Reviewerly/exHarmony huggingface dataset:
A Network Traffic Classification Method Based on Hierarchical Clustering

With the continuous expansion of civil aviation airport information system, the types and quantities of traffic carried in the network are also increasing rapidly. But during the information system construction, there was no strict sorting of the network traffic types of the information system, and network equipment could not formulate strict network access control policies, which caused the flow data of various information systems to be intertwined and increased the difficulty of information system security maintenance. At the same time, some malicious attack traffic was also mixed, which posed a great threat to the security of information systems. In order to further improve the efficiency of information security protection equipment and improve the recognition rate of abnormal traffic, this paper proposed a network traffic classification method based on hierarchical clustering, which sorts out network traffic in order to find abnormal traffic and improve the protection strategy of network security equipment, thereby improving the network security protection capability of the entire information system.
On Analysing Similarity Knowledge Transfer by Ensembles

Knowledge transfer is the task of transferring the knowledge learned by a model A to a new model B. This task is essential in Deep Learning, since there are complex models with excellent results, but computationally costly to be executed. The Similarity Knowledge Transfer (SKT) method proposes an approach to transfer the knowledge layer-by-layer between a donor model and a receiver model. This transfer is carried out through the representations learned by the layers from the teacher model. Despite presenting good results, the SKT method proposes just a way to transfer knowledge between two models. Therefore, this work presents the Similarity Knowledge Transfer Ensemble (SKTE) method, a generic form of SKT that allows the transfer from several teachers to a single student model. We carried out experiments with the CIFAR10 benchmark, where the results obtained showed promising results in this activity.
How to Delete the Dead

Formative experiences in human lives are often unpleasant. Yet social technologies today are designed to presume positive experiences; 'design for delight' is even a principle in user interface development. So when humans have negative life experiences like the loss of a loved one, which often include technology-related tasks, the designs of their technologies can exacerbate that negativity. It is these negative experiences---interactions that trigger negative affects related to human sadness or distress---that design and engineering can and must honor. My dissertation will frame death as a key example of a difficult life experience that often requires digital interactions and digital tasks, yet currently lacks adequate tools to facilitate compassionate and meaningful interactions in a digital context. Incorporating empirical work in human-computer interaction with roots in cultural anthropology, I propose a project that will examine the role of ritual in technologically mediated human interactions during times of grief. Through interviews, participant observation, and tech support, I will create ritual-based practices around postmortem data that will honor the experiences people have of the presence of the deceased within that data, while maintaining the necessary control or closure of accounts that may be preferred.
Comparisons on Scrum Team Pairing Strategies: A multi-agent Simulation

Scrum is a type of agile process that incrementally, iteratively and continuously deliver software based on sprint time box. It is composed by User Stories, product backlog, sprint backlog, scrum team and sprints. Scrum team take user stories from product backlog into sprint backlog to start each sprint and deliver products at the end of each sprint. Sprint retrospective and review occurs at the end of each sprint to evaluate the delivered products and team performance. Based on the Scrum guide, scrum is easy to be understood but hard to be measured. Especially, it is depended largely on the performance of team dynamics referring to team compositions and task allocations, as its optimization make big impact on each sprint result. A simulation tool is also designed and developed to carry on scrum team modelling and simulation which has realized all the designed strategies to carry on experiments and evaluation. The tool (Z. Wang, 2018; Zhe Wang, 2018, 2019a, 2019b)can simulate all types of scrum context and team composition to test designed strategies under various what-if assumptions in agent-based modelling.
Static Detection of Access Control Vulnerabilities in Vue Applications

Abstract In this article, we reviewed the research progress of mainstream methods for detecting access control vulnerabilities, and designed a method for detecting access control vulnerabilities in Vue applications based on previous research. After that, we tested our detection method on the vulnerability test set designed by ourselves, and basically achieved the research goal of this article.
IterG: An Iteratively Learning Graph Convolutional Network with Ontology Semantics

Knowledge reasoning aims to infer new triples based on existing triples, which is essential for the development of large knowledge graphs, especially for knowledge graph completion. With the development of neural networks, Graph Convolutional Networks (GCNs) in knowledge reasoning have been paid widespread attention in recent years. However, the GCN model only considers the structural information of knowledge graphs and ignores the ontology semantic information. In this paper, we propose a novel model named IterG, which is able to incorporate ontology semantics seamlessly into the GCN model. More specifically, IterG learns the embeddings of knowledge graphs in an unsupervised manner via GCNs and extracts the semantic ontology information via rule learning. The model is capable of propagating relation layer-wisely as well as combining both rich structural information in knowledge graphs and ontological semantics. The experimental results on five real-world datasets demonstrate that our method outperforms the state-of-the-art approaches, and IterG can effectively and efficiently fuse ontology semantics into GCNs.
Using of type-ii fuzzy sets and Z-numbers for expert group information formalization

Two models of formalizing group expert information based on fuzzy sets of the second type and Z-numbers have been developed. The construction of fuzzy sets of the second type and components of Z-numbers is carried out using full orthogonal semantic spaces. The construction of semantic spaces is carried out using statistical information or information obtained as a result of a direct survey of experts. The input information for the model based on fuzzy sets of the second type are linguistic estimates of objects. The input information for the model based on Z-numbers are linguistic estimates of objects and the reliability of these estimates. The developed models expand the possibilities of processing expert information, allow preserving the individual characteristics of expert criteria embedded in the data, and at the same time correctly process different types of uncertainty inherent in this data.
Active Scene Classification via Dynamically Learning Prototypical Views

Scene classification is an important computer vision problem with applications to a wide range of domains including remote sensing, robotics, autonomous driving, defense, and surveillance. However, many approaches to scene classification make simplifying assumptions about the data, and many algorithms for scene classification are ill-suited for real-world use cases. Specifically, scene classification algorithms generally assume that the input data consists of single views that are extremely representative of a limited set of known scene categories. In real-world applications, such perfect data is rarely encountered. In this paper, we propose an approach for active scene classification where an agent must assign a label to the scene with high confidence while minimizing the number of sensor adjustments, and the agent is also embedded with the capability to dynamically update its underlying machine learning models. Specifically, we employ the Dynamic Data-Driven Applications Systems paradigm: our machine learning model drives the sensor manipulation, and the data captured by the manipulated sensor is used to update the machine learning model in a feedback control loop. Our approach is based on learning to identify prototypical views of scenes in a streaming setting.
Digital research on field experiment of combination flooding

The IMCFS software that describes the combination flooding mechanism and the simplified geologic structural model and simulation and calculation model were used for the fitting calculation of the field experiment at Xingerxi Block Area of No. 4 Oil Production Plant, Daqing Oilfield, China. The field experiment was digitized and the digital geologic model platform containing reservoir geologic information and combination flooding information was established, on which the scheme calculation was carried out. The optimal flooding scheme was worked out via the digitization of flooding experiments and the in-depth study of field flooding experiments. With regard to the research of combination flooding technology in Daqing, the industrial field experiments of four key oil production plants were used to establish the digital research geologic model platforms and carry out the optimal flooding scheme. The good effect of an enhanced recovery target of more than 28% was then obtained. We researched the alkali-surfactant-polymer (ASP) flooding field experiments in the Warden Unit of the Sho-Vel-Tum Oilfield in Oklahoma, USA. By increasing the viscosity of the ultra-low systems for the industrial field experiments at Daqing Oilfield, the enhanced oil recovery rate can be increased to more than 30%.
The Relevance of Feedback Mechanisms to Library Databases in Academic Libraries

This chapter studies the relevance of the feedback mechanism on the library database in academic libraries. The study is a systematic literature review which uses Kitchenham and Charters methodology. The chapter has three objectives, which are to enumerate the concept of information retrieval and its problems and the relevance of feedback mechanism to the database in academic libraries. The findings indicated that feedback is very important in the information retrieval systems most especially in relation to the database in an academic library. The contributions of this chapter show that the feedback mechanism is relevant to information retrieval. It also creates awareness among librarians of the importance of these mechanisms and encouraged them to seek for a database with these components whenever they want to buy a new database for their various institutions. The chapter, however, recommends further empirical studies on the subject matter.
WITHDRAWN: Remote cloud classroom based on FPGA-based high-performance algorithms

Distributed computing offers an incredible variety of quick, flexible delivery of assets following the interests of customers, expanding the classification data to provide and prepare essentials. The rapid development of its huge interest in this innovation, and in many applications, including government, business, and the order has been accepted. At the same time, the complexity of the material helps to understand the student computer science and related topics by eliminating regional requirements, improving refinement, and rewarding experience to display computer science and related topics to Cloud. Field Programmable Gate Array (FPGA) is used by accelerating computationally expensive applications. Low power utilization drawn from industry and academia are important considerations. FPGA is the base attraction towards the treatment of intangible assets and their distributed computing circles reconfigurability of the causes and adaptability in critical mode. The basis for most universities are the use of under-utilized assets, adjusted asset utilization, happen to have flexible and innovative requirements. A new approach is introduced to software engineering, virtual or Open Distance Learning (ODL) data, and the University of Understudies e-learning Phase. This structure is calculated by measuring dispersion and tends to (cloud virtual frame-based study hall) administration layers. The basic part (in coordination with the cloud and software engineering process) is to develop a cloud program open academic expectations separation learning atmosphere. Finally, it is the way to achieve the structure proposed.
Stateful Optimization in Federated Learning of Neural Networks

Federated learning is a emerging branch of machine learning research, that is examining the methods for training models over geographically separated, unbalanced and non-iid data. In FL, on non-convex problems, as in single node training, the almost exclusively used method is mini batch gradient descent. In this work we examine the effect of using stateful training method in a federated environment. According to our empirical results with these methods, at the cost of synchronizing state variables along with model parameters, a significant improvement can be achieved.
Development of an Interface that Expresses Twinkling Eyes by Superimposing Human Shadows on Pupils

Human eyes are related to own affects and emotions, and seem to twinkle when human has positive affects and emotions such as a strong interest and eagerness. Human is subconsciously attracted to the twinkling eyes. In this study, aiming at the development of a novel communication interface that attracts human, we focused on the reflected glare on the surface of eyeballs and proposed an expression method that superimposes human shadows on pupils. We conducted an impression experiment to evaluate the proposed method and demonstrated that the method was effective for attracting human. Based on the result, we developed an advanced interface that expresses twinkling eyes for attracting human. The developed interface generates a pseudo reflected glare by superimposing human shadow on each pupil as a pseudo-self-image.
The application of CAMSHIFT algorithm based on kernel function in laser tracking moving target

Abstract According to the characteristics of laser technology, a fast moving target tracking algorithm is designed. Using the spatial information of laser diffuse reflection light points, by adding the kernel function of CAMSHIFT algorithm, the weight of light points in the center of the target is increased by using the weighted histogram, so as to improve the convergence speed of the algorithm and the tracking accuracy of the target. The simulation results show that the improved algorithm can adapt to more complex tracking environment quickly and has better anti-interference, agility and prediction ability.
Usability Evaluation of Smartphone Keyboard Design from an Approach of Structural Equation Model

Keyboard input is the mainstream input method on smartphones, and the keyboard design plays an important role in input experience. Previous studies mainly measured descriptive variables through inputting phrases to evaluate the usability of keyboard design. However, the development of screen technology brings about more precise and attributional data that could in-depth explore and assess the details of keyboard design. This study adopted a single factor (Phone screen size: Small, 5-in. vs. Large, 6.5-in.) within-subject design through inputting English character pairs to build up the usability evaluation structural equation model. Traditional measurements and new attributional measurements, including Point Pressure, Point Interval Response Time, and Point Duration, were taken as modeling indicators. The results showed that Word Per Minute was not fitted to evaluate keyboard designs with other indicators in both screen sizes, and new attributional measurements could replace this indicator. For the large-screen smartphone, the well-fitted model consists of three factors, including Input Speed (Point Duration and Point Interval Response Time), Objective Experience (Point Pressure and Word Error Rate), and Subjective Feedback. For the small-screen smartphone, the well-fitted model consists of two factors, including Efficiency (Point Interval Response Time, Word Error Rate, and Point Duration) and Subjective Feedback. These findings provide an effective approach to evaluate the usability of keyboard design for future studies, thus enlightening the keyboard optimization method.
Implementation of convolution neural network on GPDSP

With the rapid development of deep learning algorithm, convolutional neural network has been widely used in engineering. However, due to the huge amount of calculation, it is necessary to select the appropriate hardware platform to make full use of the advantages of the network and give full play to the performance of the algorithm; GPDSP is very suitable for deploying deep learning algorithm on it due to its advantages of architecture. This paper introduces the method of mapping convolution neural network on GPDSP, analyzes its performance, and expounds the advantages of GPDSP in computing convolution.
Let’s Refresh! Efficient and Private OpenPGP Certificate Updates

In the OpenPGP ecosystem clients can interact with the key server network to discover or fetch updates for other peoples’ keys or rather their certificates. In fact, it is recommended to regularly update the certificates in an OpenPGP keyring to get hold of information that validate or invalidate a certificate, such as new subkeys, certifications, or revocations. However, the HTTP Keyserver Protocol (HKP), which defines how to interact with the key servers, lacks a definition of a dedicated update procedure for certificates. In order to close this gap, this paper analyses the existing protocol and its shortcomings for regular certificate updates as well as the requirements in terms of frequency and size of updates. This paper explores the trade-off between privacy and efficiency and analyses multiple solutions including a trivial Private Information Retrieval (PIR) and a k-anonymous protocol. The data set for the investigation is obtained from the key server network currently deployed in the OpenPGP ecosystem and the proposed protocol maintains compatibility with the existing HKP in order to foster adoption of the proposed amendments.
Deep Generative Model for Single Image De-Hazing on Embedded Platform

Haze can severely undermine the visibility and can often deteriorate the performance of computer vision applications like object detection, object classification etc. Hence, image de-hazing is a very desirable task. The existing state of the art methods perform well on qualitative measures but often overlook memory efficiency, which is especially crucial when working with embedded platforms, especially for realistic applications. The proposed method in this paper utilises a deep generative network that incorporates Wasserstein loss which significantly improves the quality of images. Moreover; the proposed method is parameter efficient and utilises nearly 50 times lesser parameters than the existing state of the art deep generative models which have been used for de-hazing.
Research on Recommendation Strategies Integrating Emotional Tendency and User Influences

At present, the topic model based on LDA as an information recommendation method has the defect of neglecting the emotional information in social platform. Therefore, a microblog user recommendation strategy I-TES (influence topic emotionality similarity model) is proposed which combines emotional orientation with user influence model in this paper. Firstly, the user's theme distribution and emotional tendency is obtained based on JST model, the JS distance is used to calculate the similarity of user's theme probability distribution, and the two is combined to get the user's interest similarity score. Secondly, the user's influence measurement indicators are summarized into microblog influence, user's activity and fan's influence, and the weights of the three indicators are calculated according to AHP(analytic hierarchy process). Finally, the combination of the two is used to obtain the final score as the recommendation result. The strategy is tested on Sina Weibo user data set, and the experiment shows that this strategy has higher recommendation accuracy than the traditional recommendation method.
Fisheye image Distortion Correction Based on Spherical Perspective Projection Constraint

Fisheye lens has the prominent advantage of large field of view, which is suitable for the overall environmental monitoring during the restoration of polluted sites. But at the same time, the fisheye lens has a large distortion, which has a great influence on the image matching and stitching between different cameras. Aiming at the shortcomings of traditional wide-angle lens image correction methods, only correction is carried out in the longitude direction, and the correction effect is poor. A fisheye correction algorithm based on spherical perspective projection constraint was proposed, in the case of spherical perspective projection, the space line is no longer projected as a plane line of the image, but as a great circle on the sphere. First, adopt the scan line approximation method to extract the effective area of the fisheye image, to determine the center and radius; then in view of the shortcomings and deficiencies of the spherical coordinate positioning method and the latitude and longitude mapping method. A correction method based on spherical perspective projection constraint is quantized; finally, the experimental verification is carried out. Through the experiment, this method has a good effect on the correction of fisheye image, the shape of the object in the image has been restored, and several arcs have become parallel lines, which are very consistent with the reality.
The analysis of replacing high-resolution terrain data forforecasting solar radiation in Wuhan, China

Abstract This experiment uses the data from European Center for Medium-Term Weather Forcast and the mesoscale numerical model WRF to predict the total solar radiation in Wuhan in January, April, July and October 2018. During the experiment, the Aster global digital elevation model data was used as high-precision topographic data to replace, and two sets of data which are forecasted by WRF were compared with the observation data of Wuhan station. The experiment results show that the four-month forecast has a good correspondence with the observation station data, and which has passed the 0.01 significance test. Meanwhile, the simulation results in January were poor, and the simulation results in July and October were the best. After the improvement of replacing high-precision terrain data, the MAE and RMSE for 4 months have been reduced by 10-20W/m2, and both MAE and RRMSE have also been reduced by 5%-10%.