Reviewerly/exHarmony huggingface dataset:
An Automated Framework for Real-time Phishing URL Detection

An increasing number of services, including banking and social networking, are being integrated with world wide web in recent years. The crux of this increasing dependence on the internet is the rise of different kinds of cyberattacks on unsuspecting users. One such attack is phishing, which aims at stealing user information via deceptive websites. The primary defense against phishing consists of maintaining a black list of the phishing URLs. However, a black list approach is reactive and cannot defend against new phishing websites. For this reason, a number of research have been done on using machine learning techniques to detect previously unseen phishing URLs. While they show promising results, any such implementation is yet to be seen. This is because 1) little work has been done on developing a complete end-to-end framework for phishing URL detection 2) it is prohibitively slow to detect phishing URLs using machine learning algorithms. In this work we address these two issues by formulating a robust framework for fast and automated detection of phishing URLs. We have validated our framework with a real dataset achieving 87% accuracy in a real-time setup.
Semantically Interpretable Activation Maps: what-where-how explanations within CNNs

A main issue preventing the use of Convolutional Neural Networks (CNN) in end user applications is the low level of transparency in the decision process. Previous work on CNN interpretability has mostly focused either on localizing the regions of the image that contribute to the result or on building an external model that generates plausible explanations. However, the former does not provide any semantic information and the latter does not guarantee the faithfulness of the explanation. We propose an intermediate representation composed of multiple Semantically Interpretable Activation Maps (SIAM) indicating the presence of predefined attributes at different locations of the image. These attribute maps are then linearly combined to produce the final output. This gives the user insight into what the model has seen, where, and a final output directly linked to this information in a comprehensive and interpretable way. We test the method on the task of landscape scenicness (aesthetic value) estimation, using an intermediate representation of 33 attributes from the SUN Attributes database. The results confirm that SIAM makes it possible to understand what attributes in the image are contributing to the final score and where they are located. Since it is based on learning from multiple tasks and datasets, SIAM improve the explanability of the prediction without additional annotation efforts or computational overhead at inference time, while keeping good performances on both the final and intermediate tasks.
A pointer network based deep learning algorithm for unconstrained binary quadratic programming problem

Combinatorial optimization problems have been widely used in various fields. And many types of combinatorial optimization problems can be generalized into the model of unconstrained binary quadratic programming (UBQP). Therefore, designing an effective and efficient algorithm for UBQP problems will also contribute to solving other combinatorial optimization problems. Pointer network is an end-to-end sequential decision structure and combines with deep learning technology. With the utilization of the structural characteristics of combinatorial optimization problems and the ability to extract the rule behind the data by deep learning, pointer network has been successfully applied to solve several classical combinatorial optimization problems. In this paper, a pointer network based algorithm is designed to solve UBQP problems. The network model is trained by supervised learning (SL) and deep reinforcement learning (DRL) respectively. Trained pointer network models are evaluated by self-generated benchmark dataset and ORLIB dataset respectively. Experimental results show that pointer network model trained by SL has strong learning ability to specific distributed dataset. Pointer network model trained by DRL can learn more general distribution data characteristics. In other words, it can quickly solve problems with great generalization ability. As a result, the framework proposed in this paper for UBQP has great potential to solve large scale combinatorial optimization problems.
Sentiment Analysis using Feature Extraction and Dictionary-Based Approaches

Natural Language Processing (NLP) is a study of computational treatment of human language in order to make it understandable for computers. It is used in the research fields like artificial intelligence, information engineering, statistics, sentiment analysis and linguistics. Sentiment analysis plays significant role in NLP which performs the series of operations computationally to identify and categorize the sentiment conveyed in segment of words. The proposed approach is to detect the polarity of words from twitter using feature extraction and dictionary-based methods. Method are compared with feature engineering techniques like CountVectorizer, TF-IDF, Word2Vec by using machine learning classifier and also scoring of each word is done by SentiWordNet, VADER dictionaries. From the results, it clarifies that feature extraction outperforms the dictionary-based method. The proposed model gives better accuracy to detect the polarity.
Intelligent rebar layout in RC building frames using artificial potential field

Reinforced concrete (RC) beam-column joints are the critical components of RC building frames. Congestion and even collision of reinforcing bars (rebars) encounter frequently in the core region of a joint. The building information modeling (BIM) technology is helpful to detect the rebar collision, which has to be revised manually. In this paper, inspired by collision-free capability of intelligent agent, an automatic rebar layout framework based on artificial potential field (APF) is proposed to solve the issues of rebar collision and congestion during the design stage. The rebars in a beam-column joint are regarded as agents, and a novel APF method is proposed to guide the moving trajectory of each agent. The APF method ensures that each rebar-agent can reach its goal point and escape from the trapping of local-minimum point. An Autodesk Revit add-in is developed to save modeling time and improve accuracy. Experiments on a two-story RC frame validate the applicability and efficiency of the intelligent rebar layout framework. It can be shown that the developed framework can avoid the rebar collision and congestion at beam-column joints quickly and effectively.
A CloudSim-Extension for Simulating Distributed Functions-as-a-Service

The performance of Functions-as-a-Service (FaaS) would be significantly improved by organizing cloud servers into a hierarchical distributed architecture, resulting in low-latency access and faster response when compared to centralized cloud. However, the distributed organization introduces a new type of decision making problem for placing and executing functions to a specific cloud server. In order to handle the problem, we extended a well-known cloud computing simulator, CloudSim. The extended CloudSim enables users to define FaaS functions with various characteristics and service level objectives (SLOs), place them across geo-distributed cloud servers, and evaluate per-function performance. Proof-of-Concept (PoC) evaluation results show the potential of our CloudSim extension in terms of execution efficiency and simulation reality.
Robust Video Frame Interpolation With Exceptional Motion Map

Video frame interpolation has increasingly attracted attention in computer vision and video processing fields. When motion patterns in a video are complex, large and non-linear (exceptional motion), the generated intermediate frame is blurred and likely to have large artifacts. In this paper, we propose a novel video frame interpolation considering the exceptional motion patterns. The proposed video frame interpolation takes into account an exceptional motion map that contains the location and intensity of the exceptional motion. The proposed method consists of three parts, which are optical flow based frame interpolation, exceptional motion detection, and frame refinement. The optical flow based frame interpolation predicts an optical flow which is used to synthesize the pre-generated intermediate frame. The exceptional motion detection detects the position and intensity of complex and large motion with the current frame and the previous frame sequence. The frame refinement focuses on the exceptional motion region of the pre-generated intermediate frame by using the exceptional motion map. The proposed video frame interpolation can be robust against the exceptional motion including complex and large motion. Experimental results showed that the proposed video frame interpolation achieved high performance on various public video datasets and especially on videos with exceptional motion patterns.
Generating Multi-hop Reasoning Questions to Improve Machine Reading Comprehension

This paper focuses on the topic of multi-hop question generation, which aims to generate questions needed reasoning over multiple sentences and relations to derive answers. In particular, we first build an entity graph to integrate various entities scattered over text based on their contextual relations. We then heuristically extract the sub-graph by the evidential relations and type, so as to obtain the reasoning chain and textual related contents for each question. Guided by the chain, we propose a holistic generator-evaluator network to form the questions, where such guidance helps to ensure the rationality of generated questions which need multi-hop deduction to correspond to the answers. The generator is a sequence-to-sequence model, designed with several techniques to make the questions syntactically and semantically valid. The evaluator optimizes the generator network by employing a hybrid mechanism combined of supervised and reinforced learning. Experimental results on HotpotQA data set demonstrate the effectiveness of our approach, where the generated samples can be used as pseudo training data to alleviate the data shortage problem for neural network and assist to learn the state-of-the-arts for multi-hop machine comprehension.
Unsupervised Dual-Cascade Learning with Pseudo-Feedback Distillation for Query-Focused Extractive Summarization

We propose Dual-CES – a novel unsupervised, query-focused, multi-document extractive summarizer. Dual-CES builds on top of the Cross Entropy Summarizer (CES) and is designed to better handle the tradeoff between saliency and focus in summarization. To this end, Dual-CES employs a two-step dual-cascade optimization approach with saliency-based pseudo-feedback distillation. Overall, Dual-CES significantly outperforms all other state-of-the-art unsupervised alternatives. Dual-CES is even shown to be able to outperform strong supervised summarizers.
Review-guided Helpful Answer Identification in E-commerce

Product-specific community question answering platforms can greatly help address the concerns of potential customers. However, the user-provided answers on such platforms often vary a lot in their qualities. Helpfulness votes from the community can indicate the overall quality of the answer, but they are often missing. Accurately predicting the helpfulness of an answer to a given question and thus identifying helpful answers is becoming a demanding need. Since the helpfulness of an answer depends on multiple perspectives instead of only topical relevance investigated in typical QA tasks, common answer selection algorithms are insufficient for tackling this task. In this paper, we propose the Review-guided Answer Helpfulness Prediction (RAHP) model that not only considers the interactions between QA pairs but also investigates the opinion coherence between the answer and crowds’ opinions reflected in the reviews, which is another important factor to identify helpful answers. Moreover, we tackle the task of determining opinion coherence as a language inference problem and explore the utilization of pre-training strategy to transfer the textual inference knowledge obtained from a specifically designed trained network. Extensive experiments conducted on real-world data across seven product categories show that our proposed model achieves superior performance on the prediction task.
eDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection

Darknet crypto markets are online marketplaces using crypto currencies (e.g., Bitcoin, Monero) and advanced encryption techniques to offer anonymity to vendors and consumers trading for illegal goods or services. The exact volume of substances advertised and sold through these crypto markets is difficult to assess, at least partially, because vendors tend to maintain multiple accounts (or Sybil accounts) within and across different crypto markets. Linking these different accounts will allow us to accurately evaluate the volume of substances advertised across the different crypto markets by each vendor. In this paper, we present a multi-view unsupervised framework (eDarkFind) that helps modeling vendor characteristics and facilitates Sybil account detection. We employ a multi-view learning paradigm to generalize and improve the performance by exploiting the diverse views from multiple rich sources such as BERT, stylometric, and location representation. Our model is further tailored to take advantage of domain-specific knowledge such as the Drug Abuse Ontology to take into consideration the substance information. We performed extensive experiments and demonstrated that the multiple views obtained from diverse sources can be effective in linking Sybil accounts. Our proposed eDarkFind model achieves an accuracy of 98% on three real-world datasets which shows the generality of the approach.
Leveraging Sentiment Distributions to Distinguish Figurative From Literal Health Reports on Twitter

Harnessing data from social media to monitor health events is a promising avenue for public health surveillance. A key step is the detection of reports of a disease (referred to as ‘health mention classification’) amongst tweets that mention disease words. Prior work shows that figurative usage of disease words may prove to be challenging for health mention classification. Since the experience of a disease is associated with a negative sentiment, we present a method that utilises sentiment information to improve health mention classification. Specifically, our classifier for health mention classification combines pre-trained contextual word representations with sentiment distributions of words in the tweet. For our experiments, we extend a benchmark dataset of tweets for health mention classification, adding over 14k manually annotated tweets across diseases. We also additionally annotate each tweet with a label that indicates if the disease words are used in a figurative sense. Our classifier outperforms current SOTA approaches in detecting both health-related and figurative tweets that mention disease words. We also show that tweets containing disease words are mentioned figuratively more often than in a health-related context, proving to be challenging for classifiers targeting health-related tweets.
Research on Intelligent Vehicle Path Planning Based on Rapidly-Exploring Random Tree

Aiming at the problems of large randomness, slow convergence speed, and deviation of Rapidly-Exploring Random Tree algorithm, a new node is generated by a cyclic alternating iteration search method and a bidirectional random tree search simultaneously. A vehicle steering model is established to increase the vehicle turning angle constraint. The Rapidly-Exploring Random Tree algorithm is improved and optimized. The problems of large randomness, slow convergence speed, and deviation of the Rapidly-Exploring Random Tree algorithm are solved. Node optimization is performed on the generated path, redundant nodes are removed, the length of the path is shortened, and the feasibility of the path is improved. The B -spline curve is used to insert the local end point, and the path is smoothed to make the generated path more in line with the driving conditions of the vehicle. The feasibility of the improved algorithm is verified in different scenarios. MATLAB/CarSim is used for joint simulation. Based on the vehicle model, virtual simulation is carried out to track the planned path, which verifies the correctness of the algorithm.
Smart Meter Data Obfuscation Using Correlated Noise

In this article, we present a data obfuscation technique for smart meter data based on additive correlated noise. This noise is used to mask the data transmitted by different users to a third party, resulting in protection against eavesdroppers, but at the same time enabling the accurate recovery of statistics of the original data for use by the energy supplier. We analyze the proposed technique by studying its obfuscation performance and accuracy of statistics recovered from the masked data as a function of noise correlation with the users' data. Finally, we identify deep learning techniques such as generative adversarial networks for data obfuscation using correlated noise, and show preliminary results to demonstrate their performance in this scenario.
Large‐scale geochemical survey by pXRF spectrometry of archaeological settlements and features: new perspectives on the method

Abstract Over the past several decades the geochemical study of archaeological sediments has experienced an increase in interest from archaeologists in search of new methods to investigate spatial organization and space function. Used with other proxies like geophysics, archaeobotany and micromorphology, geochemistry provides a new insight into microscopic pollution generated by past human activities, and thus can explain or confirm the function of features and spaces. Portable X‐ray fluorescence (pXRF) spectrometry offers new perspectives to the archaeological community to test, develop and integrate geochemical surveys in the daily routine of fieldwork, especially in the developer‐funded archaeology sector. We have undertaken over 20 geochemical surveys using pXRF spectrometry, both in the field and on bulk samples, and from a wide variety of sites, sedimentological, and chronological contexts. Working exclusively on developer‐funded projects, we present a series of results detailing a range of chemical element associations, including details on how the data is organized spatially and how these data may be interpreted. For some associations, such as that between phosphorus, calcium and strontium, our work confirms and builds on existing studies. For other elements such as zirconium, or the association between potassium, chromium and vanadium, our work presents new data patterns as well as a series of new hypothesis; the interpretation of these patterns remains at an early stage and we hope that further work will be done to test and detail the origins of these chemical associations and enrichments, with the goal of confirming the human activities and/or natural processes from which they originated. The rapidity and cost‐efficiency of pXRF, especially when used in situ in the field, makes it the ideal method for documenting the geochemical characteristics of landscapes; the approach we have developed means that geochemical survey can become a regular part of archaeological practice.
Analyzing and learning the language for different types of harassment

THIS ARTICLE USES WORDS OR LANGUAGE THAT IS CONSIDERED PROFANE, VULGAR, OR OFFENSIVE BY SOME READERS. The presence of a significant amount of harassment in user-generated content and its negative impact calls for robust automatic detection approaches. This requires the identification of different types of harassment. Earlier work has classified harassing language in terms of hurtfulness, abusiveness, sentiment, and profanity. However, to identify and understand harassment more accurately, it is essential to determine the contextual type that captures the interrelated conditions in which harassing language occurs. In this paper we introduce the notion of contextual type in harassment by distinguishing between five contextual types: (i) sexual, (ii) racial, (iii) appearance-related, (iv) intellectual and (v) political. We utilize an annotated corpus from Twitter distinguishing these types of harassment. We study the context of each kind to shed light on the linguistic meaning, interpretation, and distribution, with results from two lines of investigation: an extensive linguistic analysis, and the statistical distribution of uni-grams. We then build type- aware classifiers to automate the identification of type-specific harassment. Our experiments demonstrate that these classifiers provide competitive accuracy for identifying and analyzing harassment on social media. We present extensive discussion and significant observations about the effectiveness of type-aware classifiers using a detailed comparison setup, providing insight into the role of type-dependent features.
Features of Postgraduate Programs’ Reforming as an Issue for Scientific and Pedagogical Discussion

The article discusses the problems of Russian postgraduate studies improving, the goals and objectives of postgraduate training. The peculiarities of reforming the national postgraduate school, general and specific principles and forms of its improvement are considered. The author dwells on a highly specialized orientation of postgraduate training’s educational component. The emphasis is on the need of its restructuring in order to deepen the training of postgraduate students in the beginnings of their researcher profession. The article makes the case for establishing variable postgraduate training, which means focusing of the structure and content of postgraduate training on different types of graduates’ professional employment. In order to improve the quality of scientific and scientific-pedagogical personnel training, it is necessary to examine the possibilities of higher education resources to be integrated with the scientific potential of Russian Academy of Sciences research institutes. A brief description of the international experience in the field of scientists and teachers training is presented. A two-level model of postgraduate program is proposed. The postgraduate studies at the first level ensure obtaining the primary professional researcher qualification in industrial orientation, while at the second level, the task of transforming the graduate student into an independent active researcher is solved.
Intelligence-Sharing Vehicular Networks with Mobile Edge Computing and Spatiotemporal Knowledge Transfer

Based on recent advances in MEC and knowledge transfer in artificial intelligence, we propose a novel framework named ISVN, in which the intelligence of different MEC servers can be shared to improve performance. Specifically, we present the main techniques in the ISVN framework, including aggregation and representation for context features, relationship mining and reasoning, and knowledge transfer among MEC servers. The results of object detection experiments with the proposed ISVN framework are presented. By taking advantage of MEC and knowledge transfer, the processing speed and accuracy of object detection can be significantly improved in different scenarios of vehicular networks.