Reviewerly/exHarmony huggingface dataset:
SecureAD: A Secure Video Anomaly Detection Framework on Convolutional Neural Network in Edge Computing Environment

Anomaly detection offers a powerful approach to identifying unusual activities and uncommon behaviors in real-world video scenes. At present, convolutional neural networks (CNN) have been widely used to tackle anomalous events detection, which mainly rely on its stronger ability of feature representation than traditional hand-crafted features. However, massive video data and high cost of CNN model training are a challenge to achieve satisfactory detection results for resource-limited users. In this article, we propose a secure video anomaly detection framework (SecureAD) based on CNN. Specifically, we introduce additive secret sharing to design several calculation protocols for achieving safe CNN training and video anomaly detection. Besides, we propose a Bloom filter based fine-grained access control policy to authenticate legitimate users, without leaking the privacy of raw personal attributes. In addition, edge computing instead of cloud computing is integrated into the architecture to reduce response time between servers and users in an outsourced environment. Finally, we prove that the proposed SecureAD achieves secure video anomaly detection without compromising the privacy of the related data. Also, the simulation results demonstrate the effectiveness and security of our SecureAD.
Manganese potential mapping in western Guangxi-southeastern Yunnan (China) via spatial analysis and modal-adaptive prospectivity modeling

While the region of western Guangxi-southeastern Yunan, China, is known and considered prospective for manganese deposits, carrying out prospectivity mapping in this region is challenging due to the diversity of geological factors, the complexity of geological process and the asymmetry of geo-information. In this work, the manganese potential mapping for further exploration targeting is implemented via spatial analysis and modal-adaptive prospectivity modeling. On the basis of targeting criteria developed by the mineral system approach, the spatial analysis is leveraged to extract the predictor variables to identify features of the geological process. Specifically, a metallogenic field analysis approach is proposed to extract metallogenic information that quantifies the regional impacts of the synsedimentary faults and sedimentary basins. In the integration of the extracted predictor variables, a modal-adaptive prospectivity model is built, which allows to adapt different data availability and geological process. The resulting prospective areas of high potential not only correspond to the areas of known manganese deposits but also provide a number of favorable targets in the region for future mineral exploration.
Improving Census Transform by High-Pass with Haar Wavelet Transform and Edge Detection

One of the common methods for measuring distance is to use a camera and image processing algorithm, such as an eye and brain. Mechanical stereo vision uses two cameras to shoot the same object and analyzes the disparity of the stereo vision. One of the most robust methods to calculate disparity is the well-known census transform, which has the problem of conversion window selection. In this paper, three methods are proposed to improve the performance of the census transform. The first one uses a low-pass band of the wavelet to reduce the computation loading and a high-pass band of the wavelet to modify the disparity. The main idea of the second method is the adaptive size selection of the conversion window by edge information. The third proposed method is to apply the adaptive window size to the previous sparse census transform. In the experiments, two indexes, percentage of bad matching pixels (PoBMP) and root mean squared (RMS), are used to evaluate the performance with the known ground truth data. According to the results, the computation required can be reduced by the multiresolution feature of the wavelet transform. The accuracy is also improved with the modified disparity processing. Compared with previous methods, the number of operation points is reduced by the proposed adaptive window size method.
Using thermal intensities to build conditional random fields for object segmentation at night

Surveillance systems often make use of the thermal spectrum for improved computer vision application during night time scenarios. In the proposed work we tackle a similar problem of street object segmentation using multispectral information. We propose an efficient model that utilizes spark modules to reduce network parameters and perform a multispectral feature extraction along with multi-scale feature fusion in an encoder-decoder architecture. The predictions are further refined using a conditional random field based on thermal intensity-based pairwise potentials. Overall the network achieves an average pixel-level accuracy of 0.8185 and a mean IOU of 0.6438 for the night dataset which exceeds competing models by a significant margin. The proposed method also performs well for the combined dataset of day and night images. Additionally, it has been established that the use of thermal intensity-based pairwise potential on average is superior when it comes to images captured during the night.
Interoceptive Awareness: The ‘Being’ Dimension of ‘Being There’ in Virtual Worlds

Abstract We conducted an exploratory study to introduce, operationalize and test a theoretical conceptualization of interoceptive awareness (IA) in virtual worlds that aligns with neurobiological explanations of interoception. We examined the relationship between IA and the self-location subdimension of spatial presence. Experimental subjects experienced either a 10-min meditation designed to activate IA or a control condition designed to encourage mind wandering. Participants either wore a virtual reality (VR) headset (Samsung Gear VR or HTC Vive) or had their eyes closed. Results suggest that (i) IA was not different between eyes closed or eyes open in VR; (ii) VR display technology did not influence IA; and (iii) higher IA and visual clarity were associated with significantly stronger feelings of spatial presence, whereas display technology had no relationship to spatial presence. Further examination of the role of IA in virtual worlds experiences is warranted.
Operation and Imitation Under Safety-Aware Shared Control

We describe a shared control methodology that can, without knowledge of the task, be used to improve a human’s control of a dynamic system, be used as a training mechanism, and be used in conjunction with Imitation Learning to generate autonomous policies that recreate novel behaviors. Our algorithm introduces autonomy that assists the human partner by enforcing safety and stability constraints. The autonomous agent has no a priori knowledge of the desired task and therefore only adds control information when there is concern for the safety of the system. We evaluate the efficacy of our approach with a human subjects study consisting of 20 participants. We find that our shared control algorithm significantly improves the rate at which users are able to successfully execute novel behaviors. Experimental results suggest that the benefits of our safety-aware shared control algorithm also extend to the human partner’s understanding of the system and their control skill. Finally, we demonstrate how a combination of our safety-aware shared control algorithm and Imitation Learning can be used to autonomously recreate the demonstrated behaviors.
Multi-Layer Cross Loss Model for Zero-Shot Human Activity Recognition

Most existing methods of human activity recognition are based on supervised learning. These methods can only recognize classes which appear in the training dataset, but are out of work when the classes are not in the training dataset. Zero-shot learning aims at solving this problem. In this paper, we propose a novel model termed Multi-Layer Cross Loss Model (MLCLM). Our model has two novel ideas: (1) In the model, we design a multi-nonlinear layers model to project features to semantic space for that the deeper the network is, the better the network can fit the data's distribution. (2) A novel objective function combining mean square loss and cross entropy loss is designed for the zero-shot learning task. We have conduct sufficient experiments to evaluate the proposed model on three benchmark datasets. Experiments show that our model outperforms other state-of-the-art methods significantly in zero-shot human activity recognition.
Predictive Modeling of Individual Human Cognition: Upper Bounds and a New Perspective on Performance

Abstract Model evaluation is commonly performed by relying on aggregated data as well as relative metrics for model comparison and selection. In light of recent criticism about the prevailing perspectives on cognitive modeling, we investigate models for human syllogistic reasoning in terms of predictive accuracy on individual responses. By contrasting cognitive models with statistical baselines such as random guessing or the most frequently selected response option as well as data‐driven neural networks, we obtain information about the progress cognitive modeling could achieve for syllogistic reasoning to date, its remaining potential, and upper bounds of performance future models should strive to exceed. The methods presented in this article are not restricted to the domains of reasoning but generalize to other fields of behavioral research and can serve as useful additions to the modern modeler's toolbox.
Personalized Product Service Scheme Recommendation Based on Trust and Cloud Model

Considering the low satisfaction rate and low efficiency of product service plans, a personalized product service plan recommendation method adopting the degree of trust and cloud model is proposed. The recommendation algorithm mainly includes calculating the similarity and the prediction results of the scheme. First, by fully considering the user's subjective characteristics, the user's trust is used to improve the traditional similarity. Second, considering data sparsity and discreteness, the cloud drop distance similarity calculation method is introduced in the process of calculating the trust similarity, and a new similarity is generated via the weighted synthesis to predict and fill in the gaps in the data. Then, when the new user does not have the cold start problem caused by the historical score record, the neural network method can be used to classify the users based on the user characteristics. The method is proposed and introduced to predict sparse user interest features and obtain similar user sets based on feature classification. The corresponding program offers recommendations. Finally, the effectiveness and rationality of the proposed method are verified by using the recommendations for a machine tool product for a manufacturing enterprise as an example.
A Hardware Descriptive Approach to Beetle Antennae Search

Beetle antennae search (BAS) is a newly developed meta-heuristic algorithm which is effectively used for optimizing objective functions of complex forms or even unknown forms.The common practice for implementing meta-heuristic algorithms including the BAS largely relies on programming in a high-level language and executing the code on a computer platform.However, the high-level implementation of the BAS algorithm hinders it from being used in an embedding system, where real-time operations are normally required.To address this limitation, we present an approach to implementing the BAS algorithm on a field-programmable gate array (FPGA).Specifically, we program the BAS function in the Verilog hardware description language (HDL), which provides a tractable vehicle for implementing the BAS algorithm at the gate level on the FPGA chip.We simulate our Verilog HDL based BAS module with the Modelsim platform.Simulation results validate the feasibility of our proposed Verilog HDL implementation of the BAS.Additionally, we implement the BAS model on the Zynq XC7Z010 platform, with 132.5 µ s latency for model implementation.
Machine Learning and Image Processing for Breast Cancer: A Systematic Map

Machine Learning (ML) combined with Image Processing (IP) gives a powerful tool to help physician, doctors and radiologist to make more accurate decisions. Breast cancer (BC) is a largely common disease among women worldwide; it is one of the medical sub-field that are experiencing an emergence of the use of ML and IP techniques. This paper explores the use of ML and IP techniques for BC in the form of a systematic mapping study. 530 papers published between 2000 and August 2019 were selected and analyzed according to 6 criteria: year and publication channel, empirical type, research type, medical task, machine learning objectives and datasets used. The results show that classification was the most used ML objective. As for the datasets most of the articles used private datasets belonging to hospitals, although papers using public data choose MIAS (Mammographic Image Analysis Society) which make it as the most used public dataset.
Review on Recent Advances in Human Action Recognition in Video Data

AI has achieved new heights in image recognition, human action recognition and NLP. It has a vast area of implementations such as IoT, robotics, biosciences and surveillance. Video based human action recognition has lots of potential applications that make it the most sought after field for researchers. With the constant growth of high performance computing, computer vision and GPUs, deep learning based human activity recognition is one of the constant evolving and promising stream. This review focuses on recent advancements in the field of action recognition based on deep learning. The present state of the art techniques for action recognition and prediction as well as the future scope for the research is discussed in the paper.
Group discriminative least square regression for multicategory classification

The least square regression (LSR) is a popular framework for multicategory classification because it has simple mathematical formulation and efficient solution. The classification performance of LSR based methods depends heavily on the discriminative capability of the label transformation. In this work, we aim to enhance the discriminative capability of the label transformation by imposing a new class-induced structure constraint. Specifically, we propose to regularize the label transformation matrix by the difference of l2,1 norm and l2,2 norm of the predicted labels of each class. The major advantage of the new regularity is that, it can guarantee the ideal discrimination of the label transformation matrix and make the classification more stable. For better generalization capability, we adopt the existing ε-dragging technique to relax the binary label. Leveraging the new regularity term and the label relaxation, we give a group discriminative least square regression (GDLSR) training model to learn the label transformation for multicategory classification. To solve the proposed model, we present an ADMM-like iteration algorithm, which we can guarantee a weak convergency. Experiments on several commonly used datasets show that our method outperforms both related LSR-based methods and some traditional methods.
A Deep Learning approach for the Motion Picture Content Rating

The film industry brings thousands of films to life every year. Not all of them are suitable for everyone, especially those with violent content. A content rating system is designed for evaluating the content and reporting the suitability for children, teenagers, or adults. It assists content providers during the assignment of rating levels for movies and, on the other hand, it can be useful for users to block violent content directly on their devices. However, applying for content ratings for movies can be tedious, prone to personal judgment, and also impossible if we also consider the videos on video-sharing websites. This work provides a motion picture content rating model to automatically classify and censor violent scenes using a Deep Learning (DL) approach. We collect a large amount of data searching for visual elements, such as blood or weapons, and manually label them according to a rating scale. Then we employ the Convolutional Neural Network (CNN) Inception v3 for training and validating. The CNN is modified, and additional regularization techniques are adopted to avoid overfitting during the training step. Finally, we design a video post-processing algorithm to refine the network output. Preliminary results demonstrate the effectiveness of our automatic classifier for supporting content providers to assign the rating and encourage further investigations on the use of DL.
Learning from Noisy Labels via Discrepant Collaborative Training

Noise is ubiquitous in the world around us. Difficulty in estimating the noise within a dataset makes learning from such a dataset a difficult and challenging task. In this paper, we propose a novel and effective learning framework in order to alleviate the adverse effects of noise within a dataset. Towards this aim, we modify a collaborative training framework to utilize discrepancy constraints between respective feature extractors enabling the learning of distinct, yet discriminative features, pacifying the adverse effects of noise. Empirical results of our proposed algorithm, Discrepant Collaborative Training (DCT), achieve competitive results against several current state-of-the-art algorithms across MNIST, CIFAR10 and CIFAR100, as well as large fine-grained image classification datasets such as CUBS-200-2011 and CARS196 for different levels of noise.
Verification of multi-agent systems with public actions against strategy logic

Model checking multi-agent systems, in which agents are distributed and thus may have different observations of the world, against strategic behaviours is known to be a complex problem in a number of settings. There are traditionally two ways of ameliorating this complexity: imposing a hierarchy on the observations of the agents, or restricting agent actions so that they are observable by all agents. We study systems of the latter kind, since they are more suitable for modelling rational agents. In particular, we define multi-agent systems in which all actions are public and study the model checking problem of such systems against Strategy Logic with equality, a very rich strategic logic that can express relevant concepts such as Nash equilibria, Pareto optimality, and due to the novel addition of equality, also evolutionary stable strategies. The main result is that the corresponding model checking problem is decidable.
Relationships between Dutch Youths’ Social Media Use and School Writing

Many youths are hooked on social media nowadays. Because computer-mediated communication (CMC) often deviates from standard language norms, it is feared to hurt literacy. A large-scale empirical study was conducted to examine whether social media affect school writings. 400 Dutch youths of different educational levels and ages participated; the data of 338 youths were used. We analysed whether relationships could be found between their self-reported social media use, as measured via extensive questionnaires, and the writing quality of their essays. We found more positive than negative associations between participants’ CMC use and their school writing. Results revealed that passive engagement with CMC, by heavy reliance on mobile phone and consumption of social media messages, might hinder writing skills, but active and creative language production in CMC – via various genres, from an early age, with many people, and including textisms – might help develop writing skills. Educational level turned out to be a relevant demographic factor in relationships between CMC and literacy: (pre-)vocationally or ‘lower’ educated youths’ school writing was most at risk of being affected, but could also benefit most from social media. The present study thus suggests that social media are not merely negatively associated with school writing.
A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM

We present Korbit, a large-scale, open-domain, mixed-interface, dialogue-based intelligent tutoring system (ITS). Korbit uses machine learning, natural language processing and reinforcement learning to provide interactive, personalized learning online. Korbit has been designed to easily scale to thousands of subjects, by automating, standardizing and simplifying the content creation process. Unlike other ITS, a teacher can develop new learning modules for Korbit in a matter of hours. To facilitate learning across a wide range of STEM subjects, Korbit uses a mixed-interface, which includes videos, interactive dialogue-based exercises, question-answering, conceptual diagrams, mathematical exercises and gamification elements. Korbit has been built to scale to millions of students, by utilizing a state-of-the-art cloud-based micro-service architecture. Korbit launched its first course in 2019 and has over 7, 000 students have enrolled. Although Korbit was designed to be open-domain and highly scalable, A/B testing experiments with real-world students demonstrate that both student learning outcomes and student motivation are substantially improved compared to typical online courses.
Parallel design of sparse deep belief network with multi-objective optimization

Deep belief network (DBN) is an import deep learning model and restricted Boltzmann machine (RBM) is one of its basic models. The traditional DBN and RBM have numerous redundant features. Hence an improved strategy is required to perform sparse operations on them. Previously, we have proposed our own sparse DBN (SDBN): using a multi-objective optimization (MOP) algorithm to learn sparse features, which solves the contradiction between the reconstruction error and network sparsity of RBM. Due to the optimization algorithm and millions of parameters of the network itself, the training process is difficult. Therefore, in this paper, we propose an efficient parallel strategy to speed up the training of SDBN networks. Self-adaptive Quantum Multi-objectives Evolutionary algorithm based on Decomposition (SA-QMOEA/D) that we have proposed as the multi-objective optimization algorithm has the hidden parallelism of populations. Based on this, we not only parallelize the DBN network but also realize the parallelism of the multi-objective optimization algorithm. In order to further verify the advantages of our approach, we apply it to the problem of facial expression recognition (FER). The obtained experimental results demonstrate that our parallel algorithm achieves a significant speedup performance and a higher accuracy rate over previous CPU implementations and other conventional methods.
Photonic Associative Learning Neural Network Based on VCSELs and STDP

In this article, we propose a photonic neural network (PNN) to emulate associative learning and forgetting for the first time. The PNN contains three photonic neurons based on 1550nm long-wavelength vertical-cavity surface-emitting lasers (VCSELs) subject to double polarized optical injection. A computational model of the PNN is derived based on the well-known spin-flip model. According to the learning rule named spike-time-dependent plasticity (STDP), the connection weight between neurons in the PNN is modified by self-learning. The simulation results show that, during the associative learning process, an association of feeding food and bell ringing is established within the Long-term synaptic potentiation window of the STDP; while, the association is forgotten within the Long-term depression window of the STDP in the forgetting process. Moreover, the effects of time interval between two pre-synaptic spikes on the speed of associative learning and forgetting are considered in detail. Finally, we further realize pattern recall based on the photonic associative learning, which shows promises for emerging applications of a large-scale energy-efficient PNN.