Reviewerly/exHarmony huggingface dataset:
Towards a Novel Cross-media Encryption-Cum-Obfuscation Technique

The safeguarding of sensitive information has always been the mission of researchers and cryptographers since time immemorial. People all around the world have always felt the dire need to establish and ensure information security, i.e., to ascertain that crucial and confidential information is kept secure from any unwanted access and/or attack. Moreover, it is also necessary to provide access to confidential data to legitimate and authentic users after confirming login credentials. Cryptography is a well-known technique for maintaining confidentiality, integrity, authentication, and non-repudiation of messages, whereby the sender encrypts the plain text to generate ciphertext, sends it across a communication medium which on the receipt is decrypted by the authentic receiver. This paper proposes a novel cross-media encryption-cum-obfuscation technique where the encryption technique will obfuscate itself by changing its media. To simulate the proposed obfuscation technique in the real world, a specific hardware setup is requisite at both the terminals. So, the transmitter and receiver hardware circuitry is smartly designed to dynamically change the signatures and achieve enhanced security. Thus, it can be said that the proposed technique is robust against the traditional cryptanalysis methods and can ensure information security by obfuscating the data.
Qubits in Space

The sharing of qubits along links in Space emerged as a novel arena for realizing experiments on the fundamental aspects of Quantum Mechanics as well as a tool for implementing the protocols of Quantum Communications. The test of Bell's inequalities [1] and the wave-particle duality [2] are some of the most recent examples of the first aspect, while the intercontinental QKD [3] and teleportation on a satellite [4] are remarkable findings of the second one. All these results were achieved using satellites in Low-Earth-Orbit (LEO), orbiting at an altitude of about 500 km.
Authorization Model Definition for an Adaptable Workflow within Cloud Environment

In this paper, we present a formal definition in temporal logic for an authorization model for an adaptable workflow within cloud environment. Cloud computing is a strong driving technology reshaping cyber space transactions. Security is characterized as the prime challenge for cloud computing. Workflows are core business activities which were previously running in trusted environments. Running workflows in cloud environment is a growing practice which brings agility to institutions as well as presenting a new challenge. New challenges were brought in the picture due to the factors of the distributed environment. In this paper a formal model for the access control problem of workflows within cloud environment is presented. A temporal event-based periodic role-based authorization model is given for workflows on cloud where synchronization of workflows with authorization flow and separation of duties are supported. The model combines the necessary mechanisms needed to answer the authorization challenges. The model will help to answer some of the pressing needs of security within the new developing cloud environment besides the classical needs of workflows. The model presented creates a set of mechanisms in response to those challenges, and then combines all the mechanisms in a single model. Yet another challenge this work lays ground for is presenting a formal model where it is practical to explore the security-reliability nexus. That gives support to the cloud system design process.
Self-rescue of the Dead City——the Construction of Information Entropy Self-Organizing Model Based on the Healing of Urban Aphasia Space

The Daxing fire that occurred at the end of 2017 exposed the current situation that the survivors of the urban fringe had no fixed place and the living environment was overcrowded. The essence of this was that the spatial information could not be effectively transmitted, which eventually led to the lag of local information flow and potential hidden dangers. From the perspective of urban information flow transmission, four kinds of sports patterns already existed in the city are selected, and the rules of thermal change are extracted, and a new spatial information network with self-organization ability is reconstructed at the fire occurrence site. This kind of network is based on TOD theory. It combines five kinds of functional information carried by four main groups to explore the spatial unit of local static and regional dynamics, and makes scene prediction for the systematic evolution of information particles in urban fringe.
A Brief Survey: 3D Face Reconstruction

3D reconstruction technology is an important branch in the field of computer vision. Due to the large proportion of face information when people browsing pictures and the rapid development of virtual reality applications, face reconstruction technology has attracted more attention from researchers over many years. In terms of the special structure of human face, 3D faces reconstruction is quite different from ordinary object 3D reconstruction. This paper summaries the approaches of obtaining face data and the expression of 3d face. The reconstruction effect of each method is analyzed, including the advantages and disadvantages, the issues that can be improved, as well as the prospect of the future work.
Multi-label Aspect Classification on Question-Answering Text with Contextualized Attention-Based Neural Network

In the e-commerce websites, such as Taobao and Amazon, interactive question-answering (QA) style reviews usually carry rich aspect information of products. To well automatically analyze the aspect information inside QA style reviews, it's worthwhile to perform aspect classification on them. Unfortunately, until now, there are few papers that focus on performing aspect classification on the QA style reviews. For short, we referred to this novel task as QA aspect classification (QA-AC). In this study, we model this task as a multi-label classification problem where each QA style review is explicitly mapped to multiple aspect categories instead of only one aspect category. To solve this issue, we propose a contextualized attention-based neural network approach to capture both the contextual information and the QA matching information inside QA style reviews for the task of QA-AC. Specifically, we first propose two aggregating strategies to integrate multi-layer contextualized word embeddings of the pre-trained language representation model (i.e., BERT) so as to capture contextual information. Second, we propose a bidirectional attention layer to capture the QA matching information. Experimental results demonstrate the effectiveness of our approach to QA-AC.
There is a method to it: Making meaning in information research through a mix of paradigms and methods

ABSTRACT The current information environment provides many options for individuals in finding information and acquiring resources. It is increasingly necessary for Information Science (IS) researchers to employ user‐centered research methods in order to better identify individuals' information behaviors within their contextual worlds. This panel will provide an overview of some of the novel user‐centered research methods being used globally in information research within various contexts. Focusing on some recent innovative projects ranging from information discovery and access, refugee information behaviors, and cyberbullying, to personal health information management, the panel will examine the benefits, challenges, and implications for IS researchers. The panel will have an interactive component for participants to learn how to incorporate new methodologies in innovative ways within their research to make new meaning from their user studies.
The Intelligent Brain of the Passenger Station for China Railway High-Speed (CRH)

This paper focus on the intelligent development of China Railway High-Speed (CRH), and the “intelligent brain” of the passenger station is introduced academically for the first time. The “intelligent brain” is generated and proposeddue to the greatly improved abilities of perception, transmission, analysis, evaluation, decision-making and execution of the passenger railway station. As a long-term plan, the “intelligent brain” will be contributed continually, and a new service and operation mode will be achieved to provide more suitable and lower time cost service for the passenger. To describe the station brain intuitively, the brain-like intelligence architecture, the concept of the brain, the reflexarcs are presented and discussed firstly. And then the closed-loop control model based on the intelligent brain is designed, and the main modules are analyzed. The rationality and usability of the intelligent station brain is tested in the Taiyuan railway station, and the experiment result is presented thirdly. At last a conclusion is given.
Improved AIUKF Based Aeroengine Component Deterioration Analysis

Aeroengines are complex aerodynamic thermal systems that operate in harsh environments for long periods of time. To ensure engine reliability and stability, accurate and effective prediction of engine deterioration is critical. At present, most of the filtering methods for predicting the degree of deterioration of engine components have shortcomings such as large initial state error and limited estimation accuracy. This paper proposes an improved adaptive iteration Unscented Kalman Filter (AIUKF) algorithm, combining forgetting factor with AIUKF to predict the degree of engine deterioration. The simulation results show that the method can simulate the engine deterioration process with high precision, effectively predict the deterioration degree of each component. This method has strong ability to predict nonlinear systems, and improves the accuracy of predicting engine deterioration.
Proposed Omnipresent Learning for Rising Students Persistence through Learning Network

This paper proposes a Mobile Learning network (MLSS) that allows understudies to induce to learning materials by utilizing 2nd institutionalized labels and GPS advancement. Because the pilot course of action of all comprehensive learning, we have a tendency to used camera-arranged mobile phones and 2nd scanner label marks to induce taking in data from on-line locales. By acquainting the MLSS on with their mobile phones, understudies will check the mark related to the contrastive article with show connected sight and sound materials on the screen of PDAs. Moreover, MLSS in like manner applies GPS development to develop a territory careful condition for understudies. GPS advancement is employed to acknowledge the understudies' zone and understand that 2nd scanner label names are in their proximity. Therefore, this paper permits creating to architects build ineluctable learning circumstances that are part of veritable world and automatic world resources.
Towards Dementia Things

In the early stages of dementia, care often takes place at home. In the project Dementia Things we form competence teams consisting of designers, professional caregivers, persons with dementia and their care giving relatives. We ask for the everyday problems of persons with dementia and their relatives in private environments and use methods of field research and participatory design to understand the specific contexts of dementia and improve the quality of life. From the ongoing field work we were able to identify four factors influencing the participatory design process: building up trustful relations in the field, the role of the professional caregiver, the surplus of overcoming verbal barriers with design methods, and the overall benefit of changing perspectives and knowledge transfer within the competence team.
Differences in Efficiency of Innovation Performance among Middle Income Countries: An Empirical Approach

The purpose of this paper is to determine empirically the differences in the efficiency of innovation performance in middle-income countries. To achieve this aim, it was used cluster analysis which is one of the multivariate statistical techniques. Ward’s agglomerative hierarchical method was employed for cluster analysis. In determining efficiency of innovation performance, it was followed process suggested by Kula and Ünlü (2019). So, cluster analysis was performed separately for inputs and outputs indicators. Secondly, discriminant analysis was used to identify factors that lead to differences in the efficiency. According to the World Bank's income classification, it was included a total of 54 countries, including 23 lower-middle income and 31 upper-middle income. The data used in the analysis was obtained from Global Innovation Index. The findings confirm the existence of the inefficiency problem in terms of innovation performance in the middle income countries.
Enhancing Chinese Word Embeddings from Relevant Derivative Meanings of Main-Components in Characters

Word embeddings have a significant impact on natural language processing. In morpheme writing systems, most Chinese word embeddings take a word as the basic unit, or directly use the internal structure of words. However, these models still neglect the rich relevant derivative meanings in the internal structure of Chinese characters. Based on our observations, the relevant derivative meanings of the main-components in Chinese characters are very helpful for improving Chinese word embeddings learning. In this paper, we focus on employing the relevant derivative meanings of the main-components in the Chinese characters to train and enhance the Chinese word embeddings. To this end, we propose two main-component enhanced word embedding models named MCWE-SA and MCWE-HA respectively, which incorporate the relevant derivative meanings of the main-components during the training process based on the attention mechanism. Our models can fine-grained enhance the precision of word embeddings without generating additional vectors. Experiments on word similarity and syntactic analogy tasks are conducted to validate the feasibility of our models. Furthermore, the results show that our models have a certain improvement in the similarity task over most baselines, and have nearly 3% improvement in Chinese analogical reasoning dataset compared with the state-of-the-art model.
Named Entity Recognition by Character-Based Word Classification Using a Domain Specific Dictionary

Named entity recognition is a fundamental task in natural language processing and has been widely studied. The construction of a recognizer requires training data that contains annotated named entities. However, it is expensive to construct such training data for low-resource domains. In this paper, we propose a recognizer that uses not only training data but also a domain specific dictionary that is available and easy to use. Our recognizer first uses character-based distributed representations to classify words into categories in the dictionary. The recognizer then uses the output of the classification as an additional feature. We conducted experiments to recognize named entities in recipe text and report the results to demonstrate the performance of our method.
Image thresholding segmentation based on oriented genetic algorithm and maximum entropy

Image thresholding segmentation based on entropy is classical method. Time cost of image thresholding segmentation method based on maximum entropy and enumeration is unacceptable, so that the genetic algorithms is adopted to improve efficiency. However, the performance of image thresholding segmentation based on traditional genetic algorithms is not satisfied because of the premature convergence. Therefore, we propose oriented genetic algorithm to increase speed and success rate. Oriented genetic algorithm includes an oriented crossover operator which directs generation of offspring. The blindness of genetic algorithm is reduced and efficiency of optimization is improved due to introducing oriented crossover operator. The proposed method is compared with enumeration method and standard genetic algorithm in image segmentation experiment. Experimental results show that performance of proposed method is better than traditional methods.
Modified Angstrom Solar Radiation Model for Abeokuta Nigeria

Sustainable power supply still remains the challenge in developing countries. Unfortunately the growing energy need is directly proportional to the population growth. Hence, there is the need to step-up on the alternative energy use. The adoption of solar technology in the research site is encouraging-partly because of the low cost of purchasing solar devices. However, there is the issue of short lifespan of the photovoltaic (PV) panel that operates in the area. This research used the modified Angstrom solar radiation model to determine that the main cause of short PV lifespan is solar spectrum signature over the research area. It is recommended that solar companies should start incorporating spectra filtering layer to protect the PV panel.
Research on Intelligent Speech Guide Robot Control Method Based on Machine Vision

Robotic navigation via speech guidance gain potential attention on the field artificial intelligent and it stat-of-art. This paper highlights speech guide for robot toward machine vision were designed. The robot navigation can determine and identify the objects around the blind area. The process of determining the direction and distance of the blind people to consider for deciding and predicating the next step. The information of the sensors is fused together, and a path information suitable for the blind person is transmitted to the user via speech voice broadcast are the key elements for identification part. Moreover, deep learning algorithm are involve for accurate classifiers target into categories, to archive the highest recognition accuracy. The direction and distance are judgment part to adopt include the improvements binocular ranging, angle measurement algorithm with a consideration of errors marge less than 2%. The novelsystem can-authcorrect and, guide blind people to move froward, advance, stop, turn, which greatly compensates for the visual defects of the blind, with respect of constraint of improving the indoor safety of the blind people.
Introducing Connotation Similarity

Various different measures of textual similarity exist including string-based, corpus-based, knowledge-based, and hybrid-based measures. To our knowledge, none of them examine the textual connotation of two different sentences for the purpose of establishing whether they express a similar opinion. Connotation, within the context of this work, is the negative, positive, or neutral emotional meaning of words or phrases. In this paper we define a new type of a similarity measure mathematically, namely connotation similarity. It evaluates how similar the emotional meanings of two sentences are using opinion mining, which is also known as sentiment analysis. We compare two different algorithms of our definition of connotation similarity against various algorithms of cosine similarity using one dataset of 100 pairs of sentences, and another dataset of 8 pairs. We show how connotation similarity can be used on its own to indicate whether two sentences express a similar opinion, and also how it can be used to improve other similarity measures such as cosine similarity.
Optimal State Estimation for Stochastic Systems with Measurement Delays and Auto-Correlated Noises

This paper investigates the estimation problem for stochastic systems with measurement delays and auto-correlated noises where the multiplicative noises are zero-mean white noise sequences, and the no-white additive noise is time-correlated noise which can be described by a linear system model. Different from the argument method and partial difference equation approach, the optimal linear state estimator for the systems under consideration is proposed based on the measurement difference and reorganized innovation approaches.
Secret Sharing Scheme Using Identity Based Signatures

Using identity based signatures, we propose a verifiable secret sharing scheme, that uses participants' identities in this scheme. The main advantages of the proposed scheme is that it uses signature scheme based on identities and also it uses hash functions for the verifiability of the shares as well as the reconstructed secret. It checks the honestness of either dealer or participant, which means that the dealer intentionally/unintentionally distributes a currupted/fake share to a participant and/or a participant may intentionally/unintentionally send a fake share to the reconstructor in reconstruction. Our proposed scheme efficiently works to resist the dealer's and/or participants' cheating behaviour.
Multi-point Laser Automatic Oblique Angle Shooting Correction System Based on Image Processing

When we take photos of object, due to the limitations of shooting location and direction, the images often appear distorted. This paper proposes multi-point laser automatic correction method and the method includes two correction projects: multi-point laser automatic correction method based on coordinate and multi-point laser automatic correction method based on angle. Unlike traditional method of manual annotation, this paper adopts laser dotting and ranging technology instead of manual annotation to calculate the corresponding coordinate of each laser point coordinate on correction image, and then calculates correction matrix by perspective transformation, further uses correction matrix to correct the distortion image. The results of experiments show that multi-point laser automatic correction method is simple and accurate, it effectively avoid the loss of edge information, it can automatically correct the image at real time and improve the accuracy of correction.
A Weight-Based Multi-Innovation Intelligent Tracking Algorithm with Maneuver Detection

Kalman filter is an unbiased minimum variance estimator under the linear, Gaussian circumstance. However, the robustness of Kalman filter is underdeveloped because it strongly depends on the system model and the performance may seriously deteriorate if the model turns erroneous or the target maneuvers unexpectedly. To solve this problem, a weight-based multi-innovation intelligent correction algorithm with maneuver detection is proposed. Firstly, an intelligent maneuver detection algorithm called double error ellipses is presented by setting a bilateral threshold to the error ellipse, which is able to detect the maneuver occasion by improving the sensitivity to the maneuver. Secondly, we compute the weighs of the multi-innovations through building a projection to decrease the maneuver deviations and the model errors. Particularly, the projection is constructed with respect to the Euclidean distance between the predicted and effective measurement in two-time steps. Finally, a new target tracking algorithm weight-based multi-innovation correction algorithm with maneuver detection is derived by combining the above maneuver detection approach with the multi-innovation correction method. The simulation results show the proposed algorithm is superior to the adaptive Kalman filter and the conventional Kalman filter in cost-effectiveness.
Image Segmentation on Embedded Systems via Superpixel Convolutional Networks

In this paper we describe a lightweight framework for fast image segmentation on embedded systems, based on superpixels, which leverages on convolutional and graph-convolutional neural networks. In particular, we analyzed different superpixel representation looking for the best tradeoff between the efficiency of the system and richness of the description. Similarly, we analyzed different network sizes, balancing the number of filters used and the prediction accuracy. We also compared two different convolutional architecture, one based on the classical encoder-decoder paradigm and one based on graphs, to guarantee a most accurate representation of the image structure. The architecture was tested on the KITTI dataset using an embedded system with CUDA capabilities.