OpenRaiser/Intern-Atlas huggingface dataset:
Lord of the Ring(s): Side Channel Attacks on the CPU On-Chip Ring Interconnect Are Practical
Venue: arXiv cs.CR 2021
Year: 2021

We introduce the first microarchitectural side channel attacks that leverage contention on the CPU ring interconnect. There are two challenges that make it uniquely difficult to exploit this channel. First, little is known about the ring interconnect's functioning and architecture. Second, information that can be learned by an attacker through ring contention is noisy by nature and has coarse spatial granularity. To address the first challenge, we perform a thorough reverse engineering of the sophisticated protocols that handle communication on the ring interconnect. With this knowledge, we build a cross-core covert channel over the ring interconnect with a capacity of over 4 Mbps from a single thread, the largest to date for a cross-core channel not relying on shared memory. To address the second challenge, we leverage the fine-grained temporal patterns of ring contention to infer a victim program's secrets. We demonstrate our attack by extracting key bits from vulnerable EdDSA and RSA implementations, as well as inferring the precise timing of keystrokes typed by a victim user.
Dual Pointer Network for Fast Extraction of Multiple Relations in a Sentence
Venue: arXiv cs.CL 2021
Year: 2021

Relation extraction is a type of information extraction task that recognizes semantic relationships between entities in a sentence. Many previous studies have focused on extracting only one semantic relation between two entities in a single sentence. However, multiple entities in a sentence are associated through various relations. To address this issue, we propose a relation extraction model based on a dual pointer network with a multi-head attention mechanism. The proposed model finds n-to-1 subject-object relations using a forward object decoder. Then, it finds 1-to-n subject-object relations using a backward subject decoder. Our experiments confirmed that the proposed model outperformed previous models, with an F1-score of 80.8% for the ACE-2005 corpus and an F1-score of 78.3% for the NYT corpus.
App's Auto-Login Function Security Testing via Android OS-Level Virtualization
Venue: arXiv cs.CR 2021
Year: 2021

Limited by the small keyboard, most mobile apps support the automatic login feature for better user experience. Therefore, users avoid the inconvenience of retyping their ID and password when an app runs in the foreground again. However, this auto-login function can be exploited to launch the so-called "data-clone attack": once the locally-stored, auto-login depended data are cloned by attackers and placed into their own smartphones, attackers can break through the login-device number limit and log in to the victim's account stealthily. A natural countermeasure is to check the consistency of devicespecific attributes. As long as the new device shows different device fingerprints with the previous one, the app will disable the auto-login function and thus prevent data-clone attacks. In this paper, we develop VPDroid, a transparent Android OS-level virtualization platform tailored for security testing. With VPDroid, security analysts can customize different device artifacts, such as CPU model, Android ID, and phone number, in a virtual phone without user-level API hooking. VPDroid's isolation mechanism ensures that user-mode apps in the virtual phone cannot detect device-specific discrepancies. To assess Android apps' susceptibility to the data-clone attack, we use VPDroid to simulate data-clone attacks with 234 most-downloaded apps. Our experiments on five different virtual phone environments show that VPDroid's device attribute customization can deceive all tested apps that perform device-consistency checks, such as Twitter, WeChat, and PayPal. 19 vendors have confirmed our report as a zero-day vulnerability. Our findings paint a cautionary tale: only enforcing a device-consistency check at client side is still vulnerable to an advanced data-clone attack.
Assessment of a hybrid software development process for student projects: a controlled experiment
Venue: arXiv cs.SE 2021
Year: 2021

In recent years, a vivid interest in hybrid development methods has been observed as practitioners combine various approaches to software creation to improve productivity, product quality, and adaptability of the process to react to change. Scientific papers on the subject proliferate, however evaluation of the effectiveness of hybrid methods in academic contexts has yet to follow. The work presented investigates if introducing a hybrid approach for student projects brings added value as compared to iterative and sequential development. A controlled experiment was carried out among Bachelor students of a French engineering school to assess the impacts of a given development method on the success of student computing undertakings. Its three dimensions were examined via a set of metrics: product quality, team productivity as well as human factors (teamwork quality & learning outcomes). Several patterns were observed, which can provide a starting point for educators and researchers wishing to tailor or design a software development process for academic needs.
Multilingual Byte2Speech Models for Scalable Low-resource Speech Synthesis
Venue: arXiv cs.CL 2021
Year: 2021

To scale neural speech synthesis to various real-world languages, we present a multilingual end-to-end framework that maps byte inputs to spectrograms, thus allowing arbitrary input scripts. Besides strong results on 40+ languages, the framework demonstrates capabilities to adapt to new languages under extreme low-resource and even few-shot scenarios of merely 40s transcribed recording, without the need of per-language resources like lexicon, extra corpus, auxiliary models, or linguistic expertise, thus ensuring scalability. While it retains satisfactory intelligibility and naturalness matching rich-resource models. Exhaustive comparative and ablation studies are performed to reveal the potential of the framework for low-resource languages. Furthermore, we propose a novel method to extract language-specific sub-networks in a multilingual model for a better understanding of its mechanism.
SDR-based Testbed for Real-time CQI Prediction for URLLC
Venue: arXiv cs.NI 2021
Year: 2021

Ultra-reliable Low-Latency Communication (URLLC) is a key feature of 5G systems. The quality of service (QoS) requirements imposed by URLLC are less than 10ms delay and less than $10^{-5}$ packet loss rate (PLR). To satisfy such strict requirements with minimal channel resource consumption, the devices need to accurately predict the channel quality and select Modulation and Coding Scheme (MCS) for URLLC in a proper way. This paper presents a novel real-time channel prediction system based on Software-Defined Radio that uses a neural network. The paper also describes and shares an open channel measurement dataset that can be used to compare various channel prediction approaches in different mobility scenarios in future research on URLLC
Algebra-based Synthesis of Loops and their Invariants (Invited Paper)
Venue: arXiv cs.LO 2021
Year: 2021

Provably correct software is one of the key challenges in our softwaredriven society. While formal verification establishes the correctness of a given program, the result of program synthesis is a program which is correct by construction. In this paper we overview some of our results for both of these scenarios when analysing programs with loops. The class of loops we consider can be modelled by a system of linear recurrence equations with constant coefficients, called C-finite recurrences. We first describe an algorithmic approach for synthesising all polynomial equality invariants of such non-deterministic numeric single-path loops. By reverse engineering invariant synthesis, we then describe an automated method for synthesising program loops satisfying a given set of polynomial loop invariants. Our results have applications towards proving partial correctness of programs, compiler optimisation and generating number sequences from algebraic relations. This is a preprint that was invited for publication at VMCAI 2021.
Uncertainty Quantification and Software Risk Analysis for Digital Twins in the Nearly Autonomous Management and Control Systems: A Review
Venue: arXiv cs.SE 2021
Year: 2021

A nearly autonomous management and control (NAMAC) system is designed to furnish recommendations to operators for achieving particular goals based on NAMAC's knowledge base. As a critical component in a NAMAC system, digital twins (DTs) are used to extract information from the knowledge base to support decision-making in reactor control and management during all modes of plant operations. With the advancement of artificial intelligence and data-driven methods, machine learning algorithms are used to build DTs of various functions in the NAMAC system. To evaluate the uncertainty of DTs and its impacts on the reactor digital instrumentation and control systems, uncertainty quantification (UQ) and software risk analysis is needed. As a comprehensive overview of prior research and a starting point for new investigations, this study selects and reviews relevant UQ techniques and software hazard and software risk analysis methods that may be suitable for DTs in the NAMAC system.
Predictive Coding Can Do Exact Backpropagation on Convolutional and Recurrent Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

Predictive coding networks (PCNs) are an influential model for information processing in the brain. They have appealing theoretical interpretations and offer a single mechanism that accounts for diverse perceptual phenomena of the brain. On the other hand, backpropagation (BP) is commonly regarded to be the most successful learning method in modern machine learning. Thus, it is exciting that recent work formulates inference learning (IL) that trains PCNs to approximate BP. However, there are several remaining critical issues: (i) IL is an approximation to BP with unrealistic/non-trivial requirements, (ii) IL approximates BP in single-step weight updates; whether it leads to the same point as BP after the weight updates are conducted for more steps is unknown, and (iii) IL is computationally significantly more costly than BP. To solve these issues, a variant of IL that is strictly equivalent to BP in fully connected networks has been proposed. In this work, we build on this result by showing that it also holds for more complex architectures, namely, convolutional neural networks and (many-to-one) recurrent neural networks. To our knowledge, we are the first to show that a biologically plausible algorithm is able to exactly replicate the accuracy of BP on such complex architectures, bridging the existing gap between IL and BP, and setting an unprecedented performance for PCNs, which can now be considered as efficient alternatives to BP.
Data-Driven Short-Term Voltage Stability Assessment Based on Spatial-Temporal Graph Convolutional Network
Venue: arXiv cs.LG 2021
Year: 2021

Post-fault dynamics of short-term voltage stability (SVS) present spatial-temporal characteristics, but the existing data-driven methods for online SVS assessment fail to incorporate such characteristics into their models effectively. Confronted with this dilemma, this paper develops a novel spatial-temporal graph convolutional network (STGCN) to address this problem. The proposed STGCN utilizes graph convolution to integrate network topology information into the learning model to exploit spatial information. Then, it adopts one-dimensional convolution to exploit temporal information. In this way, it models the spatial-temporal characteristics of SVS with complete convolutional structures. After that, a node layer and a system layer are strategically designed in the STGCN for SVS assessment. The proposed STGCN incorporates the characteristics of SVS into the data-driven classification model. It can result in higher assessment accuracy, better robustness and adaptability than conventional methods. Besides, parameters in the system layer can provide valuable information about the influences of individual buses on SVS. Test results on the real-world Guangdong Power Grid in South China verify the effectiveness of the proposed network.
Lotka's Law and Authorship Distribution in Coronary Artery Disease Research in South Africa
Venue: arXiv cs.DL 2021
Year: 2021

Coronary artery disease (CAD) is a major cause of death and disability in developed countries. Although CAD mortality rates worldwide have declined over the past 4 decades, CAD remains responsible for approximately one-third or more of all deaths in individuals over age 35, and it has been estimated that nearly half of all middle-aged men and one-third of middle aged women in the United States will develop clinical CAD. The present paper attempts to check the applicability of Lotka's Law on South African publication on Coronary artery disease research. The study lights on Lotka's empirical law of scientific productivity, i.e., Inverse Square Law, to measure the scientific productivity of authors, to test Lotka's Exponent value and the K.S test for the fitness of Lotka's Law.
Leveraging Recursive Processing for Neural-Symbolic Affect-Target Associations
Venue: arXiv cs.CL 2021
Year: 2021

Explaining the outcome of deep learning decisions based on affect is challenging but necessary if we expect social companion robots to interact with users on an emotional level. In this paper, we present a commonsense approach that utilizes an interpretable hybrid neural-symbolic system to associate extracted targets, noun chunks determined to be associated with the expressed emotion, with affective labels from a natural language expression. We leverage a pre-trained neural network that is well adapted to tree and sub-tree processing, the Dependency Tree-LSTM, to learn the affect labels of dynamic targets, determined through symbolic rules, in natural language. We find that making use of the unique properties of the recursive network provides higher accuracy and interpretability when compared to other unstructured and sequential methods for determining target-affect associations in an aspect-based sentiment analysis task.
Hybrid Point Cloud Semantic Compression for Automotive Sensors: A Performance Evaluation
Venue: arXiv cs.NI 2021
Year: 2021

In a fully autonomous driving framework, where vehicles operate without human intervention, information sharing plays a fundamental role. In this context, new network solutions have to be designed to handle the large volumes of data generated by the rich sensor suite of the cars in a reliable and efficient way. Among all the possible sensors, Light Detection and Ranging (LiDAR) can produce an accurate 3D point cloud representation of the surrounding environment, which in turn generates high data rates. For this reason, efficient point cloud compression is paramount to alleviate the burden of data transmission over bandwidth-constrained channels and to facilitate real-time communications. In this paper, we propose a pipeline to efficiently compress LiDAR observations in an automotive scenario. First, we leverage the capabilities of RangeNet++, a Deep Neural Network (DNN) used to semantically infer point labels, to reduce the channel load by selecting the most valuable environmental data to be disseminated. Second, we compress the selected points using Draco, a 3D compression algorithm which is able to obtain compression up to the quantization error. Our experiments, validated on the Semantic KITTI dataset, demonstrate that it is possible to compress and send the information at the frame rate of the LiDAR, thus achieving real-time performance.
AnswerQuest: A System for Generating Question-Answer Items from Multi-Paragraph Documents
Venue: arXiv cs.CL 2021
Year: 2021

One strategy for facilitating reading comprehension is to present information in a question-and-answer format. We demo a system that integrates the tasks of question answering (QA) and question generation (QG) in order to produce Q&A items that convey the content of multi-paragraph documents. We report some experiments for QA and QG that yield improvements on both tasks, and assess how they interact to produce a list of Q&A items for a text. The demo is accessible at qna.sdl.com.
A Pilot Study on Visually Stimulated Cognitive Tasks for EEG-Based Dementia Recognition
Venue: arXiv cs.LG 2021
Year: 2021

In the status quo, dementia is yet to be cured. Precise diagnosis prior to the onset of the symptoms can prevent the rapid progression of the emerging cognitive impairment. Recent progress has shown that Electroencephalography (EEG) is the promising and cost-effective test to facilitate the detection of neurocognitive disorders. However, most of the existing works have been using only resting-state EEG. The efficiencies of EEG signals from various cognitive tasks, for dementia classification, have yet to be thoroughly investigated. In this study, we designed four cognitive tasks that engage different cognitive performances: attention, working memory, and executive function. We investigated these tasks by using statistical analysis on both time and frequency domains of EEG signals from three classes of human subjects: Dementia (DEM), Mild Cognitive Impairment (MCI), and Normal Control (NC). We also further evaluated the classification performances of two features extraction methods: Principal Component Analysis (PCA) and Filter Bank Common Spatial Pattern (FBCSP). We found that the working memory related tasks yielded good performances for dementia recognition in both cases using PCA and FBCSP. Moreover, FBCSP with features combination from four tasks revealed the best sensitivity of 0.87 and the specificity of 0.80. To our best knowledge, this is the first work that concurrently investigated several cognitive tasks for dementia recognition using both statistical analysis and classification scores. Our results yielded essential information to design and aid in conducting further experimental tasks to early diagnose dementia patients.
Modal Reasoning = Metric Reasoning, via Lawvere
Venue: arXiv cs.LO 2021
Year: 2021

Graded modal types systems and coeffects are becoming a standard formalism to deal with context-dependent computations where code usage plays a central role. The theory of program equivalence for modal and coeffectful languages, however, is considerably underdeveloped if compared to the denotational and operational semantics of such languages. This raises the question of how much of the theory of ordinary program equivalence can be given in a modal scenario. In this work, we show that coinductive equivalences can be extended to a modal setting, and we do so by generalising Abramsky's applicative bisimilarity to coeffectful behaviours. To achieve this goal, we develop a general theory of ternary program relations based on the novel notion of a comonadic lax extension, on top of which we define a modal extension of Abramsky's applicative bisimilarity (which we dub modal applicative bisimilarity). We prove such a relation to be a congruence, this way obtaining a compositional technique for reasoning about modal and coeffectful behaviours. But this is not the end of the story: we also establish a correspondence between modal program relations and program distances. This correspondence shows that modal applicative bisimilarity and (a properly extended) applicative bisimilarity distance coincide, this way revealing that modal program equivalences and program distances are just two sides of the same coin.
SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models
Venue: arXiv cs.LG 2021
Year: 2021

Despite deep learning (DL) success in classification problems, DL classifiers do not provide a sound mechanism to decide when to refrain from predicting. Recent works tried to control the overall prediction risk with classification with rejection options. However, existing works overlook the different significance of different classes. We introduce Set-classifier with Class-specific RIsk Bounds (SCRIB) to tackle this problem, assigning multiple labels to each example. Given the output of a black-box model on the validation set, SCRIB constructs a set-classifier that controls the class-specific prediction risks with a theoretical guarantee. The key idea is to reject when the set classifier returns more than one label. We validated SCRIB on several medical applications, including sleep staging on electroencephalogram (EEG) data, X-ray COVID image classification, and atrial fibrillation detection based on electrocardiogram (ECG) data. SCRIB obtained desirable class-specific risks, which are 35\%-88\% closer to the target risks than baseline methods.
ODIN: A Bit-Parallel Stochastic Arithmetic Based Accelerator for In-Situ Neural Network Processing in Phase Change RAM
Venue: arXiv cs.AR 2021
Year: 2021

Due to the very rapidly growing use of Artificial Neural Networks (ANNs) in real-world applications related to machine learning and Artificial Intelligence (AI), several hardware accelerator de-signs for ANNs have been proposed recently. In this paper, we present a novel processing-in-memory (PIM) engine called ODIN that employs hybrid binary-stochastic bit-parallel arithmetic in-side phase change RAM (PCRAM) to enable a low-overhead in-situ acceleration of all essential ANN functions such as multiply-accumulate (MAC), nonlinear activation, and pooling. We mapped four ANN benchmark applications on ODIN to compare its performance with a conventional processor-centric design and a crossbar-based in-situ ANN accelerator from prior work. The results of our analysis for the considered ANN topologies indicate that our ODIN accelerator can be at least 5.8x faster and 23.2x more energy-efficient, and up to 90.8x faster and 1554x more energy-efficient, compared to the crossbar-based in-situ ANN accelerator from prior work.
Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimationand 3D Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

The ability to accurately detect and localize objects is recognized as being the most important for the perception of self-driving cars. From 2D to 3D object detection, the most difficult is to determine the distance from the ego-vehicle to objects. Expensive technology like LiDAR can provide a precise and accurate depth information, so most studies have tended to focus on this sensor showing a performance gap between LiDAR-based methods and camera-based methods. Although many authors have investigated how to fuse LiDAR with RGB cameras, as far as we know there are no studies to fuse LiDAR and stereo in a deep neural network for the 3D object detection task. This paper presents SLS-Fusion, a new approach to fuse data from 4-beam LiDAR and a stereo camera via a neural network for depth estimation to achieve better dense depth maps and thereby improves 3D object detection performance. Since 4-beam LiDAR is cheaper than the well-known 64-beam LiDAR, this approach is also classified as a low-cost sensors-based method. Through evaluation on the KITTI benchmark, it is shown that the proposed method significantly improves depth estimation performance compared to a baseline method. Also, when applying it to 3D object detection, a new state of the art on low-cost sensor based method is achieved.