Reviewerly/exHarmony huggingface dataset:
Understanding Promotion-as-a-Service on GitHub

As the world's leading software development platform, GitHub has become a social networking site for programmers and recruiters who leverage its social features, such as star and fork, for career and business development. However, in this paper, we found a group of GitHub accounts that conducted promotion services in GitHub, called "promoters", by performing paid star and fork operations on specified repositories. We also uncovered a stealthy way of tampering with historical commits, through which these promoters are able to fake commits retroactively. By exploiting such a promotion service, any GitHub user can pretend to be a skillful developer with high influence.
Applying NLP to Build a Cold Reading Chatbot

Chatbots are computer programs designed to simulate conversation by interacting with a human user. In this paper we present a chatbot framework designed specifically to aid prolonged grief disorder (PGD) sufferers by replicating the techniques performed during cold readings. Our initial framework performed an association rule analysis on transcripts of real-world cold reading performances, in order to generate the required data as used in traditional rules based chatbots. However due to the structure of cold readings the traditional approach was unable to determine a satisfactory set of rules. Therefore, in this paper we discuss the limitations of this approach and subsequently provide a generative solution using sequence-to-sequence modeling with long short-term memory. We demonstrate how our generative chatbot is therefore able to provide appropriate responses to the majority of inputs. However, as inappropriate responses can present a risk to sensitive PGD sufferers we suggest a final iteration of our chatbot which successfully adjusts to account for multi-turn conversations.
An adjacent-swap Markov chain on coalescent trees

Abstract The standard coalescent is widely used in evolutionary biology and population genetics to model the ancestral history of a sample of molecular sequences as a rooted and ranked binary tree. In this paper we present a representation of the space of ranked trees as a space of constrained ordered matched pairs. We use this representation to define ergodic Markov chains on labeled and unlabeled ranked tree shapes analogously to transposition chains on the space of permutations. We show that an adjacent-swap chain on labeled and unlabeled ranked tree shapes has a mixing time at least of order $n^3$ , and at most of order $n^{4}$ . Bayesian inference methods rely on Markov chain Monte Carlo methods on the space of trees. Thus it is important to define good Markov chains which are easy to simulate and for which rates of convergence can be studied.
Estimating the Cost of Cybersecurity Activities with CAsPeA: A Case Study and Comparative Analysis

Contemporary approaches to the estimation of cybersecurity costs in organisations tend to focus on the cost of incidents or technological investments. However, there are other, less transparent costs related to cybersecurity management that need to be properly recognised in order to get a complete picture. These costs are associated with everyday activities and the time spent by employees on cybersecurity-related actions. Such costs constitute a substantial component of cybersecurity expenditures, but because they become evident only during scrupulous analyses, often they are neglected. This paper presents new developments on CAsPeA – a method which enables estimating the cost of these activities based on a model derived from the Activity-Based Costing (ABC) and the NIST SP 800-53 guidelines. The application of the method is illustrated by a case study of a civil engineering enterprise. The method's evaluation based on comparative analysis in respect to SQUARE is described.
Employment Service System Based on Hybrid Recommendation Algorithm

The recommendation system can calculate the similarity between users and items in the system, which is based on different computational methods, analyzing of calculation results, and then calculates the items that may interest the users and recommend those items to users. Collaborative filtering algorithm is a popular algorithm in academia and industry, but it does have certain shortcomings such as cold start and sparse data. In this paper, a hybrid recommendation algorithm is proposed and applied to an employment service system by using job postings from websites obtained by web crawler as the dataset. The experimental result shows that the hybrid recommendation algorithm is able to the accuracy of employment information recommendation to some extent and meet the personalized needs of job-seeking users.
An Aircraft-Centered Multi-Frame TBD Method for Airborne Radar Systems

It is widely verified that the multi-frame track-before-detect (MF-TBD) is effective for the detection and tracking of weak targets. This paper considers the extension of MF-TBD to moving surveillance platform, specifically, the airborne radar system. For static radars, MF-TBD needs to first map the received raw data to an absolute coordinate, mostly the WGS-84 coordinate, and then perform the raw data integration between multi-frame to accumulate target energy along physically feasible states. The time-varying surveillance space and biased complex coordinate system conversion, caused by the aircraft motion, produce the unaligned multi-frame mapping. It brings the cell misalignment and dislocation error that contribute to the challenge of the tracking accuracy in the absolute coordinate. To address these limitations, the proposed aircraft-centered multi-frame TBD (AC-MF-TBD) method adopts the concept of relative kinematics making the aircraft as the center of each measurement frame and doing raw data integration in this relative coordinate, namely the inertial coordinate. It makes the mapping no longer time-varying and develops the tracking accuracy on the airborne radar. AC-MF-TBD essentially avoids the errors introduced by the aircraft motion in the absolute coordinate. In addition, the coordinate conversion using the attitude information of the aircraft is still a necessary process for AC-MF-TBD. The attitude angle calibration is used to improve the conversion accuracy. The simulation shows the performance of the proposed algorithm.
Deep Learning Based People Detection, Tracking and Re-identification in Intelligent Video Surveillance System

Intelligent video surveillance has gained increasing needs and attentions due to its greatly potential in security and social governance. In this paper, we integrate three parts of video surveillance system together, including people detection, tracking and person re-identification to construct a systematic research framework. Following a brief introduction, we first present current problems and difficulties of people detection, tracking and re-identification. Then the state-of-the-art deep learning based algorithms in recent years are summarized and evaluated. At last we put forward the prospect of current research directions, providing a relatively comprehensive knowledge base for the future development of intelligent surveillance video systems.
Woodpecker: Secret Back-of-Device Tap Rhythms to Authenticate Mobile Users

This paper presents Woodpecker, a playful mobile user authentication method that enables users to authenticate themselves by performing back-of-device tap rhythms. It uses the microphone and accelerometer data of an off-the-shelf smart-phone to compare the sequence, frequency, and intensity of tap rhythms to authenticate users. In a study, Woodpecker yielded a moderate accuracy (70%) and a low successful attack rate (17%) in an ideal shoulder surfing threat model with only three sample rhythms. Besides, most participants found the method easy-to-use and more secure than the conventional methods, thus wanted to keep using it on their devices.
Multidimensional Self-Attention for Aspect Term Extraction and Biomedical Named Entity Recognition

Wide attention has been paid to named entity recognition (NER) in specific fields. Among the representative tasks are the aspect term extraction (ATE) in user online comments and the biomedical named entity recognition (BioNER) in medical documents. Existing methods only perform well in a particular field, and it is difficult to maintain an advantage in other fields. In this article, we propose a supervised learning method that can be used for much special domain NER tasks. The model consists of two parts, a multidimensional self-attention (MDSA) network and a CNN-based model. The multidimensional self-attention mechanism can calculate the importance of the context to the current word, select the relevance according to the importance, and complete the update of the word vector. This update mechanism allows the subsequent CNN model to have variable-length memory of sentence context. We conduct experiments on benchmark datasets of ATE and BioNER tasks. The results show that our model surpasses most baseline methods.
Optical demonstration of quantum fault-tolerant threshold

A major challenge in practical quantum computation is the ineludible errors caused by the interaction of quantum systems with their environment. Fault-tolerant schemes, in which logical qubits are encoded by several physical qubits, enable to the output of a higher probability of correct logical qubits under the presence of errors. However, strict requirements to encode qubits and operators render the implementation of a full fault-tolerant computation challenging even for the achievable noisy intermediate-scale quantum technology. Especially the threshold for fault-tolerant computation still lacks experimental verification. Here, based on an all-optical setup, we experimentally demonstrate the existence of the threshold for the fault-tolerant protocol. Four physical qubits are represented as the spatial modes of two entangled photons, which are used to encode two logical qubits. The experimental results clearly show that when the error rate is below the threshold, the probability of correct output in the circuit, formed with fault-tolerant gates, is higher than that in the corresponding non-encoded circuit. In contrast, when the error rate is above the threshold, no advantage is observed in the fault-tolerant implementation. The developed high-accuracy optical system may provide a reliable platform to investigate error propagation in more complex circuits with fault-tolerant gates.
Few-Shot Scale-Insensitive Object Detection for Edge Computing Platform

In the era of the Internet of Things, the construction of edge computing platform has become more and more important, which has led lots of object detection applications being deployed on embedded devices. However, traditional object detection algorithms require lots of engery and a large amount of well labeled samples for training. The time spent on model training and data labeling also slows down the upgrade iteration of applications. Therefore, an object detection algorithm that requires only few energy and a few samples to update parameters could help the long-term benign development of IoT technology. In this paper, we propose an effective object detection method based on the few-shot learning, which could achieve considerable performance with few data for novel(new) classes. Our well-designed strategies could alleviate the impact of scale variation in support set under few-shot setting. Through extensive experiments, we prove that our model is superior to well-recognized baselines on few-shot object detection task.
Chapter 10. Challenges and strategies for beginners to solve research questions with DH methodologies on a corpus of multilingual Philippine periodicals

A usually mentioned problem in Digital Humanities (DH) is the difficult fit between Humanities research questions and DH methodologies. This chapter is therefore configured as a meta-chapter that explains the problems and strategies when exploring the multilingual repository of Philippine periodicals constructed within the project “Strenghthening Digital Research at the UP System” in order to research the evolution of the image of China in these periodicals. The two main challenges found for analysing the periodicals to find an answer have been (1) Problematic OCRs, (2) Research across multi-lingual publications. The chapter lists literature and research projects that have approached similar questions and challenges in comparable corpora. Some suggestions of tools to address them will also be provided.
Object Detection with Deep Learning on Drive PX2

Many successful deep learning networks for object detection have been proposed in recent years, but direct comparison is difficult due to different resolutions, training validation data, and frameworks used to train these networks. In particular, we use DRIVE PX2 as a reference computing platform, which is manufactured by NVIDIA Corporation for development of autonomous vehicles and evaluate the performance of methods of autonomous driving on ARM-based embedded processing cores and Tegra-based embedded graphics processing units (GPUs).
Applying Task-centric Holistic Teaching Approach in Education of Industrial Cyber Physical Systems

In order to meet the increasing demand for industrial cyber physical systems, highly qualified professionals are required who possess both excellent professional knowledge and skills as well as soft skills. The interdisciplinarity of industrial cyber physical systems makes teaching the necessary skill challenging. The approach known from the education of software engineering, in which students form a team to develop a software product, is not sufficient. There is also a lack of soft skills, which are often seen as key success factors for engineering projects. To close the existing gaps in education of industrial cyber-physical systems engineering, the task-centric holistic agile teaching approach (T-CHAT) is proposed. This approach will be implemented in a newly developed curriculum for industrial cyber physical systems at the ITMO University St. Petersburg, Russia. It focuses on teaching of both technical and soft skills.
Modernizing Healthcare by Using Blockchain

Electronic health record (EHR) systems are designed and deployed to store data accurately and to capture the state of a patient across time, and they have been one of the major drivers to advance care in the last decade. However, the EHR is not eligible in supporting a model that is beyond episodic visits, nor the idea of an integrated care plan that all care team members can view and contribute to. On the other hand, the concept of a longitudinal record and the idea of a “smart care plan” are key factors for paving the way toward Predictive, Preventive, Personalized and Participatory (P4-medicine), which arguably will be in a near future the only effective and sustainable approach for pandemics and “silent” chronic diseases. At the current state-of-the-art, the HL7 FHIR standard and distributed ledger technologies (DLTs) are two very promising areas of research and development in the context of health information management, and a proper synergy among their approaches, concepts and tools could overcome the limitations of EHR systems, giving rise to the hub of the IT infrastructure for P4-medicine. This chapter explores the potential and challenges of integrating the FHIR standard into DLTs, also through a concrete example of implementation.
Auto-Scaling System in Apache Spark Cluster Using Model-Based Deep Reinforcement Learning

Real-time processing is a fast and prompt processing technology that needs to complete the execution within a limited time constraint almost equal to the input time. Executing such real-time processing needs an efficient auto-scaling system which provides sufficient resources to compute the process within the time constraint. We use Apache Spark framework to build a cluster which supports real-time processing. The major challenge of scaling Apache Spark cluster automatically for the real-time processing is how to handle the unpredictable input data size and also the unpredictable resource availability of the underlying cloud infrastructure. If the scaling-out of the cluster is too slow then the application can not be executed within the time constraint as a result of insufficient resources. If the scaling-in of the cluster is slow, the resources are wasted without being utilized, and it leads less resource utilization. This research follows the real-world scenario where the computing resources are bounded by a certain number of computing nodes due to limited budget as well as the computing time is limited due to the nature of near real-time application. We design an auto-scaling system that applies a deep reinforcement learning technique, DQN (Deep Q-Network), to improve resource utilization efficiently. Our model-based DQN allows to automatically optimize the scaling of the cluster, because the DQN can autonomously learn the given environment features so that it can take suitable actions to get the maximum reward under the limited execution time and worker nodes.
An Improved Artificial Bee Colony Algorithm with Multiple Search Strategy

Artificial Bee Colony (ABC) has been applied to solve constrained optimization problems such as green wireless communications, path planning and so on. To solve the problem that ABC algorithm is easy to fall into local optimum, this paper proposes an Improved Artificial Bee Colony (IABC) algorithm with multiple search strategy. An opposition-based learning technique is integrated in initialization phase. Then, in order to speed up convergence rate, each employed bee searches for neighbor with adding global information. Furthermore, multiple search strategy is used to balance the exploitation and exploration during the onlooker bee phase. Inspired by Modification Rate (MR), the solution generation method of new bees whose trail have exceed limit is modified to increase disturbance in scout bee phase. Six benchmark functions are used to test the efficiency and stability of the algorithm, and the simulation results show IABC algorithm performs better than ABC algorithm in high dimensional space.
A Methodology For Drones to Learn How to Navigate And Avoid Obstacles Using Decision Trees

Over the last decade, drones and UAVs have attracted great research interest mainly due to their abilities and their potential to be used in various applications and domains. One of the most important operations that Drones must perform efficiently concerns the navigation in real-world environments. This typically includes the ability of path planning and obstacle avoidance. It is crucial that drones have the ability to perform automatically and efficiently procedures related to the avoidance of objects while navigating in environments. In this work, we present a methodology for assisting a drone to navigate in unknown environments and avoid obstacles. The methodology is based on a training-by-human concept where the drone learns how to avoid obstacles by example cases that are provided to it and it is trained on them. The results are quite interesting and indicate that the methodology is efficient and can assist drones and robotics systems to learn how to navigate and avoid obstacles in environments.
Integration of Internet of Things and blockchain for chattel asset pledge financial service

This paper demonstrates how to use IoT and blockchain to enable chattel asset pledge financial service. In the beginning, the background, the advantages and challenges for integration of IoT and blockchain technologies for industries are introduced, and then the relevant survey are studied. The actors in the chattel asset monitoring application scenario, relationships, data flow and interaction sequences among the involving actors are depicted, Finally, the conclusion has been given to summarize the paper.
Recent applications of big data in finance

The financial sector generates tremendous amounts of data daily. Consequently, more attention is being focused on transforming that data into actionable knowledge. Big data and data science techniques have revolutionized the business world; the focus of this paper is to study the extent of this transformation in the financial field by considering research works discussing big data in financial markets, banking, credit risk management, fraud detection and insurance and illustrating some real life applications and challenges of this technology.
Analogous Approach towards Performance Analysis for Software Defect Prediction and Prioritization

Abstract This exploration paper proposes the Software frameworks assume a significant part in everyday human lives, and hence making quality programming framework is an indispensable issue. A lot of work is being done here. One of the principle key among them is programming deformity forecast (SDP). Imperfection Prediction is a twofold request issue the spot a particular item module will be requested as broken however those blunder might be more great more than 0 and as Non-Defective on those slip will be identical to 0. A number for programming estimations additionally measurable models have been framed to this explanation behind existing. Larger aspect of the deformity Prediction models would make using AI in techniques. That execution of various models can be contemplated using the boundaries like Accuracy, hit rate and bogus alert rate. In this exploration covers on the different examinations done on the region of programming imperfection forecast in the previous not many years lastly another framework is proposed.
Exponents of skew polynomials over periodic rings

We investigate the properties of periodic rings R in view of studying general skew polynomials f(t)∈R[t;σ,δ]. We introduce exponents for these polynomials and give some properties of this notion. We show, in particular, that this notion is right-left symmetric. Using the skew evaluation, we generalize the classical connection between the exponent of a polynomial and the order of its companion matrix.
A Data Reconstruction Method based on Adversarial Conditional Variational Autoencoder

Aiming at the problem of sample missing for magnetic flux leakage (MFL), a data reconstruction method based on conditional autoencoder (CVAE) and generative adversarial networks (GAN) is proposed. This method combines the advantages of CVAE and GAN, and generates high-quality samples steadily. The proposed CVAE-GAN method can not only reconstruct the missing MFL samples, but also generate a large amount of real and diverse defect sample, which solves the problem of low accuracy of the defect detection model due to insufficient samples and lack of diversity of samples. The defect sample are collected from the domestic in-service oil pipelines in experiments. The experimental results illustrate that the proposed method can effectively generate high-quality samples.