Reviewerly/exHarmony huggingface dataset:
Sentiment analysis on film review in Gujarati language using machine learning

&lt;span&gt;Opinion analysis is by a long shot most basic zone of characteristic language handling. It manages the portrayal of information to choose the motivation behind the wellspring of the content. The reason might be of a type of gratefulness (positive) or study (negative). This paper offers a correlation between the outcomes accomplished by applying the calculation arrangement using various classifiers for instance K-nearest neighbor and multinomial naive Bayes. These techniques are utilized to assess a significant assessment with either a positive remark or negative remark. The gathered information considered on the grounds of the extremity film datasets and an association with the results accessible proof has been created for a careful assessment. This paper investigates the word level count vectorizer and term frequency inverse document frequency (TF-IDF) influence on film sentiment analysis. We concluded that multinomial Naive Bayes (MNB) classier generate more accurate result using TF-IDF vectorizer compared to CountVectorizer, K-nearest-neighbors (KNN) classifier has the same accuracy result in case of TF-IDF and CountVectorizer.&lt;/span&gt;
De-noising drift chambers in CLAS12 using convolutional auto encoders

Modern Nuclear Physics experimental setups run experiments with higher beam intensity resulting in increased noise in detector components used for particle track reconstruction. Increased uncorrelated signals (noise) result in decreased particle reconstruction efficiency. In this paper, we investigate the usage of Machine Learning, specifically Convolutional Neural Network Auto-Encoders (CAE), for de-noising raw hits from drift chambers in the CLAS12 detector. To the best of our knowledge, this is the first time CAE is employed to perform such an operation in this field. During the de-noising phase, it is important to remove as much noise as possible while retaining the valid hits to avoid losing crucial information about the experiment. We show that using CAE, it is possible to remove noise hits while retaining up to 94% of valid tracks for a beam current of 110nA while for lower beam currents (45−55nA), we get up to 98% efficiency. Studies on experimental conditions with increasing noise show that CAE performs better than conventional tracking algorithms in isolating hits belonging to tracks. Specifically, the de-noising algorithm results in tracking efficiency improvements greater than 15%, in real data production procedures with nominal conditions, and up to two times better efficiency in synthetically generated data with high luminosity conditions (90−110nA), indicating that machine learning can lead to significantly shorter times for conducting physics experiments.
Evolving context-aware recommender systems with users in mind

A context-aware recommender system (CARS) applies sensing and analysis of user context to provide personalized services. The contextual information can be driven from sensors in order to improve the accuracy of the recommendations. Yet, generating accurate recommendations is not enough to constitute a useful system from the users' perspective, since certain contextual information may cause different issues, such as draining the user's battery, privacy issues, and more. Adding high-dimensional contextual information may increase both the dimensionality and sparsity of the model. Previous studies suggest reducing the amount of contextual information by selecting the most suitable contextual information using a domain knowledge. Another solution is compressing it into a denser latent space, thus disrupting the ability to explain the recommendation item to the user, and damaging users' trust. In this paper we present an approach for selecting low-dimensional subsets of the contextual information and incorporating them explicitly within CARS. Specifically, we present a novel feature-selection algorithm, based on genetic algorithms (GA), that outperforms SOTA dimensional-reduction CARS algorithms, improves the accuracy and the explainability of the recommendations, and allows for controlling user aspects, such as privacy and battery consumption. Furthermore, we exploit the top subsets that are generated along the evolutionary process, by learning multiple deep context-aware models and applying a stacking technique on them, thus improving the accuracy while remaining at the explicit space. We evaluated our approach on two high-dimensional context-aware datasets driven from smartphones. An empirical analysis of our results validates that our proposed approach outperforms SOTA CARS models while improving transparency and explainability to the user.
Ethical framework for IoT deployment in SMEs: individual perspective

Purpose This study aims to investigate the ethical issues related to the internet of Things (IoT) deployment in small- and medium-sized enterprises (SMEs) from an individual employee's perspective. To provide researchers and practitioners with concrete tools for examining these matters, an ethical framework dedicated to IoT is introduced. Design/methodology/approach First, the applicability of Mason's original privacy, accuracy, property and accessibility (PAPA) framework is studied in the IoT context. Second, issue category additions are proposed based on the identified coverage limitations of PAPA. Findings While the original PAPA framework can be utilised as a generic ethical evaluation tool, it lacks coverage of several IoT-specific issue areas. To thoroughly address the ethical risks associated with IoT, two additional categories are introduced. Research limitations/implications The new framework requires further validation to ensure its applicability and to identify potential modification requirements in continuously evolving IoT ecosystems. Practical implications Considering the lack of ethical IoT frameworks, this study provides organisations with a practical framework for analysing the ethical issues in IoT deployment. Social implications Ethical standards for IoT have not been sufficiently addressed in the current literature and frameworks, making the ethical considerations dependent on subjective stances. Thus, there is an acute demand for a practical framework that outlines the general ethical standards, helping its users to thoroughly address the potential ethical issues. Originality/value While the use of IoT keeps growing in SMEs, there is an apparent lack of ethical guidelines. This study contributes to the gap by introducing a preliminary framework for both practical use and further theoretical development.
DSLs and Middleware Platforms in a Model-Driven Development Approach for Secure Predictive Maintenance Systems in Smart Factories

Abstract In many industries, traditional automation systems (operating technology) such as PLCs are being replaced with modern, networked ICT-based systems as part of a drive towards the Industrial Internet of Things (IIoT). The intention behind this is to use more cost-effective, open platforms that also integrate better with an organisation’s information technology (IT) systems. In order to deal with heterogeneity in these systems, middleware platforms such as EdgeX Foundry, IoTivity, FI-WARE for Internet of Things (IoT) systems are under development that provide integration and try to overcome interoperability issues between devices of different standards. In this paper, we consider the EdgeX Foundry IIoT middleware platform as a transformation engine between field devices and enterprise applications. We also consider security as a critical element in this and discuss how to prevent or mitigate the possibility of several security risks. Here we address secure data access control by introducing a declarative policy layer implementable using Ciphertext-Policy Attribute-Based Encryption (CP-ABE). Finally, we tackle the interoperability challenge at the application layer by connecting EdgeX with DIME, a model-driven/low-code application development platform that provides methods and techniques for systematic integration based on layered Domain-Specific Languages (DSL). Here, EdgeX services are accessed through a Native DSL, and the application logic is designed in the DIME Language DSL, lifting middleware development/configuration to a DSL abstraction level. Through the use of DSLs, this approach covers the integration space domain by domain, technology by technology, and is thus highly generalizable and reusable. We validate our approach with an example IIoT use case in smart manufacturing.
Learning continuous chaotic attractors with a reservoir computer

Neural systems are well known for their ability to learn and store information as memories. Even more impressive is their ability to abstract these memories to create complex internal representations, enabling advanced functions such as the spatial manipulation of mental representations. While recurrent neural networks (RNNs) are capable of representing complex information, the exact mechanisms of how dynamical neural systems perform abstraction are still not well-understood, thereby hindering the development of more advanced functions. Here, we train a 1000-neuron RNN-a reservoir computer (RC)-to abstract a continuous dynamical attractor memory from isolated examples of dynamical attractor memories. Furthermore, we explain the abstraction mechanism with a new theory. By training the RC on isolated and shifted examples of either stable limit cycles or chaotic Lorenz attractors, the RC learns a continuum of attractors as quantified by an extra Lyapunov exponent equal to zero. We propose a theoretical mechanism of this abstraction by combining ideas from differentiable generalized synchronization and feedback dynamics. Our results quantify abstraction in simple neural systems, enabling us to design artificial RNNs for abstraction and leading us toward a neural basis of abstraction.
Performance Analysis of Deep Neural Networks Using Computer Vision

INTRODUCTION: In recent years, deep learning techniques have been made to outperform the earlier state-of-the-art machine learning techniques in many areas, with one of the most notable cases being computer vision.Deep learning is also employed to train the neural networks with the images and to perform the various tasks such as classification and segmentation using several different models.The size and depth of current deep learning models have increased to solve certain tasks as these models provide better accuracy.As pre-trained weights may be used for further training and prevent costly computing, transfer learning is therefore of vital importance.A brief account is given of their history, structure, benefits, and drawbacks, followed by a description of their applications in the different tasks of computer vision, such as object detection, face recognition etc. OBJECTIVE:.The purpose of this paper is to train a deep neural network to properly classify the images that it has never seen before, define techniques to enhance the efficiency of deep learning and deploy deep neural networks in various applications.METHOD: The proposed approach represents that after the reading of images, 256x256 pixel image's random parts are extracted and noise, distortion, flip, or rotation transforms are applied.Multiple convolution and pooling steps are applied by controlling the stride lengths.RESULT: Data analysis and research findings showed that DNN models have been implemented in three main configurations of deep learning: CNTK, MXNet and TensorFlow.The proposed work outperforms the previous techniques in predicting the dependent variables, learning rate, image count, image mean, performance analysis of loss rate and learning rate during training, performance Analysis of Loss with respect to Epoch for Training, Validation and Accuracy.CONCLUSION: This research encompasses a large variety of computer applications, from image recognition and machine translation to enhanced learning.DNN models have been implemented in three main configurations of deep learning: CNTK, MXNet and TensorFlow.Extensive research has been conducted using the various deep architectures such as AlexNet, InceptionNet, etc.To the best of authors' knowledge, this is the first work that presents a quantitative analysis of the deep architectures mentioned above.
A Privacy-Preserving Distributed Control of Optimal Power Flow

We consider a distributed optimal power flow formulated as an optimization problem that maximizes a nondifferentiable concave function. Solving such a problem by the existing distributed algorithms can lead to data privacy issues because the solution information exchanged within the algorithms can be utilized by an adversary to infer the data. To preserve data privacy, in this paper we propose a differentially private projected subgradient (DP-PS) algorithm that includes a solution encryption step. We show that a sequence generated by DP-PS converges in expectation, in probability, and with probability 1. Moreover, we show that the rate of convergence in expectation is affected by a target privacy level of DP-PS chosen by the user. We conduct numerical experiments that demonstrate the convergence and data privacy preservation of DP-PS.
Stigmergic electronic gates and networks

Abstract Stigmergy is a communication method based on changing the surrounding environment according to reference feedbacks. It is typical within animal colonies that are able to process even complex information by releasing signals into the environment, which are subsequently received and processed by other elements of the colony. For example, ants searching for food leave traces of a pheromone, like Hansel and Gretel’s breadcrumbs, along the way. When food is found, they return to the anthill reinforcing this pheromone trace as a signal and reminder to all the others. Similar techniques are used in routing software even if stigmergic hardware might be even more efficient, fast, and energy saving. Recently, a stigmergic photonic gate based on soliton waveguides has been proposed; this particular stigmergic hardware can switch the output ratio of the channels as a result of optical feedback. Based on these results, in this study, we analyze stigmergic electronic gates that can be addressed through external feedback, as the photonic ones do. We show that the nonlinear response of such gates must be based on quadratic saturating conductances driven by feedback signals. For this purpose, networks of stigmergic gates require two parallel and communicating current circuits: one to transmit information, and another for feedback signals to control the gate switching. We also show that by increasing the number of terminals per single gate, from 2 × 2 to 3 × 3 or higher, the overall power consumption can be reduced by a few orders of magnitude.
Mobile Robot Obstacle Avoidance Based on Neural Network with a Standardization Technique

Reactive algorithm in an unknown environment is very useful to deal with dynamic obstacles that may change unexpectantly and quickly because the workspace is dynamic in real-life applications, and this work is focusing on the dynamic and unknown environment by online updating data in each step toward a specific goal; sensing and avoiding the obstacles coming across its way toward the target by training to take the corrective action for every possible offset is one of the most challenging problems in the field of robotics. This problem is solved by proposing an Artificial Intelligence System (AIS), which works on the behaviour of Intelligent Autonomous Vehicles (IAVs) like humans in recognition, learning, decision making, and action. First, the use of the AIS and some navigation methods based on Artificial Neural Networks (ANNs) to training datasets provided high Mean Square Error (MSE) from training on MATLAB Simulink tool. Standardization techniques were used to improve the performance of results from the training network on MATLAB Simulink. When it comes to knowledge-based systems, ANNs can be well adapted in an appropriate form. The adaption is related to the learning capacity since the network can consider and respond to new constraints and data related to the external environment.
Towards Globally Unique Identification of Physical Samples: Governance and Technical Implementation of the IGSN Global Sample Number

Persistent unique identifiers (PID) are a critical element in digital research data infrastructure to unambiguously identify, locate, and cite digital representations of a growing range of entities – publications, data, instruments, organizations, funding awards, field programs, and others. The IGSN was developed as the International Geo Sample Number to provide a persistent, globally unique, web resolvable identifier for physical samples. IGSN is both a governance and technical system for assigning globally unique persistent identifiers to physical samples. Even though initially developed for samples in the geosciences, the application of IGSN can be and has already been expanded to other domains that rely on physical samples and collections. This paper describes the current architecture and technical implementation of IGSN, how IGSN relates to other sample identifiers, and how its technical systems are supported by an international governance structure.
Focus is Key to Success: A Focal Loss Function for Deep Learning-Based Side-Channel Analysis

The deep learning-based side-channel analysis represents one of the most powerful side-channel attack approaches. Thanks to its capability in dealing with raw features and countermeasures, it becomes the de facto standard approach for the SCA community. The recent works significantly improved the deep learning-based attacks from various perspectives, like hyperparameter tuning, design guidelines, or custom neural network architecture elements. Still, insufficient attention has been given to the core of the learning process - the loss function. This paper analyzes the limitations of the existing loss functions and then proposes a novel side-channel analysis-optimized loss function: Focal Loss Ratio (FLR), to cope with the identified drawbacks observed in other loss functions. To validate our design, we 1) conduct a thorough experimental study considering various scenarios (datasets, leakage models, neural network architectures) and 2) compare with other loss functions used in the deep learning-based side-channel analysis (both “traditional” ones and those designed for side-channel analysis). Our results show that FLR loss outperforms other loss functions in various conditions while not having computational overhead like some recent loss function proposals.
Development of an Undergraduate Quantum Engineering Degree

Quantum technology is exploding. Computing, communication, and sensing are just a few areas likely to see breakthroughs in the next few years. Worldwide, national governments, industries, and universities are moving to create a new class of workforce - the Quantum Engineers. Demand for such engineers is predicted to be in the tens of thousands within a five-year timescale. However, how best to train this next generation of engineers is far from obvious. Quantum mechanics - long a pillar of traditional physics undergraduate degrees - must now be merged with traditional engineering offerings. This paper discusses the history, development, and first year of operation of the world's first undergraduate degree in quantum engineering. The main purpose of the paper is to inform the wider debate, now being held by many institutions worldwide, on how best to formally educate the Quantum Engineer.
Explainable Artificial Intelligence (XAI): How the Visualization of AI Predictions Affects User Cognitive Load and Confidence

Explainable Artificial Intelligence (XAI) aims to bring transparency to AI systems by translating, simplifying, and visualizing its decisions. While society remains skeptical about AI systems, studies show that transparent and explainable AI systems result in improved confidence between humans and AI. We present preliminary results from a study designed to assess two presentation-order methods and three AI decision visualization attribution models to determine each visualization's impact upon a user's cognitive load and confidence in the system by asking participants to complete a visual decision-making task. The results show that both the presentation order and the morphological clarity impact cognitive load. Furthermore, a negative correlation was revealed between cognitive load and confidence in the AI system. Our findings have implications for future AI systems design, which may facilitate better collaboration between humans and AI.
Sentiment Analysis using Optimized Feature Sets in Different Facebook/Twitter Dataset Domains using Big Data

Now days, in many real life applications, the sentiment analysis plays very vital role for automatic prediction of human being activities especially on online social networks (OSNs). Therefore since from last decade, the research on opinion mining and sentiment analysis is growing with increasing volume of online reviews available over the social media networks like Facebook OSNs. Sentiment analysis falls under the data mining domain research problem. Sentiment analysis is kind of text mining process used to determine the subjective attitude like sentiment from the written texts and hence becoming the main research interest in domain of natural language processing and data mining. The main task in sentiment analysis is classifying human sentiment with objective of classifying the sentiment or emotion of end users for their specific text on OSNs. There are number of research methods designed already for sentiment analysis. There are many factors like accuracy, efficiency, speed etc. used to evaluate the effectiveness of sentiment analysis methods. The MapReduce framework under the domain of big-data is used to minimize the speed of execution and efficiency recently with many data mining methods. The sentiment analysis for Facebook OSNs messages is very challenging tasks as compared to other sentiment analysis because of misspellings and slang words presence in twitter dataset. In this paper, different solutions recently presented are discussed in detail. Then proposed the new approach for sentiment analysis based on hybrid features extraction methods and multi-class Support Vector Machine (SVM). These algorithms are designed using the Big-data techniques to optimize the performance of sentiment analysis
Contrastive Prediction Strategies for Unsupervised Segmentation and Categorization of Phonemes and Words

We identify a performance trade-off between the tasks of phoneme categorization and phoneme and word segmentation in several self-supervised learning algorithms based on Contrastive Predictive Coding (CPC). Our experiments suggest that context building networks, albeit necessary for high performance on categorization tasks, harm segmentation performance by causing a temporal shift on the learned representations. Aiming to tackle this trade-off, we take inspiration from the leading approaches on segmentation and propose multi-level Aligned CPC (mACPC). It builds on Aligned CPC (ACPC), a variant of CPC which exhibits the best performance on categorization tasks, and incorporates multi-level modeling and optimization for detection of spectral changes. Our methods improve in all tested categorization metrics and achieve state-of-the-art performance in word segmentation.