Reviewerly/exHarmony huggingface dataset:
SeqTrans: Automatic Vulnerability Fix Via Sequence to Sequence Learning

Software vulnerabilities are now reported unprecedentedly due to the recent development of automated vulnerability hunting tools. However, fixing vulnerabilities still mainly depends on programmers’ manual efforts. Developers need to deeply understand the vulnerability and affect the system’s functions as little as possible. In this paper, with the advancement of Neural Machine Translation (NMT) techniques, we provide a novel approach called SeqTrans to exploit historical vulnerability fixes to provide suggestions and automatically fix the source code. To capture the contextual information around the vulnerable code, we propose to leverage data-flow dependencies to construct code sequences and feed them into the state-of-the-art transformer model. The fine-tuning strategy has been introduced to overcome the small sample size problem. We evaluate SeqTrans on a dataset containing 1,282 commits that fix 624 CVEs in 205 Java projects. Results show that the accuracy of SeqTrans outperforms the latest techniques and achieves 23.3% in statement-level fix and 25.3% in CVE-level fix. In the meantime, we look deep inside the result and observe that the NMT model performs very well in certain kinds of vulnerabilities like CWE-287 (Improper Authentication) and CWE-863 (Incorrect Authorization).
<scp>NER‐RAKE</scp>: An improved rapid automatic keyword extraction method for scientific literatures based on named entity recognition

Abstract The proposing of an accurate and efficient model for automatic keyword extraction of scientific literature is conducive to promoting the development of academic text mining research, such as literature retrieval optimization, research topic discovery, topic evolution analysis, emerging trend detection and so on, with the continuous expansion of digital academic resources. A keyword extraction method is proposed called NER‐RAKE which combines Named Entity Recognition (NER) process with Rapid automatic keyword extraction (RAKE), Bidirectional Long Short‐Term Memory Network Conditional Random Field (BiLSTM‐CRF) is used to recognize the domain entities in the scientific literature so as to enrich the list of candidate keywords divided by RAKE, and the frequency threshold is set to ensure the validity of candidate keywords. NER‐RAKE is better than RAKE in accuracy and recall rate through the keyword extraction experiment in the computer science field. It is effective to combine the process of NER with RAKE, which can improve the efficiency of keyword extraction.
Mario Level Generation From Mechanics Using Scene Stitching

Video game tutorials allow players to gain mastery over game skills and mechanics. To hone players' skills, it is beneficial from practicing in environments that promote individual player skill sets. However, automatically generating environments which are mechanically similar to one-another is a non-trivial problem. This paper presents a level generation method for Super Mario by stitching together pre-generated "scenes" that contain specific mechanics, using mechanic-sequences from agent playthroughs as input specifications. Given a sequence of mechanics, the proposed system uses an FI-2Pop algorithm and a corpus of scenes to perform automated level authoring. The proposed system outputs levels that can be beaten using a similar mechanical sequence to the target mechanic sequence but with a different playthrough experience. We compare the proposed system to a greedy method that selects scenes that maximize the number of matched mechanics. Unlike the greedy approach, the proposed system is able to maximize the number of matched mechanics while reducing emergent mechanics using the stitching process.
A Large Test Collection for Entity Aspect Linking

Given a text with entity links, the task of entity aspect linking is to identify which aspect of an entity is referred to in the context. For example, if a text passage mentions the entity "USA'', is USA mentioned in the context of the 2008 financial crisis, American cuisine, or else? Complementing efforts of Nanni et al (2018), we provide a large-scale test collection which is derived from Wikipedia hyperlinks in a dump from 01/01/2020. Furthermore, we offer strong baselines with results and broken-out feature sets to stimulate more research in this area.
Identifying gross post‐mortem organ images using a pre‐trained convolutional neural network

Abstract Identifying organs/tissue and pathology on radiological and microscopic images can be performed using convolutional neural networks (CNN). However, there are scant studies on applying CNN to post‐mortem gross images of visceral organs. This proof‐of‐concept study used 537 gross post‐mortem images of dissected brain, heart, lung, liver, spleen, and kidney, which were randomly divided into a training and teaching datasets for the pre‐trained CNN Xception. The CNN was trained using the training dataset and subsequently tested on the testing dataset. The overall accuracies were &gt;95% percent for both training and testing datasets and have an F1 score of &gt;0.95 for all dissected organs. This study showed that small datasets of post‐mortem images can be classified with a very high accuracy using a pre‐trained CNN. This novel area has the potential for future application in data mining, education and teaching, case review, research, quality assurance, auditing purposes, and identifying pathology.
An Autonomous Food Wastage Control Warehouse: Distributed Ledger and Machine Learning based Approach

Food is an essential aspect of our lives. Back in those days, people consumed seasonal fruits and vegetables which were available locally and on a daily basis. But things have changed when refrigeration units were invented. People were now able to store and consume food products which were nonseasonal also. In today's world, warehouses and cold chains are of striking importance because most of the food products that we see in our supermarkets and grocery stores come from them. Due to improper management of food products these warehouses have become an epicenter for food wastage. Since most of the traditional warehouses are manual or RDBMS based, they can be easily tampered. With these considerations in this paper, we have proposed an autonomous warehouse system which will tackle food wastage by using Machine Learning and Blockchain technology.
Flexible IR Pipelines with Capreolus

While a number of recent open-source toolkits for training and using neural information retrieval models have greatly simplified experiments with neural reranking methods, they essentially hard code a "search-then-rerank'' experimental pipeline. These pipelines consist of an efficient first-stage ranking method, like BM25, followed by a neural reranking method. Deviations from this setup often require hacks; some improvements, like adding a second reranking step that uses a more expensive neural method, are infeasible without major code changes. In order to improve the flexibility of such toolkits, we propose implementing experimental pipelines as dependency graphs of functional "IR primitives,'' which we call modules, that can be used and combined as needed.
A Robust Weld Seam Tracking Method by Using Intensity-Modulated Structured Light

The seam tracking method by using structured light is widely used in the automatic welding process. Most of the noise can be filtered out by a narrow-band optical filter whose center wavelength is the same as the structured light. However, it still suffers from light noise caused by sparks and fumes when the structured light is getting close to the welding pool. This limitation of the structured-light based method is inherent because light noise caused by the welding process has a wide spectrum and couldn't filter out by the optical filter. To circumvent this problem, we proposed a seam tracking method that can modulate the intensity of the structured light at a high frequency by an acoustic-optic modulator (AOM) and extract the modulated frequency from multiple seam images by Fourier transformation. As the frequency of noises such as sparks is random and different from the modulated frequency, the proposed method can remove these noises in the frequency domain directly. We demonstrated the robustness of this method by detecting seam in noisy welding conditions.
Real-Time Human-UAV Interaction: New Dataset and Two Novel Gesture-Based Interacting Systems

Two novel gesture-based Human-UAV Interaction (HUI) systems are proposed to launch and control a UAV in real-time utilizing a monocular camera and a ground computer. The first proposal is an end-to-end static Gesture-Based Interaction (GBI) system based on classifying the interacted user poses while discarding the gesture interpreting component to boost the system performance up to 99 % with a speed of 28 fps. On the other hand, the second proposal is a dynamic one that adopts a simple model to detect three parts (face and two hands) of the interacted person and tracks them for a certain number of frames till a specific dynamic gesture is recognized. The proposed dynamic method is efficient, decreases the complexity, and speeds the interaction up to 27 fps comparing with the recent multimodel ones. Its backbone is a simplified Tiny-You Only Look Once (YOLO) network saves the resources and speeds the detection process up to 120 fps. Moreover, a comprehensive new gestures dataset was established to facilitate the learning process and aid the research work. A comparative study is carried out to show the performance and efficiency of the proposed dynamic HUI system in terms of detection accuracy and speed with the baseline detector on the human gesture public dataset. Finally, a non-expert volunteer examines the proposed HUIs by launching and driving a Bebop 2 micro UAV through a set of real flights.
Job Interviewer Android with Elaborate Follow-up Question Generation

A job interview is a domain that takes advantage of an android robot's human-like appearance and behaviors. In this work, our goal is to implement a system in which an android plays the role of an interviewer so that users may practice for a real job interview. Our proposed system generates elaborate follow-up questions based on responses from the interviewee. We conducted an interactive experiment to compare the proposed system against a baseline system that asked only fixed-form questions. We found that this system was significantly better than the baseline system with respect to the impression of the interview and the quality of the questions, and that the presence of the android interviewer was enhanced by the follow-up questions. We also found a similar result when using a virtual agent interviewer, except that presence was not enhanced.
Long-Term Evaluation of Technical Debt in Open-Source Software

Background: A consistent body of research and practice have identified that technical debt provides valuable and actionable insight into the design and implementation deficiencies of complex software systems. Existing software tools enable characterizing and measuring the amount of technical debt at selective granularity levels; by providing a computational model, they enable stakeholders to measure and ultimately control this phenomenon. Aims: In this paper we aim to study the evolution and characteristics of technical debt in open-source software. For this, we carry out a longitudinal study that covers the entire development history of several complex applications. The goal is to improve our understanding of how the amount and composition of technical debt changes in evolving software. We also study how new technical debt is introduced in software, as well as identify how developers handle its accumulation over the long term. Method: We carried out our evaluation using three complex, open-source Java applications. All 110 released versions, covering more than 10 years of development history for each application were analyzed using SonarQube. We studied how the amount, composition and history of technical debt changed during development, compared our results across the studied applications and present our most important findings. Results: For each application, we identified key versions during which large amounts of technical debt were added, removed or both. This had significantly more impact when compared to the lines of code or class count increases that generally occurred during development. However, within each version, we found high correlation between file lines of code and technical debt. We observed that the Pareto principle was satisfied for the studied applications, as 20% of issue types generated around 80% of total technical debt. Interestingly, there was a large degree of overlap between the issues that generated most of the debt across the studied applications. Conclusions: Early application versions showed greater fluctuation in the amount of existing technical debt. We found application size to be an unreliable predictor for the quantity of technical debt. Most debt was introduced in applications as part of milestone releases that expanded their feature set; likewise, we identified releases where extensive refactoring significantly reduced the level of debt. We also discovered that technical debt issues persist for a long time in source code, and their removal did not appear to be prioritized according to type or severity.
Student Agency in Traditional and Distance Learning from Their Instructors' Perspective

Student agency serves as a key factor in student success at university, and a crucial factor in distance learning. The forced transition to teaching with distance educational technologies eased external regulation and showed a picture of student agency. This study aims to describe how instructors assess student agency and how it changes with distance learning. Our study is based on the data from 27 interviews with instructors from 12 Russian leading universities. Interviews were held in two waves: before and during the COVID-19 pandemic. In this study, we analyze how the learning process is organized usually and in the new conditions, how instructors assess students’ agency in traditional and distance learning, and we also present instructors’ forecast for the future and practices that they plan to continue using when the quarantine is over.
Securing Bloom Filters for Privacy-preserving Record Linkage

Privacy-preserving record linkage (PPRL) facilitates the matching of records that correspond to the same real-world entities across different databases while preserving the privacy of the individuals in these databases. A Bloom filter (BF) is a space efficient probabilistic data structure that is becoming popular in PPRL as an efficient privacy technique to encode sensitive information in records while still enabling approximate similarity computations between attribute values. However, BF encoding is susceptible to privacy attacks which can re-identify the values that are being encoded. In this paper we propose two novel techniques that can be applied on BF encoding to improve privacy against attacks. Our techniques use neighbouring bits in a BF to generate new bit values. An empirical study on large real databases shows that our techniques provide high security against privacy attacks, and achieve better similarity computation accuracy and linkage quality compared to other privacy improvements that can be applied on BF encoding.
Describing and Predicting Online Items with Reshare Cascades via Dual Mixture Self-exciting Processes

It is well-known that online behavior is long-tailed, with most cascaded actions being short and a few being very long. A prominent drawback in generative models for online events is the inability to describe unpopular items well. This work addresses these shortcomings by proposing dual mixture self-exciting processes to jointly learn from groups of cascades. We first start from the observation that maximum likelihood estimates for content virality and influence decay are separable in a Hawkes process. Next, our proposed model, which leverages a Borel mixture model and a kernel mixture model, jointly models the unfolding of a heterogeneous set of cascades. When applied to cascades of the same online items, the model directly characterizes their spread dynamics and supplies interpretable quantities, such as content virality and content influence decay, as well as methods for predicting the final content popularities. On two retweet cascade datasets --- one relating to YouTube videos and the second relating to controversial news articles --- we show that our models capture the differences between online items at the granularity of items, publishers and categories. In particular, we are able to distinguish between far-right, conspiracy, controversial and reputable online news articles based on how they diffuse through social media, achieving an F1 score of 0.945. On holdout datasets, we show that the dual mixture model provides, for reshare diffusion cascades especially unpopular ones, better generalization performance and, for online items, accurate item popularity predictions.
MARU

Information networks, such as social and citation networks, are ubiquitous in the real world so that network analysis plays an important role in data mining and knowledge discovery. To alleviate the sparsity problem of network analysis, it is common to capture the network semantics by projecting nodes onto a vector space as network embeddings. Moreover, random walks are usually exploited to efficiently learn node embeddings and preserve network proximity. In addition to proximity structure, heterogeneous networks have more knowledge about the types of nodes. However, to profit from heterogeneous knowledge, most of the existing approaches guide the random walks through predefined meta-paths or specific strategies, which can distort the understanding of network structures. Furthermore, traditional random walk-based approaches much favor the nodes with higher degrees while other nodes are equivalently important for the downstream applications. In this paper, we propose Meta-context Aware Random Walks (MARU) to overcome these challenges, thereby learning richer and more unbiased representations for heterogeneous networks. To reduce the bias in classical random walks, the algorithm of bidirectional extended random walks is introduced to improve the fairness of representation learning. Based on the enhanced random walks, the meta-context aware skip-gram model is then presented to learn robust network embeddings with dynamic meta-contexts. Therefore, MARU can not only fairly understand the overall network structures but also leverage the sophisticated heterogeneous knowledge in the networks. Extensive experiments have been conducted on three real-world large-scale publicly available datasets. The experimental results demonstrate that MARU significantly outperforms state-of-the-art heterogeneous network embedding methods across three general machine learning tasks, including multi-label node classification, node clustering, and link prediction.
Using spectral entropy and bernoulli map to handle concept drift

Data stream mining is a relevant task to extract information from large amounts of data that continuously evolve over time. In this context, learning algorithms may combine a classifier and a drift detector to identify changes in the distribution of the predictions error in order to rapidly adapt or replace the predictive model. Several proposals have been presented in the literature for the detection of concept changes based on the error rate of the predictive models. In general, the error rate distribution grounds most of the approaches based on sequential analysis and statistical process control, or by monitoring distributions using sliding windows, which assume the prediction errors are generated independently. However, empirical studies have shown that the error rate can be influenced by temporal dependence. In addition, new approaches considering dynamical system tools have been proposed for concept drift detection in unsupervised scenarios containing temporal dependencies. Motivated by these approaches, this article proposes the Spectral Entropy Drift Detector (SEDD), which is based on Spectral Entropy, Bernoulli Map and on the surrogate stability concept. Experimental results using abrupt and gradual concept drift versions of different dataset generators as well as real-world data streams, run in the Massive Online Analysis (MOA) framework, suggest that SEDD was competitive with the state-of-the-art methods, especially when considering accuracy and false alarms.
Fast-D: When Non-Smoothing Color Feature Meets Moving Object Detection in Real-Time

The moving object detection refers to the detection of physical moving objects from a video, which is applied in video surveillance, object recognition, object counting, human-computer interaction, and so on. Moreover, nowadays, real-time moving object detection is used as services in the cloud, edge, and fog computing. However, the existing methods do not meet the trade-off between accuracy and complexity. To address these issues, we present a background subtraction-based moving object detection method, called Fast-D. In this paper, we look at the `non-smoothing color feature' to make the moving object detection more robust in real-time. Each color feature is given equal significance during the classification of a pixel. Background model and threshold are initialized for each pixel. And then, the background model and threshold are updated dynamically when there are changes in the background of the video. Adaptive post-processing is considered to discard salt and pepper noise and fill holes in the detected moving object silhouettes. The evaluation of our proposed method on four complex datasets exhibits the significance.
Web Page Segmentation Revisited

Each web page can be segmented into semantically coherent units that fulfill specific purposes. Though the task of automatic web page segmentation was introduced two decades ago, along with several applications in web content analysis, its foundations are still lacking. Specifically, the developed evaluation methods and datasets presume a certain downstream task, which led to a variety of incompatible datasets and evaluation methods. To address this shortcoming, we contribute two resources: (1) An evaluation framework which can be adjusted to downstream tasks by measuring the segmentation similarity regarding visual, structural, and textual elements, and which includes measures for annotator agreement, segmentation quality, and an algorithm for segmentation fusion. (2) The Webis-WebSeg-20 dataset, comprising 42,450~crowdsourced segmentations for 8,490~web pages, outranging existing sources by an order of magnitude. Our results help to better understand the "mental segmentation model'' of human annotators: Among other things we find that annotators mostly agree on segmentations for all kinds of web page elements (visual, structural, and textual). Disagreement exists mostly regarding the right level of granularity, indicating a general agreement on the visual structure of web pages.
Mitigating Boredom Using An Empathetic Conversational Agent

In spite of their ubiquity, our interactions with contemporary conversational agents (CA), such as Alexa, are still transactional in nature and lack the expressiveness of human-human communication. Conversational agents equipped with the ability to detect and address users' emotional and cognitive states could make our interactions with them more human. In this work, we investigate whether an empathetic CA can help mitigate boredom. We design a protocol in order to first elicit boredom in users, and explore strategies that attempt to mitigate their boredom with the help of two conversational agents, an empathetic agent and a non-empathetic agent, administered in a Wizard-of-Oz setting. We quantify their efficacy by measuring the effects on user mood and task performance. Our user study with 34 participants shows that the empathetic CA outperforms the non-empathetic CA with respect to modulating users' mood and performance.