Reviewerly/exHarmony huggingface dataset:
Reform of the classification and evaluation system for scientific and technological innovation talents in the intelligent age

The existing evaluation system of scientific and technological talents is insufficient in classification and evaluation, lacks clear evaluation objectives and evaluation standards, and attaches too much importance to the achievements of scientific and technological talents and lacks the development potential of scientific and technological talents. In this paper, using intelligent era information technology reform of science and technology talent assessment system, to advocate and innovation for the purpose, classification of evaluation objects, evaluation criterion of explicit, the diversification of evaluation subject, evaluation method is scientific, the diversification of evaluation methods, to effectively identify innovative science and technology talents, motivating talents of science and technology innovation power and guide the scientific and technological personnel work to lay the foundation.
Design of a Multifunctional Operating Station Based on Augmented Reality (MOSAR)

Abstract Design principles of a novel Multifunctional Operation Station (MOS) using Augmented Reality (AR) technology (MOSAR) are proposed in this paper. AR-based design allows more ergonomic remote instrument control in real time in contrast to classical instrument-centered interfaces. Another advantage is its hierarchical software structure including multiple programming interpreters. The MOSAR approach is illustrated with a remote surgical operating station that controls intelligent surgical instruments. The implementation of the Operation Station (MOS) is based on the multiplatform open-source library Tcl/Tk, and an AR extension has been developed on a Unity platform, using Vuforia SDK.
Comprehensibility of the ontological model as a characteristic of its quality

There is a tendency to combine professional knowledge within the framework of the creation of cyber-physical systems. This encourages making them available to a wide range of stakeholders. Various models of knowledge representation are considered from the standpoint of historicism and levels of community. It is proposed to add an interdisciplinary (general scientific) level to the three levels of knowledge representation according to the degree of its generality – philosophical, narrowly disciplinary (professional) and subject level (knowledge base). It is argued that any model of knowledge representation is based on the model of a formal system and its child model, the predicate calculus. A labeled graph is a visual model for representing knowledge. A generalized model that reflects the relationship between knowledge and cognition is proposed. Understanding the model of knowledge is interpreted as a means of cognition. The term of comprehensibility of the ontological model is introduced as a property that most characterizes its quality. The comprehensibility of the ontological model is divided into verbal and systemic ones. The factors influencing these components of model comprehensibility are discussed. Indicators for measureing the clarity of the ontological model are proposed.
Improving Cross-Modal Alignment in Vision Language Navigation via Syntactic Information

Vision language navigation is the task that requires an agent to navigate through a 3D environment based on natural language instructions. One key challenge in this task is to ground instructions with the current visual information that the agent perceives. Most of the existing work employs soft attention over individual words to locate the instruction required for the next action. However, different words have different functions in a sentence (e.g., modifiers convey attributes, verbs convey actions). Syntax information like dependencies and phrase structures can aid the agent to locate important parts of the instruction. Hence, in this paper, we propose a navigation agent that utilizes syntax information derived from a dependency tree to enhance alignment between the instruction and the current visual scenes. Empirically, our agent outperforms the baseline model that does not use syntax information on the Room-to-Room dataset, especially in the unseen environment. Besides, our agent achieves the new state-of-the-art on Room-Across-Room dataset, which contains instructions in 3 languages (English, Hindi, and Telugu). We also show that our agent is better at aligning instructions with the current visual information via qualitative visualizations. Code and models: https://github.com/jialuli-luka/SyntaxVLN
Activity Recognition on Subject Independent Using Machine Learning

Abstract Recent Activity Daily Living (ADL) not only tackles simple activities, but also caters to a wide range of complex activities. Although the same activity has been carried out under the same environmental conditions, the acceleration signal obtained from each subject considerably differs. This happens due to the pattern of action generated for each subject is diverse based on several aspects such as subject age, gender, emotion and personality. This project therefore compares the accuracy of various machine learning models for ADL classification. On top of that, this research work also scrutinizes the effectiveness of various feature selection methods to identify the most relevant attribute for ADL classification. As a result, Random Forest was able to achieve the highest accuracy of 83.3% on subject independent matter in ADL classification. Meanwhile, CFS Subset Evaluator is considered to be a good feature selector as it successfully selected the 8 most relevant features compared with Correlation and Information Gain Evaluator.
Literature Review on Block Chain: Technology, Principle and Development

Abstract The block chain technology enjoys a promising development prospect. As one of the most popular technologies, block chain has been heatedly studied and researched by people from all walks of life. Based on an introduction to the technical background and basic concepts of block chain, by analyzing the technical architecture and working principle of block chain, this paper systematically explains the key technologies of block chain, namely decentralization, proof of work (POW), smart contract and Ethereum, and further discusses the development of the block chain technology, 5G, the Internet of Things and the future wireless network.
Implementation of early and late fusion methods for content-based image retrieval

Content-Based Image Retrieval (CBIR) systems retrieve images from the image repository or database in which they are visually similar to the query image. CBIR plays an important role in various fields such as medical diagnosis, crime prevention, web-based searching, and architecture. CBIR consists mainly of two stages: The first is the extraction of features and the second is the matching of similarities. There are several ways to improve the efficiency and performance of CBIR, such as segmentation, relevance feedback, expansion of queries, and fusion-based methods. The literature has suggested several methods for combining and fusing various image descriptors. In general, fusion strategies are typically divided into two groups, namely early and late fusion strategies. Early fusion is the combination of image features from more than one descriptor into a single vector before the similarity computation, while late fusion refers either to the combination of outputs produced by various retrieval systems or to the combination of different rankings of similarity. In this study, a group of color and texture features is proposed to be used for both methods of fusion strategies. Firstly, an early combination of eighteen color features and twelve texture features are combined into a single vector representation and secondly, the late fusion of three of the most common distance measures are used in the late fusion stage. Our experimental results on two common image datasets show that our proposed method has good performance retrieval results compared to the traditional way of using single features descriptor and also has an acceptable retrieval performance compared to some of the state-of-the-art methods. The overall accuracy of our proposed method is 60.6% and 39.07% for Corel-1K and GHIM-10K ‎datasets, respectively.
Chinese image caption of Inceptionv4 and double-layer GRUs based on attention mechanism

Abstract In recent years, there has been a wave of research on English image caption at home and abroad. However, due to the particularity of Chinese image caption task, the research on Chinese image caption has not made good progress. In order to solve this problem, a new Chinese image caption model is implemented. Firstly, the AI challenge dataset is enhanced, and then the Chinese text data of the dataset is preprocessed by Chinese word segmentation tool word2vec. Secondly, based on the encoder-decoder framework, the image visual features are extracted by Inceptionv4 network, the attention mechanism is incorporated in the process of feature extraction and the Chinese sentences are generated by double-layer GRUs network. In the process of training, Adam is used to optimize the algorithm. Finally, A GUI interface is designed to better show the experimental effect. Experiments show that the new Chinese image caption model can automatically generate more fluent and more accurate Chinese caption sentences, and the trained model has excellent performance in many evaluation indexes.
How the SP System May Promote Sustainability in Energy Consumption in IT Systems

The SP System (SPS), referring to the SP Theory of Intelligence and its realisation as the SP Computer Model, has the potential to reduce demands for energy from IT, especially in AI applications and in the processing of big data, in addition to reductions in CO2 emissions when the energy comes from the burning of fossil fuels. The biological foundations of the SPS suggest that with further development, the SPS may approach the extraordinarily low (20 W)energy demands of the human brain. Some of these savings may arise in the SPS because, like people, the SPS may learn usable knowledge from a single exposure or experience. As a comparison, deep neural networks (DNNs) need many repetitions, with much consumption of energy, for the learning of one concept. Another potential saving with the SPS is that like people, it can incorporate old learning in new. This contrasts with DNNs where new learning wipes out old learning (‘catastrophic forgetting’). Other ways in which the mature SPS is likely to prove relatively parsimonious in its demands for energy arise from the central role of information compression (IC) in the organisation and workings of the system: by making data smaller, there is less to process; because the efficiency of searching for matches between patterns can be improved by exploiting probabilities that arise from the intimate connection between IC and probabilities; and because, with SPS-derived ’Model-Based Codings’ of data, there can be substantial reductions in the demand for energy in transmitting data from one place to another.
Exploring the predictor of innovative teaching using the job demands-resources model

Introduction . To keep in pace and remain competitive in today's environment, the lecturer must do innovation in educational process. However, encouraging lecturers' willingness to do innovative teaching is challenging in higher education due to the dual roles as teachers and researchers, which makes the excessive workload and leads to the teacher-researcher role conflict (TRC). Therefore, it is crucial to analyse the impact of TRC on the innovative teaching of lecturers. The present study utilises the job demands-resources (JDR) model due to its high popularity but rarely used in higher education. Aim . This study is aimed to explore the predictor of innovative teaching by utilising the JDR model as a theoretical anchor. Methodology and research methods . This study uses structural equation modelling (SEM) to examine the research model on a random sample of 233 respondents.Results. The results indicated that teacher-researcher role conflict negatively predicted the innovative teaching of the lecturer. Besides, the occupational well-being is a mediating variable to explain the influence of teacher-researcher role conflict on innovative teaching. Scientific novelty . This study reveals innovative teaching predictors in higher education by using the JDR model as a theoretical anchor. The authors found out that teacher-researcher role conflict (TRC) was significantly related to innovative teaching. The high-level expression of TRC will reduce the lecturer's innovative behaviour on teaching activity, and vice versa. Practical significance . The current study provides critical insight into the related stakeholders, such as the universities and related ministries, regarding the negative predictor of innovative teaching. They should discover approaches to reduce the negative effect of TRC on the innovation behaviour of lecturer teaching activity and to address the problem of job role conflict.
OF FLEXIBLE SKILLS OF YOUNG PEOPLE IN THE CONDITIONS OF SOCIAL PARTNERSHIP

The problem of social partnership as a leading mechanism for educating students ‘ flexible skills is discussed. Social partnership is understood as cooperative activities aimed at the exchange, combination and joint use of economic, material, technical and human resources resulting in educating flexible skills of the regional young people, demanded on the labor market today. It is noted that social partnership helps balance the lower role of the public and civic society institutions in the process of young people social development. It is noted that that social partnership principles are: synergy, advance, diversification and trust of social partners flexible skills of students inventiveness, ethic, sociability, independence, self motivation, ability to cooperate, activity, vigor, desire for self education, self development etc. are specified on the basis of theoretical analysis of the modern scientific literature. It is supposed that empathy and emotional intellect may be considered as students’ flexible skills. Empirical results of the study of emotional intellect formation level and empathy as key flexible skills of students are presented.
Can NLI Models Verify QA Systems' Predictions?

To build robust question answering systems, we need the ability to verify whether answers to questions are truly correct, not just "good enough" in the context of imperfect QA datasets. We explore the use of natural language inference (NLI) as a way to achieve this goal, as NLI inherently requires the premise (document context) to contain all necessary information to support the hypothesis (proposed answer to the question). We leverage large pre-trained models and recent prior datasets to construct powerful question converter and decontextualization modules, which can reformulate QA instances as premise-hypothesis pairs with very high reliability. Then, by combining standard NLI datasets with NLI examples automatically derived from QA training data, we can train NLI models to judge the correctness of QA models' proposed answers. We show that our NLI approach can generally improve the confidence estimation of a QA model across different domains, evaluated in a selective QA setting. Careful manual analysis over the predictions of our NLI model shows that it can further identify cases where the QA model produces the right answer for the wrong reason, or where the answer cannot be verified as addressing all aspects of the question.
Cross-Domain Label-Adaptive Stance Detection

Stance detection concerns the classification of a writer's viewpoint towards a target. There are different task variants, e.g., stance of a tweet vs. a full article, or stance with respect to a claim vs. an (implicit) topic. Moreover, task definitions vary, which includes the label inventory, the data collection, and the annotation protocol. All these aspects hinder cross-domain studies, as they require changes to standard domain adaptation approaches. In this paper, we perform an in-depth analysis of 16 stance detection datasets, and we explore the possibility for cross-domain learning from them. Moreover, we propose an end-to-end unsupervised framework for out-of-domain prediction of unseen, user-defined labels. In particular, we combine domain adaptation techniques such as mixture of experts and domain-adversarial training with label embeddings, and we demonstrate sizable performance gains over strong baselines -- both (i) in-domain, i.e., for seen targets, and (ii) out-of-domain, i.e., for unseen targets. Finally, we perform an exhaustive analysis of the cross-domain results, and we highlight the important factors influencing the model performance.
Personalized Teaching Model of College English Based on Big Data

Abstract With the progress of the times and the development of economy, the state and society put forward higher requirements for talents, and college students, as the main force of socialist construction, are expected to be higher. The society requires college students to have higher comprehensive quality, more and more occupations and posts put forward higher requirements for college students’ English ability, and the market economy needs more workers with excellent English ability. The international trend of education also requires college students to have excellent language skills, especially English as a second official language. With the further development of economic globalization, more and more international cooperation and more frequent cultural exchanges, society needs more English talents, and the ability to use English effectively reaches 90%, and the ability of cross-cultural communication reaches 90%. This is an important part of personal progress.
Selective kernel networks for weakly supervised relation extraction

The purpose of relation extraction is to identify the semantic relations between entities in sentences that contain two entities. Recently, many variants of the convolution neural network (CNN) have been introduced to relation extraction for the extracting of features—the quality of the neural network model directly affects the final quality of relation extraction. However, the traditional convolution network uses a fixed convolution kernel, so it is difficult to choose the size of the convolution kernel dynamically, which results in networks with weak representation ability. To address this, a novel CNN is designed with selective kernel networks and multigranularity. In the process of feature extraction, the model can adaptively select the size of the convolution kernel, that is, give more weight to the appropriate convolution kernel. It is then combined with multigranularity convolution to obtain more abundant semantic information. Finally, a new pooling method is designed to obtain more comprehensive information and improve model performance. Experimental results indicate that this method is effective without excessively deep network layers, and it also outperforms several competitive baseline methods.
Training of Future Education Managers For Professional Activities

Large-scale changes in the social and economic spheres of Russian society revealed the demands for managers with non-standard thinking and a high cultural level. Specific and ambiguous processes in politic, economic and social spheres in different parts of the world have general trends reflecting the evolution of global civilization.
Variational Autoencoder with Learned Latent Structure

The manifold hypothesis states that high-dimensional data can be modeled as lying on or near a low-dimensional, nonlinear manifold. Variational Autoencoders (VAEs) approximate this manifold by learning mappings from low-dimensional latent vectors to high-dimensional data while encouraging a global structure in the latent space through the use of a specified prior distribution. When this prior does not match the structure of the true data manifold, it can lead to a less accurate model of the data. To resolve this mismatch, we introduce the Variational Autoencoder with Learned Latent Structure (VAELLS) which incorporates a learnable manifold model into the latent space of a VAE. This enables us to learn the nonlinear manifold structure from the data and use that structure to define a prior in the latent space. The integration of a latent manifold model not only ensures that our prior is well-matched to the data, but also allows us to define generative transformation paths in the latent space and describe class manifolds with transformations stemming from examples of each class. We validate our model on examples with known latent structure and also demonstrate its capabilities on a real-world dataset.
An Efficient Video Coding System With an Adaptive Overfitted Multi-Scale Attention Network

We herein propose an efficient video coding system (EVCS) consists of a conventional codec and an adaptive overfitted multi-scale attention network (MSAN) to improve coding efficiency. At the encoder side, the MSAN adjusts the network size adaptively and is trained in an overfitting way for a group of frames. Using only the current encoding video stream as a training set, the MSAN can easily obtain powerful restoration capability. After training, the learned parameters of the MSAN are transmitted to the decoder as part of the encoded bitstream. At the decoder side, the MSAN loaded with the transmitted parameters can restore the reconstructed frames very meticulously. Compared with the high efficiency video coding (HEVC) standard, the EVCS can achieve 12.141% Bjøntegaard-Delta bitrate reduction, which outperforms existing deep learning based compressed video restoration works with less computation complexity. Moreover, the MSAN is an additional part of the conventional codec without any structure change, thereby rendering it compatible with the existing coding systems.
The Diary of Niels: Affective Engagement Through Tangible Interaction with Museum Artifacts

This paper presents a research through design exploration using tangible interactions in order to seamlessly integrate technology in a historical house museum. The study addresses a longstanding concern in museum exhibition design that interactive technologies may distract from the artifacts on display. Through an iterative design process including user studies, a co-creation workshop with museum staff and several prototypes, we developed an interactive installation called The Diary of Niels that combines physical objects, RFID sensors and an elaborate fiction in order to facilitate increased visitor engagement. Insights from the research process and user tests indicate that the integration of technology and artifacts is meaningful and engaging for users, and helps introduce museum visitors to the historic theme of the exhibition and the meaning of the artifacts. The study also points to continued challenges in integrating such hybrid experiences fully with the rest of the exhibition.