Reviewerly/exHarmony huggingface dataset:
Basic English Training in an Effort to Instill Student Interest in Entering Health Schools

English teaching quality is not distributed evenly throughout the region in Indonesia. The tendency is for students that live in big cities, they get more qualified English teaching than remote areas. In other side, Student who come from village are far from big city. Students expected that study at Health Education must have good English skill. It effects to Senior High School student unwilling to study at Health School because they have less skill in Basic English. Moreover, they didn’t have enough money to getting facilities to study more outside of school, although they need it. Finally their human resource capabilities are down. So, the Basic English skill need to improve to improve the human resource capability in the field of Englis.in this dedication, Basic English trainee will be given to students of MA Al-Istiqomah that need increasing of capacity. There are 8 meets by enjoyable studying, beside pretest and posttest. The result was English skill increase. But its need seriously study from the students so the English skill will be increase significantly. So English Trainee Program for students can help human resouces in English
Klasifikasi Varietas Unggul Padi Menggunakan Metode Bagging, Boosting, dan Extremely Randomized Trees

Rice is one of the agricultural products which is the main commodity in Indonesia. Supporting factors that play a very important role in efforts to increase rice production are superior varieties. Superior rice varieties have characteristics that are similar to one another. Thus, farmers must choose the varieties used through a classification process to determine the appropriate type of rice. At this stage, three methods are used: bagging, boosting, and extremely randomized trees. From the analysis results, the overall method of extremely randomized trees has more optimal capabilities compared to the bagging and boosting methods. This is indicated by the three parameters, sensitivity, specificity, and accuracy, which have the highest values.
Anomaly Detection Using Machine Learning

Comparative Machine Learning Analysis on Electrocardiogram (ECG) Anomaly Detection.Anomaly Detection on the ECG dataset of 4998 patients was done with each patient having 140 data points, around 7,00,00 data points.Data is divided as 4998 patients learning data.Machine Learning (ML) model is created using Algorithms like Logistic Regression, Decision Tree Classifier, Random Forest Classifier, and Support Vector Machines on which and remaining 1000 patient data is tested to get accuracy to check whether the model has learned correctly.The same is again analyzed by Deep Learning algorithms like RMSProp, Adam Optimization, and SGD.
Usability of Augmented Reality (AR) Application in ‘Fertigation System’ among Form 1 Students at Sekolah Menengah Kebangsaan Behrang 2020, Perak, Malaysia

The purpose of teaching aid is important to attract students to always be active in the classroom. It is important to focus on educational activities among students such as the usage of teaching aid during the teaching and learning process. Therefore, the objective of this study is to identify the usability of augmented reality (AR) application 'Fertigation System' among form 1 students of SMK Behrang 2020 who took Design and Technology subject. The application was developed using the ADDIE model which has five phases including analysis, design, development, implementation, and evaluation. The design of the studies used was quantitative method (survey method). The instrument of study used was a questionnaire. In the first phase (Analysis), a questionnaire that was endorsed by experts was distributed to 35 respondents to identify the needs of the AR application development. In this phase, there were 72% of respondents agreed the needs of the AR application development. For the next phases, an AR 'Fertigation System' has been designed and developed. A questionnaire was distributed together with the AR application that has been developed and verified by three experts from different fields (language, education, content). After that, a pilot study was conducted among 50 students of form 1 who took Design and Technology subject at SMK Behrang 2020 as respondents for improvement purposes (Implementation phase). For the final phase (Evaluation), a questionnaire and application of AR 'Fertigation System' which has been verified and improved was distributed to 76 respondents comprising form 1 students who took Design and Technology subject at SMK Behrang 2020. The results of this study showed that 62.72% of the respondents strongly agreed with the usability of the AR application 'Fertigation System'. All data obtained was analyzed using the Statistical Package for Social Science (SPSS) to obtain the average value, percentage, frequency and standard deviation. In conclusion, the results of this study show that the AR application of the 'Fertigation System' that has been developed can be used among form 1 students who took Design and Technology subject at SMK Behrang 2020, Perak.
The Effectiveness of Using Picture Series to Teach Reading Comprehension

Reading is an activity carried out by someone to get information previously unknown to the reader, and picture series is an interesting media for students to understand reading texts by illustrating. This study aimed to know students' reading comprehension before and after using picture series and whether or not using picture series to teach reading comprehension was effective. This research was a pre-experimental study using one group pre-test and post-test design. This research used class X students of MA Terpadu Al-Munawaroh as the population. The sample was class X-A students, totaling 18 people who were selected through the purposive sampling method. The instrument used was a reading comprehension test, and the validity and reliability were tested first using SPSS 25. The results of this research indicated that post-test mean score was higher than the pre-test. Students find it easier to catch and summarize reading content with pictures and they can retell using their own language easily. So, it could be revealed that teaching reading using the picture series of students’ class X-A was effective. Then, it was highly recommended to use picture series for teachers or other researchers in listening or writing.
Development of Digi-Ecosmart, a smart digital economy ecosystem based on immersive technology case study at PT Nuswantoro Informasi Teknologi Indonesia

Sidoarjo Regency is a Delta Plain with an altitude between 0 to 25 m, a height of 0-the 3m with an area of ​​19,006 Ha, covering 29.99%, is a pond area located in the eastern part of the Central Region which has fresh water with an altitude of 3-10 meters above sea level is a residential area, trade, and government. Covering 40.81%. The western region with an altitude of 10-25 meters above sea level is agricultural. This paper is a brief report on application development funded by the Matching Fund of the Ministry of Culture, Research, and Technology at PSDKU Sidoarjo Information Technology. As a study program that stands in the Sidoarjo district with all of the above characteristics. The Matching Fund Program is a program funded by the Ministry of Education and Culture, and the Ministry of Research and Technology to strengthen cooperation between universities and the industrial world to jointly form the “Ekosistem Merdeka Belajar – Kampus Merdeka”. In this project, we make application software based on immersive technology. At the end of the program, a better and more sustainable working relationship will be established between universities and the industrial world, which is expected to contribute to solving real problems in the field or carrying out national strategic actions The method used on this project is prototyping, which is a system development method in which prototypes are built, tested, and then reworked as necessary until acceptable results are achieved from which a complete system or product can be developed.
MultiCAM: A multivariable framework for connecting the mass accretion history of haloes with their properties

Models that connect galaxy and halo properties often summarize a halo's mass accretion history (MAH) with a single value, and use this value as the basis for predictions. However, a single-value summary fails to capture the complexity of MAHs and information can be lost in the process. We present MultiCAM, a generalization of traditional abundance matching frameworks, which can simultaneously connect the full MAH of a halo with multiple halo and/or galaxy properties. As a first case study, we apply MultiCAM to the problem of connecting dark matter halo properties to their MAHs in the context of a dark matter-only simulation. While some halo properties, such as concentration, are more strongly correlated to the early-time mass growth of a halo, others, like the virial ratio, have stronger correlations with late-time mass growth. This highlights the necessity of considering the impact of the entire MAH on halo properties. For most of the halo properties we consider, we find that MultiCAM models that use the full MAH achieve higher accuracy than conditional abundance matching models which use a single epoch. We also demonstrate an extension of MultiCAM that captures the covariance between predicted halo properties. This extension provides a baseline model for applications where the covariance between predicted properties is important.
Optimal Heaviest Induced Ancestors

We revisit the Heaviest Induced Ancestors (HIA) problem that was introduced by Gagie, Gawrychowski, and Nekrich [CCCG 2013] and has a number of applications in string algorithms. Let $T_1$ and $T_2$ be two rooted trees whose nodes have weights that are increasing in all root-to-leaf paths, and labels on the leaves, such that no two leaves of a tree have the same label. A pair of nodes $(u, v)\in T_1 \times T_2$ is \emph{induced} if and only if there is a label shared by leaf-descendants of $u$ and $v$. In an HIA query, given nodes $x \in T_1$ and $y \in T_2$, the goal is to find an induced pair of nodes $(u, v)$ of the maximum total weight such that $u$ is an ancestor of~$x$ and $v$ is an ancestor of $y$. Let $n$ be the upper bound on the sizes of the two trees. It is known that no data structure of size $\tilde{\mathcal{O}}(n)$ can answer HIA queries in $o(\log n / \log \log n)$ time [Charalampopoulos, Gawrychowski, Pokorski; ICALP 2020]. This (unconditional) lower bound is a $\operatorname{polyloglog} n$ factor away from the query time of the fastest $\tilde{\mathcal{O}}(n)$-size data structure known to date for the HIA problem [Abedin, Hooshmand, Ganguly, Thankachan; Algorithmica 2022]. In this work, we resolve the query-time complexity of the HIA problem for the near-linear space regime by presenting a data structure that can be built in $\tilde{\mathcal{O}}(n)$ time and answers HIA queries in $\mathcal{O}(\log n/\log\log n)$ time. As a direct corollary, we obtain an $\tilde{\mathcal{O}}(n)$-size data structure that maintains the LCS of a static string and a dynamic string, both of length at most $n$, in time optimal for this space regime. The main ingredients of our approach are fractional cascading and the utilization of an $\mathcal{O}(\log n/ \log\log n)$-depth tree decomposition.
Limiting Sets in Digital Topology

Freezing sets and cold sets have been introduced as part of the theory of fixed points in digital topology. In this paper, we introduce a generalization of these notions, the limiting set, and examine properties of limiting sets.
Complexity of Solo Chess with Unlimited Moves

We analyze Solo Chess puzzles, where the input is an $n \times n$ board containing some standard Chess pieces of the same color, and the goal is to make a sequence of capture moves to reduce down to a single piece. Prior work analyzes this puzzle for a single piece type when each piece is limited to make at most two capture moves (as in the Solo Chess puzzles on chess.com). By contrast, we study when each piece can make an unlimited number of capture moves. We show that any single piece type can be solved in polynomial time in a general model of piece types, while any two standard Chess piece types are NP-complete. We also analyze the restriction (as on chess.com) that one piece type is unique and must be the last surviving piece, showing that in this case some pairs of piece types become tractable while others remain hard.
ANTM: An Aligned Neural Topic Model for Exploring Evolving Topics

This paper presents an algorithmic family of dynamic topic models called Aligned Neural Topic Models (ANTM), which combine novel data mining algorithms to provide a modular framework for discovering evolving topics. ANTM maintains the temporal continuity of evolving topics by extracting time-aware features from documents using advanced pre-trained Large Language Models (LLMs) and employing an overlapping sliding window algorithm for sequential document clustering. This overlapping sliding window algorithm identifies a different number of topics within each time frame and aligns semantically similar document clusters across time periods. This process captures emerging and fading trends across different periods and allows for a more interpretable representation of evolving topics. Experiments on four distinct datasets show that ANTM outperforms probabilistic dynamic topic models in terms of topic coherence and diversity metrics. Moreover, it improves the scalability and flexibility of dynamic topic models by being accessible and adaptable to different types of algorithms. Additionally, a Python package is developed for researchers and scientists who wish to study the trends and evolving patterns of topics in large-scale textual data.
INV: Towards Streaming Incremental Neural Videos

Recent works in spatiotemporal radiance fields can produce photorealistic free-viewpoint videos. However, they are inherently unsuitable for interactive streaming scenarios (e.g. video conferencing, telepresence) because have an inevitable lag even if the training is instantaneous. This is because these approaches consume videos and thus have to buffer chunks of frames (often seconds) before processing. In this work, we take a step towards interactive streaming via a frame-by-frame approach naturally free of lag. Conventional wisdom believes that per-frame NeRFs are impractical due to prohibitive training costs and storage. We break this belief by introducing Incremental Neural Videos (INV), a per-frame NeRF that is efficiently trained and streamable. We designed INV based on two insights: (1) Our main finding is that MLPs naturally partition themselves into Structure and Color Layers, which store structural and color/texture information respectively. (2) We leverage this property to retain and improve upon knowledge from previous frames, thus amortizing training across frames and reducing redundant learning. As a result, with negligible changes to NeRF, INV can achieve good qualities (>28.6db) in 8min/frame. It can also outperform prior SOTA in 19% less training time. Additionally, our Temporal Weight Compression reduces the per-frame size to 0.3MB/frame (6.6% of NeRF). More importantly, INV is free from buffer lag and is naturally fit for streaming. While this work does not achieve real-time training, it shows that incremental approaches like INV present new possibilities in interactive 3D streaming. Moreover, our discovery of natural information partition leads to a better understanding and manipulation of MLPs. Code and dataset will be released soon.
Improving Recommendation Relevance by simulating User Interest

Most if not all on-line item-to-item recommendation systems rely on estimation of a distance like measure (rank) of similarity between items. For on-line recommendation systems, time sensitivity of this similarity measure is extremely important. We observe that recommendation "recency" can be straightforwardly and transparently maintained by iterative reduction of ranks of inactive items. The paper briefly summarizes algorithmic developments based on this self-explanatory observation. The basic idea behind this work is patented in a context of online recommendation systems.
Revisiting Long-tailed Image Classification: Survey and Benchmarks with New Evaluation Metrics

Recently, long-tailed image classification harvests lots of research attention, since the data distribution is long-tailed in many real-world situations. Piles of algorithms are devised to address the data imbalance problem by biasing the training process towards less frequent classes. However, they usually evaluate the performance on a balanced testing set or multiple independent testing sets having distinct distributions with the training data. Considering the testing data may have arbitrary distributions, existing evaluation strategies are unable to reflect the actual classification performance objectively. We set up novel evaluation benchmarks based on a series of testing sets with evolving distributions. A corpus of metrics are designed for measuring the accuracy, robustness, and bounds of algorithms for learning with long-tailed distribution. Based on our benchmarks, we re-evaluate the performance of existing methods on CIFAR10 and CIFAR100 datasets, which is valuable for guiding the selection of data rebalancing techniques. We also revisit existing methods and categorize them into four types including data balancing, feature balancing, loss balancing, and prediction balancing, according the focused procedure during the training pipeline.
Gradient and Variable Tracking with Multiple Local SGD for Decentralized Non-Convex Learning

Stochastic distributed optimization methods that solve an optimization problem over a multi-agent network have played an important role in a variety of large-scale signal processing and machine leaning applications. Among the existing methods, the gradient tracking (GT) method is found robust against the variance between agents' local data distribution, in contrast to the distributed stochastic gradient descent (SGD) methods which have a slowed convergence speed when the agents have heterogeneous data distributions. However, the GT method can be communication expensive due to the need of a large number of iterations for convergence. In this paper, we intend to reduce the communication cost of the GT method by integrating it with the local SGD technique. Specifically, we propose a new local stochastic GT (LSGT) algorithm where, within each communication round, the agents perform multiple SGD updates locally. Theoretically, we build the convergence conditions of the LSGT algorithm and show that it can have an improved convergence rate of $\mathcal{O}(1/\sqrt{ET})$, where $E$ is the number of local SGD updates and $T$ is the number of communication rounds. We further extend the LSGT algorithm to solve a more complex learning problem which has linearly coupled variables inside the objective function. Experiment results demonstrate that the proposed algorithms have significantly improved convergence speed even under heterogeneous data distribution.
Spectral Aware Softmax for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) aims to match specific pedestrian images from different modalities. Although suffering an extra modality discrepancy, existing methods still follow the softmax loss training paradigm, which is widely used in single-modality classification tasks. The softmax loss lacks an explicit penalty for the apparent modality gap, which adversely limits the performance upper bound of the VI-ReID task. In this paper, we propose the spectral-aware softmax (SA-Softmax) loss, which can fully explore the embedding space with the modality information and has clear interpretability. Specifically, SA-Softmax loss utilizes an asynchronous optimization strategy based on the modality prototype instead of the synchronous optimization based on the identity prototype in the original softmax loss. To encourage a high overlapping between two modalities, SA-Softmax optimizes each sample by the prototype from another spectrum. Based on the observation and analysis of SA-Softmax, we modify the SA-Softmax with the Feature Mask and Absolute-Similarity Term to alleviate the ambiguous optimization during model training. Extensive experimental evaluations conducted on RegDB and SYSU-MM01 demonstrate the superior performance of the SA-Softmax over the state-of-the-art methods in such a cross-modality condition.
Learning to Decouple Complex Systems

A complex system with cluttered observations may be a coupled mixture of multiple simple sub-systems corresponding to latent entities. Such sub-systems may hold distinct dynamics in the continuous-time domain; therein, complicated interactions between sub-systems also evolve over time. This setting is fairly common in the real world but has been less considered. In this paper, we propose a sequential learning approach under this setting by decoupling a complex system for handling irregularly sampled and cluttered sequential observations. Such decoupling brings about not only subsystems describing the dynamics of each latent entity but also a meta-system capturing the interaction between entities over time. Specifically, we argue that the meta-system evolving within a simplex is governed by projected differential equations (ProjDEs). We further analyze and provide neural-friendly projection operators in the context of Bregman divergence. Experimental results on synthetic and real-world datasets show the advantages of our approach when facing complex and cluttered sequential data compared to the state-of-the-art.
A space-time adaptive low-rank method for high-dimensional parabolic partial differential equations

An adaptive method for parabolic partial differential equations that combines sparse wavelet expansions in time with adaptive low-rank approximations in the spatial variables is constructed and analyzed. The method is shown to converge and satisfy similar complexity bounds as existing adaptive low-rank methods for elliptic problems, establishing its suitability for parabolic problems on high-dimensional spatial domains. The construction also yields computable rigorous a posteriori error bounds for such problems. The results are illustrated by numerical experiments.
Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents

We investigate the challenge of task planning for multi-task embodied agents in open-world environments. Two main difficulties are identified: 1) executing plans in an open-world environment (e.g., Minecraft) necessitates accurate and multi-step reasoning due to the long-term nature of tasks, and 2) as vanilla planners do not consider how easy the current agent can achieve a given sub-task when ordering parallel sub-goals within a complicated plan, the resulting plan could be inefficient or even infeasible. To this end, we propose "$\underline{D}$escribe, $\underline{E}$xplain, $\underline{P}$lan and $\underline{S}$elect" ($\textbf{DEPS}$), an interactive planning approach based on Large Language Models (LLMs). DEPS facilitates better error correction on initial LLM-generated $\textit{plan}$ by integrating $\textit{description}$ of the plan execution process and providing self-$\textit{explanation}$ of feedback when encountering failures during the extended planning phases. Furthermore, it includes a goal $\textit{selector}$, which is a trainable module that ranks parallel candidate sub-goals based on the estimated steps of completion, consequently refining the initial plan. Our experiments mark the milestone of the first zero-shot multi-task agent that can robustly accomplish 70+ Minecraft tasks and nearly double the overall performances. Further testing reveals our method's general effectiveness in popularly adopted non-open-ended domains as well (i.e., ALFWorld and tabletop manipulation). The ablation and exploratory studies detail how our design beats the counterparts and provide a promising update on the $\texttt{ObtainDiamond}$ grand challenge with our approach. The code is released at https://github.com/CraftJarvis/MC-Planner.