Reviewerly/exHarmony huggingface dataset:
Character Building in Teaching English Vocabulary For Elementary School Students

The aim of this research was�to describe the activity of C-L-C (Central Learning and Character) program in buiding character and improving English vocabulary of Elementary School Students. C-L-C Programme has created integrated learning acvities to build students� character and improve English vocabulary for elementary��school students. The�design of this study was a descriptive qualitative research. The researcher described the activities of English insrtuctor and students in building students�character and improving English vocabulary.�There was 23 elementary School students had joinned on C-L-C program. The techniques for collecting the data were observation, documentation and questionnaire. The finding of this study showed that some�activities�did by the English instructor on C-L-C program�to build the students� character and improve the students� English vocabulary�were�reading a text, writing, painting, gaming, pronouncing the new vocabulary, retelling the story, disscussing�the character value in the story and preparing�the students� characters� book. The�result of this study, the C-L-C programme had built students� character on discipline, responsibility, honor the other people and care to the environmen. Besides C-L-C had improved the students� English vocabualry.�It can be seen from the participation of students in learning process and the recapitulation of the students� English test score. It showed that�1 students was poor, 5 students�were satisfactory, 8 students were good and 9 students were very good. It can be concluded that C-L-C programme built students� character and improved the students� English vocabulary. �Keywords: �Character Building , teaching English vocabulary
The Significance of Studying Marxist Philosophy

Marxist philosophy is the sum of the view of practice, philosophy and human nature, and learning Marxist philosophy can enable people to establish dialectical thinking and philosophical world view.Marxist philosophy plays a crucial role in using philosophical thinking to understand the relationship between man and the world, man and society, and man and man.Establishing a correct world outlook, consciously deepening philosophical cultivation, broadening theoretical space, and cultivating keen judgment and theoretical generalization are conducive to examining, guiding and regulating life with philosophical thinking.A deep understanding of philosophical thinking helps to grasp the objective law of the development of things, improve the state of life, and explore the truth of life.Consciously cultivating philosophical thinking and mastering the basic system of philosophy in life practice can continuously promote the integration of philosophy and reality, interwoven and collision between theoretical thinking and life practice, and provide spiritual nourishment for people's growth.
PEMBUATAN REPOSITORI SKRIPSI MAHASISWA JURUSAN BUDIDAYA PERAIRAN FAKULTAS PERIKANAN DAN ILMU KELAUTAN UNIVERSITAS PATTIMURA

The Aquaculture Department Library is an information service unit for library materials for students and lecturers who are required to provide information services that are fast, precise, relevant, useful for users. To further optimize the benefits of these sources of information, it is necessary to develop a library function in the form of a digital-based student thesis data storage facility. The purpose of this activity is to create a student thesis repository as an effort to facilitate student access to theses to obtain secondary scientific information in the Aquaculture Study Program at Pattimura University. The activity of creating a student thesis repository will be carried out in October – November 2022. The activities carried out include coordinating and extracting thesis data files to be uploaded, inputting data and files to Google Drive and the website, making posters and socializing the finished repository to students and lecturers majoring in Aquaculture and evaluating the effectiveness of student thesis repository. It can be concluded that the existence of a thesis repository for students of the Aquaculture Study Program can provide convenience to students and lecturers in finding information related to references, topics and research developments, especially in the field of Aquaculture.
Technology-Driven Implementation of Smart Entrances in Public Places During the COVID-19

This research proposes a smart entrance system to cope with the COVID-19 pandemic in public places. The system can help automate standard operating procedures (SOPs) for checking. The paper focuses on exploring the problem context related to the COVID-19 SOPs for public places. The research on technologies involves using thermal cameras, fingerprint recognition, face recognition, iris recognition, object detection and cloud computing. These technologies can be integrated to provide a more versatile and effective solution. The technological solutions proposed by contemporary researchers are also critically analysed by investigating their advantages and disadvantages.
Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem

The Python array API standard specifies standardized application programming interfaces (APIs) and behaviors for array and tensor objects and operations as commonly found in libraries such as NumPy Harris2020a, CuPy Okuta2017a, PyTorch Paszke2019a, JAX Bradbury2018a, TensorFlow Abadi2016a, Dask Rocklin2015a, and MXNet Chen2015a. The establishment and subsequent adoption of the standard aims to reduce ecosystem fragmentation and facilitate array library interoperability in user code and among array-consuming libraries, such as scikit-learn Pedregosa2011a and SciPy Virtanen2020a. A key benefit of array interoperability for downstream consumers of the standard is device agnosticism, whereby previously CPU-bound implementations can more readily leverage hardware acceleration via graphics processing units (GPUs), tensor processing units (TPUs), and other accelerator devices.
Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples.

Human tissue consists of complex structures that display a diversity of morphologies, forming a tissue microenvironment that is, by nature, three-dimensional (3D). However, the current standard-of-care involves slicing 3D tissue specimens into two-dimensional (2D) sections and selecting a few for microscopic evaluation1,2, with concomitant risks of sampling bias and misdiagnosis3-6. To this end, there have been intense efforts to capture 3D tissue morphology and transition to 3D pathology, with the development of multiple high-resolution 3D imaging modalities7-18. However, these tools have had little translation to clinical practice as manual evaluation of such large data by pathologists is impractical and there is a lack of computational platforms that can efficiently process the 3D images and provide patient-level clinical insights. Here we present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA), a deep-learning-based platform for processing 3D tissue images from diverse imaging modalities and predicting patient outcomes. Archived prostate cancer specimens were imaged with open-top light-sheet microscopy12-14 or microcomputed tomography15,16 and the resulting 3D datasets were used to train risk-stratification networks based on 5-year biochemical recurrence outcomes via MAMBA. With the 3D block-based approach, MAMBA achieves an area under the receiver operating characteristic curve (AUC) of 0.86 and 0.74, superior to 2D traditional single-slice-based prognostication (AUC of 0.79 and 0.57), suggesting superior prognostication with 3D morphological features. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, suggesting that there is value in capturing larger extents of spatially heterogeneous 3D morphology. With the rapid growth and adoption of 3D spatial biology and pathology techniques by researchers and clinicians, MAMBA provides a general and efficient framework for 3D weakly supervised learning for clinical decision support and can help to reveal novel 3D morphological biomarkers for prognosis and therapeutic response.
Advanced multilayer holographic technology for the realization of compact AR-HUDs

Holographic components are nowadays not only applied in optical systems for the chromatical correction combined with lenses and mirrors but also widely applied in projection, detection, lighting and filtering. It plays an important role in Augmented Reality (AR) and Virtual Reality (VR) projection systems in order to achieve high imaging performance within a very compact volume. For AR Head-up Displays (HUDs), due to the increase of the field-of-view and the display distances, it is getting more and more challenging to realize the good imaging performance within a small volume. We will introduce an innovative technology from ZEISS Microoptics, which differs from other already existing diffractive or holographic solutions. This technology is based on multilayer holographic structure. With the smart arrangement of the holograms and the complex diffraction of the light, it is possible to offer large degrees of freedom to improve the resolution and brightness of the AR-HUDs and tremendously reduce the system size. The advanced multilayer holographic technology provides large flexibilities of the shape, the size, and the position. The layers are also defined to increase the achromatism and color performance of the AR-HUDs. This work of ZEISS Microoptics provides a break-through solution, which overcomes the disadvantages of mirrors, lenses and the single layer holographic components. It provides also an insight for optical systems not only in Automotive but also in other smart functional glass technologies to realize the compact high-performance functions.
More assistance of entanglement, less rounds of classical communication

Abstract Classical communication plays a crucial role to distinguish locally a class of quantum states. Despite considerable advances, we have very little knowledge about the number of measurement and communication rounds needed to implement a discrimination task by local quantum operations and classical communications (in short, LOCC). In this paper, we are able to show the relation between round numbers with the local discrimination of a set of pure bipartite orthogonal quantum states. To demonstrate the possible strong dependence on the round numbers, we consider a class of orthogonal product states in <?CDATA $d\otimes d$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>d</mml:mi> <mml:mo>⊗</mml:mo> <mml:mi>d</mml:mi> </mml:math> , which require at least <?CDATA $2d-2$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>2</mml:mn> <mml:mi>d</mml:mi> <mml:mo>−</mml:mo> <mml:mn>2</mml:mn> </mml:math> round of classical communications. Curiously the round number can be reduced to d by the assistance of one-ebit of entanglement as resource and can be reduced further by assistance of more entanglement. We are also able to show that the number of LOCC rounds needed for a discrimination task may depend on the amount of entanglement assistance.
A Classification Framework for IoT Network Traffic Data for Provisioning 5G Network Slices in Smart Computing Applications

Existing massive deployments of IoT devices in support of smart computing applications across a range of domains must leverage critical features of 5G, such as network slicing, to receive differentiated and reliable services. However, the voluminous, dynamic, and heterogeneous nature of IoT traffic imposes complexities on the problems of network flow classification, network traffic analysis, and accurate quantification of the network requirements, thereby making the provisioning of 5G network slices across the application mix a challenging problem. To address these needs, we propose a novel network traffic classification approach that consists of a pipeline that combines Principal Component Analysis (PCA), with KMeans clustering and Hellinger distance. PCA is applied as the first step to efficiently reduce the dimensionality of features while preserving as much of the original information as possible. This significantly reduces the runtime of KMeans, which is applied as the second step. KMeans, being an unsupervised approach, eliminates the need to label data which can be cumbersome, error-prone, and time-consuming. In the third step, a Hellinger distance-based recursive KMeans algorithm is applied to merge similar clusters toward identifying the optimal number of clusters. This makes the final clustering results compact and intuitively interpretable within the context of the problem, while addressing the limitations of traditional KMeans algorithm, such as sensitivity to initialization and the requirement of manual specification of the number of clusters. Evaluation of our approach on a real-world IoT dataset demonstrates that the pipeline can compactly represent the dataset as three clusters. The service properties of these clusters can be easily inferred and directly mapped to different types of slices in the 5G network.
Sign Language Recognition using Deep Learning

Sign Language Recognition is a form of action recognition problem. The purpose of such a system is to automatically translate sign words from one language to another. While much work has been done in the SLR domain, it is a broad area of study and numerous areas still need research attention. The work that we present in this paper aims to investigate the suitability of deep learning approaches in recognizing and classifying words from video frames in different sign languages. We consider three sign languages, namely Indian Sign Language, American Sign Language, and Turkish Sign Language. Our methodology employs five different deep learning models with increasing complexities. They are a shallow four-layer Convolutional Neural Network, a basic VGG16 model, a VGG16 model with Attention Mechanism, a VGG16 model with Transformer Encoder and Gated Recurrent Units-based Decoder, and an Inflated 3D model with the same. We trained and tested the models to recognize and classify words from videos in three different sign language datasets. From our experiment, we found that the performance of the models relates quite closely to the model's complexity with the Inflated 3D model performing the best. Furthermore, we also found that all models find it more difficult to recognize words in the American Sign Language dataset than the others.
Video-Based Fall Detection Using Human Pose and Constrained Generative Adversarial Network

Falls are a major health threat for older people. A timely assistance can reduce the extent of physical injury caused by the falls. Currently, low-cost and convenient video surveillance systems based on ordinary RGB cameras are widely used for improving the safety of people. The fall detection is a research hotspot in intelligent video surveillance. In this work, we propose an unsupervised fall detection method. The proposed method first converts the RGB video frames into human pose images to eliminate the background interferences and focus on human motion and protect privacy. Afterwards, the future pose images are predicted by using the continuous historical human pose images based on a constrained generative adversarial network (GAN). Finally, the prediction errors of the human pose images and the anomaly scores of actual poses calculated by using the traditional hand-crafted features are used to realize the fall detection. As compared to the existing vision-based fall detection methods, the proposed method possesses strong generalization ability, and is robust to environmental interferences and small local occlusions, and effectively protects the privacy, and avoids time-consuming data annotations. In addition, in this work, a new large-scale and comprehensive fall dataset is created and is available for download. We perform extensive experiments on the public benchmark datasets and the proposed dataset. The results demonstrate the validity and superiority of the proposed method.
Sample Efficient Offline-to-Online Reinforcement Learning

Offline reinforcement learning (RL) makes it possible to train the agents entirely from a previously collected dataset. However, constrained by the quality of the offline dataset, offline RL agents typically have limited performance and cannot be directly deployed. Thus, it is desirable to further finetune the pretrained offline RL agents via online interactions with the environment. Existing offline-to-online RL algorithms suffer from the low sample efficiency issue, due to two inherent challenges, i.e., exploration limitation and distribution shift. To this end, we propose a sample-efficient offline-to-online RL algorithm via Optimistic Exploration and Meta Adaptation (OEMA). Specifically, we first propose an optimistic exploration strategy according to the principle of optimism in the face of uncertainty. This allows agents to sufficiently explore the environment in a stable manner. Moreover, we propose a meta learning based adaptation method, which can reduce the distribution shift and accelerate the offline-to-online adaptation process. We empirically demonstrate that OEMA improves the sample efficiency on D4RL benchmark. Besides, we provide in-depth analyses to verify the effectiveness of both optimistic exploration and meta adaptation. Our codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/guosyjlu/OEMA</uri> .
Leveraging Joint-action Embedding in Multi-agent Reinforcement Learning for Cooperative Games

State-of-the-art multi-agent policy gradient (MAPG) methods have demonstrated convincing capability in many cooperative games. However, the exponentially growing joint-action space severely challenges the critic's value evaluation and hinders performance of MAPG methods. To address this issue, we augment Central-Q policy gradient with a joint-action embedding function and propose Mutual-information Maximization MAPG (M3APG). The joint-action embedding function makes joint-actions contain information of state transitions, which will improve the critic's generalization over the joint-action space by allowing it to infer joint-actions' outcomes. We theoretically prove that with a fixed joint-action embedding function, the convergence of M3APG is guaranteed. Experiment results on the StarCraft Multi-Agent Challenge (SMAC) demonstrate that M3APG gives evaluation results with better accuracy and outperform other MAPG basic models across various maps of multiple difficulty levels. We empirically show that our joint-action embedding model can be extended to value-based multi-agent reinforcement learning methods and state-of-the-art MAPG methods. Finally, we run ablation study to show that the usage of mutual information in our method is necessary and effective.
An Optimized Sparse Response Mechanism for Differentially Private Federated Learning

Federated Learning (FL) enables geo-distributed clients to collaboratively train a learning model without exposing their private data. By only exposing local model parameters, FL well preserves data privacy of clients. Yet, it remains possible to recover raw samples from over frequently exposed parameters resulting in privacy leakage. Differentially private federated learning (DPFL) has recently been suggested to protect these parameters by introducing information noises. In this way, even if attackers get these parameters, they cannot exactly infer true parameters from these noisy information. Directly incorporating Differentially Private (DP) into FL however can severely affect model utility. In this paper, we present an optimized sparse response mechanism (OSRM) that seamlessly incorporates DP into FL to diminish privacy budget consumption and improve model accuracy. Through OSRM, each FL client only exposes a selected set of large gradients, so as not to waste privacy budgets in protecting valueless gradients. We theoretically derive the convergence rate of DPFL with OSRM under non-convex loss. Then, OSRM is optimized by minimizing the loss of the convergence rate. Based on analysis, we present an effective algorithm for optimizing OSRM. Extensive experiments are conducted with public datasets, including MNIST, Fashion-MNIST and CIFAR-10. The results suggest that OSRM can achieve the average improvement of accuracy by 18.42% as compared to state-of-the-art baselines with a fixed privacy budget.
Improve Computing Efficiency and Motion Safety by Analyzing Environment With Graphics

Exploring topologically distinctive trajectories provides more options for robot motion planning. Since computing time grows greatly with environment complexity, improving exploration efficiency and picking the optimal trajectory in complex environments are critical issues. To this end, this paper proposes a Graphic-and Timed-Elastic-Band-based approach (GraphicTEB) with spatial completeness and high computing efficiency. The environment is analyzed utilizing computer graphics, where obstacles are extracted as nodes and their relationships are built as edges. Three contributions are presented. 1) By assembling directed detours formed by nodes and segmented paths formed by edges, a generalized path consisting of nodes and edges derives various normal paths efficiently. 2) By multiplying two vectors starting from the obstacle point closest to the waypoint and the boundary point farthest from the waypoint, an novel obstacle gradient is introduced to guide safer optimization. 3) By assigning edges with asymmetric Gaussian model, a trajectory evaluation strategy is designed to reflect the motion tendency and motion uncertainty of dynamic obstacles. Qualitative and quantitative simulations demonstrate that the proposed GraphicTEB achieves spatial completeness, higher scene pass rate, and fastest computing efficiency. Experiments are implemented in long corridor and broad room scenarios, where the robot goes through gaps safely, finds trajectories quickly, and passes pedestrians politely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation stems from the fact that our daily cruising robot occasionally gets trapped in a corridor with piled obstacles or in a complex dynamic crowd due to the lack of a reliable trajectory. The solution is to search for more topologically distinctive trajectories and pick the optimal one. Considering that existing open-source approaches are either incomplete or highly time-consuming, a method for clustering and searching trajectories in the obstacle-occupied regions is proposed to achieve spatial completeness and high computing efficiency. In addition, an optimization technique and a trajectory selection strategy are proposed to improve motion safety. However, at present, the search is incomplete in the temporal-spatial dimension when dynamic obstacle are moving fast. How to perform a complete and fast search in temporal-spatial space will be developed in the future.