OpenRaiser/Intern-Atlas huggingface dataset:
Learning dynamic and hierarchical traffic spatiotemporal features with Transformer
Venue: arXiv cs.AI 2021
Year: 2021

Traffic forecasting is an indispensable part of Intelligent transportation systems (ITS), and long-term network-wide accurate traffic speed forecasting is one of the most challenging tasks. Recently, deep learning methods have become popular in this domain. As traffic data are physically associated with road networks, most proposed models treat it as a spatiotemporal graph modeling problem and use Graph Convolution Network (GCN) based methods. These GCN-based models highly depend on a predefined and fixed adjacent matrix to reflect the spatial dependency. However, the predefined fixed adjacent matrix is limited in reflecting the actual dependence of traffic flow. This paper proposes a novel model, Traffic Transformer, for spatial-temporal graph modeling and long-term traffic forecasting to overcome these limitations. Transformer is the most popular framework in Natural Language Processing (NLP). And by adapting it to the spatiotemporal problem, Traffic Transformer hierarchically extracts spatiotemporal features through data dynamically by multi-head attention and masked multi-head attention mechanism, and fuse these features for traffic forecasting. Furthermore, analyzing the attention weight matrixes can find the influential part of road networks, allowing us to learn the traffic networks better. Experimental results on the public traffic network datasets and real-world traffic network datasets generated by ourselves demonstrate our proposed model achieves better performance than the state-of-the-art ones.
Iterative Access Point Selection, MMSE Precoding and Power Allocation for Cell-Free Networks
Venue: arXiv cs.IT 2021
Year: 2021

In this work, we propose iterative access point (AP) selection (APS), linear minimum mean-square error (MMSE) precoding and power allocation techniques for Cell-Free Massive multiple-input multiple-output (MIMO) systems. We consider the downlink channel with single-antenna users and multiple-antenna APs. We derive sum-rate expressions for the proposed iterative APS techniques followed by MMSE precoding and optimal, adaptive, and uniform power allocation schemes. Simulations show that the proposed approach outperforms existing conjugate beamforming (CB) and zero-forcing (ZF) schemes and that performance remains excellent with APS, in the presence of perfect and imperfect channel state information (CSI).
Polar-Precoding: A Unitary Finite-Feedback Transmit Precoder for Polar-Coded MIMO Systems
Venue: arXiv cs.IT 2021
Year: 2021

We propose a unitary precoding scheme, namely polar-precoding, to improve the performance of polar-coded MIMO systems. In contrast to the traditional design of MIMO precoding criteria, the proposed polar-precoding scheme relies on the \emph{polarization criterion}. In particular, the precoding matrix design comprises two steps. After selecting a basic matrix for maximizing the capacity in the first step, we design a unitary matrix for maximizing the polarization effect among the data streams without degrading the capacity. Our simulation results show that the proposed polar-precoding scheme outperforms the state-of-the-art DFT precoding scheme.
Volume and leaf area calculation of cabbage with a neural network-based instance segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Fruit size and leaf area are important indicators for plant health and are of interest for plant nutrient management, plant protection and harvest. In this research, an image-based method for measuring the fruit volume as well as the leaf area for cabbage is presented. For this purpose, a mask region-based convolutional neural network (Mask R-CNN) was trained to segment the cabbage fruit from the leaves and assign it to the corresponding plant. The results indicated that even with a single camera, the developed method can provide a calculation accuracy of fruit size of 92.6% and an accuracy of leaf area of 89.8% on individual plant level.
LearningCity: Knowledge Generation for Smart Cities
Venue: arXiv cs.DC 2021
Year: 2021

Although we have reached new levels in smart city installations and systems, efforts so far have focused on providing diverse sources of data to smart city services consumers while neglecting to provide ways to simplify making good use of them. In this context, one first step that will bring added value to smart cities is knowledge creation in smart cities through anomaly detection and data annotation, supported in both an automated and a crowdsourced manner. We present here LearningCity, our solution that has been validated over an existing smart city deployment in Santander, and the OrganiCity experimentation-as-a-service ecosystem. We discuss key challenges along with characteristic use cases, and report on our design and implementation, together with some preliminary results derived from combining large smart city datasets with machine learning.
Consideration of resilience for digital farming systems
Venue: arXiv cs.CY 2021
Year: 2021

Latest and current innovations of agricultural tech industry are increasingly driven by digital technologies. These digital farming solutions provide attractive advantages for farmers. The trend is going to devices and sensors, which send the acquired data directly to the cloud. Also the number of scientific publications on cloud based solutions follows this development. Considering on the other hand the necessity of continuous agricultural production in any kind of crises, new cloud-based digital systems and applications need to be reliable, independent of internet supply. In this conceptual study the necessary resilience is defined, which is marginally taken into account by agtech industry innovations. Problems of development using web-based farming systems are identified and discussed. For digital farming systems the farmers individual needs of resilience are classified into five levels. Consequently, suggestions for soft- and hardware equipment are made. This includes the installation of a farm server, a local farm network, offline applications and consideration of edge computing, which can ensure a high level of resilience of new digital farming components.
Personalized Bundle Recommendation in Online Games
Venue: arXiv cs.IR 2021
Year: 2021

In business domains, \textit{bundling} is one of the most important marketing strategies to conduct product promotions, which is commonly used in online e-commerce and offline retailers. Existing recommender systems mostly focus on recommending individual items that users may be interested in. In this paper, we target at a practical but less explored recommendation problem named bundle recommendation, which aims to offer a combination of items to users. To tackle this specific recommendation problem in the context of the \emph{virtual mall} in online games, we formalize it as a link prediction problem on a user-item-bundle tripartite graph constructed from the historical interactions, and solve it with a neural network model that can learn directly on the graph-structure data. Extensive experiments on three public datasets and one industrial game dataset demonstrate the effectiveness of the proposed method. Further, the bundle recommendation model has been deployed in production for more than one year in a popular online game developed by Netease Games, and the launch of the model yields more than 60\% improvement on conversion rate of bundles, and a relative improvement of more than 15\% on gross merchandise volume (GMV).
Generating Code with the Help of Retrieved Template Functions and Stack Overflow Answers
Venue: arXiv cs.IR 2021
Year: 2021

We approach the important challenge of code autocompletion as an open-domain task, in which a sequence-to-sequence code generator model is enhanced with the ability to attend to reference code snippets supplied by a semantic code search engine. In this work, we present a novel framework to precisely retrieve template functions as well as intent-snippet pairs and effectively train such a retrieval-guided code generator. To demonstrate the effectiveness of our model designs, we perform extensive experiments with CodeSearchNet which contains template functions and CoNaLa which contains Stack Overflow intent-snippet pairs. We also investigate different retrieval models, including Elasticsearch, DPR, and our fusion representation search model, which currently holds the number one spot on the CodeSearchNet leaderboard. We observe improvements by leveraging multiple database elements and further gain from retrieving diverse data points by using Maximal Marginal Relevance. Overall, we see a 4% improvement to cross-entropy loss, a 15% improvement to edit distance, and a 44% improvement to BLEU score when retrieving template functions. We see subtler improvements of 2%, 11%, and 6% respectively when retrieving Stack Overflow intent-snippet pairs. We also create a novel Stack Overflow-Function Alignment dataset, which consists of 150K tuples of functions and Stack Overflow intent-snippet pairs that are of help in writing the associated function, of which 1.7K are manually curated.
Risk-Averse Biased Human Policies in Assistive Multi-Armed Bandit Settings
Venue: arXiv cs.RO 2021
Year: 2021

Assistive multi-armed bandit problems can be used to model team situations between a human and an autonomous system like a domestic service robot. To account for human biases such as the risk-aversion described in the Cumulative Prospect Theory, the setting is expanded to using observable rewards. When robots leverage knowledge about the risk-averse human model they eliminate the bias and make more rational choices. We present an algorithm that increases the utility value of such human-robot teams. A brief evaluation indicates that arbitrary reward functions can be handled.
Speaking of Trust -- Speech as a Measure of Trust
Venue: arXiv cs.HC 2021
Year: 2021

Since trust measures in human-robot interaction are often subjective or not possible to implement real-time, we propose to use speech cues (on what, when and how the user talks) as an objective real-time measure of trust. This could be implemented in the robot to calibrate towards appropriate trust. However, we would like to open the discussion on how to deal with the ethical implications surrounding this trust measure.
Visiting the Invisible: Layer-by-Layer Completed Scene Decomposition
Venue: arXiv cs.CV 2021
Year: 2021

Existing scene understanding systems mainly focus on recognizing the visible parts of a scene, ignoring the intact appearance of physical objects in the real-world. Concurrently, image completion has aimed to create plausible appearance for the invisible regions, but requires a manual mask as input. In this work, we propose a higher-level scene understanding system to tackle both visible and invisible parts of objects and backgrounds in a given scene. Particularly, we built a system to decompose a scene into individual objects, infer their underlying occlusion relationships, and even automatically learn which parts of the objects are occluded that need to be completed. In order to disentangle the occluded relationships of all objects in a complex scene, we use the fact that the front object without being occluded is easy to be identified, detected, and segmented. Our system interleaves the two tasks of instance segmentation and scene completion through multiple iterations, solving for objects layer-by-layer. We first provide a thorough experiment using a new realistically rendered dataset with ground-truths for all invisible regions. To bridge the domain gap to real imagery where ground-truths are unavailable, we then train another model with the pseudo-ground-truths generated from our trained synthesis model. We demonstrate results on a wide variety of datasets and show significant improvement over the state-of-the-art.
Dual Discriminator Adversarial Distillation for Data-free Model Compression
Venue: arXiv cs.CV 2021
Year: 2021

Knowledge distillation has been widely used to produce portable and efficient neural networks which can be well applied on edge devices for computer vision tasks. However, almost all top-performing knowledge distillation methods need to access the original training data, which usually has a huge size and is often unavailable. To tackle this problem, we propose a novel data-free approach in this paper, named Dual Discriminator Adversarial Distillation (DDAD) to distill a neural network without any training data or meta-data. To be specific, we use a generator to create samples through dual discriminator adversarial distillation, which mimics the original training data. The generator not only uses the pre-trained teacher's intrinsic statistics in existing batch normalization layers but also obtains the maximum discrepancy from the student model. Then the generated samples are used to train the compact student network under the supervision of the teacher. The proposed method obtains an efficient student network which closely approximates its teacher network, despite using no original training data. Extensive experiments are conducted to to demonstrate the effectiveness of the proposed approach on CIFAR-10, CIFAR-100 and Caltech101 datasets for classification tasks. Moreover, we extend our method to semantic segmentation tasks on several public datasets such as CamVid and NYUv2. All experiments show that our method outperforms all baselines for data-free knowledge distillation.
Energy-Efficient Coverage Enhancement of Indoor THz-MISO Systems: An FD-NOMA Approach
Venue: arXiv cs.IT 2021
Year: 2021

Terahertz (THz) communication is gaining more interest as one of the envisioned enablers of high-data-rate short-distance indoor applications in beyond 5G networks. Moreover, non-orthogonal multiple-access (NOMA)-enabled schemes are promising schemes to realize the target spectral efficiency, low latency, and user fairness requirements in future networks. In this paper, an energy-efficient cooperative NOMA (CNOMA) scheme that guarantees the minimum required rate for the cell-edge users in an indoor THz-MISO communications network is proposed. The proposed cooperative scheme consists of three stages: (i) beamforming stage that allocates base-station (BS) beams to THz cooperating cell-center users using analog beamforming with the aid of the cosine similarity metric, (ii) user pairing stage that is tackled using the Hungarian algorithm, and (iii) power allocation stage for both the BS THz-NOMA transmit power and the cooperation power of the cooperating cell-center users, which are optimized sequentially. The obtained results quantify the energy efficiency (EE) of the proposed scheme and shed new light on the performance of multi-user THz-NOMA-enabled networks.
Blazer: Laser Scanning Simulation using Physically Based Rendering
Venue: arXiv cs.CV 2021
Year: 2021

Line laser scanners are a sub-type of structured light 3D scanners that are relatively common devices to find within the industrial setting, typically in the context of assembly, process control, and welding. Despite its extensive use, scanning of some materials remain a difficult or even impossible task without additional pre-processing. For instance, materials which are shiny, or transparent. In this paper, we present a Blazer, a virtual line laser scanner that, combined with physically based rendering, produces synthetic data with a realistic light-matter interaction, and hence realistic appearance. This makes it eligible for the use as a tool in the development of novel algorithms, and in particular as a source of synthetic data for training of machine learning models. Similar systems exist for synthetic RGB-D data generation, but to our knowledge this the first publicly available implementation for synthetic line laser data. We release this implementation under an open-source license to aid further research on line laser scanners.
Trusting small training dataset for supervised change detection
Venue: arXiv cs.LG 2021
Year: 2021

Deep learning (DL) based supervised change detection (CD) models require large labeled training data. Due to the difficulty of collecting labeled multi-temporal data, unsupervised methods are preferred in the CD literature. However, unsupervised methods cannot fully exploit the potentials of data-driven deep learning and thus they are not absolute alternative to the supervised methods. This motivates us to look deeper into the supervised DL methods and investigate how they can be adopted intelligently for CD by minimizing the requirement of labeled training data. Towards this, in this work we show that geographically diverse training dataset can yield significant improvement over less diverse training datasets of the same size. We propose a simple confidence indicator for verifying the trustworthiness/confidence of supervised models trained with small labeled dataset. Moreover, we show that for the test cases where supervised CD model is found to be less confident/trustworthy, unsupervised methods often produce better result than the supervised ones.
Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate in Gradient Descent
Venue: arXiv cs.LG 2021
Year: 2021

We propose \textit{Meta-Regularization}, a novel approach for the adaptive choice of the learning rate in first-order gradient descent methods. Our approach modifies the objective function by adding a regularization term on the learning rate, and casts the joint updating process of parameters and learning rates into a maxmin problem. Given any regularization term, our approach facilitates the generation of practical algorithms. When \textit{Meta-Regularization} takes the $φ$-divergence as a regularizer, the resulting algorithms exhibit comparable theoretical convergence performance with other first-order gradient-based algorithms. Furthermore, we theoretically prove that some well-designed regularizers can improve the convergence performance under the strong-convexity condition of the objective function. Numerical experiments on benchmark problems demonstrate the effectiveness of algorithms derived from some common $φ$-divergence in full batch as well as online learning settings.
Unburdening onboarding in Software Product Lines
Venue: arXiv cs.SE 2021
Year: 2021

The number of studies focusing on onboarding in software organizations has increased significantly during the last years. However, current literature overlooks onboarding in Software Product Lines (SPLs). SPLs have been proven effective in managing the increasing variability of industry software and enabling systematic reuse of a product family. Despite these benefits, SPLs are complex and exhibit particular characteristics that distinguish them from traditional software. Due to these peculiarities, SPLs require a tailor-made onboarding process. Assistance tools might help. In this dissertation, we propose assistance tools (i.e., tools built on top of the software project that help learners understand and develop knowledge) as a means for helping newcomers during onboarding in SPLs.
Threshold-Based Data Exclusion Approach for Energy-Efficient Federated Edge Learning
Venue: arXiv cs.LG 2021
Year: 2021

Federated edge learning (FEEL) is a promising distributed learning technique for next-generation wireless networks. FEEL preserves the user's privacy, reduces the communication costs, and exploits the unprecedented capabilities of edge devices to train a shared global model by leveraging a massive amount of data generated at the network edge. However, FEEL might significantly shorten energy-constrained participating devices' lifetime due to the power consumed during the model training round. This paper proposes a novel approach that endeavors to minimize computation and communication energy consumption during FEEL rounds to address this issue. First, we introduce a modified local training algorithm that intelligently selects only the samples that enhance the model's quality based on a predetermined threshold probability. Then, the problem is formulated as joint energy minimization and resource allocation optimization problem to obtain the optimal local computation time and the optimal transmission time that minimize the total energy consumption considering the worker's energy budget, available bandwidth, channel states, beamforming, and local CPU speed. After that, we introduce a tractable solution to the formulated problem that ensures the robustness of FEEL. Our simulation results show that our solution substantially outperforms the baseline FEEL algorithm as it reduces the local consumed energy by up to 79%.
Updatable Learned Index with Precise Positions
Venue: arXiv cs.DB 2021
Year: 2021

Index plays an essential role in modern database engines to accelerate the query processing. The new paradigm of "learned index" has significantly changed the way of designing index structures in DBMS. The key insight is that indexes could be regarded as learned models that predict the position of a lookup key in the dataset. While such studies show promising results in both lookup time and index size, they cannot efficiently support update operations. Although recent studies have proposed some preliminary approaches to support update, they are at the cost of scarifying the lookup performance as they suffer from the overheads brought by imprecise predictions in the leaf nodes. In this paper, we propose LIPP, a brand new framework of learned index to address such issues. Similar with state-of-the-art learned index structures, LIPP is able to support all kinds of index operations, namely lookup query, range query, insert, delete, update and bulkload. Meanwhile, we overcome the limitations of previous studies by properly extending the tree structure when dealing with update operations so as to eliminate the deviation of location predicted by the models in the leaf nodes. Moreover, we further propose a dynamic adjustment strategy to ensure that the height of the tree index is tightly bounded and provide comprehensive theoretical analysis to illustrate it. We conduct an extensive set of experiments on several real-life and synthetic datasets. The results demonstrate that our method consistently outperforms state-of-the-art solutions, achieving by up to 4x for a broader class of workloads with different index operations.
A-FMI: Learning Attributions from Deep Networks via Feature Map Importance
Venue: arXiv cs.CV 2021
Year: 2021

Gradient-based attribution methods can aid in the understanding of convolutional neural networks (CNNs). However, the redundancy of attribution features and the gradient saturation problem, which weaken the ability to identify significant features and cause an explanation focus shift, are challenges that attribution methods still face. In this work, we propose: 1) an essential characteristic, Strong Relevance, when selecting attribution features; 2) a new concept, feature map importance (FMI), to refine the contribution of each feature map, which is faithful to the CNN model; and 3) a novel attribution method via FMI, termed A-FMI, to address the gradient saturation problem, which couples the target image with a reference image, and assigns the FMI to the difference-from-reference at the granularity of feature map. Through visual inspections and qualitative evaluations on the ImageNet dataset, we show the compelling advantages of A-FMI on its faithfulness, insensitivity to the choice of reference, class discriminability, and superior explanation performance compared with popular attribution methods across varying CNN architectures.