Reviewerly/exHarmony huggingface dataset:
Thoughts on the “14th Five-Year Plan” of Informatization Construction in University

The article reviews the completion of the 13th Five-Year Plan for Informatization of South China University of Technology, combined with the new situation of the 14th Five-Year Plan for Informatization, and proposed that the 14th Five-Year Plan for informatization of our university should focus on innovation and integration, and accelerate the deployment of “New Infrastructure”, and take the construction of educational information system supporting the “double first-class” development level as the strategic goal. This article discusses the five key construction points of our university.
Smarter Data Availability Checks in the Cloud

Cloud computing offers clients flexible and cost-effective resources. Nevertheless, past incidents indicate that the cloud may misbehave by exposing or tampering with clients' data. Therefore, it is vital for clients to protect the confidentiality and integrity of their outsourced data. To address these issues, researchers proposed cryptographic protocols called “proof of storage” that let a client efficiently verify the integrity or availability of its data stored in a remote cloud server. However, in these schemes, the client either has to be online to perform the verification itself or has to delegate the verification to a fully trusted auditor. In this chapter, a new scheme is proposed that lets the client distribute its data replicas among multiple cloud servers to achieve high availability without the need for the client to be online for the verification and without a trusted auditor's involvement. The new scheme is mainly based on blockchain smart contracts. It illustrates how a combination of cloud computing and blockchain technology can resolve real-world problems.
A Unified Summarization Model with Semantic Guide and Keyword Coverage Mechanism

Neural abstractive summarization models, based on attentional encoder-decoder architecture, can generate a summary closer to human style. However, the generated summaries often suffer from inaccuracy and irrelevance. To tackle these problems, we propose a novel unified summarization model with integrated semantic guide and keyword coverage mechanism. First, we add the integrated semantic guide in the encoder, which helps the encoder-decoder structure grasp the central idea of the full text. Second, in the decoding process, we use the keyword coverage mechanism to reward the attention distribution of keywords, which promotes the generation of more logically related expressions to the central idea. Evaluations on the CNN/Daily Mail dataset demonstrate that our model outperforms the baseline models, and the analysis shows that our model is capable of generating a more relevant and fluent summary.
An Improved Convolutional Neural Network and Its Applications to Image Super Resolution

This paper studies an improved convolutional neural network application in the image super-resolution problem. On the traditional super-resolution model, we improve the model framework, the upsampling method and the network design. And through the experiment of traditional super-resolution convolution neural network with improved super-resolution convolution neural network and compare the effect of the reconstruction image, it is concluded that the improved super-resolution network model compared with super-resolution in higher PSNR and better visual effect, stronger image resolution.
MA Mask R-CNN: MPR and AFPN Based Mask R-CNN

Multi-resolution parallel ResNet (MPR) and Attention FPN (AFPN) are presented based Mask R-CNN (MA Mask R-CNN) to achieve image instance segmentation, which aims at improving the feature extraction ability of the model, and efficiently increasing the detection and segmentation accuracy. MPR adds parallel branches to extract the feature before each down-sampling of ResNet to make full use of the features. Attention FPN (AFPN) learns the attention mechanism by adding attention modules to each layer of the FPN, so that each layer can adaptively adjust the weight, emphasize the effective information, and suppress the unimportant information. Results of the comparison of the proposed MA Mask R-CNN to the previous state-of-the-art models on COCO dataset show a wider variety of scenarios and complete labels. Our method in instance segmentation task performs better, compared with the state-of-the-art methods. mAP, mAP50 and mAP75 are improved by 1.1%, 1.6% and 1.3 respectively, to Mask R-CNN, and 0.4%, 1.3%, 0.4% respectively, to PANet, and 7.4%, 7.9%, 9.5% respectively, to FCIS. The average segmentation accuracy of small, medium, and large targets (mAPS, mAPM, mAPL) is increased by 1.4%, 1.0%, and 1.1% to Mask R-CNN, respectively.
Vision-and-Dialog Navigation by Fusing Cross-modal features

Recently, research in robotics community shows a trend that the future intelligent robots should be endowed with the capacities to understand the environment and navigate to the goal location through the communications with their human users. Such tasks usually require the smart agents to process multi-modal information effectively. Though multi-modal information processing has been long studied, the problem of how to effectively fuse different modalities of information remains challenging. In this paper, we focus on the vision-and-dialog navigation(NDH) task. The NDH task is proposed for building dialog-enabled agents which can find a path to the goal location in unexplored environments by inferring navigation actions based on the dialog history and the visual inputs. We first investigate the problem about what role the visual features are playing in NDH task. We observe the same trend which was observed in the vision-and-language Navigation(VLN) task. The conclusion is using different levels of visual features affects the model performance seriously. Particularly, using low-level visual features makes the agent models hardly generalize in unseen environments (i.e., environments not used in training). Models using only high-level visual features perform better in unseen environments. However, these models suffer a significant performance drop in seen environments, which means these models can not understand and remember the seen environments thoroughly. According to this observation, we explore several ways to fuse these features. We propose a model to fuse the dialog feature with each modality of visual feature. On the other hand, the prediction of our model is an ensemble of jointly-trained models which focus on different modalities. Our proposed method can be applied in any VLN models and NDH models. Our results show that our method can improve the performance of NDH models in both seen environments and unseen environments.
Improving Lexical-Constraint-Aware Machine Translation by Factoring Encoders

Existing lexically constrained machine translation employs data augmentation and incorporates lexical constraints during decoding period, which requires a bilingual dictionary or costs much decoding time. In this paper, we propose a simple but effective method to leverage lexical constraints. We use separate encoders to encode source sentence and lexical constraints, with self-attention layer mask to disentangle the two encoding subtasks. Our method does not require bilingual dictionaries or modify decoding process. Experiments on WMT 2016 English-German (En-De) and IWSLT 2017 English-Chinese (En-Zh) datasets show that our method gains improvement compared to baseline models.
Autonomous Language Learning with Augmented Reality – An Individual Case Study

We want to motivate and inspire children to learn languages with the help of augmented reality. In this individual case study, we evaluate the potential of our Augmented Reality learning assistant “ARTranslate”. We conduct a individual case study by letting children do everyday shopping with the help of our augmented reality learning app. We evaluate these repeatedly with queries in the following days. The respective retention rates range from 26% to 92%. We were able to show that children autonomously learn foreign language terms with the help of our augmented reality learning initiator. To start the initial motivation when learning a new language, ARTranslate has delivered strong indications of high effectiveness.
User Weighting Affect in Neighbourhood based Collaborative Filtering using Firefly Algorithm

A recommendation system can provide content that users are likely to choose because the content provided will be based on filtering information that takes preferences from the behavior and history of the user. Recently, researchers researched to improve the quality of one of the recommendations using swarm intelligence in a collaborative filtering system of traditional recommendation systems. This study aims to determine the effect of user weighting on traditional recommendation systems, one of which is the swarm intelligence, namely the firefly algorithm, to give weights to users and get active users on the dataset. We conducted several experiments to compare the performance of our proposed method, including by comparing 100 users obtained from the weighting results. As a result, the firefly algorithm was able to find 100 users who had a significant influence on the prediction results with an MAE error value of 0.8101. Another experiment with a scheme using all data can give a lower MAE error value of 0.8007.
English Master AMMU: Advanced Spoken English Chatbot

Everyone needs to express their ideas, thoughts, and emotions. For a professional or a student, they have to express or communicate their thoughts effectively. To make communication effective, it is important to understand the English language. As English is an international language, all had to have fluent English-speaking skills. Making improvements in spoken English can be done in various ways. Talking to friends or family members in English, practicing phrases and sentences, etc. are some of them. But most people feel some awkwardness talking in front of a crowd or even with friends. Here comes the helper Assistant, Miss AMMU Teacher. Miss AMMU Teacher is the advanced version of AMMU spoken English chatbot. In this work, a chatbot called AMMU, Automatic Mega-agent Managed User guide is built for helping this purpose. AMMU will communicate with the user and will help to improve the spoken English capability of users.
Towards combinational relation linking over knowledge graphs

Given a knowledge graph and a natural language phrase, relation linking aims to find relations (predicates or properties) from the underlying knowledge graph to match the phrase. It is very useful in many applications, such as natural language question answering, personalized recommendation and text summarization. However, the previous relation linking algorithms usually produce a single relation for the input phrase and pay little attention to the more general and challenging problem, i.e., combinational relation linking that extracts a subgraph pattern to match the compound phrase (e.g. father-in-law). In this paper, we focus on the task of combinational relation linking over knowledge graphs. To resolve the problem, we define several elementary meta patterns which can be used to build any combinational relation. Then we design a systematic method based on the data-driven relation assembly technique, which is performed under the guidance of meta patterns. To enhance the system’s understanding ability, we introduce external knowledge during the linking process. Finally, extensive experiments over the real knowledge graph confirm the effectiveness of the proposed method.
E-FFTF: An Extended Framework for Flexible Fault Tolerance in Cloud

Fault tolerance provisioning is extremely important in cloud. Literature suggests that the existing frameworks are service provider centric. Being a pay-per-use model, fault tolerance in the cloud should be flexible with respect to the users’ requirement. Our previously proposed framework FFTF (Flexible Fault Tolerance Framework) is further extended to E-FFTF in the present paper to provide user transparency in the selection of service category based on the task completion deadline and slack time. E-FFTF successfully establishes task execution price savings through flexible fault tolerance. E-FFTF is proved to be beneficial for both the cloud service providers and service users as per the obtained experimental results.
Structural description model for vehicle feature recognition

Precise vehicle recognition has long been neglected compared with face recognition, meanwhile, benefiting from the development of neural networks, as same as face recognition, structural vehicle feature recognition is currently feasible. In this paper, we use a CNN-based cascaded multi-task framework for vehicle detection and alignment, then we trained a backbone CNN which can learn a mapping from vehicle image to a Euclidean space. Therefore, task of vehicle recognition can be solved as face recognition. Besides, different vehicles have different attributes compared with faces, we enriched the largest open source vehicle recognition dataset VehicleID with color and direction while the branch-CNN is employed to learn multiple features from different branches, outputs. Finally, structural vehicle features can be transformed from image to text which enhances the data expression ability.
Multi-Channel Deep Networks on Sequence Data for Multi-Action Recognition

Recently, Human Activity Recognition (HAR) using deep neural network has become a hot topic in human-computer interaction. Machine can effectively identify human naturalistic activities by learning from a large collection of sensor data. Distinguishing the same activity of different human is not only an interesting research problem, but also has real applications in private identity authentication. Based on the success of convolutional networks for sequence raw data and residual network to achieve the level of aesthetic representation of the automatic learning, we propose a novel model, named Deep Convolutional and Residual Networks (ConvResNets), for identifying authentication on wearable data, in which: (1)The raw input features can be replaced by high-level features representation; (2)ConvResNets does not require expert-knowledge to extract activity data features; (3)ConvResNets can easily capture features distribution of private activities. We evaluate our model on two public datasets. Our results show that our model is effective to recognize identity authentication via wearable datasets. We discuss the influence of networks parameters on performance to provide insights about its optimization.
Two-Stage Feature Selection Pipeline for Text Classification

Text classification is one of the significant fields in NLP. It has numerous applications in the business world such as e-mail spam filtering, fraud detection, recruitment and various other tasks. The past three decades have witnessed an exponential rise in the amount of information. This has made text classification all the more challenging. There is a greater need for optimal feature subset selection which in turn enhances classification performance. In this study, we discuss a two-stage feature selection pipeline which combines conventional filter methods (Chi square and information gain) with evolutionary algorithms (particle swarm optimization and genetic algorithm). Subsequently, we aim to compare the results from these pipelines with the results from evolutionary algorithms over existing classifiers. The experiments are performed on two popular data sets—20 newsgroups and IMDB reviews. Results obtained show that the pipeline-based feature selection methods perform better than their respective wrapper methods.
Experimental research on the implementation of auxiliary teaching of track and field course based on Wechat platform

This paper uses the methods of literature, questionnaire, teaching experiment, mathematical statistics and other research methods to integrate wechat assisted teaching into the teaching of javelin throwing technology in track and field elective course, achieving sound teaching effect.
Sequential and Dynamic constraint Contrastive Learning for Reinforcement Learning

Contrastive unsupervised learning gives remarkable promise for sample-efficiency improvement in reinforcement learning, especially for high-dimensional observations by extracting latent features from raw inputs. However, prior works scarcely take sequential information and the knowledge of dynamic transitions into consideration when constructing contrastive samples. In this paper, we propose Sequential and Dynamic constraint Contrastive Reinforcement Learning (SDCRL) to improve the sample efficiency in high-dimensional inputs (e.g., images) setting. We firstly construct a sequential contrastive module to extract latent features with sequential information from raw correlated image inputs. Furthermore, we add a dynamic transition classification module to extract the knowledge of state transitions. We validate the proposed method in low sample regime (few interactions). Our algorithm surpasses prior pixel-based approaches on complex tasks in Deepmind Control Suite and even achieves or exceeds the performance of the method that uses state-based features as inputs on 11 out of 15 tasks. In Atari2600 games, SDCRL also outperforms strong baselines and achieves state-of-the-art performance on 7 out of 26 games.
Emergence of Different Modes of Tool Use in a Reaching and Dragging Task

Tool use is an important milestone in the evolution of intelligence. In this paper, we investigate different modes of tool use that emerge in a reaching and dragging task. In this task, a jointed arm with a gripper must grab a tool (T, I, or L-shaped) and drag an object down to the target location (the bottom of the arena). The simulated environment had real physics such as gravity and friction. We trained a deep-reinforcement learning based controller (with raw visual and proprioceptive input) with minimal reward shaping information to tackle this task. We observed the emergence of a wide range of unexpected behaviors, not directly encoded in the motor primitives or reward functions. Examples include hitting the object to the target location, correcting error of initial contact, throwing the tool toward the object, as well as normal expected behavior such as wide sweep. Also, we further analyzed these behaviors based on the type of tool and the initial position of the target object. Our results show a rich repertoire of behaviors, beyond the basic built-in mechanisms of the deep reinforcement learning method we used.
Learning digital skills: analytics of leading practices

The relevance of this research is substantiated by an interesting phenomenon that has occurred in the last decade: the technologies of the Fourth Industrial Revolution are based on digitalization of not only products and processes, but also people's behavior at work and outside it. &amp;nbsp;The author reviews digital skills as the &amp;ldquo;third basket&amp;rdquo;, which is equally as significant as the &amp;ldquo;baskets&amp;rdquo; of professional and &amp;ldquo;soft&amp;rdquo; skills. Therefore, analysis is conducted on the experience of facts and cases presented by dozens of Russian and international educational institutions for determining the leading practices of digital education. The author describes the key determinants of digital literacy. The following conclusions were made : 1) digital education is based on the principles that differ drastically from other tools, sets new goals, focusing on the approaches that blow up the traditional university methods; 2) IBM &amp;ldquo;Facing The Storm&amp;rdquo; research indicates that massive shortage of digital skills among personnel is a storm that is brewing on the horizon; 3) research of the BFR (Big Fast Results) institute reflects the mechanism of implementing educational and managerial processes within the paradigm of digital literacy; 4) the presented research covers a wide range of empirical data, which allows all parties to the educational process to form digital learning environment strongly contributing to development of the currently relevant &amp;ldquo;soft&amp;rdquo; skills.
RecSys in HR: Workshop on Recommender Systems for Human Resources

extended-abstract Share on RecSys in HR: Workshop on Recommender Systems for Human Resources Authors: Toine Bogers Aalborg University Copenhagen, Denmark Aalborg University Copenhagen, DenmarkView Profile , David Graus Randstad Groep Nederland, Netherlands Randstad Groep Nederland, NetherlandsView Profile , Mesut Kaya Aalborg University Copenhagen, Denmark Aalborg University Copenhagen, DenmarkView Profile , Francisco Gutiérrez Computer Science KU Leuven, Belgium Computer Science KU Leuven, BelgiumView Profile , Katrien Verbert Computer Science KU Leuven, Belgium Computer Science KU Leuven, BelgiumView Profile Authors Info & Claims RecSys '21: Fifteenth ACM Conference on Recommender SystemsSeptember 2021 Pages 799–802https://doi.org/10.1145/3460231.3470935Online:13 September 2021Publication History 0citation118DownloadsMetricsTotal Citations0Total Downloads118Last 12 Months118Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Lightweight Privacy-Preserving Similar Documents Retrieval over Encrypted Data

Document Similarity Detection (DSD) is significant in our real life applications. However, the existing methods ignore the privacy of what is contained in the documents uploaded on remote servers, thus reducing the applicability of these methods. The proposed scheme allows documents to be compared without revealing to those remote servers. For each document, the fingerprint set is calculated. The inverted index is constructed on the basis of the whole fingerprint set. The inverted index is widely used for efficient retrieval. This index is under protection by Paillier cryptosystem before it gets uploaded to the server.
Concept Matching for Low-Resource Classification

In many applications that rely on machine learning, the availability of labelled data is a matter of primary importance. However, when tackling new tasks, labels are usually missing and must be collected from scratch by the users. In this work, we address the problem of learning classifiers when the amount of labels is very scarce. We do so by learning multiple vectors, called prototypes, that represent relevant semantic concepts for the task at hand. We propose a theoretically inspired mechanism that computes probabilities of matching between the prototypes and the input elements, and we combine these probabilities to increase the expressiveness of the classifier. Moreover, by leveraging low-cost extra annotations in the training data, a simple error-boosting technique guides the learning process and provides substantial performance improvements. Empirical results confirm the benefits of the proposed approach in both balanced and unbalanced datasets. Our methodology is thus of practical use when gathering and labelling new examples is more expensive than annotating what we already have.