OpenRaiser/Intern-Atlas huggingface dataset:
Performance Analysis and Improvement on DSRC Application for V2V Communication
Venue: arXiv cs.NI 2021
Year: 2021

In this paper, we focus on the performance of vehicle-to-vehicle (V2V) communication adopting the Dedicated Short Range Communication (DSRC) application in periodic broadcast mode. An analytical model is studied and a fixed point method is used to analyze the packet delivery ratio (PDR) and mean delay based on the IEEE 802.11p standard in a fully connected network under the assumption of perfect PHY performance. With the characteristics of V2V communication, we develop the Semi-persistent Contention Density Control (SpCDC) scheme to improve the DSRC performance. We use Monte Carlo simulation to verify the results obtained by the analytical model. The simulation results show that the packet delivery ratio in SpCDC scheme increases more than 10% compared with IEEE 802.11p in heavy vehicle load scenarios. Meanwhile, the mean reception delay decreases more than 50%, which provides more reliable road safety.
ThetA -- fast and robust clustering via a distance parameter
Venue: arXiv cs.LG 2021
Year: 2021

Clustering is a fundamental problem in machine learning where distance-based approaches have dominated the field for many decades. This set of problems is often tackled by partitioning the data into K clusters where the number of clusters is chosen apriori. While significant progress has been made on these lines over the years, it is well established that as the number of clusters or dimensions increase, current approaches dwell in local minima resulting in suboptimal solutions. In this work, we propose a new set of distance threshold methods called Theta-based Algorithms (ThetA). Via experimental comparisons and complexity analyses we show that our proposed approach outperforms existing approaches in: a) clustering accuracy and b) time complexity. Additionally, we show that for a large class of problems, learning the optimal threshold is straightforward in comparison to learning K. Moreover, we show how ThetA can infer the sparsity of datasets in higher dimensions.
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
Venue: arXiv cs.CL 2021
Year: 2021

Open-domain Question Answering models which directly leverage question-answer (QA) pairs, such as closed-book QA (CBQA) models and QA-pair retrievers, show promise in terms of speed and memory compared to conventional models which retrieve and read from text corpora. QA-pair retrievers also offer interpretable answers, a high degree of control, and are trivial to update at test time with new knowledge. However, these models lack the accuracy of retrieve-and-read systems, as substantially less knowledge is covered by the available QA-pairs relative to text corpora like Wikipedia. To facilitate improved QA-pair models, we introduce Probably Asked Questions (PAQ), a very large resource of 65M automatically-generated QA-pairs. We introduce a new QA-pair retriever, RePAQ, to complement PAQ. We find that PAQ preempts and caches test questions, enabling RePAQ to match the accuracy of recent retrieve-and-read models, whilst being significantly faster. Using PAQ, we train CBQA models which outperform comparable baselines by 5%, but trail RePAQ by over 15%, indicating the effectiveness of explicit retrieval. RePAQ can be configured for size (under 500MB) or speed (over 1K questions per second) whilst retaining high accuracy. Lastly, we demonstrate RePAQ's strength at selective QA, abstaining from answering when it is likely to be incorrect. This enables RePAQ to ``back-off" to a more expensive state-of-the-art model, leading to a combined system which is both more accurate and 2x faster than the state-of-the-art model alone.
Robust Lane Detection via Expanded Self Attention
Venue: arXiv cs.CV 2021
Year: 2021

The image-based lane detection algorithm is one of the key technologies in autonomous vehicles. Modern deep learning methods achieve high performance in lane detection, but it is still difficult to accurately detect lanes in challenging situations such as congested roads and extreme lighting conditions. To be robust on these challenging situations, it is important to extract global contextual information even from limited visual cues. In this paper, we propose a simple but powerful self-attention mechanism optimized for lane detection called the Expanded Self Attention (ESA) module. Inspired by the simple geometric structure of lanes, the proposed method predicts the confidence of a lane along the vertical and horizontal directions in an image. The prediction of the confidence enables estimating occluded locations by extracting global contextual information. ESA module can be easily implemented and applied to any encoder-decoder-based model without increasing the inference time. The performance of our method is evaluated on three popular lane detection benchmarks (TuSimple, CULane and BDD100K). We achieve state-of-the-art performance in CULane and BDD100K and distinct improvement on TuSimple dataset. The experimental results show that our approach is robust to occlusion and extreme lighting conditions.
Fast Monocular Hand Pose Estimation on Embedded Systems
Venue: arXiv cs.RO 2021
Year: 2021

Hand pose estimation is a fundamental task in many human-robot interaction-related applications. However, previous approaches suffer from unsatisfying hand landmark predictions in real-world scenes and high computation burden. This paper proposes a fast and accurate framework for hand pose estimation, dubbed as "FastHand". Using a lightweight encoder-decoder network architecture, FastHand fulfills the requirements of practical applications running on embedded devices. The encoder consists of deep layers with a small number of parameters, while the decoder makes use of spatial location information to obtain more accurate results. The evaluation took place on two publicly available datasets demonstrating the improved performance of the proposed pipeline compared to other state-of-the-art approaches. FastHand offers high accuracy scores while reaching a speed of 25 frames per second on an NVIDIA Jetson TX2 graphics processing unit.
New methods for metastimuli: architecture, embeddings, and neural network optimization
Venue: arXiv cs.AI 2021
Year: 2021

Six significant new methodological developments of the previously-presented "metastimuli architecture" for human learning through machine learning of spatially correlated structural position within a user's personal information management system (PIMS), providing the basis for haptic metastimuli, are presented. These include architectural innovation, recurrent (RNN) artificial neural network (ANN) application, a variety of atom embedding techniques (including a novel technique we call "nabla" embedding inspired by linguistics), ANN hyper-parameter (one that affects the network but is not trained, e.g. the learning rate) optimization, and meta-parameter (one that determines the system performance but is not trained and not a hyper-parameter, e.g. the atom embedding technique) optimization for exploring the large design space. A technique for using the system for automatic atom categorization in a user's PIMS is outlined. ANN training and hyper- and meta-parameter optimization results are presented and discussed in service of methodological recommendations.
A New Algorithm for Hidden Markov Models Learning Problem
Venue: arXiv cs.LG 2021
Year: 2021

This research focuses on the algorithms and approaches for learning Hidden Markov Models (HMMs) and compares HMM learning methods and algorithms. HMM is a statistical Markov model in which the system being modeled is assumed to be a Markov process. One of the essential characteristics of HMMs is their learning capabilities. Learning algorithms are introduced to overcome this inconvenience. One of the main problems of the newly proposed algorithms is their validation. This research aims by using the theoretical and experimental analysis to 1) compare HMMs learning algorithms proposed in the literature, 2) provide a validation tool for new HMM learning algorithms, and 3) present a new algorithm called Asexual Reproduction Optimization (ARO) with one of its extensions - Modified ARO (MARO) - as a novel HMM learning algorithm to use the validation tool proposed. According to the literature findings, it seems that populationbased algorithms perform better among HMMs learning approaches than other algorithms. Also, the testing was done in nine benchmark datasets. The results show that MARO outperforms different algorithms in objective functions in terms of accuracy and robustness.
Sociotechnical Challenges of eHealth Technology for Patient Self-Management: A Systematic Review
Venue: arXiv cs.CY 2021
Year: 2021

Ageing of society and increase of time spent with chronic conditions challenge the traditional long-term care model. Assistive technology and eHealth are seen to play an important role when addressing these challenges. One prominent example are patient self-management systems. These systems not only transform the way patients with chronic conditions interact with the healthcare system, but also change work practices of care providers. This literature review addresses sociotechnical challenges of eHealth technologies with a strong collaborative component. As a result, four themes are identified and discussed.
Human-Robot Handshaking: A Review
Venue: arXiv cs.RO 2021
Year: 2021

For some years now, the use of social, anthropomorphic robots in various situations has been on the rise. These are robots developed to interact with humans and are equipped with corresponding extremities. They already support human users in various industries, such as retail, gastronomy, hotels, education and healthcare. During such Human-Robot Interaction (HRI) scenarios, physical touch plays a central role in the various applications of social robots as interactive non-verbal behaviour is a key factor in making the interaction more natural. Shaking hands is a simple, natural interaction used commonly in many social contexts and is seen as a symbol of greeting, farewell and congratulations. In this paper, we take a look at the existing state of Human-Robot Handshaking research, categorise the works based on their focus areas, draw out the major findings of these areas while analysing their pitfalls. We mainly see that some form of synchronisation exists during the different phases of the interaction. In addition to this, we also find that additional factors like gaze, voice facial expressions etc. can affect the perception of a robotic handshake and that internal factors like personality and mood can affect the way in which handshaking behaviours are executed by humans. Based on the findings and insights, we finally discuss possible ways forward for research on such physically interactive behaviours.
Hugo: A Cluster Scheduler that Efficiently Learns to Select Complementary Data-Parallel Jobs
Venue: arXiv cs.DC 2021
Year: 2021

Distributed data processing systems like MapReduce, Spark, and Flink are popular tools for analysis of large datasets with cluster resources. Yet, users often overprovision resources for their data processing jobs, while the resource usage of these jobs also typically fluctuates considerably. Therefore, multiple jobs usually get scheduled onto the same shared resources to increase the resource utilization and throughput of clusters. However, job runtimes and the utilization of shared resources can vary significantly depending on the specific combinations of co-located jobs. This paper presents Hugo, a cluster scheduler that continuously learns how efficiently jobs share resources, considering metrics for the resource utilization and interference among co-located jobs. The scheduler combines offline grouping of jobs with online reinforcement learning to provide a scheduling mechanism that efficiently generalizes from specific monitored job combinations yet also adapts to changes in workloads. Our evaluation of a prototype shows that the approach can reduce the runtimes of exemplary Spark jobs on a YARN cluster by up to 12.5%, while resource utilization is increased and waiting times can be bounded.
Guided Interpolation for Adversarial Training
Venue: arXiv cs.LG 2021
Year: 2021

To enhance adversarial robustness, adversarial training learns deep neural networks on the adversarial variants generated by their natural data. However, as the training progresses, the training data becomes less and less attackable, undermining the robustness enhancement. A straightforward remedy is to incorporate more training data, but sometimes incurring an unaffordable cost. In this paper, to mitigate this issue, we propose the guided interpolation framework (GIF): in each epoch, the GIF employs the previous epoch's meta information to guide the data's interpolation. Compared with the vanilla mixup, the GIF can provide a higher ratio of attackable data, which is beneficial to the robustness enhancement; it meanwhile mitigates the model's linear behavior between classes, where the linear behavior is favorable to generalization but not to the robustness. As a result, the GIF encourages the model to predict invariantly in the cluster of each class. Experiments demonstrate that the GIF can indeed enhance adversarial robustness on various adversarial training methods and various datasets.
Win-Fail Action Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Current video/action understanding systems have demonstrated impressive performance on large recognition tasks. However, they might be limiting themselves to learning to recognize spatiotemporal patterns, rather than attempting to thoroughly understand the actions. To spur progress in the direction of a truer, deeper understanding of videos, we introduce the task of win-fail action recognition -- differentiating between successful and failed attempts at various activities. We introduce a first of its kind paired win-fail action understanding dataset with samples from the following domains: "General Stunts," "Internet Wins-Fails," "Trick Shots," and "Party Games." Unlike existing action recognition datasets, intra-class variation is high making the task challenging, yet feasible. We systematically analyze the characteristics of the win-fail task/dataset with prototypical action recognition networks and a novel video retrieval task. While current action recognition methods work well on our task/dataset, they still leave a large gap to achieve high performance. We hope to motivate more work towards the true understanding of actions/videos. Dataset will be available from https://github.com/ParitoshParmar/Win-Fail-Action-Recognition.
Self-Organizing Teams in Online Work Settings
Venue: arXiv cs.HC 2021
Year: 2021

As the volume and complexity of distributed online work increases, the collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of such teams by grouping workers according to a set of predefined decision criteria. This approach micro-manages workers, who have no say in the team formation process. Depriving users of control over who they will work with stifles creativity, causes psychological discomfort and results in less-than-optimal collaboration results. In this work, we propose an alternative model, called Self-Organizing Teams (SOTs), which relies on the crowd of online workers itself to organize into effective teams. Supported but not guided by an algorithm, SOTs are a new human-centered computational structure, which enables participants to control, correct and guide the output of their collaboration as a collective. Experimental results, comparing SOTs to two benchmarks that do not offer user agency over the collaboration, reveal that participants in the SOTs condition produce results of higher quality and report higher teamwork satisfaction. We also find that, similarly to machine learning-based self-organization, human SOTs exhibit emergent collective properties, including the presence of an objective function and the tendency to form more distinct clusters of compatible teammates.
Selecting Matchings via Multiwinner Voting: How Structure Defeats a Large Candidate Space
Venue: arXiv cs.GT 2021
Year: 2021

Given a set of agents with approval preferences over each other, we study the task of finding $k$ matchings fairly representing everyone's preferences. We model the problem as an approval-based multiwinner election where the set of candidates consists of all possible matchings and agents' preferences over each other are lifted to preferences over matchings. Due to the exponential number of candidates in such elections, standard algorithms for classical sequential voting rules (such as those proposed by Thiele and Phragmén) are rendered inefficient. We show that the computational tractability of these rules can be regained by exploiting the structure of the approval preferences. Moreover, we establish algorithmic results and axiomatic guarantees that go beyond those obtainable in the general multiwinner setting. Assuming that approvals are symmetric, we show that proportional approval voting (PAV), a well-established but computationally intractable voting rule, becomes polynomial-time computable, and its sequential variant (seq-PAV), which does not provide any proportionality guarantees in general, fulfills a rather strong guarantee known as extended justified representation. Some of our positive computational results extend to other types of compactly representable elections with an exponential candidate space.
FAT: Learning Low-Bitwidth Parametric Representation via Frequency-Aware Transformation
Venue: arXiv cs.CV 2021
Year: 2021

Learning convolutional neural networks (CNNs) with low bitwidth is challenging because performance may drop significantly after quantization. Prior arts often discretize the network weights by carefully tuning hyper-parameters of quantization (e.g. non-uniform stepsize and layer-wise bitwidths), which are complicated and sub-optimal because the full-precision and low-precision models have a large discrepancy. This work presents a novel quantization pipeline, Frequency-Aware Transformation (FAT), which has several appealing benefits. (1) Rather than designing complicated quantizers like existing works, FAT learns to transform network weights in the frequency domain before quantization, making them more amenable to training in low bitwidth. (2) With FAT, CNNs can be easily trained in low precision using simple standard quantizers without tedious hyper-parameter tuning. Theoretical analysis shows that FAT improves both uniform and non-uniform quantizers. (3) FAT can be easily plugged into many CNN architectures. When training ResNet-18 and MobileNet-V2 in 4 bits, FAT plus a simple rounding operation already achieves 70.5% and 69.2% top-1 accuracy on ImageNet without bells and whistles, outperforming recent state-of-the-art by reducing 54.9X and 45.7X computations against full-precision models. We hope FAT provides a novel perspective for model quantization. Code is available at \url{https://github.com/ChaofanTao/FAT_Quantization}.
Finding Anomalies in Scratch Assignments
Venue: arXiv cs.SE 2021
Year: 2021

In programming education, teachers need to monitor and assess the progress of their students by investigating the code they write. Code quality of programs written in traditional programming languages can be automatically assessed with automated tests, verification tools, or linters. In many cases these approaches rely on some form of manually written formal specification to analyze the given programs. Writing such specifications, however, is hard for teachers, who are often not adequately trained for this task. Furthermore, automated tool support for popular block-based introductory programming languages like Scratch is lacking. Anomaly detection is an approach to automatically identify deviations of common behavior in datasets without any need for writing a specification. In this paper, we use anomaly detection to automatically find deviations of Scratch code in a classroom setting, where anomalies can represent erroneous code, alternative solutions, or distinguished work. Evaluation on solutions of different programming tasks demonstrates that anomaly detection can successfully be applied to tightly specified as well as open-ended programming tasks.
T-RACKs: A Faster Recovery Mechanism for TCP in Data Center Networks
Venue: arXiv cs.NI 2021
Year: 2021

Cloud interactive data-driven applications generate swarms of small TCP flows that compete for the small buffer space in data-center switches. Such applications require a short flow completion time (FCT) to perform their jobs effectively. However, TCP is oblivious to the composite nature of application data and artificially inflates the FCT of such flows by several orders of magnitude. This is due to TCP's Internet-centric design that fixes the retransmission timeout (RTO) to be at least hundreds of milliseconds. To better understand this problem, in this paper, we use empirical measurements in a small testbed to study, at a microscopic level, the effects of various types of packet losses on TCP's performance. In particular, we single out packet losses that impact the tail end of small flows, as well as bursty losses, that span a significant fraction of the small congestion window of TCP flows in data-centers, to show a non-negligible effect on the FCT. Based on this, we propose the so-called, timely-retransmitted ACKs (or T-RACKs), a simple loss recovery mechanism to conceal the drawbacks of the long RTO even in the presence of heavy packet losses. Interestingly enough, T-RACKS achieves this transparently to TCP itself as it does not require any change to TCP in the tenant's virtual machine (VM). T-RACKs can be implemented as a software shim layer in the hypervisor between the VMs and server's NIC or in hardware as a networking function in a SmartNIC. Simulation and real testbed results show that T-RACKs achieves remarkable performance improvements.
Interface Compliance of Inline Assembly: Automatically Check, Patch and Refine
Venue: arXiv cs.PL 2021
Year: 2021

Inline assembly is still a common practice in low-level C programming, typically for efficiency reasons or for accessing specific hardware resources. Such embedded assembly codes in the GNU syntax (supported by major compilers such as GCC, Clang and ICC) have an interface specifying how the assembly codes interact with the C environment. For simplicity reasons, the compiler treats GNU inline assembly codes as blackboxes and relies only on their interface to correctly glue them into the compiled C code. Therefore, the adequacy between the assembly chunk and its interface (named compliance) is of primary importance, as such compliance issues can lead to subtle and hard-to-find bugs. We propose RUSTInA, the first automated technique for formally checking inline assembly compliance, with the extra ability to propose (proven) patches and (optimization) refinements in certain cases. RUSTInA is based on an original formalization of the inline assembly compliance problem together with novel dedicated algorithms. Our prototype has been evaluated on 202 Debian packages with inline assembly (2656 chunks), finding 2183 issues in 85 packages -- 986 significant issues in 54 packages (including major projects such as ffmpeg or ALSA), and proposing patches for 92% of them. Currently, 38 patches have already been accepted (solving 156 significant issues), with positive feedback from development teams.