced Driving Assistant System (ADAS), which aims to detect and classify street signs in real time. However, traffic sign detection has challenges in real applications requiring high precision and real-time recall. Those challenges are due to the small object size and class imbalance. Recently, researchers have proposed several techniques to improve the detection quality by enriching the features through a multiscale network, introducing attention mechanisms and augmentation techniques to improve the features of tiny objects. To overcome class imbalance researchers proposed cascaded networks and various loss functions. However, those existing techniques and mechanisms added more complexity to the model. Meanwhile, the imbalance affects single-stage networks such as YOLO, which causes a lower recall for minor classes. We proposed a new training method for a single-stage detection network, known as Real Time Attribute Based Deep Learning Detection Network (Real Time-Attribute DL). We introduced new attributes to the loss and Non-Maximum Suppression (NMS) to reduce the class number by categorizing it based on the shape of the traffic sign while maintaining the same number of classes. Our proposed method extends the YOLO detection head to have four main parameters: objectiveness, regression, class, and attribute. We modify the loss function to train the network jointly between class and attribute. We validate our proposed technique with Tsinghua-Tencent 100K(TT100K) as a benchmark dataset. The results show that our proposed technique improves the recall index from 85.85% to 94.26% in yolov4-tiny-31 with a 0.8% improvement in precision and improves the recall index from 93.51% to 96.68% in yolov4 with a drop by 2% in precision without adding extra complexity to the main network. The proposed technique offers a better recall index than the baseline, especially for imbalanced datasets such as TT100K datasets.
The Application of Scientific Games by Artificial Intelligence in Preschool Education under the Smart City

The purpose is to explore the application and effect of scientific games based on artificial intelligence (AI) in preschool education under the smart city. An interactive scientific games model for children by virtual reality (VR) is constructed using the augmented reality (AR) technology in AI. This enables the product to continuously enrich its interactive functions in addition to realizing its own functional requirements, pay attention to user experience, and allow users to feel the pleasure and comfortable emotional communication brought by the product in the process of using it. The model mainly completes the interaction through gesture interaction and voice interaction, and it is further empirically analyzed by means of questionnaires and interviews. The results manifested that after the use of the intelligent interaction model of scientific games, 93.33% of the students in the experimental group were interested in scientific games, and 83.33% of the students in the experimental group had a clear understanding of scientific knowledge. Moreover, more students in the experimental group believed that they had received support from explanations, demonstrations, and guided conjectures, which was significantly different from the control group (P &lt; 0.05). In the analysis of teacher interviews, it testified that the scientific values of preschool teachers and the reserve of basic scientific knowledge were still relatively weak, and their knowledge reserve could be improved in the future. Therefore, the research shows that the constructed model can improve the interest of scientific games and the ability of knowledge acquisition in preschool education, and provide an experimental reference for the development of intelligence and the improvement of teaching skills in the field of preschool education.
Reputation‐based partition scheme for <scp>IoT</scp> security

Abstract With the popularity of smart terminals, such as the Internet of Things, crowdsensing is an emerging data aggregation paradigm, which plays a pivotal role in data‐driven applications. There are some key issues in the development of crowdsensing such as platform security and privacy protection. As the crowdsensing is usually managed by a centralized platform, centralized management will bring various security vulnerabilities and scalability issues. To solve these issues, an effective reputation‐based partition scheme (RSPC) is proposed in this article. The partition scheme calculates the optimal partition size by combining the node reputation value and divides the node into several disjoint partitions according to the node reputation value. By selecting the appropriate partition size, RSPC provides a mechanism to ensure that each partition is valid, as long as the maximum permissible threshold for the failed node is observed. At the same time, the RSPC reorganizes the network periodically to avoid partition attacks. In addition, for cross‐partition transactions, this paper innovatively proposes a four‐stage confirmation protocol to ensure the efficient and safe completion of cross‐partition transactions. Finally, experiments show that RSPC improves scalability, low latency, and high throughput for crowdsensing.
Real Image Super-Resolution using GAN through modeling of LR and HR process

The current existing deep image super-resolution methods usually assume that a Low Resolution (LR) image is bicubicly downscaled of a High Resolution (HR) image.However, such an ideal bicubic downsampling process is different from the real LR degradations, which usually come from complicated combinations of different degradation processes, such as camera blur, sensor noise, sharpening artifacts, JPEG compression, and further image editing, and several times image transmission over the internet and unpredictable noises.It leads to the highly ill-posed nature of the inverse upscaling problem.To address these issues, we propose a GAN-based SR approach with learnable adaptive sinusoidal nonlinearities incorporated in LR and SR models by directly learn degradation distributions and then synthesize paired LR/HR training data to train the generalized SR model to real image degradations.We demonstrate the effectiveness of our proposed approach in quantitative and qualitative experiments.
Impact of Video Compression on the Performance of Object Detection Systems for Surveillance Applications

This study examines the relationship between H.264 video compression and the performance of an object detection network (YOLOv5). We curated a set of 50 surveillance videos and annotated targets of interest (people, bikes, and vehicles). Videos were encoded at 5 quality levels using Constant Rate Factor (CRF) values in the set {22,32,37,42,47}. YOLOv5 was applied to compressed videos and detection performance was analyzed at each CRF level. Test results indicate that the detection performance is generally robust to moderate levels of compression; using a CRF value of 37 instead of 22 leads to significantly reduced bitrates/file sizes without adversely affecting detection performance. However, detection performance degrades appreciably at higher compression levels, especially in complex scenes with poor lighting and fast-moving targets. Finally, retraining YOLOv5 on compressed imagery gives up to a 1% improvement in F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score when applied to highly compressed footage.
Dynamic Background Subtraction by Generative Neural Networks

Background subtraction is a significant task in computer vision and an essential step for many real world applications. One of the challenges for background subtraction methods is dynamic background, which constitutes stochastic movements in some parts of the background. In this paper, we have proposed a new background subtraction method, called DBSGen, which uses two generative neural networks, one for dynamic motion removal and another for background generation. At the end, the foreground moving objects are obtained by a pixel-wise distance threshold based on a dynamic entropy map. DBSGen is an end-to-end, unsupervised optimization method with a near real-time frame rate. The performance of the method is evaluated over dynamic background sequences and it outperforms most of state-of-the-art unsupervised methods. Our code is publicly available at https://github.com/FatemeBahri/DBSGen.
Violence Detection using 3D Convolutional Neural Networks

Accurate detection of abnormal behavior can help improve public safety. In this work, a 3D convolutional neural network (CNN) is implemented to detect violence captured by surveillance cameras. A comprehensive study of model hyper-parameter tuning is addressed to show competitive violence detection results using a general action recognition CNN without modifying the original architecture. Experimental results on three publicly available benchmark datasets show that the proposed method outperforms other sophisticated techniques designed specifically to detect violence in videos. Our analysis further indicates that reasonable network parameter adjustments can be an effective mechanism to guide the design of computer vision models in abnormal human behavior detection.
Flexible access control mechanism for cloud stored EHR using consortium blockchain

The implementation of cloud computing to store and access electronic health records has shown substantial benefits for both clinical organizations and patients in managing electronic health records. The prime security issue of cloud-based electronic health records is that the patient is physically unable to own a medical record whereas a clinical organization can maintain one for them. The latter may collude with centralized cloud servers. So, there is a vulnerability of such records being tampered with in order to hide the medical malpractices. So, maintaining data integrity and data privacy becomes a significant challenge when deploying cloud computing. Therefore, in this paper, a consortium blockchain-based cloud-stored electronic health record is proposed which provides data integrity, data privacy, storage scalability, and fine-grained access control. Each process in outsourcing electronic health records to the cloud is incorporated as a transaction in a consortium ethereum blockchain through smart contracts. Through smart contracts, an attribute-based contract key is generated for the users that can decrypt the encrypted data stored in the cloud. The attribute-based contract key allows only users who are authorized to access the information ensuring data privacy and fine-grained access control. Moreover, the proposed scheme is proved to provide tamper-proof although the medical records are controlled by a group of clinical organizations.
When artificial parameter evolution gets real: particle filtering for time-varying parameter estimation in deterministic dynamical systems

Estimating and quantifying uncertainty in unknown system parameters from limited data remains a challenging inverse problem in a variety of real-world applications. While many approaches focus on estimating constant parameters, a subset of these problems includes time-varying parameters with unknown evolution models that often cannot be directly observed. This work develops a systematic particle filtering approach that reframes the idea behind artificial parameter evolution to estimate time-varying parameters in nonstationary inverse problems arising from deterministic dynamical systems. Focusing on systems modeled by ordinary differential equations, we present two particle filter algorithms for time-varying parameter estimation: one that relies on a fixed value for the noise variance of a parameter random walk; another that employs online estimation of the parameter evolution noise variance along with the time-varying parameter of interest. Several computed examples demonstrate the capability of the proposed algorithms in estimating time-varying parameters with different underlying functional forms and different relationships with the system states (i.e., additive vs. multiplicative).
Imbalanced binary classification under distribution uncertainty

Imbalanced binary classification plays an important role in many applications. Some popular classifiers, such as logistic regression (LR), usually underestimate the probability of the minority class. Therefore, in this paper, we introduce two novel methods under distribution uncertainty, the idea of which is to modify the predicted probability with an additional uncertainty estimation. We develop the mean-uncertain method and the volatility-uncertain method, respectively, by assuming that the disturbance term follows the maximal and the G-normal distributions, which are the most important distributions within a sublinear expectation framework. Experiments on the simulated dataset and 10real-life datasets are conducted to compare the newly proposed approaches to several existing ones, including two resampling methods and two regression-based methods. The results of experiments show that our methods outperform most of the others in common evaluation metrics, especially the accuracy of the minority class.
Lithological classification by PCA-QPSO-LSSVM method with thermal infrared hyper-spectral data

To improve the accuracy and operation efficiency of lithological classification using a thermal infrared airborne hyper-spectral imager (TASI) data, an innovative combinatorial algorithm (PCA-QPSO-LSSVM) based on principal component analysis (PCA), quantum-behaved particle swarm optimization (QPSO), and least-squares support vector machines (LSSVM) is proposed. After pre-processing, the emissivity data was extracted from TASI data, and 27 types of lithological units were selected in comparison with the measured spectrum and Johns Hopkins University spectrum library. The PCA was used for statistical analysis, and the appropriate n principal components were selected instead of the original 32 bands of TASI emissivity data. Based on the LSSVM classification algorithm, the improved QPSO algorithm was used to optimize the regularization parameter γ and the kernel function σ2 in the classification process. Finally, test samples selected from TASI data were classified by PCA-QPSO-LSSVM. The results show that, compared with the traditional LSSVM algorithm, PCA-QPOS-LSSVM has a higher recognition accuracy and greater operation efficiency. The field verification of the study area was carried out in LiuYuan town, GanSu province, China, and the accuracy of the field verification was 74.36% for the classification result, which is more consistent with the actual lithological distribution compared with the LSSVM algorithm. However, there are some flaws in this paper, such as the low classification accuracy in Moyite and plauenite. In general, The PCA-QPSO-LSSVM algorithm is a practical algorithm for lithological classification of TASI data.
A Novel Random Number Generator and Its Application in Sound Encryption Based on a Fractional-Order Chaotic System

Random number generator design is one of the practical applications of nonlinear systems. This study used random number generation and sound encryption application with a fractional chaotic system. Random numbers were generated with the Langford chaotic system, and a sound encryption application was carried out for the secure transmission of voice messages. Randomization performance of numbers was evaluated by employing NIST-800-22 statistical tests, which meet the highest international requirements. It was observed that the distributions of these generated random numbers reached the desired level of randomness after the examination. Unlike the integer-order random number generators widely used in the literature, the fractional-order Langford chaotic system was employed to generate and analyze random numbers and demonstrate their utilization in sound encryption. Random numbers generated from a fractional degree-based chaotic system developed in this study can be used in cryptology, secret writing, stamping, statistical sampling, computer simulations, dynamic information compression and coding.