Reviewerly/exHarmony huggingface dataset:
Fusion of Natural Language and Knowledge Graph for Multi-hop Reasoning

Multi-hop reasoning has been widely studied for its important application values in the domain of intelligent search and question answering. Real-world applications are often dominated by natural language input, and it is difficult to directly model the relation semantics in natural language to fit the multi-hop reasoning model. In addition, the extremely large scale and complex structure of knowledge graphs increase the challenge. We propose a natural language and knowledge graph fusion model (NLKGF) for multi-hop reasoning. NLKGF embeds knowledge graph by fusing natural language semantics during the graph propagation process and adds a relation attention mechanism to enable entity representations to perceive the contribution of different relations. A relation path encoder is designed to encode the relation path by an improved recurrent neural network, the reasoning entity is obtained by calculating the correlation score with the natural language. We tested the performance of the NLKGF model on two datasets requiring multi-hop reasoning. The experimental results show that NLKGF beats advanced benchmark models in multi-hop reasoning tasks, which proves superiority of our model.
Linking the Lewis &amp; Short Dictionary to the LiLa Knowledge Base of Interoperable Linguistic Resources for Latin

The LiLa project aims to build a Knowledge Base of linguistic resources for Latin based on the Linked Data framework, with the goal of creating interoperability between them.To this end, LiLa integrates all types of annotation applied to a particular word/text into a common representation where all linguistic information conveyed by a specific linguistic resource becomes accessible.The recent inclusion in the Knowledge Base of information on word formation raised a number of theoretical and practical issues concerning its treatment and representation.This paper discusses such issues, detailing how they are addressed in the project, and introduces the web application to query the collection of lemmas of the Knowledge Base.A number of use-case scenarios that employ the information on word formation made available in the LiLa Knowledge Base are also presented, particularly focusing on the use of the Knowledge Base to compare the perspectives on word formation in different linguistic resources.
Case Studies of Innovators’ Marketplace on Data Jackets

Many companies and organizations have been actively engaged in efforts for businesses and scientific researches via cross-disciplinary data exchange and collaboration but it is difficult to achieve results. We believe that one of the reasons is that there is a great deal of difficulty in facilitating the matching of appropriate data and analytical methods to the problem and encouraging and managing the actions among stakeholders. Since 2013, the authors have employed DJs and the related tools to overcome those obstacles and promote innovation to various business and R&D fields. In this chapter, we introduce part of our successes with data analysis cases and tools.
MAKT: A Knowledge Tracing Model Based on Meta Path and Attention Mechanism

With the deep integration of artificial intelligence technology and education, the traditional educational pattern has changed hugely. And the adaptive learning based on automatically tracing the knowledge status of students at various stages has attracted much attention. As a key technology, knowledge tracing has become an important research. Although deep learning has been used in knowledge tracing and promoted certain performance improvement, it still has drawbacks. First, current researches consider less the explicit representation of meta path between users, exercise items and knowledge points, ignoring some of the higher-order information. Secondly, the effect of higher-order information of knowledge points on prediction is ignored. Therefore, we proposes a meta-path based four-way co-attention mechanism model MAKT to inversely infer the unobservable knowledge cognitive proficiency of learners. Based on meta path, the MAKT model integrates instance information and higher-order information between nodes to effectively enhance the representation of user, exercise item and knowledge points. The effectiveness of the model was demonstrated in tests on a real data set.
A Pipeline-Based Multimodal Military Event Argument Extraction Framework

This paper presents a winning solution for the CCKS2022 Competition of Event Argument Extraction from Open Source Multimodal Military Equipment Data. The task is to match the textual information extracted from the text corpus with the visual information from images for event argument extraction. For this aim, we introduce a pipeline-based multimodal information extraction framework consisted of three models. The first one is a global pointer model which extracts entity information from the given text corpus, the second one is a yolo model which detects the bounding boxes of entities in the images and the last one is a multimodal matcher which matches the textual information with the visual information of entities for event argument extraction. With our pipeline-based multimodal information extraction framework, we have achieved the first place in the CCKS-2022 competition with a F1-score of 54.15%.
Prediction Model of Blast Furnace Gas Flow Distribution Base on improved SSA-ELM

The blast furnace is an important part of the iron and steel production process and the stable and smooth operation of the blast furnace is an important prerequisite for blast furnace production. The distribution of the blast furnace gas flow is an important representation of the production condition of the blast furnace. The pursuit of the rationality of the gas flow distribution is of high research significance to optimise the blast furnace production operation and ensure the stable smooth running of the blast furnace. To address the weakness of the traditional gas flow prediction model, a prediction model based on the improved SSA-ELM is presented in this paper. Firstly, to solve the sparrow search optimization(SSA) easily falling into the local optimal solution, the improved SSA (RSSA) is put forward, which introduces random walk strategy and spiral search strategies. Besides, the RSSA algorithm is utilized to optimized ELM algorithm instead of random initialization. The experiment results show that the algorithm has higher accuracy and is suitable for the estimation of gas flow distribution conditions.
Temporal Correlation-Diversity Representations for Video-Based Person Re-Identification

Video-based person re-identification is a challenging task due to illuminations, occlusions, viewpoint changes, and pedestrian misalignment. Most previous works focus more on temporal correlation features, which leads to a lack of detailed information. In this paper, we emphasize the importance of keeping both correlation and diversity of multi-frame features simultaneously. Thus, we propose a Temporal Correlation-Diversity Representation (TCDR) network to enhance the representation of frame-level pedestrian features and the temporal feature aggregation abilities. Specifically, in order to capture correlated but diverse temporal features, we propose a Temporal-Guided Frame Feature Enhancement (TGFE) module, which explores the temporal correlation with a global perspective and enhances frame-level features to achieve the temporal diversity. Furthermore, we propose a Temporal Feature Integration (TFI) module to aggregate multi-frame features. Finally, we propose a novel progressive smooth loss to alleviate the influence of noisy frames. Extensive experiments show that our method achieves the state-of-the-art performance on MARS, DukeMTMC-VideoReID and LS-VID datasets.
Dropout in Russian Higher Education and Ways to Reduce it

The subject of the study is the dropout rate in higher education. The number of students who were enrolled and did not graduate from educational institutions is analysed with the goal to increase the number of graduates when number of educational institutions and students is not subject to change. An empirical method is used: documents and expert opinions are studied. Method of obtaining information: open statistical databases and relevant analytical studies. The methodology of the OECD in compiling statistical databases and the levels of education in Russia are considered. The differences between «tertiary» and «higher education» are highlighted. The position of Russia in terms of the level of literacy of the population among the OECD countries is considered: a modest indicator of the level of «higher education» of the population of Russia is noted amid a high indicator of «tertiary education». The importance of reducing the dropout rate in the context of a direct correlation between the level of «higher education» and the development of a country and society is emphasized. Main reasons for dropping out of educational institutions are considered. The novelty lies in providing the definition of the dropout rate in higher education and its target indicators. The importance of using the term «dropout» in official sources and tightening the criteria indicators used for the purpose of accreditation monitoring is substantiated. The result of the study is the proposals intended to achieve the above designated goal: to provide detailed and additional information to the applicants, to increase the flexibility of curricula and educational programs, to design and aware the target audience of the affordable educational loans etc. In conclusion the importance of further research into the dropout rate to increase the level of higher education is stated.
Model transition by means of Zachman Enterprise Architecture Framework

Success of Enterprise Resource Planning Systems transition is crucial during system conversion, upgrade, or implementation. Enterprise business architects, consultants and developers commonly participate in designing new business functions, and technological deployments. Participating experts are aware of the necessary templates, know-hows, and rules at a given company. However, they determine deployment processes and steps according to their own expertise. Available standard approaches, templates, and rules can improve the quality of the implementation. This paper introduces a novel approach that applying formal frameworks, best practices, development templates to business case studies. Adaption of generic formal approaches to an arbitrary chosen enterprise can be improved by using case studies empowered by formal approaches and templates solutions.
Reform of College Public English Teaching Management in the New Media Era

The continuous derivation of new media promotes the major colleges and universities to speed up the innovation of English teaching and management.This paper expounds the influence of new media on public English teaching, comprehensively analyzes the current situation of college English teaching in the era of new media, and thus explores and discusses the strategies of college public English teaching management reform.
Hybrid Probabilistic Forecasting of Photovoltaic Power Generation Considering Weather Conditions

Rapid growing of photovoltaic (PV) power puts forward higher requirements for PV power generation forecasting. However, the volatility and fluctuation of meteorological factors bring severe forecasting uncertainties. This paper proposes a hybrid approach integrating extreme learning machine-based quantile regression and hidden Markov model (HMEQR) for probabilistic forecasting of PV power to effectively quantify the forecasting uncertainties. The constructed hidden Markov model takes the numerical weather prediction (NWP) data as observation series and succeeds to extract the latent meteorological states for every moment, which benefits the conditional modeling of probabilistic forecasting. The extreme learning machine-based quantile regression (EQR) model is constructed under various weather conditions to produce conditional predictive quantiles for forecasting distribution description. Trained with efficient linear programming, the EQR model lowers the computational complexity and maintains satisfactory overall performances. Comprehensive case studies validate the effectiveness of the proposed HMEQR model compared with mature approaches.
T4V: Exploring Neural Network Architectures that Improve the Scalability of Neural Network Verification

This paper focuses on improving the scalability of NN verification through exploring which NN architectures lead to more scalable verification. We propose a general framework for incorporating verification scalability in the training process by identifying NN properties that improve verification and incentivizing these properties through a verification loss. One natural application of our method is robustness verification, especially using tools based on interval analysis, which have shown great promise in recent years. Specifically, we show that we can greatly reduce the approximation error of interval analysis by forcing all (or most) NNs to have the same sign. Finally, we provide an extensive evaluation on the MNIST and CIFAR-10 datasets in order to illustrate the benefit of training for verification.
What process can a university follow for open data The University of Crete case

All public bodies in Greece, including universities, are obliged to comply with the national legal framework and policy on open data. An emerging concern is how such a big and diverse organisation could develop supporting procedures from an administrative, legal and technical standpoint, that will enhance and expand the level of the provided open data related services. In this paper, we describe our experience, at the University of Crete, for tackling these requirements. In particular, (a) we detail the steps of the process that we followed, (b) we show how an Open Data Catalogue can be exploited also in the first steps of this process, (c) we describe the platform that we selected, how we organised the catalogue and the metadata selection, (d) we describe extensions that were required, (e) we motivate and describe various additional services that we developed and (f) we discuss the current status and possible next steps.
Classification model for type of stroke using kernel logistic regression

Stroke is the second leading cause of death in the world and has a high contribution to disability. Many stroke sufferers do not recognize the symptoms of stroke or do not even have knowledge related to stroke. This causes many sufferers to be late to the hospital for first aid. This can lead to even greater risk. One effort that can be done is to find out the factors that influence stroke so that it can be prevented. This study aims to create a classification model that can be used to predict the type of stroke and to find out what factors have a significant effect on the type of stroke. The method used is Kernel Logistic Regression (KLR) which is the development of Logistic Regression (LR) by using a linear combination of regularized LR. In the modeling, two scenarios for the distribution of training and testing data were also carried out, namely, scenarios 7:3 and 8:2. The results of the accuracy of the two scenarios, are 75.97% for scenario 8:2 and 73.97% for scenario 7:3. The accuracy of the KLR is 92.12% which increased by 16.15% from the LR. From the modeling results for scenario 8:2, it was found that four predictors affected the type of stroke significantly, namely cholesterol level, temperature, length of stay, and disease history.
Load balancing algorithms with cluster in cloud environment

Load balancing is one of the important aspects of cloud computing. Its main goal is to improve system performance and to reduce its cost. Cloud computing has a dedicated load balancer to attain this. Sometimes, heavy traffic may overwhelm the load balancer and disrupt it to achieve its goal. The repeated disruption has an impact on customer service and results in poor performance. The clustering technique gives a solution to this by reducing the burden on a single machine and improves system performance. In this paper, an analysis has been made to test the performance of the load balancer with and without the cluster. The previously proposed capacity-based load balancing (CBLB) and artificial bee colony_CBLB algorithms are considered during this experiment. The results are compared by applying clusters on existing load balancing algorithms. In addition to this, the performance of the ABC_CBLB with cluster is also compared with two existing cluster load balancing algorithms. The results of CBLB and ABC with cluster are better than the other comparing algorithms.
Connect Your Database to the Cloud

SQL Server runs on any platform you need, edge to cloud. Regardless of where you run SQL Server, we have seen a trend in the industry and for our customers to connect SQL Server to the cloud. Connect is a pretty broad term, so in this chapter you will learn the details of exactly how SQL Server 2022 can be connected to the cloud.
DstNet: deep spatial-temporal network for real-time action recognition and localisation in untrimmed video

Action recognition is a hot research direction of computer vision. How to deal with human action in untrimmed video in real time is a very significant challenge. It can be widely used in fields such as real-time monitoring. In this paper, we propose an end-to-end Deep Spatial-Temporal Network (DstNet) for action recognition and localisation. First of all, the untrimmed video is clipped into segments with fixed length. Then, the Convolutional 3 Dimension (C3D) network is used to extract highly dimensional features for each segment. Finally, the extracted feature sequences of several continual segments are input into Long Short-Term Memory (LSTM) network to find the intrinsic relationship among clipped segments to take action recognition and localisation simultaneously in the untrimmed video. While maintaining good accuracy, our network has the function of real-time video processing and has achieved good results in the standard evaluation performance of THUMOS14.
Improvement of Spatial Localization Accuracy in Learning-Based Patch Matching Using Anisotropic Fractal Brownian Motion Data

The exhaustive search of multiple matches in an overlapping area of two multimodal remote sensing images and the accurate localization of found matches are inherent steps to an efficient registration of these two images. A supervised approach based on convolutional neural networks can address this challenge by producing a similarity map, identifying potential matches within a preset search area and estimating a covariation matrix of their location errors. The training is based on a specially designed loss function to enforce the translational and rotational invariance of the similarity map. Using synthetic samples from anisotropic fractal Brownian motion (afBm) models of different orientation, we made the experimental finding that this loss function is biased with respect to orientation. This bias problem is then addressed by beneficially adding pure afBm data to the learning process.
Case Study Approach to Conducting and Designing Qualitative Investigation for Business Researchers

Research into social problems, problems of deviation, of control and of crisis and the like, the general subject matter to which social problems are devoted is still mainly feasible through methods which yield qualitative data. Indeed, a method of analyzing qualitative interview data can be outlined as a stage-by-stage process. This research offers a resource guide for qualitative researchers in social science investigations, discussing data collection techniques, data analysis, reporting, and the issues of validity, reliability, and ethics adopting the qualitative context. This study further provides an overview of important concepts (manifest and latent content, unit of analysis, meaning unit, condensation, abstraction, content area, code, category and theme) related to qualitative content analysis. The research would serve as a resource guide for qualitative researchers in the field of business and the social sciences, discussing data collection techniques, data analysis, reporting, and the issues of trustworthiness, dependability, credibility and ethics. Keywords: Case Studies, Manual Analysis, In-depth Interviews, Qualitative Research, Research Methodology.
Development of a platform part wearable force feedback device that can present the sensation of dropping in a VR space

In recent years, force feedback devices have been developed to improve the reality of VR experiences. However, most of the general force feedback devices target the upper limbs. By realizing force feedback to the lower limbs, it will be possible to realize an experience in VR space that cannot be achieved by force feedback to the upper limbs alone. The authors have clarified the perceptual characteristics of the human dropping sensation in order to present force feedback to the lower limbs. In this study, we developed a wearable force feedback device that can present the sensation of dropping in VR space.
The E-Learning Effect on Learning Activities during Covid-19 Pandemic in Higher Education

The study aimed to identify factors influencing learning limitations using e-learning methods during the Covid 19 pandemic. The changes in learning methods previously carried out face-to-face to online learning methods. using online learning methods there are several obstacles in using online learning methods, starting from the internet which is less stable which can cause disruption of the learning process. Technology Acceptance Model (TAM) is the model used to analyze. This research used purposive sampling as research methodology and this data collected with questionnaires and the respondents specifically chosen from students in higher education with various majors. As a result of this study, e-learning has had a significant impact on the learning process, especially in the midst of the COVID-19 pandemic. When meeting in person is not possible, e-learning becomes a solution to support the learning process in an ongoing manner.
Improving Equlibrium Optimization Algorithm Performance with Optimization to Optimization Approach

In this study, the performance of the Equilibrium Optimization (EO) algorithm has been improved with the optimization to optimization (OO) approach. While the EO algorithm has been used as the basic algorithm in the OO approach, the Stochastics Multi-parameters Divergence Optimization (SMDO) algorithm is used as the auxiliary algorithm. The proposed structure has been tested on benchmark functions. In this study, three basic parameters of the Equilibrium Optimization algorithm have been optimized separately for the benchmark functions used in the study by using the SMDO method. Although these three parameters were kept constant in the original algorithm, they have been tried to be optimized with an upper algorithm without changing the basic philosophy of EO in order to increase the current optimization performance by ensuring that they take more appropriate values in this study. Thus a strong algorithm that can produce more efficient and better results for the existing problem has been tried to be created with the optimization to optimization approach. As a result, the obtained algorithm has been tested with the most well-known benchmark functions in the literature and the results obtained have been presented in comparison with the results of the original Equilibrium Optimization algorithm.