Reviewerly/exHarmony huggingface dataset:
Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence

In this paper, we propose a Spatial Robust Mixture Regression model to investigate the relationship between a response variable and a set of explanatory variables over the spatial domain, assuming that the relationships may exhibit complex spatially dynamic patterns that cannot be captured by constant regression coefficients. Our method integrates the robust finite mixture Gaussian regression model with spatial constraints, to simultaneously handle the spatial non-stationarity, local homogeneity, and outlier contaminations. Compared with existing spatial regression models, our proposed model assumes the existence a few distinct regression models that are estimated based on observations that exhibit similar response-predictor relationships. As such, the proposed model not only accounts for non-stationarity in the spatial trend, but also clusters observations into a few distinct and homogenous groups. This provides an advantage on interpretation with a few stationary sub-processes identified that capture the predominant relationships between response and predictor variables. Moreover, the proposed method incorporates robust procedures to handle contaminations from both regression outliers and spatial outliers. By doing so, we robustly segment the spatial domain into distinct local regions with similar regression coefficients, and sporadic locations that are purely outliers. Rigorous statistical hypothesis testing procedure has been designed to test the significance of such segmentation. Experimental results on many synthetic and real-world datasets demonstrate the robustness, accuracy, and effectiveness of our proposed method, compared with other robust finite mixture regression, spatial regression and spatial segmentation methods.
Network Traffic Obfuscation System for IIoT-Cloud Control Systems

One of the latest technologies enabling remote control, operational efficiency upgrades, and real-time big-data monitoring in an industrial control system (ICS) is the IIoT-Cloud ICS, which integrates the Industrial Internet of Things (IIoT) and the cloud into the ICS.Although an ICS benefits from the application of IIoT and the cloud in terms of cost reduction, efficiency improvement, and real-time monitoring, the application of this technology to an ICS poses an unprecedented security risk by exposing its terminal devices to the outside world.An adversary can collect information regarding senders, recipients, and prime-time slots through traffic analysis and use it as a linchpin for the next attack, posing a potential threat to the ICS.To address this problem, we designed a network traffic obfuscation system (NTOS) for the IIoT-Cloud ICS, based on the requirements derived from the ICS characteristics and limitations of existing NTOS models.As a strategy to solve this problem wherein a decrease in the traffic volume facilitates traffic analysis or reduces the packet transmission speed, we proposed an NTOS based on packet scrambling, wherein a packet is split into multiple pieces before transmission, thus obfuscating network analysis.To minimize the ICS modification and downtime, the proposed NTOS was designed using an agentbased model.In addition, for the ICS network traffic analyzer to operate normally in an environment wherein the NTOS is applied, a rule-based NTOS was adopted such that the actual traffic flow is known only to the device that is aware of the rule and is blocked for attackers.The experimental results verified that the same time requested for response and level of difficulty of analysis were maintained by the application of an NTOS based on packet scrambling, even when the number of requests received by the server per second was reduced.The network traffic analyzer of the ICS can capture the packet flow by using the pre-communicated NTOS rule.In addition, by designing an NTOS using an agent-based model, the impact on the ICS was minimized such that the system could be applied with short downtime.
PPML-TSA: A modular privacy-preserving time series classification framework

Privacy-preservation is of key importance for the transition of modern deep learning algorithms into everyday applications dealing with sensitive data, such as healthcare, finance and several other domains of critical infrastructure. One major impediment of research in computer science is the considerable time investment required to set up experiments and their evaluation. In the domain of privacy-preserving deep learning, this is aggravated by the dispersion of implementations throughout frameworks and libraries. This work introduces and documents PPML-TSA, a versatile framework for privacy-preserving time series classification. Our framework was initially used to evaluate privacy-preserving methods across different model architectures and datasets. PPML-TSA offers a modular design suitable for performing classification on all datasets from the entire UCR & UEA repository. Its modular implementation offers quick and easy adaptation and extension to increase the number of supported model architectures and datasets. The code supports a variety of model architectures (such as AlexNet, FCN, FDN, LSTM, and LeNet) as well as privacy-preserving deep learning methods (Differential Privacy, Federated Learning, their combination and Homomorphic Encryption) out-of-the-box. We believe that our framework facilitates further research on privacy-preserving deep learning, resulting in accelerated innovation and disruption in the field.
Isolated Vector Median Filtering Using K-Means Clustering for Impulse Noise Reduction in Digital Images with Application to Medical Diagnosis of Sars-Cov-2 Covid19 Disease

This paper proposes a new approach for filtering out impulse noise in digital color images. This work is of critical interest in biomedical imaging, visual tracking, etc. The conventional filtering methods operate by applying a noise reduction scheme, generally the vector median filtering approach and its variants, for the center pixel of a suitably chosen window that iteratively slides along the entire image. These methods consider the window in its entirety in the filtering process. This consideration, however, comprehends the noise within the noisy pixels in the filtering process and could prove detrimental to the overall output. The method proposed in this paper operates by clustering the pixels in the chosen window into two groups, one that corresponds to the pixel intensities that lie in the signal space and the other to those that lie in the noise space. The motivating rationale for this clustering scheme is to marginalize those pixels that lie in the noise space that seemingly do not contribute to the information in the image. The median filter is then applied to the pixels that contribute to the signal, in isolation of the color components, to filter out the impulse noise. Simulation results show that the proposed method outperforms conventional filtering methods in terms of noise reduction and structural similarity and thus validates the proposed approach. The proposed method is applied to analyze CT scans for improved diagnosis of SARS-COV-2 Covid19 disease. The method is also applied to a visual tracking example as a preprocessor.
Infectious Diseases Reporting System Using Naïve Bayes Classification Algorithm

Many approaches have been used in the past to address issues of spreading infectious diseases in the society with inclusion of reporting system. However, there is less or no study on the use of machine learning techniques in designing the reporting system for infectious diseases. Thus, the main objective of this study is to design an infectious disease reporting system that makes use of Naïve Bayes classification algorithm. The issue was addressed through a quantitative research approach to design a system that uses Naïve Bayes classification algorithm while reporting infectious diseases with respect to their associated symptoms. A model was developed together with system Pseudocode and the result revealed that the use of machine learning technique in the study eases the recommendation aspect of the system toward creating awareness of infectious diseases in the society. Hence, the study may serve as assistance to healthcare industry toward mitigation or eradication of infectious diseases in the societies.
Anomaly Detection and Blocking Based on Power IoT Sensing Layer Traffic Features Identification

Power IoT dumb terminals such as web cameras and network printers based on IP protocol and no user interaction interface are usually based on embedded system development, hard to update program firmware, limited computing resources, and simple security authentication mechanisms. The upgrade is easily controlled by the attacker to initiate a network attack. Aiming at the above problems, this paper designs and implements a dumb terminal security management and control system based on traffic feature recognition. The system extracts the traffic characteristics of the terminal and realizes the identity authentication and behavior supervision of the terminal. When the device is accessed, the static characteristics of the traffic of the terminal are extracted to implement identity authentication. After the device is accessed, the abnormal behavior of the network access terminal is analyzed and analyzed, and the session connection is blocked. The performance of the system is better in the experimental environment and the measured environment. The accuracy of equipment identification is 96.6%, and the accuracy of abnormal detection is 97.7%. It can effectively detect DOS, port scanning and other network attacks.
Development of exergy maps as a tool for assessing solar energy potential in Iraq

This work aims to develop three sets of maps: solar intensity, solar exergy, and the exergy efficiency of the solar and PV systems for Iraqi cities. Petela mode equations were programmed depending on the Iraqi weather data and solved using the Engineering Equation Solver software. It is found that the western regions of Iraq, which are often desert lands, suffer from the low intensity of solar radiation due to the dusty weather that characterises this region. The solar exergy is 70 W/m2 to 138 W/m2 in January, and the range in July is 260 W/m2 to 330 W/m2. The higher average exergy efficiency ranges from 15% to 19.3% in the winter, while the lower is 7.5% to 13.8% in the summer. Finally, the sites at lower latitudes have more global solar radiation. On the other hand, higher latitudes have more exergy efficiency than those at lower latitudes.
Performance Measurement of Natural Dialog System by Analyzing the Conversation

The natural dialog system, Chatbot, plays an important role in business domains by properly answering customer queries. In the pattern matching approach of Chatbot development, the user input is matched with a predefined set of responses. The machine learning approach of Chatbot development uses the principles of natural language processing to learn from conversational content. This study focuses on the performance measurement of pattern-based and machine learning-based Chatbot systems. As per the user point of view, performance measurement parameters are the ability to answer quickly, accurately, and comprehensively; to understand questions clearly; user friendliness; personalization options; ethics followed; and ability to process user feedback. The comprehensiveness of the knowledge base, robustness to handle unexpected input, scalability, and interoperability are some of the parameters considered to evaluate the Chatbot system by expert point of view. In this study, the specially designed Chatbot Usability Questionnaire is used to measure the performance of the implemented Chatbot systems.
Cryptocurrency

Since ancient times, currency has been an integral part of our lives. In the early times, we saw the barter system. No one really knows how currency came into the play. China created the world's first paper money in the early 7th century. Thousands of years later, we are living in the era of internet where after virtual games and friends, we have started using virtual currency. Cryptocurrency is an encrypted, peer-to-peer network for online payments directly between two parties without going through a financial institution. Bitcoin was the first and most popular cryptocurrency that was introduced in the year 2009 which created a huge surge in the market and got a lot of attention from the world. This new type of money is not likely to replace traditional flat currency, but it has the potential to change the way global markets interact with each other. Since the introduction of cryptocurrency, it has gathered a lot of appreciation and criticism. This chapter will provide in-depth discussion on cryptocurrency, its architecture, and also its pros and cons.
Short Text Representation via Adaptive Weighting with Two Views

This paper proposes a short text representation method via adaptive weighting with two views. Two views are established and integrated for training texts for representation. With two representations of the training set, the improved adaptive two-view weighting clustering algorithm is designed to cluster texts, view weights and the attribute weights can be obtained respectively, based on which the obtained cluster centers are utilized to represent the feature space. A weighted similarity calculation method utilizing two types of weights of clustered word vectors is established to calculate the similarity between the terms of the short text to be represented and the feature terms in the feature space. Thereafter the text mapping matrix is constructed for short text representation. The experimental results reveal that our method has a remarkable effect on representing short text.
Negative Sample Mining based Deep Feature Learning for Kinship Verification

Kinship verification is an important and challenging task. The aim of it is to find out whether there is a kin relationship between one pair of facial images. However, there are many challenges for kinship verification. One of the most important challenge is the problem of data imbalance. Specifically, if there exists N positive kinship pairs, we can get N(N-1) negative pairs. Obviously, the number of negative kinship pairs is much larger than the number of positive kinship pairs. How to make full use of positive and negative samples for training is a valuable problem. But most of the existing methods just randomly select the same number of negative sample pairs as the positive sample pairs. What is more, different negative pairs contain different discrimination information. Therefore, finding discriminative sample is also an important problem. In this paper, we propose a simple and effective method, named Negative sample Mining based Deep Feature Learning (NMDFL) to solve the above problems. Specifically, different with most existing methods, we sample N positive kinship pairs and T*N negative pairs, where T>1. Then, they are sent to the train net, and their impacts on network updating are adjusted in proportion to the number of positive and negative samples. At the same time, the corresponding weight of each negative sample is dynamically adjusted according to the training results of each time. Experimental results on the KinFaceW-I and KinFaceW-II datasets proves the effectiveness of our method.
Analysis on the Application of Cloud Computing in Music Education

Nowadays, cloud computing technology(CCT) has achieved unprecedented development, and the role of computers is getting bigger and bigger, and it has penetrated into various fields. We only have sufficient CCT to combine it with the traditional teaching(TT) model, so that students have a strong interest in music lessons and effectively improve the teaching quality of music lessons. And the overall musical quality of the students. The purpose is to study the application of cloud computing(CC) in music education(ME). This article mainly explores the application and practice of the cloud education platform model in college MT(MT) teaching. The educational effect of its creation is obviously better than that of TT. It solves the difficulties more quickly in textbooks, help students express actions faster, understand the essence of actions, and stimulate their interest. This research systematically reveals the connotation and significance of CC and the impact of CC on college music education, scientifically investigates the me curriculum, analyzes the basis and conditions of the integration of music education and CCT education, and examines CC through education. Experience. The types and effects of integration into college music education. Taking the integration of undergraduate me courses in our university as the research object, this paper uses the methods of literature, questionnaire and mathematical statistics to study and analyze, and defines the degree of integration of CC and me undergraduate courses. Research shows that more than 40% of music teachers use CC to find courseware materials and important and difficult mini-courseware on the Internet, and most music teachers hope that music websites will enhance collaboration and the construction of discussion areas.
Application of Associative Classifier for Data Sparsity in Predictive Analysis Recommendation

The researchers developed a web-based tool entitled, BizScout: Application of Associative Classifier for Data Sparsity in Predictive Analysis, which recommends an appropriate business that can be established in the chosen area of interest by utilizing associative classifier and apriori algorithm. The study aims to help individuals who plans to venture into entrepreneurship but lacks the knowledge on what business to start. Also, this study will help in regards to Computer Science students in terms of using Associative Classification on predictive analysis as well as apriori algorithm on data mining. The developed tool uses two main process the preprocessing and the Associative Classification. The developed tool resulted to 76.67% of accuracy in terms of business recommendations for the chosen area of interest and 4.44 weighted mean which is Moderately High for the appropriateness of the recommended business for the chosen location using associative classification.
Relevance of Spatio-temporal Data Visualization Techniques in Healthcare System

‘Health is Wealth’. It occupies an essential component for our survival on this planet earth. Ab antique, our ancestors and progenitors had a cherished desire for a good health. Keeping this in view, they urged for a coherence and congruity of our living patterns with the natural environment as the laws of nature are but the mathematical thoughts of GOD. On the contrary, our living patterns have been changed drastically which is the root cause of preponderance of diseases worldwide. It is interesting to note that the health that are useful in the treatment of individual’s health condition might prove a significant tool for making instant decision in case of emergency period. Besides, a growing volume of the clinical data is proving helpful to diagnose the various patients along with full understanding of their health status. The twenty-first century has witnessed a rapid advancement in science and technology particularly in the field of the Medical Sciences. However, with the advancement of information technologies in Medical Sciences has provided several opportunities before scientific communities to simplify the visualization of the health problems commensurately. Spatio-temporarily, these visualizations are important to measure the spreading rate of a particular disease in a particular region. In the present chapter, we have discussed the data pertaining to the healthcare system, various data visualization techniques, spatio-temporal data analysis, and the relevance of these data visualization techniques for the health science professionals.
Gripper integrated vision guided assembly of PEM fuel cells

Polymer electrolyte membrane (PEM) fuel cells consist of hundreds of individual layers, which need to be precisely stacked onto each other in a pick-and-place process in order to ensure a functioning product. Common automated pick-and-place systems usually feature an external camera for position detection of the individual layers. However, an external position detection increases cycle time and reduces the overall efficiency of the assembly system. Thus, this approach presents a camera, which is integrated into a flat surface vacuum gripper to detect and measure the position of components on-the-fly while grasping. The camera makes use of the suction openings necessary for grasping itself. Incoming light is detected and an aperture photometry is being applied to the image. Different methods for the component position detection will be presented and compared to a precise stereo camera reference measurement for the components actual position. With the reference the measurement accuracy of the novel gripper configuration is derived.
Base station network alarm streams modeling and prediction based on Cox proportional hazard model and copula

With the increasing development of wireless communication, telecommunication network operators value the precise prediction of the occurrence time of the alarm event, which promises high performance and reliability of the network. The usage of the correlation between the different types of alarms can effectively improve the accuracy of prediction. This article combines the Cox Proportional Hazard model with the copulas model to construct a flexible model for correlation analysis and time-to-event prediction. We use the Cox PH model to build the marginal distribution of the time-to-event variable of each alarm type and use copula to model the joint distribution. Based on our model, we analyze the real-life alarm events which occurred in cells of the base station network and estimate the coefficient parameters and covariance matrix, to clarify the correlations of different types of cell alarms, which are helpful for predicting the occurrence of alarms more precisely.
Exploring DAO Members' Individual Liability

The rise of cryptocurrency and blockchain technologies has revived the attention to decentralized autonomous organizations (DAOs) for social cooperation enabled by algorithms and the internet. They can substitute or complement conventional organizations. From a legal and governance perspective, to what extent should members and other participants be held accountable, responsible, and liable for DAOs? While some argue that DAOs should benefit from limited liability and legal immunity for the members, others favor individual liability. This paper argues for the latter approach to promote public policies and responsible innovation and explores principles and mechanisms available for holding members individually liable.
Object-Oriented Model of Knowledge for the Intelligent Tutoring Shell

Rapid development of computer technologies and Artificial Intelligence (AI) methods, and introduction of computers into schools have yielded an ever-increasing use of AI methods in design of education software. The teachers in schools have become interested in taking an active role in designing educational software, and not being only passive users of these tools. However, the advancement of AI methods and techniques makes understanding of Intelligent Tutoring Systems (ITS) more difficult, so that the teachers are less and less prepared to accept these systems. As a result, the gap between the researchers in the field of ITS and the educational staff is constantly widening. This paper describes our efforts toward developing uniform data and control structures that can be used by a wide circle of authors, e.g., domain experts, teachers, curriculum developers, etc., who are involved in the building of ITS.