Reviewerly/exHarmony huggingface dataset:
Self-supervised learning with randomized cross-sensor masked reconstruction for human activity recognition

Self-supervised learning (SSL) has gained prominence in the field of accelerometer-based human activity recognition (HAR) due to its ability to learn from both labeled and unlabeled data. While labeled data acquisition is costly, it is relatively easy to accumulate unlabeled sensor data. However, few works utilize large-scale, unlabeled datasets for pre-training despite its positive impact on downstream HAR performance, shown in recent work. Cross-sensor upstream training has also received limited attention. We introduce a new auxiliary task, randomized cross-sensor masked reconstruction (RCSMR), for SSL. We pre-train a transformer encoder on the large-scale HUNT4 dataset with RCSMR. The resulting model exhibits better performance on two downstream datasets with the same sensor setup as HUNT4 (HARTH and HAR70+), achieving an average F1-score of 74.03%, surpassing two other auxiliary tasks (70.51% to 72.78%) and five supervised baselines (47.51% to 58.84%). Moreover, when applied to three datasets with sensor configurations distinct from HUNT4 (USC-HAD, PAMAP2, MobiAct), RCSMR outperforms nine state-of-the-art SSL methods, with an F1-score of 72.99% compared to F1-scores ranging from 51.46% to 69.88%. We further show that certain activities exhibit improved separability when utilizing latent representations learned through RCSMR, indicating reduced sensor position and orientation bias. Our method is applied in large-scale epidemiological studies, offering valuable insights into the impact of physical activity behavior on public health.
Multi-level attention network: Mixed time–frequency channel attention and multi-scale self-attentive standard deviation pooling for speaker recognition

In this paper, we propose a more efficient lightweight speaker recognition network, the multi-level attention network (MANet). MANet aims to generate more robust and discriminative speaker features by emphasizing features at different levels in the speaker recognition network through multi-level attention. The multi-level attention contains mixed time–frequency channel (MTFC) attention and multi-scale self-attentive standard deviation pooling (MSSDP). MTFC attention combines channel, time, and frequency information to capture global features and model long-term contexts. MSSDP can capture changes in frame-level features and aggregate frame-level features with different scales, generating a long-term, robust, and discriminative utterance-level feature. Therefore, MANet emphasizes the features of different levels. We performed extensive experiments on two popular datasets, Voxceleb and CN-Celeb. The proposed method is compared with the current state-of-the-art speaker recognition methods. It achieved EER/minDCF of 1.82%/0.1965, 1.94%/0.2059, 3.69%/0.3626, and 11.98%/0.4814 on the test sets Voxceleb1-O, Voxceleb1-E, Voxceleb1-H, and CN-Celeb, respectively. It is a more effective lightweight speaker recognition network, superior to most large speaker recognition networks and all lightweight speaker recognition networks tested, with an improved performance of 64% compared to the baseline system ThinResNet-34. Compared to the lightest EfficientTDNN-Small, it has only 0.6 million more parameters but 63% better performance. The performance of MANet is only 4% different compared to the state-of-the-art large model LE-Conformer. In the ablation experiments, our proposed attention method and aggregation model achieved the best experimental performance in Voxceleb1-O with EER/minDCF of 2.46%/0.2708, 2.39%/0.2417, respectively, which indicates that our proposed methods are a significant improvement over previous methods.
GCCF: A lightweight and scalable network for underwater image enhancement

Underwater images suffer from color deviation and low contrast arising from light attenuation and scatter, making them act as a drag on exploring and exploiting ocean resources. Existed underwater image enhancement algorithms are either inflexible due to scene-dependent design or redundant for ignoring prior knowledge. To address the above problems, we propose a lightweight and scalable deep learning-based network that consists of a Grouped Color Compensation encoder and channel Fusion decoder, abbreviated GCCF. Concretely, the least absorbed green channel contains abundant information compared to the red and blue channels. Taking this into account, the encoder extracts information independently from three grouped features according to the color. Simultaneously, the encoder flexibly compensates for the red and blue channels from the green channel with a proposed learnable module. Then, the decoder gradually fuses the grouped features into a whole part and produces competitive outputs from the integrated features. Extensive experiments on real-world underwater images demonstrate that the proposed GCCF achieves outstanding results in subjective visual comparisons and objective metrics.
Multi-UAV airborne UV area coverage and task assignment method

To address the area coverage problem in large-scale reconnaissance and detection tasks, Unmanned Aerial Vehicles(UAVs) utilize Ultraviolet(UV) light for flight guidance. Additionally, a UV light power control method is implemented to assist UAVs in adapting to various terrains. Based on the flight characteristics of quadrotor UAVs, the target area is divided into a grid-like format. This transformation helps convert the area coverage task into a multi-UAV path planning problem on a raster map. To address this, constraints are established for assigning tasks to multiple UAVs. An algorithm is proposed to assign the target area uniformly based on the initial position of the UAV, ensuring full coverage. Additionally, the algorithm considers the differences in endurance among multiple UAVs. This improved algorithm is highly suitable for real-life application scenarios. The simulation results demonstrate the power control changes in wireless UV-guided UAV flight. Moreover, the assignment results of the improved algorithm align closely with the theoretical assignment results, considering different parameters like the number of UAVs, initial position, and endurance capacity.
Research on the construction scheme of smart library based on blockchain technology

Facial recognition technology and recommendation systems are the main technologies in the construction of intelligent libraries, but both technologies face privacy breaches and credibility issues. Blockchain, as an emerging technology, is having significant impact in many fields. This article delves into the role and core application value of blockchain technology in the construction of smart libraries in universities, and proposes smart library service architecture based on blockchain technology. This architecture provides a value-added path for smart library services, including secure storage, resource sharing, and optimizing book borrowing and returning systems. It can improve the quality of resource services and meet the increasing service needs of readers. The effectiveness and practicality of this method have been verified through experiments.
Some relations between the irreducible polynomials over a finite field and its quadratic extension

In this paper, we establish some relations between irreducible polynomials over a finite field Fq and its quadratic extension Fq2. First we consider a relation between the numbers of irreducible polynomials of a fixed degree over Fq and Fq2, and some relations between self-reciprocal irreducible polynomials over Fq and self-conjugate-reciprocal irreducible polynomials over Fq2. We also obtain formulas for the number and the product of all self-conjugate-reciprocal irreducible monic (SCRIM) polynomials over Fq2.
A differential evolution algorithm for solving mixed-integer nonlinear programming problems

Many engineering optimization problems fall into the category of Mixed-Integer Nonlinear Programming (MINLP) problems, which combine nonlinear relations, constraint conditions, and different types of variables, including continuous, integer, and/or discrete variables. Solving MINLP problems can be a challenging exploration process since their landscape can be composed of many discontinuous feasible parts with different sizes. In this scheme, Evolutionary Algorithms (EAs) often fail to generate enough diversity to explore the discontinuous feasible parts. Consequently, EAs are vulnerable to being attracted to larger discontinuous feasible parts, even if they are not promising regions. In this paper, a new proposal based on two fundamental strategies is presented to improve the performance of the differential evolution algorithm when solving MINLP problems. The first strategy considers a set of "good fitness-infeasible solutions" that contribute to exploring promising regions from infeasible contours. It reduces the vulnerability of the solutions to be attracted to larger discontinuous feasible parts with unpromising objective function values. The second is a composite trial vector generation to improve the combinatorial exploration while ensuring a robust convergence capacity toward the final solution. Sixteen well-known MINLP problems are used in several experiments to evaluate the performance of the proposed algorithm, comparing it to state-of-the-art EAs. The results provided by the proposal show a better performance in terms of quality, robustness, and computational cost.
CoCo-trie: Data-aware compression and indexing of strings

We address the problem of compressing and indexing a sorted dictionary of strings to support efficient lookups and more sophisticated operations, such as prefix, predecessor, and range searches. This problem occurs as a key task in a plethora of applications, and thus it has been deeply investigated in the literature since the introduction of tries in the ’60s. We introduce a new data structure, called the COmpressed COllapsed Trie (CoCo-trie), that hinges on a pool of techniques to compress subtries (of arbitrary depth) into succinctly-encoded and efficiently-searchable trie macro-nodes with a possibly large fan-out. Then, we observe that the choice of the subtries to compress depends on the trie structure and its edge labels. Hence, we develop a data-aware optimisation approach that selects the best subtries to compress via the above pool of succinct encodings, with the overall goal of minimising the total space occupancy and still achieving efficient query time. We also investigate some variants of this approach that induce interesting space–time trade-offs in the CoCo-trie design. Our experimental evaluation on six diverse and large datasets (representing URLs, XML data, DNA and protein sequences, database records, and search-engine dictionaries) shows that the space–time performance of well-established and highly-engineered data structures solving this problem is very input-sensitive. Conversely, our CoCo-trie provides a robust and uniform improvement over all competitors for half of the datasets, and it results on the Pareto space–time frontier for the others, thus offering new competitive trade-offs.
Properties and Styles of Software Technology Tutorials

A large number of tutorials for popular software development technologies are available online, and those about the same technology vary widely in their presentation. We studied the design of tutorials in the software documentation landscape for five popular programming languages: Java, C#, Python, Javascript, and Typescript. We investigated the extent to which tutorial pages, i.e. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">resources</i> , differ and report statistics of variations in resource properties. We developed a framework for characterizing resources based on their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distinguishing attributes</i> , i.e. properties that vary widely for the resource, relative to other resources. Additionally, we propose that a resource can be represented by its <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">resource style</i> , i.e. the combination of its distinguishing attributes. We discuss three techniques for characterizing resources based on our framework, to capture notable and relevant content and presentation properties of tutorial pages. We apply these techniques on a data set of 2551 resources to validate that our framework identifies valid and interpretable styles. We contribute this framework for reasoning about the design of resources in the online software documentation landscape.
Early analysis of requirements using NLP and Petri-nets

Scenario-based approaches are widely used for software requirements specification. Since scenarios are usually written using natural language, specifications may have statements that are ambiguous, unnecessarily complicated, missing, duplicated, or conflicting. Requirements quality is challenging since it is hard to achieve consistency in requirements products. Unfortunately, if done manually, analysis of textual scenarios can be an arduous, time-consuming, and error-prone activity. This work rethinks the unambiguity, completeness, consistency, and correctness properties of scenario-based specifications; and how static and dynamic analysis strategies could automatically evaluate them. To do so, we introduce an automated requirements analysis approach to check both structural and behavioral aspects of scenarios, which combines natural language processing, Petri-nets, and visualization techniques for: (i) identifying certain types of defects and their indicators; (ii) highlighting scenario statements or relationships among scenarios that can lead to defects; and (iii) foreseeing scenario execution paths that can lead to inconsistencies. We show the feasibility of the proposed approach through the analysis of four projects specified as scenario-based descriptions. Overall, our approach produced reasonable results, with precision greater than 89% and recall greater than 98%. Our work allows researchers, as well as practitioners, to improve the quality of scenarios through an automated analysis approach.
Dynamically retrieving knowledge via query generation for informative dialogue generation

Knowledge-driven dialogue systems have recently made remarkable breakthroughs. Compared with general dialogue systems, superior knowledge-driven dialogue systems can generate more informative and knowledgeable responses with pre-provided knowledge. However, in practical applications, the knowledge-driven dialogue systems cannot be supplied with relevant knowledge beforehand. Hence, to enhance the practicality of the knowledge-driven dialogue systems, it is crucial to devise a method to dynamically retrieve pertinent knowledge based on the context. In addressing this challenge, we introduce a knowledge-driven dialogue system called DRKQG (Dynamically Retrieving Knowledge via Query Generation for informative dialogue response). Specifically, this system is composed of two main modules: a query generation module and a response generation module. Initially, a time-aware mechanism is employed to capture contextual information, enabling the generation of a query for knowledge retrieval through a search engine. Subsequently, we incorporate the copy mechanism and transformers, empowering the response generation module to create responses based on both the context and retrieved knowledge. Experimental results at LIC2022, Language and Intelligence Technology Competition, show that our module outperforms the baseline model by a large margin on automatic evaluation metrics, while human evaluation by the Baidu Linguistics team shows that our system achieves impressive results in Factually Correct and Knowledgeable.
Teacher–student complementary sample contrastive distillation

Knowledge distillation (KD) is a widely adopted model compression technique for improving the performance of compact student models, by utilizing the "dark knowledge" of a large teacher model. However, previous studies have not adequately investigated the effectiveness of supervision from the teacher model, and overconfident predictions in the student model may degrade its performance. In this work, we propose a novel framework, Teacher-Student Complementary Sample Contrastive Distillation (TSCSCD), that alleviate these challenges. TSCSCD consists of three key components: Contrastive Sample Hardness (CSH), Supervision Signal Correction (SSC), and Student Self-Learning (SSL). Specifically, CSH evaluates the teacher's supervision for each sample by comparing the predictions of two compact models, one distilled from the teacher and the other trained from scratch. SSC corrects weak supervision according to CSH, while SSL employs integrated learning among multi-classifiers to regularize overconfident predictions. Extensive experiments on four real-world datasets demonstrate that TSCSCD outperforms recent state-of-the-art knowledge distillation techniques.
Optimized hybrid deep learning pipelines for processing heterogeneous facial expression datasets

Emotions, central to human experience, have been categorized through various theories. Biological theories, such as the James-Lange model, suggest that physiological reactions are the precursors to emotions. Cognitive theories, as highlighted by the Schachter-Singer model, argue that emotions emerge from a combination of physiological responses and cognitive appraisal. Evolutionary theories, rooted in Darwin's work, view emotions as adaptive mechanisms that have evolved to promote survival. In the realm of psychology, expressing emotions via facial expressions is termed face reading, a vital communication method. With the swift advancement of Deep Neural Networks (DNN) across various fields, they are increasingly used to derive exclusive features for automated Facial Expression Prediction and Recognition (FEPR). This research employs a Three-staged Deep Learning (TsDL) strategy for FEPR. Recognizing the pivotal role of datasets in determining system performance, we incorporate both laboratory-controlled and real-world data. Initially, the Generative Adversarial Networks (GAN) model, leveraging datasets like CK+ and Radboud Faces Database (RFD), is introduced. This model successfully identifies expressions across seven categories with an accuracy of 92.15 %. Subsequently, another model based on the Attention-Long Short Term Memory (A-LSTM) is trained using the Real-world Affective Face Database, given its capability to manage spatio-temporal features. The TsDL approach predicts and classifies six primary emotions, achieving an 89.22 % accuracy rate. In the culmination of our work, we harness Ensemble Learning Strategies to craft a refined hybrid model, ensuring systematic performance and optimal accuracy. The diverse theoretical perspectives on emotions greatly enrich our comprehension, especially when developing our FEPR model.
Introducing shape priors in Siamese networks for image classification

The efficiency of deep neural networks is increasing, and so is the amount of annotated data required for training them. We propose a solution improving the learning process of a classification network with less labeled data. Our approach is to inform the classifier of the elements it should focus on to make its decision by supplying it with some shape priors. These shape priors are expressed as binary masks, giving a rough idea of the shape of the relevant elements for a given class. We resort to Siamese architecture and feed it with image/mask pairs. By inserting shape priors, only the relevant features are retained. This provides the network with significant generalization power without requiring a specific domain adaptation step. This solution is tested on some standard cross-domain digit classification tasks and on a real-world video surveillance application. Extensive tests show that our approach outperforms the classical classifier by generating a good latent space with less training data. Code is available at https://github.com/halqasir/MG-Siamese.
Rotation-equivariant correspondence matching based on a dual-activation mixer

Learning-based correspondence matching methods have become the mainstream techniques in many computer vision and robotics applications due to their robustness to large illumination and viewpoint changes. However, it is difficult for conventional convolutional neural networks (CNNs) to extract rotation-equivariant local features. Recent work has shown that CNNs combined with group-equivariant architectures are surprisingly effective at matching correspondences even when the images are rotated to a dramatic extent. However, the inherent shape (square) of convolution kernels causes the performance bottleneck of such rotation-equivariant CNNs. To address this issue, we propose an adaptive dual rotation-equivariant correspondence matching algorithm, which performs stably at all angles. We mathematically analyze the effectiveness of our proposed rotation-equivariant correspondence matching approach and its performance with respect to different convolution kernels. Extensive experiments on the Rotated-HPatches, SIM2E, and MegaDepth datasets demonstrate the effectiveness of our proposed algorithm.
Influence of cost/loss functions on classification rate: A comparative study across diverse classifiers and domains

The classification rate is the most significant factor affecting the selection of an appropriate classification approach for achieving the desired quality of decisions. Several researchers have evaluated the influence of different features on the performance of classification approaches, but cost/loss functions, despite their theoretical impact on the classification rate, have been less considered in the comparative literature review. In this paper, a comparative study has been presented on the influence of different cost/loss functions on the classification rate of different classifiers. For this purpose, five of the most well-known and widely used categories of cost/loss functions and three of the most popular main categories of classification approaches are considered. Also, 23 benchmark datasets from six different domains are selected. In this paper, all modeling of the statistical and intelligent classifiers are run in the Eviews and MATLAB package software, respectively. In addition, all mathematical modeling of models run in the GAMS package software. Empirical results indicate that cost/loss functions can significantly affect the classification rate. Numerical results show that the discrete cost/loss function, by average 3.91% and 8.31% improvement rather than semi-continuous and continuous cost/loss functions, respectively, is the most effective one. This clearly illustrates that the consistency of the cost/loss function with the goal function of the classification approach has a positive direct relationship with the classification rate. Furthermore, nonlinear versions of semi-continuous and continuous cost/loss functions are on average slightly more efficient than linear ones. Numerical results indicate that nonlinear forms can averagely yield an 88.99% classification rate that is overall 0.98% higher than linear ones, which achieved an 88.13% classification rate. Although the type of cost/loss function can affect the classification rate of different classifiers, the improvement amount varies based on the type of classifier used. In general, the improvement amount in statistical classifiers, i.e., 15.07% is significantly higher than others, and intelligent by 7.86% and deep learning by 5.20% are in the second and third places, respectively. This overall demonstrates that the complexity of the used classifiers has a negative relationship with the improvement of the classification rate in changing cost/loss function. Finally, numerical results indicate that the difference between improvement amounts achieved by varying cost/loss functions, in general, is not significant and is independent of the type of classifier, as well as the type and domain/area of data.