Reviewerly/exHarmony huggingface dataset:
Mutltimodal AI Companion for Interactive Fairytale Co-creation

AI fairy tale companions play an important role in early childhood education as an augmentation for parents' efforts to close the participation gap and boost kids' mental and language development. Existing systems are generally designed to provide vivid materials as unidirectional entertaining reading environments, e.g, visualizing inputting texts. However, due to the limited vocabulary of kids, these systems failed to afford effective interaction to motivate kids to write their own fairy tales. In this work, we propose AI.R Taletorium, an illustrative, immersive, and inclusive multimodal AI companion, for interactive fairy tale co-creation that actively involves kids to create fairy tales with both the AI agent and their normal peers. AI.R Taletorium consists a neural story generator and a doodler-based fairy tale visualizer. We design a character-centric bidirectional connection mechanism between the story generator and visualizer equipped with Contrastive Language Image Pretraining (CLIP), thus enabling kids to participant in the story generation process by simple sketching. Extensive experiments and user studies show that our system was able to generate and visualize meaningful and vivid fairy tales with limited training data and complete the full interaction cycle under various inputs (text, doodler) through the bidirectional connection.
Continual Learning for Class- and Domain-Incremental Semantic Segmentation.

The field of continual deep learning is an emerging field and a lot of progress has been made. However, concurrently most of the approaches are only tested on the task of image classification, which is not relevant in the field of intelligent vehicles. Only recently approaches for class-incremental semantic segmentation were proposed. However, all of those approaches are based on some form of knowledge distillation. At the moment there are no investigations on replay-based approaches that are commonly used for object recognition in a continual setting. At the same time while unsupervised domain adaption for semantic segmentation gained a lot of traction, investigations regarding domain-incremental learning in an continual setting is not well-studied. Therefore, the goal of our work is to evaluate and adapt established solutions for continual object recognition to the task of semantic segmentation and to provide baseline methods and evaluation protocols for the task of continual semantic segmentation. We firstly introduce evaluation protocols for the class- and domain-incremental segmentation and analyze selected approaches. We show that the nature of the task of semantic segmentation changes which methods are most effective in mitigating forgetting compared to image classification. Especially, in class-incremental learning knowledge distillation proves to be a vital tool, whereas in domain-incremental learning replay methods are the most effective method.
Learning to Predict Persona Information forDialogue Personalization without Explicit Persona Description

Personalizing dialogue agents is important for dialogue systems to generate more specific, consistent, and engaging responses. However, most current dialogue personalization approaches rely on explicit persona descriptions during inference, which severely restricts its application. In this paper, we propose a novel approach that learns to predict persona information based on the dialogue history to personalize the dialogue agent without relying on any explicit persona descriptions during inference. Experimental results on the PersonaChat dataset show that the proposed method can improve the consistency of generated responses when conditioning on the predicted profile of the dialogue agent (i.e. "self persona"), and improve the engagingness of the generated responses when conditioning on the predicted persona of the dialogue partner (i.e. "their persona"). We also find that a trained persona prediction model can be successfully transferred to other datasets and help generate more relevant responses.
Klasifikasi Kecambah Mangrove Menggunakan Multi Layer Perceptron

Data processing in the field of machine learning and its application to environment is still an interesting field until now. It is because there have been a lot of research related to computer science and agriculture especially mangroves, so there are still many research gaps that can be executed in the future. The long-term goal of this research is to apply machine learning techniques to the data and problem domains of mangrove plants. This study aims to obtain a classification of three classes of mangrove sprouts, namely: Avicennia Marina, Sonneratia Caseolaris and Ceriops Tagal, using the Multi Layer Perceptron (MLP) method, where MLP is one of the methods in the field of Machine Learning and Artificial Intelligence. The results of this study are using the number of neurons in the hidden layer more than the number of neurons in the input layer resulting in an optimal accuracy value at the 1000th epoch with an accuracy value of 97.7% for data testing, and an accuracy value of 99% for testing data. Keywords: Machine Learning, Classification, Mangrove Sprouts, Multi-Layer Perceptron.
THE SUCCESSFUL INTERACTION BETWEEN UNIVERSITIES AND LIBRARIES TO ACQUIRE NEW PRACTICAL TRAINING AND SKILLS FOR STUDENTS DURING A PANDEMIC

I. Yankova1, K. Ivanova2, B. Hadjieva1 1University of Library Science and Information Technology (ULSIT) (BULGARIA) 2Regional Public Library "Petko Rachev Slaveykov" (BULGARIA)
Efficient Self-Ensemble Framework for Semantic Segmentation

Ensemble of predictions is known to perform better than individual predictions taken separately. However, for tasks that require heavy computational resources, \textit{e.g.} semantic segmentation, creating an ensemble of learners that needs to be trained separately is hardly tractable. In this work, we propose to leverage the performance boost offered by ensemble methods to enhance the semantic segmentation, while avoiding the traditional heavy training cost of the ensemble. Our self-ensemble framework takes advantage of the multi-scale features set produced by feature pyramid network methods to feed independent decoders, thus creating an ensemble within a single model. Similar to the ensemble, the final prediction is the aggregation of the prediction made by each learner. In contrast to previous works, our model can be trained end-to-end, alleviating the traditional cumbersome multi-stage training of ensembles. Our self-ensemble framework outperforms the current state-of-the-art on the benchmark datasets ADE20K, Pascal Context and COCO-Stuff-10K for semantic segmentation and is competitive on Cityscapes. Code will be available at github.com/WalBouss/SenFormer.
THE ROBINHOOD OF 3D: DEMOCRATIZING VOLUMETRIC CAPTURE FOR MORE ACCESSIBLE AND INNOVATIVE LEARNING ENVIRONMENTS

B. Carter, L. Robinson, E. Acuna, D. Uralov, T. Ketema, M. GordonUniversity of Arizona (UNITED STATES)
Featured Team Automata.

We propose featured team automata to support variability in the development and analysis of teams, which are systems of reactive components that communicate according to specified synchronisation types. A featured team automaton concisely describes a family of concrete product models for specific configurations determined by feature selection. We focus on the analysis of communication-safety properties, but doing so product-wise quickly becomes impractical. Therefore, we investigate how to lift notions of receptiveness (no message loss) to the level of family models. We show that featured (weak) receptiveness of featured team automata characterises (weak) receptiveness for all product instantiations. A prototypical tool supports the developed theory.
예수의 발 씻기 내러티브(요 13:1-20)의 선교적 읽기

This study aims to missionally read Jesus’ foot washing narrative (John 13:1-20) for Korean churches and Christians. This paper argues that the foot washing narrative accounts for Jesus’ and the disciples’ missional concepts. First, the structure of the narrative and sending language reflect missions. Second, Jesus’ mission was the achievement of establishing a holy community through his death. Third, the disciples were sent into the world for mission(s). Their mission was not only to bear witness to Jesus through the life-pattern of their love and service, but also to preach the gospel to the world. Finally, the missional significance of the foot washing narrative challenges today’s Korean churches and Christians to become holy communities, the communities of love and service, and the communities to spread the gospel within the truth.
Calculate The Conductivity of Some Composites of Cellulose Bacteria Mixed with Polypyrol

The manufacture of composites that have good electrical properties is to use a conductive polymer matrix. A conductive polymer is a polymer compound that has a stable bond that allows the polymer to act as a good conductor of electricity. This study aims to determine the highest conductivity value of composite materials that have been coated with polypyrrole, namely bacterial cellulose with polypyrrole (bio composite 1), tempo bacteria cellulose with polypyrrole (bio composite 2), and Gambier bacteria cellulose with polypyrrole (bio composite 3). In this study, there were four samples consisting of nata de coco (cellulosic bacteria), 2, 2, 6, 6-tetramethylpiperidine 1-oxyl (TEMPO), Gambier extract, and polypyrrole. Measurement of resistance value using the two point probe method. The results of this study obtained that the resistance and conductivity values of bio composite 1, bio composite 2, and bio composite 3 were 29.742 k? and 1.17810-3 S/cm, 20.338 k? and 1.69210-3 S/cm, 34,572 k? and 0.980710-3 S/cm. The measurement results show that the highest conductivity value is bio composite 2.
PERANCANGAN INSTALASI LISTRIK GEDUNG DINAS PENGENDALIAN PENDUDUK DAN KELUARGA BERENCANA ( P2KB) PROVINSI SULAWESI TENGAH

Electricity has become a basic need for various human activities, because electricity has functions and benefits that are vital in daily life which has become a basic need in encouraging national development in all fields. One part of the world electricity is the installation of lighting that is also me njadi one of the main points in a building, it is very necessary that human activity has remained secure and running smoothly.&#x0D; In this study, the writer will design an electrical installation for a building, namely the office building of the Population and Family Planning Control Office (P2KB) of Central Sulawesi Province, JL. RA Kartini NO. 100 Palu City. This study aims to make an electrical installation design in accordance with the Indonesian National Standard (SNI) and the General Electrical Installation regulations (PUIL), so that the installation is designed to be more effective and efficient. In the design of this installation also made design drawings for installation in buildings.&#x0D; Based on the design planning of the electrical installation that has been done, the results are obtained, the total electrical power from the electrical installation of this building is 60246 watts or 75307 VA and for electricity supply is taken from PLN, namely 3-phase electricity with a capacity of 82,500 VA. The conductor used in this installation uses NYY and NYM cable types. The security used in this installation consists of 2 types, namely NFB and MCB , in addition to this building installation system is also given a PHB box to facilitate maintenance and repair in case of damage. Taking into account the economic and safety factors of the installation, the total Budget Plan (RAB) for the installation of the building of the Population and Family Planning Control Office (P2KB) is Rp. 223,600,000
Anomaly Rule Detection in Sequence Data

Analyzing sequence data usually leads to the discovery of interesting patterns and then anomaly detection. In recent years, numerous frameworks and methods have been proposed to discover interesting patterns in sequence data as well as detect anomalous behavior. However, existing algorithms mainly focus on frequency-driven analytic, and they are challenging to be applied in real-world settings. In this work, we present a new anomaly detection framework called DUOS that enables Discovery of Utility-aware Outlier Sequential rules from a set of sequences. In this pattern-based anomaly detection algorithm, we incorporate both the anomalousness and utility of a group, and then introduce the concept of utility-aware outlier sequential rule (UOSR). We show that this is a more meaningful way for detecting anomalies. Besides, we propose some efficient pruning strategies w.r.t. upper bounds for mining UOSR, as well as the outlier detection. An extensive experimental study conducted on several real-world datasets shows that the proposed DUOS algorithm has a better effectiveness and efficiency. Finally, DUOS outperforms the baseline algorithm and has a suitable scalability.
Video Frame Interpolation without Temporal Priors

Video frame interpolation, which aims to synthesize non-exist intermediate frames in a video sequence, is an important research topic in computer vision. Existing video frame interpolation methods have achieved remarkable results under specific assumptions, such as instant or known exposure time. However, in complicated real-world situations, the temporal priors of videos, i.e. frames per second (FPS) and frame exposure time, may vary from different camera sensors. When test videos are taken under different exposure settings from training ones, the interpolated frames will suffer significant misalignment problems. In this work, we solve the video frame interpolation problem in a general situation, where input frames can be acquired under uncertain exposure (and interval) time. Unlike previous methods that can only be applied to a specific temporal prior, we derive a general curvilinear motion trajectory formula from four consecutive sharp frames or two consecutive blurry frames without temporal priors. Moreover, utilizing constraints within adjacent motion trajectories, we devise a novel optical flow refinement strategy for better interpolation results. Finally, experiments demonstrate that one well-trained model is enough for synthesizing high-quality slow-motion videos under complicated real-world situations. Codes are available on https://github.com/yjzhang96/UTI-VFI.
Theoretically and Practically Efficient Parallel Nucleus Decomposition

This paper studies the nucleus decomposition problem, which has been shown to be useful in finding dense substructures in graphs. We present a novel parallel algorithm that is efficient both in theory and in practice. Our algorithm achieves a work complexity matching the best sequential algorithm while also having low depth (parallel running time), which significantly improves upon the only existing parallel nucleus decomposition algorithm (Sariyuce et al., PVLDB 2018). The key to the theoretical efficiency of our algorithm is the use of a theoretically-efficient parallel algorithms for clique listing and bucketing. We introduce several new practical optimizations, including a new multi-level hash table structure to store information on cliques space-efficiently and a technique for traversing this structure cache-efficiently. On a 30-core machine with two-way hyper-threading on real-world graphs, we achieve up to a 55x speedup over the state-of-the-art parallel nucleus decomposition algorithm by Sariyuce et al., and up to a 40x self-relative parallel speedup. We are able to efficiently compute larger nucleus decompositions than prior work on several million-scale graphs for the first time.
Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data

We show that Gaussian process regression (GPR) allows representing multivariate functions with low-dimensional terms via kernel design. When using a kernel built with HDMR (High-dimensional model representation), one obtains a similar type of representation as the previously proposed HDMR-GPR scheme while being faster and simpler to use. We tested the approach on cases where highly accurate machine learning is required from sparse data by fitting potential energy surfaces and kinetic energy densities.
The decision making process of school managers in the Federal District public high schools and its interface with information literacy

This study analyzed the behavior in search and use of information for decision making of school managers in relation to the information literacy patterns in the high schools of the Distrito Federal, that are part of the public school system. The research is qualitative-quantitative, based on official documents of the educational system of the Distrito Federal, a questionnaire and a semi-structured interview. The results show that the main access to information is carried out through the internet and also that the volume of data along with the lack of time to obtain information hinders the decision making. In order to make decisions, besides the political pedagogical projects, managers consult information available at schools. They verify the origin of the information received and the results achieved by the school in external evaluations, which are proposed by the government. Finally, the data collected from school managers in relation to information literacy patterns show the development of few skills and the need for systematic and adequate training to learn how to deal with information.
LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-training

Natural language generation from structured data mainly focuses on surface-level descriptions, suffering from uncontrollable content selection and low fidelity. Previous works leverage logical forms to facilitate logical knowledge-conditioned text generation. Though achieving remarkable progress, they are data-hungry, which makes the adoption for real-world applications challenging with limited data. To this end, this paper proposes a unified framework for logical knowledge-conditioned text generation in the few-shot setting. With only a few seeds logical forms (e.g., 20/100 shot), our approach leverages self-training and samples pseudo logical forms based on content and structure consistency. Experimental results demonstrate that our approach can obtain better few-shot performance than baselines.
RegionCL: Can Simple Region Swapping Contribute to Contrastive Learning?

Self-supervised methods (SSL) have achieved significant success via maximizing the mutual information between two augmented views, where cropping is a popular augmentation technique. Cropped regions are widely used to construct positive pairs, while the left regions after cropping have rarely been explored in existing methods, although they together constitute the same image instance and both contribute to the description of the category. In this paper, we make the first attempt to demonstrate the importance of both regions in cropping from a complete perspective and propose a simple yet effective pretext task called Region Contrastive Learning (RegionCL). Specifically, given two different images, we randomly crop a region (called the paste view) from each image with the same size and swap them to compose two new images together with the left regions (called the canvas view), respectively. Then, contrastive pairs can be efficiently constructed according to the following simple criteria, i.e., each view is (1) positive with views augmented from the same original image and (2) negative with views augmented from other images. With minor modifications to popular SSL methods, RegionCL exploits those abundant pairs and helps the model distinguish the regions features from both canvas and paste views, therefore learning better visual representations. Experiments on ImageNet, MS COCO, and Cityscapes demonstrate that RegionCL improves MoCo v2, DenseCL, and SimSiam by large margins and achieves state-of-the-art performance on classification, detection, and segmentation tasks. The code will be available at https://github.com/Annbless/RegionCL.git.
Research on the Construction of College Music Teaching Cloud Platform Based on Data Mining Technology

As far as music teaching is concerned, music appreciation class can be said to be an effective means to arouse students' interest in music in music teaching, and to make this interest more intense and unsustainable. Through the combination of network information and computer application, the cloud platform obtains powerful learning information and corresponding materials by sharing data information, and displays them to readers intuitively by means of pictures or videos, so that readers can understand the knowledge they want to know more clearly, thus improving the learning quality. Using data mining technology, it is hoped that the recognition of music style will become a practical work. There is no doubt that the development of cloud computing and data mining technology has opened up a new way for contemporary education and teaching. This paper makes effective use of information means such as database and education cloud, and deeply explores the music curriculum model based on cloud computing and data mining technology, so as to provide better information services for schools, teachers and students.
Margin Transfer: A Knowledge Distillation Method for Face Recognition to Recuperate Network Capacity

Face Recognition (FR) systems are one of the utmost used domains in real-world applications hence a field that needs network compression techniques habitually. Within this scope, we introduce a new knowledge distillation approach for FR to improve pruned model performance, which we call as Margin Transfer (MT). MT takes advantage of the margin penalty of the deeper baseline model in the training stage. We present an end-to-end trainable deep learning face recognition model that doesn't require any special software or hardware accelerators to achieve speedup. The experiments demonstrate and validate the performance of MT. Our method reduces more than 75% floating point operations per second (FLOPs) on ResNet-l00 with the same baseline accuracy. Moreover, MT reduces more than 61% FLOPs on ResNet-50 with accuracy improvement.