Reviewerly/exHarmony huggingface dataset:
Stein variational gradient descent with local approximations

Bayesian computation plays an important role in modern machine learning and statistics to reason about uncertainty. A key computational challenge in Bayesian inference is to develop efficient techniques to approximate, or draw samples from posterior distributions. Stein variational gradient decent (SVGD) has been shown to be a powerful approximate inference algorithm for this issue. However, the vanilla SVGD requires calculating the gradient of the target density and cannot be applied when the gradient is unavailable or too expensive to evaluate. In this paper we explore one way to address this challenge by the construction of a local surrogate for the target distribution in which the gradient can be obtained in a much more computationally feasible manner. More specifically, we approximate the forward model using a deep neural network (DNN) which is trained on a carefully chosen training set, which also determines the quality of the surrogate. To this end, we propose a general adaptation procedure to refine the local approximation online without destroying the convergence of the resulting SVGD. This significantly reduces the computational cost of SVGD and leads to a suite of algorithms that are straightforward to implement. The new algorithm is illustrated on a set of challenging Bayesian inverse problems, and numerical experiments demonstrate a clear improvement in performance and applicability of standard SVGD.
Human-in-the-Loop Deep Reinforcement Learning with Application to Autonomous Driving

Due to the limited smartness and abilities of machine intelligence, currently autonomous vehicles are still unable to handle all kinds of situations and completely replace drivers. Because humans exhibit strong robustness and adaptability in complex driving scenarios, it is of great importance to introduce humans into the training loop of artificial intelligence, leveraging human intelligence to further advance machine learning algorithms. In this study, a real-time human-guidance-based deep reinforcement learning (Hug-DRL) method is developed for policy training of autonomous driving. Leveraging a newly designed control transfer mechanism between human and automation, human is able to intervene and correct the agent's unreasonable actions in real time when necessary during the model training process. Based on this human-in-the-loop guidance mechanism, an improved actor-critic architecture with modified policy and value networks is developed. The fast convergence of the proposed Hug-DRL allows real-time human guidance actions to be fused into the agent's training loop, further improving the efficiency and performance of deep reinforcement learning. The developed method is validated by human-in-the-loop experiments with 40 subjects and compared with other state-of-the-art learning approaches. The results suggest that the proposed method can effectively enhance the training efficiency and performance of the deep reinforcement learning algorithm under human guidance, without imposing specific requirements on participant expertise and experience.
Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach

This letter proposes a deep learning approach to detect a change in the antenna orientation of transmitter or receiver as a physical tamper attack in OFDM systems using channel state information. We treat the physical tamper attack problem as a semi-supervised anomaly detection problem and utilize a deep convolutional autoencoder (DCAE) to tackle it. The past observations of the estimated channel state information (CSI) are used to train the DCAE. Then, a post-processing is deployed on the trained DCAE output to perform the physical tamper detection. Our experimental results show that the proposed approach, deployed in an office and a hall environment, is able to detect on average 99.6% of tamper events (TPR = 99.6%) while creating zero false alarms (FPR = 0%).
Bootstrapping Semantic Segmentation with Regional Contrast

We present ReCo, a contrastive learning framework designed at a regional level to assist learning in semantic segmentation. ReCo performs semi-supervised or supervised pixel-level contrastive learning on a sparse set of hard negative pixels, with minimal additional memory footprint. ReCo is easy to implement, being built on top of off-the-shelf segmentation networks, and consistently improves performance in both semi-supervised and supervised semantic segmentation methods, achieving smoother segmentation boundaries and faster convergence. The strongest effect is in semi-supervised learning with very few labels. With ReCo, we achieve high-quality semantic segmentation models, requiring only 5 examples of each semantic class. Code is available at https://github.com/lorenmt/reco.
How emoji and word embedding helps to unveil emotional transitions during online messaging

During online chats, body-language and vocal characteristics are not part of the communication mechanism making it challenging to facilitate an accurate interpretation of feelings, emotions, and attitudes. The use of emojis to express emotional feeling is an alternative approach in these types of communication. In this project, we focus on modeling a customer's emotion in an online messaging session with a chatbot. We use Affect Control Theory (ACT) to predict emotional change during the interaction. To let the customer use emojis, we also extend the affective dictionaries used by ACT. For this purpose, we mapped Emoji2vec embedding to the affective space. Our framework can find emotional change during messaging and how a customer's reaction is changed accordingly.
Collaborative Classification Approach for Airline Tweets Using Sentiment Analysis

In the world there are so many airline services which facilitate different airline facilities for their customers. Those airline services may satisfy or may not satisfy their customers. Customers cannot express their comments immediately, so airline services provide the twitter blog to give the feedback on their services. Twitter has been increased to develop the quality of services[4]. This paper develop the different classification techniques to improve accuracy for sentiment analysis. The tweets of services are classified into three polarities such as positive, negative and neutral. Classification methods are Random forest(RF), Logistic Regression(LR), K-Nearest Neighbors(KNN), Naïve Baye’s(NB), Decision Tree(DTC), Extreme Gradient Boost(XGB), merging of (two, three and four) classification techniques with majority Voting Classifier, AdaBoost measuring the accuracy achieved by the function using 20-fold and 30-fold cross validation was compassed in the validation phase. In this paper proposes a new ensemble Bagging approach for different classifiers[10]. The metrics of sentiment analysis precision, recall, f1-score, micro average, macro average and accuracy are discovered for all above mentioned classification techniques. In addition average predictions of classifiers and also accuracy of average predictions of classifiers was calculated for getting good quality of services. The result describes that bagging classifiers achieve better accuracy than non-bagging classifiers.
Radar Jamming Effect Analysis Based on Bayesian Inference Network With Adaptive Clustering

Radar jamming effect analysis (RJEA) is important for improving the performance of radar sensors in jamming environments and one of the most popular methods for RJEA is Bayesian inference network (BIN). Notice the features in RJEA are usually continuous, so they should be discretized for BIN, where the feature discretization approach determines the node sizes and the discretization error, which further seriously affect the training time and inference precision of BIN, respectively. In this paper, we put forward a adaptive clustering approach for the features in RJEA to reduce the node sizes of BIN in the premise of keeping or even improving its inference precision. To do so, a modified K-means algorithm is designed, where the initial clustering centers are preset according to the density of samples, and the number of final clustering centers, not given in advance, is estimated with their locations simultaneously via iterative optimization. Finally, the performance of BIN with the adaptive clustering is validated in the experiments on pulse Doppler radar sensor with radial frequency noise, where 20 features are extracted for RJEA. Compared with uniform discretization, the proposed method can reduce the training time of BIN from 19.52 second to 4.39 second and improve its inference precision from 71 % to 82% simultaneously. Compared with K-means discretization, the proposed method results in similar inference precision but less training time of BIN.
Eluder Dimension and Generalized Rank.

We study the relationship between the eluder dimension for a function class and a generalized notion of rank, defined for any monotone activation $\sigma : \mathbb{R} \to \mathbb{R}$, which corresponds to the minimal dimension required to represent the class as a generalized linear model. When $\sigma$ has derivatives bounded away from $0$, it is known that $\sigma$-rank gives rise to an upper bound on eluder dimension for any function class; we show however that eluder dimension can be exponentially smaller than $\sigma$-rank. We also show that the condition on the derivative is necessary; namely, when $\sigma$ is the $\mathrm{relu}$ activation, we show that eluder dimension can be exponentially larger than $\sigma$-rank.
Better Neural Machine Translation by Extracting Linguistic Information from BERT

Adding linguistic information (syntax or semantics) to neural machine translation (NMT) has mostly focused on using point estimates from pre-trained models.Directly using the capacity of massive pre-trained contextual word embedding models such as BERT (Devlin et al., 2019) has been marginally useful in NMT because effective fine-tuning is difficult to obtain for NMT without making training brittle and unreliable.We augment NMT by extracting dense fine-tuned vector-based linguistic information from BERT instead of using point estimates.Experimental results show that our method of incorporating linguistic information helps NMT to generalize better in a variety of training contexts and is no more difficult to train than conventional Transformerbased NMT.Languages.Both train and test data are accessible through https://catalog.ldc.upenn.edu/LDC2015T11. 21We thank the anonymous reviewers for their valuable feedback on this procedure.
Research on personalized recommendation method based on social impact theory

Abstract With the rapid development of computer technology, the personalized needs of users become more and more prominent. The complexity of online learning resources and social networks lead to sparse data sets and low recommendation efficiency. In this paper, the existing collaborative filtering recommendation algorithm does not distinguish the degree of user’s trust when integrating the social impact theory. Therefore, in order to establish more accurate characteristics of user’s social interaction, this paper integrates the trust mechanism in the social impact theory, brings the establishment, dissemination and socialization factors of trust into the research scope, and introduces them into personalized recommendation in the recommendation process of recommendation model, it is used to solve the problem of low recommendation effect and low quality of recommendation system on sparse data set. Experimental results show that the proposed personalized recommendation method can improve the recommendation effect and quality on sparse data sets.
MISTIQS: An open-source software for performing quantum dynamics simulations on quantum computers

<h2>Abstract</h2> We present <i>MISTIQS</i>, a Multiplatform Software for Time-dependent Quantum Simulations. <i>MISTIQS</i> delivers end-to-end functionality for simulating the quantum many-body dynamics of systems governed by time-dependent Heisenberg Hamiltonians across multiple quantum computing platforms. It provides high-level programming functionality for generating intermediate representations of quantum circuits which can be translated into a variety of industry-standard representations. Furthermore, it offers a selection of circuit compilation and optimization methods and facilitates execution of the quantum circuits on currently available cloud-based quantum computing backends. <i>MISTIQS</i> serves as an accessible and highly flexible research and education platform, allowing a broader community of scientists and students to perform quantum many-body dynamics simulations on current quantum computers.
The compatibility dimension of quantum measurements

We introduce the notion of compatibility dimension for a set of quantum measurements: it is the largest dimension of a Hilbert space on which the given measurements are compatible. In the Schrödinger picture, this notion corresponds to testing compatibility with ensembles of quantum states supported on a subspace, using the incompatibility witnesses of Carmeli, Heinosaari, and Toigo [Phys. Rev. A 98,012126 (2018) and Phys. Rev. Lett. 122, 130402 (2019).]. We provide several bounds for the compatibility dimension using approximate quantum cloning or algebraic techniques inspired by quantum error correction. We analyze in detail the case of two orthonormal bases and, in particular, that of mutually unbiased bases.
Classification of Cryptocurrency: A Review of the Literature

Technological development has affected the global financial industry. The use of digital currency is increasingly gaining a place among the world’s population, so much so that there are 2486 types of digital currency on record. Scholars in Islamic finance as well as Fatwa institutions all over the world have delivered their religious decree concerning the digital currency; hence, most discussions about the use of the decree was only directed at Bitcoin as a medium of payment although some digital currencies have other functions, such as being utility and security tokens. Therefore, the decree concerning other digital currencies cannot apply the decree issued for Bitcoin only because each digital currency has a different conceptual framework. Hence, this study, which applied the qualitative approach and a descriptive research design, intended to analyse the classification of digital currencies according to their function and characteristics. The findings show that digital currency is classified into coins, currency, tokens, payment tokens, utility tokens and security or asset tokens. Coins function as a medium of payment and store of value that was developed using its own blockchain. Currency is a medium of exchange and can be exchanged with any form of money, including the crediting or debiting of an account. Tokens represent services, financial instruments or infrastructure that is developed using the blockchain technology of other digital currencies. Tokens are divided into three types according to their function, namely payment tokens, utility tokens and security or asset tokens. The classification of digital currencies provides a guideline for the public who wish to carry out transactions using digital currencies. It is important to ensure that transactions carried out in accordance with the syarak will help alleviate cases of fraud related to investment and sale of digital currencies.
DEVELOPMENT STEM-EDUCATION IN UKRAINE (ACCORDING TO THE RESULTS OF THE RESEARCH “THE STATE OF DEVELOPMENT STEM-EDUCATION IN UKRAINE”)

The article considers the results of the research “The state of development STEM-education in Ukraine”, which was conducted by the Department of STEM-education of Institute of education content modernization. The study was conducted by questionnaire. The toolkit is Google Forms, for all groups of respondents: regional coordinators, methodologists of city/district methodological centers, heads of secondary education institutions, teachers of subjects: computer science, mathematics, physics, biology, chemistry, technology. The number of respondents is 2,988. The questionnaires contained different types and content of questions, the analysis of the answers to which allows us to draw conclusions about the state of development of STEM education in Ukraine. The process of developing the field of STEM-education is in the following main areas: creating a legal framework, methodological and scientific-methodological basis, conducting research on the content and results of the process, acquainting teachers with the best examples of foreign and domestic innovation experience, training, support and support STEMteachers, etc. Research of the content, processes and results of STEM-education provides prompt receipt of information that reflects the changing pedagogical reality, helps to introduce innovation into the practice of the educational process, allows to draw conclusions and adjust educational activities at a certain stage. Thanks to the generalized results of research we have the opportunity to draw conclusions, make recommendations, and sometimes improve the theoretical foundations of the field of STEM education.
Rethinking Automatic Evaluation in Sentence Simplification

Automatic evaluation remains an open research question in Natural Language Generation. In the context of Sentence Simplification, this is particularly challenging: the task requires by nature to replace complex words with simpler ones that shares the same meaning. This limits the effectiveness of n-gram based metrics like BLEU. Going hand in hand with the recent advances in NLG, new metrics have been proposed, such as BERTScore for Machine Translation. In summarization, the QuestEval metric proposes to automatically compare two texts by questioning them. In this paper, we first propose a simple modification of QuestEval allowing it to tackle Sentence Simplification. We then extensively evaluate the correlations w.r.t. human judgement for several metrics including the recent BERTScore and QuestEval, and show that the latter obtain state-of-the-art correlations, outperforming standard metrics like BLEU and SARI. More importantly, we also show that a large part of the correlations are actually spurious for all the metrics. To investigate this phenomenon further, we release a new corpus of evaluated simplifications, this time not generated by systems but instead, written by humans. This allows us to remove the spurious correlations and draw very different conclusions from the original ones, resulting in a better understanding of these metrics. In particular, we raise concerns about very low correlations for most of traditional metrics. Our results show that the only significant measure of the Meaning Preservation is our adaptation of QuestEval.
Blockchain-Based Cyber Security

The term cyber security is referred to as security of information technology. Major goal of cyber security is to provide the protection of computers, networks, programs, and data and keep safe from unauthorized person and does not allow them to access or change the data and information. Cybercrime can be classified into three categories like cybercrime against individual, crime against property, and cybercrime against organization. Application development is a process that reduces human effort in doing the things manually such as ticket booking and banking. It is a new technology known as a next-generation solution to a wide variety of transactional blockchain. It is a collection of records which are called blocks, which are linked and secured using cryptography. Blockchain technology increasingly receives attention and recordkeeping problems. The core ideas behind blockchain technology emerged in the late 1980s and early 1990s by STUART HABER and W. SCOTT STORNETTA. Blockchain can be defined into four categories such as data structure, immutable, validated by distributed network and cryptography for security. There are two types of blockchain available public and private. In this chapter, we have described the problem with current system and their solution with the help of distributed system, block identifiers and different terms that is supported by blockchain technology such as smart contracts.
Code Complexity and Version History for Enhancing Hybrid Bug Localization

Software projects are not void from bugs when they are released, so the developers keep receiving bug reports that describe technical issues.The process of identifying the buggy code files that correspond to the submitted bug reports is called bug localization.Automating the bug localization process can speed up bug fixing and improve the productivity of the developers, especially with a large number of submitted bug reports.Several automatic bug localization approaches were proposed in the literature reviews which are based on the textual and /or semantic similarity among the bug reports and the source code files.Nevertheless, none of the previous approaches made use of the source code complexity despite its importance; as high complexity source code files have higher probabilities to be modified than the low complexity files and are prone to bug occurrences.To improve the accuracy of the automatic bug localization task, this paper proposes a Hybrid Bug Localization approach (HBL) that makes full use of textual and semantic features of source code files, previously fixed bug reports, in addition to the source code complexity and version history properties.The effectiveness of the proposed approach was assessed using three opensource Java projects, ZXing, SWT, and AspectJ, of different sizes.Experimental results showed that the proposed approach outperforms several state-of-the-art approaches in terms of the mean average precision (MAP) and the mean reciprocal rank (MRR) metrics.
Measuring and Improving Faithfulness of Attention in Neural Machine Translation

While the attention heatmaps produced by neural machine translation (NMT) models seem insightful, there is little evidence that they reflect a model's true internal reasoning.We provide a measure of faithfulness for NMT based on a variety of stress tests where attention weights which are crucial for prediction are perturbed and the model should alter its predictions if the learned weights are a faithful explanation of the predictions.We show that our proposed faithfulness measure for NMT models can be improved using a novel differentiable objective that rewards faithful behaviour by the model through probability divergence.Our experimental results on multiple language pairs show that our objective function is effective in increasing faithfulness and can lead to a useful analysis of NMT model behaviour and more trustworthy attention heatmaps.Our proposed objective improves faithfulness without reducing the translation quality and has a useful regularization effect on the NMT model and can even improve translation quality in some cases.
Usability Testing of Mobile Augmented Applications for Cultural Heritage – A Systematic Literature Review

This paper presents an overview of the existing literature to date regarding the usability testing of Mobile Augmented Reality applications developed for cultural heritage.A Systematic Literature Review was conducted with this purpose.Four databases were interrogated with specific keywords.Our of the 88 found research papers, only 31 met the eligibility criteria to be included in this survey.Four major usability testing methods were identified: questionnaires, focus groups, user testing and interviews.The questionnaire method was used in almost all of the studies, and only a few of them combined more usability testing methods.Nearly all papers targeted the outdoors use centered on location-based augmented reality.The perceived ease of use, usefulness and enjoyment were confirmed by a majority of the papers.The ease of use was also correlated to the ease of learning, as well as with the interest of using the application.Cultural heritage is one of the domains that can benefit greatly from the latest improvements in mobile augmented reality technologies.