Reviewerly/exHarmony huggingface dataset:
Exploiting auditory filter models as interpretable convolutional frontends to obtain optimal architectures for speaker gender recognition

Identifying the gender of a speaker is an important problem which can be considered as a preliminary task in many speech processing applications such as speaker identification and emotion recognition. Although many accurate models have been developed through the progress of Deep Neural networks (DNNs), the computational complexity of these models makes them unsuitable for some situations with limited hardware resources. Pruning the designed architecture of DNNs to achieve the same initial accuracies by reducing the number of network parameters is a way to create more efficient architectures. In this paper, Auditory Filter Models (AFMs) are exploited to be used as frontend convolutional filters. In addition to the fact that these meaningful filters can be represented by a few parameters, they also provide better performance compared to traditional conventional filters. The proposed method can increase efficiency, improve the generalizability of the models, and reduce the number of parameters for gender recognition. Moreover, by utilizing a clustering approach to reduce the number of learnable filters of the AFM in the first convolutional layer, the network parameters can be reduced even further. Experimental results conducted on four well-known datasets, namely TIMIT, LibriSpeech, VoxCeleb1, and RAVDESS, prove the validity of the claims made earlier. The results show that the use of this technique not only enhances the performance of the models but also facilitates the learning of a rich and informative representation of speech data. This finding emphasizes the effectiveness and robustness of the proposed approach in handling diverse datasets and real-world scenarios.
A Multi-Objective Degree-Based Network Anonymization Method

Enormous amounts of data collected from social networks or other online platforms are being published for the sake of statistics, marketing, and research, among other objectives. The consequent privacy and data security concerns have motivated the work on degree-based data anonymization. In this paper, we propose and study a new multi-objective anonymization approach that generalizes the known degree anonymization problem and attempts at improving it as a more realistic model for data security/privacy. Our suggested model guarantees a convenient privacy level, based on modifying the degrees in a way that respects some given local restrictions, per node, such that the total modifications at the global level (in the whole graph/network) are bounded by some given value. The corresponding multi-objective graph realization approach is formulated and solved using Integer Linear Programming to obtain an optimum solution. Our thorough experimental studies provide empirical evidence of the effectiveness of the new approach, by specifically showing that the introduced anonymization algorithm has a negligible effect on the way nodes are clustered, thereby preserving valuable network information while significantly improving data privacy.
AGV path planning method based on memory precursor point and physical PID model

Path planning is a core algorithm component of AGV equipment, and its quality directly affects the operating efficiency and accuracy of the equipment. The current mainstream path planning algorithm A* and the traditional Dijkstra algorithm have the disadvantages of high computational complexity and limited accuracy. Aiming at the limitations of existing algorithms, this paper improves the Dijkstra-like algorithm by using the memorable precursor point list and introduces the kinematics model of the AGV car to optimize the PID control details. The comparison test proves that the improved algorithm can effectively improve the planning efficiency and stability of equipment operation.
Llieformer: A Low-Light Image Enhancement Transformer Network with a Degraded Restoration Model

Low-light image enhancement aims at improving human perception or the effectiveness of computer vision tasks of images taken in dark. The low-light images are usually seriously lack in visual information. To tackle this problem, we propose a general Low-light Image Enhancement Transformer Network (LLIEFormer) with a degraded restoration model in this paper. The network of LLIEFormer synthesizes the advantages of Transformer to extract global information and convolutional neural networks to capture local details. We conduct extensive experiments on various low-illumination enhanced datasets including PairL1.6K and FiveK to demonstrate the effectiveness of our method. The results show that our LLIEFormer has better performance and wider applicability than other advanced methods. Our code will be available at https://github.com/xunpengyi/LLIEFormer.
Mining False Positive Examples for Text-Based Person Re-Identification

Text-based person re-identification (ReID) aims to identify images of the targeted person from a large-scale person image database according to a given textual description. Most existing methods focus on establishing instance-level and word-region-level correspondences. However, these methods generally rely heavily on the similarity contributed by matched word-region pairs while neglecting the decisive mismatched word-region pairs, resulting in false-positive matching. Accordingly, we propose to mine false-positive examples (MFPE) via a jointly optimized multi-branch architecture. Specifically, we delicately design a cross-relu loss to amplify the dissimilarity between matched and mismatched word-region pairs. Furthermore, we propose a false-positive mining (FPM) branch to highlight the role of mismatched word-region pairs, effectively preventing false-positive matching. Extensive experiments on CUHK-PEDES demonstrate the superior effectiveness of MFPE. Our code is released at https://github.com/xx-adeline/MFPE.
Lightweight Deep Deblurring Model with Discriminative Multi-Scale Feature Fusion

Although existing learning-based deblurring methods achieve significant progress, these approaches tend to require lots of network parameters and huge computational costs, which limits their practical applications. Instead of pursuing larger deep models for boosting deblurring performance, we propose a lightweight deep convolutional neural network with lower computational costs and comparable restoration performance, which is based on a multi-scale framework with an encoder and decoder network architecture. Specifically, we present an effective depth-wise separable convolution block (DSCB) as the fundamental building block of our method to reduce the model complexity. In addition, to better utilize the features from different scales, we develop a simple yet effective discriminative multi-scale feature fusion (DMFF) module for achieving high-quality results. Experimental results on the benchmarks show that our method is about 10× smaller than the state-of-the-art deblurring methods, e.g. MPRNet [1], in terms of model parameters and FLOPs while achieving competitive performance. The training code and models are available at https://github.com/cslvjt/LightweightDeblur.
Online trajectory optimization for safe autonomous overtaking with active obstacle avoidance

Autonomous driving with active obstacle avoidance in dynamic urban environment has attracted significant attention to improve road safety and traffic efficiency. In this paper, an online optimization-based trajectory planning method for autonomous overtaking is developed to prevent collision and realize safe efficient driving. Unlike traditional methods which solve the overtaking problem with segmented reference path in multi-stages with integer variables, this paper proposes a novel dual-variable trajectory planning framework to get the optimal trajectory for active collision avoidance. First, the nonlinear non-differentiable collision-free constraint between vehicles is reformulated using dual problem optimization. Then, the optimal trajectory for longitudinal and lateral movement is obtained jointly in a receding horizon optimization framework based on the optimized dual variable considering the safety and dynamic performance. The key novelty lies in the reformulation of the nonlinear optimal trajectory planning problem and the formulated whole optimization framework can be solved using efficient open-source solvers for online computation. Simulation studies in different dynamic cases are provided to show the efficiency and robustness for safe driving. The method reaches safe overtaking performance with much less calculation time. Real-world experiment with a static obstacle demonstrates its capability for the safe efficient trajectory planning and online implementation in autonomous driving.
Gradient-Based Metrics for the Evaluation of Image Defogging

Fog, haze, or smoke are standard atmospheric phenomena that dramatically compromise the overall visibility of any scene, critically affecting features such as the illumination, contrast, and contour detection of objects. The decrease in visibility compromises the performance of computer vision algorithms such as pattern recognition and segmentation, some of which are very relevant to decision-making in the field of autonomous vehicles. Several dehazing methods have been proposed that either need to estimate fog parameters through physical models or are statistically based. But physical parameters greatly depend on the scene conditions, and statistically based methods require large datasets of natural foggy images together with the original images without fog, i.e., the ground truth, for evaluation. Obtaining proper fog-less ground truth images for pixel-to-pixel evaluation is costly and time-consuming, and this fact hinders progress in the field. This paper aims to tackle this issue by proposing gradient-based metrics for image defogging evaluation that do not require a ground truth image without fog or a physical model. A comparison of the proposed metrics with metrics already used in the NTIRE 2018 defogging challenge as well as several state-of-the-art defogging evaluation metrics is performed to prove its effectiveness in a general situation, showing comparable results to conventional metrics and an improvement in the no-reference scene. A Matlab implementation of the proposed metrics has been developed and it is open-sourced in a public GitHub repository.
Maintaining Cybersecurity Awareness in Large-Scale Organizations

Research was conducted to increase the awareness of employees with regard to cyber security to fill the gap in the literature where few studies on how effective the measures implemented in organizations were reported. This research uses the outcome of the phishing drills that a public institution applied to its personnel, participation of said personnel in awareness training, and the reading statistics of regularly published information security bulletins. This has been beneficial in determining the methods to increase the cyber security awareness of personnel in organizations with 1,000 or more personnel; users were considered as a whole, and not individually evaluated. Findings report that organizations can increase users' cybersecurity awareness by systematically conducting phishing exercises, providing awareness training, and regularly publishing information security bulletins. The awareness of reading bulletins rapidly increased after phishing exercises and training and decreased in the following months; however, an increase was observed in the long term.
Revocable Signature Scheme with Implicit and Explicit Certificates

This paper addresses the certificate revocation problem and proposes the first revocable pairing-based signature scheme with implicit and explicit certificates (IE-RCBS-kCAA). We should no longer discuss whether to revoke certificates but how to do it effectively, ensuring both the scalability of the revocation operation and the non-repudiation of the signature in the short or long term. Under the computational difficulty assumptions of the modified collusion attack algorithm with k traitors (k-mCAA) and discrete logarithm (DL) problems, we demonstrate that our scheme is secure against existential unforgeability under chosen message attacks (EUF-IERCBS-kCAA-CMA) in a random oracle model. The proposed solution is scaled and allows the use of many trusted status authorities that issue explicit short-term certificates confirming the validity of explicit long-term certificates. Furthermore, we demonstrate that our signature scheme has a short-term non-repudiation property for the shell validity model.
A modified recurrent neural network (MRNN) model for and breast cancer classification system

Breast cancer is most dangerous cancer among women. Image processing techniques are used for Breast cancer detection. A Block-based cross diagonal texture matrix (BCDTM) method is used first to extract Haralick's features from each mammography ROI. Likewise, wrapper method is utilized to choose the crucial features from the condensed feature vector. There are lot of factors that affects the quality of the images such as salt or pepper noise. As a result, this is less precise and more prone to mistakes because of human. In order to address the problems, input breast image is first pre-processed via median filtering to reduce noise. ROI segmentation is done using weighted K means clustering. Feature extraction, texture and form descriptors based on Centroid Distance Functions (CDF) and BCDTM are used. Kernel Principal Component Analysis (KPCA) is used as dimensionality reduction on the extracted features. Improved Cuckoo Search Optimization (ICSO) is used to compute relevant feature selection. Modified Recurrent Neural Network (MRNN) is utilized to classify breast cancer into benign and malignant. Results show that the suggested model achieved highest accuracy, precision and recall values compared with other state-of-the-art approaches.
An Examination of Dynamic Group Particle Swarm Optimization - A New Variant of Particle Swarm Optimization

Particle Swarm Optimization (PSO) is a meta-heuristic algorithm widely used to find the global minima of a function, making it particularly useful in optimization problems. Since the algorithm's introduction, many variants have been proposed to improve the performance of the algorithm. In this study, we propose a new variant of Particle Swarm Optimization called Dynamic Group Particle Swarm Optimization (DGPSO). DGPSO introduces the novel idea of moving particles through various groups(each with different parameters) based on the current iteration and the particles' fitness. In this paper, the performance of DGPSO will be assessed by testing the algorithm on complex benchmark functions and comparing its performance with other PSO variants.
Action Anticipation with Goal Consistency

In this paper, we address the problem of short-term action anticipation, i.e., we want to predict an upcoming action one second before it happens. We propose to harness high-level intent information to anticipate actions that will take place in the future. To this end, we incorporate an additional goal prediction branch into our model and propose a consistency loss function that encourages the anticipated actions to conform to the high-level goal pursued in the video. In our experiments, we show the effectiveness of the proposed approach and demonstrate that our method achieves state-of-the-art results on two large-scale datasets: Assembly101 and COIN. The code is available at https://github.com/olga-zats/goal_consistency.
Designing Strong Baselines for Ternary Neural Network Quantization through Support and Mass Equalization

Deep neural networks (DNNs) offer the highest performance in a wide range of applications in computer vision. These results rely on over-parameterized backbones, which are expensive to run. This computational burden can be dramatically reduced by quantizing (in either data-free (DFQ), post-training (PTQ) or quantization-aware training (QAT) scenarios) floating point values to ternary values (2 bits, with each weight taking value in {−1,0,1}). In this context, we observe that rounding to nearest minimizes the expected error given a uniform distribution and thus does not account for the skewness and kurtosis of the weight distribution, which strongly affects ternary quantization performance. This raises the following question: shall one minimize the highest or average quantization error? To answer this, we design two operators: TQuant and MQuant that correspond to these respective minimization tasks. We show experimentally that our approach allows to significantly improve the performance of ternary quantization through a variety of scenarios in DFQ, PTQ and QAT and give strong insights to pave the way for future research in deep neural network quantization.
DLEN: Deep Laplacian Enhancement Networks for Low-Light Images

Enhancing low-light images is challenging as it requires simultaneously handling global and local contents. This paper presents a new solution which incorporates the vision transformer (ViT) into Laplacian pyramid and explores cross-layer dependence within the pyramid. It first applies Laplacian pyramid to decompose the low-light image into a low-frequency (LF) component and several high-frequency (HF) components. As the LF component has a low resolution and mainly includes global attributes, ViT is applied on it to explore the interdependence among global contents. Since there exists strong spatial correlation among different frequency components, the refined features from a lower pyramid layer are used to assist the refinement of upper-layer features. Experiments demonstrate that our approach achieves better performance than state-of-the-art methods, while maintaining a relative small model size and low computational complexity. Our source code and trained model will be released at https://github.com/Xinjie-Wei/DLEN.
An Optimized Error Compensation Method for Phase Measurement Profilometry

Phase measurement profilometry (PMP) is primarily employed to analyze the morphology of a functional surface with precision. Historically, one of the most complex and persistent challenges in PMP has been reducing errors stemming from inconsistent indicators at the edges of a surface. In response to this challenge, we propose an optimized error compensation methodology specifically designed to handle edge artefacts. This methodology introduces the Hilbert transform and object surface albedo as tools to detect the edges of the artefact region that need to be compensated. Moreover, we analyze the characteristics of the sinusoidal fringe waveform propagation direction and investigate the reconstruction results of the fringe vertical to the current directions to compensate for edge artefacts. The experimental results for various objects show that the optimized approach can compensate for edge artefacts by projecting in two directions and reducing the projection by half. The compensated root mean square error (RMSE) for planar objects can be reduced by over 45%.
The Influence of Educational Videos on Knowledge of Food and Beverage Consumption of Contemporary Snacks in Young Women at SMA PGRI 2 Palembang

Introduction: Consumption of snack foods and drinks contributes to adolescent intake which affects nutritional status so good knowledge is needed. Educational videos are an interesting nutrition education medium and can help increase nutrition knowledge in adolescents. This study aims to determine the influence of educational videos on knowledge of food consumption and beverages of contemporary snacks in adolescent girls at SMA PGRI 02 Palembang. Method: This research is a quasi-experiment with one group pretest posttest group design. The study was conducted at SMA PGRI 02 Palembang in March. The study sample was 106 students according to inclusion and exclusion criteria. The statistical test used is the paired sample t test. Results: There is a significant difference in educational videos on the knowledge of food consumption and contemporary snack drinks for young women at SMA 02 PGRI Palembang after being given educational videos with a p value of &lt;0.05. Conclusion: Interesting educational videos can increase teenagers' attention to the material presented so that it can change the knowledge of food consumption and snack drinks for the better.
An Analysis of the Design of the Cybersecurity Maturity Model Certification (CMMC) and Its Direct Effect on Supply Chain Management

This chapter addresses the rationale behind commissioning the Cybersecurity Maturity Model Certification by the Department of Defense to address critical supply chain issues that directly affect services provided by contractors. The fourth pillar of cybersecurity was added to the existent three-pillar model (cost, schedule, and performance) to significantly lower risk to the supply chain. Scenarios addressed in the chapter identify the need for its immediate implementation. Recommendations were made based on the literature about informing the supply chain community of the impact of cybersecurity's lack of awareness on its overall effect on business continuity and impact.
Social Media App

"Social media app” website is a project that presents a web application for users to browse, and contribute their own messages The app offers a range of features to enable users to interact with each other and share various forms of content. Users can create personalized profiles, post multimedia content such as photos and videos, and engage in discussions through comments and likes. Additionally, the app incorporates messaging capabilities, allowing users to communicate privately or in groups and public charity. The website may utilize various technologies and frameworks, such as React, HTML, CSS, JavaScript, and server-side languages, to create a seamless and engaging user experience
Efficient mining of concise and informative representations of frequent high utility itemsets

The discovery of frequent closed high utility itemsets (FCHUIs) and frequent generators of high utility itemsets (FGHUIs) is significant because they serve as important and concise representations of frequent high utility itemsets (FHUIs), offering a brief but essential summary that can be considerably smaller. Besides, they facilitate the generation of nonredundant high utility association rules that are crucial for decision-makers. However, the challenge lies in the difficulty of mining these representations due to scalability issues, high memory usage, and long runtimes, particularly when dealing with dense and large datasets. To address this issue, this paper proposes a novel approach for efficiently mining FCHUIs and FGHUIs using a novel weak lower bound named wlbu on the utility. The approach includes effective pruning strategies for early eliminating non-closed and/or non-generator high utility branches in the prefix search tree based on wlbu. These pruning strategies allow faster execution with lower memory usage. In addition, the paper presents two novel algorithms, FCGHUI-Miner and FGHUI-Miner, which can simultaneously discover both FGHUIs and FCHUIs or solely mine FGHUIs, respectively. The experimental results demonstrate that the proposed algorithms outperform state-of-the-art algorithms in terms of efficiency and effectiveness.