Reviewerly/exHarmony huggingface dataset:
Opinion Transmission Network for Jointly Improving Aspect-Oriented Opinion Words Extraction and Sentiment Classification

Aspect-level sentiment classification (ALSC) and aspect oriented opinion words extraction (AOWE) are two highly relevant aspect-based sentiment analysis (ABSA) subtasks. They respectively aim to detect the sentiment polarity and extract the corresponding opinion words toward a given aspect in a sentence. Previous works separate them and focus on one of them by training neural models on small-scale labeled data, while neglecting the connections between them. In this paper, we propose a novel joint model, Opinion Transmission Network (OTN), to exploit the potential bridge between ALSC and AOWE to achieve the goal of facilitating them simultaneously. Specifically, we design two tailor-made opinion transmission mechanisms to control opinion clues flow bidirectionally, respectively from ALSC to AOWE and AOWE to ALSC. Experiment results on two benchmark datasets show that our joint model outperforms strong baselines on the two tasks. Further analysis also validates the effectiveness of opinion transmission mechanisms.
The Development of an Interaction Mechanism between Universities and Other Innovation System Actors: Its Influence on University Innovation Activity Effectiveness

The innovative economy is based on effective interaction between the scientific and educational sphere and the business environment, free flow of innovative ideas, and active commercialization of developments in order to constantly update and develop the domestic economy through new technologies. At the moment, a model of effective interaction between universities, business structures, and government in Russia has not been built. This research is aimed to develop a mechanism for multilateral interaction between universities and other participants in the innovation system, which ensures the activation of scientific and innovative activities, acceleration of the transfer process, and effective commercialization of innovative ideas. Methods. To evaluate the effectiveness of the offered interaction mechanism based on the open innovation platform we conducted a complex multi-criteria assessment of forecast indicators of a university&rsquo;s innovation activity effectiveness. We conducted two online surveys. Heads of scientific laboratories at the university (N = 4) and representatives of business (N = 3) acted as experts. The first survey was intended to indicate the weight of innovation activity indicators reflecting the significance of these indicators in the framework of innovation development. In the second survey, experts defined the forecast indicators of the university&rsquo;s innovation activity after implementation of the proposed interaction mechanism. Results. The conducted study revealed the importance of enhancing cooperation between universities and other innovation system actors for achieving higher results of innovation activity. The calculation of the effectiveness of the offered interaction mechanism showed positive influence on the university&rsquo;s innovation activity indicators. Thus, the mechanism can be used by multidisciplinary universities to increase their innovation activity indicators as well as the potential of all interacting entities.
Image Inpainting Based on Multi-frequency Probabilistic Inference Model

Image inpainting methods usually fail to reconstruct reasonable structure and fine-grained texture simultaneously. This paper handles this problem from a novel perspective of predicting low-frequency semantic structural contents and high-frequency detailed textures respectively, and proposes a multi-frequency probabilistic inference model(MPI model) to predict the multi-frequency information of missing regions by estimating the parametric distribution of multi-frequency features over the corresponding latent spaces. Firstly, in order to extract the information of different frequencies without any interference, wavelet transform is utilized to decompose the input image into low-frequency subband and high-frequency subbands. Furthermore, an MPI model is designed to estimate the underlying multi-frequency distribution of input images. With this model, closer approximation to the true posterior distribution can be constrained and maximum-likelihood assignment can be approximated. Finally, based on the proposed MPI model, a two-path network consisting of inference network(InferenceNet) and generation network(GenerationNet) is trained parallelly to enforce the consistency of global structure and local texture between the generated image and ground truth. We qualitatively and quantitatively compare our method with other state-of-the-art methods on Paris StreetView, CelebA, CelebAMask-HQ and Places2 datasets. The results show the superior performance of our method, especially in the aspects of realistic texture details and semantic structural consistency.
Fast and Accurate Clustering of Multiple Modality Data via Feature Matching

Multiple modality clustering seeks to partition objects via leveraging cross-modality relations to provide comprehensive descriptions of the same objects. Current clustering methods rely heavily on accurate affinity measurements among samples. The samplewise affinity is costive to be constructed yet easy to corrupt by the heterogeneous gap. In the era of big data, fast and accurate clustering of multiple modality data remains challenging. To fill the gap, we propose a novel approach to achieve the clustering by focusing on feature matching across different modalities instead of samplewise affinity. First, a feature matching matrix is calculated by measuring the potential featurewise correlations. The obtained matching matrix is decomposed into two bases corresponding to the column and row spaces of feature matching, acting as coded bases within feature spaces of the different modalities. Then, the sample assignment is obtained by jointly reconstructing the samples by the two bases. The feature matching potential and sample assignment are collaboratively learned by an alternating optimization scheme. The proposed method dramatically reduces the computational cost by avoiding the costive samplewise affinity estimation, without sacrificing accuracy. Extensive experiments on the synthetic and real-world datasets demonstrate its superior speed and high accuracy.
Fast Conjugate Gradient Algorithm for Feedforward Neural Networks

The conjugate gradient (CG) algorithm is a method for learning neural networks. The highest computational load in this method is directional minimization. In this paper a new modification of the conjugate gradient algorithm is presented. The proposed solution speeds up the directional minimization, which result in a significant reduction of the calculation time. This modification of the CG algorithm was tested on selected examples. The performance of our method and the classic CG method was compared.
AR Book-Finding Behavior of Users in Library Venue

ARKit and ARCore, key technologies in recent augmented reality (AR) development, have allowed AR to become more integrated in our lives. However, how effective AR is in an auxiliary role in venue guidance and how to collect the actual behaviors of users in physical venues are worth exploring. This study used navAR, a spatial behavior analysis system app that our research team developed, to collect the actual behaviors of participants in physical space via a smartphone, such as time, distance travelled, and trajectory, and compared their book-finding behaviors in a library venue in a text scenario and an AR scenario without any additional sensors or cameras. The experiment results revealed that (1) AR targets made a significant difference in book search time, and the participants found some of the books significantly faster; (2) the participants presented no significant differences in distance travelled; (3) with an AR target, the book-finding trajectories of the participants were significantly more regular; (4) the AR guidance system had good usability. The results of this study can facilitate planning with AR in indoor venue routes, improve venue and exhibition tour experiences, and enable AR to be used for crowd flow diversion. Furthermore, this study provides a methodology for future analyses on user behavior in physical spaces.
PENGARUH PEMBELAJARAN DARING TERHADAP MINAT BELAJAR MAHASISWA PADA MASA PANDEMIK COVID-19

Pandemics that hit the world including Indonesia had an impact on all sectors, including the education sector. This has an impact on the introduction of distance learning with an online learning system. Because of this distance system the lecturer is required to be able to convey the course material well to students. This study aims to determine whether online learning carried out by lecturers at INSTIKA and UPN East Java can increase students' interest in studying the courses they take. The sample used in this study were 278 students. The analytical tool used was IBM SPSS Version 21. The results showed that the attitude of internet use, perception, experience, financial condition significantly influenced students' interest in learning. While the ability to learn independently does not have a significant effect on student interest in learning during online lectures.
UNIVERSITY-ENTERPRISE COOPERATION: EXPERIENCE OF A BASE DEPARTMENT IN ENGINEER TRAINING

This article discusses the problems and prospects of university-enterprise cooperation in the sphere of engineer training, in particular the phenomenon of ‘base departments’. The article focuses on the case of the Aeronautical Engineering Department of the Don State Technical University, whose purpose is to train engineering staff for the partner aerospace enterprise PAO Rostvertol. The study draws from the empirical data collected through the sociological survey of the Department’s faculty and students as well as the partner enterprise’s employees (graduates) and managers. Methodologically and conceptually, the study relies on the systemic approach, stakeholder theory, theory of competencies and the method of situation analysis. We sought to evaluate the sufficiency and relevance of the hard and soft skills that students developed during their studies at the Department. It is shown that the Department has managed to provide its students and graduates with a sufficient level of hard and soft skills to enhance their employability in aeronautical engineering. The Department uses a dual education system, which combines work-based learning with theoretical courses. In future, it is planned to expand this system and maximize student engagement by encouraging students to do their individual research projects at the partner enterprise. The research findings may be used to develop strategies of university-enterprise cooperation and to assess the efficiency of the training process.
Spatial Shift Point-Wise Quantization

Deep neural networks (DNN) have been applied to numerous artificial-intelligence applications because of their remarkable accuracy. However, computational requirements for deep neural networks are recently skyrocketing far beyond the Moore's Law. In addition to the importance of accuracy, the industry's demand for efficiency in model learning process is increasing. This has led to various attempts to make DNNs more lightweight. Hence, we propose a modeling technique that applies lightweight convolutional neural networks (CNN) to handle the model-learning processes for DNNs. The proposed spatial-shift pointwise quantization (SSPQ) model elegantly combines compact network-design techniques to revitalize DNN quantization efficiency with little accuracy loss. We set the depths of our SSPQ model to 20, 34, and 50 to test against CIFAR10, CIFAR100, and ImageNet datasets, respectively. By applying SSPQ20 to the CIFAR10 dataset, we reduced accuracy degradation by 2.95%, while reducing the number of parameters 8.7×. For the same dataset, our “wide”SSPQ20 variant reduced training parameters 1.96×, compared with the ResNet20 model architecture, which provided a 0.2% improvement. By applying SSPQ34 to the CIFAR100 dataset, we successfully reduced the number of learning parameters 8×, compared with the ResNet34 model with an accuracy degradation of 4.57%. By applying SSPQ50 to the ImageNet dataset, we successfully reduced the number of parameters 10.2× over ResNet50 with an accuracy degradation of only 2.68%. Therefore, using only 9.78MB of learning parameter, the SSPQ50 model guarantees 73.6% accuracy. This is an improved performance by 1.46% in terms of accuracy and 3.62MB in terms of model size compared to the MobileNetV2 lightweight model. The source code is available at https://github.com/EunhuiKim/SSPQ.
Graph Policy Network for Transferable Active Learning on Graphs

Graph neural networks (GNNs) have been attracting increasing popularity due to their simplicity and effectiveness in a variety of fields. However, a large number of labeled data is generally required to train these networks, which could be very expensive to obtain in some domains. In this paper, we study active learning for GNNs, i.e., how to efficiently label the nodes on a graph to reduce the annotation cost of training GNNs. We formulate the problem as a sequential decision process on graphs and train a GNN-based policy network with reinforcement learning to learn the optimal query strategy. By jointly training on several source graphs with full labels, we learn a transferable active learning policy which can directly generalize to unlabeled target graphs. Experimental results on multiple datasets from different domains prove the effectiveness of the learned policy in promoting active learning performance in both settings of transferring between graphs in the same domain and across different domains.
Overwater Image Dehazing via Cycle-Consistent Generative Adversarial Network

In contrast to images taken on land scenes, images taken over water are more prone to degradation due to the influence of the haze. However, existing image dehazing methods are mainly developed for land-scene images and perform poorly when applied to overwater images. To address this problem, we collect the first overwater image dehazing dataset and propose a Generative Adversial Network (GAN)-based method called OverWater Image Dehazing GAN (OWI-DehazeGAN). Due to the difficulties of collecting paired hazy and clean images, the dataset contains unpaired hazy and clean images taken over water. The proposed OWI-DehazeGAN is composed of an encoder–decoder framework, supervised by a forward-backward translation consistency loss for self-supervision and a perceptual loss for content preservation. In addition to qualitative evaluation, we design an image quality assessment neural network to rank the dehazed images. Experimental results on both real and synthetic test data demonstrate that the proposed method performs superiorly against several state-of-the-art land dehazing methods. Compared with the state-of-the-art, our method gains a significant improvement by 1.94% for SSIM, 7.13% for PSNR and 4.00% for CIEDE2000 on the synthetic test dataset.
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity

We present a differentially private learner for halfspaces over a finite grid $G$ in $\mathbb{R}^d$ with sample complexity $\approx d^{2.5}\cdot 2^{\log^*|G|}$, which improves the state-of-the-art result of [Beimel et al., COLT 2019] by a $d^2$ factor. The building block for our learner is a new differentially private algorithm for approximately solving the linear feasibility problem: Given a feasible collection of $m$ linear constraints of the form $Ax\geq b$, the task is to privately identify a solution $x$ that satisfies most of the constraints. Our algorithm is iterative, where each iteration determines the next coordinate of the constructed solution $x$.
Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations

A major challenge in visually grounded language generation is to build robust benchmark datasets and models that can generalize well in real-world settings.To do this, it is critical to ensure that our evaluation protocols are correct, and benchmarks are reliable.In this work, we set forth to design a set of experiments to understand an important but often ignored problem in visually grounded language generation: given that humans have different utilities and visual attention, how will the sample variance in multi-reference datasets affect the models' performance?Empirically, we study several multi-reference datasets and corresponding vision-and-language tasks.We show that it is of paramount importance to report variance in experiments; that humangenerated references could vary drastically in different datasets/tasks, revealing the nature of each task; that metric-wise, CIDEr has shown systematically larger variances than others.Our evaluations on reference-per-instance shed light on the design of reliable datasets in the future.
Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization

Much progress has been made in text summarization, fueled by neural architectures using large-scale training corpora.However, in the news domain, neural models easily overfit by leveraging position-related features due to the prevalence of the inverted pyramid writing style.In addition, there is an unmet need to generate a variety of summaries for different users.In this paper, we propose a neural framework that can flexibly control summary generation by introducing a set of subaspect functions (i.e.importance, diversity, position).These sub-aspect functions are regulated by a set of control codes to decide which sub-aspect to focus on during summary generation.We demonstrate that extracted summaries with minimal position bias is comparable with those generated by standard models that take advantage of position preference.We also show that news summaries generated with a focus on diversity can be more preferred by human raters.These results suggest that a more flexible neural summarization framework providing more control options could be desirable in tailoring to different user preferences, which is useful since it is often impractical to articulate such preferences for different applications a priori.
Designing Technologies with and for Youth: Traps of Privacy by Design

Media and communication scholars studying young people’s privacy often involve them in research in order to better understand their interactions with digital technologies. Yet there is a lack of research on how, when, and why it makes sense to involve young people in the design phase of new technologies and how data protection safeguards can be taken proactively by design. By engaging with the body of literature at the intersection of media and communication studies, participatory design, and child–computer interaction research, this article discusses how youth-centred design efforts risk falling into three traps of privacy by design, relating to: 1) the different degrees of decision power within and between child-centred design guidelines and participatory design with young people; 2) the involvement of young people in design as citizens versus consumers; and 3) the conditions under which their participation in design is empowerment rather than mere decoration. The contribution of this article is a critical, sociotechnical reflection on the challenges and opportunities of involving young people in privacy by design decision-making. The article concludes by outlining an agenda for participatory design within an encompassing empowerment and digital citizenship framework that invites young people to reflect on who they want to be in a data-driven society.
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynamics, we present a novel, scalable two-player RL algorithm, which is a sampling variant of the two-player policy gradient method. Our algorithm consistently outperforms existing baselines, in terms of generalization across different training and testing conditions, on several MuJoCo environments. Our experiments also show that, even for objective functions that entirely ignore potential environmental shifts, our sampling approach remains highly robust in comparison to standard RL algorithms.
A Reference Ontology of Money and Virtual Currencies

In recent years, there has been a growing interest, within the financial sector, in the adoption of ontology-based conceptual models to make the nature of conceptualizations explicit, as well as to safely establish the correct relations between them, thereby supporting semantic interoperability. Despite the wide number of efforts to create a unified view of the reality related to economic and financial domains, no comprehensive enough formal model has been developed to, on one hand, accurately describe the semantics regarding the world of money and currencies and, on the other hand, differentiate them from virtual currencies - of which cryptocurrencies are the most significant representative. This research aims at tackling these questions by conducting an ontological analysis of money and related concepts, grounded in the Unified Foundational Ontology, based on a literature review of the most relevant economic theories, and considering recent innovations in the financial industry.
Output-based transfer learning in genetic programming for document classification

Transfer learning has been studied in document classification for transferring a model trained from a source domain (SD) to a relatively similar target domain (TD). In feature-based transfer learning techniques, there is an investigation on the features being transferred from SD to TD. This paper conducts an investigation on an output-based transfer learning system using Genetic Programming (GP) in document classification tasks, which automatically selects features to construct classifiers. The proposed GP system directly generates programs from a set of sparse features and only considers the output change of the evolved programs from SD to TD. A linear model is then used to combine existing GP programs from SD as features to TD. Also, new GP programs are mutated from the programs evolved in SD to improve the accuracy. Via directly utilizing the evolved GP programs and their mutations, the feature extraction and estimation processes on TD are avoided. The results for the experiments demonstrates that the GP programs from SD can be effectively used for classifying documents in the relevant TD. The results also show that it is easy to train effective classifiers on TD when the GP programs are used as features. Furthermore, the proposed linear model, using multiple GP programs from SD as its inputs, outperforms single GP programs which are directly obtained from TD.
Planning and Generating Natural and Diverse Disfluent Texts as Augmentation for Disfluency Detection

Existing approaches to disfluency detection heavily depend on human-annotated data.Numbers of data augmentation methods have been proposed to alleviate the dependence on labeled data.However, current augmentation approaches such as random insertion or repetition fail to resemble training corpus well and usually resulted in unnatural and limited types of disfluencies.In this work, we propose a simple Planner-Generator based disfluency generation model to generate natural and diverse disfluent texts as augmented data, where the Planner decides on where to insert disfluent segments and the Generator follows the prediction to generate corresponding disfluent segments.We further utilize this augmented data for pretraining and leverage it for the task of disfluency detection.Experiments demonstrated that our two-stage disfluency generation model outperforms existing baselines; those disfluent sentences generated significantly aided the task of disfluency detection and led to state-of-the-art performance on Switchboard corpus.We have publicly released our code at https://github.com/GT-SALT/Disfluency-Generation-and-Detection.