Paper Group ANR 552
Finding Salient Context based on Semantic Matching for Relevance Ranking. FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine. Distinction Graphs and Graphtropy: A Formalized Phenomenological Layer Underlying Classical and Quantum Entropy, Observational Semantics and Cognitive Computation. Three Dimensional Route Planning for Multip …
Finding Salient Context based on Semantic Matching for Relevance Ranking
| Title | Finding Salient Context based on Semantic Matching for Relevance Ranking |
| Authors | Yuanyuan Qi, Jiayue Zhang, Weiran Xu, Jun Guo |
| Abstract | In this paper, we propose a salient-context based semantic matching method to improve relevance ranking in information retrieval. We first propose a new notion of salient context and then define how to measure it. Then we show how the most salient context can be located with a sliding window technique. Finally, we use the semantic similarity between a query term and the most salient context terms in a corpus of documents to rank those documents. Experiments on various collections from TREC show the effectiveness of our model compared to the state-of-the-art methods. |
| Tasks | Information Retrieval, Semantic Similarity, Semantic Textual Similarity |
| Published | 2019-09-03 |
| URL | https://arxiv.org/abs/1909.01165v1 |
| https://arxiv.org/pdf/1909.01165v1.pdf | |
| PWC | https://paperswithcode.com/paper/finding-salient-context-based-on-semantic |
| Repo | |
| Framework | |
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
| Title | FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine |
| Authors | Junlin Zhang, Tongwen Huang, Zhiqi Zhang |
| Abstract | Click through rate (CTR) estimation is a fundamental task in personalized advertising and recommender systems. Recent years have witnessed the success of both the deep learning based model and attention mechanism in various tasks in computer vision (CV) and natural language processing (NLP). How to combine the attention mechanism with deep CTR model is a promising direction because it may ensemble the advantages of both sides. Although some CTR model such as Attentional Factorization Machine (AFM) has been proposed to model the weight of second order interaction features, we posit the evaluation of feature importance before explicit feature interaction procedure is also important for CTR prediction tasks because the model can learn to selectively highlight the informative features and suppress less useful ones if the task has many input features. In this paper, we propose a new neural CTR model named Field Attentive Deep Field-aware Factorization Machine (FAT-DeepFFM) by combining the Deep Field-aware Factorization Machine (DeepFFM) with Compose-Excitation network (CENet) field attention mechanism which is proposed by us as an enhanced version of Squeeze-Excitation network (SENet) to highlight the feature importance. We conduct extensive experiments on two real-world datasets and the experiment results show that FAT-DeepFFM achieves the best performance and obtains different improvements over the state-of-the-art methods. We also compare two kinds of attention mechanisms (attention before explicit feature interaction vs. attention after explicit feature interaction) and demonstrate that the former one outperforms the latter one significantly. |
| Tasks | Click-Through Rate Prediction, Feature Importance, Recommendation Systems |
| Published | 2019-05-15 |
| URL | https://arxiv.org/abs/1905.06336v1 |
| https://arxiv.org/pdf/1905.06336v1.pdf | |
| PWC | https://paperswithcode.com/paper/fat-deepffm-field-attentive-deep-field-aware |
| Repo | |
| Framework | |
Distinction Graphs and Graphtropy: A Formalized Phenomenological Layer Underlying Classical and Quantum Entropy, Observational Semantics and Cognitive Computation
| Title | Distinction Graphs and Graphtropy: A Formalized Phenomenological Layer Underlying Classical and Quantum Entropy, Observational Semantics and Cognitive Computation |
| Authors | Ben Goertzel |
| Abstract | A new conceptual foundation for the notion of “information” is proposed, based on the concept of a “distinction graph”: a graph in which two nodes are connected iff they cannot be distinguished by a particular observer. The “graphtropy” of a distinction graph is defined as the average connection probability of two nodes; in the case where the distinction graph is a composed of disconnected components that are fully connected subgraphs, this is equivalent to Ellerman’s logical entropy, which has straightforward relationships to Shannon entropy. Probabilistic distinction graphs and probabilistic graphtropy are also considered, as well as connections between graphtropy and thermodynamic and quantum entropy. The semantics of the Second Law of Thermodynamics and the Maximum Entropy Production Principle are unfolded in a novel way, via analysis of the cognitive processes underlying the making of distinction graphs This evokes an interpretation in which complex intelligence is seen to correspond to states of consciousness with intermediate graphtropy, which are associated with memory imperfections that violate the assumptions leading to derivation of the Second Law. In the case where nodes of a distinction graph are labeled by computable entities, graphtropy is shown to be monotonically related to the average algorithmic information of the nodes (relative to to the algorithmic information of the observer). A quantum-mechanical version of distinction graphs is considered, in which distinctions can exist in a superposed state; this yields to graphtropy as a measure of the impurity of a mixed state, and to a concept of “quangraphtropy.” Finally, a novel computational model called Dynamic Distinction Graphs (DDGs) is formulated, via enhancing distinction graphs with additional links expressing causal implications, enabling a distinction-based model of “observers.” |
| Tasks | |
| Published | 2019-02-02 |
| URL | http://arxiv.org/abs/1902.00741v1 |
| http://arxiv.org/pdf/1902.00741v1.pdf | |
| PWC | https://paperswithcode.com/paper/distinction-graphs-and-graphtropy-a |
| Repo | |
| Framework | |
Three Dimensional Route Planning for Multiple Unmanned Aerial Vehicles using Salp Swarm Algorithm
| Title | Three Dimensional Route Planning for Multiple Unmanned Aerial Vehicles using Salp Swarm Algorithm |
| Authors | Priyansh Saxena, Shivani Tayal, Raahat Gupta, Akshat Maheshwari, Gaurav Kaushal, Ritu Tiwari |
| Abstract | Route planning for multiple Unmanned Aerial Vehicles (UAVs) is a series of translation and rotational steps from a given start location to the destination goal location. The goal of the route planning problem is to determine the most optimal route avoiding any collisions with the obstacles present in the environment. Route planning is an NP-hard optimization problem. In this paper, a newly proposed Salp Swarm Algorithm (SSA) is used, and its performance is compared with deterministic and other Nature-Inspired Algorithms (NIAs). The results illustrate that SSA outperforms all the other meta-heuristic algorithms in route planning for multiple UAVs in a 3D environment. The proposed approach improves the average cost and overall time by 1.25% and 6.035% respectively when compared to recently reported data. Route planning is involved in many real-life applications like robot navigation, self-driving car, autonomous UAV for search and rescue operations in dangerous ground-zero situations, civilian surveillance, military combat and even commercial services like package delivery by drones. |
| Tasks | Robot Navigation |
| Published | 2019-11-24 |
| URL | https://arxiv.org/abs/1911.10519v2 |
| https://arxiv.org/pdf/1911.10519v2.pdf | |
| PWC | https://paperswithcode.com/paper/three-dimensional-route-planning-for-multiple |
| Repo | |
| Framework | |
Dynamic Graph Configuration with Reinforcement Learning for Connected Autonomous Vehicle Trajectories
| Title | Dynamic Graph Configuration with Reinforcement Learning for Connected Autonomous Vehicle Trajectories |
| Authors | Udesh Gunarathna, Hairuo Xie, Egemen Tanin, Shanika Karunasekara, Renata Borovica-Gajic |
| Abstract | Traditional traffic optimization solutions assume that the graph structure of road networks is static, missing opportunities for further traffic flow optimization. We are interested in optimizing traffic flows as a new type of graph-based problem, where the graph structure of a road network can adapt to traffic conditions in real time. In particular, we focus on the dynamic configuration of traffic-lane directions, which can help balance the usage of traffic lanes in opposite directions. The rise of connected autonomous vehicles offers an opportunity to apply this type of dynamic traffic optimization at a large scale. The existing techniques for optimizing lane-directions are however not suitable for dynamic traffic environments due to their high computational complexity and the static nature. In this paper, we propose an efficient traffic optimization solution, called Coordinated Learning-based Lane Allocation (CLLA), which is suitable for dynamic configuration of lane-directions. CLLA consists of a two-layer multi-agent architecture, where the bottom-layer agents use a machine learning technique to find a suitable configuration of lane-directions around individual road intersections. The lane-direction changes proposed by the learning agents are then coordinated at a higher level to reduce the negative impact of the changes on other parts of the road network. Our experimental results show that CLLA can reduce the average travel time significantly in congested road networks. We believe our method is general enough to be applied to other types of networks as well. |
| Tasks | Autonomous Vehicles |
| Published | 2019-10-14 |
| URL | https://arxiv.org/abs/1910.06788v1 |
| https://arxiv.org/pdf/1910.06788v1.pdf | |
| PWC | https://paperswithcode.com/paper/dynamic-graph-configuration-with |
| Repo | |
| Framework | |
Is change the only constant? Profile change perspective on #LokSabhaElections2019
| Title | Is change the only constant? Profile change perspective on #LokSabhaElections2019 |
| Authors | Kumari Neha, Shashank Srikanth, Sonali Singhal, Shwetanshu Singh, Arun Balaji Buduru, Ponnurangam Kumaraguru |
| Abstract | Users on Twitter are identified with the help of their profile attributes that consists of username, display name, profile image, to name a few. The profile attributes that users adopt can reflect their interests, belief, or thematic inclinations. Literature has proposed the implications and significance of profile attribute change for a random population of users. However, the use of profile attribute for endorsements and to start a movement have been under-explored. In this work, we consider #LokSabhaElections2019 as a movement and perform a large-scale study of the profile of users who actively made changes to profile attributes centered around #LokSabhaElections2019. We collect the profile metadata for 49.4M users for a period of 2 months from April 5, 2019 to June 5, 2019 amid #LokSabhaElections2019. We investigate how the profile changes vary for the influential leaders and their followers over the social movement. We further differentiate the organic and inorganic ways to show the political inclination from the prism of profile changes. We report how the addition of election campaign related keywords lead to spread of behavior contagion and further investigate it with respect to “Chowkidar Movement” in detail. |
| Tasks | |
| Published | 2019-09-22 |
| URL | https://arxiv.org/abs/1909.10012v1 |
| https://arxiv.org/pdf/1909.10012v1.pdf | |
| PWC | https://paperswithcode.com/paper/190910012 |
| Repo | |
| Framework | |
Story-oriented Image Selection and Placement
| Title | Story-oriented Image Selection and Placement |
| Authors | Sreyasi Nag Chowdhury, Simon Razniewski, Gerhard Weikum |
| Abstract | Multimodal contents have become commonplace on the Internet today, manifested as news articles, social media posts, and personal or business blog posts. Among the various kinds of media (images, videos, graphics, icons, audio) used in such multimodal stories, images are the most popular. The selection of images from a collection - either author’s personal photo album, or web repositories - and their meticulous placement within a text, builds a succinct multimodal commentary for digital consumption. In this paper we present a system that automates the process of selecting relevant images for a story and placing them at contextual paragraphs within the story for a multimodal narration. We leverage automatic object recognition, user-provided tags, and commonsense knowledge, and use an unsupervised combinatorial optimization to solve the selection and placement problems seamlessly as a single unit. |
| Tasks | Combinatorial Optimization, Object Recognition |
| Published | 2019-09-02 |
| URL | https://arxiv.org/abs/1909.00692v1 |
| https://arxiv.org/pdf/1909.00692v1.pdf | |
| PWC | https://paperswithcode.com/paper/story-oriented-image-selection-and-placement |
| Repo | |
| Framework | |
Multi-Granular Text Encoding for Self-Explaining Categorization
| Title | Multi-Granular Text Encoding for Self-Explaining Categorization |
| Authors | Zhiguo Wang, Yue Zhang, Mo Yu, Wei Zhang, Lin Pan, Linfeng Song, Kun Xu, Yousef El-Kurdi |
| Abstract | Self-explaining text categorization requires a classifier to make a prediction along with supporting evidence. A popular type of evidence is sub-sequences extracted from the input text which are sufficient for the classifier to make the prediction. In this work, we define multi-granular ngrams as basic units for explanation, and organize all ngrams into a hierarchical structure, so that shorter ngrams can be reused while computing longer ngrams. We leverage a tree-structured LSTM to learn a context-independent representation for each unit via parameter sharing. Experiments on medical disease classification show that our model is more accurate, efficient and compact than BiLSTM and CNN baselines. More importantly, our model can extract intuitive multi-granular evidence to support its predictions. |
| Tasks | Text Categorization |
| Published | 2019-07-19 |
| URL | https://arxiv.org/abs/1907.08532v1 |
| https://arxiv.org/pdf/1907.08532v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-granular-text-encoding-for-self |
| Repo | |
| Framework | |
Robot navigation and target capturing using nature-inspired approaches in a dynamic environment
| Title | Robot navigation and target capturing using nature-inspired approaches in a dynamic environment |
| Authors | Devansh Verma, Priyansh Saxena, Ritu Tiwari |
| Abstract | Path Planning and target searching in a three-dimensional environment is a challenging task in the field of robotics. It is an optimization problem as the path from source to destination has to be optimal. This paper aims to generate a collision-free trajectory in a dynamic environment. The path planning problem has sought to be of extreme importance in the military, search and rescue missions and in life-saving tasks. During its operation, the unmanned air vehicle operates in a hostile environment, and faster replanning is needed to reach the target as optimally as possible. This paper presents a novel approach of hierarchical planning using multiresolution abstract levels for faster replanning. Economic constraints like path length, total path planning time and the number of turns are taken into consideration that mandate the use of cost functions. Experimental results show that the hierarchical version of GSO gives better performance compared to the BBO, IWO and their hierarchical versions. |
| Tasks | Robot Navigation |
| Published | 2019-11-06 |
| URL | https://arxiv.org/abs/1911.02268v1 |
| https://arxiv.org/pdf/1911.02268v1.pdf | |
| PWC | https://paperswithcode.com/paper/robot-navigation-and-target-capturing-using |
| Repo | |
| Framework | |
Consensus-based Interpretable Deep Neural Networks with Application to Mortality Prediction
| Title | Consensus-based Interpretable Deep Neural Networks with Application to Mortality Prediction |
| Authors | Shaeke Salman, Seyedeh Neelufar Payrovnaziri, Xiuwen Liu, Pablo Rengifo-Moreno, Zhe He |
| Abstract | Deep neural networks have achieved remarkable success in various challenging tasks. However, the black-box nature of such networks is not acceptable to critical applications, such as healthcare. In particular, the existence of adversarial examples and their overgeneralization to irrelevant, out-of-distribution inputs with high confidence makes it difficult, if not impossible, to explain decisions by such networks. In this paper, we analyze the underlying mechanism of generalization of deep neural networks and propose an ($n$, $k$) consensus algorithm which is insensitive to adversarial examples and can reliably reject out-of-distribution samples. Furthermore, the consensus algorithm is able to improve classification accuracy by using multiple trained deep neural networks. To handle the complexity of deep neural networks, we cluster linear approximations of individual models and identify highly correlated clusters among different models to capture feature importance robustly, resulting in improved interpretability. Motivated by the importance of building accurate and interpretable prediction models for healthcare, our experimental results on an ICU dataset show the effectiveness of our algorithm in enhancing both the prediction accuracy and the interpretability of deep neural network models on one-year patient mortality prediction. In particular, while the proposed method maintains similar interpretability as conventional shallow models such as logistic regression, it improves the prediction accuracy significantly. |
| Tasks | Feature Importance, Mortality Prediction |
| Published | 2019-05-14 |
| URL | https://arxiv.org/abs/1905.05849v2 |
| https://arxiv.org/pdf/1905.05849v2.pdf | |
| PWC | https://paperswithcode.com/paper/interpretable-deep-neural-networks-for |
| Repo | |
| Framework | |
Interactive Gibson: A Benchmark for Interactive Navigation in Cluttered Environments
| Title | Interactive Gibson: A Benchmark for Interactive Navigation in Cluttered Environments |
| Authors | Fei Xia, William B. Shen, Chengshu Li, Priya Kasimbeg, Micael Tchapmi, Alexander Toshev, Roberto Martín-Martín, Silvio Savarese |
| Abstract | We present Interactive Gibson, the first comprehensive benchmark for training and evaluating Interactive Navigation: robot navigation strategies where physical interaction with objects is allowed and even encouraged to accomplish a task. For example, the robot can move objects if needed in order to clear a path leading to the goal location. Our benchmark comprises two novel elements: 1) a new experimental setup, the Interactive Gibson Environment, which simulates high fidelity visuals of indoor scenes, and high fidelity physical dynamics of the robot and common objects found in these scenes; 2) a set of Interactive Navigation metrics which allows one to study the interplay between navigation and physical interaction. We present and evaluate multiple learning-based baselines in Interactive Gibson, and provide insights into regimes of navigation with different trade-offs between navigation path efficiency and disturbance of surrounding objects. We make our benchmark publicly available(https://sites.google.com/view/interactivegibsonenv) and encourage researchers from all disciplines in robotics (e.g. planning, learning, control) to propose, evaluate, and compare their Interactive Navigation solutions in Interactive Gibson. |
| Tasks | Robot Navigation |
| Published | 2019-10-30 |
| URL | https://arxiv.org/abs/1910.14442v1 |
| https://arxiv.org/pdf/1910.14442v1.pdf | |
| PWC | https://paperswithcode.com/paper/interactive-gibson-a-benchmark-for |
| Repo | |
| Framework | |
Crop yield probability density forecasting via quantile random forest and Epanechnikov Kernel function
| Title | Crop yield probability density forecasting via quantile random forest and Epanechnikov Kernel function |
| Authors | Samuel Asante Gyamerah, Philip Ngare, Dennis Ikpe |
| Abstract | A reliable and accurate forecasting model for crop yields is of crucial importance for efficient decision-making process in the agricultural sector. However, due to weather extremes and uncertainties, most forecasting models for crop yield are not reliable and accurate. For measuring the uncertainty and obtaining further information of future crop yields, a probability density forecasting model based on quantile random forest and Epanechnikov kernel function (QRF-SJ) is proposed. The nonlinear structure of random forest is applied to change the quantile regression model for building the probabilistic forecasting model. Epanechnikov kernel function and solve-the equation plug-in approach of Sheather and Jones are used in the kernel density estimation. A case study using the annual crop yield of groundnut and millet in Ghana is presented to illustrate the efficiency and robustness of the proposed technique. The values of the prediction interval coverage probability and prediction interval normalized average width for the two crops show that the constructed prediction intervals capture the observed yields with high coverage probability. The probability density curves show that QRF-SJ method has a very high ability to forecast quality prediction intervals with a higher coverage probability. The feature importance gave a score of the importance of each weather variable in building the quantile regression forest model. The farmer and other stakeholders are able to realize the specific weather variable that affect the yield of a selected crop through feature importance. The proposed method and its application on crop yield dataset are the first of its kind in literature. |
| Tasks | Decision Making, Density Estimation, Feature Importance |
| Published | 2019-04-23 |
| URL | https://arxiv.org/abs/1904.10959v2 |
| https://arxiv.org/pdf/1904.10959v2.pdf | |
| PWC | https://paperswithcode.com/paper/crop-yield-probability-density-forecasting |
| Repo | |
| Framework | |
Fairness-Aware Process Mining
| Title | Fairness-Aware Process Mining |
| Authors | Mahnaz Sadat Qafari, Wil van der Aalst |
| Abstract | Process mining is a multi-purpose tool enabling organizations to improve their processes. One of the primary purposes of process mining is finding the root causes of performance or compliance problems in processes. The usual way of doing so is by gathering data from the process event log and other sources and then applying some data mining and machine learning techniques. However, the results of applying such techniques are not always acceptable. In many situations, this approach is prone to making obvious or unfair diagnoses and applying them may result in conclusions that are unsurprising or even discriminating (e.g., blaming overloaded employees for delays). In this paper, we present a solution to this problem by creating a fair classifier for such situations. The undesired effects are removed at the expense of reduction on the accuracy of the resulting classifier. We have implemented this method as a plug-in in ProM. Using the implemented plug-in on two real event logs, we decreased the discrimination caused by the classifier, while losing a small fraction of its accuracy. |
| Tasks | |
| Published | 2019-08-28 |
| URL | https://arxiv.org/abs/1908.11451v1 |
| https://arxiv.org/pdf/1908.11451v1.pdf | |
| PWC | https://paperswithcode.com/paper/fairness-aware-process-mining |
| Repo | |
| Framework | |
A Quantum Search Decoder for Natural Language Processing
| Title | A Quantum Search Decoder for Natural Language Processing |
| Authors | Johannes Bausch, Sathyawageeswar Subramanian, Stephen Piddock |
| Abstract | Probabilistic language models, e.g. those based on an LSTM, often face the problem of finding a high probability prediction from a sequence of random variables over a set of words. This is commonly addressed using a form of greedy decoding such as beam search, where a limited number of highest-likelihood paths (the beam width) of the decoder are kept, and at the end the maximum-likelihood path is chosen. The resulting algorithm has linear runtime in the beam width. However, the input is not necessarily distributed such that a high-likelihood input symbol at any given time step also leads to the global optimum. Limiting the beam width can thus result in a failure to recognise long-range dependencies. In practice, only an exponentially large beam width can guarantee that the global optimum is found: for an input of length $n$ and average parser branching ratio $R$, the baseline classical algorithm needs to query the input on average $R^n$ times. In this work, we construct a quantum algorithm to find the globally optimal parse with high constant success probability. Given the input to the decoder is distributed like a power-law with exponent $k>0$, our algorithm yields a runtime $R^{n f(R,k)}$, where $f\le 1/2$, and $f\rightarrow 0$ exponentially quickly for growing $k$. This implies that our algorithm always yields a super-Grover type speedup, i.e. it is more than quadratically faster than its classical counterpart. We further modify our procedure to recover a quantum beam search variant, which enables an even stronger empirical speedup, while sacrificing accuracy. Finally, we apply this quantum beam search decoder to Mozilla’s implementation of Baidu’s DeepSpeech neural net, which we show to exhibit such a power law word rank frequency, underpinning the applicability of our model. |
| Tasks | |
| Published | 2019-09-09 |
| URL | https://arxiv.org/abs/1909.05023v1 |
| https://arxiv.org/pdf/1909.05023v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-quantum-search-decoder-for-natural-language |
| Repo | |
| Framework | |
Non-myopic Planetary Exploration Combining In Situ and Remote Measurements
| Title | Non-myopic Planetary Exploration Combining In Situ and Remote Measurements |
| Authors | Suhit Kodgule, Alberto Candela, David Wettergreen |
| Abstract | Remote sensing can provide crucial information for planetary rovers. However, they must validate these orbital observations with in situ measurements. Typically, this involves validating hyperspectral data using a spectrometer on-board the field robot. In order to achieve this, the robot must visit sampling locations that jointly improve a model of the environment while satisfying sampling constraints. However, current planners follow sub-optimal greedy strategies that are not scalable to larger regions. We demonstrate how the problem can be effectively defined in an MDP framework and propose a planning algorithm based on Monte Carlo Tree Search, which is devoid of the common drawbacks of existing planners and also provides superior performance. We evaluate our approach using hyperspectral imagery of a well-studied geologic site in Cuprite, Nevada. |
| Tasks | |
| Published | 2019-04-28 |
| URL | http://arxiv.org/abs/1904.12255v1 |
| http://arxiv.org/pdf/1904.12255v1.pdf | |
| PWC | https://paperswithcode.com/paper/non-myopic-planetary-exploration-combining-in |
| Repo | |
| Framework | |
