Reviewerly/exHarmony huggingface dataset:
A creative chain coding technique for bi-level image compression inspired by the NetLogo HIV agent-based modeling simulation

In this paper, we develop a new chain coding application stimulated by the existing NetLogo HIV agent-based model and utilize it in bi-level image compression. Our paper is an extended version of our previously published paper in the International Conference on Computational Science 2021. Our method considers converting an image into a virtual environment, which maps to the original image and consists of HIV+ and HIV− female agents depending on the distribution of the pixels. Then, the algorithm introduces HIV+ male agents the purpose of which is to move around and infect other HIV− female agents. The movements of the HIV+ male agents are designed in a way that follows the relative coding approach, utilized in different chain coding projects. The relative coding increases the likelihood of generating consecutive codes that are encoded in a similar manner, and therefore, helps in providing better compression ratios. The algorithm tracks certain HIV+ male movements and uses them along with other pieces of information to reconstruct the original image back. As the literature shows, agent-based modeling can be advantageous over mathematical techniques and it can be effectively applied in some domains. The outcomes revealed that we could outperform standardized benchmarks used by researchers in the image processing community. Additionally, the paired samples t-tests reveal that the mean differences between our results and the ones generated by the other benchmarks (e.g. JBIG2) are statistically significant.
A Novel Feature-Selection Method for Human Activity Recognition in Videos

Human Activity Recognition (HAR) is the process of identifying human actions in a specific environment. Recognizing human activities from video streams is a challenging task due to problems such as background noise, partial occlusion, changes in scale, orientation, lighting, and the unstable capturing process. Such multi-dimensional and none-linear process increases the complexity, making traditional solutions inefficient in terms of several performance indicators such as accuracy, time, and memory. This paper proposes a technique to select a set of representative features that can accurately recognize human activities from video streams, while minimizing the recognition time and memory. The extracted features are projected on a canvas, which keeps the synchronization property of the spatiotemporal information. The proposed technique is developed to select the features that refer only to progression of changes. The original RGB frames are preprocessed using background subtraction to extract the subject. Then the activity pattern is extracted through the proposed Growth method. Three experiments were conducted; the first experiment was a baseline to compare the classification task using the original RGB features. The second experiment relied on classifying activities using the proposed feature-selection method. Finally, the third experiment provided a sensitivity analysis that compares between the effect of both techniques on time and memory resources. The results indicated that the proposed method outperformed original RBG feature-selection method in terms of accuracy, time, and memory requirements.
Looking here or there? Gaze Following in 360-Degree Images

Gaze following, i.e., detecting the gaze target of a human subject, in 2D images has become an active topic in computer vision. However, it usually suffers from the out of frame issue due to the limited field-of-view (FoV) of 2D images. In this paper, we introduce a novel task, gaze following in 360-degree images which provide an omnidirectional FoV and can alleviate the out of frame issue. We collect the first dataset, "GazeFollow360" <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , for this task, containing around 10,000 360-degree images with complex gaze behaviors under various scenes. Existing 2D gaze following methods suffer from performance degradation in 360degree images since they may use the assumption that a gaze target is in the 2D gaze sight line. However, this assumption is no longer true for long-distance gaze behaviors in 360-degree images, due to the distortion brought by sphere-to-plane projection. To address this challenge, we propose a 3D sight line guided dual-pathway framework, to detect the gaze target within a local region (here) and from a distant region (there), parallelly. Specifically, the local region is obtained as a 2D cone-shaped field along the 2D projection of the sight line starting at the human subject’s head position, and the distant region is obtained by searching along the sight line in 3D sphere space. Finally, the location of the gaze target is determined by fusing the estimations from both the local region and the distant region. Experimental results show that our method achieves significant improvements over previous 2D gaze following methods on our GazeFollow360 dataset.
Repackaging FDI for Inclusive Growth: Nullifying Effects and Policy Relevant Thresholds of Governance

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RAiA-Net: A Multi-Stage Network With Refined Attention in Attention Module for Single Image Deraining

Image deraining is an important task for the outdoor computer vision system in the rain days. Deep learning with attention mechanism has shown promising performance for rain removal. However, most of existing attention mechanisms select the locations of attention module empirically, and the receptive field of network is small and fixed. Motivated by the Attention in Attention (AiA) and multi-stage strategy, we propose a multi-stage deep neural network for image deraining, which is equipped with the refined AiA (RAiA) module. Specifically, RAiA extends the original AiA by exploiting the joint dependencies of channel and spatial information to generate the dynamic allocated weights. Moreover, the proposed network is carried out by a multi-stage U-Net fashion, which can extract features progressively, enlarge the receptive field of network, and improve the ability of image structures representation. We demonstrate the superiority of proposed method on the public synthetic datasets and real rainy images through comparing to seven state-of-the-art deep learning based methods.
DNCNet: Deep Radar Signal Denoising and Recognition

Deep learning with its rapid development and advancement has achieved unparalleled performance in many areas like computer vision as well as cognitive radio and signal recognition. However, the performance of most deep neural networks would suffer from degradation in the data mismatch scenario, e.g., the test dataset has a related but nonidentical distribution with the training dataset. Considering the noise corruption, a classifier’s accuracy might drop sharply when it is tested on a dataset with much lower signal-to-noise ratio compared to its training dataset. To address this dilemma, in this work, we propose an efficient denoising and classification network (DNCNet) for radar signals. The DNCNet consists of denoising and classification subnetworks. First, a radar signal detection and synthetic mechanism is designed to generate pairwise clean data and noisy data for the DNCNet to train its denoising subnetwork. Then, a two-phase training procedure is proposed to train the denoising subnetwork in the first phase and strengthen the mapping between the denoising results and perceptual representation in the second. Experiments on synthetic and benchmark datasets validate the excellent performance of the proposed DNCNet against state-of-the-art methods in terms of both signal restoration quality and classification accuracy.
DAM: Discrepancy Alignment Metric for Face Recognition

The field of face recognition (FR) has witnessed remarkable progress with the surge of deep learning. The effective loss functions play an important role for FR. In this paper, we observe that a majority of loss functions, including the widespread triplet loss and softmax-based cross-entropy loss, embed inter-class (negative) similarity s <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> and intra-class (positive) similarity s <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> into similarity pairs and optimize to reduce (s <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> − s <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> ) in the training process. However, in the verification process, existing metrics directly take the absolute similarity between two features as the confidence of belonging to the same identity, which inevitably causes a gap between the training and verification process. To bridge the gap, we propose a new metric called Discrepancy Alignment Metric (DAM) for verification, which introduces the Local Inter-class Discrepancy (LID) for each face image to normalize the absolute similarity score. To estimate the LID of each face image in the verification process, we propose two types of LID Estimation (LIDE) methods, which are reference-based and learning-based estimation methods, respectively. The proposed DAM is plug-and-play and can be easily applied to the most existing methods. Extensive experiments on multiple popular face recognition benchmark datasets demonstrate the effectiveness of our proposed method.
ARoBERT: An ASR Robust Pre-Trained Language Model for Spoken Language Understanding

Spoken Language Understanding (SLU) aims to interpret the meanings of human speeches in order to support various human-machine interaction systems. A key technique for SLU is Automatic Speech Recognition (ASR), which transcribes speech signals into text contents. As the output texts of modern ASR systems unavoidably contain errors, mainstream SLU models either trained or tested on texts transcribed by ASR systems would not be sufficiently error robust. We present ARoBERT, an ASR Robust BERT model, which can be fine-tuned to solve a variety of SLU tasks with noisy inputs. To guarantee the robustness of ARoBERT, during pretraining, we decrease the fluctuations of language representations when some parts of the input texts are replaced by homophones or synophones. Specifically, we propose two novel self-supervised pre-training tasks for ARoBERT, namely Phonetically-aware Masked Language Modeling (PMLM) and ASR Model-adaptive Masked Language Modeling (AMMLM). The PMLM task explicitly fuses the knowledge of word phonetic similarities into the pre-training process, which forces homophones and synophones to share similar representations. In AMMLM, a data-driven algorithm is further introduced to mine typical ASR errors such that ARoBERT can tolerate ASR model errors. In the experiments, we evaluate ARoBERT over multiple datasets. The results show the superiority of ARoBERT, which consistently outperforms strong baselines. We have also shown that ARoBERT outperforms state-of-the-arts on a public benchmark. Currently, ARoBERT has been deployed in an online production system with significant improvements.
Employment Data Screening and Destination Prediction of College Students Based on Deep Learning

This paper takes the data of a university graduate as the research object. By consulting various literatures and literature analyses, we can understand the impact of students’ academic performance, English level, and other activities on students’ employment development and select data on this basis. The collected data is cleaned, integrated, and transformed to form a standard data set. The factors affecting graduates’ employment are complex and diverse, with high data feature dimensions, sparse links between features, complex and diverse attributes, and both discrete and continuous features. According to the characteristics of college students’ employment data, this paper uses the deep-seated neural network with strong learning ability and adaptability to predict college students’ employment, so as to provide guidance for college students’ employment. Firstly, based on deep learning and Feedforward Neural Network technology, a prediction model of college students’ employment destination with six influencing factors is established, and the prediction accuracy of the model is evaluated. The ACC value and loss value are used as indicators to test whether the prediction effect of the prediction model is good. The results show that the prediction effect of the model is worthy of further research and optimization. Finally, combined with the actual data of graduates, the practical application of the prediction model is analyzed. Compared with the traditional machine learning algorithm, the effectiveness of the algorithm is verified.
PointBA: Towards Backdoor Attacks in 3D Point Cloud

3D deep learning has been increasingly more popular for a variety of tasks including many safety-critical applications. However, recently several works raise the security issues of 3D deep models. Although most of them consider adversarial attacks, we identify that backdoor attack is indeed a more serious threat to 3D deep learning systems but remains unexplored. We present the backdoor attacks in 3D point cloud with a unified framework that exploits the unique properties of 3D data and networks. In particular, we design two attack approaches on point cloud: the poison-label backdoor attack (PointPBA) and the clean- label backdoor attack (PointCBA). The first one is straight-forward and effective in practice, while the latter is more sophisticated assuming there are certain data inspections. The attack algorithms are mainly motivated and developed by 1) the recent discovery of 3D adversarial samples suggesting the vulnerability of deep models under spatial transformation; 2) the proposed feature disentanglement technique that manipulates the feature of the data through optimization methods and its potential to embed a new task. Extensive experiments show the efficacy of the PointPBA with over 95% success rate across various 3D datasets and models, and the more stealthy PointCBA with around 50% success rate. Our proposed backdoor attack in 3D point cloud is expected to perform as a baseline for improving the robustness of 3D deep models.
Scene-adaptive radar tracking with deep reinforcement learning

Multi-target tracking with radars is a highly challenging problem due to detection artifacts, sensor noise, and interference sources. The traditional signal processing chain is, therefore, a complex combination of various algorithms with several tunable tracking-parameters. Usually, these are initially set by engineers and are independent of the scene tracked. For this reason, they are often non-optimal and generate poorly performing tracking. In this context, scene-adaptive radar processing refers to algorithms that can sense, understand and learn information related to detected targets as well as the environment and adapt its tracking-parameters to optimize the desired goal. In this paper, we propose a Deep Reinforcement Learning framework that guides the scene-adaptive choice of radar tracking-parameters towards an improved performance on multi-target tracking.
Adaptive of New Technology for Agriculture Online Learning by Metaverse: A Case Study in Faculty of Agriculture, National University of Laos

These papers investigated the adaption of the new technology for online learning in the COVID-19 situation, giving one more choice of online applications or programs for getting new experiences with online learning and creating ideas to resolve some of the current problems from online learning in some subjects. This study focuses to utilize the appropriate and new tools to motivate an online learning system during the Covid-19 pandemic in Lao PDR. This method had more functions of 3D for simulation the places and works by using smart mobile devices, computers, and other smart tools connecting to simulate real learning in the parts of technical and visiting were used on data collected from sample size audients from faculty of agriculture, National University of Lao, Ministry of technology and communication. The user satisfaction resulted and success rate of learning show that the positive result for technology adoption with the online learning system.
Measuring the Similarity of Concept Maps According to Pedagogical Criteria

A concept map provides a graphic hierarchical means of representing how knowledge is structured in a domain. It visually organizes a set of concepts showing their mutual relations. An analysis of the similarities between two concept maps can produce significant results in any fields where Intelligent Knowledge Management is used, such as Healthcare, Policy Development, Energy and Waste Management, Resource Consumption Sustainability, Mobility, Safety, Citizen Empowerment, and, of course, Education. In an educational setting, a concept map conveys the various concepts connected by relations of dependence that a course must cover. However, the similarity between two concept maps for education has to be measured according to criteria that take into consideration the pedagogical properties of the maps, i.e., not only considering the structural aspects of the maps themselves. An automated analysis of the similarity between two concept maps can allow the teacher to reflect on different interpretations of the knowledge domain of a certain course as well as to assess how existing learning material can be implemented in a new course. Research into this aspect of concept mapping appears to be relatively scarce. This paper proposes criteria to assess the similarity of two concept maps, also based on pedagogical features, with the aim of providing teachers with better support during the course creation process. Each criterion is implemented through a specific measure function. The measures are then shown to be sensitive to their criterion rationale by evaluating them against a collection of random case studies.
A surrogate-assisted Jaya algorithm based on optimal directional guidance and historical learning mechanism

An improved Jaya algorithm named surrogate-assisted Jaya algorithm based on optimal directional guidance and historical learning mechanism (SDH-Jaya) is proposed in this study to address the continuous optimization problems. In the SDH-Jaya, a surrogate-assisted model combined with the polynomial model and radial basis model built by the individual with real fitness is introduced to decrease the expensive computational simulations and accelerate the convergence speed. Two co-evolutionary mechanisms, which are named assisted co-evolutionary mechanism and self-learning co-evolutionary mechanism, are proposed to optimize the surrogate model and evolutionary population. Search directions and steps of the SDH-Jaya are adjusted adaptively by the differential vector resulting from the best solution and worst solution in the candidates at each generation. The historical population stored in an archive is selected randomly to provide new search areas for improving the diversity of the population during the evolution process of the SDH-Jaya. The performance of SDH-Jaya is tested on CEC2017 benchmark problems. The experimental results reveal that the effectiveness of the SDH-Jaya algorithm outperforms the classical Jaya algorithm, its variants, and state-of-the-art algorithms in terms of the quality of solution and execution time.
Teleporting quantum Fisher information under Davies-Markovian dynamics

The estimation degree of a teleported state using two different quantum channels by means of quantum Fisher information is discussed. It consists of using two different noisy channels generated from an interaction between an open quantum system and its surrounding environment under Davies-Markovian dynamics. However, when small interacted system–environment parameters and a small number of detuning parameters are taken into account, a robust estimation degree is obtained. Moreover, it is possible to estimate various system–environment parameters related to the energy relaxation time and dephasing time. By controlling these parameters, we conclude that maximally entangled channels and robust estimation degrees by means of quantum Fisher information are achieved when the energy relaxation and dephasing times vanish.
MeNTT: A Compact and Efficient Processing-in-Memory Number Theoretic Transform (NTT) Accelerator

Lattice-based cryptography (LBC), exploiting learning with error (LWE) problems, is a promising candidate for postquantum cryptography. The number theoretic transform (NTT) is the latency- and energy-dominant process in the computation of LWE problems. This article presents a compact and efficient in-MEmory NTT accelerator, named MeNTT, which explores an optimized computation in and near a 6T SRAM array. Specifically designed peripherals enable fast and efficient modular operations. Moreover, a novel mapping strategy reduces the data flow between the NTT stages into a unique pattern, which greatly simplifies the routing among processing units (i.e., the SRAM column in this work), reducing the energy and area overheads. The accelerator achieves significant latency and energy reductions over prior arts.
RVFace: Reliable Vector Guided Softmax Loss for Face Recognition

Face recognition has witnessed significant progress with the advances of deep convolutional neural networks (CNNs), and the central task of which is how to improve the feature discrimination. To this end, several margin-based (e.g., angular, additive and additive angular margins) softmax loss functions have been proposed to increase the feature margin between different classes. However, despite great achievements have been made, they mainly suffer from four issues: 1) They are based on the assumption of well-cleaned training sets, without considering the consequence of noisy labels inherently existing in most of face recognition datasets; 2) They ignore the importance of informative (e.g., semi-hard) features mining for discriminative learning; 3) They encourage the feature margin only from the perspective of ground truth class, without realizing the discriminability from other non-ground truth classes; and 4) They set the feature margin between different classes to be same and fixed, which may not adapt the situation of unbalanced data in different classes very well. To cope with these issues, this paper develops a novel loss function, which explicitly estimates the noisy labels to drop them and adaptively emphasizes the semi-hard feature vectors from the remaining reliable ones to guide the discriminative feature learning. Thus we can address all the above issues and achieve more discriminative features for face recognition. To the best of our knowledge, this is the first attempt to inherit the advantages of feature-based noisy labels detection, feature mining and feature margin into a unified loss function. Extensive experimental results on a variety of face recognition benchmarks have demonstrated the effectiveness of our method over state-of-the-art alternatives. Our source code is available at http://www.cbsr.ia.ac.cn/users/xiaobowang/.