by jsendak | Jan 28, 2024 | Computer Science
Expert Commentary:
The PaSTiLa algorithm presented in this article offers a promising approach to automated labeling of large time series on a cluster with GPUs. Time series analysis is a crucial task in various domains including finance, healthcare, and environmental monitoring. The ability to effectively search for patterns within these time series can provide valuable insights and aid decision-making processes.
One of the key contributions of the PaSTiLa algorithm is its automatic selection of snippet length values. The snippet length plays a crucial role in identifying patterns within time series data as it determines the granularity at which the data is analyzed. By proposing a new criterion for selecting snippet length values, the algorithm can effectively adapt to different types of time series and optimize pattern search performance.
An important aspect highlighted in the article is the use of GPUs for processing the large time series data. GPUs are well-known for their parallel processing capabilities, making them highly suitable for accelerating computationally-intensive tasks like pattern search in time series. By leveraging the power of GPUs, PaSTiLa demonstrates enhanced performance compared to existing analogues.
The high accuracy of pattern search achieved by the PaSTiLa algorithm is another significant finding. Accurate detection of patterns within time series is fundamental for reliable predictions and actionable insights. The article’s experiments showing the advantage of PaSTiLa over analogues suggest that this algorithm has the potential to become a valuable tool in time series analysis.
Looking ahead, further research and development can be conducted to explore potential enhancements to the PaSTiLa algorithm. This could involve investigating the algorithm’s performance on different types of time series data and exploring additional criteria for selecting snippet length values. Moreover, incorporating techniques from machine learning and deep learning could potentially improve the accuracy and efficiency of pattern search algorithms like PaSTiLa.
In conclusion, the PaSTiLa algorithm presented in this article offers a promising solution for automated labeling of large time series on a cluster with GPUs. Its automatic selection of snippet length values and high accuracy in pattern search make it a valuable addition to the field of time series analysis. Continued research and development in this area could lead to further advancements and applications of PaSTiLa and similar algorithms.
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by jsendak | Jan 27, 2024 | Computer Science
Multi-modal large language models(MLLMs) have achieved remarkable progress
and demonstrated powerful knowledge comprehension and reasoning abilities.
However, the mastery of domain-specific knowledge, which is essential for
evaluating the intelligence of MLLMs, continues to be a challenge. Current
multi-modal benchmarks for domain-specific knowledge concentrate on
multiple-choice questions and are predominantly available in English, which
imposes limitations on the comprehensiveness of the evaluation. To this end, we
introduce CMMU, a novel benchmark for multi-modal and multi-type question
understanding and reasoning in Chinese. CMMU consists of 3,603 questions in 7
subjects, covering knowledge from primary to high school. The questions can be
categorized into 3 types: multiple-choice, multiple-response, and
fill-in-the-blank, bringing greater challenges to MLLMs. In addition, we
propose a rigorous evaluation strategy called ShiftCheck for assessing
multiple-choice questions. The strategy aims to reduce position bias, minimize
the influence of randomness on correctness, and perform a quantitative analysis
of position bias. We evaluate seven open-source MLLMs along with GPT4-V,
Gemini-Pro, and Qwen-VL-Plus. The results demonstrate that CMMU poses a
significant challenge to the recent MLLMs.
Multi-modal large language models (MLLMs) have been making significant advancements in natural language processing, showing impressive comprehension and reasoning abilities. However, evaluating their intelligence and domain-specific knowledge has remained a challenge. In order to address this, the authors of this article have introduced a new benchmark called CMMU, which focuses on multi-modal and multi-type question understanding and reasoning in Chinese.
CMMU consists of 3,603 questions across 7 subjects, covering knowledge from primary to high school levels. The questions are of three types: multiple-choice, multiple-response, and fill-in-the-blank, which present greater challenges for MLLMs to handle. This benchmark serves as a platform for evaluating the performance of MLLMs in Chinese, enabling a more comprehensive assessment of their domain-specific knowledge.
In addition to introducing CMMU benchmark, the article also proposes a rigorous evaluation strategy called ShiftCheck for assessing multiple-choice questions. This strategy aims to minimize position bias, reduce the impact of randomness on correctness, and provide a quantitative analysis of position bias. By implementing ShiftCheck, the authors aim to further enhance the evaluation process and ensure fair assessment of MLLMs’ performance.
The results of the evaluation conducted on seven open-source MLLMs, along with GPT4-V, Gemini-Pro, and Qwen-VL-Plus, indicate that CMMU indeed presents a significant challenge to these state-of-the-art models. The findings highlight the need for further improvements in MLLMs’ domain-specific knowledge and their ability to handle multi-modal and multi-type questions.
This article has important implications for the wider field of multimedia information systems and related technologies such as animations, artificial reality, augmented reality, and virtual realities. As MLLMs continue to advance and demonstrate powerful knowledge comprehension and reasoning abilities, they are expected to play a crucial role in various multimedia applications. The ability of these models to understand and reason with different types of information, including visual and textual data, is particularly relevant in the context of multimedia systems.
Moreover, the introduction of CMMU as a benchmark for Chinese language evaluations expands the scope of assessment beyond English, which has predominantly been the focus in existing benchmarks. This highlights the importance of considering different languages and cultures when evaluating the performance of MLLMs. It also underscores the multi-disciplinary nature of MLLMs, as they need to incorporate various linguistic and cultural aspects to achieve proficient understanding and reasoning.
By addressing the limitations in evaluating MLLMs’ domain-specific knowledge and expanding the evaluation to other languages, the article contributes to advancing the field of natural language processing and its intersection with multimedia information systems. It encourages researchers and practitioners to strive for more comprehensive evaluations and overcome the challenges posed by multi-modal and multi-type questions in different languages, thereby advancing the overall capabilities of MLLMs in understanding and reasoning across diverse domains.
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by jsendak | Jan 27, 2024 | Computer Science
Abstract:
The application of process mining for unstructured data might significantly elevate novel insights into disciplines where unstructured data is a common data format. To efficiently analyze unstructured data by process mining and to convey confidence into the analysis result, requires bridging multiple challenges. The purpose of this paper is to discuss these challenges, present initial solutions and describe future research directions. We hope that this article lays the foundations for future collaboration on this topic.
Introduction
In today’s digital era, unstructured data has become a ubiquitous and valuable resource in various disciplines. However, the analysis of unstructured data presents unique challenges due to its lack of pre-defined structure and its diverse formats. Process mining, on the other hand, is a powerful technique that allows organizations to extract valuable insights from their process-related data.
However, the application of process mining for unstructured data poses several challenges that need to be addressed for efficient analysis and reliable results. This article aims to shed light on these challenges, present initial solutions, and outline future research directions to pave the way for collaboration in this domain.
Challenges in Analyzing Unstructured Data with Process Mining
When it comes to analyzing unstructured data using process mining techniques, several challenges arise. These challenges include:
- Lack of standardization: Unstructured data comes in various formats and lacks a predefined structure. This heterogeneity makes it difficult to apply traditional process mining techniques directly.
- Data integration: Unstructured data often resides in different systems and sources, requiring effective integration to extract meaningful insights through process mining.
- Data quality and completeness: Unstructured data might suffer from inconsistencies, errors, and missing information, which can affect the accuracy and reliability of process mining analyses.
- Text analysis and natural language processing: Unstructured data often contains text-based information, requiring advanced techniques in text analysis and natural language processing to extract and analyze relevant process-related information.
- Scalability: Unstructured data sets can be massive in size, making it challenging to scale process mining techniques to handle such volumes of data efficiently.
Solutions and Future Research Directions
To address these challenges, initial solutions have been proposed, but further research is still needed. Some potential solutions and future research directions include:
- Standardization frameworks: Developing frameworks or standards for representing unstructured data in a structured manner to enable its effective analysis using process mining techniques.
- Integration methods: Designing efficient methods and tools for integrating unstructured data from disparate sources, ensuring data consistency and usability in process mining analyses.
- Data cleansing and enrichment: Advancing techniques for cleaning and enriching unstructured data to improve its quality and completeness, enhancing the reliability of process mining results.
- Text mining and NLP advancements: Investing in research to improve text analysis and natural language processing techniques that can effectively handle unstructured data and extract valuable process-related information.
- Scalable process mining algorithms: Developing scalable algorithms and approaches that can handle the volume and velocity of unstructured data, considering factors like distributed computing and parallel processing.
Conclusion
The analysis of unstructured data using process mining holds immense potential for various disciplines. However, several challenges need to be overcome to ensure effective analysis and reliable results. This article has highlighted the challenges involved, presented initial solutions, and outlined future research directions. It is our hope that this article will stimulate collaboration among researchers, practitioners, and organizations working on leveraging process mining for unstructured data.
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by jsendak | Jan 26, 2024 | Computer Science
In this paper, we extend our prior research named DKIC and propose the
perceptual-oriented learned image compression method, PO-DKIC. Specifically,
DKIC adopts a dynamic kernel-based dynamic residual block group to enhance the
transform coding and an asymmetric space-channel context entropy model to
facilitate the estimation of gaussian parameters. Based on DKIC, PO-DKIC
introduces PatchGAN and LPIPS loss to enhance visual quality. Furthermore, to
maximize the overall perceptual quality under a rate constraint, we formulate
this challenge into a constrained programming problem and use the Linear
Integer Programming method for resolution. The experiments demonstrate that our
proposed method can generate realistic images with richer textures and finer
details when compared to state-of-the-art image compression techniques.
Expert Commentary: The Multi-Disciplinary Nature of Perceptual-Oriented Learned Image Compression
In this paper, the authors propose a perceptual-oriented learned image compression method called PO-DKIC, which builds upon their prior research named DKIC. This method aims to enhance the visual quality and compression efficiency of images by incorporating various techniques from different disciplines.
One of the key components of DKIC is the dynamic kernel-based dynamic residual block group, which improves the transform coding process. Transform coding is a fundamental technique used in image and video compression, and by enhancing it, DKIC can achieve better compression results. This aspect of the method relates to multimedia information systems, as it involves optimizing the representation and storage of multimedia data.
Additionally, DKIC utilizes an asymmetric space-channel context entropy model to facilitate the estimation of gaussian parameters. This model takes into account both spatial and channel dependencies in the image data, allowing for more accurate estimation of the statistical properties. Estimating such parameters is crucial for efficient compression algorithms, and the use of this model showcases the integration of concepts from statistics and information theory into image compression.
Building upon DKIC, PO-DKIC introduces PatchGAN and LPIPS loss to further enhance visual quality. PatchGAN is a type of discriminator network commonly used in image synthesis tasks, while LPIPS loss measures perceptual similarity between images based on learned feature representations. These techniques leverage concepts from computer vision and deep learning to improve the visual fidelity of compressed images.
To address the trade-off between compression efficiency and visual quality, the authors formulate the problem as a constrained programming problem and utilize Linear Integer Programming (LIP) for resolution. By formulating the problem in this manner, the method aims to find an optimal solution that maximizes overall perceptual quality under a rate constraint. The application of optimization techniques from operations research and mathematical programming illustrates the interdisciplinary nature of the research.
The experimental results presented in the paper demonstrate the effectiveness of the proposed method. It is shown that PO-DKIC is capable of generating realistic images with richer textures and finer details compared to state-of-the-art image compression techniques. This exemplifies the advancements made in the field of image compression, which is a crucial component of various multimedia systems and applications, including animations, artificial reality, augmented reality, and virtual realities.
In conclusion,
the paper presents a perceptual-oriented learned image compression method that leverages concepts and techniques from multiple disciplines. By incorporating ideas from multimedia information systems, computer vision, deep learning, statistics, and optimization, the proposed method successfully enhances the visual quality and compression efficiency of images. The results obtained highlight the potential impact of this research on various domains that rely on efficient and high-quality image compression, such as animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Jan 26, 2024 | Computer Science
Analysis of Factors Influencing Renewable Energy Consumption in Madagascar
In this study, the aim was to identify the factors that have influenced renewable energy consumption in Madagascar over the period of 1990 to 2021. The researchers focused on 12 features that covered various aspects including macroeconomic, financial, social, and environmental factors.
The features that were considered in this analysis are:
- Economic growth
- Domestic investment
- Foreign direct investment
- Financial development
- Industrial development
- Inflation
- Income distribution
- Trade openness
- Exchange rate
- Tourism development
- Environmental quality
- Urbanization
In order to assess the significance of these features, the researchers assumed a linear relationship between renewable energy consumption and the selected factors. They then applied different machine learning feature selection algorithms to determine the importance of each feature.
The machine learning algorithms used for feature selection were classified into three categories: filter-based methods, embedded methods, and wrapper-based methods.
Filter-based Methods
The researchers employed two filter-based methods: relative importance for linear regression and correlation method. Filter-based methods rank the features based on their individual importance rather than considering interactions between features. These methods provide a quick and efficient way to identify the most influential features.
Embedded Methods
The LASSO (Least Absolute Shrinkage and Selection Operator) method was used as an embedded method in this analysis. Embedded methods incorporate feature selection within the model training process. The LASSO method is known for its ability to perform both feature selection and regularization, which helps to prevent overfitting and improve model performance.
Wrapper-based Methods
Several wrapper-based methods were utilized in this study, including best subset regression, stepwise regression, recursive feature elimination, iterative predictor weighting partial least squares, Boruta, simulated annealing, and genetic algorithms. Wrapper-based methods evaluate subsets of features and select the one that achieves the best model performance. These methods are computationally intensive but often yield more accurate results compared to filter-based or embedded methods.
The findings of the analysis revealed that the five most influential drivers of renewable energy consumption in Madagascar are related to macroeconomic aspects.
Firstly, domestic investment was found to have a positive impact on the adoption of renewable energy sources. This suggests that increased domestic investment in renewable energy projects can contribute to the growth of the sector in Madagascar.
Secondly, foreign direct investment was identified as another positive driver. This implies that foreign financial inflows specifically targeted at renewable energy projects can stimulate the adoption and development of clean energy sources in the country.
Thirdly, inflation was found to positively contribute to renewable energy consumption. This result may indicate that higher inflation rates lead to increased investment in renewable energy as a hedge against inflationary pressures.
On the other hand, industrial development and trade openness were found to negatively affect renewable energy consumption in Madagascar. This suggests that as industrialization and trade activities increase, there may be a tendency to rely more on conventional energy sources rather than investing in renewable alternatives.
This analysis provides valuable insights into the factors influencing renewable energy consumption in Madagascar. Policymakers and stakeholders in the energy sector can use these findings to design effective strategies and policies that promote sustainable and renewable energy sources in the country. Future research could further explore the interactions between these factors and consider additional variables to enhance the understanding of renewable energy adoption in Madagascar.
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by jsendak | Jan 25, 2024 | Computer Science
Automatic Mean Opinion Score (MOS) prediction is employed to evaluate the
quality of synthetic speech. This study extends the application of predicted
MOS to the task of Fake Audio Detection (FAD), as we expect that MOS can be
used to assess how close synthesized speech is to the natural human voice. We
propose MOS-FAD, where MOS can be leveraged at two key points in FAD: training
data selection and model fusion. In training data selection, we demonstrate
that MOS enables effective filtering of samples from unbalanced datasets. In
the model fusion, our results demonstrate that incorporating MOS as a gating
mechanism in FAD model fusion enhances overall performance.
Expert Commentary: The Role of Predicted MOS in Fake Audio Detection
Automatic Mean Opinion Score (MOS) prediction has been widely used in the field of multimedia information systems to evaluate the quality of synthetic speech. However, this study takes a step further by extending the application of predicted MOS to the task of Fake Audio Detection (FAD). By leveraging MOS, we can now assess how close synthesized speech is to the natural human voice, which is crucial in determining the authenticity of audio content.
Multi-disciplinary Nature of the Concepts
The concepts discussed in this article highlight the multi-disciplinary nature of multimedia information systems. It brings together expertise from various domains such as speech synthesis, audio analysis, and machine learning. By combining these fields, researchers and practitioners can develop more robust systems for detecting fake audio.
Animations, Artificial Reality, Augmented Reality, and Virtual Realities are closely related to multimedia information systems. While this article specifically focuses on audio content, these technologies often involve the integration of audiovisual elements to create immersive experiences. The ability to accurately detect fake audio is essential in maintaining the integrity of such systems and preventing misinformation or malicious manipulation.
Training Data Selection
The use of MOS in training data selection is a significant advancement in the field of Fake Audio Detection. Unbalanced datasets can pose challenges in accurately training models, as the imbalance may lead to biased results. By leveraging MOS, researchers can effectively filter samples and ensure that the training dataset represents a diverse range of voice qualities. This ultimately improves the performance and generalizability of the FAD models.
Model Fusion
Incorporating MOS as a gating mechanism in FAD model fusion is another key contribution highlighted in this article. Model fusion involves combining multiple models or techniques to enhance overall performance. By using MOS as a gating mechanism, the FAD system can prioritize the models with higher MOS values, indicating a closer resemblance to the natural human voice. This approach not only improves the accuracy of fake audio detection but also provides insights into the quality of synthesized speech.
Future Directions
As the field of multimedia information systems continues to evolve, the integration of MOS in various applications holds promise for future advancements. Predicted MOS can be further employed in areas such as video analysis, virtual reality experiences, and even deepfake detection. By considering MOS as a metric for assessing quality and authenticity, researchers can develop more comprehensive and reliable systems.
In conclusion, this article showcases the potential of predicted MOS in Fake Audio Detection. The multi-disciplinary nature of the concepts discussed highlights the interconnectedness of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By incorporating MOS in training data selection and model fusion, researchers pave the way for more accurate and robust systems in the detection of fake audio.
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