OpenRaiser/Intern-Atlas huggingface dataset:
BMPQ: Bit-Gradient Sensitivity Driven Mixed-Precision Quantization of DNNs from Scratch
Venue: arXiv cs.CV 2021
Year: 2021

Large DNNs with mixed-precision quantization can achieve ultra-high compression while retaining high classification performance. However, because of the challenges in finding an accurate metric that can guide the optimization process, these methods either sacrifice significant performance compared to the 32-bit floating-point (FP-32) baseline or rely on a compute-expensive, iterative training policy that requires the availability of a pre-trained baseline. To address this issue, this paper presents BMPQ, a training method that uses bit gradients to analyze layer sensitivities and yield mixed-precision quantized models. BMPQ requires a single training iteration but does not need a pre-trained baseline. It uses an integer linear program (ILP) to dynamically adjust the precision of layers during training, subject to a fixed hardware budget. To evaluate the efficacy of BMPQ, we conduct extensive experiments with VGG16 and ResNet18 on CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets. Compared to the baseline FP-32 models, BMPQ can yield models that have 15.4x fewer parameter bits with a negligible drop in accuracy. Compared to the SOTA "during training", mixed-precision training scheme, our models are 2.1x, 2.2x, and 2.9x smaller, on CIFAR-10, CIFAR-100, and Tiny-ImageNet, respectively, with an improved accuracy of up to 14.54%.
Raw Produce Quality Detection with Shifted Window Self-Attention
Venue: arXiv cs.CV 2021
Year: 2021

Global food insecurity is expected to worsen in the coming decades with the accelerated rate of climate change and the rapidly increasing population. In this vein, it is important to remove inefficiencies at every level of food production. The recent advances in deep learning can help reduce such inefficiencies, yet their application has not yet become mainstream throughout the industry, inducing economic costs at a massive scale. To this point, modern techniques such as CNNs (Convolutional Neural Networks) have been applied to RPQD (Raw Produce Quality Detection) tasks. On the other hand, Transformer's successful debut in the vision among other modalities led us to expect a better performance with these Transformer-based models in RPQD. In this work, we exclusively investigate the recent state-of-the-art Swin (Shifted Windows) Transformer which computes self-attention in both intra- and inter-window fashion. We compare Swin Transformer against CNN models on four RPQD image datasets, each containing different kinds of raw produce: fruits and vegetables, fish, pork, and beef. We observe that Swin Transformer not only achieves better or competitive performance but also is data- and compute-efficient, making it ideal for actual deployment in real-world setting. To the best of our knowledge, this is the first large-scale empirical study on RPQD task, which we hope will gain more attention in future works.
Improving Depth Estimation using Location Information
Venue: arXiv cs.CV 2021
Year: 2021

The ability to accurately estimate depth information is crucial for many autonomous applications to recognize the surrounded environment and predict the depth of important objects. One of the most recently used techniques is monocular depth estimation where the depth map is inferred from a single image. This paper improves the self-supervised deep learning techniques to perform accurate generalized monocular depth estimation. The main idea is to train the deep model to take into account a sequence of the different frames, each frame is geotagged with its location information. This makes the model able to enhance depth estimation given area semantics. We demonstrate the effectiveness of our model to improve depth estimation results. The model is trained in a realistic environment and the results show improvements in the depth map after adding the location data to the model training phase.
Mechanics-based Analysis on Flagellated Robots
Venue: arXiv cs.RO 2021
Year: 2021

We explore the locomotion of soft robots in granular medium (GM) resulting from the elastic deformation of slender rods. A low-cost, rapidly fabricable robot inspired by the physiological structure of bacteria is presented. It consists of a rigid head, with a motor and batteries embedded, and multiple elastic rods (our model for flagella) to investigate locomotion in GM. The elastic flagella are rotated at one end by the motor, and they deform due to the drag from GM, propelling the robot. The external drag is determined by the flagellar shape, while the latter changes due to the competition between external loading and elastic forces. In this coupled fluid-structure interaction problem, we observe that increasing the number of flagella can decrease or increase the propulsive speed of the robot, depending on the physical parameters of the system. This nonlinearity in the functional relation between propulsion and the parameters of this simple robot motivates us to fundamentally analyze its mechanics using theory, numerical simulation, and experiments. We present a simple Euler-Bernoulli beam theory-based analytical framework that is capable of qualitatively capturing both cases. Theoretical prediction quantitatively matches experiments when the flagellar deformation is small. To account for the geometrically nonlinear deformation often encountered in soft robots and microbes, we implement a simulation framework that incorporates discrete differential geometry-based simulations of elastic rods, a resistive force theory-based model for drag, and a modified Stokes law for the hydrodynamics of the robot head. Comparison with experimental data indicates that the simulations can quantitatively predict robotic motion. Overall, the theoretical and numerical tools presented in this paper can shed light on the design and control of this class of articulated robots in granular or fluid media.
AET-SGD: Asynchronous Event-triggered Stochastic Gradient Descent
Venue: arXiv cs.LG 2021
Year: 2021

Communication cost is the main bottleneck for the design of effective distributed learning algorithms. Recently, event-triggered techniques have been proposed to reduce the exchanged information among compute nodes and thus alleviate the communication cost. However, most existing event-triggered approaches only consider heuristic event-triggered thresholds. They also ignore the impact of computation and network delay, which play an important role on the training performance. In this paper, we propose an Asynchronous Event-triggered Stochastic Gradient Descent (SGD) framework, called AET-SGD, to i) reduce the communication cost among the compute nodes, and ii) mitigate the impact of the delay. Compared with baseline event-triggered methods, AET-SGD employs a linear increasing sample size event-triggered threshold, and can significantly reduce the communication cost while keeping good convergence performance. We implement AET-SGD and evaluate its performance on multiple representative data sets, including MNIST, FashionMNIST, KMNIST and CIFAR10. The experimental results validate the correctness of the design and show a significant communication cost reduction from 44x to 120x, compared to the state of the art. Our results also show that AET-SGD can resist large delay from the straggler nodes while obtaining a decent performance and a desired speedup ratio.
Source Feature Compression for Object Classification in Vision-Based Underwater Robotics
Venue: arXiv cs.CV 2021
Year: 2021

New efficient source feature compression solutions are proposed based on a two-stage Walsh-Hadamard Transform (WHT) for Convolutional Neural Network (CNN)-based object classification in underwater robotics. The object images are firstly transformed by WHT following a two-stage process. The transform-domain tensors have large values concentrated in the upper left corner of the matrices in the RGB channels. By observing this property, the transform-domain matrix is partitioned into inner and outer regions. Consequently, two novel partitioning methods are proposed in this work: (i) fixing the size of inner and outer regions; and (ii) adjusting the size of inner and outer regions adaptively per image. The proposals are evaluated with an underwater object dataset captured from the Raritan River in New Jersey, USA. It is demonstrated and verified that the proposals reduce the training time effectively for learning-based underwater object classification task and increase the accuracy compared with the competing methods. The object classification is an essential part of a vision-based underwater robot that can sense the environment and navigate autonomously. Therefore, the proposed method is well-suited for efficient computer vision-based tasks in underwater robotics applications.
A Compositional Approach to Parity Games
Venue: arXiv cs.GT 2021
Year: 2021

In this paper, we introduce open parity games, which is a compositional approach to parity games. This is achieved by adding open ends to the usual notion of parity games. We introduce the category of open parity games, which is defined using standard definitions for graph games. We also define a graphical language for open parity games as a prop, which have recently been used in many applications as graphical languages. We introduce a suitable semantic category inspired by the work by Grellois and Melliès on the semantics of higher-order model checking. Computing the set of winning positions in open parity games yields a functor to the semantic category. Finally, by interpreting the graphical language in the semantic category, we show that this computation can be carried out compositionally.
State Compression and Quantitative Assessment Model for Assessing Security Risks in the Oil and Gas Transmission Systems
Venue: arXiv cs.CR 2021
Year: 2021

The SCADA system is the foundation of the large-scale industrial control system. It is widely used in industries of petrochemistry, electric power, pipeline, etc. The natural gas SCADA system is among the critical infrastructure systems that have security issues related to trusted communications in transactions at the control system layer, and lack quantitative risk assessment and mitigation models. However, to guarantee the security of the Oil and Gas Transmission SCADA systems (OGTSS), there should be a holistic security system that considers the nature of these SCADA systems. In this paper, we augment our Security Awareness Framework with two new contributions, (i) a Data Quantization and State Compression Approach (DQSCA) that improves the classification accuracy, speeds up the detection algorithm, and reduces the computational resource consumption. DQSCA reduces the size of processed data while preserving original key events and patterns within the datasets. (ii) A quantitative risk assessment model that carries out regular system information security evaluation and assessment on the SCADA system using a deductive process. Our experiments denote that DQSCA has a low negative impact on the reduction of the detection accuracy (2.45% and 4.45%) while it reduces the detection time much (27.74% and 42.06%) for the Turnipseed and Gao datasets respectively. Furthermore, the mean absolute percentage error (MAPE) rate for the proposed risk assessment model is lower than the intrusion response system (Suricata) for the DOS, Response Injection, and Command Injection attacks by 59.80%, 73.72%, and 66.96% respectively.
Processing M.A. Castrén's Materials: Multilingual Typed and Handwritten Manuscripts
Venue: arXiv cs.CL 2021
Year: 2021

The study forms a technical report of various tasks that have been performed on the materials collected and published by Finnish ethnographer and linguist, Matthias Alexander Castrén (1813-1852). The Finno-Ugrian Society is publishing Castrén's manuscripts as new critical and digital editions, and at the same time different research groups have also paid attention to these materials. We discuss the workflows and technical infrastructure used, and consider how datasets that benefit different computational tasks could be created to further improve the usability of these materials, and also to aid the further processing of similar archived collections. We specifically focus on the parts of the collections that are processed in a way that improves their usability in more technical applications, complementing the earlier work on the cultural and linguistic aspects of these materials. Most of these datasets are openly available in Zenodo. The study points to specific areas where further research is needed, and provides benchmarks for text recognition tasks.
A Survey on Gender Bias in Natural Language Processing
Venue: arXiv cs.CL 2021
Year: 2021

Language can be used as a means of reproducing and enforcing harmful stereotypes and biases and has been analysed as such in numerous research. In this paper, we present a survey of 304 papers on gender bias in natural language processing. We analyse definitions of gender and its categories within social sciences and connect them to formal definitions of gender bias in NLP research. We survey lexica and datasets applied in research on gender bias and then compare and contrast approaches to detecting and mitigating gender bias. We find that research on gender bias suffers from four core limitations. 1) Most research treats gender as a binary variable neglecting its fluidity and continuity. 2) Most of the work has been conducted in monolingual setups for English or other high-resource languages. 3) Despite a myriad of papers on gender bias in NLP methods, we find that most of the newly developed algorithms do not test their models for bias and disregard possible ethical considerations of their work. 4) Finally, methodologies developed in this line of research are fundamentally flawed covering very limited definitions of gender bias and lacking evaluation baselines and pipelines. We suggest recommendations towards overcoming these limitations as a guide for future research.
Recruiting credible participants for field studies in software engineering research
Venue: arXiv cs.SE 2021
Year: 2021

Context: Software practitioners are a primary provider of information for field studies in software engineering. Research typically recruits practitioners through some kind of sampling. But sampling may not in itself recruit credible participants. Objectives: To propose and demonstrate a framework for recruiting professional practitioners as credible participants in field studies of software engineering. Method: We review existing guidelines, checklists and other advisory sources on recruiting participants for field studies. We develop a framework, partly based on our prior research and on the research of others. We search for and select three exemplar studies (a case study, an interview study and a survey study) and use those to demonstrate the application of the framework. Results: Whilst existing guidelines etc. recognise the importance of recruiting participants, there is limited guidance on how to recruit the right participants. Our demonstration of the framework with three exemplars shows that at least some members of the research community are aware of the need to carefully recruit participants. Conclusions: The framework provides a new perspective for thinking about the recruitment of credible practitioners for field studies of software engineering. In particular, the framework identifies a number of characteristics not explicitly addressed by existing guidelines.
Robust Convergence in Federated Learning through Label-wise Clustering
Venue: arXiv cs.LG 2021
Year: 2021

Non-IID dataset and heterogeneous environment of the local clients are regarded as a major issue in Federated Learning (FL), causing a downturn in the convergence without achieving satisfactory performance. In this paper, we propose a novel Label-wise clustering algorithm that guarantees the trainability among geographically dispersed heterogeneous local clients, by selecting only the local models trained with a dataset that approximates into uniformly distributed class labels, which is likely to obtain faster minimization of the loss and increment the accuracy among the FL network. Through conducting experiments on the suggested six common non-IID scenarios, we empirically show that the vanilla FL aggregation model is incapable of gaining robust convergence generating biased pre-trained local models and drifting the local weights to mislead the trainability in the worst case. Moreover, we quantitatively estimate the expected performance of the local models before training, which offers a global server to select the optimal clients, saving additional computational costs. Ultimately, in order to gain resolution of the non-convergence in such non-IID situations, we design clustering algorithms based on local input class labels, accommodating the diversity and assorting clients that could lead the overall system to attain the swift convergence as global training continues. Our paper shows that proposed Label-wise clustering demonstrates prompt and robust convergence compared to other FL algorithms when local training datasets are non-IID or coexist with IID through multiple experiments.
Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization
Venue: arXiv cs.LG 2021
Year: 2021

The finite invert Beta-Liouville mixture model (IBLMM) has recently gained some attention due to its positive data modeling capability. Under the conventional variational inference (VI) framework, the analytically tractable solution to the optimization of the variational posterior distribution cannot be obtained, since the variational object function involves evaluation of intractable moments. With the recently proposed extended variational inference (EVI) framework, a new function is proposed to replace the original variational object function in order to avoid intractable moment computation, so that the analytically tractable solution of the IBLMM can be derived in an elegant way. The good performance of the proposed approach is demonstrated by experiments with both synthesized data and a real-world application namely text categorization.
Training Time Minimization for Federated Edge Learning with Optimized Gradient Quantization and Bandwidth Allocation
Venue: arXiv cs.IT 2021
Year: 2021

Training a machine learning model with federated edge learning (FEEL) is typically time-consuming due to the constrained computation power of edge devices and limited wireless resources in edge networks. In this paper, the training time minimization problem is investigated in a quantized FEEL system, where the heterogeneous edge devices send quantized gradients to the edge server via orthogonal channels. In particular, a stochastic quantization scheme is adopted for compression of uploaded gradients, which can reduce the burden of per-round communication but may come at the cost of increasing number of communication rounds. The training time is modeled by taking into account the communication time, computation time and the number of communication rounds. Based on the proposed training time model, the intrinsic trade-off between the number of communication rounds and per-round latency is characterized. Specifically, we analyze the convergence behavior of the quantized FEEL in terms of the optimality gap. Further, a joint data-and-model-driven fitting method is proposed to obtain the exact optimality gap, based on which the closed-form expressions for the number of communication rounds and the total training time are obtained. Constrained by total bandwidth, the training time minimization problem is formulated as a joint quantization level and bandwidth allocation optimization problem. To this end, an algorithm based on alternating optimization is proposed, which alternatively solves the subproblem of quantization optimization via successive convex approximation and the subproblem of bandwidth allocation via bisection search. With different learning tasks and models, the validation of our analysis and the near-optimal performance of the proposed optimization algorithm are demonstrated by the experimental results.
Overcoming Mode Collapse with Adaptive Multi Adversarial Training
Venue: arXiv cs.CV 2021
Year: 2021

Generative Adversarial Networks (GANs) are a class of generative models used for various applications, but they have been known to suffer from the mode collapse problem, in which some modes of the target distribution are ignored by the generator. Investigative study using a new data generation procedure indicates that the mode collapse of the generator is driven by the discriminator's inability to maintain classification accuracy on previously seen samples, a phenomenon called Catastrophic Forgetting in continual learning. Motivated by this observation, we introduce a novel training procedure that adaptively spawns additional discriminators to remember previous modes of generation. On several datasets, we show that our training scheme can be plugged-in to existing GAN frameworks to mitigate mode collapse and improve standard metrics for GAN evaluation.
ADAPQUEST: A Software for Web-Based Adaptive Questionnaires based on Bayesian Networks
Venue: arXiv cs.AI 2021
Year: 2021

We introduce ADAPQUEST, a software tool written in Java for the development of adaptive questionnaires based on Bayesian networks. Adaptiveness is intended here as the dynamical choice of the question sequence on the basis of an evolving model of the skill level of the test taker. Bayesian networks offer a flexible and highly interpretable framework to describe such testing process, especially when coping with multiple skills. ADAPQUEST embeds dedicated elicitation strategies to simplify the elicitation of the questionnaire parameters. An application of this tool for the diagnosis of mental disorders is also discussed together with some implementation details.
The link between countries' economic and scientific wealth has a complex dependence on technological activity and research policy
Venue: arXiv cs.DL 2021
Year: 2021

We studied the research performance of 69 countries by considering two different types of new knowledge: incremental (normal) and fundamental (radical). In principle, these two types of new knowledge should be assessed at two very different levels of citations, but we demonstrate that a simpler assessment can be performed based on the total number of papers (P) and the ratio of the number of papers in the global top 10% of most cited papers divided to the total number of papers (Ptop 10%/P). P represents the quantity, whereas the Ptop 10%/P ratio represents the efficiency. In ideal countries, P and the Ptop 10%/P ratio are linked to the gross domestic product (GDP) and GDP the per capita, respectively. Only countries with high Ptop 10%/P ratios participate actively in the creation of fundamental new knowledge and have Noble laureates. In real countries, the link between economic and scientific wealth can be modified by the technological activity and the research policy. We discuss how technological activity may decrease the Ptop 10%/P ratio while only slightly affecting the capacity to create fundamental new knowledge; in such countries, many papers may report incremental innovations that do not drive the advancement of knowledge. Japan is the clearest example of this, although there are many less extreme examples. Independently of technological activity, research policy has a strong influence on the Ptop 10%/P ratio, which may be higher or lower than expected from the GDP per capita depending on the success of the research policy.