Reviewerly/exHarmony huggingface dataset:
Convolutional and Recurrent Neural Networks for Opinion Mining on Drug Reviews

Online health forums are places where patients share their experiences about their disease(s), treatment(s), etc. Under the cover of anonymity, they express their personal experiences very freely. These forums are therefore a very useful source of information for healthcare professionals to better identify and understand the problems, behaviors, and sentiments of their patients. In this study, our goal is to unveil the secrets of sentiment analysis on drug reviews using deep learning-based approaches. More specifically, we focus our research on genericity, to reduce human intervention in the excavation process. We, therefore, explore the problem of opinion mining using methods based on deep convolutional and recurrent neuronal networks like CNN, LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and hybrid models to reach complementarity, instead of standard methods which require a priori resources such as vocabulary, and sentence structure. In this work, we attempt to extend the genericity of our process by reducing this need for resources a priori.
Modified-PIP with Deep Neural Network (DNN) Architecture: A Coherent Recommendation Framework for Capturing User Behaviour

The use of online services such as e-commerce is rapidly growing, resulting in an exponential rise in the amount of information that is available. Recommender systems assist consumers in selecting relevant information from a vast pool of alternatives. Furthermore, varied recommendation contexts have their own challenges, necessitating the use of diverse recommendation approaches. Collaborative filtering techniques have been predominantly used owing to their high accuracy, simplistic approach, and low computational needs, whereas content-based filtering faces the problem of high computational needs. This chapter aims to capture the behaviour of users using a modified Proximity Impact Popularity (Modified-PIP) technique augmented with Deep Neural Network (DNN) to make efficient movie recommendations. The dominance of Modified-PIP over other similarity measures such as PIP similarity, Cosine similarity, and PCC (Pearson Correlation Coefficient) is tested on the MovieLens-100K, MovieLens-1M, and Jester datasets. A matching imputation technique is applied to reduce the sparsity of the dataset. The performance of the proposed Deep Neural Network (DNN) architecture in conjunction with the Modified-PIP similarity measure is evaluated using RMSE (root mean squared error), MAE (mean absolute error), R (recall), and P (precision). Low error scores obtained by the proposed model give justification for the high efficacy of the proposed model.
Social Sentiment Analysis Using Features Based Intelligent Learning Techniques

In these days, sentiment analysis has become increasingly popular. It examines how people feel about organizations, individuals, products, and services. Opinions from customers are collected through various resources such as social media networks, surveys, and forms. Social sentiment analysis is quite useful in determining how the product or brand is regarded in comparison to the competitors. Business organizations make use of reviews given by the customer to enhance product quality. In this study, sentiment analysis is applied to a variety of datasets, including Twitter sentiment Analysis, Internet Movie Database (IMDB) movie reviews, and Amazon mobile reviews. Desired aspects are extracted from the datasets, and the sentiment polarity of the aspects is identified. The sentiment associated with the reviews can have a positive, negative, or neutral tone. Various intelligence learning techniques are considered for the classification of sentiment. Parameters such as recall, precision, and accuracy are considered in order to estimate performance of the classification algorithms.
The spread of electronic blogs on the Internet and the circulation of news through there platforms .. Twitter as a model

This research deals with the importance of blogging and its spread, as it has become of great importance in the content industry and the transfer of news and information through it to many users over the Internet, through which experiences and opinions are exchanged, which creates an interactive virtual environment between users, and the most important research questions are: What is the importance of Electronic blogs in dealing with news, what topics are covered through electronic blogs, what is the importance of Twitter in news circulation.. Through the study, the researcher sought to identify the importance of electronic blogs, and the research is considered a descriptive research. The study reached a set of results, the most important of which are: the importance of electronic blogs in creating interactive environments between users via the Internet, what topics are addressed through blogs, and the importance of Twitter in transmitting news.
Public Key Protocols over Skew Dihedral Group Rings

This paper introduces skew dihedral group rings and their applications for public-key cryptography. We present a specific skew group ring that is the underlying algebraic platform for our cryptographic constructions. We then build a two-party key exchange protocol and present an analysis of its security. We then exploit it to derive a group key agreement protocol, a probabilistic public-key scheme, and a key encapsulation mechanism. In addition to the security analysis of our cryptographic constructions, we present a proof-of-concept implementation.
Building Trust in Business Ecosystems: The Interplay of Technology and Community in Governing Data Sharing

Data sharing remains one of the biggest challenges for emerging industrial ecosystems. Lack of trust, and perceived risks are holding companies back, as no one wants to go first and get it wrong. At the core of this challenge is a lack of understanding how data sharing could, and should be governed in business ecosystems. Despite growing scholarly interest in data sharing and data markets, research on the governance mechanisms of data sharing is scarce. In this paper, we analyze the governance of data sharing in two emerging business ecosystems. Based on our findings, we elaborate on the complementary role of community- and technology-based governance mechanisms. We discuss our findings in light of emerging industry 4.0 business ecosystems.
A Rubric for Implementing Explainable AI in Production Logistics

With the advent of Industry 4.0, the world is witnessing increasing use of data and data-driven services. This phenomenon has penetrated through different sectors of production including logistics. The purpose of this study is to explore the use of Artificial Intelligence (AI) and Machine Learning (ML) in production logistics. This paper is the first step in the direction of understanding the complexity of AI and ML algorithms and thus explaining the black-box-like characteristics of these algorithms. This is coupled with the definition of eXplainable AI (XAI) in the domain. The paper furthers describes the needs for XAI and consequently presents a rubric for implementing XAI in the domain of production logistics and discusses it in detail.
Two-Headed Chicken by Tom Angleberger

Reviewed by: Two-Headed Chicken by Tom Angleberger Kate Quealy-Gainer, Editor Angleberger, Tom Two-Headed Chicken; illus. by Joey Ellis. Walker, 2022 [208p] Trade ed. ISBN 9781536323217 $12.99 Reviewed from digital galleys Ad Gr.1-4 The mere existence of a two-headed chicken is a wacky premise in itself, but in Angleberger's goofy graphic novel, that anomaly is just one of many in the multiverse. One of the chicken heads is not especially bright (though quite funny), but fortunately the other is smart enough to have invented the Astrocap, which allows them to escape an angry moose, Kernel Antlers, who is intent on frying them for a tasty snack. The contraption takes them through the multiverses, and they become mer-chickens, wizards, and even the Mona Lisa, depending on the logic (or illogic) of their current universe. Kernel Antlers is persistent, however, and eventually the chicken must put its heads together and face its antlered predator. The story starts off the rails then gleefully freefalls into a chasm of chaos, and the jokes (some of which will likely go over young readers' heads) come at a frenetic pace. The energy is initially balanced with commentary from a mustachioed fish [End Page 40] and other direct address asides, but even the chicken heads acknowledge the lack of plot; eventually, the multiverse travels begin to feel like an unending stream of unconnected TikTok videos, amusing but overwhelming. Shifting panel shapes zip readers through the chickens' travels, and the thick lines and exaggerated expressions call up Dav Pilkey's Dog Man, though with a bit more visual detail. Fans of that title or Scieszka's Astronuts (BCCB 10/19) who are willing to overlook the loose narrative and lose themselves in the medley of jokes might appreciate the intergalactic adventures here. Copyright © 2022 The Board of Trustees of the University of Illinois
Evaluation of Potential Benefits of Augmented Reality for Industrial Services

Augmented reality seems to offer great potential benefits in the field of industrial services. However, the question of the exact benefits, both monetary and qualitative, is difficult to evaluate, as is the case with IT investments in general. Within the framework of the DM4AR research project, an evaluation model was therefore developed. Based on group discussions and interviews on potential AR use cases, a list of monetary and qualitative benefits was compiled to form the basis for selecting suitable evaluation modules in the existing literature. These include an impact chain analysis in the form of a strategy map, a monetary evaluation as a calculation of the return on investment, based on the assumptions of the use case as well as existing studies, and a qualitative evaluation in the form of a utility analysis. The outcome is an evaluation model in the form of a multi-perspective approach that considers the impact of AR in the four perspectives of the balanced scorecard (financial, customer, internal business processes, learning and growth). The results of the qualitative and monetary evaluation can be summarized in a 2D matrix to support decision-making.
A Fault Detection Method for Power Systems Based on Random Matrix Theory

The safety and stability of power systems are closely related to industrial production and people’s livelihood [1–3]. Timely and efficiently detecting the faults can prevent the further expansion of the faults and avoid serious consequences [4]. As a result, the security and stability of power systems have long been the focus of power researchers.
A hyper-heuristic based reinforcement-learning algorithm to train feedforward neural networks

Artificial Neural Networks (ANNs) offer unique opportunities in numerous research fields. Due to their remarkable generalization capabilities, they have grabbed attention in solving challenging problems such as classification, function approximation, pattern recognition and image processing that can be quite complex to model mathematically in practice. One of the most vital issues regarding the ANNs is the training process. The aim at this stage is to find the optimum values of ANN parameters such as weights and biases, which indeed embed the whole information of the network. Traditional gradient-descent-based training methods include various algorithms, of which the backpropagation is one of the best-known. Such methods have been shown to exhibit outstanding results, however, they are known have two major theoretical and computational limitations, which are slow convergence speed and possible local minima issues. For this purpose, numerous stochastic search algorithms and heuristic methods have been individually used to train ANNs. However, methods, bringing diverse features of different optimizers together are still lacking in the related literature. In this regard, this paper aims to develop a training algorithm operating based on a hyper-heuristic (HH) framework, which indeed resembles reinforcement learning-based machine learning algorithm. The proposed method is used to train Feed-forward Neural Networks, which are specific forms of ANNs. The proposed HH employs individual metaheuristic algorithms such as Particle Swarm Optimization (PSO), Differential Evolution (DE) Algorithm and Flower Pollination Algorithm (FPA) as low-level heuristics. Based on a feedback mechanism, the proposed HH learns throughout epochs and encourages or discourages the related metaheuristic. Thus, due its stochastic nature, HH attempts to avoid local minima, while utilizing promising regions in search space more conveniently by increasing the probability of invoking relatively more promising heuristics during training. The proposed method is tested in both function approximation and classification problems, which have been adopted from UCI machine learning repository and existing literature. According to the comprehensive experimental study and statistically verified results, which point out significant improvements, the developed HH based training algorithm can achieve significantly superior results to some of the compared optimizers.
A Fault Location Method for Power Systems Based on Random Matrix Theory

Fault location is an important prerequisite to ensure the safety and stability of power systems [1]. Traditional model-based methods are facing new challenges with the development of new power systems, of which the scale is continuously expanding and the structure is tending to be more and more complex [2–4]. On the other hand, the digital construction of power systems and the introduction of wide area measurement system (WAMS) shed the light on fault location for power systems.
Mediated Authorships: The Designer as the Instructor of Machines

The implementation and use of artificial intelligence (AI), and automatic learning in particular, are ushering in a methodological and conceptual updating of creative practices and their associated theory. The roots of these changes lie in disciplinary traditions associated with research into automatism promoted in the 1960s, with the introduction of cybernetics and computational sciences. This article focuses on the spread of the notion of authorship in architecture in the digitalization process and takes the discussion of systematic automatism in home design as a case study. For doing so it revises the main theoretical references, particularly the work by Gordon Pask, unfolds projects from the 60 s to post the main questions raised by AI today in the Design Field in respect to Authorship. While Gordon Pask suggested the transformation of the architect into a systems designer, now we can state that this is an environment in which the architect is transformed into an educator and a critic, an educator in the sense that they have to design the datasets, or conditions of possibility of learning; they have to instruct the generative and discriminative neural networks through numerous groups of “examples” and guide this learning through weighted assessment protocols.
Digital Security Narratives in the Time of COVID-19: A Case for Kindness

The COVID-19 pandemic has shined a light on the digital divide and its implications in a digital-first society. In the UK, where our research is focused, parts of society still lack the infrastructure and/or basic skills needed to access essential online services like health, welfare, food, housing and education. During the pandemic, these services became digital by necessity, forcing many people to seek help through informal networks such as community hubs. Based on our focus groups and interviews with voluntary and third sector organisations in the UK, we make a case in this chapter for a kinder, more holistic approach to the accessibility of essential online services, based on the hypothesis that such an approach creates the types of spaces in which the benefits of such services can be more safely realised.
Bearing Fault Diagnosis of Wind Motor Based on Shared-FedAvg with Non-IID Data

Different Wind motors have great differences in operating status and fault cases in distributed wind farms, data of which is of Non-IID. Using Non-IID data for training will reduce the accuracy of the model. Bearing usually causes pitting corrosion, rolling body peeling, cracks, and other defects, worsening the performance of wind motor [1]. Fault diagnosis of wind motor bearings can judge the operation of the wind motors, eliminate hidden trouble, and avoid more economic losses.
Re-Engineered Word Embeddings for Improved Document-Level Sentiment Analysis

In this paper, a novel re-engineering mechanism for the generation of word embeddings is proposed for document-level sentiment analysis. Current approaches to sentiment analysis often integrate feature engineering with classification, without optimizing the feature vectors explicitly. Engineering feature vectors to match the data between the training set and query sample as proposed in this paper could be a promising way for boosting the classification performance in machine learning applications. The proposed mechanism is designed to re-engineer the feature components from a set of embedding vectors for greatly increased between-class separation, hence better leveraging the informative content of the documents. The proposed mechanism was evaluated using four public benchmarking datasets for both two-way and five-way semantic classifications. The resulting embeddings have demonstrated substantially improved performance for a range of sentiment analysis tasks. Tests using all the four datasets achieved by far the best classification results compared with the state-of-the-art.
The link between learner performance in early reading literacy and what is happening in the Grade 1 classroom

Background: Developing reading skills depends on how learners are taught to read in the classroom. Previous research showed that academic achievement is strongly affected by pedagogical content knowledge and lack of appropriate professional development.Aim: This article aims to examine the relationship between learner performance in early reading literacy and what is happening in the Grade 1 classroom.Setting: The study was conducted in five different schools in Mopani district in the Limpopo province.Methods: An exploratory mixed methods research design was used, where quantitative and qualitative methods were used for data collection and analysis. Five teachers who teach reading in Grade 1 classrooms were observed. Seventy-five Grade 1 learners were assessed twice (March and September 2018) in Xitsonga as the home language and as the first additional language using the early grade reading assessment toolkit.Results: The quantitative results showed an improvement in scores from baseline to endline; however, this was not good enough, given that performance in all the reading measures was low and very slow, reflecting inadequate mastery of foundational reading skills by the end of Grade 1. The qualitative findings provided some insights into how this comes about. Although teachers made efforts to implement reading methodologies in line with the national curriculum, they showed lack of understanding of how early reading develops and how each of the reading activities contributes to different aspects of this development.Conclusion: The present teachers’ understanding of early reading development and their enactments of reading in Grade 1 classrooms are insufficient to develop grade-level readers by the end of the year. Recommendations are made to help teachers improve their classroom practices.
Encrypted Transfer of Traffic Sign Information for Advanced Driving Assistance Systems Using Invisible Security Patches

Adversarial attacks pose serious traffic safety risks that could potentially impede the deployment of automated driving systems (ADS). Recent discoveries show that imperceptible perturbations (also referred to as adversarial attacks) to inputs of deep neural networks employed in these systems could impair their image recognition and prediction capabilities. Adversarial attacks can misguide advanced driving assistance systems (ADAS) to misclassify a traffic sign (e.g., classify a stop sign as a speed limit sign), causing the vehicle to accelerate instead of stop, which may potentially result in catastrophic incidents. This paper investigates encrypted invisible security patches placed on traffic signs and an onboard machine-vision system that can be integrated into the ADAS (i.e., ADS and cooperative adaptive cruise control systems). The security patches are essentially a visual representation of encrypted hash values generated via a one-way cryptographic algorithm. In this way, every traffic sign will be associated with a unique security pattern that can only be recognized by the proposed machine-vision technology. Additionally, these patches will be made invisible with the help of ultraviolet reflective sign technology. The proposed methodology aims to bring an additional security layer to sign recognition to enhance the reliability of ADAS. If a traffic sign is physically altered to attempt an adversarial threat, the security patch on the sign will not match with the sign information, and the driver will receive a warning to take control of the driving. For self-driving vehicles, a signal will be sent to the autonomous vehicle to enforce a safe driving action.
Acoustic Model with Multiple Lexicon Types for Indonesian Speech Recognition

Currently, speech recognition datasets are increasingly available freely in various languages. However, speech recognition datasets in the Indonesian language are still challenging to obtain. Consequently, research focusing on speech recognition is challenging to carry out. This research creates Indonesian speech recognition datasets from YouTube channels with subtitles by validating all utterances of downloaded audio to improve the data quality. The quality of the dataset was evaluated using a deep neural network. The time delay neural network (TDNN) was used to build the acoustic model by applying the alignment data from the Gaussian mixture model-hidden Markov model (GMM-HMM). Data augmentation was used to increase the number of validated datasets and enhance the performance of the acoustic model. The results show that the acoustic model built using the validated datasets is better than the unvalidated datasets for all types of lexicons. Utilizing the four lexicon types and increasing the data through augmentation to train the acoustic models can lower the word error rate percentage in the GMM-HMM, TDNN factorization (TDNNF), and CNN-TDNNF-augmented models to 40.85%, 24.96%, and 19.03%, respectively.