OpenRaiser/Intern-Atlas huggingface dataset:
Dijkstra-Through-Time: Ahead of time hardware scheduling method for deterministic workloads
Venue: arXiv cs.AR 2021
Year: 2021

Most of the previous works on data flow optimizations for Machine Learning hardware accelerators try to find algorithmic re-factorization such as loop-reordering and loop-tiling. However, the analysis and information they provide are still at very high level and one must further map them onto instructions that hardware can understand. This paper presents "Dijkstra-Through-Time" (DTT), an ahead of time compute and memory scheduling-mapping algorithm for deterministic workloads. It provides a simple implementation and supports accelerators with complex NoC configurations, at the expense of a long compilation process. This initial paper illustrates a proof of concept implementation to merge scheduling and data cache coherence mechanisms to get more optimized data flows.
Centralized QoS-Aware Resource Allocation for D2D Communications with Multiple D2D Pairs in One Resource Block
Venue: arXiv cs.IT 2021
Year: 2021

Device-to-device (D2D) communications can result in spectral efficiency (SE) improvement of cellular networks when interference is addressed properly. For further improvement of overall network sum-rate, it can be assumed that multiple D2D pairs reuse the same resource block (RB) of a cellular user equipment (CUE). In this paper, we study a centralized joint power and spectrum resource allocation problem to maximize the overall sum-rate of the network while guaranteeing the quality-of-service (QoS) requirements of CUEs and admitted D2D pairs. The proposed method uses a distance based approach to select a CUE at each step with a distance and power based approach to increase the number of admitted D2D pairs. A power minimization approach when the number of admitted D2D pairs are increasing and a power maximization approach at the end of resource allocation for each RB is proposed. Numerical results show that the proposed algorithm significantly improves SE compared to the situation where at most one D2D pair can reuse the RB of a CUE. It also shows that the proposed algorithm has a slight lower performance compared to the optimal scheme but dramatically decreases the matching complexity and signaling overhead.
Distributed pricing-based resource allocation for dense device-to-device communications in beyond 5G networks
Venue: arXiv cs.IT 2021
Year: 2021

Considering the dramatic increase of data rate demand in beyond fifth generation (5G) networks due to the numerous transmitting nodes, dense device-to-device (D2D) communications where multiple D2D pairs can simultaneously reuse a cellular link can be considered as a communication paradigm for future wireless networks. Since distributed methods can be more practical compared to complex centralized schemes, we propose a low-complexity distributed pricing-based resource allocation algorithm to allocate power to cellular user equipments (CUEs) and D2D pairs constrained to the minimum quality-of-service (QoS) requirements of CUEs and D2D pairs in two phases. The price of each link is set by the base station (BS). CUEs and D2D pairs maximize their rate-and-interference-dependent utility function by adjusting their power in the first phase, while the price of each link is updated at the BS based on the minimum QoS requirements of CUEs and D2D pairs in the second phase. The proposed utility function controls D2D admission, hence it is suitable for dense scenarios. The proposed method is fast due to the closed-form solution for the first phase power allocation. Numerical results verify the effectiveness of the proposed method for practical dense beyond 5G scenarios by allocating resources to multiple D2D pairs and taking advantage of the spatial reuse gain of D2D pairs. Furthermore, the utility function definition is discussed to illustrate its effectiveness for SE maximization. Finally, the SE of the proposed method is compared to other centralized and distributed algorithms to demonstrate the higher sum-rate performance of the proposed algorithm.
Scope and Sense of Explainability for AI-Systems
Venue: arXiv cs.LG 2021
Year: 2021

Certain aspects of the explainability of AI systems will be critically discussed. This especially with focus on the feasibility of the task of making every AI system explainable. Emphasis will be given to difficulties related to the explainability of highly complex and efficient AI systems which deliver decisions whose explanation defies classical logical schemes of cause and effect. AI systems have provably delivered unintelligible solutions which in retrospect were characterized as ingenious (for example move 37 of the game 2 of AlphaGo). It will be elaborated on arguments supporting the notion that if AI-solutions were to be discarded in advance because of their not being thoroughly comprehensible, a great deal of the potentiality of intelligent systems would be wasted.
Image-free multi-character recognition
Venue: arXiv cs.CV 2021
Year: 2021

The recently developed image-free sensing technique maintains the advantages of both the light hardware and software, which has been applied in simple target classification and motion tracking. In practical applications, however, there usually exist multiple targets in the field of view, where existing trials fail to produce multi-semantic information. In this letter, we report a novel image-free sensing technique to tackle the multi-target recognition challenge for the first time. Different from the convolutional layer stack of image-free single-pixel networks, the reported CRNN network utilities the bidirectional LSTM architecture to predict the distribution of multiple characters simultaneously. The framework enables to capture the long-range dependencies, providing a high recognition accuracy of multiple characters. We demonstrated the technique's effectiveness in license plate detection, which achieved 87.60% recognition accuracy at a 5% sampling rate with a higher than 100 FPS refresh rate.
DeePaste -- Inpainting for Pasting
Venue: arXiv cs.CV 2021
Year: 2021

One of the challenges of supervised learning training is the need to procure an substantial amount of tagged data. A well-known method of solving this problem is to use synthetic data in a copy-paste fashion, so that we cut objects and paste them onto relevant backgrounds. Pasting the objects naively results in artifacts that cause models to give poor results on real data. We present a new method for cleanly pasting objects on different backgrounds so that the dataset created gives competitive performance on real data. The main emphasis is on the treatment of the border of the pasted object using inpainting. We show state-of-the-art results both on instance detection and foreground segmentation
Context-Based Music Recommendation Algorithm Evaluation
Venue: arXiv cs.IR 2021
Year: 2021

Artificial Intelligence (AI ) has been very successful in creating and predicting music playlists for online users based on their data; data received from users experience using the app such as searching the songs they like. There are lots of current technological advancements in AI due to the competition between music platform owners such as Spotify, Pandora, and more. In this paper, 6 machine learning algorithms and their individual accuracy for predicting whether a user will like a song are explored across 3 different platforms including Weka, SKLearn, and Orange. The algorithms explored include Logistic Regression, Naive Bayes, Sequential Minimal Optimization (SMO), Multilayer Perceptron (Neural Network), Nearest Neighbor, and Random Forest. With the analysis of the specific characteristics of each song provided by the Spotify API [1], Random Forest is the most successful algorithm for predicting whether a user will like a song with an accuracy of 84%. This is higher than the accuracy of 82.72% found by Mungekar using the Random Forest technique and slightly different characteristics of a song [2]. The characteristics in Mungekars Random Forest algorithm focus more on the artist and popularity rather than the sonic features of the songs. Removing the popularity aspect and focusing purely on the sonic qualities improve the accuracy of recommendations. Finally, this paper shows how song prediction can be accomplished without any monetary investments, and thus, inspires an idea of what amazing results can be accomplished with full financial research.
REFORM: Reputation Based Fair and Temporal Reward Framework for Crowdsourcing
Venue: arXiv cs.GT 2021
Year: 2021

Crowdsourcing is an effective method to collect data by employing distributed human population. Researchers introduce appropriate reward mechanisms to incentivize agents to report accurately. In particular, this paper focuses on Peer-Based Mechanisms (PBMs). We observe that with PBMs, crowdsourcing systems may not be fair, i.e., agents may not receive the deserved rewards despite investing efforts and reporting truthfully. Unfair rewards for the agents may discourage participation. This paper aims to build a general framework that assures fairness for PBMs in temporal settings, i.e., settings that prefer early reports. Towards this, we introduce two general notions of fairness for PBMs, namely gamma-fairness and qualitative fairness. To satisfy these notions, our framework provides trustworthy agents with additional chances of pairing. We introduce Temporal Reputation Model (TERM) to quantify agents' trustworthiness across tasks. With TERM as the key constituent, we present our iterative framework, REFORM, that can adopt the reward scheme of any existing PBM. We demonstrate REFORM's significance by deploying the framework with RPTSC's reward scheme. Specifically, we prove that REFORM with RPTSC considerably improves fairness; while incentivizing truthful and early reports. We conduct synthetic simulations and show that our framework provides improved fairness over RPTSC.
SelFSR: Self-Conditioned Face Super-Resolution in the Wild via Flow Field Degradation Network
Venue: arXiv cs.CV 2021
Year: 2021

In spite of the success on benchmark datasets, most advanced face super-resolution models perform poorly in real scenarios since the remarkable domain gap between the real images and the synthesized training pairs. To tackle this problem, we propose a novel domain-adaptive degradation network for face super-resolution in the wild. This degradation network predicts a flow field along with an intermediate low resolution image. Then, the degraded counterpart is generated by warping the intermediate image. With the preference of capturing motion blur, such a model performs better at preserving identity consistency between the original images and the degraded. We further present the self-conditioned block for super-resolution network. This block takes the input image as a condition term to effectively utilize facial structure information, eliminating the reliance on explicit priors, e.g. facial landmarks or boundary. Our model achieves state-of-the-art performance on both CelebA and real-world face dataset. The former demonstrates the powerful generative ability of our proposed architecture while the latter shows great identity consistency and perceptual quality in real-world images.
BAPose: Bottom-Up Pose Estimation with Disentangled Waterfall Representations
Venue: arXiv cs.CV 2021
Year: 2021

We propose BAPose, a novel bottom-up approach that achieves state-of-the-art results for multi-person pose estimation. Our end-to-end trainable framework leverages a disentangled multi-scale waterfall architecture and incorporates adaptive convolutions to infer keypoints more precisely in crowded scenes with occlusions. The multi-scale representations, obtained by the disentangled waterfall module in BAPose, leverage the efficiency of progressive filtering in the cascade architecture, while maintaining multi-scale fields-of-view comparable to spatial pyramid configurations. Our results on the challenging COCO and CrowdPose datasets demonstrate that BAPose is an efficient and robust framework for multi-person pose estimation, achieving significant improvements on state-of-the-art accuracy.
A Grid-Structured Model of Tubular Reactors
Venue: arXiv cs.LG 2021
Year: 2021

We propose a grid-like computational model of tubular reactors. The architecture is inspired by the computations performed by solvers of partial differential equations which describe the dynamics of the chemical process inside a tubular reactor. The proposed model may be entirely based on the known form of the partial differential equations or it may contain generic machine learning components such as multi-layer perceptrons. We show that the proposed model can be trained using limited amounts of data to describe the state of a fixed-bed catalytic reactor. The trained model can reconstruct unmeasured states such as the catalyst activity using the measurements of inlet concentrations and temperatures along the reactor.
Provable Hierarchical Lifelong Learning with a Sketch-based Modular Architecture
Venue: arXiv cs.LG 2021
Year: 2021

We propose a modular architecture for the lifelong learning of hierarchically structured tasks. Specifically, we prove that our architecture is theoretically able to learn tasks that can be solved by functions that are learnable given access to functions for other, previously learned tasks as subroutines. We empirically show that some tasks that we can learn in this way are not learned by standard training methods in practice; indeed, prior work suggests that some such tasks cannot be learned by any efficient method without the aid of the simpler tasks. We also consider methods for identifying the tasks automatically, without relying on explicitly given indicators.
How Do Developers Search for Architectural Information? An Industrial Survey
Venue: arXiv cs.SE 2021
Year: 2021

Building software systems often requires knowledge and skills beyond what developers already possess. In such cases, developers have to leverage different sources of information to seek help. A growing number of researchers and practitioners have started investigating what programming-related information developers seek during software development. However, being a high level and a type of the most important development-related information, architectural information search activity is seldom explored. To fill this gap, we conducted an industrial survey completed by 103 participants to understand how developers search for architectural information to solve their architectural problems in development. Our main findings are: (1) searching for architectural information to learn about the pros and cons of certain architectural solutions (e.g., patterns, tactics) and to make an architecture decision among multiple choices are the most frequent purposes or tasks; (2) developers find difficulties mostly in getting relevant architectural information for addressing quality concerns and making design decisions among multiple choices when seeking architectural information; (3) taking too much time to go through architectural information retrieved from various sources and feeling overwhelmed due to the dispersion and abundance of architectural information in various sources are the top two major challenges developers face when searching for architectural information. Our findings (1) provide researchers with future directions, such as the design and development of approaches and tools for searching architectural information from multiple sources, and (2) can be used to provide guidelines for practitioners to refer to when seeking architectural information and providing architectural information that could be considered useful.
Hiding Signal Strength Interference from Outside Adversaries
Venue: arXiv cs.NI 2021
Year: 2021

The presence of people can be detected by passively observing the signal strength of Wifi and related forms of communication. This paper tackles the question of how and when can this be prevented by adjustments to the transmitted signal strength, and other similar measures. The main contribution of this paper is a formal framework to analyze this problem, and the identification of several scenarios and corresponding protocols which can prevent or limit the inference from passive signal strength snooping.
Point spread function estimation for blind image deblurring problems based on framelet transform
Venue: arXiv cs.CV 2021
Year: 2021

One of the most important issues in the image processing is the approximation of the image that has been lost due to the blurring process. These types of matters are divided into non-blind and blind problems. The second type of problem is more complex in terms of calculations than the first problems due to the unknown of original image and point spread function estimation. In the present paper, an algorithm based on coarse-to-fine iterative by $l_0-αl_1$ regularization and framelet transform is introduced to approximate the spread function estimation. Framelet transfer improves the restored kernel due to the decomposition of the kernel to different frequencies. Also in the proposed model fraction gradient operator is used instead of ordinary gradient operator. The proposed method is investigated on different kinds of images such as text, face, natural. The output of the proposed method reflects the effectiveness of the proposed algorithm in restoring the images from blind problems.
Pseudo-Haptic Button for Improving User Experience of Mid-Air Interaction in VR
Venue: arXiv cs.HC 2021
Year: 2021

Mid-air interaction is one of the promising interaction modalities in virtual reality (VR) due to its merits in naturalness and intuitiveness, but the interaction suffers from the lack of haptic feedback as no force or vibrotactile feedback can be provided in mid-air. As a breakthrough to compensate for this insufficiency, the application of pseudo-haptic features which create the visuo-haptic illusion without actual physical haptic stimulus can be explored. Therefore, this study aimed to investigate the effect of four pseudo-haptic features: proximity feedback, protrusion, hit effect, and penetration blocking on user experience for free-hand mid-air button interaction in VR. We conducted a user study on 21 young subjects to collect user ratings on various aspects of user experience while users were freely interacting with 16 buttons with different combinations of four features. Results indicated that all investigated features significantly improved user experience in terms of haptic illusion, embodiment, sense of reality, spatiotemporal perception, satisfaction, and hedonic quality. In addition, protrusion and hit effect were more beneficial in comparison with the other two features. It is recommended to utilize the four proposed pseudo-haptic features in 3D user interfaces (UIs) to make users feel more pleased and amused, but caution is needed when using proximity feedback together with other features. The findings of this study could be helpful for VR developers and UI designers in providing better interactive buttons in the 3D interfaces.
Maxwell: a hardware and software highly integrated compute-storage system
Venue: arXiv cs.DC 2021
Year: 2021

The compute-storage framework is responsible for data storage and processing, and acts as the digital chassis of all upper-level businesses. The performance of the framework affects the business's processing throughput, latency, jitter, and etc., and also determines the theoretical performance upper bound that the business can achieve. In financial applications, the compute-storage framework must have high reliability and high throughput, but with low latency as well as low jitter characteristics. For some scenarios such as hot-spot account update, the performance of the compute-storage framework even surfaces to become a server performance bottleneck of the whole business system. In this paper, we study the hot-spot account issue faced by Alipay and present our exciting solution to this problem by developing a new compute-storage system, called Maxwell. Maxwell is a distributed compute-storage system with integrated hardware and software optimizations. Maxwell does not rely on any specific hardware (e.g. GPUs or FPGAs). Instead, it takes deep advantage of computer components' characteristics, such as disk, network, operating system and CPU, and aims to emit the ultimate performance of both hardware and software. In comparison with the existing hot-spot account updating solutions deployed online, Maxwell achieves three orders of magnitude performance improvement for end-to-end evaluation. Meanwhile, Maxwell also demonstrates remarkable performance gains in other related businesses of Ant Group.
Geometry-Aware Unsupervised Domain Adaptation
Venue: arXiv cs.LG 2021
Year: 2021

Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain in the presence of dataset shift. Most existing methods cannot address the domain alignment and class discrimination well, which may distort the intrinsic data structure for downstream tasks (e.g., classification). To this end, we propose a novel geometry-aware model to learn the transferability and discriminability simultaneously via nuclear norm optimization. We introduce the domain coherence and class orthogonality for UDA from the perspective of subspace geometry. The domain coherence will ensure the model has a larger capacity for learning separable representations, and class orthogonality will minimize the correlation between clusters to alleviate the misalignment. So, they are consistent and can benefit from each other. Besides, we provide a theoretical insight into the norm-based learning literature in UDA, which ensures the interpretability of our model. We show that the norms of domains and clusters are expected to be larger and smaller to enhance the transferability and discriminability, respectively. Extensive experimental results on standard UDA datasets demonstrate the effectiveness of our theory and model.
Extending Merge Resolution to a Family of Proof Systems
Venue: arXiv cs.CC 2021
Year: 2021

Merge Resolution (MRes [Beyersdorff et al. J. Autom. Reason.'2021]) is a recently introduced proof system for false QBFs. It stores the countermodels as merge maps. Merge maps are deterministic branching programs in which isomorphism checking is efficient making MRes a polynomial time verifiable proof system. In this paper, we introduce a family of proof systems MRes-R in which, the countermodels are stored in any pre-fixed complete representation R, instead of merge maps. Hence corresponding to each such R, we have a sound and refutationally complete QBF-proof system in MRes-R. To handle arbitrary representations for the strategies, we introduce consistency checking rules in MRes-R instead of isomorphism checking. As a result these proof systems are not polynomial time verifiable. Consequently, the paper shows that using merge maps is too restrictive and can be replaced with arbitrary representations leading to several interesting proof systems. Exploring proof theoretic properties of MRes-R, we show that eFrege+$\forall$red simulates all valid refutations from proof systems in MRes-R. In order to simulate arbitrary representations in MRes-R, we first represent the steps used by the proof systems as a new complete structure. Consequently, the corresponding proof system belonging to MRes-R is able to simulate all proof systems in MRes-R. Finally, we simulate this proof system via eFrege+$\forall$red using the ideas from [Chew et al. ECCC.'2021]. On the lower bound side, we show that the completion principle formulas from [Jonata et al. Theor. Comput. Sci.'2015] which are shown to be hard for regular MRes in [Beyersdorff et al. FSTTCS.'2020], are also hard for any regular proof system in MRes-R. Thereby, the paper lifts the lower bound of regular MRes to an entire class of proof systems, which use some complete representation, including those undiscovered, instead of merge maps.