Reviewerly/exHarmony huggingface dataset:
Smart Contract-Based Secure Model for Miner Registration and Block Validation

Rewriting-history attacks can erase all valid records of blockchain-based systems, which is extremely devastating. To deter such attacks, we design a new smart contract-based secure model to make such attacks ineffective. Each node who creates a new block is required to register with the smart contract to get a voucher required for the subsequent block validation. We introduce the design idea and the structure of the proposed model in detail. We also present the algorithms of the smart contract and analyze the security performance of the proposed model in different cases. To prove the feasibility of the proposed model, we use the Solidity language to implement the smart contract in this model and simulate it on the Ethereum test network. We also show simulation results in terms of runtime and resource consumption.
Semantic human activity annotation tool using skeletonized surveillance videos

Human activity data sets are fundamental for intelligent activity recognition in context-aware computing and intelligent video analysis. Surveillance videos include rich human activity data that are more realistic compared to data collected from a controlled environment. However, there are several challenges in annotating large data sets: 1) inappropriateness for crowd-sourcing because of public privacy, and 2) tediousness to manually select activities of people from busy scenes.
Color image encryption algorithm based on the position index and chaos theory

A color image encryption algorithm is proposed based on the position index and chaos theory. First, the plain image is decomposed into its constituent red, green, and blue channels, each of which is resized to form one-dimensional arrays. Then, a permutation mechanism based on the position index values is introduced to effectively decrease the correlation between adjacent pixels by dynamically swapping the pixels of the three components. The Tent–Sine system is employed as a pseudorandom sequence generator to provide chaotic sequences in which the initial values and parameters are calculated from the Secure Hash Algorithm (SHA)-256 hash of the plain image. As a result, the algorithm is sensitive to the plain image. In addition, in contrast to the traditional ciphertext feedback mode, each pixel is modified based on the order of the position index sequences. The results of experimental simulations and extensive cryptanalyses indicate that the algorithm satisfactorily resists various typical attacks.
Developing EFL Writing Skills through WhatsApp Dialogue Journaling

The present study attempts to explore the effects of WhatsApp and Pen/Paper dialogue journal writing techniques on university students’ writing performance. 45 freshmen students from Sociology department of State University in Turkey, participated in the current study forming three homogenous groups of 15, namely WhatsApp dialogue journaling (WDJ), pen/paper dialogue journaling (Pap DJW) and the control group. The experimental groups (WDJ and Pap DJW) received 12 treatment sessions expecting students to create dialogues through dialogue journaling while the control group had no treatment except weekly class requirements. Data were collected through a pre- and post-writing test administered to all groups and an open-ended questionnaire on attitudes administered just to the experimental groups. Paired-samples T-test and one-way analysis of variance (ANOVA) were conducted to determine the differences among the three groups while a content analysis was carried out to examine the attitudes. The findings related to the pre- and post-tests of all groups indicated a significant progress in overall writing production. When considering each of writing components separately; however, the differences between the pre and post tests revealed no improvement in vocabulary competence of WhatsApp group or organization skills of control group. On the other hand, pen and paper group students made a remarkable progress in all writing components. In addition, ANOVA results showed no significant difference among the groups regarding writing overall production or any component performance. Finally, both pen and paper and WhatsApp group indicated positive attitudes and a great desire towards utilizing dialogue journaling in EFL writing classes. This study will help teachers and learners of English and researchers to find out the efficacy of target writing techniques and thus make appropriate decisions concerning EFL learning.
Speech emotion classification using fractal dimension-based features

During the last 10–20 years, a great deal of new ideas have been proposed to improve the accuracy of speech emotion recognition: e.g., effective feature sets, complex classification schemes, and multi-modal data acquisition. Nevertheless, speech emotion recognition is still the task in limited success. Considering the nonlinear and fluctuating nature of the emotional speech, in this paper, we present fractal dimension-based features for speech emotion classification. We employed Katz, Castiglioni, Higuchi, and Hurst exponent-based features and their statistical functionals to establish the 224-dimensional full feature set. The dimension was downsized by applying the Sequential Forward Selection technique. The results of experimental study show a clear superiority of fractal dimension-based feature sets against the acoustic ones. The average accuracy of 96.5% was obtained using the reduced feature sets. The feature selection enabled us to obtain the 4-dimensional and 8-dimensional sets for Lithuanian and German emotions, respectively.
PLANET: A radial layout algorithm for network visualization

Tree layouts are among the key approaches for network visualization, and are of particular importance for exploring the hierarchical structure of networks. However, visualizations of the overall network structure and its hierarchical relationships can rarely be optimized simultaneously. This paper presents a radial layout algorithm called PLANET that enables users to explore the network structure from a root node, while maintaining readability. In order to distribute the nodes evenly and minimize edge crossings, we define a list of angle assignment rules for displaying child nodes which can automatically maximize the tunable angles between parent and child nodes, and to uniformly divide the angles of child nodes. Using these rules, the structural properties of the network such as hubs can be properly conveyed, and the readability of nodes that are far from the root can be guaranteed. Our experimental results show that PLANET is comparable to similar algorithms in terms of execution time, and gives better performance in terms of node distribution, variance of edge length and number of edge crossing; these advantages become greater for networks with large diameters.
Centralized Fusion Methods for Multi-Sensor System With Bounded Disturbances

The set-membership information fusion problem is studied for general multi-sensor dynamic systems. Based on set-membership theory, three centralized state fusion estimation algorithms in the presence of bounded disturbances are proposed, namely augmented algorithm, combined measurement filtering algorithm and pseudo-sequential filtering algorithm. Theoretical discussions on the convergence and boundedness of the proposed fusion algorithms are provided and their stability is proved. The estimate accuracy, computational complexity and flexibility of these three fusion algorithms are compared through theoretical analysis and simulation. And their exchanging property of measurement update order is discussed. Results show that these algorithms are functionally equivalent in terms of the estimation accuracy and the exchangeability of the measurement update order can be guaranteed as long as the parameters satisfy certain conditions. Meanwhile the simulation results prove the role of the proposed algorithms in improving state estimation accuracy. In addition, the combined measurement filtering algorithm has the highest calculation speed due to lower dimension. But it is less flexible because the sensor measurement matrices need to satisfy some additional conditions. These conclusions are valuable in applications.
Effectiveness Analysis of Edmodo-Based Blended English Learning Mode

This paper attempts to overcome the defects of traditional English learning mode, including poor real-time communication ability, heavy reliance on individual self-discipline, and undesirable learning effect. Drawing on the theories of English learning motivation, the author analyzed the relationship between learning motiva-tion and learning behavior in light of the Edmodo-based blended English learning mode, and put forward the English learning persistence rate formula, thus reveal-ing the correlations between learning cost, objective external learning environ-ment, learning motivation, and learning persistent rate. In addition, the English courses were made more pertinent according to the Ebbinghaus forgetting curve. The course test data were fitted and weighted, outputting intuitive curves on learning effect. The research results show that the Edmodo-based blended Eng-lish learning mode has a significant learning effect, and a guiding significance for English learning and English teaching reform.
Target-Specific Action Classification for Automated Assessment of Human Motor Behavior from Video

Objective monitoring and assessment of human motor behavior can improve the diagnosis and management of several medical conditions. Over the past decade, significant advances have been made in the use of wearable technology for continuously monitoring human motor behavior in free-living conditions. However, wearable technology remains ill-suited for applications which require monitoring and interpretation of complex motor behaviors (e.g., involving interactions with the environment). Recent advances in computer vision and deep learning have opened up new possibilities for extracting information from video recordings. In this paper, we present a hierarchical vision-based behavior phenotyping method for classification of basic human actions in video recordings performed using a single RGB camera. Our method addresses challenges associated with tracking multiple human actors and classification of actions in videos recorded in changing environments with different fields of view. We implement a cascaded pose tracker that uses temporal relationships between detections for short-term tracking and appearance based tracklet fusion for long-term tracking. Furthermore, for action classification, we use pose evolution maps derived from the cascaded pose tracker as low-dimensional and interpretable representations of the movement sequences for training a convolutional neural network. The cascaded pose tracker achieves an average accuracy of 88% in tracking the target human actor in our video recordings, and overall system achieves average test accuracy of 84% for target-specific action classification in untrimmed video recordings.
A single fault localization technique based on failed test input

Testing and debugging are very important tasks in software development. Fault localization is a very critical activity in the debugging process and also is one of the most difficult and time-consuming activities. The demand for effective fault localization techniques that can aid developers to the location of faults is high. In this paper, a fault localization technique based on complex network theory named FLCN-S is proposed to improve localization effectiveness on single-fault subject programs. The proposed technique diagnoses and ranks faulty program statements based on their behavioral anomalies and distance between statements in failed tests execution by utilizing two network centrality measures (degree centrality and closeness centrality). The proposed technique is evaluated on a well-known standard benchmark (Siemens test suite) and four Unix real-life utility subject programs (gzip, sed, flex, and grep). Overall, the results show that FLCN-S is significantly more effective in locating faults in comparison with other techniques. Furthermore, we observed that both degree and closeness centrality play a vital role in the identification of faults.
Difference Based Metrics for Deep Reinforcement Learning Algorithms

Although state-of-the-art deep reinforcement learning often achieves superhuman levels on some tasks, the authors still struggle to analyze, compare or report the obtained results due to the unstable nature of the algorithms and the diversity of metrics used in the literature. Furthermore, these metrics fail to show some characteristics of the learning process, leading to misinterpretations by the analyst. The objective of this paper is to propose, implement and analyze a difference based evaluation metric that highlight different aspects of the learning process, allowing for more detailed results and analysis that can be used by automated software or analysts, experienced or not. A possible applicability for the proposed metric is to create automated evaluation systems, that can detect anomalies during the training process automatically.
Clustering Student's Satisfaction in Complex Adaptive Blended Learning with the Six Value System Using the K-Means Algorithm

Complex adaptive blended learning with the six value system is expected to improve the quality of learning.It is also expected to improve high order thinking skills.But the level of student's satisfaction in Complex adaptive blended learning with the six value system varies in results.This study aims to determine the grouping of student's satisfaction in complex adaptive blended learning with the six value system.The object of the study was conducted in 3 (three) vocational high schools (SMK) in Cirebon City, West Java Province, Indonesia for digital simulation subjects with a purposive sample of 150 students.Data about student's satisfaction is grouped using the K-Means algorithm with the optimization of generation method.Several stages are carried out in grouping student's satisfaction, starting with randomly determining initial centroid values.The K-Means algorithm process ends if there is no change in centroid value between one iteration and another iteration.Furthermore, performance measurements are performed using the Cluster Distance Performance method.The results are obtained by the performance Vector with parameters K = 4.The average distance in the centroid is -0.107, the average distance in the center of cluster 0 is -0.109, the average distance in the center of cluster 1 is -0.109, the average distance in the center of cluster 2 of -0,100, and the average distance in the center of cluster 3 is -0,106, with Davies Bouldin index of -1,049.
Robust parameter estimation of chaotic systems

Reliable estimation of parameters of chaotic dynamical systems is a long standing problem important in numerous applications. We present a robust method for parameter estimation and uncertainty quantification that requires neither the knowledge of initial values for the system nor good guesses for the unknown model parameters. The method uses a new distance concept recently introduced to characterize the variability of chaotic dynamical systems. We apply it to cases where more traditional methods, such as those based on state space filtering, are no more applicable. Indeed, the approach combines concepts from chaos theory, optimization and statistics in a way that enables solving problems considered as 'intractable and unsolved' in prior literature. We illustrate the results with a large number of chaotic test cases, and extend the method in ways that increase the accuracy of the estimation results.
Combining model finder and genetic programming into a general purpose automatic program synthesizer

Abstract Program synthesis aims to mechanize the task of programming from the user intent (expressed in various forms like pre/post conditions, examples, sketches, etc). There are many approaches to program synthesis that are usually implemented in isolation: deductive, syntax-based, inductive, etc. In this paper, we describe PSMF2, a program synthesizer that combines model finder and genetic programming. PSMF2 takes as user intent examples and a soft sketch: a new kind of user intent defined as a set of commands that must appear in the synthesized program (and that are in no particular order of execution). The output of PSMF2 is a general purpose imperative program. The combination of inductive synthesis and genetic programming has allowed PSMF2 to synthesize 7 programs (IntSQRT, Majority of 5, Majority of 8, Max of 4, Modulo, Factorial, and Fibonacci) found in the SyGuS competition, the iJava and IntroClass, and the Genetic programming communities. We carried out an empirical evaluation on the synthesis time of these 7 programs and the mean time varied from 56.4 seconds (Majority of 5) to 15.9 minutes (Fibonacci).
Splitting obsidian: Assessing a multiproxy approach for sourcing obsidian artifacts in British Columbia

Archaeology and geology inherently intersect when attempting to determine the geographic origin of lithic materials manufactured into artifacts by ancient people. Obsidian, or volcanic glass, is frequently found in archaeological sites, and researchers commonly use the trace element characteristics of obsidian artifacts to identify their source. However, the trace element concentrations of obsidian flows are not necessarily unique, potentially leading to inaccurate conclusions when used in isolation, especially if all possible sources have not been identified and systematically characterized. In this pilot study, we assess variations among the trace element, structural, and isotopic characteristics of obsidian sources in British Columbia and describe a multiproxy approach for provenancing obsidian artifacts. Our approach combines the survey of an artifact assemblage using non-destructive techniques with further analysis of a pre-selected subset of representative artifacts using minimally-invasive analytical methods. The initial non-destructive survey step includes determination of trace element concentrations by portable X-ray fluorescence and structural characteristics by Raman spectroscopy. The minimally-invasive step includes the measurement of lead isotope ratios and more precise trace element concentrations with split stream-laser ablation-inductively coupled plasma-mass spectrometry (SS-LA-ICP-MS) on only a select few representative artifacts. We tested this approach on 14 obsidian artifacts from an archaeological site in British Columbia. Our results provide multiple lines of evidence which indicate that two of the analysed artifacts likely originated from Mount Edziza, ~360 km north of the site, and all others likely originated from Anahim Peak, ~275 km south of the site. The combined use of multiple proxies significantly enhances confidence during sourcing studies and highlights the strength of multidisciplinary and multivariate geochemical approaches to archaeological challenges.
SAST: Learning Semantic Action-Aware Spatial-Temporal Features for Efficient Action Recognition

The state-of-the-arts in action recognition are suffering from three challenges: (1) How to model spatial transformations of action since it is always geometric variation over time in videos.(2) How to develop the semantic action-aware temporal features from one video with a large proportion of irrelevant frames to the labeled action class, which hurt the final performance.(3) The action recognition speed of most existing models is too slow to be applied to actual scenes.In this paper, to address these three challenges, we propose a novel CNN-based action recognition method called SAST including three important modules, which can effectively learn semantic action-aware spatial-temporal features with a faster speed.Firstly, to learn action-aware spatial features (spatial transformations), we design a weight shared 2D Deformable Convolutional network named 2DDC with deformable convolutions whose receptive fields can be adaptively adjusted according to the complex geometric structure of actions.Then, we propose a light Temporal Attention model called TA to develop the action-aware temporal features that are discriminative for the labeled action category.Finally, we apply an effective 3D network to learn the temporal context between frames for building the final video-level representation.To improve the efficiency, we only utilize the raw RGB rather than optical flow and RGB as the input to our model.Experimental results on four challenging video recognition datasets Kinetics-400, Something-Something-V1, UCF101 and HMDB51 demonstrate that our proposed method can not only achieve comparable performances but be 10x to 50x faster than most of state-of-the-art action recognition methods.
Joint image compression and encryption based on sparse Bayesian learning and bit-level 3D Arnold cat maps

Image compression and image encryption are two essential tasks in image processing. The former aims to reduce the cost for storage or transmission of images while the latter aims to change the positions or values of pixels to protect image content. Nowadays, an increasing number of researchers are focusing on the combination of these two tasks. In this paper, we propose a novel joint image compression and encryption approach that integrates a quantum chaotic system, sparse Bayesian learning (SBL) and a bit-level 3D Arnold cat map, so-called QSBLA, for such a purpose. Specifically, the QSBLA consists of 6 stages. First, a quantum chaotic system is employed to generate chaotic sequences for subsequent compression and encryption. Second, as one method of compressive sensing, SBL is used to compress images. Third, an operation of diffusion is performed on the compressed image. Fourth, the compressed and diffused image is transformed into several bit-level cubes. Fifth, 3D Arnold cat maps are used to permute each bit-level cube. Finally, all the bit-level cubes are integrated and transformed into a 2D pixel-level image, resulting in the compressed and encrypted image. Extensive experiments on 8 publicly-accessed images demonstrate that the proposed QSBLA is superior or comparable to some state-of-the-art approaches in terms of several measurement indices, indicating that the QSBLA is promising for joint image compression and encryption.
Polygonal approximation based on coarse-grained parallel genetic algorithm

This paper proposes to apply coarse-grained parallel genetic algorithm (CGPGA) to solve polygonal approximation problem. Chromosomes are used to represent digital curves and genes correspond to points of curves. This method divides the whole population into several subpopulations, each of which performs evolutionary process independently. After every migration interval number of generations, these subpopulations exchange their information with each other. Inspired by the designing theory of ensemble learning in machine learning, this paper further improves the basic CGPGA through adopting different but effective genetic algorithms, respectively, in different subpopulations. Both the diversity among different subpopulations and the accuracy in each individual subpopulation are ensured. Experimental results, based on four benchmark curves and four real image curves extracted from the lake maps, show that the basic CGPGA outperforms the used genetic algorithm, and further the improved CGPGA (ICGPGA) is more effective than the basic CGPGA, in terms of the quality of best solutions, the average solutions, and the variance of best solutions. Especially for those larger approximation problems, the ICGPGA is more remarkably superior to some representative genetic algorithms.