Reviewerly/exHarmony huggingface dataset:
Application of Role Playing Techniques to Increase Students’ Learning Motivation in the New Normal Period

The title of this study is "Application of Role Playing Techniques to Increase Students' Learning Motivation in Class V SDN 89 Uru in the New Normal Period." The pupils in this research were all in fifth grade at SDN 89 Uru. When provided to pupils, little learning motivation is shown, and just a few repetitions are appropriate to complete the job. The goal of this research was to see if using role-playing strategies may help pupils become more motivated to learn. A pre-experimental approach was used to perform this study. In this study, descriptive and inferential statistics were used to analyze the data. This is a class V research project involving 19 pupils. To make observations and distribute surveys, and subsequently draw conclusions, data gathering techniques were utilized. Learning motivation in class V SDN 89 Uru is in the high group, according to the research findings, with an average score of 84.65, a standard deviation of 7.03, and an ideal score of 200. The use of role playing tactics is now more common than it was previously.
KAJIAN TEOLOGI SOSIAL TERHADAP BENCANA ALAM BANJIR BAGI MASYARAKAT KAROMBASAN DI KOTA MANADO TAHUN 2021

Flood is one of most dangerous disasters to all living things on earth. Flood natural disaster having occurred Manado city, especially Karombasan village, was caused by heavy rains for days, surface water runoff, low soil infiltration capacity, and littering. Floods that hit Karombasan sub-district have killed 3 persons, and made 1 person missing, 8 houses damaged: 3 heavily damaged and 5 lightly damaged. This paper was organized aiming to know, understand, and analyze the causes of flood in the Karombasan village and what the government's social action is in dealing with the Karombasan community affected by the flood disaster. This paper spotlighted the inventory of flood victims, the causes, how to overcome them, and social actions taken by the government to overcome the floods occurring. A qualitative research method with a descriptive approach was used in this study and data collection techniques used was in-depth interviews. The results show natural factors of heavy rain, human factors such as littering, and illegal logging causing the flood natural disaster. As a result of flood, casualties, missing victims, injuries, lost valuable documents, lost and damaged properties, damaged houses and economic disruption can be found in local community.
Teaching Practices in the Covid-19 Emergency. The Italian Teachers’ Perspective

The worldwide imposed lockdowns forced schools and universities to digitise conventional teaching in a very short time and to convert teaching and learning formats partially or completely to Distance Learning. With 22 double blind peerreviewed articles of researchers reporting on 17 different countries, the editors of this book want to shed light on the effects of Distance Learning in different regions of the world.
Learning from Student Feedback – Developing University-Wide Guidelines to Support Distance Learning after COVID-19

The worldwide imposed lockdowns forced schools and universities to digitise conventional teaching in a very short time and to convert teaching and learning formats partially or completely to Distance Learning. With 22 double blind peerreviewed articles of researchers reporting on 17 different countries, the editors of this book want to shed light on the effects of Distance Learning in different regions of the world.
Communication

Communication -- the sending and receiving of signals -- underlies nearly all interactions, and requires at least 2 individuals. Understanding signal function and evolution requires exploring a suite of interacting (and possibly conflicting) selection pressures on traits in both the signal producer, and signal receiver. Signals also can be modified or masked by biotic and abiotic environmental features (including anthropogenic effects) during transmission. Signals encode information via diverse mechanisms influenced by selection on structures used for signal production, receiver sensory and cognitive capacities, and the degree to which signaler and receiver interests align (in terms of the outcome of the interaction). Despite a long history of study regarding signal form and signaling behavior, however, we still lack a predictive framework of signal-information relationships. Advances in analytical and methodological approaches -- especially for quantifying signal exchanges and remote recording of signaling interactions -- promise new insights in signaling behavior and evolution.
Redefining consumer relationships through voice technologies

Voice technologies are becoming pervasive in consumers' lives, offering brands a wide range of opportunities to develop deeper, para-social consumer relationships that engender brand loyalty. Much of the growing popularity of voice technologies is due to characteristics that closely mirror human-to-human interactions. By examining how consumers interact with voice technologies, we delineate their core functionalities (searchability, actionability, and sociability) and explain how these functionalities prompt consumers to anthropomorphize the technologies' mechanisms and outputs. This process can result in the attribution of human traits of warmth and competence to voice technologies, leading to the formation of para-social relationships. By leveraging voice technologies, brands can potentially develop more intimate consumer relationships and enhance brand loyalty.
PENERAPAN DATA MINING UNTUK PEMBUATAN PAKET PROMOSI PENJUALAN MENGGUNAKAN KOMBINASI FP-TREE DAN TID-LIST

The manual preparation strategy of a promosi package can encounter difficulties in determining the right product to be promosited caused of no sales data analysis and the large size of sales database.The solution to this problem is to apply data mining science to find buying patterns that consumers often make in a collection of sales transactions, so that company can create the right promosi packages to encourage increased sales turnover. The study scanned the sales database obtained from the UCI maching learning repository and intelligent system dataset with 12,224 sales transaction records and 126,898 record of goods sold. Furthermore, the data mining process carries out the process of constructing an FP-Tree tree structure, constructing a conditional FP-Tree and TID List, then extracting a combination of items and calculating support (S), confidence (C) and sorting association rules based on the value of S x C descending, from the highest to the lowest value. The combination of FP-Tree and TID-List algorithms can be used to help formulate promosi package strategies, by analyzing the sales database and finding the most frequently sold combinations of items.
Localized Quantum Walks in Quasiperiodic Photonics Lattices

Localized quantum walks are theoretically investigated in new class of quasiperiodic photonics lattices. Theoretical results have been validated quantitatively by experiments of quantum walks in Fibonacci multicore fibers.
Q-MARINA: Quantum Mapping Algorithm of Resonator Interaction with N Atoms

We devise a quantum circuit mapping of the Tavis-Cummings open system dynamics in the single-excitation regime, suitable for execution on a star-connected NISQ quantum computer in linear space and time.
Building a Prototype of a Memory-assisted Quantum Repeater Network

We report on progress toward building a first prototype of an entanglement-based Type IT quantum repeater, comprising two cavity enhanced sources of entangled photon pairs, four room temperature Rb vapor quantum memories and a Bell-state measurement station.
Reducing g(2)(0) of a Parametric Down-Conversion Source via Photon-Number Resolution with Superconducting Nanowire Detectors

We demonstrate a 13 ± 0.4% reduction of the second-order correlation function g (2) (0) by harnessing the photon-number resolving capabilities of commercial super-conducting nanowire single-photon detectors, improving the quality of a heralded single-photon source.
Quantum photo-thermodynamics on a programmable photonic quantum processor

We experimentally investigate the relation between thermodynamics and quantum mechanics by demonstrating equilibration of a quantum state towards a thermal state in an integrated quantum photonics platform.
Optimizing Metrological Devices with Memory-Efficient Automatic Differentiation

We present a computational framework for the direct optimization of measures of metrological gain without analytic gradients, such as the quantum Fisher information. The method is enabled by a new memory-efficient formulation of automatic differentiation.
Deep Learning based Image Captioning Models: A Critical Analysis

The main goal of image captioning is to produce moat appropriate caption for any image. It is required to find out objects in a picture, their connection and also some hidden features which might be not present in a picture. When the recognition is done, our succeeding step is to produce the most suitable, accurate and short description for a picture which must be grammatically correct. Computer vision concept is used for recognition of objects and description is generated by methods of natural image processing. Sometimes it is not easy for a machine to emulate exactly like human brain or thinking like that. However, in this field researches have shown their great achievements. By using deep learning algorithms like CNN and RNN, we are capable enough to deal with such type of problems. In intelligent control system and IOT based devices this is mostly used. In this research paper, we are introducing distinct models of picture description like retrieval based, template based or deep learning based and distinct evaluation methods also.
Movie Reviews Sentiment Analysis through Deep Learning

Sentiment analysis is one of the widely used methods to predict reviews provided by us and a lot more emotions through texts and words. A huge load of AI techniques has been performed to procure proper gauges about the data. Long stretch dependence and max pooling is not considered by the classifier. To further develop assumptions using these features, in this paper we cluster surveys utilizing Deep learning methods. We utilize Neural Network and Long Short-Term Memory Recurrent Neural Network to get the highest point of accuracy with fewer misfortune and with a smaller registering time.
Keynote Talk 2 Training Large Language Models: Challenges and Opportunities

Language models with large number of parameters trained on massive datasets can achieve state-of-the-art accuracies in various natural language processing applications including summarization, automatic dialogue generation, translation, semantic search, and code autocompletion. However, training such models is challenging as these models no longer fit in the largest GPU memory and can require a very long training time. Therefore, numerous innovations and breakthroughs are required in dataset, algorithms, software, and hardware altogether to make training these models a reality. In this talk, I present our efforts to train the Megatron-Turing Natural Language Generation model (MT-NLG), the largest and the most powerful monolithic transformer language model trained to date, with 530 billion parameters. I will also showcase several applications of MT-NLG and discuss future research and numerous opportunities that this model presents.
Adaptive Optimization for Sparse Data on Heterogeneous GPUs

Motivated by extreme multi-label classification applications, we consider training deep learning models over sparse data in multi-GPU servers. The variance in the number of non-zero features across training batches and the intrinsic GPU heterogeneity combine to limit accuracy and increase the time to convergence. We address these challenges with Adaptive SGD, an adaptive elastic model averaging stochastic gradient descent algorithm for heterogeneous multi-GPUs that is characterized by dynamic scheduling, adaptive batch size scaling, and normalized model merging. Instead of statically partitioning batches to GPUs, batches are routed based on the relative processing speed. Batch size scaling assigns larger batches to the faster GPUs and smaller batches to the slower ones, with the goal to arrive at a steady state in which all the GPUs perform the same number of model updates. Normalized model merging computes optimal weights for every GPU based on the assigned batches such that the combined model achieves better accuracy. We show experimentally that Adaptive SGD outperforms four state-of-the-art solutions in time-to-accuracy and is scalable with the number of GPUs.
When Moore Just Isn't Enough: Scaling ML in the Datacenter

As the industry drives towards more capable ML, workloads are rapidly evolving and the need for performance is nearly unlimited. Performance has vastly outstripped the pace of Moore's Law as measured by MLPerf™ through software/hardware co-design. This talk will discuss challenges in benchmarking ML Training and explore the design space and also identify opportunities in future ML systems.
When and How to Retrain Machine Learning-based Cloud Management Systems

Cloud management systems increasingly rely on machine learning (ML) models to predict incoming workload rates, load, and other system behaviours for efficient dynamic resource management. Current state-of-the-art prediction models demonstrate high accuracy but assume that data patterns remain stable. However, in production use, systems may face hardware upgrades, changes in user behaviour etc. that lead to concept drifts - significant changes in the characteristics of data streams over time. To mitigate prediction deterioration, ML models need to be updated - but questions of when and how to best retrain these models are unsolved in the context of cloud management. We present a pilot study that addresses these questions for one of the most common models for adaptive prediction - Long Short Term Memory (LSTM) - using synthetic and real-world workload data. Our analysis of when to retrain explores approaches for detecting when retraining is required using both concept drift detection and prediction error thresholds, and at what point retraining should actually take place. Our analysis of how to retrain focuses on the data required for retraining, and what proportion should be taken from before and after the need for retraining is detected. We present initial results that indicate that retraining of existing models can achieve prediction accuracy close to that of newly trained models but for much less cost, and present initial advice for how to provide cloud management systems with support for automatic retraining of ML-based models.
Dynamic Batch Parallel Algorithms for Updating PageRank

The design and implementation of parallel algorithms for dynamic graph problems is attracting significant research attention in the recent years, driven by numerous applications to social network analysis, neuroscience, and protein interaction networks. One such problem is the computation of PageRank values of vertices in a directed graph. This paper presents two new parallel algorithms for recomputing the PageRank values of vertices in a dynamic graph. Our techniques require the recomputation of the PageRank of only the vertices affected by the insertion/deletion of a batch of edges. We conduct detailed experimental studies of our algorithm on a set of 11 real-world graphs. Our results on Intel Xeon Silver 4116 CPU and NVIDIA Tesla V100 PCIe 16GB GPU indicate that our algorithms outperform static and dynamic update algorithms by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$6.1\times$</tex> : and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8.6\times \mathbf{on}$</tex> the CPU, and by 9.8×and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$9.3\times\mathbf{on}$</tex> the GPU respectively. We also compare the performance of the algorithms in batched mode to cumulative single-edge updates.
RAW 2022 Keynote Speaker 1: Using FPGAs in datacenters and the cloud

Several trends in the IT industry are driving an increasing specialization of the hardware layers. On the one hand, demanding workloads, large data volumes, diversity in data types, etc. are all factors contributing to make general purpose computing too inefficient. On the other hand, cloud computing and its economies of scale allow vendors to invest on specialized hardware for particular tasks that otherwise would be too expensive or consume resources needed elsewhere. In this talk I will discuss the shift towards hardware acceleration and show with several examples from industry and from research the large role that FPGAs could play.
Multi-agent Exploration with Reinforcement Learning

Modern robots are used in many exploration, search and rescue applications nowadays. They are essentially coordinated by human operators and collaborate with inspection or rescue teams. Over time, robots (agents) have become more sophisticated with more autonomy, operating in complex environments. Therefore, the purpose of this paper is to present an approach for autonomous multi-agent coordination for exploring and covering unknown environments. The method we suggest combines reinforcement learning with multiple neural networks (Deep Learning) to plan the path for each agent separately and achieve collaborative behavior amongst them. Specifically, we have applied two recent techniques, namely the target neural network and the prioritized experience replay, which have been proven to stabilize and accelerate the training process. Agents should also avoid obstacles (walls, objects, etc.) throughout the exploration without prior information/knowledge about the environment; thus we use only local information available at any time instant to make the decision of each agent. Furthermore, two neural networks are used for generating actions, accompanied by an extra neural network with a switching logic that chooses one of them. The exploration of the unknown environment is conducted in a two-dimensional model (2D) using multiple agents for various maps, ranging from small to large size. Finally, the efficiency of the exploration is investigated for a different number of agents and various types of neural networks.
Coordinated Navigation of Holonomic Robot Swarms in Complex Workspaces via Dynamic Formation Tracking Control

This paper deals with the planar navigation problem of a swarm of robotic agents within an obstacle cluttered workspace, under time varying formation specifications. The robots are considered homogeneous and achieve the desired formation by operating inside a moving and shape-varying region. The complexity of the cluttered workspace is confronted by the transformation of the original planar world into a punctured unit disk. The navigation of the swarm is accomplished by a simultaneous path and motion planning strategy with regards to the centroid of the desired region. Towards this direction, a motion planner based on artificial potential fields drives successfully the centroid to the desired position while avoiding collisions with the workspace. Additionally, the formation orientation is aligned with the linear velocity of its centroid. The robots are also equipped with a distributed controller to avoid collisions with each other. Finally, simulation results validate the efficiency of our method and illustrate the ability of the algorithm to appropriately adapt the formation in situations of narrow path crossing.
A study on communication characteristics experienced by parents in innovation elementary school

Objectives The purpose of this study is to explore the characteristics of communication experienced by parents in order to find the possibility of rational communication in innovation elementary schools.&#x0D; Methods This study used a case study method and analyzed how parents of Deulkkot Elementary School, an innovation plus school, experienced communication with teachers. Seven parents were interviewed in-depth with semi-structured questionnaires on experience in innovative schools, communication experience with homeroom teachers, communication experience in school management, general conversation, and school participation consciousness. The collected data were derived from three topics and six categories by applying sequential and cyclical method of Merriam(2009).&#x0D; Results First, Deulkkot Elementary School strived for reasonable communication between teachers and parents through online and offline information opening and sharing, participation in equal relationships, and bottom-up communication. Second, the teacher tried to communicate with parents with truthfulness through an unauthorized attitude, a mind of consideration and respect, and a reasonable explanation. Third, parents actively supported schools and teachers through expectations for schools, understanding of schools, and mutual exchange activities between parents. The limitations revealed in rational communication were the inconvenience and disappointment that parents felt from some teachers, and internal confrontation and dissatisfaction in parent interaction.&#x0D; Conclusions In order to realize a democratic autonomous community through rational communication in innovative schools, efforts such as the appropriate size of schools that can communicate, sincere attitudes of teacher, parents' communication skills through parent training, and participation of graduated parents in schools are necessary.&#x0D; &#x0D;
Navigational Aid for Open-Ended Surveillance, by Fusing Estimated Depth and Scene Segmentation Maps, Using RGB Images of Indoor Scenes

Open-ended surveillance task for a robot in an unspecified environment using only an RGB camera, has not been addressed at length in literature. This is unlike the popular scenario of path planning where both the target and environments are often known. We focus on the task of a robot which needs to estimate a realistic depiction of the surrounding 3D environment, including the location of obstacles and free space to navigate in the scene within the view field. In this paper, we propose an unsupervised algorithm to iteratively compute an optimal direction for maximal unhindered movement in the scene. This task is challenging when presented with only a single RGB view of the scene, without the use of any online depth sensor. Our process combines cues from two deep-learning processes - semantic segmentation and depth map estimation, to automatically decide plausible robot movement paths while avoiding hindrance posed by objects in the scene. We make assumptions of the use of a low-end RGB USB camera, pre-set camera view direction (angle) and field of view, incremental movement of the robot in the view field, and iterative analysis of the scene, all catering to any open-ended (target-free) surveillance/patrolling applications. Inverse perspective geometry has been used to map the optimal direction estimated in the view field, to that on the floor of the scene for navigation. Results of evaluation using a dataset of videos of scenes captured from indoor (office, labs, meeting/class-rooms, corridors, lounge) environments, reveal the success of the proposed approach.