Reviewerly/exHarmony huggingface dataset:
Fast response fuzzy logic controller optimized by bats sonar algorithm

This paper proposes a DC servo motor that is used for the crane system where the system is implemented by a controller that has been optimized by a bat sonar algorithm. With the new designed optimized controller, a faster angular position of the crane system can be generated. Unlike the normal approach, this method of an optimization technique is applied to the designed controller to tune the controller’s parameters can improve the stability and also the transportation time. The method involving the comparison between the system with and without the fuzzy logic controller. Besides, there are also comparisons of the fuzzy controller optimized by the bat sonar algorithm. This application is conducted in the real-time experiment setup. The system performance is evaluated based on the system responses such as rise time, settling time, steady-state error and percentage of overshoot. From the experiment, it found that designing fuzzy with optimizing algorithms gives a 20% faster response compared with the normal one. Looking into the future, this work will lead to faster dynamic response.
Inference in receiver operating characteristic surface analysis via a trinormal model‐based testing approach

Receiver operating characteristic (ROC) analysis is the methodological framework of choice for the assessment of diagnostic markers and classification procedures in general, in both two‐class and multiple‐class classification problems. We focus on the three‐class problem for which inference usually involves formal hypothesis testing using a proxy metric such as the volume under the ROC surface (VUS). In this article, we develop an existing approach from the two‐class ROC framework. We define a hypothesis‐testing procedure that directly compares two ROC surfaces under the assumption of the trinormal model. In the case of the assessment of a single marker, the corresponding ROC surface is compared with the chance plane, that is, to an uninformative marker. A simulation study investigating the proposed tests with existing ones on the basis of the VUS metric follows. Finally, the proposed methodology is applied to a dataset of a panel of pancreatic cancer diagnostic markers. The described testing procedures along with related graphical tools are supported in the corresponding R‐package trinROC , which we have developed for this purpose.
Hierarchical Target-Attentive Diagnosis Prediction in Heterogeneous Information Networks

We introduce HTAD, a novel model for diagnosis prediction using Electronic Health Records (EHR) represented as Heterogeneous Information Networks. Recent studies on modeling EHR have shown success in automatically learning representations of the clinical records in order to avoid the need for manual feature selection. However, these representations are often learned and aggregated without specificity for the different possible targets being predicted. Our model introduces a target-aware hierarchical attention mechanism that allows it to learn to attend to the most important clinical records when aggregating their representations for prediction of a diagnosis. We evaluate our model using a publicly available benchmark dataset and demonstrate that the use of target-aware attention significantly improves performance compared to the current state of the art. Additionally, we propose a method for incorporating non-categorical data into our predictions and demonstrate that this technique leads to further performance improvements. Lastly, we demonstrate that the predictions made by our proposed model are easily interpretable.
ExCoDE: a Tool for Discovering and Visualizing Regions of Correlation in Dynamic Networks

Dynamic graphs are valuable means to represent the volatility of real-world networks. In such scenarios, dense subgraph mining is a widely studied task, as it can give insights about how the relationships change over time. However, there are cases in which these changes are correlated. For example, in a road network, a traffic accident affects also the traffic in the adjacent road segments. We consider the problem of detecting dense regions of correlation in dynamically evolving networks, and demonstrate EXCODE, a system that solves two variants of the problem, which are based on two different density measures. It enumerates all the subgraphs satisfying certain density and correlation constraints, but can also detect compact subsets of limited overlap. In this demonstration, the audience can try this tool with real-world datasets, hence visualizing, interacting, and exploring the dense correlated subgraphs discovered in the mining process. An interactive panel allows them to learn where the correlations are located in the network, how the regions of correlation are related to each other, and how they evolve over time.
Design and Testing of a Corpus for Forensic Speaker Recognition Using MFCC, GMM and MLE

The importance of applying speaker recognition systems in forensic environments has increased in this century. One reason is the use of audio recordings as evidence in trials of every kind. This article presents a voice corpus design with this use in mind, based in recordings from speakers of the Spanish language dialect used in Central Mexico, distinct from any other corpus currently in use. For its evaluation, we used a Mel frequency Cepstral coefficient with a Gaussian Mixture Models for parametrization, and a Maximum Likelihood Estimation approach for classification. Results show an accuracy of more than 93% identification of the speaker at any condition, proving a good recognition model.
A Small Target Recognition Algorithm Based on Improved SSD

In the power industry, there are a lot of image and video data need to be analyzed to find out equipment abnormalities such as bolt looseness and out of stock. Using artificial intelligence technology for image analysis can reduce the artificial dependence, but the general object detection algorithms, such as SSD, can not get a good detection effect for small targets. In this paper, we proposed an improved SSD object recognition algorithm to improve the recognition accuracy of small targets, based on the characteristics of power image recognition requirements. The method can improve the ability of SSD framework to understand small objects and enhance the location information and semantic information of the small targets. The method introduce the deconvolution structure to act on the shallow feature layer Conv4_3 and then use pooling layer to get the fixed size. Finally the method made Conv14 upsampled to the same size, and make full use of the information of small targets. Experimental results show that the proposed method can achieve the good recognition accuracy.
Problems and Vulnerabilities of Ethical Hacking in Pakistan

The latest evolving threats to modern cyber security is making difficult for public and private sector organizations to protect their intellectual property and business information in digital form against theft, damage and misuse. Therefore, businesses need to become proactive and must have an adaptive approach in order to cater for this constant evolution of technology-related threats. In order to ensure successful cyber security strategy, Pakistan is facing two major problems. Firstly, there are no in-country formal trainings related to hacking. Therefore, only option is to have ethical hacker certification from abroad, which is expensive and beyond the approach of a mediocre citizen. Secondly, there is an acute crisis of job opportunities in the IT sector. In order to address these glaring issues; the Ministry of Defense, Interior Ministry and IT Ministry in liaison with Public and Private sector Universities of Pakistan has launched various programs for the development of legitimate hackers and to address the issue of un-employment in IT sector of Pakistan. This paper identifies the gaps in existing ethical hacking practices in vogue and helps to develop its own activity plan for self-defense against such cyber-attacks. The improved practices in ethical hacking will not only address the issues like availability of certified ethical hackers, un-employment in IT sector and defense against cyber-attacks, but will also promote the image of Pakistan as a peaceful nation across the world.
Forecasting trade balance of Czech Republic and People´s Republic of China in equalizing time series and considering seasonal fluctuations

The objective of the contribution is to introduce a methodology for considering seasonal fluctuations in equalizing time series using artificial neural networks on the example of the Czech Republic and the People´s Republic of China trade balance. The data available is the data on monthly balance for the period between January 2000 and July 2018, that is, 223 input data. The unit is Euro. The data for the analysis are available on the World Bank web pages etc. Regression analysis is carried out using artificial neural networks. There are two types on neural networks generated, multilayer perceptron networks (MLP) and radial basis function networks (RBF). In order to achieve the optimal result, two sets of neural structures are generated. There are generated a total of 10,000 neural structures, out of which only 5 with the best characteristics are retained. Finally, the results of both groups of retained neural networks are compared. The contribution this paper brings is the involvement of variables that are able to forecast a possible seasonal fluctuation in the time series development when using artificial neural networks. Moreover, neural networks have been identified that achieve slightly better results than other networks, specifically these are the neural networks 1. MLP 13-6-1 and 3. MLP 13-8-1.
&lt;i&gt;FlowScan&lt;/i&gt;: Estimating People Flows on Sidewalks Using Dashboard Cameras Based on Deep Learning

In this paper, we propose FlowScan: a pedestrian flow estimation technique based on a dashboard camera. Grasping flows of people is important for various purposes such as city planning and event detection. FlowScan can estimate pedestrian flows on sidewalks without taking much cost. Currently, dashboard cameras have been becoming so popular for preserving the evidence of traffic accidents and security reasons. FlowScan assumes that an application which analyzes video from the camera is installed on an on-board device. To realize such an application, we need to design a method for pedestrian recognition and occlusion-proof tracking of pedestrians. For pedestrian recognition, the application uses Deep Learning-based techniques; CNN (Convolutional Neural Networks) and LSTM (Long-Short-Term-Memory). In this process, the faces and backs of their heads are searched in the video separately to detect not only the number of pedestrians but also their directions. Then, a series of detected positions of heads are arranged into tracks depending on the similarity of locations and colors considering the knowledge about the movement of the vehicle and pedestrians. We have evaluated FlowScan using real video data recorded by a dashboard camera. The mean absolute error rate for people flow estimation of both directions was 18.5%, highlighting its effectiveness compared with the state-of-the-art.
User interface redesign in a point rewards mobile application using usability testing method

The e-commerce market is projected to grow years by years, including in Indonesia. This growth was triggered by the increasing number of internet users both in the world and in Indonesia. But along with the e-commerce market grow in Indonesia, this was followed by a decline in the number of visits to the retail store. The object is an ad tech company in Indonesia that tries to increase the number of visits to the retail store. The firm uses the mobile application platform to run its business. However, according to a preliminary study, 75% of respondents said they were still having problems when using the application. Therefore we need usability testing for this application to find out how easily the mobile application is used. This study uses the usability testing method with think aloud, after scenario questionnaire (ASQ), system usability scale (SUS), and questionnaire for user interface satisfaction (QUIS). After gathered the usability results from the mobile application, this research was continued by making improvements to the user interface design of the application using the user-centered design approach and using 10 usability heuristics for user interface design and Material Design as literature in the making of usability improvements.
ARMAX: A Mobile Geospatial Augmented Reality Platform for Serious Gaming

The AR concept can be considered as one of the key technologies in the context of Industry 4.0 as it can be applied in many domains to provide context-aware interaction with the environment. While the concept of mixed reality is not new, the technology can now be found in different shapes and sizes, each tailored for a particular use case. In this paper we aim at defining a broader context while developing a universal platform for Serious Gaming for different industries that benefits from the most advanced AR implementation combined with the ubiquity of mobile devices. Some of the main challenges related to the 3D registration and tracking of virtual objects are discussed and the results are presented as obtained from real world testing. The calibration method is validated by simulation and while not being the most sophisticated solution it shows the experimental work and testing that is required for the accurate alignment of the real world and the virtual objects on the AR. The solution is presented with a focus on gamification and user experience in the context of a crowdsensing platform that aims to increase collaboration and participation in a Smart City environment.
A Method of Detecting the Abnormal Encrypted Traffic Based on Machine Learning and Behavior Characteristics

Classification of network traffic using port-based or deep packet-based analysis is becoming increasingly difficult with many peer-to-peer(P2P) applications using dynamic port numbers, especially in massive data streams. In view of the problem that traditional method cannot be self-learning and self-evolving in dynamic networks, this paper proposed an abnormally encrypted traffic detection method based on machine learning and behavior characteristics, this approach can not only identify unknown abnormal traffic, but eliminate specific feature extraction in advance, which can effectively improve the accuracy of the abnormal encrypted traffic detection system. In this paper, we processed the network traffic data with using a machine learning approach combined behavior characteristics of applications, the experimental results show that in the complex network, the abnormal encrypted data stream detection method based on machine learning and behavior characteristics has higher recognition accuracy and can more effectively solve the problem of abnormally encrypted traffic identification.
Development of Matrix Methods for Genetic Analysis and Noise-Immune Coding

The article discusses new methods of studying and using Hadamard matrices. This class of matrices is commonly used in developing AI systems, noise-immune coding of data and simulation of bioinformational entities. The relations between Hadamard matrices and hyper-complex numbers are discussed. A special attention is given to the new method of pre-orthogonal sequences for building multi-block Hadamard matrices of large orders. This method, due to its mathematical characteristics, should also be useful in modeling the noise-immunity features of genetic coding.
On ‘Artificial Intelligence-User Interface’ Approach

This paper considers the basic concepts of automated design and implementation of application graphical user interface supported by artificial neural networks and application of this approach to child computer interaction educational projects.
GPGPU Implementation of Variational Bayesian Gaussian Mixture Models

Machine learning technology has been developed along with the performance improvement of CPUs and GPGPUs, and it has been used for analyses of big data. Clustering is one task of them. Gaussian Mixture Models are a representation method that realizes clustering, and widely used in many applications for probability density modeling and soft clustering. However, the parameters of a GMM need to be estimated from data. There are some methods for estimate parameters, Variational Bayesian Gaussian Mixture Models (VB-GMM) are one of them, and it is hardly to over-fitting than other methods. But, it is computationally demanding. In this paper, we explain a parallel implementation of VB-GMM by GPGPU, and its evaluation. As a result, its execution speed is 524 times faster than that of CPU implementation.
Efficient Retrieval of Data from Cloud Databases using Hash Partitioned Buckets

Cloud computing has become a cornerstone of development during this era of computing. More and more users are turning to the cloud services provided by the third party service providers at cheaper costs and high availability. But protection and privacy of the data stored in the cloud is still a major concern since the users data is being controlled by a third -party in between. Encryption becomes an obvious choice though but is is still questionable on how well the data in its encrypted form can be managed at the cloud . We have proposed a method of applying partitioning techniques on the encrypted structured database in cloud . We have used the hash partitioning technique to partition our database. Our proposed method is compared against a traditional coding table method and it is found to be comaparitively efficient in terms of the running time of the queries.
NDBC OWL Wave System Development

The U.S. National Oceanic and Atmospheric Administration's (NOAA) National Data Buoy Center (NDBC) is developing a new wave observation system named Ocean Wave Linux (OWL). OWL will replace the NDBC's older and now obsolete Digital Directional Wave Module (DDWM). The OWL uses the existing DDWM processing algorithm but is implemented with Linux-based modern processing hardware and a high performance motion sensor. These improvements avoid parts obsolescence and lower power demand. The OWL consists of a nine axis motion sensor, a Linux processing board, and a custom NDBC circuit board that provides control and data collection. The NDBC performed extensive tests to verify proper code operation, motion sensing, and operation of the electronics. Tests included using the NDBC's Desktop Wave Simulator (DTWS) and the Ocean Wave Instrumentation Facility (OWIF) to simulate wind and swell ocean waves, respectively. Other laboratory tests were conducted to confirm proper operation at extreme cold and heat (e.g. twenty degrees below Celsius and fifty degrees above Celsius) and to measure power consumption. The OWL achieves a sixty-five percent reduction in power consumption as compared to DDWM. Test systems that incorporate both the older DDWM and OWL systems in one enclosure have been fabricated, bench tested, and one deployed for field evaluations at the NDBC 46022 station. The OWL's wave height and period compared well with the collocated DDWM and a Waverider located approximately twenty-five kilometers away. Further collocated tests between the DDWM and OWL and a Datawell Waverider are planned for other West Coast and East Coast locations.
Biometric Detection Using Stroke Dynamics

The process of preventing unauthorized use of computers is called computer security. A convenient user authentication device is more essential than ever before. The major intend of this paper is to provide a suitable and reliable authentication method using keystroke and mouse dynamics combined with face recognition. Using user-id and password is considered as the most basic type of authentication. An unauthorized person can access a genuine user’s resources by exploiting an unreached unlocked computer or possibly by stealing the password of user. Without continuous authentication it is not possible to say who the real person is. So the paper is to provide a combination of static and continuous authentication. This is a biometric system to avoid impersonation and is a hybrid security system. Typing pattern in the keyboard of a user is combined with mouse interaction behavior to perform continuous analysis of a user and extracting the face features to perform static authentication. Dataset was collected and analysis was done continuously and transparently during the whole session. The keyboard, mouse interaction behaviors, and face features are unique to each individual. The method provides a probable solution to authenticate rightful users statistically and endlessly.
Probabilistic routing in wireless networks with privacy guarantees

We consider the source–destination location privacy problem for routing in wireless networks. Previous routing schemes mainly provided privacy protection by minimizing the average detection probability of traffic analysis attempts. However, they do not seek to provide strict privacy guarantees of the vulnerable source–destination pairs, which could still be relatively easy to identify. To address this gap in the literature, we propose the (k,ϵ)-anonymity property for routing in wireless networks with privacy guarantees. We consider a Bayesian maximum-a-posteriori (MAP) inference-based adversary and design a probabilistic routing scheme that uses a statistical decision-making framework to compute the minimum-cost (k,ϵ)-anonymous paths. A routing scheme is (k,ϵ)-anonymous if there are k or more distinct source–destination pairs within an ϵ-tolerance of the MAP probability. We compare our approach against a baseline routing scheme that minimizes the average detection probability of the adversary, and our simulation results show that our approach provides significantly better (k,ϵ)-anonymity privacy guarantees while achieving comparable average adversarial detection probability. We also studied how the adversary’s prior beliefs affect its detection probability and Bayes risk.
How are exemplar representations transformed by encoding, retrieval, and explicit knowledge? A commentary on Ambridge (2020)

The radical exemplar model resonates with work on perceptual classification and categorization highlighting the role of exemplars in memory representations. Further development of the model requires acknowledgment of both the fleeting and fragile nature of perceptual representations and the gist-based, good-enough quality of long-term memory representations. Retrieval operations potentially serve as a mechanism for abstraction as representations of exemplars are distorted through reconstructive processes. As a framework applicable to both first and second language acquisition, the model needs to account for how explicit knowledge arises and its role in filtering input via selective attention.
Proposed Recommender System for Open Educational Resources for Informatics Institute for Postgraduate Studies

finding user appropriate information among the enormous amount of data on the internet is a challenging method. Therefore, an information filtering method is required to assist users to discover their contents of interest. The Recommendation system is the most popular method used in many websites today to assist the process of creating suggestions. This paper will explain the design of an OER (Open Educational Resources) recommender system using KNN and Naïve Bayes classification algorithms, and present the evaluation of the results of their performance. The proposed system follows the content- based recommendation approach, by analyzing the user's actions, such as ratings and compare with resource features to create a user-profile model and generate an accurate recommendation.