OpenRaiser/Intern-Atlas huggingface dataset:
Asymmetric Scalable Cross-modal Hashing
Venue: arXiv cs.CV 2022
Year: 2022

Cross-modal hashing is a successful method to solve large-scale multimedia retrieval issue. A lot of matrix factorization-based hashing methods are proposed. However, the existing methods still struggle with a few problems, such as how to generate the binary codes efficiently rather than directly relax them to continuity. In addition, most of the existing methods choose to use an $n\times n$ similarity matrix for optimization, which makes the memory and computation unaffordable. In this paper we propose a novel Asymmetric Scalable Cross-Modal Hashing (ASCMH) to address these issues. It firstly introduces a collective matrix factorization to learn a common latent space from the kernelized features of different modalities, and then transforms the similarity matrix optimization to a distance-distance difference problem minimization with the help of semantic labels and common latent space. Hence, the computational complexity of the $n\times n$ asymmetric optimization is relieved. In the generation of hash codes we also employ an orthogonal constraint of label information, which is indispensable for search accuracy. So the redundancy of computation can be much reduced. For efficient optimization and scalable to large-scale datasets, we adopt the two-step approach rather than optimizing simultaneously. Extensive experiments on three benchmark datasets: Wiki, MIRFlickr-25K, and NUS-WIDE, demonstrate that our ASCMH outperforms the state-of-the-art cross-modal hashing methods in terms of accuracy and efficiency.
EEG-Based Detection of Braking Intention During Simulated Driving
Venue: arXiv cs.HC 2022
Year: 2022

Accurately detecting and identifying drivers' braking intention is the basis of man-machine driving. In this paper, we proposed an electroencephalographic (EEG)-based braking intention measurement strategy. We used the Car Learning to Act (Carla) platform to build the simulated driving environment. 11 subjects participated in our study, and each subject drove a simulated vehicle to complete emergency braking and normal braking tasks. We compared the EEG topographic maps in different braking situations and used three different classifiers to predict the subjects' braking intention through EEG signals. The experimental results showed that the average response time of subjects in emergency braking was 762 ms; emergency braking and no braking can be well distinguished, while normal braking and no braking were not easy to be classified; for the two different types of braking, emergency braking and normal braking had obvious differences in EEG topographic maps, and the classification results also showed that the two were highly distinguishable. This study provides a user-centered driver-assistance system and a good framework to combine with advanced shared control algorithms, which has the potential to be applied to achieve a more friendly interaction between the driver and vehicle in real driving environment.
Active Learning of Ordinal Embeddings: A User Study on Football Data
Venue: arXiv cs.LG 2022
Year: 2022

Humans innately measure distance between instances in an unlabeled dataset using an unknown similarity function. Distance metrics can only serve as proxy for similarity in information retrieval of similar instances. Learning a good similarity function from human annotations improves the quality of retrievals. This work uses deep metric learning to learn these user-defined similarity functions from few annotations for a large football trajectory dataset. We adapt an entropy-based active learning method with recent work from triplet mining to collect easy-to-answer but still informative annotations from human participants and use them to train a deep convolutional network that generalizes to unseen samples. Our user study shows that our approach improves the quality of the information retrieval compared to a previous deep metric learning approach that relies on a Siamese network. Specifically, we shed light on the strengths and weaknesses of passive sampling heuristics and active learners alike by analyzing the participants' response efficacy. To this end, we collect accuracy, algorithmic time complexity, the participants' fatigue and time-to-response, qualitative self-assessment and statements, as well as the effects of mixed-expertise annotators and their consistency on model performance and transfer-learning.
MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular Backbones
Venue: arXiv cs.CV 2022
Year: 2022

In this technical report, we present our solution, dubbed MV-FCOS3D++, for the Camera-Only 3D Detection track in Waymo Open Dataset Challenge 2022. For multi-view camera-only 3D detection, methods based on bird-eye-view or 3D geometric representations can leverage the stereo cues from overlapped regions between adjacent views and directly perform 3D detection without hand-crafted post-processing. However, it lacks direct semantic supervision for 2D backbones, which can be complemented by pretraining simple monocular-based detectors. Our solution is a multi-view framework for 4D detection following this paradigm. It is built upon a simple monocular detector FCOS3D++, pretrained only with object annotations of Waymo, and converts multi-view features to a 3D grid space to detect 3D objects thereon. A dual-path neck for single-frame understanding and temporal stereo matching is devised to incorporate multi-frame information. Our method finally achieves 49.75% mAPL with a single model and wins 2nd place in the WOD challenge, without any LiDAR-based depth supervision during training. The code will be released at https://github.com/Tai-Wang/Depth-from-Motion.
Voreen -- An Open-source Framework for Interactive Visualization and Processing of Large Volume Data
Venue: arXiv cs.GR 2022
Year: 2022

Technological advances for measuring or simulating volume data have led to large data sizes in many research areas such as biology, medicine, physics, and geoscience. Here, large data can refer to individual data sets with high spatial and/or temporal resolution as well as collections of data sets in the sense of cohorts or ensembles. Therefore, general-purpose and customizable volume visualization and processing systems have to provide out-of-core mechanisms that allow for handling and analyzing such data. Voreen is an open-source rapid-prototyping framework that was originally designed to quickly create custom visualization applications for volumetric imaging data using the meanwhile quite common data flow graph paradigm. In recent years, Voreen has been used in various interdisciplinary research projects with an increasing demand for large data processing capabilities without relying on cluster compute resources. In its latest release, Voreen has thus been extended by out-of-core techniques for processing and visualization of volume data with very high spatial resolution as well as collections of volume data sets including spatio-temporal multi-field simulation ensembles. In this paper we compare state-of-the-art volume processing and visualization systems and conclude that Voreen is the first system combining out-of-core processing and rendering capabilities for large volume data on consumer hardware with features important for interdisciplinary research. We describe how Voreen achieves these goals and show-case its use, performance, and capability to support interdisciplinary research by presenting typical workflows within two large volume data case studies.
Controllable User Dialogue Act Augmentation for Dialogue State Tracking
Venue: arXiv cs.CL 2022
Year: 2022

Prior work has demonstrated that data augmentation is useful for improving dialogue state tracking. However, there are many types of user utterances, while the prior method only considered the simplest one for augmentation, raising the concern about poor generalization capability. In order to better cover diverse dialogue acts and control the generation quality, this paper proposes controllable user dialogue act augmentation (CUDA-DST) to augment user utterances with diverse behaviors. With the augmented data, different state trackers gain improvement and show better robustness, achieving the state-of-the-art performance on MultiWOZ 2.1
Productivity meets Performance: Julia on A64FX
Venue: arXiv cs.DC 2022
Year: 2022

The Fujitsu A64FX ARM-based processor is used in supercomputers such as Fugaku in Japan and Isambard 2 in the UK and provides an interesting combination of hardware features such as Scalable Vector Extension (SVE), and native support for reduced-precision floating-point arithmetic. The goal of this paper is to explore performance of the Julia programming language on the A64FX processor, with a particular focus on reduced precision. Here, we present a performance study on axpy to verify the compilation pipeline, demonstrating that Julia can match the performance of tuned libraries. Additionally, we investigate Message Passing Interface (MPI) scalability and throughput analysis on Fugaku showing next to no significant overheads of Julia of its MPI interface. To explore the usability of Julia to target various floating-point precisions, we present results of ShallowWaters.jl, a shallow water model that can be executed a various levels of precision. Even for such complex applications, Julia's type-flexible programming paradigm offers both, productivity and performance.
Using Abstraction for Interpretable Robot Programs in Stochastic Domains
Venue: arXiv cs.AI 2022
Year: 2022

A robot's actions are inherently stochastic, as its sensors are noisy and its actions do not always have the intended effects. For this reason, the agent language Golog has been extended to models with degrees of belief and stochastic actions. While this allows more precise robot models, the resulting programs are much harder to comprehend, because they need to deal with the noise, e.g., by looping until some desired state has been reached with certainty, and because the resulting action traces consist of a large number of actions cluttered with sensor noise. To alleviate these issues, we propose to use abstraction. We define a high-level and nonstochastic model of the robot and then map the high-level model into the lower-level stochastic model. The resulting programs are much easier to understand, often do not require belief operators or loops, and produce much shorter action traces.
Partial-Monotone Adaptive Submodular Maximization
Venue: arXiv cs.LG 2022
Year: 2022

Many sequential decision making problems, including pool-based active learning and adaptive viral marketing, can be formulated as an adaptive submodular maximization problem. Most of existing studies on adaptive submodular optimization focus on either monotone case or non-monotone case. Specifically, if the utility function is monotone and adaptive submodular, \cite{golovin2011adaptive} developed a greedy policy that achieves a $(1-1/e)$ approximation ratio subject to a cardinality constraint. If the utility function is non-monotone and adaptive submodular, \cite{tang2021beyond} showed that a random greedy policy achieves a $1/e$ approximation ratio subject to a cardinality constraint. In this work, we aim to generalize the above mentioned results by studying the partial-monotone adaptive submodular maximization problem. To this end, we introduce the notation of adaptive monotonicity ratio $m\in[0,1]$ to measure the degree of monotonicity of a function. Our main result is to show that a random greedy policy achieves an approximation ratio of $m(1-1/e)+(1-m)(1/e)$ if the utility function is $m$-adaptive monotone and adaptive submodular. Notably this result recovers the aforementioned $(1-1/e)$ and $1/e$ approximation ratios when $m = 0$ and $m = 1$, respectively. We further extend our results to consider a knapsack constraint. We show that a sampling-based policy achieves an approximation ratio of $(m+1)/10$ if the utility function is $m$-adaptive monotone and adaptive submodular. One important implication of our results is that even for a non-monotone utility function, we still can achieve an approximation ratio close to $(1-1/e)$ if this function is ``close'' to a monotone function. This leads to improved performance bounds for many machine learning applications whose utility functions are almost adaptive monotone.
Balanced Knowledge Distribution among Software Development Teams -- Observations from Open-Source and Closed-Source Software Development
Venue: arXiv cs.SE 2022
Year: 2022

In software development teams, developer turnover is among the primary reasons for project failures as it leads to a great void of knowledge and strain for the newcomers. Unfortunately, no established methods exist to measure how knowledge is distributed among development teams. Knowing how this knowledge evolves and is owned by key developers in a project helps managers reduce risks caused by turnover. To this end, this paper introduces a novel, realistic representation of domain knowledge distribution: the ConceptRealm. To construct the ConceptRealm, we employ a latent Dirichlet allocation model to represent textual features obtained from 300k issues and 1.3M comments from 518 open-source projects. We analyze whether the newly emerged issues and developers share similar concepts or how aligned the developers' concepts are with the team over time. We also investigate the impact of leaving members on the frequency of concepts. Finally, we evaluate the soundness of our approach to closed-source software, thus allowing the validation of the results from a practical standpoint. We find out that the ConceptRealm can represent the high-level domain knowledge within a team and can be utilized to predict the alignment of developers with issues. We also observe that projects exhibit many keepers independent of project maturity and that abruptly leaving keepers harm the team's concept familiarity.
"I Used To Carry A Wallet, Now I Just Need To Carry My Phone": Understanding Current Banking Practices and Challenges Among Older Adults in China
Venue: arXiv cs.HC 2022
Year: 2022

Managing finances is crucial for older adults who are retired and may rely on savings to ensure their life quality. As digital banking platforms (e.g., mobile apps, electronic payment) gradually replace physical ones, it is critical to understand how they adapt to digital banking and the potential frictions they experience. We conducted semi-structured interviews with 16 older adults in China, where the aging population is the largest and digital banking grows fast. We also interviewed bank employees to gain complementary perspectives of these help givers. Our findings show that older adults used both physical and digital platforms as an ecosystem based on perceived pros and cons. Perceived usefulness, self-confidence, and social influence were key motivators for learning digital banking. They experienced app-related (e.g., insufficient error-recovery support) and user-related challenges (e.g., trust, security and privacy concerns, low perceived self-efficacy) and developed coping strategies. We discuss design considerations to improve their banking experiences.
Review of Peer-to-Peer Botnets and Detection Mechanisms
Venue: arXiv cs.CR 2022
Year: 2022

Cybercrimes are becoming a bigger menace to both people and corporations. It poses a serious challenge to the modern digital world. According to a press release from 2019 Cisco and Cybersecurity Ventures, Cisco stopped seven trillion threats in 2018, or 20 billion threats every day, on behalf of its clients. According to Cybersecurity Ventures, the global cost of cybercrime will reach \$6 trillion annually by 2021, which is significantly more than the annual damage caused by all natural disasters and more profitable than the global trade in all major illegal narcotics put together. Malware software, including viruses, worms, spyware, keyloggers, Trojan horses, and botnets, is therefore frequently used in cybercrime. The most common malware employed by attackers to carry out cybercrimes is the botnet, which is available in a variety of forms and for a variety of purposes when attacking computer assets. However, the issue continues to exist and worsen, seriously harming both enterprises and people who conduct their business online. The detection of P2P (Peer to Peer) botnet, which has emerged as one of the primary hazards in network cyberspace for acting as the infrastructure for several cyber-crimes, has proven more difficult than regular botnets using a few existing approaches. As a result, this study will explore various P2P botnet detection algorithms by outlining their essential characteristics, advantages and disadvantages, obstacles, and future research.
Extensible Proof Systems for Infinite-State Systems
Venue: arXiv cs.LO 2022
Year: 2022

This paper revisits soundness and completeness of proof systems for proving that sets of states in infinite-state labeled transition systems satisfy formulas in the modal mu-calculus. Our results rely on novel results in lattice theory, which give constructive characterizations of both greatest and least fixpoints of monotonic functions over complete lattices. We show how these results may be used to reconstruct the sound and complete tableau method for this problem due to Bradfield and Stirling. We also show how the flexibility of our lattice-theoretic basis simplifies reasoning about tableau-based proof strategies for alternative classes of systems. In particular, we extend the modal mu-calculus with timed modalities, and prove that the resulting tableaux method is sound and complete for timed transition systems.
Users and Contemporary SERPs: A (Re-)Investigation Examining User Interactions and Experiences
Venue: arXiv cs.IR 2022
Year: 2022

The Search Engine Results Page (SERP) has evolved significantly over the last two decades, moving away from the simple ten blue links paradigm to considerably more complex presentations that contain results from multiple verticals and granularities of textual information. Prior works have investigated how user interactions on the SERP are influenced by the presence or absence of heterogeneous content (e.g., images, videos, or news content), the layout of the SERP (list vs. grid layout), and task complexity. In this paper, we reproduce the user studies conducted in prior works-specifically those of Arguello et al. [4] and Siu and Chaparro [29]-to explore to what extent the findings from research conducted five to ten years ago still hold today as the average web user has become accustomed to SERPs with ever-increasing presentational complexity. To this end, we designed and ran a user study with four different SERP interfaces: (i) a heterogeneous grid; (ii) a heterogeneous list; (iii) a simple grid; and (iv) a simple list. We collected the interactions of 41 study participants over 12 search tasks for our analyses. We observed that SERP types and task complexity affect user interactions with search results. We also find evidence to support most (6 out of 8) observations from [4 , 29] indicating that user interactions with different interfaces and to solve tasks of different complexity have remained mostly similar over time.
Random Test Generation of Application Programming Interfaces
Venue: arXiv cs.SE 2022
Year: 2022

Cloud high quality API (Application Programming Interface) testing is essential for supporting the API economy. Autotest is a random test generator that addresses this need. It reads the API specification and deduces a model used in the test generation. This paper describes Autotest. It also address the topic of API specification pitfalls which Autotest may reveal when reading the specification. A best practice is to add an appropriate test to the regression once a problem is revealed and solved. How to do that in the context of Autotest's random test generation is covered.
Call Scheduling to Reduce Response Time of a FaaS System
Venue: arXiv cs.DC 2022
Year: 2022

In an overloaded FaaS cluster, individual worker nodes strain under lengthening queues of requests. Although the cluster might be eventually horizontally-scaled, adding a new node takes dozens of seconds. As serving applications are tuned for tail serving latencies, and these greatly increase under heavier loads, the current workaround is resource over-provisioning. In fact, even though a service can withstand a steady load of, e.g., 70% CPU utilization, the autoscaler is triggered at, e.g., 30-40% (thus the service uses twice as many nodes as it would be needed). We propose an alternative: a worker-level method handling heavy load without increasing the number of nodes. FaaS executions are not interactive, compared to, e.g., text editors: end-users do not benefit from the CPU allocated to processes often, yet for short periods. Inspired by scheduling methods for High Performance Computing, we take a radical step of replacing the classic OS preemption by (1) queuing requests based on their historical characteristics; (2) once a request is being processed, setting its CPU limit to exactly one core (with no CPU oversubscription). We extend OpenWhisk and measure the efficiency of the proposed solutions using the SeBS benchmark. In a loaded system, our method decreases the average response time by a factor of 4. The improvement is even higher for shorter requests, as the average stretch is decreased by a factor of 18. This leads us to show that we can provide better response-time statistics with 3 machines compared to a 4-machine baseline.
Reliable Web Services Approaches of Mobile CloudComputing: A Comparative study
Venue: arXiv cs.SE 2022
Year: 2022

The Mobile intermittent wireless connectivity limits the evolution of the mobile landscape, such that this progress coupled with the ubiquitous nature of the Internet and the web services. The web service Reliability achieves the low overhead communication and retrieves the appropriate state response from the web service consumption. This paper discusses and analyzes the most recent approaches that achieve the reliability of web services consumed by mobile devices. Achieving the reliable web service consumption istended to two approaches: Middleware approach and Mobile Agent(MA) approach. Both of them focus on ensuring the request execution under the communication limitations and services temporary unavailability.
GCN-WP -- Semi-Supervised Graph Convolutional Networks for Win Prediction in Esports
Venue: arXiv cs.LG 2022
Year: 2022

Win prediction is crucial to understanding skill modeling, teamwork and matchmaking in esports. In this paper we propose GCN-WP, a semi-supervised win prediction model for esports based on graph convolutional networks. This model learns the structure of an esports league over the course of a season (1 year) and makes predictions on another similar league. This model integrates over 30 features about the match and players and employs graph convolution to classify games based on their neighborhood. Our model achieves state-of-the-art prediction accuracy when compared to machine learning or skill rating models for LoL. The framework is generalizable so it can easily be extended to other multiplayer online games.