Reviewerly/exHarmony huggingface dataset:
FedLinked: A client-wise distilled representation based semi-supervised collaborative multitask learning scheme

Multitask learning in distributed environments is an important problem in the field of federated learning. In many practical problems, human or machine experts try to solve different, but highly correlated tasks, thus their performance can be improved through collaboration, but their private datasets and model parameters cannot be shared with other participants because of business and privacy issues. Semi-supervised learning is also actively researched nowadays, which is motivated by the utilization of the usually free, large set of unlabeled data in concept and representation learning. These unlabeled samples can be utilized in the knowledge sharing of the participants during their collaboration. We propose a method, called FedLinked, which combines the capabilities of these area (federated, multitask and semi-supervised learning) related algorithms in the form of a privacy preserving complex solution-block targeting semi-supervised multitask cross-silo problems. FedLinked is a collaborative learning method, which is implemented through the regularization of representation learning based on the cross-utility of participating clients. We evaluate FedLinked on image classification problems and compare its performance to FedAvg based and non-cooperating clients based solutions in multitask scenarios. Besides, a detailed analysis of our proposed method is provided.
Immersive Authoring of 360 Degree Interactive Applications

Although there are proposals in the literature for authoring mulsemedia ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mul</i> tiple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">se</i> nsorial <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">media</i> ) applications with 2D content, there are no suitable solutions when it comes to 360° content. Moreover, little consensus on 360° mulsemedia authoring methodology exists. Aiming at filling this gap, we propose the concept of immersive authoring of 360° multisensory applications. Our proposal comprises an immersive 360° authoring environment to bring the author closer to the final user presentation environment. We implemented our proposal in AMUSEVR, a virtual-reality (VR) environment for authoring 360° mulsemedia applications. We see it as an alternative or a possible complement to available 2D mulsemedia authoring tools. AMUSEVR provides creation and editing of interactive multiple sensorial media scenes by directly arranging objects in a 3D space using VR technology. Also, the tool allows users to run their applications through AMUSEVR viewer mode.We used the Goal Question Metric (GQM) approach to plan our tests and a group of users evaluated the tool with the SUS and UEQ questionnaires, obtaining a SUS score of 82.25 and an excellent UEQ benchmark, which are very promising results.
A Domain Adaptive Adversarial Training Method Based on Self-Supervised Learning

Image classification technology based on neural network is an important task in computer vision, and the introduction of transfer learning can solve the problems of lack of data sets and long training time. To address this problem, this paper proposes a self-supervised domain-adaptive adversarial network approach. The algorithm uses the VGG network to extract image features, realizes the transfer learning of different image styles through domain adversarial training, and introduces a data augmentation model and self-supervised learning method based on pseudo-label to improve the accuracy of model classification. The experimental results show that the model can effectively improve the accuracy of image transfer learning of different styles in the image classification problem. When the number of pseudo-labels is 10, the classification effect is the best, and the accuracy rate is improved to 12.99%, which greatly saves training time and computing power while solving the problem of missing training data.
College English Teaching Design Based on WE Learn Computer Platform

With the rapid development of computer technology, the language teaching mode has undergone tremendous changes, form sitting in the classroom with face-to-face teaching to using the online computer platforms for teaching. Online computer platforms have opened a new world for college English teaching and learning, not only with the advantage for college students to learn without the restrictions of time and space, but also with the advantage for college teachers to guide the students in a prompt and individualized way. Firstly, this paper analyzes the development and changes of computer technology in college English teaching. Secondly, it provides the theoretical basis and feasibility of online computer platform in college English teaching. Thirdly, it demonstrates the advantages of online computer platform in college English teaching and makes college English teaching design based on online computer platform. Finally, through the analysis of the data recorded on the online computer platform, it is found that students can promote their autonomic learning ability and teachers can achieve the purpose of individualized teaching and manage the teaching process accurately and scientifically.
Sim-2-Sim Transfer for Vision-and-Language Navigation in Continuous Environments

Recent work in Vision-and-Language Navigation (VLN) has presented two environmental paradigms with differing realism – the standard VLN setting built on topological environments where navigation is abstracted away [3], and the VLN-CE setting where agents must navigate continuous 3D environments using low-level actions [21]. Despite sharing the high-level task and even the underlying instruction-path data, performance on VLN-CE lags behind VLN significantly. In this work, we explore this gap by transferring an agent from the abstract environment of VLN to the continuous environment of VLN-CE. We find that this sim-2-sim transfer is highly effective, improving over the prior state of the art in VLN-CE by +12% success rate. While this demonstrates the potential for this direction, the transfer does not fully retain the original performance of the agent in the abstract setting. We present a sequence of experiments to identify what differences result in performance degradation, providing clear directions for further improvement.
SASD: A Shape-Aware Saliency Object Detection Approach for RGB-D Images

Saliency object detection is a fundamental problem in the field of computer vision. With the commercial success of consumer-grade depth sensors such as Microsoft Kinect, the captured RGB-D images provide users with a higher viewing experience, but also pose a higher challenge to the current saliency detection technology. In this paper, we propose a shape-aware saliency object detection approach SASD, manifesting in two aspects: 1) obtaining high quality saliency maps, especially the salient details; 2) displaying irregular salient regions, such as circles, ellipses, and splines. The proposed SASD approach consists of three steps. First, the initial saliency map is obtained by combing depth information and edge details. On this basis, the rules for the dominant factors of saliency detection are designed and the method of enhanced saliency map calculation is proposed. Finally, an irregular shape display method is demonstrated, the purpose of which is to fit the obtained saliency map to different shapes. The experiment demonstrates the effectiveness of our approach in subjective and objective aspects.
A Focused Garbage Collection Approach for Primary Deduplicated Storage with Low Memory Overhead

Since one chunk could be shared by many files after data deduplication, Garbage Collection (GC) is an essential but complex task to reclaim stale chunks in large-scale primary deduplication systems. Traditional Mark&Sweep is a widely used approach but suffers from the increasingly traversing time and huge memory overhead of Liveness Array (i.e., a data structure reflects the liveness of alive chunks) in the Mark phase. This paper proposes a new method named Focused Garbage Collection (FGC) to accelerate the Mark phase for primary deduplication storage significantly. Specifically, we design a global Austere Reference Graph with low memory cost that efficiently represents files’ reference relationships (i.e., sharing chunks after deduplication) by considering the deduplication characteristics of workloads in primary systems. Austere Reference Graph helps FGC focus on the deleted files and their correlative files to quickly mark stale chunks, while traditional approaches need to traverse all files. Consequently, FGC’s traversing time and Liveness Array size will be greatly reduced in the Mark phase. Evaluation results show that compared with traditional Mark&Sweep, FGC decreases the time consumption in the Mark phase 1.3×-7.34× in a stand-alone primary deduplication system and 128×-256× network traffic reduction for the Mark phase while only introducing < 0.05% extra memory overhead for the reference graph.
Game mechanics of gamification in the formation of the digital footprint of younger school children in the process of career guidance

The authors of the article substantiate that at present there is an objective need to update and introduce into practice new models of career guidance and professional self-determination of younger students using modern digital resources in the process of lifelong education. The purpose of this study is to develop and experimentally test career guidance game mechanics in the formation of a digital footprint of a younger student in the process of career guidance. In the course of the study, the following were used: modeling method, computer modeling method, testing method, observation, method of professional tests, method of static processing of empirical data, classification method. The article reflects: a model of vocational guidance and professional self-determination of children based on the principles, methods, techniques of gamification, which is implemented on a digital platform; content and technological components of the model, game mechanics, which are the main tools of the technological component. The study presents the game mechanics developed by the authors in the formation of a digital footprint of junior schoolchildren in the process of vocational guidance using gamification resources.
Multi Recursive Residual Dense Attention GAN for Perceptual Image Super Resolution

Single image super-resolution (SISR) has achieved great progress based on convolutional neural networks (CNNs) such as generative adversarial network (GAN). However, most deep learning architectures cannot utilize the hierarchical features in original low-resolution images, which may result in the loss of image details. To recover visually high-quality high-resolution images, we propose a novel Multi-recursive residual dense Attention Generative Adversarial Network (MAGAN). Our MAGAN enjoys the ability to learn more texture details and overcome the weakness of conventional GAN-based models, which easily generate redundant information. In particular, we design a new multi-recursive residual dense network as a module in our generator to take advantage of the information from hierarchical features. We also introduce a multi-attention mechanism to our MAGAN to capture more informative features. Moreover, we present a new convolutional block in our discriminator by utilizing switchable normalization and spectral normalization to stabilize the training and accelerate convergence. Experimental results on benchmark datasets indicate that MAGAN yields finer texture details and does not produce redundant information in comparison with existing methods.
Mixing Static Word Embeddings and RoBERTa for Spatial Role Labeling

Language model pretraining has yielded significant results in diverse natural language processing tasks. RoberTa, an efficient method for pretraining self-supervised NLP systems, is a good example. Our hypothesis in this paper is that the performance of Spatial Role Labeling (SpRL) can be improved by combining static word vectors and bags of features with RoberTa vectors. Furthermore, we show that our method is successful in several SpRL datasets.
Cross-modal Retrieval Using Contrastive Learning of Visual-Semantic Embeddings

Contrastive learning is a powerful technique to learn representations that are semantically distinctive and geometrically invariant. While most of the earlier approaches have demonstrated its effectiveness on single-modality learning tasks such as image classification, recently there have been a few attempts towards extending this idea to multi-modal data. In this paper, we propose two loss functions based on normalized cross-entropy to perform the task of learning joint visual-semantic embedding using batch contrastive training. In a batch, for a given anchor point from one modality, we consider its negatives only from another modality, and define our first contrastive loss based on the expected violations incurred by all the negatives. Next, we update this loss and define the second contrastive loss based on the violation incurred only by the hardest negative. We compare our results with existing visual-semantic embedding methods on cross-modal image-to-text and text-to-image retrieval tasks using the MS-COCO and Flickr30K datasets, where we achieve competitive results and are outperformed only by adaptations of the n-pairs symmetric angular loss for multi-modal data. We have also shared our code and pre-trained models for reproducibility.
Named Entity Recognition via Interlayer Attention Residual LSTM

As a type of powerful technique in neural networks, attention mechanisms have been successfully applied to many state-of-the-art models for NLP tasks. In this paper, we present an interlayer attention mechanism and introduce it to the stacked residual LSTM network for named entity recognition (NER). Compared to the conventional residual structure which adds the features from all the previous layers directly, the interlayer attention mechanism allows the model to focus on the most relevant features by assigning different weights to the features from different layers. Experimental results show that our model achieves state-of-the-art performance on both English and Chinese datasets, which demonstrates that the proposed interlayer attention mechanism can improve the performance of stacked residual LSTM network significantly.
Collision Avoidance for Underactuated Surface Vehicles in the Presence of Ocean Currents

This paper considers the problem of collision avoidance for surface vehicles moving under the influence of ocean currents. The vehicles we consider have underactuated dynamics, where the vehicle cannot directly control its lateral motion, which is a common trait of marine vehicles. We propose a reactive algorithm where the vehicle dynamics, including its underactuation and the effects of an ocean current disturbance, are handled directly in the avoidance strategy. Moreover, the algorithm only requires a limited amount of information about the obstacle and is proven to guarantee collision avoidance, as well as the completion of a nominal goal, under explicitly derived conditions. The theory is validated by simulations.
Research on Chinese Word Segmentation Algorithm based on Dictionary and Statistical Method and Its Application in the field of Power Grid Control

In view of the particularity of Chinese word segmentation in the professional field of power grid regulation, the performance of Chinese word segmentation based on traditional statistical algorithm is frequently limited by the corpus training in the proprietary field, and the insufficient ability of Chinese word segmentation based on dictionary algorithm in identifying new words and disambiguation in the proprietary field. This paper proposes a Chinese word segmentation method based on the combination of dictionary and statistical method, and applies it to the application scenario of querying the operation status of power grid. This method constructs a professional dictionary in the field of power grid regulation by using the Double-Array Tree storage structure. Then using the CRF(Conditional Random Fields) train the text data to generate the model file, and the FMM(Forward Maximum Matching) method is used for Chinese word segmentation combined with the professional dictionary in the field of power grid regulation. A fast and accurate online word segmentation method in the field of power grid regulation is designed and implemented, it performs well in some main performance indexes. In the application scenario of querying the power grid operation status, the Chinese word segmentation results obtained by this method combined with the standard query sentence pattern realize the rapid classification of application scenario query and the accurate execution of operation commands, greatly improve the efficiency and accuracy of querying the power grid operation status, and improve the human-computer interaction experience.
Development of a support system for learning foot massage movement skills using IoT technology

In this study, we developed a learning support system based on the Internet of Things technology to extract, visualize, communicate, and share expert-performed aroma foot massage movement skills. The support functions include sensors for the movement of the expert's arms and fingers, an eye tracker for the movement of the expert's viewpoint, a wearable video camera for movements, and a voice that explains them. Furthermore, the movements are converted into posture and skeletal data, and the information from each is integrated and visualized. In this study, we develop a prototype of a learning support system and report on the results of trial experiments to show that the system functions properly.
Addressing Classification on Highly Imbalanced Clinical Datasets

During the last twenty years, machine learning provided a myriad of frameworks and tools to improve data analyses in several fields. Classification, regression, clustering and dimensionality reduction techniques have been widely used in clinical studies to assist health professionals in screening, risk estimation, diagnostics and prognostics. Prospective studies often involve a long follow-up period and a large sample, therefore many investigations rely on a retrospective technique to develop precise classifiers. However, biological data usually presents a limited number of samples and imbalanced number of classes, which affects classification performance. These issues can be alleviated by employing balancing techniques, which increase the number of samples of the minority classes (oversampling) and/or decrease the number of samples of the majority classes (undersampling). In this work, we propose an original framework to assess several balancing techniques, combining them with 3 out-of-the-box classifiers. We applied the combination of techniques to the AVOCADO clinical study, which consists of a database of patient information including cardiovascular death or survival. Our results from the retrospective analysis of this database showed that for training the algorithm to predict cardiovascular outcomes in both sexes, the best undersampling techniques were ENN, RENN and Near-Miss 3, while ADASYN and SMOTE were the best oversampling techniques. Regarding the classifier algorithms, Random Forest and Logistic Regression (with internal balancing parameter enabled) achieved the best results with both families of balancing techniques. Proper balancing techniques associated with feature importance analysis improved the identification of clinical patterns in the data, enabling detection of high risk patients. This approach can be used for personalized medicine, for improving patients survival and recovery.
Analyzing Echo-state Networks Using Fractal Dimension

This work joins aspects of reservoir optimization, information-theoretic optimal encoding, and at its center fractal analysis. We build on the observation that, due to the recursive nature of recurrent neural networks, input sequences appear as fractal patterns in their hidden state representation. These patterns have a fractal dimension that is lower than the number of units in the reservoir. We show potential usage of this fractal dimension with regard to optimization of recurrent neural network initialization. We connect the idea of “ideal” reservoirs to lossless optimal encoding using arithmetic encoders. Our investigation suggests that the fractal dimension of the mapping from input to hidden state shall be close to the number of units in the network. This connection between fractal dimension and network connectivity is an interesting new direction for recurrent neural network initialization and reservoir computing.
Blockchain technology in sustainable supply chain management: from theoretical expectations to application perspective. Case of the fisheries sector

The development of the supply chain between the requirement of competitiveness and the imperative of sustainability, induces the supply chains and the companies that support them to engage in a sustainable development approach. Nevertheless, to achieve this end, new devices have been adopted, among which we find sustainable technological innovations. Therefore, this research highlights the blockchain technology considered as a real lever for the anchoring of sustainable development. However, this ambition of sustainable digitization encounters many obstacles in terms of acceptance of technology, its use and its compatibility with the sustainable development strategy. Through the analysis of the literature review, we have identified the main factors that influence the adoption of blockchain technology that can improve the sustainability of a supply chain. The exploratory study with actors in the fisheries industrial supply chain allowed us to analyze the feasibility of implementing this technology in the sector.
Structural Complexity and Performance of Support Vector Machines

Support vector machines (SVM) are often applied in the context of machine learning analysis of various data. Given the nature of SVMs, these operate always in the sub-interpolation range as a machine learning method. Here we explore the impact of structural complexity on the performance and statistical reliability of SVMs applied for text mining. We set a theoretical framework for our analysis. We found experimentally that the statistical reliability and performance reduce exponentially with the increase of the structural complexity of the SVMs. This is an important result for the understanding of how the prediction error of SVM predictive data models behaves.
Identifying factors and risk sources of pedagogical activity in conditions of digitalization

The paper explores issues and sources of risk associated with pedagogical activity in the context of digitalization of the educational environment. The authors identified risks that lower the efficiency of teacher’s work, studied the probability of their appearance, and assessed the degree of probability of appearance of the most common risks. In addition, classification of risks associated with teacher’s work is presented: risks for teachers working in a digital environment, risks of interactions with students, infrastructure risks, cybersecurity risks. This research allows for the development of directions for further training of teachers in the sphere of digital competence. The results may become a basis for developing a system of managing school risks in the context of digitalization.