Reviewerly/exHarmony huggingface dataset:
Empirical Study of Semi-Supervised Deep Fuzzy C-Mean Clustering Algorithm

Nowadays machine learning is most widely used in different applications to enhance the quality of system. In our study, we are going to propose an Empirical study of semi supervised Deep Fuzzy C-Mean clustering algorithm on different state-of-the-art pre-processing approach. However, the performance of the models depends on the quality of datasets. In this paper, we initially split our data into labeled and unlabeled data and simultaneously compare the feature between labeled and unlabeled data to extract the knowledge from unlabeled data. In second stage of feature selection and instance reduction, we apply Information Gain (IG) to conduct redundancy control and Random under sampling and Random over sampling to handle the imbalance problem. In our paper, we use the dataset (MNIST) to check the demonstration performance of our given approaches.
Mapping of TOGAF ADM and TMForum Frameworx in the Telecommunication Industry

Nowadays, the telecommunication market is developing actively and dynamically. Flexibility and agility are required, in which the alignment between business process and IT strategies is a mandatory attribute to be able to sustain in the hard competition for the open market. To allow the alignment, several companies adopt the use of an Enterprise Architecture (EA), which in the process of designing, it is necessary to use specific framework such as TOGAF based on certain best practice. In the telecommunication industry, a collaboration framework is needed due to several factors such as availability, novelty and technicality, in which one of the telecommunication associations, known as TMForum, provides frameworks, namely, eTOM. To identify the mapping between these frameworks, interviews and observations are used in this study. The secondary data source is the result of a review of publications on the implementation of EA and the use of a broader framework in the telecommunication industry such as the utilization of subcomponents of eTOM such as SID and TAM. Therefore, in this study, TOGAF will be mapped to eTOM.
A new context-aware approach for automatic Chinese poetry generation

Chinese poetry has been a favorite literary genre for thousands of years. Chinese ancient poetry is still being read and practiced, and many famous ancient Chinese poets are honored and adorned. Recently, deep learning has been widely adopted for poetry generation. In this paper, we present a new context-aware Chinese poetry generation method based on sequence-to-sequence framework. We generate a new concept called keyword team, which is a combination of all the keywords to capture the context of the Chinese poetry. Then we use the keyword, the keyword team and the previously generated lines to generate the present line in the poetry. We find that, by including keyword teams into the generation of the poetry, it can additionally perceive the keywords of preceding and succeeding lines to generate the present line, which can effectively improve the adhesion among the overall lines. The comprehensive evaluation results show that our proposed model outperforms many of the state-of-the-art poetry generation models.
DISTANCE EDUCATION - The School of Tomorrow

This paper reviews the importance and demerits of distance education and provides a short history of it - from its beginnings to the current day and how it can affect the future of students.
User Engagement Assessment for Adaptive Learning Systems

Modern trends in educational software development are associated with adaptive learning systems, i.e. systems that can personalize the user’s interaction with the system. One of the key quality indicators for educational software, which is characterized by intensive user interaction with the system, is engagement. The paper discusses applicability of the known methods of assessing engagement to adapt learning process in learning systems, as well as the metrics used by these methods and ways of interpreting them in terms of engagement. A method of combined engagement assessment is presented, which makes it possible to take into account various aspects of possible manifestation of user engagement while interacting with the learning system. Metrics, indicators and rules for their convolution are proposed that are applicable to the online assessment of engagement in adaptive learning systems. Ways of using the developed method to adaptation of the learning process in learning systems are consider.
FA-IATI: A Framework of Frequency Adaptive and Iterative Attention Interaction for Image-Text Matching

The matching relationship between language and vision, which extensively involves various fields such as search engines and social media, is a hot topic that researchers are exploring. Existing matching methods pay more attention to alignment of features and lack the reasoning of high-level semantic concepts, especially the difference in visual expression, inside the modal. Therefore, we propose a frequency adaptive and iterative attention interaction for image-text matching (FA-IATI) framework, starting from the perspective of capturing visual semantic relationships. Specifically, we adaptively aggregate low-frequency and high-frequency signals by using graph convolutional networks to enhance the contextual information between image regions. An attention interaction module generates global features through an iterative mechanism and gradually achieves semantic alignment during the aggregation of words and image regions. Experiments show that our FA-IATI model achieves the best results of 98.4% (R@10) and 94.9% (R@10) on the MS COCO dataset (using 1Ktesting) compared with the baseline model on text query and image query, respectively. Compared with other current advanced matching models, FA-IATI has superior performance and strong competitiveness.
Network Embedding For Link Prediction in Bipartite Networks

Many social networks have a bipartite nature.Link prediction in social networks has been the focus of interest for many researchers recently.Network embedding, which maps each node in the network to a low-dimensional feature vector is used to solve many problems.The aim of this study is to investigate how network embedding enhance the link prediction performance in bipartite networks.A network embedding and a supervised learning based link prediction model has been presented for bipartite networks.The input of the supervised learning model is learned embedding vectors of node pairs obtained from network embedding method.The target feature of prediction is a binary label indicating the existence or absence of a link between these node pairs.Ensemble learning algorithms have been applied for supervised link prediction.The experiments performed on two bipartite social networks built from public datasets led promising results with 0.939 and 0.974 AUC values.Random Forest models trained with embedding vectors obtained from BiNE method achieved the highest performances.
Blockchain Technology Adoption in Healthcare Sector for Challenges Posed by COVID-19

Industry operations in health care have majorly developed in the past decade, with policies gradually accepting technological changes brought by digitalization. However, healthcare's operational structure is not designed to deal with an unpredictable crisis and functional uncertainties. Therefore, these operations need a digital tool to streamline industry services/processes to render optimal results. Blockchain technology (BCT) is one of the effective tools that is a distributed ledger technology (DLT) based on a peer-to-peer network that enables storage and access to data that is shared on them. Continuous evolution and developments in this technology have established its potential to address and disrupt the healthcare sector, to mitigate risk and manage operations. This paper provides insights on implementation of BCT in the health care to optimize its performance in case of uncertain situations such as the COVID-19 pandemic. A conceptual, blockchain-enabled framework of 4As (Aggregation–Analysis–Arrangement–Action) is provided to tackle the challenges in the healthcare industries. It is evidently drawn that, blockchain application in these industries through the given framework can effectively elucidate uncertain threats and minimizes functional disparities in industry operations of healthcare sector.
Search and Reconnaissance Robot for Disaster Management

Natural disasters like earthquakes/landslides are sudden events that cause widespread destruction, major collateral damage including loss of life. The loss cannot be completely prevented but it can be reduced. People stuck in rubble/debris from the collapsed buildings can be rescued, but that operation must be performed in a very short span of time and requires the intervention of skilled personnel to avoid further risking, and the lives of the victims trapped underneath. In this paper, we present the design and control of search and reconnaissance robot (SRR)—a robot that aims at traversing on all terrains and locating the position of survivors inside the debris for quick rescue operations and cutting down on the necessity of trained manpower. The standout feature of SRR from other existing ATVs is the active articulating chassis to allow climbing and overcoming obstacles of size much greater than its wheel diameter. Module separation allows the robot to go into tight spaces where the whole body does not fit. The design of the active articulating chassis, modularity with a locking mechanism, the entire control of the vehicle and the various modes of operation are presented.
Neural networks and neurodiversity

The Photogrammetric RecordVolume 36, Issue 175 p. 192-196 Editorial Neural networks and neurodiversity Stuart I. Granshaw, Corresponding Author Stuart I. Granshaw stuart.granshaw@btinternet.com Harlech, WalesSearch for more papers by this author Stuart I. Granshaw, Corresponding Author Stuart I. Granshaw stuart.granshaw@btinternet.com Harlech, WalesSearch for more papers by this author First published: 18 September 2021 https://doi.org/10.1111/phor.12376Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume36, Issue175September 2021Pages 192-196 RelatedInformation
An Optimized Inner Product Argument with More Application Scenarios

The inner product argument is an effective tool to reduce communication complexity in many cryptographic protocols. Bootle et al. (EUROCRYPT’16) presented an inner product argument with a statement including two vector commitments to two vectors and the inner product of the two vectors equals to a public scalar. Bünz et al. (S&P’18) then presented an inner product argument with a statement including only one vector commitment to two vectors. In this paper, we first summarize the scenarios to use inner product arguments based on Bootle et al. and Bünz et al. Then we propose and implement an improved inner product argument for the same statement as Bootle et al. Our argument has a lower communication complexity of \(4\log _2n\) which improves by about 30% when \(n=8192\). Moreover, as most existing inner product argument protocols have a recursive structure, we find the most appropriate recursive round that decides a better communication complexity.
Low-Quality 3D Face Recognition with Soft Thresholding

3D face recognition has developed rapidly in the past decade for its robustness to large variations of illumination and pose. Plenty of researches in the past have been done based on high-quality 3D faces, which, however, have limitations in real applications because of expensive 3D scanners and high computational cost. Recently, low-quality 3D face recognition is attracting increasing attention because of its lower acquisition cost and faster acquisition speed, making it easier to generalize in practice. However, identity feature in low-quality 3D face data is easily damaged by massive noise, leading to very low accuracy in face recognition. To deal with this problem, we propose a trainable Soft Thresholding Module (STM) to explicitly recover 3D faces from highly noised inputs, which is different from existing methods that either use untrainable preprocessing techniques or implicitly learn robust feature representations. Experimental results show that the proposed method is effective and achieves state-of-the-art performance in low-quality 3D face recognition.
PHGNN: Position-aware Graph Neural Network for Heterogeneous Graph Embedding

Heterogeneous graph embedding is to learn node representations that preserve both heterogeneity and semantic information of a heterogeneous graph, so as to facilitate various downstream tasks, such as node classification and link prediction. Most of the existing methods process the graph structure based on the meta-path to learn low-dimensional feature vectors. However, these methods need to manually set the meta-path which requires strong prior knowledge and omit the content features of the intermediate nodes along the meta-path. In this paper, we propose a novel heterogeneous graph neural network based on node position information (PHGNN). With this method, the meta-path is no longer required, and heterogeneous information can be automatically preserved. Specifically, the proposed framework consists of two main components: content-level learning and type-level learning. Content-level learning utilizes the features information of nodes to learn the importance of the edge-type based nodes to the target node, while type-level learning focuses on the importance of different types of nodes. With the two-level mechanism, PHGNN can hierarchically aggregate multi-type information. The experiments on multiple public datasets show that compared with state-of-the-art baselines, our proposed PHGNN model achieves significant and consistent improvements.
Disentangled-based Adversarial Network for Multiplex Network Embedding

Multiplex networks contain multiple types of relations between nodes, where each relation type is modeled as one layer. In the real world, a relation type may only depend on certain attribute features of nodes. Most existing multiplex network embedding methods only focus on preserving consistent information or complementary information from multiplex networks. However, these methods ignore the dependency between node attributes and the topology of each relation. To address the problem, we propose a model called DAME (Disentangled-based Adversarial Network for Multiplex Network Embedding). We utilize generative adversarial learning to preserve the consistent and complementary information between different relation types. Meanwhile, we develop a disentangled graph convolution network (DGCN) based on disentangled learning, enabling DAME to capture the dependency between node attributes and each relation type. We conduct extensive experiments on five realworld datasets. Experimental results indicate the effectiveness of DAME on link prediction and node classification tasks.
Identification of Clickbait in Video Sharing Platforms

People nowadays spend most of their time learning or entertaining themselves on video-sharing platforms. These platforms (e.g., YouTube, Vimeo) have already begun to supplant television day by day. However, some content creators use irrelevant and eye-catching titles (clickbait) that do not correspond to the video’s original content. This video title increases video engagement or view counts and ad revenue but wastes viewers’ valuable time. These videos are spreading like a contagion in the video-sharing platforms. In this research, we have targeted to solve this issue by identifying clickbait videos by creating a dataset containing video titles (Bangla, English, and Phonetic Bangla) and numerical features. We have adopted various feature extraction techniques for proper analysis and applied Logistic Regression, Support Vector Machine, Decision Tree, and Gradient Boost algorithm to create classification models. Remarkably, both the Decision Tree and Gradient Boost models have accomplished a 99% accuracy score in detecting clickbait videos.
Evaluating Functional Suitability of Domain-Specific Languages Oriented to Create Interactions for Cognitive Training

To address problems related to people’s memory, several techniques have been developed; one of them consists of cognitive training that seeks to maintain and improve the brain’s essential executive functions. Today this technique has made use of software applications to provide such training. One way to support these applications’ development is through Domain-Specific Languages (DSLs), which has reduced focus but produces an efficient solution. However, the applications’ growth and complexity have generated systematic and specific approaches to evaluating the products developed. This study proposes to assess DSLs aimed at creating interactions for cognitive training. A quality model based on the ISO 25010 standard is presented with an evaluation method that references the ISO 25040 standard. The quality model focuses on Functional Suitability, while the evaluation method focuses on the Running the Assessment phase. The inputs generated were tested in an experimental environment. The results show that an effective, efficient, and satisfactory evaluation of DSLs within the domain can be developed from the generated quality and evaluation methods.
Implementation of tailored requirements engineering and management principles in a supplier to the oil and gas industry

Abstract This article investigates the implementation of a tailored requirements management system. Requirements management is becoming increasingly important, due to the growing complexity of umbilical systems coupled with efforts to reduce both project duration and project cost. We have investigated the use of a new system for requirements engineering and management, by interviewing stakeholders and analyzing the as‐is state of the company. Based on the findings, this paper proposes a requirements template and tailored functionalities to aid with requirements engineering and management. Results indicate that the use of requirement elicitation increased by 62 % through implementation of the proposed system. A survey with stakeholders reveals that they are positive to future implementation of the proposed system. In conclusion, the tailored requirements management system will be a step in the right direction for management of requirements and better control for members of the project teams.
Multi-Response Awareness for Retrieval-Based Conversations: Respond with Diversity via Dynamic Representation Learning

Conversational systems now attract great attention due to their promising potential and commercial values. To build a conversational system with moderate intelligence is challenging and requires big (conversational) data, as well as interdisciplinary techniques. Thanks to the prosperity of the Web, the massive data available greatly facilitate data-driven methods such as deep learning for human-computer conversational systems. In general, retrieval-based conversational systems apply various matching schema between query utterances and responses, but the classic retrieval paradigm suffers from prominent weakness for conversations: the system finds similar responses given a particular query. For real human-to-human conversations, on the contrary, responses can be greatly different yet all are possibly appropriate. The observation reveals the diversity phenomenon in conversations. In this article, we ascribe the lack of conversational diversity to the reason that the query utterances are statically modeled regardless of candidate responses through traditional methods. To this end, we propose a dynamic representation learning strategy that models the query utterances and different response candidates in an interactive way. To be more specific, we propose a Respond-with-Diversity model augmented by the memory module interacting with both the query utterances and multiple candidate responses. Hence, we obtain dynamic representations for the input queries conditioned on different response candidates. We frame the model as an end-to-end learnable neural network. In the experiments, we demonstrate the effectiveness of the proposed model by achieving a good appropriateness score and much better diversity in retrieval-based conversations between humans and computers.
Adaptive Consensus-Based Ensemble for Improved Deep Learning Inference Cost

Deep learning models are continuously improving the state-of-the-art in nearly every domain, achieving increased levels of accuracy. To sustain, however, this performance, these models have become larger and more computationally intensive at a staggering rate. Using an ensemble of deep learning models to improve the accuracy (in comparison to running a single model) is a well-known approach, but using it in real-world settings is challenging due to its exuberant inference computational cost. In this paper we present a novel method for reducing the cost associated with an ensemble of models by \(\sim \)50% on average while maintaining comparable accuracy. The method proposed is simple to implement, and is fully agnostic to the model and the problem domain. The experimental results presented demonstrate that our method can be used in a number of configurations, all of which provide a much better “performance per cost” than standard ensembles, whether using an ensemble of N instances of the same model architecture (trained from scratch each time), or an ensemble of completely different models.
Learning Optimal Primary Capsules by Information Bottleneck

Capsule network provides comparable results on several benchmark datasets with only two convolutional layers. The shortcomings of highly cost in dynamic routing and unsatisfactory performance on complex datasets are mainly caused by primary capsules, they are filled with trivial redundant features mainly because of the lack of feature compression process. We introduce a novel algorithm for exploiting better performance with limited capacity based on the information bottleneck theory, which defines optimal representation of supervised learning that it is to extract minimal sufficient statistics of the input with respect to the output. Optimal primary capsules are solved by injecting constraint variable in the representation instead of variational information bottleneck, this process can be in turn to add a regularization term to loss function. Our algorithm allows more flexible prior distribution, and it is convenient to apply and optimize. Our experiments validate that our method can make Capsule Network achieve better performance with fewer primary capsules and reduce computation complexity significantly.