OpenRaiser/Intern-Atlas huggingface dataset:
Cell-Free Massive MIMO-OFDM Transmission over Frequency-Selective Fading Channels
Venue: arXiv cs.IT 2021
Year: 2021

This letter presents and analyzes orthogonal frequency-division multiplexing (OFDM)-based multi-carrier transmission for cell-free massive multi-input multi-output (CFmMIMO) over frequency-selective fading channels. Frequency-domain conjugate beamforming, pilot assignment, and user-specific resource allocation are proposed. CFmMIMO-OFDM is scalable to serve a massive number of users and is flexible to offer diverse data rates for heterogeneous applications.
Scene-aware Learning Network for Radar Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

Object detection is essential to safe autonomous or assisted driving. Previous works usually utilize RGB images or LiDAR point clouds to identify and localize multiple objects in self-driving. However, cameras tend to fail in bad driving conditions, e.g. bad weather or weak lighting, while LiDAR scanners are too expensive to get widely deployed in commercial applications. Radar has been drawing more and more attention due to its robustness and low cost. In this paper, we propose a scene-aware radar learning framework for accurate and robust object detection. First, the learning framework contains branches conditioning on the scene category of the radar sequence; with each branch optimized for a specific type of scene. Second, three different 3D autoencoder-based architectures are proposed for radar object detection and ensemble learning is performed over the different architectures to further boost the final performance. Third, we propose novel scene-aware sequence mix augmentation (SceneMix) and scene-specific post-processing to generate more robust detection results. In the ROD2021 Challenge, we achieved a final result of average precision of 75.0% and an average recall of 81.0%. Moreover, in the parking lot scene, our framework ranks first with an average precision of 97.8% and an average recall of 98.6%, which demonstrates the effectiveness of our framework.
SSPNet: Scale Selection Pyramid Network for Tiny Person Detection from UAV Images
Venue: arXiv cs.CV 2021
Year: 2021

With the increasing demand for search and rescue, it is highly demanded to detect objects of interest in large-scale images captured by Unmanned Aerial Vehicles (UAVs), which is quite challenging due to extremely small scales of objects. Most existing methods employed Feature Pyramid Network (FPN) to enrich shallow layers' features by combing deep layers' contextual features. However, under the limitation of the inconsistency in gradient computation across different layers, the shallow layers in FPN are not fully exploited to detect tiny objects. In this paper, we propose a Scale Selection Pyramid network (SSPNet) for tiny person detection, which consists of three components: Context Attention Module (CAM), Scale Enhancement Module (SEM), and Scale Selection Module (SSM). CAM takes account of context information to produce hierarchical attention heatmaps. SEM highlights features of specific scales at different layers, leading the detector to focus on objects of specific scales instead of vast backgrounds. SSM exploits adjacent layers' relationships to fulfill suitable feature sharing between deep layers and shallow layers, thereby avoiding the inconsistency in gradient computation across different layers. Besides, we propose a Weighted Negative Sampling (WNS) strategy to guide the detector to select more representative samples. Experiments on the TinyPerson benchmark show that our method outperforms other state-of-the-art (SOTA) detectors.
Certifying DFA Bounds for Recognition and Separation
Venue: arXiv cs.FL 2021
Year: 2021

The automation of decision procedures makes certification essential. We suggest to use determinacy of turn-based two-player games with regular winning conditions in order to generate certificates for the number of states that a deterministic finite automaton (DFA) needs in order to recognize a given language. Given a language $L$ and a bound $k$, recognizability of $L$ by a DFA with $k$ states is reduced to a game between Prover and Refuter. The interaction along the game then serves as a certificate. Certificates generated by Prover are minimal DFAs. Certificates generated by Refuter are faulty attempts to define the required DFA. We compare the length of offline certificates, which are generated with no interaction between Prover and Refuter, and online certificates, which are based on such an interaction, and are thus shorter. We show that our approach is useful also for certification of separability of regular languages by a DFA of a given size. Unlike DFA minimization, which can be solved in polynomial time, separation is NP-complete, and thus the certification approach is essential. In addition, we prove NP-completeness of a strict version of separation.
A Typology of Data Anomalies
Venue: arXiv cs.LG 2021
Year: 2021

Anomalies are cases that are in some way unusual and do not appear to fit the general patterns present in the dataset. Several conceptualizations exist to distinguish between different types of anomalies. However, these are either too specific to be generally applicable or so abstract that they neither provide concrete insight into the nature of anomaly types nor facilitate the functional evaluation of anomaly detection algorithms. With the recent criticism on 'black box' algorithms and analytics it has become clear that this is an undesirable situation. This paper therefore introduces a general typology of anomalies that offers a clear and tangible definition of the different types of anomalies in datasets. The typology also facilitates the evaluation of the functional capabilities of anomaly detection algorithms and as a framework assists in analyzing the conceptual levels of data, patterns and anomalies. Finally, it serves as an analytical tool for studying anomaly types from other typologies.
Attribute-aware Explainable Complementary Clothing Recommendation
Venue: arXiv cs.IR 2021
Year: 2021

Modelling mix-and-match relationships among fashion items has become increasingly demanding yet challenging for modern E-commerce recommender systems. When performing clothes matching, most existing approaches leverage the latent visual features extracted from fashion item images for compatibility modelling, which lacks explainability of generated matching results and can hardly convince users of the recommendations. Though recent methods start to incorporate pre-defined attribute information (e.g., colour, style, length, etc.) for learning item representations and improving the model interpretability, their utilisation of attribute information is still mainly reserved for enhancing the learned item representations and generating explanations via post-processing. As a result, this creates a severe bottleneck when we are trying to advance the recommendation accuracy and generating fine-grained explanations since the explicit attributes have only loose connections to the actual recommendation process. This work aims to tackle the explainability challenge in fashion recommendation tasks by proposing a novel Attribute-aware Fashion Recommender (AFRec). Specifically, AFRec recommender assesses the outfit compatibility by explicitly leveraging the extracted attribute-level representations from each item's visual feature. The attributes serve as the bridge between two fashion items, where we quantify the affinity of a pair of items through the learned compatibility between their attributes. Extensive experiments have demonstrated that, by making full use of the explicit attributes in the recommendation process, AFRec is able to achieve state-of-the-art recommendation accuracy and generate intuitive explanations at the same time.
IITP at WAT 2021: System description for English-Hindi Multimodal Translation Task
Venue: arXiv cs.CL 2021
Year: 2021

Neural Machine Translation (NMT) is a predominant machine translation technology nowadays because of its end-to-end trainable flexibility. However, NMT still struggles to translate properly in low-resource settings specifically on distant language pairs. One way to overcome this is to use the information from other modalities if available. The idea is that despite differences in languages, both the source and target language speakers see the same thing and the visual representation of both the source and target is the same, which can positively assist the system. Multimodal information can help the NMT system to improve the translation by removing ambiguity on some phrases or words. We participate in the 8th Workshop on Asian Translation (WAT - 2021) for English-Hindi multimodal translation task and achieve 42.47 and 37.50 BLEU points for Evaluation and Challenge subset, respectively.
Repulsive Surfaces
Venue: arXiv cs.GR 2021
Year: 2021

Functionals that penalize bending or stretching of a surface play a key role in geometric and scientific computing, but to date have ignored a very basic requirement: in many situations, surfaces must not pass through themselves or each other. This paper develops a numerical framework for optimization of surface geometry while avoiding (self-)collision. The starting point is the tangent-point energy, which effectively pushes apart pairs of points that are close in space but distant along the surface. We develop a discretization of this energy for triangle meshes, and introduce a novel acceleration scheme based on a fractional Sobolev inner product. In contrast to similar schemes developed for curves, we avoid the complexity of building a multiresolution mesh hierarchy by decomposing our preconditioner into two ordinary Poisson equations, plus forward application of a fractional differential operator. We further accelerate this scheme via hierarchical approximation, and describe how to incorporate a variety of constraints (on area, volume, etc.). Finally, we explore how this machinery might be applied to problems in mathematical visualization, geometric modeling, and geometry processing.
PyLUSAT: An open-source Python toolkit for GIS-based land use suitability analysis
Venue: arXiv cs.CY 2021
Year: 2021

Desktop GIS applications, such as ArcGIS and QGIS, provide tools essential for conducting suitability analysis, an activity that is central in formulating a land-use plan. But, when it comes to building complicated land-use suitability models, these applications have several limitations, including operating system-dependence, lack of dedicated modules, insufficient reproducibility, and difficult, if not impossible, deployment on a computing cluster. To address the challenges, this paper introduces PyLUSAT: Python for Land Use Suitability Analysis Tools. PyLUSAT is an open-source software package that provides a series of tools (functions) to conduct various tasks in a suitability modeling workflow. These tools were evaluated against comparable tools in ArcMap 10.4 with respect to both accuracy and computational efficiency. Results showed that PyLUSAT functions were two to ten times more efficient depending on the job's complexity, while generating outputs with similar accuracy compared to the ArcMap tools. PyLUSAT also features extensibility and cross-platform compatibility. It has been used to develop fourteen QGIS Processing Algorithms and implemented on a high-performance computational cluster (HiPerGator at the University of Florida) to expedite the process of suitability analysis. All these properties make PyLUSAT a competitive alternative solution for urban planners/researchers to customize and automate suitability analysis as well as integrate the technique into a larger analytical framework.
A Comprehensive Survey on the State-of-the-art Data Provenance Approaches for Security Enforcement
Venue: arXiv cs.CR 2021
Year: 2021

Data provenance collects comprehensive information about the events and operations in a computer system at both application and system levels. It provides a detailed and accurate history of transactions that help delineate the data flow scenario across the whole system. Data provenance helps achieve system resilience by uncovering several malicious attack traces after a system compromise that are leveraged by the analyzer to understand the attack behavior and discover the level of damage. Existing literature demonstrates a number of research efforts on information capture, management, and analysis of data provenance. In recent years, provenance in IoT devices attracts several research efforts because of the proliferation of commodity IoT devices. In this survey paper, we present a comparative study of the state-of-the-art approaches to provenance by classifying them based on frameworks, deployed techniques, and subjects of interest. We also discuss the emergence and scope of data provenance in IoT networks. Finally, we present the urgency in several directions that data provenance needs to pursue, including data management and analysis.
Linear-Time Model Checking Branching Processes
Venue: arXiv cs.LO 2021
Year: 2021

(Multi-type) branching processes are a natural and well-studied model for generating random infinite trees. Branching processes feature both nondeterministic and probabilistic branching, generalizing both transition systems and Markov chains (but not generally Markov decision processes). We study the complexity of model checking branching processes against linear-time omega-regular specifications: is it the case almost surely that every branch of a tree randomly generated by the branching process satisfies the omega-regular specification? The main result is that for LTL specifications this problem is in PSPACE, subsuming classical results for transition systems and Markov chains, respectively. The underlying general model-checking algorithm is based on the automata-theoretic approach, using unambiguous Büchi automata.
Protected probabilistic classification
Venue: arXiv cs.LG 2021
Year: 2021

This paper proposes a way of protecting probabilistic prediction models against changes in the data distribution, concentrating on the case of classification and paying particular attention to binary classification. This is important in applications of machine learning, where the quality of a trained prediction algorithm may drop significantly in the process of its exploitation. Our techniques are based on recent work on conformal test martingales and older work on prediction with expert advice, namely tracking the best expert.
Single Model for Influenza Forecasting of Multiple Countries by Multi-task Learning
Venue: arXiv cs.LG 2021
Year: 2021

The accurate forecasting of infectious epidemic diseases such as influenza is a crucial task undertaken by medical institutions. Although numerous flu forecasting methods and models based mainly on historical flu activity data and online user-generated contents have been proposed in previous studies, no flu forecasting model targeting multiple countries using two types of data exists at present. Our paper leverages multi-task learning to tackle the challenge of building one flu forecasting model targeting multiple countries; each country as each task. Also, to develop the flu prediction model with higher performance, we solved two issues; finding suitable search queries, which are part of the user-generated contents, and how to leverage search queries efficiently in the model creation. For the first issue, we propose the transfer approaches from English to other languages. For the second issue, we propose a novel flu forecasting model that takes advantage of search queries using an attention mechanism and extend the model to a multi-task model for multiple countries' flu forecasts. Experiments on forecasting flu epidemics in five countries demonstrate that our model significantly improved the performance by leveraging the search queries and multi-task learning compared to the baselines.
E-SC4R: Explaining Software Clustering for Remodularisation
Venue: arXiv cs.SE 2021
Year: 2021

Maintenance of existing software requires a large amount of time for comprehending the source code. The architecture of a software, however, may not be clear to maintainers if up to date documentations are not available. Software clustering is often used as a remodularisation and architecture recovery technique to help recover a semantic representation of the software design. Due to the diverse domains, structure, and behaviour of software systems, the suitability of different clustering algorithms for different software systems are not investigated thoroughly. Research that introduce new clustering techniques usually validate their approaches on a specific domain, which might limit its generalisability. If the chosen test subjects could only represent a narrow perspective of the whole picture, researchers might risk not being able to address the external validity of their findings. This work aims to fill this gap by introducing a new approach, Explaining Software Clustering for Remodularisation, to evaluate the effectiveness of different software clustering approaches. This work focuses on hierarchical clustering and Bunch clustering algorithms and provides information about their suitability according to the features of the software, which as a consequence, enables the selection of the most optimum algorithm and configuration from our existing pool of choices for a particular software system. The proposed framework is tested on 30 open source software systems with varying sizes and domains, and demonstrates that it can characterise both the strengths and weaknesses of the analysed software clustering algorithms using software features extracted from the code. The proposed approach also provides a better understanding of the algorithms behaviour through the application of dimensionality reduction techniques.
A contextual analysis of multi-layer perceptron models in classifying hand-written digits and letters: limited resources
Venue: arXiv cs.LG 2021
Year: 2021

Classifying hand-written digits and letters has taken a big leap with the introduction of ConvNets. However, on very constrained hardware the time necessary to train such models would be high. Our main contribution is twofold. First, we extensively test an end-to-end vanilla neural network (MLP) approach in pure numpy without any pre-processing or feature extraction done beforehand. Second, we show that basic data mining operations can significantly improve the performance of the models in terms of computational time, without sacrificing much accuracy. We illustrate our claims on a simpler variant of the Extended MNIST dataset, called Balanced EMNIST dataset. Our experiments show that, without any data mining, we get increased generalization performance when using more hidden layers and regularization techniques, the best model achieving 84.83% accuracy on a test dataset. Using dimensionality reduction done by PCA we were able to increase that figure to 85.08% with only 10% of the original feature space, reducing the memory size needed by 64%. Finally, adding methods to remove possibly harmful training samples like deviation from the mean helped us to still achieve over 84% test accuracy but with only 32.8% of the original memory size for the training set. This compares favorably to the majority of literature results obtained through similar architectures. Although this approach gets outshined by state-of-the-art models, it does scale to some (AlexNet, VGGNet) trained on 50% of the same dataset.
Poisoning Attack against Estimating from Pairwise Comparisons
Venue: arXiv cs.LG 2021
Year: 2021

As pairwise ranking becomes broadly employed for elections, sports competitions, recommendations, and so on, attackers have strong motivation and incentives to manipulate the ranking list. They could inject malicious comparisons into the training data to fool the victim. Such a technique is called poisoning attack in regression and classification tasks. In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attacks on pairwise ranking algorithms, which can be formalized as the dynamic and static games between the ranker and the attacker and can be modeled as certain kinds of integer programming problems. To break the computational hurdle of the underlying integer programming problems, we reformulate them into the distributionally robust optimization (DRO) problems, which are computationally tractable. Based on such DRO formulations, we propose two efficient poisoning attack algorithms and establish the associated theoretical guarantees. The effectiveness of the suggested poisoning attack strategies is demonstrated by a series of toy simulations and several real data experiments. These experimental results show that the proposed methods can significantly reduce the performance of the ranker in the sense that the correlation between the true ranking list and the aggregated results can be decreased dramatically.
Control of rough terrain vehicles using deep reinforcement learning
Venue: arXiv cs.RO 2021
Year: 2021

We explore the potential to control terrain vehicles using deep reinforcement in scenarios where human operators and traditional control methods are inadequate. This letter presents a controller that perceives, plans, and successfully controls a 16-tonne forestry vehicle with two frame articulation joints, six wheels, and their actively articulated suspensions to traverse rough terrain. The carefully shaped reward signal promotes safe, environmental, and efficient driving, which leads to the emergence of unprecedented driving skills. We test learned skills in a virtual environment, including terrains reconstructed from high-density laser scans of forest sites. The controller displays the ability to handle obstructing obstacles, slopes up to 27$^\circ$, and a variety of natural terrains, all with limited wheel slip, smooth, and upright traversal with intelligent use of the active suspensions. The results confirm that deep reinforcement learning has the potential to enhance control of vehicles with complex dynamics and high-dimensional observation data compared to human operators or traditional control methods, especially in rough terrain.
Faster-LTN: a neuro-symbolic, end-to-end object detection architecture
Venue: arXiv cs.CV 2021
Year: 2021

The detection of semantic relationships between objects represented in an image is one of the fundamental challenges in image interpretation. Neural-Symbolic techniques, such as Logic Tensor Networks (LTNs), allow the combination of semantic knowledge representation and reasoning with the ability to efficiently learn from examples typical of neural networks. We here propose Faster-LTN, an object detector composed of a convolutional backbone and an LTN. To the best of our knowledge, this is the first attempt to combine both frameworks in an end-to-end training setting. This architecture is trained by optimizing a grounded theory which combines labelled examples with prior knowledge, in the form of logical axioms. Experimental comparisons show competitive performance with respect to the traditional Faster R-CNN architecture.