Reviewerly/exHarmony huggingface dataset:
Argus++: Robust Real-time Activity Detection for Unconstrained Video Streams with Overlapping Cube Proposals

Activity detection is one of the attractive computer vision tasks to exploit the video streams captured by widely installed cameras. Although achieving impressive performance, conventional activity detection algorithms are usually designed under certain constraints, such as using trimmed and/or object-centered video clips as inputs. Therefore, they failed to deal with the multi-scale multi-instance cases in real-world unconstrained video streams, which are untrimmed and have large field-of-views. Real-time requirements for streaming analysis also mark brute force expansion of them unfeasible. To overcome these issues, we propose Argus++, a robust real-time activity detection system for analyzing unconstrained video streams. The design of Argus++ introduces overlapping spatio-temporal cubes as an intermediate concept of activity proposals to ensure coverage and completeness of activity detection through over-sampling. The overall system is optimized for real-time processing on standalone consumer-level hardware. Extensive experiments on different surveillance and driving scenarios demonstrated its superior performance in a series of activity detection benchmarks, including CVPR ActivityNet ActEV 2021, NIST ActEV SDL UF/KF, TRECVID ActEV 2020/2021, and ICCV ROAD 2021.
Mobilenetv3-YOLOv4-Sonar: Object Detection Model Based on Lightweight Network for Forward-Looking Sonar Image

Real-time performance and accuracy are the standards to measure the detection algorithm performance. The existing detection methods for forward-looking sonar target image are difficult to achieve ideal balance between the two standards. Due to the influence of background interference and non-rigid deformation, it hardly obtains the high detection accuracy in fast detection. In this paper, we propose a Mobilenetv3-YOLOv4-Sonar algorithm, which is a more accurate and lightweight target detection model. The backbone of YOLOv4 adopts Mobilenetv3, which is improved by Convolutional Block Attention Module (CBMA) and modified Squeeze-and-Excitation Network (SENet). Consequently, we can eliminate the influence of high-light background interference and reduce the complexity of the model. Meanwhile, the improved SPPnet is used as the neck of YOLOv4 to reduce the failure of target detection caused by non-rigid deformation. Extensive experiments and comparisons with state-of-arts detectors illustrate that the accuracy of our model has increased by 4.6% compared with YOLOv4 on the dataset, and the model parameters are only one tenth of the traditional YOLOv4, which has practical significance for target detection in forward-looking sonar images.
A Framework for Analyzing Resource Allocation Policies for Multi-Access Edge Computing

Multi-Access Edge Computing (MEC) is a promising new paradigm enabling low-latency access to services deployed on edge servers. This helps to avert network latencies often encountered in accessing cloud services. The cornerstone of a MEC environment is a resource allocation policy used to partition and allocate computational resources such as bandwidth, memory available on the edge server to user service invocations availing such services. In this work, we propose a generic data-driven framework to model and analyze such MEC resource allocation policies. We model a MEC system as a Turn-Based Stochastic Multi-Player Game and use Probabilistic Model Checking to derive quantitative guarantees on resource allocation policies against requirements expressed in Probabilistic Alternating-Time Temporal Logic with Rewards. We present results on state-of-the-art MEC resource allocation policies to demonstrate the effectiveness of our framework.
Free resources for forced phonetic alignment in Brazilian Portuguese based on Kaldi toolkit

Abstract Phonetic analysis of speech, in general, requires the alignment of audio samples to its phonetic transcription. This could be done manually for a couple of files, but as the corpus grows large, it becomes infeasibly time-consuming. This paper describes the evolution process toward creating free resources for phonetic alignment in Brazilian Portuguese (BP) using Kaldi, a toolkit that achieves state of the art for open-source speech recognition, within a toolkit we call UFPAlign. The contributions of this work are then twofold: developing resources to perform forced alignment in BP, including the release of scripts to train acoustic models via Kaldi, as well as the resources themselves under open licenses; and bringing forth a comparison to other two phonetic aligners that provide resources for BP, namely EasyAlign and Montreal Forced Aligner (MFA), the latter being also Kaldi-based. Evaluation took place in terms of phone boundary and intersection over union metrics over a dataset of 385 hand-aligned utterances, and results show that Kaldi-based aligners perform better overall, and that UFPAlign models are more accurate than MFA’s. Furthermore, complex deep-learning-based approaches still do not improve performance compared to simpler models.
Towards improving fast adversarial training in multi-exit network

Adversarial examples are usually generated by adding adversarial perturbations on clean samples, designed to deceive the model to make wrong classifications. Adversarial robustness refers to the ability of a model to resist adversarial attacks. And currently, a mainstream method to enhance adversarial robustness is the Projected Gradient Descent (PGD). However, PGD is often criticized for being time-consuming during constructing adversarial examples. Fast adversarial training can improve the adversarial robustness in shorter time, but it only can train for a limited number of epochs, leading to sub-optimal performance. This paper demonstrates that the multi-exit network can reduce the impact of adversarial perturbations by outputting easily identified samples at early exits. Therefore, we can improve the adversarial robustness. Further, we find that the multi-exit network can prevent catastrophic overfitting existing in single-step adversarial training. Specifically, we find that, in the multi-exit network, (1) the norm of weights at a fully connected layer in a non-overfitted exit is much smaller than that in an overfitted exit; and (2) catastrophic overfitting occurs when the late exits have weight norms larger than the early exits. Based on these findings, we propose an approach to alleviating the catastrophic overfitting of the multi-exit network. Compared to PGD adversarial training, our approach can train a model with decreased time complexity and increased empirical robustness. Extensive experiments have been conducted to evaluate our approach against various adversarial attacks, and the experimental results demonstrate superior robustness accuracies on CIFAR-10, CIFAR-100 and SVHN.
Hate Speech &amp; Offensive Language Detection Using ML &amp;NLP

To restore peace and harmony in this cross-cultural Internet era, it is of utmost importance for every citizen to behave and spread brotherhood. Under the given circumstances of 5G evolution citizens have taken their role onto the internet very seriously thereby most of the netizens spend their time condemning, judging, and trolling other netizens, public figures for that matter. Because of the consequences in an unprejudiced society involving race, gender, or religion, the challenge of automatically detecting hate speech and objectionable language in social media material is critical. However, existing research in this field is mostly focused on several languages, which limits its relevance to certain groups. The use of harsh language on social media platforms, as well as the consequences that this has, has become a serious problem in modern culture. Automatic ways to recognize and deal with this sort of content are necessary due to the large volume of content produced every day. Machine Learning & Natural Language processing has cutting-edge algorithms and classifiers that have benefitted mankind in impossible ways. Hence, our effort in this project is to make use of this impeccable technology to create an efficient system that automatically detects hate speech and offensive language from the Twitter dataset.
Application of Fuzzy PID Position Control Algorithm in Motion Control System Design of Palletizing Robot

In view of the low efficiency in traditional palletizing robot problem of poor control precision, this paper introduces fuzzy PID position control algorithm, based on the actual operation situation of palletizing robot; determined as palletizing robot FPGA hardware platform, hardware platform based on this fuzzy PID position control algorithm is applied to implement palletizing robot motion control system design. The simulation model of fuzzy PID motion control was established by MATLAB software for testing to determine that the fuzzy PID position control algorithm reflects the time quickly in the motion control of palletizing robot, and the actual overshooting is small, which is more suitable for the motion control algorithm of palletizing robot. Under this condition, the modular method is adopted to complete the system application design on the FPGA hardware platform.
Semantic‐meshed and content‐guided transformer for image captioning

The transformer architecture has been the dominant framework for today's image captioning tasks because of its superior performance. However, existing methods based on transformer often lack the integrated use of multi-level semantic information and are weak in maintaining the relevance of captions to the image. In this paper, a semantic-meshed and content-guided transformer network is introduced for image captioning to solve these problems. The semantic-meshed mechanism allows the model to generate words by selecting semantic information of multiple interaction levels adaptively through attention-based reconstruction. And the content-guided module guides the words generation by using attribute features that represent the image content, which aims to keep the generated caption consistent with the main content of the image. Experiments on dataset on the MSCOCO captioning dataset are conducted to validate the authors’ model and achieve superior results compared to other state-of-the-art method approaches.
The Impact of Feature Selection on Different Machine Learning Models for Breast Cancer Classification

Breast cancer appears to be a common type of cancer suffered by women globally, with considered high death rates. The survival rate of breast cancer patients decreases considerably for patients diagnosed at an advanced stage compared to those diagnosed at an early stage. The objective of this study is to investigate breast cancer classification and diagnosis task using the data from WBCD dataset. In our methodology, first, the breast cancer data was scaled. Then, four features selection methods were used to analyze the features. Pearson's Correlation method, Forward Selection method, Mutual Information and Univariate ROC-AUC were the used feature selectors. Next, different Machine Leaning models were applied including Support Vector Machine, Logistic Regression and XGBoost. Finally, the three models were cross-validated by 5-fold method. The ML models with different classifiers were evaluated based on several performance measures including accuracy, precision, recall, and F1-score. results show that Logistic Regression (LR) model with Forward Selection appeared to be the most successful classifier. The obtained classification accuracy, precision, and F1-score were 0.982, 0.983, 0.986; respectively. However, the highest recall score was 0.992 achieved by SVM model with Correlation feature selection. The developed model could potentially help the medical experts for the early diagnosis of breast cancer to decrease potential risk.
Image features for quality analysis of thick blood smears employed in malaria diagnosis

Abstract Background The World Health Organization (WHO) provides protocols for the diagnosis of malaria. One of them is related to the staining process of blood samples to guarantee the correct parasite visualization. Ensuring the quality of the staining procedure on thick blood smears (TBS) is a difficult task, especially in rural centres, where there are factors that can affect the smear quality (e.g. types of reagents employed, place of sample preparation, among others). This work presents an analysis of an image-based approach to evaluate the coloration quality of the staining process of TBS used for malaria diagnosis. Methods According to the WHO, there are different coloration quality descriptors of smears. Among those, the background colour is one of the best indicators of how well the staining process was conducted. An image database with 420 images (corresponding to 42 TBS samples) was created for analysing and testing image-based algorithms to detect the quality of the coloration of TBS. Background segmentation techniques were explored (based on RGB and HSV colour spaces) to separate the background and foreground (leukocytes, platelets, parasites) information. Then, different features (PCA, correlation, Histograms, variance) were explored as image criteria of coloration quality on the extracted background information; and evaluated according to their capability to classify images as with Good or Bad coloration quality from TBS. Results For background segmentation, a thresholding-based approach in the SV components of the HSV colour space was selected. It provided robustness separating the background information independently of its coloration quality. On the other hand, as image criteria of coloration quality, among the 19 feature vectors explored, the best one corresponds to the 15-bins histogram of the Hue component with classification rates of &gt; 97%. Conclusions An analysis of an image-based approach to describe the coloration quality of TBS was presented. It was demonstrated that if a robust background segmentation is conducted, the histogram of the H component from the HSV colour space is the best feature vector to discriminate the coloration quality of the smears. These results are the baseline for automating the estimation of the coloration quality, which has not been studied before, but that can be crucial for automating TBS’s analysis for assisting malaria diagnosis process.
Radial basis function-based vector field algorithm for wildfire boundary tracking with UAVs

Abstract This paper tackles the problem of dynamic wildfire boundary tracking with UAVs. Wildfire boundary is treated as the zero-level set curve of an implicit function and is approximated with radial basis functions. Its propagation is modeled with the Hamilton–Jacobi equation with an arbitrary initial boundary as the input. To navigate UAVs to the wildfire boundary, an analytical velocity vector field, whose integral curves converge to the wildfire boundary, is constructed on the basis of the typical radial basis function thin-plate spline. Computer simulations with a single UAV and multiple UAVs have been conducted for the evaluation of the proposed solution, and numerical results show that the proposed algorithm can ensure the successful tracking of an arbitrarily shaped wildfire boundary.
OntoJob: Automated Ontology Learning from Labor Market Data

Due to the rapidly changing labor market and the consequently widening information gap between the labor market and education, there is a need for methods that can tackle, or at least ease, the construction of labor market ontologies. The current study set out to examine the viability of Ontology Learning (OL) methods for the (semi-)automated construction of labor market ontologies and / or taxonomies. The purpose of this paper is to propose an unsupervised framework, OntoJob, that can identify and extract from raw vacancy text instances, attributes, and relations, such as job titles, worker qualities, and the non-taxonomic “is-a” relations between those concepts, and convert those to an expressive descriptive logic. Evaluation of the extracted worker qualities from OntoJob, using a small sample of 5621 job postings representing 1048 occupations, showed an overall lexical precision of 0.36 and recall of 0.22.
Adversarial counterfactual augmentation: application in Alzheimer’s disease classification

Due to the limited availability of medical data, deep learning approaches for medical image analysis tend to generalise poorly to unseen data. Augmenting data during training with random transformations has been shown to help and became a ubiquitous technique for training neural networks. Here, we propose a novel adversarial counterfactual augmentation scheme that aims at finding the most effective synthesised images to improve downstream tasks, given a pre-trained generative model. Specifically, we construct an adversarial game where we update the input conditional factor of the generator and the downstream classifier with gradient backpropagation alternatively and iteratively. This can be viewed as finding the ‘ weakness ’ of the classifier and purposely forcing it to overcome its weakness via the generative model. To demonstrate the effectiveness of the proposed approach, we validate the method with the classification of Alzheimer’s Disease (AD) as a downstream task. The pre-trained generative model synthesises brain images using age as conditional factor. Extensive experiments and ablation studies have been performed to show that the proposed approach improves classification performance and has potential to alleviate spurious correlations and catastrophic forgetting. Code: https://github.com/xiat0616/adversarial_counterfactual_augmentation
Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents

We present the HIPE-2022 shared task on named entity processing in multilingual historical documents. Following the success of the first CLEF-HIPE-2020 evaluation lab, this edition confronts systems with the challenges of dealing with more languages, learning domain-specific entities, and adapting to diverse annotation tag sets. HIPE-2022 is part of the ongoing efforts of the natural language processing and digital humanities communities to adapt and develop appropriate technologies to efficiently retrieve and explore information from historical texts. On such material, however, named entity processing techniques face the challenges of domain heterogeneity, input noisiness, dynamics of language, and lack of resources. In this context, the main objective of the evaluation lab is to gain new insights into the transferability of named entity processing approaches across languages, time periods, document types, and annotation tag sets.
Automated Segmentation of Olivine Phenocrysts in a Volcanic Rock Thin Section Using a Fully Convolutional Neural Network

An example of automated characterization and interpretation of the textural and compositional characteristics of solids phases in thin sections using machine learning (ML) is presented. In our study, we focus on the characterization of olivine in volcanic rocks, which is a phase that is often chemically zoned with variable Mg/(Mg + Fe) ratios, so-called magnesian number or mg# . As the olivine crystals represent only less than 10 vol% of the volcanic rock, a pre-processing step is necessary to automatically detect the phases of interest in the images on a pixel level, which is achieved using Deep Learning. A major contribution of the presented approach is to use backscattered electron (BSE) images to: 1) automatically segment all olivine crystals present in the thin section; 2) determine quantitatively their mg# ; and 3) identify different populations depending on zoning type (e.g., normal vs reversal zoning) and textural characteristics (e.g., microlites vs phenocrysts). The segmentation of the olivine crystals is implemented with a pretrained fully convolutional neural network model with DeepLabV3 architecture. The model is trained to identify olivine crystals in backscattered electron images using automatically generated training data. The training data are generated automatically from images which can easily be created from X-Ray element maps. Once the olivines are identified in the BSE images, the relationship between BSE intensity value and mg# is determined using a simple regression based on a set of microprobe measurements. This learned functional relationship can then be applied to all olivine pixels of the thin section. If the highest possible map resolution (1 micron per 1 pixel) is selected for the data acquisition, the full processing time of an entire thin section of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mo>∼</mml:mo><mml:mn>3</mml:mn><mml:mo>×</mml:mo><mml:mn>4</mml:mn><mml:mi>c</mml:mi><mml:mi>m</mml:mi></mml:math> containing more than 1,500 phenocrysts and 20.000 microliths required 140 h of data acquisition (BSE + X-Ray element maps), 8 h of training and 16 h of segmentation and classification. Our further tests demonstrated that the 140 h of data acquisition can be reduced at least by a factor of 4 since only a part of the thin section area (25% or even less) needs to be used for training. The characterization of each additional thin section would only require the BSE data acquisition time (less than 48 h for a whole thin section), without an additional training step. The paper describes the training and processing in detail, shows analytical results and outlines the potential of this Deep Learning approach for petrological applications, resulting in the automatic characterization and interpretation of mineral textures and compositions with an unprecedented high resolution.
Software Development Effort Estimation Techniques Using Long Short Term Memory

The Software Effort Estimation (SEE) process is used to predict the effort involved in developing a software project inaccuracy. This process permeates the development stages of the software project. Although there are many models for estimating the effort, we will do our best to follow the best model for estimating the effort. The main objective of this research is to apply the Long Short-Term Memory (LSTM) algorithm and discover its accuracy in estimating the software effort. Then, compare the results with other models taken from previous work and find the best one among the models. The LSTM algorithm has been used with two data sets: China, which contains 499 projects, and Kitchenham, 145 projects. The metrics adopted to measure the accuracy of the models are Root Mean Squared Error (RMSE), respectively, Mean Absolute Error (MAE), and R_Squared. The results of RMSE, MAE, and R_Squared are 0.016, 0.019, and 0.972, respectively, in the china dataset. At the same time, the RMSE, MAE, and R_Squared results are also 0.017, 0.058, and 0.896, respectively, in the Kitchenham dataset. Therefore, the experiments&#x0027; results showed the LSTM algorithm&#x0027;s superiority over the other algorithms in both data sets.
A survey on machine learning-based facial recognition algorithm

A Method of perceiving a human face through innovation is cleared by face recognition. Face identification and recognition have been utilized in the security frameworks, access control which has acquired sufficient ubiquity over the most recent couple of years. Biometrics is utilized to plan facial highlights from a photo or video in a facial recognition framework. A database of known faces is used to compare the information to find a match. Facial recognition can verify personal identity, but it also raises privacy issues. This paper compares the different facial recognition algorithms and reviews them. We aim to come up with an approach that best suits a database and gives high accuracy. This paper intends to focus on different face recognition methods and gives a brief view of the application. It also gives a comparative study of various parameters for the measurement of the performance of an algorithm for a particular database system, Yale Database, and ORL Database. The comparative study on different approaches depicts each algorithm’s performance and its limitations.