Reviewerly/exHarmony huggingface dataset:
Predicting Request Success with Objective Features in German Multimodal Speech Assistants

We investigate whether objective features, like occurrence of an error and number of turns, can automatically predict success in interactions with multimodal speech assistants. We used interactions from the SmartKom corpus, a data set on multimodal interactions with virtual assistants in German. In a first step, we segmented the interactions into requests and labeled them as successful or unsuccessful. Afterwards, we defined task success as the average of request success rate. Next, we investigated whether subjective features such as emotions expressed by users show a relation to task success. We find no significant correlation. Finally, we exploited objective features, e.g., number of turns to predict request success. We find that objective features suffice to reach $$F_1$$ scores over 0.9 (prediction of successful requests) and $$F_0$$ scores above 0.83 (prediction of unsuccessful requests). Finally, we discuss implications of our findings for automatic evaluation of pragmatic aspects of user experience.
Automatic Speech Recognition Model Adaptation to Medical Domain Using Untranscribed Audio

Automatic speech recognition (ASR) technologies can provide significant efficiency gains in the health sector, by saving time and financial resources, allowing specialists to shift more time to high-value activities. Creating customized ASR models requires domain- and task-related transcribed speech data. Unfortunately, producing such data usually is too expensive for medical institutions: it requires a lot of financial, human resources, and expertise. Consequently, his paper explores a semi-supervised medical domain adaptation method for the Latvian language that benefits from the untranscribed speech recordings. For the initial model, we use the currently available general-purpose hybrid ASR system with the core of a lattice-free maximum mutual information method used to train its acoustic model. The initial system is applied to the domain-related untranscribed data to extract sequences of pseudo-labels. Such automatic transcriptions are later added to the supervised and used together to update the acoustic model. To improve our ASR system further, we have also updated its language model with additional in-domain texts. We have achieved significant improvements in the quality of speech recognition on all evaluation datasets. On the epicrises, psychiatry, and radiology datasets word error rate (WER) decreased by 39%, 27%–29%, and 21%, respectively.
Adversarial Examples and Defenses

Perturbed data, called adversarial examples, is known to cause deep neural networks (DNNs) to make erroneous predictions, although the data is indistinguishable from clean data. The notion of adversarial examples has raised concerns about where DNNs are to be deployed in security-sensitive applications such as self-driving vehicles, healthcare, and finance. There are various ways of crafting adversarial examples on the basis of the knowledge available to an attacker. With the development of various attacks, researchers have proposed numerous adversarial defenses. In this chapter, a comprehensive survey of state-of-the-art adversarial examples and defenses is put forward.
Deep Learning Approach for Predicting the Price of Cryptocurrencies

Cryptocurrencies can be treated as a digital tokens; their market capitalization is going up day-by-day as cryptocurrencies can be exchanged globally without a mediator. Therefore, the prediction of prices of different cryptocurrencies is an active area of research. Knowledge of future prices can not only build trust among the investors but can also help to build global acceptance. In this paper, the main objective is to predict the prices of cryptocurrencies using deep learning methodology. The long-term memory model is used for different activation functions, i.e., tanh and ReLU. Our model(tanh) has performed better than model (ReLU) as evaluation matrices, Root Mean Squared Error, Mean Absolute Error, Mean Absolute percentage error of former is lesser than that of latter. The RMSE of former and latter are .074, 0.169 respectively.
Development and Performance Evaluation of an Augmented Reality Instructional System (Easy-AR) for Assembly Support

This work develops and evaluates an Augmented Reality Instructional System for assembly support and its role in improving efficiency on manual, customized, or modular manufacturing. The prototype application is a tool designed to unite real and computer-based scenes and images to deliver a combined view of assembly with a marker-less based tracking system, the “Easy-AR”. Normally, such assembly process is conducted by referring to the manual instruction, often via printed graphics which are inefficient especially for new workers. To overcome, industries need to provide series of training sessions to develop worker skills until necessary performance is obtained. This will likely contribute to an increment of direct and indirect cost towards industries. The development of the “Easy-AR” application for assembly support prototype is hypothesized to help to enhance the efficiency and reduce errors of the manual assembly process with minimum training requirement. The prototype was tested in a controlled environment to minimize unnecessary variables. Time study, efficiency, and error of assembly process was done under assistance of the prototype while assistance of a printed manual is introduced as control. Time taken to assemble each component of a prototype was recorded and analyzed to identify the difference of efficiency and assembly quality under both situations and analyzed statistically. Based on testing on 60 participants, assembly time with the prototype reported to be improved by 38.9% in comparison with control. Furthermore, a 50% reduction of errors has been recorded with the prototype. As conclusion, the problem in industry related to the manual processes in daily tasks can be overcome by the implementation of the prototype app which is scalable, and the assembling process will be more interesting, attractive, and effective, in line with the fourth industrial revolution.
Radar Fusion Monocular Depth Estimation Based on Dual Attention

In this article, we explore the integration of multimodal data into monocular depth estimation. Monocular depth estimation is performed by fusing RGB data with sparse radar data. Since the existing fusion method does not take into account the correlation between the two types of data in the channel and in space, it lacks the representation of the global information relationship on the channel and in space. Therefore, we propose a feature fusion module (DAF) based on the dual attention mechanism. The dual attention fusion module improves the global information representation capability of the model by modeling the dynamic and non-linear relationship of the two kinds of data in the channel and space, adaptively recalibrates the response to each feature, and maximizes the use of radar data. At the same time, DAF can reduce noise interference in radar data by weighting features, avoiding the loss of secondary details caused by filtering operations, and alleviating the problem of excessive noise in radar data. Finally, due to the influence of the complex weather environment and the model itself, it is difficult for the model to obtain an effective feature representation in the complex weather environment. Therefore, we introduced a batch loss function to enable the model to focus on feature extraction in a complex environment, so as to obtain a more accurate representation of feature information. It reduces model errors and speeds up the convergence of the model. The experiment was conducted on the recently released nuScenes dataset, which provides data records of the entire sensor suite of autonomous vehicles. Experiments prove that our method is superior to other fusion methods.
Detection of Credit Card Frauds with Machine Learning Solutions: An Experimental Approach

In many cases frauds in payment transactions could be detected by analysing the customer’s behaviour. Only in the United States fraudulent transactions led to financial losses of 300 billion a year. Machine learning (ML) and Data Mining techniques were shown to be efficient for detection of fraudulent transactions. This paper proposes an experimental way for designing a ML solution to the problem, which allows practitioners to minimise financial losses by analysing the customer’s behaviour and common patterns of using credit cards. The solution designed within a Random Forest (RF) strategy is examined on a public data set available for the research community. The results obtained on the benchmark data show that the proposed approach provides a high accuracy of detecting fraudulent transaction based on the customer’s behaviour patterns that were learnt from data. This allow us to conclude that the use of the RF models for detecting credit card fraud transactions allows practitioners to design an efficient solution in terms of sensitivity and specificity. Our experimental results show that practitioners using the RF models can find new insights into the problem and minimise the losses.
Archives Cultural and Creative Cloud Service System Based on VR Virtual Reality Technology

With the popularization of the Internet and mobile terminals, VR technology has gradually been applied to people’s daily life, playing an important role in many fields. The technology is designed and developed based on human-computer interaction, using computer simulation and digital image processing to realize the collection and analysis of three-dimensional spatial information to achieve display, communication and interactive effects. While the rapid development of virtual reality technology, it also brings development opportunities to various modern office, office equipment and other digital service products and systems. Applying VR technology to the development process of the archives cultural and creative cloud service system will make the system more intuitive and shareable. This article adopts experimental analysis method and data analysis method to better understand the performance of this archives cultural and creative cloud service system, so as to realize the sharing of archive resources more efficiently. According to the experimental research results, the response time of the archives cultural and creative cloud service system is relatively short and controlled within 3 s. The performance of the system is good and can meet the basic needs of users.
Multilevel Feature Interaction Learning for Session-Based Recommendation via Graph Neural Networks

Predicting users’ actions based on anonymous sessions is a challenging problem due to the uncertainty of user behavior and limited information. Recent advances in graph neural networks (GNN) have led to a promising approach for addressing this problem. However, existing methods have three major issues. First, they are incapable of modeling the transitions between inconsecutive items. Second, they are infeasible for learning the cross-feature interactions when learning the item relationships. Third, very few models can adapt to the improvement of embedding quality to help improve recommendation performance. Therefore, to address these issues, we propose a novel model named M ultilevel F eature I nteractions L earning (MFIL) that effectively learns item and session representation using GNN. By leveraging item side information, e.g., brands and categories, MFIL can model transitions between inconsecutive items in the session graph (session-level). We further design hierarchical structures to learn the feature interactions, which is effective to estimate the importance weights of different neighboring items in the global graph (global-level). In addition, an effective learning strategy is employed to enhance MFIL’s capability, and it performs better than the classic regularization methods. Extensive experiments conducted on real-world datasets demonstrate that MFIL, significantly outperforms existing state-of-the-art graph-based methods.
Predicting the Health of the System Based on the Sounds

A fundamental challenge in artificial intelligence is to predict the system’s state by detecting anomalies generated due to the faults in the systems. Sound data that deviates significantly from the default sounds generated by the system is referred to as anomalous sounds. Predicting anomalous sounds has gained importance in various applications as it helps in maintaining and monitoring machine conditions. The goal of anomaly detection involves training the system to distinguish default sounds from abnormal sounds. As self-supervised learning helps in improvising representations when labeled data are used, it is employed where only the normal sounds are collected and used. The largest interval on the feature space defines the support vector machine, which is a linear classifier. We propose a self-supervised support vector machine (SVM) to develop a health prediction model that helps understand the current status of the machinery activities and their maintenance, enhancing the system’s health accuracy and efficiency. This work uses a subset of MIMII and ToyADMOS datasets. The implemented system would be tested for the performance measure by obtaining the training accuracy, validation accuracy, testing accuracy, and overall mean accuracy. The proposed work would benefit from faster prediction and better accuracy.
LC-BiDet: Laterally Connected Binary Detector With Efficient Image Processing

Recently, binary neural networks have received increasing interest in object detection community, since compared to the full-precision detection networks, they can reduce the memory and computation requirements significantly due to the efficient XNOR and BITCOUNT operations introduced by binarization. However, the final detection performance of binary detectors always suffers from a severe degradation compared with the full-precision counterparts. In this letter, to achieve a better trade-off between the inference efficiency and the detection performance, we propose to learn a laterally connected binary detector based on the multiple parallel binary subnets with a group sparse regularization term. Firstly, we simply adopt the parallel structure to improve the representation capacity of binary detectors. Secondly, we introduce the dense lateral connections between binary subnets in each convolutional layer to increase the information flows. Finally, to reduce the redundancy of the dense connections, we take the L21 norm as the regularization term to optimize the lateral connections in an end-to-end manner. Experimental results on PASCAL VOC and COCO datasets show that our proposed laterally connected binary detector could outperform the other state-ofthe- art binary detectors.
Comparison of Encryption Techniques to Encrypt Private Parts of an Image

This article compares two methods for encrypting private information of the image and to make the image data highly secure. One method implements the advanced least significant bit (LSB) steganography, AES encryption, and RSA algorithm by matching data to an image, and there is much less chance of an attacker having the capacity to apply steganography analysis to recover original data. Before concealing the key in an image encrypted using AES algorithm with same key, the application first encrypts it using RSA and LSB methods (Moumen and Sissaoui in Nonlinear Engineering 6:53–59, 2017 [1]). The other method implements an image encryption algorithm derived from SHA-512 (Seyedzade, et al. in Iranian conference on machine vision and image processing, No. 5941167, 2010 [2]). In this algorithm, first equal half of data in image is used to encrypt opposite half of the image reciprocally. In order to assess performance, both the algorithms are examined to analyze the measure of the security, quality, and efficiency. These assessments consist of histogram analysis, coefficient of correlation analysis, information entropy analysis, degree of quality in encryption.
Review of Research on Named Entity Recognition

With the development of Web2.0, huge amount of text information is produced. It's important to extract useful information from data. This paper systematically analyzes the main research progress and methods of named entity recognition (NER), and grasps the development context to help researchers quickly understand NER. [Method/process] We select representative literature for review, summarize and comb the mainstream methods by bibliometrics and literature research, and count the keywords of relevant papers in Web of Science to support this view, and finally summarize the applications and the development trends of NER. [Result/conclusion] Research shows that common recognition methods include rule-based, statistics-based, hybrid methods, and more and more tend to integrate multiple methods; in recent 5 years, hybrid and joint models based on deep learning are currently dominating the latest technology.
An Efficient Classifier Model for Opinion Mining to Analyze Drugs Satisfaction Among Patients

Now day's people opinion or sentiment matters a lot in the field of research. There are many patients who stay away from their family, friends and live in the place where hospital, facilities are not available. Then if they have any health related problems and they have to take a medicine in emergency then which medicine will be better for them. Therefore to overcome the problem developed the proposed predictive classifier model in which experience patients who used a medicine in the past, their reviews or feedback will be mined. In the developed model the worthless model has been removed by the stop word removing and stemming algorithms. The developed model will train the simplex decision tree classification algorithm rather than the complex classification algorithm employed in the model used in the previous years. The model used in the previous years were processed over the entire sentence of the reviews, which caused the model to take a long time to process the drug reviews because the worthless words exists in the sentence.
Instinctive Data Analysis in Machine Learning and Summary Exhibitor

The process of Data Analysis in Machine Learning (ML) is very huge, it involves a task beginning with defining the business objective, collecting data, preprocessing the data, selecting, building and testing models, monitoring and validating against stated objectives. This requires more time for the user to get the result when each step is done manually. During analysis, not everyone checks with accuracy for all the models that exist. While dealing with ML, the data analysts usually come across lots of errors that are difficult to analyze and solve. The main objective of the paper is to perform the instinctive data analysis tool for Machine Learning in an easier way. This tool just needs the dataset, and all the data analysis required is done automatically and the result is generated within a short period. Different kinds of datasets can be provided for analysis, Eg: Numerical Dataset, Categorical Dataset, unlabelled data, etc. Around 40 regression and classifier models are available for testing here. The two main categories of Machine learning techniques have been used which are Supervised and Unsupervised. For the demo, Kaggle datasets are used, the iris dataset is used for classification, and the vegetable dataset is used for regression. This will be immensely useful for individual purposes, software companies, new budding ML engineering, and data scientists.
Advanced Technique for Causing Immediate Threats to Prevent Social Engineering Attacks

When people try to get into systems or networks by tricking people, this is an attack on information security. When people don't know how to avoid being scammed, they end up being scammed themselves. The current research talks about user studies and how to evaluate these studies to stop social engineering attacks. Unfortunately, there has never been a study that looked at social engineering attacks in a proper and methodical way. Social engineering attacks can be stopped in many different ways, such as through health campaigns, human security sensor frameworks, user-centered frameworks, and models of user vulnerability. Cybersecurity as super-recognizers needs direction, which is likely to be a police act for the secure system, for the human sensor framework. We used Bryman and Bell's method for reviewing the literature to do a systematic review of the literature. During our evaluation of approaches, frameworks, models, and evaluations, we found a protocol. This is a new way to stop social engineering attacks. Some things that have been found to help stop social engineering attacks are health campaigns, victims who are easy to trick, and co-use protocols that can control how information is shared on a network. This in-depth research review is meant to stop social engineering attacks.
An Efficient Learning Automaton Scheme for Massive-Action Environments

As a primary tool in reinforcement learning, learning automaton (LA) has been widely applied in the area of electronic engineerings such as signal processing and wireless communications. The scenario that an LA operates may have a large number of actions. The existing LA schemes, which are designed in a fixed manner, are inefficient in massive-action environments. In this paper, we propose an efficient LA (ELA) scheme based on a novel learning framework. The well-designed framework includes a brand-new action set adjustment strategy, a convergence judgment strategy, and an exploration-exploitation strategy. Experimental results in various massive-action environments indicate that the proposed ELA scheme achieves significant improvements in both convergence rate and convergence time, especially in environments with an extremely large number of actions.
The art of cyber security in the age of the digital supply chain

As the digitalization of supply chains accelerates and the importance of cyber risk management across the supply chain is recognized, both academics and practitioners are paying much more attention to the topic. However, there is a lack of theoretical foundations and practical solutions to underpin and sustain the effective management of supply chain cyber security. This chapter provides a holistic definition for supply chain cyber security and conducts a thorough review of both nonacademic industry sources and the academic literature. Through analysis of industry sources, 18 common best practice principles are identified. These are classified hierarchically—strategic (e.g., supply chain cyber vulnerability and threat identification), tactical (e.g., cyber security education of employees), and operational (e.g., real-time cyber security monitoring). The review of the academic literature complements existing reviews by including the most recent studies, identifying the most significant, and classifying the research. The most common research topics are noted, the main quantitative methodologies deployed are identified, and technology-specific supply chain security research is highlighted. Lastly, 19 key questions for supply chain cyber security research are posed under four categories—modeling and theoretical foundations, implementation of security strategies, interactions between theory and practice, and the analysis of real-world cases.