OpenRaiser/Intern-Atlas huggingface dataset:
Cause-and-Effect Analysis of ADAS: A Comparison Study between Literature Review and Complaint Data
Venue: arXiv cs.CL 2022
Year: 2022

Advanced driver assistance systems (ADAS) are designed to improve vehicle safety. However, it is difficult to achieve such benefits without understanding the causes and limitations of the current ADAS and their possible solutions. This study 1) investigated the limitations and solutions of ADAS through a literature review, 2) identified the causes and effects of ADAS through consumer complaints using natural language processing models, and 3) compared the major differences between the two. These two lines of research identified similar categories of ADAS causes, including human factors, environmental factors, and vehicle factors. However, academic research focused more on human factors of ADAS issues and proposed advanced algorithms to mitigate such issues while drivers complained more of vehicle factors of ADAS failures, which led to associated top consequences. The findings from these two sources tend to complement each other and provide important implications for the improvement of ADAS in the future.
Privacy-Preserving Edge Caching: A Probabilistic Approach
Venue: arXiv cs.NI 2022
Year: 2022

Edge caching (EC) decreases the average access delay of the end-users through caching popular content at the edge network, however, it increases the leakage probability of valuable information such as users preferences. Most of the existing privacy-preserving approaches focus on adding layers of encryption, which confronts the network with more challenges such as energy and computation limitations. We employ a chunk-based joint probabilistic caching (JPC) approach to mislead an adversary eavesdropping on the communication inside an EC and maximizing the adversary's error in estimating the requested file and requesting cache. In JPC, we optimize the probability of each cache placement to minimize the communication cost while guaranteeing the desired privacy and then, formulate the optimization problem as a linear programming (LP) problem. Since JPC inherits the curse of dimensionality, we also propose scalable JPC (SPC), which reduces the number of feasible cache placements by dividing files into non-overlapping subsets. We also compare the JPC and SPC approaches against an existing probabilistic method, referred to as disjoint probabilistic caching (DPC) and random dummy-based approach (RDA). Results obtained through extensive numerical evaluations confirm the validity of the analytical approach, the superiority of JPC and SPC over DPC and RDA.
Involutory permutation automorphisms of binary linear codes
Venue: arXiv cs.IT 2022
Year: 2022

We investigate the properties of binary linear codes of even length whose permutation automorphism group is a cyclic group generated by an involution. Up to dimension or co-dimension $4$, we show that there is no quasi group code whose permutation automorphism group is isomorphic to $C_2$. By generalizing the method we use to prove this result, we obtain results on the structure of putative extremal self-dual $[72, 36, 16]$ and $[96, 48, 20]$ codes in the presence of an involutory permutation automorphism.
Conceptual Modeling of Objects
Venue: arXiv cs.SE 2022
Year: 2022

In this paper, we concentrate on object-related analysis in the field of general ontology of reality as related to software engineering (e.g., UML classes). Such a venture is similar to many studies in which researchers have enhanced modeling through ontological analysis of the underlying paradigm of UML models. We attempt to develop a conceptual model that consists of a foundation of things that is supplemented with a second level of designated objects. According to some researchers, the problem of the difference between things and objects is one of the most decisive issues for the conception of reality. In software engineering, objects serve two purposes: they promote understanding of the real world and provide a practical basis for computer implementation. The notion of object plays a central role in the object-oriented approach, in which other notions are viewed by decomposing them into objects and their relationships. This paper contributes to the establishment of a broader understanding of the notion of object in conceptual modeling based on things that are simultaneously machines. In this study, we explored the underlying hypothesis of conceptual models (e.g., UML) to enhance their ontological analysis by using the thing/machine (TM) model, which presents the domain as thimacs. Following the philosophical distinction between things and objects, we can specify modeling at two levels: the thinging stage and the objectification stage. Objects are thimacs that control the handleablity of their sub-parts when interacting with the outside of the object (analogous to the body parts holding together in an assemblage when interacting with the outside). The results promise a more refined modeling process to develop a high-level description of the involved domain.
Electromagnetic Signal Injection Attacks on Differential Signaling
Venue: arXiv cs.CR 2022
Year: 2022

Differential signaling is a method of data transmission that uses two complementary electrical signals to encode information. This allows a receiver to reject any noise by looking at the difference between the two signals, assuming the noise affects both signals in the same way. Many protocols such as USB, Ethernet, and HDMI use differential signaling to achieve a robust communication channel in a noisy environment. This generally works well and has led many to believe that it is infeasible to remotely inject attacking signals into such a differential pair. In this paper we challenge this assumption and show that an adversary can in fact inject malicious signals from a distance, purely using common-mode injection, i.e., injecting into both wires at the same time. We show how this allows an attacker to inject bits or even arbitrary messages into a communication line. Such an attack is a significant threat to many applications, from home security and privacy to automotive systems, critical infrastructure, or implantable medical devices; in which incorrect data or unauthorized control could cause significant damage, or even fatal accidents. We show in detail the principles of how an electromagnetic signal can bypass the noise rejection of differential signaling, and eventually result in incorrect bits in the receiver. We show how an attacker can exploit this to achieve a successful injection of an arbitrary bit, and we analyze the success rate of injecting longer arbitrary messages. We demonstrate the attack on a real system and show that the success rate can reach as high as $90\%$. Finally, we present a case study where we wirelessly inject a message into a Controller Area Network (CAN) bus, which is a differential signaling bus protocol used in many critical applications, including the automotive and aviation sector.
DA$^2$ Dataset: Toward Dexterity-Aware Dual-Arm Grasping
Venue: arXiv cs.RO 2022
Year: 2022

In this paper, we introduce DA$^2$, the first large-scale dual-arm dexterity-aware dataset for the generation of optimal bimanual grasping pairs for arbitrary large objects. The dataset contains about 9M pairs of parallel-jaw grasps, generated from more than 6000 objects and each labeled with various grasp dexterity measures. In addition, we propose an end-to-end dual-arm grasp evaluation model trained on the rendered scenes from this dataset. We utilize the evaluation model as our baseline to show the value of this novel and nontrivial dataset by both online analysis and real robot experiments. All data and related code will be open-sourced at https://sites.google.com/view/da2dataset.
A heuristic technique for decomposing multisets of non-negative integers according to the Minkowski sum
Venue: arXiv cs.DM 2022
Year: 2022

We study the following problem. Given a multiset $M$ of non-negative integers, decide whether there exist and, in the positive case, compute two non-trivial multisets whose Minkowski sum is equal to $M$. The Minkowski sum of two multisets A and B is a multiset containing all possible sums of any element of A and any element of B. This problem was proved to be NP-complete when multisets are replaced by sets. This version of the problem is strictly related to the factorization of boolean polynomials that turns out to be NP-complete as well. When multisets are considered, the problem is equivalent to the factorization of polynomials with non-negative integer coefficients. The computational complexity of both these problems is still unknown. The main contribution of this paper is a heuristic technique for decomposing multisets of non-negative integers. Experimental results show that our heuristic decomposes multisets of hundreds of elements within seconds independently of the magnitude of numbers belonging to the multisets. Our heuristic can be used also for factoring polynomials in N[x]. We show that, when the degree of the polynomials gets larger, our technique is much faster than the state-of-the-art algorithms implemented in commercial software like Mathematica and MatLab.
Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

In this paper, we study the post-hoc calibration of modern neural networks, a problem that has drawn a lot of attention in recent years. Many calibration methods of varying complexity have been proposed for the task, but there is no consensus about how expressive these should be. We focus on the task of confidence scaling, specifically on post-hoc methods that generalize Temperature Scaling, we call these the Adaptive Temperature Scaling family. We analyse expressive functions that improve calibration and propose interpretable methods. We show that when there is plenty of data complex models like neural networks yield better performance, but are prone to fail when the amount of data is limited, a common situation in certain post-hoc calibration applications like medical diagnosis. We study the functions that expressive methods learn under ideal conditions and design simpler methods but with a strong inductive bias towards these well-performing functions. Concretely, we propose Entropy-based Temperature Scaling, a simple method that scales the confidence of a prediction according to its entropy. Results show that our method obtains state-of-the-art performance when compared to others and, unlike complex models, it is robust against data scarcity. Moreover, our proposed model enables a deeper interpretation of the calibration process.
Augmenting Vision Language Pretraining by Learning Codebook with Visual Semantics
Venue: arXiv cs.CV 2022
Year: 2022

Language modality within the vision language pretraining framework is innately discretized, endowing each word in the language vocabulary a semantic meaning. In contrast, visual modality is inherently continuous and high-dimensional, which potentially prohibits the alignment as well as fusion between vision and language modalities. We therefore propose to "discretize" the visual representation by joint learning a codebook that imbues each visual token a semantic. We then utilize these discretized visual semantics as self-supervised ground-truths for building our Masked Image Modeling objective, a counterpart of Masked Language Modeling which proves successful for language models. To optimize the codebook, we extend the formulation of VQ-VAE which gives a theoretic guarantee. Experiments validate the effectiveness of our approach across common vision-language benchmarks.
Repairing $\mathcal{EL}$ Ontologies Using Weakening and Completing
Venue: arXiv cs.AI 2022
Year: 2022

The quality of ontologies in terms of their correctness and completeness is crucial for developing high-quality ontology-based applications. Traditional debugging techniques repair ontologies by removing unwanted axioms, but may thereby remove consequences that are correct in the domain of the ontology. In this paper we propose an interactive approach to mitigate this for $\mathcal{EL}$ ontologies by axiom weakening and completing. We present algorithms for weakening and completing and present the first approach for repairing that takes into account removing, weakening and completing. We show different combination strategies, discuss the influence on the final ontologies and show experimental results. We show that previous work has only considered special cases and that there is a trade-off between the amount of validation work for a domain expert and the quality of the ontology in terms of correctness and completeness.
Editorial: Special Issue on Collaborative Aspects of Open Data in Software EngineeringJohan
Venue: arXiv cs.SE 2022
Year: 2022

High-quality data has become increasingly important to software engineers in designing and implementing today's software, for example, as an input to machine-learning algorithms and visualisation- and analytics-based features. Open data - i.e., data shared under a licence that gives users the right to study, process, and distribute the data to anyone and for any purpose - offers a mechanism to address this need. Data may originate from multiple sources, whether crowdsourced, shared by government agencies, or shared between commercial entities, and is undoubtedly inherent to all business and revenue models across the public sector, business and industry today. In this guest editorial for the Special Issue on Collaborative Aspects of Open Data in Software Engineering, we explore the collaborative aspects of open data in software engineering. We highlight how these aspects can benefit organisations, what challenges may exist and how these may be addressed based on current practice, and introduce the four papers included in this special issue.
Deep Active Learning with Budget Annotation
Venue: arXiv cs.LG 2022
Year: 2022

Digital data collected over the decades and data currently being produced with use of information technology is vastly the unlabeled data or data without description. The unlabeled data is relatively easy to acquire but expensive to label even with use of domain experts. Most of the recent works focus on use of active learning with uncertainty metrics measure to address this problem. Although most uncertainty selection strategies are very effective, they fail to take informativeness of the unlabeled instances into account and are prone to querying outliers. In order to address these challenges we propose an hybrid approach of computing both the uncertainty and informativeness of an instance, then automaticaly label the computed instances using budget annotator. To reduce the annotation cost, we employ the state-of-the-art pre-trained models in order to avoid querying information already contained in those models. Our extensive experiments on different sets of datasets demonstrate the efficacy of the proposed approach.
ERC-20R and ERC-721R: Reversible Transactions on Ethereum
Venue: arXiv cs.CR 2022
Year: 2022

Blockchains are meant to be persistent: posted transactions are immutable and cannot be changed. When a theft takes place, there are limited options for reversing the disputed transaction, and this has led to significant losses in the blockchain ecosystem. In this paper we propose reversible versions of ERC-20 and ERC-721, the most widely used token standards. With these new standards, a transaction is eligible for reversal for a short period of time after it has been posted on chain. After the dispute period has elapsed, the transaction can no longer be reversed. Within the short dispute period, a sender can request to reverse a transaction by convincing a decentralized set of judges to first freeze the disputed assets, and then later convincing them to reverse the transaction. Supporting reversibility in the context of ERC-20 and ERC-721 raises many interesting technical challenges. This paper explores these challenges and proposes a design for our ERC-20R and ERC-721R standards, the reversible versions of ERC-20 and ERC-721. We also provide a prototype implementation. Our goal is to initiate a deeper conversation about reversibility in the hope of reducing some of the losses in the blockchain ecosystem.
XOOD: Extreme Value Based Out-Of-Distribution Detection For Image Classification
Venue: arXiv cs.LG 2022
Year: 2022

Detecting out-of-distribution (OOD) data at inference time is crucial for many applications of machine learning. We present XOOD: a novel extreme value-based OOD detection framework for image classification that consists of two algorithms. The first, XOOD-M, is completely unsupervised, while the second XOOD-L is self-supervised. Both algorithms rely on the signals captured by the extreme values of the data in the activation layers of the neural network in order to distinguish between in-distribution and OOD instances. We show experimentally that both XOOD-M and XOOD-L outperform state-of-the-art OOD detection methods on many benchmark data sets in both efficiency and accuracy, reducing false-positive rate (FPR95) by 50%, while improving the inferencing time by an order of magnitude.
Fashion Recommendation Based on Style and Social Events
Venue: arXiv cs.CV 2022
Year: 2022

Fashion recommendation is often declined as the task of finding complementary items given a query garment or retrieving outfits that are suitable for a given user. In this work we address the problem by adding an additional semantic layer based on the style of the proposed dressing. We model style according to two important aspects: the mood and the emotion concealed behind color combination patterns and the appropriateness of the retrieved garments for a given type of social event. To address the former we rely on Shigenobu Kobayashi's color image scale, which associated emotional patterns and moods to color triples. The latter instead is analyzed by extracting garments from images of social events. Overall, we integrate in a state of the art garment recommendation framework a style classifier and an event classifier in order to condition recommendation on a given query.
100 prisoners and a lightbulb -- looking back
Venue: arXiv cs.DM 2022
Year: 2022

100 prisoners and a light bulb is a long standing mathematical puzzle. The problem was studied mostly in 2002 [5], 2003 [1], and 2004 [3]. Solutions in published articles had average number of visits above 3850, but best solutions on forums had (declared) average number of visits around 3500. I spent some time in 2007-2009 to optimize the communication strategy and I pushed the average number of visits below 3390, seems no new ideas appear after it. Recently I have met several people familiar with published papers from 2002-2003 but not knowing newer results. Even after 2009 several papers on the topic were published where the new results were not mentioned [4]. Whole book was written about the problem [2]. This is why I am writing this summary.
Improving the Reliability of Next Generation SSDs using WOM-v Codes
Venue: arXiv cs.AR 2022
Year: 2022

High density Solid State Drives, such as QLC drives, offer increased storage capacity, but a magnitude lower Program and Erase (P/E) cycles, limiting their endurance and hence usability. We present the design and implementation of non-binary, Voltage-Based Write-Once-Memory (WOM-v) Codes to improve the lifetime of QLC drives. First, we develop a FEMU based simulator test-bed to evaluate the gains of WOM-v codes on real world workloads. Second, we propose and implement two optimizations, an efficient garbage collection mechanism and an encoding optimization to drastically improve WOM-v code endurance without compromising performance. A careful evaluation, including microbenchmarks and trace-driven evaluation, demonstrates that WOM-v codes can reduce Erase cycles for QLC drives by 4.4x-11.1x for real world workloads with minimal performance overheads resulting in improved QLC SSD lifetime.
Partial Connection Based on Channel Attention for Differentiable Neural Architecture Search
Venue: arXiv cs.LG 2022
Year: 2022

Differentiable neural architecture search (DARTS), as a gradient-guided search method, greatly reduces the cost of computation and speeds up the search. In DARTS, the architecture parameters are introduced to the candidate operations, but the parameters of some weight-equipped operations may not be trained well in the initial stage, which causes unfair competition between candidate operations. The weight-free operations appear in large numbers which results in the phenomenon of performance crash. Besides, a lot of memory will be occupied during training supernet which causes the memory utilization to be low. In this paper, a partial channel connection based on channel attention for differentiable neural architecture search (ADARTS) is proposed. Some channels with higher weights are selected through the attention mechanism and sent into the operation space while the other channels are directly contacted with the processed channels. Selecting a few channels with higher attention weights can better transmit important feature information into the search space and greatly improve search efficiency and memory utilization. The instability of network structure caused by random selection can also be avoided. The experimental results show that ADARTS achieved 2.46% and 17.06% classification error rates on CIFAR-10 and CIFAR-100, respectively. ADARTS can effectively solve the problem that too many skip connections appear in the search process and obtain network structures with better performance.
GANDSE: Generative Adversarial Network based Design Space Exploration for Neural Network Accelerator Design
Venue: arXiv cs.LG 2022
Year: 2022

With the popularity of deep learning, the hardware implementation platform of deep learning has received increasing interest. Unlike the general purpose devices, e.g., CPU, or GPU, where the deep learning algorithms are executed at the software level, neural network hardware accelerators directly execute the algorithms to achieve higher both energy efficiency and performance improvements. However, as the deep learning algorithms evolve frequently, the engineering effort and cost of designing the hardware accelerators are greatly increased. To improve the design quality while saving the cost, design automation for neural network accelerators was proposed, where design space exploration algorithms are used to automatically search the optimized accelerator design within a design space. Nevertheless, the increasing complexity of the neural network accelerators brings the increasing dimensions to the design space. As a result, the previous design space exploration algorithms are no longer effective enough to find an optimized design. In this work, we propose a neural network accelerator design automation framework named GANDSE, where we rethink the problem of design space exploration, and propose a novel approach based on the generative adversarial network (GAN) to support an optimized exploration for high dimension large design space. The experiments show that GANDSE is able to find the more optimized designs in negligible time compared with approaches including multilayer perceptron and deep reinforcement learning.