Reviewerly/exHarmony huggingface dataset:
Japanese Language Review Mining using Translators, Word Embedding and ML Techniques

Most individuals rely on translation to communicate and comprehend other languages. The main driving force behind this study is determining which translator, along with the blend of word embedding and Machine Learning(ML)techniques, has greater accuracy when working with the Japanese Language. Business owners struggle to understand how their products perform in Japan and receive feedback to improve it. Japanese is among the most complex languages to read and write. The issue can be resolved by classifying reviews and ratings into separate groups, such as excellent and negative so business owners can improve their products based on the reviews. This study examines the reviews and ratings from the Japanese-language Amazon Product Review dataset. These reviews have been translated into English using three different translators (Google - GT, Microsoft - MT, and Python - PT). The data for each translator is then subjected to a combination of word embedding techniques (Count Vector, TF-IDF, and Word2vec) and machine learning techniques (Logistic Regression (LR), Support Vector Machine (SVM), Random Forest Classifier (RF), Decision Tree Classifier (DT), and XG Boost Classifier (XGB). According to the study's findings, the Python Translator, combined with a TF-IDF and LR mix, exceeds all other translators with an accuracy of 89.96%.
Image Encryption and Hiding Algorithm Based on Hyper Chaotic System and Compressed Sensing

An image encryption and hiding algorithm combining hyperchaotic system and compressed sensing is proposed. The key to the algorithm is jointly generated by SHA-512 function and 2D-IDCT. In compressed sensing, part of the random block weight matrix is used as the measurement matrix, the information of the original image is destroyed by scrambling and diffusion technology, and the security of the encryption algorithm is improved by using DNA encoded triploid mutation technology. Finally, the obtained ciphertext image is embedded into the carrier image through IWT and bit plane decomposition technology, Reduce the probability of ciphertext images being attacked. Experiments have shown that this algorithm has high security while ensuring the visual effect of ciphertext images. Even if the encrypted image is extracted from the carrier image, it is difficult to obtain effective information from the encrypted image.
A Learnable Noise Exploration Method for Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) has demonstrated to be an attractive approach for addressing the challenge of multi-agent collaboration. Numerous existing MARL algorithms require hand-craft setting of exploration parameters to alleviate instability and low efficiency in the exploration-exploitation dilemma. However, the meticulous hand-craft exploration parameters limited the efficiency and adaptability of algorithms. In this paper, we propose a learnable noise exploration method for multi-agent reinforcement learning. By introducing noise into the individual value network, the proposed method brings about a level of randomness in agents to drive exploration. The parameters of noise are updated along with the network through gradient descent, avoiding the handcraft setting of exploration parameters. The experiment results demonstrate the effectiveness of our method in the single-state matrix game, and show the stability and significant performance improvement in the predator-prey game and hard multi-agent particle environment.
Continuous Learning at the Individual Level: a study in software development organizations

This study aims to understand the occurrence of continuous learning at the individual level in Software Development Organizations – SDOs according to the framework proposed by Tannenbaum (1997). The objectives are to understand and describe the strategies and tools applied by developers during their learning process and if the studied SDOs offer a work environment conducive to continuous learning at the individual level. Thus, qualitative research was conducted with 12 Software Development Professionals in four SDOs applying semi-structured interviews. The subjects interviewed were involved in the software development process. The analyses were performed by the qualitative data analysis software Weft_QDA, which allowed the identification and analysis of speeches related to the theoretical concept of this study. The results show that SDOs offer an environment conducive to continuous learning at the individual level by allowing meaningful learning experiences, motivating constant participation in these experiences and, especially, engaging in complex projects whose results are influenced by the application of acquired knowledge.
Peningkatan Hasil Belajar IPA Dengan Penerapan Model Bertukar Pasangan Pada Siswa Sekolah Dasar

This research is motivated by the low learning result of science at the four year students of state elementary school 020 Pangkalan Baru District of Siak Hulu Regency of Kampar. One of solution to overcome this problem by implementation of exchange partners models. The objective of study to know increase learning result of science material part of the plant by implementation of exchange partners models at the four year students of state elementary school school 020 Pangkalan Baru District of Siak Hulu Regency of Kampar. This research is a kind of Classroom Action Research (CAR) executed in two cycles. Every cycles consisted of bay one meeting and four phase, thas is planning, action, observation, and reflection. Research time executed to August s/d September 2017. For the subjects in this study is the fourth grade students of 020 Pangkalan Baru District of Siak Hulu Regency of Kampar 2017-2018 school year the number of students of 20 people, consisting of 9 men and 11 women. Data collection techniques used in this study is the observation, testing, and documentation. Having conducted research note an increase in student learning outcomes of prior actions, the first cycle and second cycle. In the pre-action mastery students only reached 45,83% or 11 students who completed, the students who pass the first cycle increased to 14 people or completeness only reached 70,00%. In the second cycle turns completeness students at 19 students or the percentage of 95,00%. Thereby that is the implementation of exchange partners models to increase learning result of science material part of the plant at the four year students of state elementary school 020 Pangkalan Baru District of Siak Hulu Regency of Kampar.
An Improved Deep Netural Network for Small Ship Detection in SAR Imagery

Ship detection by using remote-sensing images based on a synthetic aperture radar (SAR) plays an important role in managing water transportation and marine safety. However, complex background, a small ship size, and low focus on small ships results in difficulties in feature extraction and low detection accuracy. This study proposes a new small SAR ship-detection network. First, a transformer-based dynamic sparse attention module is used to improve the focus and extraction of small ship features. Second, the feature maps are fused with deep layers, and small target-friendly detection heads are used to improve the processing of global information in the network. Third, a more suitable fused loss function is used for small ships to ensure the multi-scale detection capability. Experimental results on publicly available datasets, LS-SSDD_v1.0 and AIR-SARShip-1.0, show that the proposed method effectively improves the detection accuracy of small ships on SAR images without computational burden boost. Compared with other methods based on the convolutional neural network, the proposed method demonstrates better multiscale detection performance.
Evidence of Synchronization between Solar Activity and Agricultural Performance in Germany

Background:Numerous authors have extensively reported the causal relationship between Space Weather, solar eruptive activity, and the Biosphere, prompting an exploration into these connections for mitigating adverse effects and harnessing potential benefits. Recent observations highlight the synchronization between agricultural performance and multi-year cycles of solar activity, notably the eleven-year cycles (11±1 years) recorded since 1700. This insight offers the anticipation of fluctuations in agricultural productivity, aiding resource planning and aiming for a more favorable cost-benefit ratio and profitability in the agri-food industry, serving as the focal point of this research.Methodology:The research employed the Fast Fourier Transform (FFT) and tools from Microcal Origin 6.0 for data analysis.Results:The study presents the analysis of time series data encompassing the yield of ten economically significant agricultural products in Germany concerning solar activity cycles. Graphical analysis, including cross-correlation and spectral analysis, was performed on these datasets.Conclusion:The obtained results corroborate the hypothesis of a temporal association between the studied phenomena, affirming the synchronization between solar activity cycles and agricultural yield variations.
Prediction of Soil Moisture Based on BP Neural Network

Grassland is the largest green ecological barrier in China, and reasonable grazing policies are key to ensuring people's livelihoods. The soil moisture data in this article is time series data and is related to multiple factors. On the premise of maintaining the same grazing strategy, first expand the collected data and use it as input. The L-BFGS algorithm was used to iteratively update the parameters, and the ReLU activation function was used to select the final model by comparing the Goodness of fit of models with different structures. A BP neural network model was established to predict the soil moisture at a depth of 40cm in 2022, providing theoretical reference for the research on grazing strategies of grasslands in pastoral areas in China
SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes

Novel view synthesis for dynamic scenes is still a challenging problem in computer vision and graphics. Recently, Gaussian splatting has emerged as a robust technique to represent static scenes and enable high-quality and real-time novel view synthesis. Building upon this technique, we propose a new representation that explicitly decomposes the motion and appearance of dynamic scenes into sparse control points and dense Gaussians, respectively. Our key idea is to use sparse control points, significantly fewer in number than the Gaussians, to learn compact 6 DoF transformation bases, which can be locally interpolated through learned interpolation weights to yield the motion field of 3D Gaussians. We employ a deformation MLP to predict time-varying 6 DoF transformations for each control point, which reduces learning complexities, enhances learning abilities, and facilitates obtaining temporal and spatial coherent motion patterns. Then, we jointly learn the 3D Gaussians, the canonical space locations of control points, and the deformation MLP to reconstruct the appearance, geometry, and dynamics of 3D scenes. During learning, the location and number of control points are adaptively adjusted to accommodate varying motion complexities in different regions, and an ARAP loss following the principle of as rigid as possible is developed to enforce spatial continuity and local rigidity of learned motions. Finally, thanks to the explicit sparse motion representation and its decomposition from appearance, our method can enable user-controlled motion editing while retaining high-fidelity appearances. Extensive experiments demonstrate that our approach outperforms existing approaches on novel view synthesis with a high rendering speed and enables novel appearance-preserved motion editing applications. Project page: https://yihua7.github.io/SC-GS-web/
Gemini vs GPT-4V: A Preliminary Comparison and Combination of Vision-Language Models Through Qualitative Cases

The rapidly evolving sector of Multi-modal Large Language Models (MLLMs) is at the forefront of integrating linguistic and visual processing in artificial intelligence. This paper presents an in-depth comparative study of two pioneering models: Google's Gemini and OpenAI's GPT-4V(ision). Our study involves a multi-faceted evaluation of both models across key dimensions such as Vision-Language Capability, Interaction with Humans, Temporal Understanding, and assessments in both Intelligence and Emotional Quotients. The core of our analysis delves into the distinct visual comprehension abilities of each model. We conducted a series of structured experiments to evaluate their performance in various industrial application scenarios, offering a comprehensive perspective on their practical utility. We not only involve direct performance comparisons but also include adjustments in prompts and scenarios to ensure a balanced and fair analysis. Our findings illuminate the unique strengths and niches of both models. GPT-4V distinguishes itself with its precision and succinctness in responses, while Gemini excels in providing detailed, expansive answers accompanied by relevant imagery and links. These understandings not only shed light on the comparative merits of Gemini and GPT-4V but also underscore the evolving landscape of multimodal foundation models, paving the way for future advancements in this area. After the comparison, we attempted to achieve better results by combining the two models. Finally, We would like to express our profound gratitude to the teams behind GPT-4V and Gemini for their pioneering contributions to the field. Our acknowledgments are also extended to the comprehensive qualitative analysis presented in 'Dawn' by Yang et al. This work, with its extensive collection of image samples, prompts, and GPT-4V-related results, provided a foundational basis for our analysis.
Scalable Volume Visualization for Big Scientific Data Modeled by Functional Approximation

Considering the challenges posed by the space and time complexities in handling extensive scientific volumetric data, various data representations have been developed for the analysis of large-scale scientific data. Multivariate functional approximation (MFA) is an innovative data model designed to tackle substantial challenges in scientific data analysis. It computes values and derivatives with high-order accuracy throughout the spatial domain, mitigating artifacts associated with zero- or first-order interpolation. However, the slow query time through MFA makes it less suitable for interactively visualizing a large MFA model. In this work, we develop the first scalable interactive volume visualization pipeline, MFA-DVV, for the MFA model encoded from large-scale datasets. Our method achieves low input latency through distributed architecture, and its performance can be further enhanced by utilizing a compressed MFA model while still maintaining a high-quality rendering result for scientific datasets. We conduct comprehensive experiments to show that MFA-DVV can decrease the input latency and achieve superior visualization results for big scientific data compared with existing approaches.
Consistent, multidimensional differential histogramming and summary statistics with YODA 2

Histogramming is often taken for granted, but the power and compactness of partially aggregated, multidimensional summary statistics, and their fundamental connection to differential and integral calculus make them formidable statistical objects, especially when very large data volumes are involved. But expressing these concepts robustly and efficiently in high-dimensional parameter spaces and for large data samples is a highly non-trivial challenge -- doubly so if the resulting library is to remain usable by scientists as opposed to software engineers. In this paper we summarise the core principles required for consistent generalised histogramming, and use them to motivate the design principles and implementation mechanics of the re-engineered YODA histogramming library, a key component of physics data-model comparison and statistical interpretation in collider physics.
QUBO Dual Bounds via SDP Plane Projection Method

In this paper, we present a new method to solve a certain type of Semidefinite Programming (SDP) problems. These types of SDPs naturally arise in the Quadratic Convex Reformulation (QCR) method and can be used to obtain dual bounds of Quadratic Unconstrained Binary Optimization (QUBO) problems. QUBO problems have recently become the focus of attention in the quantum computing and optimization communities as they are well suited to both gate-based and annealing-based quantum computers on one side, and can encompass an exceptional variety of combinatorial optimization problems on the other. Our new method can be easily warm-started, making it very effective when embedded into a branch-and-bound scheme and used to solve the QUBO problem to global optimality. We test our method in this setting and present computational results showing the effectiveness of our approach.
Spectrally Decomposed Diffusion Models for Generative Turbulence Recovery

We investigate the statistical recovery of missing physics and turbulent phenomena in fluid flows using generative machine learning. Here we develop a two-stage super-resolution method using spectral filtering to restore the high-wavenumber components of a Kolmogorov flow. We include a rigorous examination of generated samples through the lens of statistical turbulence. By extending the prior methods to a combined super-resolution and conditional high-wavenumber generation, we demonstrate turbulence recovery on a $8\times$ upsampling task, effectively doubling the range of recovered wavenumbers.
Generation and Preservation of Large Entangled States on Physical Quantum Devices

As quantum technology advances and the size of quantum computers grow, it becomes increasingly important to understand the extent of quality in the devices. As large-scale entanglement is a quantum resource crucial for achieving quantum advantage, the challenge in its generation makes it a valuable benchmark for measuring the performance of universal quantum devices. In this work, we study entanglement in Greenberger-Horne-Zeilinger (GHZ) and graph states prepared on the range of IBM Quantum devices. We generate GHZ states and investigate their coherence times with respect to state size and dynamical decoupling techniques. A GHZ fidelity of $0.519 \pm 0.014$ is measured on a 32-qubit GHZ state, certifying its genuine multipartite entanglement (GME). We show a substantial improvement in GHZ decoherence rates for a 7-qubit GHZ state after implementing dynamical decoupling, and observe a linear trend in the decoherence rate of $\alpha=(7.13N+5.54)10^{-3} \mu s^{-1}$ for up to $N=15$ qubits, confirming the absence of superdecoherence. Additionally, we prepare and characterise fully bipartite entangled native graph states on 22 IBM Quantum devices with qubit counts as high as 414 qubits, all active qubits of the 433-qubit Osprey device. Analysis of the decay of 2-qubit entanglement within the prepared states shows suppression of coherent noise signals with the implementation of dynamical decoupling techniques. Additionally, we observe that the entanglement in some qubit pairs oscillates over time, which is likely caused by residual ZZ-interactions. Characterising entanglement in native graph states, along with detecting entanglement oscillations, can be an effective approach to low-level device benchmarking that encapsulates 2-qubit error rates along with additional sources of noise, with possible applications to quantum circuit compilation.
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling

We introduce SOLAR 10.7B, a large language model (LLM) with 10.7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks. Inspired by recent efforts to efficiently up-scale LLMs, we present a method for scaling LLMs called depth up-scaling (DUS), which encompasses depthwise scaling and continued pretraining. In contrast to other LLM up-scaling methods that use mixture-of-experts, DUS does not require complex changes to train and inference efficiently. We show experimentally that DUS is simple yet effective in scaling up high-performance LLMs from small ones. Building on the DUS model, we additionally present SOLAR 10.7B-Instruct, a variant fine-tuned for instruction-following capabilities, surpassing Mixtral-8x7B-Instruct. SOLAR 10.7B is publicly available under the Apache 2.0 license, promoting broad access and application in the LLM field.
PACE: Pose Annotations in Cluttered Environments

Pose estimation is a crucial task in computer vision and robotics, enabling the tracking and manipulation of objects in images or videos. While several datasets exist for pose estimation, there is a lack of large-scale datasets specifically focusing on cluttered scenes with occlusions. We introduce PACE (Pose Annotations in Cluttered Environments), a large-scale benchmark designed to advance the development and evaluation of pose estimation methods in cluttered scenarios. PACE consists of 54,945 frames with 257,673 annotations across 300 videos, covering 576 objects from 44 categories and featuring a mix of rigid and articulated items in cluttered scenes. To annotate the real-world data efficiently, we developed an innovative annotation system utilizing a calibrated 3-camera setup. We test state-of-the-art algorithms in PACE along two tracks: pose estimation, and object pose tracking, revealing the benchmark's challenges and research opportunities. Our code and data is available on https://github.com/qq456cvb/PACE.
A dynamic approach to ceteris paribus counterfactuals

Counterfactuals are conditional statements describing consequences of states of affairs that might not have occurred. Ceteris paribus counterfactuals take into account specific conditions which hold at the actual state of affairs, and which ought to be kept unchanged throughout the evaluation. Inspired by the approach of Girard and Triplett, we define ceteris paribus conditionals as dynamic operators over (weakly) centered sphere models. We introduce three ways of evaluating the operators, by updating sphere models based on ceteris paribus sets of formulas. Our approach features two main novelties: on the one hand, we define our ceteris paribus conditionals as proper dynamic operators, thus allowing for iterated evaluations. On the other hand, when updating the worlds within a system of sphere we additionally take into account a notion of 'significance' of the formulas that ought to be kept unchanged. We discuss the relations of our model updates with other prioritisations introduced in the literature, and show completeness of the set of valid formulas in the updated centered (resp. weakly centered) sphere models with respect to Lewis' axiomatization of the conditional logic VC (resp. VW).
Multilingual Bias Detection and Mitigation for Indian Languages

Lack of diverse perspectives causes neutrality bias in Wikipedia content leading to millions of worldwide readers getting exposed by potentially inaccurate information. Hence, neutrality bias detection and mitigation is a critical problem. Although previous studies have proposed effective solutions for English, no work exists for Indian languages. First, we contribute two large datasets, mWikiBias and mWNC, covering 8 languages, for the bias detection and mitigation tasks respectively. Next, we investigate the effectiveness of popular multilingual Transformer-based models for the two tasks by modeling detection as a binary classification problem and mitigation as a style transfer problem. We make the code and data publicly available.