Reviewerly/exHarmony huggingface dataset:
MODELS OF INTELLIGENT INTERFACES OF INFORMATION SYSTEMS SEARCH

Currently, there are many models of intelligent interfaces of search information systems for search using natural for the user modalities of speech, face, gestures and their recognition. In this article, on the example of the intelligent interface of the unmanned aerial vehicle, two models of user modality recognition will be studied, these are hidden markov models and bayesian network, these models will have to recognize the hand gestures of the operator of the unmanned aerial vehicle.
Thread-Level CPU and Memory Usage Control of Custom Code in Multi-tenant SaaS

Software-as-a-Service (SaaS) providers commonly support customization of their services to allow them to attract larger tenant bases. The nature of these customizations in practice ranges from anticipated configuration options to sophisticated code extensions. From a SaaS provider viewpoint, the latter category is particularly challenging as it involves executing untrusted tenant custom code in the SaaS production environment. Proper isolation of custom code in turn requires the ability to control the CPU and memory usage of each tenant. In current practice, OS-level virtualization tools such as hypervisors or containers are predominantly used for this purpose. These techniques, however, constrain the number of tenants that a single node can cost-effectively accommodate. In this paper, we present a practical solution for thread-level tenant isolation, vis-à-vis CPU and memory usage in presence of tenant-provided custom code. Both Java Runtime Environment (JRE) bytecode and tenant code are instrumented with usage control checkpoints which, based on data gathered using the Java Resource Consumption Management API (JSR-284), ensures that CPU and memory usage of tenants remain within their Service-level Agreements (SLA) limits. Our experiments show that the tenant accommodation capacity of single node increases 59 times with the proposed solution instead of containers. This scalability improvement comes at the average cost of $$0.31\,\mathrm{ns}$$ performance overhead per control checkpoint.
Through an Inference Rule, Darkly

Mathematical logic provides a formal language to describe complex abstract phenomena whereby a finite formula written in a finite alphabet states a property of an object that may even be infinite. Thus, the complexity of the underlying objects is abstracted away to give way for a simple syntactic description, a kind of mathesis universalis. The complexity, however, continues affecting which ways of reasoning are valid. Structural proof theory reasons using proof objects more complex than individual formulas. One of its goals is to find minimal additional structures, depending on the complexity of the underlying objects, sufficient for efficient and modular reasoning about them. In this paper, we are primarily interested in both the global structure used for reasoning and the local part of this structure employed to justify single inference steps. We provide recently developed examples where adapting the global structure of the sequent to the local structure of (potentially infinite) Kripke models yielded both quantitative and qualitative benefits in establishing fundamental logical properties such as complexity and interpolation.
Bayesian Network for Development of Expert System in Pest Management

Timely availability of decision support to the farmers on ‘whether and what management option is required’ is imperative for effective pest management. In the absence of knowledge and expertise, farmers are over-dependent on pesticide dealers for pest management decision support in the country, which results in excessive, injudicious and irrational use of chemicals for the pest control and thus not only degrades the environment but also affects human health. For decades, the pest economic threshold level (ETL) has been the basis to select appropriate pest management option for decision support to the farmers, used in ICT-based expert systems for decision support in agriculture. This process requires quantitative information about pest activity, which needs to be scientifically observed in the farmers’ fields. Moreover, in current pest management, decision-making depends upon a large range of agro-ecological information, besides pest activity. However, a large section of the farming community is not able to obtain this kind of information rather can observe qualitative information consisting of uncertainty. Neither there are the methods of selecting appropriate pest management option based on tentative pest relevant information, provided by farmers that can be used in expert systems of decision support in pest management. Bayesian Network (BN), the most suitable probabilistic reasoning approach for dealing with uncertain information has been effectively used in ecological decision-making nevertheless, very little efforts have been for its usage in pest management in the country. Application of Bayesian network may greatly assist in development of innovative methods of selecting appropriate management option based on uncertain or tentative information of identified agro-ecological factors besides crop condition that farmers provide. Application of the methods thus developed in information and communication technology (ICT) based expert systems (ESs) of agriculture would automate and speed up the process of providing real-time decision support on insect-pest management to the farmers.
Linear Filters for Image Energy

Four families of new matrix linear filters are established and being used for the approximation to directional derivatives. These filters are also used for representations of image energy, which, in turn, are used for representing image edges. The first three families are for approximations of first order directional derivatives, while the fourth family is for the approximations to the second order directional derivatives. Examples are demonstrated.
Text Difficulty Classification Based on Lexile Levels Using K-Means Clustering and Multinomial Naive Bayes

Reading is an important thing for one's education development. Even so, every single person has the different reading ability, this kind of differences becomes a problem when the complexity of reading material exceeds the reader's reading ability. Therefore, it is necessary to determine the difficulty level for reading material. One of the solutions that can be applied is grouping and classifying text difficulty based on the Lexile Level. Lexile Level is a value that represents a person's reading ability and the difficulty level of reading material. In this study, there were 4 experimental scenarios. First, Lexile levels are grouped into 2 clusters, “Difficult” and “Easy”. Second, Lexile levels are grouped into 3 clusters, “Difficult”, “Normal”, and Easy”. Furthermore, the oversampling process is carried out on each clustering result to overcome unbalanced data distribution using SMOTE (Synthetic Minority Over-Sampling Technique). Of the four processing results, text classification is done to determine the difficulty level of reading material using Multinomial Naive Bayes. The study showed that classification on 2 clusters with oversampling produces the highest average accuracy to 84%, while classification on 2 clusters without oversampling reached 71% of average accuracy. The result of classification on 3 clusters with oversampling showed an average accuracy of 78%, and the classification on 3 clusters without oversampling showed the lowest average accuracy value, which is 53%.
Frequency Shift Triangulation: A Robust Fringe Projection Technique for 3D Shape Acquisition in the Presence of Strong Interreflections

We present the Frequency Shift Method, a new structured light technique allowing 3D shape acquisition in the presence of strong interreflections. The intensity signal of each camera pixel is represented by one or several peaks in the Fourier domain. If there is no interreflection, only a single peak appears, otherwise several peaks are present. Each peak represents a projector pixel participating in the illumination of the surface point imaged at the camera pixel. In the baseline version of the proposed approach, the number of patterns required is proportional to the number of projector pixels. We also propose a modification that significantly reduces the required number of patterns by subdividing and encoding the projector into multiple virtual low-resolution projectors. A method based on dynamic programming is used to separate direct and indirect illuminations. Our experimental results illustrate the effectiveness of our method compared to existing ones.
Human Body Segmentation Using Level Set-Based Active Contours With Application on Activity Recognition

Activity recognition (AR) methods require the ability to identify activities of crucial parts in a human body. Body segmentation techniques employed in video-based AR methods outlines significance of areas (parts) in a human body image that are essential for the follow-up methods, such as feature extraction. Existing body segmentation techniques generally make use of body modeling with or without its background and essentially entail large training datasets. These techniques cannot cope with the changes that may occur in images over time. For example, illumination and clothing changes are some of the key concerns in practical AR systems. This paper presents a new approach to employ active contour-based model that exhibits robustness to illumination and clothing changes. Existing techniques based on active contours effectively address this problem in still images and hence lack the ability to cope with illumination and changes. We present an optical flow that 1) ensures the smooth working of the model over the data that is used for estimation into contiguous frames, and 2) ensures to place the initial contour within the current frame. Our proposed approach does not depend on prior knowledge about the data and and does not require the training data, hence it is an unsupervised approach. We use the existing effective method curvelet transform to extract prominent features from activity frames. Curvelet transform keeps the line and edge information in an image intact. We employ the linear discriminant analysis (LDA) for dimension space reduction and the hidden Markov model (HMM) for activity recognition. We show that AR using our proposed method for segmentation has the accuracy comparable to those that make use of manual segmentation methods and prove its feasibility in practical AR systems.
RefineNet4Dehaze: Single Image Dehazing Network Based on RefineNet

In recent years, the image dehazing technology with deep learning has been updated rapidly. These deep learning method shows more potential compared with traditional methods which need to estimate the atmospheric scattering model and prior information. In this paper, we proposed an end-to-end multi-scale single image dehazing method, and the method does not rely on the parameter estimation of atmospheric scattering model. We use the residual network (ResNet) to extract the residual features of hazy images at different scales, and then we use a kind of enhanced RefineNet to fuse these features. Finally we use recursively network to directly learn the nonlinear mapping from hazy images to haze-free images. We assessed our method on two datasets, and compared with two representative and effective methods. The results demonstrated that the proposed method has better performance on image dehazing.
Electric Maternal, Child and Doctor Handbook as Personal Data Storage for Disaster Preparedness

Today, personal data management has become a big issue in our society. Especially, when large-scale devastating disaster happen, personal data management is much more important issue. One of the most important problems was the poor information collection methods of people who need urgent medical care. In this paper, we propose an online system, focusing on obstetric medical care, called “Electric Maternal, Child and Doctor Handbook” (eMCDH), which manages location information, medical information of pregnant women and their fetuses. An eMCDH user can control how to share her data with disaster medical support at the disaster time. We have developed a pilot system and conducted an experiment on Shelter Assessment Drill. As a result, we found that eMCDH was useful for grasping pregnant information in a smooth manner in the assessment process. On the other hands, the extent of its effect largely depended on the assessment skill at the shelter.
Adaptive Neuro-Fuzzy Inference System implementation for farmer’s term of trade forecasting in West Sumatra

Abstract The farmer’s term of trade (NTP) is used by the Central Bureau of Statistics (BPS) as one of the indicators for measuring the level of welfare or purchasing power of farmers. To prepare preventive measures when the NTP index falls from the previous period, the relevant parties need to predict NTP for the coming period. The purpose of this study is to measure the performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting NTP of West Sumatra Province in the coming month. The data used are monthly NTP data of West Sumatra Province obtained from the BPS website www.sumbar.bps.go.id from 2013 to 2016 for the network training process. Model evaluation was done by comparing the test results with actual data in 2017. Forecasting systems are divided into two types, namely time series model and the multivariate model. The test results show that the time series model has the smallest RMSE of 0.3430 for the training set and the RMSE value is 1.4570 for the testing set.
Implementation of Radiation Safety Utilization of Fluoroscopy Baggage Scanner at Port

The effort to ensure radiation safety guarantees for officers and the general public on the use of fluoroscopy baggage scanner is used at the port passenger terminal due to using ionizing radiation. The aim to be achieved is to find out the management of the use of ionizing radiation and related radiation protection according to national and international standards. The research method in this study was an observational survey by observing, interviewing and documenting safety facilities and procedures by performing management for operating fluoroscopy baggage scanner devices at the Port. Based on the results of this study: no operational license for fluoroscopy baggage scanner, radiation workers operator didn’t certify, they don’t have personal monitoring dose, they get health monitoring but not well documented, radiation testing has not been carried out for radiation workers and passengers. Radiation exposure measurements using surveymeter detected both for passengers and radiation workers around the fluoroscopy baggage scanner device was declared safe because the radiation exposure rate detected was below 1 µSv/h (FDA), PP No. 33 of 2007, Perka Bapeten No. 7 of 2009 and Perka Bapeten No. 4 of 2013. But it can be optimized by lead glass or lead apron.
Enhancing Face Recognition through Overlaying Training Images

In the usual face recognition approach, system is getting trained through a large number of training samples. It means in the process of training, features are extracted from all the training images individually. In this process many redundant features are required to be eliminate also. During feature elimination, some features also get suppressed due to inappropriate thresholds. So, this approach is typically time consuming and costly in the part of training. Hence, there is a requirement of feature extraction in such a way that it reduces the chance of data redundancy and system complexity. This paper presents a facial recognition technique by inclusion of superimposed version of all relevant images which improves the accuracy of the model by roughly 43 percent. The algorithm aims to establish the importance of superimposition strategy in the field of face recognition. The Haar feature based classifier is used, where a cascade function is trained from a set of images. We have used the open source database of faces from the archives of AT&T Laboratories Cambridge to train and test our model.
Formalising Document Structure and Automatically Recognising Document Elements: A Case Study on Automobile Repair Manuals

Correct understanding of document structures is vital to enhancing various practices and applications, such as authoring and information retrieval. In this paper, with a focus on procedural documents of automobile repair manuals, we report a formalisation of document structure and an experiment to automatically classify sentences according to document structure. To formalise document structure, we first investigated what types of content are included in the target documents. Through manual annotation, we identified 26 indicative content categories (content elements). We then employed an existing standard for technical documents—the Darwin Information Typing Architecture—and formalised a detailed document structure for automobile repair tasks that specifies the arrangement of the content elements. We also examined the feasibility of automatic content element recognition in given documents by implementing sentence classifiers using multiple machine learning methods. The evaluation results revealed that support vector machine-based classifiers generally demonstrated high performance for in-domain data, while convolutional neural network-based classifiers are advantageous for out-of-domain data. We also conducted in-depth analyses to identify classification difficulties.
EKMC: Ensemble of kNN using MetaCost for Efficient Anomaly Detection

Anomaly detection aims at identification of suspicious items, observations or events by differing from most of the data.Intrusion Detection, Fault Detection, and Fraud Detection are some of the various applications of Anomaly Detection.The Machine learning classifier algorithms used in these applications would greatly affect the overall efficiency.This work is an extension of our previous work ERCRTV: Ensemble of Random Committee and Random Tree for Efficient Anomaly Classification using Voting.In the current work, we propose SDMR a simple Feature Selection Technique to select significant features from the data set.Furthermore, to reduce the dimensionality, we use PCA in the pre-processing stage.The EKMC (Ensemble of kNN using MetaCost) with ten-fold cross validation is then applied on the pre-processed data.The performance of EKMC is evaluated on UNSW_NB15 and NSL KDD data sets.The results of EKMC indicate better detection rate and prediction accuracy with a lesser error rate than other existing methods.
Deformable Part Model-Based Vehicle Detection for UAV Vision System

Unmanned Aerial Vehicles(UAV) have been widely applied in many areas of civilian and military. Vehicle detection is one of the practical and necessary applications of UAV. To improve the detection ability of the UAV vision system, we adopt the effective deformable part model in the vehicle detection of UAV. We evaluate our method with simulative images and videos and the results show that our desired goals have been achieved.
Research on University’s Cyber Threat Intelligence Sharing Platform Based on New Types of STIX and TAXII Standards

With the systematization of cyber threats, the variety of intrusion tools and intrusion methods has greatly reduced the cost of attackers' threats to network security.Due to a large number of colleges and universities, teachers and students are highly educated and the Internet access rate is nearly 100%.The social status makes the university network become the main target of threat.The traditional defense method cannot cope with the current complex network attacks.In order to solve this problem, the threat intelligence sharing platform based on various threat intelligence sharing standards is established, which STIX and TAXII It is a widely used sharing standard in various sharing platforms.This paper analyzes the existing standards of STIX and TAXII, improves the STIX and TAXII standards based on the analysis results, and proposes a new type of STIX and TAXII based on the improved results.The standard design scheme of threat intelligence sharing platform suitable for college network environment features.The experimental results show that the threat intelligence sharing platform designed in this paper can be effectively applied to the network environment of colleges and universities.
A New Evolutionary Algorithm Based on Self-Adaptive Grouping and Efficient Resource Allocation

To solve large scale optimization problem, a new evolutionary algorithm using variable grouping and allocating computational resource based on contribution is proposed in this paper. First, we propose a new grouping scheme in which we adopt an existing and effective one called FBG for separable problems and design a new grouping scheme called a self-adaptive grouping (SAG) method for non-separable problems. By using these two schemes, any large scale problem can be effectively divided into small scale sub-problems. Second, in order to solve the sub-problems efficiently by making full use of limited computational resource, we design an efficient resource allocation method (ERA) which can reasonably allocate the resource to each sub-problem according to its contribution. The more contribution a sub-problem gives to whole problem, the more resource the sub-problem will get. Based on these, a new evolutionary algorithm is proposed. Finally, the experiments are conducted on widely used benchmark suite CEC'2010, and the proposed algorithm is compared with some state-of-the-art algorithms. The experimental results show the competitive performance of the proposed algorithm to solve these problems.
Substantiation of multinomial classification using ensemble learning approach

Abstract The software defect prediction is the most operative research domain in software engineering as it enhances its reliability. The availability of defect data related to different projects leads to cross project defect prediction an open issue. This paper instanced on multiclass/multinomial classification of defect prediction on different categories of cross projects. The ensemble learning statistical models – Random forest and Gradient Boosting are used for classification. An empirical study is carried out to determine the predictive performance of the within project and cross project prediction models. Depending on the number of defects, class level information is classified into one of three defined multiclass. The homogeneous set of object oriented metrics is used for training the model. Furthermore, k-fold cross validation is done to evaluate the training accuracy of the statistical models. Major outcome of the paper concludes that multinomial/multiclass classification is applicable on cross project data and has comparable results to within project defect data with statistical significance.
Self-Calibrating Scene Understanding Based on Motifnet

Scene understanding has made significant progress recently that motivates the development of practical applications and accelerates the prosperity of general artificial intelligence. As one of the most important tools in scene understanding, scene graph generation inspires abundant researches focusing on the reasoning of different relationships among objects. However, as one of the basic concepts constructing physical hierarchies of our world, mutual promotion and calibration within different perceptual contexts are sparsely considered in current work. In this paper, we introduce the Self-Calibrating Scene Understanding Model based on the combination between Motifnet and Wasserstein GAN [1], which emulates the methodologies people cognize the world and brings out an idea that scene understanding, recognized as one of the core technologies towards general artificial intelligence, should be fused with calibrating mechanism.