Reviewerly/exHarmony huggingface dataset:
A Holistic Approach Towards Human Factors in Information Security and Risk

Businesses take various precautions and measures to protect their assets, and at the centre of their computer systems are users. Many data breaches originate from accidental human error, which has lasting damaging financial or reputation loss. Although companies intend to change behaviour, one of the biggest problems with this approach is the lack of Psychology informed theories to understand why and how users are targeted. To understand why users defy compliance procedures and policy, despite warnings and training, we need to understand every internal and external factor that contributes to such behaviour. The literature proposes that users are the main cause for system dysfunction, and this is accentuated by media headlines that portray users as the source of the problem. One of the biggest problems is that, research continues to evaluate surface level problems, rather than explore or acknowledge more systemic factors that can have damaging results. In this paper, we discuss factors, that could impact the way that information is processed and how this is translated into action or no action. Also we, identify how an environment can encourage or discourage desired behaviour.
Visual object tracking via precise localization

Recently, trackers composed of a target estimation module and a target classification module have presented excellent accuracy with high efficiency. However, they underperform when encountering background semantic interference, large-scale variation, and long-term tracking. To address these problems, we propose a two-stage tracking framework. First, we propose a more applicable objective function for tracking tasks named metrizable intersection over union by considering the alignment mode and the center distance between two bounding boxes. Second, multilevel features are used to eliminate the semantic ambiguity by exploring diverse semantic information. Third, a meta-synthetic decision strategy is proposed to determine the optimum location of the target. In comprehensive experiments on OTB100, Lasot, TrackingNet, TColor-128, UAV123, and UAV20L, our method performs favorably against state-of-the-art trackers.
The Potential For Agricultural Area Development In Bedadung Watershed

Abstract Food security can be achieved through the development of agricultural area and water supply. This study aims to figure out the potential of land development for food crop cultivation, both of the technical irrigation land and rainfed land in Bedadung watershed. The suitability principle of natural resources for food crops is utilized to determine the potential for agricultural area development. In this case, the parameters are land suitability based on the water availability which can be seen from the Oldeman classification, land elevation, and land topography. The results showed that Bedadung Watershed more suitable for corn ( S2 ). The rice cultivation can be done by considering the S3 classification and it must be supported by good water availability for providing an adequate irrigation system.
Obtaining Deep Learning Models for Automatic Classification of Leukocytes

In this work, the authors classify leukocyte images using the neural network architectures that won the annual ILSVRC competition. The classification of leukocytes is made using pretrained networks and the same networks trained from scratch in order to select the ones that achieve the best performance for the intended task. The categories used are eosinophils, lymphocytes, monocytes, and neutrophils. The analysis of the results takes into account the amount of training required, the regularization techniques used, the training time, and the accuracy in image classification. The best classification results, on the order of 98%, suggest that it is possible, considering a competent preprocessing, to train a network like the DenseNet with 169 or 201 layers, in about 100 epochs, to classify leukocytes in microscopy images.
Interpersonal Metaphor Used in Different Discursive Moves in Reply Posts of an Online Health Forum

Online health communities have found to be important healthcare information resources for patients and caregivers through a study of 25,448 websites, for “increased access to online health information can empower patients to manage health better” (Nath et al. in Journal of Medical Internet Research 18(1):e11, 2016: 2).
Entity Hyponymy Extraction of Complex Sentence Combining Bootstrapping and At-BiLSTM in Special Domain

Acquiring entity hyponymy of complex sentences can be a highly difficult process in special domain. To tackle this problem, this paper proposes a novel method that combines Bootstrapping method and Attention-Based Bidirectional Long Short-Term Memory Networks (Bo-At-BiLSTM). The experimental corpus is in the field of tourism in China. First, the bootstrapping method is used to obtain the patterns set. Then, pattern matching is employed to acquire the candidate sentences and word embedding. Next, import into the bidirectional Long Short-Term Memory Networks and introduce attention mechanism. Finally, output the results by Softmax classifier. The experimental results on the tourism corpus show that the proposed approach outperforms the baseline methods.
Machine Translation System for Translation of Malayalam Morphological Causative Constructions into English Periphrastic Causative

Malayalam is the regional as well as the official language of Kerala, the southern state of India, and Union Territory of Lakshadweep. The Government of Kerala follows a trilingual (Malayalam, English and Hindi) educational system from primary level to graduation level. But in the context of Machine translation, Malayalam is far behind English and Hindi. Here starts the relevance of study of Malayalam-to-English Machine Translation (MT) Systems. In a sentence, the pattern of verb phrase (VP) demonstrates language-specific features, and the causative sentence construction is one among them. The part of the semantics of the verb can be represented by the causation. The sense of causation is expressed through the verb or verb phrase. Any existing online MT system has not handled causative pattern of sentence. This research studies and analyses the problems in Malayalam-to-English causative sentence translations. Based on the study, this research designs and develops a system to improve the accuracy of existing Malayalam–English by including causative sentence translation mechanism. The output was compared with the output of Google Translate and identified that the Malayalam-to-English causative sentence MT system performs translations of simple causative sentences with outstanding accuracy.
Approximations for Statistics Based on Random Sample Sizes

In practice, we often encounter situations where a sample size is not defined in advance and can be random itself. It is known that the asymptotic properties of the statistics can be radically changed when the non-random sample size is replaced by a random value. In this chapter, we consider the second-order Chebyshev–Edgeworth type and Cornish–Fisher type expansions based on Student’s t- and Laplace distributions and their quantiles for samples with random size of a special kind. This is accomplished using the general transfer theorem, which allows constructing asymptotic expansions for distributions of randomly normalized statistics from the distributions of the considered non-randomly normalized statistics and of the random size of the underlying sample.
A Reflective Neural Network Based Learning Framework for Intelligent Physical Systems

Existing functionality for intelligent physical systems (IPS), such as autonomous vehicles (AV), is generally built around rigid rule-sets, which allow predictable behavior in their expected operational design domain. This manner of system design is desirable for scenarios where a human is generally able to take control, such as on-road driving. In situations where, human intervention is unlikely to be available and long-term autonomous operation is desired in potentially dynamic environments, the limitations of rigid rule-sets will need to be addressed. To address these issues, an IPS will need to be able to derive the properties of items in its environment, since those objects may not be available for training prior to exposure. Moreover, in order to verify that the IPS is properly interpreting the environment, metacognition will be added through the integration of a reflective neural network (RNN) that can identify high- and low-value training inputs, provide confidence for outputs, and reduce the size of training sets. In this paper, we have proposed RNN-based learning framework to address these IPS issues. The preliminary experimental results addressing these research challenges are promising.
SQL User Queries Execution Model

Here, a Generalized Net model is developed, modelling the processes of utilization of relational databases, generalizing the execution of the main types of SQL queries. On the basis of the created model and accumulated statistical data from real life processes, evaluations can be made and solutions can be sought of problems arising in servicing the processes. Additional model parameters can be introduced or new tokens’ characteristics can be formulated by taking consideration of various factors that affect the process.
How to Enhance Focused Attention of Organization Members on Their Learning Process

In the process of learning, the duration of focused attention may vary from sustained concentration to short-lived attention or just momentary notice.This article is meant to describe the psychological behaviors displayed by organization members in their learning process: no response to the object of learning, attention focused on learning and sustained attention through various activities.The analysis of causes of "distraction" herein is followed by a presentation of strategies devoted to focused attention.Heuristic teaching methods and creation of appropriate conditions may give rise to concentration of attention.
Development of a Score-Based Virtual Instrument for Beginners, That Combines Composition With Performance

This paper describes the virtual instrument "Musicurves" that plays sounds based on curves drawn on a blank score. The concept was to create a program that allows beginners to engage in both performing and composing music at the same time, by allowing the users to play sounds just by visually drawing their image of a melody as a line.
MEAN-VARIANCE MODEL WITH FUZZY RANDOM DATA

This paper recommends discourse feeling acknowledgment from discourse signal dependent on highlights examination and PNN-classifier. The arrangement of acknowledgment incorporates discovery of discourse feelings, extraction and determination of highlights, lastly characterization. These highlights are valued to segregate the greatest number of tests precisely and the PNN classifier dependent on discriminant investigation is utilized to characterize the six distinctive articulations. The reproduced outcomes will be indicated that the channel occupied component extortion with utilized distribution presents much better exactness with less algorithmic unpredictability than other discourse feeling articulation acknowledgment draws near.
Linear and Quadratic Discriminant Functions

This chapter is concerned with theoretical accuracies for asymptotic approximations of the expected probabilities of misclassification (EPMC) when the linear discriminant function and the quadratic discriminant function are used. The method in this chapter is based on asymptotic bounds for asymptotic approximations of a location and scale mixture. The asymptotic approximations considered in detail are those in which both the sample size and the dimension are large, and the sample size is large.
Reasoning over Ontologies with DLV

The paper presents DLV, an advanced AI system from the area of Answer Set Programming (ASP), showing its high potential for reasoning over ontologies. Ontological reasoning services represent fundamental features in the development of the Semantic Web. Among them, scientists are focusing their attention on the so-called ontology-based query answering (OBQA) task where a (conjunctive) query has to be evaluated over a logical theory (a.k.a. Knowledge Base, or simply KB) consisting of an extensional database (a.k.a. ABox) paired with an ontology (a.k.a. TBox). From a theoretical viewpoint, much has been done. Indeed, Description logics and Datalog$$^\pm $$ have been recognized as the two main families of formal ontology specification languages to specify KBs, while OWL has been identified as the official W3C standard language to physically represent and share them; moreover sophisticated algorithms and techniques have been proposed. Conversely, from a practical point of view, only a few systems for solving complex ontological reasoning services such as OBQA have been developed, and no official standard has been identified yet. The aim of the present paper is to illustrate the applicability of the well-known ASP system DLV for powerful ontology-based reasoning.
Improvement of Project Management Knowledge Areas Using Scrum Technique

Before the popularity of agile methodology, many software organizations were using the waterfall model. Due to scope changes there are limitations of waterfall model over agile methodology. Agile methodology is popular due to its incremental approach and transparency. Scrum is the popular agile project management approach in software development organization. Study was conducted before and after scrum implementation. Numerical analysis was done based on surveys of project managers and its impact on project knowledge areas.
Random Projections and α-Shape to Support the Kernel Design (Student Abstract)

We demonstrate that projecting data points into hyperplanes is good strategy for general-purpose kernel design. We used three different hyperplanes generation schemes, random, convex hull and α-shape, and evaluated the results on two synthetic and three well known image-based datasets. The results showed considerable improvement in the classification performance in almost all scenarios, corroborating the claim that such an approach can be used as a general-purpose kernel transformation. Also, we discuss some connection with Convolutional Neural Networks and how such an approach could be used to understand such networks better.
Genetic Algorithm Based Task Scheduling for Load Balancing in Cloud

Due to on-demand services for online resources like processing power, storage, software, infrastructure, etc., provided by cloud computing, it becomes incredibly popular today. So, intensity of Web data is increasing day by day. To balance the load of different nodes is the biggest challenge in this era. Load balancing method makes sure that no any node is over utilized or underutilized. It is considered to be an optimization problem. This paper proposes a genetic algorithm-based task scheduling for load balancing. The proposed strategy is simulated using cloud analyst. The results demonstrate that proposed method is better than existing algorithm like Round Robin (RR), Equally Spread Current Execution Load algorithm (ESCELEA) and Throttled algorithm (TA).
Scale-Mixed Distributions

In this chapter we present a general theory of approximation of scale-mixed distributions or distributions of scale mixtures, including simple examples of Student’s t-distribution and F-distribution as a scale mixtures of the normal and chi-square distribution, respectively. Such scale mixtures appear as sampling distributions of various statistics such as the studentized version of some estimators. Errors of the approximation are evaluated in $$\sup $$ and $$L_1$$ -norms. Extension to multivariate scale mixtures with error bounds evaluated in $$L_1$$ -norm shall be discussed in Chap. 3 .
Multi-level Query Analysis for NLT-based Synthesizer Interface

A synthesizer is an electronic musical instrument, which can produce a variety of tones, but requires long-term training and highly skilled knowledge to obtain the desired tone. Therefore we developed an interface for software synthesizers utilizing natural language processing so that even beginners can use it easily. This paper describes a new method to select a timbre from a database of presets by using word-level information from a sentence, and an additional function that applies a sound effect to the produced tone using a sentence-level information. We have been researching ways to use natural language processing for synthesizers to select timbre from words and phrases input by users. In this research, by analyzing input sentences at multi-level basis, it becomes possible to call out timbres with more variety than ever. In addition to calling timbre at the word level, the system performs language analysis at the sentence level and uses the results to apply certain sound effects to the synthesizer sounds. We plan to explore more appropriate relationships between sentences and effects to provide easy-to-use synthesizer interface.
A Novel Invisible Watermarking Approach for Double Authentication of Image Content

Nowadays, digital watermarking approaches are proposed for content authentication of digital images. Digital watermarking is a technique that is used to embed watermarks into a digital image. In this work, a novel double authentication mechanism is proposed to ensure the authenticity of image content based on the hash function and signature value. The suggested technique is capable of finding malicious image manipulation even if simple image processing operations have unintentionally corrupted them. Using the hash function, the difficulty level of finding embedded bits is increased and this scheme also withstands any kind of attacks. Moreover, the proposed technique is simple which is based on modifying a single pixel value in the host image itself using the surrounding pixels i.e., neighboring pixels. Experimental results are shown to illustrate the system’s capability to detect unauthorized image alteration.
Prediction of Wart Treatment Using Deep Learning with Implicit Feature Engineering

Warts are skin growths that are caused by the human papillomavirus (HPV) through direct or indirect contact with an object used by a person with the virus. There are different types of warts and different treatment methods accordingly. Of which the cryotherapy method is preferably used to enhance the existing conventional treatment methods. As the enhanced method, the treatment decision process is very important to keep tracked and support for similar cases in future. In this study, a decision support system using machine learning is proposed to predict whether the selected wart treatment method could be successful or not using actual samples from a public dataset. There are some machine learning researches in this field using artificial neuron network (ANN) to solve the problem due to the structured dataset which is more suitable for other methods like k-nearest neighbors (kNN) or Random Forest (RF). This study uses a deep learning neuron network (DNN) approach with an implicit feature engineering method to deal with categorical features to learn implicit interactions among them. Furthermore, the k-fold cross validation is used to evaluate the proposed algorithm and the proposed model achieves the results up to 97.78% of Accuracy, 99.94% of the Area Under the Receiver Operating Characteristics Curve (AUC), 98.00% of Sensitivity and 98.00% of Specificity to predict the wart treatment method using the public cryotherapy dataset from the UCI Machine Learning Repository. This confirms that the proposed framework outperforms other methods using the same dataset.
Assessing the Perception of Abstract Paintings with Elements of Augmented Reality

The materials of this research are devoted to implementation of the main aspects of the problem, in particular, the analysis of the perception by visitors group of abstract visual objects of modern art with elements of augmented reality. This aspect is associated with the need to take into account the perception of the artefact by a group of visitors with a view to its subsequent transformation in new paintings or understanding of visitors. The approach to assessing the perception of abstract paintings with elements of augmented reality is described. The application of a special clustering method within the framework of the approach is substantiated, which allows reducing the variety of estimates in the formation of the initial data for the verbal semantic differential method (MSD). Application of the approach will make it possible to implement the triad "MSD — technology of collective expert assessment — ontological engineering" will serve as a methodological basis for developing an IoT-system in the form of an intellectual agency, for automatically changing the digital visual object of modern art with augmented reality, in accordance with its reflection in the mass consciousness of the visitors.
RGBD Scene Flow Estimation with Global Nonrigid and Local Rigid Assumption

RGBD scene flow has attracted increasing attention in the computer vision with the popularity of depth sensor. To estimate the 3D motion of object accurately, a RGBD scene flow estimation method with global nonrigid and local rigid motion assumption is proposed in this paper. Firstly, the preprocessing is implemented, which includes the colour-depth registration and depth image inpainting, to processing holes and noises in the depth image; secondly, the depth image is segmented to obtain different motion regions with different depth values; thirdly, scene flow is estimated based on the global nonrigid and local rigid assumption and spatial-temporal correlation of RGBD information. In the global nonrigid and local rigid assumption, each segmented region is divided into several blocks, and each block has a rigid motion. With this assumption, the interaction of motion from different parts in the same segmented region is avoided, especially the nonrigid object, e.g., a human body. Experiments are implemented on RGBD tracking dataset and deformable 3D reconstruction dataset. The visual comparison shows that the proposed method can distinguish the motion parts from the static parts in the same region better, and the quantitative comparisons proved more accurate scene flow can be obtained.
Compendious and Succinct Data Structures for Big Data

Recent growth of cloud data and cloud computing has been expediter and predecessor to the appearance of big data. Cloud computing has improved data storage by calculating and saving time with the means of identical and relevant technologies. While cloud computing provides important benefits over conventional physical deployments, its platform has also originated in numerous forms from time to time (Gog and Petri in Softw Pract Exp 44:1287–1314 [1]). In this proposed paper, the main data structure used in big data is tree. Quad tree is used for graphics and spatial data in the main memory. Traditional sub-linear algorithms that are used to handle Quad tree were inefficient. Also, SDS can be optimized for query handling and space. Optimized SDS can improve functionality of different SDS like rank and select, FM index (Blandford et al. in Proceedings of the fourteenth annual ACM-SIAM symposium on discrete algorithms, pp 679–688, 2003 [2]). As geometric data, proteins database, Gnome data, DNA data are large databases for main memory, an efficient and simple representation is required in main memory of computer system. However, overall quantity of storing area is not a vital problem in recent times, considering the fact that external memory can store large quantity of data and may be inexpensive, time needed to get access to information is a vital blockage in numerous programs. Number of access for hitting outside of memory is conventionally lower than number of access for hitting into main memory which has caused examine of recent compressed demonstrations of information that might be capable to save identical data in a reduced area.