Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis for Product Rating Using Classification

This paper proposes a machine learning technique for implementation of Sentiment Analysis in rating the product. This system would detect hidden viewpoints in the comment section of an online marketing site and subsequently give the rating of the product. It utilizes the assessment investigation approach with the end goal to accomplish the coveted usefulness. This depends on an E-Commerce web application where clients should enroll themselves and after that they view the item and its highlights and along these lines remark about its highlights. The framework will then break down the item and dependent on the surveys given by the clients rank the item. This will utilize a database of assessment-based watchwords alongside energy or antagonism weight in database and after that dependent on these slant catchphrases mined in client remark is positioned. The remarks will be dissected by contrasting them and the catchphrases put away in the database. The framework takes in different remarks of the clients of that item in this manner indicating whether the item is great, terrible, or normal. The framework likewise holds the capacity of giving the clients a chance to see the remarks posted by different customers or clients. The job of the administrator is to add item to the framework and to include catchphrases in database. This empowers the client to effortlessly discover the right item for utilization. This is likewise valuable for those clients who need to peruse audits about the item.
On public request for reforming the postgraduate studies system in the Russian Federation

The author offers a model of reforming the system of postgraduate studies in the Russian Federation. The Higher Attestation Commission should become an autonomous body of state administration under the federal authority and carry out administrative functions in the field of scientific and pedagogical personnel certification.
To Be or Not To Be a Verbal Multiword Expression: A Quest for Discriminating Features

Automatic identification of mutiword expressions (MWEs) is a pre-requisite for semantically-oriented downstream applications. This task is challenging because MWEs, especially verbal ones (VMWEs), exhibit surface variability. However, this variability is usually more restricted than in regular (non-VMWE) constructions, which leads to various variability profiles. We use this fact to determine the optimal set of features which could be used in a supervised classification setting to solve a subproblem of VMWE identification: the identification of occurrences of previously seen VMWEs. Surprisingly, a simple custom frequency-based feature selection method proves more efficient than other standard methods such as Chi-squared test, information gain or decision trees. An SVM classifier using the optimal set of only 6 features outperforms the best systems from a recent shared task on the French seen data.
A Deep Ordinal Distortion Estimation Approach for Distortion Rectification

Distortion is widely existed in the images captured by popular wide-angle cameras and fisheye cameras. Despite the long history of distortion rectification, accurately estimating the distortion parameters from a single distorted image is still challenging. The main reason is these parameters are implicit to image features, influencing the networks to fully learn the distortion information. In this work, we propose a novel distortion rectification approach that can obtain more accurate parameters with higher efficiency. Our key insight is that distortion rectification can be cast as a problem of learning an ordinal distortion from a single distorted image. To solve this problem, we design a local-global associated estimation network that learns the ordinal distortion to approximate the realistic distortion distribution. In contrast to the implicit distortion parameters, the proposed ordinal distortion have more explicit relationship with image features, and thus significantly boosts the distortion perception of neural networks. Considering the redundancy of distortion information, our approach only uses a part of distorted image for the ordinal distortion estimation, showing promising applications in the efficient distortion rectification. To our knowledge, we first unify the heterogeneous distortion parameters into a learning-friendly intermediate representation through ordinal distortion, bridging the gap between image feature and distortion rectification. The experimental results demonstrate that our approach outperforms the state-of-the-art methods by a significant margin, with approximately 23% improvement on the quantitative evaluation while displaying the best performance on visual appearance.
Peranan Pojok Baca dalam Menumbuhkan Minat Baca Siswa Sekolah Dasar

The purpose of this study is to describe the role of the reading corner in fostering students' interest in reading in elementary school. This type of research is phenomenology with a qualitative approach. The informants of this study were the homeroom teacher 1,2,3,4,5, and 6 primary schools 34/1 Lotus, the data collection instrument used observation sheets, interviews, and documentation. The results of this study are seen that the reading corner has a very important role in fostering students' interest in reading in elementary school, this is evident from the presence of several reading corner roles that can foster interest in reading students in elementary school. This study can be concluded that the reading corner is very helpful in fostering students 'interest in reading in the classroom, the role of the reading corner in growing students' interest in reading is, as a facilitator where to read, as the closest reading material, a comfortable reading place and an interesting reading place, so that of some the role helps foster students' interest in reading in elementary school.
INION’s Library as a “Mirror” of the Social Sciences Development in Russia: From the First Socialist Experiments to the Modern Digital Age

The article deals with the modern specifics of digitization of the fundamental library functioning in one of the leading Russian scientific institutes for social sciences – Institute of Scientific Information for Social Sciences of the Russian Academy of Sciences (INION). It is shown that after the fire in January 2015, the INION’s library never stopped to work. Nowadays, more than 3 million books and journals are available to readers in 18 departments at academic institutions in Moscow. INION receives mandatory copies of Russian publications and new foreign books and journals due to international exchange. Other funds are still preserved in the warehouse due to insufficient funding from the Russian Ministry of Science and Higher Education, but after moving to a new building on the historical site on Nakhimovsky Prospekt in Moscow, the INION’s library will expand its opportunities for readers. In preparation for this move, several digitization projects are being implemented at once in INION – from the development of famous bibliographic databases, which are still in commercial demand not only in Russia but also abroad, to the digitization of books and the creation of electronic catalogues of the library. The authors draw parallels between the modern objectives and methods of the modernization of the INION’s library in accordance with the challenges of the digital era and specifics of creation of the Library at the Socialist Academy of Social Sciences (which after several reorganizations in 1969 originated the INION) by difficult financial and political conditions of 1920-30-ies. It is concluded that any steps towards digitalization should be focused primarily on the needs of readers (postgraduates, researchers and university teachers) and not based on the proposals of technocrats who have little idea in which direction social and humanitarian sciences are developing. The ultimate goal of the development of the INION’s library should be to increase the competitiveness of scientific products of Russian social scientists in the world.
Hide-and-Seek Privacy Challenge

The clinical time-series setting poses a unique combination of challenges to data modeling and sharing. Due to the high dimensionality of clinical time series, adequate de-identification to preserve privacy while retaining data utility is difficult to achieve using common de-identification techniques. An innovative approach to this problem is synthetic data generation. From a technical perspective, a good generative model for time-series data should preserve temporal dynamics, in the sense that new sequences respect the original relationships between high-dimensional variables across time. From the privacy perspective, the model should prevent patient re-identification by limiting vulnerability to membership inference attacks. The NeurIPS 2020 Hide-and-Seek Privacy Challenge is a novel two-tracked competition to simultaneously accelerate progress in tackling both problems. In our head-to-head format, participants in the synthetic data generation track (i.e. "hiders") and the patient re-identification track (i.e. "seekers") are directly pitted against each other by way of a new, high-quality intensive care time-series dataset: the AmsterdamUMCdb dataset. Ultimately, we seek to advance generative techniques for dense and high-dimensional temporal data streams that are (1) clinically meaningful in terms of fidelity and predictivity, as well as (2) capable of minimizing membership privacy risks in terms of the concrete notion of patient re-identification.
Self-Adapting Recurrent Models for Object Pushing from Learning in Simulation

Planar pushing remains a challenging research topic, where building the dynamic model of the interaction is the core issue. Even an accurate analytical dynamic model is inherently unstable because physics parameters such as inertia and friction can only be approximated. Data-driven models usually rely on large amounts of training data, but data collection is time consuming when working with real robots. In this paper, we collect all training data in a physics simulator and build an LSTM-based model to fit the pushing dynamics. Domain Randomization is applied to capture the pushing trajectories of a generalized class of objects. When executed on the real robot, the trained recursive model adapts to the tracked object's real dynamics within a few steps. We propose the algorithm \emph{Recurrent} Model Predictive Path Integral (RMPPI) as a variation of the original MPPI approach, employing state-dependent recurrent models. As a comparison, we also train a Deep Deterministic Policy Gradient (DDPG) network as a model-free baseline, which is also used as the action generator in the data collection phase. During policy training, Hindsight Experience Replay is used to improve exploration efficiency. Pushing experiments on our UR5 platform demonstrate the model's adaptability and the effectiveness of the proposed framework.
Efficient Machine Unlearning Using General Adversarial Network

According to a recent study conducted by Forbes, the collective world data is expected to raise by 175 ZB in 2025 accounting to a 61% increase in data generation. As human, we are favoured with the nature of forgetting irrelevant data. The neurons in our brain are designed to filtrate the noise in the information in order to create more space to store relevant data. This neurophysical principle is witnessing its significance in the field of artificial intelligence due to data intensification. There is a great need for developing systems that have the capability to forget the data on the user’s request without compromising its system accuracy as data are the backbone of any deep learning system. Our approach combines the process of batching training data and general adversarial networks for data augmentation networks to achieve a complete and faster data forgetting model.
Intuitive, VR- and Gesture-based Physical Interaction with Virtual Commissioning Simulation Models

To manage complexity in modern, highly automated production systems, functional simulation for logic validation, known as virtual commissioning, is an important enabler. However, established systems for virtual commissioning are highly specialized software, which are solely used by simulation experts. In this contribution, we present a system for intuitive, user-integrated virtual commissioning. Using virtual reality, we allow for a first person experience of the simulation at runtime. By implementing a stereo-camera-based hand tracking system, we facilitate intuitive manual interaction with the simulation, including physical manipulation. Furthermore, we also integrate common human-machine interfaces, like touch panels, which are directly streamed from the industrial controller, offering the operator comparable interaction experience as with the real system. We conduct a user study quantifying the intuitiveness of interaction for defined tasks of our system compared to a standard simulation setup.
Study of Information Needs in the Historical Retrospect

The article presents an overview of the history of study of reading and information needs in Russia based on the analysis of publications in the professional press. The author highlights the main stages of development in this research area and notes the achievements of librarians who have made the greatest scientific contribution to the development of theory of information needs. The author shows that even before the emergence of the term (in the mid-twentieth century), the phenomenon of information needs was studied indirectly — through the analysis of reader’s interests and preferences. The article focuses on the formation and development of the methodology of studying information needs, considers the practice of using various research methods, including special ones: quantitative, qualitative, complex and computer. The author notes the role of the founder of bibliopsychology Nikolai A. Rubakin, who for the first time compared the qualitative and quantitative characteristics of reading with the social status of reader, and the significant contribution to the development of theory of information needs made by S.D. Kogotkov, who described the mechanisms and stages of their formation based on the activity approach. The article highlights the large-scale projects for studying reading of various social groups, implemented in the 1960s and 1980s. At the turn of the 20th and 21st centuries, the interest to the problem of information needs and its various aspects raised again. Research issues expanded mainly due to the increase in the number of works on the transformation of information needs in the electronic environment, professional reading, including specialists in the library sphere. The article shows the process of identifying the problems of information needs in the independent scientific direction of library science. It is concluded that despite the intensive research in recent decades, the scientific potential of the problem of information needs is far from exhausted, due to the complexity of the phenomenon being studied. The author believes that the priority task of library science in the near future should be research aimed at studying the peculiarities of the formation of professional information needs of specialists with the emphasis on the subjective component and involvement of modern methodological tools.
A Case based Online Trajectory Planning Method of Autonomous Unmanned Combat Aerial Vehicles with Weapon Release Constraints

As a challenging and highly complex problem, the trajectory planning for unmanned combat aerial vehicle (UCAV) focuses on optimising flight trajectory under such constraints as kinematics and complicated battlefield environment. An online case-based trajectory planning strategy is proposed in this study to achieve rapid control variables solution of UCAV flight trajectory for the of delivery airborne guided bombs. Firstly, with an analysis of the ballistic model of airborne guided bombs, the trajectory planning model of UCAVs is established with launch acceptable region (LAR) as a terminal constraint. Secondly, a case-based planning strategy is presented, which involves four cases depending on the situation of UCAVs at the current moment. Finally, the feasibility and efficiency of the proposed planning strategy is validated by numerical simulations, and the results show that the presented strategy is suitable for UCAV performing airborne guided delivery missions in dynamic environments.
Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration

Non-local self-similarity and sparsity principles have proven to be powerful priors for natural image modeling. We propose a novel differentiable relaxation of joint sparsity that exploits both principles and leads to a general framework for image restoration which is (1) trainable end to end, (2) fully interpretable, and (3) much more compact than competing deep learning architectures. We apply this approach to denoising, jpeg deblocking, and demosaicking, and show that, with as few as 100K parameters, its performance on several standard benchmarks is on par or better than state-of-the-art methods that may have an order of magnitude or more parameters.
English Language Learning Strategies Adopted by Bachelor Level Students (A Case Study of Nepal)

This research paper is an attempt to find out the difficulties faced by the students of bachelor level in Nepal in learning English language at class. In order to achieve these objectives, two research tools – questionnaire for students and class observation form were prepared. Two colleges of Banke district, Nepalgunj were selected for the collection of data. Twenty-five students were selected from each college. Questionnaires were distributed to the selected students and also classes were observed focusing on the strategies and difficulties of language learning. A very divergent situation was found in the strategies of language learning. It was varied from students to students. It was found out that the maximum use of memory strategy; they were using other strategies too. It was found that students were facing with different problems in language learning.
Recurrent Exposure Generation for Low-Light Face Detection

Face detection from low-light images is challenging due to limited photos and inevitable noise, which, to make the task even harder, are often spatially unevenly distributed. A natural solution is to borrow the idea from multi-exposure, which captures multiple shots to obtain well-exposed images under challenging conditions. High-quality implementation/approximation of multi-exposure from a single image is however nontrivial. Fortunately, as shown in this paper, neither is such high-quality necessary since our task is face detection rather than image enhancement. Specifically, we propose a novel Recurrent Exposure Generation (REG) module and couple it seamlessly with a Multi-Exposure Detection (MED) module, and thus significantly improve face detection performance by effectively inhibiting non-uniform illumination and noise issues. REG produces progressively and efficiently intermediate images corresponding to various exposure settings, and such pseudo-exposures are then fused by MED to detect faces across different lighting conditions. The proposed method, named REGDet, is the first `detection-with-enhancement' framework for low-light face detection. It not only encourages rich interaction and feature fusion across different illumination levels, but also enables effective end-to-end learning of the REG component to be better tailored for face detection. Moreover, as clearly shown in our experiments, REG can be flexibly coupled with different face detectors without extra low/normal-light image pairs for training. We tested REGDet on the DARK FACE low-light face benchmark with thorough ablation study, where REGDet outperforms previous state-of-the-arts by a significant margin, with only negligible extra parameters.
College English Teaching System Based on Resource Library and Network Support Platform

The current situation of university English teaching in the modern internet technology environment, the college English teaching environment featuring personalized and interactive learning and active learning. University English teaching is to help students lay a solid language foundation and improve their language use ability, innovation ability, collaboration ability, independence ability, English communication ability, open thinking ability and quality shared with others, so that students can better adapt to society and serve the society. The online teaching platform provides a wide range of learning resources, convenient learning tools and flexible learning strategies for the teaching of college English teachers, and supports teachers’ tutoring and assistance to students. This paper analyzes the current situation of university English teaching mode and related problems, and proposes a strategy based on English education resources library and internet platform to implement English teaching reform practice.
Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets

Monocular depth estimation plays a crucial role in 3D recognition and understanding. One key limitation of existing approaches lies in their lack of structural information exploitation, which leads to inaccurate spatial layout, discontinuous surface, and ambiguous boundaries. In this paper, we tackle this problem in three aspects. First, to exploit the spatial relationship of visual features, we propose a structure-aware neural network with spatial attention blocks. These blocks guide the network attention to global structures or local details across different feature layers. Second, we introduce a global focal relative loss for uniform point pairs to enhance spatial constraint in the prediction, and explicitly increase the penalty on errors in depth-wise discontinuous regions, which helps preserve the sharpness of estimation results. Finally, based on analysis of failure cases for prior methods, we collect a new Hard Case (HC) Depth dataset of challenging scenes, such as special lighting conditions, dynamic objects, and tilted camera angles. The new dataset is leveraged by an informed learning curriculum that mixes training examples incrementally to handle diverse data distributions. Experimental results show that our method outperforms state-of-the-art approaches by a large margin in terms of both prediction accuracy on NYUDv2 dataset and generalization performance on unseen datasets.
도메인 적응을 이용한 단일 파노라마 깊이 추정

In this paper,we propose a deep learning framework for predicting a depth map of a 360° panorama image. Previous works use synthetic 360° panorama datasets to train networks due to the lack of realistic datasets. However,the synthetic nature of the datasets induces features extracted by the networks to differ from those of real 360° panorama images,which inevitably leads previous methods to fail in depth prediction of real 360° panorama images. To address this gap, we use domain adaptation to learn features shared by real and synthetic panorama images. Experimental results show that our approach can greatly improve the accuracy of depth estimation on real panorama images while achieving the state-of-the-art performance on synthetic images.