OpenRaiser/Intern-Atlas huggingface dataset:
To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning
Venue: arXiv cs.CL 2022
Year: 2022

Machine Reading Comprehension with Unanswerable Questions is a difficult NLP task, challenged by the questions which can not be answered from passages. It is observed that subtle literal changes often make an answerable question unanswerable, however, most MRC models fail to recognize such changes. To address this problem, in this paper, we propose a span-based method of Contrastive Learning (spanCL) which explicitly contrast answerable questions with their answerable and unanswerable counterparts at the answer span level. With spanCL, MRC models are forced to perceive crucial semantic changes from slight literal differences. Experiments on SQuAD 2.0 dataset show that spanCL can improve baselines significantly, yielding 0.86-2.14 absolute EM improvements. Additional experiments also show that spanCL is an effective way to utilize generated questions.
Application of Blockchain Smart Contracts in E-Commerce and Government
Venue: arXiv cs.CY 2022
Year: 2022

With technological advances and the establishment of e-commerce models, business challenges have shifted to online platforms. The promise of embedding self-executing and autonomous programs into blockchain technologies has attracted increased interest and its use in niche solutions. Using qualitative interviews, this paper sought the opinions of the eleven industry leaders regarding smart contracts. Findings reveal that the technology is gaining momentum in e-commerce, particularly in financial transfer, record-keeping, real estate, and property management, insurance, mortgage, supply chain management, data storage, authorization of credit, denaturalized intelligence, aviation sector, shipping of products, invoice financing and other domains. The significant benefits of widespread adoption and deployment of smart contracts include their capability to deliver decentralization, efficacy, cost-effectiveness, transparency, speed, autonomy, transparency, privacy, and security, encouraging the emergence of novel business models. Albeit these benefits that revolutionize online transactions, the technology faced multifaceted challenges. Smart technologies are only a decade old and are not advanced in security, transparency, cost-effectiveness, and regulatory framework. Furthermore, organizational, and technical challenges limit their deployment: incompatibility with legacy systems, scalability, bugs, speed, and lack of talent and understanding regarding smart contracts. Consequently, policymakers, developers, researchers, practitioners, and other stakeholders need to invest effort and time to foster the technologies and address pertinent issues to enable the global adoption of smart contracts by small and big businesses.
A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models
Venue: arXiv cs.CL 2022
Year: 2022

The rapid advancement of social networks and the convenience of internet availability have accelerated the rampant spread of false news and rumors on social media sites. Amid the COVID 19 epidemic, this misleading information has aggravated the situation by putting peoples mental and physical lives in danger. To limit the spread of such inaccuracies, identifying the fake news from online platforms could be the first and foremost step. In this research, the authors have conducted a comparative analysis by implementing five transformer based models such as BERT, BERT without LSTM, ALBERT, RoBERTa, and a Hybrid of BERT & ALBERT in order to detect the fraudulent news of COVID 19 from the internet. COVID 19 Fake News Dataset has been used for training and testing the models. Among all these models, the RoBERTa model has performed better than other models by obtaining an F1 score of 0.98 in both real and fake classes.
SCFI: State Machine Control-Flow Hardening Against Fault Attacks
Venue: arXiv cs.CR 2022
Year: 2022

Fault injection (FI) is a powerful attack methodology allowing an adversary to entirely break the security of a target device. As finite-state machines (FSMs) are fundamental hardware building blocks responsible for controlling systems, inducing faults into these controllers enables an adversary to hijack the execution of the integrated circuit. A common defense strategy mitigating these attacks is to manually instantiate FSMs multiple times and detect faults using a majority voting logic. However, as each additional FSM instance only provides security against one additional induced fault, this approach scales poorly in a multi-fault attack scenario. In this paper, we present SCFI: a strong, probabilistic FSM protection mechanism ensuring that control-flow deviations from the intended control-flow are detected even in the presence of multiple faults. At its core, SCFI consists of a hardened next-state function absorbing the execution history as well as the FSM's control signals to derive the next state. When either the absorbed inputs, the state registers, or the function itself are affected by faults, SCFI triggers an error with no detection latency. We integrate SCFI into a synthesis tool capable of automatically hardening arbitrary unprotected FSMs without user interaction and open-source the tool. Our evaluation shows that SCFI provides strong protection guarantees with a better area-time product than FSMs protected using classical redundancy-based approaches. Finally, we formally verify the resilience of the protected state machines using a pre-silicon fault analysis tool.
GaitGL: Learning Discriminative Global-Local Feature Representations for Gait Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Existing gait recognition methods either directly establish Global Feature Representation (GFR) from original gait sequences or generate Local Feature Representation (LFR) from several local parts. However, GFR tends to neglect local details of human postures as the receptive fields become larger in the deeper network layers. Although LFR allows the network to focus on the detailed posture information of each local region, it neglects the relations among different local parts and thus only exploits limited local information of several specific regions. To solve these issues, we propose a global-local based gait recognition network, named GaitGL, to generate more discriminative feature representations. To be specific, a novel Global and Local Convolutional Layer (GLCL) is developed to take full advantage of both global visual information and local region details in each layer. GLCL is a dual-branch structure that consists of a GFR extractor and a mask-based LFR extractor. GFR extractor aims to extract contextual information, e.g., the relationship among various body parts, and the mask-based LFR extractor is presented to exploit the detailed posture changes of local regions. In addition, we introduce a novel mask-based strategy to improve the local feature extraction capability. Specifically, we design pairs of complementary masks to randomly occlude feature maps, and then train our mask-based LFR extractor on various occluded feature maps. In this manner, the LFR extractor will learn to fully exploit local information. Extensive experiments demonstrate that GaitGL achieves better performance than state-of-the-art gait recognition methods. The average rank-1 accuracy on CASIA-B, OU-MVLP, GREW and Gait3D is 93.6%, 98.7%, 68.0% and 63.8%, respectively, significantly outperforming the competing methods. The proposed method has won the first prize in two competitions: HID 2020 and HID 2021.
Bounds on Covering Codes in RT spaces using Ordered Covering Arrays
Venue: arXiv cs.DM 2022
Year: 2022

In this work, constructions of ordered covering arrays are discussed and applied to obtain new upper bounds on covering codes in Rosenbloom-Tsfasman spaces (RT spaces), improving or extending some previous results.
T4DT: Tensorizing Time for Learning Temporal 3D Visual Data
Venue: arXiv cs.CV 2022
Year: 2022

Unlike 2D raster images, there is no single dominant representation for 3D visual data processing. Different formats like point clouds, meshes, or implicit functions each have their strengths and weaknesses. Still, grid representations such as signed distance functions have attractive properties also in 3D. In particular, they offer constant-time random access and are eminently suitable for modern machine learning. Unfortunately, the storage size of a grid grows exponentially with its dimension. Hence they often exceed memory limits even at moderate resolution. This work proposes using low-rank tensor formats, including the Tucker, tensor train, and quantics tensor train decompositions, to compress time-varying 3D data. Our method iteratively computes, voxelizes, and compresses each frame's truncated signed distance function and applies tensor rank truncation to condense all frames into a single, compressed tensor that represents the entire 4D scene. We show that low-rank tensor compression is extremely compact to store and query time-varying signed distance functions. It significantly reduces the memory footprint of 4D scenes while remarkably preserving their geometric quality. Unlike existing, iterative learning-based approaches like DeepSDF and NeRF, our method uses a closed-form algorithm with theoretical guarantees.
Connection Reduction of DenseNet for Image Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Convolutional Neural Networks (CNN) increase depth by stacking convolutional layers, and deeper network models perform better in image recognition. Empirical research shows that simply stacking convolutional layers does not make the network train better, and skip connection (residual learning) can improve network model performance. For the image classification task, models with global densely connected architectures perform well in large datasets like ImageNet, but are not suitable for small datasets such as CIFAR-10 and SVHN. Different from dense connections, we propose two new algorithms to connect layers. Baseline is a densely connected network, and the networks connected by the two new algorithms are named ShortNet1 and ShortNet2 respectively. The experimental results of image classification on CIFAR-10 and SVHN show that ShortNet1 has a 5% lower test error rate and 25% faster inference time than Baseline. ShortNet2 speeds up inference time by 40% with less loss in test accuracy. Code and pre-trained models are available at https://github.com/RuiyangJu/Connection_Reduction.
Improvement of algebraic attacks for solving superdetermined MinRank instances
Venue: arXiv cs.CR 2022
Year: 2022

The MinRank (MR) problem is a computational problem that arises in many cryptographic applications. In Verbel et al. (PQCrypto 2019), the authors introduced a new way to solve superdetermined instances of the MinRank problem, starting from the bilinear Kipnis-Shamir (KS) modeling. They use linear algebra on specific Macaulay matrices, considering only multiples of the initial equations by one block of variables, the so called ''kernel'' variables. Later, Bardet et al. (Asiacrypt 2020) introduced a new Support Minors modeling (SM), that consider the Pl{ü}cker coordinates associated to the kernel variables, i.e. the maximal minors of the Kernel matrix in the KS modeling. In this paper, we give a complete algebraic explanation of the link between the (KS) and (SM) modelings (for any instance). We then show that superdetermined MinRank instances can be seen as easy instances of the SM modeling. In particular, we show that performing computation at the smallest possible degree (the ''first degree fall'') and the smallest possible number of variables is not always the best strategy. We give complexity estimates of the attack for generic random instances.We apply those results to the DAGS cryptosystem, that was submitted to the first round of the NIST standardization process. We show that the algebraic attack from Barelli and Couvreur (Asiacrypt 2018), improved in Bardet et al. (CBC 2019), is a particular superdetermined MinRank instance.Here, the instances are not generic, but we show that it is possible to analyse the particular instances from DAGS and provide a way toselect the optimal parameters (number of shortened positions) to solve a particular instance.
Procedural Generation and Rendering of Realistic, Navigable Forest Environments: An Open-Source Tool
Venue: arXiv cs.GR 2022
Year: 2022

Simulation of forest environments has applications from entertainment and art creation to commercial and scientific modelling. Due to the unique features and lighting in forests, a forest-specific simulator is desirable, however many current forest simulators are proprietary or highly tailored to a particular application. Here we review several areas of procedural generation and rendering specific to forest generation, and utilise this to create a generalised, open-source tool for generating and rendering interactive, realistic forest scenes. The system uses specialised L-systems to generate trees which are distributed using an ecosystem simulation algorithm. The resulting scene is rendered using a deferred rendering pipeline, a Blinn-Phong lighting model with real-time leaf transparency and post-processing lighting effects. The result is a system that achieves a balance between high natural realism and visual appeal, suitable for tasks including training computer vision algorithms for autonomous robots and visual media generation.
RankAxis: Towards a Systematic Combination of Projection and Ranking in Multi-Attribute Data Exploration
Venue: arXiv cs.DB 2022
Year: 2022

Projection and ranking are frequently used analysis techniques in multi-attribute data exploration. Both families of techniques help analysts with tasks such as identifying similarities between observations and determining ordered subgroups, and have shown good performances in multi-attribute data exploration. However, they often exhibit problems such as distorted projection layouts, obscure semantic interpretations, and non-intuitive effects produced by selecting a subset of (weighted) attributes. Moreover, few studies have attempted to combine projection and ranking into the same exploration space to complement each other's strengths and weaknesses. For this reason, we propose RankAxis, a visual analytics system that systematically combines projection and ranking to facilitate the mutual interpretation of these two techniques and jointly support multi-attribute data exploration. A real-world case study, expert feedback, and a user study demonstrate the efficacy of RankAxis.
Characterizing Vaccination Movements on YouTube in the United States and Brazil
Venue: arXiv cs.SI 2022
Year: 2022

In the context of COVID-19 pandemic, social networks such as Twitter and YouTube stand out as important sources of information. YouTube, as the largest and most engaging online media consumption platform, has a large influence in the spread of information and misinformation, which makes it important to study how it deals with the problems that arise from disinformation, as well as how its users interact with different types of content. Considering that United States (USA) and Brazil (BR) are two countries with the highest COVID-19 death tolls, we asked the following question: What are the nuances of vaccination campaigns in the two countries? With that in mind, we engage in a comparative analysis of pro and anti-vaccine movements on YouTube. We also investigate the role of YouTube in countering online vaccine misinformation in USA and BR. For this means, we monitored the removal of vaccine related content on the platform and also applied various techniques to analyze the differences in discourse and engagement in pro and anti-vaccine "comment sections". We found that American anti-vaccine content tend to lead to considerably more toxic and negative discussion than their pro-vaccine counterparts while also leading to 18% higher user-user engagement, while Brazilian anti-vaccine content was significantly less engaging. We also found that pro-vaccine and anti-vaccine discourses are considerably different as the former is associated with conspiracy theories (e.g. ccp), misinformation and alternative medicine (e.g. hydroxychloroquine), while the latter is associated with protective measures. Finally, it was observed that YouTube content removals are still insufficient, with only approximately 16% of the anti-vaccine content being removed by the end of the studied period, with the USA registering the highest percentage of removed anti-vaccine content(34%) and BR registering the lowest(9.8%).
On the Expressiveness of a Logic of Separated Relations
Venue: arXiv cs.LO 2022
Year: 2022

We compare the model-theoretic expressiveness of the existential fragment of Separation Logic over unrestricted relational signatures (SLR) -- with only separating conjunction as logical connective and higher-order inductive definitions, traditionally known as the symbolic heap fragment -- with the expressiveness of (Monadic) Second Order Logic ((M)SO). While SLR and MSO are incomparable on structures of unbounded treewidth, it turns out that SLR can be embedded in SO, in general, and that MSO becomes a strict subset of SLR, when the treewidth of the models is bounded by a parameter given as input. We also discuss the problem of defining a fragment of SLR that is equivalent to MSO over models of bounded treewidth. Such a fragment would then become the most general Separation Logic with a decidable entailment problem, a key ingredient of practical verification methods for self-adapting (reconfigurable) component-based and distributed systems.
Optimal Friendly Jamming and Transmit Power Allocation in RIS-assisted Secure Communication
Venue: arXiv cs.IT 2022
Year: 2022

This paper analyzes the secrecy performance of a reconfigurable intelligent surface (RIS) assisted wireless communication system with a friendly jammer in the presence of an eavesdropper. The friendly jammer enhances the secrecy by introducing artificial noise towards the eavesdropper without degrading the reception at the destination. Approximate secrecy outage probability (SOP) is derived in closed form. We also provide a simpler approximate closed-form expression for the SOP in order to understand the effect of system parameters on the performance and to find the optimal power allocation for the transmitter and jammer. The optimal transmit and jamming power allocation factor is derived by minimizing the SOP assuming a total power constraint. It is shown that the SOP performance is significantly improved by the introduction of the jammer and a gain of approximately $3$ dB is achieved at an SOP of $10^{-4}$ by optimally allocating power compared to the case of equal power allocation.
A Turning Point for Verified Spectre Sandboxing
Venue: arXiv cs.CR 2022
Year: 2022

Spectre attacks enable an attacker to access restricted data in an application's memory. Both the academic community and industry veterans have developed several mitigations to block Spectre attacks, but to date, very few have been formally vetted; most are "best effort" strategies. Formal guarantees are particularly crucial for protecting isolated environments like sandboxing against Spectre attacks. In such environments, a subtle flaw in the mitigation would allow untrusted code to break out of the sandbox and access trusted memory regions. In our work, we develop principled foundations to build isolated environments resistant against Spectre attacks. We propose a formal framework for reasoning about sandbox execution and Spectre attacks. We formalize properties that sound mitigation strategies must fulfill and we show how various existing mitigations satisfy (or fail to satisfy!) these properties.
Do I really need all this work to find vulnerabilities? An empirical case study comparing vulnerability detection techniques on a Java application
Venue: arXiv cs.SE 2022
Year: 2022

CONTEXT: Applying vulnerability detection techniques is one of many tasks using the limited resources of a software project. OBJECTIVE: The goal of this research is to assist managers and other decision-makers in making informed choices about the use of software vulnerability detection techniques through an empirical study of the efficiency and effectiveness of four techniques on a Java-based web application. METHOD: We apply four different categories of vulnerability detection techniques \textendash~ systematic manual penetration testing (SMPT), exploratory manual penetration testing (EMPT), dynamic application security testing (DAST), and static application security testing (SAST) \textendash\ to an open-source medical records system. RESULTS: We found the most vulnerabilities using SAST. However, EMPT found more severe vulnerabilities. With each technique, we found unique vulnerabilities not found using the other techniques. The efficiency of manual techniques (EMPT, SMPT) was comparable to or better than the efficiency of automated techniques (DAST, SAST) in terms of Vulnerabilities per Hour (VpH). CONCLUSIONS: The vulnerability detection technique practitioners should select may vary based on the goals and available resources of the project. If the goal of an organization is to find "all" vulnerabilities in a project, they need to use as many techniques as their resources allow.
The Importance of the Instantaneous Phase in Detecting Faces with Convolutional Neural Networks
Venue: arXiv cs.CV 2022
Year: 2022

Convolutional Neural Networks (CNN) have provided new and accurate methods for processing digital images and videos. Yet, training CNNs is extremely demanding in terms of computational resources. Also, for specific applications, the standard use of transfer learning also tends to require far more resources than what may be needed. Furthermore, the final systems tend to operate as black boxes that are difficult to interpret. The current thesis considers the problem of detecting faces from the AOLME video dataset. The AOLME dataset consists of a large video collection of group interactions that are recorded in unconstrained classroom environments. For the thesis, still image frames were extracted at every minute from 18 24-minute videos. Then, each video frame was divided into 9x5 blocks with 50x50 pixels each. For each of the 19440 blocks, the percentage of face pixels was set as ground truth. Face detection was then defined as a regression problem for determining the face pixel percentage for each block. For testing different methods, 12 videos were used for training and validation. The remaining 6 videos were used for testing. The thesis examines the impact of using the instantaneous phase for the AOLME block-based face detection application. For comparison, the thesis compares the use of the Frequency Modulation image based on the instantaneous phase, the use of the instantaneous amplitude, and the original gray scale image. To generate the FM and AM inputs, the thesis uses dominant component analysis that aims to decrease the training overhead while maintaining interpretability.