Reviewerly/exHarmony huggingface dataset:
Effectiveness Evaluation of Humanware Service in Novel Decision System

Humanware service (HwS) is a critical component of Novel Decision System (NDS). The technology of effectiveness evaluation is an important one in the technological system for HwS. The result can provide a reference for the evolution of them, and it also provides an objective basis in the evolution process of NDS. HwS has the characteristics of multi-object and multi-critical. Hence, PCA method is used to abate the number of multi-criteria of HwS, which may cut down the accurate and objective assessment of the effectiveness evaluation; Then, the TOPSIS method is used to evaluation of HwS based on the principal component; Last, by the simulation and the compare result of compatibility degree shows that the PCA-TOPSIS method is feasibility and effectiveness.
Assessing the Impact of Mobile Educational Games on Student’s Success within Mathematics Subject in Primary Schools

The research focus is set in investigating the impact of educational games as instructional strategy in student's success within the course of Mathematics in High School. In order to assess the impact we have developed a mathematical mobile Game for Android Devices, to be used as a case study, and assessed the students' achievements. We have used quantitative research methodology and as research method used a questionnaire to collect the data. We collected the data before and after playing the game, so we can assess the difference of the time spent on educational games and the level of transferred knowledge. The system is expected to help high school students to easily orient and engage during lecture classes and easily find and use the learning content. The system has been evaluated on its functionalities and the usability, on two groups of users, one group with computer science background and the second group of users as novices with different field background. Afterwards we used regression analyses to determine the impact. The research study tries to contribute with identified impacting factors for such systems, analyses of the improvement in user friendliness, and usability. Insights and recommendations are provided.
Monocular Reconstruction of Conformal Surfaces through Optimization

In this paper we study monocular reconstruction of extensible surfaces undergoing conformal deformation. Given a 3D template, its image and a set of point correspondences from a specific image of the deformed surface, the goal is to determine the 3D positions of the points as well as the stretching factor at each point. To perform such reconstruction, we define an optimization procedure that makes use of the re-projection error, so-called upper-bound model, constraints associated with conformal deformation, and those resulting from the assumption that the motion amplitude of boundary points is restricted. These points lie on the surface border which varies by the camera viewpoint. The upper-bound model is used in a variational form where the bound on the Euclidean distance of point pairs decreases in a sequence of optimizations, from which the best reconstruction is selected. Our experimental study revealed that this approach achieves accurate results.
Improved Automatic Speaker Verification System Using Deep Learning

In this paper we have implemented and designed an accurate and robust automatic speaker verification (ASV) system. There is a constant need for improving the performance of the ASV because of its many advantages over other biometric means. In this work we have experimented with the VoxCeleb dataset. The VoxCeleb Dataset which has more than 1200 individuals and around 300–500 utterances each. We have proposed a new approach to authenticate users at a text independent level. Firstly the audio files are converted to spectrograms which are pre-processed and then are classified using Convolutional Neural Networks (CNN) and a model is created at a text independent level. The classification of the individual speakers are made on the bases of Spectrogram. Finally, the performance evaluation is done using the training and validation accuracy plots.
Generating High-fidelity Cybersecurity Data With Generative Adversarial Networks

Machine learning for cyber defense offers the promise of detecting adversarial activity against the ground data systems managing critical space assets. A fundamental challenge facing machine learning research in cybersecurity is the lack of high-fidelity, shareable datasets for robust evaluation and testing of machine learning-based solutions. High-fidelity, real-world datasets are necessary for reliable benchmarking of nominal system behavior and malicious activity. Unfortunately, such realistic datasets of both nominal and adversarial activity are rarely shared publicly by data owners due to security and privacy concerns. Besides, the available adversarial data is sparse, which makes training models on malicious activity much harder. This situation has impeded and continues to impede the research and successful adoption of machine learning methods for cyber defense. Researchers have dealt with this problem by generating data within a low-fidelity lab environment, using classified and thus unshareable datasets, or downloading low-fidelity public datasets made available by others. We propose an innovative solution to the problem by employing machine learning methods to generate high-fidelity data. Specifically, we propose the use of Generative Adversarial Networks (GANs) to generate high-fidelity data for cybersecurity purposes. GANs have found successful image processing and natural language applications, but have not yet been investigated for cyber data generation. Our proposed approach first involves training the `discriminator' network of the GAN with a sample of real-world data consisting of malicious and nominal samples. We then use the `generator' network to generate new high-fidelity data samples consisting of an appropriate mix of malicious and nominal activity. We demonstrate applications of our architecture by generating high-fidelity cybersecurity data containing both malicious and nominal samples. We thoroughly evaluate the fidelity of our generated data using heuristics and evaluate its usefulness for machine learning applications using three different datasets. Overall, our approach results in high-fidelity, shareable datasets.
Technological Advancement and Pandemic

On 31 December 2019, the World Health Organization (WHO, Pneumonia of unknown cause in China, 2020) was notified of a cluster of cases with unknown reasons of the people being affected by pneumonia in the Wuhan City, China. On January 7, 2020, the Chinese authorities and experts declared the novel coronavirus as the root cause. It was in that very first instance labeled as Novel Coronavirus (WHO 2019) on February 11, 2020. A new type that has not been found before in human beings, Coronaviruses (CoV) are a diverse family of viruses that cause infections ranging from common colds to greater serious and grave illnesses. Augmenting their surveillance, countries around the world are tracking and finding new 2019-nCoV (novel coronavirus) cases. Use of modern technologies along with healthcare/medical systems is pervasive around the globe for an effective response and reduction of disease outbreak. Along with the evolution of biomedical response, an advanced technological response is also evolving to combat the pandemic. Technologies such as Big data, artificial intelligence, blockchain, remote sensing and others are linked to medical technologies for a prompt, rapid and effective response (Shaw et al., Governance, technology and citizen behavior in pandemic: lessons from COVID-19 in East Asia, Prog Disaster Sci 6:10009, 2020). This chapter covers a brief history of epidemics and technology, technology in healthcare, manufacturing production and surveillance under COVID-19 control measures.
Number of quantum measurement outcomes as a resource

Recently there have been fruitful results on resource theories of quantum measurements. Here we investigate the number of measurement outcomes as a kind of resource. We cast the robustness of the resource as a semi-definite positive program. Its dual problem confirms that if a measurement cannot be simulated by a set of smaller number of outcomes, there exists a state discrimination task where it can outperforms the whole latter set. An upper bound of this advantage that can be saturated under certain condition is derived. We also show that the possible tasks to reveal the advantage are not restricted to state discrimination and can be more general.
A General Modeling Framework for Open Wildlife Populations Based on the Polya Tree Prior

Abstract Wildlife monitoring for open populations can be performed using a number of different survey methods. Each survey method gives rise to a type of data and, in the last five decades, a large number of associated statistical models have been developed for analyzing these data. Although these models have been parameterized and fitted using different approaches, they have all been designed to either model the pattern with which individuals enter and/or exit the population, or to estimate the population size by accounting for the corresponding observation process, or both. However, existing approaches rely on a predefined model structure and complexity, either by assuming that parameters linked to the entry and exit pattern (EEP) are specific to sampling occasions, or by employing parametric curves to describe the EEP. Instead, we propose a novel Bayesian nonparametric framework for modeling EEPs based on the Polya tree (PT) prior for densities. Our Bayesian nonparametric approach avoids overfitting when inferring EEPs, while simultaneously allowing more flexibility than is possible using parametric curves. Finally, we introduce the replicate PT prior for defining classes of models for these data allowing us to impose constraints on the EEPs, when required. We demonstrate our new approach using capture–recapture, count, and ring-recovery data for two different case studies.
Challenges and Countermeasures of Network Security Law Enforcement

Abstract With the rapid development of the Internet, there are more and more illegal crimes on the Internet. In order to comply with the development of the times and meet the challenge of law enforcement, the requirements for the enforcement of network security enforcement are increasingly urgent. This article first elaborates the connotation and characteristics of network security law enforcement. Secondly, it analyzes the challenges faced by current network law enforcement. Finally, it points out the strategies for enforcing network security law enforcement.
RCPID: retina color perception-based image dehazing

Images captured from outdoor scenes are usually suffer from the atmospheric scattering and absorption, these natural phenomenon of hazy cause low contrast, poor quality of images, which severely affect their subsequent applications. Despite numerous image dehazing methods have been developed to improve the quality of hazy images, effective image dehazing method remains a challenging problem. In this paper, we propose a new method for retina color perception-based image dehazing, which considers the light response model based on the retina color perception mechanism, and the transmission estimation model combines with the dark channel prior. Specifically, an adaptive light response model is designed based on stimulus perception of retinal ganglion cells and lateral geniculate nucleus (LGN) neurones in the receptive field; and a new transmission estimation model combine with the dark channel prior is developed, thus we propose a retina color perception-based image dehazing method. Moreover, to validate the effectiveness of the proposed dehazing method, we test the method on various synthetic hazy images, and compare with several state-of-the-art image dehazing methods quantitatively. Extensive experimental results shown that the proposed method could improve the clarity of the hazy corrupted images, and the details of dehazed images could be protected moderately.
Vehicle tracking based on Adaboost cascade classifier

Abstract Driving safety and unobstructed travel have always been the common pursuit of drivers. In the actual environment, traffic elements are complex and changeable, and a large number of dynamic and uncertain factors bring great challenges to the driving behavior decision of smart vehicles. Machine vision because of its the advantages of wide detection range and complete road information are widely used inIn this paper, we take the road environment as the main research object and carry out research on target detection and tracking in smart vehicles. First, we propose a vehicle detection method based on multi-class feature fusion, which combines the SVM classifier. The method can effectively improve the accuracy of vehicle recognition. Then a vehicle detection method based on the Adaboost cascade classifier algorithm is proposed, and the experimental verification of the accuracy and robustness of the vehicle detection algorithm in different road scenarios is completed. Based on the principles and ideas of Kalman filtering, vehicle tracking is realized on the basis of vehicle detection, and the effect of vehicle tracking algorithm based on Klaman filtering is verified under different road scenarios.
Video-Based Human Authentication System for Access Control

The issue of security is paramount in any organisation. Therefore, the authors intend to aid in the security of such organisations by bringing a video based human authentication system for access control which is a type of cyber physical system (CPS). CPS is an integration of computation and physical processes; here the computation is provided by face detection and recognition algorithm and physical process is the input human face. This system aims to provide a platform that allows any authorized person to enter or leave the premise automatically by using face detection and recognition technology. The system also provides the administrator with the access to the logs, wherein he/she would be able to access the details of the people entering or leaving the organisation along with the live video streaming so that there is no sneaking of any unauthorized person with any other authorized person. The administrator can also do registration on behalf of a new person who requires access to the premises for a restricted amount of time only as specified by the administrator.
A Novel Plastic Neural Model with Dendritic Computation for Classification Problems

This paper proposes a novel plastic neural model (PNM) at the single neuron level and a specified learning algorithm to train it. The dendritic structure of PNM presents its diversity to fulfill each particular task. During the training process, PNM divides the Euclidean space of the training instances into several appropriate hypercubes, which have the same dimensional number. And then, each hypercube is transformed into a corresponding dendritic branch in PNM. A suitable dendritic structure of PNM has been proved to have powerful computational capabilities to solve the classification problems. Both theoretical analysis and empirical study of the proposed model are demonstrated in this paper. Five benchmark problems are employed to verify the effectiveness of PNM in our experiment. The results have verified that PNM can provide competitive classification performance compared with several widely-used classifiers.
Analysis of secure data deletion and recovery with common digital forensic tools and procedures

This paper presents how students practical's is developed and used for the important task forensic specialist have to do when using common digital forensic tools for data deletion and data recovery from various types of digital media and live systems. Digital forensic tools like EnCase, FTK imager, BlackLight, and open source tools are discussed in developed practical's scenarios. This paper shows how these tools can be used to train and enhance student understanding of the capabilities and limitations of digital forensic tools in uncommon digital forensic scenarios. Students' practicals encourage students to efficiently use digital forensic tools in the various professional scenarios that they will encounter.
Early Childhood Gymnastic Motion Recognition System Using Image Processing Technology

It is very important to assess the motor development of students in Early Childhood Education (PAUD) schools. The motor skills of students can be evaluated through the ability of children to follow the gymnastic movements taught by the teacher. A large number of students makes it difficult to monitor the development of children's movements directly. In this study, it is proposed to monitor the development of students through video recordings of learning exercises in the classroom. Learning videos of students' gymnastics are turned into digital images. The object (students) on the frame are recognized using the Histogram of Oriented Gradient (HOG) method and the student's gymnastic movements are detected using Principal Component Analysis (PCA). The experiment used 280 pictures as training data, the training data consisted of 8 students' gymnastic movements, each movement used 35 training data. In the testing phase using 4 video input learning exercises of students' gymnastics, 1 video input demonstrates 8 gymnastic movements with a total of 4 students. The experimental results show an accuracy rate of 96,09%.
The Use of ICT in Education According to the Perception of Regular Basic Education Teaching Engineers in Peru

The objective of this work is to know the perception of the engineers dedicated to teaching at the regular basic level in Peru regarding ICT. For this, the National Survey of Teachers (ENDO) that was carried out by the Ministry of Education (Minedu) of Peru in 2018, and whose results were published in 2019, was used. A data cleaning was carried out to exclusively analyze the 280 teaching engineers who participated in the survey. After the analysis, we conclude that the teaching engineers do not fully recognize the benefits of ICT in the educational field, they have a complex and contradictory perception, because despite accepting its importance for student learning, they also believe that it promotes ease and the lack of communication between them. Likewise, these results can also be explained due to various factors and difficulties (socioeconomic, educational, and technological) that teaching engineers in Peru must face.
Review of Semi-Supervised Learning Methods for Medical Computer-Aided Diagnosis Systems

Building computer-aided diagnosis systems remain one of the biggest challenges because of the complexity of the medical data and lack of massive labeled dataset. Semi-supervised methods provide the framework to reuse available unlabeled data to improve model accuracy and generality. This article considers two main strategies that are used in medical application to work with unlabeled data: label propagation and representation learning. Our review shows how these approaches used in the medical application and which modern strategies can be used to apply them.
Dense Captioning Using Abstract Meaning Representation

The world around us is composed of images that often need to be translated into words. This translation can take place in parts, converting regions of the image into textual descriptions what is also known as dense captioning. By doing so, the information present in this region is converted into words expressing the way objects relate to each other. Computational models have been proposed to perform this task in a similar way to human beings, mainly using deep neural networks. As the same region of the image can be described in several different forms, this study proposes to use the Abstract Meaning Representation (AMR) in the generation of descriptions for a given region. We hypothesize that by using AMR it would be possible to extract the meaning of the text and, as a consequence, improve the quality of the sentences produced by the models. AMR was investigated as a semantic representation formalism evolving the so far proposed models that are based only on purely natural language. The results show that the models trained with sentences in the form of AMR led to better descriptions and the performance achieved was superior in almost all evaluations.
Content Based Temporal Segmentation for Video Analysis

This temporal video segmentation aims at getting video segments so that the object's action should be enveloped by one segment. It is more challenging when each frame in the video is having multiple objects. Hence, it provides overlapped segments as a solution based on object-level information of the video rather than abrupt and gradual transitions. Temporal coherence between the content of frames is found to represent object-level information followed by a histogram intersection method to find a level of coincidence between the frames. The outcome of this work is suitable for many further video content analysis tasks such as understanding multimedia databases, tracking an object motion completely, efficient video content indexing, and so on. The segmentation method was tested on the sports genre which is one of the challenging domains because these video genres are composed of diverse scenes with multiple objects.
Research on Step-Speed Detection Device and Prediction Algorithm of VR Motion Platform

In order to solve the problem of dizziness caused by the inconsistency between visual and somatosensory movements of people in a virtual reality system, a set of stride detection device composed of 2×11 array of foot pressure sensors was designed. The piezoresistive characteristics and rebound time of the foot pressure sensor were studied. A footstep pressure signal detection circuit is designed, then the footstep pressure signal through the MCU is collected, and calculated the pace. At the same time, the ARIMA model is used to predict speed changes. The experimental results show that the piezoresistive characteristics of the foot pressure sensor meet the requirements of foot detection, and the rebound time is less than 170 ms; the pace detection device detects the step detection accuracy and prediction accuracy are both greater than 95%, which meets the design requirements of VR motion platforms.
Computer and Translation

Abstract In the big data era, due to the fast pace of society and development of information technology, translation and computer technology have become increasingly related with each other. Computer translation, with its efficiency and accuracy, has aroused more and more concern from all over the world. This paper starts from the history of computer translation. Then it illustrates five forms of computer translation, including translation automation software, computer aided translation, localization tools, translation tools in internet, and other computer tools; Afterwards the paper discusses the benefits and drawbacks of computer translation compared with human translation. Although there are some shortcomings, artificial intelligence is a trend, computer translation technology will be greatly improved and bring about more convenience for human beings.