Reviewerly/exHarmony huggingface dataset:
Mingling of Clear and Muddy Water: Understanding and Detecting Semantic Confusion in Blackhat SEO

Search Engine Optimization (SEO) is a set of techniques that help website operators increase the visibility of their webpages to search engine users. However, there are also many unethical practices that abuse ranking algorithms of a search engine to promote illegal online content, called blackhat SEO. In this paper, we make the first attempt to systematically investigate a recent trend in blackhat SEO, semantic confusion, which mingles the content of a webpage to deceive existing detection of blackhat SEO. In particular, from a new perspective of content semantics, we propose an effective defense against the semantic confusion based blackhat SEO. We built a prototype of our defense called SCDS, and then we validated its effectiveness based on 4.5 million domains randomly selected from 11 zone files and passive DNS records. Our evaluation results show that SCDS can detect more than 82 thousand blackhat SEO websites with a precision of 98.35%. We further analyzed 57,477 long-tail keywords promoted by blackhat SEO and found more than 157 SEO campaigns. Finally, we deployed SCDS into the gateway of a campus network for ten months and detected 23,093 domains with malicious semantic confusion content, showing the effectiveness of SCDS in practice.
On the Specification and Monitoring of Timed Normative Systems

In this article we explore different issues and design choices that arise when considering how to fully embrace timed aspects in the formalisation of normative systems, e.g., by using deontic modalities, looking primarily through the lens of monitoring. We primarily focus on expressivity and computational aspects, discussing issues such as duration, superposition, conflicts, attempts, discharge, and complexity, while identifying semantic choices which arise and the challenges these pose for full monitoring of legal contracts.
Nonstationary Gaussian Process Discriminant Analysis With Variable Selection for High-Dimensional Functional Data

High-dimensional classification and feature selection tasks are ubiquitous with the recent advancement in data acquisition technology. In several application areas such as biology, genomics, and proteomics, the data are often functional in their nature and exhibit a degree of roughness and nonstationarity. These structures pose additional challenges to commonly used methods that rely mainly on a two-stage approach performing variable selection and classification separately. We propose in this work a novel Gaussian process discriminant analysis (GPDA) that combines these steps in a unified framework. Our model is a two-layer nonstationary Gaussian process coupled with an Ising prior to identify differentially-distributed locations. Scalable inference is achieved via developing a variational scheme that exploits advances in the use of sparse inverse covariance matrices. We demonstrate the performance of our methodology on simulated datasets and two proteomics datasets: breast cancer and SARS-CoV-2. Our approach distinguishes itself by offering explainability as well as uncertainty quantification in addition to low computational cost, which are crucial to increase trust and social acceptance of data-driven tools. Supplementary materials for this article are available online.
Parallel Vision and Learning for Intelligent Perception in Smart Driving

Modern vision models are built upon large-scale diversified dataset, which is labor-intensive and cost-ineffective to collect and label. They are usually effective under simple constrained conditions, while the performance gets worse under actual traffic scenes. To address aforementioned problems, we propose a visual analysis framework based on parallel vision and learning for intelligent perception in smart driving. Specifically, our framework first generate and select data exploiting artificial image system. Then, computational experiments are conducted with predictive learning for model design, learning, and evaluation. Our framework is powered with virtual-actual interactive ability through parallel execution, integrating information from different scenes. The difficulty of the visual model in the actual scene can be applied to guide the model training in the artificial scene. We also carried out a case study to preliminarily demonstrate the effectiveness of the proposed framework.
Multi-Edge Concept used for Image Steganography

Digital Steganography means hiding sensitive data inside a cover object ina way that is invisible to un-authorized persons. Many proposed steganography techniques in spatial domain may achieve high invisibility requirement but sacrifice the good robustness against attacks. In some cases, weneed to take in account not just the invisibility but also we need to thinkabout other requirement which is the robustness of recovering the embedded secrete messages. In this paper we propose a new steganoraphicscheme that aims to achieve the robustness even the stego image attackedby steganalyzers. Furthermore, we proposed a scheme which is more robust against JPEG compression attack compared with other traditionalsteganography schemes.
Review and Investigation of Merkle Tree’s Technical Principles and Related Application Fields

In cryptography, many different data structures are used to store and encrypt data. Merkle Tree is such a data structure that provides a way to prove data integrity and validity. It requires less memory or disk space than other data structures, resulting in simple and fast computation. This paper first explains the detailed principles and then introduces the application fields of Merkle Tree. Merkle Tree uses the hash value of the data block as leaf node tags and the encrypted hash of the child node tags as other node tags. As data structures evolve and innovate, Merkle Tree provides help and inspiration for the future new data structures. Furthermore, we investigate various applications using Merkle Tree technology, including Smart Grid, Bitcoin, Ethereum, Interplanetary File System, Image Authentication, Public Security Audit of Cloud Computing, SIMulator for Blockchain Applications, Hardware Memory Integrity, and Certificate Transparency. These researches show that Merkle Tree applies to a wide range of fields and has great potential. In brief, the paper aims to help researchers better understand the Merkle Tree features and various roles and effects in different environments, contributing to the development of cryptography.
Differential Privacy Protection with Group Onion Routing based on AI-based URL Classification

Due to the rapid spread of tablet computers, smartphones, and other mobile information devices, wireless communication technology is fully developed and deployed widely. Mobile Internet access has been a main and important way to communicate the world in daily and it privacy protection also catches much attentions. The Onion Router, better known as Tor, is a technique for anonymous communication over internet without regional restrictions for privacy protection. Apart from this, Tor can reach sites that normal search engine cannot search. As the opinion of Tor, the transmitted data has been encrypted layer by layer, just like onion, before it reaching server. Our research proposed a system predicting URL's category with the use of machine learning technique before visiting. According to the category prediction, we represent different privacy level with three kinds of RSA key lengths on onion routing. Depending on various situations, our system can obtain the balance between security and time cost. Hence, our proposed scheme can make onion routing more flexible and efficiently.
Dynamic Verification of C11 Concurrency over Multi Copy Atomics

We investigate the problem of runtime analysis of concurrent C11 programs under Multi-Copy-Atomic semantics (MCA). Under MCA, one can analyze program outcomes solely through interleaving and reordering of thread events. As a result, obtaining intuitive explanations of program outcomes becomes straightforward. Newer versions of ARM (ARMv8 and later), Alpha, and Intel’s x-86 support MCA. Our tests reveal that state-of-the-art dynamic verification techniques that analyze program executions under the C11 memory model report safety property violations that can be interpreted as false alarms under MCA semantics. Sorting the true from false violations puts an undesirable burden on the user.In this work, we provide a dynamic verification technique (MoCA) to analyze C11 program executions which are permitted under the MCA model. We restrict C11 happens-before relation and propose coherence rules to capture precisely those C11 program executions which are allowed under the MCA model. MoCA’s exploration of the state-space is based on the state-of-the-art dynamic verification algorithm, source-DPOR. Our experiments validate that MoCA captures all coherent C11 program executions, and is precise for the MCA model.
Research on Consensus Mechanism in Microgrid Transaction

With the development and wide application of new energy, the transaction information is huge. In this paper, a delegated power authority mechanism (DPPGC) is designed to achieve local block consensus, to ensure transaction security and the stability of the blockchain network. Through experimental simulation, it can be seen that the proposed DPPGC consensus mechanism can improve the security of the consensus and make the proportion of malicious nodes in the consensus node less than 20%.
Effective Machine Learning Solutions for Punctual Weather Parameter Forecasting in a Real Missing Data Scenario

This work considers the Internet of Things (IoT) and machine learning (ML) applied to the agricultural sector within a real-working scenario. More specifically, the aim is to punctually forecast two of the most important meteorological parameters (solar radiation and the rainfall) to determine the amount of water needed by a specific plantation under different contour conditions. Three different state-of-the-art ML approaches, coupled with boosting techniques, have been adopted and compared to obtain hourly forecasting. Real-working conditions are referred to the situation in which training data are missing for a specific weather station near the specific field to be irrigated. A simple but effective approach, based on correlation between available weather stations, is considered to cope with this problem. Results, evaluated considering different metrics as well as the execution time, demonstrate the viability of the proposed solution in real IoT working scenario in which these forecasting are input data to successively evaluate irrigation needing.
Coping with Covid-19: Forging Creative Pathways to Support Educational Continuity Amidst the Pandemic

Abstract The pandemic has forced all educational institutions to grapple with challenges. Throughout this time Symbiosis International University (SIU) in India has been proactive in leading change not only at the university but also in K-12 schools. While the university transitioned to virtual teaching and learning, a methodical approach was laid out in assisting its eight elementary and senior secondary schools in both urban and rural areas, through the Symbiosis Schools Central Directorate (SSCD), to adapt to the needs of a public health crisis. While connectivity challenges continue to haunt schools, especially in rural areas, training and capacity building of K-12 teachers and administrators by university professors and experts has been a saving grace in navigating the pandemic. The focus of this case is understanding the parallels and the partnership between SIU and its K-12 schools. It reflects a bottom-up approach in dealing with the pandemic where Symbiosis Society, the non-profit organization that has established the schools as well as the University, invested in teacher capacity building at its elementary and secondary schools through its Symbiosis Schools Central Directorate (SSCD) in both rural and urban areas to ensure continuity of teaching and learning while adapting to this new normal. The investment in teacher capacity building has enabled the leadership to address the emerging circumstances, stimulate momentum to create or demand needed change at their institutions, inspire peer learning, and foster innovation in strategy and practice for the greater benefit of its stakeholders including students and parents. This case study reflects on SIU experiences in dealing with the dynamic circumstances such as training and capacity building with respect to supporting teachers in developing skills to adapt their content to virtual mode, blended learning, and integrating Collaborative Online International Learning (COIL) into the curriculum. In addition, SIU had to counsel students and parents to adapt to this new way of learning. SIU’s experience encompasses a coordinated approach of working with internal and external stakeholders to develop a response to the crisis, short-to-medium-term strategic planning in the face of uncertainty, exploring technology solutions, partnership management, and effective communication processes with its stakeholders. Special emphasis has been put on ensuring the mental and physical wellbeing of the learner, constant communication and guidance to parents, and virtual activities to promote community engagement to mitigate the loss of physical social interactions at this crucial time.
The Real-Time Trajectory Planning of UAV Formation Transformation Based on Safety Flight Corridor

According to different mission scenarios, realistic environment, or other reasons, UAVs ought to be transformed into the corresponding formation. When formation transformed, it is the most important thing to avoid collision among UAVs. This paper introduces the method of the real-time trajectory of the formation transformation based on safety flight corridor. At first, the formation information, formation control model and concept of safety flight corridor are introduced. Secondly, the real-time trajectory planning strategy and algorithm with collision avoidance are designed. The algorithm proof and efficiency model are analyzed. Finally, the effectiveness of UAVs formation transformation is revealed by simulation results.
The Design and Implementation of Intelligent English Learning Chabot based on Transfer Learning Technology

Chatbot operates task-oriented customer services in special and open domains at different mobile devices.Its related products such as knowledge base Question-Answer System also benefit daily activities.Chatbot functions generally include automatic speech recognition (ASR), natural language understanding (NLU), dialogue management (DM), natural language generation (NLG) and speech synthesis (SS).In this paper, we proposed a Transfer-based English Language learning chatbot with three learning system levels for real-world application, which integrate recognition service from Google and GPT-2 Open AI with dialogue tasks in NLU and NLG at a WeChat mini-program.From operational perspective, three levels for learning languages systematically were devised: phonetics, semantic and "free-style conversation" simulation in English.First level is to correct pronunciation in voice recognition and learning sentence syntactic.Second is a converse special-domain and the highest third level is a language chatbot communication as freestyle conversation agent.From implementation perspective, the Language Learning agent integrates into a WeChat mini-program to devise three user interface levels and to finetune transfer learning GPT-2 as back-end language model to generate responses for users.With the combination of the two parts about operation and implementation, based on the Neural Network model of transfer learning technology, different users test the system with open-domain topic acquiring good communication experience and proved it ready to be the industrial application to be used.All of our source codes had uploaded to GitHub: https://github.com/p930203110/EnglishLanguageRobot.
A practical residual block-based no-reference quality metric for neutron radiographic images

Objective image quality metric is a challenging problem for neutron radiography due to the lack of reference images. The existing no-reference image quality metric (NR-IQM) method designed for neutron radiographic images (NRIs) still exists two main shortcomings. Firstly, the limited L/D of the collimator would inevitably lead to geometric blur in the NRIs. However, the special distortion type of mixed Poisson–Gaussian noise is merely considered but ignores the blur distortion. Secondly, the distortion levels of Poisson noise added to an image cannot be adjusted. In order to address these two issues, a more practical objective NR-IQM method is proposed to predict the perceptual quality of NRIs in this paper. Experimental results demonstrate that the proposed method shows good consistency with the quality of authentic NRIs and also performs well in reflecting the tiny quality difference of enhanced NRIs, thus demonstrating its application potential in neutron radiography.
Manipulator Motion Planning based on Actor-Critic Reinforcement Learning

The manipulator control model has the characteristics of high-order, nonlinear, multivariable and strong coupling, which makes it difficult for the manipulator to have good adaptability and autonomy. Aiming at the problem of poor reusability and poor autonomy of manipulator applications, a motion planning algorithm based on reinforcement learning is proposed. In this paper, the reinforcement learning continuous control algorithm Actor-Critic is applied to the motion planning of the manipulator to increase the environmental applicability and autonomy of the manipulator, and realize the intelligent control of the manipulator under simple kinematics modeling. At first, the simulation environment of the hand-eye system of the manipulator is constructed, then the reinforcement learning algorithm model is established according to the simulation environment, and finally, the motion planning training of the manipulator is completed in the simulation environment. Experimental results demonstrate that the proposed manipulator motion planning algorithm based on Actor-Critic reinforcement learning has good environmental adaptability and stability.
Really natural adversarial examples

Abstract The phenomenon of Adversarial Examples has become one of the most intriguing topics associated to deep learning. The so-called adversarial attacks have the ability to fool deep neural networks with inappreciable perturbations. While the effect is striking, it has been suggested that such carefully selected injected noise does not necessarily appear in real-world scenarios. In contrast to this, some authors have looked for ways to generate adversarial noise in physical scenarios (traffic signs, shirts, etc.), thus showing that attackers can indeed fool the networks. In this paper we go beyond that and show that adversarial examples also appear in the real-world without any attacker or maliciously selected noise involved. We show this by using images from tasks related to microscopy and also general object recognition with the well-known ImageNet dataset. A comparison between these natural and the artificially generated adversarial examples is performed using distance metrics and image quality metrics. We also show that the natural adversarial examples are in fact at a higher distance from the originals that in the case of artificially generated adversarial examples.
Network Public Opinion Monitoring Based on MapReduce

Public opinion information on the network is abundantly decentralized in Internet, these false ones, easily misleading, not legally binding, it is difficult to deal with. Quickly master information is a prerequisite to deal with the network of public opinion, clustering algorithm in data mining plays an important role in in the collection of information statistics. Disadvantages of classical clustering methods are high resources consumption, low efficiency, high time and space complexity, and difficult to deal with large-scale data processing in massive network. To the network public opinion text as the research object, in-depth study of summarizing the network public opinion monitoring technology based on distributed MapReduce, including parallel technology, improved clustering algorithm, relational database and distributed database construction, which is in order to improve the efficiency of information processing, to reduce the pin.
Mean Error Rate Weighted Online Boosting Method

Abstract Boosting is a generally known technique to convert a group of weak learners into a powerful ensemble. To reach this desired objective successfully, the modules are trained with distinct data samples and the hypotheses are combined in order to achieve an optimal prediction. To make use of boosting technique in online condition is a new approach. It motivates to meet the requirements due to its success in offline conditions. This work presents new online boosting method. We make use of mean error rate of individual base learners to achieve effective weight distribution of the instances to closely match the behavior of OzaBoost. Experimental results show that, in most of the situations, the proposed method achieves better accuracies, outperforming the other state-of-art methods.
Multi Objective Decision Making for Virtual Machine Placement in Cloud Computing

Cloud computing is an innovative process that delivers on-demand services over the internet. Virtualization is considered as the key concept of cloud computing since it handles running multiple virtual resources in a single physical resource. Mapping the virtual machine (VM) to the appropriate physical machine (PM) is called virtual machine placement (VMP). In this context, the dilemma of placing VMs in the cloud environment presents a significant challenge that has been wholly addressed but not yet totally fixed. This paper provides a multi-objective decision-making approach for VMP in a cloud computing infrastructure. We propose a conic scalarization method to solve the optimization problem. Simulation results prove that the offline algorithm yields good results compared to online deterministic algorithms.
A Sparse TFD Reconstruction Approach Using the S-method and Local Entropies Information

This paper aims to investigate the S-method (SM) as an alternative for the Wigner-Ville Distribution (WVD) when used as the starting point for a sparse time-frequency distribution (TFD) reconstruction of non-stationary signals. The motivation comes from the SM's ability of providing a high-resolution TFD with satisfactory cross- and inner-artefact suppression, which should lead to a reconstructed TFD performance improvement over the WVD. The comparison between the WVD and the SM has been conducted using several state-of-the-art algorithms optimized with the multi-objective meta-heuristic optimization method (by minimizing the mean squared error between the local number of components in the starting and reconstructed TFDs and the number of regions with continuously connected samples). The results are shown for single and multi-component noisy synthetic signals.
Motivational Orientation As a Factor in the Formation of Professional Competence of University Students

This article reveals the process of forming the professional competence of university students. In conditions when the learning process becomes more and more formalized and depersonalized, the teacher should focus on clarifying the motivational sphere in order to form the most important cognitive motives in students. Motivational orientation influences not only the student's learning outcomes, but also changes the educational process itself. What is this influence - positive or negative, developing or standardizing, how universal and intense is it? The strength, effectiveness, form of influence, the predominance of certain functions are determined by the scientific and methodological guidance and management of the educational process by the teacher.