Reviewerly/exHarmony huggingface dataset:
On generalization of deep learning recognizers in overhead imagery

In many applications, access to large quantities of labeled data is prohibitive due to its cost or lack of access to classes of interest. This problem is exacerbated in the context of specific subclasses and data types that are not easily accessible, such as remotes sensing data. The problem of limited data for specific classes of data is referred to as the low-shot or few-shot problem. Typically in the low-shot problem, there is a wealth of data from a source domain that is leveraged to train a convolutional feature extractor that is then applied to a target domain in innovative ways. In this work we apply this framework to the low-shot and fully sampled problem, in which the convolutional neural network is used as a feature extractor and paired with an alternate classifier. We evaluate the benefits of this approach in two contexts, a baseline problem, and limited training data. Additionally, we investigate the impact of loss function selection and sequestering of low-shot data on the classification performance of this approach. We present an applications of these techniques on the recent public xView dataset.
Calibration of Aircraft Airdata Systems Using Adaptive Filtering Algorithm

Abstract This paper focuses on the calibration of aircraft airdata system to obtain online accurate Angle of Attack(AOA) and Angle of Sideslip (AOSS) from measured quantities contain error. For this purpose, an adaptive version of the extended Kalman filter (EKF) is employed to estimate accurately flight path trajectories of an aircraft. The investigations are made with flight simulated data with turbulence effects. Results are analyzed and found that adaptive EKF provides more accurate results over EKF.
Hardware/Software Co-designed Security Extensions for Embedded Devices

The rise of the Internet of Things (IoT) has dramatically increased the number of low-cost embedded devices. Being introduced into today’s connected cyber-physical world, these devices now become vulnerable, especially if they offer no protection mechanisms. In this work we present a hardware/software co-designed memory protection approach that provides efficient, cheap, and effective isolation of tasks. The security extensions are implemented into a RISC-V-based MCU and a microkernel-based operating system. Our FPGA prototype shows that the hardware extensions use less than 5.5% of its area in terms of LUTs, and 24.7% in terms of FFs. They impose an extra 28% of context switch time, while providing protection of shared on-chip peripherals and authenticated communication via shared memory.
Robust Declassification by Incremental Typing

Security of software systems has to be preserved while they grow and change incrementally. The problem is to make the analysis of their security properties adhere to such a development. In particular we concentrate here on static type systems. Given a non-incremental type system, the algorithm we propose permits using it incrementally, so avoiding to develop new incremental versions of it. As a proof-of-concept we show how our technique permits an incremental checking of non-interference with robust declassification, starting from the classical type system by Myers, Sabelfeld and Zdancewic.
On the Construction of Digital Campus in the Development of Higher Vocational Colleges

The digital campus system is one of the most important issue in the development of information system in higher vocational colleges. To implement the construction of digital campus needs exerting the subjective consciousness of teachers, renewing their ideas and improving their quality of informatization. In the processing of digital campus construction, we should pay more attention to the fields of education and teaching, the management, the scientific research and community service work and jointly promote their development.
Secure Computing Systems Design Through Formal Micro-Contracts

Two enduring concepts in computer system design are abstraction levels and layered composition. The design generally takes a layered approach where each layer implements a different abstraction of the system. The layers communicate through interfaces that are designed to support functional specification of the system as a whole. Traditionally, these layers and interfaces have primarily focused on functionality and efficiency --- performance, power and area. Security-related issues are often overlooked or deferred until later in the design cycle or applied as add-ons when some security features are explicitly required. The challenge with this approach is that chasing security implications of certain design decisions along the multiple layers is a complex and error-prone task. Therefore, in this work, we are introducing the notion of "security micro-contracts" or simply "micro-contracts". We propose a novel secure computer systems design approach through minimal contracts --- micro-contracts --- between adjacent layers. These contracts have strict structures that contain security-relevant details of each connected layer and the secure-properties that have to be preserved to assure confidentiality, integrity and availability of the data of interest. Micro-contracts may be used as (i) basic formalism for proving security properties of computing systems both in the software and hardware layers and across them or (ii) run time security policy checks.
Dim Sum: Light Clock Tree by Small Diameter Sum

By retrospecting the classical deferred-merge embedding (DME) algorithm, we found an intrinsic relationship between the zero-skew tree (ZST) problem and the hierarchical clustering (HC) problem. To be more specific, the wire length of a ZST is proved a linear function of the sum of diameters of its corresponding HC. With this new insight, an effective O(n log n)-time O(1)-approximation algorithm and an optimal dynamic programming for ZST are designed. Using the ZST construction black box and a linear-time optimal tree decomposition algorithm, an improved algorithm for constructing the bounded-skew tree (BST) is derived. In the experiment, our approach shows superior wire length compared with previous methods for both ZST and BST.
ARNature: augmented reality style colorization for enhancing tourism experience

Augmented Reality (AR) can seamlessly combine a real scene viewed by a user and a virtual component generated by a computer. This work introduces a system architecture integrating augmented reality technology with state-of-art computer vison techniques such as image semantic segmentation and style colorization. The proposed production system, ARNature, is able to superimpose a virtual scene, audio, and other enhancements in real time over a realworld environment for enhancing tourism experience. ARNature: A lot times, tourists have limited money and time to experience a tourist site during different seasons or weather conditions. The visitors are able to go on an augmented reality journey using an AR device, such as HoloLens, tablet or cellphones, and interact with real objects in a natural scene. Different enhancements of a tourism site are digitally overlaid onto visitors’ direct field of vision in real time. In addition, a voice module can be used to play music and provide additional information. Related algorithms, system design, and simulation results for a prototype ARNature system are presented. Furthermore, a no-reference image quality measure, Naturalness Image Quality Evaluator (NIQE), was utilized to evaluate the immersiveness and naturalness of ARNature. The results demonstrate that ARNature has the ability to enhance tourist experience in a truly immersive manner.
Cycle-Consistent Adversarial Network for Facial Local-Region Exchange in Wild

Recently, Generative Adversarial Networks are popularly used in face generation and get the state-of-art result. However, it's hard to swap the local area of face while many of previous work has focused on either generating face from a noise vector which belongs to some kind of data distribution or swaps the whole face. In this paper, we proposed a Cycle-Consistent Region Exchange Generative Adversarial Network(CREGAN) for facial local area exchange in the wild facial database. The Cycle-Consistent guaranteed that the exchanged area keeps the another facial feature and a novel approach to achieve face local region exchange and other region remain unchanged. At the same time, the characteristics of generative adversarial network can make ensure the quality of the generated images. And, it will shows that the generated images can reach photo-realistic results by CREGAN.
Linked Data on theWeb and its Relationship with Distributed Ledgers (LDOW/LDDL)

Decentralised data solutions bring their own sets of capabilities, requirements and issues not necessarily present in centralised solutions. In order to compare the properties of different approaches or tools for management of decentralised data, it is important to have a common evaluation framework. We present a set of dimensions relevant to data management in decentralised contexts and use them to define principles extending the FAIR framework, initially developed for open research data. By characterising a range of different data solutions or approaches by how TRusted, Autonomous, Distributed and dEcentralised, in addition to how Findable, Accessible, Interoperable and Reusable, they are, we show that our FAIR TRADE framework is useful for describing and evaluating the management of decentralised data solutions, and aim to contribute to the development of best practice in a developing field.
Back-Projection Lightweight Network for Accurate Image Super Resolution

Recently, deep neural networks have led to tremendous advances in image super-resolution and achieved remarkable results. However, most deep learning methods attain appreciable performance by increasing model parameters and complexity. Consequently, they are difficult to utilize in common devices despite significant results. With respect to application in resource-limited devices, the goal of the present study involves designing a compact network that exhibits good scalability. We proposed a back projection network (BPnet) as we were inspired by a traditional image super-resolution technique, namely iterative back projection (IBP). Our proposed network is composed of different modules. Each module performs the enlarging function based on the result of the previous module, which is similar to an iteration in IBP. The input of each module network corresponds to the previous down-sampled output minus the low-resolution input image. Additionally, the output of the network is the residual between the ground truth high-resolution image and previous output. The non-linear property of a neural network is maximized through the sparsity of residual input/output. Thus, we can achieve a lightweight network without sacrificing the quality of the results. The experiments indicate that the number of parameters of the proposed BPnet can be less than quarter of those proposed in the state-of-the-art papers and still have comparable results.
Application of Type-2 Fuzzy Sets for Analyzing Production Processes

The manufacturing processes of the aircraft factory are analyzed to improve the quality of management decisions. Production processes models based on time series models are proposed. The applying of fuzzy smoothing of time series is considered. A new technique for extracting fuzzy trends for forecasting time series proposed. The use of type-2 fuzzy sets for making new models of time series with the aim of improving the quality of the forecast considered. An information system is being built to calculate the production capacity using these models. The system implements the algorithms for the calculation of production capacity based on a methodology approved in the industry. The information extracted from the production processes is supposed to be used as a component of the models. An experiment with checking the quality of smoothing of time series is described. The experiment shows the possibility and advantages of modeling time series using type-2 fuzzy sets.
D3CAS: Distributed Clustering Algorithm Applied to Short-Text Stream Processing

In this article, a proof of concept of a dynamic clustering algorithm based on density, called D3CAS, is presented. This algorithm was implemented to be run under the Spark Streaming framework, and it allows processing data streams. The algorithm was tested using a stream of short texts consisting of requirements generated by social media users, in particular, from a dataset called Pizza Request Dataset. The results, obtained in a virtualized environment, were analyzed with different configurations for algorithm parameters, which allowed establishing which are the configurations that yield the best results. Since the dataset used includes the label for each text in the stream, cluster purity could be measured and the results obtained could be compared to those presented by the authors of the dataset.
Physics Teaching in the 21st Century

The aims of physics education have evolved alongside the society in which we live, to be able to adapt to the rapid pace of change imposed by the application of scientiﬁc discoveries in the industry. We could deﬁne the quality of physics’ teaching as its ability to learn how to reason. This approach covers both teaching competency-based and is student-centered. Each topic should be an excuse to teach how deduce it or induce it. The teacher decides if he/she deduces it or induces it from a demonstration or from experimental data. Experiments allows us to appreciate the ability of reasoning of the students, since they need to combine all they know in conducting the experiment. To evaluate the learning of a topic, we need to write questions to test the required reasoning, that it is not easy. PISA succeeds for students 15 years old, the challenge is to do it with our students.
An Alternative Deep Model for Turn-Based Conversations

In this paper, we propose a new model for building a conversational dialogue system which provides natural, realistic and flexible interaction between human and machine based on large movie subtitles dataset. Our models are a generative model that is autonomously generated word-by-word, opening up the possibility of working on many different languages. To address this goal, we extend the hierarchical recurrent encoder-decoder neural network (HRED) for dealing with extracting features from long input turns and generating long output turns. Furthermore, we also apply an attention mechanism in order to attend to particular sentences on source side when predicting a turn response. The models are trained end-to-end without labeling data. The experiments show that our proposed model has improved 36% on BLUE score compared to the HRED model. It also shows many potential improvements in chatbot models.
Application of reflection transform imaging in handwriting trace detection

Handwriting is a special kind of trace, which carries important information such as writing content, writing tools and writing habits. Reflection transform imaging (RTI) is a computational photography method that creates an interactive image to further display the 2D and 3D texture features of the inspection surface. In this paper, Reflection Transform Imaging is applied to study the colored and colorless handwriting traces produced by pen and water-based pen. By combining different lighting directions and rendering modes, not only the writing content of different handwriting traces can be obtained, but also the three-dimensional texture information of handwriting traces can be obtained. Especially for the colorless handwriting trace, the two-dimensional and three-dimensional texture of the handwriting detail features can reveal the differences between different writing tools and show the shape of the brush. The experimental results show that reflection transform imaging technique can clearly display the colored and colorless handwriting traces, and it can provide powerful help for the case detection by revealing various information hidden in the handwriting traces.
AUTONOMOUS POSITION OF THE STUDENT’S PERSONALITY IN LEARNING ACTIVITY

The article describes the manifestations of the autonomous position of the students personality in the educational process. Autonomy, manifested in the ability to make an informed, subject and responsible choice, is considered as the main prerequisite for future professional success. The study of manifestations of the students autonomous position in the educational process was carried out over two years. The level of student autonomy was determined through the ability to set goals, individual responsibility, and subject activity in the educational process. The study allowed us to identify explicit and implicit forms of autonomy. Explicitly, the measure of autonomy is determined through the setting of goals and priorities in the educational process, the degree of synchronization of actions, the construction of prospective plans for self-realization and development. The implicit basis of the autonomous position of the students personality is reflection and awareness of the significance of the events.The study describes the factors that determine the autonomous, responsible and independent behavior of the student in learning activities. Statistical data processing reveals differences in the signs of successful and not successful groups of students. The relationship between academic performance and the level of autonomy of the students personality is defined. Specific manifestations of autonomy in behavior are described.The autonomous position of a students personality is manifested in responsible and independent behavior, interest in current learning outcomes, the ability to independently make decisions, exercise control, show initiative and criticality when studying educational material; high adaptive activity, focus on the interests of others, desire to plan their actions, taking into account group tasks. A low level of student autonomy in learning activity is manifested through the strategy of subordinate adaptive behavior: the desire to live without conflict in the professional community, lack of initiative, avoidance of independent actions; desire to be careful and pragmatic when making decisions; propensity to avoid problem tasks, to receive support and encouragement for the efforts made, rather than the result achieved.The autonomous position of a students personality influences the quality of learning outcomes due to the ability to align learning objectives with individual interests and needs.
Design and Application of Integrated Monitoring Model for Provincial Meteorological Observation Data Transmission

With the development of meteorological services, there are more and more types of real-time observation data, and the timeliness requirements are getting higher and higher.The monitoring methods of existing meteorological observation data transmission can no longer meet the needs.This paper proposes a new monitoring model, namely the "integrated monitoring model" for provincial meteorological observation data transmission.The model can complete the whole network monitoring of meteorological observation data transmission process.Based on this model, the integrated monitoring system for meteorological observation data transmission in Guangdong Province is developed.The system uses Java as the programming language, and integrates J2EE, Hibernate, Quartz, Snmp4j and Slf4j frameworks, and uses Oracle database as the data storage carrier, following the MVC specification and agile development concept.The system development uses four key technologies, including simple network management protocol, network connectivity detection technology, remote host management technology and thread pool technology.The integrated monitoring system has been put into business application.As a highlight of Guangdong's meteorological modernization, it has played an active role in many major meteorological services.
Multi-Path and Multi-Loss Network for Person Re-Identification

In person re-identification (re-ID), most state-of-the-art models extract features by convolutional neural networks to do similarity comparison. Feature representation becomes the key task for person re-ID. However, the learned features are not good enough based on a single-path and single-loss network because the learned objective only achieves one of the multiple minima. To improve feature representation, we propose a multi-path and multi-loss network (MPMLN) and concatenate multi-path features to represent pedestrian. Subsequently, we design MPMLN based on ResNet-50 and construct an end-to-end architecture. The backbone of our proposed network shares the local parameters for multiple paths and multiple losses. It has fewer parameters than multiple independent networks. Experimental results show that our MPMLN achieves the state-of-the-art performance on the public Market1501, DukeMTMC-reID and CUHK03 person re-ID benchmarks.
PLVSNs: A Privacy-Preserving Location-Sharing System for Vehicular Social Networks

Combining Vehicular Ad Hoc Networks (VANET) and mobile online social networks (mOSNs), Vehicular Social Networks (VSNs) can provide more complex and rich services, including location sharing services, which brings more convenient communication and information sharing. However, location sharing in VSNs demands advanced privacy-preserving schemes to protect vehicles’ social relationships and location information. Previous solutions employ Location Based Server (LBS) to manage users’ location information. However, the server may collect the messages it has received to get more information of vehicles. Moreover, the threshold distances privacy is not properly protected, which may disclose users’ identities. In order to overcome the shortage, we propose PLVSNs, a privacy-enhanced location-sharing scheme in VSNs. It takes advantage of vehicles’ increasingly superior computing power, allowing vehicles and SNS to implement the distance comparison without LBS. Furthermore, we introduce a privacy enhanced distance comparison protocol, which provides a more efficient comparison between the actual distance and the threshold distance while not disclosing vehicles’ privacy. We also show that the new construction is secure. Finally, we implement PLVSNs and the experimental results demonstrate the efficiency of our proposed scheme.
Rethinking Print Periodicals Holdings Retention Policy in Small Academic Libraries

Collection development policies in small academic libraries may lack a formal policy statement about print periodical holdings retention. However, there is a need for a distinct policy about print periodicals holdings and their retention. Periodicals collections at academic libraries have been greatly affected by publishers’ decisions to discontinue print journal formats and move to online-only electronic versions. The move from one format to another produces challenges to the retention of an effective print periodicals collection. Given these continuous changes in publication format, it is necessary for academic libraries to rethink their print periodicals holdings retention. This article will present a literature review on and case study of periodicals collection management and explore strategies for developing holdings policies and guidelines for retention. It will argue that collection development policies ought to include a separate policy for the print periodicals collection and that unlike their reputation for being time-consuming and inflexible, periodical retention policies can improve flexibility and guide in decision making, helping to preserve core titles and acquire new titles that support academic programs and the work of the college community.