Reviewerly/exHarmony huggingface dataset:
Sememes-Based Framework for Knowledge Graph Embedding with Comprehensive-Information

The goal of knowledge graph embedding is to represent both entities and relationships as low-dimensional, dense vectors that can be used to empower other machine learning models. While most approaches concentrate on modeling the structural information of the graph, part of the work also focuses on fusing entity descriptions, allowing entities to be fused with richer semantics. However, the complex entity text descriptions contain a lot of noise, which reduces the semantic purity. Therefore, in this paper, we propose a novel sememes-based framework for knowledge graph to streamline the semantic space of entities. More specifically, We replace entity descriptions with a finite set of semantics and encode the sememe labels of entities using a pre-trained Bert model, and finally jointly learning the symbolic triples and sememe labels. The experimental results show that our method outperforms other baselines on the task of link prediction and entity classification.
A new chaotic image cryptosystem based on plaintext-associated mechanism and integrated confusion-diffusion operation

A new chaotic image cryptosystem based on plaintext-associated mechanism A new chaotic image cryptosystem based on plaintext-associated mechanism and integrated confusion-diffusion operation and integrated confusion-diffusion operation
A Minimax Probability Machine for Nondecomposable Performance Measures

Imbalanced classification tasks are widespread in many real-world applications. For such classification tasks, in comparison with the accuracy rate (AR), it is usually much more appropriate to use nondecomposable performance measures such as the area under the receiver operating characteristic curve (AUC) and the <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex>$F_β$</tex> </formula> measure as the classification criterion since the label class is imbalanced. On the other hand, the minimax probability machine is a popular method for binary classification problems and aims at learning a linear classifier by maximizing the AR, which makes it unsuitable to deal with imbalanced classification tasks. The purpose of this article is to develop a new minimax probability machine for the <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex>$F_β$</tex> </formula> measure, called minimax probability machine for the <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex>$F_β$</tex> </formula> -measures (MPMF), which can be used to deal with imbalanced classification tasks. A brief discussion is also given on how to extend the MPMF model for several other nondecomposable performance measures listed in the article. To solve the MPMF model effectively, we derive its equivalent form which can then be solved by an alternating descent method to learn a linear classifier. Further, the kernel trick is employed to derive a nonlinear MPMF model to learn a nonlinear classifier. Several experiments on real-world benchmark datasets demonstrate the effectiveness of our new model.
Safe Learning for Near-Optimal Scheduling

In this paper, we investigate the combination of synthesis, model-based learning, and online sampling techniques to obtain safe and near-optimal schedulers for a preemptible task scheduling problem. Our algorithms can handle Markov decision processes (MDPs) that have $$10^{20}$$ states and beyond which cannot be handled with state-of-the art probabilistic model-checkers. We provide probably approximately correct (PAC) guarantees for learning the model. Additionally, we extend Monte-Carlo tree search with advice, computed using safety games or obtained using the earliest-deadline-first scheduler, to safely explore the learned model online. Finally, we implemented and compared our algorithms empirically against shielded deep Q-learning on large task systems.
Beanpole. Dir. Kantemir Balagov. Moscow: Non-stop Production, 2019. 130 minutes. AR Content. Color.

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Explainable Problem in clingo-dl Programs

Research in explainable planning is becoming increasingly important as human-AI collaborations become more pervasive. An explanation is needed when the planning system’s solution does not match the human’s expectation. In this paper, we introduce the explainability problem in clingo-dl programs (XASP-D) because clingo-dl can effectively work with numerical scheduling, a problem similar to the explainable planning.
Online Inference with Multi-modal Likelihood Functions

Let $(Y_t)_{t\geq 1}$ be a sequence of i.i.d. observations and $\{f_\theta,\theta\in \mathbb{R}^d\}$ be a parametric model. We introduce a new online algorithm for computing a sequence $(\hat{\theta}_t)_{t\geq 1}$ which is shown to converge almost surely to $\text{argmax}_{\theta\in \mathbb{R}^d}\mathbb{E}[\log f_\theta(Y_1)]$ at rate $ \mathcal{O}(\log (t)^{(1+\varepsilon)/2}t^{-1/2})$, with $\varepsilon>0$ a user specified parameter. This convergence result is obtained under standard conditions on the statistical model and, most notably, we allow the mapping $\theta\mapsto \mathbb{E}[\log f_\theta(Y_1)]$ to be multi-modal. However, the computational cost to process each observation grows exponentially with the dimension of $\theta$, which makes the proposed approach applicable to low or moderate dimensional problems only. We also derive a version of the estimator $\hat{\theta}_t$ which is well suited to Student-t linear regression models. The corresponding estimator of the regression coefficients is robust to the presence of outliers, as shown by experiments on simulated and real data, and thus, as a by-product of this work, we obtain a new online and adaptive robust estimation method for linear regression models.
Dynamic Gesture Recognition Method Based on Millimeter-wave Radar by One-Dimensional Series Neural Network

For the most of the existing gesture recognition methods based on the radar sensor, the parameters such as the distance, Doppler, and angle are estimated using the radar echo at first. And then the obtained data spectra are inputted into the convolutional neural networks to classify the gestures. The implementation process is complicated. A dynamic gesture recognition method is proposed based on the millimeter-wave radar using the One-Dimensional Series connection Neural Networks (1D-ScNN) in this paper. Firstly, the original echo of dynamic gesture is obtained by the millimeter-wave radar. The gesture features are extracted by the one-dimensional convolution and pooling operations, and then are inputted into the one-dimensional inception v3 structure. In order to aggregate the one-dimensional features, the Long Short-Term Memory (LSTM) modular is connected to the end of the network. The inter-frame correlation of dynamic gestures echo is fully utilized to improve the recognition accuracy and the convergence speed of training. The experimental results show that the proposed method is simple in implementation and has a fast convergence speed. The classification accuracy can reach more than 96.0%, which is higher than the traditional gesture classification methods.
Combination of Biosignal and Head Movement to Control Robot Manipulator

A proactive approach is needed to develop the right technology for disabled people to carry out their daily activities. Therefore, in this study, we propose an alternative method of controlling a robot manipulator by combining biosignal, EOG from eyes activity, and EMG from jaw contraction operation. Head movement is also considered in the control system by detecting its movement using a flex sensor. A fuzzy logic algorithm is used to determine the direction and distance in horizontal gaze motion. Experiments involving fifteen volunteers were conducted. Data were obtained from picked and placed tasks in which the volunteers need to repeat the tasks three times. The result showed that the combination of biosignals and head movement can be employed to operate the robot with minimum error. This research provides alternative ways to control the robot manipulator by combining the signal inputs.
New View of Expanding Perspectives in the Geosciences

Earth and environmental sciences have some of the least diverse racial and ethnic representation in academia. To face profound future challenges, the fields need to address the inequities of the past and how they inform the present.
Fingerspelling Identification For American Sign Language Based On Resnet-18

Sign language as the main communication channel for deaf and hearing people, plays a very important role in daily life.With the rapid development of the field of deep learning, the field of sign language recognition has ushered in new opportunities.Aiming at the small number of sign language samples and low detection accuracy, an American sign language detection method based on Resnet-18 and data augmentation is proposed.First, the sign language picture is adjusted to 64×64 size using the filling method, and then the data is augmented by methods such as chromaticity change, rotation, and noise addition to increase the diversity of data samples and improve the robustness of the detection method.The experimental results show that the accuracy of the sign language recognition method based on Resnet-18 can reach 99%, which provides a new method for sign language recognition.
Piloting a Community of Student Data Consultants that Supports and Enhances Research Data Services

Research ecosystems within university environments are continuously evolving and requiring more resources and domain specialists to assist with the data lifecycle. Typically, academic researchers and professionals are overcommitted, making it challenging to be up-to-date on recent developments in best practices of data management, curation, transformation, analysis, and visualization. Recently, research groups, university core centers, and Libraries are revitalizing these services to fill in the gaps to aid researchers in finding new tools and approaches to make their work more impactful, sustainable, and replicable. In this paper, we report on a student consultation program built within the University Libraries, that takes an innovative, student-centered approach to meeting the research data needs in a university environment while also providing students with experiential learning opportunities. This student program, DataBridge, trains students to work in multi-disciplinary teams and as student consultants to assist faculty, staff, and students with their real-world, data-intensive research challenges. Centering DataBridge in the Libraries allows students the unique opportunity to work across all disciplines, on problems and in domains that some students may not interact with during their college careers. To encourage students from multiple disciplines to participate, we developed a scaffolded curriculum that allows students from any discipline and skill level to quickly develop the essential data science skill sets and begin contributing their own unique perspectives and specializations to the research consultations. These students, mentored by Informatics faculty in the Libraries, provide research support that can ultimately impact the entire research process. Through our pilot phase, we have found that DataBridge enhances the utilization and openness of data created through research, extends the reach and impact of the work beyond the researcher’s specialized community, and creates a network of student “data champions” across the University who see the value in working with the Library. Here, we describe the evolution of the DataBridge program and outline its unique role in both training the data stewards of the future with regard to FAIR data practices, and in contributing significant value to research projects at Virginia Tech. Ultimately, this work highlights the need for innovative, strategic programs that encourage and enable real-world experience of data curation, data analysis, and data publication for current researchers, all while training the next generation of researchers in these best practices.
Boundary-Sensitive Approach for Approximate Nearest-Neighbor Classification.

The problem of nearest-neighbor classification is a fundamental technique in machine-learning. Given a training set P of n labeled points in ℝ^d, and an approximation parameter 0 < e ≤ 1/2, any unlabeled query point should be classified with the class of any of its e-approximate nearest-neighbors in P. Answering these queries efficiently has been the focus of extensive research, proposing techniques that are mainly tailored towards resolving the more general problem of e-approximate nearest-neighbor search. While the latest can only hope to provide query time and space complexities dependent on n, the problem of nearest-neighbor classification accepts other parameters more suitable to its analysis. Such is the number k_e of e-border points, which describes the complexity of boundaries between sets of points of different classes.This paper presents a new data structure called Chromatic AVD. This is the first approach for e-approximate nearest-neighbor classification whose space and query time complexities are only dependent on e, k_e and d, while being independent on both n and Δ, the spread of P.
INFIN: An Efficient Algorithm for Fast Mining Frequent Itemsets

The negFIN is the current state-of-the art algorithm for frequent itemsets mining. It employs a novel BMC (bitmap code) encoding model for nodes in a prefix tree based on the bitmap representation of sets. The encoding of each node is a binary number of which bit number is the number of frequent items, and is stored in the form of decimal integer number. The key operations of negFIN are all performed based on the bitwise operation of the encoding. The main problem of BMC is that the maximal bit number of the data type which is used to store the decimal integer number in current general compiling systems is 64, so if the number of frequent items exceeds 64, the encoding cannot work effectively. In this work, we propose B-BMC (block bitmap code) encoding model, a more efficient encoding model. The B-BMC is a dividing of BMC based on the block size in essential. For facilitating the work of B-BMC, the B-BMC tree and TNC(terminal node code) table are devised as an alternative to the BMC tree of negFIN. Based on these two structures, we present an efficient algorithm called INFIN (improved negFIN) to mining frequent itemsets. Our experiments illustrate that the B-BMC can overcome the drawback of BMC, and the INFIN is the most efficient one in time and space when the block size takes value 64 on condition that the number of frequent items exceeds 64.
APiCS-Ligt: Towards Semantic Enrichment of Interlinear Glossed Text.

This paper presents APiCS-Ligt, an LLOD version of a collection of interlinear glossed linguistic examples from APiCS, the Atlas of Pidgin and Creole Language Structures. Interlinear glossed text (IGT) plays an important role in typological and theoretical linguistic research, especially with understudied and endangered languages: It provides a way to understand linguistic phenomena without necessarily knowing the source language which is crucial for these languages since native speakers are not always easily accessible.Previously, we presented Ligt, RDF vocabulary created for representing interlinear glosses in text segments. In this paper, we present our conversion of the APiCS IGT dataset into this model and describe our efforts in linking linguistic annotations to an external ontology to add semantic representation.
A New Test Method for Zinc Oxide Arrester with Gap in Power Transmission Wires

At present, the zinc oxide arrester with gap is widely used on 220kV transmission wires, because preventive tests should be carried out on the conventional zinc oxide arrester without gap every year before the thunderstorm season, which will judge whether the arrester can work safely. It is not necessary to carry out preventive tests because of the absence of working voltage and transient overvoltage for the zinc oxide arrester with gap, and thus a lot of manpower and material resources will be saved. However, according to the investigation of the working condition of the zinc oxide arrester with gap, it is found that some of the arresters with gap have got damp and lose the hydrophobicity of the silicone rubber umbrella sleeve. In order to prevent the gap zinc oxide arrester from out of action, it is proved by theoretical analysis and practical test that it is feasible to study a new method which can be tested without dismounting the arrester.
Using Query Expansion in Manifold Ranking for Query-Oriented Multi-document Summarization

Manifold ranking has been successfully applied in query-oriented multi-document summarization. It not only makes use of the relationships among the sentences, but also the relationships between the given query and the sentences. However, the information of original query is often insufficient. So we present a query expansion method, which is combined in the manifold ranking to resolve this problem. Our method not only utilizes the information of the query term itself and the knowledge base WordNet to expand it by synonyms, but also uses the information of the document set itself to expand the query in various ways (mean expansion, variance expansion and TextRank expansion). Compared with the previous query expansion methods, our method combines multiple query expansion methods to better represent query information, and at the same time, it makes a useful attempt on manifold ranking. In addition, we use the degree of word overlap and the proximity between words to calculate the similarity between sentences. We performed experiments on the datasets of DUC 2006 and DUC2007, and the evaluation results show that the proposed query expansion method can significantly improve the system performance and make our system comparable to the state-of-the-art systems.
Solar Power System: Dual-Axis Tracker & Monitoring System for a Gateway Station using Arduino & NodeMCU

Sunlight is a non-disappearing and atmospheric-free source of renewable energy. The disadvantage of solar energy, however, it relies precisely on the strength of the sun. In order to create the utmost amount of energy, the solar panel has to be perpendicular to the source of light. When the photovoltaic panel is perpendicular to the sun, the solar energy conversion efficiency increases. Therefore, the purpose of this study is to propose an innovative method of tracking the sun using a resistor dependent on light (LDR). It is rational to conclude that the sun position indicates the points of the brightest region in the sky. The sun central location data is sent to the embedded processor to power two servo motors that can move both horizontally and vertically to track the sunlight. The IoT is used in this study purposely for monitoring the performance of the solar power system and the dual-axis solar tracker. The data helps to make the decision to plan operations, repairs, and preventive maintenance.
A Novel Two-Stage Generation Framework for Promoting the Persona-Consistency and Diversity of Responses in Neural Dialog Systems

Although quite natural for human beings to communicate based on their own personality in daily life, it is rather challenging for neural dialog systems to do the same. This is because the general dialog systems are difficult to generate diverse responses while at the same time maintaining consistent persona information. Existing methods basically focus on merely one of them, ignoring either of them will reduce the quality of dialog. In this work, we propose a two-stage generation framework to promote the persona-consistency and diversity of responses. In the first stage, we propose a persona-guided conditional variational autoencoder (persona-guided CVAE) to generate diverse responses, and the main difference when compared with general CVAE-based model is that we use additional dialog attribute to assist the latent variables to encode the effective information in the response and further use it as a guiding vector for response generation. In the second stage, we employ persona-consistency checking module and the response rewriting module to mask the inconsistent word in the generated response prototype and rewrite it to more consistent. Automatic evaluation results demonstrate that the proposed model is able to generate diverse and persona-consistent responses.
Stanje i perspektive interkulturnog dijaloga s aspekta medijske i informacijske pismenosti

This paper analyses position and perspectives of intercultural dialogue in the context of media and information literacy. Besides author's presentation of several basic elements regarding intercultural dialogue concept, the article also points out significant obstructions regarding intercultural dialogue emphasising new media. In the focus of this article are media and informational literacy, which contribute to the creation of competencies (knowledge, skills and viwes) for developing an intercultural dialogue. In modern societies across the world, media and information literacy are an important part of wider education, which immanently strengthens intercultural dialogue within the entire society, as well as better understanding and mutual respect among different members of community. This is mostly referred to children and young people that are among the most numerous users of new media.
On Sampling-Based Training Criteria for Neural Language Modeling

As the vocabulary size of modern word-based language models becomes ever larger, many sampling-based training criteria are proposed and investigated. The essence of these sampling methods is that the softmax-related traversal over the entire vocabulary can be simplified, giving speedups compared to the baseline. A problem we notice about the current landscape of such sampling methods is the lack of a systematic comparison and some myths about preferring one over another. In this work, we consider Monte Carlo sampling, importance sampling, a novel method we call compensated partial summation, and noise contrastive estimation. Linking back to the three traditional criteria, namely mean squared error, binary cross-entropy, and cross-entropy, we derive the theoretical solutions to the training problems. Contrary to some common belief, we show that all these sampling methods can perform equally well, as long as we correct for the intended class posterior probabilities. Experimental results in language modeling and automatic speech recognition on Switchboard and LibriSpeech support our claim, with all sampling-based methods showing similar perplexities and word error rates while giving the expected speedups.
Image Denoising Method based on Deep Learning using Improved U-net

Various methods, including block-matching and 3D filtering (BM3D), have been proposed for image denoising. Recently, studies on deep learning methods for image denoising have been on the rise. In this paper, we propose a new structure for a deep neural network that improves image denoising performance. Among the existing deep neural networks, we improve U-net, which is widely used for image restoration, through the inclusion of pre-processing and post-processing and by modifying each of its stages. Extensive simulations show that the proposed structure performs very well for a wide range of noise levels with a single trained parameter, and it exhibits superior image denoising performance compared to conventional deep neural networks.