Reviewerly/exHarmony huggingface dataset:
Towards a Human-AI Hybrid for Adversarial Authorship

In this paper, we compare two types of masking methods for Adversarial Authorship. One method is a human-based interactive form of masking (referred to as AuthorCAAT-V) while the second method is hybrid of three state-of-the-art author masking techniques (referred to as AIM-IT). Our results show that the performances of AuthorCAAT-V and AIM-IT are equal to or better than the performances of the three state-of-the-art author masking techniques in reducing the identification rate of four well-known authorship attribution systems (AASs). Furthermore, our results show that the hybridization of AuthorCAAT-V and AIM-IT provides a greater reduction in the identification rate.
Equivalent Transformation of the Reasoning Model in Production Zeroth-Order Logic

Reasoning models play an important role in artificial intelligence. One of such models is logic-based production systems. These systems are based on rules, or productions, of the form "if A is true then B is true" where A, B are the elements of logic of a particular order. In this paper propositional logic is under consideration thus A, B are elementary facts or expressions over elementary facts constructed using propositional logic operators. There are two types of logical inference performed in production systems: forward chaining and backward chaining. In the backward chaining the problem of decreasing the number of queries to external sources of information arises. In this work, an approach to solving this problem is proposed in the case of logic-based production systems that use conjunctions and disjunctions in their rules. This approach is based on representing a set of rules and their implications with the help of an additional relation over a distributive lattice (the resulting algebraic system is so-called LP structure). The process of backward chaining is modeled as a solution of a production-logical equation on an LP-structure. The specified algebraic model is reduced equivalently to a simpler model of the form of an LP-structure over an atom-based lattice.
Automatically Discovering Relevant Images From Web Pages

Web pages contain irrelevant images along with relevant images. The classification of these images is an error-prone process due to the number of design variations of web pages. Using multiple web pages provides additional features that improve the performance of relevant image extraction. Traditional studies use the features extracted from a single web page. However, in this study, we enhance the performance of relevant image extraction by employing the features extracted from different web pages consisting of standard news, galleries, video pages, and link pages. The dataset obtained from these web pages contains 100 different web pages for each 200 online news websites from 58 different countries. For discovering relevant images, the most straightforward approach extracts the largest image on the web page. This approach achieves a 0.451 F-Measure score as a baseline. Then, we apply several machine learning methods using features in this dataset to find the most suitable machine learning method. The best f-Measure score is 0.822 using the AdaBoost classifier. Some of these features have been utilized in previous web data extraction studies. To the best of our knowledge, 15 new features are proposed for the first time in this study for discovering the relevant images. We compare the performance of the AdaBoost classifier on different feature sets. The proposed features improve the f-Measure by 35 percent. Besides, using only the cache feature, which is the most prominent feature, corresponds to 7 percent of this improvement.
KNCR: Knowledge-Aware Neural Collaborative Ranking for Recommender Systems

The recommendation system is designed to generate a personalized sorting list of items that users may be interested in. With the unprecedented success of deep learning in the field of Computer Vision and Voice recognition, how to reasonably introduce deep learning into the recommendation system has also aroused the thinking of researchers. Knowledge graph, as a new research hotspot, contains abundant new auxiliary information of entity semantic association. The researchers found that when the knowledge map is introduced into the recommendation system, it can reduce the data sparsity and cold start problem, and it is a good assistant for neural network in the recommendation system.In the traditional recommendation system, because it relies on the matrix decomposition and collaborative filtering algorithm for recommendation, there will inevitably be problems of cold start and data sparsity. The problem of data sparsity often refers to the large number of users and items in platforms such as large-scale e-commerce, but in the user-item matrix obtained, the average number of users interacting with the project is small, which will cause the user-item matrix to be sparse. The cold start problem refers to how to make personalized recommendation for new users without a large number of user data. The sparsity of data will eventually lead to the inability to capture the relationship between different users and different items, thus reducing the accuracy of the recommendation system. As an implicit expression, implicit feedback can get users’ preferences in many ways, rather than limited to the display of expression preferences, so as to enrich the user-item matrix and alleviate the problem of data sparsity. Neural network can analyze the relationship between things from a higher dimension, and improve the data sparsity. The knowledge graph contains the fact relationship of a thing in the real world, which is equivalent to providing additional information dimension for the data, so as to solve the cold start problem to a certain extent.In this paper, we propose an enhanced collaborative filtering recommendation algorithm based on implicit feedback and representation learning of the knowledge graph combined with neural network (KNCR). KNCR can bridge intrinsic relationship between items that are not considered by the traditional collaborative filtering algorithm, and effectively solve the problems of sparse scoring matrix and cold start. The experimental results from the real-world public dataset demonstrate that KNCR can improve the performance of personalized recommendations.
A QoE-based DASH Multicast Grouping Algorithm for Mobile Edge Computing

Dynamic Adaptive Streaming over HTTP (DASH) can adaptively select the appropriate video bitrate for mobile users. Mobile Edge Computing (MEC) scenario is of great benefit to improve the performance of mobile networks by providing computing and storage capabilities. And the utilization of spectrum resources can be improved by multicast transmission, but the performance of the multicast transmission will be directly affected by the selected grouping algorithm. Therefore, we propose a grouping algorithm for DASH multicast in MEC to complete a more reasonable grouping of users, thereby improving the Quality of Experience (QoE) of mobile users. QoE is not only our optimization goal but also the grouping basis of the algorithm proposed in this paper. We dynamically group multiple users in the same Multicast Broadcast Single Frequency Network (MBSFN) area in three dimensions based on the three components of QoE. The simulation results show that the proposed grouping algorithm performs well in QoE and fairness.
Towards Passive Authentication using Inertia Variations: An Experimental Study on Smartphones

Passive biometrics and behavioral analytics seek to identify users based on their unique patterns of activities. In this paper, we test the feasibility of using time-varying inertia data as passive biometrics to be used for user identification and authentication. We present a deep learning model for inertia pattern recognition that achieved a high accuracy of 87.17%. A fully-connected sequential deep neural network was trained on 6730 sensor data samples, each having 15 features: triaxial measurements from accelerometer, gyroscope, magnetometer, and rotational vector. We further discuss the potential impact of inertia pattern recognition for user identification and authentication.
Image Processing Based Billing System with Edge Computing

The idea of this paper is to introduce an Image Detection based billing system which leverages edge computing. The idea for the study of this product comes observing the C-shops, where billing of products is generally done manually. All the shops are not technologically advanced or financially rich to use devices like OCR. Also, the mix-up of prices of different products also remains a problem in manual billing. To counter such issues the authors, propose the idea of an image-based billing system, which produces fast and accurate results and works with simple camera devices like a mobile phone. The specialty of this model is that it will differentiate the objects accurately but based on its size meaning that if there are two versions of the same product, one of small size (of price x) and the other of larger size (of price 2x) then it will successfully recognize them and add the price to the billing system accordingly. The precision of the model for object detection is 90%. It has been developed in the form of a web application. Thus, this paper proposes a fully developed software for billing in the local C-Shops.
A Novel Interpretable Model for Solar Radiation Prediction based on Adaptive Fuzzy Clustering and Linguistic Hedges

The designs of solar energy systems depend mainly on the solar radiation that reaches the earth's surface, as it is difficult to determine the amount of solar radiation precisely due to several climatic, geographical and temporal factors. Therefore, forecasting of solar radiation is necessary before using solar energy systems. In this paper, the researchers present an accuracy MAMDANI fuzzy inference system for solar radiation prediction with meteorological data. This system is based with a two-stage method for Fuzzy Rules Learning through Clustering (FRLC). In the first stage, the subtractive clustering is used to extract the fuzzy rules, the second stage is a linguistic approximation and a refinement of the learned solutions with linguistic hedges. FRLC is compared to multilayer feed-forward neural network and support vector regression. The results of the experiments show the efficacy of linguistic fuzzy rules in the forecasting of solar radiation. In parallel with the prediction, the model provides a good balance between interpretability and accuracy.
Identifying users from the interaction with a door handle

Ambient intelligence pursues the integration of intelligent approaches on an IoT infrastructure, mainly using everyday objects of the environment. The main hypothesis of the work is that the way in which a user interacts with a door handle is suitable to be used in the identification task. Our proposal contributes with a new method to identify persons in a seamless and unobstrusive way, suitable to be used in a smart building scenery without the need to bring any additional device. In this case, we embed accelerometers and gyroscopes in a door handle in order to obtain a data set comprising samples of 47 individuals. A parametric approximation is adopted to reduce each sample to a feature vector by using a dynamic time warping technique. A study has been made of the outcomes of different classification techniques over six different feature sets in order to assess the feasibility of this identification challenge. The AUC values observed with the selected feature set show promising results above 0.90 using neural networks and SVM classifiers.
Development of a Feature Matrix for Classifying Network Traffic in SDN in Real-Time Based on Machine Learning Algorithms

The paper explores the prospects of using a set of features for classifying network traffic using Machine Learning (ML) methods, in which the statistical characteristics of each of the first 10-15 packets themselves act as features, such as the size of each packet and the interarrival time difference (ITD) between consecutive packets. It is shown that the use of this approach provides an increase in the results of the classification of TCP and UDP applications using the Random Forest method. The composition of the resulting feature matrix allows to use it for determination the QoS class of the flow in SDN in real-time.
A Novel Fusion-Based Texture Descriptor to Improve the Detection of Architectural Distortion in Digital Mammography

Architectural distortion (AD) is the earliest sign of breast cancer that can be detected on a mammogram, and it is usually associated with malignant tumors. Breast cancer is one of the major causes of death among women, and the chance of cure can increase significantly when detected early. Computer-aided detection (CAD) systems have been used in clinical practice to assist radiologists with the task of detecting breast lesions. However, due to the complexity and subtlety of AD, its detection is still a challenge, even with the assistance of CAD. Recently, the fusion of descriptors has become a trend for improving the performance of computer vision algorithms. In this work, we evaluated some local texture descriptors and their possible combinations, considering different fusion approaches, for application in CAD systems to improve AD detection. In addition, we present a novel fusion-based texture descriptor, the Completed Mean Local Mapped Pattern (CMLMP), which is based on complementary information between three LMP operators (signal, magnitude and center) and the local differences between pixel values and the mean value of a neighborhood. We compared the performance of the proposed descriptor with two other well-known descriptors: the Completed Local Binary Pattern (CLBP) and the Completed Local Mapped Pattern (CLMP), for the task of detecting AD in 350 digital mammography clinical images. The results showed that the descriptor proposed in this work outperforms the others, for both individual and fused approaches. Moreover, the choice of the fusion operator is crucial because it results in different detection performances.
Automatic Dropout for Deep Neural Networks

A greater demand for accuracy and performance in neural networks has led to deeper networks with a large number of parameters. Overfitting is a major problem for such deeper networks. Dropout is a popular regularization strategy used in deep neural networks to mitigate overfitting. However, dropout requires a hyperparameter to be chosen for every dropout layer. This process becomes tedious when the network has several dropout layers. In this paper, we introduce a method of sampling a dropout rate from an automatically determined distribution. We further build on this automatic selection of dropout rate by clustering the activations and adaptively applying different rates to each cluster. We have evaluated both our approaches using the CIFAR-10, CIFAR-100, and Fashion-MNIST datasets, using two state-of-the-art Wide ResNet variants as well as a simpler network. We show that our methods outperform standard dropout across all datasets and neural networks.
Opportunities in the Cross-Scale Collaborative Human Sensing of 'Developing' Device-Free and Wearable Systems

This is a position paper that discusses the challenges of emerging new sensing modalities for both device-free and wearable sensing systems, as well as opportunities lying in the combination of them across multiple information scales. With the development of the Internet of Things (IoT), many devices with sensing-ability have entered people's life. These systems mainly fall into two categories: wearables and infrastructure sensing (device-free). In this paper, wefirst briefly summarize the state-of-the-art sensing modalities of these two categories, then we discuss the challenges faced by them. We envision a future of IoT human sensing systems that achieves seamless sensing across multiple scales through collaborative information inference by both categories of modalities. Finally, we discuss the opportunities to expand the boundaries of sensing modalities that lie in their collaborative adaptation.
Continuity of Earthquake and Tsunami Monitoring by Japan Meteorological Agency under Critical Conditions

Abstract The Japan Meteorological Agency (JMA) is a governmental organization that has responsibilities for mitigation of natural disasters. JMA issues warnings and information about natural disasters, in addition to daily weather forecasts. When an earthquake occurs, JMA analyzes seismic data to issue an earthquake early warning and to warn of possible tsunamis when a tsunami is expected to strike coastal areas of Japan. During tsunami warning in effect, JMA monitors tsunami meters and updates the warning. JMA also provides several types of macroseismic information. To fulfill these responsibilities, JMA collects data from 4400 seismic intensity meters, 1800 seismometers, 400 tsunami meters, and 39 strainmeters. Monitoring must be continued even under difficult situations such as times following great earthquakes, volcanic eruptions, severe weather conditions, and pandemics. JMA has dual operations centers located in Tokyo and Osaka. When one loses functionality due to a disaster or infection, the other continues 24/7 operations including warnings and issuing other information. Disastrous situations often cause power and communication failures and insufficient numbers of technical specialists. Following the 2011 Tohoku-oki earthquake, JMA enhanced power and communication capabilities by adding large capacity batteries at each station and satellite communication links. During the coronavirus disease 2019 (COVID-19) pandemic, JMA has taken several measures to prevent technical specialists’ infection to continue the full range of functions for issuing of warnings and conveying needed information.
Forced Edges and Graph Structure

For a degree sequence, we define the set of edges that appear in every labeled realization of that sequence as forced, while the edges that appear in none are define as forbidden. We examine the structure of graphs in which the degree sequences contain either forced or forbidden edges. The results include the determination of the structure of the forced or forbidden edge sets, the relationship between the sizes of forced and forbidden sets for a sequence, and the structural consequences to their realizations. This includes showing that the diameter of every realization of a degree sequence containing forced or forbidden edges is no greater than 3, and that these graphs are maximally edge-connected.
Multi-Class Categorization of Design-Build Contract Requirements Using Text Mining and Natural Language Processing Techniques

Contract requirement processing is one of the most crucial tasks in the construction projects practicing design-build project delivery method. The contract requirements and technical specifications of design-build projects are typically enormous and associated with many different disciplines and project stages. In many cases, the design-build contractor needs to classify these general contract requirements into different categories to prepare the subcontracts for the specialized works. The success of a design-build project highly relies on the completeness of subcontracts to avoid disputes which may lead to delays and cost overruns. The conventional practices for the preparation of subcontracts require professionals much time and effort to read the complete contract package and extract the requirements related to the specialized works. This paper introduces an effective method to prepare the subcontracts scope by developing an automated framework for information retrieval using natural language processing techniques. The proposed technique classifies the text describing project requirements into three distinct classes associated with different construction project stages namely as design, construction, and operation and maintenance. The requirement classification model was developed using six different supervised machine learning approaches, including naïve Bayes, support vector machine, logistic regression, K-nearest number, decision tree, and feedforward neural network. The experimental results revealed the logistic regression as the highest performing algorithm with an accuracy of 94.12%. This study is expected to help reduce reading time and improve the quality of subcontracts.
An Experimental Study on Hyper Parameters for Training Deep Convolutional Networks

When training deep networks, it is crucial to obtain a network that offers optimum performance by trying different values of many hyper parameters and combinations of these values. Theoretically, the optimal set of values, which ensure the maximum performance of the network, can be found by giving these parameters numerous different values. However, it is not feasible to try all combinations of values. The a priori information regarding the contribution levels of hyper parameters and their values to the performance of the network will narrow the search space and enable researchers to easily and quickly obtain the network with optimum performance. In this study, a priori information is investigated that will guide in searching for most important hyper parameters and their ideal values that ensure optimum performance of a typical convolutional neural network in single image super resolution. For this purpose, the importance levels of the 5 most commonly used hyper parameters in training, and their optimum values were investigated. By giving two different values that are widely used or known to give good results from previous works in the literature for each hyper parameter, in total, 32 different training procedure were performed. The results showed that the learning rate has the most important effect on the performance of the network, then normalization, and then the size of the input image given to the model during training. It has also been found that the batch number and step count parameter values do not make a significant change in the performance of the network. The results obtained from this study could help researchers in determining the training parameters and their values in order to efficiently and rapidly obtain optimum network performance.
Consistency of the MLE under a two-parameter Gamma mixture model with a structural shape parameter

Finite Gamma mixture models are often used to describe randomness in income data, insurance data, and data in applications where the response values are intrinsically positive. The popular likelihood approach for model fitting, however, does not work for this model because its likelihood function is unbounded. Because of this, the maximum likelihood estimator is not well-defined. Other approaches have been developed to achieve consistent estimation of the mixing distribution, such as placing an upper bound on the shape parameter or adding a penalty to the log-likelihood function. In this paper, we show that if the shape parameter in the finite Gamma mixture model is structural, then the direct maximum likelihood estimator of the mixing distribution is well-defined and strongly consistent. We also present simulation results demonstrating the consistency of the estimator. We illustrate the application of the model with a structural shape parameter to household income data. The fitted mixture distribution leads to several possible subpopulation structures with regard to the level of disposable income.