Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Reduction Methods Applied on a Convolutional Neural Network

With the emergence of smartphones, video surveillance cameras, social networks, and multimedia engines, as well as the development of the internet and connected objects (the Internet of Things—IoT), the number of available images is increasing very quickly. This leads to the necessity of managing a huge amount of data using Big Data technologies. In this context, several sectors, such as security and medicine, need to extract image features (index) in order to quickly and efficiently find these data with high precision. To reach this first goal, two main approaches exist in the literature. The first one uses classical methods based on the extraction of visual features, such as color, texture, and shape for indexation. The accuracy of these methods was acceptable until the early 2010s. The second approach is based on convolutional neuronal networks (CNN), which offer better precision due to the largeness of the descriptors, but they can cause an increase in research time and storage space. To decrease the research time, one needs to reduce the size of these vectors (descriptors) by using dimensionality reduction methods. In this paper, we propose an approach that allows the problem of the “curse of dimensionality” to be solved thanks to an efficient combination of convolutional neural networks and dimensionality reduction methods. Our contribution consists of defining the best combination approach between the CNN layers and the regional maximum activation of convolutions (RMAC) method and its variants. With our combined approach, we propose providing reduced descriptors that will accelerate the research time and reduce the storage space while maintaining precision. We conclude by proposing the best position of an RMAC layer with an increase in accuracy ranging from 4.03% to 27.34%, a decrease in research time ranging from 89.66% to 98.14% in the function of CNN architecture, and a reduction in the size of the descriptor vector by 97.96% on the GHIM-10K benchmark database.
Quality Evaluation for Physical Education Teaching in Colleges with Joint Neural Network

A new teaching curriculum for physical education in colleges has been proposed as part of the teaching reform. New developments in content, goals, and teaching methods have also occurred, and these have gradually become the focus of current physical education researchers. By developing a model for objectively, truly, comprehensively, and accurately evaluating college physical education teaching, it will be possible not only to improve the quality of physical education instruction and the effectiveness of faculty in achieving their goals but also to improve the quality of programs. This research work proposes a Joint Neural Network (JNN) composed of an Improved Support Vector Machine (ISVM) and improved Back Propagation (BP) network for evaluating physical education quality in colleges. When using a standard SVM to classify feature data, the parameters used have an impact on the SVM’s classification. This study introduces an improved SVM with hybrid optimization of PSO and GA algorithms, combining the properties of genetic algorithms and particle swarm optimization. As the existing BP network is trapped with a local optimum, this work proposes an optimized BP network algorithm using the bat algorithm. The improved SVM and improved BP are then combined to form a joint neural network for evaluating the quality of physical education in colleges. Comprehensive and systematic experiments validate the correctness and effectiveness of the algorithm proposed in this paper.
Interpolating Happiness: Understanding the Intensity Gradations of Face Emojis Across Cultures

We frequently utilize face emojis to express emotions in digital communication. But how wholly and precisely do such pictographs sample the emotional spectrum, and are there gaps to be closed? Our research establishes emoji intensity scales for seven basic emotions: happiness, anger, disgust, sadness, shock, annoyance, and love. In our survey (N = 1195), participants worldwide assigned emotions and intensities to 68 face emojis. According to our results, certain feelings, such as happiness or shock, are visualized by manifold emojis covering a broad spectrum of intensities. Other feelings, such as anger, have limited and only very intense representative visualizations. We further emphasize that the cultural background influences emojis’ perception: for instance, linear-active cultures (e.g., UK, Germany) rate the intensity of such visualizations higher than multi-active (e.g., Brazil, Russia) or reactive cultures (e.g., Indonesia, Singapore). To summarize, our manuscript promotes future research on more expressive, culture-aware emoji design.
Optimal location of logistics distribution centres with swarm intelligent clustering algorithms

A clustering algorithm is a solution for grouping a set of objects and for distribution centre location problems. But the common K-means clustering algorithm may give local optimal solutions. Swarm intelligent algorithms simulate the social behaviours of animals and avoid local optimal solutions. We employ three swarm intelligent algorithms to avoid these solutions. We propose a new algorithm for the clustering problem, the fruit-fly optimization K-means algorithm (FOA K-means). We designed a distribution centre location problem and three clustering indicators to evaluate the performance of algorithms. We compare the algorithms of K-means with the ant colony optimization algorithm (ACO K-means), particle swarm optimization algorithm (PSO K-means), and fruit-fly optimization algorithm. We find K-Means modified by the fruit-fly optimization algorithm (FOA K-means) has the best performance on convergence speed and three clustering indicators, compactness, separation, and integration. Thus, we can apply FOA K-means to improve the distribution centre location solution and the efficiency for distribution in the future.
PCformer: an MVI recognition method via classification of the MVI boundary according to histopathological images of liver cancer

Liver cancer is one of the most common cancers leading to death in the world. Microvascular invasion (MVI) is a principal reason for the poor long-term survival rate after liver cancer surgery. Early detection and treatment are very important for improving the survival rate. Manual examination of MVI based on histopathological images is very inefficient and time consuming. MVI automatic diagnosis based on deep learning methods can effectively deal with this problem, reduce examination time, and improve detection efficiency. In recent years, deep learning-based methods have been widely used in histopathological image analysis because of their impressive performance. However, it is very challenging to identify MVI directly using deep learning methods, especially under the interference of hepatocellular carcinoma (HCC) because there is no obvious difference in the histopathological level between HCC and MVI. To cope with this problem, we adopt a method of classifying the MVI boundary to avoid interference from HCC. Nonetheless, due to the specificity of the histopathological tissue structure with the MVI boundary, the effect of transfer learning using the existing models is not obvious. Therefore, in this paper, according to the features of the MVI boundary histopathological tissue structure, we propose a new classification model, i.e., the PCformer, which combines the convolutional neural network (CNN) method with a visual transformer and improves the recognition performance of the MVI boundary histopathological image. Experimental results show that our method has better performance than other models based on a CNN or a transformer.
Effective qubit dephasing induced by spectator-qubit relaxation

Abstract In many leading architectures for quantum computing, it remains to be understood if we can equate single-qubit coherence times measured in isolation, to that of coherence times measured in multi-qubit devices. On a multi-qubit superconducting circuit platform we show an increase in the dephasing rate of a control qubit due to the spontaneous relaxation of spectator qubits coupled to the control qubit. We attribute this increased dephasing to random in time Z -phase kicks on the control qubit due to the interplay between spectator relaxation and the control-spectator ZZ -interaction. We measure the magnitude of this extra dephasing using Ramsey decay experiments, show how it can be corrected via dynamical decoupling pulse sequences, and demonstrate that randomized benchmarking is insensitive to the effect. Our experimental results are supported by a robust theoretical model that captures an arbitrary number of spectator qubits, and gives a simple, intuitive picture for the mechanism behind the enhanced dephasing.
Lightweight AAC Audio Steganalysis Model Based on ResNeXt

Traditional AAC (Advanced Audio Coding) audio steganalysis methods rely on manual feature extraction, which results in low detection accuracy and low efficiency. Nowadays, the new steganalysis model based on neural network is very attractive, but its scale is large and its detection accuracy needs further improvement. Aiming at the above problems, this paper proposes a lightweight AAC audio general steganalysis model based on ResNeXt network. Firstly, the residual signal of QMDCT (Quantized Modified Discrete Cosine Transform) coefficients is calculated through a fixed convolution layer composed of multiple sets of high-pass filters. Then, based on the original structure of ResNeXt network, two ResNeXt blocks are designed to form a residual learning module, by which the steganalysis features in the QMDCT coefficients are further extracted. Finally, the classification module consisting of the fully connected layer and the Softmax layer is designed to obtain the classification result. The experimental results show that the model detection accuracy can reach more than 94% under all relative embedding rates when it operates on both the steganography algorithm based on the small value area of the QMDCT coefficient and the steganography algorithm based on the Huffman code sign bit. For the algorithm based on Huffman codeword mapping, even with the relative embedding rate of 0.1, the detection accuracy of the model can reach 85.5%, which is obviously better than the existing steganalysis schemes. Compared with other steganalysis schemes based on neural network, the model in this paper has fewer parameters, and reduces the scale by more than 40%, which is more lightweight and more efficient.
Evaluating the Cranfield Paradigm for Conversational Search Systems

Due to the sequential and interactive nature of conversations, the application of traditional Information Retrieval (IR) methods like the Cranfield paradigm require stronger assumptions. When building a test collection for Ad Hoc search, it is fair to assume that the relevance judgments provided by an annotator correlate well with the relevance judgments perceived by an actual user of the search engine. However, when building a test collection for conversational search, we do not know if it is fair to assume the same. In this paper, we perform a crowdsourcing study to evaluate the applicability of the Cranfield paradigm to conversational search systems. Our main aim is to understand what is the agreement in terms of user satisfaction between the users performing a search task in a conversational search system (i.e., directly assessing the system) and the users observing the search task being performed (i.e., indirectly assessing the system). The results of this study are paramount because they underpin and guide 1) the development of more realistic user models and simulators, and 2) the design of more reliable and robust evaluation measures for conversational search systems. Our results show that there is a fair agreement between direct and indirect assessments in terms of user satisfaction and that these two kinds of assessments share similar conversational patterns. Indeed, by collecting relevance assessments for each system utterance, we tested several conversational patterns that show a promising ability to predict user satisfaction.
U-BERT for Fast and Scalable Text-Image Retrieval

Exploiting cross-modal attention on image region features and text features, cross-modal BERT models have achieved higher accuracy than the embedding-based methods in cross-modal text-image retrieval. Nevertheless, cross-modal BERT models take image-text pairs as input, requiring a quadratic computational complexity. Thus, cross-modal BERT models are prohibitively slow and not scalable. A remedy is a two-stage strategy, wherein the first stage uses an embedding-based method to retrieve top K items and the second stage deploys the heavy cross-modal BERT to re-rank these K items. Nevertheless, to achieve a satisfying accuracy, K should be large, making the retrieval in the second phase still slow. In this paper, we propose a U-BERT model to achieve an effective and efficient cross-modal retrieval. The proposed U-BERT decomposes each image/text feature into an intra-modal component and an inter-modal component. In the first stage, U-BERT only uses the intra-modal component of the image/text features to obtain the text-image similarity scores based on two independent encoders, with a linear computation complexity. In the second stage, U-BERT reuses the intra-modal component and additionally use the inter-modal component as the complementary residual to enhance the intra-modal component's discriminating capability. Benefited from reusing the intra-modal component, we only need few Transformer layers to generate the inter-modal component, making the second phase efficient even facing a large K. Extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of the proposed U-BERT.
DeepTLF: robust deep neural networks for heterogeneous tabular data

Abstract Although deep neural networks (DNNs) constitute the state of the art in many tasks based on visual, audio, or text data, their performance on heterogeneous, tabular data is typically inferior to that of decision tree ensembles. To bridge the gap between the difficulty of DNNs to handle tabular data and leverage the flexibility of deep learning under input heterogeneity, we propose DeepTLF , a framework for deep tabular learning. The core idea of our method is to transform the heterogeneous input data into homogeneous data to boost the performance of DNNs considerably. For the transformation step, we develop a novel knowledge distillations approach, TreeDrivenEncoder , which exploits the structure of decision trees trained on the available heterogeneous data to map the original input vectors onto homogeneous vectors that a DNN can use to improve the predictive performance. Within the proposed framework, we also address the issue of the multimodal learning, since it is challenging to apply decision tree ensemble methods when other data modalities are present. Through extensive and challenging experiments on various real-world datasets, we demonstrate that the DeepTLF pipeline leads to higher predictive performance. On average, our framework shows 19.6% performance improvement in comparison to DNNs. The DeepTLF code is publicly available .
Fault tolerant cooperative localization using diagnosis based on Jensen Shannon divergence

Multi-robot systems have taken an important place in various applications, where at all times, the integrity of the location of robots must be ensured. This can be achieved by integrating a step of detecting and excluding sensor faults. In this article, a multi-sensor multi-vehicle Cooperative Positioning System (CPS) method tolerant to sensor faults is presented. The estimator used in this work is the informational form of the Kalman filter (KF), namely the Informational Filter (IF). To detect and isolate faults, the residuals generated are based on the divergence of Jensen Shannon <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(D_{JS})$</tex> between the probability distributions predicted and corrected by the IF. These residuals, as they are a sum of two divergences of Kullback-Leibler include two tests: one compares the means and the other compares the covariance matrices. For the optimization of the threshold of the residuals, the operating characteristic of the receiver (frequently referred to as the ROC curve) is used. The approach is tested on real data acquired by three Turtlebot3 equipped with wheel encoders (for odometry), a gyroscope (for the yaw angle), a Marvelmind localization system (for the position), and an Optitrack system (for ground truth).
Projected Gradient Descent Adversarial Attack and Its Defense on a Fault Diagnosis System

Knowledge-based fault diagnosis methods have become more preferred as they do not need precise model and signal patterns required in model-based and signal-based diagnosis methods, respectively. Machine learning (ML) techniques provide notable results on fault diagnosis by mapping information from raw signals to health condition. However, their vulnerabilities against malicious attacks arises as in other industrial application employing ML methods. In this paper, first, a common white-box adversarial attack called projected gradient descent (PGD) adversarial attack is injected into a deep residual learning (DRL) network model, which decides health condition of a rolling bearing. Then, robustness of the DRL model is analyzed to examine the effect of the implemented adversarial machine learning (AML). After that, adversarial training technique is used to improve the robustness of the DRL model. The experimental results show that it is possible to implement AML with existing methods to force model to misclassification. Even for a quite perturbation, the average classification accuracy of the DRL model is decreased from 99.98% to 61.25%. The results also indicate that the adversarial training technique increases the robustness of the model.
Multi-Robot Motion Planning via Parabolic Relaxation

Multi-robot systems offer enhanced capability over their monolithic counterparts, but they come at a cost of increased complexity in coordination. To reduce complexity and to make the problem tractable, multi-robot motion planning (MRMP) methods in the literature adopt de-coupled approaches that sacrifice either optimality or dynamic feasibility. In this letter, we present a convexification method, namely “parabolic relaxation,” to generate dynamically feasible trajectories for MRMP in the coupled joint-space of all robots. Furthermore, we prove that the resulting trajectories satisfy the Karush-Kuhn-Tucker optimality conditions. We leverage upon the proposed relaxation to tackle the problem complexity and to attain computational tractability for planning over one hundred robots in extremely clustered environments. We take a multi-stage optimization approach that consists of i) mathematically formulating MRMP as a non-convex optimization, ii) lifting the problem into a higher dimensional space, iii) convexifying the problem through the proposed computationally efficient parabolic relaxation, and iv) penalizing with sequential search to ensure the recovery of feasible and near-optimal solutions to the original problem. Our numerical experiments demonstrate that the proposed approach is capable of tackling challenging motion planning problems with higher success rate than the state-of-the-art, yet remain computationally tractable for over one hundred robots in a highly dense environment.
Register variation across text lengths

Abstract This paper explores variation in lexico-grammatical register features across text lengths in a large-scale sample of Reddit comments. Very short texts are known to be problematic for many statistical methods, so understanding their nature is important for the corpus-linguistic study of social media, where most contributions are short. I show that the frequencies of linguistic features change with comment length, even between longer comments, although longer texts are often considered similar in statistical terms. Moreover, I classify the variation found between short comments of different lengths into two main patterns, although other patterns can also be found, and there is variation even within these patterns. Furthermore, I interpret the observed differences in terms of register variation. For example, shorter comments appear to be more casual and less edited in terms of their feature makeup, whereas narrative and informational registers seem to favor longer comments.
Non-invasive anaemia detection by examining palm pallor: A smartphone-based approach

Anaemia, caused due to lack of blood haemoglobin level, is one of the most common diseases that affects billions of people across the world. According to WHO statistics, India is one of the developing countries with highest prevalence of anaemia. Conventional invasive methods are cost-prohibitive and difficult to administer globally which essentially demands non-invasive, inexpensive and reliable solution. This work targets to combine state-of-the computational approaches with the idea of age-old practice of rough estimation of blood haemoglobin level by observing pallor in palm to develop a non-invasive anaemia detection system. The proposed system works with the basis of inducing changes in palm pallor with suitable pressure application and release, measuring the rate of change of colour and performing time-domain analysis thereof to correlate with blood haemoglobin concentration. The video of colour changes in palm induced through a customised device, is captured using smartphone camera and is processed and analysed through a set of image processing and analysis techniques. Estimated parameters are provided as input to the ensemble-based prediction models comprising of regression and multi-layer perceptron. The proposed system ensures a sensitivity of 93% with MSE and RMSE of 0.701 and 0.698, respectively, determined on palm pallor video samples of 131 participants.
Variability in data visualization

Data visualization aims to effectively communicate quantitative information by understanding which techniques and displays work better for different circumstances and why. There are a variety of software solutions capable of generating a multitude of different visualizations of the same dataset. However, data visualization exposes a large space of visual configurations depending on the type of data to be visualized, the different displays (e.g., scatter plots, line graphs, pie charts), the visual components to encode the data (e.g., lines, dots, bars), or the specific visual attributes of those components (e.g., color, shape, size, length). Researchers and developers are not usually aware about best practices in data visualization, and they are required to learn about both the design practices that make communication effective and the low level details of the specific software tool used to generate the visualization. This paper proposes a software product line approach to model and materialize the variability of the visualization design process, guided by feature models. We encode the visualization knowledge regarding the best design practices, resolve the variability following a step-wise configuration approach, and then evaluate our proposal for a specific software visualization tool. Our solution helps researchers and developers communicate their quantitative results effectively by assisting them in the selection and generation of the visualizations that work best for each case. We open a new window of research where data visualization and variability meet each other.