Reviewerly/exHarmony huggingface dataset:
Pemanfaatan Biopori dan Rainwater harvesting guna Mereduksi Tinggi Banjir (Studi Kasus: Permuahan PGRI Balikpapan)

Flood is a disaster which is impact of the land use changing from land for rainwater absorption to resident area or others. Rainwater has a high potential that can be used by people. Every stakeholder, especially residents, can take participation in flood prevention. Methods that can be used by the community in anticipating or reducing the impact of floods are the bio-pore and rainwater harvesting. This community service activity aims to provide information and training on the use of bio-pore and harvesting rainwater to reduce the risk of flooding. The counseling was conducted on RT 10 Blok K Housing PGRI Graha Indah Balikpapan residents. This activity involves the community implementing bio-pore in residential areas and working together to install rainwater reservoirs in residential mosques to be used as a water source for ablution.
FEASIBILITY OF 12 W SOLAR POWER PLANT FOR STREET LIGHTING IN RURAL AREA

Solar power plants are a suitable alternative for solving the energy crisis in rural areas in Indonesia because it has abundant solar energy potential. South Sumatra is the second area with the largest solar energy potential in Indonesia, after East Nusa Tenggara. The Dabuk Rejo village is not all area covered by street lighting facilities in South Sumatra. Hence, the solar power plant is suitable for solving the crisis energy problem in Dabuk Rejo because it is cheap, easy to maintain, and easy to operate. Based on the observation field, the solar power plant positively impacts the Dabuk Rejo community because the activity time increases due to lighting.
Performance Evaluation of LSTM and RNN Models in the Detection of Email Spam Messages

Email spam is an unwanted bulk message that is sent to a recipient’s email address without explicit consent from the recipient. This is usually considered a means of advertising and maximizing profit, especially with the increase in the usage of the internet for social networking, but can also be very frustrating and annoying to the recipients of these messages. Recent research has shown that about 14.7 billion spam messages are sent out every single day of which more than 45% of these messages are promotional sales content that the recipient did not specifically opt-in. This has gotten the attention of many researchers in the area of natural language processing. In this paper, we used the Long Short-Time Memory (LSTM) for classification tasks between spam and Ham messages. The performance of LSTM is compared with that of a Recurrent Neural Network( RNN) which can also be used for a classification task of this nature but suffers from short-time memory and tends to leave out important information from earlier time steps to later ones in terms of prediction. The evaluation of the result shows that LSTM achieved 97% accuracy with both Adams and RMSprop optimizers compared to RNN with an accuracy of 94% with RMSprop and 87% accuracy with Adams optimizer.
Carboxymethyl Cellulose Nanoadsorbent for Remediation of Polluted Water

The development of nanoadsorbents for remediation of polluted water in order to obtain clean and healthy water quality has been carried out, namely the incorporation of chitosan, magnetic, and activated carbon materials.The activated carbon used is the result of the synthesis of banana peel waste nanocrystals, while the magnetic is Fe 3 O 4 .The method used in this study is an experimental method with coprecipitation through several stages, namely (1) magnetic synthesis of Fe 3 O 4 by thecoprecipitation method, (2) preparation of chitosan solution, (3) synthesis of activated carbon nanocrystals from banana peel waste by the milling process, (4) merger of the three materials, and (5) characterization with SEM/EDX, XRD, FTIR, BET, PSA, TGA, and AAS to test the performance of the material against polluted water.The study found that 210 minutes was the optimal time for the heavy metal ions Fe, Mn, Zn, and Pb to adsorption.The best sample was sample S4 with a ratio of 1:2:2 with adsorption for Zn 92.43%, Fe 95.44%, Mn 89.54%, and Pb 84.38%.For the heavy metal ions: Mn 5624 mg/g, Fe 5849.4 mg/g, Zn 4894.22 mg/g, and Pb 468.2 mg/g, the Langmuir model was used.The adsorption kinetics showed that the reaction order for Pb, Mn, Zn, and Fe ions varied with pseudo-first order and pseudo-second order.Carboxymethyl cellulose nanoadsorbents are effective in remediating the water contaminated with heavy metals, such as Pb, Mn, Zn, and Fe, meeting the environmental health quality standards for water media for sanitation hygiene purposes.
Opportunities for the Formation of University Students’ Digital Competence

The relevance of this article lies in the fact that during the period of the accelerating process of the technological development of society, the active introduction of electronic and distance learning technologies, electronic educational resources, the creation of a digital educational environment, it is necessary to prevent emerging very controversial problems. One of which is the demand for primary school teachers who have the necessary skills to work in a digital educational environment and the urgent need that arises against this background to prepare students and undergraduates for professional activities in accordance with modern educational trends. Thus, the article presents an analysis of the problem of preparing modern undergraduates in the direction “Pedagogical education”, profile “Primary education” for the implementation of their professional activities in the context of creating a unified digital educational environment of elementary school. A study developed on the basis of the methods of K. Zamfir (modified by A. A. Rean), V. I. Andreeva, studies by S. A. Zaitseva, where undergraduates were asked to complete tasks and answer questions related to the organization of a unified digital educational environment in elementary school. The study has made it possible to identify the level of their readiness to use digital technologies in their professional activities that contribute to the creation of a digital educational environment in elementary school. The task of the study was also to identify “gaps” in the process of developing the digital competence of undergraduates and further work to eliminate them, in order to prepare highly qualified specialists with skills that will allow them to independently develop and improve their digital competence and maintain professional mobility. The author of the article offers practical tasks that will improve the level of digital knowledge and skills of undergraduates: knowledge about digital technologies, understanding the possibilities of their use, understanding the essence of the digital transformation of the educational process, the skill of working with information, the ability to work in a unified digital educational environment of elementary school, etc. The research materials can be useful for teachers of universities, secondary vocational education, as well as additional education in organizing the process of professional training of primary school teachers. Keywords: digital transformation of education, digital competence, digital competence, digitalization of the educational process, digital technologies, primary education
The Contribution of Banking Sector to Economic Growth in Nigeria (1980 -2019)

The banking system remains one of the major development sectors in emerging markets and developed economies. The study examines the contribution of banking sector to economic growth for a period of forty (40) years. The Nigerian banking sector has undergone remarkable changes over the years in terms of technological payment system, digital assets development, increase in number of institutions and ownership structures. The data used in this study are collected from the period of 1980 to 2019. Augmented Dickey Fuller (ADF) and Philip Perron Unit root test and Ordinary Least Square method are adopted. Unit root test confirms the stationary of all variables at first difference. Results show that deposits, advances, investments and profit after tax have significant different proportional effects on economic growth in Nigeria. The paper recommends a two-year interval reforms in the banking subsector in order to stimulate economic growth and to achieve financial stability in Nigeria.
Better Generalized Few-Shot Learning Even Without Base Data

This paper introduces and studies zero-base generalized few-shot learning (zero-base GFSL), which is an extreme yet practical version of few-shot learning problem. Motivated by the cases where base data is not available due to privacy or ethical issues, the goal of zero-base GFSL is to newly incorporate the knowledge of few samples of novel classes into a pretrained model without any samples of base classes. According to our analysis, we discover the fact that both mean and variance of the weight distribution of novel classes are not properly established, compared to those of base classes. The existing GFSL methods attempt to make the weight norms balanced, which we find helps only the variance part, but discard the importance of mean of weights particularly for novel classes, leading to the limited performance in the GFSL problem even with base data. In this paper, we overcome this limitation by proposing a simple yet effective normalization method that can effectively control both mean and variance of the weight distribution of novel classes without using any base samples and thereby achieve a satisfactory performance on both novel and base classes. Our experimental results somewhat surprisingly show that the proposed zero-base GFSL method that does not utilize any base samples even outperforms the existing GFSL methods that make the best use of base data. Our implementation is available at: https://github.com/bigdata-inha/Zero-Base-GFSL.
Soft Alignment Objectives for Robust Adaptation of Language Generation

Domain adaptation allows generative language models to address specific flaws caused by the domain shift of their application. However, the traditional adaptation by further training on in-domain data rapidly weakens the model's ability to generalize to other domains, making the open-ended deployments of the adapted models prone to errors. This work introduces novel training objectives built upon a semantic similarity of the predicted tokens to the reference. Our results show that (1) avoiding the common assumption of a single correct prediction by constructing the training target from tokens' semantic similarity can mitigate catastrophic forgetting during domain adaptation, while (2) preserving the quality of the adaptation, (3) with negligible additions to compute costs. In the broader context, the objectives grounded in a continuous token similarity pioneer the exploration of the middle ground between the efficient but na\"{\i}ve exact-match token-level objectives and expressive but computationally- and resource-intensive sequential objectives.
Performance Evaluation of Vanilla, Residual, and Dense 2D U-Net Architectures for Skull Stripping of Augmented 3D T1-weighted MRI Head Scans

Skull Stripping is a requisite preliminary step in most diagnostic neuroimaging applications. Manual Skull Stripping methods define the gold standard for the domain but are time-consuming and challenging to integrate into processing pipelines with a high number of data samples. Automated methods are an active area of research for head MRI segmentation, especially deep learning methods such as U-Net architecture implementations. This study compares Vanilla, Residual, and Dense 2D U-Net architectures for Skull Stripping. The Dense 2D U-Net architecture outperforms the Vanilla and Residual counterparts by achieving an accuracy of 99.75% on a test dataset. It is observed that dense interconnections in a U-Net encourage feature reuse across layers of the architecture and allow for shallower models with the strengths of a deeper network.
SinDDM: A Single Image Denoising Diffusion Model

Denoising diffusion models (DDMs) have led to staggering performance leaps in image generation, editing and restoration. However, existing DDMs use very large datasets for training. Here, we introduce a framework for training a DDM on a single image. Our method, which we coin SinDDM, learns the internal statistics of the training image by using a multi-scale diffusion process. To drive the reverse diffusion process, we use a fully-convolutional light-weight denoiser, which is conditioned on both the noise level and the scale. This architecture allows generating samples of arbitrary dimensions, in a coarse-to-fine manner. As we illustrate, SinDDM generates diverse high-quality samples, and is applicable in a wide array of tasks, including style transfer and harmonization. Furthermore, it can be easily guided by external supervision. Particularly, we demonstrate text-guided generation from a single image using a pre-trained CLIP model.
Exploiting Category Names for Few-Shot Classification with Vision-Language Models

Vision-language foundation models pretrained on large-scale data provide a powerful tool for many visual understanding tasks. Notably, many vision-language models build two encoders (visual and textual) that can map two modalities into the same embedding space. As a result, the learned representations achieve good zero-shot performance on tasks like image classification. However, when there are only a few examples per category, the potential of large vision-language models is often underperformed, mainly due to the gap between a large number of parameters and a relatively small amount of training data. This paper shows that we can significantly improve the performance of few-shot classification by using the category names to initialize the classification head. With the proposed category name initialization method, our model obtains the state-of-the-art performance on a number of few-shot image classification benchmarks (e.g., 87.37% on ImageNet and 96.08% on Stanford Cars, both using five-shot learning).
Trifocal Relative Pose from Lines at Points and its Efficient Solution

We present a method for solving two minimal problems for relative camera pose estimation from three views, which are based on three view correspondences of i) three points and one line and the novel case of ii) three points and two lines through two of the points. These problems are too difficult to be efficiently solved by the state of the art Groebner basis methods. Our method is based on a new efficient homotopy continuation (HC) solver framework MINUS, which dramatically speeds up previous HC solving by specializing HC methods to generic cases of our problems. We characterize their number of solutions and show with simulated experiments that our solvers are numerically robust and stable under image noise, a key contribution given the borderline intractable degree of nonlinearity of trinocular constraints. We show in real experiments that i) SIFT feature location and orientation provide good enough point-and-line correspondences for three-view reconstruction and ii) that we can solve difficult cases with too few or too noisy tentative matches, where the state of the art structure from motion initialization fails.
Complexity of Deciding Syntactic Equivalence up to Renaming for Term Rewriting Systems (Extended Version)

Motivated by questions from program transformations, eight notions of isomorphisms between term rewriting systems are defined, analysed, and classified. The notions include global isomorphisms, where the renaming of variables and function symbols is the same for all term rewriting rules of the system, local ones, where a single renaming for every rule is used, and a combination, where one symbol set is renamed globally while the other set is renamed locally. Preservation of semantic properties like convertibility and termination is analysed for the different isomorphism notions. The notions of templates and maximal normal forms of term rewriting systems are introduced and algorithms to efficiently compute them are presented. Equipped with these techniques, the complexity of the underlying decision problems of the isomorphisms are analysed and either shown to be efficiently solvable or proved to be complete for the graph isomorphism complexity class.
Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer

When presented with a data stream of two statistically dependent variables, predicting the future of one of the variables (the target stream) can benefit from information about both its history and the history of the other variable (the source stream). For example, fluctuations in temperature at a weather station can be predicted using both temperatures and barometric readings. However, a challenge when modelling such data is that it is easy for a neural network to rely on the greatest joint correlations within the target stream, which may ignore a crucial but small information transfer from the source to the target stream. As well, there are often situations where the target stream may have previously been modelled independently and it would be useful to use that model to inform a new joint model. Here, we develop an information bottleneck approach for conditional learning on two dependent streams of data. Our method, which we call Transfer Entropy Bottleneck (TEB), allows one to learn a model that bottlenecks the directed information transferred from the source variable to the target variable, while quantifying this information transfer within the model. As such, TEB provides a useful new information bottleneck approach for modelling two statistically dependent streams of data in order to make predictions about one of them.
Proof complexity of CSP

The CSP (constraint satisfaction problems) is a class of problems deciding whether there exists a homomorphism from an instance relational structure to a target one. The CSP dichotomy is a profound result recently proved by Zhuk (2020, J. ACM, 67) and Bulatov (2017, FOCS, 58). It establishes that for any fixed target structure, CSP is either NP-complete or $p$-time solvable. Zhuk's algorithm solves CSP in polynomial time for constraint languages having a weak near-unanimity polymorphism. For negative instances of $p$-time CSPs, it is reasonable to explore their proof complexity. We show that the soundness of Zhuk's algorithm can be proved in a theory of bounded arithmetic, namely in the theory $V^1$ augmented by three special universal algebra axioms. This implies that any propositional proof system that simulates both Extended Resolution and a theory that proves the three axioms admits $p$-size proofs of all negative instances of a fixed $p$-time CSP.
DINER: Depth-aware Image-based NEural Radiance fields

We present Depth-aware Image-based NEural Radiance fields (DINER). Given a sparse set of RGB input views, we predict depth and feature maps to guide the reconstruction of a volumetric scene representation that allows us to render 3D objects under novel views. Specifically, we propose novel techniques to incorporate depth information into feature fusion and efficient scene sampling. In comparison to the previous state of the art, DINER achieves higher synthesis quality and can process input views with greater disparity. This allows us to capture scenes more completely without changing capturing hardware requirements and ultimately enables larger viewpoint changes during novel view synthesis. We evaluate our method by synthesizing novel views, both for human heads and for general objects, and observe significantly improved qualitative results and increased perceptual metrics compared to the previous state of the art. The code is publicly available for research purposes.
CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation

Household environments are visually diverse. Embodied agents performing Vision-and-Language Navigation (VLN) in the wild must be able to handle this diversity, while also following arbitrary language instructions. Recently, Vision-Language models like CLIP have shown great performance on the task of zero-shot object recognition. In this work, we ask if these models are also capable of zero-shot language grounding. In particular, we utilize CLIP to tackle the novel problem of zero-shot VLN using natural language referring expressions that describe target objects, in contrast to past work that used simple language templates describing object classes. We examine CLIP's capability in making sequential navigational decisions without any dataset-specific finetuning, and study how it influences the path that an agent takes. Our results on the coarse-grained instruction following task of REVERIE demonstrate the navigational capability of CLIP, surpassing the supervised baseline in terms of both success rate (SR) and success weighted by path length (SPL). More importantly, we quantitatively show that our CLIP-based zero-shot approach generalizes better to show consistent performance across environments when compared to SOTA, fully supervised learning approaches when evaluated via Relative Change in Success (RCS).
Approximating binary longest common subsequence in almost-linear time

The Longest Common Subsequence (LCS) is a fundamental string similarity measure, and computing the LCS of two strings is a classic algorithms question. A textbook dynamic programming algorithm gives an exact algorithm in quadratic time, and this is essentially best possible under plausible fine-grained complexity assumptions, so a natural problem is to find faster approximation algorithms. When the inputs are two binary strings, there is a simple $\frac{1}{2}$-approximation in linear time: compute the longest common all-0s or all-1s subsequence. It has been open whether a better approximation is possible even in truly subquadratic time. Rubinstein and Song showed that the answer is yes under the assumption that the two input strings have equal lengths. We settle the question, generalizing their result to unequal length strings, proving that, for any $\varepsilon>0$, there exists $\delta>0$ and a $(\frac{1}{2}+\delta)$-approximation algorithm for binary LCS that runs in $n^{1+\varepsilon}$ time. As a consequence of our result and a result of Akmal and Vassilevska-Williams, for any $\varepsilon>0$, there exists a $(\frac{1}{q}+\delta)$-approximation for LCS over $q$-ary strings in $n^{1+\varepsilon}$ time. Our techniques build on the recent work of Guruswami, He, and Li who proved new bounds for error-correcting codes tolerating deletion errors. They prove a combinatorial "structure lemma" for strings which classifies them according to their oscillation patterns. We prove and use an algorithmic generalization of this structure lemma, which may be of independent interest.
Policy Optimization over General State and Action Spaces

Reinforcement learning (RL) problems over general state and action spaces are notoriously challenging. In contrast to the tableau setting, one can not enumerate all the states and then iteratively update the policies for each state. This prevents the application of many well-studied RL methods especially those with provable convergence guarantees. In this paper, we first present a substantial generalization of the recently developed policy mirror descent method to deal with general state and action spaces. We introduce new approaches to incorporate function approximation into this method, so that we do not need to use explicit policy parameterization at all. Moreover, we present a novel policy dual averaging method for which possibly simpler function approximation techniques can be applied. We establish linear convergence rate to global optimality or sublinear convergence to stationarity for these methods applied to solve different classes of RL problems under exact policy evaluation. We then define proper notions of the approximation errors for policy evaluation and investigate their impact on the convergence of these methods applied to general-state RL problems with either finite-action or continuous-action spaces. To the best of our knowledge, the development of these algorithmic frameworks as well as their convergence analysis appear to be new in the literature.