Reviewerly/exHarmony huggingface dataset:
Underscoring archival authenticity with blockchain technology

Archives have well-established practices which have been developed over years of working with analogue records. Now they face huge challenges due to the inexorable development of digital technologies. Not only is the heterogeneous nature of the records, their instability and the rapid pace of technological development a threat to the records’ survival, but the ease with which digital records can be altered has put archives in a technology arms race with those parties who would seek to falsify our digital inheritance and undermine democracy.In order to tackle these challenges, the ARCHANGEL project is breaking new ground by using blockchain to record checksums (cryptographic hashes) and other metadata derived from either scanned physical records or born-digital records to allow verification of their integrity over decade- or century-long time spans. This data is permanently preserved through peer-to-peer distribution and consensus checking without the need for a trusted third party, thereby enabling archives to prove the authenticity of the records in their custody.
Impact of organizational culture, organizational communication and supervisor support on the job satisfaction of employees working in online IT based distance learning institutions of Pakistan

Article: Impact of organizational culture, organizational communication and supervisor support on the job satisfaction of employees working in online IT based distance learning institutions of Pakistan
Graphical User Authentication System Resistant to Shoulder Surfing Attack

User authentication is one of the most significant issues in the field of Information Security. The most common and convenient authentication method used is the alphanumeric password which has significant drawbacks. To overcome the vulnerabilities of traditional methods, graphical password schemes have been developed as possible alternative solutions to text-based scheme. A potential drawback of graphical password schemes is that they are more vulnerable to shoulder surfing than conventional alphanumeric text passwords due to their visual interface. To overcome the shortcoming of existing graphical password schemes this project focuses on developing a graphical authentication system that is resistant to shoulder surfing attack.
Decision Analysis for Big Data Platform Selection

The accumulation of enormous quantities of structured and unstructured data in the organizations is a prerequisite for the appearance of different IT solutions for data warehousing and fast processing of huge collections of information.The objective of this research is to compare frequently used cloudbased resources for large volumes of data focusing on their specific characteristics.This comparison will be a stepping stone in the creation of a fuzzy multi-criteria system for evaluating cloud platforms for deploying, operating and analysis of big data.
Forecasting Feature Selection based on Single Exponential Smoothing using Wrapper Method

Feature selection is one way to simplify classification process.The purpose is only the selected features are used for classification process and without decreasing its performance when compared without feature selection.This research uses new feature matrix as the base for selection.This feature matrix contains forecasting result using Single Exponential Smoothing (FMF(SES)).The method uses wrapper method of GASVM and it is named FMF(SES)-GASVM.The result of this research is compared with other methods such as GA Bayes, Forward Bayes and Backward Bayes.The result shows that FMF(SES)-GASVM has maximum accuracy when compared of FMF(SES)-GA Bayes, FMF(SES)-Forward Bayes, FMF(SES)-Backward Bayes, however the number of selected features are more than if compared with FMF(SES)-GA Bayes and FMF(SES)-Forward Bayes.
Emotional Impact of Video Quality: Self-Assessment and Facial Expression Recognition

As known from everyday contexts of multimedia usage, suddenly occurring quality impairments are capable of causing strong negative emotions in human users. This is particularly the case if the displayed content is highly relevant to current motives and behavioral goals. The present study investigated the effects of visual degradations on quality perception and emotional state of participants who were exposed to a series of short video clips. After each video playback, participants had to decide whether a certain event happened in the video. For data collection, subjective measures of quality and emotion were complemented by behavioral measures derived from capturing participants' spontaneous facial expressions. For data analysis, two general approaches were combined: First, a multivariate analysis of variance approach allowed to examine the effects of visual degradation factors on perceived quality and subjective emotional dimensions. It mainly revealed that perceived quality and emotional valence were both sensitive to degradation intensity, whereas the impact of degradation length was limited when task-relevant video content had already been obscured. Second, using a machine learning approach, an automatic Video Quality of Experience (VQoE) prediction system based on the recorded facial expressions was derived, demonstrating a strong correlation between facial expressions and perceived quality. Hereby, estimates of VQoE might be delivered in an objective, continuous and concealed manner, thus diminishing any further need for subjective self-reports.
Orthogonal Decomposition Network for Pixel-Wise Binary Classification

The weight sharing scheme and spatial pooling operations in Convolutional Neural Networks (CNNs) introduce semantic correlation to neighboring pixels on feature maps and therefore deteriorate their pixel-wise classification performance. In this paper, we implement an Orthogonal Decomposition Unit (ODU) that transforms a convolutional feature map into orthogonal bases targeting at de-correlating neighboring pixels on convolutional features. In theory, complete orthogonal decomposition produces orthogonal bases which can perfectly reconstruct any binary mask (ground-truth). In practice, we further design incomplete orthogonal decomposition focusing on de-correlating local patches which balances the reconstruction performance and computational cost. Fully Convolutional Networks (FCNs) implemented with ODUs, referred to as Orthogonal Decomposition Networks (ODNs), learn de-correlated and complementary convolutional features and fuse such features in a pixel-wise selective manner. Over pixel-wise binary classification tasks for two-dimensional image processing, specifically skeleton detection, edge detection, and saliency detection, and one-dimensional keypoint detection, specifically S-wave arrival time detection for earthquake localization, ODNs consistently improves the state-of-the-arts with significant margins.
Precise Position Detection of Linear Motor Movers Based on Extended Joint Transformation Correlation

The extended joint transformation correlation (EJTC) algorithm is proposed in this paper to meet the high-precision and anti-interference requirements of linear motor mover position measurement. An image measurement system for linear motor mover position is established. Stripe image movement sequences are captured using a high-speed camera. First, the image is preprocessed using a bilateral filtering method. Second, the joint power spectral function of adjacent stripe images is obtained on the basis of the joint transform correlation method. The zero-order diffraction peaks are eliminated, and interference from cross-correlated peak side lobes is avoided. Third, subpixel positioning is obtained using the oversampled discrete Fourier transform. Finally, the actual displacement of the stripe images can be obtained by combining the system calibration coefficients. An experimental measurement platform for a linear motor mover is established to verify the validity of the EJTC algorithm. The EJTC shows high measurement accuracy and strong anti-interference ability.
A Semantic Cover Approach for Topic Modeling

We introduce a novel topic modeling approach based on constructing a semantic set cover for clusters of similar documents.Specifically, our approach first clusters documents using their Tf-Idf representation, and then covers each cluster with a set of topic words based on semantic similarity, defined in terms of a word embedding.Computing a topic cover amounts to solving a minimum set cover problem.Our evaluation compares our topic modeling approach to Latent Dirichlet Allocation (LDA) on three metrics: 1) qualitative topic match, measured using evaluations by Amazon Mechanical Turk (MTurk) workers, 2) performance on classification tasks using each topic model as a sparse feature representation, and 3) topic coherence.We find that qualitative judgments significantly favor our approach, the method outperforms LDA on topic coherence, and is comparable to LDA on document classification tasks.
Comparative Study of Database Security In Cloud Computing Using AES and DES Encryption Algorithms

Security is consider as one of the largest part important aspects in daily computing. The security is important in cloud computing especially for data save in cloud because it have sensitivity and import data as well many user can access to same data. Unfortunately the increase of the cloud user has been accompanied with a increase in malicious action in the cloud and data not be completely trustworthy. Because of that the cloud computing security become big issue in the cloud data. The danger of malicious in the cloud and the crash of cloud services have received a strong interest by researchers. Here, we present a comparative study between state-of-art approaches to overcome these issues. This paper test and compare between the Data Encryption Standard (DES) and Advanced Encryption Standard (AES) in term of different input size that result the AES is faster than DES in the encryption time but in decryption the DES faster than AES from 20KB to 100 KB after that the DES rise sharply and AES rise slightly that make ASE faster than DES in the decryption time from 120 KB to 300KB.
Resource Allocation Based on Matchmaking Services in Multiple Clouds Using Trustworthy and Scalable Service Providers Algorithm

Nowadays, cloud server adoption becomes more popular and highly demanded due to unlimited data contribution and retrieval from anywhere and anytime.Existing methods have lacks of adaptability issues with a trust value calculation based on multi-dimensional cloud service providers.Some, existing methods believed in expert opinion to evaluate the trust factors.However, the techniques have many adaptability issues and trust evaluation results have many errors.To bring the better solution, Trustworthy and Scalable Service Providers Algorithm is proposed for analyzing the design the relationship among the users, the broker, and cloud service providers.The proposed method works for resource allocation based on matchmaking service among multiple clouds.The trust-based proposed method efficiently minimizes the cloud user burden and enhances the system stabilities.Proposed method works based on information entropy theory to evaluate the multi-attribute based decision-making.Here, cloud user efficiently can find trustable cloud service providers in advance.Where, cloud service providers are more dependent on cloud users.Based on the excremental results, the proposed method reduces the System execution time 2 milliseconds; communication cost 9.33 % and improves the 39.33 % Trust score compare than existing methodologies in multiple cloud environments.
The Influence of Audit Committee Characteristics on Modified Audit Opinion in Jordan

The Jordanian Corporate Governance Code (JCGC) was first enacted in 2004 and revised in 2017.It offers standards for ethical and decent practices in the corporates.Law in Jordan in 2009 has enforced the formation of an audit committee for all listed companies.Literature has substantiated that the audit committee characteristic (ACCs) impact the quality of financial reporting.This work investigates the role of ACCs in lessening the prospect of corporates in obtaining modified audit opinion in the context of Jordan.Four ACC problems (expertise, independent, meeting, and size) have been studied and the modified audit opinion.The total sample of 117 listed companies on the Amman Stock Exchange (ASE) was studied.The relationship between the modified audit opinions (dependent variable), and ACCs (expertise, independent, meeting, and size; as independent variables) was analyzed using logistic regression.The ACCs is projected to effectively improve the quality of financial reporting, and thus, decrease the prospect of corporate in obtaining modified audit opinion.The findings according to the listed companies from 2012 to 2017 in Jordan showed that audit committee (AC) expertise validates this likelihood.Lastly, there is no effect of AC independent, size, and the number of meetings held on the modified audit opinion.General, the findings have policy implications on enhancing corporate governance (CG) efficacy concerning the quality of financial reporting.
Modeling Word Emotion in Historical Language: Quantity Beats Supposed Stability in Seed Word Selection

To understand historical texts, we must be aware that language-including the emotional connotation attached to words-changes over time.In this paper, we aim at estimating the emotion which is associated with a given word in former language stages of English and German.Emotion is represented following the popular Valence-Arousal-Dominance (VAD) annotation scheme.While being more expressive than polarity alone, existing word emotion induction methods are typically not suited for addressing it.To overcome this limitation, we present adaptations of two popular algorithms to VAD.To measure their effectiveness in diachronic settings, we present the first gold standard for historical word emotions, which was created by scholars with proficiency in the respective language stages and covers both English and German.In contrast to claims in previous work, our findings indicate that hand-selecting small sets of seed words with supposedly stable emotional meaning is actually harm-rather than helpful.* These authors contributed equally to this work.Johannes Hellrich was responsible for selecting historical text corpora and training embedding models.Sven Buechel selected existing emotion lexicons and was responsible for modeling word emotions.The adaptation of polarity-based algorithms (Section 3), the creation of the German and English historical gold standard lexicons (Section 5.1), as well as the overall study design were done jointly.
Image Encryption Algorithm with Plaintext Related Chaining

This paper describes a plaintext related image encryption algorithm that utilizes the Mojette transform for computation of bins that are subsequently combined with pixels of the processed image. While the bins are computed solely from pixel intensities of a plain image and also the combination depends only on intensities of plain image pixels, the parameters of bins are rearranged according to used key. This design results in a great sensitivity of the proposed image encryption algorithm to both plain images and keys, which is verified by a set of experiments. The paper also tests the resistance of the proposal against statistical and differential attacks by means of commonly used measures as correlation coefficients, entropy, NPCR and UACI. Furthermore, the paper analyses computation speed reached by the proposed solution. Computed values of all parameters are discussed and then compared with results obtained by some recent plaintext related image encryption algorithms.
Informational Model of Consciousness: From Philosophic Concepts to an Information Science of Consciousness

Since ancient times, the humanity has contemplated his relationship with the universe and with itself existence, wondering "who we are", "how our body/mind works", and "where we are going to".Understanding consciousness has been and still remains an issue of debate in this broad context (Gaiseanu, 2018b;2018d;2019b).From the perspective of the society which we live in today, flooded by the high technology products of micro-electronics and micro-systems, making our lives easier and substituting many of our hard activities, but On the long and well-worn road of many, but justifiable attempts of human to discover his origin, his trajectory as a species, and a suitable understanding consciousness, his system allowing the connection to the environment and to his own organism, the concepts and models of philosophy enunciated or experienced by millennia, meet today with modern science concepts of physics and of science of information.Based on recent discoveries of quantum physics and astrophysics, revealing a new understanding of our environment and starting from some philosophical concepts on information of matter and of living structures, this work discusses the dynamics of information within the frame of the Informational Model of Consciousness as an informational system of the human body, connected both to the environment and to the body itself, to control the adaptation for survival.It is shown that consciousness is actually an informational projection in the mind of seven informational subsystems, three of which forming the operative system of consciousness for the short-term adaptation, and other three forming the programmed operating system, dedicated to the maintenance of body and to the long-term survival of species, showing various inputs and outputs of information.The seventh subsystem is the information pole, connecting the organism with the external information, especially related to the extra-sensorial properties of the mind, the human body appearing as a bipolar info-matter structure, managed by the brain.The received information is progressively integrated into the informational system of the organism, which absorbs and emanates information as a reactive system for adaptation, able to operate both with matter-related (codified) and non-matter related (virtual) information.As both connections with external and internal environment (body itself) can be described in terms of information, this model opens the gate to investigate consciousness by means of the tools of the information science, offering also answers to the philosophic "mind-body" problem and to the "hard" problem and showing correspondences with some ancient philosophies.
Using TVD Simulation to Improve Collaboration

Target Value Design (TVD), a lean approach, has been implemented successfully in the past decade in various countries and its process mandates the collaboration of project participants.However, issues of adapting collaborative practices and the time it takes firsttime users to understand TVD practices have been a challenge in TVD projects.Recently, there has been an increase in the creation, reinvention and use of simulations and serious games to teach TVD and other lean principles to project stakeholders encountering them for the first time.The 50 minute version of the simulation game developed in Texas A & M University was used to illustrate TVD practice and collaboration in this study.The study used 24 industry stakeholders from a reputable real estate developer during the implementation of TVD on a live project in Nigeria.The results reported that the simulation is effective in illustrating the practices of TVD including collaboration and designing to set targets.Finally, this study recommends the inclusion of the TVD simulation game in training and workshops for project team before the commencement of construction projects because it demonstrated to be a simple and practical method of understanding collaboration and TVD practices.
Text Classification Using LDA-W2V Hybrid Algorithm

Text classification is a vivid research topic due to practical applications in organizing textual information which is expanding enormously. In this paper, we propose a hybrid approach making use of well-examined Latent Dirichlet Allocation algorithm expanded by the knowledge acquired via word embeddings representing words. The preliminary results for the proposed approach are verified and compared with those known from latest publications.
Cross-Domain Complementary Learning with Synthetic Data for Multi-Person Part Segmentation.

The success of supervised deep learning depends on the training labels. However, data labeling at pixel-level is very expensive, and people have been exploring synthetic data as an alternative. Even though it is easy to generate labels for synthetic data, the quality gap makes it challenging to transfer knowledge from synthetic data to real data. In this paper, we propose a novel technique, called cross-domain complementary learning that takes advantage of the rich variations of real data and the easily obtainable labels of synthetic data to learn multi-person part segmentation on real images without any human-annotated segmentation labels. To make sure the synthetic data and real data are aligned in a common latent space, we use an auxiliary task of human pose estimation to bridge the two domains. Without any real part segmentation training data, our method performs comparably to several supervised state-of-the-art approaches which require real part segmentation training data on Pascal-Person-Parts and COCO-DensePose datasets. We further demonstrate the generalizability of our method on predicting novel keypoints in the wild where no real data labels are available for the novel keypoints.
Dynamic swarm particle for fast motion vehicle tracking

Nowadays, the broad availability of cameras and embedded systems makes the application of computer vision very promising as a supporting technology for intelligent transportation systems, particularly in the field of vehicle tracking. Although there are several existing trackers, the limitation of using low-cost cameras, besides the relatively low processing power in embedded systems, makes most of these trackers useless. For the tracker to work under those conditions, the video frame rate must be reduced to decrease the burden on computation. However, doing this will make the vehicle seem to move faster on the observer's side. This phenomenon is called the fast motion challenge. This paper proposes a tracker called dynamic swarm particle (DSP), which solves the challenge. The term particle refers to the particle filter, while the term swarm refers to particle swarm optimization (PSO). The fundamental concept of our method is to exploit the continuity of vehicle dynamic motions by creating dynamic models based on PSO. Based on the experiments, DSP achieves a precision of 0.896 and success rate of 0.755. These results are better than those obtained by several other benchmark trackers.
Quantifications for multimode entanglement

We introduce two independent quantifications for 3-mode and 4-mode entanglement. We investigate the conversion of one type of nonclassicality, i.e. single-mode nonclassicality, into another type of nonclassicality, i.e. multi-mode entanglement, in beam-splitters. We observe parallel behavior of the two quantifications. The methods can be generalized to the quantification of any multi-mode entanglement.