Reviewerly/exHarmony huggingface dataset:
GAZEATTENTIONNET: Gaze Estimation with Attentions

Predicting gaze point on mobile devices without calibration in unconstrained environments has great significance on human computer interaction. Appearance-based gaze estimation methods have been improved due to the recent advance in convolutional neural network (CNN) models and the availability of large-scale datasets. CNN models have limitations on extracting the global information of features and ignore the important information of local features. In this paper, we propose a novel structure named GazeAttentionNet. To improve the accuracy of gaze estimation, we use the global and local attention modules to utilize both global and local features. Firstly, we use MobileNetV2 and the self-attention layers as the global attention module to extract global features. Secondly, we add the local attention module containing the spatial attention to extract local features. With GazeAttentionNet, we achieve an excellent result on the GazeCapture dataset. The average errors of mobile phones and tablets are 1.67 cm and 2.37 cm.
An Approach to Control the PC with Hand Gesture Recognition using Computer Vision Technique

Human-Computer Interaction (HCI) is the interface between humans and computers. Traditionally, mouse and keyboards are used to interact with computers. An approach recently introduced to interact with computers is hand gestures. In this research paper, an approach to recognize hand gestures is introduced, and a virtual mouse and keyboard with hand gesture recognition using Computer Vision techniques are implemented. Full keyboard features and mouse cursor movement and click events are implemented to control the computer virtually. The recognition rate and response rate of all the considered inputs are calculated and presented in the results. The accuracy of the presented approach is compared with the other state-of-the-art algorithms that show the method presented here performs better with the accuracy of 95&#x0025;.
An Efficient Framework for Detection and Recognition of Numerical Traffic Signs

Due to the variety of categories and uneven distribution of available samples, automatic traffic sign detection and recognition is still a challenging task. For those categories with less training data, existing deep learning methods cannot achieve desirable performance, and the overall detection effect is not satisfactory as well. In this letter, we fully explore the relationship between different traffic signs with digital characters and transform the category objects into multi-level classes to alleviate the uneven distribution of samples. We design a lightweight two-stage object detection framework with high real-time performance. The first stage network is proposed to obtain the category groups of traffic signs, and then we construct another object detection network to identify the digital characters of the detected traffic signs. To make the prediction in the first stage more accurate, we put forward a boxes fusion algorithm in the post-processing process and a refine module to improve the recognition performance. Experimental results show that our approach possesses significantly improved performance compared with the latest object detection networks and other traffic sign detectors. Even some traffic signs that only exist in testset can also be recognized accurately by our method.
Phoneme Mispronunciation Detection By Jointly Learning To Align

Phoneme mispronunciation detection plays an important role in Computer-Assisted Pronunciation Training. Traditional methods either rely on phone recognition, which has the limitation of incapability of detecting mispronounced phonemes out of the dictionary, or the need of external phoneme alignment for extracting acoustic features. In this paper, we propose a method for phoneme mispronunciation detection by jointly learning to align. Specifically, we first obtain acoustic and canonical phoneme representations utilizing acoustic and phoneme encoders. Second, we utilize an attention mechanism to fuse acoustic features of each frame and phoneme representations. Finally, a convolutional neural network (CNN)-based layer following the fused representations is utilized for better exploring local context. The network is jointly optimized for phoneme mispronunciation and phoneme alignment based on a multi-task learning framework. Experimental results based on a public dataset L2-ARCTIC show the state-of-the-art (SOTA) performance with an F1-score of 63.04%. It is also found that optimizing the phoneme alignment can further improve the performance of phoneme mispronunciation detection.
A Survey on Face and Body Based Human Recognition Robust to Image Blurring and Low Illumination

Many studies have been actively conducted on human recognition in indoor and outdoor environments. This is because human recognition methods in such environments are closely related to everyday life situations. Besides, these methods can be applied for finding missing children and identifying criminals. Methods for human recognition in indoor and outdoor environments can be classified into three categories: face-, body-, and gait-based methods. There are various factors that hinder indoor and outdoor human recognition, for example, blurring of captured images, cutoff in images due to the camera angle, and poor recognition in images acquired in low-illumination environments. Previous studies conducted to solve these problems focused on facial recognition only. This is because the face is typically assumed to contain more important information for human recognition than the body. However, when a human face captured by a distant camera is small, or even impossible to identify with the naked eye, the body’s information can help with recognition. For this reason, this survey paper reviews both face- and body-based human recognition methods. In previous surveys, recognition on low-resolution images were reviewed. However, survey papers on blurred images are not comprehensive. Therefore, in this paper, we review studies on blurred image restoration in detail by classifying them based on whether deep learning was used and whether the human face and body were combined. Although previous survey papers on recognition covered low-illumination environments as well, they excluded deep learning methods. Therefore, in this survey, we also include details on deep-learning-based low-illumination image recognition methods. We aim to help researchers who will study related fields in the future.
Phonotactic Language Recognition Using A Universal Phoneme Recognizer and A Transformer Architecture

In this paper, we describe a phonotactic language recognition model that effectively manages long and short n-gram input sequences to learn contextual phonotactic-based vector embeddings. Our approach uses a transformer-based encoder that integrates a sliding window attention to attempt finding discriminative short and long cooccurrences of language dependent n-gram phonetic units. We then evaluate and compare the use of different phoneme recognizers (Brno and Allosaurus) and sub-unit tokenizers to help select the more discriminative n-grams. The proposed architecture is evaluated using the Kalaka-3 database that contains clean and noisy audio recordings for very similar languages (i.e. Iberian languages, e.g., Spanish, Galician, Catalan). We provide results using the Cavg and accuracy metrics used in NIST evaluations. The experimental results show that our proposed approach outperforms by 21% of relative improvement to the best system presented in the Albayzin LR competition.
Disseminating Science and Education through Social Media: The Experience of a Students’ Editorial Team at the University of Padova

"WeSocial: Online Learning Community" is a project aiming to provide students with the basic skills in science communication via social media as a useful tool in their future careers and to disseminate the University Department of Comparative Biomedicine and Food Science activities to the general public. The project is based on two main actions: professional training on science communication and social media strategies, and the establishment of an editorial team composed of students supervised by the teaching staff. When the training phase was concluded, official department accounts on Instagram (bca_campus_unipd) and Facebook (BCA_campus_unipd) were opened. Currently, the students' editorial team (SET) oversees publishing a maximum of 3 posts per week, whose content deals with the academic, research, and educational areas of the department seen through the students' eyes. The social media accounts are constantly growing and becoming a "place" for the virtual community of the department. Since students are both "information producers" and the "audience" of the project, they propose and focus on issues particularly important to them. As a result, the department's social media has become a meaningful and relevant experience for students, enhancing their sense of belonging to the departmental and university community life. Moreover, the project is fostering the interaction between students and teaching staff and, thanks to peer communication, is increasing the awareness of department activities especially in the student audience.
MOOCs Recommender System with Siamese Neural Network

Massive open online courses (MOOCs) are becoming a popular method of education, as they offer students a large-scale learning opportunity. However, the variety of MOOC courses and their frequent changes make it more difficult for students to identify relevant new information. To pique students&#x0027; attention, a recommendation system (RS) is used to match the learner with the best learning resources. Most research on recommender system relies mainly on the presence of explicit feedback, while this information is commonly scarce or unavailable in MOOCs. Therefore, in this paper we use implicit feedback which is gathered passively by tracking different sorts of students&#x0027; behavior to model user positive and negative preferences. We propose using Siamese Neural Networks (SNNs) to extract latent representations of students and courses based on a loss function that gives observed courses a higher preference than unobserved courses. Then, users and courses similarity are determined based on new representations. Furthermore, the other challenge is recommending courses to students with little available interaction data (cold start). To solve this problem, we employ user and course content information, which aids in the creation of more accurate representations as well. We analyze the proposed model on a real dataset obtained from XuetangX-one of China&#x0027;s largest MOOCs-. Experiment results show that the proposed algorithm outperforms numerous baseline algorithms.
New Diagnostic Forensic Protocol for Damaged Secure Digital Memory Cards

Over the past twenty years, Secure Digital memory cards have become the most popular digital storage media. Therefore, forensic experts need to develop forensic techniques to enable recovery of data, especially from cards severely damaged by accidents, air crashes, terrorist attacks or deliberate attempts by criminals to destroy evidence. The paper discusses the non-invasive and invasive diagnostics available to forensic experts, with descriptions of the necessary equipment, including binocular microscopes, X-Ray equipment, scanning acoustic microscopes, chemical benches, and, for the first time, infrared cameras. All of these techniques can be used to methodically determine the best treatment in order to repair damaged storage media and extract data from them. The main contributions of the paper include the development of an innovative systematic forensic protocol for diagnostics on damaged cards, which involves a decision-making diagram. Finally, a concrete case study is presented using the new forensic decision diagram-based protocol and the panel of techniques available to diagnose a card damage.
Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures

Recent publications on automatic-speech-recognition (ASR) have a strong focus on attention encoder-decoder (AED) architectures which tend to suffer from over-fitting in low resource scenarios. One solution to tackle this issue is to generate synthetic data with a trained text-to-speech system (TTS) if additional text is available. This was successfully applied in many publications with AED systems, but only very limited in the context of other ASR architectures. We investigate the effect of varying pre-processing, the speaker embedding and input encoding of the TTS system w.r.t. the effectiveness of the synthesized data for AED-ASR training. Additionally, we also consider internal language model subtraction for the first time, resulting in up to 38% relative improvement. We compare the AED results to a state-of-the-art hybrid ASR system, a monophone based system using connectionist-temporal-classification (CTC) and a monotonic transducer based system. We show that for the later systems the addition of synthetic data has no relevant effect, but they still outperform the AED systems on LibriSpeech-100h. We achieve a final word-error-rate of 3.3%/10.0% with a hybrid system on the clean/noisy test-sets, surpassing any previous state-of-the-art systems on Librispeech-100h that do not include unlabeled audio data.
How to build data-driven Strategy Maps? A methodological framework proposition

The Strategy Map is a strategic tool that enables companies to formulate, control and communicate their strategy and positively influence their performance. Introduced in 2000, the methodology for developing Strategy Maps has evolved over the past two decades, but still relies exclusively on human input. In practice, Strategy Map causalities – the core elements of this tool – are determined by managers’ opinions and judgments, which can lead to a lack of accuracy, completeness and longitudinal perspective. Although authors in the literature have pointed out these problems in the past, there are few recommendations on how to address them. In this paper, we propose a methodological framework which uses operational data and data mining techniques to systematize the detection of causalities in Strategy Maps. We apply time series techniques and Granger causality tests to increase the efficiency of such strategic tool. We demonstrate the feasibility and relevance of this methodology using data from skeyes, the Belgian air traffic control company. 1
Simulation Paths for Quantum Circuit Simulation With Decision Diagrams What to Learn From Tensor Networks, and What Not

Simulating quantum circuits on classical computers is a notoriously hard, yet increasingly important task for the development and testing of quantum algorithms. In order to alleviate this inherent complexity, efficient data structures and methods such as tensor networks and decision diagrams have been proposed. However, their efficiency heavily depends on the order in which the individual computations are performed. For tensor networks the order is defined by so-called contraction plans and a plethora of methods has been developed to determine suitable plans. On the other hand, simulation based on decision diagrams is mostly conducted in a straight-forward, i.e., sequential, fashion thus far. In this work, we study the importance of the path that is chosen when simulating quantum circuits using decision diagrams and show, conceptually and experimentally, that choosing the right simulation path can make a vast difference in the efficiency of classical simulations using decision diagrams. We propose an open-source framework (available at github.com/cda-tum/ddsim) that not only allows to investigate dedicated simulation paths, but also to re-use existing findings, e.g., obtained from determining contraction plans for tensor networks. Experimental evaluations show that translating strategies from the domain of tensor networks may yield speedups of several factors compared to the state of the art. Furthermore, we design a dedicated simulation path heuristic that allows to improve the performance even further -- frequently yielding speedups of several orders of magnitude. Finally, we provide an extensive discussion on what can be learned from tensor networks and what cannot.
Deep hashing for global registration of untracked 2D laparoscopic ultrasound to CT

The registration of Laparoscopic Ultrasound (LUS) to CT can enhance the safety of laparoscopic liver surgery by providing the surgeon with awareness on the relative positioning between critical vessels and a tumour. In an effort to provide a translatable solution for this poorly constrained problem, Content-based Image Retrieval (CBIR) based on vessel information has been suggested as a method for obtaining a global coarse registration without using tracking information. However, the performance of these frameworks is limited by the use of non-generalisable handcrafted vessel features.We propose the use of a Deep Hashing (DH) network to directly convert vessel images from both LUS and CT into fixed size hash codes. During training, these codes are learnt from a patient-specific CT scan by supplying the network with triplets of vessel images which include both a registered and a mis-registered pair. Once hash codes have been learnt, they can be used to perform registration with CBIR methods.We test a CBIR pipeline on 11 sequences of untracked LUS distributed across 5 clinical cases. Compared to a handcrafted feature approach, our model improves the registration success rate significantly from 48% to 61%, considering a 20 mm error as the threshold for a successful coarse registration.We present the first DH framework for interventional multi-modal registration tasks. The presented approach is easily generalisable to other registration problems, does not require annotated data for training, and may promote the translation of these techniques.
Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection System

Human fall detection (FD) acts as an important part in creating sensor based alarm system, enabling physical therapists to minimize the effect of fall events and save human lives.Generally, elderly people suffer from several diseases, and fall action is a common situation which can occur at any time.In this view, this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection (IAOA-DLFD) model to identify the fall/non-fall events.The proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image quality.Besides, the IAOA with Capsule Network based feature extractor is derived to produce an optimal set of feature vectors.In addition, the IAOA uses to significantly boost the overall FD performance by optimal choice of CapsNet hyperparameters.Lastly, radial basis function (RBF) network is applied for determining the proper class labels of the test images.To showcase the enhanced performance of the IAOA-DLFD technique, a wide range of experiments are executed and the outcomes stated the enhanced detection outcome of the IAOA-DLFD approach over the recent methods with the accuracy of 0.997.
Crowd counting method via a dynamic-refined density map network

At present, most existing crowd counting methods use density maps to estimate the number of people, so the quality of density maps is particularly important to the counting results. In practical application, the density map generated by the existing methods is easily affected by the change of head proportion, which cannot truly reflect the size of head in the image and affect the counting accuracy. To effectively overcome the above problems, a crowd counting method via a dynamic-refined density map network is proposed in this paper. This method refines the existing ground-truth density maps by jointly training Counting Net and learnable Refine Net, and counts through Counting Net. Specifically, we design a Refine Net, which adaptively adjusts the relationship between the head sizes at different positions in the dot map through the U-shaped network structure and the regional attention module (RAM), so as to obtain a ground truth that is more in line with the real head size. In addition, we propose a Counting Net based on coprime dilation rate convolution groups to ensure the continuity of information. Extensive experiments on four benchmark datasets (ShanghaiTech, UCF_CC_50, UCF-QNRF and JHU-CROWD++) indicate that our method can achieve state-of-the-art counting performance and high robustness.
Search for Generalized MPS and Barker Sequences

The lowest achievable PSL for m<sup>th</sup>-root-of-unity codes are found by several search approaches, including exhaustive search and an evolutionary algorithm, for lengths 2 &#x2264; <tex>$N$</tex> &#x2264; 15 and 2 &#x2264; m &#x2264; 20. Tabulation of the findings reveals patterns that may be used to inform searches for cases that remain un-resolved. Generalized Barker sequences were found for m &#x003D; 6 for each <tex>$N$</tex> between 2 and 15, which implies that for these lengths, the same holds true when <tex>$m$</tex> is any multiple of 6. The findings suggest that generalized Barker sequences are not that rare, at least as far as cases N &#x00D7; m that harbor them.
Multistage pseudo-spectral method (method of collocations) for the approximate solution of an ordinary differential equation of the first order

The classical pseudospectral collocation method based on the expansion of the solution in a basis of Chebyshev polynomials is considered. A new approach to constructing systems of linear algebraic equations for solving ordinary differential equations with variable coefficients and with initial (and/or boundary) conditions makes possible a significant simplification of the structure of matrices, reducing it to a diagonal form. The solution of the system is reduced to multiplying the matrix of values of the Chebyshev polynomials on the selected collocation grid by the vector of values of the function describing the given derivative at the collocation points. The subsequent multiplication of the obtained vector by the two-diagonal spectral matrix, inverse with respect to the Chebyshev differentiation matrix, yields all the expansion coefficients of the sought solution except for the first one. This first coefficient is determined at the second stage based on a given initial (and/or boundary) condition. The novelty of the approach is to first select a class (set) of functions that satisfy the differential equation, using a stable and computationally simple method of interpolation (collocation) of the derivative of the future solution. Then the coefficients (except for the first one) of the expansion of the future solution are determined in terms of the calculated expansion coefficients of the derivative using the integration matrix. Finally, from this set of solutions only those that correspond to the given initial conditions are selected.