pwc-archive/papers-with-abstracts huggingface dataset:
New mechanism of combination crossover operators in genetic algorithm for solving the traveling salesman problem
Authors: Pham Dinh Thanh, Huynh Thi Thanh Binh, Bui Thu Lam
Date: 2020-01-14 00:00:00
Tasks: Traveling Salesman Problem

Traveling salesman problem (TSP) is a well-known in computing field. There are many researches to improve the genetic algorithm for solving TSP. In this paper, we propose two new crossover operators and new mechanism of combination crossover operators in genetic algorithm for solving TSP. We experimented on TSP instances from TSP-Lib and compared the results of proposed algorithm with genetic algorithm (GA), which used MSCX. Experimental results show that, our proposed algorithm is better than the GA using MSCX on the min, mean cost values.
Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift
Authors: Alexandre Dey, Marc Velay, Jean-Philippe Fauvelle, Sylvain Navers
Date: 2020-01-13 00:00:00
Tasks: Active Learning, Anomaly Detection

Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security consisting in the detection of hostile action based on the unusual nature of events observed on the Information System.In our previous work (presented at C\&ESAR 2018 and FIC 2019), we have associated deep neural networks auto-encoders for anomaly detection and graph-based events correlation to address major limitations in UEBA systems. This resulted in reduced false positive and false negative rates, improved alert explainability, while maintaining real-time performances and scalability. However, we did not address the natural evolution of behaviours through time, also known as concept drift. To maintain effective detection capabilities, an anomaly-based detection system must be continually trained, which opens a door to an adversary that can conduct the so-called "frog-boiling" attack by progressively distilling unnoticed attack traces inside the behavioural models until the complete attack is considered normal. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise. We also present preliminary work on adversarial AI conducting deception attack, which, in term, will be used to help assess and improve the defense system. These defensive and offensive AI implement joint, continual and active learning, in a step that is necessary in assessing, validating and certifying AI-based defensive solutions.
Cat Swarm Optimization Algorithm -- A Survey and Performance Evaluation
Authors: Aram M. Ahmed, Tarik A. Rashid, Soran Ab. M. Saeed
Date: 2020-01-10 00:00:00
Tasks: Survey

This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.
Unwanted Advances in Higher Education: Uncovering Sexual Harassment Experiences in Academia with Text Mining
Authors: Amir Karami, Cynthia Nicole White, Kayla Ford, Suzanne Swan, Melek Yildiz Spinel
Date: 2019-12-11 00:00:00

Sexual harassment in academia is often a hidden problem because victims are usually reluctant to report their experiences. Recently, a web survey was developed to provide an opportunity to share thousands of sexual harassment experiences in academia. Using an efficient approach, this study collected and investigated more than 2,000 sexual harassment experiences to better understand these unwanted advances in higher education. This paper utilized text mining to disclose hidden topics and explore their weight across three variables: harasser gender, institution type, and victim's field of study. We mapped the topics on five themes drawn from the sexual harassment literature and found that more than 50% of the topics were assigned to the unwanted sexual attention theme. Fourteen percent of the topics were in the gender harassment theme, in which insulting, sexist, or degrading comments or behavior was directed towards women. Five percent of the topics involved sexual coercion (a benefit is offered in exchange for sexual favors), 5% involved sex discrimination, and 7% of the topics discussed retaliation against the victim for reporting the harassment, or for simply not complying with the harasser. Findings highlight the power differential between faculty and students, and the toll on students when professors abuse their power. While some topics did differ based on type of institution, there were no differences between the topics based on gender of harasser or field of study. This research can be beneficial to researchers in further investigation of this paper's dataset, and to policymakers in improving existing policies to create a safe and supportive environment in academia.
a high efficiency fully convolutional networks for pixel wise surface defect detection
Authors: Lingteng Qiu, XiaoJun Wu, Zhiyang Yu
Date: 2019-01-23 00:00:00
Proceeding: journal 2019 1
Tasks: Defect Detection, Image Matting, Segmentation

In this paper, we propose a highly efficient deep learning-based method for pixel-wise surface defect segmentation algorithm in machine vision. Our method is composed of a segmentation stage (stage 1), a detection stage (stage 2), and a matting stage (stage 3). In the segmentation stage, a lightweight fully convolutional network (FCN) is employed to make a pixel-wise prediction of the defect areas. Those predicted defect areas act as the initialization of stage 2, guiding the process of detection to correct the improper segmentation. In the matting stage, a guided filter is utilized to refine the contour of the defect area to reflect the real abnormal region. Besides that, aiming to achieve the tradeoff between efficiency and accuracy, and simultaneously we use depthwise & pointwise convolution layer, strided depthwise convolution layer, and upsample depthwise convolution layer to replace the standard convolution layer, pooling layer, and deconvolution layer, respectively. We validate our findings by analyzing the performance obtained on the dataset of DAGM 2007
A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory
Authors: Yanlin Qi a, Qi Lia, Hamed Karimiana, Di Liub
Date: 2019-02-01 00:00:00
Proceeding: Science for the Total environment 2019 2

Increasing availability of data related to air quality from ground monitoring stations has provided the chance
for data mining researchers to propose sophisticated models for predicting the concentrations of different
air pollutants. In this paper, we proposed a hybrid model based on deep learning methods that integrates
Graph Convolutional networks and Long Short-Term Memory networks (GC-LSTM) to model and forecast
the spatiotemporal variation of PM2.5 concentrations. Specifically, historical observations on different stations are constructed as spatiotemporal graph series, and historical air quality variables, meteorological
factors, spatial terms and temporal attributes are defined as graph signals. To evaluate the performance
of the GC-LSTM, we compared our results with several state-of-the-art methods in different time intervals. Based on the results, our GC-LSTM model achieved the best performance for predictions. Moreover,
evaluations of recall rate (68.45%), false alarm rate (4.65%) (both of threshold: 115 lg/m3) and correlation coefficient R2 (0.72) for 72-hour predictions also verify the feasibility of our proposed model. This
methodology can be used for concentration forecasting of different air pollutants in future.
Defining a Sandbox for Responsible AI
Authors: Shaun C. D'Souza, Bhavin Mehta
Date: 2018-09-25 00:00:00
Proceeding: SSRN 2018 9
Tasks: Ethics

Technology evolution present a unique set of challenges around the governance and ethics of the technology. These questions have dodged human civilization since the invention of the wheel. With every iteration of our journey we have to reevaluate our role in the larger planet. The human is a social animal. Technology is designed to augment our human lives and on occasion despite our best efforts can find its place in the wrong hands. This is no more real today with the advent of Artificial intelligence where we have given machines a cognition enabling them to function in more human centric roles. While this is not the first time that we have faced this future, it is important to take cognizance of the broader issues around the ethics and governance that are prevalent today in what are early days in an industry with a smaller number of large technology players. Like all our predecessor technologies through to today yet again we are dealing with a mortal creation that comes with an off switch and lesser divine origins. The human mind continues to question the singularity and what is to be the theory of everything. In this paper we explore some of the macro nuances centered around human actors.
Novel Entity Discovery from Web Tables
Authors: Shuo Zhang, Edgar Meij, Krisztian Balog, Ridho Reinanda
Date: 2020-02-01 00:00:00

When working with any sort of knowledge base (KB) one has to make sure it is as complete and also as up-to-date as possible. Both tasks are non-trivial as they require recall-oriented efforts to determine which entities and relationships are missing from the KB. As such they require a significant amount of labor. Tables on the Web, on the other hand, are abundant and have the distinct potential to assist with these tasks. In particular, we can leverage the content in such tables to discover new entities, properties, and relationships. Because web tables typically only contain raw textual content we first need to determine which cells refer to which known entities---a task we dub table-to-KB matching. This first task aims to infer table semantics by linking table cells and heading columns to elements of a KB. Then second task builds upon these linked entities and properties to not only identify novel ones in the same table but also to bootstrap their type and additional relationships. We refer to this process as novel entity discovery and, to the best of our knowledge, it is the first endeavor on mining the unlinked cells in web tables. Our method identifies not only out-of-KB (``novel'') information but also novel aliases for in-KB (``known'') entities. When evaluated using three purpose-built test collections, we find that our proposed approaches obtain a marked improvement in terms of precision over our baselines whilst keeping recall stable.
Web Table Extraction, Retrieval and Augmentation: A Survey
Authors: Shuo Zhang, Krisztian Balog
Date: 2020-02-01 00:00:00
Tasks: Question Answering, Retrieval, Survey, Table Extraction, Table Search

Tables are a powerful and popular tool for organizing and manipulating data. A vast number of tables can be found on the Web, which represents a valuable knowledge resource. The objective of this survey is to synthesize and present two decades of research on web tables. In particular, we organize existing literature into six main categories of information access tasks: table extraction, table interpretation, table search, question answering, knowledge base augmentation, and table augmentation. For each of these tasks, we identify and describe seminal approaches, present relevant resources, and point out interdependencies among the different tasks.
Safe Exploration for Optimizing Contextual Bandits
Authors: Rolf Jagerman, Ilya Markov, Maarten de Rijke
Date: 2020-02-02 00:00:00
Tasks: counterfactual, Information Retrieval, Learning-To-Rank, Multi-Armed Bandits, Off-policy evaluation, Retrieval, Safe Exploration, text-classification, Text Classification

Contextual bandit problems are a natural fit for many information retrieval tasks, such as learning to rank, text classification, recommendation, etc. However, existing learning methods for contextual bandit problems have one of two drawbacks: they either do not explore the space of all possible document rankings (i.e., actions) and, thus, may miss the optimal ranking, or they present suboptimal rankings to a user and, thus, may harm the user experience. We introduce a new learning method for contextual bandit problems, Safe Exploration Algorithm (SEA), which overcomes the above drawbacks. SEA starts by using a baseline (or production) ranking system (i.e., policy), which does not harm the user experience and, thus, is safe to execute, but has suboptimal performance and, thus, needs to be improved. Then SEA uses counterfactual learning to learn a new policy based on the behavior of the baseline policy. SEA also uses high-confidence off-policy evaluation to estimate the performance of the newly learned policy. Once the performance of the newly learned policy is at least as good as the performance of the baseline policy, SEA starts using the new policy to execute new actions, allowing it to actively explore favorable regions of the action space. This way, SEA never performs worse than the baseline policy and, thus, does not harm the user experience, while still exploring the action space and, thus, being able to find an optimal policy. Our experiments using text classification and document retrieval confirm the above by comparing SEA (and a boundless variant called BSEA) to online and offline learning methods for contextual bandit problems.
IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems
Authors: Liu Yang, Minghui Qiu, Chen Qu, Cen Chen, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Haiqing Chen
Date: 2020-02-03 00:00:00
Tasks: Representation Learning

Personal assistant systems, such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana, are becoming ever more widely used. Understanding user intent such as clarification questions, potential answers and user feedback in information-seeking conversations is critical for retrieving good responses. In this paper, we analyze user intent patterns in information-seeking conversations and propose an intent-aware neural response ranking model "IART", which refers to "Intent-Aware Ranking with Transformers". IART is built on top of the integration of user intent modeling and language representation learning with the Transformer architecture, which relies entirely on a self-attention mechanism instead of recurrent nets. It incorporates intent-aware utterance attention to derive an importance weighting scheme of utterances in conversation context with the aim of better conversation history understanding. We conduct extensive experiments with three information-seeking conversation data sets including both standard benchmarks and commercial data. Our proposed model outperforms all baseline methods with respect to a variety of metrics. We also perform case studies and analysis of learned user intent and its impact on response ranking in information-seeking conversations to provide interpretation of results.
Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation
Authors: Jibang Wu, Renqin Cai, Hongning Wang
Date: 2020-01-29 00:00:00
Tasks: Recommendation Systems, Sequential Recommendation

Predicting users' preferences based on their sequential behaviors in history is challenging and crucial for modern recommender systems. Most existing sequential recommendation algorithms focus on transitional structure among the sequential actions, but largely ignore the temporal and context information, when modeling the influence of a historical event to current prediction. In this paper, we argue that the influence from the past events on a user's current action should vary over the course of time and under different context. Thus, we propose a Contextualized Temporal Attention Mechanism that learns to weigh historical actions' influence on not only what action it is, but also when and how the action took place. More specifically, to dynamically calibrate the relative input dependence from the self-attention mechanism, we deploy multiple parameterized kernel functions to learn various temporal dynamics, and then use the context information to determine which of these reweighing kernels to follow for each input. In empirical evaluations on two large public recommendation datasets, our model consistently outperformed an extensive set of state-of-the-art sequential recommendation methods.
Multi-Modal Music Information Retrieval: Augmenting Audio-Analysis with Visual Computing for Improved Music Video Analysis
Authors: Alexander Schindler
Date: 2020-02-01 00:00:00
Tasks: Classification, General Classification, Genre classification, Information Retrieval, Music Genre Classification, Music Information Retrieval, Retrieval

This thesis combines audio-analysis with computer vision to approach Music Information Retrieval (MIR) tasks from a multi-modal perspective. This thesis focuses on the information provided by the visual layer of music videos and how it can be harnessed to augment and improve tasks of the MIR research domain. The main hypothesis of this work is based on the observation that certain expressive categories such as genre or theme can be recognized on the basis of the visual content alone, without the sound being heard. This leads to the hypothesis that there exists a visual language that is used to express mood or genre. In a further consequence it can be concluded that this visual information is music related and thus should be beneficial for the corresponding MIR tasks such as music genre classification or mood recognition. A series of comprehensive experiments and evaluations are conducted which are focused on the extraction of visual information and its application in different MIR tasks. A custom dataset is created, suitable to develop and test visual features which are able to represent music related information. Evaluations range from low-level visual features to high-level concepts retrieved by means of Deep Convolutional Neural Networks. Additionally, new visual features are introduced capturing rhythmic visual patterns. In all of these experiments the audio-based results serve as benchmark for the visual and audio-visual approaches. The experiments are conducted for three MIR tasks Artist Identification, Music Genre Classification and Cross-Genre Classification. Experiments show that an audio-visual approach harnessing high-level semantic information gained from visual concept detection, outperforms audio-only genre-classification accuracy by 16.43%.
Analysis of the quotation corpus of the Russian Wiktionary
Authors: A. Smirnov, T. Levashova, A. Karpov, I. Kipyatkova, A. Ronzhin, A. Krizhanovsky, N. Krizhanovsky
Date: 2020-01-20 00:00:00

The quantitative evaluation of quotations in the Russian Wiktionary was performed using the developed Wiktionary parser. It was found that the number of quotations in the dictionary is growing fast (51.5 thousands in 2011, 62 thousands in 2012). These quotations were extracted and saved in the relational database of a machine-readable dictionary. For this database, tables related to the quotations were designed. A histogram of distribution of quotations of literary works written in different years was built. It was made an attempt to explain the characteristics of the histogram by associating it with the years of the most popular and cited (in the Russian Wiktionary) writers of the nineteenth century. It was found that more than one-third of all the quotations (the example sentences) contained in the Russian Wiktionary are taken by the editors of a Wiktionary entry from the Russian National Corpus.
Conversations with Documents. An Exploration of Document-Centered Assistance
Authors: Maartje ter Hoeve, Robert Sim, Elnaz Nouri, Adam Fourney, Maarten de Rijke, Ryen W. White
Date: 2020-01-27 00:00:00

The role of conversational assistants has become more prevalent in helping people increase their productivity. Document-centered assistance, for example to help an individual quickly review a document, has seen less significant progress, even though it has the potential to tremendously increase a user's productivity. This type of document-centered assistance is the focus of this paper. Our contributions are three-fold: (1) We first present a survey to understand the space of document-centered assistance and the capabilities people expect in this scenario. (2) We investigate the types of queries that users will pose while seeking assistance with documents, and show that document-centered questions form the majority of these queries. (3) We present a set of initial machine learned models that show that (a) we can accurately detect document-centered questions, and (b) we can build reasonably accurate models for answering such questions. These positive results are encouraging, and suggest that even greater results may be attained with continued study of this interesting and novel problem space. Our findings have implications for the design of intelligent systems to support task completion via natural interactions with documents.
Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover's Distance
Authors: Ahmed El-Kishky, Francisco Guzmán
Date: 2020-01-31 00:00:00
Proceeding: Asian Chapter of the Association for Computational Linguistics 2020
Tasks: Machine Translation, Sentence, Sentence Embeddings, Translation

Document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. Such aligned data can be used for a variety of NLP tasks from training cross-lingual representations to mining parallel data for machine translation. In this paper we develop an unsupervised scoring function that leverages cross-lingual sentence embeddings to compute the semantic distance between documents in different languages. These semantic distances are then used to guide a document alignment algorithm to properly pair cross-lingual web documents across a variety of low, mid, and high-resource language pairs. Recognizing that our proposed scoring function and other state of the art methods are computationally intractable for long web documents, we utilize a more tractable greedy algorithm that performs comparably. We experimentally demonstrate that our distance metric performs better alignment than current baselines outperforming them by 7% on high-resource language pairs, 15% on mid-resource language pairs, and 22% on low-resource language pairs.