Reviewerly/exHarmony huggingface dataset:
Developing E-Book Contained Character Values in PPKn Lesson Content Grade V Elementary School

Teachers are not innovated in developing media, therefore students’ interaction in online learning was not optimal. This issue raised a new problem such as, lack of deepening character values for students. The media developed was an E-Book containing character values. Therefore, this research aims to describe the design process of E-Book product with character values and find out the results of E-Book validation according to the results of expert reviews, individual try-outs, and small group try-outs. The type of this research was a development research by applying ADDIE model (analyze, design, development, implementation, evaluation). Then, data collection method which used was questionnaire and quantitative descriptive analysis method. The results of data analysis were gained based on media validation assessed by content learning experts obtained a score of 91,67% with very good qualifications, instructional design expert obtained a score of 100% with very good qualifications, learning media expert obtained a score of 95,00% with very good qualifications. The results of individual user test on 3 students obtained a score of 95,83% with very good qualifications, and the user test through small group test on 12 student obtained a score of 97,50% with very good qualifications. Based on the results of the product test data analysis by experts (content learning expert, instructional design expert, learning media expert) and user subject test results (students), it can be concluded that E-Book media containing character values were suitable to be used in PPKn learning subject for 5th grade elementary school students. The implication of using E-Book as technology-based learning media supported by existing facilities and infrastructure in schools such as internet access (WiFi), computers / laptops, LCDs, projectors, as well as the readiness and skills of teachers and students in accessing E-Book media.
A Privacy-Preserving Proximity Testing Using Private Set Intersection for Vehicular <i>Ad-Hoc</i> Networks

Proximity testing technologies have been of increasing importance in vehicular <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad-hoc</i> networks (VANETs), especially in location-based services. However, there exist several known challenges in most existing proximity testing methods. For instance, during proximity testing, how to protect the location privacy of users, guarantee the fairness trait of both communication parties, and reduce computational costs is challenging. In this article, we present an efficient privacy-preserving proximity testing scheme using private set intersection (PSI) and differential privacy. In our design, a Chebyshev-based PSI is constructed to achieve location privacy with low energy consumption during the proximity testing process. Furthermore, geo-indistinguishability is employed in our scheme to generate virtual points as inputs set of PSI, which further protects the location privacy from exposure and provides resistance to collusion attacks. Fairness requirement is alsosatisfied in our scheme. The performance evaluation shows that the proposed scheme achieves good efficiency and is suitable for VANETs.
Public Key Cryptosystem Based on Singular Matrix

The algorithms such as RSA, ElGamal and ECC work on integers. Commutative operations on integer multiplication leave these algorithms vulnerable to attack by eavesdroppers. For this reason, experts develop the concept of non-commutative algebra in the public key cryptosystem by adding non-commutative properties to groups, semirings, semiring division, matrices and matrix decomposition. However, the key generating process in some public key cryptosystems is quite complicated to carry out. Therefore, in previous research, Liu used nonsingular matrices to form a simpler public key cryptosystem. However, eavesdroppers use the inverse of nonsingular matrices to construct the private key. As a result, this public key cryptosystem is still vulnerable to attacks. Therefore, we use a singular matrix to modify and build the proposed public key cryptosystem in this study. This study indicates that the singular matrix can be used to modify the public key cryptosystem. The results also show that the key generating algorithm only uses ordinary matrix multiplication (without using matrix power operations), so it is not too complicated. Furthermore, the proposed public key cryptosystem works on a matrix over integers so that the possible brute force attack trials are endless. The proposed public key cryptosystem also cannot be attacked by matrix inversion because it uses a singular matrix.&#x0D; HIGHLIGHTS&#x0D; &#x0D; Public key cryptosystems that use commutative operations are vulnerable to eavesdropping attacks&#x0D; This study uses a singular matrix to modify and build a public key cryptosystem&#x0D; The proposed public key cryptosystem works on ordinary matrix multiplication operations and cannot be attacked by matrix inversion&#x0D;
Implementation Drought Area Identifier Application Of East Bali Based On Remote Sensing

Drought is a natural disaster whose frequency is quite high and occurs almost every year. According to the radarbali.jawapos.com page on June 15, 2019, rice fields in Karangasem are threatened by drought. 2000 hectares of agricultural land threatened with drought or did not get irrigation supply because during the dry season the water source used to irrigate the land was drastically reduced and even partly dried up. From the observations of the UPTD of the Horticultural Agricultural Plant Protection Agency (BPTPH) of the Bali Provincial Agriculture Office, until June 2019 there was damage to rice fields due to drought where observations were made since the start of the planting season in May. Drought area information is used to map and calculate the area of drought areas, so that areas with drought potential can be identified. One way to get drought area data is by remote sensing technology. Identification of potential drought areas is the basic idea for the formation of drought identification applications in East Bali. The remote sensing application uses the NDDI (Normalized Difference Drought Index) method. NDDI is the result of calculation of the Normalized Difference Vegetation Index (NDVI) which is an index of vegetation parameters and Normalized Difference Water Index (NDWI) which is the value of wetness of vegetation. The images used in this study are Landsat 8 imagery with OLI (Onboard Operational Land Imager) sensor and TIRS (Thermal Infrared Sensor) sensor. The result of the application of drought area identification is the visualization of drought area and the extent of drought area based on the category of very dry, dry, mild dry, normal and non-classification using hectare units.
Bi-Lingual (English, Punjabi) Sarcastic Sentiment Analysis by using Classification Methods

Sentiment analysis is one of the heated topic in the field of text mining. As the social media data is increased day by day the main need of the data scientists is to classify the data so that it can be further used for decision making or knowledge discovery. Now –a-days everything and everyone available online so to check the latest trends in business or in daily life one must consider the online data. The main focus of sentiment analysis is to focus on positive or negative comments so that a well define picture is created that what is trending or not but the sarcasm manipulates the data as in sarcastic comment negative comment consider as positive because of the presence of positive words in the comment or data so it is necessary to detect the sarcasm in online data . The data on social media is available in various languages so sentiment analysis in regional languages is also a main step . In the proposed work we focus on two languages i.e Punjabi and English. Here we use deep learning based neural networks for the sarcasm detection in English as well as Punjabi language. In the proposed work we consider three datasets i.e. balanced English dataset, Balanced Punjabi Dataset and unbalanced Punjabi dataset. We used six different models to check the accuracy of the classified data the models we used are LSTM with word embedding layer, BiLSTM with , LSTM+LSTM, BiLSTM+BiLSTM, LSTM+BiLSTM, CNN respectively. LSTM provide better accuracy for balanced Punjabi and English dataset i.e. 95.63% and 94.17% respectively. The accuracy for unbalanced Punjabi dataset is provided by BiLSTM i.e.96.31%.
Consistency and Decidability in Some Paraconsistent Arithmetics

&#x0D; &#x0D; &#x0D; The standard style of argument used to prove that a theory is unde- cidable relies on certain consistency assumptions, usually that some fragment or other is negation consistent. In a non-paraconsistent set- ting, this amounts to an assumption that the theory is non-trivial, but these diverge when theories are couched in paraconsistent logics. Furthermore, there are general methods for constructing inconsistent models of arithmetic from consistent models, and the theories of such inconsistent models seem likely to differ in terms of complexity. In this paper, I begin to explore this terrain, working, particularly, in incon- sistent theories of arithmetic couched in three-valued paraconsistent logics which have strong (i.e. detaching) conditionals. &#x0D; &#x0D; &#x0D;
Characterizing Fine-Grained Resource Utilization for Multitasking GPGPU in Cloud Systems

Managing GPGPU resources in cloud systems is challenging as workloads with various resource usage patterns coexist. To determine the co-location of workloads, previous studies have shown that run-time performance profiling and dynamic relocation of workloads is necessary due to interference between workloads. However, this makes instant scheduling difficult and also affects the performance of workload executions. In this article, we show that efficient resource sharing in GPGPU is possible without run-time profiling if resource usage characteristics of workloads are analyzed down to a fine-grained unit level. To extract workload characteristics, we do not perform profiling at scheduling time, but separates profiling from scheduling, thereby reducing the run-time complexity of previous approaches. Specifically, we anatomize the characteristics of various GPGPU workloads and present a new scheduling policy that aims at balancing resource utilization by co-locating workloads with complementary resource demands. Simulation experiments under various virtual machine scenarios show that the proposed policy improves the GPGPU throughput by 119.5% on average and up to 191.7%.
Human Digital Twins in Acquiring Information About Human Mental Processes for Cognitive Mimetics

Modern information technology makes it possible to redesign the ways people work. In the future, machines can carry out intelligence-requiring tasks, which previously were done by people. It is thus good to develop methodologies for designing intelligent systems. An example of such methods is cognitive mimetics, i.e. imitating human information processing. Today, machines cannot by themselves navigate in archipelagos. However, the fact that people can take care of ship steering and navigation means that there is an information process, which makes it possible to navigate ships. This information process takes place inside the minds of navigating people. If we are able to explicate the information processing in the navigator’s mind, the knowledge of it can be used in designing intelligent machines. Replicating physical objects and industrial processes by means of digital computers is called digital twinning. Digital twins (DTs), which are digital replicas of physical systems and processes, have recently become tools for working with complex industrial processes. A crucial question for DTs is should human actions be added to them? As the answer is positive, such models of human information processing can be called human digital twins (HDTs). The knowledge of human tacit and explicit information processes can be represented by human digital twins. Models can be used in the search for a deeper understanding of human intelligent information processes. Human digital twins can thus be used as methodological tools in cognitive mimetics. In our present study, we modeled paper machine operators’ thinking. Specifically, we developed an ideal-exception-correction (IEC) model for paper operators’ control logic. The model illustrates how research and HDT-modeling can be used for explicating the subconscious or tacit information processing of people for the design of intelligent systems. In this article a model for design processes using cognitive modelling will be suggested. The concepts of cognitive mimetics and human digital twins enable us to outline a model for using the long tradition of simulating human thinking as a tool in designing intelligent systems.
Towards a New Metamodel Approach of Scrum, XP and Ignite Methods

The agile approach is a philosophy that aims to avoid the traditional management approach problems.It concentrates on the collaborative approach, using iterative and incremental development.The client receives a first production version (increment) of his software, faster thanks to agile methodologies.Project needs are influenced by the rapid expansion of technologies, particularly after the emergence of the Internet of Things (IoT).They are becoming larger and more complex.IoT provides a standardization and unification of electronic identities, digital entities, and physical objects.Consequently, interconnected devices can retrieve, store, send, and process data easier from both physical and virtual worlds.Scalable methods such as SAFe, LeSS, SPS, and others are existing methodologies ameliorated and dedicated to large projects.These methods are tough to adopt and do not consider the physical side of the project, according to IoT enterprise teams.Based on their managerial and IoT expertise, they suggest their own methods (Ignite | IoT Methodology and IoT Methodology).Model Driven Architecture (MDA) was coined by the Object Management Group (OMG) in 2000 to develop perpetual models that are independent of the technical intricacies of the execution platforms.The purpose of this paper is to propose a metamodel for each methodology among: Scrum, XP, and Ignite.
PASSWORDLESS AUTHENTICATION USING MAGIC LINK TECHNOLOGY

Nowadays, the problem of identification and authentication on the Internet is more urgent than ever.There are several reasons for this: on the one hand, there are many Internet services that keep recordsof users and differentiate their access rights to certain resources; on the other hand, cybercriminals'attacks on web services have become much more frequent lately. At the same time, in many cases, theweak point of systems exposed to attacks is precisely the authentication system.Authentication methods based on the knowledge factor (e. g. password protection) are the mostcommon and are applied almost everywhere. Their advantages are ease and low cost ofimplementation. On the other hand, such systems are often vulnerable to various kinds of attacks. It isestimated that up to 80% of successful hacker attacks (including attacks on the largest services withmillions of users) succeeded precisely because of the weakness of the password protection system.This paper presents a solution to the problem of passwordless authentication, which can be applied ina number of online services and systems. In particular, we consider the magic link technology andpresent an authentication system implemented using Keycloak, an open-source software product thatimplements single sign-on technology. In the future, it is possible to further improve the system, inparticular, using adaptive authentication, which allows switching between different authenticationmechanisms depending on certain factors.
Listening Effort Informed Quality of Experience Evaluation

Perceived quality of experience for speech listening is influenced by cognitive processing and can affect a listener's comprehension, engagement and responsiveness. Quality of Experience (QoE) is a paradigm used within the media technology community to assess media quality by linking quantifiable media parameters to perceived quality. The established QoE framework provides a general definition of QoE, categories of possible quality influencing factors, and an identified QoE formation pathway. These assist researchers to implement experiments and to evaluate perceived quality for any applications. The QoE formation pathways in the current framework do not attempt to capture cognitive effort effects and the standard experimental assessments of QoE minimize the influence from cognitive processes. The impact of cognitive processes and how they can be captured within the QoE framework have not been systematically studied by the QoE research community. This article reviews research from the fields of audiology and cognitive science regarding how cognitive processes influence the quality of listening experience. The cognitive listening mechanism theories are compared with the QoE formation mechanism in terms of the quality contributing factors, experience formation pathways, and measures for experience. The review prompts a proposal to integrate mechanisms from audiology and cognitive science into the existing QoE framework in order to properly account for cognitive load in speech listening. The article concludes with a discussion regarding how an extended framework could facilitate measurement of QoE in broader and more realistic application scenarios where cognitive effort is a material consideration.
Deep Learning for Arabic Image Captioning: A Comparative Study of Main Factors and Preprocessing Recommendations

Captioning of images has been a major concern for the last decade, with most of the efforts aimed at English captioning.Due to the lack of work done for Arabic, relying on translation as an alternative to creating Arabic captions will lead to accumulating errors during translation and caption prediction.When working with Arabic datasets, preprocessing is crucial, and handling Arabic morphological features such as Nunation requires additional steps.We tested 32 different variables combinations that affect caption generation, including preprocessing, deep learning techniques (LSTM and GRU), dropout, and features extraction (Inception V3, VGG16).Moreover, our results on the only publicly avail-able Arabic Dataset outperform the best result with BLEU-1=36.5,BLEU-2=21.4,BLEU-3=12 and BLEU4=6.6.As a result of this study, we demonstrated that using Arabic preprocessing and VGG16 image features extraction enhanced Arabic caption quality, but we saw no measurable difference when using Dropout or LSTM instead of GRU.
A Survey on Deep Learning Face Age Estimation Model: Method and Ethnicity

Face age estimation is a type of study in computer vision and pattern recognition.Designing an age estimation or classification model requires data as training samples for the machine to learn.Deep learning method has improved estimation accuracy and the number of deep learning age estimation models developed.Furthermore, numerous datasets availability is making the method an increasingly attractive approach.However, face age databases mostly have limited ethnic subjects, only one or two ethnicities and may result in ethnic bias during age estimation, thus impeding progress in understanding face age estimation.This paper reviewed available face age databases, deep learning age estimation models, and discussed issues related to ethnicity when estimating age.The review revealed changes in deep learning architectural designs from 2015 to 2020, frequently used face databases, and the number of different ethnicities considered.Although model performance has improved, the widespread use of specific few multi-races databases, such as the MORPH and FG-NET databases, suggests that most age estimation studies are biased against non-Caucasians/non-white subjects.Two primary reasons for face age research's failure to further discover and understand ethnic traits effects on a person's facial aging process: lack of multi-race databases and ethnic traits exclusion.Additionally, this study presented a framework for accounting ethnic in face age estimation research and several suggestions on collecting and expanding multi-race databases.The given framework and suggestions are also applicable for other secondary factors (e.g.gender) that affect face age progression and may help further improve future face age estimation research.
Exploring privacy-enhancing technologies in the automotive value chain

Privacy-enhancing technologies (PETs) are becoming increasingly crucial for addressing customer needs, security, privacy (e. g., enhancing anonymity and confidentiality), and regulatory requirements. However, applying PETs in organizations requires a precise understanding of use cases, technologies, and limitations. This paper investigates several industrial use cases, their characteristics, and the potential applicability of PETs to these. We conduct expert interviews to identify and classify uses cases, a gray literature review of relevant open-source PET tools, and discuss how the use case characteristics can be addressed using PETs’ capabilities. While we focus mainly on automotive use cases, the results also apply to other use case domains.
Consortium Blockchain based Reputation Incentive Mechanism for Recommendation System

Recommendation systems have been widely used in many e-commerce services, but it is difficult to gather enough participants to supply their recommendations. Moreover, participants in the system may make malicious recommendations, which will affect the accuracy of recommendation results. In order to provide better recommendation service for users, incentive mechanisms are needed to attract more participants in recommendation and curb their malicious behaviors. In this paper, we propose a consortium blockchain based reputation incentive mechanism for recommendation systems(CRIM). Firstly, the monetary rewards are used to attract participants and motivate them to take part in the recommendation. Secondly, we design the incentive mechanism with reputation which is attached to the rewards. Honest participants will gain more rewards while malicious participants will be penalized. Meanwhile, we adopt the Stackelberg game to maximize the utility of participants, and prove that the mechanism can reach a unique Nash equilibrium. Thirdly, the decentralization and immutability of blockchain can guarantee the credibility and security of the stored data, thus ensuring the openness and transparency of the recommendation. Finally, we implement the system for education resources recommendation and conduct experiments, and the results demonstrate that our incentive mechanism is effective and has significant performance when compared with other incentive mechanisms.