Reviewerly/exHarmony huggingface dataset:
A Robust Incentive Consensus Propagation Design for Consortium-Chain Based Wireless Network

In the consortium-chain based wireless networks, edge service providers (ESPs) can act as pre-selected miners to maintain the blockchain network and obtained reward from the blockchain user. To make the network more secure, ESPs can recruit nearby mobile devices (NMDs) as lightweight verifiers during the block propagation process. However, with the increasing of verifiers, the propagation overhead is also increasing. Therefore, there exists a trade-off between the recruit ratios of ESPs and the transaction fee of blockchain user. In this paper, we propose a consortium-chain based wireless network architecture, and investigate the trade-off under the circumstance that some NMDs maybe crashed. The problem can be formulated as a two-stage robust Stackelberg game (RSG) approach. We provide the theoretical analysis of the existence and uniqueness of the Nash equilibrium point, and use the Lagrange dual decomposition method (LDDM) to obtain the Nash equilibrium point. Numerical results demonstrate that the proposed RSG approach outperforms other schemes in efficiency and robustness during the block propagation process.
Smart buckets

In literature a lot has been done to investigate the possible advantages of integrating the virtual and physical world, combining the benefits of both and improving the overall user experience. Plenty of tangible user interfaces were designed, to facilitate the development of cognitive skills, motor skills, social skills, or more concrete notions such as STEM concepts for children. Also, thanks to the flexibility provided by digital systems, it is recently growing a sub-research field that specifically addressed people with a cognitive disability. In this scenario, immersive multi-sensory environments are gaining more and more interest as a support tool for learning and therapeutic methods. In this paper, we present Smart Buckets, an interactive interface integrated into "Magika", a multi-sensory system; the result of co-design with therapists specialized in the treatment and education of children with a cognitive disability. The paper illustrates the multi-step co-design process used to produce this idea as a tailored support for their therapeutic activities. Finally, it highlights the importance of adding a new customizable tangible interaction combined within the multi-sensory experience of Magika system.
Pointing Direction Estimation for Attention Target Extraction Using Body-mounted Camera

In this paper, we propose a pointing-directionestimation method using a body-mounted camera. The opportunities to capture a large amount of image data in daily life are increasing due to the spread of smartphones and wearable cameras. In order to efficiently look back at the captured images, we aim to extract attention targets from the image sequences because the attention target is important for reminding people of their memories. Toward this purpose, in this paper, we propose a method for estimating the pointing direction from wearable camera images. The proposed method consists of two steps: arm skeleton estimation and pointing direction estimation. We model three types of pointing-directionestimation models and compare the estimations' accuracy for evaluating which parts are important for pointing direction estimation. The experimental results show that the model based on the wrists and elbows had the best results.
Prediction Analysis Of Criminal Data Using Machine Learning

Abstract The human-being life necessities have encouraged the commission of crime since ancient times. Nowadays, many crimes factor and techniques have developed in a more sadistic way, causing more victims and loss. The authorized parties need to find a way to minimize the commission of a crime. This research aims to utilize linear regression algorithm for analyzing crime data to generate predictions for crime, which shows a quite reliable result. The result can be used by the authorized parties to help them prevent and handle upcoming crime.
UVCA: a Unified Vocal Commands Approach for Mobile Applications

Humans prefer to interact with their smart devices through speech rather than using pointing tools and keyboards. Recent mobile devices provide the capability to automatically convert voice to text but they still suffer from many issues when it comes to efficiency goal. In this paper, we present a new approach for vocal commands processing based on Information Retrieval concepts (i.e. text pre-processing, cosine similarity) as well as machine learning algorithms and which enables mobile smart devices to better and faster access to applications' functionalities.
Secure and Efficient Sensitive Info-Hiding for Data Sharing via DACES Method in Cloud

Cloud computing has been developed very quickly within the past few years. With public cloud storage services, users can store remotely to servers in cloud and also recognize the sharing of data with other users. The public cloud servers cannot be completely trusted by users and cannot manage records kept on cloud like local storage devices. However, within cloud storage systems, stored data would possibly contain sensitive info which should not be visible to other users while sharing record in cloud. This becomes a challenge to realize secure and efficient information sharing within cloud. However, existing cryptographic mechanisms do not protect shared sensitive data which are typically protected using a low-entropy password. To tackle this, Division and Auditing of information in Cloud for Efficiency and Security (DACES) is recommended in order to treat the above issues for public cloud storage. In DACES: (i) Erasure code is applied to divide a single record into blocks and distributed them to different cloud servers. (ii) Privacy of data owner and data sharing with sensitive info-hiding is achieved by secure data integrity auditing scheme. (iii) Identity-based access management is implemented to recognize that only authorized users will access the sensitive information. Furthermore, the proposed method is secure and efficient for information sharing in cloud environment. At last, its application is in electronic health (E-health) record.
Experience-based analysis and modeling for cognitive vehicle data

Herein, we discuss an experience-based approach to cognitive vehicle research. We prepared six subjects with individual experiences and skills. We made them drive their cars on the same test course and collected data on their mental status and operations, as driving data, using a simple brain wave sensor, and the acceleration sensor in a smartphone. We analyzed the data and constructed models using our previously developed qualitative cognitive analysis and modeling tool. We then experimentally compared the variation between the analysis and the modeling results. Consequently, we could find a parameter that indicates the degree of experience and skill of each driver. We could also derive rules from models, which explained the experience and skill of each driver. Our experience-based approach yields an understanding of unconscious operation and situated cognition based on a driver's experiences, and how it allows novice or experienced drivers to retain and re-apply driving experiences and skills.
Forecast model for dephosphorization process of ferromanganese steels using artificial neural networks

One of the main problems affecting the quality of steel products is the existence of contaminants in alloy steel, being phosphorus (P) a major contamination element interfering with the steelmaking process.The increased P concentration levels can severely affect physical integrity of steel bonds, thus threatening the quality of the final product.The dephosphorization process of Ferromanganese consists by carbothermic reaction that involves the control of the manganese volatilization and reduction of manganese oxide in injection of oxygen.Therefore, we propose to forecast model for dephosphorization process of Ferromanganese steels in a steelmaker industry, that allows estimating the phosphorus concentration levels at the final refining process.We chose the artificial neural network models because it is computational models inspired in the human nervous system and an architecture of neural network with the Levenberg-Marquadt algorithm and Kolmogorov theorem for improving the estimation technique.The developed model presented excellent performance with a percentage error of 0.09%.Based on this created estimation model it is possible to estimate the impact of certain P concentration levels in FeMnMC beforehand, with a considerable amount of reliability.
An Efficient Certificateless Arbitrated Signature Scheme for IoT Environments

Recently, researches on lightweight cryptosystems for the IoT (Internet of Things) environment have been continuously conducted. Among the public key cryptosystems, ID-based cryptography has the advantage of verify the public key by using an identifier in the form of a public key. However, a key escrow problem occurs that requires the generation of both public and private key pairs in the Key Generation Center (KGC). To solve this problem, Certificateless Public Key Cryptography (CL-PKC), which is a public key cryptosystem without certificate was proposed. The CL-PKC is actively researched in various cryptography fields such as authentication, key agreement, signature, and encryption. In this paper, we analyze the CLS (Certificateless Signature), a signature scheme using CL-PKC for IoT environment. The existing CLS schemes may have problems such as public key replacement attack due to the weakness of the nonrepudiation function, so we propose a CL-based arbitrated signature scheme, CLAS.
Phrase Structures and Message Frames for Generation of Cognitive messages

This paper proposes a methodology to generate a variety of unique personalized cognitively charged messages intended to improve the rate of resonation between the message and its audience for communication campaigns in any given discourse. It presents an end-to-end methodology for incorporating cognitive elements in messages, structuring them, parsing them, transforming them into word embeddings and using custom message frames to generate cognitively charged messages. The use of a plethora of psychological principles related to compliance (principles of persuasion) and quick decision making (heuristics) have been employed in the design for this purpose. Additionally, this method ensures uniqueness and diversity in the messages generated.
Feature Selection in Cross-Project Software Defect Prediction

Abstract Advances in technology have increased the use and complexity of software. The complexity of the software can increase the possibility of defects. Defective software can cause high losses. Fixing defective software requires a high cost because it can spend up 50% of the project schedule. Most software developers don’t document their work properly so that making it difficult to analyse software development history data. Software metrics which use in cross-project software defects prediction have many features. Software metrics usually consist of various measurement techniques, so there are possibilities for their features to be similar. It is possible that these features are similar or irrelevant so that they can cause a decrease in the performance of classifiers. In this study, several feature selection techniques were proposed to select the relevant features. The classification algorithm used is Naive Bayes. Based on the analysis using ANOVA, the SBS and SBFS models can significantly improve the performance of the Naïve Bayes model.
Analysis on the Mechanism of Enrollment, Training and Employment Linkage in Colleges and Universities under Large-Scale Enrollment Mode

As a new type of talent training model, the large-scale enrollment and diversion training model has been adopted by more and more colleges and universities. However, some colleges and universities have blind enrollment categories, the management system still adopts the old-style professional management mode, the importance of employment guidance is not highlighted in the diversion of large categories, and the status quo of employment and the development of disciplines have not put forward scientific guidance for enrollment work, resulting in a series of problems such as the decline of student quality, training and employment difficulties. Colleges and universities should innovate and adjust the linkage mechanism of enrollment, training and employment in order to adapt to the current reform of enrollment mode and promote the benign development of enrollment mode.
Deploying Personalized Oblivious Mobile Identity

Mobile Id approach provides a contactless and secure identification process. Standardization towards a common scheme is important to ensure wide-spread adoption through cross-country and cross-region interoperability. Therefore mobile identification has seen enormous attention both by platform vendors and researchers. In this work we discuss a novel mobile identification approach that is based on oblivious identity to ensure improved privacy protection and identification data at rest protection. The mobile identification approach addresses the development of the interfaces to connect users' identities to their real-world physical identities by integrating with officially issued eIDs through bridging with eIDAS, STORK, or electronic driving licenses. In particular we present the initial validation for mobile identification of distributed virtual iDP cryptographic infrastructure for mobile ID use cases. The key aim is to show the particular steps needed in order to perform thorough testing in new cryptographic approaches that aim to be applied in real life application through presented first pilots. Evaluating these prototypes and also evaluating them with other stakeholders in order to identify a wider deployment for business development and exploitation. Initial testing and validation results of the proposed mobile identification approach are encouraging and promising showing strong privacy preservation outcomes.
Determining the Direction of Images Using Sum and Difference Histograms and Texture Descriptors

One of the most used statistical approaches in the literature for computing texture information is the Gray Level Cooccurrence Matrix (GLCM). The use of GLCM and textures descriptors calculated from there, allows, among other applications, the detection of the main direction of the elements of texture. This is possible essentially because the GLCM uses different angles to compute the relationship between the gray tones in the image. An alternative to computing this relationship is the Sum of Difference Histograms (SDH). In contrast to GLCM, which is a bidimensional structure, SDHs are one-dimensional. Once there is a relationship between the data stored in both structures, the behavior of the descriptors is similar, and the SDH have a lower computational cost being, for this, more efficient in a number of applications. In this sense, this work leads to a series of investigations regarding the correspondence between these two approaches. Moreover, it investigates which texture descriptors, calculated from the SDH, can collaborate in the process of identifying the predominant direction of a texture.
Gaussian copula-based zero-inflated power series joint models to analyze correlated count data

A Gaussian Copula-based regression model is proposed that accounts for associations between count responses with extra zeros. Our approach entails underlying latent variables to indicate the latent mechanisms which generate the count responses where some of the count responses are inflated in a zero point. The model contains, as special sub-models, several important distributions such as the power series distributions with and without extra zeros, for example, Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial distributions. The full likelihood-based inference method is applied for the estimation of parameters to obtain maximum likelihood estimates of the parameters. Modified Pearson residuals, where the correlation between responses is taken into account, are used for finding abnormal observations. To illustrate the utility of the models, some simulations are illustrated. Finally, the proposed models are applied to an insurance data set for insurers, obtained from an observational study, where the number of automobile claims and the number of third party claims are the correlated count responses. The effects of car age and the type of car, driving place on both responses are investigated simultaneously.
Cryptanalysis of Modification in Hill Cipher for Cryptographic Application

This paper focuses on the strengths and weaknesses of the algorithm proposed by Qazi et al. [1]. The cipher proposed by the authors is highly predictable and suffers from lack of sufficiently strong and complex intermediate operations along with Hill cipher. The Hill cipher, on its own, cannot offer foolproof security. It should be strengthened with sufficiently complex and nonlinear operations which offsets the known plaintext vulnerability which the Hill cipher exhibits. All intermediate operations introduced in the scheme proposed by Qazi et al. are very simple, linear and predictable. Such operations do not strengthen an existing cipher which is proven to be vulnerable to the known plaintext attack. The algorithm cannot stand even moderate cryptanalysis and can be very easily broken. To be more precise, the algorithm can be broken with the help of one chosen ciphertext and four chosen plaintexts. This paper also proposes suitable modifications to offset the vulnerabilities exhibited by the original cipher.
Machine Learning in Information Visualization: Using Rule-based Learning Algorithms to Personalize Interfaces

Machine Learning has offered great potential in areas such as data mining and information retrieval, while less attention has been given to its use in the design of Information Visualization tools. Based on user needs, a properly designed interface can enhance augmented perception; however, the project is usually conducted without the inclusion of users. Machine Learning techniques can be useful to promote the users' involvement in the interface development process, contributing to offer personalized visualizations. Based on a dataset from a User Experience experiment with an interactive 3D environment to visualize medical temporal data, we present an approach using rule-based learning algorithms to promote the user's involvement. Our results indicate that Association Rule Learning can be a valuable resource to improve the design of Information Visualization interface tools as well as to customize visualization according to the user's preferences.
Characteristic of Student Informal Gathering Space (SIGS) Faculty of Engineering Hasanuddin University at Gowa Campus

Abstract The purpose of this study are: 1) What are the characteristics of Student Informal Gathering Room (SIGS) in the Faculty of Engineering, and 2) What attributes are felt by students in the Student Informal Gathering Room (SIGS). To get the results of the research purposed applied qualitative research methods with purposive sampling techniques. Data collection is done by distributing questionnaires to students, in-depth interviews and mapping behavior with place-centered maps. The results of the study is the closeness of the gathering place with the main activities of students in the teaching and learning process which is a characteristic of SIGS found on the campus of engineering faculty at Gowa campus. While the environmental attributes felt by engineering students at SIGS are accessibility, comfort, sociality, visibility, and activities.
Security and Privacy Challenges of Deep Learning

Deep learning is the buzz word in recent times in the research field due to its various advantages in the fields of healthcare, medicine, automobiles, etc. A huge amount of data is required for deep learning to achieve better accuracy; thus, it is important to protect the data from security and privacy breaches. In this chapter, a comprehensive survey of security and privacy challenges in deep learning is presented. The security attacks such as poisoning attacks, evasion attacks, and black-box attacks are explored with its prevention and defence techniques. A comparative analysis is done on various techniques to prevent the data from such security attacks. Privacy is another major challenge in deep learning. In this chapter, the authors presented an in-depth survey on various privacy-preserving techniques for deep learning such as differential privacy, homomorphic encryption, secret sharing, and secure multi-party computation. A detailed comparison table to compare the various privacy-preserving techniques and approaches is also presented.
3D Interactive Multimedia in English Language Learning

Abstract Language has an important role in communicating. English is the most widely international language used in the world. Based on the results of observations made at Elementary School that the media used during the learning process are worksheets and textbooks, and it has not used learning media yet. This research aims to determine the feasibility of the media and the effect of using interactive learning media toward learning outcomes in English material. The method of this research used “Research and Development” with a development model ADDIE (Analysis-Design-Development-Implementation-Evaluation). This research uses design Matching pretest posttest control group. The media validation is categorized as feasible with an average value of 79.37%. And the material content validation in the category is very feasible with an average value of 96.97%. The measurement of student learning outcomes is done by providing tests in the form of multiple choices. Based on the average value of learning outcomes control class are smaller than the experimental class that is 67.43 &lt; 82.30. The statistical analysis used in this research is normality test, homogeneity test, hypothesis test, and n-gain test. Assessment of students are categorized as acceptable with an average value of 85%. The results of the data analysis were strengthened using the independent sample t test, with the value obtained sig = 0,451 &lt; α = 0.05 then H0 is rejected and H1 is accepted. The n-gain test results obtained a value of 66.90 for the experimental class and the control class of 32.53. The result of this research is the use of learning media in which it has a greater influence than conventional methods and there are significant differences in the student learning outcomes.
Quartet For Violin, Tenor Saxophone, Double Bass, and Piano

Author(s): Adler, Sawyer | Abstract: This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.
The Role of Augmented Reality on Public Relations and Advertising

Along with the changes in information communication technologies, changes are observed in today's communication practices. Particularly the personal and corporate usage of the internet-based applications is remarkable. At this point, augmented reality applications that take their place as a communication strategy in the brand communication process draw attention. It can be said that institutions prefer augmented reality applications in their communication processes with the target audience. In this process, augmented reality is a preferred communication element in public relations and advertising applications. In this study, the situation of augmented reality applications in public relations and advertising applications is examined. Firstly, the concept and scope of augmented reality is explained. Then, historical development process and how it develops and changes are emphasized. In the light of the aforementioned literature information, applications were tried to be described with the examples determined from the augmented reality applications.