Reviewerly/exHarmony huggingface dataset:
Quickest physical‐layer MGD anomaly detection for jamming attacks in centralized modulated wideband converter‐based ROC curve

Summary In wireless communication systems, physical‐layer security menaces have evolved from jammers. Jammers, due to their furtive nature, make wireless communication systems vulnerable. The novelty in this work is to combine centralized modulated wideband converter, which is a networking system developed from the modulated wideband converter–based sub‐Nyquist sampling theory with a multivariate Gaussian distribution (MGD) anomaly detector‐based receiver operating characteristic curve that plot the detection rate (DR) versus false alarm rate (FAR) at various threshold values. We supposed the presence of a group of jammers in the spectrum corrupted with the primary source signal and noise. The received primary signal at each cognitive radio (CR) receiver is converted in to a digital signal using an analog‐to‐information converter. Each CR receiver give minimum number of samples denoted N 1 . All these compressed samples from every CR receiver are collected in the form of matrix called compressed sampling matrix, which is considered directly as the input of the MGD detector. The intelligent MGD detector proposed in the level of fusion center is based on the characteristics of the MGD. The numerical results show that this new system of combination detects faster anomalies perfectly in the presence of jammers in the spectrum in real‐time scenarios. Performance evaluation is performed in terms of DR versus FAR at different detection threshold values, under the presence of attacks in the system. By employing well‐known machine learning algorithms called MGD, the performance of this new proposed system shows good.
Named Entity Recognition (NER) for Nepali

Named Entity Recognition (NER) is the process of automatic extraction of Named Entities (NEs) by means of recognizing (finding the entities in a given text) and their classification (assigning a type). A lot of work has been done in English and other languages for NER but very little research has been done for Nepali NER. Nepali language has its own issues and challenges which need to be addressed. In this paper, we present the NE Tagset and features that can be potentially used for Nepali NER. To train the model classifiers like Support Vector Machine (SVM), Multinomial Naïve Bayes and Logistic Regression are used and their precision, recall and F1-score have been calculated. 5 fold cross validation is used to evaluate and validate our model.
Real-Time Light Field Depth Estimation via GPU-Accelerated Muti-View Semi-Global Matching

The structured and redundant imagery of light field cameras can provide more robust depth estimation results while on the other hand demands a huge computation power, which limits its real-time applications, such as online industrial monitoring, 3D endoscopic surgery etc. This paper extends the classical SGM(Semi-global matching) [1] algorithm to light filed multi-view stereo framework, which can acquire sub-pixel level disparity estimation to cope with the micro-baseline of light field cameras. The whole algorithm is tailored for parallelization on GPU exploiting multi-stream asynchronization, multi-thread allocation, and multi-type memory management. The results show that our method’s execution time is less than 50ms on NVIDIA 1080Ti, which to our knowledge is the fastest among reported geometry based methods while keeping a comparative accuracy performance.
New Optimization Algorithm Based on Free Dynamic Schema

In this paper, we describe and test a new evolutionary algorithm based on the notion of a schema, which is designed to solve global optimization problems. We call it Free Dynamic Schema (FDS). It is a more refined variant of our previous DSC, DSDSC and MDSDSC algorithms. FDS processes two populations which are partially composed of the same chromosomes. The algorithm divides each population into several groups to which various genetic operators are applied: free dynamic schema, dissimilarity, similarity, and dynamic dissimilarity. Also, some new chromosomes are regenerated randomly. The FDS algorithm is applied to 22 test functions in 2, 4 and 10 dimensions. It is also compared with the classical GA, CMA-ES and DE algorithms. Moreover, the FDS algorithm is compared with the BA and PSA algorithms for some functions. In most cases, we have found the FDS algorithm to be superior to the classical GA and BA.
Lazy Learned Screening for Efficient Recruitment

The transition from traditional paper based systems for recruitment to the internet has resulted in companies in getting a lot more applications. A majority of these applications are often unstructured documents sent over email. This results in a lot of work sorting through the applicants. Due to this, a number of systems have been implemented in an effort to make the screening phase more efficient. The main problems consist of extracting information from resumes and ranking the candidates for positions based on their relevance. We develop a system that can learn how to rank candidates for a position based on knowledge obtained from earlier screening phases. This Candidate Ranking System (CRS) is based on Case-based Reasoning, combined with semantic data models. The systems performance is evaluated in conjunction with a large international Job company and a software company in an actual recruitment process.
3D Driver Pose Estimation Based on Joint 2d-3d Network

3D driver pose estimation is a promising and challenging problem for computer-human interaction. Recently convolutional neural networks (CNNs) have been introduced into 3D pose estimation, but these methods have the problem of slow running speed and are not suitable for driving scenario. In this paper, our method is based on two type of inputs, IR image and point cloud obtained from TOF camera. We propose a Joint 2D-3D network incorporating image-based and point-based feature to promote the performance of 3D human pose estimation and run in a high speed. For point cloud with invalid points, we firstly do preprocess and then design a denoising module to handle this problem. Experiments on private Driver dataset and public ITOP dataset show that our method achieves efficient and competitive performance on 3D human pose estimation.
Robustification of Deep Net Classifiers by Key Based Diversified Aggregation with Pre-Filtering

In this paper, we address a problem of machine learning system vulnerability to adversarial attacks. We propose and investigate a Key based Diversified Aggregation (KDA) mechanism as a defense strategy. The KDA assumes that the attacker (i) knows the architecture of classifier and the used de-fense strategy, (ii) has an access to the training data set but (iii) does not know the secret key. The robustness of the system is achieved by a specially designed key based randomization. The proposed randomization prevents the gradients' back propagation or the creating of a "bypass" system. The randomization is performed simultaneously in several channels and a multi-channel aggregation stabilizes the results of randomization by aggregating soft outputs from each classifier in multi-channel system. The performed experimental evaluation demonstrates a high robustness and universality of the KDA against the most efficient gradient based attacks like those proposed by N. Carlini and D. Wagner [1] and the non-gradient based sparse adversarial perturbations like OnePixel attacks [2].
Combining Machine Learning and Natural Language Processing for Language-Specific, Multi-Lingual, and Cross-Lingual Text Summarization

The recent advances in multimedia and web-based applications have eased the accessibility to large collections of textual documents. To automate the process of document analysis, the research community has put relevant efforts into extracting short summaries of the document content. However, most of the early proposed summarization methods were tailored to English-written textual corpora or to collections of documents all written in the same language. More recently, the joint efforts of the machine learning and the natural language processing communities have produced more portable and flexible solutions, which can be applied to documents written in different languages. This chapter first overviews the most relevant language-specific summarization algorithms. Then, it presents the most recent advances in multi- and cross-lingual text summarization. The chapter classifies the presented methodology, highlights the main pros and cons, and discusses the perspectives of the extension of the current research towards cross-lingual summarization systems.
An Improved Local Ternary Pattern for Texture Classification

In this study, we proposed a new operator known as the synchronized rotation local ternary pattern (SRLTP) for texture classification. The proposed SRLTP descriptor improves on the local ternary pattern (LTP) method with an additional process on the generated lower and upper LTPs. The lower and upper patterns are encoded to a rotation invariant pattern histogram and a uniform pattern histogram, respectively. Thus, the feature vector can utilize the advantages offered by the rotation invariant pattern histogram while retaining the original information in the uniform pattern histogram. Moreover, in this study, a two-dimensional discrete wavelet transform (DWT) and a discrete Fourier transform (DFT) enhanced the robustness of the texture classification. The experimental results demonstrate that the performance of the SRLTP descriptor is better than those of the existing descriptors.
Digital Image Processing for Character Detection of Captcha Login Internet Banking Image using Matching Template

Abstract The development of the number of online shopping consumers in Indonesia is increasing. In 2016, research from e-Marketer estimated that it would reach 8.6 million people who shop through the internet. This figure increased from 7.9 million people in the previous year. The increasing number of people who know the internet along with the birth of generation Z (Gen Z) in the digital era makes changes in shopping habits that previously conventionally turned into online. Online transaction is an automatic ticket number that is used to differentiate transactions for each buyer. For example in the purchase transaction of goods in the online shop, each purchase transaction is distinguished through an additional three digits behind the nominal to facilitate payment into the seller’s account. The problem faced by the owner of the online shop is when BRI internet banking login to see account mutations, user authentication by entering the Captcha code. This process is repeated because there is a duration limitation for access to the system. It is necessary to check the account mutation automatically on a computer system that is able to read and recognize the character of the Captcha image. In this study, we propose the Template Matching method in recognizing characters in the captcha image. The dataset contains 300 images taken from the BRI internet banking website. The first step is to convert the extension from .png to .jpg. After the image is converted to extension .jpg the next step is pre-processing to correct the image of the noise that exists. Pre-processing result images that have been fixed are quality, then the separation of objects and background using Otsu. Then the process of labelling and segmentation. The characters are then processed using the Template Matching method so that they can be identified as numbers. The results of the experiments that have been carried out, found that the method we propose is able to recognize characters in the image with an accuracy rate of 90%. Errors in character recognition are due to characters that intersect, so when the results are processed the results do not match the ground truth.
Improving Human Parsing by Extracting Global Information Using the Non-Local Operation

Human parsing has recently attracted considerable interests due to its wide application potentials. However, developing an accurate human parsing system is still a challenge for researchers. In this paper, we demonstrate that global information are critical for accurate prediction by applying a non-local operation for effectively extracting global information. Meanwhile several training data refinement methodologies are proposed to further boost the performance. Benefiting from all the approaches, the proposed single human parsing model NLGINet achieves the state-of-the-art segmentation accuracy on two human parsing benchmark datasets LIP and Pascal-Person-Parts.
Fundamentals of Cloud Computing

The objective of this chapter is to introduce the fundamentals of cloud computing. This covers the features of cloud computing, the architecture of cloud computing, and different service classes and deployment models of cloud computing. In addition, it gives a brief overview of the different public cloud service providers.
A Comparative Study of 2D-to-3D Reconstruction Techniques

Image reconstruction is primarily an inverse problem, which directs the attention to reconstructing the original image from the given 2D image which may be blurred, consisting of some noise, and/or some damaged regions. The conversion to 3D involves finding out missing data. Rotation in conjunction with visualization from numerous perspectives of the image is facilitated by the third dimension for 3D reconstruction. Detailed and swift 3D reconstruction has established its applications in various fields such as Computer Vision, Medical Imaging, Virtual Reality, etc. The purpose of this paper is to bring attention to the thorough study of some state-of-the-art methods, which is used for fast 3D reconstruction. These methods include techniques, namely “Patch based Multi-View Stereo (PMVS), DAISY Descriptors, 3D point cloud, 3D Template matching, Recurrent Neural Network”. In the end, a comparative study of these methods based on various parameters is done to guide the readers to check the suitability of each approach.
What usage can tell us about grammar: Embedded verb second in Scandinavian

This paper uses large-scale data extracted from a series of Swedish corpora to investigate the factors responsible for conditioning the choice of (optional) embedded V2 in Swedish. Embedded V2 has been argued to represent a more general kind of syntactic optionality found across languages: syntactic structures typically found in matrix clauses, but which are also available in certain types of embedded environments (so called Main Clause Phenomena). While the received view, going back to Hooper &amp;amp; Thompson (1973), is that the availability of main clause syntax has a semantic-pragmatic correlate in the presence of Illocutionary Force, pinpointing exactly what this amounts to has remained an open problem. Through statistical analysis of the Swedish corpus data, combined with results from a semantic-inference task, we are able to falsify certain previous (theoretical and empirical) claims about the distribution and interpretation of embedded V2. We additionally evaluate, and find no evidence to support, a processing or usage-based view of optionality in embedded V2. We argue instead that the interpretive notion driving the distribution of embedded V2 is discourse novelty; whether the embedded proposition is treated as discourse-old or new information. We argue that embedded V2 is licensed in contexts where p is discourse novel. While this is fundamentally a pragmatic notion, it is nevertheless tightly constrained by both lexical-semantic properties of the matrix predicate and other aspects of the grammatical context. An important methodological consequence of this work is that by looking at particular interactions of lexical and grammatical contexts, statistical analysis of usage data can be used to test specific predictions made by syntactic and semantic theory.
A Multi-Layer Perceptron Model in Analyzing Parametric Classification of Students’ Assessment Results in K12

This paper focuses on assessment results based on the analysis of parametric classification with the support of the multi-layer perceptron (MLP) model. MLP is the most effective tool or model to assess and evaluate training sets, such as the score of each student, exam results, and the like. Domains are evaluated as to know the career pathing of students in the different categorical aspects. Indispensable to each categorical response are the results of the completed curriculum under the K12 educational approach. Career assessment should be administered to classify students in the different categorical domains namely: arts and design, business, information and communication technology, criminal justice and law, culinary arts, education, engineering and architecture, health care, liberal arts, math and science, and vocational respectively. MLP model has been implemented to assess students' scores and compare them to what specific categorical domain do they belong. The three main components of MLP as the computational model are the input layer, hidden layer, and the output layer. Employment of MLP in a supervised learning problem helps to pair all the training sets of input and output to train the dependence between them. This paper gives the reader the results of parametric training through associating MLP in all training datasets and the 87% accuracy results being garnered to sustain relevant parametric results.
Effective approximation of parametrized closure systems over transactional data streams

Strongly closed itemsets, defined by a parameterized closure operator, are a generalization of ordinary closed itemsets. Depending on the strength of closedness, the family of strongly closed itemsets typically forms a tiny subfamily of ordinary closed itemsets that is stable against changes in the input. In this paper we consider the problem of mining strongly closed itemsets from transactional data streams. Utilizing their algebraic and algorithmic properties, we propose an algorithm based on reservoir sampling for approximating this type of itemsets in the landmark streaming setting, prove its correctness, and show empirically that it yields a considerable speed-up over a straightforward naive algorithm without any significant loss in precision and recall. We motivate the problem setting considered by two practical applications. In particular, we first experimentally demonstrate that the above properties, i.e., compactness and stability, make strongly closed itemsets an excellent indicator of certain types of concept drifts in transactional data streams. As a second application we consider computer-aided product configuration, a real-world problem raised by an industrial project. For this problem, which is essentially exact concept identification, we propose a learning algorithm based on a certain type of subset queries formed by strongly closed itemsets and show on real-world datasets that it requires significantly less query evaluations than a naive algorithm based on membership queries.
Multiple-Action Transform of Quaternion for Color Images

Color image is an important information medium in many multimedia applications. Quaternionic representation (QR), a popular technique for color image processing, is capable of considering the interactions among color channels. However, some commonly used quaternionic operators, such as Clifford translation, rotation, and reflection, only explore the shallow relationships among different color channels. To address this limitation, we propose a simple yet effective operator, named Multiple-Action Transform of Quaternion (MATQ), for color images. MATQ cascades some basic quaternionic operators to form a multiple architecture such that it is able to take account of more complicated relationships among color channels. Three examples of MATQ operators are given and detailedly investigated. Two applications, impulse noise detection and image classification of color images, are provided to show the effectiveness of MATQ. Experiments and comparisons demonstrate that the developed MATQ is a useful tool for color image processing.
Interval Differential Evolution Using Structural Information of Global Optimization Problems

Differential Evolution (DE) algorithms are a promising strategy to Numerical Constrained Global Optimization Problems (NCOP). Most DE recent variants are applied to black-box optimization problems, where the analytical structure of the NCOP instance is unknown. In this paper we present an Interval Differential Evolution (InDE) algorithm that explores the structural information of the problem. The instance structure is represented by a hypergraph Epiphytic decomposition, where the variables are intervals. InDE algorithm is based on several strategies used in state-of-the-art DE implementations. Based on structural information, our approach extracts a subset of variables of the instance that are critical to the search process. The DE population individuals encode only this subset of variables. The other variables of the instance are valuated by a linear cost constraint propagation over the hypergraph structure. Our experiments show that the use of structural information associated with interval local consistency techniques significantly improves the performance of DE algorithm.
SandBOX - An Intuitive Conceptual Design System

We describe the development and use of a new conceptual design system, called SandBOX, which combines a range of intuitive interfaces with real-time analysis, thus enabling a wide variety of users to develop performative concept designs. We show how this interactive design platform can overcome some of the limitations of current physical model-based design processes, whilst retaining many of their advantages.
NashDB

Modern elastic computing systems allow applications to scale up and down automatically, increasing capacity for workload spikes and ensuring cost savings during lulls in activity. Adapting database management systems to work on top of such elastic infrastructure is not a trivial task, and requires a deep understanding of the sophisticated interplay between data fragmentation, replica allocation, and cluster provisioning. This demonstration showcases NashDB, an end-to-end method for addressing these concerns in an automatic way. NashDB relies on economic models to maximize query performance while staying within a user's budget. This demonstration will (1) allow audience members to see how NashDB handles shifting workloads in an adaptive way, and (2) allow audience members to test NashDB themselves by constructing synthetic workloads and seeing how NashDB adapts a cluster to them in real time.
Decision-Making with Temporal Association Rule Mining and Clustering in Supply Chains

Timely identification of recently rising patterns is required in business process. Data mining methods are most appropriate for the characterization, valuable examples extraction, and predications which are essential for business support and decision-making. Some research studies have also expanded the use of this idea in inventory management. However, not very many research analyzes have considered the utilization of the data mining approach for supply chain inventory management. In this chapter, two unique cases for supply chain inventory management dependent on cross-selling effect are presented. First, the cross-selling effect in different clusters is characterized as a basis for deciding the significance of items. Second, the cross-selling in different time periods is considered as a criterion for ranking inventory items. An example is devised to approve the outcomes. It is illustrated that by using this modified approach, the ranking of items may get affected resulting in higher profit.