Reviewerly/exHarmony huggingface dataset:
An Improved Blockchain Consensus Protocol with Distributed Verifiable Delay Function

The difficulty adjustment algorithm (DAA) embeded in a proof of work (PoW)-based blockchain often causes a fluctuation on the block generation, which results in either no blocks for a long time or many blocks for a short time. In addition, this strategy also leads to potential attacks on the DAA (such as jumping mining attack, selfish attack, etc.). To solve these problems, we propose an improved blockchain consensus protocol which utilizes the PoW with a distributed verifiable delay function (DVDF). Our protocol can retain the proof of work mechanism to ensure that the probability of finding a suitable block. Also, With the DVDF, our protocol can ensure that all the miners perform a calculation that requires a relatively fixed time despite his/her considerable hashrate. Consequently, the fluctuation of the hashrate of the blockchain caused by the DAA can be dismissed by our scheme. Eventually, our improved consensus protocol can resist the attacks caused by this weakness. Finally, simulation experiments are proposed to support our improvements.
ONTOLOGICAL MODEL OF INTERACTION BETWEEN EDUCATIONAL CONTENT AND LEARNER IN INTELLIGENT TUTORIAL SYSTEM ARCHITECTURE

The article discusses using the educational content in the context of the changed requirements for the educational process. Organization of the modern educational process is analyzed from the point of view of the ecosystem concept, which allows the interaction of biotic (actors) and abiotic components (information technology architecture, standards, and specifications). To describe the use of educational content in the educational information system the university presents an “as-is” model in EPC notation. There have been determined the main promising functions of using educational content for implementation in information technology architecture. Based on a detailed analysis of literary sources, the concept of implementing the intellectual support for using educational content in the context of the architecture of information technologies of the university has been determined. From the perspective of creating a Personal Learning Environment and introducing intelligent means of supporting the educational process, it is proposed to implement two models in the format of ontologies: an educational content model and a learner's model. The educational content model is presented in the form of the Protégé ontology. The classes of ontology, the connections between them, and the data available in the university's information system are briefly characterized. A general approach to integrating such &#x0D; a model into the typical architecture of the university information systems is described for the subsequent development of means of intellectual support for the actors of the educational process (namely, content developers). The UML case model is used to display the approach. When con-sidering integration issues, the experience of using such specifications and standards as LOM IEEE, SCORM, TinCanApi, and the principles of modern learning and content management sys-tems was taken into account. There are described the prospects for further work, general meth-odology of further research, the first stage of which is the presented work
Analysis on the Application of Electronic Diagnosis Technology in the Components of Energy Meters in Intelligent Equipment

The gradual intelligentization of EEM (electric energy meter) is inseparable from the development of electronic information technology. Due to some unavoidable factors, the EEM will inevitably fail. The failure of the EEM is not only related to the vital interests of every household. At the same time, it will also affect the reputation of EEM manufacturers. In order to prevent this from happening, this puts forward high requirements on the reliability of smart device EEM. To strengthen the diagnosis technology of the EEM of the smart device, it is far from enough to rely on the traditional backward fault diagnosis technology. It is necessary to combine the electronic diagnosis technology. Therefore, the application of ED (electronic diagnostic) technology in the components of smart equipment watt-hour meters is the focus of this article. First, analyze the health monitoring of electronic diagnostic technology in the circuit. The results show that the average method, ripple voltage method and wavelet method in electronic diagnostic technology can effectively monitor the health of the circuit. When using the mean value method, the measured early warning time was only 86 days, and the failure time was 101 days. Secondly, it analyzes the fault diagnosis process of electronic diagnosis technology in the circuit. The results show that using wavelet analysis combined with the electronic diagnosis technology of Mahalanobis distance, a higher fault diagnosis accuracy rate can be obtained in the circuit diagnosis process. When the inductance is reduced by 0.5 times, the R resistance is short-circuited and increased by 2 times, the diagnostic rate of wavelet analysis is as high as 100%.
Research on Core Function of Adjacency Pairs Prediction Based on BERT-BIGRU-CRF∗

Based on how to predict the core functions of the antecedent and the afterword belongs to the adjacency pairs according to different speaker's turn in the process of dialogue, So as to achieve overall control of the dialogue process, The core function prediction model based on BERT-BIGRU-CRF is put forward. The inputs of the model are the different turns of the conversationalist, Firstly, the model used BERT get sentence of vectorization, then it is input into BIGRU to make the two directional encoding, Finally, CRF is used to form the mark of its core function and the output of core function by S, R, Q, A and DO adjacent is completed. Experiments show that this prediction method has better performance than the previous sequential labeling model and its accuracy and F1 score are significantly improved.
Adversarial Scan Attack against Scan Matching Algorithm for Pose Estimation in LiDAR-Based SLAM

Autonomous robots are controlled using physical information acquired by various sensors. The sensors are susceptible to physical attacks, which tamper with the observed values and interfere with control of the autonomous robots. Recently, sensor spoofing attacks targeting subsequent algorithms which use sensor data have become large threats. In this paper, we introduce a new attack against the LiDAR-based simultaneous localization and mapping (SLAM) algorithm. The attack uses an adversarial LiDAR scan to fool a pose graph and a generated map. The adversary calculates a falsification amount for deceiving pose estimation and physically injects the spoofed distance against LiDAR. The falsification amount is calculated by gradient method against a cost function of the scan matching algorithm. The SLAM algorithm generates the wrong map from the deceived movement path estimated by scan matching. We evaluated our attack on two typical scan matching algorithms, iterative closest point (ICP) and normal distribution transform (NDT). Our experimental results show that SLAM can be fooled by tampering with the scan. Simple odometry sensor fusion is not a sufficient countermeasure. We argue that it is important to detect or prevent tampering with LiDAR scans and to notice inconsistencies in sensors caused by physical attacks.
Generic Construction of Black-Box Traceable Attribute-Based Encryption

Attribute-based encryption (ABE) has been widely used to provide fine-grained access control to encrypted data in cloud computing. However, in practice, since the attributes are shared by multiple users in the ABE system, it is difficult to identify malicious users which intentionally but implicitly reveal their own ABE secret keys for some purpose. In recent years, a substantial amount of work focused on designing efficient tracing mechanisms to identify such misbehaving users on ABE. In this article, we propose an efficient generic construction of black-box traceable ABE to identify the traitors. Our construction supports (public) adaptive black-box traceability and has short keys and short ciphertexts that are independent of the number of users. Technically, our construction relies on a generic transformation from any attribute-based inner-product functional encryption (ABIPFE) scheme to black-box traceable ABE system. Furthermore, to instantiate our generic construction from standard lattices, we propose the first lattice-based ABIPFE scheme, which is proven weakly selective secure in the standard model.
Deep Learning for Self-tuning of Control systems

This paper proposes an innovative two-dimensional multi-layered deep neural network (DNN) to achieve adaptive, physics-informed, model-free and data-based control of stochastic, sensitive and highly nonlinear systems. The algorithm design exploits the DNN features of adaptive learning, inference of latent variables and time-series prediction to update the controller parameters on-the-run in real-time while compensating for the loop delays at the same time in addition to the system processing time. The proposed deep learning based self-tuning algorithm (DLSTA) is generic and can be used for online self-tuning of controller parameters in general. For the purpose of this paper, it is applied on the speed control of the brushless DC (BLDC) motor in an electric vehicle (EV) using PID controller on the front-end. The simulation results demonstrate the superiority of the proposed scheme over other conventional tuning methods.
Robot-Generated Mixed Reality Gestures Improve Human-Robot Interaction

We investigate the effectiveness of robot-generated mixed reality gestures. Our findings demonstrate how these gestures increase user effectiveness by decreasing user response time, and that robots can pair long referring expressions with mixed reality gestures without cognitively overloading users.
Leveraging Semantic Information to Facilitate the Discovery of Underserved Podcasts

Podcasts are a popular medium for rapid dissemination of information, entertainment, and casual conversations. Content aggregators are taking an increased interest in recommending podcasts to listeners to help them build larger audiences. With many podcasts released every day, many podcasts that would be of interest to listeners remain underserved by these recommendation systems. In this paper, we study variables related to podcast appeal to listeners selected at random in a large online study, in a production setting, involving more than five million recommendations. We present the results of two observational studies, which suggests that underserved podcast have the potential to grow their audiences. To mitigate the rich-get-richer effect, we propose leveraging semantic information, via means of knowledge graphs, to recommend underserved podcasts to listeners. Finally, we conduct empirical experiments that show our method is effective at recommending underserved podcasts, in comparison to baseline methods that rely on listening behavior.
A Simplified Skeleton Joints Based Approach For Human Action Recognition

The growing technological development in the field of computer vision in general, and human action recognition (HAR), in particular, have attracted increasing number of researchers from various disciplines. Amid the variety of challenges in the field of human action recognition, one of the major issues is complex modelling which requires multiple parameters leading to troublesome training which further requires heavy configuration machines for real-time recognition. Therefore, there is a need to develop a simplified method that could result in reduced complexity, without compromising the performance accuracy. In order to address the mentioned issue, this paper proposes an approach that extracts the mean, variance and median from the skeleton joint locations and directly uses them in the classification process. The system used MCAD dataset for extracting 2D skeleton features with the help of OpenPose technique, which is suitable for the extraction of skeleton features from the 2D image instead of 3D image or using a depth sensor. Henceforth, we avoid using either the RGB images or the skeleton images in the recognition process. The method shows a promising performance with an accuracy of 73.8% when tested with MCAD dataset.
A Theory of Auto-Scaling for Resource Reservation in Cloud Services

We consider a distributed server system consisting of a large number of servers, each with limited capacity on multiple resources (CPU, memory, disk, etc.). Jobs with different rewards arrive over time and require certain amounts of resources for the duration of their service. When a job arrives, the system must decide whether to admit it or reject it, and if admitted, in which server to schedule the job. The objective is to maximize the expected total reward received by the system. This problem is motivated by control of cloud computing clusters, in which, jobs are requests for Virtual Machines or Containers that reserve resources for various services, and rewards represent service priority of requests or price paid per time unit of service by clients. We study this problem in an asymptotic regime where the number of servers and jobs' arrival rates scale by a factor L, as L becomes large. We propose a resource reservation policy that asymptotically achieves at least 1/2, and under certain monotone property on jobs' rewards and resources, at least 11/4 of the optimal expected reward. The policy automatically scales the number of VM slots for each job type as the demand changes, and decides in which servers the slots should be created in advance, without the knowledge of traffic rates. It effectively tracks a low-complexity greedy packing of existing jobs in the system while maintaining only a small number, g(L) = w(logL), of reserved VM slots for high priority jobs that pack well.
An Analytical Survey of Defuzzification Techniques

Defuzzification is the final unit of a fuzzy system that provides the crisp output from the fuzzy output. There are several defuzzification techniques. The selection of defuzzification technique greatly influences the outcome of a fuzzy inference system. Here, we provide a comprehensive and critical survey of significant defuzzification techniques including both traditional techniques and recently proposed techniques. Also we have pointed out the current challenges in developing an unique defuzzification technique. Finally, we conclude with a new proposal of defuzzification method for selection of appropriately and optimally suited defuzzification technique for a given application.
Interpretable Early Prediction of Lane Changes Using a Constrained Neural Network Architecture

This paper proposes an interpretable machine learning structure for the early prediction of lane changes. The interpretability relies on interpretable templates, as well as constrained weights during the training process of a neural network. It is shown, that each template is separable and interpretable by means of automatically generated rule sets. For the validation of the proposed method, a publicly available dataset is used. The architecture is compared to reference publications that apply recurrent neural networks to the task of lane change prediction. The proposed method significantly improves the maximum prediction time of the lane changes while keeping low false alarm rates.
Plan CEIBAL and the Incorporation of Digital Tools and Platforms in the Teaching of Mathematics According to the Teachers’ Perceptions

This article is based on research in which the three authors participated. The main objective is to identify, describe and analyze the practices of using digital tools and platforms for teaching mathematics in lower secondary education. Framed within a set of ICT policies promoted since 2007 by Plan CEIBAL in Uruguay, a mixed study was conducted, 176 teachers participated in a digital survey, and 15 of the participants were interviewed. Among the study’s main results, it is worth mentioning that mathematics teachers reported a low-frequency use of digital tools and platforms, and mainly an instrumental use of digital technology. It is necessary to foster and boost the development of best practices on the use of digital tools with pedagogical meaning in the context of teacher education degree courses through more prescriptive proposals that give meaning to innovation.
Topic Modeling for Multi-Aspect Listwise Comparisons

As a well-established probabilistic method, topic models seek to uncover latent semantics from plain text. In addition to having textual content, we observe that documents are usually compared in listwise rankings based on their content. For instance, world-wide countries are compared in an international ranking in terms of electricity production based on their national reports. Such document comparisons constitute additional information that reveal documents' relative similarities. Incorporating them into topic modeling could yield comparative topics that help to differentiate and rank documents. Furthermore, based on different comparison criteria, the observed document comparisons usually cover multiple aspects, each expressing a distinct ranked list. For example, a country may be ranked higher in terms of electricity production, but fall behind others in terms of life expectancy or government budget. Each comparison criterion, or aspect, observes a distinct ranking. Considering such multiple aspects of comparisons based on different ranking criteria allows us to derive one set of topics that inform heterogeneous document similarities. We propose a generative topic model aimed at learning topics that are well aligned to multi-aspect listwise comparisons. Experiments on public datasets demonstrate the advantage of the proposed method in jointly modeling topics and ranked lists against baselines comprehensively.
Learning to capture dependencies between global features of different convolution layers

NLNet has been considered as one milestone in the study of capturing long-range dependencies. Many recent studies modify the internal structure of NLNet directly and apply them to video object detection and semantic segmentation tasks. The dependencies between local and global features have been well developed, but the dependencies between global features of different convolution layers are rarely considered. Convolution is a local operation, so the global features of different convolution layers cannot be directly related, resulting in the loss of dependencies between global features. Given the vulnerability, this study designs a network that can efficiently capture the dependencies between the global features of different convolution layers, potentially further improving the accuracy. Furthermore, for the calculation of the dependency matrix, based on the Dot-product used in NLNet, we propose RELU-Dot-product, which can achieve higher accuracy. We evaluate the proposed method on image classification and object detection tasks. The data sets involved are CIFAR10, CIFAR100, Tiny-imagenet, VOC2007, VOC2012 and MS COCO. Experiments show that our method can significantly improve network performance by introducing a few parameters.
Research on improved image edge detection based on Hough transform

Line detection in images has always been one of the important research fields of artificial intelligence computer vision for image processing.Hough transform (HT) is one of the most extensive detection algorithms in image processing and machine vision applications, and has the advantages of anti-noise. However, it involves a huge amount of computation and too much memory.In view of its shortcomings, it is recommended to improve its performance. In this paper, a new detection method is cited, and the statistical probability Hough line transformation is added.The first step is to use the Canny operator to obtain the edge information of the gray image and use the improved method to detect the straight line segment in the image. This algorithm enhances the accuracy of edge detection and optimizes the extraction of the straight line. At the same time, it has the simplicity and wide applicability of Hough transform.
A Method of Recommending Physical Education Network Course Resources Based on Machine Learning Algorithms

Aiming at the problem of difficult selection of physical education online course resources, a method of recommending online course resources based on machine learning algorithms is proposed. The information recommendation model is established through the expression of a collaborative filtering algorithm and resource feedback matrix. According to the feedback score of any user on the same data resource in the project set, the interest matching degree is established by comparative analysis, and the matching degree is substituted into the cosine similarity function to calculate the similarity threshold between each item and so on, calculate the similarity threshold number of all items, select the project resource that best matches the user according to the threshold number, and complete the recommendation. The experimental results show that the recommended method of physical education network curriculum resources based on machine learning algorithm is relatively excellent in recommendation accuracy and efficiency; this method can realize the innovation of higher physical education network curriculum teaching mode.
CANN

Unsupervised domain adaptation (UDA) methods aim to transfer knowledge from a labeled source domain to an unlabeled target domain. Most existing UDA methods try to learn domain-invariant features so that the classifier trained by the source labels can automatically be adapted to the target domain. However, recent works have shown the limitations of these methods when label distributions differ between the source and target domains. Especially, in partial domain adaptation (PDA) where the source domain holds plenty of individual labels (private labels) not appeared in the target domain, the domain-invariant features can cause catastrophic performance degradation. In this paper, based on the originally favorable underlying structures of the two domains, we learn two kinds of target features, i.e., the source-approximate features and target-approximate features instead of the domain-invariant features. The source-approximate features utilize the consistency of the two domains to estimate the distribution of the source private labels. The target-approximate features enhance the feature discrimination in the target domain while detecting the hard (outlier) target samples. A novel Coupled Approximation Neural Network (CANN) has been proposed to co-train the source-approximate and target-approximate features by two parallel sub-networks without sharing the parameters. We apply CANN to three prevalent transfer learning benchmark datasets, Office-Home, Office-31, and Visda2017 with both UDA and PDA settings. The results show that CANN outperforms all baselines by a large margin in PDA and also performs best in UDA.
Emotions as an Object of Information Exchange in Digital Culture

In this article, we substantiate the factors for developing a discussion of the problem of emotions as an object of information exchange within the framework of the documentary sciences and propose directions for using the potential of the emotional function of social information in the process of interaction between a specialist in the field of scientific information and a user in a digital culture.