OpenRaiser/Intern-Atlas huggingface dataset:
aiSTROM -- A roadmap for developing a successful AI strategy
Venue: arXiv cs.AI 2021
Year: 2021

A total of 34% of AI research and development projects fails or are abandoned, according to a recent survey by Rackspace Technology of 1,870 companies. We propose a new strategic framework, aiSTROM, that empowers managers to create a successful AI strategy based on a thorough literature review. This provides a unique and integrated approach that guides managers and lead developers through the various challenges in the implementation process. In the aiSTROM framework, we start by identifying the top n potential projects (typically 3-5). For each of those, seven areas of focus are thoroughly analysed. These areas include creating a data strategy that takes into account unique cross-departmental machine learning data requirements, security, and legal requirements. aiSTROM then guides managers to think about how to put together an interdisciplinary artificial intelligence (AI) implementation team given the scarcity of AI talent. Once an AI team strategy has been established, it needs to be positioned within the organization, either cross-departmental or as a separate division. Other considerations include AI as a service (AIaas), or outsourcing development. Looking at new technologies, we have to consider challenges such as bias, legality of black-box-models, and keeping humans in the loop. Next, like any project, we need value-based key performance indicators (KPIs) to track and validate the progress. Depending on the company's risk-strategy, a SWOT analysis (strengths, weaknesses, opportunities, and threats) can help further classify the shortlisted projects. Finally, we should make sure that our strategy includes continuous education of employees to enable a culture of adoption. This unique and comprehensive framework offers a valuable, literature supported, tool for managers and lead developers.
A Novel Dual Quaternion Based Dynamic Motion Primitives for Acrobatic Flight
Venue: arXiv cs.RO 2021
Year: 2021

The realization of motion description is a challenging work for fixed-wing Unmanned Aerial Vehicle (UAV) acrobatic flight, due to the inherent coupling problem in ranslational-rotational motion. This paper aims to develop a novel maneuver description method through the idea of imitation learning, and there are two main contributions of our work: 1) A dual quaternion based dynamic motion primitives (DQ-DMP) is proposed and the state equations of the position and attitude can be combined without loss of accuracy. 2) An online hardware-inthe-loop (HITL) training system is established. Based on the DQDMP method, the geometric features of the demonstrated maneuver can be obtained in real-time, and the stability of the DQ-DMP is theoretically proved. The simulation results illustrate the superiority of the proposed method compared to the traditional position/attitude decoupling method.
Ontology-Based Process Modelling -- Will we live to see it?
Venue: arXiv cs.AI 2021
Year: 2021

In theory, ontology-based process modelling (OBPM) bares great potential to extend business process management. Many works have studied OBPM and are clear on the potential amenities, such as eliminating ambiguities or enabling advanced reasoning over company processes. However, despite this approval in academia, a widespread industry adoption is still nowhere to be seen. This can be mainly attributed to the fact, that it still requires high amounts of manual labour to initially create ontologies and annotations to process models. As long as these problems are not addressed, implementing OBPM seems unfeasible in practice. In this work, we therefore identify requirements needed for a successful implementation of OBPM and assess the current state of research w.r.t. these requirements. Our results indicate that the research progress for means to facilitate OBPM are still alarmingly low and there needs to be urgent work on extending existing approaches.
From Identity to Difference: A Quantitative Interpretation of the Identity Type
Venue: arXiv cs.LO 2021
Year: 2021

We explore a quantitative interpretation of 2-dimensional intuitionistic type theory (ITT) in which the identity type is interpreted as a "type of differences". We show that a fragment of ITT, that we call difference type theory (dTT), yields a general logical framework to talk about quantitative properties of programs like approximate equivalence and metric preservation. To demonstrate this fact, we show that dTT can be used to capture compositional reasoning in presence of errors, since any program can be associated with a "derivative" relating errors in input with errors in output. Moreover, after relating the semantics of dTT to the standard weak factorization systems semantics of ITT, we describe the interpretation of dTT in some quantitative models developed for approximate program transformations, incremental computing, program differentiation and differential privacy.
Deep Ranking with Adaptive Margin Triplet Loss
Venue: arXiv cs.CV 2021
Year: 2021

We propose a simple modification from a fixed margin triplet loss to an adaptive margin triplet loss. While the original triplet loss is used widely in classification problems such as face recognition, face re-identification and fine-grained similarity, our proposed loss is well suited for rating datasets in which the ratings are continuous values. In contrast to original triplet loss where we have to sample data carefully, in out method, we can generate triplets using the whole dataset, and the optimization can still converge without frequently running into a model collapsing issue. The adaptive margins only need to be computed once before the training, which is much less expensive than generating triplets after every epoch as in the fixed margin case. Besides substantially improved training stability (the proposed model never collapsed in our experiments compared to a couple of times that the training collapsed on existing triplet loss), we achieved slightly better performance than the original triplet loss on various rating datasets and network architectures.
Transfer Learning in Multi-Agent Reinforcement Learning with Double Q-Networks for Distributed Resource Sharing in V2X Communication
Venue: arXiv cs.LG 2021
Year: 2021

This paper addresses the problem of decentralized spectrum sharing in vehicle-to-everything (V2X) communication networks. The aim is to provide resource-efficient coexistence of vehicle-to-infrastructure(V2I) and vehicle-to-vehicle(V2V) links. A recent work on the topic proposes a multi-agent reinforcement learning (MARL) approach based on deep Q-learning, which leverages a fingerprint-based deep Q-network (DQN) architecture. This work considers an extension of this framework by combining Double Q-learning (via Double DQN) and transfer learning. The motivation behind is that Double Q-learning can alleviate the problem of overestimation of the action values present in conventional Q-learning, while transfer learning can leverage knowledge acquired by an expert model to accelerate learning in the MARL setting. The proposed algorithm is evaluated in a realistic V2X setting, with synthetic data generated based on a geometry-based propagation model that incorporates location-specific geographical descriptors of the simulated environment(outlines of buildings, foliage, and vehicles). The advantages of the proposed approach are demonstrated via numerical simulations.
ML-Quest: A Game for Introducing Machine Learning Concepts to K-12 Students
Venue: arXiv cs.HC 2021
Year: 2021

Today, Machine Learning (ML) is of a great importance to society due to the availability of huge data and high computational resources. This ultimately led to the introduction of ML concepts at multiple levels of education including K-12 students to promote computational thinking. However, teaching these concepts to K-12 through traditional methodologies such as video lectures and books is challenging. Many studies in the literature have reported that using interactive environments such as games to teach computational thinking and programming improves retention capacity and motivation among students. Therefore, introducing ML concepts using a game might enhance students' understanding of the subject and motivate them to learn further. However, we are not aware of any existing game which explicitly focuses on introducing ML concepts to students using game play. Hence, in this paper, we propose ML-Quest, a 3D video game to provide conceptual overview of three ML concepts: Supervised Learning, Gradient Descent and K-Nearest Neighbor (KNN) Classification. The crux of the game is to introduce the definition and working of these concepts, which we call conceptual overview, in a simulated scenario without overwhelming students with the intricacies of ML. The game has been predominantly evaluated for its usefulness and player experience using the Technology Acceptance Model (TAM) model with the help of 23 higher-secondary school students. The survey result shows that around 70% of the participants either agree or strongly agree that the ML-Quest is quite interactive and useful in introducing them to ML concepts.
Learning a Discriminant Latent Space with Neural Discriminant Analysis
Venue: arXiv cs.CV 2021
Year: 2021

Discriminative features play an important role in image and object classification and also in other fields of research such as semi-supervised learning, fine-grained classification, out of distribution detection. Inspired by Linear Discriminant Analysis (LDA), we propose an optimization called Neural Discriminant Analysis (NDA) for Deep Convolutional Neural Networks (DCNNs). NDA transforms deep features to become more discriminative and, therefore, improves the performances in various tasks. Our proposed optimization has two primary goals for inter- and intra-class variances. The first one is to minimize variances within each individual class. The second goal is to maximize pairwise distances between features coming from different classes. We evaluate our NDA optimization in different research fields: general supervised classification, fine-grained classification, semi-supervised learning, and out of distribution detection. We achieve performance improvements in all the fields compared to baseline methods that do not use NDA. Besides, using NDA, we also surpass the state of the art on the four tasks on various testing datasets.
Bag-of-Tasks Scheduling on Related Machines
Venue: arXiv cs.DS 2021
Year: 2021

We consider online scheduling to minimize weighted completion time on related machines, where each job consists of several tasks that can be concurrently executed. A job gets completed when all its component tasks finish. We obtain an $O(K^3 \log^2 K)$-competitive algorithm in the non-clairvoyant setting, where $K$ denotes the number of distinct machine speeds. The analysis is based on dual-fitting on a precedence-constrained LP relaxation that may be of independent interest.
Dance2Music: Automatic Dance-driven Music Generation
Venue: arXiv cs.SD 2021
Year: 2021

Dance and music typically go hand in hand. The complexities in dance, music, and their synchronisation make them fascinating to study from a computational creativity perspective. While several works have looked at generating dance for a given music, automatically generating music for a given dance remains under-explored. This capability could have several creative expression and entertainment applications. We present some early explorations in this direction. We present a search-based offline approach that generates music after processing the entire dance video and an online approach that uses a deep neural network to generate music on-the-fly as the video proceeds. We compare these approaches to a strong heuristic baseline via human studies and present our findings. We have integrated our online approach in a live demo! A video of the demo can be found here: https://sites.google.com/view/dance2music/live-demo.
LookAtChat: Visualizing Gaze Awareness for Remote Small-Group Conversations
Venue: arXiv cs.HC 2021
Year: 2021

Video conferences play a vital role in our daily lives. However, many nonverbal cues are missing, including gaze and spatial information. We introduce LookAtChat, a web-based video conferencing system, which empowers remote users to identify gaze awareness and spatial relationships in small-group conversations. Leveraging real-time eye-tracking technology available with ordinary webcams, LookAtChat tracks each user's gaze direction, identifies who is looking at whom, and provides corresponding spatial cues. Informed by formative interviews with 5 participants who regularly use videoconferencing software, we explored the design space of gaze visualization in both 2D and 3D layouts. We further conducted an exploratory user study (N=20) to evaluate LookAtChat in three conditions: baseline layout, 2D directional layout, and 3D perspective layout. Our findings demonstrate how LookAtChat engages participants in small-group conversations, how gaze and spatial information improve conversation quality, and the potential benefits and challenges to incorporating gaze awareness visualization into existing videoconferencing systems.
How Much Can CLIP Benefit Vision-and-Language Tasks?
Venue: arXiv cs.CV 2021
Year: 2021

Most existing Vision-and-Language (V&L) models rely on pre-trained visual encoders, using a relatively small set of manually-annotated data (as compared to web-crawled data), to perceive the visual world. However, it has been observed that large-scale pretraining usually can result in better generalization performance, e.g., CLIP (Contrastive Language-Image Pre-training), trained on a massive amount of image-caption pairs, has shown a strong zero-shot capability on various vision tasks. To further study the advantage brought by CLIP, we propose to use CLIP as the visual encoder in various V&L models in two typical scenarios: 1) plugging CLIP into task-specific fine-tuning; 2) combining CLIP with V&L pre-training and transferring to downstream tasks. We show that CLIP significantly outperforms widely-used visual encoders trained with in-domain annotated data, such as BottomUp-TopDown. We achieve competitive or better results on diverse V&L tasks, while establishing new state-of-the-art results on Visual Question Answering, Visual Entailment, and V&L Navigation tasks. We release our code at https://github.com/clip-vil/CLIP-ViL.
The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions
Venue: arXiv cs.LG 2021
Year: 2021

Datasets often contain input dimensions that are unnecessary to predict the output label, e.g. background in object recognition, which lead to more trainable parameters. Deep Neural Networks (DNNs) are robust to increasing the number of parameters in the hidden layers, but it is unclear whether this holds true for the input layer. In this letter, we investigate the impact of unnecessary input dimensions on a central issue of DNNs: their data efficiency, ie. the amount of examples needed to achieve certain generalization performance. Our results show that unnecessary input dimensions that are task-unrelated substantially degrade data efficiency. This highlights the need for mechanisms that remove {task-unrelated} dimensions to enable data efficiency gains.
Hierarchical Associative Memory
Venue: arXiv cs.NE 2021
Year: 2021

Dense Associative Memories or Modern Hopfield Networks have many appealing properties of associative memory. They can do pattern completion, store a large number of memories, and can be described using a recurrent neural network with a degree of biological plausibility and rich feedback between the neurons. At the same time, up until now all the models of this class have had only one hidden layer, and have only been formulated with densely connected network architectures, two aspects that hinder their machine learning applications. This paper tackles this gap and describes a fully recurrent model of associative memory with an arbitrary large number of layers, some of which can be locally connected (convolutional), and a corresponding energy function that decreases on the dynamical trajectory of the neurons' activations. The memories of the full network are dynamically "assembled" using primitives encoded in the synaptic weights of the lower layers, with the "assembling rules" encoded in the synaptic weights of the higher layers. In addition to the bottom-up propagation of information, typical of commonly used feedforward neural networks, the model described has rich top-down feedback from higher layers that help the lower-layer neurons to decide on their response to the input stimuli.
CNN-Cap: Effective Convolutional Neural Network Based Capacitance Models for Full-Chip Parasitic Extraction
Venue: arXiv cs.LG 2021
Year: 2021

Accurate capacitance extraction is becoming more important for designing integrated circuits under advanced process technology. The pattern matching based full-chip extraction methodology delivers fast computational speed, but suffers from large error, and tedious efforts on building capacitance models of the increasing structure patterns. In this work, we propose an effective method for building convolutional neural network (CNN) based capacitance models (called CNN-Cap) for two-dimensional (2-D) structures in full-chip capacitance extraction. With a novel grid-based data representation, the proposed method is able to model the pattern with a variable number of conductors, so that largely reduce the number of patterns. Based on the ability of ResNet architecture on capturing spatial information and the proposed training skills, the obtained CNN-Cap exhibits much better performance over the multilayer perception neural network based capacitance model while being more versatile. Extensive experiments on a 55nm and a 15nm process technologies have demonstrated that the error of total capacitance produced with CNN-Cap is always within 1.3% and the error of produced coupling capacitance is less than 10% in over 99.5% probability. CNN-Cap runs more than 4000X faster than 2-D field solver on a GPU server, while it consumes negligible memory compared to the look-up table based capacitance model.
On the Analysis of Adaptive-Rate Applications in Data-Centric Wireless Ad-Hoc Networks
Venue: arXiv cs.NI 2021
Year: 2021

Adapting applications' data rates in multi-hop wireless ad-hoc networks is inherently challenging. Packet collision, channel contention, and queue buildup contribute to packet loss but are difficult to manage in conventional TCP/IP architecture. This work explores a data-centric approach based on Name Data Networking (NDN) architecture, which is considered more suitable for wireless ad-hoc networks. We show that the default NDN transport offers better performance in linear topologies but struggles in more extensive networks due to high collision and contention caused by excessive Interests from out-of-order data retrieval and redundant data transmission from improper Interest lifetime setting as well as in-network caching. To fix these, we use round-trip hop count to limit Interest rate and Dynamic Interest Lifetime to minimize the negative effect of improper Interest lifetime. Finally, we analyze the effect of in-network caching on transport performance and which scenarios may benefit or suffer from it.
Graph Jigsaw Learning for Cartoon Face Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Cartoon face recognition is challenging as they typically have smooth color regions and emphasized edges, the key to recognize cartoon faces is to precisely perceive their sparse and critical shape patterns. However, it is quite difficult to learn a shape-oriented representation for cartoon face recognition with convolutional neural networks (CNNs). To mitigate this issue, we propose the GraphJigsaw that constructs jigsaw puzzles at various stages in the classification network and solves the puzzles with the graph convolutional network (GCN) in a progressive manner. Solving the puzzles requires the model to spot the shape patterns of the cartoon faces as the texture information is quite limited. The key idea of GraphJigsaw is constructing a jigsaw puzzle by randomly shuffling the intermediate convolutional feature maps in the spatial dimension and exploiting the GCN to reason and recover the correct layout of the jigsaw fragments in a self-supervised manner. The proposed GraphJigsaw avoids training the classification model with the deconstructed images that would introduce noisy patterns and are harmful for the final classification. Specially, GraphJigsaw can be incorporated at various stages in a top-down manner within the classification model, which facilitates propagating the learned shape patterns gradually. GraphJigsaw does not rely on any extra manual annotation during the training process and incorporates no extra computation burden at inference time. Both quantitative and qualitative experimental results have verified the feasibility of our proposed GraphJigsaw, which consistently outperforms other face recognition or jigsaw-based methods on two popular cartoon face datasets with considerable improvements.
Useful Open Call-by-Need
Venue: arXiv cs.LO 2021
Year: 2021

This paper studies useful sharing, which is a sophisticated optimization for lambda-calculi, in the context of call-by-need evaluation in presence of open terms. Useful sharing turns out to be harder in call-by-need than in call-by-name or call-by-value, because call-by-need evaluates inside environments, making it harder to specify when a substitution step is useful. We isolate the key involved concepts and prove the correctness and the completeness of useful sharing in this setting.
AutoMCM: Maneuver Coordination Service with Abstracted Functions for Autonomous Driving
Venue: arXiv cs.RO 2021
Year: 2021

A cooperative intelligent transport system (C-ITS) uses vehicle-to-everything (V2X) technology to make self-driving vehicles safer and more efficient. Current C-ITS applications have mainly focused on real-time information sharing, such as for cooperative perception. In addition to better real-time perception, self-driving vehicles need to achieve higher safety and efficiency by coordinating action plans. This study designs a maneuver coordination (MC) protocol that uses seven messages to cover various scenarios and an abstracted MC support service. We implement our proposal as AutoMCM by extending two open-source software tools: Autoware for autonomous driving and OpenC2X for C-ITS. The results show that our system effectively reduces the communication bandwidth by limiting message exchange in an event-driven manner. Furthermore, it shows that the vehicles run 15% faster when the vehicle speed is 30 km/h and 28% faster when the vehicle speed is 50 km/h using our scheme. Our system shows robustness against packet loss in experiments when the message timeout parameters are appropriately set.
Online Evaluation Methods for the Causal Effect of Recommendations
Venue: arXiv cs.LG 2021
Year: 2021

Evaluating the causal effect of recommendations is an important objective because the causal effect on user interactions can directly leads to an increase in sales and user engagement. To select an optimal recommendation model, it is common to conduct A/B testing to compare model performance. However, A/B testing of causal effects requires a large number of users, making such experiments costly and risky. We therefore propose the first interleaving methods that can efficiently compare recommendation models in terms of causal effects. In contrast to conventional interleaving methods, we measure the outcomes of both items on an interleaved list and items not on the interleaved list, since the causal effect is the difference between outcomes with and without recommendations. To ensure that the evaluations are unbiased, we either select items with equal probability or weight the outcomes using inverse propensity scores. We then verify the unbiasedness and efficiency of online evaluation methods through simulated online experiments. The results indicate that our proposed methods are unbiased and that they have superior efficiency to A/B testing.