Miracle-dz/UniFAR-FTUs huggingface dataset:
Large Language Models Are State-of-the-Art Evaluators of Code Generation

Recent advancements in the field of natural language generation have facilitated the use of large language models to assess the quality of generated text. Although these models have shown promising results in tasks such as machine translation and summarization, their applicability in code generation tasks remains limited without human involvement. The complexity of programming concepts required for such tasks makes it difficult to develop evaluation metrics that align with human judgment. Token-matching-based metrics, such as BLEU, have demonstrated weak correlations with human practitioners in code generation tasks. Moreover, the utilization of human-written test suites to evaluate functional correctness can be challenging in domains with low resources. To overcome these obstacles, we propose a new evaluation framework based on the GPT-3.5 (GPT-3.5-turbo), for code generation assessments. Our framework addresses the limitations of existing approaches by achieving superior correlations with functional correctness and human preferences, without the need for test oracles or references. We evaluate the efficacy of our framework on two different aspects (human preference and execution success) and four programming languages, comparing its performance with the state-of-the-art CodeBERTScore metric, which relies on a pre-trained model. Our results demonstrate that our framework surpasses CodeBERTScore, delivering high levels of accuracy and consistency across various programming languages and tasks. We also make our evaluation framework and datasets available to the public at https://github.com/terryyz/llm-code-eval, encouraging further research in the evaluation of code generation.1
Multiple Acoustic Model-Based Discriminative Likelihood Ratio Weighting for Voice Activity Detection

In this letter, we propose a novel statistical voice activity detection (VAD) technique. The proposed technique employs probabilistically derived multiple acoustic models to effectively optimize the weights on frequency domain likelihood ratios with the discriminative training approach for more accurate voice activity detection. Experiments performed on various AURORA noisy environments showed that the proposed approach produces meaningful performance improvements over the single acoustic model-based conventional approaches.
The Therapeutic Potential of Modifying Inflammasomes and NOD-Like Receptors

Inflammasomes are the central processing units (CPUs) responsible for decoding and integrating signals of foreignness, damage, danger, and distress released by pathogens, cells, and tissues. It was initially thought that the inflammasomes participated only in pathogen recognition and in the pathogenesis of a few, rare, hereditary inflammatory disorders. On the contrary, it is now clear that they have a central role in the pathogenesis of basically all types of chronic inflammation, in metabolic diseases and cancer. So far, six or possibly eight inflammasome subtypes have been identified. Their main, but by no means exclusive, function is to catalyze conversion of pro-IL-1β and pro-IL-18 into their respective mature forms. However, the different inflammasome subtypes may also participate in additional responses, e.g., proliferation, regulation of glycolytic metabolism, or cell activation, albeit it is not clear whether these effects are still mediated through IL-1β release or via modulation of other caspase-1-dependent or -independent pathways. Central to inflammasome organization and activity are proteins belonging to the nucleotide binding domain, leucine-rich repeat, or NOD-like receptor family. One relevant exception is the AIM2 inflammasome. NOD-like receptors belong to the superfamily of pattern recognition receptors, a group of highly conserved molecules specialized in the recognition of invariant molecular patterns diffused across species. Given their potent proinflammatory activity, it is anticipated that inflammasome activation is tightly controlled. In this review, I will summarize essential features of the known NOD-like receptors, the basic molecular structure of inflammasomes, their participation in pathophysiological responses, and their possible exploitation for therapy.
Community-Augmented Meta-Analyses

We present the concept of a community-augmented meta-analysis (CAMA), a simple yet novel tool that significantly facilitates the accumulation and evaluation of previous studies within a specific scientific field. A CAMA is a combination of a meta-analysis and an open repository. Like a meta-analysis, it is centered around a psychologically relevant topic and includes methodological details and standardized effect sizes. As in a repository, data do not remain undisclosed and static after publication but can be used and extended by the research community, as anyone can download all information and can add new data via simple forms. Based on our experiences with building three CAMAs, we illustrate the concept and explain how CAMAs can facilitate improving our research practices via the integration of past research, the accumulation of knowledge, and the documentation of file-drawer studies.
Synthesizing Soundscapes: Leveraging Text-to-Audio Models for Environmental Sound Classification

In the past few years, text-to-audio models have emerged as a significant advancement in automatic audio generation. Although they represent impressive technological progress, the effectiveness of their use in the development of audio applications remains uncertain. This paper aims to investigate these aspects, specifically focusing on the task of classification of environmental sounds. This study analyzes the performance of two different environmental classification systems when data generated from text-to-audio models is used for training. Two cases are considered: a) when the training dataset is augmented by data coming from two different text-to-audio models; and b) when the training dataset consists solely of synthetic audio generated. In both cases, the performance of the classification task is tested on real data. Results indicate that text-to-audio models are effective for dataset augmentation, whereas the performance of the models drops when relying on only generated audio.
Java Classes with"-Er"and"-Utils"Suffixes Have Higher Complexity

In object-oriented programming languages, a belief exists that classes with -Er/-Or and -Utils suffixes are"code smells"because they take over a lot of functional responsibility, turning out to be bulky and complicated, and therefore making it more difficult to maintain the code. In order to validate this intuition, we analyzed complexity and cohesion of 13,861 Java classes from 212 unique open-source GitHub repositories. We found out that average values of Cyclomatic Complexity and Cognitive Complexity metrics are at least 2.5 times higher when suffixes are present.
Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians

The recent 3D Gaussian splatting (3D-GS) has shown remarkable rendering fidelity and efficiency compared to NeRF-based neural scene representations. While demonstrating the potential for real-time rendering, 3D-GS encounters rendering bottlenecks in large scenes with complex details due to an excessive number of Gaussian primitives located within the viewing frustum. This limitation is particularly noticeable in zoom-out views and can lead to inconsistent rendering speeds in scenes with varying details. Moreover, it often struggles to capture the corresponding level of details at different scales with its heuristic density control operation. Inspired by the Level-of-Detail (LOD) techniques, we introduce Octree-GS, featuring an LOD-structured 3D Gaussian approach supporting level-of-detail decomposition for scene representation that contributes to the final rendering results. Our model dynamically selects the appropriate level from the set of multi-resolution anchor points, ensuring consistent rendering performance with adaptive LOD adjustments while maintaining high-fidelity rendering results.
Comprehensive microRNA Analysis Identifies miR-24 and miR-125a-5p as Plasma Biomarkers for Rheumatoid Arthritis

MicroRNAs (miRNAs) are present in human plasma and known as a non-invasive biomarker for cancer detection. Our study was designed to identify plasma miRNAs specific for rheumatoid arthritis (RA) by a comprehensive array approach. We performed a systematic, array-based miRNA analysis on plasma samples from three RA patients and three healthy controls (HCs). Plasma miRNAs with more than four times change or with significant (P<0.05) change in expression, or detectable only in RA plasma, were confirmed with plasma from eight RA patients and eight HCs using real-time quantitative PCR. Consistently detectable miRNAs that were significantly different between RA patients and HCs were chosen for further validation with 102 RA patients and 104 HCs. The area under curves (AUC) were calculated after plotting the receiver operating characteristic (ROC) curves. To determine if these miRNAs are specific for RA, the concentrations of these miRNAs were analyzed in 24 patients with osteoarthritis (OA), and 11 patients with systemic lupus erythematosus (SLE). The array analysis and the subsequent confirmation in larger patient cohort identified significant alterations in plasma levels of seven miRNAs. The highest AUC was found for miR-125a-5p, followed in order by miR-24 and miR-26a. Multivariable logistic regression analysis showed that miR-24, miR-30a-5p, and miR-125a-5p were crucial factors for making detection model of RA and provided a formula for Estimated Probability of RA by plasma MiRNA (ePRAM), employing miR-24, miR-30a-5p and miR-125a-5p, which showed increased diagnostic accuracy (AUC: 0.89). The level of miR-24, miR-125a-5p, and ePRAM in OA and SLE patients were lower than that in RA. There was no significant difference in detection for anti-citrullinated protein antibody (ACPA)-positive and ACPA-negative RA patients. These results suggest that the plasma concentrations of miR-24 and miR-125a-5p, and ePRAM are potential diagnostic markers of RA even if patients were ACPA-negative.
Ontology Completion with Natural Language Inference and Concept Embeddings: An Analysis

We consider the problem of finding plausible knowledge that is missing from a given ontology, as a generalisation of the well-studied taxonomy expansion task. One line of work treats this task as a Natural Language Inference (NLI) problem, thus relying on the knowledge captured by language models to identify the missing knowledge. Another line of work uses concept embeddings to identify what different concepts have in common, taking inspiration from cognitive models for category based induction. These two approaches are intuitively complementary, but their effectiveness has not yet been compared. In this paper, we introduce a benchmark for evaluating ontology completion methods and thoroughly analyse the strengths and weaknesses of both approaches. We find that both approaches are indeed complementary, with hybrid strategies achieving the best overall results. We also find that the task is highly challenging for Large Language Models, even after fine-tuning.
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks

Graph representation learning has become a crucial task in machine learning and data mining due to its potential for modeling complex structures such as social networks, chemical compounds, and biological systems. Spiking neural networks (SNNs) have recently emerged as a promising alternative to traditional neural networks for graph learning tasks, benefiting from their ability to efficiently encode and process temporal and spatial information. In this paper, we propose a novel approach that integrates attention mechanisms with SNNs to improve graph representation learning. Specifically, we introduce an attention mechanism for SNN that can selectively focus on important nodes and corresponding features in a graph during the learning process. We evaluate our proposed method on several benchmark datasets and show that it achieves comparable performance compared to existing graph learning techniques.
The Complexity of the Sigma Chromatic Number of Cubic Graphs

The sigma chromatic number of a graph G, denoted by σ(G), is the minimum number k that the vertices can be partitioned into k disjoint sets V1,. ,Vk such that for every two adjacent vertices u and v there is an index i that u and v have different numbers of neighbors in Vi. We show that, it is NP-complete to decide for a given 3-regular graph G, whether σ(G) = 2. Also, we prove that for every k ≥ 3, it is NP-complete to decide whether σ(G) = k for a given graph G. Furthermore, for planar 3-regular graphs with σ = 2, we show that the problem of minimizing the size of a set is NP-hard.
Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice

Our paper provides empirical comparisons between recent IDSs to provide an objective comparison between them to help users choose the most appropriate solution based on their requirements. Our results show that no one solution is the best, but is dependent on external variables such as the types of attacks, complexity, and network environment in the dataset. For example, BoT_IoT and Stratosphere IoT datasets both capture IoT-related attacks, but the deep neural network performed the best when tested using the BoT_IoT dataset while HELAD performed the best when tested using the Stratosphere IoT dataset. So although we found that a deep neural network solution had the highest average F1 scores on tested datasets, it is not always the best-performing one. We further discuss difficulties in using IDS from literature and project repositories, which complicated drawing definitive conclusions regarding IDS selection.
Climate Downscaling: A Deep-Learning Based Super-resolution Model of Precipitation Data with Attention Block and Skip Connections

Human activities accelerate consumption of fossil fuels and produce greenhouse gases, resulting in urgent issues today: global warming and the climate change. These indirectly cause severe natural disasters, plenty of lives suffering and huge losses of agricultural properties. To mitigate impacts on our lands, scientists are developing renewable, reusable, and clean energies and climatologists are trying to predict the extremes. Meanwhile, governments are publicizing resource-saving policies for a more eco-friendly society and arousing environment awareness. One of the most influencing factors is the precipitation, bringing condensed water vapor onto lands. Water resources are the most significant but basic needs in society, not only supporting our livings, but also economics. In Taiwan, although the average annual precipitation is up to 2,500 millimeter (mm), the water allocation for each person is lower than the global average due to drastically geographical elevation changes and uneven distribution through the year. Thus, it is crucial to track and predict the rainfall to make the most use of it and to prevent the floods. However, climate models have limited resolution and require intensive computational power for local-scale use. Therefore, we proposed a deep convolutional neural network with skip connections, attention blocks, and auxiliary data concatenation, in order to downscale the low-resolution precipitation data into high-resolution one. Eventually, we compare with other climate downscaling methods and show better performance in metrics of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Pearson Correlation, structural similarity index (SSIM), and forecast indicators.
Robust Stability for Multiagent Systems with Spatio-Temporally Correlated Packet Loss

A problem with considering correlations in the analysis of multiagent system with stochastic packet loss is that they induce dependencies between agents that are otherwise decoupled, preventing the application of decomposition methods required for efficient evaluation. To circumvent that issue, this paper is proposing an approach based on analysing sets of networks with independent communication links, only considering the correlations in an implicit fashion. Combining ideas from the robust stabilization of Markov jump linear systems with recently proposed techniques for analysing packet loss in multiagent systems, we obtain a linear matrix inequality based stability condition which is independent of the number of agents. The main result is that the set of stabilized probability distributions has non-empty interior such that small correlations cannot lead to instability, even though only distributions of independent links were analysed. Moreover, two examples are provided to demonstrate the applicability of the results to practically relevant scenarios.
Multi-Objective Quality-Diversity for Crystal Structure Prediction

Crystal structures are indispensable across various domains, from batteries to solar cells, and extensive research has been dedicated to predicting their properties based on their atomic configurations. However, prevailing Crystal Structure Prediction methods focus on identifying the most stable solutions that lie at the global minimum of the energy function. This approach overlooks other potentially interesting materials that lie in neighbouring local minima and have different material properties such as conductivity or resistance to deformation. By contrast, Quality-Diversity algorithms provide a promising avenue for Crystal Structure Prediction as they aim to find a collection of high-performing solutions that have diverse characteristics. However, it may also be valuable to optimise for the stability of crystal structures alongside other objectives such as magnetism or thermoelectric efficiency. Therefore, in this work, we harness the power of Multi-Objective Quality-Diversity algorithms in order to find crystal structures which have diverse features and achieve different trade-offs of objectives. We analyse our approach on 5 crystal systems and demonstrate that it is not only able to re-discover known real-life structures, but also find promising new ones. Moreover, we propose a method for illuminating the objective space to gain an understanding of what trade-offs can be achieved.
Histogram Layers for Neural Engineered Features

In the computer vision literature, many effective histogram-based features have been developed. These engineered features include local binary patterns and edge histogram descriptors among others and they have been shown to be informative features for a variety of computer vision tasks. In this paper, we explore whether these features can be learned through histogram layers embedded in a neural network and, therefore, be leveraged within deep learning frameworks. By using histogram features, local statistics of the feature maps from the convolution neural networks can be used to better represent the data. We present neural versions of local binary pattern and edge histogram descriptors that jointly improve the feature representation and perform image classification. Experiments are presented on benchmark and real-world datasets.
A Comparative Analysis of Visual Odometry in Virtual and Real-World Railways Environments

Perception tasks play a crucial role in the development of automated operations and systems across multiple application fields. In the railway transportation domain, these tasks can improve the safety, reliability, and efficiency of various perations, including train localization, signal recognition, and track discrimination. However, collecting considerable and precisely labeled datasets for testing such novel algorithms poses extreme challenges in the railway environment due to the severe restrictions in accessing the infrastructures and the practical difficulties associated with properly equipping trains with the required sensors, such as cameras and LiDARs. The remarkable innovations of graphic engine tools offer new solutions to craft realistic synthetic datasets. To illustrate the advantages of employing graphic simulation for early-stage testing of perception tasks in the railway domain, this paper presents a comparative analysis of the performance of a SLAM algorithm applied both in a virtual synthetic environment and a real-world scenario. The analysis leverages virtual railway environments created with the latest version of Unreal Engine, facilitating data collection and allowing the examination of challenging scenarios, including low-visibility, dangerous operational modes, and complex environments. The results highlight the feasibility and potentiality of graphic simulation to advance perception tasks in the railway domain.
Steering Feedback in Dynamic Driving Simulators: The Influence of Steering Wheel Vibration and Vehicle Motion Frequency

The validity of the subjective evaluation of steering feedback in driving simulators is crucial for modern vehicle development. Although there are established objective steering characteristics for the assessment of both stationary and dynamic feedback behaviour, factors such as steering wheel vibrations and vehicle body motion, particularly in high-frequency ranges, present challenges in simulator fidelity. This work investigates the influence of steering wheel vibration and vehicle body motion frequency content on the subjective evaluation of steering feedback during closed-loop driving in a dynamic driving simulator. A controlled subject study with 30 participants consisting of a back-to-back comparison of a reference vehicle with an electrical power steering system and three variants of its virtual representation on a dynamic driving simulator was performed. Subjective evaluation focused on the representation of road feedback in comparison to the reference vehicle. The statistical analysis of subjective results show that there is a significant influence of the frequency content of both steering wheel torque and vehicle motion on the subjective evaluation of steering feedback in a dynamic driving simulator. The results suggest an influence of frequency content on the subjective evaluation quality of steering feedback characteristics that are not associated with the dynamic feedback behaviour in the context of established performance indicators.
Reciprocity Calibration of Dual-Antenna Repeaters

We present a reciprocity calibration method for dual-antenna repeaters in wireless networks. The method uses bi-directional measurements between two network nodes, A and B, where for each bi-directional measurement, the repeaters are configured in different states. The nodes A and B could be two access points in a distributed MIMO system, or they could be a base station and a mobile user terminal, for example. From the calibration measurements, the differences between the repeaters' forward and reverse gains are estimated. The repeaters are then (re-)configured to compensate for these differences such that the repeaters appear, transparently to the network, as reciprocal components of the propagation environment, enabling reciprocity-based beamforming in the network.