Reviewerly/exHarmony huggingface dataset:
Research on E-healthcare Security Evaluation in Cloud-Based System

As healthcare adopts electronic methods of storing patient health records, cloud technology allows mining and analyzing this data and accessing the general public health information. Patient’s privacy and security of personal information is crucial when considering using electronic health records in the healthcare industry (E-Healthcare). The need of the hour is to be able to store and recover patient’s data efficiently. However, moving patient’s medical information to the Cloud involves major security risks and destructs robust data privacy of health records. The threat of data breaches and the need to manage huge amounts of data are issues that have affected the healthcare industry for years. The privacy issues and threats faced by E-healthcare systems include network attacks and threats to hosts and unsanctioned access to EHR records. The major EHR system functions such as appointment scheduling, dietary planning, admission and discharge, transfer radiology/lab orders and prescription order entry are vulnerable to security threats. Security issues need to be assessed thoroughly in order for hospitals and other such medical institutions to shift to electronic standards of maintaining patient's data. Before moving to a cloud-based architecture, health organizations should perform a risk assessment. This will help to identify the security measures required to mitigate these risks. This approach provides a valid assessment of the security risk management procedure approved by field experts. It not only identifies threats to E-healthcare but also allows for a structured flow in risk management. The risk management process is documented and reported in an efficient and easy way for concerned stakeholders of the E-healthcare to understand. It will compare existing solutions on how to protect the confidentiality of patient information. Some of these methods include implementation of a Role-Based Access Control (RBAC) model, digital encryption and signature for cloud security and EHR service continuity management. Moreover, it will consider security matters that CSP’s should look into while storing patient’s record. Ultimately, it will come up with the most suitable solution to make E-Healthcare systems more secure. In this paper, the focus will be on identifying security encounters that E-health systems face by discussing means in which these systems are susceptible to attacks.
The Nature of Information Security of Living and Non-Living Objects

The author sees synergetics as a very interesting scientific approach to cognition. It seems to be particularly effective in combining the theories of legal and natural sciences, for the simultaneous study of the laws of society and the universe. For the purpose of his study of the possibilities of this theory, the author will use synergetics in defining information processes, and especially in the field of information security. Therefore, considering the conceptual foundations of synergetics, the emphasis will be shifted not so much to the study of the mechanisms of self-organization of the information society as to the exploration of its management capabilities. This is especially important in the context of the coexistence of organizational and self-organizing components, the reduction of the role of anthropogenic factor, the ability to exist in full entropy and to predict bifurcation periods. In this article, the author will try to investigate the natural genesis of information accumulation, natural ways of storing information and known storage devices. However, by comparing the natural and the artificial, ways of transmitting and stealing information from natural and technical storage devices will be considered. An analogy will be made between psychological intervention in the human mind (hypnosis, magic) to obtain information from the brain or subconscious mind and modern hacking attacks for the same purposes.
Multi-belt Onion-ring Visualization of OF@TEIN Testbed for SmartX Multi-View Visibility

ICT infrastructure visualization based on collected visibility data is essential for the effect ive operation of contemporary SDN (Software-Defined Networking)-enabled cloud playground.More precisely, collecting and visualizing visib ility info rmation about various physical, virtual, and container (p+v +c) types of resources is one of the key requirement for mu lt isite cloud playground operators.In order to address the playground visibility challenges, we have been developing a mult i-layered visibility and visualization solution denoted as SmartX Mult i-View Visibility Framework (M VF).By extending our earlier effort, in this paper, we attempt to realize mu lti-belt onion-ring style visualization software to assist both playground developers and operators in capturing the end-to-end visualization of infrastructure resources and flows.
A distributed cross layer recommender system incorporating product diffusion

In this era of online retailing, personalisation of web content has become very essential. Recommender system is a tool for extraction of relevant information to render personalisation in web information retrieval systems. With an inclination towards customer oriented service, there is a need to understand the adaptability of customers, to provide products/services of interest at the right time. In this paper, a model for distributed context aware cross layer recommender system incorporating the principle of product diffusion is proposed. The offline-online modelled recommender system learns offline about the adaptation time of users using the principle of product diffusion and then, uses online explore-then-exploit strategy to make effective recommendations to the user at the most probable time of consumption. Also, an algorithm based on product adaptability is proposed for recommending new items to the most probable users. The extensive experiments and results demonstrate the efficiency, scalability, reliability and enhanced retrieval effectiveness of the proposed recommender system model.
Identifying environmental homologous complaint reports based on Siamese network

Aiming at the problem that it is difficult to efficiently identify homologous complaints in complaint reporting, this paper proposes a Siamese Text Convolutional Neural Network (TextCNN) method to detect homologous complaints in complaint reporting. TextCNN is good at capturing local features in text, while two TextCNN with the same structure can capture similar features in two complaint reporting samples and judge whether the two complaint reporting events are homology events by comparing these features. Meanwhile, attention mechanism is used to enhance model performance. This paper compares the accuracy of multiple neural networks in complaint reporting data, and the experimental results of these neural network models show that the Siamese TextCNN model in this paper has better effect and stronger stability.
Emotion Detection for Afaan Oromo Using Deep Learning

Emotion detection in text has become more popular due to its various useful applications in a different area, such as tracking product perception, public opinion detection about political tendencies, stock market monitoring, text summarization, information extraction, recommendation system, question answering and etc.However, manually identifying emotion of a million of people and aggregating them towards a rapid and efficient decision is quite a challenging task due to the rapid growth of social media user.This study aimed to develop Afaan Oromo emotion detection model in order to tackle this challenge.This study adopts artificial neural network approach.We used python tools with Keras library.We conduct our experiments on five emotion class (anger(arii), love(jaalala), joy(gamachu), disgust(jibba), and sadness(gadda)) by collecting a total of 1005 emotional sentence of Afaan Oromo language that have been manually annotated.The sentence has been scraped from different official Facebook page such as Oromia Broadcasting Network (OBN) pages, Fana Broadcasting Corporation (FBC) Afaan Oromo page, and British Broadcasting Corporation (BBC) Afaan Oromo pages using Facepager tools by creating Facebook API id.After collecting these data all preprocessing steps like tokenization, stop word removal and normalization have been undertaken.We used word embedding's for feature extraction of preprocessed data.Subsequently, we have applied three artificial neural network algorithms such as Feed forward neural network, long short-term Memory and Bidirectional long short-term memory for classification purpose of the vectorized sentence into their emotion class.We compared the three artificial neural network algorithms and found out that Bidirectional long short-term memory achieved the best performance.We have achieved an average accuracy of 66%, 78%, 83% using Feed Forward Neural Network, Long Short-Term Memory and Bidirectional Long Short-Term Memory respectively.Based on experimental result, the researcher concluded that increasing amount of dataset, tuning hyper parameters properly and trying by different algorithms can, in some case, improve the performance of the model.
Introduction to the Minitrack on Software Development for Mobile Devices, the Internet-of-Things, and Cyber-Physical Systems

Applications for mobile devices (apps) have facilitated the success of smartphones and tablets.By using apps, the multi-purpose hardware of modern devices can be utilized to the full extent.Despite much progress with regard to development methods, software development kits, and frameworks, app development poses many challenges.This is even more so for novel mobile devices such as wearables and for the hardware that constitutes the Internet-of-Things (IoT) and Cyber-Physical Systems (CPS).Compatibility, performance, battery-saving, and security and safety are only some of the issues that are mainly driven by the quality of the used software.A satisfying level of this quality in many cases is very hard to achieve and proper techniques for testing and formal verification are needed.
Role of Pre-processing Phase in Document Clustering Technique for Gurmukhi Script

Document clustering plays a central role in knowledge discovery and data mining by representing large data-sets into a certain number of data objects called clusters. Each cluster consists similar data objects in such a way that data objects in the same cluster are more similar and dissimilar to the data objects of other clusters. Document clustering technique for Gurmukhi script consists two phases namely: 1) Pre-processing phase 2) Processing phase. This paper concentrates pre-processing phase of document clustering technique for Gurmukhi script. The purpose of pre-processing phase is to convert unstructured text into structured text format. Various sub-phases of pre-processing phase are: segmentation, tokenization, removal of stop words, stemming, and normalization. The purpose of this paper is to present the significant role of pre-processing phase in an overall performance of document clustering technique for Gurmukhi script. The experimental results represent the significant role of pre-processing phase in terms of performance regarding assignment of data objects to the relevant clusters as well as in creation of meaningful cluster title list.
Student Aid Application using Service Now Platform

Student Aid System acts as an interface between the students and the management in an organization. The overall application is categorized into two categories. They are Complaint Portal and Stationary Ordering. The application permits the new person to enroll and listed person is permitted to login. Complaint gateway includes complaints concerning class room, transport and hostels. Stationary ordering is carried out by the user to order the accessories required by them. The users need to pay the amount based on the bill generated by the portal. Impressive advances, even breakthroughs, have been made during the last decades in understanding the relationship between knowledge and growth. The principal theme of this paper is therefore to shed lightweight on using recent advances i.e. service-now to be simple and interactable.
Miscellaneous manufactured articles (SITC Section 8)

In 2019, the value (in current US$) of exports of "prefabricated buildings" (SITC group 811) decreased by 2.0 percent (compared to 3.0 percent average growth rate from 2015-2019) to reach 9.2 bln US$ (see table 2), while imports increased by 0.2 percent to reach 7.8 bln US$ (see table 3). Exports of this commodity accounted for 0.4 percent of world exports of SITC section 8, and less than 0.1 percent of total world merchandise exports (see table 1). China, Netherlands and Estonia were the top exporters in 2019 (see table 2). They accounted for 18.1, 8.2 and 5.4 percent of world exports, respectively. Germany, Norway and Uzbekistan were the top destinations, with respectively 8.8, 6.4 and 5.4 percent of world imports (see table 3).
First Impressions

Research Article| January 01 2020 First Impressions Julie Ezelle Patton Julie Ezelle Patton JULIE EZELLE PATTON is a sculptor of sound, image, and text. Her visual poetics take the form of found object assemblage, scrolls, extended texts, limited edition work, performances, ephemeral libraries, and site-specific installations. Her noted Womb Room Tomb installation honoring her mother, Virgie Ezelle Patton (1928-2015), was featured in the Front International Triennial 2018. Patton is the author of Notes For Some (Nominally) Awake. Her book-length serial poem B is forthcoming from Tender Buttons Press. Patton bridges musical and literary improvisations in performances emphasizing “in-the-moment” compositions. She has performed at the Stone, Jazz Standard, the Vision Festival, Woodland Pattern, Poesie Sonore, the Festival Internacional de Poesía in Medellín in Colombia, and many other venues and festivals in the United States and abroad. Patton enjoys collaborating with composing-instrumentalists and poets, including Nasheet Waits, Henry Grimes, Ravi Coltrane, Abou Farman, Janice Lowe, Drew Gardner, Anne Waldman, and Lee Ann Brown and Tony Torn. She performs with improvising guitarist Paul Van Curen as the duo Cloud Flower. Search for other works by this author on: This Site Google Jazz and Culture (2020) 3 (1): 82–84. https://doi.org/10.5406/jazzculture.3.1.0082 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Julie Ezelle Patton; First Impressions. Jazz and Culture 1 January 2020; 3 (1): 82–84. doi: https://doi.org/10.5406/jazzculture.3.1.0082 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressJazz and Culture Search Advanced Search The text of this article is only available as a PDF. Copyright 2019 by the Board of Trustees of the University of Illinois2019 Article PDF first page preview Close Modal You do not currently have access to this content.
CYBERSECURITY INERTIA AND SOCIAL ENGINEERING: WHO'S WORSE, EMPLOYEES OR HACKERS?

Subject matter experts assert that cybersecurity inertia critically contributes to successful cyberattacks that steal business confidential data and personal private information, capable of influencing major elections, crippling businesses, and causing devastating identify theft.The role cyber warfare plays in our daily lives cannot be underestimated.According to a 2018 Gallup Poll, 71% of Americans are more worried about cybercrime than violent crimes, including terrorism, murder, and sexual assault.Human error or negligent behavior is being increasingly blamed for cyberattacks, costing an organization an average of more than $15 million per year.Our research focuses on social engineering, an attack vector that relies on human interaction by manipulating people into breaking normal security protocols and best practices, permitting actors access to computer systems, networks or physical locations for fraudulent purposes.We discuss these constructs by exploring the most prevalent types of cyberattacks, the actors, and their prey, the human targets, perceived to be the weakest links within the organizational system.We close by offering recommendations to disrupt cybersecurity inertia and mitigation strategies to curb the influence of social engineering upon unsuspecting organizations.
Mitigating the Side Channel Power Analysis Attacks using New Variable Mapping Substitution Technique

Side-channel attack has been a real threat against many cryptographic embedded systems. In this attack, the internal data is retrieved directly by analyzing the power magnitude according to the fact that there is a considerable difference in power when manipulating 0’s and 1’s. A commonly used algorithmic countermeasures incur large execution delay and resources overheads. In this paper, a novel technique using Variable Mapping Substitution (VMS) is proposed for mitigating side channel power analysis attack against Advanced Encryption Standard (AES). VMS-AES is a novel AES-like algorithm which uses Linear Feedback Shift Register (LFSR) to generate the required parameters used to remap the values of substitution box (S-box) randomly to another location depending on a secret key. This remapping also keeps the same good linear and differential properties of the AES S-box. VMS-AES algorithm can be easily deployed in most embedded applications because no architectural change is needed and only software modifications are performed. In our proposal, chipwisperer side channel attack analysis tool is used to verify the effectiveness of the proposed algorithm. Also VMS-AES with different number of rounds is evaluated using three methods: NIST statistical suite tests, correlation coefficient analysis, and cryptographic parameters evaluation to study the effects of this change upon the AES security
A Strange Algorithm for Fingerprint Based Age Estimation using Frequency Domain and Pattern Recognition Techniques

identifying the age from the unique mark of a new individual is a developing field of constructing studies. to perform this age component diverse systems had been extensively applied. Dominant a part of strategies makes use of the parameters like facet check and area broadness of a given particular finger influence for identity of the age estimation of that distinct person. Unique mark exam has enormous potential as a functioning machine for recognizable evidence of an person depending on both medical just as on restorative parameters. on this exploration work to select the age along their relationship recurrence vicinity and example acknowledgment methods were actualized for a hit grouping of finger prints two Dimensional Discrete Wavelet's (DWT) alongside independent component evaluation (ICA) are utilized to gauge the age factor from the unique mark statistics base. The statistics base which contains round 500 fingerprints of humans involving extraordinary age bunches inside the scope of 10 to 60 is considered for our exploration work. Multi-modular aid Vector gadget primarily based classifier is utilized for particular mark grouping. The exploratory consequences were arranged and from alongside those strains were given plots it changed into discovered that the talent of the proposed method is good.
Pixel Value Differencing Based Image Steganography using AES and SHA-2 Cryptography Method

The growing use of Internet and availability of public and private digital data and its sharing among professionals and researchers need particular attention to information security. This information needs to be protected against unauthorized access and attacks. Digital communication witnesses a noticeable and continuous development in many applications in the Internet. Hence, secure communication sessions must be provided. The security of data transmitted across a global network has turned into a key factor on the network performance measures. So, the confidentiality and the integrity of data are needed to prevent eavesdroppers from accessing and using transmitted data. Presently, three main methods of information security being used: watermarking, cryptography and steganography. The aim of this paper is to develop a new approach to hiding a secret information in an image by taking advantage of benefits of combining cryptography and steganography. In this method first, the message is encrypted by using AES algorithm and hashed the key using SHA-2 to prevent from attacks. After that, we performed some modifications on PVD algorithm by adding a key to make hiding process non sequential. Results achieved indicate that our proposed method is encouraging in terms of robustness and security.
Improved Variational Methodology Towards Enhancement of Marathi Printed Degraded Documents

Optical Character Recognition (OCR) system aims to translate scanned text to a machine understandable text. To do so, numerous tactics exist for several scripts and so far for good quality documents. Conversely, only a delimited permutation of the same has been investigated for degraded printed Marathi documents. This work offers learning which aims to discover and fetch out a marginal and competent policy of pre-processing in treating OCR for degraded printed Marathi documents. An effective estimation of the offered substitute has been considered by exposing it to documents having bleed-through, border smear, smear inside, low illuminations, unclearness etc. Proposed methodology’s results are examined in MATLAB R2015a. The work produces preprocessed images having better lucidity. Subsequent phases like segmentation, feature extraction, classification etc., offers better results with such preprocessed images having better clarity.
Structured-light 3D shape measurements using deep learning

Over the past few decades, tremendous efforts have been devoted to developing various techniques for fringe analysis, and they can be broadly classified into two categories: (1) phase-shifting (PS) methods which require multiple fringe patterns to extract phase information and (2) spatial phase demodulation methods which allow phase retrieval from a single fringe pattern, such as the Fourier transform (FT), windowed Fourier transform (WFT), and wavelet transform (WT) methods. Compared with spatial phase demodulation methods, the multiple-shot phase-shifting techniques are generally more robust and can achieve pixel-wise phase measurement with higher resolution and accuracy. Furthermore, the phase-shifting measurements are quite insensitive to non-uniform background intensity and fringe modulation. Nevertheless, due to their multi-shot nature, these methods are difficult to apply to dynamic measurements and are more susceptible to external disturbance and vibrations. Thus, for many applications, phase extraction from a single fringe pattern is desired, which falls under the purview of spatial fringe analysis. Recently, we demonstrated that the use of convolutional neural networks can substantially enhance the accuracy of phase demodulation from a single fringe pattern. Moreover, we find the powerful learning ability of deep neural network (DNN) enables the phase unwrapping, super-fast 3D shape measurement of transient events, multi-view fringe projection and so on. From comparative results, the DNN shows better performance over traditional state-of-the-art methods in terms of the phase accuracy and efficiency. We believe the deep learning technique is a powerful technique to handle fringe images and will find wide applications in 3D measurements with structured-light illumination.