Reviewerly/exHarmony huggingface dataset:
Learning Social Spatio-Temporal Relation Graph in the Wild and a Video Benchmark

Social relations are ubiquitous and form the basis of social structure in our daily life. However, existing studies mainly focus on recognizing social relations from still images and movie clips, which are different from real-world scenarios. For example, movie-based datasets define the task as the video classification, only recognizing one relation in the scene. In this article, we aim to study the problem of social relation recognition in an open environment. To close the gap, we provide the first video dataset collected from real-life scenarios, named social relation in the wild (SRIW), where the number of people can be huge and vary, and each pair of relations needs to be recognized. To overcome new challenges, we propose a spatio-temporal relation graph convolutional network (STRGCN) architecture, utilizing correlative visual features to recognize social relations intuitively. Our method decouples the task into two classification tasks: person-level and pair-level relation recognition. Specifically, we propose a person behavior and character module to encode moving and static features in two explicit ways. Then we take them as node features to build a relation graph with meaningful edges in a scene. Based on the relation graph, we introduce the graph convolutional network (GCN) and local GCN to encode social relation features which are used for both recognitions. Experimental results demonstrate the effectiveness of the proposed framework, achieving 83.1% and 40.8% mAP in person-level and pair-level classification. Moreover, the study also contributes to the practicality in this field.
A Variable Memory Length Auto Encoder

Auto-encoders typically require batch learning to be effective. There is a lack of online learning mechanisms for auto-encoders. To address this shortcoming in the literature, we propose an auto-encoder that can not only learn on a sample-by-sample basis without back-propagation but also has a memory to benefit from past learning. The memory can be adapted to fit the current state of the data by varying the memory length of the auto-encoder. Simulation supports our approach, especially when the data is nonstationary.
Artificial Neural Network Based Automatic Modulation Classification System Applied to FPGA

The wireless communication systems face rapid growth, driven by advances in new technologies such as the 5G and the Internet of Things. However, this growth faces a limitation: the scarcity of frequencies in the electromagnetic spectrum, demanding efficient technologies to improve its utilization. For this reason, this work aimed to construct an Automatic Modulation Classification system and implement it in both software and hardware, using an FPGA. The resulting models can classify five modulations and a noise-only signal, using an Artificial Neural Network architecture, which was constructed based on the test of over 2000 different topologies, resulting in distinct configurations for each technology due to their intrinsic limitations. Both setups achieved approximately 90% of accuracy when the SNR is ≥4 dB and are capable of outperforming similar works developed so far, as it uses a set of inputs that require less computational time and resource utilization on its execution.
Stylized image resolution enhancement scheme based on an improved convolutional neural network in cyber‐physical systems

Abstract Edge computing based on intelligent techniques provides powerful processing abilities in cyber‐physical systems (CPS), thereby enabling real‐time insights, and analyses for applications. To decrease network redundancy and improve the speed of convolutional neural networks (CNNs) when performing high‐resolution stylization in image style transfer tasks, a resolution enhancement scheme is proposed based on an improved CNN in CPS. A style transfer network (RESTN) model and a two‐stage neural network training method are presented. In the first stage, the resolution enhancement ability of RESTN is trained with high‐resolution and low‐resolution image pairs, and in the second stage, the style transfer ability of RESTN is supplemented. The parameters of the feature extraction phase are shared for learning different styles, and the octave convolution operation is adopted to reduce network redundancy. Experimental results show that compared with the traditional model‐based iterative method, the proposed RESTN model can conduct training three times faster and with half of the memory cost.
Frequency Response of Linear Time-Varying Circuits Using Iterated Laplace Transform

The concept of iterated bilateral Laplace transform (ILT) is used to obtain the frequency response of linear time-varying (LTV) causal circuits and systems. It is shown that the application of this commonly overlooked transform is essential in the analysis of time-varying systems.
Investigation of In-home Augmented Reality assisted Rehabilitation Therapies for Disabled Patient

Rehabilitation is the key process in bringing back the cognitive, mental, or physical functions of the disabled persons mainly caused by the effects of accidents, diseases, medical treatment side effects, etc. The conventional rehabilitation process is highly monotonous and uninspired among the patients which in turn pretend to use such methods. To overcome these limitations in the traditional methods of the rehabilitation process, Augmented Reality (AR) is introduced in this field. AR provides comfortable experience in handling the applications for both patients and medical therapists. This research article provides complete insight into AR technology in various rehabilitation applications, its major design and deployment challenges. It also highlights the major concentration of adapting AR methodologies in clinical practices for in-home usage by the patients in a convenient manner.
Model Compression in Object Detection

Compressed neural-network models have a growing relevance in the Deep Learning literature, since they allow the deployment of AI on devices with computational constraints for many automation purposes. Despite the amount and diversity of work for this purpose, there is no standard benchmark in the literature, and it is often difficult to choose the proper approach due to the large difference between models, datasets, and training details that are tested. Therefore, this paper proposes a standard experimental benchmark for different model compression approaches for the object detection task, using a fixed model (the well-known YOLOv3) and training scheme. Between Pruning, Knowledge Distillation, and Neural Architecture Search, our experiments reveal that the best trade-off is by using pruning, which enables the creation of a model with 80.67% mAP of the original model but removing 98.8% of the parameters, 96.53% of the Multiply-Accumulate Operations, and reducing the storage size from 235.44MB to 11.61MB.
What Kind of Opposition-Forming Operator is Privation?

In this paper, a new kind of opposition relations is presented. Taking privation as a main negative operation on predicates, in this paper is presented a relative opposition theory, i.e., a sub-theory of oppositions.
The Application of Active Query K-Means in Text Classification

Active learning is a state-of-art machine learning approach to deal with an abundance of unlabeled data. In the field of Natural Language Processing, typically it is costly and time-consuming to have all the data annotated. This inefficiency inspires out application of active learning in text classification. Traditional unsupervised k-means clustering is first modified into a semi-supervised version in this research. Then, a novel attempt is applied to extend the algorithm into active learning scenario with Penalized Min-Max-selection, so as to make limited queries that yield more stable initial centroids. This method utilizes both the interactive query results from users and the underlying distance representation. After tested on a Chinese news dataset, it shows a consistent increase in accuracy while lowering the cost in training.
Differential Eye Movements in Verbal and Nonverbal Search

In addition to “nonverbal search” for objects, modern life also necessitates “verbal search” for written words in variable configurations. We know less about how we locate words in novel spatial arrangements, as occurs on websites and menus, than when words are located in passages. In this study we leveraged eye tracking technology to examine the hypothesis that objects are simultaneously screened in parallel while words can only be found when each are directly foveated in serial fashion. Participants were provided with a cue (e.g. rabbit) and tasked with finding a thematically-related target (e.g. carrot) embedded within an array including a dozen distractors. The cues and arrays were comprised of object pictures on nonverbal trials, and of written words on verbal trials. In keeping with the well-established “picture superiority effect,” picture targets were identified more rapidly than word targets. Eye movement analysis showed that picture superiority was promoted by parallel viewing of objects, while words were viewed serially. Different factors influenced performance in each stimulus modality; lexical characteristics such as word frequency modulated viewing times during verbal search, while taxonomic category affected viewing times during nonverbal search. In addition to within-platform task conditions, performance was examined in cross-platform conditions where picture cues were followed by word arrays, and vice versa. Although taxonomically-related words did not capture gaze on verbal trials, they were viewed disproportionately when preceded by cross-platform picture cues. Our findings suggest that verbal and nonverbal search are associated with qualitatively different search strategies and forms of distraction, and cross-platform search incorporates characteristics of both.
Performance Analysis of Medical Image Denoising Techniques

Noise reduction in digital image is a daunting assignment for the analysts in Digital Image Processing. As per survey, images are affected by some kind of noises such as gaussian noise, speckle noise and salt and pepper noise. Noise destroys the quality of the active radar, synthetic aperture radar (SAR), medical images. As per the literature, medical images are affected by low resolution, low contrast and geometric deformations, thereby reducing the diagnostic value of medical image. Predominantly, ultrasound images are influenced by speckle noise. Denoising is used to improve visual appearance of an organ and helps in better understanding of disease and accordingly decide the line of treatment for medical field. The proposed work focuses the importance of Spatial filtering techniques that improves resolution, contrast, edge preservation. This paper emphasis on preprocessing techniques that improves the statistical parameter peak signal to noise ratio (PSNR) which are the integral attributes of image quality. The generated result shows that the Wiener and Noise Adaptive Fuzzy switching median filter performs better PNSR values for reduction of low (10%), medium (50%) and high (80%) densities of speckle noise while Adaptive median and Noise Adaptive fuzzy switching median filters achieves good PSNR value for low (10%), medium (50%) and high (80%) noise densities of Gaussian noise among various types of filters. Hence Noise Adaptive Fuzzy switching median filter performs well for low, medium and high noise densities of both types speckle and gaussian noise.
A domain-mismatch speech recognition system in radio communication based on improved spectrum augmentation

Most of the current automatic speech recognition systems (ASR) require that the data domain of the test set and training set are similar to obtain satisfactory accuracy. However, when the data style of the two are different, such as different background noise, different sampling rates, and different speech speeds, etc., they will have a bad influence on the recognition results. In some areas like radio communications, the performance of the acoustics models trained by the pure and noise-free Mandarin speech data which is relatively easy to obtain is still not comparable to that in some other areas because of the different data styles. Therefore, we investigate an audio-level speech augmentation method that directly processes the raw signal. The augmentation policy consists of masking blocks of frequency channels randomly and specifically based on Discrete Fourier Transformation (DFT). We apply our method on the radio Mandarin speech recognition task and achieve 24% improvement of Character Error Rate (CER) and 22% of Word Error Rate (WER) over other augmentation policies relatively. Besides, we propose a method of eliminating the impact of the original sampling rate and the experimental results verify the effectiveness of it.
Evaluation of Strong and Weak Signifiers in a Web Interface Using Eye-Tracking Heatmaps and Machine Learning

The eye-tracking heatmap is a quantitative research tool that shows the user’s gaze points. Most of the eye-tracking heatmap is a 2D visualization comprising different colors. The heatmap colors indicate gaze duration, and the color cell’s position indicates gaze position. The eye-tracking heatmap has often been used to evaluate the usability of web interfaces to understand user behavior. For example, web designers have used heatmaps to obtain actual evidence for how users use their website. Further, the collection of eye-tracking heatmap data during website viewing facilitates measurement of improvements in site usability. However, although the eye-tracking heatmap provides rich information about how users watch, focus, and interact with a site, the high informational requirements substantially increase computational burden. In many cases, the distribution of gaze points in an eye-tracking heatmap may not be easily understood and interpreted. Accordingly, manual evaluation of heatmaps is inefficient. This study aimed to evaluate web usability by focusing on signifiers as an interface element using eye-tracking heatmaps and machine learning algorithms. We also used the dimensionality reduction technique to reduce the complexity of heatmap data. The results showed that the proposed classification model that combined the decision tree and PCA technique provided more than 90% accuracy when compared with the other nine classical machine learning methods. This finding indicated that the machine learning process reached the correct decision about the interface’s usability.
Matrix Factorization for Collaborative Filtering Is Just Solving an Adjoint Latent Dirichlet Allocation Model After All

Matrix factorization-based methods are among the most popular methods for collaborative filtering tasks with implicit feedback. The most effective of these methods do not apply sign constraints, such as non-negativity, to their factors. Despite their simplicity, the latent factors for users and items lack interpretability, which is becoming an increasingly important requirement. In this work, we provide a theoretical link between unconstrained and the interpretable non-negative matrix factorization in terms of the personalized ranking induced by these methods. We also introduce a novel, latent Dirichlet allocation-inspired model for recommenders and extend our theoretical link to also allow the interpretation of an unconstrained matrix factorization as an adjoint formulation of our new model. Our experiments indicate that this novel approach represents the unknown processes of implicit user-item interactions in the real world much better than unconstrained matrix factorization while being interpretable.
Few-Shot Learning with Random Erasing and Task-Relevant Feature Transforming

Few-shot learning for visual recognition aims to classify images from unseen classes with only a few labeled samples. Many previous works address such a challenge by using a base set consisting of massive labeled samples to learn a feature extractor, which is transferred to categorize unseen classes from a novel set. However, a challenging issue is how to make the learned feature extractor transferable in few-shot learning because the categories extracted from the base set are different from those in the novel set. To address this issue, this paper proposes a novel Random Erasing Network(RENet) to make the network better utilize the full context of the input image, yielding a more transferable network than previous networks that only use the most discriminative features. Further, we present a Task-Relevant Feature Transforming(TRFT) framework based on CrossTransformers to generate embedding that can better exploit the information within the current task. Then, we combine RENet and TRFT to implement a cooperative training model RE-TRFT for the episodic training. We conduct extensive experiments on two benchmarks and the results show that our approach outperforms recent state-of-the-art methods.
Sparse Information Filter for Fast Gaussian Process Regression

Gaussian processes (GPs) are an important tool in machine learning and applied mathematics with applications ranging from Bayesian optimization to calibration of computer experiments. They constitute a powerful kernelized non-parametric method with well-calibrated uncertainty estimates, however, off-the-shelf GP inference procedures are limited to datasets with a few thousand data points because of their cubic computational complexity. For this reason, many sparse GPs techniques were developed over the past years. In this paper, we focus on GP regression tasks and propose a new algorithm to train variational sparse GP models. An analytical posterior update expression based on the Information Filter is derived for the variational sparse GP model. We benchmark our method on several real datasets with millions of data points against the state-of-the-art Stochastic Variational GP (SVGP) and sparse orthogonal variational inference for Gaussian Processes (SOLVEGP). Our method achieves comparable performances to SVGP and SOLVEGP while providing considerable speed-ups. Specifically, it is consistently four times faster than SVGP and on average 2.5 times faster than SOLVEGP.
Implementation of Efficient Anonymous Certificate-Based Multi-Message and Multi-Receiver Signcryption On Raspberry Pi-Based Internet of Things Monitoring System

Internet of things as a technology that connect internet and physical world has been implemented in many diverse fields and has been proven very useful and flexible. In every implementation of technology that involve internet, security must be a great concern, including the implementation of IoT technology. A lot of alternatives can be used to achieve security of IoT. Ming et al. has proposed novel signcryption scheme to secure IoT of monitoring health data. In this work, proposed signcryption scheme from Ming et al. has been successfully implemented using Raspberry Pi and ESP32 and has proven work in securing IoT data.
Time-Based Model of the Success of a Malefactor’s Multistep Social Engineering Attack on a User

Multistep social engineering attacks (that involve a chain of users) are a serious threat to an organization’s information security. Usually such attacks require an integrated approach to reduce the probability of their success. This approach can be analysis of the social graph with modeling of scenarios for the spread of multistep social engineering attacks, highlighting the most critical among them, the development of ways to reduce criticality and directly implement the most effective measures. The goal of this work was to improve the approach to modeling multistep social engineering attack by including the factor of accidents in the model of a malefactor’s actions. The novelty of the research lies in the proposal of the new approach to the analysis of multistep social engineering attacks, taking into account the factor of accidents of the malefactor’s actions. The theoretical significance of the work is to create a foundation for further modeling and analysis of multistep social engineering attacks. The practical significance of the study lies in the formation of a tool for a comprehensive analysis of the organization to identify the most critical scenarios for the development of social engineering attacks.
A Multi-task Learning Method for Human Motion Classification and Person Identification

Wearable robotic systems have been widely studied in recent years, but it still remains a challenge to design a user-adaptive controller for wearable robotic systems to ensure personalized and accurate human-robot interaction. Accurate human motion classification and person identification are two premises helping design user-adaptive controllers for wearable robotic systems. In this paper, we proposed a multi-task learning method for human motion classification and person identification with a single neural network, which can serve as a solution to personalized human-robot interaction, and can also serve as a benchmark for the following studies in related fields. The multi-task learning neural network was trained and tested on a public human motion data set. The proposed method was capable to classify human motions and identify the person, with 99.13% and 96.51% accuracy, respectively. We also compared the proposed method with a benchmark single task learning method for human motion classification, the results showed that the performance of the multi-task learning method is more superior.
Deep recurrent networks predicting the gap evolution in adiabatic quantum computing

In adiabatic quantum computing finding the dependence of the gap of the Hamiltonian as a function of the parameter varied during the adiabatic sweep is crucial in order to optimize the speed of the computation. Inspired by this challenge, in this work, we explore the potential of deep learning for discovering a mapping from the parameters that fully identify a problem Hamiltonian to the aforementioned parametric dependence of the gap applying different network architectures. Through this example, we conjecture that a limiting factor for the learnability of such problems is the size of the input, that is, how the number of parameters needed to identify the Hamiltonian scales with the system size. We show that a long short-term memory network succeeds in predicting the gap when the parameter space scales linearly with system size. Remarkably, we show that once this architecture is combined with a convolutional neural network to deal with the spatial structure of the model, the gap evolution can even be predicted for system sizes larger than the ones seen by the neural network during training. This provides a significant speedup in comparison with the existing exact and approximate algorithms in calculating the gap.
The Improved Artificial Bee Colony Method and Its Application on UAV Disaster Rescue

An improved Artificial Bee Colony (ABC) algorithm is proposed in this paper. In contrast to the traditional method, which may drop into the local optimization and meet the slow convergence problem, a global population optimization-based method is developed. The equilibrium searching factors are designed to speed up the convergence effect and expand the searching ability. To evaluate the effectiveness of proposed method, on one hand the algorithm computation effect is assessed. The convergence effect of fitness value can be improved. The experiment results show that the proposed method is better than the ordinary ABC algorithm. On the other hand, an optimal path planning mission of the UAV disaster rescue is also compared between the traditional ABC and our proposed methods. The results also illustrate that our proposed method has a better performance to avoid the threaten sources on ground.