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Articles 301 - 330 of 3475
Full-Text Articles in Computer Sciences
Early Prediction Of Hate Speech Propagation, Ken-Yu Lin, Roy Ka-Wei Lee, Wei Gao, Wen-Chih Peng
Early Prediction Of Hate Speech Propagation, Ken-Yu Lin, Roy Ka-Wei Lee, Wei Gao, Wen-Chih Peng
Research Collection School Of Computing and Information Systems
Online hate speech has disrupted the social connectedness in online communities and raises public safety concerns in our societies. Motivated by this rising issue, researchers have developed many machine learning and deep learning methods to detect hate speech in social media automatically. However, most of the existing automated solutions have focused on detecting hate speech in a single post, neglecting the network and information propagation effects of social media platforms. Ideally, the content moderators would want to identify the hateful posts and monitor posts and threads that are likely to induce hate. This paper aims to address this research gap …
Curriculum Complexity And Graduation Rates At Utah State University, Hayden Hoopes
Curriculum Complexity And Graduation Rates At Utah State University, Hayden Hoopes
Undergraduate Honors Capstone Projects
This study utilizes a curricular analytics framework developed by Heileman et al. (2018) to examine the relationship between curriculum complexity and graduation rates in academic programs at Utah State University. The goal in quantifying the complexity of curricula is to determine whether or not prerequisite courses and other factors of curricula structure impacts graduation from the university. To accomplish this goal, curriculum complexity spreadsheets were developed for 96 degree programs at the university, which facilitated the assignment of curriculum complexity scores to the 6,337 students who qualified for the quasi-experimental study. Logistic regression was then applied to the resulting data …
Estimation And Interpretation Of Machine Learning Models With Customized Surrogate Model, Mudabbir Ali, Asad Masood Khattak, Zain Ali, Bashir Hayat, Muhammad Idrees, Zeeshan Pervez, Kashif Rizwan, Tae Eung Sung, Ki Il Kim
Estimation And Interpretation Of Machine Learning Models With Customized Surrogate Model, Mudabbir Ali, Asad Masood Khattak, Zain Ali, Bashir Hayat, Muhammad Idrees, Zeeshan Pervez, Kashif Rizwan, Tae Eung Sung, Ki Il Kim
All Works
Machine learning has the potential to predict unseen data and thus improve the productivity and processes of daily life activities. Notwithstanding its adaptiveness, several sensitive applications based on such technology cannot compromise our trust in them; thus, highly accurate machine learning models require reason. Such models are black boxes for end-users. Therefore, the concept of interpretability plays the role if assisting users in a couple of ways. Interpretable models are models that possess the quality of explaining predictions. Different strategies have been proposed for the aforementioned concept but some of these require an excessive amount of effort, lack generalization, are …
Modelling Customers Credit Card Behaviour Using Bidirectional Lstm Neural Networks, Maher Ala’Raj, Maysam F. Abbod, Munir Majdalawieh
Modelling Customers Credit Card Behaviour Using Bidirectional Lstm Neural Networks, Maher Ala’Raj, Maysam F. Abbod, Munir Majdalawieh
All Works
With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this paper is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behaviour with respect to two aspects: the probability of single and consecutive …
Theoretical Models Of Integration Of Interactive Learning Technologies Into Teaching: A Systematic Literature Review, Laila Mohebi
Theoretical Models Of Integration Of Interactive Learning Technologies Into Teaching: A Systematic Literature Review, Laila Mohebi
All Works
With the fast progress of technology and the vast amount of research papers related to technology integration in education being published yearly, a study that reviews models used in these papers is needed. Therefore, this paper (1) reviewed and analysed theoretical frameworks with models used for integration of technology in classrooms, (2) reviewed studies that discussed the impact of technology integration on students' learning capabilities, and (3) discussed the importance of preparing teachers to effectively integrate technology in teaching. The models reviewed were: Teacher Thoughts and Action Process (TTAP), Theory of Planned Behavior, Expectancy-Value Theory of Achievement Motivation (EVAM), Substitution …
Hybrid Feature Selection Approach To Identify Optimal Features Of Profile Metadata To Detect Social Bots In Twitter, Eiman Alothali, Kadhim Hayawi, Hany Alashwal
Hybrid Feature Selection Approach To Identify Optimal Features Of Profile Metadata To Detect Social Bots In Twitter, Eiman Alothali, Kadhim Hayawi, Hany Alashwal
All Works
The last few years have revealed that social bots in social networks have become more sophisticated in design as they adapt their features to avoid detection systems. The deceptive nature of bots to mimic human users is due to the advancement of artificial intelligence and chatbots, where these bots learn and adjust very quickly. Therefore, finding the optimal features needed to detect them is an area for further investigation. In this paper, we propose a hybrid feature selection (FS) method to evaluate profile metadata features to find these optimal features, which are evaluated using random forest, naïve Bayes, support vector …
The Use Of Mobile Payment Systems In Post-Covid-19 Economic Recovery: Primary Research On An Emerging Market For Experience Goods, Maiya M. Suyunchaliyeva, Raghav Nautiyal, Aijaz A. Shaikh, Ravishankar Sharma
The Use Of Mobile Payment Systems In Post-Covid-19 Economic Recovery: Primary Research On An Emerging Market For Experience Goods, Maiya M. Suyunchaliyeva, Raghav Nautiyal, Aijaz A. Shaikh, Ravishankar Sharma
All Works
This study investigated whether mobile payment services could drive post-COVID-19 pandemic recovery in the ‘experience goods’ sector (e.g., tourism) utilising Bandura’s self-efficacy or social cognitive theory. It explored the factors influencing the intention to continue using mobile payment services and the intention to recommend these to others. An empirical survey was conducted to assess the study variables, and the data obtained therefrom were analysed using the industry-standard Cross-Industry Standard Process for Data Mining method. The study results suggest that personal innovativeness and perceived trust influence consumers’ intention to continue using mobile payment services and that perceived trust, personal innovativeness and …
From Language Comprehension Towards General Ai, Binay Dahal
From Language Comprehension Towards General Ai, Binay Dahal
UNLV Theses, Dissertations, Professional Papers, and Capstones
Language comprehension or more formally, natural language understanding is one of the major undertakings in Artificial Intelligence. In this work, we explore a few of the problems in language understanding using fixed deep learning models. Specifically, first, we look into question generation. Asking questions relates to the cognitive ability of language comprehension and context understanding. For that reason, making progress in question generation is significant. We introduce a novel task called “question generation with masked target answer” and propose various models and present the baseline result for the task. Next, we extend on the question generation task and develop a …
Material Handling With Embodied Loco-Manipulation, Jean Chagas Vaz
Material Handling With Embodied Loco-Manipulation, Jean Chagas Vaz
UNLV Theses, Dissertations, Professional Papers, and Capstones
Material handling is an intrinsic component of disaster response. Typically, first responders, such as firefighters and/or paramedics, must carry, push, pull, and handle objects, facilitating the transportation of goods. For many years, researchers from around the globe have sought to enable full-sized humanoid robots to perform such essential material handling tasks. This work aims to tackle current limitations of humanoids in the realm of interaction with common objects such as carts, wheelbarrows, etc. Throughout this research, many methods will be applied to ensure a stable Zero Moment Point (ZMP) trajectory to allow a robust gait while loco-manipulating a cart. The …
Exploring The Latent Space Of Image Captioning Networks, Mikian J. Musser
Exploring The Latent Space Of Image Captioning Networks, Mikian J. Musser
UNLV Theses, Dissertations, Professional Papers, and Capstones
State-of-the-art image captioning models can successfully produce a diverse set of accurate captions. Previous research has focused on improving caption diversity while maintaining a high level of fidelity. We shift the focus from accuracy and diversity to controllability. We use a modified version of the traditional encoder-decoder network that allows the model to produce a meaningful and structured latent space. We then explore the latent space using several latent cartographic methods: lerp, slerp, analogy completion, attribute vector rotation, and interpolation graphs. Additionally, we discuss different categories of latent space and provide modifications for each of the cartographic methods. Finally, we …
Ferris Bakes, Ferris Dehart
A Fine-Grained Attribute Based Data Retrieval With Proxy Re-Encryption Scheme For Data Outsourcing Systems, Hanshu Hong, Ximeng Liu, Zhixin Sun
A Fine-Grained Attribute Based Data Retrieval With Proxy Re-Encryption Scheme For Data Outsourcing Systems, Hanshu Hong, Ximeng Liu, Zhixin Sun
Research Collection School Of Computing and Information Systems
Attribute based encryption is suitable for data protection in data outsourcing systems such as cloud computing. However, the leveraging of encryption technique may retrain some routine operations over the encrypted data, particularly in the field of data retrieval. This paper presents an attribute based date retrieval with proxy re-encryption (ABDR-PRE) to provide both fine-grained access control and retrieval over the ciphertexts. The proposed scheme achieves fine-grained data access management by adopting KP-ABE mechanism, a delegator can generate the re-encryption key and search indexes for the ciphertexts to be shared over the target delegatee’s attributes. Throughout the process of data sharing, …
Rmm: Reinforced Memory Management For Class-Incremental Learning, Yaoyao Liu, Qianru Sun, Qianru Sun
Rmm: Reinforced Memory Management For Class-Incremental Learning, Yaoyao Liu, Qianru Sun, Qianru Sun
Research Collection School Of Computing and Information Systems
Class-Incremental Learning (CIL) [38] trains classifiers under a strict memory budget: in each incremental phase, learning is done for new data, most of which is abandoned to free space for the next phase. The preserved data are exemplars used for replaying. However, existing methods use a static and ad hoc strategy for memory allocation, which is often sub-optimal. In this work, we propose a dynamic memory management strategy that is optimized for the incremental phases and different object classes. We call our method reinforced memory management (RMM), leveraging reinforcement learning. RMM training is not naturally compatible with CIL as the …
Beyond Smoothness : Incorporating Low-Rank Analysis Into Nonparametric Density Estimation, Rob Vandermeulen, Antoine Ledent
Beyond Smoothness : Incorporating Low-Rank Analysis Into Nonparametric Density Estimation, Rob Vandermeulen, Antoine Ledent
Research Collection School Of Computing and Information Systems
The construction and theoretical analysis of the most popular universally consistent nonparametric density estimators hinge on one functional property: smoothness. In this paper we investigate the theoretical implications of incorporating a multi-view latent variable model, a type of low-rank model, into nonparametric density estimation. To do this we perform extensive analysis on histogram-style estimators that integrate a multi-view model. Our analysis culminates in showing that there exists a universally consistent histogram-style estimator that converges to any multi-view model with a finite number of Lipschitz continuous components at a rate of ˜O(1/3√n) in L1 error. In contrast, the standard histogram estimator …
Decentralized Deterministic Multi-Agent Reinforcement Learning, Antoine Grosnit, Desmond Cai, Laura Wynter
Decentralized Deterministic Multi-Agent Reinforcement Learning, Antoine Grosnit, Desmond Cai, Laura Wynter
Research Collection School Of Computing and Information Systems
Recent work in multi-agent reinforcement learning (MARL) by [Zhang, ICML12018] provided the first decentralized actor-critic algorithm to offer convergence guarantees. In that work, policies are stochastic and are defined on finite action spaces. We extend those results to develop a provably-convergent decentralized actor-critic algorithm for learning deterministic policies on continuous action spaces. Deterministic policies are important in many real-world settings. To handle the lack of exploration inherent in deterministic policies we provide results for the off-policy setting as well as the on-policy setting. We provide the main ingredients needed for this problem: the expression of a local deterministic policy gradient, …
Canita: Faster Rates For Distributed Convex Optimization With Communication Compression, Zhize Li, Peter Richtarik
Canita: Faster Rates For Distributed Convex Optimization With Communication Compression, Zhize Li, Peter Richtarik
Research Collection School Of Computing and Information Systems
Due to the high communication cost in distributed and federated learning, methods relying on compressed communication are becoming increasingly popular. Besides, the best theoretically and practically performing gradient-type methods invariably rely on some form of acceleration/momentum to reduce the number of communications (faster convergence), e.g., Nesterov's accelerated gradient descent (Nesterov, 1983, 2004) and Adam (Kingma and Ba, 2014). In order to combine the benefits of communication compression and convergence acceleration, we propose a \emph{compressed and accelerated} gradient method based on ANITA (Li, 2021) for distributed optimization, which we call CANITA. Our CANITA achieves the \emph{first accelerated rate} $O\bigg(\sqrt{\Big(1+\sqrt{\frac{\omega^3}{n}}\Big)\frac{L}{\epsilon}} + \omega\big(\frac{1}{\epsilon}\big)^{\frac{1}{3}}\bigg)$, …
Graph Learning Assisted Multi-Objective Integer Programming, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Abhishek Gupta, Mingyan Simon Lin
Graph Learning Assisted Multi-Objective Integer Programming, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Abhishek Gupta, Mingyan Simon Lin
Research Collection School Of Computing and Information Systems
Objective-space decomposition algorithms (ODAs) are widely studied for solvingmulti-objective integer programs. However, they often encounter difficulties inhandling scalarized problems, which could cause infeasibility or repetitive nondominatedpoints and thus induce redundant runtime. To mitigate the issue, we presenta graph neural network (GNN) based method to learn the reduction rule in the ODA.We formulate the algorithmic procedure of generic ODAs as a Markov decisionprocess, and parameterize the policy (reduction rule) with a novel two-stage GNNto fuse information from variables, constraints and especially objectives for betterstate representation. We train our model with imitation learning and deploy it ona state-of-the-art ODA. Results show that …
Fine-Grained Generalization Analysis Of Inductive Matrix Completion, Antoine Ledent, Rodrigo Alves, Yunwen Lei, Marius Kloft
Fine-Grained Generalization Analysis Of Inductive Matrix Completion, Antoine Ledent, Rodrigo Alves, Yunwen Lei, Marius Kloft
Research Collection School Of Computing and Information Systems
In this paper, we bridge the gap between the state-of-the-art theoretical results for matrix completion with the nuclear norm and their equivalent in \textit{inductive matrix completion}: (1) In the distribution-free setting, we prove bounds improving the previously best scaling of \widetilde{O}(rd2) to \widetilde{O}(d3/2√r), where d is the dimension of the side information and rr is the rank. (2) We introduce the (smoothed) \textit{adjusted trace-norm minimization} strategy, an inductive analogue of the weighted trace norm, for which we show guarantees of the order \widetilde{O}(dr) under arbitrary sampling. In the inductive case, a similar rate was previously achieved only under uniform sampling …
Strategic Behavior And Market Inefficiency In Blockchain-Based Auctions, Ping Fan Ke, Jianqing Chen, Zhiling Guo
Strategic Behavior And Market Inefficiency In Blockchain-Based Auctions, Ping Fan Ke, Jianqing Chen, Zhiling Guo
Research Collection School Of Computing and Information Systems
Blockchain-based auctions play a key role in decentralized finance, such as liquidation of collaterals in crypto-lending. In this research, we show that a Blockchain-based auction is subject to the threat to availability because of the characteristics of the Blockchain platform, which could lead to auction inefficiency or even market failure. Specifically, an adversary could occupy all of the transaction capacity of an auction by sending transactions with sufficiently high transaction fees, and then win the item in an auction with a nearly zero bid price as there are no competitors available. We discuss how to prevent this kind of strategic …
Broadcast Authenticated Encryption With Keyword Search, Xueqiao Liu, Kai He, Guomin Yang, Willy Susilo, Joseph Tonien, Qiong Huang
Broadcast Authenticated Encryption With Keyword Search, Xueqiao Liu, Kai He, Guomin Yang, Willy Susilo, Joseph Tonien, Qiong Huang
Research Collection School Of Computing and Information Systems
The emergence of public-key encryption with keyword search (PEKS) has provided an elegant approach to enable keyword search over encrypted content. Due to its high computational complexity proportional to the number of intended receivers, the trivial way of deploying PEKS for data sharing with multiple receivers is impractical, which motivates the development of a new PEKS framework for broadcast mode. However, existing works suffer from either the vulnerability to keyword guessing attacks (KGA) or high computation and communication complexity. In this work, a new primitive for keyword search in broadcast mode, named broadcast authenticated encryption with keyword search (BAEKS), is …
Degree Doesn't Matter: Identifying The Drivers Of Interaction In Software Development Ecosystem, Amrita Bhattacharjee, Subhajit Datta, Subhashis Majumder
Degree Doesn't Matter: Identifying The Drivers Of Interaction In Software Development Ecosystem, Amrita Bhattacharjee, Subhajit Datta, Subhashis Majumder
Research Collection School Of Computing and Information Systems
Large scale software development ecosystems represent one of the most complex human enterprises. In such settings, developers are embedded in a web of shared concerns, responsibilities, and objectives at individual and collective levels. A deep understanding of the factors that influence developers to connect with one another is crucial in appreciating the challenges of such ecosystems as well as formulating strategies to overcome those challenges. We use real world data from multiple software development ecosystems to construct developer interaction networks and examine the mechanisms of such network formation using statistical models to identify developer attributes that have maximal influence on …
Adadeep: A Usage-Driven, Automated Deep Model Compression Framework For Enabling Ubiquitous Intelligent Mobiles, Sicong Liu, Junzhao Du, Kaiming Nan, Zimu Zhou, Hui Liu, Zhangyang Wang, Yingyan Lin
Adadeep: A Usage-Driven, Automated Deep Model Compression Framework For Enabling Ubiquitous Intelligent Mobiles, Sicong Liu, Junzhao Du, Kaiming Nan, Zimu Zhou, Hui Liu, Zhangyang Wang, Yingyan Lin
Research Collection School Of Computing and Information Systems
Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendously growing demand for bringing DNN-powered intelligence into mobile platforms. While the potential of deploying DNNs on resource-constrained platforms has been demonstrated by DNN compression techniques, the current practice suffers from two limitations: 1) merely stand-alone compression schemes are investigated even though each compression technique only suit for certain types of DNN layers; and 2) mostly compression techniques are optimized for DNNs’ inference accuracy, without explicitly considering other application-driven system performance (e.g., latency and energy cost) and the varying resource availability across platforms (e.g., storage and processing capability). To this …
Towards Non-Intrusive Camera-Based Heart Rate Variability Estimation In The Car Under Naturalistic Condition, Shu Liu, Kevin Koch, Zimu Zhou, Martin Maritsch, Xiaoxi He, Elgar Fleisch, Felix Wortmann
Towards Non-Intrusive Camera-Based Heart Rate Variability Estimation In The Car Under Naturalistic Condition, Shu Liu, Kevin Koch, Zimu Zhou, Martin Maritsch, Xiaoxi He, Elgar Fleisch, Felix Wortmann
Research Collection School Of Computing and Information Systems
Driver status monitoring systems are a vital component of smart cars in the future, especially in the era when an increasing amount of time is spent in the vehicle. The heart rate (HR) is one of the most important physiological signals of driver status. To infer HR of drivers, the mainstream of existing research focused on capturing subtle heartbeat-induced vibration of the torso or leveraged photoplethysmography (PPG) that detects cardiac cycle-related blood volume changes in the microvascular. However, existing approaches rely on dedicated sensors that are expensive and cumbersome to be integrated or are vulnerable to ambient noise. Moreover, their …
Imon: Appearance-Based Gaze Tracking System On Mobile Devices, Sinh Huynh, Rajesh Krishna Balan, Jeonggil Ko
Imon: Appearance-Based Gaze Tracking System On Mobile Devices, Sinh Huynh, Rajesh Krishna Balan, Jeonggil Ko
Research Collection School Of Computing and Information Systems
Gaze tracking is a key building block used in many mobile applications including entertainment, personal productivity, accessibility, medical diagnosis, and visual attention monitoring. In this paper, we present iMon, an appearance-based gaze tracking system that is both designed for use on mobile phones and has significantly greater accuracy compared to prior state-of-the-art solutions. iMon achieves this by comprehensively considering the gaze estimation pipeline and then overcoming three different sources of errors. First, instead of assuming that the user's gaze is fixed to a single 2D coordinate, we construct each gaze label using a probabilistic 2D heatmap gaze representation input to …
Efficient Reinforcement Learning In Resource Allocation Problems Through Permutation Invariant Multi-Task Learning, Desmond Cai, Shiau Hong Lim, Laura Wynter
Efficient Reinforcement Learning In Resource Allocation Problems Through Permutation Invariant Multi-Task Learning, Desmond Cai, Shiau Hong Lim, Laura Wynter
Research Collection School Of Computing and Information Systems
One of the main challenges in real-world reinforcement learning is to learn successfully from limited training samples. We show that in certain settings, the available data can be dramatically increased through a form of multi-task learning, by exploiting an invariance property in the tasks. We provide a theoretical performance bound for the gain in sample efficiency under this setting. This motivates a new approach to multi-task learning, which involves the design of an appropriate neural network architecture and a prioritized task-sampling strategy. We demonstrate empirically the effectiveness of the proposed approach on two real-world sequential resource allocation tasks where this …
Context-Aware Graph Convolutional Network For Dynamic Origin-Destination Prediction, Juan Nathaniel, Baihua Zheng
Context-Aware Graph Convolutional Network For Dynamic Origin-Destination Prediction, Juan Nathaniel, Baihua Zheng
Research Collection School Of Computing and Information Systems
A robust Origin-Destination (OD) prediction is key to urban mobility. A good forecasting model can reduce operational risks and improve service availability, among many other upsides. Here, we examine the use of Graph Convolutional Net-work (GCN) and its hybrid Markov-Chain (GCN-MC) variant to perform a context-aware OD prediction based on a large-scale public transportation dataset in Singapore. Compared with the baseline Markov-Chain algorithm and GCN, the proposed hybrid GCN-MC model improves the prediction accuracy by 37% and 12% respectively. Lastly, the addition of temporal and historical contextual information further improves the performance of the proposed hybrid model by 4 –12%.
Linear Algebra For Computer Science, M. Thulasidas
Linear Algebra For Computer Science, M. Thulasidas
Research Collection School Of Computing and Information Systems
This textbook introduces the essential concepts and practice of Linear Algebra to the undergraduate student of computer science. The focus of this book is on the elegance and beauty of the numerical techniques and algorithms originating from Linear Algebra. As a practical handbook for computer and data scientists, LA4CS restricts itself mostly to real fields and tractable discourses, rather than deep and theoretical mathematics.
Robust Bipoly-Matching For Multi-Granular Entities, Ween Jiann Lee, Maksim Tkachenko, Hady W. Lauw
Robust Bipoly-Matching For Multi-Granular Entities, Ween Jiann Lee, Maksim Tkachenko, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Entity matching across two data sources is a prevalent need in many domains, including e-commerce. Of interest is the scenario where entities have varying granularity, e.g., a coarse product category may match multiple finer categories. Previous work in one-to-many matching generally presumes the `one' necessarily comes from a designated source and the `many' from the other source. In contrast, we propose a novel formulation that allows concurrent one-to-many bidirectional matching in any direction. Beyond flexibility, we also seek matching that is more robust to noisy similarity values arising from diverse entity descriptions, by introducing receptivity and reclusivity notions. In addition …
On Analysing Student Resilience In Higher Education Programs Using A Data-Driven Approach, Audrey Tedja Widjaja, Ee Peng Lim, Aldy Gunawan
On Analysing Student Resilience In Higher Education Programs Using A Data-Driven Approach, Audrey Tedja Widjaja, Ee Peng Lim, Aldy Gunawan
Research Collection School Of Computing and Information Systems
Analysing student resilience is important as research has shown that resilience is related to students’ academic performance and their persistence through academic setbacks. While questionnaires can be conducted to assess student resilience directly, they suffer from human recall errors and deliberate suppression of true responses. In this paper, we propose ACREA, ACademic REsilience Analytics framework which adopts a datadriven approach to analyse student resilient behavior with the use of student-course data. ACREA defines academic setbacks experienced by students and measures how well students overcome such setbacks using a quasi-experimental design. By applying ACREA on a real world student-course dataset, we …
Vireo @ Trecvid 2021 Ad-Hoc Video Search, Jiaxin Wu, Phuong Anh Nguyen, Chong-Wah Ngo
Vireo @ Trecvid 2021 Ad-Hoc Video Search, Jiaxin Wu, Phuong Anh Nguyen, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
In this paper, we summarize our submitted runs and results for Ad-hoc Video Search (AVS) task at TRECVid 2020