Reviewerly/exHarmony huggingface dataset:
FalconEye: A High-Performance Distributed Security Scanning System

Web applications, as a conventional platform for sensitive data and important transactions, are of great significance to human society. But with its open source framework, the existing security vulnerabilities can easily be exploited by malicious users, especially when web developers fail to follow the secure practices. Here we present a distributed scanning system, FalconEye, with great precision and high performance, it will help prevent potential threats to Web applications. Besides, our system is also capable of covering basically all the web vulnerabilities registered in the Common Vulnerabilities and Exposures (CVE). The FalconEye system is consists of three modules, an input source module, a scanner module and a support platform module. The input module is used to improve the coverage of target server, and other modules make the system capable of generic vulnerabilities scanning. We then experimentally demonstrate this system in some of the most common vulnerabilities test environment. The results proved that the FalconEye system can be a strong contender among the various detection systems in existence today.
Analyzing CNN Model Performance Sensitivity to the Ordering of Non-Natural Data

Convolutional Neural Networks (CNN) have had significant success in identifying and classifying image datasets. CNNs have also been used effectively in classifying non-visual datasets such as malware and gene expression. In all of these applications, CNNs require data to be organized in a certain order. In the case of images, this order is naturally presented. However, in the case of non-visual data, this order is sometimes not naturally defined and hence requires an artificially defined order. The sensitivity of a CNN model's performance to various artificial orders of non-natural datasets is not well-understood. In this paper, we investigate this problem by experimenting with various orders of a dataset derived from malware behavior in a cloud auto-scaling environment. We show that the ordering can have a major impact on the performance of the CNN and offer some insights on how to derive one or more orderings that could provide better performance.
Optimization of data resampling through GA for the classification of imbalanced datasets

Classification of imbalanced datasets is a critical problem in numerous contexts. In these applications, standard methods are not able to satisfactorily detect rare patterns due to multiple factors that bias the classifiers toward the frequent class. This paper overview a novel family of methods for the resampling of an imbalanced dataset in order to maximize the performance of arbitrary data-driven classifiers. The presented approaches exploit genetic algorithms (GA) for the optimization of the data selection process according to a set of criteria that assess each candidate sample suitability. A comparison among the presented techniques on a set of industrial and literature datasets put into evidence the validity of this family of approaches, which is able not only to improve the performance of a standard classifier but also to determine the optimal resampling rate automatically. Future activities for the improvement of the proposed approach will include the development of new criteria for the assessment of sample suitability.
Cross-Domain-Classification of Tsunami Damage Via Data Simulation and Residual-Network-Derived Features From Multi-Source Images

This paper presents a novel application of remote sensing data and machine learning technologies for damage classification in a real-world cross-domain application. The proposed methodology trains models to learn the building damage characteristics recorded in the 2011 Tohoku Tsunami from multi-sensor and multi-temporal remote sensing images. Then, the trained models are tested in the recent 2018 Sulawesi Tsunami. Additionally, a simulation of high-resolution SAR image was carried to deal with missing data modality. Our initial results show that the ResNet-derived features from optical images acquired after the disaster together with moderate- and high-resolution synthetic aperture radar (SAR) post-event intensity data showed significant accuracy in classifying two levels of tsunami-induced damage, with an average f-score of approximately 0.72. Taking into account that no training data from the 2018 Sulawesi Tsunami was used, our methodology shows excellent potential for future implementation of a rapid response system based on a database of building damage constructed from previous majors disasters.
Porting Multilingual Morphological Resources to OntoLex-Lemon

We describe work consisting in porting various morphological resources to the OntoLex-Lemon model.A main objective of this work is to offer a uniform representation of different morphological data sets in order to be able to compare and interlink multilingual resources and to cross-check and interlink or merge the content of morphological resources of one and the same language.The results of our work will be published on the Linguistic Linked Open Data cloud.Listing 6: The DE_morph_dict entry for "Abgang" (departure or leaving etc)
A Temporal Synchronization Scheme to reduce and compensate for temporal asynchrony in wireless 3D video streaming

Although 3D video has become popular, successful 3D video streaming over wireless networks involves a number of challenges. Due to the frequent frame damages and losses in wireless networks, temporal asynchrony occurs and results in serious visual fatigue for viewers. In order to provide higher qua lity 3D video, this paper proposes a new scheme termed the Temporal Synchronization Scheme (TSS) for live 3D video streaming. TSS delivers video frames for the left and right views in the same frame sequence with the same transmission priority, and it compensates for frame damage and losses during the decoding phase. In addition, a new metric called the Stereoscopic Temporal Variation Index (STVI) is proposed; it measures the degree of temporal asynchrony in 3D video. Subjective assessments demonstrate that STVI is an objective metric for measuring subjective quality given that it exhibits a strong positive correlation with the subjective MOS rating. This paper demonstrates that TSS significantly improves the visual quality of 3D videos given the use of STVI and the Mean Opinion Score (MOS) even when frame damage and losses occur. The contributions of the paper are as follows: i) The proposed TSS is the first scheme to address the temporal asynchrony issue in live 3D video streaming over wireless networks. ii) TSS only requires slight modifications to decoders. iii) A new metric (i.e., STVI) is proposed to measure the degree of temporal asynchrony in 3D videos. Therefore, 3D video streaming over wireless networks can be performed with temporal synchronization, and it is expected using TSS will reduce visual fatigue.
HMM Based Approach

Approaches based on hidden Markov models (HMMs) have been devoted particular attention in the last years. AFAMAP (Additive Factorial Approximate Maximum a Posteriori) has been introduced in Kolter and Jaakkola to reduce the computational burden of FHMM. The algorithm bases its operation on additive and difference FHMM, and it constrains the posterior probability to require only one HMM change state at any given time.
Tailored Controls

User interfaces rarely adapt to the specific user preferences or the task at hand. We present a method that allows to quickly and inexpensively create personalized interfaces from plain paper. Users can cut out shapes and assign control functions to these paper snippets via a simple configuration interface. After configuration, control takes place entirely through the manipulation of the paper shapes, providing the experience of a tailored tangible user interface. The shapes and assignments can be dynamically changed during use. Our system is based on markerless tracking of the user's fingers and the paper shapes on a surface using an RGBD camera mounted above the interaction space, which is the only hardware sensor required. Our approach and system are backed up by two studies where we determined what shapes and interaction abstractions users prefer, and verified that users can indeed employ our system to build real applications with paper snippet interfaces.
Multi-lane traffic flow monitoring and detection system based on video detection

The paper adopts the improved ViBe algorithm, and initializes the background sample according to the original algorithm. Using the idea of image binarization, the current frame image is subjected to the foreground detection of the traditional ViBe, and then the second judgment is made according to the number of times the target is judged, and the original is changed. The fixed background factor in the algorithm background update strategy increases the background update probability during shadow elimination. Experiments show that the method can eliminate shadows in time and improve the accuracy of motion vehicle detection in different scenarios. At the same time, the paper designs a multi-lane traffic flow monitoring system based on improved ViBe algorithm combined with IoT ZigBee technology. The flow monitoring accuracy of the algorithm is verified.
Optimization of house purchase recommendation system (HPRS) using genetic algorithm

&lt;span&gt;This paper presents the optimization of house purchase recommendation system (HRPS) using Genetic Algorithm. Everyone in this world has their own dream house and plan to purchase it depending on their budget and based on the house preferences. Homebuyers face problem in comparing the house property websites according to the factors during the house survey. Subsequently, it is time-consuming in making the decision and they need to bear the transportation cost as they will need to travel to one house developer office to another. In addition, some of the homebuyers felt disappointed when their expectations were not met. Thus, in order to optimize the preferences, in this paper, we present a web-based house purchase recommendation system (HPRS) using a genetic algorithm. Then, it follows by test the functionality and usability of the system. As a result, the system found to function accordingly and obtain more than the average score of system usability scale testing. For further research, it is recommended to add more data to the database and compare with other algorithms.&lt;/span&gt;
Analysing Coreference in Transformer Outputs

We analyse coreference phenomena in three neural machine translation systems trained with different data settings with or without access to explicit intra-and cross-sentential anaphoric information.We compare system performance on two different genres: news and TED talks.To do this, we manually annotate (the possibly incorrect) coreference chains in the MT outputs and evaluate the coreference chain translations.We define an error typology that aims to go further than pronoun translation adequacy and includes types such as incorrect word selection or missing words.The features of coreference chains in automatic translations are also compared to those of the source texts and human translations.The analysis shows stronger potential translationese effects in machine translated outputs than in human translations.
Application of Internet of Things and GIS in Power Grid Emergency Command System

The power grid is vulnerable to natural disasters and causes grid faults. When power disasters in the process of handling, the effective implementation of emergency command is important for timely restoring of power supply and increasing its reliability. The article briefly describes the related theories and key technologies of the power Internet of Things and GIS. It mainly focuses on the emergency command application scenario of the power system under natural disasters, and designs and constructs the power grid emergency command technology architecture based on the Internet of Things and space information technology. The deep application of ubiquitous power Internet of Things and power grid GIS in disaster emergency monitoring, analysis and early warning, resource allocation, command decision, and emergency drills. It provides useful exploration for building a new generation of grid disaster emergency command system and promoting the construction of ubiquitous power Internet of Things.
Visual Object Tracking via an Improved Lightweight Siamese Network

Object tracking has recently raised a great research interest, and many Siamese network-based trackers have achieved the state-of-the-art performances. However, by analyzing their network structure, it can be found that those feature extraction modules still use AlexNet or its variants. Compared with some recent lightweight networks, there is still much room for improvement in accuracy. In this paper, we proposed an improved lightweight Siamese network, which combine the MobileNetV2 and SiamRPN. By utilizing strategy of the improved network which expanding the feature channels at first then compressing it, more feature information of object can be obtained for object tracking and the accuracy of the tracking algorithm can be effectively improved. We demonstrate the tracking performance of the proposed algorithm on OTB-100 dataset, and the proposed algorithm has a better performance in AUC and accuracy than some state-of-the-art trackers, with a 0.646 AUC and a 0.877 precision.
Friend Recommendation Model Based on Multi-dimensional Academic Feature and Attention Mechanism

The academic social network platform is different from the popular social network platform. Most users of the academic social network platform are researchers and they have more academic related information. How to effectively recommend friend for them according to the characteristics of academic social network have become one of the current research directions. Because of traditional recommendation methods only consider the superficial interaction characteristics, it cannot obtain the more complex and diverse nonlinear relationship between the target user and the recommendation item. The method based on deep learning also has problems such as information loss. In this paper, we propose a friend recommendation model based on multi-dimensional academic characteristics and attention mechanism (MLP–AMAF). We capture the attribute features, relation features and text features of the user from academic social networking. Then we combine these academic features and get the key information related to the current recommendation task automatically by attention mechanism. According to the different preferences of the user, we can get more personalized recommendation results. In our experiments, we use friend data of SCHOLAT, a real academic social network platform, to evaluate our model and get better recommendation results than other widely used recommendation algorithms.
A Web Application for Glasses Virtual Try-on in 3D Space

Virtual try-on is a technology that allows people to virtually check the appearance of accessories, makeup, hairstyle, hair color, clothes and potentially more on themselves. The virtual try-on presents many advantages over real try-on, it speeds up the process providing the possibility to test hundreds of products without the need to reach a physical store. In this paper we propose a virtual try-on web application specific for eyeglasses and sunglasses that can be easily used by simply taking a picture of a face and selecting the desired frames. The try-on process is performed on a 3D face reconstructed from the input image allowing the user to see the virtual face and glasses from different viewpoints. The try-on process is fully automated and does not require the user to provide anything else but the picture and selection of the glasses frames to test.
Drone Image Stitching Guided by Robust Elastic Warping and Locality Preserving Matching

Image stitching stitches multiple overlapping images into a seamless image according to the corresponding geometric relationship between the reference and source images. In this study, the parallax-tolerant image stitching method based on robust elastic warping is applied to the stitching of drone images, and locality-preserving feature matching is used to effectively remove outliers from the drone images. The method can be divided into three stages, namely, locality-preserving feature matching, robust elastic warping, and global projectivity preservation. First, a set of high- precision point matching is provided for a drone image, and local matching is used. Second, the robust elastic warping function eliminates the parallax error, and the input image is distorted according to the calculated deformation on the grid plane. Finally, the global projectivity-preserving method is applied to obtain high-precision result panoramas. Experiments on several sets of drone images demonstrate that our method can generate better panoramas over the competitors.
Blockchain Platform for Real-Time Payments: A Less Costly and More Secure Alternative to ACH

This paper examines the historical development of banking and automated clearing house legacy systems and offers blockchain platform for real-time payments as an alternative. Institutions with legacy systems resist disruptive change unless there are demands by businesses and customers and an opportunity for new products at reduced costs and additional revenues. Although the Automated Clearing House (ACH) in the U. S. has phased in a twice-a-day clearing and settlement system, it is still behind real-time models in use in several other countries. ACH uses individual clearance and settlements for large payments and batches for smaller transactions. Using ACH data, this paper calculates the annual opportunity cost of using a real-time model such as blockchain versus ACH's discrete, twice-a-day clearance and settlement procedure. For 2016, the real-time protocol would have resulted in benefits for businesses and customers of $10 billion.
Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&amp;A Platforms

Question categorization and expert retrieval methods have been crucial for information organization and accessibility in community question & answering (CQA) platforms. Research in this area, however, has dealt with only the text modality. With the increasingly multimodal nature of web content, we focus on extending these methods for CQA questions accompanied by images. Specifically, we leverage the success of representation learning for text and images in the visual question answering (VQA) domain and adapt the underlying concept and architecture for automated category classification and expert retrieval on image-based questions posted on Yahoo! Chiebukuro, the Japanese counterpart of Yahoo! Answers. To the best of our knowledge, this is the first work to tackle the multimodality challenge in CQA, and to adapt VQA models for tasks on a more ecologically valid source of visual questions. Our analysis of the differences between visual QA and community QA data drives our proposal of novel augmentations of an attention method tailored for CQA and use of auxiliary tasks for learning better grounding features. Our final model markedly outperforms the text-only and VQA model baselines for both tasks of classification and expert retrieval on real-world multimodal CQA data.
Gaze Region Estimation Algorithm without Calibration Using Convolutional Neural Network

The Japanese government conducted measures for persons with disabilities in 2013 and they reported that about 2.2 million of challenged children aged 17 or younger live in Japan. Those children cannot communicate with others without somebody's help because of severe physical disabilities and they are wholeheartedly keen to become a member of a society and crave a device/application to communicate with other people.
Detection of Down Syndrome Using Deep Facial Recognition

Down syndrome is a genetic disorder that affects 1 in every 1000 babies born worldwide. The cases of Down syndrome have increased in the past decade. It has been observed that humans with Down syndrome generally tend to have distinct facial features. This paper proposes a model to identify people suffering from Down syndrome based on their facial features. Deep representation from different parts of the face is extracted and combined with the aid of Deep Convolutional Neural Networks. The combined representations are then classified using a Random Forest-based pipeline. The model was tested on a dataset of over 800 individuals suffering from Down syndrome and was able to achieve a recognition rate of 98.47%.
Robust Hand Gesture Recognition Using Multimodal Deep Learning for Touchless Visualization of 3D Medical Images

Three-dimensional (3D) visualization of medical images is an important technology for efficiently conducting a surgery. However, efficient review of 3D anatomical models is required to maintain sterile field conditions. An operation using touchless interface for gesture recognition is one of the review methods. Real-time hand gesture application for supporting a surgery requires a robust recognition of various gestures. This study proposes a robust hand gesture recognition using multimodal deep learning to perform recognition using color and depth images. We evaluated the recognition accuracy of 25 different gestures and compared its recognition accuracy with conventional recognition methods. Resultantly, it was found that the proposed system achieves better real-time robust recognition than conventional methods.
Capsule Networks Need an Improved Routing Algorithm.

In capsule networks, the routing algorithm connects capsules in consecutive layers, enabling the upper-level capsules to learn higher-level concepts by combining the concepts of the lower-level capsules. Capsule networks are known to have a few advantages over conventional neural networks, including robustness to 3D viewpoint changes and generalization capability. However, some studies have reported negative experimental results. Nevertheless, the reason for this phenomenon has not been analyzed yet. We empirically analyzed the effect of five different routing algorithms. The experimental results show that the routing algorithms do not behave as expected and often produce results that are worse than simple baseline algorithms that assign the connection strengths uniformly or randomly. We also show that, in most cases, the routing algorithms do not change the classification result but polarize the link strengths, and the polarization can be extreme when they continue to repeat without stopping. In order to realize the true potential of the capsule network, it is essential to develop an improved routing algorithm.
Towards the Design and Deployment of an Item Bank: An Analysis of the Requirements Elicited

Assessments are an important phase in the learning process. Information and communication technologies advancements determined the development of e-learning software tools which support e-learning activities, including e-assessment. The increasing usage of summative and formative e-assessments led to the challenge of managing items. The concept of an item bank is meant to support teachers and students alike, to provide an overview when taking assessments or creating exams. This article presents an on-going R&D project towards the design and deployment of an item bank for computer-based tests, and discusses its role within a service-oriented system architecture which enables the execution of activities related to e-assessment, ranging from item design and test creation, to the analysis of event logs generated by test-takers. The research methodology followed for the requirements elicitation and main findings are presented, and directions for future work are discussed.