Reviewerly/exHarmony huggingface dataset:
SAR Object Detection With a Saliency Method Based on PCA and Global Contrast

Object detection is a significant step in synthetic aperture radar (SAR) image interpretation. This paper contributes to propose a saliency method based on principle component analysis (PCA) and global contrast for SAR object detection. Firstly, the original SAR image is smoothed by Gaussian filter and then segmented into superpixels by the linear iterative cluster (SLIC) algorithm. Secondly, two feature maps are created based on PCA and global contrast respectively, and are fused together to construct the saliency map. Lastly, we adopt an adaptive threshold to segment the saliency map and extract object areas. Experimental results on real SAR images indicate that the constructed saliency map is able to highlight objects and inhibit other areas effectively. Moreover, the proposed method performs well in both detection accuracy and time cost.
Design and Implementation of Virtual Examination System Based on Unity 3D

Test anxiety is a situational trait anxiety caused by excessive stress, which has seriously affected the physical and mental health of adolescent students. It is an urgent concern of school and society to help teenagers to eliminate test anxiety. Based on virtual reality exposure therapy (VRET) and Unity3D engine and HTC VIVE head-mounted display for virtual reality, researchers combine virtual reality technology with exposure therapy to design the virtual examination system. It contains three immersive interactive scenarios, including the preparation for pre-test anxiety, the entrance scene for on-the-spot anxiety and the examination scene. The user can realize the interactive experience in the virtual examination room through VR device. The test shows that the virtual examination system has the characteristics of higher authenticity, intuitiveness and so on. Can effectively induce the anxiety of candidates, which provides a scientific intervention method for the study of adolescent mental health.
Improved Secure Computation over Real Numbers and Its Application to Reliability Engineering

Secure computation over real numbers has recently been proposed for outsourcing computations while maintaining high security. This scheme has had difficulty in practice because it causes an unreasonably large computational complexity in decryption. While previous research resolved this difficulty, problems remain because the encryption is based on a kind of Caesar cipher known to be weak in terms of security. This paper proposes an improvement on the previous research. The key idea is replacing the differential operator used in the encryption and decryption process with another one so that encryption can be altered to use additional secret real numbers. This addition realizes stronger security because hackers must find not only keys but also these secret real numbers.
Stardust: A deep learning serving system in IoT

The deep neural network becomes an increasingly crucial component in recent intelligent applications. The excessive resource consumptions of state-of-the-art neural networks, however, remains a huge impediment towards their widespread deployment in the Internet of Things (IoT). In this paper, we propose an IoT-oriented deep learning serving system, Stardust, that accelerates the neural network inference to improve the quality of IoT services. Stardust integrates several joint contributions from both the system and AI perspectives, including system performance predictor, model compression, and compressive offloading. On one hand, the performance predictor profiles and predicts the runtime characteristics of neural network operations on a particular device with the targeted runtime environment, which enables a hardware and software oriented performance optimization during model compression and offloading. On the other hand, the model compression minimizes the computation time of neural networks on different devices, and the compressive offloading diminishes the network data transferring time during the mobile-edge offloading. Moreover, all these optimizations can be done with almost no compromise on inference accuracy. The integration of these modules, therefore, collaboratively reduce the end-to-end latency of serving deep learning services that reside across embedded/mobile devices and edge servers. We deploy illustrative applications on Stardust, performing human perception tasks with on-device camera microphone and motion sensors to demonstrate the capability of Stardust serving system.
Dependency and Span, Cross-Style Semantic Role Labeling on PropBank and NomBank

The latest developments in neural semantic role labeling (SRL) have shown great performance improvements with both the dependency and span formalism/styles. Although the two styles share many similarities in linguistic meaning and computation, most previous studies focus on a single style. In this article, we define a new cross-style semantic role label convention and propose a new cross-style joint optimization model designed around the most basic linguistic meaning of a semantic role. Our work provides a solution to make the results of the two styles more comparable and allowing both formalisms of SRL to benefit from their natural connections in both linguistics and computation. Our model learns a general semantic argument structure and is capable of outputting in either style. Additionally, we propose a syntax-aided method to uniformly enhance the learning of both dependency and span representations. Experiments show that the proposed methods are effective on both span and dependency SRL benchmarks.
Co-design methods for eliciting patient needs for wrist splint design

Wrist splints are a common treatment for rheumatoid arthritis, however, their effectiveness is compromised by patients not wearing splints as prescribed. Previous research has identified reasons for non-compliance, but typically lacks insights that could lead to improved splint design. A three-part study, using design probes, context mapping and a personalization toolkit as co-design methods for eliciting patient needs for wrist splint designs, is described. It identifies three themes and nine sub-themes for situations that affect compliance in wearing a splint. Additionally, four motivating factors to wear and 10 motivating factors not to wear a splint are presented. Nine requirements for improved splint design are established and form the basis of the design for a prototype personalization toolkit. Testing of this toolkit reveals participants are keen to wear splints whose appearance matches the clothes they are wearing or activities being undertaken. Co-design methods are shown to be capable of identifying determinants of compliance not previously discussed in the literature, as well as eliciting patient-specific needs for splint design.
Data Preprocessing for ANN-based Industrial Time-Series Forecasting with Imbalanced Data

The evolution of Industry towards the 4.0 paradigm has motivated the adoption of Artificial Neural Networks (ANNs) to deal with applications where predictive and maintenance tasks are performed. These tasks become difficult to carry out when rare events are present due to the imbalance of data. This is because training of ANN can be biased. Conventional techniques addressing this problem are mainly based on resampling-based approaches. However, these are not always feasible when dealing with time-series forecasting tasks in industrial scenarios. For that reason, this work proposes the application of data preprocessing techniques especially designed to face this scenario, a problem which has not been covered enough in the state-of-the-art. Considered techniques are applied over time-series data coming from Wastewater Treatment Plants (WWTPs). Our proposal significantly outperforms current strategies showing a 68% of improvement in terms of RMSE when rare events are addressed.
RSSI Estimation for wireless sensor network through-the-earth communication at frequency 433 MHz

To explore wireless sensor network signal transmission characteristics in through-the-earth communication, this paper focused on wireless network signal transmission attenuation by using 433MHz carrier frequency. Sensor nodes electromagnetic wave transmission characteristics were studied under the condition the different receiving node height, horizontal distance between the transmitting node and the receiving node through the wheat field experiments and computer simulation, the relationship between the received signal strength was established, transmission characteristics of field soil information collection sensor nodes in the soil under four wheat stages were put forwarded. The experiment demonstrated that 8 model goodness of fit R2 of effection of receiving node high and inter-nodes horizontal distance on RSSI(Received Signal Strength Indicator). Besides, three-dimensional surface of RSSI was built in four wheat growing periods, which the fitting model and goodness of fit of RSSI are obtained, and the model verification was conducted through SPASS software. Validation results showed that the model could better predict the received signal strength at different condition through-the-earth communication. The study can provide the technical support for sensor network node deployment and the establishment of the system in soil information acquisition.
Compile-Time Security Certification of Imperative Programming Languages

With the ever increase in the demand of building secure systems, recent years are witnessing a plethora of research on information flow control (IFC) techniques in programming languages to enforce a finer-grained restriction on the propagation of information among untrusted objects. In this paper, we introduce a dynamic labelling (DL) algorithm (This paper is an extended version of the article [1] presented in SECRYPT’18.) for security certification of imperative programming languages that follows a combination of mutable and immutable labelling referred to as hybrid labelling approach. First, we study the possible methods of binding security labels with the subjects and objects of the program which include program counter that represent implicit flow within a program and compare the precision achieved by the applications of methods on benchmark programs. Next, we describe our labelling algorithm that generates labels for intermediate subjects/objects of a program from the given set of initial labels (some of which could be immutable throughout the computation) adhering to the constraints defined in [2] for a program to be information-flow secure. Apart from the usual control statements found in the imperative languages, we also present the labelling approach for a procedure call highlighting subtleties of different parameter passing mechanisms adopted in modern languages. Further, we discuss a variant of the algorithm for concurrent programs. It is shown that our algorithm always terminates after a finite number of iterations, also establish the soundness concerning non-interference as given by [3]. We compare the labelling precision realizable by our approach with the existing approaches in the literature.
Semantic modeling for education of library and information sciences in Iran, based on Soft Systems Methodology

Reviewing the recently published texts in the field of library and information science education indicates some fundamental problems in this pedagogic process. According to different factors dealing with the process, confronting the challenges is considered as complex issues. Therefore, in this research Soft Systems Methodology, an action research method, was chosen to propose a comprehensive model to solve the mentioned problems. Based on the Checkland seven proposed stages, the problem situation was identified, and then it was expressed in the form of a rich picture. Driving root definitions and the CATWOE model were cleared to accomplish the conceptual model. Comparison of the conceptual model to the real world, also proposing feasible and desired changes are the fifth and sixth stages of the research. Finally, taking action to improve the current situation in the field of LIS education finished the procedure. Utilizing the steps of Soft Systems Methodology, this research draws the rich picture illustrating the process of LIS education and its issues dealing with the related ecosystem. Accordingly, the final model consisting of three ontologies was attained. To validate the semantic model, Cohen’s kappa coefficient was calculated. The model, approved by high level of expert agreement, not only can be an appropriate solution for the problems involved in library and information science education in Iran, but also can be considered as a pattern for future researches in designation and implementation of a semantic model of education in other disciplines.
Primary gold mineralization in the Lisungwe Valley area, Kirk Range, southern Malawi

Abstract A conspicuous paucity in gold resources in Malawi’s mineral inventory may be partially attributed to a general low resolution in the knowledge of Malawi’s geology, coupled with historical under-exploration for this important economic commodity. To address this knowledge gap, the present study focusses on developing a regional, local and microscopic understanding of the characteristics of gold occurrences in the mineralized Little Chisumbwiti river valley, located in the Kirk Range region which forms part of Southern Irumide mobile belt in southern Malawi. Regional scale interpretations are afforded by in-depth investigation of airborne geophysical data, which are then supported by ground geological mapping and by microscopic observations using optical and electron microscopy, and X-ray computed tomography (XCT). Structural mapping and regional geophysics indicate that the area has been affected by at least two periods of deformation (D1 to D2). The D1 deformation event is characterized by northwest – southeast directed crustal shortening (tentatively associated with the Southern Irumide orogeny) which produced northeast – southwest structures. Gold mineralization in the Little Chisumbwiti river valley is hosted in northeast – southwest trending quartz—sulphide vein sets, where it occurs as flakes ranging in size between 0.24 and 4 mm. The gold is associated with a paragenetically-late pyrite-chalcopyrite-sphalerite generation of sulphide precipitation, and to a lesser extent, with sericitised biotite schist wall rock. Exploration in the Kirk Range region should focus on the northeast-southwest structures, which represent potential conduits for fluid flow during the D1-related hypogene Au mineralization event, however the impacts of subsequent D2 deformation must also be considered. Locally, wall rock lithology (e.g., Fe (II)-rich biotite schists) may have an additional second order control on the siting of gold mineralization. The new insights reported in this study contribute meaningfully towards advancing the status of Malawi’s regional geology and should serve as a useful resource for future exploration efforts undertaken in the Kirk Range, and in other parts of south/central Malawi occurring within the Southern Irumide tectonic block.
Fused voxel autoencoder for single image to 3D object reconstruction

The encoder–decoder based models become popular for many computer vision problems from image generation to image segmentation tasks. Due to the impressive performance of these models and Generative Adversarial Networks on image generation, these models have also adopted to 3D domains for 3D reconstruction and generation tasks. In this Letter, the authors have also attempted to solve a single image to 3D reconstruction problem by a novel encoder–decoder based model that is based on a fusion of encoders with the weak-supervision approach. Thus, they have focused on multi-category models inspiring the outstanding single-category models on literature for real-world tasks. In the experiments, it is seen that the proposed model is capable of generating the 3D objects from a single image by benefiting a wide range of features with weak class annotations.
Unsupervised Anomaly Thresholding from Reconstruction Errors

Internet of Things (IoT) sensors generate massive streaming data which needs to be processed in real-time for many applications. Anomaly detection is one popular way to process such data and discover nuggets of information. Various machine learning techniques for anomaly detection rely on pre-labelled data which is very expensive and not feasible for streaming scenarios. Autoencoders have been found effective for unsupervised outlier removal because of their inherent ability to better reconstruct data with higher density. Our work aims to leverage this principle to investigate approaches through which the optimal threshold for anomaly detection can be obtained in an automated and adaptive fashion for streaming scenarios. Rather than experimentally setting an optimal threshold through trial and error, we obtain the threshold from the reconstruction errors of the training data. Inspired by image processing, we investigate how thresholds set by various statistical approaches can perform in an image dataset.
NLP-based error analysis and dynamic motivation techniques in mobile learning

Mobile learning uncovers new dimensions of learning and personal growth. Mobile phones have completely dominated our lives from communication and entertainment to socializing and learning. In view of providing more individualized learning through mobile phones, several intelligent techniques should be incorporated in mobile-assisted learning systems. As such, this paper presents an effective analysis of students' errors during the assessment process in mobile learning using Natural Language Processing (NLP) techniques. The error analysis can reason between grammatical, syntax and careless errors using the Levenshtein distance. Moreover, it describes dynamic methods for motivating students in order to improve their learning experience. As such, students can receive motivation in case of making errors, cognitive inconsistencies, etc. Dynamic motivation is enriched with the delivery of badges as a means to further enhance knowledge acquisition. As a testbed for our research, a mobile language learning application for tutoring the English language has been designed, fully developed and evaluated. Concluding, this paper presents real examples of operation of the presented system and the evaluation results show the acceptance of the NLP-based error analysis and the dynamic motivation techniques by students.
Multipartite quantum coherence under electromagnetic vacuum fluctuation with a boundary

We investigate the dynamics of multipartite quantum coherence (QC) for accelerated detector qubit coupled with fluctuating electromagnetic field by presence of a perfectly reflecting boundary. We firstly derive the master equation that the detector evolution obeys. We find that without boundary, QC declines under the impact of Unruh thermal effect and vacuum fluctuation. However, with a boundary, the degradation, floating and protection of QC are closely related to boundary effect, detector polarization and acceleration. The presence of boundary can effectively protect QC under the influence of vacuum fluctuation and Unruh thermal effect in some certain conditions and give us more freedom of adjusting the QC behaviors. Besides, it is shown that the QC of W state manifests better robustness than that of GHZ state under the influence of the vacuum fluctuation and acceleration.
Taxonomy Extraction for Customer Service Knowledge Base Construction

Customer service agents play an important role in bridging the gap between customers' vocabulary and business terms. In a scenario where organisations are moving into semi-automatic customer service, semantic technologies with capacity to bridge this gap become a necessity. In this paper we explore the use of automatic taxonomy extraction from text as a means to reconstruct a customer-agent taxonomic vocabulary. We evaluate our proposed solution in an industry use case scenario in the financial domain and show that our approaches for automated term extraction and using in-domain training for taxonomy construction can improve the quality of automatically constructed taxonomic knowledge bases.
Demonstration of Flow-in-Cloud: A Multi-FPGA System

Flow-in-Cloud(FiC) is an acceleration platform designed to make a virtual monolithic large FPGA image from a number of mid-range economical FPGAs. We will show the live demonstration of the acceleration example of FiC with 24 boards through the network.
Block Analysis in Bitcoin System Using Clustering with Dimension Reduction

The online cryptocurrency bitcoin, created based in blockchain technology, is attracting the attention of individuals, businesses and the government as well. As interest in blockchain technology and cryptocurrency has steadily increased over the past few years, trading volume and market size of cryptocurrency have increased at an astonishing speed. As a result, analysis and monitoring measures for blockchain networks, blocks, and transactions have become an important issue. In this paper, the method of clustering applied dimension reduction as a method of bitcoin network analysis is proposed. The proposed method applies the analysis way using K-means algorithm with PCA to block data in bitcoin collected by this research team.
The EnMAP Mission: From Observation Request to Data Delivery

EnMAP (Environmental Mapping and Analysis Program, www.enmap.org) is a German, Earth observing, imaging spectroscopy, spaceborne mission planned for launch in 2020. The data products will cover the spectral range from 420 nm to 2450 nm with a spectral sampling distance between 5 and 12 nm with an expected signal-to-noise-ratio of 400:1 in the visible near-infrared and 180:1 in the shortwave infrared parts of the electro-magnetic spectrum. The resulting images will cover an area of 30 km in the across- track direction with a ground sampling distance of 30 m. The across-track tilt-capability of 30° enables revisit times of less than four days. The resulting data products will be freely available to the scientific user community for measuring, deriving, and analyzing diagnostic parameters, which describe vital processes on the Earth's surface comprising agriculture, forestry, soil and geological environments, as well as coastal zones and inland waters. This work concentrates on the description of activities performed and facilities involved for the preparation of these products. It starts out by the description of the User Portals for observation requests and acquisition planning, touches the aspects of creating the time-lines, the commanding and controlling of the satellite, the downlink of the telemetry and payload data, the design of the processing chain and the archiving of data plus a set of activities flanking the above for the provision of high-quality data products.