Reviewerly/exHarmony huggingface dataset:
TFPGAN: Tiny Face Detection with Prior Information and GAN

This paper addresses two challenging tasks: detecting small faces in unconstrained conditions and improving the quality of very low-resolution facial images. Tiny faces are so fuzzy that the facial patterns are not clear or even ambiguous resulting in greatly reduced detection. In this paper, we proposed an algorithm to directly generate a clear high-resolution face from a blurry small one by adopting a generative adversarial network (GAN). Besides, we also designed a prior information estimation network which extracts the facial image features, and estimates landmark heatmaps respectively. By combining these two networks, we propose the end-to-end system that addresses both tasks simultaneously, i.e. both improves face resolution and detects the tiny faces. Extensive experiments on the challenging dataset WIDER FACE demonstrate the effectiveness of our proposed method in restoring a clear high-resolution face from a blurry small one, and show that the detection performance outperforms other state-of-the-art methods.
Comparison of Machine Learning Algorithms to Recognize Human Activities from Images and Videos Using Pose Estimation and Feature Extraction

In the last few years, there have been a lot of attempts to solve human activity recognition. Few of them are trying to solve the problem by using video streams or images. In machine learning algorithms, the conventional approach for classifying images or frames is to compare them as pixel by pixel which often leads to wrong assumptions. To solve this problem, pose estimation and visual feature extraction plays a vital role. In this research, these two techniques are applied to six different machine learning algorithms for image classification, and furthermore, a comparative analysis is being conducted among them.
Calibration Method of Binocular Fisheye Lens Based on Global Parameters

In view of the large distortion of the image taken by the fisheye lens and the error in the assembly process, a method of overall parameter calibration is adopted to calibrate the binocular fisheye lens. Firstly, the geometric model of the fisheye lens is established by using the isometric projection model, and then the internal and external parameters of the two fisheye lenses are calibrated by using the whole parameter calibration method. Finally, the reliability of the calibration method is verified by substituting the calibration parameters into the correction model. The experimental results show that the calibration results obtained by the whole parameter calibration method are reliable.
User Comfort Achievement by Fuzzy Preferences Through an Emotion Communication System

An emotion communication system based on linearly ordered fuzzy preferences is proposed, whose objective is to determine the appropriate actions on the environment in order to enhance user comfort. Once a subset of elementary preferences is obtained via basic human-machine interactions, the rest of preference relations are determined in such a way that the consistency of the system is guaranteed. For this purpose, the Łukasiewicz t-conorm is applied as a rule for aggregation.
Map Task Deviation Scores: A Reconstruction

The Human Communication Research Centre Map Task Corpus is a landmark data source for analysis of multimodal dialogue corpora. The task in this dialogue is the communication by an information giver of a path on a map to an information follower. The corpus includes a measure of task success for each dialogue: the deviation from path scores (the information follower's path from the information giver's path). The original HCRC Map Task works gives information on how the scores for each map were calculated. However, upon analysis and inspection of the corpus materials, we find that it is not possible to reproduce the recorded scores exactly. We report four candidate reconstructions that all appear rational, and note the correlations these lead to with the deviation scores recorded with the corpus. This is important to anyone wishing to reproduce the HCRC method on map-task corpora.
Implications of Quantum Superposition in Cryptography: A True Random Number Generation Algorithm

This paper addresses the problem of generation of true random numbers by using superposition of qubits in a quantum computer and its application in cryptography. We used QISKIT to create quantum circuits and executed those circuits on a publicly available IBM quantum computer to create an equal probability of getting all possible outcomes i.e. unbiased random output using a few limited random bits (qubits). Furthermore, we explain the physical and mathematical aspects of superposition and random number generation. The results of this study show that in theory, true random number generation is possible on quantum computers and has a lot of application in cryptography. In addition, we show how a truly random seed generated from one of the IBM quantum computers can be used to generate public and private encryption keys using RSA encryption.
Research on Trusted Terminal System Based on Trust Chain

This paper presents a system in the trusted platform called Trusted Terminal System (TTS). Based on Trusted Cryptography Module (TCM), TTS establishes an comprehensive protection system of the terminal host via several key technologies such as trusted boot, kernel measurement and integrity report. Also, TTS sends the integrity status of terminal to the Trusted Terminal Attestation Server (TTAS) by remote attestation technology for the purpose of ensuring the terminal within a trusted environment. The paper illustrates the fundamental of the TTS, and also the key technologies and implementation method based on it. Finally, the paper describes the construction technology of TTS based on the TCM and analyzes its performance.
Detection of Ethnic Minority’s Symbols Based on Deep Learning

Abstract Symbols are the embodiments of the ideology of ethnic minorities, the detection of ethnic minority’s symbols is an important part of the protection and inheritance of ethnic cultural heritage. Nevertheless, the ethnic symbols are miscellaneous and referred to some special combination style, it is time-consuming and inaccurate to detect them only by traditional detection methods based on machine learning. Therefore, in this paper, we propose a deep learning method based on Faster R-CNN to detect various kinds of Zhuang minority’s symbols from the Z-S dataset. Firstly, the original images of Zhuang minority’s symbols are prepocessed. Secondly, the processed images are sent to the ResNet-50 with FPN to extract feature maps. Moreover, RPN processes those feature maps to generate bounding boxes. Finally, ROI pooling layer in R-CNN converts those bounding boxes into fixed-length feature vectors, which fed into two sibling fully-connected layers for further detection tasks. Through the ablation experiments based on a certain amount of ethnic symbol images, the results indicate that the proposed method has higher detection quality (mAP), and can effectively reduce the training cost.
Computing Object Selection Difficulty in VR using Run-time Contextual Analysis

This paper introduces a method for computing the difficulty of selection tasks in virtual environments using pointing metaphors by operationalizing an established human motor behavior model. In contrast to previous work, the difficulty is calculated automatically at run-time for arbitrary environments. We present and provide the implementation of our method within Unity 3D. The difficulty is computed based on a contextual analysis of spatial boundary conditions, i.e., target object size and shape, distance to the user, and occlusion. We believe our method will enable developers to build adaptive systems that automatically equip the user with the most appropriate selection technique according to the context. Further, it provides a standard metric to better evaluate and compare different selection techniques.
An improved phase compensation noise model for the continuous-variable quantum key distribution with real local oscillators

In the continuous-variable quantum key distribution with real local oscillators, the reference pulses transmitted in the insecure quantum channel results in phase compensation noises on the homodyne detection of quantum signals. Traditionally, the phase compensation noise is modelled as a zero-mean Gaussian distribution for simplicity. However, it is more likely to be the one with non-zero mean in the actual environment. In this paper, the phase compensation noise is considered to be with a non-zero mean, and then the secure bound is analyzed according to the imperfect phase compensation noise model. The actual transmittance and excess noise are compared between the evaluation values derived from the theoretical analysis and the estimation values calculated by training signals. Simulation results show that the security analysis is consistent with the practical implementation, and the secret key rate decreases with the increasing mean value and noise variance.
The Role of a Web Portal to Facilitate Higher Learning Institutions Students’ Field Attachment in Tanzania

One of the approaches applied by Higher Learning Institutions to equip students with the practical skills is through field attachment in relevant companies based on field of studies. There are so many challenges in the process of coordination and allocation of students to relevant companies including expenses in terms of time and resources due to prolonged process involved. A web-based portal was developed to address the challenges for Tanzanian context. The development approach based on Scrum framework was employed to allow users involvement. To ensure information completeness, mixed-methods approach including key informant interviews, observations and requirements workshop were applied for portal’s requirements elicitation. The requirements determined from users were further used to guide initial interface designs which were then converted to clickable wireframe pages using pencil software. The prototype was sent to real users via email for testing and improvement suggestions before real portal development. Scrum development approach was employed where increments development progress were frequently inspected to detect undesirable variances. The portal was finally validated and tested for usability and indicated to have improved field attachment process and open doors for more collaboration between Higher Learning Institutions and companies. This study provides insight on the approach used to come up with the solution regarding current challenges. Moreover, the contribution that the research based solution has brought to the students’ field practices process is identified.
An Authentication Scheme with Computable Password for Wireless Sensor Networks

With the development of wireless communication and mobile technologies, Wireless Sensor Networks (WSNs) have been applied in many fields. However, due to the limitations of sensor nodes resource, traditional security measures do not apply to WSNs. Therefore, how to coordinate the efficiency and safety performance of WSNs has become a hot spot. Meanwhile, the widely used static password authentication have been proven to be insecure in recent years. In this paper, we propose a secure and efficient computable password authentication scheme for WSNs. Through a detailed comparison on security and performance, our proposed scheme can resist various attacks with less overhead.
Finding Feasible Policies for Extreme Risk-Averse Agents in Probabilistic Planning

An important and often neglected aspect in probabilistic planning is how to account for different attitudes towards risk in the process. In goal-driven problems, modeled as Shortest Stochastic Path (ssp) problems, risk arises from the uncertainties on future events and how they can lead to goal states. An ssp agent that minimizes the expected accumulated cost is considered a risk-neutral agent, while with a different optimization criterion it could choose between two extreme attitudes: risk-aversion or risk-prone. In this work we consider a Risk Sensitive ssp (called rs-ssp) that uses an expected exponential utility parameterized by the risk factor \(\lambda \) that is used to define the agent’s risk attitude. Moreover, a \(\lambda \)-value is feasible if it admits a policy with finite expected cost. There are several algorithms capable of determining an optimal policy for rs-ssp s when we fix a feasible value for \(\lambda \). However, so far, there has been only one approach to find an extreme \(\lambda \) feasible i.e., an extreme risk-averse policy. In this work we propose and compare new approaches to finding the extreme feasible \(\lambda \) value for a given rs-ssp, and to return the corresponding extreme risk-averse policy. Experiments on three benchmark domains show that our proposals outperform previous approach, allowing the solution of larger problems.
Automatic Brain Extraction in Nissl-Stained Histological Sections

A simple and robust algorithm for automatic segmentation of Nissl-stained histological brain images is presented. The algorithm preprocesses the input image with anisotropic diffusion filter prior to applying standard binary image manipulations to isolate the brain from the background. Shape smoothing filter is applied to mimic the common practice of human evaluators. The developed code is tested on histological sections of mouse brains and shows good agreement with manual segmentation results.
Hierarchy Decomposition Pipeline: A Toolbox for Comparison of Model Induction Algorithms on Hierarchical Multi-label Classification Problems

Hierarchical multi-label classification (HMC) is a supervised machine learning task, where each example can be assigned more than one label and the possible labels are organized in a hierarchy. HMC problems emerge in domains like functional genomics, habitat modelling, text and image categorization. They can be addressed with global model induction algorithms, which induce a single model that predicts the complete hierarchy, as well as with local algorithms, which induce multiple models that predict different segments of the hierarchy. However, there is no consensus about which of these approaches perform the best over different domains, especially in the setting of learning ensembles.
Partition-Oriented Subgraph Matching on GPU

Subgraph isomorphismis a well known NP-hard problem that finds all the matched subgraphs of a query graph in a large data graph. The state-of-the-art GPU-based solution is the vertex-oriented joining strategy, which is proposed by GSI. It effectively solves the problem of parallel write conflicts by taking vertices as processing units. However, this strategy might result in load-imbalance and redundant memory transactions when dealing with dense query graph. In this paper, we design a new storage structure Level-CSR and a new partition-oriented joining strategy. To avoid the influence of vertices with large degrees, we divide the dense vertices in traditional CSR into several GPU-friendly tasks and store them in Level-CSR. Then, an efficient execution strategy is designed based on the partitioned tasks. The partition strategy can improve the load imbalance caused by the irregularity of real-world graphs, and further reduce the redundant global memory access caused by the redundant neighbor set accessing. Besides, to further improve the performance, we propose a well-directed filtering strategy by exploiting a property of real-world graphs. The experiments show that compared with the state-of-the-art GPU based solutions, our approach can effectively reduce the number of unrelated candidates, minimize memory transactions, and achieve load balance between processors.
Meta-Analysis of Cross-Language Plagiarism and Self-Plagiarism Detection Methods for Russian-English Language Pair

Scientists need to publish the results of their work to remain demanded and stay relevant. The well-known principle of “publish or perish” often forces scientists to pursue an increase in quantity, not quality. Along with the problems of authorship, paid research, fabrication of results, plagiarism and self-plagiarism are among the most common violations. Their impact is more subtle, but no less destructive for the scientific community. Statistics shows that the reuse of texts in different languages is very common in various studies. Identification of translated plagiarism is a complex task, and there are almost no such tools on the Russian market now. In this article, we provided an overview of the existing methods for the identification of cross-language borrowings in the scientific articles of the authors. We did analysis of solutions by studying the works on various language pairs and paid the great attention to the Russian-English language pair.
Knowledge-based system for data modelling based on verbalisation – an architecture proposal

Data modelling is a complex process that requires knowledge and experience of designers, where the quality of the data model significantly affects the quality of further phases in the development of the information system. The paper presents an architecture proposal for knowledge-based (KB) system that serves as a support for creating data models based on verbalisation. The system will use verbalized expert knowledge, which will be formalized using the methods of the theory of formal languages. Through the methodology for developing system architecture, system parts are described, and Purpose, Context, Inputs, Activities, Outputs, Effects for KB system are given through a variant of a logic model. Also, the functionalities which the verbalisation-based KB system for data modelling should have, are identified, along with all the potential beneficiaries. The paper presents the application of the theory of formal languages for translating business descriptions expressed in natural language into a data model expressed using the entity-relationship (ER) method. Example of the translation process is also given.
QoS Metrics-in-the-Loop for Better Robot Navigation

Navigation is an essential capability in most robotic solutions, being basic in scenarios such as inspection and maintenance, or factory intralogistics. Moreover, with the advance of service robotics and its increasing use in all kinds of real-world applications, nowadays, robots are expected to operate well in unstructured and dynamic environments, where for instance they could work close to human counterparts. In this context, traditional navigation approaches are no longer valid due to their constrained flexibility. Dealing appropriately with the intrinsic variability of open-ended environments requires robots to adapt themselves according to the situation they perceive in order to achieve the required quality of service. This paper describes a model-based framework for dealing with adaptive robot navigation. Our proposal relies on the systematic use of models for dynamically reconfiguring the robot navigation behavior, defined in terms of Behavior Trees, according to the runtime prediction and estimation of quality of service metrics based on system-level non-functional properties.
On the Performance of Planning Through Backpropagation

Planning problems with continuous state and action spaces are difficult to solve with existing planning techniques, specially when the state transition is defined by a high-dimension non-linear dynamics. Recently, a technique called Planning through Backpropagation (PtB) was introduced as an efficient and scalable alternative to traditional optimization-based methods for continuous planning problems. PtB leverages modern gradient descent algorithms and highly optimized automatic differentiation libraries to obtain approximate solutions. However, to date there have been no empirical evaluations comparing PtB with Linear-Quadratic (LQ) control problems. In this work, we compare PtB with an optimal algorithm from control theory called LQR, and its iterative version iLQR, when solving linear and non-linear continuous deterministic planning problems. The empirical results suggest that PtB can be an efficient alternative to optimizing non-linear continuous deterministic planning, being much easier to be implemented and stabilized than classical model-predictive control methods.
Robust Optimization of Convolution Natural Network

Abstract Deep learning has played a very important role in computer vision. However, most of the methods used in computer vision highly rely on human to adjust the hyperparameter. That takes researchers lots of time, but the results sometime could not be most optimized. Besides, many architectures cannot perform robustly in training with noised data. This essay aims to solve the hyperparameter optimization problem by adapting the fruit fly optimization algorithm and suppose a high robust Convolution Natural Network including a Gaussian filter. Compared with methods such as FaceNe, InceptionV3 and Resnet5, GauCNN perform higher efficiency and accuracy with noise data.
Meaningful metrics for multi-level modelling

One of the key enablers of further growth of multi-level modeling will be the development of objective ways to allow multi-level modeling approaches to be compared to one another and to two-level modeling approaches. While significant strides have been made regarding qualitative comparisons, there is currently no adequate way to quantitatively assess to what extent a multi-level model may be preferable over another model with respect to high-level qualities such as understandability, maintainability, and control capacity. In this paper, we propose deep metrics, as an approach to quantitatively measure high-level model concerns of multi-level models that are of interest to certain stakeholders. Beyond the stated goals, we see deep metrics as furthermore supporting the comparison of modeling styles and aiding modelers in making individual design decisions. We discuss what makes a metric "depth-aware" so that it can appropriately capture multi-level model properties, and present two concrete proposals for metrics that measure high-level multi-level model qualities.
3-D Reconstruction of Indoor Landscape Panorama Based on RGB-D Video

In order to improve the analysis and recognition ability of indoor landscape full-motion video images, super-resolution reconstruction of indoor landscape full-motion video images is needed. A virtual reality reconstruction method of indoor landscape full-motion video images based on RGB-D video analysis is proposed. The color feature decomposition rules of RGB images are adopted to decompose multi-pixel features of indoor landscape full-motion video images, information fusion is carried out according to the edge contour feature distribution of the images, multi-dimensional pixel spatial distribution feature quantities of the indoor landscape full-motion video images are extracted, super-resolution feature reorganization and block region matching of the indoor landscape full-motion video are carried out through a block grid region feature matching method, and an edge contour detection model of the indoor landscape full-motion video images is established. RGB decomposition of indoor landscape full-motion video image is carried out by wavelet multi-level structure decomposition method, pixel fusion model of indoor landscape full-motion video image is established, and super-resolution visual reconstruction of indoor landscape full-motion video image is realized by pixel region reconstruction method. The simulation results show that the super-resolution reconstruction of indoor landscape full-motion video images using this method has better visual expression ability and higher output peak signal-to-noise ratio.