Reviewerly/exHarmony huggingface dataset:
How Do Individuals Justify and Rationalize their Criminal Behaviors in Online Romance Fraud?

Online romance fraud (ORF) is a growing concern with such serious negative consequences as financial loss or suicide to the victim. Majority of empirical studies on online romance fraud using attachment, deception, protection motivation and relation theories focus on the victim. While neutralization offers insights into how individuals justify their deviant behaviors, the results have not been consistent in different contexts. In the ORF context, offenders may not only rely on justifying techniques but also rationalize their actions by denying risk both to the victim and the offender. Thus, drawing from the neutralization and denial of risk theories, we develop a research model to explain how online romance offenders justify and rationalize their intended criminal activities. To confirm our theoretical model, we collected 320 responses from individuals at Internet Cafés alleged to be online romance fraud hotspots. Our results highlight the boundary conditions of neutralization techniques in the context of online romance fraud. The study shows that denial of risk, a rationalization mechanism, moderates the relationship between denial of victim, a justification technique, and intention to commit romance fraud. This insight advances the frontiers of neutralization theory. We offer both theoretical and managerial implications of the findings.
M-SVD based Quality Measurement in Hybrid non-blind Watermarked Medical Images

Image and video quality measurement is an important subject to find out distortion between multimedia elements such as images, videos, and animations. There are several methods implemented in image quality such as Quality Index (Q), Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSIM). In this work, we proposed a hybrid image quality degree which is measure of singular value decomposition (M-SVD), it can express image and video quality for both grey and color images numerically and graphically in medical images. M-SVD performs better results in watermarked medical images even after common attacks. In this work, we embedded binary logo as a watermark into the medical images using Discrete Wavelet Transformation (DWT). To measure the quality of the watermarked images, we applied several image processing geometric and statistical attacks. Results show that MSVD based quality measurement gives very promising result in both grey scale and color images after common attacks.
A More Unified Approach to Free Logics

Abstract Free logics is a family of first-order logics which came about as a result of examining the existence assumptions of classical logic. What those assumptions are varies, but the central ones are that (i) the domain of interpretation is not empty, (ii) every name denotes exactly one object in the domain and (iii) the quantifiers have existential import. Free logics usually reject the claim that names need to denote in (ii), and of the systems considered in this paper, the positive free logic concedes that some atomic formulas containing non-denoting names (namely self-identity) are true, while negative free logic rejects even the latter claim. Inclusive logics, which reject (i), are likewise considered. These logics have complex and varied axiomatizations and semantics, and the goal of this paper is to present an orderly examination of the various systems and their mutual relations. This is done by first offering a formalization, using sequent calculi which possess all the desired structural properties of a good proof system, including admissibility of contraction and cut, while streamlining free logics in a way no other approach has. We then present a simple and unified system of abstract semantics, which allows for a straightforward demonstration of the meta-theoretical properties, and offers insights into the relationship between different logics (free and classical). The final part of this paper is dedicated to extending the system with modalities by using a labeled sequent calculus, and here we are again able to map out the different approaches and their mutual relations using the same framework.
Enhanced security‐aware technique and ontology data access control in cloud computing

Summary Nowadays, security and data access control are some of the major concerns in the cloud storage unit, especially in the medical field. Therefore, a security‐aware mechanism and ontology‐based data access control (SA‐ODAC) has been developed to improve security and access control in cloud computing. The model proposed in this research work is based on two operational methods, namely, secure awareness technique (SAT) and ontology‐based data access control (ODAC), to improve security and data access control in cloud computing. The SAT technique is developed to provide security for medical data in cloud computing, based on encryption, splitting and adding files, and decryption. The ODAC ontology is launched to control unauthorized persons accessing data from storage and create owner and administrator rules to allow access to data and is proposed to improve security and restrict access to data. To manage the key of the SAT technique, the secret sharing scheme is introduced in the proposed framework. The implementation of the algorithm is performed by MATLAB, and its performance is verified in terms of delay, encryption time, encryption time, and ontology processing time and is compared with role‐based access control (RBAC), context‐aware RBAC and context‐aware task RBAC, and security analysis of advanced encryption standard and data encryption standard. Ultimately, the proposed data access control and security scheme in SA‐ODAC have achieved better performance and outperform the conventional technique.
Secure Cattle Stock Infrastructure for the Internet of Things using Blockchain

The current livestock industry in certain parts of the world still runs on archaic infrastructure. Using dated systems built on inefficient database structures are not ideal moving into the next millennial as the central point of failure. A lack of data sharing and data transfer also limits the methods by which cattle farmers can operate. In this paper, we examine the use of a novel infrastructure that is in line with the current push of many industries to move into the age of Internet of Things. We propose an efficient, secure, decentralized, and distributed system that can modernize the way the cattle industry runs in dominantly rural settings. Motivated by a use case in Brazil, we propose the use of cloud storage ad blockchain connectivity being used to monitor the overall health of livestock through their natural lives. We also describe a system that can easily transfer livestock from one organization to another through blockchain for ease of buying/selling procedures that are quite common for cattle framers.
A Graph Based Approach to Prioritization of Software Functional Requirements

Requirements prioritization plays important role for a successful requirements implementation. Functional requirements, in specific, represents the specification of behavior between the inputs and outputs. They are prioritized based on the high-level requirements of the system and subsystems functionalities, as well as the type of software, expected users and the type of system where the software is used. Nonetheless, prioritization of functional requirements is very challenging considering a project where the requirements are huge. In practice, prioritization of functional requirements highly depends on stakeholders preference for giving priorities in features selection instead of based on its internal structure and characteristics. This is because the key information in functional requirements concern on business processes, security, performance, data migration and conversion. This paper proposes a graph-based approach for prioritizing functional requirements using directed acyclic graphs for relating requirements with one another on the basis of its importance to overall project and how much it is required for other requirements.The proposed approach is then evaluated in terms of total time estimation to project completion. The experimental results showed that the graph-based approach is able to effectively prioritize functional requirements with lower estimated project completion time as compared to non-prioritized requirements. The approach will help software vendors to deliver projects well within the total project duration
An Ensembled Scale-Space Model of Deep Convolutional Neural Networks for Sign Language Recognition

A sign language translator is a utilitarian in facilitating communication between the deaf community and the hearing majority. This paper proffers an innovative specialized convolutional neural network (CNN) model, Sign-Net, to recognize hand gesture signs by incorporating scale-space theory to deep learning framework. The proposed model is an ensemble of CNNs—a low resolution network (LRN) and a high resolution network (HRN). This architecture of the proposed model allows the ensemble to work at different spatial resolutions and at varying depths of CNN. The Sign-Net model was assessed with static signs of American Sign Language—alphabets and digits. Since there exists no sign dataset for deep learning, the ensemble performance is evaluated on the synthetic dataset which we have collected for this task. Assessment of the synthetic dataset by Sign-Net reported an impressive accuracy of 74.5%, notably superior to the other existing models.
Learning Causal Structures Based on Divide and Conquer

This article addresses two important issues of causal inference in the high-dimensional situation. One is how to reduce redundant conditional independence (CI) tests, which heavily impact the efficiency and accuracy of existing constraint-based methods. Another is how to construct the true causal graph from a set of Markov equivalence classes returned by these methods. For the first issue, we design a recursive decomposition approach where the original data (a set of variables) are first decomposed into two small subsets, each of which is then recursively decomposed into two smaller subsets until none of these subsets can be decomposed further. Redundant CI tests can be reduced by inferring causalities from these subsets. The advantage of this decomposition scheme lies in two aspects: 1) it requires only low-order CI tests and 2) it does not violate d -separation. The complete causality can be reconstructed by merging all the partial results of the subsets. For the second issue, we employ regression-based CI tests to check CIs in linear non-Gaussian additive noise cases, which can identify more causal directions by [Formula: see text] (or [Formula: see text]). Consequently, causal direction learning is no longer limited by the number of returned V -structures and consistent propagation. Extensive experiments show that the proposed method can not only substantially reduce redundant CI tests but also effectively distinguish the equivalence classes.
Relating Mori’s Uncanny Valley in generating conversations with artificial affective communication and natural language processing

Abstract Human beings express affinity (Shinwa‐kan in Japanese language) in communicating transactive engagements among healthcare providers, patients and healthcare robots. The appearance of healthcare robots and their language capabilities often feature characteristic and appropriate compassionate dialogical functions in human–robot interactions. Elements of healthcare robot configurations comprising its physiognomy and communication properties are founded on the positivist philosophical perspective of being the summation of composite parts, thereby mimicking human persons. This article reviews Mori's theory of the Uncanny Valley and its consequent debates, and examines "Uncanny" relations with generating healthcare robot conversational content with artificial affective communication (AAC) using natural language processing. With healthcare robots provoking influential physical composition and sensory expressions, the relations in human–healthcare robot transactive engagements are argued as supportive of the design and development in natural language processing. This implies that maintaining human–healthcare robot interaction and assessing the eeriness situations explained in the Uncanny Valley theory are crucial positions for healthcare robot functioning as a valuable commodity in health care. As such, physical features, language capabilities and mobility of healthcare robots establish the primacy of the AAC with natural language processing as integral to healthcare robot–human healthcare practice.
Photovoltaic Power Prediction for Solar Car Park Lighting Office Energy Management

Abstract The photovoltaic energy is widely used in modern power network due to its environmental and economic benefits. Solar car park is one of the solar photovoltaic system applications. The photovoltaic energy has disadvantages of intermittence and weather's variation. Thus, photovoltaic power prediction is very necessary to guarantee a balance between the produced energy and the solar car park requirements. The prediction of the photovoltaic energy is related to solar irradiation and ambient temperature forecasting. The aim of this study was to evaluate various methodologies for weather data estimation, namely, the empirical models, the multilayer perceptron neural network (MLPNN), and the adaptive neuro-fuzzy inference system (ANFIS). The simulation results show that the ANFIS model can be successfully used to forecast the photovoltaic power. The forecasted photovoltaic energy was used for the solar car park lighting office management algorithm.
Disgusting Face, Disease-Ridden Place?: Emoji Influence on the Interpretation of Restaurant Inspection Reports

Every year, millions of Americans get sick from foodborne illness and it is estimated half of all reported instances occur at restaurants. To protect the public, regulators are encouraged to conduct restaurant inspections and disclose reports to consumers. However, inspection reporting format is inconsistent and typically contains information unclear to most consumers who often misinterpret the inspection results. Additionally, consumers are increasingly searching for this information in a digital context. Limited research explores inspection reports as communication tools. Using affect-as-information and ELM as theoretical frameworks, this experiment investigated how discrete emotions (e.g., disgust) conveyed through pictorial cues (i.e., emojis) influenced consumers' processing of inspection reports. Participants, recruited from Amazon's MTurk, were randomly assigned to one of six experimental conditions in a 3 (emoji: smiling vs. disgusted vs. none) x 2 (violation level: low vs. high) between-subjects design. Then, participants completed a questionnaire regarding perceptions and cognitive processing of the message. Results revealed that, compared to text, disgusted face emoji increased risk perceptions and avoidance behavior. In terms of emotion, smiling face emoji motivated participants to feel more emotions related to sanitation. In turn, positive feelings decreased elaboration likelihood. As predicted by ELM, involvement also predicted elaboration, such that participants who were highly involved with inspection reports elaborated more than those less involved. Involvement also moderated the relationship between emoji presented and elaboration. Practical implications are also discussed.
A Blockchain-based Method for Decentralizing the ACME Protocol to Enhance Trust in PKI

Blockchain technology is the cornerstone of digital trust and systems' decentralization. The necessity of eliminating trust in computing systems has triggered researchers to investigate the applicability of Blockchain to decentralize the conventional security models. Specifically, researchers continuously aim at minimizing trust in the well-known Public Key Infrastructure (PKI) model which currently requires a trusted Certificate Authority (CA) to sign digital certificates. Recently, the Automated Certificate Management Environment (ACME) was standardized as a certificate issuance automation protocol. It minimizes the human interaction by enabling certificates to be automatically requested, verified, and installed on servers. ACME only solved the automation issue, but the trust concerns remain as a trusted CA is required. In this paper we propose decentralizing the ACME protocol by using the Blockchain technology to enhance the current trust issues of the existing PKI model and to eliminate the need for a trusted CA. The system was implemented and tested on Ethereum Blockchain, and the results showed that the system is feasible in terms of cost, speed, and applicability on a wide range of devices including Internet of Things (IoT) devices.
A path planning algorithm based on RRT and SARSA (λ) in unknown and complex conditions

The path planning problem of wheeled mobile robot is different from the traditional method in the environment of outer space which is completely unknown. If robots are needed to explore unknown environments while adapting to terrain changes, it is a more challenging problem. In this paper, a Rapidly-exploring Random Tree(RRT) path planning algorithm based on reinforcement learning SARSA(λ) optimization(RL-RRT) is proposed to solve the local path planning problem in unknown and complex conditions. RL-RRT method improves the performance of RRT algorithm by adding optimization for selection of extension points, introducing the idea of biased goal, using task return function, target distance function and angle restriction, ensuring the randomness of RRT, reducing the number of invalid nodes and optimizing the planning performance. A simulation platform is built under Robot Operating System(ROS) and MATLAB to test the role of multi-objective optimization in path planning. The simulation results show that the RL-RRT method enables the wheeled mobile robot to reach the target node smoothly and steadily in complex and unknown environments without collision with obstacles, which verifies the reliability and effectiveness of the method.
Efficient Outlier Detection in Text Corpus Using Rare Frequency and Ranking

Outlier detection in text data collections has become significant due to the need of finding anomalies in the myriad of text data sources. High feature dimensionality, together with the larger size of these document collections, presents a need for developing accurate outlier detection methods with high efficiency. Traditional outlier detection methods face several challenges including data sparseness, distance concentration, and the presence of a larger number of sub-groups when dealing with text data. In this article, we propose to address these issues by developing novel concepts such as presenting documents with the rare document frequency, finding ranking-based neighborhood for similarity computation, and identifying sub-dense local neighborhoods in high dimensions. To improve the proposed primary method based on rare document frequency, we present several novel ensemble approaches using the ranking concept to reduce the false identifications while finding the higher number of true outliers. Extensive empirical analysis shows that the proposed method and its ensemble variations improve the quality of outlier detection in document repositories as well as they are found scalable compared to the relevant benchmarking methods.
Object Scale Selection of Hierarchical Image Segmentation Using Reliable Regions

Hierarchical image segmentation is a prevailing trend in the literature for improving the segmentation quality. These methods described the meaningful regions of objects at different scales. However, to find the object of the scene we need to search in different hierarchy levels. In this paper, a new framework is introduced to use multiple segmentations of the scene from different hierarchy levels to improve the output of object segmentation. Therefore, the main idea of our work is to find reliable regions from different hierarchy levels to train CNN classifier to predict the object class of each region with the graphical model. We perform the proposed method on the MSRC and PASCAL VOC 2012 datasets. The experimental results demonstrate that the proposed method improves the quality of the segmentation of objects and facilitates the use of hierarchical image segmentation for vision tasks of high level.
High spatial resolution analysis of H2O in silicate glass using attenuated total reflection FTIR spectroscopy coupled with a focal plane array detector

High resolution spectroscopic techniques that are used to determine volatile concentration in geological glasses are on the forefront for determining small scale degassing or hydration processes. Several spectroscopic methods are used for this purpose but they mostly require complicated sample preparation. Here, these methods are extended by the use of attenuated total reflectance (ATR) FTIR spectroscopy coupled with a focal plane array detector (FPA). ATR-FTIR spectroscopy requires only singly polished samples. The coupling to an FPA detector, which is novel for the analysis of volatile bearing glasses, enables spatial resolution on a micrometer scale. An ATR-FPA calibration to quantify H2O concentration in peralkaline rhyolitic glass is presented, which is comparable to conventional ATR calibrations using a single element detector. The results confirm that calibrations for different glass compositions are comparable after correction for glass density as suggested by a previous study. A method is developed to compare calibrations obtained with ATR objectives that have different angles of incidence. Despite the larger errors that come with the use of an FPA detector, the method allows the detection of H2O concentration gradients on a micrometer scale. The ATR-FPA calibration was applied to experimentally vesiculated melts that were quenched to glasses. It was possible to resolve only several μm-sized H2O resorption halos around vesicles that formed during isobaric cooling. Besides vesiculated glasses, the new method may enable the analysis of small melt inclusions and partially crystallized glasses and may allow the spatially resolved analysis of CO2 dissolved in silicate glasses.
Reasoning Under Uncertainty in Knowledge Graphs

We provide a framework for probabilistic reasoning in Vadalog-based Knowledge Graphs (KGs), able to satisfy the requirements of ontological reasoning: full recursion, powerful existential quantification, and the ability to express inductive definitions. Vadalog is based on Warded Datalog+/−, an existential rule language that strikes a good balance between computational complexity: with tractable reasoning in data complexity, and expressive power covering SPARQL under set semantics and the entailment regime for OWL 2 QL. Vadalog and its logical core Warded Datalog+/− are not covered by existing probabilistic programming and statistical relational models for many reasons including weak support for existentials, recursion and the impossibility to express inductive definitions. We introduce Soft Vadalog, a probabilistic extension to Vadalog satisfying these desiderata. It defines a probability distribution over the nodes of a chase network, a structure induced by the grounding of a Soft Vadalog program with the chase procedure.
Rule-Based Multi-label Classification: Challenges and Opportunities

In the context of multi-label classification (MLC), rule-based learning algorithms have a number of appealing properties that are not, at least not as a whole, shared by other approaches. This includes the potential interpretability of rules, their ability to model (local) label dependencies in a flexible way, and the facile customization of a predictor to different loss functions. In this paper, we present a modular framework for rule-based MLC and discuss related challenges and opportunities for multi-label rule learning.