Reviewerly/exHarmony huggingface dataset:
Enviromental Geochemistry of Bawakaraeng Mountain Soil: Implication for Anthropogenic Impact Gowa South Sulawesi Indonesia

Abstract Determination of heavy metal are done on Mount Bawakaraeng soil to find out the impact of anthropogenic, using range proximity matrix independent variables on camps, fired, garbage. There are two concentrated heavy metal zone and two particular zone (sample point 29, zone I; sample point 3 and 5, zone II) as concentrated heavy metal on rocks fractures, lavene statistic probability show the source of heavy metal Cr are different. Surface soil samples are taken on mapping variable, forest fires, especially for top and bottom of soil only garbage variable are taken. Furthermore, using Inductively Coupled Plasma Oscilloscope Emission Spectrometer (ICP-OES), X-Ray Fluorescence (XRF). Rock Samples analysis are done using thin section method on polarized microscope to find out the composition of volcanic minerals. EF range are higher and lower than 2 are showing the enrichment of heavy metal are naturally formed and EF relatively moderate by human activities. (Igeo) camp are Cr (0.606), Cu (0,529), Mn (0.383). Zn (0.590) Cd (-0.779). Pb (0.440); fired are Cr (0.586), Cu (0.514), Mn (0.383), Zn (0.590), Cd (-0.770), Pb (0.447); garbage is Cr (0.584), Cu (0.461), Mn (0.343), Zn (0.579), Cd (-1.110), Pb (0.458). the difference of EF and Igeo value are affected by sample point 3;5;29 and indexes value 1.5 on volcanic rocks soil. Friedman test are showing relatively small differences between human activities and nature factor, which indicating that Bawakaraeng Mount morphology are easily affected by exogenic and endogenic factors.
Zip Zap Data—A Framework for ‘Personal Data Preservation’

In the era of Mobile devices, a huge volume of mobile applications produces vast and variety of data in their own formats. There is every chance that most of these data will be lost or gone forever. Let us explain how, technically any digital data becomes irretrievable if there is a change of data format or a change in application interpreting it or a change in platform feature used by the application and your app did not change to accommodate it. In simple terms, if you try to upgrade your mobile—there are a lot of possibilities that data from your previous mobile could be lost forever. Reason for loss could be your newer device comes with some obsolete platform features and hence some of your applications no longer run in the new device as developer decided to discontinue that app. Even more pressing issue is there is no way to transfer application data from old to a new device and this is very common with mobile apps. The root cause for this is the lack of backup and retrieval mechanisms in devices and applications. We studied various approaches and research works related to this problem and proposed ‘Zip Zap Data’, a framework for effective backup and recovery of personal digital data.
Does customer satisfaction exist in purchasing and usage of Electronic Home Appliances in Bangladesh through interaction effects of Social Media

The objective of the study was to investigate the impact of product quality, quality of service and perceived value on customer satisfaction with the intensity of social media effect. The study was conducted based on quantitative research methodology for which a structured questionnaire was developed to collect data from 323 households in Bangladesh. A judgmental sampling technique was adopted for data collection by using the social media. Collected data was analysed using SEM-AMOS and SPSS. The result revealed that product quality, quality of service and perceived value have positive and significant effects on customer satisfaction with usage level of social media. The outcomes of this study will help managers promote their brands through social media to build a strong customer base. This study also contributes to the body of knowledge by providing some evidence of relationships among product quality, quality of service, perceived value and customer satisfaction.
Combining Concept Graph with Improved Neural Networks for Chinese Short Text Classification

With the development of the Internet, network information is booming, and a large amount of short text data has brought more timely and comprehensive information to people. How to find the required information quickly and accurately from these pieces of information is the focus of the industry. Short text processing is one of the key technologies. Because of the sparse and noisy features of short texts, the traditional classification method can not provide good support. At present, the research on short text classification mainly focuses on two aspects: feature processing and classification algorithm. Most feature processing methods only use text literal information when performing feature expansion, which lacks the ability to discriminate the polysemy that is common in Chinese. In the classification algorithm, there are also problems such as insufficient input characteristics and insufficient classification effect. In order to improve the accuracy of Chinese short text classification, this paper proposes a method of Chinese short text classification based on improved convolutional recurrent neural network and concept graph, which achieves better classification results than existing algorithms.
Computer Arithmetic Research to Accelerate Privacy-Protecting Encrypted Computing Such as Homomorphic Encryption

One of the first major breakthroughs of computer science in the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century has been the discovery and practical demonstration of encrypted computing technologies such as Fully Homomorphic Encryption (FHE). Encrypted computing technologies allow sensitive data to be encrypted such that arbitrary programs can be securely run over the encrypted data where the output, when decrypted, is equivalent to the result of running the original algorithm on the unencrypted data. In this talk we focus on the use of and potential for computer arithmetic research to enable more practical encrypted computing, such as to accelerate advanced encryption implementations on custom hardware. These technologies are ground-breaking in their ability for privacy-preserving data science on sensitive data sets with minimal costs in terms of engineering effort, power, compute resources, etc... We discuss theory, design, algorithmic, hardware, software engineering and systems research that is enabling applications of encrypted computing in regulated data industries, such as in medical and financial domains. We use our work using accelerating the PALISADE open-source homomorphic encryption software library for practical applications in case studies.
Color image encryption based on vector decomposition operations and secondary image phase masks

This paper presents a method aimed at encrypting color images using vector operations and secondary image phase masks (SPMs). In this encryption, the optical coherent superposition principle is used to separate and overlay the original image into two phase plates on the three channels R, G and B, respectively. Then the double random phase encoding in the Fresnel domain is performed using the SPMs to encrypt one of the phase plates. Finally, the Kronecker product of two random matrices is directly perform randomization on the encoded image, achieving multilevel encryption of color images. Computer simulations are presented to demonstrate its performance, and the feasibility and security of the proposed system are analyzed as well.
Liver Segmentation using Weighted Contrast based Chan-Vese Method

Liver cancer is the leading causes for death globally. Segmentation of liver from liver CT images is very crucial to evaluate the diagnostic pattern of liver disease and is claimed to be a challenging task owing to the complexity of the liver shapes in different slices of CT. In this work, Chan vese level set segmentation algorithm coupled with contrast driven elastic optimization model is used to achieve good segmentation accuracy. Global contrast approach has been implemented in bottom-up saliency detection in curvature optimization. Saliency map and weighted coefficient technique is measured using mean shift filter which improves the accuracy of saliency detection. The proposed segmentation technique has accurate results compared to the existing segmentation technique.
Semantic Enhanced Encoder-Decoder Network (SEN) for Video Captioning

Video captioning is a challenging problem in neural networks, computer vision, and natural language processing. It aims to translate a given video into a sequence of words which can be understood by humans. The dynamic information in videos and the complexity in linguistic cause the difficulty of this task. This paper proposes a semantic enhanced encoder-decoder network to tackle this problem. To explore a more abundant variety of video information, it implements a three path fusion strategy in the encoder side which combines complementary features. In the decoding stage, the model adopts an attention mechanism to consider the different contributions of the fused features. In both the encoder and decoder side, the video information is well obtained. Furthermore, we use the idea of reinforcement learning to calculate rewards based on semantic designed computation. Experimental results on Microsoft Video Description Corpus (MSVD) dataset show the effectiveness of the proposed approach.
A novel virtual disk bandwidth allocation framework for data-intensive applications in cloud environments

Recently, cloud computing has become a promising distributed processing paradigm to deploy various kinds of non-trivial applications. In those applications, most of them are considered data-intensive and therefore require the cloud system to provide massive storage space as well as desirable I/O performance. As a result, virtual disk technique has been widely applied in many real-world platforms to meet the requirements of these applications. Therefore, how to efficiently allocate the virtual disk bandwidth has become an important issue that needs to be addressed. In this paper, we present a novel virtual disk bandwidth allocation framework, in which a set of virtual bandwidth brokers are introduced to make allocation decisions by playing two game models. Theoretical analysis and solution are presented to prove the effectiveness of the proposed game models. Extensive experiments are conducted on a real-world cloud platform, and the results indicate that the proposed framework can significantly improve the utilisation of virtual disk bandwidth comparing with other existing approaches.
CAN K-NEAREST NEIGHBOR METHOD BE USED TO PREDICT SUCCESS IN INDONESIA STATE UNIVERSITY STUDENT SELECTION

Seleksi Nasional Masuk Perguruan Tinggi Negeri (SNMPTN) is one of the selection pathways for student admissions to enter state universities (PTN) in Indonesia. This study aims to predict the chance of being accepted in the desired PTN and the lack of early monitoring of students for SNMPTN. The data source from the grades reports card of SMAN 1 Pakong, SMAN 8 Kediri, and SMAN 1 Pamekasan by using the average input of compulsory subjects, majors (Science / Social Sciences) and semester 1 to semester 5 which later the output to be accepted or not accepted An imbalanced dataset potentially affect the performance of the classification method used. Hence, we need to eliminate the imbalance class using SMOTE. Using 10-fold cross validation, this study compared K-Nearest Neighbor (KNN) without SMOTE and K-NN with SMOTE. The goal is to find the best prediction model between the two methods. The prediction model is applied to software for teachers to monitor student grades and ensuring students to pass the SNMPTN. The results show that KNN without SMOTE has higher accuracy than KNN with SMOTE. However, KNN with SMOTE outperform than KNN without SMOTE in precision and recall, KNN with SMOTE with K = 3 reached 80.08% Accuracy, 74.42% Precision and 91.68% Recall.
Decision making processes for data sharing: a framework for data trusts

Introduction: while there is as yet no agreed definition of a data trust [1], it can be generally understood as a legal, technical and organisational structure for enabling the sharing of data. In a data trust context, trustees act on behalf of beneficiaries and trustors [2] and hence must rigorously interrogate the benefits and risks of data sharing to decide access. The authors have experience in implementing a data-sharing decision process in the Horizon2020 Data Pitch project and in the Interreg 2Seas Smart City Innovation Framework Implementation project, from which we derive these insights as to the elements that comprise robust approval. Requirement for decision-making processes: unlike open data, which has minimal restrictions on use, shared data is often linked to terms and conditions, with which the purpose of use must comply. A governance process must be established to decide who gets access to the data and why. Preliminary Framework: we have modelled an initial framework that identifies key considerations in the data sharing process. A combination of rights/consents and potential uses defines the space of possible uses of the data [3], which directs related action. Understanding this space and the best way to explore it is the cornerstone of the framework, as it protects the data, data subjects (if any), the data holders, the beneficiaries and the trustees. However, it is vital to further consider several dimensions that will impact the process: how benefit is measured; how eligibility of users is assessed; how the period of time for which the data is shared is decided (and how the end of the period is managed); how the process is made transparent and, finally, how issues such as preserving purpose over time and reducing movement of data can be incorporated. CCS CONCEPTS • H.3.5 Online Information Services – Data Sharing KEYWORDS Data sharing, Data trusts, Decision-making processes ACKNOWLEDGMENTS This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732506. REFERENCES [1]J. Hardinges, (2018). What is a Daa Trust? https://theodi.org/article/what-is-a-data-trust/ [2]K. O’Hara, (2019) Data Trusts: Ethics, Architecture and Governance for Trustworthy Data Stewardship. Web Science Institute White Paper #1 [3]S. Stalla Bourdillon and A. Knight. (2017) Legal and Privacy Toolkit v.1.0 http://www.datapitch.eu/wp-content/uploads/2017/06/PUBLIC-LEGAL-AND-PRIVACY-TOOLKIT-VERSION-1.0-DELIVERABLE-8.1-FINAL-30-JUNE-2017.pdf Price:$15.00
SAT Solvers and their Limits with NFSR-based Stream Ciphers

Many modern stream ciphers combine linear and nonlinear operations with a certain amount of initial clock steps without producing keystream bits for encryption. As a result of this behaviour, the resulting system of equations get more and more complex and difficult to solve. Due to the increasing number of monomials, as well as the increasing algebraic degree, the resulting system of equations get immune to a SAT-based cryptanalysis. A stream cipher realizing this principle is Grain v1. Grain is a stream cipher family, whose first version was submitted as a proposal to the eSTREAM project in the year 2004. Through a key recovery attack in the year 2006, a new revised version was published, referred as Grain Version 1. Within this paper, we examine the initialization phase of Grain v1 for SAT-based cryptanalysis. In the first step, the paper presents the necessary steps for establishing the Boolean system of equations. The second step describes the formulation of the SAT-Problem and the related possibilities and limitations.
SpliceRadar: A Learned Method For Blind Image Forensics

Detection and localization of image manipulations like splices are gaining in importance with the easy accessibility of image editing softwares. While detection generates a verdict for an image it provides no insight into the manipulation. Localization helps explain a positive detection by identifying the pixels of the image which have been tampered. We propose a deep learning based method for splice localization without prior knowledge of a test image's camera-model. It comprises a novel approach for learning rich filters and for suppressing image-edges. Additionally, we train our model on a surrogate task of camera model identification, which allows us to leverage large and widely available, unmanipulated, camera-tagged image databases. During inference, we assume that the spliced and host regions come from different camera-models and we segment these regions using a Gaussian-mixture model. Experiments on three test databases demonstrate results on par with and above the state-of-the-art and a good generalization ability to unknown datasets.
An Exergame with SAR Bike: a First Prototype

The games that incorporate interfaces that engage players in physical activity during the gaming experience have attracted the attention of researchers and companies. In this paper we present an ongoing work of an exergame as a result from an R&D initiative in a partnership between a company and the academy. Having a strategic position in the intersection between entertainment and health and well-being, our proposal intends to be innovative and competitive in the commercial market. The system combines a static bike as an input device with an AR device, providing exergame's experiences in single-player or multiplayer mode. Personalization is one of its main characteristics, allowing players to select scenarios and control the game environment and difficulty level by managing the intensity of the physical exercise.
Implementing Stochastic Bayesian Inference : Design of the Stochastic Number Generators

Bayes' rule can be computed stochastically using a simple Muller C-element, providing an efficient means of implementing Bayesian inference for autonomous robots. This provides such machines with the ability to make decisions when there is uncertainty in the data or models, enabling adaptation to unknown or dynamic environments. Because of the inherent memory effect in the C-element, the accuracy of this approach is sensitive to autocorrelation in the input signals. Previous work has shown that a simple rotating buffer is effective in mitigating the autocorrelation effects. However, prior studies have not addressed the practical aspects of designing the stochastic number generators (SNGs) required for creating the probabilistically-encoded values at the inputs. In this work, two approaches are evaluated: the use of linear feedback shift registers (LFSRs) and the use of low discrepancy sequence (LDS) generators. In the case of the LFSRs, this paper shows that there is no significant degradation in performance when several SNGs are shared with one LFSR provided a sufficient amount of circular shifting is used between each SNG. Results show an LDS generator is beneficial for lowering the amount of autocorrelation that is input into the C-element, resulting in improved performance.
Wireless Signal Service Type Identification Based on Convolutional Neural Network

Nowadays, with the birth of 5G and the Internet of Things, more and more business signals have emerged. The identification of signal service types has become a hot research topic. Whether it is suitable for daily life in the military field, there is a wide application prospect. Using the power spectrum data of the wireless signal, the characteristics of the power spectrum waveform are captured to identify the type of traffic of the wireless signal. This paper proposes the use of convolutional neural networks to extract and classify the wireless signal power spectrum data. After hundreds of iterative training, it can achieve an ideal recognition effect.
The 2009 and the 2015 Revised Curriculum Comparative Analysis of 5th and 6th Grade Science Textbooks : Focused on Matter Domain

The study is to comparative analysis the contents and compositions, the types and roles of illustrations in the 5th and 6th grade science textbooks compiled by the 2009 revised curriculum and compiled by the 2015 revised curriculum. Through this, the changes and flows of textbooks in accordance with the curriculum revision are suggested to provide basic data that can effectively use the 2015 revision curriculum science textbooks, and suggest implications for the development of science textbooks in the future revision curriculum. The results of the comparative analysis of the 2009 revised curriculum and the 5th and 6th grade science textbooks of the 2015 revised curriculum are as follows. First, the content of science textbooks of the 2015 revised curriculum decreased by unit. In the 2009 revised curriculum, the number of unit members was reduced by presenting only one science story about two or three units per unit at the end of the unit. In addition, the scientific writing presented at the end of each unit was not presented in the 2015 revised curriculum, and the total number of pages and the number of sentences in the unit were reduced by incorporating overlapping periods in the inquiry process. Second, as a result of analyzing the types and roles of the illustrations, the proportion of the pictures was higher than that of the 2009 revised curriculum science textbooks. This is because photographs are more efficient than manga and pictures, depending on the nature of the science subject that should be highly inquisitive and objective. Among the roles of illustrations, the role of learning guidance increased compared to the 2009 revised curriculum.
A product reputation framework based on social multimedia content

Purpose The purpose of this paper is to bring together the textual and multimedia opinions, since the use of social data has become the new trend that enables to gather the product reputation traded in social media. Integrating a product reputation process into the companies' strategy will bring several benefits such as helping in decision-making regarding the current and the new generation of the product by understanding the customers’ needs. However, image-centric sentiment analysis has received much less attention than text-based sentiment detection. Design/methodology/approach In this work, the authors propose a multimedia content-based product reputation framework that helps in detecting opinions from social media. Thus, in this case, the analysis of a certain publication is made by combining their textual and multimedia parts. Findings To test the effectiveness of the proposed framework, a case study based on YouTube videos has been established, as it brings together the image, the audio and the video processing at the same time. Originality/value The key novelty is the implication of multimedia content in addition of the textual one with the goal of gathering opinions about a certain product. The multimedia analysis brings together facial sentiment detection, printed text analysis, opinion detection from speeches and textual opinion analysis.
Bayesian Hierarchical Models in Psychological Science

This chapter provides a gentle introduction to Bayesian hierarchical modeling. It aims to make the material accessible to a wide audience including those who have a limited aptitude in mathematics and statistics. Bayesian analysis starts with a different definition of probability than what most psychologists learn and teach in their statistics courses. Bayesian probability offers an elegant and intellectually pleasing alternative to traditional notions of probability. To illustrate the advantages of Bayesian hierarchical modeling, we provide an application for the process of lexical access during reading. In the course of reading, printed strings on the page must be matched to words in the lexicon to build semantic meaning. With modern advances in Bayesian analysis, Bayesian analysis of hierarchical nonlinear models of psychological processes is possible and relatively straightforward. Researchers sometimes draw a sharp distinction between models that specify plausible psychological mechanisms and those that just stipulate noise.