Reviewerly/exHarmony huggingface dataset:
Semantic Sentence Matching Based on Multiple Parallelly Organized Interaction Layers at Various Granularity Combinations With Two-Stage Aggregation Strategy

Semantic sentence matching plays an essential role in resolving many problems in natural language processing (NLP) field, it has gained increasing research focus and shown great improvements in recent years. However, most currently existing researches are for English sentence matching, research on Chinese semantic matching are relatively less. Moreover, due to the rather complicated contextual expressions and grammatical structure of Chinese language, many existing models are still unable to quite effectively capture interaction information between sentences. Thus, in this work, we attempt to propose a novel deep model to better address Chinese semantic sentence matching. Specifically, the convolutional neural networks with various kernel sizes are first employed for the multi-granular contextual encoding of sentences, combined with multiple different cross-sentence alignment mechanisms, the semantic interactions can be more clearly and profoundly performed at various granularity combinations between sentences. Additionally, rather than serially stacking multiple interaction layers, we organize multiple interaction layers in a parallel manner, and by further introduction of attention pooling, the semantically aligned sentence attentive vectors would be adaptively aggregated from both perspectives of alignment mechanisms and granularity combinations, thus more stable and effective sentence interactive features can be extracted while attempting to alleviate potential sentence alignment error propagation issue existed in hierarchically stacked interaction structure. Finally, extensive experiments are conducted to evaluate the performance of our model, the experimental results demonstrate that our proposed approach outperforms many state-of-the-art models on sentence matching and is capable of gaining a more accurate understanding of semantic relationships between Chinese sentences.
Gaze Estimation Based on Attention Mechanism Combined With Temporal Network

Due to the continuous and dynamic nature of gaze estimation, the true gaze point at each moment is closely related to the previous moment. Simply detecting individual frames of facial images cannot yield accurate gaze information. In current CNN-based gaze estimation methods, the effective utilization of eye movement temporal information and the ability to capture global relationships in the feature extraction process remain problematic. Addressing these concerns, this paper proposes a novel gaze estimation framework, named FE-net, which incorporates a temporal network. This framework introduces channel attention modules and self-attention modules, enhancing the comprehensive utilization of extracted features and reinforcing the contribution of valuable regions to gaze estimation. We further integrate an RNN structure to learn the temporal dynamics of eye movement processes, significantly improving gaze direction prediction accuracy. This framework predicts the gaze directions of left and right eyes separately using monocular and facial features and computes the overall gaze direction. FE-net achieves state-of-the-art accuracy of 3.19° and 3.16° on the EVE dataset and the MPIIFaceGaze dataset, respectively.
FGBRSN: Flow-Guided Gated Bi-Directional Recurrent Separated Network for Video Super-Resolution

Video Super-Resolution (VSR) is the task of reconstructing high-resolution (HR) video sequences from low-resolution (LR) video sequences. Apart from spatial information of reference frames, temporal information of neighboring frames is also important for reconstruction. Current VSR methods usually take advantage of the temporal information through optical flow estimation and compensation. However, optical flow estimation is often inaccurate and difficult, which may result in artifacts or blurring in the reconstructed frames. In this paper, we propose a Novel Flow-guided Deformable Alignment Module (NFDAM) for frame alignment. In this module, a lightweight optical network FNet is designed to estimate optical flow as a coarse offset, which then guides the deformable convolution at the feature and image level. On this basis, we propose a Flow-guided Gated Bidirectional Recurrent Separated Network (FGBRSN) for VSR, in which a gated recurrent structure is designed to leverage long-term information and a multilevel residual fusion approach is used in upsampling module. Our experiments on public datasets show that the proposed method improves both quantitative evaluation and visual effects compared with the existing methods.
CLiCK: Continual Learning Exploiting Intermediate Network Models With A Slack Class

We propose a continual learning (CL) method (called CLiCK), a hybrid of an architecture-based approach that increments a model when it detects that the dataset characteristics have changed significantly, and a rehearsal-based approach that exploits an episodic memory to store past dataset samples. The proposed CLiCK makes the final decision by taking the ensemble of the inference results for the current and a series of past models. A novelty of CLiCK is to introduce a concept for a slack class, which is an auxiliary class to represent unseen or undetermined classes that do not belong to the current dataset. Because the models trained with a slack class have the capability to differentiate between the classes that they were trained on and unseen classes, the inference results of the models that do not have knowledge about input can be automatically neglected in the final decision. Our experiments show that the proposed CLiCK achieves performance comparable to joint learning, which uses the entire dataset for each task, in domain-incremental scenarios on the MNIST dataset. In class-incremental scenarios on the MNIST and CIFAR-100 datasets, CLiCK outperforms other existing CL methods significantly.
Spatio-Temporal Attention Fusion SlowFast for Interrogation Violation Recognition

The use of video surveillance to monitor interrogation behavior can effectively maintain judicial civility in the context of law enforcement cases. However, analyzing and reviewing law enforcement videos can be a time-consuming and resource-intensive process, particularly in the manual identification of interrogation violations. This work is dedicated to the development of an intelligent recognition system for interrogation violations by using a spatio-temporal attention fusion SlowFast Network. To address the issue of feature information underutilization in the slow path of the traditional SlowFast, a slow-to-fast path is incorporated into the original SlowFast to enhance learning. The model fuses the attention of spatial and temporal channels, replacing the traditional convolution module with this new approach. The proposed model was evaluated using the publicly available UCF101 action recognition dataset, resulting in a 1.52% improvement in Top-1 recognition accuracy compared to the traditional SlowFast. Based on two custom interrogation misconduct datasets, the proposed model was evaluated, achieving a recognition rate of 99.16% for interrogation misconduct. This demonstrates its effectiveness in identifying misconduct behaviors inside interrogation rooms. Compared to some advanced behavior recognition models, the proposed model demonstrates strong competitiveness in identifying misconduct during interrogations.
Recurrent Feature Updating Network for Video Super-Resolution

Temporal modeling is the essential to achieve video super-resolution. Most models use alignment or recurrent methods to directly exploit the temporal information of consecutive frames. However, the feature information extracted directly from the input frames is coarse, which affects the performance and generalization ability of the model. Thus, in this paper, we propose to investigate the role of recurrent feature updating networks. The feature update module is proposed to update and optimize the input information to improve the accuracy of features. We design the model as a multi-stage form to improve performance and generalization. Moreover, to further improve the performance of the model, we propose the difference supplement module. It allows the model to exploit the temporal differences between groups to complement the missing information in the output of each stage. Experiments demonstrate that our proposed model achieves 27.78 dB, 30.44 dB, and 39.38 dB on Vid4, SPMCS, and UDM10, respectively, which indicates that our proposed model achieves state-of-the-art performance on video super-resolution. Code is available at https://github.com/gfli123/RFUN.
The Effect of Synthetic Voice Data Augmentation on Spoken Language Identification on Indian Languages

Multilingual based voice activated human computer interaction systems are currently in high demand. The Spoken Language Identification Unit (SPLID) is an inevitable front end unit of such a multilingual system. These systems will be a great boon to a country like India where around 24 official languages are spoken. Deep learning architectures for spoken language identification have progressed to the point that they can now perform well, even in the presence of various background noises. However, a strong phonetic relationship across various Indian languages leads to increased confusion in the SPLID unit. Therefore, the goal of this study is to propose a synthetic voice data augmentation method based on speech synthesis to improve the spoken Indian language identification system. Here the research attempts to determine how well pre-trained computer vision models recognize spoken languages in synthetic and classical audio augmentation environments. The accuracy of the models was compared using bottleneck features extracted from three different pre-trained models VGG16, RESNET50, and Inception-v3 while using an Artificial Neural Network (ANN), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), Decision Tree (DT) and KNN (K-Nearest Neighbors) as classifiers. The proposed system was tested on three Indian language datasets - two comprising seven Indian languages (Hindi, Malayalam, Tamil, Telugu, Marathi, Kannada and Bengali), one containing five Indian languages (Tamil, Hindi, Malayalam, Oria and Assamese), and on a foreign language dataset. It was found that the addition of synthetic audio samples improved the accuracy by 17%. Among the pre-trained models, VGG16 and Inception-v3 combined with PCA and ANN were found to have the maximum accuracy of 97%.
A New Metaphor-Free Metaheuristic Approach Based on Complex Networks and Bezier Curves

A metaheuristic method is an optimization technique that is generally inspired by natural or physical processes. The use of metaphors has created a tendency to reproduce existing algorithms with slight modifications or variations rather than encouraging the development of novel algorithmic techniques and principles. On the other hand, a complex network is a mathematical structure whose main characteristic is the ability to capture and analyze the intricate patterns and properties that emerge from the interactions between the elements that it connects. In this paper, a new metaphor-free metaheuristic algorithm based on complex networks and Bezier curves is presented. In this approach, candidate solutions are represented as nodes in a graph, whereas the connections between nodes or edges reflect the differences in their objective function values. Therefore, the graph provides a higher-level representation that captures the essential relationships and dependencies among the solutions. Once the graph is generated, the shortest path between each solution and the best solution is obtained. Then, the nodes obtained from this process are used as control points in the Bezier equation to generate the new agent position. Therefore, during the optimization process, the graph is continuously modified based on the evaluation of new candidate solutions and their objective function values, producing trajectories that allow the exploration and exploitation of the search space. The experimental results demonstrated the effectiveness of our approach by achieving competitive results compared to other well-known metaheuristic algorithms on various benchmark functions.
A Method to Improve Parameter Estimation Success

This paper introduces a method for improving parameter estimation in statistical models. Parameter estimation is a popular area of study in statistics, and recent years have seen the introduction of new distributions with more parameters to enhance modelling success. While finding a suitable model for a dataset is crucial, accurately estimating parameters is equally important. In some cases, classical parameter estimation methods fail to provide a closed form of estimation for parameters. As a result, researchers commonly resort to numerical methods and software programs for parameter estimation in models. The success rates of models have gained significance with the rising popularity of novel techniques like machine learning algorithms and artificial neural networks. Robust and reliable models are built on the strong theoretical foundations of statistical distributions. Specific distributions are used in various research fields to model datasets, and the assumptions associated with these distributions provide valuable insights into observations. Additionally, parameter estimation results sometimes lead researchers to direct conclusions. This paper presents an improvement method that relies on the estimation of parameters from other statistical distributions. This novel approach aims to make parameter estimation easier and more successful in certain situations. In the applications in this paper, the proposed methodology improves the success rate by up to 10% which provides an additional 6% success in the models.
Towards Reliable Utilization of AIGC: Blockchain-Empowered Ownership Verification Mechanism

With the development of the blockchain technology, a decentralized and de-trusted network paradigm has been constructed, enabling multiple digital assets like NFT, to be permanently recorded and authenticated by blockchain. Also, the uniqueness and verifiability of these assets allows them to flow and generate value between any network entities. With the emergence of AI Generative Content (AIGC), the ownership of models and generative contents, which are also digital assets, has not been well protected. Both because the black-box nature of neural networks makes it difficult to mark models' ownership and because the lack of a reliable third-party verification platform. Meanwhile, the existing model-attack threat and raising ethical problems driven the research on model watermark embedding for traceability and verification, and thus the reliable basic algorithm and the verification platform are needed. In this survey, while emphasizing the importance and reason of the ownership protection in AIGC and summarizing the recent research using model watermarking, we will also introduce the achievements of blockchain in copyright in order to summarize the research history and point out future direction of model copyright validation from both the underlying technology and the supporting platform.
Dynamic Mathematical Models of Theory of Mind for Socially Assistive Robots

Interactive machines should establish and maintain meaningful social interactions with humans. Thus, they need to understand and predict the mental states and actions of humans. Based on Theory of Mind (ToM), in order to understand and interact with each other, humans develop cognitive models of one another. Our main goal is to provide a mathematical framework based on ToM to improve the understanding of interactive machines regarding the perception, cognition, and decision-making of humans. Most state-of-the-art models of behavioral theories based on machine learning are focused on input-output black-box representations. Thus, they lack transparency and generalizability, and exhaustive training procedures are needed to personalize them for various humans. Moreover, these models lack dynamics, i.e., they do not mathematically describe the evolution of the mental states and actions of humans in time. Following a systems-and-control-theoretic point-of-view, we represent for the first time the perception, cognition, and decision-making of humans via a dynamic, mathematical framework by introducing a novel formalization and an extension to Fuzzy Cognitive Maps (FCMs). The resulting models are given in a general state-space representation, which can be used by interactive machines within known model-based state estimation and control methods. In a case study, the resulting models were identified and validated for 21 participants, in scenarios where predicting the intentions and behavior of the participants required understanding the dynamics of their mental procedures. The results of these experiments show that our model is capable of incorporating the dynamics to estimate the intentions and predict the behavior of the participants, with an accuracy of, respectively, 81.55% and 66.06%. Moreover, we compared our model with a state-of-the-art formalization of human cognition, which was made dynamic using our introduced FCM framework. Our model, which in addition to the elements of the state-of-the-art model included emotions, personality traits, and biases (thus providing a more transparent insight about the mental procedures of the participants) showed 6.25% and 2.45% more accuracy in, respectively, estimating the intentions and predicting the behavior of the participants.
New Estimates and Existence Results About Irreducible Polynomials and Self-Reciprocal Irreducible Polynomials with Prescribed Coefficients Over a Finite Field

A polynomial is called self-reciprocal (or palindromic) if the sequence of its coefficients is palindromic. In this paper we obtain improved error bounds for the numbers of irreducible monic polynomials and self-reciprocal irreducible monic polynomials with prescribed coefficients over a finite field $${\mathbb F}_{q}$$ . The new lower bounds are used to derive some existence results about irreducible monic polynomials of degree d and self-reciprocal irreducible monic polynomials of degree 2d with roughly d/2 coefficients prescribed at positions including the middle range $$d/2-\log _q d\le j\le d/2+\log _q d$$ .
Brain-Inspired Remote Sensing Foundation Models and Open Problems: A Comprehensive Survey

The foundation model (FM) has garnered significant attention for its remarkable transfer performance in downstream tasks. Typically, it undergoes task-agnosticpre-training on a large dataset and can be efficiently adapted to various downstream applications through fine-tuning. While FMs have been extensively explored in language and other domains, their potential in remote sensing has also begun to attract scholarly interest. However, comprehensive investigations and performance comparisons of these models on remote sensing tasks are currently lacking. In this survey, we provide essential background knowledge by introducing key technologies and recent developments in FMs. Subsequently, we explore essential downstream applications in remote sensing, covering classification, localization, and understanding. Our analysis encompasses over thirty FMs in both natural and remote sensing fields, and we conduct extensive experiments on more than ten datasets, evaluating global feature representation, local feature representation, and target localization. Through quantitative assessments, we highlight the distinctions among various foundation models and confirm that pre-trained large-scale natural FMs can also deliver outstanding performance in remote sensing tasks. After that, we systematically presented a brain-inspired framework for remote sensing foundation models (RSFMs). We delve into the brain-inspired characteristics in this framework, including structure, perception, learning, and cognition. To conclude, we summarize twelve open problems in RSFMs, providing potential research directions. Our survey offers valuable insights into the burgeoning field of RSFMs and aims to foster further advancements in this exciting area
A Grounded Theory of Cross-Community SECOs: Feedback Diversity Versus Synchronization

Despite their proliferation, growing sustainable software ecosystems (SECOs) remains a substantial challenge. One approach to mitigate this challenge is by collecting and integrating feedback from distributors (distros) and end-users of the SECO releases into future SECO releases, tools, or policies. This paper performs a socio-technical analysis of cross-community collaboration in the OpenStack SECO, which consists of the upstream OpenStack project and 21 distribution (distro) communities. First, we followed Masood et al.'s adaptation of Strauss-Corbinian grounded theory methodology for socio-technical contexts on data from an open-ended unstructured interview, a survey, focus groups, and 384 mailing list threads to investigate how SECOs manage to sustain cross-community collaboration. Our theory has 15 constructs divided into four categories: diverse feedback types and mechanisms (2), characteristics of feedback (2), challenges (7), and the benefits (4) of cross-community collaboration. We then empirically study the salient aspects of the theory, i.e., diversity and synchronization, among 21 OpenStack distros. We empirically mined feedback that distros contribute to upstream, i.e., 140,261 mailing list threads, 142,914 bugs reported, 65,179 bugs resolved, and 4,349 new features. Then, we use influence maximization social network analysis to model the synchronization of feedback in the OpenStack SECO. Our results suggest that distros contribute substantially towards the sustainability of the SECO in the form of 25.6% of new features, 30.7% of emails, 44.3% of bug reports, and 30.7% of bug fixes. Finally, we found evidence of distros playing different roles in a SECO, with nine distros contributing all four types of feedback in equal proportions, while 12 distros specialize in one type of feedback. Distros that are influential in propagating a given type of feedback to the SECO community are not necessarily specialized in that feedback type.
Reg-TuneV2: A Hardware-Aware and Multiobjective Regression-Based Fine-Tuning Approach for Deep Neural Networks on Embedded Platforms

Fine-tuning Deep Neural Networks (DNNs) for deployment has traditionally relied on computationally intensive methods such as grid search and neural architecture search (NAS), which may not consider hardware-aware metrics.Moreover, it is essential to consider multiple objectives to develop a range of solutions for tinyML hardware deployment with real-time latency and low power constraints.To address these problems, we propose Reg-TuneV2, a systematic approach to fine-tune DNNs for hardware deployment by considering multiple objectives, including accuracy, power, and latency contours.In addition, this approach uses metric learning to achieve smaller and better-suited configurations for deployment, achieving 90.5% accuracy with only 340 KB of memory for keyword spotting on FPGA.When compared to baselines for keyword spotting and image classification on the Nvidia Jetson Nano 4 GB SDK, the proposed method achieves a 14.5× and 101.8× reduction in model size coupled with 2.5× and 5.9× better inference efficiency, respectively.