Reviewerly/exHarmony huggingface dataset:
Prior wavelet knowledge for multi-modal medical image segmentation using a lightweight neural network with attention guided features

Medical image segmentation plays a crucial role in diagnosing and staging diseases. It facilitates image analysis and quantification in multiple applications, but building the right appropriate solutions is essential and highly reliant on the features of different datasets and computational resources. Most existing approaches provide segmentation for a specific anatomical region of interest and are limited to multiple imaging modalities in a clinical setting due to their generalizability with high computational requirements. To mitigate these issues, we propose a robust and lightweight deep learning real-time segmentation network for multi-modality medical images called MISegNet. We incorporate discrete wavelet transform (DWT) of the input to extract salient features in the frequency domain. This mechanism allows the neurons' receptive field to enlarge within the network. We propose a self-attention-based global context-aware (SGCA) module with varying dilation rates to enlarge the field of view and designate the importance of each scale that enhances the network's ability to discriminate features. We build a residual shuffle attention (RSA) mechanism to improve the feature representation of the proposed model and formulate a new boundary-aware loss function called Farid End Point Error (FEPE) that correctly segments regions with ambiguous boundaries for edge detection. We confirm the versatility of the proposed model by performing experiments against eleven state-of-the-art segmentation methods on four datasets of different organs, including two publicly available datasets (i.e., ISBI2017, and COVID-19 CT) and two private datasets (i.e., ovary and liver ultrasound images). Experimental results prove that the MISegNet with 1.5M parameters, outperforms the state-of-the-art methods by 1.5%–7% (i.e., dice coefficient score) with a corresponding 23× decrease in the number of parameters and multiply-accumulate operations respectively compared to U-Net.
AMESoS

Serverless computing, and in particular Function as a Service (FaaS), has become an increasingly popular cloud programming model in recent years. The serverless computing model offers an intuitive, event-based interface for developing cloud-based applications, that makes the writing and deployment of scalable microservices easier and cost-effective. Existing orchestrators in serverless systems are mainly designed for short-lived functions. Nevertheless, an increasing number of applications are deployed as pipelines that comprise a sequence of functions that execute in a specific order or pattern and must meet a wide range of throughput and latency targets to be practical. In this paper, we present AMESoS, our scalable and elastic framework for latency-sensitive streaming pipelines. AMESoS (i) enables developers to build predictable pipelines that meet their latency demands, (ii) employs prediction to proactively estimate the most appropriate number of active replicas needed for each function in the pipeline, and (iii) dynamically scales the number of replicas for each pipeline's functions in the presence of overloads. Our experimental results demonstrate the efficiency and benefits of our approach over state of the art systems.
Integrating User Stories in the Design of Augmented Reality Application

Agile development methodology has become popular among software developers. Some non-agile developers integrate certain agile and design thinking practices in their development. Among these practices include identifying personas, creating empathy and journey maps and writing user stories. This research adopts the Design Science Research Methodology, focusing on integrating agile and design thinking practices in generating quality user stories in the design of augmented reality (AR) applications. An integrated development process is modelled and applied in an example application, an AR application for student welfare services events. The study goes through the process of eliciting user requirements by identifying user personas, creating empathy maps and customer journeys and writing user stories as part of the discovery stage of the AR application development. The user stories are evaluated by fifteen agile practitioners based on fourteen criteria adapted from Quality User Story Framework. The user stories are scored and assessed to determine the level of quality.
Learning Ergodic Averages in Chaotic Systems

We propose a physics-informed machine learning method to predict the time average of a chaotic attractor. The method is based on the hybrid echo state network (hESN). We assume that the system is ergodic, so the time average is equal to the ergodic average. Compared to conventional echo state networks (ESN) (purely data-driven), the hESN uses additional information from an incomplete, or imperfect, physical model. We evaluate the performance of the hESN and compare it to that of an ESN. This approach is demonstrated on a chaotic time-delayed thermoacoustic system, where the inclusion of a physical model significantly improves the accuracy of the prediction, reducing the relative error from 48% to 1%. This improvement is obtained at the low extra cost of solving a small number of ordinary differential equations that contain physical information. This framework shows the potential of using machine learning techniques combined with prior physical knowledge to improve the prediction of time-averaged quantities in chaotic systems.
HVLM: Exploring Human-Like Visual Cognition and Language-Memory Network for Visual Dialog

Visual dialog, a visual-language task, enables an AI agent to engage in conversation with humans grounded in a given image. To generate appropriate answers for a series of questions in the dialog, the agent is required to understand the comprehensive visual content of an image and the fine-grained textual context of the dialog. However, previous studies typically utilized the object-level visual feature to represent a whole image, which only focuses on the local perspective of an image but ignores the importance of the global information in an image. In this paper, we proposed a novel model Human-Like Visual Cognitive and Language-Memory Network for Visual Dialog (HVLM), to simulate global and local dual-perspective cognitions in the human visual system and understand an image comprehensively. HVLM consists of two key modules, Local-to-Global Graph Convolutional Visual Cognition (LG-GCVC) and Question-guided Language Topic Memory (T-Mem). Specifically, in the LG-GCVC module, we design a question-guided dual-perspective reasoning to jointly learn visual contents from both local and global perspectives through a simple spectral graph convolution network. Furthermore, in the T-Mem module, we design an iterative learning strategy to gradually enhance fine-grained textual context details via an attention mechanism. Experimental results demonstrate the superiority of our proposed model, which obtains the comparable performance on benchmark datasets VisDial v1.0 and VisDial v0.9.
Human-computer interactive physical education teaching method based on speech recognition engine technology

With the advent of the era of artificial intelligence, speech recognition engine technology has a profound impact on social production, life, education, and other fields. Voice interaction is the most basic and practical type of human-computer interaction. To build an intelligent and automatic physical education teaching mode, this paper combines human-computer interaction based on speech recognition technology with physical education teaching. Students input through voice signals, and the system receives signals, analyzes signals, recognizes signals, and feeds back information to students in multiple forms. For the system to process the external speech signal, this paper uses the Mel cepstral coefficient algorithm to extract the speech information. By comparing the speech recognition rate and antinoise rate of Hidden Markov Model, Probabilistic Statistics Neural Network, and Hybrid Model (Hidden Markov and Rate Statistical Neural Network combination), the speech recognition engine uses the hybrid model, and its speech recognition rate is 98.3%, and the average antinoise rate can reach 85%. By comparing the human-computer interaction physical education teaching method with the traditional teaching method, the human-computer interaction method is superior to the traditional teaching method in the acquisition of physical knowledge, the acquisition of physical skills, the satisfaction of physical education courses and the ability of active learning. It effectively solves the drawbacks of traditional physical education and rationally uses human-computer interaction technology. On the basis of not violating physical education, realize the diversification of physical education, improve the quality of teaching, improve students' individual development and students' autonomous learning ability. Therefore, the combination of human-computer interaction and physical education based on recognition engine technology is the trend of today's physical education development.
Real-Time CLAHE Algorithm Implementation in SoC FPGA Device for 4K UHD Video Stream

One of the problems encountered in the field of computer vision and video data analysis is the extraction of information from low-contrast images. This problem can be addressed in several ways, including the use of histogram equalisation algorithms. In this work, a method designed for this purpose—the Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm—is implemented in hardware. An FPGA platform is used for this purpose due to the ability to run parallel computations and very low power consumption. To enable the processing of a 4K resolution (UHD, 3840 × 2160 pixels) video stream at 60 fps (frames per second) by using the CLAHE method, it is necessary to use a vector data format and process multiple pixels simultaneously. The algorithm realised in this work can be a component of a larger vision system, such as in autonomous vehicles or drones, but it can also support the analysis of underwater, thermal, or medical images both by humans and in an automated system.
Weakly Supervised Text-based Actor-Action Video Segmentation by Clip-level Multi-instance Learning

In real-world scenarios, it is common that a video contains multiple actors and their activities. Selectively localizing one specific actor and its action spatially and temporally via a language query becomes a vital and challenging task. Existing fully supervised methods require extensive elaborately annotated data and are sensitive to the class labels, which cannot satisfy real-world applications’ needs. Thus, we introduce the task of weakly supervised actor-action video segmentation from a sentence query (AAVSS) in this work, where only the video-sentence pairs are provided. To the best of our knowledge, our work is the first to perform AAVSS under weakly supervised situations. However, this task is extremely challenging not only because the task aims to learn the complex interactions between two heterogeneous modalities but also because the task needs to learn fine-grained analysis of video content without pixel-level annotations. To overcome the challenges, we propose a two-stage network. The network first follows the sentence guidance to localize the candidate region and then performs segmentation to achieve selective segmentation. Specifically, a novel tracker-based clip-level multiple instance learning paradigm is proposed in this article to learn the matches between regions and sentences, which makes our two-stage network robust to the region proposal network. Furthermore, two intrinsic characteristics of the video, temporal consistency and motion information, are utilized in companion with the weak supervision to facilitate the region-query matching. Through extensive experiments, the proposed method achieves comparable performance to state-of-the-art fully supervised approaches on two large-scale benchmarks, including A2D Sentences and J-HMDB Sentences.
Designing a Cruciform Specimen via Topology and Shape Optimisations under Equal Biaxial Tension Using Elastic Simulations

Stress uniformity within the gauge zone of a cruciform specimen significantly affects materials' in-plane biaxial mechanical properties in material testing. The stress uniformity depends on the load transmission of the cruciform specimen from the fixtures to the gauge zone. Previous studies failed to alter the nature of the load transmission of the geometric features using parametric optimisations. To improve stress uniformity in the gauge zone, we optimised the cross-arms to design a centre-reduced cruciform specimen with topology and shape optimisations. The simulations show that the optimised specimen obtains significantly less stress variation and range in the gauge zone than the optimised specimen under different observed areas, directions, and load ratios of von Mises, S11, S22, and S12. In the quantified gauge zone, a more uniform stress distribution could be generated by optimizing specimen geometry, whose value should be estimated indirectly each time through simulations. We found that topology and shape optimisations could markedly improve stress uniformity in the gauge zone, and stress concentration at the cross-arms intersection. We first optimised the cruciform specimen structure by combining topology and shape optimisations, which provided a cost-effective way to improve stress uniformity in the gauge zone and reduce stress concentration at the cross-arms intersection, helping obtain reliable data to perform large strains in the in-plane biaxial tensile test.
Iterated local search with perturbation based on variables interaction for pseudo-boolean optimization

Perturbing solutions is a key factor in iterated local search (ILS). The standard approach for perturbing a solution is to randomly change a fixed number of decision variables from the current local optimum. Finding suitable values of perturbation strength is difficult. It is desirable that consecutive local optima generated by ILS be close to each other and correlated in fitness. However, if the perturbation is too small, we can get stuck in the same local optimum. We propose a new perturbation strategy for ILS applied to pseudo-Boolean optimization problems where decision variables that interact are perturbed. These interactions are identified in a variable interaction graph (VIG), that is available in gray-box optimization. For black-box optimization, we propose a local search strategy that estimates an empirical VIG. Theoretical and experimental results show that perturbation based on the VIG is efficient in random and adjacent NK landscapes. Results also show that the proposed local search strategy was able to build empirical VIGs with more than 97% of the edges of the true VIG.
A survey on text classification: Practical perspectives on the Italian language

Text Classification methods have been improving at an unparalleled speed in the last decade thanks to the success brought about by deep learning. Historically, state-of-the-art approaches have been developed for and benchmarked against English datasets, while other languages have had to catch up and deal with inevitable linguistic challenges. This paper offers a survey with practical and linguistic connotations, showcasing the complications and challenges tied to the application of modern Text Classification algorithms to languages other than English. We engage this subject from the perspective of the Italian language, and we discuss in detail issues related to the scarcity of task-specific datasets, as well as the issues posed by the computational expensiveness of modern approaches. We substantiate this by providing an extensively researched list of available datasets in Italian, comparing it with a similarly sought list for French, which we use for comparison. In order to simulate a real-world practical scenario, we apply a number of representative methods to custom-tailored multilabel classification datasets in Italian, French, and English. We conclude by discussing results, future challenges, and research directions from a linguistically inclusive perspective.
Performance Evaluation Metrics

In the previous chapters, we introduced the concept of training a model either through a supervised or unsupervised learning approach. Evaluating a model's performance is a fundamental stage in the ML pipeline. It provides us with the opportunity to determine the optimal hyperparameters for the model and an indication of how the trained model will perform in production using unseen data. There are a variety of different evaluation techniques which can be used depending on the method used and the type of problem you are trying to solve. These evaluation metrics will be split into distinct categories, one for supervised learning and one for unsupervised learning. As with previous sections, there will be an opportunity to develop your skills further using the supplementary material.
3D Multi-Object Tracking Using Graph Neural Networks With Cross-Edge Modality Attention

Online 3D multi-object tracking (MOT) has witnessed significant research interest in recent years, largely driven by demand from the autonomous systems community. However, 3D offline MOT is relatively less explored. Labeling 3D trajectory scene data at a large scale while not relying on high-cost human experts is still an open research question. In this work, we propose Batch3DMOT which follows the tracking-by-detection paradigm and represents real-world scenes as directed, acyclic, and category-disjoint tracking graphs that are attributed using various modalities such as camera, LiDAR, and radar. We present a multi-modal graph neural network that uses a cross-edge attention mechanism mitigating modality intermittence, which translates into sparsity in the graph domain. Additionally, we present attention-weighted convolutions over frame-wise k-NN neighborhoods as suitable means to allow information exchange across disconnected graph components. We evaluate our approach using various sensor modalities and model configurations on the challenging nuScenes and KITTI datasets. Extensive experiments demonstrate that our proposed approach yields an overall improvement of 3.3% in the AMOTA score on nuScenes thereby setting the new state-of-the-art for 3D tracking and further enhancing false positive filtering.
Speckle Noise Suppression in Ultrasound Images Using Modular Neural Networks

In spite of the advancement in computerized imaging, many image modalities produce images with commotion influencing both the visual quality and upsetting quantitative image analysis.In this way, the research in the zone of image denoising is very dynamic.Among an extraordinary assortment of image restoration and denoising techniques the neural network system-based noise suppression is a basic and productive methodology.In this paper, Bilateral Filter (BF) based Modular Neural Networks (MNN) has been utilized for speckle noise suppression in the ultrasound image.Initial step the BF filter is used to filter the input image.From the output of BF, statistical features such as mean, standard deviation, median and kurtosis have been extracted and these features are used to train the MNN.Then, the filtered images from the BF are again denoised using MNN.The ultrasound dataset from the Kaggle site is used for the training and testing process.The simulation outcomes demonstrate that the BF-MNN filtering method performs better for the multiplicative noise concealment in UltraSound (US) images.From the simulation results, it has been observed that BF-MNN performs better than the existing techniques in terms of peak signal to noise ratio (34.89),Structural Similarity Index (0.89) and Edge Preservation Index (0.67).
A Parallel Approach for Sentiment Analysis on Social Networks Using Spark

The public is increasingly using social media platforms such as Twitter and Facebook to express their views on a variety of topics.As a result, social media has emerged as the most effective and largest open source for obtaining public opinion.Single node computational methods are inefficient for sentiment analysis on such large datasets.Supercomputers or parallel or distributed processing are two options for dealing with such large amounts of data.Most parallel programming frameworks, such as MPI (Message Processing Interface), are difficult to use and scale in environments where supercomputers are expensive.Using the Apache Spark Parallel Model, this proposed work presents a scalable system for sentiment analysis on Twitter.A Spark-based Naive Bayes training technique is suggested for this purpose; unlike prior research, this algorithm does not need any disk access.Millions of tweets have been classified using the trained model.Experiments with various-sized clusters reveal that the suggested strategy is extremely scalable and cost-effective for larger data sets.It is nearly 12 times quicker than the Map Reduce-based model and nearly 21 times faster than the Naive Bayes Classifier in Apache Mahout.To evaluate the framework's scalability, we gathered a large training corpus from Twitter.The accuracy of the classifier trained with this new dataset was more than 80%.
PD-FAC: Probability Density Factorized Multi-Agent Distributional Reinforcement Learning for Multi-Robot Reliable Search

This letter presents a new range of multi-robot search for a non-adversarial moving target problems, namely multi-robot reliable search (MuRRS). The term ‘reliability’ in MuRRS is defined as the expectation of a predefined utility function over the probability density function (PDF) of the target’s capture time. We argue that MuRRS subsumes the canonical multi-robot efficient search (MuRES) problem, which minimizes the target’s expected capture time, as its special case, and offers the end user with a wide range of objective selection options. Since state-of-the-art algorithms are usually targeting the MuRES problem, and cannot offer up-to-standard performance to the various MuRRS objectives, we, thereby, propose a probability density factorized multi-agent distributional reinforcement learning method, namely PD-FAC, as a unified solution to the MuRRS problem. PD-FAC decomposes the PDF of the multi-robot system’s overall value distribution into a set of individual value distributions and guarantees that any reliability objective defined as a function of the overall system’s value distribution can be linearly approximated by the same reliability metric defined over the agent’s individual value distribution. In this way, the individual global maximum (IGM) principle is satisfied for all the pre-defined reliability metrics. It means that when each reinforcement learning agent is executing the individual policy, which maximizes its own reliability metric, the system’s overall reliability performance is also maximized. We evaluate and compare the performance of PD-FAC with state of the arts in a range of canonical multi-robot search environments with satisfying results, and also deploy PD-FAC to a real multi-robot system for non-adversarial moving target search.