Reviewerly/exHarmony huggingface dataset:
Democratizing AI for Condition-Based Maintenance Leveraging Probabilistic Programming for Symbolic Reasoning

SUMMARY AND CONCLUSIONSAdvances in AI/ML have demonstrated enormous potential in improving and optimizing condition-based maintenance processes; however, AI/ML solutions themselves inevitably become a maintenance liability, wherein the end users must repeatedly work with a data scientist to update the AI/ML application. In this paper, we present novel research that alleviates this problem by enabling non-AI/ML experts to create and maintain AI/ML applications with minimal guidance from a data scientist. We describe this research as the "Democratization" of AI/ML and accomplish this by leveraging cutting edge techniques in knowledge capture and probabilistic programming as a way to represent higher level symbolic reasoning and delegating sub-symbolic reasoning to traditional data driven approaches.
A Generative Reinforcement Learning Framework for Predictive Analytics

SummaryIn this work, we present a new approach for latent system dynamics and remaining useful life (RUL) estimation of complex degrading systems using generative modeling and reinforcement learning. The main contributions of the proposed method are two-fold. First, we show how a deep generative model can approximate the functionality of high-fidelity simulators and, thus, is able to substitute expensive and complex physics-based models with data-driven surrogate ones. In other words, we can use the generative model in lieu of the actual system as a surrogate model of the system. Furthermore, we show how to use such surrogate models for predictive analytics. Our method follows two main steps. First, we use a deep variational autoencoder (VAE) to learn the distribution over the latent state-space that characterizes the dynamics of the system under monitoring. After model training, the probabilistic VAE decoder becomes the surrogate system model. Then, we develop a scalable reinforcement learning framework using the decoder as the environment, to train an agent for identifying adequate approximate values of the latent dynamics, as well as the RUL.To our knowledge, the method presented in this paper is the first in industrial prognostics that utilizes generative models and reinforcement learning in that capacity. While the process requires extensive data preprocessing and environment tailored design, which is not always possible, it demonstrates the ability of generative models working in conjunction with reinforcement learning to provide proper value estimations for system dynamics and their RUL. To validate the quality of the proposed method, we conducted numerical experiments using the train_FD002 dataset provided by the NASA CMAPSS data repository. Different subsets were used to train the VAE and the RL agent, and a leftover set was then used for model validation. The results shown prove the merit of our method and will further assist us in developing a data-driven RL environment that incorporates more complex latent dynamic layers, such as normal/faulty operating conditions and hazard processes.
Containerizing Fault Detection and Fault Isolation: A Pathway To Prognostics And Health Management

SUMMARY & CONCLUSIONSDiscussions around the digital transformation of reliability and maintainability practices provide challenging objectives, opposing points of views, and many complex and intimidating aspects. This paper seeks to break down some of these concepts to help shed light on digital transformation, by introducing technology that brings together the capability of Fault detection / Fault Isolation and Containerization of software. This paper will give a baseline concept for how Prognostics and Health Management is achievable, and how this capability can be a great step towards this goal.The utilization of Fault detection and Fault isolation (FD/FI) capabilities is the perfect starting point to examine the use of containerization. When this process is combined with emerging technology, this use case will help define the link to enable prognostics and predictive health management within the reliability and maintenance Industry. It is just a single use case, but through its adaptation into digital transformation it will allow the understanding of how rapidly these new technologies can provide capability.The standard for producing Fault Detection / Fault Isolation has been a practice that has been long utilized across many industries, with the goal of rooting the capability directly into the design of the product, and have it work harmoniously with the core of the operation. This paper will also discuss some of the practices of the past and how these practices can be improved upon. This paper shares new concepts that when applied to a simple use case like FD/FI, will shed light on the pathway needed to produce prognostics and health management.
How to Compare Sets of Repair Data

SUMMARY & CONCLUSIONS This article has two purposes: (1) to provide a brief introduction to repair data analysis and (2) to propose needed methods for comparing two or more samples of repair data with respect to their Mean Cumulative Functions (MCFs, defined below). Available nonparametric methods of Nelson [1] allow only pointwise comparison of MCFs at a single specified age t'. This paper provides two nonparametric and two nonhomogeneous Poisson process comparisons of two sample MCFs over the entire range of their data, where the data may be the number or costs of repairs. The methods are illustrated with repair data on automatic and manual car transmissions.
Deep Reinforcement Learning in Maximum Entropy Framework with Automatic Adjustment of Mixed Temperature Parameters for Path Planning

Deep reinforcement learning in maximum entropy framework is sample-efficient and has a strong exploration capacity, making it effective and favorable to solve problems like path planning. Properly tuning the temperature parameters can improve the performance of policy learning, but manual tuning is inefficient. In this paper, we propose a mixed algorithm named SAC-M which is inspired by adaptive soft actor-critic (A-SAC) and soft actor-critic with automatic entropy (SAC-A). The proposed method achieves automatic adjustment of temperature parameters so that the entropy can vary among different states to control the degree of exploration, reducing the possibility of learning suboptimal policies to some extent. The experimental results illustrate that the proposed SAC-M outperforms A-SAC and SAC-A in path planning tasks in different scenes, especially when A-SAC and SAC-A are mixed in a proper ratio.
Phishing Website Detection using XGBoost and Catboost Classifiers

Malicious websites infecting users' devices has become a common phenomenon. Users seldom pay attention to the URL details and ultimately fall prey to these websites, which results in personal data theft. Attackers try to mimic legitimate URLs making it difficult to identify them. Therefore, it is imperative to identify these websites. In this paper we evaluated the performance of XGBoost and Catboost: tree-based classifiers in detecting these phishing websites. Both the classifiers performed well in terms of accuracy. The result particularly shows the XGBoost performing slightly better than Catboost. We analyzed these classifiers over two datasets. To strengthen this outcome, k-fold validation as well as train-test validation are used in this study. Furthermore, we compared the performance of XGBoost and Catboost with other conventional classifiers.
Intelligent ResNet-18 based Approach for Recognizing and Assessing Arabic Children's Handwriting

Studies tackling handwriting recognition and its applications using deep learning have been promoted by developing advanced machine learning techniques. Yet, a shortage in research that serves the Arabic language and helps develop teaching and learning processes still exists. Moreover, COVID-19 pandemic affected the education system considerably in many countries and yielded an immediate shift to distance learning and extensive use of e-learning tools. An intelligent system was proposed and used in this paper to recognize isolated Arabic handwritten characters. Particularly, pre-trained CNN models were exploited and fine-tuned to meet the requirements of the considered application. Specifically, the designed system automatically supports teaching Arabic letters and evaluating children's writing skills. The Arabic Handwritten Character Dataset (AHCD) was used to train the models built upon ResNet-18 and assess the overall system performance. Furthermore, several models were investigated using various hyper-parameter settings in order to determine the most accurate one. The best model with the highest accuracy rate of 99% was used and integrated into the proposed system to recognize the Arabic alphabets.
Adaptive Batch Normalization for Training Data with Heterogeneous Features

Batch Normalization (BN) is an important preprocessing step to many deep learning applications. Since it is a data-dependent process, for some homogeneous datasets it is a redundant or even a performance-degrading process. In this paper, we propose an early-stage feasibility assessment method for estimating the benefits of applying BN on the given data batches. The proposed method uses a novel threshold-based approach to classify the training data batches into two sets according to their need for normalization. The need for normalization is decided based on the feature heterogeneity of the considered batch. The proposed approach is a pre-training processing, which implies no training overhead. The evaluation results show that the proposed approach achieves better performance mostly in small batch sizes than the traditional BN using MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets. Additionally, the network stability is increased by reducing the occurrence of internal variable transformation.
Multi-culture Sign Language Detection and Recognition Using Fine-tuned Convolutional Neural Network

Communication through hand gestures is an active research topic in computer vision and Human Computer Interaction (HCI). Deaf and speech-impaired community may benefit from the advancement of sign gesture recognition as an intermediary visual communication technique. There is no universal sign language, but different nationalities have their own sign languages. Usually sign gestures ought to be distinct and independently distinguishable and researchers in this domain have always focused on techniques for a particular sign language. However, recognizing sign languages from multiple nations could be helpful in various situations such as dealing with multiple signing persons without knowing their nationalities. This paper presents a novel approach where a two-phased system is designed based on deep learning for identification of the sign language and recognition of the sign gesture. This approach opens a new research direction for the advancement of multi-sign language development. The proposed approach has been implemented and evaluated using two custom-based CNN models and compared to some other popular CNN models. More than 98% accuracy can be achieved when tested on two different datasets.
Enhancing Data Security in the Cloud Platform by Encrypting the Used Key Dynamically

The smart city is a basic part of the future. It has many advantages on different levels. However, the most concerning aspect is the sensitivity of its data, which faces several risks that could harm individuals' lives. This paper focused on the shortages in the preservation and handling of the encryption key. In addition, to propose an approach that aims to enhance the application of encryption in the cloud platform, which is one of the technologies that are used to store the data of smart city, since most studies indicated that encryption is the most recommended security mechanism. The current paper provided a brief description of the smart city and its related topics. The experimental method was adapted, and the experiment of the study included two stages, which are testing and building. And through the experiment, the performance of AES (Advanced Encryption Standard) and Blowfish encryption algorithms was investigated by applying them on multiple Word documents. Eventually, it was found that AES had consumed 54%, 47%, and 52% of the consumed time by Blowfish in the first round, and 32%, 44%, and 39% in the second round, respectively, in each document encryption process. Hence, AES was employed in the proposed approach, which suggests encrypting the stored data in the cloud, then encrypting the used key dynamically by using a constantly changing generated key based on a specified period of time. By adapting the encryption with a Dynamic Key (DK), the level of data security increases, which was confirmed by a variety of reviewed literature.
Blockchain-Based Online E-voting System

Recently, blockchain technology has become popular. Through a highly secure, decentralised system enabled by this technology, anyone can interact securely without the need for a middleman. Machine learning can help with many of the constraints that blockchain-based systems have in addition to its own strengths. Blockchain technology and machine learning together have the potential to produce very effective and beneficial outcomes. Hence, when blockchain and ML converge, they surely would benefit from each other. Blockchain can enhance the security of ML platforms, and ML can provide automation and optimization to blockchain solutions. In this work, we advocate the importance of enhancing blockchain with ML algorithms, as a proof of principle we address the issue of secure and intelligent e-voting. The use of blockchain technology has brought tremendous different application domains, e-voting is one of them. Most existing e-Voting systems require central authority during the process of authentication and verification of the voter. In this paper, we propose a safe online voting approach based on blockchain and ML to provide a solution to this issue. We use blockchain to ensure integrity and transparency of the votes, and ML for automating the verification process of eligible voters based on AI- for face authentication. The proposed solution offers automation, security, and mobility to the voting system.
AraDS: Arabic Datasets for Text Mining Approaches

With the digital transformation, life started depending on the digital world. Hence, there is a massive amount of unstructured textual data produced and accumulated faster. Such data used in many applications such sentiment analysis, topic modeling, summarization, classifications, and clustering. However, researcher's wastes time and effort in collecting data and constructing a dataset to examine and evaluate their models or algorithms. In the Universal Language of English, there are many benchmark datasets. The Arabic language lacks datasets in many domains. This paper introduces an Arabic dataset called AraDS. AraDS consist of three Arabic datasets namely; Arabic Dataset on Herbal Treatments for Diabetes (ADHTD), Arabic Multi-Classification Dataset (AMCD), and the Arabic Sentiment Dataset - Khat (ASDK). These datasets were collected from social media platforms such as YouTube. It contains user-generated comments and video metadata. AraDS publicly available dataset. The assessment of the annotation process has been carried out and evaluated using four methods. They are Cohen's kappa, three Arabic native speaker annotators, accuracy, and F-measure. AMCD and ASDK datasets are balanced, whereas ADHTD is an imbalanced dataset. Two datasets are sided with binary classes and the third one is a multi-class dataset. These datasets can be beneficial for many data, text mining, and sentiment analysis researchers to apply methods and algorithms.
Synchronized Speech and Video Synthesis

The paper proposes a method to generate expressive talking head videos artificially in any context by synthesizing and syncing speech and video, with the only provided inputs of a facial image and an audio. The method allows the audio either to be provided directly in a pre-recorded format, or to be synthesized using a Text-to-Speech architecture from the textual input words provided. In contrast to previous methods, emphasis has been on generating more expressive, lively and appealing talking head videos through enhanced facial expressions, head dynamics and naturally enhanced audio. The proposed method works by training Long Short Term Memory (LSTM) and Multi-Layer Perceptron (MLP) networks to disentangle content and speaker identity from the input audio for generating facial landmarks dependent on these representations, in contrast to directly mapping audio with the pixels. The disentangled audio content enhances the lip movements and facial regions motions, whereas the speaker information enhances expressions and head dynamics. The method also proposes the prediction of facial landmarks, allowing enhanced, faster and more appropriate head dynamics. The paper also presents extensive qualitative and quantitative evaluation of the proposed methodology, and demonstrations of some generated talking head videos in comparison to previous methods.
Efficient Policy Learning for General Robotic Tasks with Adaptive Dual-memory Hindsight Experience Replay Based on Deep Reinforcement Learning

Deep reinforcement learning (DRL) features powerful ability of perception and decision-making, which is reward-driven and learns strategies through the interaction between the agent and the environment. However, the discrete reward mechanism makes it difficult for DRL to obtain positive feedback in the early stage of interaction, resulting in low learning efficiency. The hindsight experience replay (HER) mechanism can improve the deficiency of the discrete reward, but it also causes a lot of data redundancy. This paper proposes an adaptive dual-memory hindsight experience replay structure. The success rate of the algorithm can be improved while the training efficiency can be ensured by using the dual-memory bank structure to split the empirical data and adjusting the proportion of HER mechanism. The proposed method is applied to the DRL algorithm and verified on a 7-DoF robot, and experimental results show that the algorithm has good performance.
Upper bounds of complexity of quantum oracle for problem of finding shortest vector on integer lattice

Due to the development of quantum computing, there is a need for development and analysis of cryptosystems resistant to attacks with using a quantum computer - post-quantum algorithms cryptography. The strength of many well-known post-quantum cryptosystems, based on the theory of lattices, is based on the complexity of solving the problem finding the shortest vector in the lattice (SVP). For the previously proposed models of the quantum oracle used in the hybrid quantum-classical algorithm for solving the SVP problem, new refined estimates of implementation complexity. Designed and analyzed a new quantum oracle model that uses classical memory to storing a list of vectors.
Looking Back to the Future: A Glimpse at Twenty Years of Data Science

This paper carries out a lightweight review to explore the potentials of data science in the last two decades and especially focuses on the four essential components: data resources, technologies, data infrastructures, and data education. Considering the barriers of data science, the analysis has been mapped into four essential components, highlighting priorities and challenges in social and cultural, epistemological, scientific and technical, economic, legal, and ethical aspects. As a result, the future development of data science tends to shift toward datafication, data technicity, infrastructuralism, and data literacy empowerment. The data ecosystem, at the macro level, has also been analyzed under the open science umbrella, providing a snapshot for the future development of data science.
Style, Taste, Trend Perceptions, Statements and Misunderstandings in Design Theoretical Discourse

The striking and contemporary conditions in the life of everyone, the needed massive replacement of in-person meetings and events have seen a super-exposure of private and nested contexts. More than ever, the concepts’ perception of style, taste and trend comes to be interwoven with the personal narrative and ‘adjectivation’ in the loss or oblivion of what these concepts scholarly bring alongside. The theoretical discourse aims to nourish the understanding of the connection between design application and its society, through the observation of the role of trends’ and taste’s evolutions as the natural processes of time passing. Old and new behaviours and paradigms in oneself depiction, through design choices for the personal environment, help in grasping which direction society is going to and which might be the challenges in design theory for the next and the far future. What seems to be only an academic debate about literal statements becomes pivotal when the original concepts go so far from the original definition till turn themselves into misunderstandings. The issue of orienting society’s taste and angling self-representation cannot be unhinged from objects’ choices and owns’ story. The matter becomes nodal when the channel is under everyone’s eyes, as it is with the most known social media, and rated as sacred according to the sharing platform or the contributor. If the historical exactness and literal statement give way to shared narratives and personal perception, how to handle in the ‘right’ manner the role of visualization within social media?
What Images Say/What Users See. Exploring Mobile Augmented Reality for Visual History-Telling of Architectural Heritage

Aim of the paper is the study of the potentialities offered by augmented reality visualizations of 3D models to communicate the history of architectural heritage, also for its valorization. The case study is the Basilica of Collemaggio in L’Aquila (IT). The church was the subject of a stylistic restoration that returned the building to an ideal former medieval appearance. Based on the digital survey of the current church, a 3D model of the baroque structure of the main nave, which no longer exists, is created by studying the graphic and photographic documentation of the period. An augmented reality application is developed at the University of L’Aquila, and used with the aim of visualising the 3D model to tell the story of the Basilica, so that superimposed information can be displayed dynamically and in real time. Young people aged around 20 years were asked to navigate the visualisations and then to answer a form with questions in order to evaluate the effect of computer based visualisations. The answers are analysed, and in particular it can be seen that while there is not a high level of explicit awareness of digital tools and techniques, there is clearly a habit of frequenting digital environments. Visualizations in augmented reality arouse particular interest.