Reviewerly/exHarmony huggingface dataset:
Human Distraction Detection from Video Stream Using Artificial Emotional Intelligence

This paper addresses the problem of identifying certain human behavior such as distraction and also predicting the pattern of it.This paper proposes an artificial emotional intelligent or emotional AI algorithm to detect any change in visual attention for individuals.Simply, this algorithm detects human's attentive and distracted periods from video stream.The algorithm uses deviation of normal facial alignment to identify any change in attentive and distractive activities, e.g., looking to a different direction, speaking, yawning, sleeping, attention deficit hyperactivity and so on.For detecting facial deviation we use facial landmarks but, not all landmarks are related to any change in human behavior.This paper proposes an attribute model to identify relevant attributes that best defines human's distraction using necessary facial landmark deviations.Once the change in those attributes is identified, the deviations are evaluated against a threshold based emotional AI model in order to detect any change in the corresponding behavior.These changes are then evaluated using time constraints to detect attention levels.Finally, another threshold model against the attention level is used to recognize inattentiveness.Our proposed algorithm is evaluated using video recording of human classroom learning activity to identify inattentive learners.Experimental results show that this algorithm can successfully identify the change in human attention which can be used as a learner or driver distraction detector.It can also be very useful for human distraction detection, adaptive learning and human computer interaction.This algorithm can also be used for early attention deficit hyperactivity disorder (ADHD) or dyslexia detection among patients.
Relating the entanglement and optical nonclassicality of multimode states of a bosonic quantum field

The quantum nature of the state of a bosonic quantum field may manifest itself in its entanglement, coherence, or optical nonclassicality. Each of these distinct properties is known to be a resource for quantum computing or metrology and can be evaluated via a variety of measures, witnesses, or monotones. Here, we provide quantitative and computable bounds relating, in particular, some entanglement measures with optical nonclassicality measures. Overall, these bounds capture the fact that strongly entangled states must necessarily be strongly optically nonclassical. As an application, we infer strong bounds on the entanglement that can be produced with an optically nonclassical state impinging on a beam splitter. Then, focusing on Gaussian states, we analyze the link between the logarithmic negativity and a specific nonclassicality measure called quadrature coherence scale.
Exploring Shodan From the Perspective of Industrial Control Systems

As an essential component of the critical infrastructure, the Industrial Control System (ICS) is facing increasing cyber threats. The emergence of the Shodan search engine also magnified this threat. Since it can identify and index Internet-connected industrial control devices, the Shodan search engine has become a favorite toolkit for attackers and penetration testers. In this paper, we use honeypot technology to conduct a comprehensive exploring on Shodan search engine. We first deploy six distributed honeypot systems and collect three-month traffic data. For exploring Shodan, we design a hierarchical DFA-SVM recognition model to identify Shodan scans based on the function code and traffic feature, which is adapted to find the Shodan and Shodan-like scanners superior to the predominant method of reverse resolving IPs. Finally, we conduct an in-depth analysis for Shodan scans and evaluate the impact of Shodan on industrial control systems in terms of scanning time, scanning frequency, scanning port, region preferences, ICS protocol preferences and ICS protocol function code proportion. Accordingly, we provide some defensive measures to mitigate Shodan threat.
Optimized and Secure Pairing-Friendly Elliptic Curves Suitable for One Layer Proof Composition

A zero-knowledge proof is a method by which one can prove knowledge of general non-deterministic polynomial (NP) statements. SNARKs are in addition non-interactive, short and cheap to verify. This property makes them suitable for recursive proof composition, that is proofs attesting to the validity of other proofs. To achieve this, one moves the arithmetic operations to the exponents. Recursive proof composition has been empirically demonstrated for pairing-based SNARKs via tailored constructions of expensive pairing-friendly elliptic curves namely a pair of 753-bit MNT curves, so that one curve's order is the other curve's base field order and vice-versa. The ZEXE construction restricts to one layer proof composition and uses a pair of curves, BLS12-377 and CP6-782, which improve significantly the arithmetic on the first curve. In this work we construct a new pairing-friendly elliptic curve to be used with BLS12-377, which is STNFS-secure and fully optimized for one layer composition. We propose to name the new curve BW6-761. This work shows that it is at least five times faster to verify a composed SNARK proof on this curve compared to the previous state-of-the-art, and proposes an optimized Rust implementation that is almost thirty times faster than the one available in ZEXE library.
Internal Control Systems and Operating Performance: Evidence from Small and Medium Enterprises (SMEs) in Ondo State

This study examined the internal control systems and operating performance of SMEs in Ondo State. Specifically, the study analyzed the impact of internal control system components on the profitability of SMEs in Nigeria and a total of 120 SMEs were sampled for the study. Data used in the study were collected with a questionnaire. Collected and collated data were analyzed using logistic regression estimation. The result showed that the probability that an SMEs will have high level of operating performance in terms of higher actual annual profit increases insignificantly by 0.78% and 1.95% respectively, when there is a unit measure of improvement in the control environment and control activities and that the probability declines insignificantly by 0.051%, 0.33% and 4.53% respectively, with a unit measure of improvement in risk assessment, information and communication and monitoring activities. The study established that the components of internal control system has no significant impact on the operating performance of SMEs in Ondo State, though the control environment and control activities have a positive impact on the probability of an enterprise recording a high operating performance. Hence, SMEs should maintain and leverage the positive impact of the control environment and control activities to boost their operating performance, However, issues surrounding risk assessment, information and communication as well as monitoring activities should be addressed systematically based on the reality of the operational structure of each enterprise.
End-to-end Conversion Speed Analysis of an FPT.AI-based Text-to-Speech Application

In this paper, an FPT.AI-based text-to-speech (TTS) application is developed that converts Vietnamese text into spoken words. The application is developed based on Django for Python and in the form of an interactive web page which is connected to an FPT.AI server through its application programming interface (API). The application supports conversion of text to seven different Vietnamese speeches. Four out of seven voices can be used to convert up to 500 characters in a single transaction while the others support that of 400 characters. Based on the results obtained, the first conversion time takes up to 10 s to convert 400-character text into speech while the subsequent times, given same text, it takes under 1.8 s for the conversion. This is applicable to all voices.
Detecting the occluding contours of the uterus to automatise augmented laparoscopy: score, loss, dataset, evaluation and user study

The registration of a preoperative 3D model, reconstructed, for example, from MRI, to intraoperative laparoscopy 2D images, is the main challenge to achieve augmented reality in laparoscopy. The current systems have a major limitation: they require that the surgeon manually marks the occluding contours during surgery. This requires the surgeon to fully comprehend the non-trivial concept of occluding contours and surgeon time, directly impacting acceptance and usability. To overcome this limitation, we propose a complete framework for object-class occluding contour detection (OC2D), with application to uterus surgery. Our first contribution is a new distance-based evaluation score complying with all the relevant performance criteria. Our second contribution is a loss function combining cross-entropy and two new penalties designed to boost 1-pixel thickness responses. This allows us to train a U-Net end to end, outperforming all competing methods, which tends to produce thick responses. Our third contribution is a dataset of 3818 carefully labelled laparoscopy images of the uterus, which was used to train and evaluate our detector. Evaluation shows that the proposed detector has a similar false false-negative rate to existing methods but substantially reduces both false-positive rate and response thickness. Finally, we ran a user study to evaluate the impact of OC2D against manually marked occluding contours in augmented laparoscopy. We used 10 recorded gynecologic laparoscopies and involved 5 surgeons. Using OC2D led to a reduction of 3 min and 53 s in surgeon time without sacrificing registration accuracy. We provide a new set of criteria and a distance-based measure to evaluate an OC2D method. We propose an OC2D method which outperforms the state-of-the-art methods. The results obtained from the user study indicate that fully automatic augmented laparoscopy is feasible.
Optical implementation of image encryption based on digital holography and computer generated hologram

An experimental implement method of optical image encryption based on digital holography and computer generated hologram (CGH) is proposed in this paper. The actual object is encrypted by double random phase in the digital holography system and CGH encoding process, and after that the decryption is achieved in optical Fourier transform and a Fresnel diffraction system based on a spatial light modulator. In this method, both physical and digital images can be encrypted in optical systems. Meanwhile, besides the double random phase keys, Fresnel diffraction distance, wavelength key in digital holography systems and CGH coding parameters improve the security. Experimental results are in agreement with numerical calculations which show the feasibility and practicability of the proposed experimental method.
Foreground-Background Separation via Generalized Nuclear Norm and Structured Sparse Norm Based Low-Rank and Sparse Decomposition

Low-rank and sparse decomposition (LRSD) has attracted wide attention in video foregroundbackground separation and many other fields.However, the traditional LRSD methods have many tough problems, such as the problems of the low accuracy of the surrogate functions of rank and sparsity, ignoring the spatial information of the videos and sensitivity to noise, etc.To deal with these problems, this paper proposes the generalized nuclear norm and structured sparse norm (GNNSSN) method based LRSD for video foreground-background separation, which introduces the generalized nuclear norm (GNN) and the structured sparse norm (SSN) to approximate the rank function and the l 0 -norm of the LRSD method.In addition, we extend our proposed model to a robust model against noise for practical applications, and we called the extended method as the robust generalized nuclear norm and structured sparse norm (RGNNSSN) method.At last, we use the alternating direction method of multipliers (ADMM) to solve our proposed two methods.Experimental results and discussions on video foreground-background separation demonstrate that our proposed two methods have better performances than other LRSD based foreground-background separation methods.
Particle Swarm Optimization for Evolving Deep Convolutional Neural Networks for Image Classification: Single- and Multi-Objective Approaches

Convolutional neural networks (CNNs) are one of the most effective deep learning methods to solve image classification problems, but the design of the CNN architectures is mainly done manually, which is very time consuming and requires expertise in both problem domains and CNNs. In this chapter, we will describe an approach to the use of particle swarm optimization (PSO) for automatically searching for and learning the optimal CNN architectures. We will provide an encoding strategy inspired by computer networks to encode CNN layers and to allow the proposed method to learn variable-length CNN architectures by focusing only on the single objective of maximizing the classification accuracy. A surrogate dataset will be used to speed up the evolutionary learning process. We will also include a multi-objective way for PSO to evolve CNN architectures in the chapter. The PSO-based algorithms are examined and compared with state-of-the-art algorithms on a number of widely used image classification benchmark datasets. The experimental results show that the proposed algorithms are strong competitors to the state-of-the-art algorithms in terms of classification error. A major advantage of the proposed methods is the automated design of CNN architectures without requiring human intervention and good performance of the learned CNNs.
HRL4IN: Hierarchical Reinforcement Learning for Interactive Navigation with Mobile Manipulators.

Most common navigation tasks in human environments require auxiliary arm interactions, e.g. opening doors, pressing buttons and pushing obstacles away. This type of navigation tasks, which we call Interactive Navigation, requires the use of mobile manipulators: mobile bases with manipulation capabilities. Interactive Navigation tasks are usually long-horizon and composed of heterogeneous phases of pure navigation, pure manipulation, and their combination. Using the wrong part of the embodiment is inefficient and hinders progress. We propose HRL4IN, a novel Hierarchical RL architecture for Interactive Navigation tasks. HRL4IN exploits the exploration benefits of HRL over flat RL for long-horizon tasks thanks to temporally extended commitments towards subgoals. Different from other HRL solutions, HRL4IN handles the heterogeneous nature of the Interactive Navigation task by creating subgoals in different spaces in different phases of the task. Moreover, HRL4IN selects different parts of the embodiment to use for each phase, improving energy efficiency. We evaluate HRL4IN against flat PPO and HAC, a state-of-the-art HRL algorithm, on Interactive Navigation in two environments - a 2D grid-world environment and a 3D environment with physics simulation. We show that HRL4IN significantly outperforms its baselines in terms of task performance and energy efficiency. More information is available at this https URL.
Evaluation of educational quality performance on virtual campuses using fuzzy inference systems

The general objective of this study is to analyze student satisfaction with the use of virtual campuses in university teaching in order to discover the main variables influencing the overall online teaching-learning process that give quality to the virtual educational process. To this end, an ex-post-facto research methodology was applied to 1084 university students, who completed an ad hoc designed questionnaire, which allowed us to carry out descriptive analysis, classification trees and fuzzy inference systems using SPSS and Matlab software. The results suggest that four variables predominantly influence the quality of the teaching-learning processes in virtual campuses: satisfactory teacher responses to student questions and observations, the positive attitude of teachers towards the use of information and communication technologies, students having appropriate digital skills, and activities that encourage ideas and debate.
VIRET Tool with Advanced Visual Browsing and Feedback

VIRET is a video retrieval system that performed competitively in several recent interactive multimedia search competitions. For the Lifelog Search Challenge 2020, we implement several new features that focus on result set presentation based on dynamically computed self-organized maps, as well as additional feedback about the distribution of relevance scores for a query. The latter feature provides users with more insight to assess input queries and could help with the design of new ranking models in the future.
A Reinforcement Learning-Based Framework for Robot Manipulation Skill Acquisition

This paper studies robot manipulation skill acquisition based on a proposed reinforcement learning framework. Robot can learn policy autonomously by interacting with environment with a better learning efficiency. Aiming at the manipulator operation task, a reward function design method based on objects configuration matching (OCM) is proposed. It is simple and suitable for most Pick and Place skills learning. Integrating robot and object state, high-level action set and the designed reward function, the Markov model of robot manipulator is built. An improved Proximal Policy Optimize algorithm with manipulation set as the output of Actor (MAPPO) is proposed as the main structure to construct the robot reinforcement learning framework. The framework combines with the Markov model to learn and optimize the skill policy. A same simulation environment as the real robot is set up, and three robot manipulation tasks are designed to verify the effectiveness and feasibility of the reinforcement learning framework for skill acquisition.
Encryption of RGB Images by Means of a Novel Cryptosystem using Elliptic Curves and Chaos

A new symmetric cryptosystem is proposed in this paper which will be used to carry out image encryption without compression. The proposed system has been developed to encrypt images in BMP format. A significant contribution of this investigation is the use of one solution point of a zero valued constant elliptic curve in order to generate a set of 15 encryption keys. On the other hand, using a differential equation that produce chaotic numbers a permutation and its inverse permutation are generated. Every encryption key and both permutations are the same size as the image. The proposed cryptosystem is tested for the purpose of verifying its resistance against several cryptographic attacks as well as the randomness and quality of its ciphering. Results are showed and compared with some recent scientific articles published between 2017 and 2019.
Neural Mixed Counting Models for Dispersed Topic Discovery

Mixed counting models that use the negative binomial distribution as the prior can well model over-dispersed and hierarchically dependent random variables; thus they have attracted much attention in mining dispersed document topics. However, the existing parameter inference method like Monte Carlo sampling is quite time-consuming. In this paper, we propose two efficient neural mixed counting models, i.e., the Negative Binomial-Neural Topic Model (NB-NTM) and the Gamma Negative Binomial-Neural Topic Model (GNB-NTM) for dispersed topic discovery. Neural variational inference algorithms are developed to infer model parameters by using the reparameterization of Gamma distribution and the Gaussian approximation of Poisson distribution. Experiments on real-world datasets indicate that our models outperform state-of-the-art baseline models in terms of perplexity and topic coherence. The results also validate that both NB-NTM and GNB-NTM can produce explainable intermediate variables by generating dispersed proportions of document topics.
An empirical analysis of binary transformation strategies and base algorithms for multi-label learning

Investigating strategies that are able to efficiently deal with multi-label classification tasks is a current research topic in machine learning. Many methods have been proposed, making the selection of the most suitable strategy a challenging issue. From this premise, this paper presents an extensive empirical analysis of the binary transformation strategies and base algorithms for multi-label learning. This subset of strategies uses the one-versus-all approach to transform the original data, generating one binary data set per label, upon which any binary base algorithm can be applied. Considering that the influence of the base algorithm on the predictive performance obtained by the strategies has not been considered in depth by many empirical studies, we investigated the influence of distinct base algorithms on the performance of several strategies. Thus, this study covers a family of multi-label strategies using a diversified range of base algorithms, exploring their relationship over different perspectives. This finding has significant implications concerning the methodology of evaluation adopted in multi-label experiments containing binary transformation strategies, given that multiple base algorithms should be considered. Despite these improvements in strategy and base algorithms, for many data sets, a large number of labels, mainly those less frequent, were either never predicted, or always misclassified. We conclude the experimental analysis by recommending strategies and base algorithms in accordance with different performance criteria.
Query-Adaptive Remote Sensing Image Retrieval Based on Image Rank Similarity and Image-to-Query Class Similarity

Many image features have been proposed for image retrieval; hence, effectively fusing these features to alleviate the large variation in performance among image queries when using single image features has become a major challenge in remote sensing (RS) image retrieval. Because high-resolution remote sensing images have abundant and complex visual contents, accurately measuring the similarity between two images is another important problem. To address these challenges, we propose a novel RS image retrieval method that uses query-adaptive feature weights to fuse features and utilizes two image similarities to improve retrieval performance. First, we use the image rank similarity, which measures the similarity between two images according to their corresponding top-m image lists from a reference image collection, to calculate the similarity of each feature between a query image and each retrieved image. Then, we assign a weight to each feature to fuse these features via our query-adaptive weighting method. Finally, we take the query image and its neighborhood set selected from the retrieval dataset as the query class and utilize the image-to-query class similarity to re-rank the retrieval results. Extensive experiments are conducted on two publicly available RS image databases. Compared with the state-of-the-art methods, the proposed method can significantly enhance the retrieval precision.
New Techniques for Pairwise Symmetry Breaking in Multi-Agent Path Finding

We consider two new types of pairwise path symmetries which appear in the context of Multi-Agent Path Finding (MAPF). The first of them, corridor symmetry, arises when two agents attempt to pass through the same narrow passage but in opposite directions. The second, target symmetry, arises when the shortest path of one agent requires the target location of a second agent after the second agent has already arrived. These symmetries can produce an exponential blowup in the space of possible collision resolutions, leading to timeout failure even for state-of-the-art algorithms such as Conflict-Based Search. We propose to break symmetries using new reasoning techniques that: (1) detect each type of situation and, (2) resolve them by introducing specialized constraints. We implement our ideas in the context of Conflict-Based Search where, in a range of experiments, we report up to an order-of-magnitude improvement in runtime performance and, in some cases, more than a doubling in success rate.