OpenRaiser/Intern-Atlas huggingface dataset:
A Survey of Deep Learning Techniques for Weed Detection from Images
Venue: arXiv cs.CV 2021
Year: 2021

The rapid advances in Deep Learning (DL) techniques have enabled rapid detection, localisation, and recognition of objects from images or videos. DL techniques are now being used in many applications related to agriculture and farming. Automatic detection and classification of weeds can play an important role in weed management and so contribute to higher yields. Weed detection in crops from imagery is inherently a challenging problem because both weeds and crops have similar colours ('green-on-green'), and their shapes and texture can be very similar at the growth phase. Also, a crop in one setting can be considered a weed in another. In addition to their detection, the recognition of specific weed species is essential so that targeted controlling mechanisms (e.g. appropriate herbicides and correct doses) can be applied. In this paper, we review existing deep learning-based weed detection and classification techniques. We cover the detailed literature on four main procedures, i.e., data acquisition, dataset preparation, DL techniques employed for detection, location and classification of weeds in crops, and evaluation metrics approaches. We found that most studies applied supervised learning techniques, they achieved high classification accuracy by fine-tuning pre-trained models on any plant dataset, and past experiments have already achieved high accuracy when a large amount of labelled data is available.
Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we study asynchronous federated learning (FL) in a wireless distributed learning network (WDLN). To allow each edge device to use its local data more efficiently via asynchronous FL, transmission scheduling in the WDLN for asynchronous FL should be carefully determined considering system uncertainties, such as time-varying channel and stochastic data arrivals, and the scarce radio resources in the WDLN. To address this, we propose a metric, called an effectivity score, which represents the amount of learning from asynchronous FL. We then formulate an Asynchronous Learning-aware transmission Scheduling (ALS) problem to maximize the effectivity score and develop three ALS algorithms, called ALSA-PI, BALSA, and BALSA-PO, to solve it. If the statistical information about the uncertainties is known, the problem can be optimally and efficiently solved by ALSA-PI. Even if not, it can be still optimally solved by BALSA that learns the uncertainties based on a Bayesian approach using the state information reported from devices. BALSA-PO suboptimally solves the problem, but it addresses a more restrictive WDLN in practice, where the AP can observe a limited state information compared with the information used in BALSA. We show via simulations that the models trained by our ALS algorithms achieve performances close to that by an ideal benchmark and outperform those by other state-of-the-art baseline scheduling algorithms in terms of model accuracy, training loss, learning speed, and robustness of learning. These results demonstrate that the adaptive scheduling strategy in our ALS algorithms is effective to asynchronous FL.
Botcha: Detecting Malicious Non-Human Traffic in the Wild
Venue: arXiv cs.LG 2021
Year: 2021

Malicious bots make up about a quarter of all traffic on the web, and degrade the performance of personalization and recommendation algorithms that operate on e-commerce sites. Positive-Unlabeled learning (PU learning) provides the ability to train a binary classifier using only positive (P) and unlabeled (U) instances. The unlabeled data comprises of both positive and negative classes. It is possible to find labels for strict subsets of non-malicious actors, e.g., the assumption that only humans purchase during web sessions, or clear CAPTCHAs. However, finding signals of malicious behavior is almost impossible due to the ever-evolving and adversarial nature of bots. Such a set-up naturally lends itself to PU learning. Unfortunately, standard PU learning approaches assume that the labeled set of positives are a random sample of all positives, this is unlikely to hold in practice. In this work, we propose two modifications to PU learning that make it more robust to violations of the selected-completely-at-random assumption, leading to a system that can filter out malicious bots. In one public and one proprietary dataset, we show that proposed approaches are better at identifying humans in web data than standard PU learning methods.
Learning Robotic Manipulation Tasks via Task Progress based Gaussian Reward and Loss Adjusted Exploration
Venue: arXiv cs.RO 2021
Year: 2021

Multi-step manipulation tasks in unstructured environments are extremely challenging for a robot to learn. Such tasks interlace high-level reasoning that consists of the expected states that can be attained to achieve an overall task and low-level reasoning that decides what actions will yield these states. We propose a model-free deep reinforcement learning method to learn multi-step manipulation tasks. We introduce a Robotic Manipulation Network (RoManNet), which is a vision-based model architecture, to learn the action-value functions and predict manipulation action candidates. We define a Task Progress based Gaussian (TPG) reward function that computes the reward based on actions that lead to successful motion primitives and progress towards the overall task goal. To balance the ratio of exploration/exploitation, we introduce a Loss Adjusted Exploration (LAE) policy that determines actions from the action candidates according to the Boltzmann distribution of loss estimates. We demonstrate the effectiveness of our approach by training RoManNet to learn several challenging multi-step robotic manipulation tasks in both simulation and real-world. Experimental results show that our method outperforms the existing methods and achieves state-of-the-art performance in terms of success rate and action efficiency. The ablation studies show that TPG and LAE are especially beneficial for tasks like multiple block stacking. Code is available at: https://github.com/skumra/romannet
Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths
Venue: arXiv cs.CV 2021
Year: 2021

Quantizing deep networks with adaptive bit-widths is a promising technique for efficient inference across many devices and resource constraints. In contrast to static methods that repeat the quantization process and train different models for different constraints, adaptive quantization enables us to flexibly adjust the bit-widths of a single deep network during inference for instant adaptation in different scenarios. While existing research shows encouraging results on common image classification benchmarks, this paper investigates how to train such adaptive networks more effectively. Specifically, we present two novel techniques for quantizing deep neural networks with adaptive bit-widths of weights and activations. First, we propose a collaborative strategy to choose a high-precision teacher for transferring knowledge to the low-precision student while jointly optimizing the model with all bit-widths. Second, to effectively transfer knowledge, we develop a dynamic block swapping method by randomly replacing the blocks in the lower-precision student network with the corresponding blocks in the higher-precision teacher network. Extensive experiments on multiple image classification datasets including video classification benchmarks for the first time, well demonstrate the efficacy of our approach over state-of-the-art methods.
Online Orthogonal Dictionary Learning Based on Frank-Wolfe Method
Venue: arXiv cs.LG 2021
Year: 2021

Dictionary learning is a widely used unsupervised learning method in signal processing and machine learning. Most existing works of dictionary learning are in an offline manner. There are mainly two offline ways for dictionary learning. One is to do an alternative optimization of both the dictionary and the sparse code; the other way is to optimize the dictionary by restricting it over the orthogonal group. The latter one is called orthogonal dictionary learning which has a lower complexity implementation, hence, it is more favorable for lowcost devices. However, existing schemes on orthogonal dictionary learning only work with batch data and can not be implemented online, which is not applicable for real-time applications. This paper proposes a novel online orthogonal dictionary scheme to dynamically learn the dictionary from streaming data without storing the historical data. The proposed scheme includes a novel problem formulation and an efficient online algorithm design with convergence analysis. In the problem formulation, we relax the orthogonal constraint to enable an efficient online algorithm. In the algorithm design, we propose a new Frank-Wolfe-based online algorithm with a convergence rate of O(ln t/t^(1/4)). The convergence rate in terms of key system parameters is also derived. Experiments with synthetic data and real-world sensor readings demonstrate the effectiveness and efficiency of the proposed online orthogonal dictionary learning scheme.
LoBSTr: Real-time Lower-body Pose Prediction from Sparse Upper-body Tracking Signals
Venue: arXiv cs.GR 2021
Year: 2021

With the popularization of game and VR/AR devices, there is a growing need for capturing human motion with a sparse set of tracking data. In this paper, we introduce a deep neural-network (DNN) based method for real-time prediction of the lower-body pose only from the tracking signals of the upper-body joints. Specifically, our Gated Recurrent Unit (GRU)-based recurrent architecture predicts the lower-body pose and feet contact probability from past sequence of tracking signals of the head, hands and pelvis. A major feature of our method is that the input signal is represented with the velocity of tracking signals. We show that the velocity representation better models the correlation between the upper-body and lower-body motions and increase the robustness against the diverse scales and proportions of the user body than position-orientation representations. In addition, to remove foot-skating and floating artifacts, our network predicts feet contact state, which is used to post-process the lower-body pose with inverse kinematics to preserve the contact. Our network is lightweight so as to run in real-time applications. We show the effectiveness of our method through several quantitative evaluations against other architectures and input representations, with respect to wild tracking data obtained from commercial VR devices.
On the Memory Mechanism of Tensor-Power Recurrent Models
Venue: arXiv cs.LG 2021
Year: 2021

Tensor-power (TP) recurrent model is a family of non-linear dynamical systems, of which the recurrence relation consists of a p-fold (a.k.a., degree-p) tensor product. Despite such the model frequently appears in the advanced recurrent neural networks (RNNs), to this date there is limited study on its memory property, a critical characteristic in sequence tasks. In this work, we conduct a thorough investigation of the memory mechanism of TP recurrent models. Theoretically, we prove that a large degree p is an essential condition to achieve the long memory effect, yet it would lead to unstable dynamical behaviors. Empirically, we tackle this issue by extending the degree p from discrete to a differentiable domain, such that it is efficiently learnable from a variety of datasets. Taken together, the new model is expected to benefit from the long memory effect in a stable manner. We experimentally show that the proposed model achieves competitive performance compared to various advanced RNNs in both the single-cell and seq2seq architectures.
Explicit Construction of Minimum Bandwidth Rack-Aware Regenerating Codes
Venue: arXiv cs.IT 2021
Year: 2021

In large data centers, storage nodes are organized in racks, and the cross-rack transmission dominates the bandwidth cost. For the repair of single node failures, codes achieving the tradeoff between the storage redundancy and cross-rack repair bandwidth are called rack-aware regenerating codes (RRCs). In this work, we give the first explicit construction of RRCs with the minimum repair bandwidth (i.e., the cross-rack bandwidth equals the storage size per node). Our construction applies to all admissible parameters and has the lowest sub-packetization level. Moreover, the underlying finite fields are of size comparable to the number of storage nodes, which makes our codes more implementable in practice. Finally, for the convenience of practical use, we also establish a transformation to convert our codes into systematic codes.
Careful with That! Observation of Human Movements to Estimate Objects Properties
Venue: arXiv cs.RO 2021
Year: 2021

Humans are very effective at interpreting subtle properties of the partner's movement and use this skill to promote smooth interactions. Therefore, robotic platforms that support human partners in daily activities should acquire similar abilities. In this work we focused on the features of human motor actions that communicate insights on the weight of an object and the carefulness required in its manipulation. Our final goal is to enable a robot to autonomously infer the degree of care required in object handling and to discriminate whether the item is light or heavy, just by observing a human manipulation. This preliminary study represents a promising step towards the implementation of those abilities on a robot observing the scene with its camera. Indeed, we succeeded in demonstrating that it is possible to reliably deduct if the human operator is careful when handling an object, through machine learning algorithms relying on the stream of visual acquisition from either a robot camera or from a motion capture system. On the other hand, we observed that the same approach is inadequate to discriminate between light and heavy objects.
The High-Assurance ROS Framework
Venue: arXiv cs.SE 2021
Year: 2021

This tool paper presents the High-Assurance ROS (HAROS) framework. HAROS is a framework for the analysis and quality improvement of robotics software developed using the popular Robot Operating System (ROS). It builds on a static analysis foundation to automatically extract models from the source code. Such models are later used to enable other sorts of analyses, such as Model Checking, Runtime Verification, and Property-based Testing. It has been applied to multiple real-world examples, helping developers find and correct various issues.
Interpretable Multi-Modal Hate Speech Detection
Venue: arXiv cs.CL 2021
Year: 2021

With growing role of social media in shaping public opinions and beliefs across the world, there has been an increased attention to identify and counter the problem of hate speech on social media. Hate speech on online spaces has serious manifestations, including social polarization and hate crimes. While prior works have proposed automated techniques to detect hate speech online, these techniques primarily fail to look beyond the textual content. Moreover, few attempts have been made to focus on the aspects of interpretability of such models given the social and legal implications of incorrect predictions. In this work, we propose a deep neural multi-modal model that can: (a) detect hate speech by effectively capturing the semantics of the text along with socio-cultural context in which a particular hate expression is made, and (b) provide interpretable insights into decisions of our model. By performing a thorough evaluation of different modeling techniques, we demonstrate that our model is able to outperform the existing state-of-the-art hate speech classification approaches. Finally, we show the importance of social and cultural context features towards unearthing clusters associated with different categories of hate.
Towards a Better Understanding of Social Acceptability
Venue: arXiv cs.HC 2021
Year: 2021

Social contexts play an important role in understanding acceptance and use of technology. However, current approaches used in HCI to describe contextual influence do not capture it appropriately. On the one hand, the often used Technology Acceptance Model and related frameworks are too rigid to account for the nuanced variations of social situations. On the other hand, Goffman's dramaturgical model of social interactions emphasizes interpersonal relations but mostly overlooks the material (e.g., technology) that is central to HCI. As an alternative, we suggest an approach based on Social Practice Theory. We conceptualize social context as interactions between co-located social practices and acceptability as a matter of their (in)compatibilities. Finally, we outline how this approach provides designers with a better understanding of different types of social acceptability problems and helps finding appropriate solutions.
On Levenshtein Balls with Radius One
Venue: arXiv cs.IT 2021
Year: 2021

The rapid development of DNA storage has brought the deletion and insertion channel, once again, to the front line of research. When the number of deletions is equal to the number of insertions, the Fixed Length Levenshtein (FLL) metric is the right measure for the distance between two words of the same length. The size of a ball is one of the most fundamental parameters in any metric. The size of the ball with radius one in the FLL metric depends on the number of runs and the length of the alternating segments of the given word. In this work, we find the minimum, maximum, and average size of a ball with radius one, in the FLL metric. The related minimum and maximum sizes of a maximal anticode with diameter one are also calculated.
Conversational Norms for Human-Robot Dialogues
Venue: arXiv cs.CL 2021
Year: 2021

This paper describes a recently initiated research project aiming at supporting development of computerised dialogue systems that handle breaches of conversational norms such as the Gricean maxims, which describe how dialogue participants ideally form their utterances in order to be informative, relevant, brief, etc. Our approach is to model dialogue and norms with co-operating distributed grammar systems (CDGSs), and to develop methods to detect breaches and to handle them in dialogue systems for verbal human-robot interaction.
An Event-Based Stack For Data Transmission Through Underwater Multimodal Networks
Venue: arXiv cs.NI 2021
Year: 2021

The DESERT Underwater framework (http://desert-underwater.dei.unipd.it/), originally designed for simulating and testing underwater acoustic networks in sea trials, has recently been extended to support real payload data transmission through underwater multimodal networks. Specifically, the new version of the framework is now able to transmit data in real time through the EvoLogics S2C low-rate and high-rate acoustic modems, the SmartPORT low-cost acoustic underwater modem prototype (AHOI) for IoT applications, as well as Ethernet, surface WiFi, and the BlueComm optical modem. The system can also be tested in the lab by employing a simulated channel, and the EvoLogics S2C DMAC Emulator (DMACE)
Human error in motorcycle crashes: a methodology based on in-depth data to identify the skills needed and support training interventions for safe riding
Venue: arXiv cs.CY 2021
Year: 2021

This paper defines a methodology with in-depth data to identify the skills needed by riders in the highest risk crash configurations to reduce casualty rates. We present a case study using in-depth data of 803 powered-two-wheeler crashes. Seven high-risk crash configuration based on the pre-crash trajectories of the road-users involved were considered to investigate the human errors as crash contributors. Primary crash contributing factor, evasive manoeuvres performed, horizontal roadway alignment and speed-related factors were identified, along with the most frequent configurations and those with the greatest risk of severe injury. Straight Crossing Path/Lateral Direction was the most frequent crash configuration and Turn Across Path/ Opposing Direction that with the greatest risk of serious injury were identified. Multi-vehicle crashes cannot be considered as a homogenous category of crashes to which the same human failure is attributed, as different interactions between motorcyclists and other road users are associated with both different types of human error and different rider reactions. Human error in multiple-vehicle crashes related to crossing paths configurations were different from errors related to rear-end or head-on crashes. Multi-vehicle head-on crashes and single-vehicle collisions frequently occur along curves. The involved collision avoidance manoeuvres of the riders differed significantly among the highest risk crash configurations. The most relevant lack of skills are identified and linked to their most representative context. In most cases a combination of different skills was required simultaneously to avoid the crash. The findings underline the need to group accident cases, beyond the usual single-vehicle versus multi-vehicle collision approach. Our methodology can also be applied to support preventive actions based on riders training and eventually ADAS design.
Nonparametric estimation of the preferential attachment function from one network snapshot
Venue: arXiv cs.SI 2021
Year: 2021

Preferential attachment is commonly invoked to explain the emergence of those heavy-tailed degree distributions characteristic of growing network representations of diverse real-world phenomena. Experimentally confirming this hypothesis in real-world growing networks is an important frontier in network science research. Conventional preferential attachment estimation methods require that a growing network be observed across at least two snapshots in time. Numerous publicly available growing network datasets are, however, only available as single snapshots, leaving the applied network scientist with no means of measuring preferential attachment in these cases. We propose a nonparametric method, called PAFit-oneshot, for estimating preferential attachment in a growing network from one snapshot. PAFit-oneshot corrects for a previously unnoticed bias that arises when estimating preferential attachment values only for degrees observed in the single snapshot. Our work provides a means of measuring preferential attachment in a large number of publicly available one-snapshot networks. As a demonstration, we estimated preferential attachment in three such networks, and found sublinear preferential attachment in all cases. PAFit-oneshot is implemented in the R package PAFit.