Reviewerly/exHarmony huggingface dataset:
Detection of activities of daily living with decision trees through a technical assistance system

Abstract To cope with the increasing demands in care due to the aging society and the simultaneous lack of professional caregivers， a technical assistance system can help to monitor elderly people in their own homes and to support professional caregivers and caring relatives. The technical assistance system consists of a smart sensor with an omnidirectional camera on the ceiling of each room and additionally, other smart home sensors in an apartment for elderly persons. Based on smart sensor data, the positions, poses, and activities of the patient are detected with the help of machine learning (ML) techniques. In this work a temporal behaviour model of the patient is developed to recognize activities of daily living (ADL) such as "eating", "sleeping" or "emergency". For this, the actions (e.g., walking, sitting) and the location in the current room of the patient, as well as the data of other sensors in the apartment are used. This input data is fed into a trained decision tree of depth 14, which ultimately determines the patient’s activity. The accuracy for detecting activities of daily living with the decision tree is 96.47%, where the activities can be detected in real-time.
Improving the Students’ Speaking Ability by Using Suggestopedia Method at the Eighth Grade of SMP Negeri 1 Mandrehe in 2022/2023

&#x0D; &#x0D; &#x0D; &#x0D; Speaking ability is one of the ability to produce language in English learning. There are some problems that students faced when speaking, namely: lack of vocabulary, difficulty in conveying their ideas, rarely to speak, incorrect pronunciation, and the strategy that used by the teacher that monotous and bored. These problems could solve by using suggestopedia method. This research aimed to improve the students’ speaking ability by using suggestopedia method at the eighth grade of SMP Negeri 1 Mandrehe in 2022/2023. The researcher conducted this research by using Classroom Action Research (CAR). Instrument of the research were observation sheets, field notes and oral test. In Cycle I, the average percentage of the researcher's completed activities was 80% (good), and the percentage of the students' completed activities was 64,50% (enough). In this cycle, the students could not achieve the Minimum Competence Criterion (MCC) of 70, students' average score was 54 (less). All the students scored ? 70, with the highest score was 80 and the lowest score was 30. Furthermore, in Cycle II, the average percentage of the researcher's completed activities was 96% (excellent), and the students' completed activities was 89,50% (excellent). In this cycle, the students had achieved the Minimum Competence Criterion (MCC) of 70, the students' average score was 79 (good). None of the students scored ? 70, with the highest score was 90 and the lowest score was 65. Based on the results of this research, the researcher concluded that Suggestopedia Method could improve the students' speaking ability at the eighth grade of SMP Negeri 1 Mandrehe in 2022/2023.&#x0D; &#x0D; &#x0D; &#x0D;
Testing fuzzy hypotheses with crisp data based on p-value

In this paper, we propose the p-value of fuzzy hypothesis by the concept of projection of the fuzzy relation, the small p-value gives evidence that research hypothesis is true. For the crisp hypothesis, we give the fuzzy set description of the test and prove that the rejection region of the test is the cut set of the fuzzy set. Finally, the p-values of fuzzy hypothesis are given by specific examples.
The design method of primary school English learning system based on intelligent push

In the reform and development of modern education, electronic learning system with computer technology as the core has been popularized and applied, which not only reflects the unique advantages of modern information technology, but also fully mobilizes students' interest in learning, improves the traditional education guidance model, and enhances the effect of primary school English learning. Especially in the context of the new curriculum reform, the traditional monotonous primary school English learning mode has been unable to meet the needs of students, and it is difficult to cultivate professional English talents needed in the new era, so it is necessary to combine artificial intelligence to build a primary school English learning system. On the basis of understanding the research status of primary school English learning system, this paper mainly explores the architecture of primary school English learning system with intelligent push as the core, and designs and implements learning resource push application by taking Android mobile phone as an example. The final result proves that the primary school English learning system based on intelligent push is more in line with the needs of education reform.
A Balanced Relation Prediction Framework for Scene Graph Generation

It has become a consensus that regular scene graph generation (SGG) is limited in actual applications due to the overfitting of head predicates. A series of debiasing methods, i.e. unbiased SGG, have been proposed to solve the problem. However, existing unbiased SGG methods have a tendency to fit the tail predicates, which is another type of bias. This paper aims to eliminate the one-way overfitting of head or tail predicates. In order to provide more balanced relationship prediction, we propose a new framework DCL (Dual-branch Cumulative Learning) which integrates regular relation prediction process and debiasing relation prediction process by employing cumulative learning mechanism. The learning process of DCL enhances the discrimination of tail predicates without reducing the discrimination performance of the model on head predicates. DCL is model-agnostic and compatible with existed different type of debiasing methods. Experiments on Visual Genome dataset show that, among all the model-agnostic methods, DCL achieves the best comprehensive performance while considering both R@K and mR@K.
QuasiNet: A Neural Network with Trainable Product Layers

Classical neural networks achieve only limited convergence in hard problems such as XOR or parity when the number of hidden neurons is small. With the motivation to improve the success rate of neural networks in these problems, we propose a new neural network model inspired by existing neural network models with so called product neurons and a learning rule derived from classical error backpropagation, which elegantly solves the problem of mutually exclusive situations. Unlike existing product neurons, which have weights that are preset and not adaptable, our product layers of neurons also do learn. We tested the model and compared its success rate to a classical multilayer perceptron in the aforementioned problems as well as in other hard problems such as the two spirals. Our results indicate that our model is clearly more successful than the classical MLP and has the potential to be used in many tasks and applications.
A magmatic copper and fluid source for the sediment-hosted Mount Isa deposit

The world class Mount Isa deposit is a unique, sediment-hosted, copper deposit with no known equivalent around the world and a controversial origin.We report δ 65 Cu values (n = 90) from chalcopyrite grains collected systematically across the entire deposit.The δ 65 Cu shows a unimodal distribution with limited variability (min = -0.87‰; max = 0.88 ‰) and an average value (þ0.13 ‰) comparable to average igneous rocks.In general, the δ 65 Cu values in chalcopyrite are lower near major structures and heavier further away, consistent with equilibrium fractionation with distance from the fluid source.The range in δ 65 Cu of chalcopyrite from the Mount Isa deposit is less variable compared to sedimentary copper, VMS and porphyry/ epithermal deposits, but similar to Michigan deposits; meanwhile, average δ 65 Cu at Mt. Isa is distinctly higher than sedimentary copper deposits, but similar to VMS, porphyry/epithermal and Michigan deposits.These data suggest that, from a copper isotope perspective, the Mount Isa deposit is clearly different from sedimentary copper deposits and more like VMS, porphyry copper/ epithermal and Michigan style deposits.The average δ 65 Cu (þ0.13 ‰) is almost identical to the average δ 65 Cu (þ0.14 ‰) from Proterozoic basalts and suggests that copper was sourced from the underlying mafic rocks; the limited fractionation and the normal distribution of the δ 65 Cu indicate a very effective leaching mechanism and transport by a hot fluid from which chalcopyrite precipitated without significant fractionation of copper isotopes.
Reimagining writing education as a liberal art in the age of generative AI and literacy

This paper discusses the transformative potential of writing education and literacy within the framework of liberal arts, particularly in the context of the ongoing discourse regarding the capabilities of generative AI, such as ChatGPT, to replace human-generated writing.&#x0D; Generative AI is characterized by its ability to easily generate various texts based on learning from large volumes of data. In light of actively integrating generative AI into writing education, measures to efficiently utilize it and address its challenges (loss of human subjectivity, reliability of data, etc.) are proposed. However, the intrinsic value of literacy cultivated through human writing experiences is often overlooked in such writing education. Therefore, this paper discusses the need for conceiving writing education as a facet of liberal arts, with a focus on the “writing experience” rather than the mechanical “production of texts.” When the objective of writing education revolves around nurturing literacy, such proficiency is mostly manifested in the writing process itself, rather than the production of texts, due to the meta-linguistic and cognitive nature of writing. Based on the above discussion, this paper advocates for utilizing generative AI as a tool for enhancing reading experiences rather than writing endeavors, thus enabling learning literacy based on learners’ writing process experiences.
PELATIHAN PETA TEMATIK DAN PETA ADMINISTRASI DESA LENGGAHJAYA KECAMATAN CABANGBUNGIN

Participate and be sensitive to the surrounding environment with the aim of being able to apply the fields of knowledge that students have studied and provide benefits to the place of service. Awareness of the importance of a healthy lifestyle is an important factor that must be socialized in order to increase the general knowledge of village communities in order to achieve better welfare. This service is carried out using methods such as training for sub-district officials regarding understanding such as recognizing maps that have identified flood-prone areas. With this service, it is hoped that village communities will become more aware of their potential and care more about the environment. The results of this service show success in the form of creating a thematic map of flood events to identify flood-prone points and creating an administrative map for Branchbungin sub-district in managing the area and providing services to the community.
EFL Students’ Reader Response-driven Self-reflection Strategy in Learning Literature

This study aimed to observe students’ self-reflection activities are evident in the classroom to stimulate critical reading activities.. The study used a qualitative case study method in the process of data taken from observation. The participant of this study was chosen purposively which was 15 students second-level of English Education Program at one of the Universities in Ciamis who was observed. The result of this study revealed that it can be seen that there are reflection activities through reader response in the literature class, students are encouraged to read critically. This study concludes that the used of reflection through reader response in digital literature learning is effective in use and can stimulate critical reading activities.
CKNA: Kernel Hyperparameters Optimization Method for Group-Wise CNNs

Traditional CNNs lack specific interpretable indicators for configuring kernel hyperparameters. Practitioners typically tune model performance via tedious trial and error experiments, resulting in longer training time and expensive computational costs. To address this issue, a novel systematic method, namely CKNA, that considers comprehensive kernel hyperparameters to optimize the network architecture is proposed. Firstly, we innovatively define channel-dispersion and pseudo-kernel to characterize behaviors differentiated by kernel values across model layers. Based on these concepts, the kernel scaling algorithm and the flat wave algorithm are proposed to automatically adjust kernel size and numbers, respectively. Furthermore, we decompose the channel-expand and down-sample operations into adjacent layers to break the bottleneck effect existing in kernel elements. CKNA is applied to classic group-wise CNNs, and experimental results on ImageNet demonstrate that the optimized models obtain at least a 5.73% improvement in classification accuracy.
A Novel Encoder and Label Assignment for Instance Segmentation

SparseInst, a recent lightweight instance segmentation network, achieves better balance between efficiency and precision. However, the information contained in the single-layer features output by the encoder is not rich enough and the label assignment strategy leads to imbalance between positive and negative samples. In order to further improve the instance segmentation performance, we propose LAIS network including a novel feature encoding module and a Multi-Step Hungarian matching strategy (MSH). By combining multi-scale feature extraction and inter-layer information fusion, the encoder outputs features with more detailed and comprehensive information. By performing multiple rounds of one-to-one Hungarian matching, MSH eliminates the imbalance and duplication during the sample allocation. Experiments show that LAIS is more accurate than SparseInst without significantly increasing parameters and GFLOPs. In particular, LAIS reached 33.8% AP on the COCO val, 1.0% higher than SparseInst when using the same ResNet-50 Backbone.
OWS-Seg: Online Weakly Supervised Video Instance Segmentation via Contrastive Learning

Video Instance Segmentation (VIS) aims to detect, segment, and track instances appearing in a video. To reduce annotation costs, some existing VIS methods use the Weakly Supervised Scheme (WSVIS). However, those WSVIS methods usually run in an offline manner, which fails in handling ongoing and long videos due to the limited computational resources. It would be considerable benefits if online models could match or surpass the performance of offline models. In this paper, we propose OWS-Seg, an end-to-end, simple, and efficient online WSVIS network with box annotations. Concretely, OWS-Seg consists of two novel contrastive learning branches: the Instance Contrastive Learning (ICL) branch learns instance level discriminative features to distinguish different instances in each frame, and the Mask Contrastive Learning (MCL) branch with Boxccam learns pixel level discriminative features to differentiate foreground and background. Experimental results show that OWS-Seg achieves promising performance, e.g., 43.5% AP on YouTube-VIS 2019, 36.6% AP on YouTube-VIS 2021, and 21.9% AP on OVIS. Besides, OWS-Seg achieves comparable performance to offline WSVIS and surpasses recent fully supervised methods, demonstrating its wide range of practical applications.
PATH FINDING OF STATIC INDOOR MOBILE ROBOT VIA AOR ITERATIVE METHOD USING HARMONIC POTENTIALS

Mobile robots often have to discover a path of collision-free towards a specific goal point in their environment. We are trying to resolve the mobile robot problem iteratively by means of numerical technique. It is built on a method of potential field that count on the use of Laplace’s equation in the mobile robot’s configuration space to constrain/which reduces the generation of a potential function over regions. This paper proposed an iterative approach in solving robot path finding problem known as Accelerated Over-Relaxation (AOR). The experiment shows that these suggested approach can establish a smooth pat between the starting and goal points by engaging with a finite-difference technique. The simulation results also show that a more rapidly solution with smoother path than the previous work is achieved via this numerical approach.
Future Practices of Breast Pathology Using Digital and Computational Pathology

Pathology clinical practice has evolved by adopting technological advancements initially regarded as potentially disruptive, such as electron microscopy, immunohistochemistry, and genomic sequencing. Breast pathology has a critical role as a medical domain, where the patient’s pathology diagnosis has significant implications for prognostication and treatment of diseases. The advent of digital and computational pathology has brought about significant advancements in the field, offering new possibilities for enhancing diagnostic accuracy and improving patient care. Digital slide scanning enables to conversion of glass slides into high-fidelity digital images, supporting the review of cases in a digital workflow. Digitization offers the capability to render specimen diagnoses, digital archival of patient specimens, collaboration, and telepathology. Integration of image analysis and machine learning–based systems layered atop the high-resolution digital images offers novel workflows to assist breast pathologists in their clinical, educational, and research endeavors. Decision support tools may improve the detection and classification of breast lesions and the quantification of immunohistochemical studies. Computational biomarkers may help to contribute to patient management or outcomes. Furthermore, using digital and computational pathology may increase standardization and quality assurance, especially in areas with high interobserver variability. This review explores the current landscape and possible future applications of digital and computational techniques in the field of breast pathology.
INNOVATIVE APPROACHES TO TEACHING SPEAKING: A CASE STUDY OF SHORT FILM PROJECTS

This study was conducted as an attempt to find out the effect of applying Transactional Strategies Instruction (TSI) on students' reading comprehension. This study used an experimental design. The population were students from SMA Negeri 1 Onan Ganjang. The sample for this study consisted of sixty first-graders at a senior high school. Two randomly generated groups—one for the experimental group and one for the control group—were used. The control group was taught without applying TSI, while the experimental group was taught with TSI. The instrument for collecting data was an objective multiple-choice test, which consisted of 40 items. The data were analyzed using a t-test. This study shows that teaching reading comprehension by using TSI has a more significant effect than teaching without it.
Camera Pose-Based Background Modeling for Video Coding in Moving Cameras

For moving cameras, the video content changes significantly, which leads to inaccurate prediction in traditional inter prediction and results in limited compression efficiency. To solve these problems, first, we propose a camera pose-based background modeling (CP-BM) framework that uses the camera motion and the textures of reconstructed frames to model the background of the current frame. Compared with the reconstructed frames, the predicted background frame generated by CP-BM is more geometrically similar to the current frame in position and is more strongly correlated with it at the pixel level; thus, it can serve as a higher-quality reference for inter prediction, and the compression efficiency can be improved. Second, to compensate the motion of the background pixels, we construct a pixel-level motion vector field that can accurately describe various complex motions with only a small overhead. Our method is more general than other motion models because it has more degrees of freedom, and when the degrees of freedom are decreased, it encompasses other motion models as special cases. Third, we propose an optical flow-based depth estimation (OF-DE) method to synchronize the depth information at the codec, which is used to build the motion vector field. Finally, we integrate the overall scheme into the High Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC) reference software HM-16.7 and VTM-10.0. Experimental results demonstrate that in HM-16.7, for in-vehicle video sequences, our solution has an average Bjøntegaard delta bit rate (BD-rate) gain of 8.02% and reduces the encoding time by 20.9% due to the superiority of our scheme in motion estimation. Moreover, in VTM-10.0 with affine motion compensation (MC) turned off and turned on, our method has average BD-rate gains of 5.68% and 0.56%, respectively.
A Physics-Informed Neural Operator for the Simulation of Surface Waves

Abstract We develop and implement a Neural Operator (NOp) to predict the evolution of waves on the surface of water. The NOp uses a Graph Neural Network (GNN) to connect randomly sampled points on the water surface and exchange information between them to make the prediction. Our main contribution is adding physical knowledge to the implementation, which allows the model to be to be more general and able to be used in domains of different geometries with no retraining. Our implementation also takes advantage of the fact that the governing equations are independent of rotation and translation to make training easier. In this work, the model is trained with data from a single domain with fixed dimensions and evaluated in domains of different dimensions with little impact to performance. We believe this technique could be extended to more complex problems, such as simulating water currents in environments with more complicated geometries. It may also be possible to use encoding models that are aware of temporal evolution to predict a full time series instead of just a snapshot.
Using an anomaly detection approach for the segmentation of colorectal cancer tumors in whole slide images

Colorectal cancer (CRC) is the second most commonly diagnosed cancer in the United States. Genetic testing is critical in assisting in the early detection of CRC and selection of individualized treatment plans, which have shown to improve the survival rate of CRC patients. The tissue slide review (TSR), a tumor tissue macro-dissection procedure, is a required pre-analytical step to perform genetic testing. Due to the subjective nature of the process, major discrepancies in CRC diagnostics by pathologists are reported, and metrics for quality are often only qualitative. Progressive context encoder anomaly detection (P-CEAD) is an anomaly detection approach to detect tumor tissue from whole slide images (WSIs), since tumor tissue is by its nature, an anomaly. P-CEAD-based CRC tumor segmentation achieves a 71% 26% sensitivity, 92% 7% specificity, and 63% 23% F1 score. The proposed approach provides an automated CRC tumor segmentation pipeline with a quantitatively reproducible quality compared with the conventional manual tumor segmentation procedure.
Asynchronous Muddy Children Puzzle (work in progress)

In this work-in-progress paper we explore using the recently introduced VLSM formalism to define and reason about the dynamics of agent-based systems. To this aim we use VLSMs to formally present several possible approaches to modeling the interactions in the Muddy Children Puzzle as protocols that reach consensus asynchronously.