Reviewerly/exHarmony huggingface dataset:
CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data

Hyperbolic space can naturally embed hierarchies that often exist in real-world data and semantics. While high-dimensional hyperbolic embeddings lead to better representations, most hyperbolic models utilize low-dimensional embeddings, due to non-trivial optimization and visualization of high-dimensional hyperbolic data. We propose CO-SNE, which extends the Euclidean space visualization tool, t-SNE, to hyperbolic space. Like t-SNE, it converts distances between data points to joint probabilities and tries to minimize the Kullback-Leibler divergence between the joint probabilities of high-dimensional data $X$ and low-dimensional embedding $Y$. However, unlike Euclidean space, hyperbolic space is inhomogeneous: A volume could contain a lot more points at a location far from the origin. CO-SNE thus uses hyperbolic normal distributions for $X$ and hyperbolic \underline{C}auchy instead of t-SNE's Student's t-distribution for $Y$, and it additionally seeks to preserve $X$'s individual distances to the \underline{O}rigin in $Y$. We apply CO-SNE to naturally hyperbolic data and supervisedly learned hyperbolic features. Our results demonstrate that CO-SNE deflates high-dimensional hyperbolic data into a low-dimensional space without losing their hyperbolic characteristics, significantly outperforming popular visualization tools such as PCA, t-SNE, UMAP, and HoroPCA which is also designed for hyperbolic data.
INTELLECTUAL PROPERTY POLICY AS A TOOL FOR PROMOTING AND ENHANCING INTELLECTUAL PROPERTY LITERACY

T. Trencheva1, S. Dimitrova1, M. Lazarova2 1University of Library Studies and Information Technologies (BULGARIA) 2National Academy for Theatre and Film Arts (BULGARIA)
FBNetV5: Neural Architecture Search for Multiple Tasks in One Run

Neural Architecture Search (NAS) has been widely adopted to design accurate and efficient image classification models. However, applying NAS to a new computer vision task still requires a huge amount of effort. This is because 1) previous NAS research has been over-prioritized on image classification while largely ignoring other tasks; 2) many NAS works focus on optimizing task-specific components that cannot be favorably transferred to other tasks; and 3) existing NAS methods are typically designed to be "proxyless" and require significant effort to be integrated with each new task's training pipelines. To tackle these challenges, we propose FBNetV5, a NAS framework that can search for neural architectures for a variety of vision tasks with much reduced computational cost and human effort. Specifically, we design 1) a search space that is simple yet inclusive and transferable; 2) a multitask search process that is disentangled with target tasks' training pipeline; and 3) an algorithm to simultaneously search for architectures for multiple tasks with a computational cost agnostic to the number of tasks. We evaluate the proposed FBNetV5 targeting three fundamental vision tasks -- image classification, object detection, and semantic segmentation. Models searched by FBNetV5 in a single run of search have outperformed the previous stateof-the-art in all the three tasks: image classification (e.g., +1.3% ImageNet top-1 accuracy under the same FLOPs as compared to FBNetV3), semantic segmentation (e.g., +1.8% higher ADE20K val. mIoU than SegFormer with 3.6x fewer FLOPs), and object detection (e.g., +1.1% COCO val. mAP with 1.2x fewer FLOPs as compared to YOLOX).
Toward Compact Parameter Representations for Architecture-Agnostic Neural Network Compression

This paper investigates deep neural network (DNN) compression from the perspective of compactly representing and storing trained parameters. We explore the previously overlooked opportunity of cross-layer architecture-agnostic representation sharing for DNN parameters. To do this, we decouple feedforward parameters from DNN architectures and leverage additive quantization, an extreme lossy compression method invented for image descriptors, to compactly represent the parameters. The representations are then finetuned on task objectives to improve task accuracy. We conduct extensive experiments on MobileNet-v2, VGG-11, ResNet-50, Feature Pyramid Networks, and pruned DNNs trained for classification, detection, and segmentation tasks. The conceptually simple scheme consistently outperforms iterative unstructured pruning. Applied to ResNet-50 with 76.1% top-1 accuracy on the ILSVRC12 classification challenge, it achieves a $7.2\times$ compression ratio with no accuracy loss and a $15.3\times$ compression ratio at 74.79% accuracy. Further analyses suggest that representation sharing can frequently happen across network layers and that learning shared representations for an entire DNN can achieve better accuracy at the same compression ratio than compressing the model as multiple separate parts. We release PyTorch code to facilitate DNN deployment on resource-constrained devices and spur future research on efficient representations and storage of DNN parameters.
VisQuelle : Visual Question based Elementary Learning Companion a system to Facilitate Learning Word Object Associations

We present an automated, visual question answering based companion – VisQuelle - to facilitate elementary learning of word-object associations. In particular, we attempt to harness the power of machine learning models for object recognition and the understanding of combined processing of images and text data from visual-question answering to provide variety and nuance in the images associated with letters or words presented to the elementary learner. We incorporate elements such as gamification to motivate the learner by recording scores, errors, etc., to track the learner’s progress. Translation is also provided to reinforce word-object associations in the user’s native tongue, if the learner is using VisQuelle to learn a second language.
Semantic-Sparse Colorization Network for Deep Exemplar-based Colorization

Exemplar-based colorization approaches rely on reference image to provide plausible colors for target gray-scale image. The key and difficulty of exemplar-based colorization is to establish an accurate correspondence between these two images. Previous approaches have attempted to construct such a correspondence but are faced with two obstacles. First, using luminance channels for the calculation of correspondence is inaccurate. Second, the dense correspondence they built introduces wrong matching results and increases the computation burden. To address these two problems, we propose Semantic-Sparse Colorization Network (SSCN) to transfer both the global image style and detailed semantic-related colors to the gray-scale image in a coarse-to-fine manner. Our network can perfectly balance the global and local colors while alleviating the ambiguous matching problem. Experiments show that our method outperforms existing methods in both quantitative and qualitative evaluation and achieves state-of-the-art performance.
Insights into How to Improve Chinese Undergraduate Students' Autonomous Learning Ability

This paper firstly explores the learning characteristics of undergraduates at different stages of learning in China's universities and the self-learning abilities they are supposed to possess, as well as the reasons that affect their active learning. Finally, the paper proposes recommendations on how to improve the autonomous learning ability of students at different grades.
Attention-based Feature Decomposition-Reconstruction Network for Scene Text Detection

Recently, scene text detection has been a challenging task. Texts with arbitrary shape or large aspect ratio are usually hard to detect. Previous segmentation-based methods can describe curve text more accurately but suffer from over segmentation and text adhesion. In this paper, we propose attention-based feature decomposition-reconstruction network for scene text detection, which utilizes contextual information and low-level feature to enhance the performance of segmentation-based text detector. In the phase of feature fusion, we introduce cross level attention module to enrich contextual information of text by adding attention mechanism on fused multi-scaled feature. In the phase of probability map generation, a feature decomposition-reconstruction module is proposed to alleviate the over segmentation problem of large aspect ratio text, which decomposes text feature according to their frequency characteristic and then reconstructs it by adding low-level feature. Experiments have been conducted on two public benchmark datasets and results show that our proposed method achieves state-of-the-art performance.
Literasi Digital Taman Baca Pelita Di Kota Tanjung Pura Kabupaten Langkat

The low participation in children's education and the high drop out rate were social problems for the community in Tanjung Pura City. There fore, Pelita reading park was established as an effort to solve this problem. However, in its operation there were several problems faced including: 1) the lack of ability and knowledge of managers in the management of reading park documentation, 2) the service was still manual, 3) most of the dominant reading park activities were in the form of reading books and story telling, 4) the absence of mobile facilities. to be used as a means of service and mobile socialization, 5). lack of facilities and infrastructure. 6) limited used of information technology. The solutions offered were in the form of: assisting partners in building management of reading park based on information systems, designing tools (gerobak pintar) and preparing reading park development programs. The method of activity carried out is mentoring partners to improve the management of reading park so that they have the ability to manage reading park based on information technology and design tools in the form of smart carts. The results of this activity were 1) There was an increase in commitment and a sense of shared responsibility between the manager and the surrounding community as indicated by the increase in the number of the management team to 11 people, 2) an increase in the expertise and skills of partners in reading park documentation management. 3) the availability of gerobak pintar suitable for storing collections of reading materials, 4) the existence of an application system for managing collections of reading [ark based on information technology and 5) increasing public interest in using the Pelita reading park as a means of learning and reading.
A Universal High Performance Intelligent Processor Of Satellite

With the development and maturity of artificial intelligence technology, more and more space remote sensing satellites begin to use artificial intelligence technology to solve the problem of remote sensing image real-time processing. Payload image processing of remote sensing satellite is a complex system, involving process control, data acquisition, data transmission, data storage, analysis and processing. At present, the image processing process of most remote sensing satellites is roughly the same, but the requirements for image processing are different, resulting in different image processing algorithms, and then the hardware computing power requirements are different. This paper presents a design of universal high-performance space-borne intelligent processor, which has the characteristics of high performance, high reliability and redundancy, supports configurable computing power, and has strong versatility.
Utilizing Resource-Rich Language Datasets for End-to-End Scene Text Recognition in Resource-Poor Languages

This paper presents a novel training method for end-to-end scene text recognition. End-to-end scene text recognition offers high recognition accuracy, especially when using the encoder-decoder model based on Transformer. To train a highly accurate end-to-end model, we need to prepare a large image-to-text paired dataset for the target language. However, it is difficult to collect this data, especially for resource-poor languages. To overcome this difficulty, our proposed method utilizes well-prepared large datasets in resource-rich languages such as English, to train the resource-poor encoder-decoder model. Our key idea is to build a model in which the encoder reflects knowledge of multiple languages while the decoder specializes in knowledge of just the resource-poor language. To this end, the proposed method pre-trains the encoder by using a multilingual dataset that combines the resource-poor language’s dataset and the resource-rich language’s dataset to learn language-invariant knowledge for scene text recognition. The proposed method also pre-trains the decoder by using the resource-poor language’s dataset to make the decoder better suited to the resource-poor language. Experiments on Japanese scene text recognition using a small, publicly available dataset demonstrate the effectiveness of the proposed method.
BEYOND 3D: DESIGNING POSSIBLE FUTURES THROUGH THE POWER OF THE FOURTH DIMENSION: Versões expandidas de cinco artigos selecionados do Graphica 2019.

With advances of technologies such as Virtual Reality (VR) and Augmented Reality (AR), there is a constant need to integrate 3D elements in interaction design. This paper2 discusses the implications of the design of the fourth dimension (4D) as a speculative design element for the development of interactive experiences. Through the discussion of mixed disciplines like mathematics, physics, design and architecture, the current paper describes the potential of looking dimensions that go beyond 3D in order to augment the future applications of interactive design and reimagine the way we build new spaces and interactions.Com os avancos de tecnologias como Realidade Virtual (VR) e Realidade Aumentada (AR), ha uma necessidade constante de integrar elementos 3D no design de interacao. Este artigo discute as implicacoes do design da quarta dimensao (4D), como elemento especulativo do design para o desenvolvimento de experiencias interativas. Atraves da discussao de disciplinas mistas, como matematica, fisica, design e arquitetura, este artigo descreve o potencial de olhar dimensoes que vao alem do 3D, a fim de aumentar as futuras aplicacoes do design interativo e re-imaginar a maneira como construimos novos espacos e interacoes.
Private Reverse Top-k Algorithms Applied on Public Data of COVID-19 in the State of Ceará

In this article we propose a differentially private reverse top-k query. Our strategy allows obtaining the less frequent data according to a search criteria, with a high guarantee of privacy of the individuals who contributed with personal data in the original database. We apply our strategy on public data for COVID-19 in the State of Ceará using two different queries. Our experimental results show that the result of the proposed top-k query returns a high degree of similarity to the result of a conventional top-k query, when the chosen budget is suitable, providing useful results for researchers, while ensuring a low probability of re-identification of individuals arising from the properties of differential privacy.
Sample Efficient Imitation Learning via Reward Function Trained in Advance

Imitation learning (IL) is a framework that learns to imitate expert behavior from demonstrations. Recently, IL shows promising results on high dimensional and control tasks. However, IL typically suffers from sample inefficiency in terms of environment interaction, which severely limits their application to simulated domains. In industrial applications, learner usually have a high interaction cost, the more interactions with environment, the more damage it causes to the environment and the learner itself. In this article, we make an effort to improve sample efficiency by introducing a novel scheme of inverse reinforcement learning. Our method, which we call \textit{Model Reward Function Based Imitation Learning} (MRFIL), uses an ensemble dynamic model as a reward function, what is trained with expert demonstrations. The key idea is to provide the agent with an incentive to match the demonstrations over a long horizon, by providing a positive reward upon encountering states in line with the expert demonstration distribution. In addition, we demonstrate the convergence guarantee for new objective function. Experimental results show that our algorithm reaches the competitive performance and significantly reducing the environment interactions compared to IL methods.
Dashboard Monitoring Website Dosen Studi Kasus Universitas Bina Darma

The concept of dashboard performance is an information system application model provided for managers to present performance quality information, from a company or organizational institution, dashboards have been widely adopted by companies or businesses. In this study, the authors designed a dashboard that was used as a monitoring system for lecturer posting activities at Binadarma University, Palembang. The limitation of the dashboard in this study only displays data based on lecturer posts which will be developed in the form of a website. Dashboard design is more effective and efficient than viewing data manually, which requires managers to open a database to view data one by one from lecturers' posts. In making the lecturer website dashboard, the data taken is in the form of posting data stored in the lecturer database, the data that has been taken will be presented in the dashboard in the form of graphs, tables and dashboards.
AN INVESTIGATION OF MACROLINGUISTIC ERRORS IN INDONESIAN EFL LEARNERS’ ESSAYS

Abstract&#x0D; This present study tries to investigate macrolinguistic errors and to know the possible causes of the errors committed by Indonesian EFL Learners in their essay writing. This study employed qualitative approach with content analysis method. The data were taken from 40 university students’ essay and interview in one public university located in Banten province. The result of the data analysis reveals that the students commit seven types of error at the discoursal level and two types of error at the pragmatic level. In the discoursal level, the students committed run-on sentences, unawareness of logical relation, poor paragraph development, cohesion problem as well as coherence problem, improper use of discourse markers, and absence of parallel structure. In the pragmatic level, the researchers found miss-selection of lexical item and violation of the cooperative principles. Moreover, the possible causes of the errors included students have lack interest in writing; inadequate grammatical mastery; arranging ideas in a meaningful and logical order; lack of vocabulary and formulating their idea of writing. It is hoped that the result of this study can be the consideration for both the teachers when they design the material, and students to recognize their weaknesses in doing the writing and find the best solution to cope with this problem.&#x0D; Keywords: macrolinguistic error; students’ essay; Indonesia EFL Learners
ROBIN : A Benchmark for Robustness to Individual Nuisancesin Real-World Out-of-Distribution Shifts

Enhancing the robustness in real-world scenarios has been proven very challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or they simply measure robustness as generalization between datasets and hence ignore the effects of individual nuisance factors. In this work, we introduce ROBIN, a benchmark dataset for diagnosing the robustness of vision algorithms to individual nuisances in real-world images. ROBIN builds on 10 rigid categories from the PASCAL VOC 2012 and ImageNet datasets and includes out-of-distribution examples of the objects 3D pose, shape, texture, context and weather conditions. ROBIN is richly annotated to enable benchmark models for image classification, object detection, and 3D pose estimation. We provide results for a number of popular baselines and make several interesting observations: 1. Some nuisance factors have a much stronger negative effect on the performance compared to others. Moreover, the negative effect of an OODnuisance depends on the downstream vision task. 2. Current approaches to enhance OOD robustness using strong data augmentation have only marginal effects in real-world OOD scenarios, and sometimes even reduce the OOD performance. 3. We do not observe any significant differences between convolutional and transformer architectures in terms of OOD robustness. We believe our dataset provides a rich testbed to study the OOD robustness of vision algorithms and will help to significantly push forward research in this area.
Post-Quantum Zero Knowledge, Revisited (or: How to do Quantum Rewinding Undetectably)

A major difficulty in quantum rewinding is the fact that measurement is destructive: extracting information from a quantum state irreversibly changes it. This is especially problematic in the context of zero-knowledge simulation, where preserving the adversary's state is essential. In this work, we develop new techniques for quantum rewinding in the context of extraction and zero-knowledge simulation: (1) We show how to extract information from a quantum adversary by rewinding it without disturbing its internal state. We use this technique to prove that important interactive protocols, such as the Goldreich-Micali-Wigderson protocol for graph non-isomorphism and the Feige-Shamir protocol for NP, are zero-knowledge against quantum adversaries. (2) We prove that the Goldreich-Kahan protocol for NP is post-quantum zero knowledge using a simulator that can be seen as a natural quantum extension of the classical simulator. Our results achieve (constant-round) black-box zero-knowledge with negligible simulation error, appearing to contradict a recent impossibility result due to Chia-Chung-Liu-Yamakawa (FOCS 2021). This brings us to our final contribution: (3) We introduce coherent-runtime expected quantum polynomial time, a computational model that (a) captures all of our zero-knowledge simulators, (b) cannot break any polynomial hardness assumptions, and (c) is not subject to the CCLY impossibility. In light of our positive results and the CCLY negative results, we propose coherent-runtime simulation to be the right quantum analogue of classical expected polynomial-time simulation.
ARE RUSSIAN STUDENTS SATISFIED WITH THEIR EDUCATION?

G. ZborovskyUral Federal University named after the first President of Russia B.N. Yeltsin (RUSSIAN FEDERATION)
Efficient 2D Sound Propagation in Video Games

Sound is well known for improving immersion in video games. In many games sound also serves as an important game mechanism; in stealth games, for example, sounds triggered by player actions may attract enemy agents, and sounds created by enemy agents give useful information on where to target or avoid combat. Modelling sound, however, is computationally complex, and thus sound propagation is often restricted to highly localized contexts, or based on trivial propagation models such as a fixed radius that ignores the impact of obstacles. In this work, we describe an efficient approach to propagating sound in a real-time 2D game level. We use a heuristic ray-based approach, adapted to include diffraction, reflection, and transmission. Our design allows for dynamic geometries, avoids sound discontinuities, and can be tuned in various ways to trade-off accuracy for computation cost.