Reviewerly/exHarmony huggingface dataset:
Autonomous learning of foreign language based on facial emotion recognition and cloud computing

Due to the limitations of the learning environment and unguided guidance, students’ autonomous learning of foreign languages after class is not effective. In order to improve the efficiency of autonomous learning of foreign languages, this paper builds a foreign language self-learning system based on facial emotion recognition algorithm and cloud computing platform. Moreover, this paper uses emotion recognition algorithms to identify students’ status and guide them to improve students’ autonomous learning and improve the system’s operating efficiency through cloud computing platforms. In addition, this article combines the needs of autonomous learning to perform facial emotion matching and builds the corresponding functional modules of the system according to the requirements of autonomous learning and designs a sophisticated three-level network structure to achieve a balance between detection performance and real-time performance. In order to verify the performance of the system, an experiment was carried out through the accuracy rate of student’s autonomous state emotion recognition and the English improvement of students’ autonomous learning. The research results show that the foreign language autonomous learning system constructed in this paper has good performance.
Decomposition of Attributes Oriented Software Trustworthiness Measure Based on Axiomatic Approaches

In order to make the software trustworthiness measures more rigorous, we once applied axiomatic approaches to measure software trustworthiness, and established a software trustworthiness measure based on the decomposition of attributes (STMBDA for short). For the sake of validating the effectiveness of STMBDA, in this paper we use it to assess the trustworthiness of 23 spacecraft softwares whose total code is about 300,000 lines. The validation result shows that STMBDA can effectively evaluate the trustworthiness of the spacecraft softwares and identify the weak links in the development process.
Data Processing for Optimizing Naturalness of Vietnamese Text-to-speech System

End-to-end text-to-speech (TTS) systems has proved its great success in the presence of a large amount of high-quality training data recorded in an anechoic room with high-quality microphones. Another approach is to use available source of found data like radio broadcast news. We aim to optimize the naturalness of TTS system on the found data using a novel data processing method. The data processing method includes 1) utterance selection and 2) prosodic punctuation insertion to prepare training data which can optimize the naturalness of TTS systems. We showed that using the processing data method, an end-to-end TTS achieved a mean opinion score (MOS) of 4.1 compared to 4.3 of natural speech. We showed that the punctuation insertion contributed the most to the result. To facilitate the research and development of TTS systems, we distributed the processed data, which is known as Zalo-TTS database at https://forms.gle/6Hk5YkqgDxAaC2BU6; It consists of 18-hours of speech at a sampling rate of 44.1 kHz of one speaker with Hanoi dialect.
An Assessment of Unmanned Aircraft System Operations with the Extensible Trajectory Optimization Library

In the not too distant future, Unmanned Aircraft Systems (UAS) will be a source of economic power in urban environments. These systems will be used for package delivery, building inspections, filming, and many other tasks. However, economic benefits should not outweigh public safety. These systems must not collide into buildings, and they must maintain a safe separation distance from neighboring systems. In other words, UAS operations should be assessed for safety. This paper assesses how safety requirements in the urban environment influence UAS operations. It presents a novel way of performing this assessment by using the Extensible Trajectory Optimization Library (ETOL) to continuously solve a vehicle guidance problem (VGP) in a multi-agent robot simulator. This paper introduces the common structure of a VGP, along with a VGP formulation for a UAS in an urban environment. In addition, a platform for safety assessments is presented, along with recommendations for improving the safety of UAS operations.
Docker-Based Energy Management System Development and Deployment Methods

This paper proposes a Docker-based energy management system (DEMS) architecture to solve the problems of slow deployment and low flexibility of traditional energy management system (TEMS). Compared with TEMS, DEMS uses containerized packaging, the deployment environment and the number of Web Service releases are reduced from at least three times to one, and the workload is reduced by about two times. When the system has high concurrency, the application can be elastically expanded by adding or subtracting instances to calmly cope with peak traffic. DEMS applications use a mirrored development method. During the update process, only the Dockerfile needs to be modified first, and then the container instance is regenerated according to the new Dockerfile, which speeds up and simplifies the application update process. On the other hand, Docker virtualization is more lightweight and convenient than VMs, effectively reducing virtualization costs. This architecture has been applied to the upgrade and transformation of an actual power system EMS in China, which has good actual operating results.
Three-layer Authentication in Keystroke Dynamics using Time based Tool

Abstract Having a secure and sound system is the most important need of the end-user. Confidential and authentic information about the system must be available to the genuine user when required. This paper presents a generalized Keystroke Dynamics Technique for identification of genuine users. The method works for the authentication of the user while user is entering the password to using the system. It is a three-layer approach which first check the typing pattern while entering the password then it also monitors the system while user is using the system. Different users have different typing pattern which could be used to recognize a user. For identification of the user a time-based tool is used to collect data pertaining to the typing time of each user for words of different lengths. This is a very easy and cost-effective way of collecting data for differentiating between a genuine user and imposter. At first layer elbow method is used to know unknown targets depending upon different word combinations. For the second layer principal component analysis (PCA) is used to find suitable factors where user typing pattern is making users indistinguishable. For the third step Long-Short Term Memory (LSTM) technique is used to forecast whether the user is a genuine user or an imposter.
Interactive adaptation of Hand-over Motion by a Robot Partner for Comfort of receiving

Recently various types of robot partners have been developed to conduct human-friendly physical support to people. In this study, we proposed a system that enables human beings to adapt interactively the hand-over motion of the robot for each human posture. Basically, it is difficult for a robot partner to plan such a human-friendly hand-over motions beforehand. Therefore, we focus on the degree of human comfortability on hand-over motion when the human receive an object. To evaluate the current hand-over motion, a human applies a force to the force sensor at the gripper when receiving an object. We apply fuzzy inference to adjust the position and direction of the robot gripper in hand-over motions according to the magnitude of applied force. And the position and direction at the next hand-over motion are determined. However, the adjustment is strongly dependent on the human postures. Therefore, we apply a neural network to classify the types of human postures. Finally, we show several experimental results of the interactive adaptation on hand-over motions, and discuss the effectiveness of the proposed method.
A Load Balancing VMs Migration Approach for Multi-tier Application in Cloud Computing Based on Fuzzy Set and Q-Learning Algorithm

Cloud computing technology provides computing resource as utilities which face several resources management challenges. In the infrastructure service cloud, how to efficiently manage VMs to balance both economical cost and ensure QoS is focused by many researchers. VMs migration solution for cloud computing aims at different purposes such as cost, energy, performance, stability, availability, and depending on stakeholder’s objectives such as service providers, customers, and end users. However, its objectives are usually conflicted with each other. In addition, the optimal resource utilization problem in cloud infrastructure is usually the NP-Hard or NP-Complete class. In this paper, we formalize the VMs migration problem and develop a corresponding algorithm using Markov decision process and Q-Learning. The results of the simulation demonstrate the efficiency of our proposal. The proposed VMs migration algorithm has been benchmarked to the Round-Robin algorithm in order to highlight their strength and feasibility.
Digital Marketing Technologies Selection Under Z-Environment

As a result of advanced technological changes, the customer-company relationship becomes more digital. Recent digital marketing technologies have profoundly changed the: products produced; prices offered to customers to boost the profit gained; places of brick-and-mortar to virtual ones; interaction and communication way to enhance the customer satisfaction, and personalized promotion to influence customer buying decision; and targeting customer segments. As a result, companies can influence the consumer buying behavior at the right time, with the right product/service, in an enhanced manner. In this paper, we investigate the selection of optimal digital marketing technology among Artificial Intelligence (AI), Internet of Things (IoT), and Virtual or Augmented Reality (VR/AR). Our research is based on consistency driven approach for determination of the weights of criteria that are product, price, place, promotion and targeting the customer, and distance between ideal positive and negative solution under Z-number. A numerical example is provided in order to check the validity of the considered approach.
IC4Windows–Hand Gesture for Controlling MS Windows

IC4Windows (Intelligent Companion for Windows) provides a natural Human computer interaction through hand gesture to avoid direct contact with computer device. The hand gestures trigger a windows command that usually used by a user when they use MS windows system. Some gesture has a different purpose depending on the application being operated by the user. The proposed system using external depth camera to capture the frame as an input. The depth data is used to remove unnecessary inputs such as background, face, etc., so that our input is more specific to help get a high accuracy. The gestures are consists of two type, first is static gesture and second is dynamic gestures. These two types of gestures have a different characteristic, the static gestures use each single frame as an input while the dynamic gestures require a sequence of frame as an input. We use a different method to handle each type of gestures, CNN is used for static gestures and 3DCNN is used for dynamic gestures. The proposed method provides a recognition rate of up to 92% and average speed is up to 30 FPS.
Efficient Real-Time Radial Distortion Correction for UAVs

In this paper we present a novel algorithm for onboard radial distortion correction for unmanned aerial vehicles (UAVs) equipped with an inertial measurement unit (IMU), that runs in real-time. This approach makes calibration procedures redundant, thus allowing for exchange of optics extemporaneously. By utilizing the IMU data, the cameras can be aligned with the gravity direction. This allows us to work with fewer degrees of freedom, and opens up for further intrinsic calibration. We propose a fast and robust minimal solver for simultaneously estimating the focal length, radial distortion profile and motion parameters from homographies. The proposed solver is tested on both synthetic and real data, and perform better or on par with state-of-the-art methods relying on pre-calibration procedures. Code available at: https://github.com/marcusvaltonen/HomLib. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Detection of transcoded HEVC videos based on in-loop filtering and PU partitioning analyses

With the increasing maturity of video editing technology, forgers are more inclined to transcode videos to High Efficiency Video Coding (HEVC) videos, as HEVC not only enables people to enjoy high-definition videos but also allows broadcasters to stream it more efficiently across networks. Therefore, to verify the originality and authenticity, it is of great significance to propose an algorithm for detecting transcoded HEVC videos. In this paper, a theoretical model of video transcoding is first constructed, and a novel transcoding detection algorithm based on In-loop Filtering and Prediction Units (PU) Partition (IFPP) is proposed. By analyzing the statistical characteristics of strong and normal filtering modes in deblocking filtering and calculating offset values in Sample Adaptive Offset (SAO) filtering, the transcoding traces in inter-coded frames can be captured. In addition, PU partition statistics are also extracted to make full use of traces in intra-coded frames. By fusing these subfeatures, the proposed IFPP feature with 17 dimensions can be obtained, which is further fed to the Support Vector Machine (SVM) classifier. Finally, experiments are conducted on datasets with various coding parameters. Results show that the proposed algorithm outperforms existing algorithms and has better robustness.
Does co-presence affect the way we perceive and respond to emotional interactions?

Abstract This study compared how two virtual display conditions of human body expressions influenced explicit and implicit dimensions of emotion perception and response behavior in women and men. Two avatars displayed emotional interactions (angry, sad, affectionate, happy) in a “pictorial” condition depicting the emotional interactive partners on a screen within a virtual environment and a “visual” condition allowing participants to share space with the avatars, thereby enhancing co-presence and agency. Subsequently to stimulus presentation, explicit valence perception and response tendency (i.e. the explicit tendency to avoid or approach the situation) were assessed on rating scales. Implicit responses, i.e. postural and autonomic responses towards the observed interactions were measured by means of postural displacement and changes in skin conductance. Results showed that self-reported presence differed between pictorial and visual conditions, however, it was not correlated with skin conductance responses. Valence perception was only marginally influenced by the virtual condition and not at all by explicit response behavior. There were gender-mediated effects on postural response tendencies as well as gender differences in explicit response behavior but not in valence perception. Exploratory analyses revealed a link between valence perception and preferred behavioral response in women but not in men. We conclude that the display condition seems to influence automatic motivational tendencies but not higher level cognitive evaluations. Moreover, intragroup differences in explicit and implicit response behavior highlight the importance of individual factors beyond gender.
Comparison of the accuracy and the execution time of classification algorithms for Bulgarian literary works

The purpose of this paper is to compare the accuracy and the execution time of machine learning algorithms for classification of texts, written by Bulgarian authors. The algorithms examined are: Multinomial Naive Bayes classifier, Support Vector Machines, Random Forest and AdaBoost. The results show that the Multinomial Naive Bayes classifier is the most accurate and fastest algorithm for classifying texts by two authors with an equal number of poems in Bulgarian language. The ensemble algorithm AdaBoost is the most accurate for unbalanced data classification. The Support Vector Classification has the highest accuracy. In a classification with an unbalanced set of data, the fastest algorithm is Bernoulli Naive Bayes classifier.
New color image encry, tion algorithm based on double chaos system

Abstract Due to the specific characteristics of a chaotic system described by a set of nonlinear deterministic dynamic equations, encryption based on chaos has high security. Therefore, this paper proposes a very safe and effective new color image encryption algorithm based on double chaos system. First, a true color image is divided into three channels, and under the operation of the control signal, different pseudo-random sequences generated by the Chirikov standard image are selected for XOR operation with the image pixels of a certain channel. Secondly, this paper provides a process of generating replacement boxes using one-dimensionaltial attacks and entropy-based attacks. Therefore, the algorithm has a good encryption effect and has broad application prospects in the field of image encryption.
Data Embedding in Color Images: A Secure Data Communication Framework Based on Modular Arithmetic

Usage of the Internet has boomed since its inception. This has given rise to potent tools that can breach the privacy and security of the data over the insecure transmission lines. In such a situation, data embedding has evolved as an efficient technology to cater to the problems of security and privacy. A data embedding algorithm is said to be efficient and secure if it provides high payload while maintaining imperceptivity. In this chapter, a high capacity data embedding technique based on modular arithmetic has been proposed and analyzed using color images. The cover image is divided into its constituent planes, and data is embedded into each plane using modular operation. The experimentation of the proposed technique has been done on several color images, and the results have been evaluated in terms of various quality indices including peak signal-to-noise ratio (PSNR), mean square error (MSE), structural content (SC), and normalized absolute error (NAE). The results reveal that the proposed work provides high imperceptivity for a fairly high payload. Histogram analysis of the scheme shows the effectiveness of the proposed scheme to statistical attack. The invariance between the histogram of the original image and stego image is the testimony of its resilience to statistical attacks. It is pertinent to mention here that the proposed data hiding system yields stego images with an average PSNR of around 46 dB for the payload of two bits per pixel (2bpp).
VAGAN: Vehicle-Aware Generative Adversarial Networks for Vehicle Detection in Rain

Vision-based vehicle detection under bad weather conditions is still a challenging problem. Adhesive raindrops on windshield have been known to diffract light and distort parts of the scene behind them. In this paper, we propose a Vehicle-Aware Generative Adversarial Networks (VAGAN) to improve vehicle detection from rain images. We train a Generative Adversarial Network (GAN) on image pairs, each comprising of an original rain image and one that is manually labelled with colored bounding boxes of the vehicles therein. The latter represents a fake version of the original image emphasizing the regions of interest. To further enhance vehicle awareness, we exploit the fact that the vehicle rear lights are usually turned on during rainy conditions to compute a saliency map of image, and use it formulate a background preserving constrain on the learning vehicle loss function. We show that this novel adversarial framework is able to generate new images with colored regions overlay over vehicles, hence effectively learning to differentiate image background from vehicles. The final vehicle detection in the generated images is not affected by image translation noise because we can simply use color segmentation to localize the vehicles. Experimental results on a large dataset show that our approach is an effective way to solve vehicle detection in rain images, achieving state-of-the-art performance.
Date of Birth Information Extraction from the Internet by Deep Learning Approach

The problem of ambiguity, incompleteness, and high expense of information extraction by human could be alleviated by using computer to extract such information, however, basic pattern-matching approach still has limited performance especially against a language with no punctuation or clear mark like Thai. This work proposes a Deep-learning approach based on BERT-th and Keras. Comparison with manually maintained websites and pattern matching approach, this paper shows that the deep learning approach can achieve better recall and precision especially for Thai language.
Batch-Normalization-based Soft Filter Pruning for Deep Convolutional Neural Networks

As convolutional neural network contains many redundant parameters, a lot of methods have been developed to compress the network for accelerating inference. Among these, network pruning, which is a kind of widely used approaches, can effectively decrease the memory capacity and reduce the computation cost. Herein, we propose a competitive pruning approach based on Soft Filter Pruning (SFP) by taking account of the scaling factors y of Batch Normalization (BN) layers as the criterion of filter selection strategy. During the soft pruning procedure, in each epoch only y values of BN layers less than threshold are set to zero instead of setting the weights of selected filters in convolutional layers to zero. Compared to the existing approaches, the proposed method can obtain a highly increased accuracy on image recognition. Notably, on CIFAR-10, the proposed method reduces the same 40.8% FLOPs as SFP on ResNet-110 with even 0.87% top-1 accuracy improvement.
Considering Reliability of Deep Learning Function to Boost Data Suitability and Anomaly Detection

The increased demand of Deep Neural Networks (DNNs) in safety-critical systems, such as autonomous vehicles, leads to increasing importance of training data suitability. Firstly, we focus on how to extract the relevant data content for ensuring DNN reliability. Then, we identify error categories and propose mitigation measures with emphasis on data suitability. Despite all efforts to boost data suitability, not all possible variations of a real application can be identified. Hence, we analyse the case of unknown out-of-distribution data. In this case, we suggest to complement data suitability with online anomaly detection using FACER that supervises the behaviour of the DNN.
Should Simplicity Be Always Preferred to Complexity in Supervised Machine Learning?

In this short paper, a theoretical analysis of Occam’s razor formulation through statistical learning theory is presented, showing that pathological situations exist for which regularization may slow down supervised learning instead of making it faster.