Reviewerly/exHarmony huggingface dataset:
Soil Heavy Metal Content Prediction Based on a Deep Belief Network and Random Forest Model

In order to extract useful information from X-ray fluorescence (XRF) spectra and establish a high-accuracy prediction model of soil heavy metal contents, a hybrid model combining a deep belief network (DBN) with a tree-based model was proposed. The DBN was first introduced into feature extraction of XRF spectral data, which can obtain deep layer features of spectra. Owing to the strong regression ability of the tree-based model, it can offset the deficiency of DBN in prediction ability so it was used for predicting heavy metal contents based on the extracted features. In order to further improve the performance of the model, the parameters of model can be optimized according to the prediction error, which was completed by sparrow search algorithm and the gird search. The hybrid model was applied to predict the contents of As and Pb based on spectral data of overlapping peaks. It can be obtained that R2 of As and Pb reached 0.9884 and 0.9358, the mean square error of As and Pb are as low as 0.0011 and 0.0058, which outperform other commonly used models. That proved the combination of DBN and tree-based model can obtain more accurate prediction results.
Small-Footprint Keyword Spotting Based on Gated Channel Transformation Sandglass Residual Neural Network

Keyword spotting plays a crucial role in realizing voice-based user interaction on intelligent equipment terminals and service robots. In this task, it remains challenging to achieve the balance between low memory and high precision. To better satisfy this requirement, we propose an end-to-end neural architecture with sandglass residual blocks embedded with the gated channel-wise attention mechanism. The sandglass residual blocks utilize 1D separable convolutions to extract bottleneck temporal features, which can effectively drive the model to focus more on the speech segment with lower parameters. Especially, the gated attention mechanism helps the model enhance the critical speech temporal features and suppress the useless ones and further focus on the most important part of the human speech region for keyword spotting. The experimental results on Google Speech Commands Dataset show that our proposed model has an accuracy of 97.4[Formula: see text] with only 46K parameters. Compared with the baseline method with the highest accuracy, our model parameters are decreased by 54[Formula: see text] and accuracy is increased by 0.8[Formula: see text]. That makes us take further step in achieving the goal of low memory and high precision.
Fast Algorithms for the Simplified Partial Digest Problem

The simplified partial digest problem (SPDP) models an effective and robust method for the building of a physical map using restriction site analysis. The best known algorithm requires O(n2n) time, using O(n2n) working space. The high complexities in time and space impede its application to genomes of a large number of sites. This article gives two new algorithms. The first improves the time by a factor of O(n) and significantly reduces the space to O(n2). The second improves both the time and space to O(n1.52n/2). Extensive experiments are conducted on real genomes. For instances that can be solved by the best known algorithm, the new algorithms achieve a speedup of up to 4000 times; in addition, due to the reduction in space, the new algorithms can solve many more instances. Experiments also reveal the following advantage of the SPDP method: almost every instance has at most four feasible solutions and for an instance that does not contain any pair of symmetric restriction sites, in all observed examples, the solution is unique.
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications

There are many ways to express similar things in text, which makes evaluating natural language generation (NLG) systems difficult.Compounding this difficulty is the need to assess varying quality criteria depending on the deployment setting.While the landscape of NLG evaluation has been well-mapped, practitioners' goals, assumptions, and constraintswhich inform decisions about what, when, and how to evaluate-are often partially or implicitly stated, or not stated at all.Combining a formative semi-structured interview study of NLG practitioners (N=18) with a survey study of a broader sample of practitioners (N=61), we surface goals, community practices, assumptions, and constraints that shape NLG evaluations, examining their implications and how they embody ethical considerations.
OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval

Aligning parallel sentences in multilingual corpora is essential to curating data for downstream applications such as Machine Translation.In this work, we present OneAligner, an alignment model specially designed for sentence retrieval tasks.This model is able to train on only one language pair and transfers, in a cross-lingual fashion, to low-resource language pairs with negligible degradation in performance.When trained with all language pairs of a large-scale parallel multilingual corpus (OPUS-100), this model achieves the state-ofthe-art result on the Tateoba dataset, outperforming an equally-sized previous model by 8.0 points in accuracy while using less than 0.6% of their parallel data.When finetuned on a single rich-resource language pair, be it English-centered or not, our model is able to match the performance of the ones finetuned on all language pairs under the same data budget with less than 2.0 points decrease in accuracy.Furthermore, with the same setup, scaling up the number of rich-resource language pairs monotonically improves the performance, reaching a minimum of 0.4 points discrepancy in accuracy, making it less mandatory to collect any low-resource parallel data.Finally, we conclude through empirical results and analyses that the performance of the sentence alignment task depends mostly on the monolingual and parallel data size, up to a certain size threshold, rather than on what language pairs are used for training or evaluation.
Selecting a clustering algorithm: A semi-automated hyperparameter tuning framework for effective persona development

When approaching a clustering problem, such as during persona development, selecting the most appropriate algorithm and parameter combination is essential. The hyperparameter tuning process required to determine the best combination is often tedious and thus automated through evaluation metrics. However, there are no ground truth values available for the empirical evaluation of clustering algorithms and existing internal metrics cannot comment on the quality of a set of clusters for their proposed use case. This paper presents a semi-automated framework for the hyperparameter tuning of clustering algorithms for persona development, HyPersona, which minimises the manual intervention required through simple evaluation and the production of informative graphs and early-stage personas. Within HyPersona, an internal metric focused on aspects necessary to developing quality persons, average feature significance (AFS), is proposed to assist in the evaluation of results. HyPersona was validated through application to a real-world persona development problem, evaluating the three most widely used clustering algorithms for persona development. HyPersona was compared to existing hyperparameter tuning and persona development methods and developed personas of a comparable quality whilst reducing manual intervention. The proposed internal metric, AFS, was found to provide a unique insight into the performance of cluster sets for persona development.
Low-Light Image Enhancement with an Anti-Attention Block-Based Generative Adversarial Network

High-quality images are difficult to obtain in complex environments, such as underground or underwater. The low performance of images that are captured under low-light conditions significantly restricts the development of various engineering applications. However, existing algorithms exhibit color distortion or under/overexposure when addressing non-uniform illumination images. Furthermore, they introduce high-level noise when processing extremely dark images. In this paper, we propose a novel generative adversarial network (GAN) structure to generate high-quality enhanced images, which is called anti-attention block (AAB)-based generative adversarial networks (AABGAN). Specifically, we propose AAB to suppress undesired chromatic aberrations and establish a mapping relationship between different channels. The deep aggregation pyramid pooling module guides the network when combining multi-scale context information. Furthermore, we design a new multiple loss function to adjust images to the most suitable range for human vision. The results of extensive experiments show that our method outperforms state-of-the-art unsupervised image enhancement methods in terms of noise reduction and has a well-perceived result.
From Pose to Part: Weakly-Supervised Pose Evolution for Human Part Segmentation

Human part segmentation is a crucial but challenging task in computer vision. Recent works have achieved progress with the help of pixel-wise annotations. However, annotating pixel-wise masks especially at part-level is a tedious and labor-intensive procedure. To overcome this problem, we propose a part evolution framework to learn reliable predictions from weak pose annotations, which are much easier to collect. Our framework is composed of two essential modules: the first part adaptation module is designed to learn the deep prior knowledge from three related tasks, i.e., pose estimation, part-level and object-level segmentation; the second module is the part evolution module, which refines the part priors from deep predictions with the boundary-aware optimization algorithm. These two modules are conducted iteratively to evolve pose keypoint annotations into reliable part priors. Experimental evidence shows that our weakly-supervised approach generates comparable results with the state-of-the-art strongly-supervised methods on public benchmarks, and also validates the potential of notable improvements when combining weak labels with existing part segmentation masks.
A tool for federated training of segmentation models on whole slide images

The largest bottleneck to the development of convolutional neural network (CNN) models in the computational pathology domain is the collection and curation of diverse training datasets. Training CNNs requires large cohorts of image data, and model generalizability is dependent on training data heterogeneity. Including data from multiple centers enhances the generalizability of CNN-based models, but this is hindered by the logistical challenges of sharing medical data. In this paper, we explore the feasibility of training our recently developed cloud-based segmentation tool (Histo-Cloud) using federated learning. Using a dataset of renal tissue biopsies we show that federated training to segment interstitial fibrosis and tubular atrophy (IFTA) using datasets from three institutions is not found to be different from a training by pooling the data on one server when tested on a fourth (holdout) institution's data. Further, training a model to segment glomeruli for a federated dataset (split by staining) demonstrates similar performance.
Based on adaptive improved genetic algorithm of optimal path planning

Aiming at the problem that simple genetic algorithm is easy to fall into local optimum when solving the path planning of mobile robots, an improved adaptive genetic algorithm is proposed for robot path planning. First, use the discontinuous continuity method to initialize the population, and introduce the elitist replacement strategy, so that the individual has a better gene structure and excellent characteristics, and ensure the global optimization; introduce an adaptive adjustment strategy for the crossover and mutation operators to improve the convergence speed of the algorithm. After the mutation operation, the mutation high-quality operator is proposed to keep the mutated individual always optimal; the smoothness index is added to the fitness function, and the penalty factor is introduced to make the planned path more smooth and efficient. Finally, the algorithm is compared with the traditional genetic algorithm. Experimental results show that the improved algorithm has higher search efficiency and can obtain better path planning results.
Video Reversible Data Hiding Based on Motion Vector

Aiming at the problem that the existing motion vector data hiding algorithms based on two-dimensional histogram modification do not fully utilize the embedding space, a large-capacity video reversible data hiding algorithm is proposed. Firstly, the prediction error is calculated by using the prediction algorithm based on the spatial location of the motion vector, and the prediction error pair is obtained. Then, the secret information is embedded by the designed embedding strategy combining two-dimensional and one-dimensional histograms. Finally, the idea of two-layer embedding is combined to further increase the embedding capacity. Compared with the latest data hiding algorithms of the same type, the embedding capacity of this algorithm is increased by an average of 2.23 times.
A Decoder-free Transformer-like Architecture for High-efficiency Single Image Deraining

Despite the success of vision Transformers for the image deraining task, they are limited by computation-heavy and slow runtime. In this work, we investigate Transformer decoder is not necessary and has huge computational costs. Therefore, we revisit the standard vision Transformer as well as its successful variants and propose a novel Decoder-Free Transformer-Like (DFTL) architecture for fast and accurate single image deraining. Specifically, we adopt a cheap linear projection to represent visual information with lower computational costs than previous linear projections. Then we replace standard Transformer decoder block with designed Progressive Patch Merging (PPM), which attains comparable performance and efficiency. DFTL could significantly alleviate the computation and GPU memory requirements through proposed modules. Extensive experiments demonstrate the superiority of DFTL compared with competitive Transformer architectures, e.g., ViT, DETR, IPT, Uformer, and Restormer. The code is available at https://github.com/XiaoXiao-Woo/derain.
Motivational support as a factor of formation of students’ professional competence in the conditions of digitalization of education

The internal nature of the educational process, first of all, considers the potential capabilities and motivational basis of the individual. The article focuses on the need for the use of active forms and methods of teaching in the conditions of digitalization of education. The process of digitalization affects not only changes in the requirements for learning outcomes, but also changes the educational process itself. What is this change – positive or negative, developing or standardizing, how universal and intense is it? The strength, effectiveness, form of influence, the predominance of certain functions performed by these means in higher education are determined and conditioned by the scientific and methodological guidance and management of the educational process.
A hybrid encryption transmission scheme for industrial control systems

With the convergence of information technologies such as the Industrial Internet of Things (IoT) and cloud computing with industrial control systems (ICS), the security of industrial data is at great risk. To ensure the confidentiality and integrity of industrial data, this paper designs a hybrid encryption, access control and blockchain-assisted logging communication scheme using Advanced Encryption Standard (AES) and Ciphertext-Policy Attribute-Based Encryption (CP-ABE) algorithms. The algorithm is validated by simulation experiments and has the advantage of low decryption overhead.
Quality Assessment of a Novel Camera-Based Measurement System for Roughness Determination of Concrete Surfaces—Accuracy Evaluation and Validation

The roughness of a surface is a decisive parameter of a material. In rehabilitation of concrete structures, for example, it significantly affects the adhesion between the coating material and the base concrete. However, the standard measurement procedure in construction suffers from considerable disadvantages, which leads to the demand for more sophisticated methods. In a research project, we, therefore, developed a novel camera-based measurement system, which is customized to meet the prevailing requirements for practical use on construction sites. In this article, we provide an overview of the measurement system and present comprehensive examinations to evaluate the accuracy and to provide evidence of validity. First, we examined the accuracy of the system by empirically assessing both trueness and precision of measurements using three concrete specimens. Trueness was determined by comparing the surface measurements to those of a highly accurate microscope system, revealing RMSE values of around 40-50 µm. Precision, on the other hand, was assessed considering the scattering of the roughness measurements under repeat conditions, which led to standard deviations of less than 6 µm. Furthermore, to proof validity, a comparative study was conducted based on sixteen concrete specimens, which includes the sand patch method and laser triangulation as established roughness measurement methods in practice. The empirically determined correlation coefficients between all three methods were greater than 0.99, indicating extraordinarily high linear relationships. Among them, the greatest correlation was between the camera-based system and laser triangulation.
A Local Density-Based Abnormal Case Removal Method for Industrial Operational Optimization under the CBR Framework

Operational optimization is essential in modern industry and unsuitable operations will deteriorate the performance of industrial processes. Since measuring error and multiple working conditions are inevitable in practice, it is necessary to reduce their negative impacts on operational optimization under the case-based reasoning (CBR) framework. In this paper, a local density-based abnormal case removal method is proposed to remove the abnormal cases in a case retrieval step, so as to prevent performance deterioration in industrial operational optimization. More specifically, the reasons as to why classic CBR would retrieve abnormal cases are analyzed from the perspective of case retrieval in industry. Then, a local density-based abnormal case removal algorithm is designed based on the Local Outlier Factor (LOF), and properly integrated into the traditional case retrieval step. Finally, the effectiveness and the superiority of the local density-based abnormal case removal method was tested by a numerical simulation and an industrial case study of the cut-made process of cigarette production. The results show that the proposed method improved the operational optimization performance of an industrial cut-made process by 23.5% compared with classic CBR, and by 13.3% compared with case-based fuzzy reasoning.
An efficient and Highly Scalable Listless SPIHT Image Compression Framework

The set partitioning in hierarchical trees is a powerful image compression algorithm. It has reasonable complexity and produces a rate scalable bit-stream. Unfortunately, SPIHT fails to explore the multi-resolution nature of the wavelet transform as the output bit-stream doesn't support resolution scalability. Moreover it requires huge memory and has complex memory management as it depends on utilizing lists with memory of about 2.5 the image size. This paper proposes three related algorithms. The first one modifies SPIHT to reduce its complexity and improve its efficiency especially at low rates. The second is the main contribution of the paper. It provides a simultaneous solution to the memory and scalability problems of SPIHT. Memory is reduced by utilizing status bits of average 2.5 bits per pixel instead of the lists. Resolution scalability is maintained by encoding the resolution levels in increasing order within each coding pass. Another important attribute of our algorithm is that it has very little increment in complexity in comparison with the original SPIHT algorithm. In contrast, the existing solutions have much more complexity, and/or more memory resources. The third has slightly lower complexity and memory than the second but at the same time, it has slightly lower performance.
Complete hyperentangled Greenberger-Horne-Zeilinger state analysis for polarization and time-bin hyperentanglement

We present an efficient scheme for the complete analysis of hyperentangled Greenberger-Horne-Zeilinger (GHZ) state in polarization and time-bin degrees of freedom with two steps. First, the polarization GHZ state is distinguished completely and nondestructively, resorting to the controlled phase flip (CPF) gate constructed by the cavity-assisted interaction. Subsequently, the time-bin GHZ state is analyzed by using the preserved polarization entanglement. With the help of CPF gate and self-assisted mechanism, our scheme can be directly generalized to the complete N-photon hyperentangled GHZ state analysis, and it may have potential applications in the hyperentanglement-based quantum communication.
Combinatorial coverage framework for machine learning in multi-domain operations

Multi-domain operations (MDO) are characterized by simultaneous and sequential operations; rapid and continuous integration; and surprise. Machine learning (ML) for MDO is no different. Translated into ML, MDO requires highly assured yet rapid data and model fusion. Assurance demands robustness, reliability, and explain-ability, while speed demands computational efficiency and sample efficiency. Combinatorial interaction testing offers explainable and rigorous techniques to ML for fusing data and models with runtime guarantees. But such methods are underexplored in the literature. Combinatorial coverage has been applied to neuron- and layer-levels of neural networks, but only recently to ML in general. There are also ongoing debates of efficacy in the literature, but these debates are scoped to explainable deep learning. This work presents a framework for using combinatorial coverage for multi-domain operations. We discuss how coverage metrics can incorporate multi-modal meta-data and mission context into fusion processes, how coverage is oriented towards identifying gaps in and between sets of data, and how coverage can identify cases where performance is expected to be difficult. We conclude that combinatorial coverage should be considered a core capability for supporting ML in MDO.
Load balancing in cloud computing: an analytical review and proposal

Task scheduling &lt;span&gt;or mapping of requests to machines with specified requirements becomes a very important problem to get optimized results. With the requests of many users, the machine can go under different conditions such as heavy load and minimum load. These various aspects can lead to system failure and loss of data. Hence the load must be balanced to beat the losses of the system. A load balancing concept described how different types of loads could be balanced with each machine. The load can be of memory, computation, and network. The paper mainly focuses on all kinds of load to provide a big view for load balancing in cloud computing. Along with a review of all load balancing techniques, the paper is also presenting a proposal to balance the load with decision theory. The proposed paper introduces the basic techniques and types for load balancing and their implications for innovation.&lt;/span&gt;