Reviewerly/exHarmony huggingface dataset:
Human Assembly Task Recognition in Human-Robot Collaboration based on 3D CNN

We focus on the problem of high-precision and long-timespan human assembly task recognition from videos in human-robot collaboration area. Toward this 3D Convolutional Neural Networks (3D CNN) model is used in this paper. 3D CNN has been proved behaving well in human action recognition. But, when it comes to human assembly task recognition, there are two new problems. Firstly, human assembly task recognition needs higher recognition precision as the background of the assembly station never changes and the assembled products is always the same one. Secondly, recognizing an assembly task needs much longer timespans than just recognizing an action as a task always consists of many actions. Aiming at the first problem, we augment the diversity of the data by rotation and mirroring process. To solve the second problem, we increase the number of input frames from 10 to 100. Experiment result shows that the recognition accuracy finally reaches to 0.82.
High-Resolution Remote Sensing Image Integrity Authentication Method Considering Both Global and Local Features

Content integrity of high-resolution remote sensing (HRRS) images is the premise of its usability. Existing HRRS image integrity authentication methods are mostly binary decision-making processes, which cannot provide a further interpretable information (e.g., tamper localization, tamper type determination). Due to this reason, a robust HRRS images integrity authentication algorithm using perceptual hashing technology considering both global and local features is proposed in this paper. It extracts global features by the efficient recognition ability of Zernike moments to texture information. Meanwhile, Features from Accelerated Segment Test (FAST) key points are applied to local features construction and tamper localization. By applying the concept of multi-feature combination to the integrity authentication of HRRS images, the authentication process is more convincing in comparison to existing algorithms. Furthermore, an interpretable authentication result can be given. The experimental results show that the algorithm proposed in this paper is highly robust to the content retention operation, has a strong sensitivity to the content changing operations, and the result of tampering localization is more precise comparing with existing algorithms.
Mejora de los procesos de evaluación mediante analítica visual del aprendizaje

Current trends in higher education involve the integration of face-to-face learning, online learning and the use of information technologies that support diverse aspects of the learning-teaching process. One of the main aspects considered in all the educational tendencies is the use of effective assessment processes that entail a continuous analysis of students’ results in order to detect anomalies and provide adequate feedback so as to solve them. However, in learning-teaching environments where there is a great amount of heterogeneous information, this must be integrated in order to be adequately analyzed. This paper presents COBLE, a tool that makes possible the integration of information from different sources to analyze it using visual learning analytics techniques. COBLE incorporates a feedback module that provides both teachers and students with visual information related to the assessment process in order to facilitate their decision-making processes. The provided visualizations can be adapted to the requirements of each course or user. The evaluation of COBLE has been carried out using it in a real blended learning environment where different teachers have integrated information from different sources (Moodle, personal spreadsheets, etc.), related to the performance of students. Next, both teachers and students used COBLE and its visualizations to extract information regarding the assessment process. The result of the system’s evaluation has been very satisfactory, obtaining a good acceptance of the diverse users involved.
Effect of Training Data Order for Machine Learning

For many Machine Learning algorithms on supervised learning problems, the order of training data samples can affect the quality of the derived model and the accuracy of predictions. This paper describes a project to quantify this effect, and to statistically quantify the variation exhibited by several algorithms using permutations of a given training data set. It is demonstrated that this variation can be quite significant, and that training data set ordering should be an important consideration when approaching a classification task.
A Scalable and Semantic Data as a Service Marketplace for Enhancing Cloud-Based Applications

Data handling and provisioning play a dominant role in the structure of modern cloud–fog-based architectures. Without a strict, fast, and deterministic method of exchanging data we cannot be sure about the performance and efficiency of transactions and applications. In the present work we propose an architecture for a Data as a Service (DaaS) Marketplace, hosted exclusively in a cloud environment. The architecture includes a storage management engine that ensures the Quality of Service (QoS) requirements, a monitoring component that enables real time decisions about the resources used, and a resolution engine that provides semantic data discovery and ranking based on user queries. We show that the proposed system outperforms the classic ElasticSearch queries in data discovery use cases, providing more accurate results. Furthermore, the semantic enhancement of the process adds extra results which extend the user query with a more abstract definition to each notion. Finally, we show that the real-time scaling, provided by the data storage manager component, limits QoS requirements by decreasing the latency of the read and write data requests.
Reversible data hiding in binary images by flipping pattern pair with opposite center pixel

In this paper, a novel RDH scheme by flipping pattern pairs with opposite center pixel (PPOCPs) in binary images is proposed, aiming at decreasing the distortion while increasing the embedding payload. First, 25 patterns in the 3×3 block are designed which construct the PPOCPs according to the distance level providing a guarantee for reversibility. Then, a balanced score is designed which considers both visual distortion and embedding payload to select the optimal PPOCP, and the secret messages are embedded in the optimal PPOCP. For the receiver, the secret messages can be extracted precisely and the original binary image can be recovered by scanning the optimal PPOCP. PPOCP is a novel RDH model which fully considers the visual distortion caused by flipping pixels. Experimental results demonstrate the feasibility of the proposed RDH method for binary images, and the visual quality is satisfactory under high embedding payload and smallest pure flipping rate.
A Novel Method of Seismic Signal Detection Using Waveform Features

The detection of seismic signals is vital in seismic data processing and analysis. Many algorithms have been proposed to resolve this issue, such as the ratio of short-term and long-term power averages (STA/LTA), F detector, Generalize F, and etc. However, the detection performance will be affected by the noise signals severely. In this paper, we propose a novel seismic signal detection method based on the historical waveform features to improve the seismic signals detection performance and reduce the affection from the noise signals. We use the historical events location information in a specific area and waveform features information to build the joint probability model. For the new signal from this area, we can determine whether it is the seismic signal according to the value of the joint probability. The waveform features used to construct the model include the average spectral energy on a specific frequency band, the energy of the component obtained by decomposing the signal through empirical mode decomposition (EMD), and the peak and the ratio of STA/LTA trace. We use the Gaussian process (GP) to build each feature model and finally get a multi-features joint probability model. The historical events location information is used as the kernel of the GP, and the historical waveform features are used to train the hyperparameters of GP. The beamforming data of the seismic array KSRS of International Monitoring System are used to train and test the model. The testing results show the effectiveness of the proposed method.
MINE: A method of Multi-Interaction heterogeneous information Network Embedding

Interactivity is the most significant feature of network data, especially in social networks. Existing network embedding methods have achieved remarkable results in learning network structure and node attributes, but do not pay attention to the multiinteraction between nodes, which limits the extraction and mining of potential deep interactions between nodes. To tackle the problem, we propose a method called MultiInteraction heterogeneous information Network Embedding (MINE). Firstly, we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm. Secondly, we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse multiple interactional relationships. Finally, applying a multitasking model makes the learned vector contain richer semantic relationships. A large number of practical experiments prove that our proposed method outperforms existing methods on multiple data sets.
A New Kinect-Based Posture Recognition Method in Physical Sports Training Based on Urban Data

Physical data is an important aspect of urban data, which provides a guarantee for the healthy development of smart cities. Students’ physical health evaluation is an important part of school physical education, and postural recognition plays a significant role in physical sports. Traditional posture recognition methods are with low accuracy and high error rate due to the influence of environmental factors. Therefore, we propose a new Kinect-based posture recognition method in a physical sports training system based on urban data. First, Kinect is used to obtain the spatial coordinates of human body joints. Then, the angle is calculated by the two-point method and the body posture library is defined. Finally, angle matching with posture library is used to analyze posture recognition. We adopt this method to automatically test the effect of physical sports training, and it can be applied to the pull-up of students’ sports. The position of the crossbar is determined according to the depth sensor information, and the position of the mandible is determined by using bone tracking. The bending degree of the arm is determined through the three key joints of the arm. The distance from the jaw to the bar and the length of the arm are used to score and count the movements. Meanwhile, the user can adjust his position by playing back the action video and scoring, so as to achieve a better training effect.
Differential Privacy for Weighted Network Based on Probability Model

Weighted network contains a lot of sensitive information and may seriously jeopardize individual privacy. In this paper, we study the problem of differential privacy for weighted network. We found most existing methods add noise to edge weights directly and neglect the structural role of node. These methods perform with low accuracy. To address the above issue, we propose two approaches. One approach describes a differential privacy method for Stochastic Block Model. This private SBM reveals and the structural role of node and respects the privacy of it. Another approach develops a differential privacy method for weighted network through structuring a private probability model. We use Variational Bayes to learn the private model parameters. It adds noise to the parameters of the probability model instead of edge weights, and achieve high data utility. Experiments on real datasets illustrate that our algorithm privately releases weighted network and achieves high accuracy.
A Stacking-based Ensemble Learning Method for Outlier Detection

Outlier detection is considered as one of the crucial research areas for data mining. Many methods have been studied widely and utilized for achieving better results in outlier detection from existing literature; however, the effects of these few ways are inadequate. In this paper, a stacking-based ensemble classifier has been proposed along with four base learners (namely, Rotation Forest, Random Forest, Bagging and Boosting) and a Meta-learner (namely, Logistic Regression) to progress the outlier detection performance. The proposed mechanism is evaluated on five datasets from the ODDS library by adopting five performance criteria. The experimental outcomes demonstrate that the proposed method outperforms than the conventional ensemble approaches concerning the accuracy, AUC (Area Under Curve), precision, recall and F-measure values. This method can be used for image recognition and machine learning problems, such as binary classification.
Removing ROP Gadgets from OpenBSD

Return Oriented Programming (ROP) is a common exploitation technique that reuses existing code fragments (gadgets) to construct shellcode in a compromised program.Recent changes in OpenBSD's compiler have started to reduce the number of gadgets in x86 and arm64 binaries, with the aim of making ROP exploitation more difficult or impossible.This paper will cover how ROP gadgets emerge from legitimate code, how OpenBSD's compiler removes these gadgets, and the effects on performance, code size, and ROP tool capabilities.We find that it is possible to meaningfully reduce the number of ROP gadgets in programs, and to effectively hinder ROP tool capabilities.
New Perspectives in the Implementation of Smart-Technologies in Higher Education

The digital environment, through its characteristic tools, contributes substantially to shaping the personality and creativity of students in university education.This paper deals with research into the development trends of higher education by application of smart-technologies as a tool with high innovative and intellectual strengths for training qualitative real skills of the students in the didactic process.Finally, it can be concluded that the application and development of smart technologies is a central point of an advanced educational strategy to develop an efficient and sustainable learning style that correlates different operational concepts for maintaining high academic standards, according to the requirements from our modern society.
Enhanced Prediction of Solar Radiation Using NARX Models with Corrected Input Vectors

The main objective of this work is to analyze and configure appropriately the input vectors to enhance the performance of NARX models to forecast solar radiation one hour ahead. For this study, Engle–Granger causality tests were implemented. Additionally, collinearity among the meteorological variables of the databases was examined. Different databases were used to test the contribution of these analyses in the improvement of the input vectors. For that, databases from three cities of Mexico with different climates were obtained, namely: Chihuahua, Temixco, and Zacatecas. These databases consisted of hourly measurements of the following variables: solar radiation (SR), wind speed (WS), relative humidity (RH), pressure (P), and temperature (T). Results showed that, in all three cases, proper NARX models were produced even when using input vectors formed only with solar radiation and temperature data. Consequently, it was inferred that pressure, wind speed, and relative humidity could be excluded from the input vectors of the forecasting models since, according to the causality tests, they did not provide relevant information to improve the solar radiation forecast in the studied cases. Conversely, these variables could generate spurious results. Forecasting results obtained with the NARX model were compared to the smart persistence model, commonly used to validate SR prediction. Error measures, such as mean absolute error (MAE) and root mean squared error (RMSE), were used to compare prediction results obtained from different models. In all cases, results obtained from the enhanced NARX model surpassed the results of the smart persistence, namely: in Chihuahua up to 11.5 % , in Temixco up to 15.7 % , and in Zacatecas up to 27.2 % .
On-Site Earthquake Early Warning Using Smartphones

In this study, the measured accelerations of a single smartphone were used to provide an earthquake early warning system. In the presented system, after the smartphone is triggered, the triggering event is then classified as an earthquake event or not. Once an earthquake event is detected, the peak ground acceleration is then predicted every second until 10 s after the trigger. These predictions are made by the neural network classifier and predictor embedded in the smartphone, and an alert can be issued if a large peak ground acceleration is predicted. The proposed system is unique among approaches that use crowdsourcing ideas for earthquake early warning because the proposed system provides on-site earthquake early warning. In general, the accuracy rates of the earthquake classifications and peak ground acceleration predictions of the system were quite high according to the results of large amounts of earthquake and non-earthquake data. More specifically, according to said earthquake data, 96.9% of the issued alerts would be correct and 61.9% of the earthquakes that exceeded the threshold would have resulted in an alert being issued before the arrival of the peak ground acceleration. Among the false negative cases, approximate 97.8% would occur because of negative lead time. Using the shake table tests of worldwide and Meinong earthquake datasets, the proposed approach is confirmed to be quite promising.
Improving Classification of Imbalanced Datasets Based on KM++ SMOTE Algorithm

Imbalanced datasets are common in the area of cancer diagnosis, blood sample centres and industrial equipment. Based on these imbalanced datasets, the training model using traditional machine learning algorithms predicts a poor performance of the minority class samples, which may bring huge losses. Therefore, how to improve the classification of imbalanced datasets is a research hotspot. Motivated by this problem, a new improved SMOTE algorithm is proposed in this paper. The algorithm named KM++ SMOTE aims to deal with the problem of the new synthetic sample distribution marginalization and the uneven distribution of the overall dataset caused by the SMOTE algorithm. By using three different datasets of Bearing Data Center Seeded Fault Test Data and comparing the experimental results of KM++ SMOTE algorithm and random forest algorithm with other improved SMOTE algorithm and random forest algorithm, KM++ SMOTE algorithm and random forest algorithm have better performance.
Beyond the use of Code Mixing by Social Media Influencers in Instagram

The purpose of this study is to analyse code-mixing used by social media influencers in Instagram. The method of this research is qualitative method and observation is uded to find the posts of influencers where they use code-mixing. Questioning to the influencers about the reasons they often use code-mixing through Instagram direct message or email are also done. The writer decided that the two familiar influencers from public figures are the main samples of this research. The result from this research is that the influencers use code mixing because they want Indonesian people to develop themselves by mastering English language and they want to give example to people, especially their followers that English can be fun and everyone can practice English. Being habit is also one of the reasons that they like to use code mixing. The Influencers use code-mixing in instagram in certain time, such as lunch time and after office hours, because those are the active time for them and their followers online in Instagram. It is evident that the use of code-mixing by the influencers arouse the followers interested and motivated to sharpen their English language and it could be as one of the parts in practicing the followers’ English through social media. It then becomes one of the way to learn more a foreign language through social media.
A Multi-class SVM Based Content Based Image Retrieval System Using Hybrid Optimization Techniques

Due to the increasing usage of multimedia and storage devices accessible, searching for large image databases has become imperative.Furthermore, the handiness of high-speed internet has escalated the exchange of images by users enormously.Content-Based Image Retrieval is proposed in this work, taking features based on Exact Legendre Moments, HVS color quantization with dc coefficient and statistical properties such as variance, mean, and skew of Conjugate Symmetric Sequency Complex Hadamard Transform (CS-SCHT).In most of the machine learning tasks, the quality of the learning process depends on dimensionality.High dimensional datasets can influence the classification outcome and training time.To overcome this problem, we use DE (Differential Evolution) to generate the optimal feature subsets.The features scaled by weights derived from the firefly algorithm, which fed to Multi-Class SVM.The fitness function taken for the firefly algorithm is the classification error of SVM.By minimizing fitness function, optimum weights are obtained.When these optimal weights are applied to SVM, the proposed algorithm exhibits better precision, recall, and accuracy when compared to some of the existing algorithms in the literature.
Part–Whole Relations: New Insights about the Dynamics of Complex Geochemical Riverine Systems

Nature is often characterized by systems that are far from thermodynamic equilibrium, and rivers are not an exception for the Earth’s critical zone. When the chemical composition of stream waters is investigated, it emerges that riverine systems behave as complex systems. This means that the compositions have properties that depend on the integrity of the whole (i.e., the composition with all the chemical constituents), properties that arise thanks to the innumerable nonlinear interactions between the elements of the composition. The presence of interconnections indicates that the properties of the whole cannot be fully understood by examining the parts of the system in isolation. In this work, we propose investigating the complexity of riverine chemistry by using the CoDA (Compositional Data Analysis) methodology and the performance of the perturbation operator in the simplex geometry. With riverine bicarbonate considered as a key component of regional and global biogeochemical cycles and Ca2+ considered as mostly related to the weathering of carbonatic rocks, perturbations were calculated for subsequent couples of compositions after ranking the data for increasing values of the log-ratio ln(Ca2+/HCO3−). Numerical values were analyzed by using robust principal component analysis and non-parametric correlations between compositional parts (heat map) associated with distributional and multifractal methods. The results indicate that HCO3−, Ca2+, Mg2+ and Sr2+ are more resilient, thus contributing to compositional changes for all the values of ln(Ca2+/HCO3−) to a lesser degree with respect to the other chemical elements/components. Moreover, the complementary cumulative distribution function of all the sequences tracing the compositional change and the nonlinear relationship between the Q-th moment versus the scaling exponents for each of them indicate the presence of multifractal variability, thus revealing scaling properties of the fluctuations.