pwc-archive/papers-with-abstracts huggingface dataset:
Vertical Allocation-based Fair Exposure Amortizing in Ranking
Authors: Tao Yang, Zhichao Xu, Qingyao Ai
Date: 2022-04-06 00:00:00
Tasks: Exposure Fairness, Fairness, Recommendation Systems

Result ranking often affects consumer satisfaction as well as the amount of exposure each item receives in the ranking services. Myopically maximizing customer satisfaction by ranking items only according to relevance will lead to unfair distribution of exposure for items, followed by unfair opportunities and economic gains for item producers/providers. Such unfairness will force providers to leave the system and discourage new providers from coming in. Eventually, fewer purchase options would be left for consumers, and the utilities of both consumers and providers would be harmed. Thus, to maintain a balance between ranking relevance and fairness is crucial for both parties. In this paper, we focus on the exposure fairness in ranking services. We demonstrate that existing methods for amortized fairness optimization could be suboptimal in terms of fairness-relevance tradeoff because they fail to utilize the prior knowledge of consumers. We further propose a novel algorithm named Vertical Allocation-based Fair Exposure Amortizing in Ranking, or VerFair, to reach a better balance between exposure fairness and ranking performance. Extensive experiments on three real-world datasets show that VerFair significantly outperforms state-of-the-art fair ranking algorithms in fairness-performance trade-offs from both the individual level and the group level.
Binary Spatial Random Field Reconstruction from Non-Gaussian Inhomogeneous Time-series Observations
Authors: Shunan Sheng, Qikun Xiang, Ido Nevat, Ariel Neufeld
Date: 2022-04-07 00:00:00
Tasks: Time Series, Time Series Analysis

We develop a new model for spatial random field reconstruction of a binary-valued spatial phenomenon. In our model, sensors are deployed in a wireless sensor network across a large geographical region. Each sensor measures a non-Gaussian inhomogeneous temporal process which depends on the spatial phenomenon. Two types of sensors are employed: one collects point observations at specific time points, while the other collects integral observations over time intervals. Subsequently, the sensors transmit these time-series observations to a Fusion Center (FC), and the FC infers the spatial phenomenon from these observations. We show that the resulting posterior predictive distribution is intractable and develop a tractable two-step procedure to perform inference. Firstly, we develop algorithms to perform approximate Likelihood Ratio Tests on the time-series observations, compressing them to a single bit for both point sensors and integral sensors. Secondly, once the compressed observations are transmitted to the FC, we utilize a Spatial Best Linear Unbiased Estimator (S-BLUE) to reconstruct the binary spatial random field at any desired spatial location. The performance of the proposed approach is studied using simulation. We further illustrate the effectiveness of our method using a weather dataset from the National Environment Agency (NEA) of Singapore with fields including temperature and relative humidity.
Seeing is Not Believing: Camouflage Attacks on Image Scaling Algorithms
Authors: Qixue Xiao, Yufei Chen, Chao Shen, Yu Chen, Kang Li
Date: 2019-08-01 00:00:00
Proceeding: USENIX Security Symposium 2019 8
Tasks: Data Poisoning, image-classification, Image Classification

Image scaling algorithms are intended to preserve the visual features before and after scaling, which is commonly used in numerous visual and image processing applications. In this paper, we demonstrate an automated attack against common scaling algorithms, i.e. to automatically generate camouflage images whose visual semantics change dramatically after scaling. To illustrate the threats from such camouflage attacks, we choose several computer vision applications as targeted victims, including multiple image classification applications based on popular deep learning frameworks, as well as main-stream web browsers. Our experimental results show that such attacks can cause different visual results after scaling and thus create evasion or data poisoning effect to these victim applications. We also present an algorithm that can successfully enable attacks against famous cloud-based image services (such as those from Microsoft Azure, Aliyun, Baidu, and Tencent) and cause obvious misclassification effects, even when the details of image processing (such as the exact scaling algorithm and scale dimension parameters) are hidden in the cloud. To defend against such attacks, this paper suggests a few potential countermeasures from attack prevention to detection.
To Participate Or Not To Participate: An Investigation Of Strategic Participation In Standards
Authors: Paras Bhatt, Claire Vishik, Govind Hariharan, H. Raghav Rao
Date: 2022-04-06 00:00:00

Essential functionality in the ICT (Information and Communication Technology) space draws from standards such as HTTP (IETF RFC 2616, Bluetooth (IEEE 802.15) and various telecommunication standards (4G, 5G). They have fuelled rapid growth of ICT sector in the last decades by ensuring interoperability and consistency in computing environment. Research shows that firms that backed ICT standards and participated in standards development, have emerged as industry innovators. Standards development thus clearly has benefits for participating companies as well as technology development and innovation in general. However, significant costs are also associated with development of standards and need to be better understood to support investment in standardization necessary for todays ICT environment. We present a conceptual model that considers the potential for market innovation across a standards lifecycle and efficiency from standardization work, to build a forward-looking decision model that can guide an organizations standards development activities. We investigate and formalize motivations that drive firms to participate in standardization, specifically, changes in market innovation. Our model can serve as a strategic decision framework to drive assessments of a firms participation in standards development. We test our model with a use case on an established access control approach that was standardized more than two decades ago, Role Based Access Control (RBAC) using historical data. The investigation of the case study shows that change in market innovation is a significant indicator of success in standards development and are viable criteria to model a firms decision to participate (or not to participate) in a specific area of standardization.
Semantic Sensor Network Ontology based Decision Support System for Forest Fire Management
Authors: Ritesh Chandra, Kumar Abhishek, Sonali Agarwal, Navjot Singh
Date: 2022-04-03 00:00:00
Tasks: Fire Detection, Management

The forests are significant assets for every country. When it gets destroyed, it may negatively impact the environment, and forest fire is one of the primary causes. Fire weather indices are widely used to measure fire danger and are used to issue bushfire warnings. It can also be used to predict the demand for emergency management resources. Sensor networks have grown in popularity in data collection and processing capabilities for a variety of applications in industries such as medical, environmental monitoring, home automation etc. Semantic sensor networks can collect various climatic circumstances like wind speed, temperature, and relative humidity. However, estimating fire weather indices is challenging due to the various issues involved in processing the data streams generated by the sensors. Hence, the importance of forest fire detection has increased day by day. The underlying Semantic Sensor Network (SSN) ontologies are built to allow developers to create rules for calculating fire weather indices and also the convert dataset into Resource Description Framework (RDF). This research describes the various steps involved in developing rules for calculating fire weather indices. Besides, this work presents a Web-based mapping interface to help users visualize the changes in fire weather indices over time. With the help of the inference rule, it designed a decision support system using the SSN ontology and query on it through SPARQL. The proposed fire management system acts according to the situation, supports reasoning and the general semantics of the open-world followed by all the ontologies
Improving Urban Mobility: using artificial intelligence and new technologies to connect supply and demand
Authors: Ana L. C. Bazzan
Date: 2022-03-18 00:00:00
Tasks: Autonomous Vehicles, Management

As the demand for mobility in our society seems to increase, the various issues centered on urban mobility are among those that worry most city inhabitants in this planet. For instance, how to go from A to B in an efficient (but also less stressful) way? These questions and concerns have not changed even during the covid-19 pandemic; on the contrary, as the current stand, people who are avoiding public transportation are only contributing to an increase in the vehicular traffic. The are of intelligent transportation systems (ITS) aims at investigating how to employ information and communication technologies to problems related to transportation. This may mean monitoring and managing the infrastructure (e.g., traffic roads, traffic signals, etc.). However, currently, ITS is also targeting the management of demand. In this panorama, artificial intelligence plays an important role, especially with the advances in machine learning that translates in the use of computational vision, connected and autonomous vehicles, agent-based simulation, among others. In the present work, a survey of several works developed by our group are discussed in a holistic perspective, i.e., they cover not only the supply side (as commonly found in ITS works), but also the demand side, and, in an novel perspective, the integration of both.
QCRI's COVID-19 Disinformation Detector: A System to Fight the COVID-19 Infodemic in Social Media
Authors: Preslav Nakov, Firoj Alam, Yifan Zhang, Animesh Prakash, Fahim Dalvi
Date: 2022-03-08 00:00:00

Fighting the ongoing COVID-19 infodemic has been declared as one of the most important focus areas by the World Health Organization since the onset of the COVID-19 pandemic. While the information that is consumed and disseminated consists of promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic, at the same time there is information (e.g., containing advice, promoting cure) that can help different stakeholders such as policy-makers. Social media platforms enable the infodemic and there has been an effort to curate the content on such platforms, analyze and debunk them. While a majority of the research efforts consider one or two aspects (e.g., detecting factuality) of such information, in this study we focus on a multifaceted approach, including an API,\url{https://app.swaggerhub.com/apis/yifan2019/Tanbih/0.8.0/} and a demo system,\url{https://covid19.tanbih.org}, which we made freely and publicly available. We believe that this will facilitate researchers and different stakeholders. A screencast of the API services and demo is available.\url{https://youtu.be/zhbcSvxEKMk}
An optimized hybrid solution for IoT based lifestyle disease classification using stress data
Authors: Sadhana Tiwari, Sonali Agarwal
Date: 2022-04-04 00:00:00
Tasks: feature selection

Stress, anxiety, and nervousness are all high-risk health states in everyday life. Previously, stress levels were determined by speaking with people and gaining insight into what they had experienced recently or in the past. Typically, stress is caused by an incidence that occurred a long time ago, but sometimes it is triggered by unknown factors. This is a challenging and complex task, but recent research advances have provided numerous opportunities to automate it. The fundamental features of most of these techniques are electro dermal activity (EDA) and heart rate values (HRV). We utilized an accelerometer to measure body motions to solve this challenge. The proposed novel method employs a test that measures a subject's electrocardiogram (ECG), galvanic skin values (GSV), HRV values, and body movements in order to provide a low-cost and time-saving solution for detecting stress lifestyle disease in modern times using cyber physical systems. This study provides a new hybrid model for lifestyle disease classification that decreases execution time while picking the best collection of characteristics and increases classification accuracy. The developed approach is capable of dealing with the class imbalance problem by using WESAD (wearable stress and affect dataset) dataset. The new model uses the Grid search (GS) method to select an optimized set of hyper parameters, and it uses a combination of the Correlation coefficient based Recursive feature elimination (CoC-RFE) method for optimal feature selection and gradient boosting as an estimator to classify the dataset, which achieves high accuracy and helps to provide smart, accurate, and high-quality healthcare systems. To demonstrate the validity and utility of the proposed methodology, its performance is compared to those of other well-established machine learning models.
Transfinite Modal Logic: a Semi-quantitative Explanation for Bayesian Reasoning
Authors: Xinyu Wang
Date: 2022-04-02 00:00:00

Bayesian reasoning plays a significant role both in human rationality and in machine learning. In this paper, we introduce transfinite modal logic, which combines modal logic with ordinal arithmetic, in order to formalize Bayesian reasoning semi-quantitatively. Technically, we first investigate some nontrivial properties of ordinal arithmetic, which then enable us to expand normal modal logic's semantics naturally and elegantly onto the novel transfinite modal logic, while still keeping the ordinary definition of Kripke models totally intact. Despite all the transfinite mathematical definition, we argue that in practice, this logic can actually fit into a completely finite interpretation as well. We suggest that transfinite modal logic captures the essence of Bayesian reasoning in a rather clear and simple form, in particular, it provides a perfect explanation for Sherlock Holmes' famous saying, "When you have eliminated the impossible, whatever remains, however improbable, must be the truth." We also prove a counterpart of finite model property theorem for our logic.
Unsupervised Quantized Prosody Representation for Controllable Speech Synthesis
Authors: Yutian Wang, Yuankun Xie, Kun Zhao, Hui Wang, Qin Zhang
Date: 2022-04-07 00:00:00
Tasks: Quantization, Speech Synthesis, text-to-speech, Text to Speech

In this paper, we propose a novel prosody disentangle method for prosodic Text-to-Speech (TTS) model, which introduces the vector quantization (VQ) method to the auxiliary prosody encoder to obtain the decomposed prosody representations in an unsupervised manner. Rely on its advantages, the speaking styles, such as pitch, speaking velocity, local pitch variance, etc., are decomposed automatically into the latent quantize vectors. We also investigate the internal mechanism of VQ disentangle process by means of a latent variables counter and find that higher value dimensions usually represent prosody information. Experiments show that our model can control the speaking styles of synthesis results by directly manipulating the latent variables. The objective and subjective evaluations illustrated that our model outperforms the popular models.
Heterogeneous Ultra-Dense Networks with Traffic Hotspots: A Unified Handover Analysis
Authors: He Zhou, Haibo Zhou, Jianguo Li, Kai Yang, Jianping An, Xuemin, Shen
Date: 2022-04-07 00:00:00
Tasks: Point Processes

With the ever-growing communication demands and the unceasing miniaturization of mobile devices, the Internet of Things is expanding the amount of mobile terminals to an enormous level. To deal with such numbers of communication data, plenty of base stations (BSs) need to be deployed. However, denser deployments of heterogeneous networks (HetNets) lead to more frequent handovers, which could increase network burden and degrade the users experience, especially in traffic hotspot areas. In this paper, we develop a unified framework to investigate the handover performance of wireless networks with traffic hotspots. Using the stochastic geometry, we derive the theoretical expressions of average distances and handover metrics in HetNets, where the correlations between users and BSs in hotspots are captured. Specifically, the distributions of macro cells are modeled as independent Poisson point processes (PPPs), and the two tiers of small cells outside and inside the hotspots are modeled as PPP and Poisson cluster process (PCP) separately. A modified random waypoint (MRWP) model is also proposed to eliminate the density wave phenomenon in traditional models and to increase the accuracy of handover decision. By combining the PCP and MRWP model, the distributions of distances from a typical terminal to the BSs in different tiers are derived. Afterwards, we derive the expressions of average distances from a typical terminal to different BSs, and reveal that the handover rate, handover failure rate, and ping-pong rate are deduced as the functions of BS density, scattering variance of clustered small cell, user velocity, and threshold of triggered time. Simulation results verify the accuracy of the proposed analytical model and closed-form theoretical expressions.
AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles
Authors: Haijun Zhang, Minghui Jiang, Xiangnan Liu, Keping Long, Victor C. M. Leung
Date: 2022-03-05 00:00:00

Due to the rapid growth of data transmissions in internet of vehicles (IoV), finding schemes that can effectively alleviate access congestion has become an important issue. Recently, many traffic control schemes have been studied. Nevertheless, the dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies, which is significant for the random access resource allocation. In this paper, we consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it. Firstly, IoV devices are divided into various classes based on delay characteristics. The target of maximizing the successful transmission of packets with the success rate constraint is established. Then, the optimization objective is transformed into a markov decision process (MDP) model. Finally, the access class barring (ACB) factors are obtained based on the PPO method to maximize the number of successful access devices. The performance of the proposal algorithm in respect of successful events and delay compared to existing schemes is verified by simulations.
Musical Information Extraction from the Singing Voice
Authors: Preeti Rao
Date: 2022-04-07 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Recommendation Systems, Retrieval

Music information retrieval is currently an active research area that addresses the extraction of musically important information from audio signals, and the applications of such information. The extracted information can be used for search and retrieval of music in recommendation systems, or to aid musicological studies or even in music learning. Sophisticated signal processing techniques are applied to convert low-level acoustic signal properties to musical attributes which are further embedded in a rule-based or statistical classification framework to link with high-level descriptions such as melody, genre, mood and artist type. Vocal music comprises a large and interesting category of music where the lead instrument is the singing voice. The singing voice is more versatile than many musical instruments and therefore poses interesting challenges to information retrieval systems. In this paper, we provide a brief overview of research in vocal music processing followed by a description of related work at IIT Bombay leading to the development of an interface for melody detection of singing voice in polyphony.
Control Barrier Functions with Actuation Constraints under Signal Temporal Logic Specifications
Authors: Ali Tevfik Buyukkocak, Derya Aksaray, Yasin Yazıcıoğlu
Date: 2022-04-07 00:00:00

We propose control barrier functions (CBFs) for a family of dynamical systems to satisfy a broad fragment of Signal Temporal Logic (STL) specifications, which may include subtasks with nested temporal operators or conflicting requirements (e.g., achieving multiple subtasks within the same time interval). The proposed CBFs take into account the actuation limits of the dynamical system as well as a feasible sequence of subtasks, and they define time-varying feasible sets of states the system must always stay inside. We show some theoretical results on the correctness of the proposed method. We illustrate the benefits of the proposed CBFs and compare their performance with the existing methods via simulations.
Unsignalized Intersection Management Strategy for Mixed Autonomy Traffic Streams
Authors: Junjie Zhou, Zhaokun Shen, Xiaofan Wang, Lin Wang
Date: 2022-04-07 00:00:00
Tasks: Decision Making, Management

With the rapid development of connected and automated vehicles (CAVs) and intelligent transportation infrastructure, CAVs, connected human-driven vehicles (CHVs), and un-connected human-driven vehicles (HVs) will coexist on the roads in the future for a long time. This paper comprehensively considers the different traffic characteristics of CHVs, CAVs, and HVs, and systemically investigates the unsignalized intersection management strategy from the upper decision-making level to the lower execution level. The unsignalized intersection management strategy consists of two parts: the heuristic priority queues based right of way allocation (HPQ) algorithm and the vehicle planning and control algorithm. In the HPQ algorithm, a vehicle priority management model considering the difference between CAVs, CHVs, and HVs, is built to design the right of way management for different types of vehicles. In the lower level for vehicle planning and control algorithm, different control modes of CAVs are designed according to the upper-level decision made by the HPQ algorithm. Moreover, the vehicle control execution is realized by the model predictive controller combined with the geographical environment constraints and the unsignalized intersection management strategy. The proposed strategy is evaluated by simulations, which show that the proposed intersection management strategy can effectively reduce travel time and improve traffic efficiency. Results show that the proposed method can decrease the average travel time by 5% to 65% for different traffic flows compared with the comparative methods. The intersection management strategy captures the real-world balance between efficiency and safety for future intelligent traffic systems.
Nonlinear Kalman Filter Using Cramer Rao Bound
Authors: Xin Liang, Yi Jiang
Date: 2022-04-07 00:00:00
Tasks: Autonomous Driving, Position, State Estimation

This paper studies the optimal state estimation for a dynamic system, whose transfer function can be nonlinear and the input noise can be of arbitrary distribution. Our algorithm differs from the conventional extended Kalman filter (EKF) and the particle filter (PF) in that it estimates not only the state vector but also the Cramer-Rao bound (CRB), which serves as an accuracy indicator. Combining the state estimation, the CRB, and the incoming new measurement, the algorithm updates the state estimation according to the maximum likelihood (ML) criterion. To illustrate the effectiveness of the proposed method for autonomous driving, we apply it to estimate the position and velocity of a vehicle based on the noisy measurements of distance and Doppler offset. Simulation results show that the proposed algorithm can achieve estimation significantly more accurate than the standard EKF and the PF.