Reviewerly/exHarmony huggingface dataset:
Analysis of babi reservoir water management for irigation and raw water in Puyung Village Jonggat District Central Lombok Regency

&lt;p&gt;Babi Reservoir is located at Puyung Village Jonggat District Central Lombok Regency, which accomodate water from HLD (High Level Diversion) Channel of Jurang Sate located in DAS Dodokan is being used to fulfil the irrigation requirement during dry seasons and utilized for various requirement in agriculture fields, livestock and communities. The area of DTA (Catchment Area) of Babi Reservoir are 6,43 km2, potential areal at the location of Babi Reservoir are 150 ha ((Balai Wilayah Nusa Tenggara I, 2021)). Embung Babi services has been not fully utilized by surrounding communities. The availability of water is insufficient, thus the optimization of Embung Babi is essential so the reserved water can be optimally used. This research used evaluative method, by doing collection of primary and secondary data. For maximizing the area of irrigation land, optimization of the irrigation area is carried out by applying cropping pattern, namely Paddy-Paddy-Palawija with type of Palawija is soybean. In optimization model that used is monthly optimization within 1 year by calculation the area of irrigation land that available, area of fulfilled irrigation area, the amount of water availability, and the fulfilled requirement of water for irrigation. Optimization method that being used in this calculation is Program Solver. Based on the result of optimization of Babi Reservoir water potential that available for maximum area of irrigation land with cropping pattern Paddy-Paddy-Palawija in early planting of November II with land area of 138,52 ha and planting intentisity of 92,35% within one year. During MT I the irrigation area are 18,83 ha and planting intensity of 12,55%. During MT II the irrigation area are 89,98 ha and planting intensity of 59,99%. Amd for MT III the irrigation area are 29,71 ha and planting intensity are 19,81%.&lt;/p&gt;
Community of analysis mitigation and adaptation to the flood disaster overload of Lake Tempe, Wajo Regency

&lt;p&gt;In South Sulawesi, Wajo Regency is one of the areas prone to flooding during the rainy season. Floods that occur in the area cause inundation. The inundation that occurred was caused by the overflow of Lake Tempe. This research was conducted in Laelo Village, Tempe District, which is located around the Tempe Lake Coast. This location was chosen purposively with the consideration that the area has become a frequent flood in the western part of the kelurahan (the part near the lake). The village has a low contour in the west and high in the east. The research design is descriptive and qualitative which aims to describe the state or status of the phenomenon. The purpose of the research is to analyze the forms of adaptation and mitigation that will be carried out by communities in flood-affected areas. This research uses field surveys, in-depth interviews, making field notes, and documentation. The data collection method was carried out using purposive sampling because the desired sample in the study was only people living in the coastal area of the lake, namely in Laelo Village which was only affected by flooding, not the entire Laelo Village community. Data analysis techniques in qualitative descriptive research are carried out with interactive analysis in the form of cycles. Data analysis was carried out simultaneously with the implementation process of data collection. The results of interviews with the community show that they understand that flooding is an ordinary natural phenomenon which is an overflow of a lake, but they do not know why there is a flood. Some people who live on the shores of the lake feel that almost every year they are exposed to floods from the lake. Where the form of adaptation they do is to raise the pole of the house and prepare a boat that is moored under the house to be used as a means of transportation in the event of a flood. Several forms of mitigation that have been carried out are water hyacinth retaining embankments, and making taller bridges with concrete connecting roads from house to house. Information has been found regarding evacuation signs with the evacuation route installed, the mosque is used as a place to disseminate information related to the incident and development of the flood. It is better to form a disaster-resilient village in the area to create an area that is more prepared to face disasters.&lt;/p&gt;
Management of artesian wells at bumi purnawira housing sukabumi city with water distribution application

The problem of clean water supply to meet the needs of the community is the main obstacle experienced by residents of Bumi Purnawira housing. Water management is completely left to the residents starting from installation of pipes to each house, installation of water meters, calculation of the cost of water usage per cubic meter, as well as administration and maintenance of the artesian well. Some problem that arise include: frequent miscalculation of cost to be paid; monthly meter recapitulation records in the form of paper prone to damage and data lost; meter recapitulation records is only kept by the treasurer, so if the residents want to know the history of meter records and the payment, they have to see the treasurer; the meter’s monthly status is vulnerable to manipulation whether done by the residents or the treasurer; There is often a discrepancy between the financial recapitulation recorded and the physical cash; and financial expenditures are sometimes not properly recorded, so that it triggers the discrepancy between the recorded amount and the physical cash. Seeing the existing reality, an idea to make an application for the management of artesian well administration has arisen. The initial step is to form two teams, namely the development team and the socialization team. The development team is responsible for developing the management system of the artesian well’s administration and finance. Meanwhile, the socialization team is responsible for conducting the socialization and the application training. The result of this project is an application and socialization that can help both the administrators and the residents in managing the administration and finance of the artesian well.
Persistence-Based Discretization for Learning Discrete Event Systems from Time Series

To get a good understanding of a dynamical system, it is convenient to have an interpretable and versatile model of it. Timed discrete event systems are a kind of model that respond to these requirements. However, such models can be inferred from timestamped event sequences but not directly from numerical data. To solve this problem, a discretization step must be done to identify events or symbols in the time series. Persist is a discretization method that intends to create persisting symbols by using a score called persistence score. This allows to mitigate the risk of undesirable symbol changes that would lead to a too complex model. After the study of the persistence score, we point out that it tends to favor excessive cases making it miss interesting persisting symbols. To correct this behavior, we replace the metric used in the persistence score, the Kullback-Leibler divergence, with the Wasserstein distance. Experiments show that the improved persistence score enhances Persist's ability to capture the information of the original time series and that it makes it better suited for discrete event systems learning.
Circuit Complexity through phase transitions: consequences in quantum state preparation

In this paper, we analyze the circuit complexity for preparing ground states of quantum many-body systems. In particular, how this complexity grows as the ground state approaches a quantum phase transition. We discuss different definitions of complexity, namely the one following the Fubini-Study metric or the Nielsen complexity. We also explore different models: Ising, ZZXZ or Dicke. In addition, different forms of state preparation are investigated: analytic or exact diagonalization techniques, adiabatic algorithms (with and without shortcuts), and Quantum Variational Eigensolvers. We find that the divergence (or lack thereof) of the complexity near a phase transition depends on the non-local character of the operations used to reach the ground state. For Fubini-Study based complexity, we extract the universal properties and their critical exponents. In practical algorithms, we find that the complexity depends crucially on whether or not the system passes close to a quantum critical point when preparing the state. For both VQE and Adiabatic algorithms, we provide explicit expressions and bound the growth of complexity with respect to the system size and the execution time, respectively.
SHUNIT: Style Harmonization for Unpaired Image-to-Image Translation

We propose a novel solution for unpaired image-to-image (I2I) translation. To translate complex images with a wide range of objects to a different domain, recent approaches often use the object annotations to perform per-class source-to-target style mapping. However, there remains a point for us to exploit in the I2I. An object in each class consists of multiple components, and all the sub-object components have different characteristics. For example, a car in CAR class consists of a car body, tires, windows and head and tail lamps, etc., and they should be handled separately for realistic I2I translation. The simplest solution to the problem will be to use more detailed annotations with sub-object component annotations than the simple object annotations, but it is not possible. The key idea of this paper is to bypass the sub-object component annotations by leveraging the original style of the input image because the original style will include the information about the characteristics of the sub-object components. Specifically, for each pixel, we use not only the per-class style gap between the source and target domains but also the pixel's original style to determine the target style of a pixel. To this end, we present Style Harmonization for unpaired I2I translation (SHUNIT). Our SHUNIT generates a new style by harmonizing the target domain style retrieved from a class memory and an original source image style. Instead of direct source-to-target style mapping, we aim for source and target styles harmonization. We validate our method with extensive experiments and achieve state-of-the-art performance on the latest benchmark sets. The source code is available online: https://github.com/bluejangbaljang/SHUNIT.
Semantic Web Enabled Geographic Question Answering Framework: GeoTR

With the considerable growth of linked data, researchers have focused on how to increase the availability of semantic web technologies to provide practical usages for real life systems. Question answering systems are an example of real-life systems that communicate directly with end users, understand user intention and generate answers. End users do not care about the structural query language or the vocabulary of the knowledge base where the point of a problem arises. In this study, a question answering framework that converts Turkish natural language input into SPARQL queries in the geographical domain is proposed. Additionally, a novel Turkish ontology, which covers a 10th grade geography lesson named Spatial Synthesis Turkey, has been developed to be used as a linked data provider. Moreover, a gap in the literature on Turkish question answering systems, which utilizes linked data in the geographical domain, is addressed. A hybrid system architecture that combines natural language processing techniques with linked data technologies to generate answers is also proposed. Further related research areas are suggested.
NarrowBERT: Accelerating Masked Language Model Pretraining and Inference

Large-scale language model pretraining is a very successful form of self-supervised learning in natural language processing, but it is increasingly expensive to perform as the models and pretraining corpora have become larger over time. We propose NarrowBERT, a modified transformer encoder that increases the throughput for masked language model pretraining by more than $2\times$. NarrowBERT sparsifies the transformer model such that the self-attention queries and feedforward layers only operate on the masked tokens of each sentence during pretraining, rather than all of the tokens as with the usual transformer encoder. We also show that NarrowBERT increases the throughput at inference time by as much as $3.5\times$ with minimal (or no) performance degradation on sentence encoding tasks like MNLI. Finally, we examine the performance of NarrowBERT on the IMDB and Amazon reviews classification and CoNLL NER tasks and show that it is also comparable to standard BERT performance.
Color-NeuraCrypt: Privacy-Preserving Color-Image Classification Using Extended Random Neural Networks

In recent years, with the development of cloud computing platforms, privacy-preserving methods for deep learning have become an urgent problem. NeuraCrypt is a private random neural network for privacy-preserving that allows data owners to encrypt the medical data before the data uploading, and data owners can train and then test their models in a cloud server with the encrypted data directly. However, we point out that the performance of NeuraCrypt is heavily degraded when using color images. In this paper, we propose a Color-NeuraCrypt to solve this problem. Experiment results show that our proposed Color-NeuraCrypt can achieve a better classification accuracy than the original one and other privacy-preserving methods.
Sharpening Ponzi Schemes Detection on Ethereum with Machine Learning

Blockchain technology has been successfully exploited for deploying new economic applications. However, it has started arousing the interest of malicious users who deliver scams to deceive honest users and to gain economic advantages. Among the various scams, Ponzi schemes are one of the most common. Here, we present an automatic technique for detecting smart Ponzi contracts on Ethereum. We release a reusable data set with 4422 unique real-world smart contracts. Then, we introduce a new set of features that allow us to improve the classification. Finally, we identify a small and effective set of features that ensures a good classification quality.
A Stochastic Proximal Polyak Step Size

Recently, the stochastic Polyak step size (SPS) has emerged as a competitive adaptive step size scheme for stochastic gradient descent. Here we develop ProxSPS, a proximal variant of SPS that can handle regularization terms. Developing a proximal variant of SPS is particularly important, since SPS requires a lower bound of the objective function to work well. When the objective function is the sum of a loss and a regularizer, available estimates of a lower bound of the sum can be loose. In contrast, ProxSPS only requires a lower bound for the loss which is often readily available. As a consequence, we show that ProxSPS is easier to tune and more stable in the presence of regularization. Furthermore for image classification tasks, ProxSPS performs as well as AdamW with little to no tuning, and results in a network with smaller weight parameters. We also provide an extensive convergence analysis for ProxSPS that includes the non-smooth, smooth, weakly convex and strongly convex setting.
Semantic Segmentation via Pixel-to-Center Similarity Calculation

Since the fully convolutional network has achieved great success in semantic segmentation, lots of works have been proposed focusing on extracting discriminative pixel feature representations. However, we observe that existing methods still suffer from two typical challenges, i.e. (i) large intra-class feature variation in different scenes, (ii) small inter-class feature distinction in the same scene. In this paper, we first rethink semantic segmentation from a perspective of similarity between pixels and class centers. Each weight vector of the segmentation head represents its corresponding semantic class in the whole dataset, which can be regarded as the embedding of the class center. Thus, the pixel-wise classification amounts to computing similarity in the final feature space between pixels and the class centers. Under this novel view, we propose a Class Center Similarity layer (CCS layer) to address the above-mentioned challenges by generating adaptive class centers conditioned on different scenes and supervising the similarities between class centers. It utilizes a Adaptive Class Center Module (ACCM) to generate class centers conditioned on each scene, which adapt the large intra-class variation between different scenes. Specially designed loss functions are introduced to control both inter-class and intra-class distances based on predicted center-to-center and pixel-to-center similarity, respectively. Finally, the CCS layer outputs the processed pixel-to-center similarity as the segmentation prediction. Extensive experiments demonstrate that our model performs favourably against the state-of-the-art CNN-based methods.
Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues

Maintaining engagement and consistency is particularly important in dialogue systems. Existing works have improved the performance of dialogue systems by intentionally learning interlocutor personas with sophisticated network structures. One issue with this approach is that it requires more personal corpora with annotations. Additionally, these models typically perform the next utterance prediction to generate a response but neglect the discourse coherence in the entire conversation. To address these issues, this study proposes a method of learning to memorize entailment and discourse relations for persona-consistent dialogue tasks. Entailment text pairs in natural language inference dataset were applied to learn latent entailment relations as external memories by premise-to-hypothesis generation task. Furthermore, an internal memory with a similar architecture was applied to the discourse information in the dialogue. Placing orthogonality restrictions on these two memory spaces ensures that the latent entailment relations remain dialogue-independent. Both memories collaborate to obtain entailment and discourse representation for the generation, allowing a deeper understanding of both consistency and coherence. Experiments on two large public datasets, PersonaChat and DSTC7-AVSD, demonstrated the effectiveness of the proposed method. Both automatic and human evaluations indicate that the proposed model outperforms several strong baselines in terms of both persona consistency and response coherence. Our source code is available at https://github.com/Chenrj233/LMEDR.
Analysis of Students’ Difficulties in English Conversation Practice

Speaking skill is the most important language skills to support communication, especially in English speaking skills are needed for two-way communication that can be understood by one another. However, there are still many students who are not maximal in carrying out English conversations practice to improving their English speaking skills. Therefore, this study aims to analyze students' problems and difficulties in practicing English conversation in order to improve speaking skills. The method used in this research is qualitative by using a descriptive approach. Data were obtained through direct observation and interviews related to the conditions experienced by students in improving their English speaking skills. After the data has been obtained, the data is analyzed through three stages, namely data reduction, data display and data conclusion. The results showed that students had difficulty practicing English conversations due to a lack of confidence, lack of courage, fear of being wrong, not having enough English vocabulary and not having a supportive environment to practice English conversations. Therefore, it is necessary to have a good learning strategy to encourage students to be able to practice speaking in English.
Modeling the evolution of temporal knowledge graphs with uncertainty

Forecasting future events is a fundamental challenge for temporal knowledge graphs (tKG). As in real life predicting a mean function is most of the time not sufficient, but the question remains how confident can we be about our prediction? Thus, in this work, we will introduce a novel graph neural network architecture (WGP-NN) employing (weighted) Gaussian processes (GP) to jointly model the temporal evolution of the occurrence probability of events and their time-dependent uncertainty. Especially we employ Gaussian processes to model the uncertainty of future links by their ability to predict predictive variance. This is in contrast to existing works, which are only able to express uncertainties in the learned entity representations. Moreover, WGP-NN can model parameter-free complex temporal and structural dynamics of tKGs in continuous time. We further demonstrate the model's state-of-the-art performance on two real-world benchmark datasets.
Electroweak Phase Transition in a Right-Handed Neutrino Superfield Extended NMSSM

Supersymmetric models with singlet extensions can accommodate single- or multi-step first-order phase transitions (FOPT) along the various constituent field directions. Such a framework can also produce Gravitational Waves, detectable at the upcoming space-based interferometers, e.g., U-DECIGO. We explore the dynamics of electroweak phase transition and the production of Gravitational Waves in an extended set-up of the Next-to-Minimal Supersymmetric Standard Model (NMSSM) with a Standard Model singlet right-handed neutrino superfield. We examine the role of the new parameters compared to NMSSM on the phase transition dynamics and observe that the occurrence of a FOPT, an essential requirement for Electroweak Baryogenesis, typically favours a right-handed sneutrino state below 125 GeV. Our investigation shows how the analysis can offer complementary probes for physics beyond the Standard Model besides the collider searches.
Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks

Foundation models or pre-trained models have substantially improved the performance of various language, vision, and vision-language understanding tasks. However, existing foundation models can only perform the best in one type of tasks, namely language, vision, or vision-language. It is still an open question whether it is possible to construct a foundation model performing the best for all the understanding tasks, which we call a general foundation model. In this paper, we propose a new general foundation model, X-FM (the X-Foundation Model). X-FM has one language encoder, one vision encoder, and one fusion encoder, as well as a new training method. The training method includes two new techniques for learning X-FM from text, image, and image-text pair data. One is to stop gradients from the vision-language training when learning the language encoder. The other is to leverage the vision-language training to guide the learning of the vision encoder. Extensive experiments on benchmark datasets show that X-FM can significantly outperform existing general foundation models and perform better than or comparable to existing foundation models specifically for language, vision, or vision-language understanding. Code and pre-trained models are released at https://github.com/zhangxinsong-nlp/XFM.