pwc-archive/papers-with-abstracts huggingface dataset:
MOEF: Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate Prediction
Authors: Xiaofeng Pan, Yibin Shen, Jing Zhang, Xu He, Yang Huang, Hong Wen, Chengjun Mao, Bo Cao
Date: 2021-12-27 00:00:00
Tasks: Click-Through Rate Prediction, Recommendation Systems, Time Series, Time Series Analysis, Time Series Prediction

Promotions are becoming more important and prevalent in e-commerce to attract customers and boost sales, leading to frequent changes of occasions, which drives users to behave differently. In such situations, most existing Click-Through Rate (CTR) models can't generalize well to online serving due to distribution uncertainty of the upcoming occasion. In this paper, we propose a novel CTR model named MOEF for recommendations under frequent changes of occasions. Firstly, we design a time series that consists of occasion signals generated from the online business scenario. Since occasion signals are more discriminative in the frequency domain, we apply Fourier Transformation to sliding time windows upon the time series, obtaining a sequence of frequency spectrum which is then processed by Occasion Evolution Layer (OEL). In this way, a high-order occasion representation can be learned to handle the online distribution uncertainty. Moreover, we adopt multiple experts to learn feature representations from multiple aspects, which are guided by the occasion representation via an attention mechanism. Accordingly, a mixture of feature representations is obtained adaptively for different occasions to predict the final CTR. Experimental results on real-world datasets validate the superiority of MOEF and online A/B tests also show MOEF outperforms representative CTR models significantly.
Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement
Authors: Fuwei Zhang, Zhao Zhang, Xiang Ao, Dehong Gao, Fuzhen Zhuang, Yi Wei, Qing He
Date: 2021-12-27 00:00:00
Tasks: Cross-Lingual Information Retrieval, Information Retrieval, Retrieval

Cross-Lingual Information Retrieval (CLIR) aims to rank the documents written in a language different from the user's query. The intrinsic gap between different languages is an essential challenge for CLIR. In this paper, we introduce the multilingual knowledge graph (KG) to the CLIR task due to the sufficient information of entities in multiple languages. It is regarded as a "silver bullet" to simultaneously perform explicit alignment between queries and documents and also broaden the representations of queries. And we propose a model named CLIR with hierarchical knowledge enhancement (HIKE) for our task. The proposed model encodes the textual information in queries, documents and the KG with multilingual BERT, and incorporates the KG information in the query-document matching process with a hierarchical information fusion mechanism. Particularly, HIKE first integrates the entities and their neighborhood in KG into query representations with a knowledge-level fusion, then combines the knowledge from both source and target languages to further mitigate the linguistic gap with a language-level fusion. Finally, experimental results demonstrate that HIKE achieves substantial improvements over state-of-the-art competitors.
Automatic extraction of requirements expressed in industrial standards : a way towards machine readable standards ?
Authors: Helene de Ribaupierre, Anne-Francoise Cutting-Decelle, Nathalie Baumier, Serge Blumental
Date: 2021-12-24 00:00:00
Tasks: Sentence, Specificity

The project, under industrial funding, presented in this publication aims at the semantic analysis of a normative document describing requirements applicable to electrical appliances. The objective of the project is to build a semantic approach to extract and automatically process information related to the requirements contained in the standard. To this end, the project has been divided into three parts, covering the analysis of the requirements document, the extraction of relevant information and creation of the ontology and the comparison with other approaches. The first part of our work deals with the analysis of the requirements document under study. The study focuses on the specificity of the sentence structure, the use of particular words and vocabulary related to the representation of the requirements. The aim is to propose a representation facilitating the extraction of information, used in the second part of the study. In the second part, the extraction of relevant information is conducted in two ways: manual (the ontology being built by hand), semi-automatic (using semantic annotation software and natural language processing techniques). Whatever the method used, the aim of this extraction is to create the concept dictionary, then the ontology, enriched as the document is scanned and understood by the system. Once the relevant terms have been identified, the work focuses on identifying and representing the requirements, separating the textual writing from the information given in the tables. The automatic processing of requirements involves the extraction of sentences containing terms identified as relevant to a requirement. The identified requirement is then indexed and stored in a representation that can be used for query processing.
Data Driven Safe Gain-Scheduling Control
Authors: Amir Modares, Nasser Sadati, Hamidreza Modares
Date: 2022-01-03 00:00:00
Tasks: Scheduling

Data-based safe gain-scheduling controllers are presented for discrete-time linear parameter-varying systems (LPV) with polytopic models. First, $\lambda$-contractivity conditions are provided under which safety and stability of the LPV systems are unified through Minkowski functions of the safe sets. Then, to bypass the requirement to identify the system dynamics, a data-based representation of the closed-loop LPV system is provided to directly exploit collected data and construct a safe controller. It is shown that weaker data richness requirements are needed to directly learn a closed-loop safe control policy than to identify the LPV system. The closed-loop data-based representation is leveraged to directly design data-driven gain-scheduling controllers that guarantee $\lambda$-contractiveness, and, thus, invariance of the safe sets. It is also shown that the problem of designing a data-driven gain-scheduling controller for a polyhedral (ellipsoidal) safe set amounts to a linear program (a semi-definite program). A simulation example is provided to show the effectiveness of the presented approach.
Biased or Not?: The Story of Two Search Engines
Authors: Gizem Gezici
Date: 2021-12-23 00:00:00
Tasks: Vocal Bursts Valence Prediction

Search engines can be considered as a gate to the world of WEB, and they also decide what we see for a given search query. Since many people are exposed to information through search engines, it is fair to expect that search engines should be neutral; i.e. the returned results must cover all the elements or aspects of the search topic, and they should be impartial where the results are returned based on relevance. However, the search engine results are based on many features and sophisticated algorithms where search neutrality is not necessarily the focal point. In this work we performed an empirical study on two popular search engines and analysed the search engine result pages for controversial topics such as abortion, medical marijuana, and gay marriage. Our analysis is based on the sentiment in search results to identify their viewpoint as conservative or liberal. We also propose three sentiment-based metrics to show the existence of bias as well as to compare viewpoints of the two search engines. Extensive experiments performed on controversial topics show that both search engines are biased, moreover they have the same kind of bias towards a given controversial topic.
Customising Ranking Models for Enterprise Search on Bilingual Click-Through Dataset
Authors: Gizem Gezici
Date: 2021-12-23 00:00:00

In this work, we provide the details about the process of establishing an end-to-end system for enterprise search on bilingual click-through dataset. The first part of the paper will be about the high-level workflow of the system. Then, in the second part we will elaborately mention about the ranking models to improve the search results in the vertical search of the technical documents in enterprise domain. Throughout the paper, we will mention the way which we combine the methods in IR literature. Finally, in the last part of the paper we will report our results using different ranking algorithms with $NDCG@k$ where k is the cut-off value.
Movie Recommender System using critic consensus
Authors: A Nayan Varma, Kedareshwara Petluri
Date: 2021-12-22 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Recommendation systems are perhaps one of the most important agents for industry growth through the modern Internet world. Previous approaches on recommendation systems include collaborative filtering and content based filtering recommendation systems. These 2 methods are disjointed in nature and require the continuous storage of user preferences for a better recommendation. To provide better integration of the two processes, we propose a hybrid recommendation system based on the integration of collaborative and content-based content, taking into account the top critic consensus and movie rating score. We would like to present a novel model that recommends movies based on the combination of user preferences and critical consensus scores.
H$_2$ Optimal Model Order Reduction over a Finite Time Interval
Authors: Kasturi Das, Srinivasan Krishnaswamy, Somanath Majhi
Date: 2022-01-03 00:00:00

For a time-limited version of the H$_2$ norm defined over a fixed time interval, we obtain a closed form expression of the gradients. After that, we use the gradients to propose a time-limited model order reduction method. The method involves obtaining a reduced model which minimizes the time-limited H$_2$ norm, formulated as a nonlinear optimization problem. The optimization problem is solved using standard optimization software.
Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation
Authors: Yiming Zhang, Lingfei Wu, Qi Shen, Yitong Pang, Zhihua Wei, Fangli Xu, Bo Long, Jian Pei
Date: 2021-12-22 00:00:00
Tasks: Attribute, Conversational Recommendation, Multiple-choice

Conversational recommendation system (CRS) is able to obtain fine-grained and dynamic user preferences based on interactive dialogue. Previous CRS assumes that the user has a clear target item. However, for many users who resort to CRS, they might not have a clear idea about what they really like. Specifically, the user may have a clear single preference for some attribute types (e.g. color) of items, while for other attribute types, the user may have multiple preferences or even no clear preferences, which leads to multiple acceptable attribute instances (e.g. black and red) of one attribute type. Therefore, the users could show their preferences over items under multiple combinations of attribute instances rather than a single item with unique combination of all attribute instances. As a result, we first propose a more realistic CRS learning setting, namely Multi-Interest Multi-round Conversational Recommendation, where users may have multiple interests in attribute instance combinations and accept multiple items with partially overlapped combinations of attribute instances. To effectively cope with the new CRS learning setting, in this paper, we propose a novel learning framework namely, Multi-Choice questions based Multi-Interest Policy Learning . In order to obtain user preferences more efficiently, the agent generates multi-choice questions rather than binary yes/no ones on specific attribute instance. Besides, we propose a union set strategy to select candidate items instead of existing intersection set strategy in order to overcome over-filtering items during the conversation. Finally, we design a Multi-Interest Policy Learning module, which utilizes captured multiple interests of the user to decide next action, either asking attribute instances or recommending items. Extensive experimental results on four datasets verify the superiority of our method for the proposed setting.
Simulating the gravity in S11 parameters and friis transmission equation
Authors: Sunit Shantanu Digamber Fulari
Date: 2022-01-03 00:00:00
Tasks: object-detection, Object Detection, Optical Flow Estimation

Antennas are taking design shapes by the orientation of its material and the final structural design they take. Irrespective of the shape, the function and efficiency they produce does not change and vary to much extent. We are trying to simulate a design of antenna which is using the artificial intelligence model to Specific absorption rate minimization. We have successfully used antenna magus and CST suite to produce our improved simulation results. Motion models such as optical flow is used to smartly detect communicating devices inorder to connect and link the devices. The algorithm used by us is closest neighbour algorithm which is connected with antenna to apply it in object detection of mobile devices and antennas. We are trying to have a six second break in which the antenna will operate at minimum frequency but at the same time operating in its functioning. After every six seconds the antenna will radiate its frequency of vibration to detect if there is any new object or mobile device in sight.
Ant colonization processed algorithm for design of a toroidal shaped mobile 5G antenna
Authors: Sunit Shantanu Digamber Fulari
Date: 2022-01-03 00:00:00

There is great potential if we understand how nature functions, particularly the animals taking down from the ant to the larger animals. In this paper we will make an attempt to learn about ants colonization processing by studying their behaviour. Earlier there was particle swarm optimization which helped to solve many scientific problems. Ants communication with each other, their peculiar behaviour of working, colonization, their movements from one place to another, there is large potential in understanding their entire life to design a better 5G antenna.
An Efficient Pruning Process with Locality Aware Exploration and Dynamic Graph Editing for Subgraph Matching
Authors: Zite Jiang, Boxiao Liu, Shuai Zhang, Xingzhong Hou, Mengting Yuan, Haihang You
Date: 2021-12-22 00:00:00

Subgraph matching is a NP-complete problem that extracts isomorphic embeddings of a query graph $q$ in a data graph $G$. In this paper, we present a framework with three components: Preprocessing, Reordering and Enumeration. While pruning is the core technique for almost all existing subgraph matching solvers, it mainly eliminates unnecessary enumeration over data graph without alternation of query graph. By formulating a problem: Assignment under Conditional Candidate Set(ACCS), which is proven to be equivalent to Subgraph matching problem, we propose Dynamic Graph Editing(DGE) that is for the first time designed to tailor the query graph to achieve pruning effect and performance acceleration. As a result, we proposed DGEE(Dynamic Graph Editing Enumeration), a novel enumeration algorithm combines Dynamic Graph Editing and Failing Set optimization. Our second contribution is proposing fGQL , an optimized version of GQL algorithm, that is utilized during the Preprocessing phase. Extensive experimental results show that the DGEE-based framework can outperform state-of-the-art subgraph matching algorithms.
Sum-of-Squares Program and Safe Learning On Maximizing the Region of Attraction of Partially Unknown Systems
Authors: Dongkun Han, Hejun Huang
Date: 2022-01-01 00:00:00
Tasks: Gaussian Processes

Recent advances in learning techniques have enabled the modelling of unknown dynamical systems directly from data. However, in many contexts, these learning-based methods are short of safety guarantee and strict stability verification. To address this issue, this paper first approximates the partially unknown nonlinear systems by using a learned state space with Gaussian Processes and Chebyshev interpolants. A Sum-of-Squares Programming based approach is then proposed to synthesize a controller by searching an optimal control Lyapunov Barrier function. In this way, we maximize the estimated region of attraction of partially unknown nonlinear systems, while guaranteeing both safety and stability. It is shown that the proposed method improves the extrapolation performance, and at the same time, generates a significantly larger estimated region of attraction.
Stealth Data Injection Attacks with Sparsity Constraints
Authors: Xiuzhen Ye, Iñaki Esnaola, Samir M. Perlaza, Robert F. Harrison
Date: 2021-12-31 00:00:00

Sparse stealth attack constructions that minimize the mutual information between the state variables and the observations are proposed. The attack construction is formulated as the design of a multivariate Gaussian distribution that aims to minimize the mutual information while limiting the Kullback-Leibler divergence between the distribution of the observations under attack and the distribution of the observations without attack. The sparsity constraint is incorporated as a support constraint of the attack distribution. Two heuristic greedy algorithms for the attack construction are proposed. The first algorithm assumes that the attack vector consists of independent entries, and therefore, requires no communication between different attacked locations. The second algorithm considers correlation between the attack vector entries which results in better attack performance at the expense of coordination between different locations. We numerically evaluate the performance of the proposed attack constructions on IEEE test systems and show that it is feasible to construct stealth attacks that generate significant disruption with a low number of compromised sensors.
Validation and Transparency in AI systems for pharmacovigilance: a case study applied to the medical literature monitoring of adverse events
Authors: Bruno Ohana, Jack Sullivan, Nicole Baker
Date: 2021-12-21 00:00:00
Tasks: Articles, Pharmacovigilance

Recent advances in artificial intelligence applied to biomedical text are opening exciting opportunities for improving pharmacovigilance activities currently burdened by the ever growing volumes of real world data. To fully realize these opportunities, existing regulatory guidance and industry best practices should be taken into consideration in order to increase the overall trustworthiness of the system and enable broader adoption. In this paper we present a case study on how to operationalize existing guidance for validated AI systems in pharmacovigilance focusing on the specific task of medical literature monitoring (MLM) of adverse events from the scientific literature. We describe an AI system designed with the goal of reducing effort in MLM activities built in close collaboration with subject matter experts and considering guidance for validated systems in pharmacovigilance and AI transparency. In particular we make use of public disclosures as a useful risk control measure to mitigate system misuse and earn user trust. In addition we present experimental results showing the system can significantly remove screening effort while maintaining high levels of recall (filtering 55% of irrelevant articles on average, for a target recall of 0.99 on suspected adverse articles) and provide a robust method for tuning the desired recall to suit a particular risk profile.
Coupled Tank Non-linear System; Modeling and Level Control using PID and Fuzzy Logic Techniques
Authors: Akram Muntaser, Nagi Buaossa
Date: 2021-12-31 00:00:00

Liquid level control is very important in industrial field, where the liquid level is required, and to prevent overflows. The coupled-tank is a common system in industrial control processes. The system consists of two tanks connected together and the liquid flows between them. Tanks contain an inlet and outlet for each tank. The main principle of controlling this system is to maintain a constant level of liquid in both tanks when there are an inflow and outflow of liquid in each tank. To control liquid level in the coupled tank system, the mathematical model of the system had been derived and evaluated as a form of linear model. The mathematical model of coupled tank was developed to apply to both conventional and fuzzy control systems where the dynamic behavior of the system was considered. When the system had been designed the corresponding model was implemented in simulation by using Matlab and Simulink tools.
An Off-grid Compressive Sensing Strategy for the Subarray Synthesis of Non-uniform Linear Arrays
Authors: Songjie Yang, Wanting Lyu, Zhongpei Zhang
Date: 2021-12-31 00:00:00
Tasks: Compressive Sensing

With the increasing popularity of large-scale antenna arrays, the subarraying technology becomes more attractive. In this paper, we propose two effective subarraying methods right after formulating the subarray synthesis as a compressive sensing (CS) problem: i) Orthogonal matching pursuit based subarray synthesis (OMP-SS), a common CS approach which can be used for the subarray synthesis to attain the subarray information (the subarray number, the number of elements per subarray and corresponding excitation coeffcients) and ii) Off-grid orthogonal matching pursuit based subarray synthesis (OGOMP-SS), an advanced approach for optimizing antenna elements positions and the subarray information mentioned above simultaneously. In addition, two user-defined modes are designed for different application scenarios, wherein, mode-1 is to optimize the pattern synthesis performance for the given the number of subarrays, and mode-2 is to obtain the minimum number of subarrays for the cases when the pattern synthsis accuracy is satisfied. Finally, our simulation results reveal that it is of paramount significance to optimize antenna elements positions for the subarray synthesis performance on the one hand and demonstrate the excellent performances of proposed schemes in comparison with other competitive state-of-the-art subarray synthesis methods on the other hand.
Understanding and Mitigating the Effect of Outliers in Fair Ranking
Authors: Fatemeh Sarvi, Maria Heuss, Mohammad Aliannejadi, Sebastian Schelter, Maarten de Rijke
Date: 2021-12-21 00:00:00
Tasks: Fairness, Outlier Detection, Position

Traditional ranking systems are expected to sort items in the order of their relevance and thereby maximize their utility. In fair ranking, utility is complemented with fairness as an optimization goal. Recent work on fair ranking focuses on developing algorithms to optimize for fairness, given position-based exposure. In contrast, we identify the potential of outliers in a ranking to influence exposure and thereby negatively impact fairness. An outlier in a list of items can alter the examination probabilities, which can lead to different distributions of attention, compared to position-based exposure. We formalize outlierness in a ranking, show that outliers are present in realistic datasets, and present the results of an eye-tracking study, showing that users scanning order and the exposure of items are influenced by the presence of outliers. We then introduce OMIT, a method for fair ranking in the presence of outliers. Given an outlier detection method, OMIT improves fair allocation of exposure by suppressing outliers in the top-k ranking. Using an academic search dataset, we show that outlierness optimization leads to a fairer policy that displays fewer outliers in the top-k, while maintaining a reasonable trade-off between fairness and utility.