Reviewerly/exHarmony huggingface dataset:
Compact loss for visual identification of cattle in the wild

Visual cattle identification from a random view image taking in real farm environments conducts to provide an essential way for real time cattle monitoring applicable to precision livestock farming. This work utilizes multi-view images of cattle in the wild, synthetically taking advantage of the intrinsic physical characteristics of the breed to perform cattle identification with an image from any point of view. The main challenge in feature learning using Deep Convolutional Neural Networks (DCNNs) for identification tasks, such as face recognition (FR) and cattle identification, is the design of efficient loss functions that strengthen the discriminative power of the deeply learned features. Aiming at directly obtaining separable and discriminative metric for visual cattle identification under open-set protocol, the contributions of this work is as follows: firstly, based on analysis of the influence of bias in the last fully connected layer experimentally we reformulate the SoftMax as SoftMax-nB by eliminating the bias item in it to obtain more separable features on the hypersphere after normalization. Then we propose compact loss by combining SoftMax-nB and distance metric learning, including triplet loss and tight loss. The learning of DCNNs is jointly supervised to directly maximize inter-class distance and minimize intra-class variance simultaneously and thus highly enhances the discriminative power of the features. We present a series of experiments to evaluate the performance of compact loss on our MVCAID100 and OpenCows2020 datasets. It is encouraging to find that our compact loss not only outperforms the state-of-the-art models for face recognition tasks, including ArcFace and NPT Loss, on our MVCAID100 dataset, and it also surpasses SoftMax-based reciprocal triplet loss on OpenCows2020 under the same open-set protocol. MVCAID100 datasets will be available publicly right after the paper is accepted.
Intelligent Rule Extraction in Complex Event Processing Platform for Health Monitoring Systems

Taking care of people with disabilities is essential in any situation and its cost is increasing every day. Many intelligent remote health monitoring systems have been developed from the past till now. These systems face challenges such as managing large volumes of data, real-time processing, and rule setting human errors. In this research, a complex event processing (CEP)-based platform is proposed for remote health monitoring and user behavior modeling in a health system. This platform can manage massive amounts of data in real-time. In addition, it can solve human errors in rule setting by extracting rules from the previous data using a rule-based learning method. Moreover, rule-based learning is an explainable method to get feedback from the domain experts. Experimental results in the hospital dataset showed that the PART rule-based learning has better accuracy (about 98.61%) in classifying the health conditions of the patients in comparison to other rule-based methods. On the other hand, the JRip rule-based learning has produced 16 fewer rules with similar accuracy (about 97.32%) compared to the PART classification method. Therefore, the JRip classifier is a better option for generating a small set of rules for using in the CEP engine.
Fuzzy identification of nonlinear dynamic system based on selection of important input variables

Input variables selection (IVS) is proved to be pivotal in nonlinear dynamic system modeling.In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied.Firstly, a simplified two stage fuzzy curves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the structure.Secondly, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively.Then, the effectiveness of IVS is verified by two well-known issues.Finally, the proposed identification method is applied to a realistic variable load pneumatic system.The simulation experiments indi cate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno (T-S) fuzzy modeling.
Extreme depolarization for any spin

The opportunity to build quantum technologies operating with elementary quantum systems with more than two levels is now increasingly being examined, not least because of the availability of such systems in the laboratory. It is therefore essential to understand how these single systems initially in highly non-classical states decohere on different time scales due to their coupling with the environment. In this work, we consider the depolarization, both isotropic and anisotropic, of a quantum spin of arbitrary spin quantum number $j$ and focus on the study of the most superdecoherent states. We approach this problem from the perspective of the collective dynamics of a system of $N=2j$ constituent spin-$1/2$, initially in a symmetric state, undergoing collective depolarization. This allows us to use the powerful language of quantum information theory to analyze the fading of quantum properties of spin states caused by depolarization. In this framework, we establish a precise link between superdecoherence and entanglement. We present extensive numerical results on the scaling of the entanglement survival time with the Hilbert space dimension for collective depolarization. We also highlight the specific role played by anticoherent spin states and show how their Markovian isotropic depolarization alone can lead to the generation of bound entangled states.
Beyond Normal Distribution: More Factual Feature Generation Network for Generalized Zero-Shot Learning

Due to the prosperous development of generative models, research works have achieved great success on the generalized zero-shot learning (GZSL) task. In most generative methods of GZSL, researchers try to utilize attributes and normally distributed noise to generate visual features, which ignores whether the normal distribution can perfectly represent all categories. Therefore, in this article, we exploit variational auto-encoders (VAE) and visual features to generate image-level noise that can preserve class-level characteristics in more detail and propose a mechanism called more factual generative network (MFGN) to achieve more authentic generative process. In other words, it is to transfer the seen feature distribution to the unseen domains and regulate the knowledge to correct the generation of unseen samples. Extensive experiments are conducted on four popular datasets and the results demonstrate the effectiveness of the proposed work.
Ultra-high speed holographic shape and displacement measurements in the hearing sciences

The auditory system of mammals enables the perception of sound from our surrounding world. Containing some of the smallest bones in the body, the ear transduces complex acoustic signals with high-temporal sensitivity to complex mechanical vibrations with magnitudes as small as tens of picometers. Measurements of the shape and acoustically induced motions of different components of the ear are essential if we are to expand our understanding of hearing mechanisms, and also provide quantitative information for the development of numerical ear models that can be used to improve hearing protection, clinical diagnosis, and repair of damaged or diseased ears.We are developing digital holographic methods and instrumentation using an ultra-high speed camera to measure shape and acoustically-induced motions in the middle ear. Specifically we study the eardrum, the first structure of the middle ear which initializes the acoustic-mechanical transduction of sound for hearing. Our measurement system is capable of performing holographic measurement at rates up to 2.1 M frames per second. Two shape measurement modalities had previously been implemented into our holographic systems: (1) a multi-wavelength method with a wavelength tunable laser; and (2) a multi-angle illumination method with a single wavelength laser. In this paper, we present a third method using a miniaturized fringe projection system with a microelectromechanical system (MEMS) mirror. Further, we optimize the processing of large data sets of holographic displacement measurements using a vectorized Pearson's correlation algorithm. We validate and compare the shape and displacement measurements of our methodologies using a National Institute of Standards and Technology (NIST) traceable gauge and sound-activated latex membranes and human eardrums.
Automated Feature Reduction in Machine Learning

Supervised machine learning models are developed by selecting features that can be related to a dependent variable. This selection may involve a mix of automated procedures and manual exploration. Large data sets often include many features that are not suitable for the intended model. This paper proposes a framework for automated feature reduction (AFR) to identify features that can be removed from consideration for the model. It applies exclusion criteria based on the form of the data to determine whether each feature would pose practical issues for the design of the model. AFR is proposed as a preprocessing step prior to the use of automated or manual feature selection techniques. By reducing the set of possible features, this exploration can search a less complex space of potentially relevant features. AFR can be widely applied to many machine learning models and demonstrates clear benefits in improving the efficiency of feature selection algorithms. This paper provides details on the AFR algorithm along with examples of feature reduction in model development.
Design guidelines for narrative maps in sensemaking tasks

Narrative sensemaking is a fundamental process to understand sequential information. Narrative maps are a visual representation framework that can aid analysts in their narrative sensemaking process. Narrative maps allow analysts to understand the big picture of a narrative, uncover new relationships between events, and model the connection between storylines. We seek to understand how analysts create and use narrative maps in order to obtain design guidelines for an interactive visualization tool for narrative maps that can aid analysts in narrative sensemaking. We perform two experiments with a data set of news articles. The insights extracted from our studies can be used to design narrative maps, extraction algorithms, and visual analytics tools to support the narrative sensemaking process. The contributions of this paper are three-fold: (1) an analysis of how analysts construct narrative maps; (2) a user evaluation of specific narrative map features; and (3) design guidelines for narrative maps. Our findings suggest ways for designing narrative maps and extraction algorithms, as well as providing insights toward useful interactions. We discuss these insights and design guidelines and reflect on the potential challenges involved. As key highlights, we find that narrative maps should avoid redundant connections that can be inferred by using the transitive property of event connections, reducing the overall complexity of the map. Moreover, narrative maps should use multiple types of cognitive connections between events such as topical and causal connections, as this emulates the strategies that analysts use in the narrative sensemaking process.
Transparent Interaction Based Learning for Human-Robot Collaboration

The number of collaborative robots that perform different tasks in close proximity to humans is increasing. Previous studies showed that enabling non-expert users to program a cobot reduces the cost of robot maintenance and reprogramming. Since this approach is based on an interaction between the cobot and human partners, in this study, we investigate whether making this interaction more transparent can improve the interaction and lead to better performance for non-expert users. To evaluate the proposed methodology, an experiment with 67 participants is conducted. The obtained results show that providing explanation leads to higher performance, in terms of efficiency and efficacy, i.e., the number of times the task is completed without teaching a wrong instruction to the cobot is two times higher when explanations are provided. In addition, providing explanation also increases users' satisfaction and trust in working with the cobot.
A comparative analysis of Buffer Overflow vulnerabilities in High-End IoT devices

The increasing number of available interconnected Internet of Things (IoT) devices has exposed many security vulnerabilities. This paper presents an analysis of those security concerns related to Buffer Overflow (BOF) attacks. The problem is well known, but little research has been carried out in the context of IoT Operating Systems with respect to ARM processors as previous work has tended to focus on testing individual OSs. However, this makes it difficult to extract general conclusions. The work presented here leverages the RIPE tool and a Buffer Overflow testbed of 850 attacks to comprehensively analyse several IoT Operating Systems on a Raspberry Pi (RPi) platform. This testing allows for the presentation of trends and commonalities in BOF vulnerabilities among those Linux Operating Systems widely used in High-End IoT devices.
A method for the early prediction of chronic diseases based on short sequential medical data

Noncommunicable diseases (NCDs) have become the leading cause of death worldwide. NCDs' chronicity, hiddenness, and irreversibility make patients' disease self-awareness extremely important in disease control but hard to achieve. With an accumulation of electronic health record (EHR) data, it has become possible to predict NCDs early through machine learning approaches. However, EHR data from latent NCD patients are often irregularly sampled temporally, and the data sequences are short and imbalanced, which prevents researchers from fully and effectively using such data. Here, we outline the characteristics of typical short sequential data for NCD early prediction and emphasize the importance of using such data in machine learning schemes. We then propose a novel NCD early prediction method: the short sequential medical data-based early prediction method (SSEPM). The SSEPM network contains two stacked subnetworks for multilabel enhancement. In each subnetwork, long short-term memory (LSTM) and attention layers are implemented to extract both temporal and nontemporal embedded features. During training, with prior clinical knowledge of the NCD characteristics, a random connection (RC) process is proposed for data augmentation. Comparative experiments involving ten-fold cross-validation are performed with real-world medical data to predict 5 NCDs. The result shows that the SSEPM outperforms the state-of-the-art NCD early prediction algorithms and works well in dealing with short sequential data. The results also suggest that the direct use of short sequential data could be more effective than formatting datasets with temporal exclusion limitations.
Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography

Nowadays, eye fatigue is becoming more common globally. However, there was no objective and effective method for eye fatigue detection except the sample survey questionnaire. An eye fatigue detection method by machine learning based on the Single-Channel Electrooculography-based System is proposed. Subjects are required to finish the industry-standard questionnaires of eye fatigue; the results are used as data labels. Then, we collect their electrooculography signals through a single-channel device. From the electrooculography signals, the five most relevant feature values of eye fatigue are extracted. A machine learning model that uses the five feature values as its input is designed for eye fatigue detection. Experimental results show that there is an objective link between electrooculography and eye fatigue. This method could be used in daily eye fatigue detection and it is promised in the future.
Robust $H_{2}$-OFIR Filtering: Improving Tracking of Disturbed Systems Under Initial and Data Errors

In harsh environments, tracking is organized assuming disturbances, initial errors, and data errors that requires robust algorithms. In this article, we develop, in discrete-time state space, a robust <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{2}$</tex-math></inline-formula> optimal finite impulse response ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{2}$</tex-math></inline-formula> -OFIR) filter of disturbed systems under initial and measurement errors. The derivation is provided using a novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{2}$</tex-math></inline-formula> finite impulse response ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{2}$</tex-math></inline-formula> -FIR) state estimation approach by minimizing the squared Frobenius norm of the weighted transfer function. The robust <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{2}$</tex-math></inline-formula> -OFIR filter is designed for full block error matrices, and its recursive forms are shown for diagonal error matrices. Also presented is the suboptimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{2}$</tex-math></inline-formula> -FIR filtering algorithm using the linear matrix inequality. It is shown that, in global-positioning-system-based tracking of moving vehicles, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{2}$</tex-math></inline-formula> -OFIR filter outperforms the Kalman and unbiased FIR filters in terms of accuracy and robustness. Moreover, the ability to operate on short horizons makes the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{2}$</tex-math></inline-formula> -OFIR filter computationally efficient.
Image Encoder and Sentence Decoder Based Video Event Description Generating Model: A Storytelling

In the Multimedia era, the production of video data is massive due to advancements in computing and network infrastructure. To summarize the video events, the entire video needs to be watched, which may take a few hours to summarize events in the video. Manually generating a summary of the video events requires considerable human time and effort. In this work, a six-layer Convolutional Neural Network (CNN) is proposed to characterize images input to the Encoder-Decoder framework. The framework summarizes video events by taking images as input and generates a summary like a natural language rather than machine-generated text. It produces the synopsis in sentences for each sequence of images as output. The quantitative and qualitative analysis shows that the model outperforms the existing state-of-the-art model in terms of Training loss, Metric for Evaluation of Translation with Explicit ORdering (METEOR) score, and BiLingual Evaluation Understudy (BLEU) score. Applications of the proposed model include visual storytelling identifying critical events in the entire video.
Lightweight Image Super-Resolution Based on Local Interaction of Multi-Scale Features and Global Fusion

In recent years, computer vision technology has been widely applied in various fields, making super-resolution (SR), a low-level visual task, a research hotspot. Although deep convolutional neural network has made good progress in the field of single-image super-resolution (SISR), its adaptability to real-time interactive devices that require fast response is poor due to the excessive amount of network model parameters, the long inference image time, and the complex training model. To solve this problem, we propose a lightweight image reconstruction network (MSFN) for multi-scale feature local interaction based on global connection of the local feature channel. Then, we develop a multi-scale feature interaction block (FIB) in MSFN to fully extract spatial information of different regions of the original image by using convolution layers of different scales. On this basis, we use the channel stripping operation to compress the model, and reduce the number of model parameters as much as possible on the premise of ensuring the reconstructed image quality. Finally, we test the proposed MSFN model with the benchmark datasets. The experimental results show that the MSFN model is better than the other state-of-the-art SR methods in reconstruction effect, computational complexity, and inference time.
Linguistic Taboos and Euphemisms in Nepali

Languages across the world have words, phrases, and behaviors—the taboos—that are avoided in public communication considering them as obscene or disturbing to the social, religious, and ethical values of society. However, people deliberately use these linguistic taboos and other language constructs to make hurtful, derogatory, and obscene comments. It is nearly impossible to construct a universal set of offensive or taboo terms because offensiveness is determined entirely by different factors such as socio-physical setting, speaker-listener relationship, and word choices. In this article, we present a detailed corpus-based study of offensive language in Nepali. We identify and describe more than 18 different categories of linguistic offenses including politics, religion, race, and sex. We discuss 12 common euphemisms, such as synonym, metaphor, and circumlocution. In addition, we introduce a manually constructed dataset of more than 1,000 offensive and taboo terms popular among contemporary speakers. We describe the first experiments that provide baseline results in detecting offensive language in Nepali. This in-depth study of offensive language and resource will provide a foundation for several downstream tasks, such as offensive language detection and language learning.
RetTag

Memory-corruption-based return address hijacking, such as Return-oriented Programming (ROP), is a prevalent attack technique that compromises the program's control flow integrity. So far, software-based defenses against these attacks either introduce heavy performance overhead or trade-off security for performance. Meanwhile, some hardware-assisted defense mechanisms are not practical for large-scale deployment due to additional requirements of hardware features and flaws caused by complicated design.