Reviewerly/exHarmony huggingface dataset:
The HTTP/2 Server Push and Its Implications on Mobile Web Quality of Experience

In recent years, an unprecedented growth in the usage of mobile devices for web browsing poses a challenge for the service providers to assure the user-perceived quality. In the context of web quality of experience (QoE), quality perception is mostly dominated by the page load time (PLT). HTTP/2 protocol, with the server push feature, promises to address the design limitations of HTTP/1.1 that inhibit optimal web performance. However, it remains largely unclear if HTTP/2 can really improve web QoE for mobile browsing. In this paper, we experimentally investigate the web QoE with HTTP/2. We assess the web QoE for several popular websites on a controlled testbed emulated with real 4G/LTE and 3G network traces. Our experiments investigate the impact of both network latency and packet loss ratio on the mobile web QoE. The results clearly show 24% improvement in the PLT, on an average, with HTTP/2 over mobile networks. However, we identify that HTTP/2 with server push is necessarily not the fail-safe solution for improving mobile web QoE under all conditions. We noticed that HTTP/2 loads the web pages slower than HTTP/1.1 when the network packet loss ratio is more than 2%. Our study could be used as the basis to derive a set of guidelines on the usage of the HTTP/2 server push to improve the end-user web QoE, especially in mobile devices.
Interaction of Information Content and Frequency as Predictors of Verbs’ Lengths

The topic of this paper is the interaction of Average Information Content (IC) and frequency of aspect-coded verbs in Linear Mixed Effect Models as predictors of the verbs' lengths. For 30 languages in focus, it came to light that IC and frequency do not have a simultaneous, positive impact on the length of verb forms: the effect of the IC is high, when the effect of frequency is low and vice versa. This is an indication of Uniform Information Density [13–16]. Additionally, the predictors IC and frequency yield high correlations between predicted and actual verbs' lengths.
Visual Enhancement Techniques For Underwater Images

In the recent days, wide range of research has been carried out on visual enhancement of under images in submarine and military operations to discover submerged structural designing and sea floor exploration. But, diving in the deep ocean for a long time has increased the difficulties for analysis of underwater images. Further, other factors such as scattering resulting from presence of particles inside the water and blurring effects reduce the quality of images being captured by underwater optic camera. There are several algorithms have been introduced to improve the visual quality of deep water images. Therefore, in this project, a novel algorithm based on bidirectional Empirical Mode Decomposition (BEMD) to enhance the visual quality of the underwater images will be implemented and comparison of data with conventional enhancement technique will be illustrated. The implementation will be done using MATLAB software.
A semi-supervised method for classifying unknown protocols

Network protocol classification is the foundation and vital method of network management, and plays an important role in intrusion detection. However, with the emergence of a number of new applications, there are more and more unknown application layer protocols. The traditional protocol classification method can efficiently and accurately classify known protocols, but it cannot accurately classify unknown protocols. In this study, we proposed a semi-supervised unknown application layer protocol classification method. In this method, the appropriate protocol flow statistical features are selected by the deep neural network with the aid of known application layer protocols. And then, the protocols are divided into different sets using an improved semi-supervised clustering algorithm. Using the protocol data experiment collected by the real world, the results show that our method can effectively classify unknown protocols and has high accuracy. The feature selection method based on deep neural network also has practical value.
A Comparative Study of Illumination Invariant Techniques in Video Tracking Perspective

Object tracking is being utilized in the field of computer vision over decades for video surveillance, human–computer interaction and robotic applications. Even though the state-of-the-art tracking technology is rapidly growing, few issues are still challenging such as illumination variation, pose variation, scale changes, occlusion, etc. Among these challenges, sudden illumination variation is more complicated which is not solved completely. Most of the current trackers, indeed work under controlled illumination conditions in outdoor and indoor environments. In this work, we study the effect of adding the photometric normalization techniques prior to tracking in order to minimize the drift during abrupt light changes of the median flow tracker (MFT). The tracker under investigation is based on the optical flow method and achieved remarkable results in the tracking literature. However, it drifts off during sudden illumination variation. To resolve this problem, pre-processing technique is incorporated just before tracking. Hence, we present an experimental study of various pre-processing techniques to improve the accuracy of the MFT. A total of eight state-of-the-art normalization techniques are summarized and tested in video tracking perspective. The experiments are carried out with the video sequences obtained from the object tracking benchmark dataset posing sudden illumination change as a challenge to analyze the modified tracker. A comparative analysis indicates that the modified tracker outperforms the baseline tracker in terms of precision score and overlap score.
Robust Evolving Granular Feedback Linearization

This paper develops an adaptive feedback linearization approach to control nonlinear systems under model mismatch conditions. The approach uses the participatory learning modeling algorithm to estimate the nonlinearities from data streams online, and the certainty equivalence principle to compute the control signal. Simulation experiments with the classic surge tank level control benchmark show that evolving robust granular feedback linearization outperforms exact feedback linearization.
Mental-Map Preserving Visualisation of Partitioned Networks in Vanted

Biological networks can be large and complex, often consisting of different sub-networks or parts. Separation of networks into parts, network partitioning and layouts of overview and sub-graphs are of importance for understandable visualisations of those networks. This article presents NetPartVis to visualise non-overlapping clusters or partitions of graphs in the Vanted framework based on a method for laying out overview graph and several sub-graphs (partitions) in a coordinated, mental-map preserving way.
Camera Zoom Detection and Classification Based on Application of Histogram Intersection and Kullback Leibler Divergence

This paper presents a novel compressed domain technique for detecting zooming camera in video sequences and its further classification into zoom-in camera and zoom-out camera. The inter-frame block motion vector field serves as the input to the proposed system which is partitioned into four representative quadrants for analysis purposes. The histograms of these four quadrants are analyzed utilizing histogram intersection feature for zoom motion detection while the cumulative histogram of these four quadrants are analyzed utilizing Kullback-Leibler divergence feature for zoom motion classification purposes. Experimental validation carried out utilizing block motion vectors extracted using Exhaustive Search Motion Estimation algorithm as well as H.264 decoded block motion vectors demonstrate superior performance in comparison to existing techniques.
On the Application of Domain Adaptation in SHM

Machine learning has been widely and successfully used in many Structural Health Monitoring (SHM) applications. However, many machine learning models can only make accurate predictions when the training and test data are measured from the same system; this is because most traditional machine learning methods assume that all the data are drawn from the same distribution. Therefore, to train a robust predictor, it is often required to recollect and label new training data every time when considering a new structure, which can be significantly expensive, and sometimes impossible in the SHM context. In such cases, the idea of transfer learning may be employed, which aims to transfer knowledge between task domains to improve learners. In this paper, a subfield of transfer learning i.e. domain adaptation, is considered, and its utility in SHM applications is briefly investigated.
Merging Attribute Characteristics in Collaborative Filtering to Alleviate Data Sparsity and Cold Start

Collaborative filtering algorithm is one of the most important technologies in the current e-commerce recommendation system. The effect of the similarity measurement method directly determines the accuracy of the recommendation system. Aiming at the shortcomings of the similarity calculation of traditional Item-based collaborative filtering recommendation algorithm in the case of extremely sparse user score data, an Item-based collaborative filtering algorithm based on attribute similarity is proposed. The algorithm uses the similarity of project attributes to correct the original similarity calculation, comprehensively considers the impact of project attribute values and user evaluation on recommendations, and dynamically adjusts the calculated values of similarity according to the sparseness of the score data to truly reflect each other similarity. The experimental results show that the improved algorithm can significantly improve the recommendation quality of the recommendation system when the user score data is extremely sparse, and can solve the recommendation problem of the new project.
Mahalanobis Distance based Adaptive Unscented Kalman Filter and Its Application in GPS/MEMS-IMU Integration

In this paper, a Mahalanobis distance based adaptive unscented Kalman filter (MDAUKF) is proposed, and it is applied to the tightly coupled GPS/MEMS-IMU integration for navigation solution. The MDAUKF aims to address the performance degradation of the unscented Kalman filter due to process model error. Based on a statistical function defined by the Mahalanobis distance of innovation vector, the MDAUKF adopts hypothesis testing to identify process model error and further introduces an adaptive factor into the prediction covariance to decrease the weight of state prediction and make more use of the accurate observation information. The MDAUKF is achievable to suppress the influence of process model error on filtering solution, and improve the adaptability for data fusion in GPS/MEMS-IMU (global positioning system/Micro-electro mechanical system based on inertial measurement unit) integration. Simulation results and comparison analysis demonstrate the effectiveness of the proposed method.
The Focus-Aspect-Value Model for Explainable Prediction of Subjective Visual Interpretation

Subjective visual interpretation is a challenging yet important topic in computer vision. Many approaches reduce this problem to the prediction of adjective- or attribute-labels from images. However,most of these do not take attribute semantics into account, or only process the image in a holistic manner. Furthermore, there is alack of relevant datasets with fine-grained subjective labels. In this paper, we propose the Focus-Aspect-Value (FAV) model to structure the process of capturing subjectivity in image processing,and introduce a novel dataset following this way of modeling. We run experiments on this dataset to compare several deep learning methods and find that incorporating context information based on tensor multiplication outperforms the default way of information fusion (concatenation).
Discourse-Driven Argument Mining in Scientific Abstracts

Argument mining consists in the automatic identification of argumentative structures in texts. In this work we address the open question of whether discourse-level annotations can contribute to facilitate the identification of argumentative components and relations in scientific literature. We conduct a pilot study by enriching a corpus of computational linguistics abstracts that contains discourse annotations with a new argumentative annotation level. The results obtained from preliminary experiments confirm the potential value of the proposed approach.
Wavelet series representation and geometric properties of harmonizable fractional stable sheets

Let Z H = {Z H (t), t ∈ R N } be a real-valued N -parameter harmonizable fractional stable sheet with index H = (H 1 , . . ., H N ) ∈ (0, 1) N .We establish a random wavelet series expansion for Z H which is almost surely convergent in all the Hölder spaces C γ ([-M, M ] N ), where M > 0 and γ ∈ (0, min{H 1 , . . ., H N }) are arbitrary.One of the main ingredients for proving the latter result is the LePage representation for a rotationally invariant stable random measure.Also, let X = {X(t), t ∈ R N } be an R d -valued harmonizable fractional stable sheet whose components are independent copies of Z H .By making essential use of the regularity of its local times, we prove that, on an event of positive probability, the formula for the Hausdorff dimension of the inverse image X -1 (F ) holds for all Borel sets F ⊆ R d .This is referred to as a uniform Hausdorff dimension result for the inverse images.
An Efficient and Recoverable Data Sharing Mechanism for Edge Storage

With data growing exponentially, more and more people prefer to share data with others by storing the data in edge servers. However, edge server cannot be deemed completely trustable as the sensitive data my be disclosed. Therefore, in this paper, we propose an efficient and secure data sharing scheme for edge storage by employing Ciphertext-Policy Attribute-Based Encryption (CP-ABE) which can be utilized to conduct fine-grained control. This scheme can not only support data recovery when some edge servers break down by employing Secret Sharing Scheme, but also can support semi-trusted third party authority via employing re-encryption method. That is, the third party authority can not either reveal the private data stored in edge servers. Finally, we analyze security of our scheme to demonstrate that this scheme is resistant to eavesdropping attack and colluding attack. Additionally, relevant experiments results are shown that the scheme is feasibility and efficiency.
FGreat: Focused Graph Query Autocompletion

Composing queries is evidently a tedious task. This is particularly true of graph queries as they are typically complex and prone to errors. This is compounded by the fact that graph schemas can be missing or too loose to be helpful for query formulation. Graph Query AutoCompletion (gQAC) alleviates users from the potentially painstaking task of graph query formulation. This demonstration presents an interactive visual Focused GRaph quEry AutocompleTion framework, called FGreat. Its novelty relies on the user focus for gQAC, which is a subgraph of the current query that a user is focusing on. FGreat automatically computes a focus and completes the query at the focus, as opposed to an arbitrary query subgraph. This demonstration presents two complementary approaches to compute the user focus for different circumstances. It computes the focus from either (i) the sequence of edges that a user recently added to his/her query, or (ii) the position of the mouse cursor, if it is available. We demonstrate that the user focus enhances both the effectiveness and efficiency of graph query autocompletion.
Stacon: Self-Stabilizing Context Neighborhood for Mobile IoT Devices

Retrieving context information from on-board sensors is critical to many scenarios in the Internet of Things (IoT). The battery capacity and physical size of smart devices can restrict the on-board sensors that can be supported and thereby limit the potential of applications for multi-agent IoT systems. In this work, we propose a novel scheme for building a context neighborhood among nearby devices through infrastructure-less collaboration. A context neighborhood is an ad hoc grouping of sensing devices that can collaborate to sense physical attributes of the environment. In this way, not every device needs to directly sense every context attribute to be able to leverage the information in its applications. Our approach entails a distributed algorithm that dynamically adjusts a device's sensing and sharing strategy based on the heterogeneity of resources in the proximity. We develop a prototype system using off-the-shelf IoT sensor kits; our demonstration exploits the self-stabilization feature to show the benefits this system could bring to higher-layer applications.
Analysis and Experimental Research of Modifications of the Image Segmentation Method Using Graph Theory

We investigate the problem of image segmentation based on the color difference of regions. The aim of the research is to increase the processing speed of the image segmentation and improve the segmentation quality on textured images. We study the aspects of effective implementation of the image segmentation algorithm based on the minimum spanning tree graph, in particular, the use of different data structures for displaying segments. It is shown the dependence of the segmentation result on the color difference metrics. It is suggested the modification algorithm with the use of an array of singly linked list and with the sorting of the graph edges over linear time, which has resulted in 4 times speed gain. We propose the modification algorithm with the use of superpixelization, which avoids the resegmentation on the textured areas of the image achieved through the superpixel construction and its use as the graph nodes.
NEIVA

With the popularization of advanced cellular networks, mobile video occupies nearly three quarters of cellular network traffic. While previous adaptive bitrate (ABR) algorithms perform well under broadband network, their performance degrades in cellular networks due to throughput fluctuation. Through real world 4G/LTE network measurement, we find that throughput in cellular networks exhibits high fluctuation. It follows Markov behaviors with different states and different transition probability among states. We further find that the transition probability is stable along time but varies significantly under different environments. This inspires us to design ABR algorithms by improving throughput prediction in cellular networks. We propose NEIVA, a network environment identification based video bitrate adaption method in cellular networks. NEIVA trains a network environment identifier based on throughput data and trains a hidden Markov model (HMM) based throughput predictor for different environments. In online video bitrate selection, NEIVA utilizes the environment identifier to select the model for corresponding environment. Then NEIVA predicts future network performance by combining offline model and online throughput data. We implement NEIVA with MPC and evaluate it in real environment. The evaluation results show that with manually identifying environment, NEIVA improves 20% -- 25% bandwidth prediction accuracy and 11% -- 20% QoE improvement over the baseline predictors. With online environment identification, online NEIVA achieves 3.8% and 11.1% average QoE improvement over MPC and HMM, respectively.
An Efficient Base Conversion Using Variable Length Segmentation and Remainder Transfer

This letter addresses the problems of conversion overflows, losses, and faults in the radix number system. Different from the fixed length truncation conversion strategy, the proposed scheme determines the optimal length of each segment adaptively according to the bases of source and destination and the representable numbers range limited by the maximum storage capacity of registers. In the segmentation repeated division scheme, the term of segment weight is presented to reflect the influence of different segment positions on the size of corresponding segment value, and a remainder transfer function that can interrelate individual segments is established to correct the dividend automatically. Theoretical and experimental results prove that the proposed scheme can realize the defect-free radix conversion for any integers between any bases and improve conversion efficiency up to the theoretical value.
Visual tracking based on semantic and similarity learning

We present a method by combining the similarity and semantic features of a target to improve tracking performance in video sequences. Trackers based on Siamese networks have achieved success in recent competitions and databases through learning similarity according to binary labels. Unfortunately, such weak labels result in limiting the discriminative ability of the learned feature, thus it is difficult to identify the target itself from the distractors that have the same class. The authors observe that the inter‐class semantic features benefit to increase the separation between the target and the background, even distractors. Therefore, they proposed a network architecture which uses both similarity and semantic branches to obtain more discriminative features for locating the target accuracy in new frames. The large‐scale ImageNet VID dataset is employed to train the network. Even in the presence of background clutter, visual distortion, and distractors, the proposed method still maintains following the target. They test their method with the open benchmarks OTB and UAV123. The results show that their combined approach significantly improves the tracking ability relative to trackers using similarity or semantic features alone.
The Development of Information Educational Environment

Within the framework of the research, the information-educational environment “Practice” has been developed: functional requirements have been identified, software and development tools have been selected, a database has been designed, a client-server web-application for its access has been implemented. The created information educational environment will effectively organize the process of students' practical training in terms of increasing the level of formation of the most important “XXI century skills”: bachelors' ICT-competence, communicative reflection, critical thinking.
An Intelligent Radio monitoring Architecture Based on Edge Computing

Radio spectrum resources are owned by the state, and radio spectrum security is the premise and foundation of national cyberspace security. In this paper, the current status and development trend of radio monitoring and management of china is introduced, and an intelligent radio monitoring framework based on edge computing is proposed. The intelligent radio monitoring framework consists of the edge computing layer, cloud computing layer and data visualization layer. Finally, taking the radiolocation, interference analysis and hazard judgment as examples implementation of the system is discussed.
Credulity to Phishing Attacks: A Real-World Study of Personnel with Higher Education

Phishing is an attack where the attacker creates the fake object (e-mail, web site, etc) to fool an online user into eliciting personal information. This is a base for a big variety of other security attacks as during this social engineering attack user provides sensitive personal information. In order to protect against phishing attacks a variety of tools are proposed; however, the majority agrees that the best countermeasure is user training and education rather than hardware or software solutions. In this paper, we analyze how credulous to phishing attacks are educated persons (people with no less than bachelor degree). The real-world study was executed to inspect how personnel of higher education organization react to phishing email, how credulous, suspicious they are and how far they will go in revealing their personal data.