Reviewerly/exHarmony huggingface dataset:
A label noise filtering and label missing supplement framework based on game theory

Labeled data is widely used in various classification tasks. However, there is a huge challenge that labels are often added artificially. Wrong labels added by malicious users will affect the training effect of the model. The unreliability of labeled data has hindered the research. In order to solve the above problems, we propose a framework of Label Noise Filtering and Missing Label Supplement (LNFS). And we take location labels in Location-Based Social Networks (LBSN) as an example to implement our framework. For the problem of label noise filtering, we first use FastText to transform the restaurant's labels into vectors, and then based on the assumption that the label most similar to all other labels in the location is most representative. We use cosine similarity to judge and select the most representative label. For the problem of label missing, we use simple common word similarity to judge the similarity of users' comments, and then use the label of the similar restaurant to supplement the missing labels. To optimize the performance of the model, we introduce game theory into our model to simulate the game between the malicious users and the model to improve the reliability of the model. Finally, a case study is given to illustrate the effectiveness and reliability of LNFS.
Seismic Intensity Estimation Using Multilayer Perceptron for Onsite Earthquake Early Warning

Automated early preventive measures are necessary to reduce secondary hazards in case of high-intensity earthquakes, as these have the potential to damage civil structures over a larger distance from the epicentre. The warning in case of a low-intensity earthquake can, however, disrupt human life and cause unnecessary losses. Hence, in this article, a Multilayer Perceptron-classifier is modeled to provide ensuing severity-based warning by predicting the possibility of the onsite intensity exceeding a pre-trained Peak Ground Acceleration threshold related to damaging intensities in the MMI scale. The supervised learning of the classifier utilized seismic features extracted from the strong-motion signal after the onset of the p-wave. A stratified differential feature-window resampling is applied, which enhanced the F-score of intensity class prediction from 79% to 91%. The trained model is realized on a Faster Than Real-Time (FTRT) simulation environment and tested with sliding windows for estimation of earthquake intensity classes for early warning, predicting with 89% accuracy. A seismic dataset consisting of around eight thousand non-earthquake seismic events from the National Capital Region, India, is generated using a fleet of developed seismic sensing nodes (SSNs) over a period of four years. The cross-dataset validation on this generated dataset showed 95.98% accuracy. A novel Factor of Early Warning (FoEW) metric is introduced to measure the effectiveness of warning by normalizing the achieved lead time with the corresponding maximum warning time. Mean FoEW exceeded 85% for the earthquakes with moderate to heavy damage potentials, i.e., MMI ≥ VI, suitable for onsite earthquake early warning.
Improving low-resource Tibetan end-to-end ASR by multilingual and multilevel unit modeling

Abstract Conventional automatic speech recognition (ASR) and emerging end-to-end (E2E) speech recognition have achieved promising results after being provided with sufficient resources. However, for low-resource language, the current ASR is still challenging. The Lhasa dialect is the most widespread Tibetan dialect and has a wealth of speakers and transcriptions. Hence, it is meaningful to apply the ASR technique to the Lhasa dialect for historical heritage protection and cultural exchange. Previous work on Tibetan speech recognition focused on selecting phone-level acoustic modeling units and incorporating tonal information but underestimated the influence of limited data. The purpose of this paper is to improve the speech recognition performance of the low-resource Lhasa dialect by adopting multilingual speech recognition technology on the E2E structure based on the transfer learning framework. Using transfer learning, we first establish a monolingual E2E ASR system for the Lhasa dialect with different source languages to initialize the ASR model to compare the positive effects of source languages on the Tibetan ASR model. We further propose a multilingual E2E ASR system by utilizing initialization strategies with different source languages and multilevel units, which is proposed for the first time. Our experiments show that the performance of the proposed method-based ASR system exceeds that of the E2E baseline ASR system. Our proposed method effectively models the low-resource Lhasa dialect and achieves a relative 14.2% performance improvement in character error rate (CER) compared to DNN-HMM systems. Moreover, from the best monolingual E2E model to the best multilingual E2E model of the Lhasa dialect, the system’s performance increased by 8.4% in CER.
Diffusion theory, transnational antecedents and International Standards on Auditing adoption around the world

This paper investigates the impact of four key transnational factors (i.e., cultural, educational, legal and political factors) on the diffusion and early adoption of International Standards on Auditing (ISAs). Using data for 162 countries over 1995–2014 (i.e., 3240 observations) and drawing on distinctive insights from diffusion theory, we found that common law countries tend to adopt ISAs faster than civil law countries. Our findings also show that vigorous legal enforcement and shareholder protection regimes promote the early adoption of ISAs, whereas robust judicial efficiency systems constrain the early adoption of ISAs. In addition, our results show that higher literacy rates and educational attainment simulate the earlier diffusion of ISAs. We also find that higher levels of individualism and uncertainty avoidance, but low levels of power distance cultural values, boost the early adoption of ISAs. Finally, our results report that countries with strong political stability and control of corruption tend to adopt ISAs faster than those with solid government effectiveness.
A Novel Online Education Reform Model Based on Risky Decision-Making under the Situation of Internet Plus

In the new situation of Internet plus, information technology has been widely applied in education, and hence online education has attracted wide attention from all walks of life. Today’s society is a risk society, and risk is everywhere. Online education reform is also risky, which is determined by many reasons. Some risks will cause certain losses to the online education reform, so based on risky decision-making, it is necessary to carry out online education reform under the new situation of Internet plus. At first, the risky decision-making in online education reform is analyzed, which is the risk of online education reform in risk society and the allocation logic of online education reform. Then, taking interval type-2 fuzzy logic (IT2FL) as the information environment, this study proposes the optimal risky decision-making method based on IT2FL utility functions, IT2FL entropy, and risk preference factor of online education reform to solve the multipath risky decision-making problem of online education reform. Finally, the experimental results show that, in the risky decision-making model, the decision-maker’s risk preference has an impact on the path weight and the ranking of the scheme, and the idea has a certain reference role for risky decision-making. Compared with the three benchmarks, the proposed method has the fewest ranking time with the same ranking results.
Low-Rank Subspaces for Unsupervised Entity Linking

Entity linking is an important problem with many applications.Most previous solutions were designed for settings where annotated training data is available, which is, however, not the case in numerous domains.We propose a light-weight and scalable entity linking method, EIGENTHEMES, that relies solely on the availability of entity names and a referent knowledge base.EIGENTHEMES exploits the fact that the entities that are truly mentioned in a document (the "gold entities") tend to form a semantically dense subset of the set of all candidate entities in the document.Geometrically speaking, when representing entities as vectors via some given embedding, the gold entities tend to lie in a low-rank subspace of the full embedding space.EIGENTHEMES identifies this subspace using the singular value decomposition and scores candidate entities according to their proximity to the subspace.On the empirical front, we introduce multiple strong baselines that compare favorably to (and sometimes even outperform) the existing state of the art.Extensive experiments on benchmark datasets from a variety of real-world domains showcase the effectiveness of our approach.
Perceptually Quasi-Lossless Compression of Screen Content Data Via Visibility Modeling and Deep Forecasting

Screen content data, such as computer-generated photographs, desktop sharing, remote education, video game streaming and screenshot, is one of the most popular visual information carriers in Internet of Video Things. Although lossless compression can guarantee high quality of service for these screen content based industrial applications, it also causes considerable storage space and transmission bandwidth issues. To alleviate these challenges, in this article, we present a visually quasi-lossless coding approach to control the compression distortion below <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visibility threshold</i> in the human visual system. Specifically, to better quantify the visual redundancy for screen content data, a new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visibility threshold</i> method is designed by incorporating blur sensitivity and oblique correction effects. Then, an end-to-end mapping between the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visibility threshold</i> and quality control factor is learned and represented as a deep convolutional neural network. The experimental results demonstrate that the proposed method saves the average encoding bits up to 23.15% compared with the latest scheme under the same perceptual quality.
Financial Management System for Undergraduate Innovation and Entrepreneurship Education Based on Grid Algorithm

The integration of undergraduate innovation and entrepreneurship education and financial management education is in line with the trend of deepening the reform of higher education. The two have a deep foundation of integration, which requires universities to carry out top-down integration design, and provide necessary guarantee for the implementation of the integration scheme in terms of system, organization and resources. Based on grid algorithm, it has a new development and further improves the quality of education.
Routing and secret key assignment for secure multicast services in quantum satellite networks

Multicast is an important service type in both terrestrial and space communication networks but has security issues because more than one destination node can receive information. Point-to-point quantum key distribution can be achieved end-to-end over long distances by using a quantum satellite (e.g., Micius) as a relay node. Hence, how to provide secret keys for remote multicast services and efficiently distribute secret keys between the source node and multiple destination nodes for long-distance secure multicast services (SMSs) has become a critical problem. In this paper, a quantum satellite network with a special node structure is designed to complete point-to-multipoint key distribution, and a point-to-multipoint satellite relay scheme is proposed. The weight information of the link is dynamically described by the method of auxiliary topology, and a satellite relay tree is constructed. Finally, this paper proposes a satellite relay algorithm for multicast services to route and allocate key resources to solve the problem of long-distance point-to-multipoint key relay and improve the success probability of multicast service requests. Simulation results show that the proposed algorithm can improve the success probability of SMS requests by about 51.32% in a static scenario and 17.33% in a dynamic scenario.
On efficient model selection for sparse hard and fuzzy center-based clustering algorithms

The class of center-based clustering algorithms offers methods to efficiently identify clusters in data sets, making them applicable to larger data sets. While a data set may contain several features, not all of them may be equally informative or helpful towards cluster detection. Therefore, sparse center-based clustering methods offer a way to select only those features that may be useful in identifying the clusters present in a data set. However, to automatically determine the degree to which features should be selected, these methods use the Permutation Method which involves generating and clustering multiple randomly permuted data sets, leading to much higher computation costs. In this paper, we propose an improved approach towards model selection for sparse clustering by using expressions of Bayesian Information Criterion (BIC) derived for the center-based clustering methods of k-Means and Fuzzy c-Means. The derived expressions of BIC require significantly lower computation costs, yet allow us to compare and select a suitable sparse clustering among several possible sparse partitions that may have selected different subsets of features. Experiments on synthetic and real-world data sets show that using BIC for model selection leads to remarkable improvements in the identification of sparse clusterings for both Sparse k-Means and Sparse Fuzzy c-Means.
A Programming Language for Sound Self-Adaptive Systems

The ability for systems to adapt at runtime by hot-swapping their logic, seamlessly and without any apparent interruption, allows a program to adjust its behavior to its context. Research in adaptive systems support has to date focused on the basic mechanics of hot-swapping code at runtime, with the soundness of a system after each hot-swap left to the developer to assure on a case-by-case basis. Providing this assurance in existing programming languages is sufficiently difficult that self-adaptive systems using hot-swapping remain largely untrusted for production use. In this context we study two research questions: (i) what is the general soundness principle for self-adaptive systems; and (ii) how can we embed this soundness principle in a general-purpose programming language? We answer these questions partly by theoretical analysis, and partly through developing a novel general-purpose programming language which embeds our soundness principle - allowing any module to be hot-swapped with the soundness of the wider system guaranteed.
Chatbot User Interface for Customer Relationship Management using NLP models

NLP is the most researched field. Speech-totext conversions, fake-news detection, and text summarization are the hot topics of NLP. ChatBot User Interface(UI) using NLP, allows machines to understand customers better. The aim was to use different NLP and machine learning techniques and to add ChatBot UI to guide customers or clients through the CRM software and help them whenever they get stuck. Different approaches, libraries, and algorithms like 'RASA', python's 'Chatterbot', 'Cosine similarity', and Google's embedder were used to train the model and then later compared to see which gave the best results. After that, during the deployment other 2 approaches were tried, one was fetching questions from the database and then training the model, the other was to maintain a local text document and train the model from that. The advantages and disadvantages of each approach, plus challenges and better methods for deployment is also discussed.
On Security of TrustZone-M-Based IoT Systems

Internet of Things (IoT) devices have been increasingly integrated into our daily life. However, such smart devices suffer a broad attack surface. Particularly, attacks targeting the device software at runtime are challenging to defend against if IoT devices use resource-constrained microcontrollers (MCUs). TrustZone-M, a TrustZone extension designed specifically for MCUs, is an emerging hardware security technique fortifying software security of MCU-based IoT devices. This article introduces a comprehensive security framework for IoT devices using TrustZone-M-enabled MCUs, in which device security is protected in five dimensions, i.e., hardware, boot-time software, runtime software, network, and over-the-air (OTA) update. Along developing the framework, we also present the first security analysis of potential runtime software security issues in TrustZone-M-enabled MCUs. In particular, we explore the feasibility of launching stack-based buffer overflow (BOF) attack for code injection, return-oriented programming (ROP) attack, heap-based BOF attack, format string attack, and attacks against nonsecure callable (NSC) functions in the context of TrustZone-M. We validate these attacks using SAM L11, a microchip MCU with TrustZone-M and provide defense mechanisms in the runtime software dimension of the proposed framework. The security framework is implemented with a full-fledged secure and trustworthy air quality monitoring device using SAM L11 as its MCU.
MRDFlow: Unsupervised Optical Flow Estimation Network With Multi-Scale Recurrent Decoder

Optical flow estimation is a fundamental task in computer vision and image processing. Due to the difficulty in obtaining the ground truth of flow field, unsupervised learning approaches attract more and more research interests in recent years. However, despite of their good generalization capability, unsupervised optical flow methods suffer in the scenarios with large displacement, small objects, and occlusions. In this work, we propose a novel optical flow network based on decoder with multi-scale kernels. Different from previous U-Net like or pyramidal methods, we design our network based on RAFT architecture that with a 4D correlation layer and recurrent decoder. More importantly, we incorporate three novel ideas with regard to the input, information processing and output of the update units improve the performance. Firstly, we utilize various motion-related information as input to the update units. Secondly, we propose a module of multi-scale update unit. Thirdly, for the final flow up-sampling procedure, we propose an image-guided up-sampling loss to guide the learning of up-sampling masks. Our model is trained by the occlusion-aware photometric loss, edge-aware smoothness loss, self-supervised loss, and image-guided up-sampling loss. Experimental results demonstrate that our model achieves the state-of-the-art performance on both Sintel and KITTI and outperforms other unsupervised optical flow methods remarkably.
Symbolic DNN-Tuner: A Python and ProbLog-based system for optimizing Deep Neural Networks hyperparameters

The application of deep learning models to increasingly complex contexts has led to a rise in the complexity of the models themselves. Due to this, there is an increase in the number of hyper-parameters (HPs) to be set and Hyper-Parameter Optimization (HPO) algorithms occupy a fundamental role in deep learning. Bayesian Optimization (BO) is the state-of-the-art of HPO for deep learning models. BO keeps track of past results and uses them to build a probabilistic model, building a probability density of HPs. This work aims to improve BO applied to Deep Neural Networks (DNNs) by an analysis of the results of the network on training and validation sets. This analysis is obtained by applying symbolic tuning rules, implemented in Probabilistic Logic Programming (PLP). The resulting system, called Symbolic DNN-Tuner, logically evaluates the results obtained from the training and the validation phase and, by applying symbolic tuning rules, fixes the network architecture, and its HPs, leading to improved performance. In this paper, we present the general system and its implementation. We also show its graphical interface and a simple example of execution.
A Comparative Study of Different Deep Learning Architectures for Benign-Malignant Mass Classification

Deep Learning, due to its promising performance in computer vision applications, has also seen tremendous success in medical image analysis. Thus, this paper is mainly concentrated on the study of various deep learning models in classification of masses as benign or malignant. To carry out the comparative analysis, VGG-16, Inception-V3, DenseNet-121, ResNet-50, and AlexNet are considered due to its performance in the ImageNet challenge. The evaluation is conducted on the DDSM database with hold-out method having 80:20 ratios for training and testing, respectively. The efficiency of the models are satisfactory in which VGG-16 outperforms other techniques with an area under curve (AUC) value of 0.82 in case of without augmentation while the same with data augmentation is 0.91 for DenseNet-121.
WebMap: A Concept for WebEngine Version 3.0

We introduce a concept for the Web Engine 3.0. It is based on previous research conducted in the fields of graph-based text analysis, namely, co-occurrence graphs and text representing centroids (TRC). We intend to construct an extensive static linking structure for the web, which we call WebMap, in analogy to sitemaps. Aspects of, and ideas for the intended web engine are aggregated on a comprehensive level, and open work regarding the development of algorithms and system specifications necessary for implementation are unveiled.
Enhancement of Security of Diffie-Hellman Key Exchange Protocol using RSA Cryptography.

Abstract Cryptography is related and referred to as the secured transmission of messages amongst the sender and the intended receiver by ensuring confidentiality, integrity, and authentication. Diffie – Hellman (DH) key exchange protocol is a well-known algorithm that would generate a shared secret key among the sender and the intended receiver, and the basis of cryptosystems for using public and private key for encryption and decryption process. But it is severely affected by the Man in the Middle (MITM) attack that would intercept and manipulate thus eavesdropping the shared secret key. This paper proposes a model of integrating the public-key RSA cryptography system with the DH key exchange to prevent the MITM attack. The performance of the proposed work has been compared to the DH Key Exchange algorithm as well as RSA Cryptosystem to conclude for effectiveness of the proposed model.
Wave Function Adapted Hamiltonians for Quantum Computing

The use of the variational quantum eigensolver (VQE) for quantum chemistry is one of the most promising applications for noisy intermediate-scale quantum (NISQ) devices. A major limitation is represented by the need to build compact and shallow circuit ansatzes having the variational flexibility to catch the complexity of the electronic structure problem. To alleviate this drawback, we introduce a modified VQE scheme in which the form of the molecular Hamiltonian is adapted to the circuit ansatz through an optimization procedure. Exploiting the invariance of the Hamiltonian by molecular orbital rotations, we can optimize it using gradients that can be calculated without significant computational overload. The proposed method, named Wavefunction Adapted Hamiltonian Through Orbital Rotation (WAHTOR), has been applied to small molecules in numerical state vector simulations. The results demonstrate that, at variance with standard VQE, the method is less dependent on circuit topology and less prone to be trapped into high-energy local minima. It is able to recover a significant amount of electron correlation even with only empirical ansatzes with shallow circuit depth. Noisy calculations demonstrate the robustness and feasibility of the proposed methodology and indicate the hardware requirements to effectively apply the procedure using forthcoming NISQ devices.