Reviewerly/exHarmony huggingface dataset:
Design and Implementation of a Web Crawler System based on an Adaptive Page-Rank algorithm

Abstract Web crawlers have the ability to automatically extract web page information, but there exists the issue that some pages reuse keywords to improve their search rankings. Therefore, we propose an adaptive Page-rank algorithm to build a crawler system to resolve the issue mentioned above. Specifically, we generate a relationship matrix based on the crawled web page access relationships, and then an probability matrix based on the number of web pages is generated iteratively, and finally the web pages crawled are displayed in descending order of calculated weights. Besides, we propose to control the iterative process in Page-rank with the coherence of anchor texts. The system uses Python language to realize the functions of web crawling. Experimental results demonstrate that this system has a high speed in data collection. Comparing with Hints and classical Page-rank crawler systems, The results show that the proposed method outperforms in precision and recall.
Blind Image Quality Assessment Based on Classification Guidance and Feature Aggregation

In this work, we present a convolutional neural network (CNN) named CGFA-CNN for blind image quality assessment (BIQA). A unique two-stage strategy is utilized which firstly identifies the distortion type in an image using Sub-Network I and then quantifies this distortion using Sub-Network II. Different from most deep neural networks, we extract hierarchical features as descriptors to enhance the image representation and design a feature aggregation layer in an end-to-end training manner applying Fisher encoding to visual vocabularies modeled by Gaussian mixture models (GMMs). Considering the authentic distortions and synthetic distortions, the hierarchical feature contains the characteristics of a CNN trained on the self-built dataset and a CNN trained on ImageNet. We evaluated our algorithm on four publicly available databases, and the results demonstrate that our CGFA-CNN has superior performance over other methods both on synthetic and authentic databases.
Event Classification in Surabaya on Twitter with Support Vector Machine

Twitter is a social media that is often used by many people in the world. The information is spread and obtained through social media. For example, there is a company that is organizing a new event that many people need to know. This allows the creation of a system that supports the presentation of user information by detecting certain events from Twitter's social media data. In this study, tweet data will be retrieved using Twitter API and stored in JSON format. Furthermore, there will be a pre-processing which includes the deletion of characters, number, URL, stemming, and lower case. Furthermore, feature extraction is performed using Global Vector for Word Representation. we will classify into four classes, which are Competitions, Seminars, Festivals, and Other events. The classification is using SVM to predict the type of event. There are three experimental methods used, there is SVM C, SVM linear, and SVM Nu. SVM Nu was conducted with changes in the SVC parameters in the form of kernel and Nu to produce the best accuracy. Based on the experiments we have done, the best results are obtained with an accuracy of 85.2% by classification using the NuSVC method with an RBF kernel and nu parameter of 0.2.
Robustness analytics to data heterogeneity in edge computing

Federated Learning is a framework that jointly trains a model with complete knowledge on a remotely placed centralized server, but without the requirement of accessing the data stored in distributed machines. Some work assumes that the data generated from edge devices are identically and independently sampled from a common population distribution. However, such ideal sampling may not be realistic in many contexts. Also, models based on intrinsic agency, such as active sampling schemes, may lead to highly biased sampling. So an imminent question is how robust Federated Learning is to biased sampling? In this work1 , we experimentally investigate two such scenarios. First, we study a centralized classifier aggregated from a collection of local classifiers trained with data having categorical heterogeneity. Second, we study a classifier aggregated from a collection of local classifiers trained by data through active sampling at the edge. We present evidence in both scenarios that Federated Learning is robust to data heterogeneity when local training iterations and communication frequency are appropriately chosen.
Analysis of Attack Actions on the Railway Infrastructure Based on the Integrated Model

In this paper we present analysis of attack actions on the railway infrastructure based on the integrated model. The novelty of the presented solution is in combination of the component-based approach, which is used to detect attack vectors based on the presence of vulnerabilities, the semi-natural model, which is used to model vulnerabilities exploitation, the simulation model, which is used to analyze attack scenarios that are affecting timetable planning process and the analytical model, which is used to analyze multi-step attack scenarios. The integrated model also contains the model of attacker, which distinguish them by type of access to the railway infrastructure as well as by level of capabilities and resources. The integrated model is used due to the fact that none of the listed approaches can effectively analyze all classes of attacks, while combining these approaches allows one to represent various aspects of the investigated object and provides the ability for attack actions effective analysis. The proposed solution has a strong focus on security, which determines the main contribution to the research field. The objective of the proposed model is to increase the security of critical infrastructure by improving the quality of attack actions analysis. The correctness of the proposed model is validated by various application examples.
Robustness analysis for secure software design

A common type of security analysis involves checking whether a system is capable of establishing a set of security requirements under a particular threat model. Building an accurate threat model, however, is a challenging task due to the uncertain and evolving nature of a malicious environment in which the system is deployed. In this paper, as a complementary analysis, we propose a systematic approach for evaluating the design of a system with respect to its robustness against an adversarial environment; i.e., the degree of assumptions about attacker capabilities under which the system is capable of maintaining its security requirements. We argue that robustness is an important property that should be considered as part of any secure development process. In this paper, we propose a formal definition of robustness, and describe a technique for automatically evaluating the robustness of a system. We demonstrate potential applications of the robustness concept using an example involving the OAuth authentication protocol.
Universal Bayes Consistency in Metric Spaces

We show that a recently proposed 1-nearest-neighbor-based multiclass learning algorithm is universally strongly Bayes consistent in all metric spaces where such Bayes consistency is possible, making it an "optimistically universal" Bayes-consistent learner. This is the first learning algorithm known to enjoy this property; by comparison, k-NN and its variants are not generally universally Bayes consistent, except under additional structural assumptions, such as an inner product, a norm, finite doubling dimension, or a Besicovitch-type property.The metric spaces in which universal Bayes consistency is possible are the "essentially separable" ones - a new notion that we define, which is more general than standard separability. The existence of metric spaces that are not essentially separable is independent of the ZFC axioms of set theory. We prove that essential separability exactly characterizes the existence of a universal Bayes-consistent learner for the given metric space. In particular, this yields the first impossibility result for universal Bayes consistency.Taken together, these positive and negative results resolve the open problems posed in Kontorovich, Sabato, Weiss (2017).
UAV Remote Sensing Image Mosaic Technology Combined with Improved SPHP Algorithm

The paper proposes an improved algorithm, for the problem that SPHP algorithm is not suitable for the UAV Remote sensing image mosaic algorithm because it is susceptible to geomorphic factors, resulting in deformation or ghosting. The algorithm firstly extracts feature points by combining SURF algorithm and Harris algorithm. The obtained feature points are coarsely matched by KNN algorithm, then matched by PROSAC algorithm. Finally, the weight coefficient is introduced to calculate the spatial transformation model of the image overlap region. This model replaces the original spatial model of SPHP algorithm. The ghosting of the image overlap area is reduced, and the stitched image is caused to have a smaller deformation. The final result shows that this improved SPHP algorithm can effectively remove the ghost image of the stitched image and generate better stitching results.
Designing privacy preserving data sharing middleware for internet of things

The rise of low-cost Internet of Things (IoT) sensing and communication capabilities has given rise to a range of new smart services that rely on heterogeneous data from devices embedded in our everyday lives. The provision of such IoT services relies on environmental or user data from other data controllers (e.g. network provider, water agency, building management). Recent privacy regulations such as the European General Data Protection Requirement (GDPR) and California Consumer Privacy Act (CCPA) have made it mandatory for data controllers to perform enhanced processing of the shared data with appropriate privacy-preserving mechanisms before release to service providers. To facilitate this, we propose PE-IoT, a system for orchestrating privacy-enhanced data flows that (a) provides users (data subjects) with capabilities to opt-in/opt-out in the data that is shared with the service providers and (b) enable data controllers to invoke a range of Privacy Enhancing Technologies (PETs) such as anonymization, randomization, and perturbation to transform data streams into their privacy preserving counterparts. PE-IoT is based on a new model for privacy compliant data sharing and we describe the design and architecture of the PE-IoT system based on this model.
Central-Eye

With development of computing technology, virtual reality devices are widely used in Internet-enabled industrial platforms. The problem of virtual reality stuns caused by the increase in visual frequency and real-world scenarios in industrial applications is still remaining the core problem. We proposes a new method called Central-Eye. It adopts a new context-aware solution for eye tracking to obtain the focus of the human eye on the screen, and then based on the distribution of human visual acuity, down-sampling the rendering in the visual edge area to increase the rendering speed to solve the user experience problem. Aiming at the characteristics of industrial virtual reality equipment with large field of view, high frame rate requirements, and eyepiece distortion, we propose a new gaze tracking method combining pattern recognition with local linear fitting. From experimental evaluation, we finally conclude the research that Central-Eye method can virtually improve the user experiences.
MD-MinerP: Interaction Profiling Bipartite Graph Mining for Malware-Control Domain Detection

Despite the efforts of information security experts, cybercrimes are still emerging at an alarming rate. Among the tools used by cybercriminals, malicious domains are indispensable and harm from the Internet has become a global problem. Malicious domains play an important role from SPAM and Cross-Site Scripting (XSS) threats to Botnet and Advanced Persistent Threat (APT) attacks at large scales. To ensure there is not a single point of failure or to prevent their detection and blocking, malware authors have employed domain generation algorithms (DGAs) and domain-flux techniques to generate a large number of domain names for malicious servers. As a result, malicious servers are difficult to detect and remove. Furthermore, the clues of cybercrime are stored in network traffic logs, but analyzing long-term big network traffic data is a challenge. To adapt the technology of cybercrimes and automatically detect unknown malicious threats, we previously proposed a system called MD-Miner. To improve its efficiency and accuracy, we propose the MD-MinerP here, which generates more features with identification capabilities in the feature extraction stage. Moreover, MD-MinerP adapts interaction profiling bipartite graphs instead of annotated bipartite graphs. The experimental results show that MD-MinerP has better area under curve (AUC) results and found new malicious domains that could not be recognized by other threat intelligence systems. The MD-MinerP exhibits both scalability and applicability, which has been experimentally validated on actual enterprise network traffic.
Emergent Languages from Pretrained Embeddings Characterize Latent Concepts in Dynamic Imagery

Recent unsupervised learning approaches have explored the feasibility of semantic analysis and interpretation of imagery using Emergent Language (EL) models. As EL requires some form of numerical embedding as input, it remains unclear which type is required in order for the EL to properly capture key semantic concepts associated with a given domain. In this paper, we compare unsupervised and supervised approaches for generating embeddings across two experiments. In Experiment 1, data are produced using a single-agent simulator. In each episode, a goal-driven agent attempts to accomplish a number of tasks in a synthetic cityscape environment which includes houses, banks, theaters and restaurants. In Experiment 2, a comparatively smaller dataset is produced where one or more objects demonstrate various types of physical motion in a 3D simulator environment. We investigate whether EL models generated from embeddings of raw pixel data produce expressions that capture key latent concepts (i.e. an agent’s motivations or physical motion types) in each environment. Our initial experiments show that the supervised learning approaches yield embeddings and EL descriptions that capture meaningful concepts from raw pixel inputs. Alternatively, embeddings from an unsupervised learning approach result in greater ambiguity with respect to latent concepts.
Improved Long Short-Term Memory Network with Multi-Attention for Human Action Flow Evaluation in Workshop

As an indispensable part of workshops, the normalization of workers’ manufacturing processes is an important factor that affects product quality. How to effectively supervise the manufacturing process of workers has always been a difficult problem in intelligent manufacturing. This paper proposes a method for action detection and process evaluation of workers based on a deep learning model. In this method, the human skeleton and workpiece features are separately obtained by the monitoring frame and then input into an action detection network in chronological order. The model uses two inputs to predict frame-by-frame classification results, which are then merged into a continuous action flow, and finally, input into the action flow evaluation network. The network effectively improves the ability to evaluate action flow through the attention mechanism of key actions in the process. The experimental results show that our method can effectively recognize operation actions in workshops, and can evaluate the manufacturing process with 99% accuracy using the experimental verification dataset.
Host Behavior in Computer Network: One-Year Study

An analysis of a host behavior is an essential key for modern network management and security. A robust behavior profile enables the network managers to detect anomalies with high accuracy, predict the host behavior, or group host to clusters for better management. Hence, host profiling methods attract the interest of many researchers, and novel methods for host profiling are being introduced. However, these methods are frequently developed on preprocessed and small datasets. Therefore, they do not reflect the real-world artifacts of the host profiling, such as missing observations, temporal patterns, or variability in the profile characteristics in time. To provide the needed insight into the artifacts of host profiling in real-world settings, we present a study of the host behavior in a network conducted on a one-year-long real-world network dataset. In the study, we inspect the availability of the data for host profiling, identify the temporal patterns in host behavior, introduce a method for stable labeling of the hosts, and assess the variability of the host characteristics in the course of the year using the coefficient of variance. Moreover, we make the one-year dataset containing nine characteristics used for host behavior analysis available for public use and further research, including selected use cases representing host profiling caveats. We also share the record of analysis presented in the paper.
Investigating the Effectiveness of Locked Dwell Time-based Point and Tap Gesture for Selection of Nail-sized Objects in Dense Virtual Environment

In immersive VR environments, object selection is an essential interaction. However, current object selection techniques suffer from issues of hand jitter, accuracy, and fatigue, especially to select nail-size objects. Here, we present locked dwell time-based point and tap, a novel object selection technique designed for nail-size object selection in a dense virtual environment. The objects are within arm’s reach. We also compare locked dwell time-based point and tap with magnetic grasp, pinch and raycasting. 40 participants evaluated the effectiveness and efficiency of these techniques. The results found that locked dwell time-based point and tap took significantly less task completion time and error rate. It was also the most preferred and caused least effort among all the techniques. We also measured easy to use, easy to learn and perceived naturalness of the techniques.
Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban Areas

Nowadays, our mobile devices have become smart computing platforms, incorporating a wide number of embedded sensors such as accelerometers, gyroscopes, barometers, GPS receivers, and magnetometers. Smartphones are valuable devices for gathering user-related data and transforming it into value-added information for the user. In this study, a novel mechanism to process sensor data from mobile devices in order to detect the type of area the user is crossing while walking in an urban setting is presented. The method is based on combining outlier data analysis and classification techniques from data collected by several pedestrians while traversing an urban environment. A theoretical framework, composed of methods for detecting multivariate outliers combined with supervised classification techniques, has been proposed in order to identify different situations and physical barriers while walking. Each type of element to be detected is characterized by using a feature vector computed based on the outliers detected. Finally, a radial SVM is used for the classification task. The classifier is trained in a supervised way with data from 20 different segments containing several physical barriers and used later to assign a class to new un-labelled data. The results obtained with this approach are very promising with an average accuracy around 95% when detecting different types of physical barriers.
Research on aircraft route planning optimization problem with multi-constraints and dual-targets

Abstract As the core technology in the field of aircraft, the route planning has attracted much attention. However, due to the complexity of the structure and performance constraints of the aircraft, the route planning algorithm does not have well universality, so it cannot be used in a complex environment. In the paper, a multi-constraints and dual-targets aircraft route planning model was established for the real-time requirements of space flight, the dynamic changes of flight environment with time, the accuracy requirements of positioning errors in the safety area, and the minimum turning radius constraints. Based on the directed graph and dynamic programming ideas, the model simulation and model validation were carried out with the data of F question in the “16th Graduate Mathematical Modeling Contest” . The results showed that the optimal path length obtained in data set 1 was 124.61 km , the number of corrections was 11 times , the solution time was 2.3768 seconds , the optimal path length obtained in data set 2 was 110.00 km , and the number of corrections was 12 times . The solution time was 0.0168 seconds . Multi-constraints and dual-targets aircraft route planning model can plan the flight path of the aircraft intuitively and timely, which confirmed the effectiveness of the model.
Making Tactful Objects for Sensitive Settings

This contribution illustrates the Research through Design process of Mr.V the Spaceman, a tactful object meant to support families of children with cancer maintain a healthy domestic life during stressful times. By recounting insights from the field and unfolding changes to the original artifact over a period of two years, the paper illuminates how an understanding of tactfulness has emerged and developed into a key design quality for this work. This complex and entangled process of making is surfaced and illustrated by a rich and scrupulous annotation of the artisanal craft and technological explorations that led from the original Mr.V prototype to what is now Mr.V the Spaceman.
Building Knowledge Graph using Pre-trained Language Model for Learning Entity-aware Relationships

Relations exhibited among entities from textual content can be a potential source of information for any business domain. This paper encompasses a wholesome approach to mine entity-relation and building knowledge graph from textual documents. The paper concentrates on two approaches to classify directional entity relations. We build on extending pretrained language model i.e. BERT for text classification along-side providing entity and directionality information as input making it entity-aware BERT classifier. We also did ablation studies of presented model in terms of various ways of providing entity information on the learning capabilities of model. We demonstrate the end to end pipeline for building an entity-relation extraction system in a business application. The techniques proposed in the paper are also evaluated against SemEval-2010 Task 8, a popular relation classification dataset. The experimental results demonstrate that learning entity-aware relations through language models outperforms almost all the previous state-of-the-art (SOTA) models.