Reviewerly/exHarmony huggingface dataset:
Uncertainty-Aware Policy Sampling and Mixing for Safe Interactive Imitation Learning

Teaching robots how to execute tasks through demonstrations is appealing since it sidesteps the need to explicitly specify a reward function. However, posing imitation learning as a simple supervised learning problem suffers from the well-known problem of distributional shift - the teacher will only demonstrate the optimal trajectory and therefore the learner is unable to recover if it deviates even slightly from this trajectory since it has no training data for this case. This problem has been overcome in the literature by some element of interactivity in the learning process - usually be somehow interleaving the execution of the learner and the teacher so that the teacher can demonstrate to the learner also how to recover from mistakes. In this paper, we consider the cases where the robot has the potential to do harm, and therefore safety must be imposed at every step in the learning process. We show that uncertainty is an appropriate measure of safety and that both the mixing of the policies and the data sampling procedure benefit from considering the uncertainty of both the learner and the teacher. Our method, uncertainty-aware policy sampling and mixing (UPMS), is used to teach an agent to drive down a lane with less safety violations and less queries to the teacher than state-of-the-art methods.
People with Disabilities and AR/VR Usage Trends: Overview, Estimation, Forecasts

COVID-19 has illustrated that humans should be ready for life in another dimension. Social isolation was one of the reasons that has led to increase of interest for AR/VR technologies in different areas of human activities as long as they can give the sense of being n some place. Such technologies could also be useful not only in conditions of limits due to pandemic, but also in common life as long as these technologies could help people with disabilities, for example, to visit some places. In this paper we overview current state of VR/AR, forecasts of its distribution, tendencies of the demand of this technologies and also consider different approaches in this area and we pay attention to the examples of existing solutions with application to people with disabilities.
An Empirical Study on Data Sampling for Just-in-Time Defect Prediction

In this paper, the impact of Data Sampling on Just-in-Time defect prediction is explored. We find that there is a significant negative relationship between the class imbalance ratio of the dataset and the performance of the instant software defect prediction model. Secondly although most software defect data are not as unbalanced as expected, a moderate degree of imbalance is sufficient to affect the performance of traditional learning. This means that if the training data for immediate software defects show moderate or more severe imbalances, one need not expect good defect prediction performance and the data sampling approach to balancing the training data can improve the performance of the model. Finally, the empirical approach shows that although the under-sampling method slightly improves model performance, the different sampling methods do not have a substantial impact on the evaluation of immediate software defect prediction models.
A Graph-Based Opinion Mining Approach for Reducing Information Loss and Overload in Product Reviews Analysis

Information overload is a real challenge that product designers face whiles trying to glean insight from online product reviews. Opinion summaries are limited in the richness of insight and text summarizations pose the risk of information loss for aspect based analysis. Although much effort has been spent yearly to advance the research in search, opinion analysis and text summarization, the same cannot be said for the provision of practical models and tools to leverage these advancements. In this work, we proposed a graph-based method centred on the Labelled Property Graph, sentiment analysis and text summarization. It indexes all tokens and opinions and allows for an explorative approach to aspect sentiment analysis whiles providing targeted sentence extracts for text summarizations through opinion-aware search. We show the limitations of text summarization for designers and show how our model can avoid them with expressive pattern matching and property filtering without reprocessing aspect sentiments.
Exploring Pseudo Hand-Eye Interaction on the Head-Mounted Display

Virtual and augmented reality devices and applications have enabled the user to experience a variety of simulated real-life experiences through first-person visual, auditory, and haptic feedback. However, among the numerous everyday interactions that have been emulated, the familiar interaction of touching or rubbing the eyes is yet to be explored and remains to be understood. In this paper, we aim to understand the components of natural hand-eye interaction, propose an interaction technique through a proof-of-concept prototype head-mounted display, and evaluate the user experience of the prototype through a user study. In addition, we share insights emerged from the studies with suggestions for further development of interaction techniques based on combinations of hardware and software.
Comparing Persona Analytics and Social Media Analytics for a User-Centric Task Using Eye-Tracking and Think-Aloud

We compare a data-driven persona system and an analytics system for efficiency and effectiveness for a user identification task. Findings from the 34-participant experiment show that the data-driven persona system affords faster task completion, is easier for users to engage with, and provides better user identification accuracy. Eye-tracking data indicates that the participants focus most of their attention on the persona content while focusing more on navigation features when using the analytics system. The combined results provide empirical support for the use of data-driven personas for a user identification task, which we surmise to be a result of the persona system following a user-centered design paradigm instead of an information-centered paradigm. That analytics system afforded capabilities and insights that the persona system did not suggest that the triangulation of features may lead to a better overall user understanding.
Power Blockchain Guarantee Mechanism Based on Trusted Computing

The power Internet of Things is an effective way to solve the inefficiency of traditional Internet of Things services in the era of big data. However, as the power Internet of Things is full of sensitive data, how to ensure the security of the information transmission and exchange process is an urgent problem in the power Internet of Things system. This paper combines the characteristics of blockchain and power Internet of Things to propose a power blockchain guarantee mechanism based on trusted computing for the above problems. The decentralized technology of the blockchain provides a reliable solution to the massive data and high concurrency problems in the power Internet of Things. The trusted platform module is added to the blockchain node server to ensure the credibility of the server operating environment. At the collection layer, the system designs an algorithm for real-time monitoring of malicious collection terminals based on reputation, which prevents malicious terminals from publishing malicious data. At the same time, the system detects the visitor’s access authority through integrity certification and access policy verification, ensuring the credibility of the access terminal, maintaining the safe and reliable operation of the power blockchain system in all aspects, and protecting the private data in the power blockchain.
Automated design of accurate and robust image classifiers with brain programming

Foster the mechanical design of artificial vision requires a delicate balance between high-level analytical methods and the discovery through metaheuristics of near-optimal functions working towards complex visual problems. Evolutionary computation and swarm intelligence have developed strategies that automatically design meaningful deep convolutional neural network architectures to create better image classifiers. However, these architectures have not surpassed hand-craft models working with outdated problems with datasets of icon images. Nowadays, recent concerns about deep convolutional neural networks to adversarial attacks in the form of modifications to the input image can manipulate their output to make them untrustworthy. Brain programming is a hyper-heuristic whose aim is to work at a higher level of abstraction to develop automatically artificial visual cortex algorithms for a problem domain like image classification. This work's primary goal is to employ brain programming to design an artificial visual cortex to produce accurate and robust image classifiers in two problems. We analyze the final models designed by brain programming with the assumption of fooling the system using two adversarial attacks. In both experiments, brain programming constructed artificial brain models capable of competing with hand-crafted deep convolutional neural networks without any influence in the predictions when an adversarial attack is present.
An Effective Mechanism for the Secure Transmission of Medical Images Using Compression and Public Key Encryption Mechanism

In medical image applications, the diagnostic observations of a patient are transmitted to a remote location through internet. Since the information being transmitted is really sensitive as far as the diagnosis is concerned, the security of the image is a major concern. The size of the image being transmitted is another major concern in the scenario. We propose a methodology to mitigate problems relating to both security and size of image. The proposed mechanism first compresses the image to reduce its size using a lossless compression mechanism. The compressed image is then encrypted using the well-known public key cryptography algorithm ECC to ensure its security. The encrypted cipher image is transmitted to the remote center where the image undergoes decryption and decompression mechanisms to generate the actual image. The performance analysis shows that the system works well and is effective in ensuring the security of the image.
Learning low-dimensional manifolds under the L0-norm constraint for unsupervised outlier detection

Abstract Unsupervised outlier detection without the need for clean data has attracted great attention because it is suitable for real-world problems as a result of its low data collection costs. Reconstruction-based methods are popular approaches for unsupervised outlier detection. These methods decompose a data matrix into low-dimensional manifolds and an error matrix. Then, samples with a large error are detected as outliers. To achieve high outlier detection accuracy, when data are corrupted by large noise, the detection method should have the following two properties: (1) it should be able to decompose the data under the L0-norm constraint on the error matrix and (2) it should be able to reflect the nonlinear features of the data in the manifolds. Despite significant efforts, no method with both of these properties exists. To address this issue, we propose a novel reconstruction-based method: “L0-norm constrained autoencoders (L0-AE).” L0-AE uses autoencoders to learn low-dimensional manifolds that capture the nonlinear features of the data and uses a novel optimization algorithm that can decompose the data under the L0-norm constraints on the error matrix. This novel L0-AE algorithm provably guarantees the convergence of the optimization if the autoencoder is trained appropriately. The experimental results show that L0-AE is more robust, accurate and stable than other unsupervised outlier detection methods not only for artificial datasets with corrupted samples but also artificial datasets with well-known outlier distributions and real datasets. Additionally, the results show that the accuracy of L0-AE is moderately stable to changes in the parameter of the constrained term, and for real datasets, L0-AE achieves higher accuracy than the baseline non-robustified method for most parameter values.
Towards population-based fitness landscape analysis using local optima networks

A fitness landscape describes the interaction of a search domain, a cost function on designs drawn from the domain, and a neighbourhood function defining the adjacency of designs --- induced by the optimisation method used. Fitness landscapes can be represented in a compact form as Local Optima Networks (LONs). Although research has been conducted on LONs in continuous domains, the majority of work has focused on combinatorial landscapes. LONs are often used to understand the landscape encountered by population-based search heuristics, but are usually constructed via point-based search. This paper proposes the first construction of LONs by a population-based algorithm, applied to continuous optimisation problems. We construct LONs for three benchmark functions with well-known global structure using the widely used Nelder-Mead downhill simplex algorithm, and contrast these to the LONs from a point-based approach. We also investigate the sensitivity of the LON visualisation to the downhill simplex algorithm's hyperparameters, by varying the initial step size of the simplex and the step size for connectivity of optima. Our results suggest that large initial simplex sizes fragment the landscape structure, and exclude some local optima from the fitness landscape.
Metaheuristic Optimization on Tensor-Type Solution via Swarm Intelligence and Its Application in the Profit Optimization in Designing Selling Scheme

Nature-inspired metaheuristic optimization has been widely used in many problems in industry and scientific investigations, but their applications in designing selling scheme are rare because the solution space in this kind of problems is usually high-dimensional, and their constraints are sometimes cross-dimensional. Recently, the Swarm Intelligence Based (SIB) method is proposed for problems in discrete domains, and it is widely applied in many mathematical and statistical problems that common metaheuristic methods seldom approach. In this work, we introduce an extension of the SIB method that handles solutions with many dimensions, or tensor solution in mathematics. We further speed up our method by implementing our algorithm with the use of CPU parallelization. We then apply this extended framework to real applications in designing selling scheme, showing that our proposed method helps to increase the profit of a selling scheme compared to those suggested by traditional methods.
Higher Education Evaluation System Based on NET

In order to overcome the shortcomings of the current higher education evaluation system, this paper starts with the current situation of higher education, and puts forward a new higher education evaluation system combining NET (national level, education level and comprehensive evaluation). The evaluation system refers to the 2010 higher education evaluation system of four countries, namely the United States, the United Kingdom, China and Japan. In addition, the evaluation system also constructs a system dynamics model to evaluate the education system of the four countries, and uses the linear regression method to calculate different variables, and then simulates and forecasts the data changes in the next ten years. The results show that the evaluation system can deeply study the shortcomings of the current higher education evaluation system, and can accurately predict the data changes of the higher education evaluation system in the next ten years, which undoubtedly lays a good foundation for improving the level of higher education evaluation and perfecting the higher education evaluation system in China.
Evolutionary Ontology Matching Technique with User Involvement

Ontology matching is able to identify the entity correspondences between two heterogeneous ontologies, which is an effective method to solve the data heterogeneous problem on the Semantic Web. Traditional fully-automatic ontology matching techniques suffers from the limitation of similarity measure, whose alignment’s quality can not be ensured. To overcome this drawback, in this work, an Evolutionary Ontology Matching technique with User Involvement (EOM-UI) is proposed, which utilizes both the Compact Evolutionary Algorithm and user knowledge to improve the algorithm’s performance and the alignment’s quality. In addition, an optimization model is established to formally define the ontology entity matching problem, and an efficient interacting strategy is proposed to reduce the user’s workload and maximize his working value. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s benchmark to test our proposal’s performance. The experimental results show that our approach is able to make use of the user knowledge to improve the alignment’s quality, and it also outperforms OAEI’s participants.
Identifying Chemical Reactions and Their Associated Attributes in Patents

Chemical patents are an essential source of information about novel chemicals and chemical reactions. However, with the increasing volume of such patents, mining information about these chemicals and chemical reactions has become a time-intensive and laborious endeavor. In this study, we present a system to extract chemical reaction events from patents automatically. Our approach consists of two steps: 1) named entity recognition (NER)—the automatic identification of chemical reaction parameters from the corresponding text, and 2) event extraction (EE)—the automatic classifying and linking of entities based on their relationships to each other. For our NER system, we evaluate bidirectional long short-term memory (BiLSTM)-based and bidirectional encoder representations from transformer (BERT)-based methods. For our EE system, we evaluate BERT-based, convolutional neural network (CNN)-based, and rule-based methods. We evaluate our NER and EE components independently and as an end-to-end system, reporting the precision, recall, and F 1 score. Our results show that the BiLSTM-based method performed best at identifying the entities, and the CNN-based method performed best at extracting events.
Interactive Infographics Improve Learning Outcomes in a Food Science Laboratory Exercise Environment

The Journal of Career and Technical Education is a peer-reviewed, open access, electronic journal, publishing papers on issues and trends in career and technical education. The scope of the journal includes research reports conducted in the context of academic and career/technical education integration, agricultural education, business education, technology education, family and consumer sciences, and other related disciplines. The journal seeks submissions of manuscripts based on empirical research, quantitative and qualitative, contributing to the body of knowledge about the organization, implementation, and outcomes of programs and practices in career and technical education.
Fast Manifold Ranking With Local Bipartite Graph

During the past decades, manifold ranking has been widely applied to content-based image retrieval and shown excellent performance. However, manifold ranking is computationally expensive in both graph construction and ranking learning. Much effort has been devoted to improve its performance by introducing approximating techniques. In this paper, we propose a fast manifold ranking method, namely Local Bipartite Manifold Ranking (LBMR). Given a set of images, we first extract multiple regions from each image to form a large image descriptor matrix, and then use the anchor-based strategy to construct a local bipartite graph in which a regional k-means (RKM) is proposed to obtain high quality anchors. We propose an iterative method to directly solve the manifold ranking problem from the local bipartite graph, which monotonically decreases the objective function value in each iteration until the algorithm converges. Experimental results on several real-world image datasets demonstrate the effectiveness and efficiency of our proposed method.
Investigation of Informativeness and Stability of Mel-Frequency Cepstral Coefficients Estimates Based on Voice Signal Phase Data of Authentication System User

The problems of increasing the reliability of using various resources, access to which is carried out by means of infocommunication networks, are considered. It is known that the first barrier in ensuring high reliability of access is a high-quality user authentication system. Currently preference is given to access systems based on biometric characteristics of a user. Initially, the priority was given to static biometric characteristics of a user (face image, finger papillary picture and iris). These biometric features did not meet the expectations of developers and users due to the simplicity of their forgery. At present, the main attention of developers is focused on implementation of dynamic (behavioral) biometric features of users and, first of all, voice authentication systems. Voice authentication systems have a number of significant advantages: simplicity, compactness, low cost, and a number of others. It is also important that the user's passphrase can be quickly changed and extended in the process of voice authentication. The quality indicators of voice authentication systems, like all biometric access systems, do not meet the increasing requirements. In the process of voice authentication, the amplitude-frequency spectrum of the recording materials is analyzed. The main efforts of researchers are focused on using estimates of the pitch frequency and formant frequencies associated with it, cepstral coefficients, mel-frequency cepstral coefficients, and linear prediction coefficients as a user template. Some attention is paid to decision-making procedures based on Gaussian Mixture Model, Support Vector Machine, and Hidden Markov Models or artificial neural networks. In the presented work, it is proposed to supplement the analysis of the amplitude-frequency spectrum with studies of phase data, which is currently receiving less focus in the process of voice authentication. The results of research on estimates of pitch frequency and mel-frequency cepstral coefficients based on the amplitude and phase information of the voice signal are presented. The purpose of this work is to analyze the informativeness of phase data of a voice signal, as well as to study the stability of estimates of the user's template and, first of all, mel-frequency cepstral coefficients calculated from the phase data. The studies performed have shown high informativeness and stability of the investigated estimates, which emphasizes the importance of the phase information of the voice signal for improving quality characteristics of voice authentication systems.
Novelty and MCTS

Novelty search has become a popular technique in different fields such as evolutionary computing, classical AI planning, and deep reinforcement learning. Searching for novelty instead of, or in addition to, directly maximizing the search objective, aims at avoiding dead ends and local minima, and overall improving exploration. We propose and test the integration of novelty into Monte Carlo Tree Search (MCTS), a state-of-the-art framework for online RL planning, by linearly combining value estimates with novelty scores during the selection phase of MCTS. Three different novelty measures are adapted from the literature, integrated into MCTS, and tested in four different board games. The initial results are promising and point towards potential for novelty as "online generalization for uncertainty" in more challenging search settings.