Reviewerly/exHarmony huggingface dataset:
GOW-Stream: A novel approach of graph-of-words based mixture model for semantic-enhanced text stream clustering

Recently, rapid growth of social networks and online news resources from Internet have made text stream clustering become an insufficient application in multiple domains (e.g.: text retrieval diversification, social event detection, text summarization, etc.) Different from traditional static text clustering approach, text stream clustering task has specific key challenges related to the rapid change of topics/clusters and high-velocity of coming streaming document batches. Recent well-known model-based text stream clustering models, such as: DTM, DCT, MStream, etc. are considered as word-independent evaluation approach which means largely ignoring the relations between words while sampling clusters/topics. It definitely leads to the decrease of overall model accuracy performance, especially for short-length text documents such as comments, microblogs, etc. in social networks. To tackle these existing problems, in this paper we propose a novel approach of graph-of-words (GOWs) based text stream clustering, called GOW-Stream. The application of common GOWs which are generated from each document batch while sampling clusters/topics can support to overcome the word-independent evaluation challenge. Our proposed GOW-Stream is promising to significantly achieve better text stream clustering performance than recent state-of-the-art baselines. Extensive experiments on multiple benchmark real-world datasets demonstrate the effectiveness of our proposed model in both accuracy and time-consuming performances.
A Study on Analysis of Satisfaction for Engineering Convergence Subject

Our world is quickly moving towards the fourth industrial revolution including mobile, big data, AI, IoT, cloud computing, VR, etc. Recently, South Korea has been emphasizing convergence education to university. Thus, university has begun doing convergence education on their own by linking major subjects and liberal arts courses or linking different departments. In this paper, we analyzed learnersâ€™ satisfaction for operating convergence education effectively to increase education satisfaction and developed convergence curriculum and convergence skills required by society. for this study, a satisfaction survey is conducted for students majoring in engineering colleges. And the studentsâ€™ experiences are collected through interviews and questionnaires for suggesting improved the convergence curriculum operation. We also did interviews and asked students about the meaning convergence education had for them, the impressions they had after taking the classes, and any opinions for further improvements. As a result of the analysis of student's satisfaction and satisfaction of convergence curriculum, it was analyzed as â€œapproximately satisfiedâ€ with 3.6. Additionally, the correlation between student satisfaction and convergence curriculum satisfaction was analyzed, and the correlation coefficient showed a significant correlation with 0.732. In other words, it can be seen that students with high-student satisfaction are also highly satisfied with the convergence curriculum. Based on the result of the research and the studentâ€™s opinions, we would like to suggest that there should be subject development that is connected to careers or job searching for senior students, and additional research of practical educational methods are also needed.
Nested Conceptual Graphs for Information Fusion Traceability

InSyTo is a toolbox of algorithms for information fusion and query relying on the conceptual graphs formalism and subgraph isomorphism search. InSyTo was used in order to develop many applications in different domains. Although the framework was used in several application domain and well received by end-users, they highlighted an urgent need for traceability within the information fusion process. We propose here an improvement of the toolbox, in order to embed traceability feature inside the fusion algorithm. The underlying conceptual graph representation of the information was extended from basic conceptual graph to Nested Typed Graphs. A lineage nested graph is added to each concept of the initial information graph, that contains it’s processing history through the several processing steps. The lineage graph contains the information concerning the initial sources of each elementary information item (concept), as well as the fusion operations that were applied on them. The main advantage of this new development is the capacity of having a trustworthy framework aware of the current observed situation, as well as the interpretations that were used to build this situation from elementary observations coming from different sources. In this paper, after presenting the context of our work, we recall of the InSyTo toolbox approach and functionalities. We then define the new information representation and operations that we proposed for a matter of traceability handling.
Private FLI: Anti-Gradient Leakage Recovery Data Privacy Architecture

While machine learning brings convenience, it also faces the issue of data privacy. For privacy issues, most researches focus on implementing homomorphic encryption or differential privacy to protect data, while ignoring the potential threats caused by the leakage of model parameters. However, a malicious attacker can still recover sensitive data information through model parameters. On the one hand, traditional methods cannot take both high accuracy and low computation time into account. On the other hand, they cannot resist the reconstruction attack from the model's parameter. In order to address this problem, this paper designs a privacy protection framework named FLI, which is inspired by public key infrastructure. In FLI, all participants and the server are trained and aggregated under one framework based on federated learning, which includes key exchange and shares with the idea of homomorphic encryption. Under the algorithm we design, the malicious adversary cannot recover effective information after obtaining the transformed parameters, while the server can still perform effective parameter aggregation. To evaluate the performance of FLI, we conduct extensive experiments. The experimental results show that the computation time is within an acceptable range while ensuring high accuracy.
Effect of Online and Offline Hybrid Teaching on Self-esteem and Self-confidence of College Students with Physical Weakness

Objective: This study aims to understand the effect of online and offline hybrid teaching on the self-esteem and self-confidence of college students with physical weakness and provide a new perspective and approach for their physical education. Method: Freshmen in the sports health care class of a university in Guangzhou were selected as experimental objects and randomly divided into an experimental group and a control group based on the single-blind method. Online and offline hybrid teaching was carried out for the experimental group, and traditional classroom teaching for the control group. Results: (1) Before the experiment, there was no difference in self-esteem and self-confidence scores of students in both groups; (2) After the experiment, the self-confidence and self-esteem of college students with physical weakness were significantly improved. Conclusion: Online and offline hybrid teaching can effectively improve the self-esteem and self-confidence of college students with physical weakness. It is of higher value to adopt the mode in the sports health care class of colleges and universities.
Deep Thompson Sampling for Length of Stay Prediction

Length of stay (LoS) in the intensive care unit (ICU) is a general outcome measure used as an indicator of both quality of care and resource use. Existing LoS prediction methods usually model the problem as a binary classification or regression problem. This paper proposes to address this problem under a sequential learning framework using an embedded representation of patients. First, we exploit electronic health records (EHR) to learn patient distributed representation. Second, we formulate the LoS prediction task as a Deep Bayesian contextual bandit problem, which sequentially predicts LoS based on contextual distributed information about patients, with the goal of maximizing overall prediction outcomes. Experimental results show that our model has superior performance than other sequential learning approaches in the LoS task using the publicly available MIMIC-III ICU dataset. We also evaluate the robustness of our framework under different patient representations.
Implementing Inductive Logic into Embedded Industrial Control System with Polynomial Ring on F2

In many industrial applications, planning control strategies in a way to guarantee that the collective behaviors of the system satisfies a certain steady specification is crucial. Motivated by this problem, in this paper, we develop an inductive logic representation method with polynomial ring on ${\mathbb{F}_2}$ and automatic control framework for industrial equipments. The goal of the industrial equipments is to accomplish diverse complex tasks, captured by learned rules set with inductive logic formulas. Specifically, we introduce a novel inductive logic with anonymous predicates to deal with the variability of industrial systems, and representation with polynomial ring on ${\mathbb{F}_2}$ is designed to make inductive logic reasoning computable. An industrial control simulation dataset containing multiple tasks is also constructed to evaluate the applicability of the method. In experimental simulation and practicing based on single-chip microcomputer, we illustrate the feasibility and efficiency of our proposed method compared with baselines on different industrial controlling scenarios by both rich numerical results and theoretical analysis.
Graph-based KB and Text Fusion Interaction Network for Open Domain Question Answering

The incompleteness of the knowledge base (KB) limits the performance of open domain question answering (QA).Represent the incomplete KB with graph attention network (GAT) and complement the incomplete KB by extra text achieves great success to boost the QA system when the KB is incomplete. In this paper, we propose a Graph-based KB and Text Fusion Interaction Network (GTFIN) to improve the performance of the incomplete QA system by utilizing the KB and text information.In GTFIN, to reduce the influence of the query-unrelated noisy information of GAT on final answer prediction, we first design a global-normalization graph attention network (GGAT) by determining the query-related edge weights from the global perspective, and then a coarse-to-fine text reader (CFReader) is proposed to both exploit the relation information and obtain the entity mention representation in the text to enhance the incomplete KB. We further incorporate a bi-attention mechanism to enhance the interaction between question and entity representation which could find more query-related entities for final answer prediction. On the widely used KBQA benchmark WebQSP, our model achieves state-of-the-art performance.
Toward Mobile Distributed Ledgers

Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on Device-to-Device (D2D) ecosystems (e.g., crowdsensing). Sophisticated applications, like cryptocurrencies, need distributed ledgers (DLs) to function. DLs, such as blockchains and directed acyclic graphs (DAGs), employ consensus protocols to add data in the form of blocks. However, such protocols are designed for resourceful devices that are interconnected via the Internet. Moreover, existing DLs are not deployable to D2D ecosystems since their storage needs are continuously increasing. In this work, we introduce and analyze Mneme, a DAG-based DL that can be maintained solely by mobile devices. Mneme utilizes two novel consensus protocols: 1) Proof of Context (PoC) and 2) Proof of Equivalence (PoE). PoC employs users’ context to add data on Mneme. PoE is executed periodically to summarize data and produce equivalent blocks that require less storage. We analyze Mneme’s security and justify the ability of PoC and PoE to guarantee the characteristics of DLs: persistence and liveness. Furthermore, we analyze potential attacks from malicious users and prove that the probability of a successful attack is inversely proportional to the square of the number of mobile users who maintain Mneme.
Performance Analysis of $${\varvec{\pi}}$$, AL and CT for Consistency Regularization Using Semi-Supervised Learning

A semi-supervised learning problem starts with a series of labeled data points as well as some data point for which labels are not known. The primary motive of a typical semi-supervised model is to categorize some of the unlabeled data by means of the labeled information set. The training procedures used for semi-supervised learning paradigm show different levels of consistency loss. These losses are mainly caused by small disruptions or disturbances of its inputs and parameters, and it helps in improvisation of generalization performance in comparison to supervised learning. This research article analyzes the performance of Π, active learning (AL) and interpolation consistency training (ICT).
Quantum Computing and More!

Presents the introductory editorial for this issue of the publication.
Training Many-to-Many Recurrent Neural Networks with Target Propagation

Deep neural networks trained with back-propagation have been the driving force for the progress in fields such as computer vision, natural language processing. However, back-propagation has often been criticized for its biological implausibility. More biologically plausible alternatives to backpropagation such as target propagation and feedback alignment have been proposed. But most of these learning algorithms are originally designed and tested for feedforward networks, and their ability for training recurrent networks and arbitrary computation graphs is not fully studied nor understood. In this paper, we propose a learning procedure based on target propagation for training multi-output recurrent networks. It opens doors to extending such biologically plausible models as general learning algorithms for arbitrary graphs.
Towards Handling Unconstrained User Preferences in Dialogue

A user input to a schema-driven dialogue information navigation system, such as venue search, is typically constrained by the underlying database which restricts the user to specify a predefined set of preferences, or slots, corresponding to the database fields. We envision a more natural information navigation dialogue interface where a user has flexibility to specify unconstrained preferences that may not match a predefined schema. We propose to use information retrieval from unstructured knowledge to identify entities relevant to a user request. We construct an up-to-date database of restaurants in Cambridge, including unstructured knowledge snippets (reviews and information from the web) and annotate a set of query-snippet pairs with relevance labels. We use the annotated dataset to train and evaluate snippet relevance classifiers, as a proxy to evaluating recommendation accuracy. We show that with a pretrained transformer model as an encoder, an unsupervised/supervised classifier achieves a weighted F1 of 0.661/0.856.
Design and implementation of a general SQL parser

Abstract With the rapid development of computer network, the information security of database becomes more and more important. In database security protection, SQL statements need to be parsed and reconstructed. However, the database audit products on the market lack accurate analysis of SQL statements. Therefore, this paper constructs an SQL parser from four aspects: lexical analysis, syntax analysis, SQL spanning tree optimization and error detection.
Research on Factors Influencing the Masses’ Will of Browsing Anti-Epidemic Posters Online under the COVID-19 Epidemic

As the outbreak of novel coronavirus in 2020 is a public crisis event sweeping across the globe, electronic posters as a kind of information transmission medium featuring outstanding visual effect and easy transmission are playing a significant role in timely issuing information, properly guiding the public opinion and alleviating the mental anxiety of the masses under the epidemic situation. In this paper, in order to optimize the transmission efficiency of electronic posters, special effort is made to screen main factors influencing the masses’ will of browsing electronic posters online under the epidemic situation from three aspects (Platform, Posters and Audiences) based on the literature research. Then, through the questionnaire survey and DEMATEL method, analysis is made on various factors and the relevance between factors. According to the research results, Information Visualization is the most important demand factor, the basic function of platform the core impact factor and the audiences’ anxiety the core impacted factor. Through comparing the result respectively concluded from the DEMATEL and the binary logistic regression analytical method, the research’s conclusions can be supplemented and extended. At last, based on the specific analysis and comparison result, suggestions on optimizing online anti-epidemic popular science posters are proposed to enhance the masses’ will of browsing and improve the transmission efficiency.
Rancang Bangun Aplikasi Panduan Peduli Jemaah Haji (APPJI) Berbasis Android

Hajj is one of the 5 pillars of Islam. In performing the pilgrimage, there are many problems and obstacles that are often experienced by pilgrims, including: the number of pilgrims who still need guidance regarding Hajj materials and various supporting features as well as many pilgrims who are still often left behind from their entourage, this makes it easy for pilgrims to perform Hajj. lost or separated from his hajj tour group. The purpose of this research is expected to be able to help pilgrims to overcome the problems that often occur by implementing the location sharing feature with Google Maps integration for tracking using GPS so that pilgrims can send accurate locations of their whereabouts. This application uses the Waterfall development method, UML as the design design. This research has produced an android-based application called APPJI (Application Peduli Jamaah Haji) that can be used for Mobile Android Smartphones with 89.2% of test results stating that the application has met the software's ideal score.
CcGL-GAN: Criss-Cross Attention and Global-Local Discriminator Generative Adversarial Networks for text-to-image synthesis

Text-to-image synthesis aims to generate a visually realistic image according to a linguistic text description. Visual quality and semantic consistency are two key objectives. Although remarkable progress has been made in improving visual resolutions leveraging Generative Adversarial Networks (GANs), guaranteeing the semantic conformity remains challenging. In this paper, we address it by proposing a novel Criss-Cross Attention and Global-Local Discriminator Generative Adversarial Networks(CcGL-GAN). CcGL-GAN exploits a Criss-Cross Attention mechanism to capture the variation of contextual description, which enables back generators to generate images more efficiently. Moreover, it utilizes Global-Local discriminators to project low-resolution images onto global linguistic representations, and high-resolution images onto local linguistic representations, which ensures that our model narrows the gap between images and descriptions. Experiments conducted on two publicly available datasets, the CUB and Oxford-102, demonstrate the effectiveness of the proposed CcGL-GAN model.
Holistic Co-occurrence Prior for High-Density Face Detection

Though object detection has made tremendous strides, small object detection remains one of the challenges. Aggregating information from context becomes a natural choice for the detection of small objects with appearance degradation. This paper discusses how to properly utilize high-level contextual prior knowledge to enhance the capabilities of anchor-based detectors for small and crowded face detection. We propose density-aware face co-occurrence prior for inferred box harmonization. Specifically, We mine the face spatial co-occurrence information on the holistic view according to the density estimation. We also collect a challenging face detection dataset - Crowd Face, to provide adequate samples to prominent the bottleneck of detecting crowded faces. We integrate our proposed method with the anchor-based detectors and test it on benchmarked datasets (WIDER FACE and Crowd Face). Experiments indicate it obviously enhances the performance of existing face detectors in crowd scenes. Especially for the detectors with poor inferring performance, the improvements of the detectors integrated with the proposed scheme are obvious. Our approach is plug-and-play and model-independent, which could be integrated into the existing anchor-based detectors without extra learning.
9th International Workshop on News Recommendation and Analytics

News portals, social media, and news recommender systems have a strong influence on the perception of events. The way with which people engage with news has changed. Today, we encounter personalized access to news. On the one hand, personalization allows us to manage the overwhelming amount of information. On the other hand, personalization can create a set of problems such as filter bubbles, privacy and disinformation related problems. News analytics helps us to develop solutions towards the challenges created by personalization. News analytics helps us to understand the news ecosystem better and develop solutions towards the challenges of news recommender systems. The 9th International Workshop on News Recommendation and Analytics (INRA 2021) provides a forum to discuss recent trends and observations related to news recommendation, personalization, and analytics. The interdisciplinary workshop connects research from machine learning and analytics, algorithmic modelling and prediction, as well as results from ethical and psychological research.
Needs and potential use of augmented reality in teaching and learning computer architecture and organization course: a pilot study / Anis Salwani Abu Bakar, Hasliza A Rahim & Noraini Desa

Augmented reality generally defined as an enhanced version of the real physical world accomplished through the use of visual elements, sound, or other sensory stimulation delivered through various platforms of technology application. In the Electrical Engineering Department of Politeknik Sultan Haji Ahmad Shah, the Computer Architecture and Organization Course is still being delivered to students via conventional methods of interactive lectures and discussion in the classroom. Despite the fact that blended learning approach has been implied, students’ understanding of the course is still at a low level based on their achievement in continuous assessment and final examination. Considering that this course is one of the difficult courses offered to students majoring in Diploma In Electronic Engineering (Technology Computer), the teaching and learning process always ends up with merely teacher centred learning rather than student centred learning. Hence this study aims to identify the needs and potential constructs or topics that are suitable to adopt augmented reality (AR) innovation in teaching and learning of Computer Architecture and Organization Course in order to help in increasing the students’ understanding and achievement. Further findings reveal that students and lecturers showed very high perception of the necessity of using AR in Computer Architecture and Organization Course teaching and learning processes. The adoption of AR is expected to become an effective teaching innovation method for lecturers and consequently assist the students to improve their understanding and achievement in the course. Further study is also expected in reviewing the effectiveness and implementation of AR innovation model that will be developed later on.