Reviewerly/exHarmony huggingface dataset:
Face Beauty: Improving Quality of Face with Semantic Segmentation Prior and Style Encoder

Despite image super-resolution has great progress in recent years, state-of-the-art face super-resolution still has much potential to promote visual quality. Most of these methods utilize a deep convolutional neural network to explore a mapping between low resolution and high resolution, but it cannot well explore facial structures and local knowledge. In this work, we propose a face hallucination method that explicitly incorporates semantic segmentation prior and style encoder to improve the quality of low resolution face images. To enhance the feature mapping and color mapping of the face, we focus on transferring the prior information extracted from the segmentation mask to the super-resolution process. Furthermore, we add the color attention residual block as a color fidelity unit to preserve the color information of the mapped area. In this way, we can input the latent code generated by the style encoder as parameters into the network to improve the image quality. Experimental results demonstrate that our proposed model achieves superior performance over state-of-the-art approaches including the enhanced super-resolution generative adversarial networks (ESRGAN) and Residual Channel Attention Networks (RCAN).
FlightSense: A Spoofer Detection and Aircraft Identification System using Raw ADS-B Data

We introduce a robust neural network based method for classifying aircraft, using raw I/Q data obtained from the Automatic Dependent Surveillance-Broadcast (ADS-B) data from airplanes. ADS-B has become the de-facto standard for air traffic control and forms the basis of the Next Generation Air Transportation System (NextGen). Although ADS-B is at the core of modern day air traffic control, the standard lacks basic security features such as encryption and authentication. As a result, it is possible to spoof ADS-B data and in the process create unprecedented operational havoc in the skies. In this work we propose FlightSense: a robust adversarial learning based system for filtering out spoofed ADS-B data and subsequent identification of airplanes operating in the airspace from the filtered signal. We use the framework of a generative adversarial network (GAN) for our implementation, which is end-to-end in that it uses the raw I/Q signal data as input and no preprocessing steps are required. We present experiments and results to demonstrate the efficacy of our methods using a real world standardized ADS-B dataset.
Off-line Signature Verification Using Multidirectional Run-Length Features

Run-length features have shown its effectivity in off-line signature verification. Configurations of these handcrafted features include four direction-based features. In this paper, we propose to add further spatial information of the signature to the standard run-length features. Such information is worked out in two stages: firstly, beyond classical four directions, more directions are studied. Secondly, improving the knowledge of each direction by combining the information of the neighbor directions. This new configuration has been used in two classifiers, one based on Euclidean distance and another based on a one-class support vector machine. We outperformed previous performances with the proposed configuration regarding the last ICFHR 2018 competition on Thai student signatures and GPDS-960 signature database.
Research on cloud computing task scheduling based on improved evolutionary algorithm

In the research of cloud computing, the advantages and disadvantages of cloud task scheduling algorithm will affect the operation efficiency and service quality of the whole cloud computing system. Evolutionary algorithm is the sum of a series of specific algorithms inspired by the phenomenon of biological evolution in nature. One of the common points of these algorithms is that individuals must be mutated according to certain rules in the running process, so as to avoid falling into local optimum. In order to improve the efficiency of cloud task scheduling in cloud computing, this paper proposes a new mutation strategy which changes the genetic algorithm in evolutionary algorithm. It uses cloudsim platform to simulate cloud task scheduling in cloud computing, and uses particle swarm optimization algorithm to optimize its parameters. The experimental results show that the proposed evolutionary algorithm with improved mutation strategy has the function of cloud task scheduling, and its performance is also improved after the parameters are optimized by particle swarm optimization algorithm. The proposed algorithm improves the mutation step and explores the essence of mutation in evolutionary algorithm, which provides a reference for other research.
Preadolescent Advanced Readers: Exploring Attitudes, Beliefs, and Behaviors

Despite dedication of tremendous resources to developing literary proficiencies, advanced readers may remain an underserved and understudied population. This qualitative study included nine preadolescent participants aged 10–12 years who demonstrated reading comprehension abilities within the top 10% on a national normed achievement battery. The researchers gathered interview data from participants with corroborating evidence from their parents and their book club teacher. The grounded theory analyses found advanced readers to demonstrate superior reading comprehension and the ability to read entire books quickly. Participants reported positive attitudes toward reading in general and preferred out of school reading over the limiting structures of school reading. Some evidence supported a connection between reading and identity exploration through narrative imagination and empathetic relations to characters and narratives. Advanced readers may present cognitive characteristics, as well as behaviors and motivations that require differentiated learning designs.
A Survey on Streaming Data Analytics: Research Issues, Algorithms, Evaluation Metrics, and Platforms

Data is the most significant element for any organization in today's cut-throat competition, which is growing tremendously large and exchanged over the Internet due to the development of numerous digital devices. Commercial firms are focusing on predicting responses in advance before actually spending valuable resources. Nowadays most of the companies are moving from traditional static data analytics to real-time streaming data analytics to renovate the predictive model to be adaptable with currently available data which changes over time. Although building a predictive model for the real-time streaming data is an immense challenge, it is a current area of Research and Development which gained much attention from the Academia, IT Industries, and Researchers. In this survey paper, we have instigated with discussing some of the open research issues of streaming data and then we deliberate several classification algorithms proposed by various researchers by analyzing it's merits and limitations. Besides, we studied the evaluation metrics used by the researchers for the purpose of measuring the existing algorithms. Finally, we surveyed the platforms used by the researchers to handle streaming data and summarized a comparative study of streaming data platforms.
Futuristic Technology in Architecture &amp; Planning - Augmented and Virtual Reality: an Overview

Speed has become a way of life. We are asymptotically piling data. Speed can be achieved with new design processes, techniques, and Technology. Innovations AR and VR are just some of the many forms of technologies that will play a key role in shaping the Architecture and Planning of tomorrow, making it future-ready and ushering in a new age of innovation. AR and VR in Architecture &amp; Planning were introduced as assisting tools and has helped generate multiple design options, expanded possibilities of visualization, and provided us with more enhanced, detailed, and specific experience in real-time; enabling us to see the resultsof work on hand well before the commencement of the project. These tools are further developed for city development decisions, helping citizens interact with local authorities, access public services, and plan their commute. After reviewing multiple research papers, it had been observed that each one is moving forward with the changes brought by it, without entirely understanding its role. This paper provides a summary of theappliance of AR &amp; VR in architecture and planning.
Conditional Graph Pattern Matching with a Basic Static Analysis

We propose conditional graph patterns (CGPs) that extend conventional ones with simple counting quantifiers on edges, attributes on nodes, positive and negative predicates. In emerging applications such as social network marketing, CGPs allow to express complex search conditions and to find more sensible information than their traditional counterparts. We show that the CGPs expressivity does not come with a much higher price. Indeed, we propose a matching algorithm that is based on a revised notion of graph simulation and allows to match CGPs over any data graphs in quadratic time, as opposed to the prohibitive solutions based on subgraph isomorphism. We discuss a parallel version of our algorithm that makes it very efficient over real-life data. We investigate the satisfiability and containment problems of CGPs and we show that they are in quadratic time by providing a non-trivial checking algorithm for each one. This paper is the first effort that investigates static analysis of non-conventional graph patterns containing important features that are widely used in practice. An extensive experimental study has been conducted to show effectiveness and efficiency of our results.
Dual-axis solar tracking system based on Raspberry Pi imaging

Abstract Photovoltaic can transform daylight into electrical power. The application of solar tracking system improves the efficiency of photovoltaic system by increasing solar radiation fall on PV’s surface. This paper provides a low-cost solar tracking system based on image processing. Using Raspberry Pi Camera Board as a sensor to eliminate the need of another sensor. Image captured by camera fed to Raspberry Pi to calculate the position of the brightest sky body from the system’s perspective. the proposed system will face to the east and go to sleep mode after the sun set and start to work after the sun rise. This dual-axis solar tracking system based on Raspberry Pi imaging works properly whether it’s a sunny or heavily cloudy day.
A Multi-party Secure E-commerce Voting Scheme Based on SDGHV Algorithm

Abstract In the era of continuous development of e-commerce, the selection of e-commerce affects the development of e-commerce business and management efficiency. Based on the multi-party secure computing technology, this paper designs a set of the electronic voting scheme based on SDGHV multi-party secure data platform for the feedback on customers’ opinions after the goods are sold, which provides a useful reference for the selection of e-commerce products.
LPPRS: New Location Privacy Preserving Schemes Based on Ring Signature over Mobile Social Networks

There are two popular location-based service (LBS) applications: searching k-nearest neighbor Points of Interests (kNN POIs) and finding Nearby Friends (NF) via a social network server (SNS). Nevertheless, both applications are based on users’ current locations, and no scheme has been devised yet to merge POIs, NF and SNS together. A series of works were proposed to preserve users’ query privacy leaked from service attributes of POIs or location privacy over Mobile Social Networks (MSNs). However, their communication and computation costs are heavy. In this paper, we design a novel LBS application named NFPOI, which allows users to search NF based on a given POI via an SNS. To preserve users’ identity privacy, location privacy and query privacy, we firstly propose Location Privacy Preserving schemes based on Ring Signature (LPPRS). In our LPPRS, (1) Both user’s real identity and real location are kept secret from others effectively. (2) Due to the anonymity of ring signature, the SNS was allowed to return query results while it cannot distinguish the real sender when processing a query message. Thus, the sender’s query privacy is preserved even though the SNS knows the actual attributes and locations of POIs. (3) Neither a fully trusted third party (TTP) nor a pre-shared secret key with friends is required. A semi-TTP scheme and a TTP-free scheme were proposed respectively with different trade-offs in efficiency and security level. (4) Communication and computation costs for user side are less than existing works.
A lightweight encryption scheme using Chebyshev polynomial maps

Speaker Verification (SV) system is the popularly used user’s identity authentication. From this perspective, Improved Encryption with Optimized Variable Size Windowing based SV (IE-OVSW-SV) system has been designed which authenticates the speaker’s identities based on watermarking and encrypting the optimal speech signal features. For encryption, an enhanced AES algorithm was combined with improved RC4 keystream generator to generate the AES key. But it has a high computational complexity and does not ensure the maximum security level due to the utilization of the same symmetric key for cryptography. Therefore, in this article, Chaotic-based Advanced Encryption Standard (CAES) is proposed in which simplified enhanced AES is proposed by removing few rounds of the permutation process to reduce its computational complexity and finally combined with an improved quadratic chaotic map encryption in addition to increase the security of encrypted speech signal features during communication. Further, Gaussian Mixture Models (GMM) classification is used for verifying the speaker’s identity. Finally, the experimental results show the performance efficiency of CAES compared to the enhanced AES algorithm.
Cyber Situational Awareness in Critical Infrastructure Organizations

The capability related to cybersecurity plays an ever-growing role on overall national security and securing the functions vital to society. The national cyber capability is mainly composed by resilience of companies running critical infrastructures and their cyber situational awareness (CSA). According to a common view, components of critical infrastructures become more complex and interdependent on each other and, as a consequence, ramifications of incidents multiply. In practice, the actions relate to developing better CSA and understanding of a critical infrastructure organization. The aim is to prepare for incidents and their management in a whole-of-society approach. The arrangement is based on drawing correct situation-specific conclusions and, when needed, on sharing critical knowledge in the cyber networks of society. The target state is achieved with an efficient process that includes a three-leveled (strategic, operational and technical/tactical) operating model related to the organization’s decision-making. The cyber environment is dynamic and hence especially the strategic agility is required when preparing for incidents. The pervasive incidents targeting society are a challenging cyber environment when it comes to the critical reaction speed required by the situation management.
Mobilizing Developmental Education: The Causal Effect of Mobile App Courseware on the College Outcomes of Developmental Education Students

Developmental education, in which college students deemed unprepared for college-level coursework enroll in non-credit-bearing courses, is widespread in American higher education. This study evaluates the effect of mobile app courseware on the college outcomes of developmental education students. We used a research design that randomly assigned course sections to receive access to the apps or not. The results show that access to the apps significantly improved student performance in developmental education outcomes and marginally improved medium-term college persistence and performance but did not affect credential attainment in the study timeframe. Despite a number of barriers to implementation, the results suggest the intervention has the potential to improve the short-term outcomes of developmental education students in addition to being low-cost and scalable.
Utilizing visitor simulations in exhibition design process: Evaluating designers’ perspectives

Throughout the design development process, simulations provide the ability to aid the designer in exploring the relationship between different variables, in various configurations, for as many iterations as needed without any physical commitment. The goal of this qualitative research is presenting in-depth perspectives of practicing design professionals on opportunities and challenges of utilising simulations in exhibition design process. Data were collected via a series of focus group interviews, adhering to a semi-structured framework. The persisting shortcomings and emerging benefits of simulations were identified and interpreted in relation to existing research. Notable findings indicated a significant demand towards a clear and intuitive user interface and visual output, stressed the minimal presence in work and educational environments and the resulting lack of synergy, and lastly, in addition to the skill requirements and steep learning curve, underlined the reliance on specialised experience to interpret and compensate the output.
The use of new pedagogical practices and modern technologies to increase the competence level of I and II stage students of the specialty “ Physical Training” of Polotsk State University

The changes taking place in the higher education system in the Republic of Belarus require the search for new pedagogical teaching technologies, the updating of tools, methods and methodological techniques. At Polotsk State University, innovative pedagogical practices and technologies, information and communication technologies and distance learning technologies are widely used in the training of specialists in physical education. When choosing and implementing new pedagogical practices, technologies, forms and methods of teaching, constant requests for the formation of future specialists ’ competencies are taken into account. The use of innovative teaching methods increases the quality of students ’ knowledge, interest in studying the course and future professional activity.
Graph Clustering with Embedding Propagation

In the past decade, the amount of attributed network data has skyrocketed, and the problem of identifying their underlying group structures has received significant attention. By leveraging both attribute and link information, recent state-of-the-art network clustering methods have achieved significant improvements on relatively clean datasets. However, the noisy nature of real-world attributed networks has long been over-looked, which leads to degraded performance facing missing or inaccurate attributes and links. In this work, we overcome such weaknesses by marrying the strengths of clustering and embedding on attributed networks. Specifically, we propose GRACE (GRAph Clustering with Embedding propagation), to simultaneously learn network representations and identify net-work clusters in an end-to-end manner. It employs deep denoise autoencoders to generate robust network embeddings from node attributes, propagates the embeddings in the network to capture node interactions, and detects clusters based on the stable state of embedding propagation. To provide more insight, we further analyze GRACE in a theoretical manner and find its underlying connections with two canonical approaches for network modeling. Extensive experiments on six real-world attributed networks demonstrate the superiority of GRACE over various baselines from the state-of-the-art. Remarkably, GRACE improves the averaged performance of the strongest baseline from 0.43 to 0.52, yielding a 21% relative improvement. Controlled experiments and case studies further verify our intuitions and demonstrate the ability of GRACE to handle noisy information in real-world attributed networks.
An optimized run-length based algorithm for sparse remote sensing image labeling

Labeling of the connected components is the key operation of the target recognition and segmentation in remote sensing images. The conventional connected-component labeling (CCL) algorithms for ordinary optical images are considered time-consuming in processing the remote sensing images because of the larger size. A dynamic run-length based CCL algorithm (DyRLC) is proposed in this paper for the large size, big granularity sparse remote sensing image, such as space debris images and ship images. In addition, the equivalence matrix method is proposed to help design the pre-processing method to accelerate the equivalence labels resolving. The result shows our algorithm outperforms 22.86% on execution time than the other algorithms in space debris image dataset. The proposed algorithm also can be implemented on the field programming logical array (FPGA) to enable the realization of the real-time processing on-board.
Resolving Lexical Ambiguity in English-Japanese Neural Machine Translation

Although Lexical ambiguity, i.e., the presence of two or more meanings for a single word, is an inherent and challenging problem for machine translation systems. Even though the use of recurrent neural networks (RNN) and attention mechanisms are expected to solve this problem, machine translation systems are not always able to correctly translate lexically ambiguous sentences. In this work, we attempt to resolve the problem of lexical ambiguity in English-Japanese neural machine translation systems by combining a pretrained Bidirectional Encoder Representations from Transformer (BERT) language model that can produce contextualized word embeddings and a Transformer translation model, which is a state-of-the-art architecture for the machine translation task. These two proposed architectures have been shown to be more effective in translating ambiguous sentences than a vanilla Transformer model and the Google Translate system. Furthermore, one of the proposed models, the TransformerBERT-WE, achieves a higher BLEU score compared to the vanilla Transformer model in terms of general translation, which is concrete proof that the use of contextualized word embeddings from BERT can not only solve the problem of lexical ambiguity, but also boosts the translation quality in general.
Knowledge translation

We introduce Kensho, a tool for generating mapping rules between two Knowledge Bases (KBs). To create the mapping rules, Kensho starts with a set of correspondences and enriches them with additional semantic information automatically identified from the structure and constraints of the KBs. Our approach works in two phases. In the first phase, semantic associations between resources of each KB are captured. In the second phase, mapping rules are generated by interpreting the correspondences in a way that respects the discovered semantic associations among elements of each KB. Kensho's mapping rules are expressed using SPARQL queries and can be used directly to exchange knowledge from source to target. Kensho is able to automatically rank the generated mapping rules using a set of heuristics. We present an experimental evaluation of Kensho and assess our mapping generation and ranking strategies using more than 50 synthesized and real world settings, chosen to showcase some of the most important applications of knowledge translation. In addition, we use three existing benchmarks to demonstrate Kensho's ability to deal with different mapping scenarios.