Reviewerly/exHarmony huggingface dataset:
Similarity analysis of dam behavior characterized by multi-monitoring points based on Cloud model

The availability of massive amount of dam safety monitoring data can make it difficult to analyze and characterize dam behavior. This article describes the use of the Cloud model to transform quantitative monitoring data into qualitative information. Each monitoring point returning dam safety data is regarded as a cloud drop, and parameters such as the expectation, entropy, and hyper-entropy of the monitoring data are obtained through a backward cloud generator to represent the operational state of the dam. The monitoring points are then treated as vectors, and the cloud similarity is calculated using the cosine value of the angle between them. The cloud similarity coefficient is then determined to characterize the similarity of dam behavior. Experimental analysis shows that the process of identifying cloud parameters has a good effect on the discovery of abnormal monitoring values regarding dam safety and demonstrates the feasibility of characterizing the dam behavior. Clustering analysis is applied to the similarity coefficients to further achieve the hierarchical management of dam monitoring points.
Evolved explainable classifications for lymph node metastases

A novel evolutionary approach for Explainable Artificial Intelligence is presented: the "Evolved Explanations" model (EvEx). This methodology combines Local Interpretable Model Agnostic Explanations (LIME) with Multi-Objective Genetic Algorithms to allow for automated segmentation parameter tuning in image classification tasks. In this case, the dataset studied is Patch-Camelyon, comprised of patches from pathology whole slide images. A publicly available Convolutional Neural Network (CNN) was trained on this dataset to provide a binary classification for presence/absence of lymph node metastatic tissue. In turn, the classifications are explained by means of evolving segmentations, seeking to optimize three evaluation goals simultaneously. The final explanation is computed as the mean of all explanations generated by Pareto front individuals, evolved by the developed genetic algorithm. To enhance reproducibility and traceability of the explanations, each of them was generated from several different seeds, randomly chosen. The observed results show remarkable agreement between different seeds. Despite the stochastic nature of LIME explanations, regions of high explanation weights proved to have good agreement in the heat maps, as computed by pixel-wise relative standard deviations. The found heat maps coincide with expert medical segmentations, which demonstrates that this methodology can find high quality explanations (according to the evaluation metrics), with the novel advantage of automated parameter fine tuning. These results give additional insight into the inner workings of neural network black box decision making for medical data.
Active Learning for Constrained Document Clustering with Uncertainty Region

Constrained clustering is intended to improve accuracy and personalization based on the constraints expressed by an Oracle. In this paper, a new constrained clustering algorithm is proposed and some of the informative data pairs are selected during an iterative process. Then, they are presented to the Oracle and their relation is answered with “Must-link (ML) or Cannot-link (CL).” In each iteration, first, the support vector machine (SVM) is utilized based on the label produced by the current clustering. According to the distance of each document from the hyperplane, the distance matrix is created. Also, based on cosine similarity of word2vector of each document, the similarity matrix is created. Two types of probability (similarity and degree of similarity) are calculated and they are smoothed for belonging to neighborhoods. Neighborhoods form the samples that are labeled by Oracle, to be in the same cluster. Finally, at the end of each iteration, the data with a greater level of uncertainty (in term of probability) is selected for questioning the oracle. In order to evaluate, the proposed method is compared with famous state-of-the-art methods based on two criteria and over a standard dataset. The result demonstrates an increased accuracy and stability of the obtained result with fewer questions.
Digital Law Journal: Introduction

I am glad to greet you in the pages of the international, peer-reviewed Digital Law Journal. The mission of the Journal is to provide a platform to exchange and discuss information around various research aspects and best practices concerned with the legalization of digital technologies. Another fundamental goal is to elucidate the peculiarities of these technologies and their implementation prospects within a legal and regulatory framework.
Liver Guided Pancreas Segmentation

In this paper, we propose and validate a location prior guided automatic pancreas segmentation framework based on 3D convolutional neural network (CNN). To guide pancreas segmentation, centroid of the pancreas used to determine its bounding box is calculated using the location of the liver which is firstly segmented by a 2D CNN. A linear relationship between centroids of the pancreas and the liver is proposed. After that, a 3D CNN is employed the input of which is the bounding box of the pancreas to get the final segmentation. A publicly accessible pancreas dataset including 54 subjects is used to quantify the performance of the proposed framework. Experimental results reveal outstanding performance of the proposed method in terms of both computational efficiency and segmentation accuracy compared to non location guided segmentation. To be specific, the running time is 15 times faster and the segmentation accuracy in terms of Dice is higher by 4.29% (76.42% versus 80.71%).
THE EXPERT SYSTEM OF CONTROL AND KNOWLEDGE ASSESSMENT

The article deals with the problem of objective knowledge assessment using expert automated systems for monitoring and evaluating knowledge. The expert systems of control and knowledge estimation are usually developed on the basis of different approaches to constructing a question and answer on test. The analysis of existing testing methods made it possible to conclude that questions with selective and selectively constructed types of answer do not always allow objectively assessing the knowledge of trainees, which reduces the stimulating effect of pedagogical evaluation on cognitive activity of trainees, and the quality of the learning process as a whole. The article proposes a method of knowledge estimation, which is based on a new approach to constructing a question and answer that allows a freely designed form of a test response. Such answer is analyzed by a set of criteria for its formation based on the developed algorithms for the criteria for assessing their quality. This makes it possible to apply the obtained results to solve the scientific problem of objective knowledge assessment using testing. The proposed approach gives an opportunity to expand the functions of tests, thereby increasing the approximation degree of the estimation for the test to the level of the real knowledge of students. The purpose of the article is to justify the effectiveness and develop the expert system of control and knowledge assessment. The article is addressed to a wide range of researchers and experts in the field of education and information technologies.
Handling Imbalance Data in Classification Model with Nominal Predictors

Decision tree, one of classification method, can be done to find out the factors that predict something with interpretable result. However, a small and unbalanced percentage will make the classification only lead to the majority class. Therefore, handling imbalance class needs to be done. One method that often used in nominal predictor data is SMOTE-N. For accuracy improving, a hybrid SMOTE-N and ADASYN-N was developed. SMOTE-N-ENN and ADASYN-N were developed for accuracy improvement. In this study, SMOTE-N, SMOTE-N-ENN and ADASYN-N will be compared in handling imbalance class in the classification of premarital sex among adolescent using base class CART. The conclusion obtained regarding the best method for handling class imbalance is ADASYN-N because it provides the highest AUC compared to SMOTE-N and SMOTE-N-ENN. The best decision tree provides information that factors that can predict adolescents having premarital sexual relations are dating style, knowledge of the fertile period, knowledge of the risk of young marriage, gender, recent education, and area of residence.
Predicting Business Process Bottlenecks In Online Events Streams Under Concept Drifts

Process performance analysis is an important subtask of process mining that aims at optimizing the discovered process models.In this paper we focus on improving process throughput by predicting congestions in the process execution (bottlenecks).We discuss an ongoing work on incorporating gradual and seasonal concept drift in this bottleneck prediction.In the field of process mining, we develop a method of predicting whether and which bottleneck will likely appear based on data known before a case starts.We introduce GRAHOF, a Gradual and Recurrent Adaptive Hoeffding Option Forest approach, which adapts to gradual and seasonal concept drifts when predicting bottlenecks of business processes in an online setting.We evaluate the parameters involved in GRAHOF using a synthetic event stream and a real-world event log.
A Dynamic Resource Allocation Algorithm Based on Auction Model in Mobile Blockchain Network

Blockchain has been widely used in many fields such as bitcoin, smart contract, supply chain. However, because of the high requirement of computation and energy resource in mining process, blockchain application is restricted in mobile environment. In this paper, we introduce edge servers offered by Edge Service Provider into mobile blockchain network. Thus mining task can be offloaded from mobile devices to the edge server. Since there is competition among users, we propose an auction-based model for edge server resource allocation. Moreover, in order to maximize the revenue of all mobile users, we design a mechanism to achieve the allocation strategy and pricing rule for Edge Service Provider. Considering different resource demands, bids and usage time, we use Benders decomposition to get the dynamic resource allocation strategy. We evaluate the performance of the proposed auction model through extensive simulation. Results show that dynamic allocation can solve the resource allocation problem for Edge Service Provider and will get higher revenue with more winners than fixed allocation.
Role of the Flow In Physical Education Class Between School Life Stress and Aggressiveness Among Adolescents

This study examines the relationships between the flow in physical education (P.E.) class, school life stress, and aggression in adolescents. A total of 470 surveys collected from five different high schools in Daejeon, South Korea, were analyzed using structural equation modeling. Study results indicated that the flow in P.E. class had a significant negative direct effect on school life stress, but did not have a significant direct effect on aggression. Furthermore, school life stress had a significant positive direct effect on aggression. Finally, flow in P.E. class had a significant negative indirect effect on aggression mediated by school life stress. The findings of this study are valuable for education experts to establish more effective P.E. curriculums in reducing school life stress and aggression.
A Study of Cellular Traffic Data Prediction by Kernel ELM with Parameter Optimization

Accurate and efficient prediction of mobile network traffic in a public setting with changing flow of people can not only ensure a stable network but also help operators make resource scheduling decisions before reasonably allocating resources. Therefore, this paper proposes a method based on kernel extreme learning machine (kELM) for traffic data prediction. Particle swarm optimization (PSO), multiverse optimizer (MVO), and moth–flame optimization (MFO) were adopted to optimize kELM parameters for finding the best solution. To verify the predictive performance of the kernel ELM model, backpropagation (BP) neural network, v-support vector regression (vSVR), and ELM were also applied to traffic prediction, and the results were compared with kELM. Experimental results showed that the smallest mean absolute percentage error in the test (11.150%) was achieved when kELM was optimized by MFO with Gaussian as the kernel function, that is, the prediction result of MFO-kELM was the best. This study can provide significant guidance for network stability and resource conservation.
Multi-View Low-Rank Coding-Based Network Data De-Anonymization

Social networks are extensively exploited by third-party consumers such as researchers and advertisers to understand user characteristics and behaviors. In general, before network data is published, sensitive relationships should be anonymized to prevent the compromise of individual privacy. To quantify the guarantee level of privacy-preserving mechanisms and mitigate users’ privacy concerns, numerous studies concerning network data de-anonymization have been carried out. However, most existing studies focus on single-view data, and privacy protection for multi-view data that is ubiquitous in the era of big data has not been yet extensively explored. In this study, we are interested in answering the following question: Are the traditional privacy protection methods still valid for the anonymization of multi-view data? In this study, we propose a Multi-View Low-Rank Coding (MVLRC) based network data de-anonymization framework to assess the vulnerability of privacy protection techniques by accurately reconstructing a large portion of the original data. Specifically, the framework assumes that in principle, the target and auxiliary networks have common structural patterns, and they can be modeled together to infer the hidden structure of the target network. The essential components of our work include the following: (1) a robust network representation model for structural pattern learning; (2) the network representation based multi-view modeling of target network and auxiliary network; (3) the inference of the anonymized links via target network reconstruction. Experimental results on synthetic networks and three real-world networks demonstrate that auxiliary networks can be utilized by malicious adversaries for privacy inference attacks. Thus, the privacy protection of multi-view network data needs more sophisticated anonymization techniques.
A Median Filter With Evaluating of Temporal Ultrasound Image for Impulse Noise Removal for Kidney Diagnosis

Ultrasound imaging helps the doctor to view the tissues and organs in the body's abdominal area with no ionization risks compared to other internal organ examination methods dependent on radiation. It offers highly precise renal imaging of suspected acute kidney diseases. This paper proposes temporary filtering methods to improve ultrasound images from ultrasonic kidney video. The proposed filters focus on the detection and diagnosis of kidney disease by processing consecutive images of the acquired kidney video. Extending the spatial median image filters to temporal dimensions after the picture frames are manually clipped and aligned in MATLAB by image processing Toolbox to suppress speckle noise, and enhance a doctor's diagnostic information quality.
A Weakly Supervised Method for Mud Detection in Ores Based on Deep Active Learning

Automatically detecting mud in bauxite ores is important and valuable, with which we can improve productivity and reduce pollution. However, distinguishing mud and ores in a real scene is challenging for their similarity in shape, color, and texture. Moreover, training a deep learning model needs a large amount of exactly labeled samples, which is expensive and time consuming. Aiming at the challenging problem, this paper proposed a novel weakly supervised method based on deep active learning (AL), named YOLO-AL. The method uses the YOLO-v3 model as the basic detector, which is initialized with the pretrained weights on the MS COCO dataset. Then, an AL framework-embedded YOLO-v3 model is constructed. In the AL process, it iteratively fine-tunes the last few layers of the YOLO-v3 model with the most valuable samples, which is selected by a Less Confident (LC) strategy. Experimental results show that the proposed method can effectively detect mud in ores. More importantly, the proposed method can obviously reduce the labeled samples without decreasing the detection accuracy.
Distinguishability theory for time-resolved photodetection and boson sampling

We study the distinguishability of photons in multiphoton interference on a multiport when fast detectors, capable of precise time resolution, are employed. Such a setup was previously suggested for experimental realization of boson sampling with single photons. We investigate if fast photodetection allows us to circumvent distinguishability of realistic single photons in mixed states. To this goal, we compare distinguishability of photons in two setups: (a) with photons in the same average (temporal) profile on a spatial interferometer and photodetection incapable of (or with strongly imprecise) time resolution and (b) with photons in generally different average temporal profiles on the same spatial interferometer and photodetection with precise time resolution. Exact analytical results are obtained for Gaussian-shaped single photons with Gaussian distribution of photon arrival time. Distinguishability of photons in the two setups is found to be strikingly similar. For the same purity of photon states, only the same quality experimental boson sampling can be achieved using either of the two setups. The upshot of our results is that distinguishability due to mixed states is an intrinsic property of photons, whatever the photodetection scheme.
Pandemia COVID‐19: Quais são os Riscos para a Saúde Mental?

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Learning What Others Know

We propose a number of powerful dynamic-epistemic logics for multi-agent information sharing and acts of publicly or privately accessing other agents’ information databases. The static base of our logics is obtained by adding to standard epistemic logic comparative epistemic assertions for groups or individuals, as well as a common distributed knowledge operator (that combines features of both common knowledge and distributed knowledge). On the dynamic side, we introduce actions by which epistemic superiority can be acquired: “sharing all one knows” (by e.g. giving access to one’s information database to all or some of the other agents), as well as more complex informational events, such as hacking. We completely axiomatize several such logics and prove their decidability.
Bipartite entanglement in the spin-1/2 Ising-Heisenberg planar lattice constituted of identical trigonal bipyramidal plaquettes

The bipartite entanglement is rigorously examined in the spin-1/2 Ising-Heisenberg planar lattice composed of identical inter-connected bipyramidal plaquettes at zero and finite temperatures using the quantity called concurrence. It is shown that the Heisenberg spins of the same plaquette are twice stronger entangled in the two-fold degenerate quantum ground state than in the macroscopically degenerate quantum chiral one. The bipartite entanglement with chiral features completely disappears below or exactly at the critical temperature of the model, while that with no chirality may survive even above the critical temperature of the model. Non-monotonous temperature variations of the concurrence clearly evidence the activation of the entangled Heisenberg states also above classical ground state as well as their re-appearance above the critical temperature of the model.
Low-cost data partitioning and encrypted backup scheme for defending against co-resident attacks

Abstract Aiming at preventing user data leakage and the damage that is caused by co-resident attacks in the cloud environment, a data partitioning and encryption backup (P&amp;XE) scheme is proposed. After the data have been divided into blocks, the data are backed up using the XOR operation between the data. Then, the backup data are encrypted using a random string. Compared with the existing scheme, the proposed scheme resolves the conflict between data security and survivability via encrypted backup. At the same time, because the XOR-encrypted backup causes multiple data blocks to share the same backup data, the storage overhead of the user is reduced. In this paper, existing probabilistic models are used to compare the performances of an existing scheme and the P&amp;XE scheme in terms of data security, data survivability and user storage overhead, and the overall performances of the two schemes in terms of these three aspects that are compared using control variables. Finally, the experimental results demonstrate the effectiveness of the P&amp;XE scheme at improving user data security and survivability and reducing user storage overhead.
Cold Comfort Matters - How Channel-Wise Emotional Strategies Help in a Customer Service Chatbot

Smart service chatbots, aiming to provide efficient customer service, have increased rapidly. Currently, few task-oriented chatbots had employed emotion strategies together with problem solving process. Meanwhile, customers can have different preference across channels (e.g., social media and webpages). This paper presented the design for a series of emotional strategies (ES) combined with task-solving, and tested its effectiveness across channels. In the multi-channel service chatbot, Moli, we compared ES with brief apology (BA) and no emotion responses (NER) in a Wizard of Oz study. The results indicated the effectiveness of the emotion strategies borrowed from psychotherapy technologies. Meanwhile, we found customers preferred brief and direct strategies on webpage, whereas rich emotional strategies in all stages were more favorable on social media. With detailed strategy design, the "cold comfort" was found helpful for building human-machine relationships, pacifying emotions, and improving satisfaction and system usability.
A Deep Learning Approach to Aspect-Based Sentiment Prediction

Sentiment analysis is a vigorous research area, with many application domains. In this work, aspect-based sentiment prediction is examined as a component of a larger architecture that crawls, indexes and stores documents from a wide variety of online sources, including the most popular social networks. The textual part of the collected information is processed by a hybrid bi-directional long short-term memory architecture, coupled with convolutional layers along with an attention mechanism. The extracted textual features are then combined with other characteristics, such as the number of repetitions, the type and frequency of emoji ideograms in a fully-connected, feed-forward artificial neural network that performs the final prediction task. The obtained results, especially for the negative sentiment class, which is of particular importance in certain cases, are encouraging, underlying the robustness of the proposed approach.