Reviewerly/exHarmony huggingface dataset:
DELP: Dynamic Epistemic Logic for Security Protocols

The formal analysis of security protocols is a challenging field, with various approaches being studied nowadays. The famous Burrows-Abadi-Needham Logic was the first logical system aiming to validate security protocols. Combining ideas from previous approaches, in this paper we define a complete system of dynamic epistemic logic for modeling security protocols. Our logic is implemented, and few of its properties are verified, using the theorem prover Lean.
A Hybrid Autoencoder Architecture for Text Encryption

With the advent of modern computing technologies, there is a wide scope for introduction of advanced and highly secure encryption standards that cannot be rendered obsolete even when subjected to these techniques. This paper introduces one such encryption paradigm, implemented using autoencoders. The proposed algorithm trains an autoencoder to replicate an input 8-bit ASCII value and takes advantage of the bottle-neck architecture to encrypt the data in a way that can only be retrieved using the trained weights. Various analyses are utilized to exemplify the speed as well as security of the proposed cryptosystem.
Sentiment Analysis of COVID-19 Vaccines from Indonesian Tweets and News Headlines using Various Machine Learning Techniques

COVID-19 vaccine is a hot topic in online platforms due to the ongoing pandemic. Most studies on sentiment analysis of COVID-19 vaccines on Indonesian social media posts only used one or two classifiers with few modifications. This research investigated sentiment analysis using seven machine learning techniques on Twitter dataset in which the one with the highest evaluation value will be used to predict on other unlabeled Twitter datasets as well as news headlines dataset. The same classifier is also used to build a visualization dashboard that reflect the result of the sentiments. The result from the sentiment classification is then used to identify the topics, by using word cloud. The experiment revealed that SVM classifier has the highest accuracy and micro average F1-measure, which is 84% and 0.76. This classifier managed to capture similar patterns of sentiments in Twitter and news headlines datasets, which is dominated by neutral sentiment. Some of the topics from each sentiment, managed to reflect the real condition when the datasets were collected.
Online Retail Pattern Quality Improvement: From Frequent Sequential Pattern to High-Utility Sequential Pattern

There has been a change in people's shopping behavior, especially during the Covid-19 pandemic, from what traditionally requires direct face-to-face meetings between sellers and buyers, to virtual face-to-face through various shopping media. All activities carried out by customers, click streams performed, items purchased, the number of items including the price will be recorded in a log. Activity records in the log are very useful to be able to find out the pattern of activity sequences from customers, especially the order of items purchased by customers. However, the management certainly needs more knowledge, not just the order of goods that are often purchased by customers. Does the order of items purchased also provide maximum profit? There have been many methods to get frequent sequential patterns from customer activities, but getting a pattern that chooses more quality by adding utility value needs to be considered. In this research, the method used to obtain frequent sequential patterns is using PrefixSpan (Prefix-projected Sequential PAtterN) and the method used to obtain a high-utility sequential pattern is the USpan (Utility Sequential PAtterN) method. USpan is applied to the BMS (Blue Martini DataSet) dataset, which is the dataset used in KDD (Knowledge Discovery in Databases) CUP 2000 which consists of clickstream data from an e-commerce. The experimental results show that the frequent sequential pattern will always appear in the high-utility sequential pattern but not vice versa. It is certain that a high-utility pattern must be sequential, but a sequential pattern is not necessarily a high-utility sequential. From the results of the high-utility sequential pattern, it can be used as input to provide recommendations to customers to carry out the shopping process on items that can provide greater profits. The conclusion of the research conducted is that the high-utility sequential pattern mining can produce a higher quality pattern than just getting a frequent sequential pattern.
Application of artificial intelligence computer control technology in management information system

The enterprise economy integrated with artificial intelligence computer information system is an important part of the modern enterprise system. We need to do a good job in the economy of computer information system integration to perfect the system of modern enterprises. Based on this research background, the paper designs an enterprise management project management information system. At the same time, we gave the overall structure of the design system and the functional modules of each subsystem, and finally completed the system development. The various functions of this system meet the design requirements, and can improve and standardize the management strategy of the enterprise.
Deep Learning Assisted Technology for MIMO OFDM 5G Application

5G network complexity has come to a noticeable point with the help of the deep learning method. Deep learning also can optimize the performance parameter values and reduce the network cost as well. This paper delineates the deep neural network (DNN) model for the improvement of signal-to-noise ratio (SNR). Also, the channel estimation problem is focused and the BER; i.e., bit error rate is simulated by the subcarrier signals. The channel state information (CSI) is also portrayed using the robustness of the DNN-based working model. The DNN toolbox of MATLAB 2020b and Python 4.2.3 are used to construct deep learning models.
A Mobile App to Pin Media to the Real World

Smartphones have become popular also due to the innovative pervasive services they enable thanks to their sensors and ubiquitous connectivity. In this paper we present an innovative app merging the real world with the virtual one thanks to the possibility to link users&#x2019; generated media with specific geographic locations. Our app is a proof of concept built on Android OS and allows users to post geolocated media pinning them on specific coordinates. Other users can interact with these media by consuming them, leaving comments or adding new ones. Furthermore, users can contribute to extending their visibility by upvoting them, bringing a social component to the mix.
Considerations for Fitting Dynamic Bayesian Networks With Latent Variables: A Monte Carlo Study

Dynamic Bayesian networks (DBNs; Reye, 2004) are a promising tool for modeling student proficiency under rich measurement scenarios (Reichenberg, 2018). These scenarios often present assessment conditions far more complex than what is seen with more traditional assessments and require assessment arguments and psychometric models capable of integrating those complexities. Unfortunately, DBNs remain understudied and their psychometric properties relatively unknown. The current work aimed at exploring the properties of DBNs under a variety of realistic psychometric conditions. A Monte Carlo simulation study was conducted in order to evaluate parameter recovery for DBNs using maximum likelihood estimation. Manipulated factors included sample size, measurement quality, test length, the number of measurement occasions. Results suggested that measurement quality has the most prominent impact on estimation quality with more distinct performance categories yielding better estimation. From a practical perspective, parameter recovery appeared to be sufficient with samples as low as N = 400 as long as measurement quality was not poor and at least three items were present at each measurement occasion. Tests consisting of only a single item required exceptional measurement quality in order to adequately recover model parameters.
Machine Learning based Fitness Landscape Smoothing for Guiding Evolutionary Optimization

Simplifying optimization problems by smoothing their rugged and rough fitness landscapes is an effective approach to improve the performance of evolutionary algorithms (EA). However, due to the dimension curse, existing smoothing methods can only handle low dimensional fitness functions by traditional digital signal processing methods or smooth a very local region with special crossover operators. To solve this problem, we propose a methodology to smooth fitness landscapes by utilizing the low-pass filtering performance of machine learning (ML) models. Firstly, we design a framework called simplified landscape assisted evolutionary algorithm (SLEA) to utilize the fitness landscapes smoothed by ML models to guide evolutionary search. To verify its effectiveness, we design several example algorithms by combining variant ML models with the particle swarm optimization under the proposed framework and compare their optimization performances on benchmark functions. Experimental results show that by embedding an EA into the SLEA framework with suitable ML smoothing models, the global search ability of the EA in rugged and rough fitness landscapes can be improved without increasing the total computational cost.
HRS-3K: A Hybrid Encryption System Based on Matrix Computation and RSA With Disordered alphabet in ℤ/44ℤ

Data security is one of the research areas exploited by researchers today, particularly due to the emergence of the Internet, generating a large amount of data every day. Nowdays, to guarantee data confidentiality, several cryptographic systems based on Hill&#x2019;s cipher and matrix calculation use bi-graphic encryption (square matrices of order 2) with the normal alphabet modulo 26. However, this encryption method is not secure and reliable because of the linearity of its combination. This paper aims at proposing a hybrid encryption system to guarantee a better data confidentiality. This new system adds speed to the symmetric encryption based on matrix calculation, the asymmetric encryption strength proposed by RSA (Riverst, Shamir, and Adleman). The obtained results and the complexity of the new method show reliability and robustness of the proposed hybrid system.
Privacy Interpretation of Behaviour-based Anomaly Detection Approaches

This paper introduces the notion of &#x2018;Privacy-Anomaly Detection&#x2019; and considers the question of whether behaviour-based anomaly detection approaches can have a privacy semantic interpretation and whether the detected anomalies can be related to the conventional (formal) definitions of privacy semantics. The idea is to learn user&#x0027;s past querying behaviour in terms of privacy and then identify deviations from past behaviour in order to detect privacy violations. Privacy attacks, violations of formal privacy definition, based on a sequence of SQL queries (query correlations) are considered in this paper and it is shown that interactive querying settings are vulnerable to privacy attacks based on query sequences. Investigation on whether these types of privacy attacks can potentially manifest themselves as anomalies, specifically as privacy-anomalies was carried out. It is shown, in this paper, that behaviour-based anomaly detection approaches have the potential to detect privacy attacks based on query sequences (violation of formal privacy definition) as privacy-anomalies.
Industrialized Print

We now imagine ourselves to be living in an age of nearly frictionless authorship, one in which ideas – both wild and mundane – can be instantly published on a blog, in a Facebook comment, or on platforms from Reddit to Amazon that thrive on what we still awkwardly refer to as “self-published” manuscripts. Authorship – whether defined broadly as the mere production of text or more narrowly as the creation of a work of art – has become ubiquitous. Rapid developments in the field of artificial intelligence, moreover, have eerily extended authorship into the realm of objects and machines: nefarious “bots” flood Twitter streams and, in 2016, an algorithmically generated science-fiction novel advanced past the first round of cuts for the Hoshi Shinichi Literary Award.1 The origin of such ubiquitous authorship cannot be dated exactly, but its effects registered vividly on the cluttered newsstands of the 1920s, where hundreds of magazines, newspapers, paperbacks, gazettes, pamphlets, and books jostled with one another for attention.
Camera Calibration Using Catenary

In this work, we introduce a novel calibration technique based on a hanging chain curve replacing the checkerboard-based methods. It is a known physical phenomenon that a hanging chain or a flexible rope under gravity can be modeled by a special curve called a catenary. Therefore, instead of the commonly-used planar calibrator, we propose using multiple shots of a catenary-shaped chain for calibration. This approach can solve the out-of-focus problem which is faced in checkerboard calibration methods when the size of the board is not large enough. Although enlarging a planar calibrator increases the manufacturing time and cost, a simple label chain can create large planar areas as precise as a rigid checkerboard, is easily foldable and transportable. We compare the results of our proposed approach against the widely used checkerboard-based calibration as well as the state-of-the-art calibration methods and show that catenary-based calibration is much more accurate than checkerboard-based calibration by a very large margin and is also very competitive among the other approaches.
The public transport navigation system

Abstract Public transport maps are typically designed in a way to support route finding tasks for passengers, while they also provide an overview about stations, metro lines, and city-specific attractions. Most of those maps are designed as a static representation, maybe placed in a metro station or printed in a travel guide. In this paper, we describe a dynamic, interactive public transport map visualization enhanced by additional views for the dynamic passenger data on different levels of temporal granularity. Moreover, we also allow extra statistical information in form of density plots, calendar-based visualizations, and line graphs. All this information is linked to the contextual metro map to give a viewer insights into the relations between time points and typical routes taken by the passengers. We also integrated a graph-based view on user-selected routes, a way to interactively compare those routes, an attribute- and property-driven automatic computation of specific routes for one map as well as for all available maps in our repertoire, and finally, also the most important sights in each city are included as extra information to include in a user-selected route. We illustrate the usefulness of our interactive visualization and map navigation system by applying it to the railway system of Hamburg in Germany while also taking into account the extra passenger data. As another indication for the usefulness of the interactively enhanced metro maps we conducted a controlled user experiment with 20 participants. Graphical abstract
East Nusa Tenggara Weaving Image Retrieval Using Convolutional Neural Network

The popularity of East Nusa Tenggara (ENT) province is attributed to a variety of traditional woven fabrics with local cultural attributes. Each tribe in the province has its design and colors that differentiate the fabrics leading to diverse decorative motifs. Due to different varieties, it is challenging for users to know both the type of motif and its origins. In this research, several Convolutional neural network (CNN) architecture benchmarks were carried out for ENT weaving images retrieval. The image retrieval method was chosen for the study since it has feature extraction and similarity measurement, which make searching and selection relatively easier. Furthermore, the CNN method is often used for feature extraction due to its ability to recognize objects while hashing and hamming distance algorithms help reduce the computation time for similarity testing. This study was conducted by comparing several pre-trained CNN architectures, including VGG16, ResNet101, InceptionV3, and Discrete Wavelet Transform. The results showed that the highest accuracy is ResNet101 architecture with 100%, 88.50%, and 55% at top=1, top=5, and top=10, respectively. The pre-trained CNN model and Discrete Wavelet Transform combination provided better results in case the feature dimensions were above 16-bit. The feature dimensions are generally based on the best 6-bit hashing code, though they are computationally time-consuming.
A Comparative Study of Deep Learning Neural Networks in Sentiment Classification from Texts

Nowadays, text data has become the dominating medium for expressing views and opinions on various issues on online platforms. The necessity of mining information and polarity from public opinion is outgrowing. The analysis of sentiment on social networks, such as Twitter, Facebook and E-commerce websites is an effective way of learning about the users’ opinions and reviews. In recent years, it has been experimentally shown in some research works that deep learning neural networks are a promising solution to the challenges of Natural Language Processing. This research conducts a comparative study among some neural networks such as LSTM, Bi-LSTM, GRU-CNN and the combination of CNN and RNN. Two datasets from different domains have been used: The IMDB Movie Review (50 k), which is a benchmark dataset and an Amazon Product Review (10 k) dataset. Amazon is also a standard platform for collecting datasets. Analyzing the experimental results, we realize that CNNs can sometimes outperform RNNs. Also the comparative performance may differ in different domains and datasets.
Assessing a Voice-Based Conversational AI prototype for Banking Application

Conversational AI has tremendous potential in different application domains due to its rapid development and improved accuracy in recognizing natural languages. Researchers have developed numerous applications and have shown state-of-the-art results. However, acceptability by users for such Conversational AI applications is imperative for successful deployment. This paper aims to assess a Conversational AI for a banking application in terms of usability, attractiveness, and intuitiveness. For this purpose, two different prototype versions were developed with varying dialog design and visual backgrounds. The experiment was conducted by letting 40 participants interact with the prototype versions, exploiting the Wizard-of-Oz (WoZ) paradigm, and administering three questionnaires to measure their perception of the Conversational AI prototype. Qualitative and Quantitative assessment of the questionnaires suggests that the Conversational AI prototype is highly usable, attractive, and intuitive, providing evidence that users will appreciate such Conversational AI in banking applications.
Grammar-Based Vectorial Genetic Programming for Symbolic Regression

Vectorial Genetic Programming (GP) is a young branch of GP, where the training data for symbolic models not only include regular, scalar variables, but also allow vector variables. Also, the model’s abilities are extended to allow operations on vectors, where most vector operations are simply performed component-wise. Additionally, new aggregation functions are introduced that reduce vectors into scalars, allowing the model to extract information from vectors by itself, thus eliminating the need of prior feature engineering that is otherwise necessary for traditional GP to utilize vector data. And due to the white-box nature of symbolic models, the operations on vectors can be as easily interpreted as regular operations on scalars. In this paper, we extend the ideas of vectorial GP of previous authors, and propose a grammar-based approach for vectorial GP that can deal with various challenges noted. To evaluate grammar-based vectorial GP, we have designed new benchmark functions that contain both scalar and vector variables, and show that traditional GP falls short very quickly for certain scenarios. Grammar-based vectorial GP, however, is able to solve all presented benchmarks.
Real-Time Mobile Application for Handwritten Digit Recognition Using MobileNet

Real-time mobile application for handwritten digit recognition helps children learn how to write a single digit at their own pace, in a fun and straight forward approach. It can improve and strengthen the children's knowledge and skills in writing digits. One of the problems children have in learning how to write digits is mirror-writing in which the digits or numbers are written as if they are a reflection from a mirror. Convolutional Neural Networks (CNNs) have shown tremendous performance on mobile devices, including Android and iOS, with low computational cost and yet producing high recognition accuracy. Two popular CNNs for mobile applications are MobileNet and ShuffleNet. This project reports our preliminary investigation, comparing the real-time mobile application performance of MobileNet and ShuffleNet for handwritten digit recognition. The preliminary experiment involving training these models with some randomly selected images from the MNIST dataset indicates that MobileNet produces the accuracy of 0.9442 while ShuffleNet only achieves 0.6883. Thus, our mobile application employs MobileNet for the handwritten digit recognition with a simple and user-friendly interface to be tested by children of four to six years old. In this application, children can write using one of their fingers or a stylus using the mobile phone. The findings show that MobileNet is beneficial for a real-time mobile application for children to learn writing digits.
Channel Treatment for Internet Addiction with Puskesmas Application: Design Approach with Usability Heuristics

Developed since 1968, Puskesmas is the most important healthcare facility and is at the forefront of providing basic healthcare services at the community level. Basically, its role is very important and should be one of the keys to Indonesia’s success in improving the health and nutritional status of the community. Meanwhile, Internet addiction become potential threat to the community that might be more dangerous compare to drug and sex addiction in term of social consequences. This study offer the integrated solution through usability heuristics design for the purpose of redesigning current apps after the identification of business process in several Puskesmas in the Bandung. It also want to increase the quality of service in order to provide proper communication between health workers and patients. In evaluation phase, the 4 criteria was used to generate sketches in relation to contextual settings. Then, the mockups will be assessed with 12 transaction features to ensure the readiness of its financial management before the application launching.