Reviewerly/exHarmony huggingface dataset:
Machine Learning Techniques for Sentiment Analysis of Hotel Reviews

In today&#x0027;s scenario online reviews on various digital platforms plays a vital role for customers to buy products. Based on the reviews and ratings by the consumer on E-commerce platform like flipkart, amazon etc. products are widely accepted or rejected. Apart from products people also look for the reviews of the services provided from restaurants, hotels, airlines etc. Sentiment analysis helps the developers to easily analyze the reviews and categorize them as positive or negative. In this paper, service of a hotel is analyzed by finding out the polarity of the reviews in order to get the subject information. Aspect detection and sentiment classification are the main tasks focused here. For aspect detection latent dirichlet allocation (LDA) is used for building the topics. Different machine learning classifiers like naive bayes classifier, SVM, decision tree and logistic regression are used for classification of reviews. Evaluation is done by computing the accuracy, recall, precision and F score of these algorithms.
Bitcoin as a digital commodity

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VehicleLang: A probabilistic modeling and simulation language for modern vehicle IT infrastructures

Attack simulations are a feasible means of assessing the cyber security of various systems. Simulations can replicate the steps taken by an attacker to compromise sensitive system assets, and the time required for the acquisition of assets of interests can be calculated. One widely accepted approach to such simulations is the modelling of attack steps and their dependencies in a formal manner using attack graphs. To reduce the effort of creating new attack graphs for each system in a given domain, one can employ domain-specific attack-modeling languages to codify common attack logic. The Meta Attack Language has been proposed as a framework for developing domain-specific attack languages. In this article, we propose vehicleLang as a domain-specific language for modeling vehicles in the context of corresponding information technology infrastructures and analyzing weaknesses related to known attacks. To model domain-specific attributes, we reviewed existing literature to develop a comprehensive language, which was then verified through a series of interviews with domain experts from the automotive industry. Specifically, a systematic literature review was performed to identify possible attacks against vehicles. The identified attacks served as a blueprint for the evaluation of vehicleLang's simulation capabilities. Finally, the language was validated using the Feigenbaum test methodology.
Information Security Strategies for Information-Sharing Firms Considering a Strategic Hacker

Information resources have been shared to promote the business operations of firms. However, the connection of business information sharing interfaces between firms has increased the attack surface and created opportunities for the hacker. We examine the benefits and risks of business information sharing for firms who exert security efforts against a strategic hacker that launches attacks subjectively. We show that two kinds of security efforts, security investment and security knowledge sharing, act as strategic substitutes when the business-sharing degree is low and act as strategic complements otherwise. Besides, the strategic hacker is not always aggressive, who will give up launching attack activities when the business-sharing degree is relatively low. Moreover, as a specific characteristic in the security domain, the risk interdependency first enhances and then suppresses both firms’ security investments and the hacker’s attack effort, which causes a free-riding problem for two firms. Then, two coordination mechanisms, an investment-based mechanism and liability-based mechanism, are proposed to help firms coordinate their strategies to reach socially optimal security levels. Last, we extend the main model to three cases to make our model more general. This paper provides the first evidence to assess the security risks exacerbated by business information sharing while considering a strategic hacker. Some management insights to managers for making security decisions are provided.
Explainable AI for Industry 4.0: Semantic Representation of Deep Learning Models

Artificial Intelligence is an important asset of Industry 4.0. Current discoveries within machine learning and particularly in deep learning enable qualitative change within the industrial processes, applications, systems and products. However, there is an important challenge related to explainability of (and, therefore, trust to) the decisions made by the deep learning models (aka black-boxes) and their poor capacity for being integrated with each other. Explainable artificial intelligence is needed instead but without loss of effectiveness of the deep learning models. In this paper we present the transformation technique between black-box models and explainable (as well as interoperable) classifiers on the basis of semantic rules via automatic recreation of the training datasets and retraining the decision trees (explainable models) in between. Our transformation technique results to explainable rule-based classifiers with good performance and efficient training process due to embedded incremental ignorance discovery and adversarial samples (“corner cases”) generation algorithms. We have also shown the use-case scenario for such explainable and interoperable classifiers, which is collaborative condition monitoring, diagnostics and predictive maintenance of distributed (and isolated) smart industrial assets while preserving data and knowledge privacy of the users.
Revisiting Negative Sampling vs. Non-sampling in Implicit Recommendation

Recommendation systems play an important role in alleviating the information overload issue. Generally, a recommendation model is trained to discern between positive (liked) and negative (disliked) instances for each user. However, under the open-world assumption, there are only positive instances but no negative instances from users’ implicit feedback, which poses the imbalanced learning challenge of lacking negative samples. To address this, two types of learning strategies have been proposed before, the negative sampling strategy and non-sampling strategy. The first strategy samples negative instances from missing data (i.e., unlabeled data), while the non-sampling strategy regards all the missing data as negative. Although learning strategies are known to be essential for algorithm performance, the in-depth comparison of negative sampling and non-sampling has not been sufficiently explored by far. To bridge this gap, we systematically analyze the role of negative sampling and non-sampling for implicit recommendation in this work. Specifically, we first theoretically revisit the objection of negative sampling and non-sampling. Then, with a careful setup of various representative recommendation methods, we explore the performance of negative sampling and non-sampling in different scenarios. Our results empirically show that although negative sampling has been widely applied to recent recommendation models, it is non-trivial for uniform sampling methods to show comparable performance to non-sampling learning methods. Finally, we discuss the scalability and complexity of negative sampling and non-sampling and present some open problems and future research topics that are worth being further explored.
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations

Predictive models for clinical outcomes that are accurate on average in a patient population may underperform drastically for some subpopulations, potentially introducing or reinforcing inequities in care access and quality. Model training approaches that aim to maximize worst-case model performance across subpopulations, such as distributionally robust optimization (DRO), attempt to address this problem without introducing additional harms. We conduct a large-scale empirical study of DRO and several variations of standard learning procedures to identify approaches for model development and selection that consistently improve disaggregated and worst-case performance over subpopulations compared to standard approaches for learning predictive models from electronic health records data. In the course of our evaluation, we introduce an extension to DRO approaches that allows for specification of the metric used to assess worst-case performance. We conduct the analysis for models that predict in-hospital mortality, prolonged length of stay, and 30-day readmission for inpatient admissions, and predict in-hospital mortality using intensive care data. We find that, with relatively few exceptions, no approach performs better, for each patient subpopulation examined, than standard learning procedures using the entire training dataset. These results imply that when it is of interest to improve model performance for patient subpopulations beyond what can be achieved with standard practices, it may be necessary to do so via data collection techniques that increase the effective sample size or reduce the level of noise in the prediction problem.
Effectiveness of virtual reality in discrete event simulation models for manufacturing systems

• A measurement of virtual reality effectiveness in simulation is presented. • The experimentation time is reduced when users are immersed in virtual reality. • Virtual reality systems foster a lower percentage of error in decision-making. • The user can interact with discrete event simulation without previous experience. Virtual reality (VR) and discrete event simulation (DES) have become fundamental technologies of industry 4.0. There is a growing interest in such technologies in both academic and industry sectors. Nevertheless, few studies have reported the use of them simultaneously. This study develops a DES model and proposes an integrated approach combining self-reported and objective measurements to determine the VR effectiveness, based on data collection and analysis of 72 volunteers randomly assigned either a VR system or a laptop computer. The model represents a real manufacturing system where the user can move around, interact with objects, modify variables, run different scenarios, and identify the best one based on the system’s key performance indicators. Results showed that experimentation time is significantly less when users interact with the VR interface, and there is no significant difference between the two interfaces when comparing the decision-making time. However, the VR users obtained a higher percentage of correct answers. Finally, self-reported feedback indicated users preferred the VR system, in addition to the fact that discomfort and presence questionnaires exhibit typical values of the VR experience, and the usability questionnaire yields higher values than the laptop computer.
Experimental implementation of secure anonymous protocols on an eight-user quantum key distribution network

Abstract Anonymity in networked communication is vital for many privacy-preserving tasks. Secure key distribution alone is insufficient for high-security communications. Often, knowing who transmits a message to whom and when must also be kept hidden from an adversary. Here, we experimentally demonstrate five information-theoretically secure anonymity protocols on an eight user city-wide quantum network using polarisation entangled photon pairs. At the heart of these protocols is anonymous broadcasting, which is a cryptographic primitive that allows one user to reveal one bit of information while keeping their identity anonymous. For a network of n users, the protocols retain anonymity for the sender, given that no more than n − 2 users are colluding. This is an implementation of genuine multi-user cryptographic protocols beyond standard QKD. Our anonymous protocols enhance the functionality of any fully-connected Quantum Key Distribution network without trusted nodes.
Research on Sports Dance Movement Detection Based on Pose Recognition

In view of the poor effect of current sports dance movement detection, this paper puts forward a sports dance movement detection method based on posture recognition. By using the principle of posture recognition to obtain the characteristic information of sports dance movement, construct the action characteristic evaluation system, optimize the action detection algorithm combined with posture recognition technology, and realize the classified detection of sports dance movements. Finally, experiments show that the sports dance movement detection based on pose recognition has high practicability and effectiveness in the process of practical application and fully meets the research requirements.
GLASS: A Citizen-Centric Distributed Data-Sharing Model within an e-Governance Architecture

E-governance is a process that aims to enhance a government's ability to simplify all the processes that may involve government, citizens, businesses, and so on. The rapid evolution of digital technologies has often created the necessity for the establishment of an e-Governance model. There is often a need for an inclusive e-governance model with integrated multiactor governance services and where a single market approach can be adopted. e-Governance often aims to minimise bureaucratic processes, while at the same time including a digital-by-default approach to public services. This aims at administrative efficiency and the reduction of bureaucratic processes. It can also improve government capabilities, and enhances trust and security, which brings confidence in governmental transactions. However, solid implementations of a distributed data sharing model within an e-governance architecture is far from a reality; hence, citizens of European countries often go through the tedious process of having their confidential information verified. This paper focuses on the sinGLe sign-on e-GovernAnce Paradigm based on a distributed file-exchange network for security, transparency, cost-effectiveness and trust (GLASS) model, which aims to ensure that a citizen can control their relationship with governmental agencies. The paper thus proposes an approach that integrates a permissioned blockchain with the InterPlanetary File System (IPFS). This method demonstrates how we may encrypt and store verifiable credentials of the GLASS ecosystem, such as academic awards, ID documents and so on, within IPFS in a secure manner and thus only allow trusted users to read a blockchain record, and obtain the encryption key. This allows for the decryption of a given verifiable credential that stored on IPFS. This paper outlines the creation of a demonstrator that proves the principles of the GLASS approach.
ETEH: Unified Attention-Based End-to-End ASR and KWS Architecture

Even though attention-based end-to-end (E2E) automatic speech recognition (ASR) models have been yielding state-of-the-art recognition accuracy, they still fall behind many of the ASR models deployed in the industry in some crucial functionalities such as online processing and precise timestamps generating. This weakness prevents attention-based E2E ASR models from being applied in several essential speech tasks, such as online speech recognition and keyword searching (KWS). In this paper, we describe our proposed unified attention-based E2E ASR and KWS architecture–ETEH, which supports, in one model, both online and offline ASR decoding modes, thus allowing for precise and reliable KWS. “ETE” stands for attention-based E2E modeling, whereas “H” represents the hybrid gaussian mixture model and hidden Markov model (GMM-HMM). As a combination of both, ETEH is an attention-based E2E ASR architecture which utilizes the frame-wise time alignment (FTA) generated by GMM-HMM ASR models. This FTA is used to better the model in two ways: first, it helps the monotonic attentions of ETEH models to capture more accurate word time stamps, thus resulting in lower latency for online decoding; second, it helps ETEH models to provide accurate and reliable KWS results. Furthermore, we are able to combine both offline and online modes in one ETEH model and establish a concise system by adopt the universal training strategy. ETEH is functional and unique, and to the best of our knowledge, we can hardly find a comparable single attention-based E2E ASR system as the baseline. To evaluate ASR accuracy and latency for ETEH, we use our previously proposed monotonic truncated attention (MTA) based online CTC/attention (OCA) ASR models as baselines. Experimental results show that ETEH ASR models gain significant improvement in ASR latency compared to the baseline. To evaluate KWS performance, we compare ETEH models with CTC-based KWS models. Results demonstrate that our ETEH models achieve significantly better KWS performance compared to the CTC baselines.
Implementing efficient selective quantum process tomography of superconducting quantum gates on IBM quantum experience

The experimental implementation of selective quantum process tomography (SQPT) involves computing individual elements of the process matrix with the help of a special set of states called quantum 2-design states. However, the number of experimental settings required to prepare input states from quantum 2-design states to selectively and precisely compute a desired element of the process matrix is still high, and hence constructing the corresponding unitary operations in the lab is a daunting task. In order to reduce the experimental complexity, we mathematically reformulated the standard SQPT problem, which we term the modified SQPT (MSQPT) method. We designed the generalized quantum circuit to prepare the required set of input states and formulated an efficient measurement strategy aimed at minimizing the experimental cost of SQPT. We experimentally demonstrated the MSQPT protocol on the IBM QX2 cloud quantum processor and selectively characterized various two- and three-qubit quantum gates.
An efficient self-attention network for skeleton-based action recognition

There has been significant progress in skeleton-based action recognition. Human skeleton can be naturally structured into graph, so graph convolution networks have become the most popular method in this task. Most of these state-of-the-art methods optimized the structure of human skeleton graph to obtain better performance. Based on these advanced algorithms, a simple but strong network is proposed with three major contributions. Firstly, inspired by some adaptive graph convolution networks and non-local blocks, some kinds of self-attention modules are designed to exploit spatial and temporal dependencies and dynamically optimize the graph structure. Secondly, a light but efficient architecture of network is designed for skeleton-based action recognition. Moreover, a trick is proposed to enrich the skeleton data with bones connection information and make obvious improvement to the performance. The method achieves 90.5% accuracy on cross-subjects setting (NTU60), with 0.89M parameters and 0.32 GMACs of computation cost. This work is expected to inspire new ideas for the field.
Multi-Fitting Detection on Transmission Line Based on Cascade Reasoning Graph Network

Aiming at the problems of complex background, diverse shapes, and object occlusion in aerial images, a cascade reasoning graph network (CRGN) is proposed for multi-fitting detection on transmission lines. First of all, for these three problems mentioned above, co-occurrence knowledge, semantic knowledge, and spatial knowledge were constructed to represent the co-relation of objects by analyzing the characteristics of the transmission line fittings. Next, the Supervised Graph Learning (SGL), Graph Attention network (GAT), and Graph Convolutional Network (GCN) were employed to reason corresponding knowledge. In addition, to generate more accurate proposals for the graph reasoning module, resampling was carried out through the cascade network. Finally, the enhanced features were fused with the original visual features to recognize and position the fittings. Test results show that CRGN can improve the detection effect of multi-fittings on the transmission line, especially for some hard-detection fittings.
Broad learning approach to Surrogate-Assisted Multi-Objective evolutionary fuzzy clustering algorithm based on reference points for color image segmentation

For some real-world problems, the objective function evaluation is time-consuming and computationally expensive in multi-objective evolutionary algorithms. Surrogate-assistance approaches can be used to estimate the objective function and solve the computationally expensive problem. Taking advantages of the flatted structure and incremental learning, broad learning system (BLS) has shown a great improvement of time efficiency on the premise of ensuring the performance on the classification and regression problems. In this paper, BLS is adopted to design a BLS surrogate-assisted evolutionary framework to boost the time efficiency of clustering-based image segmentation. A BLS-assisted multi-objective evolutionary fuzzy clustering algorithm with reference points (BLS-MOEFC) is proposed. Firstly, two fuzzy clustering objective functions integrating image region information are constructed as the optimized fitness functions for obtaining satisfactory segmentation quality. Secondly, in order to improve the optimization efficiency and reduce the computational time cost, a BLS surrogate model-assisted multi-objective evolutionary framework with reference points is designed to optimize the constructed objective functions. Thirdly, an adaptively updating strategy of parameters and a model update mechanism based on the non-dominated sorting are adopted to improve the prediction accuracy of the BLS surrogate model. Finally, a novel fuzzy clustering validity index integrating region information is designed to select an optimal solution from the final set of non-dominated solutions. Experimental results on Berkeley and Weizmann images show that the proposed algorithm behaves well in the segmentation performance and time cost.
Limited text speech synthesis with electroglottograph based on Bi-LSTM and modified Tacotron-2

Abstract This paper proposes a framework of applying only the EGG signal for speech synthesis in the limited categories of contents scenario. EGG is a sort of physiological signal which can reflect the trends of the vocal cord movement. Note that EGG’s different acquisition method contrasted with speech signals, we exploit its application in speech synthesis under the following two scenarios. (1) To synthesize speeches under high noise circumstances, where clean speech signals are unavailable. (2) To enable dumb people who retain vocal cord vibration to speak again. Our study consists of two stages, EGG to text and text to speech. The first is a text content recognition model based on Bi-LSTM, which converts each EGG signal sample into the corresponding text with a limited class of contents. This model achieves 91.12% accuracy on the validation set in a 20-class content recognition experiment. Then the second step synthesizes speeches with the corresponding text and the EGG signal. Based on modified Tacotron-2, our model gains the Mel cepstral distortion (MCD) of 5.877 and the mean opinion score (MOS) of 3.87, which is comparable with the state-of-the-art performance and achieves an improvement by 0.42 and a relatively smaller model size than the origin Tacotron-2. Considering to introduce the characteristics of speakers contained in EGG to the final synthesized speech, we put forward a fine-grained fundamental frequency modification method, which adjusts the fundamental frequency according to EGG signals and achieves a lower MCD of 5.781 and a higher MOS of 3.94 than that without modification.