OpenRaiser/Intern-Atlas huggingface dataset:
A transformer based approach for fighting COVID-19 fake news
Venue: arXiv cs.CL 2021
Year: 2021

The rapid outbreak of COVID-19 has caused humanity to come to a stand-still and brought with it a plethora of other problems. COVID-19 is the first pandemic in history when humanity is the most technologically advanced and relies heavily on social media platforms for connectivity and other benefits. Unfortunately, fake news and misinformation regarding this virus is also available to people and causing some massive problems. So, fighting this infodemic has become a significant challenge. We present our solution for the "Constraint@AAAI2021 - COVID19 Fake News Detection in English" challenge in this work. After extensive experimentation with numerous architectures and techniques, we use eight different transformer-based pre-trained models with additional layers to construct a stacking ensemble classifier and fine-tuned them for our purpose. We achieved 0.979906542 accuracy, 0.979913119 precision, 0.979906542 recall, and 0.979907901 f1-score on the test dataset of the competition.
Stagnation Detection with Randomized Local Search
Venue: arXiv cs.NE 2021
Year: 2021

Recently a mechanism called stagnation detection was proposed that automatically adjusts the mutation rate of evolutionary algorithms when they encounter local optima. The so-called $SD-(1+1)EA$ introduced by Rajabi and Witt (GECCO 2020) adds stagnation detection to the classical $(1+1)EA$ with standard bit mutation, which flips each bit independently with some mutation rate, and raises the mutation rate when the algorithm is likely to have encountered local optima. In this paper, we investigate stagnation detection in the context of the $k$-bit flip operator of randomized local search that flips $k$ bits chosen uniformly at random and let stagnation detection adjust the parameter $k$. We obtain improved runtime results compared to the $SD-(1+1)EA$ amounting to a speed-up of up to $e=2.71\dots$ Moreover, we propose additional schemes that prevent infinite optimization times even if the algorithm misses a working choice of $k$ due to unlucky events. Finally, we present an example where standard bit mutation still outperforms the local $k$-bit flip with stagnation detection.
Visualization of Nonlinear Programming for Robot Motion Planning
Venue: arXiv cs.RO 2021
Year: 2021

Nonlinear programming targets nonlinear optimization with constraints, which is a generic yet complex methodology involving humans for problem modeling and algorithms for problem solving. We address the particularly hard challenge of supporting domain experts in handling, understanding, and trouble-shooting high-dimensional optimization with a large number of constraints. Leveraging visual analytics, users are supported in exploring the computation process of nonlinear constraint optimization. Our system was designed for robot motion planning problems and developed in tight collaboration with domain experts in nonlinear programming and robotics. We report on the experiences from this design study, illustrate the usefulness for relevant example cases, and discuss the extension to visual analytics for nonlinear programming in general.
Two-Sided Matching Markets in the ELLIS 2020 PhD Program
Venue: arXiv cs.GT 2021
Year: 2021

The ELLIS PhD program is a European initiative that supports excellent young researchers by connecting them to leading researchers in AI. In particular, PhD students are supervised by two advisors from different countries: an advisor and a co-advisor. In this work we summarize the procedure that, in its final step, matches students to advisors in the ELLIS 2020 PhD program. The steps of the procedure are based on the extensive literature of two-sided matching markets and the college admissions problem [Knuth and De Bruijn, 1997, Gale and Shapley, 1962, Rothand Sotomayor, 1992]. We introduce PolyGS, an algorithm for the case of two-sided markets with quotas on both sides (also known as many-to-many markets) which we use throughout the selection procedure of pre-screening, interview matching and final matching with advisors. The algorithm returns a stable matching in the sense that no unmatched persons prefer to be matched together rather than with their current partners (given their indicated preferences). Roth [1984] gives evidence that only stable matchings are likely to be adhered to over time. Additionally, the matching is student-optimal. Preferences are constructed based on the rankings each side gives to the other side and the overlaps of research fields. We present and discuss the matchings that the algorithm produces in the ELLIS 2020 PhD program.
Adversarial Machine Learning Attacks on Condition-Based Maintenance Capabilities
Venue: arXiv cs.LG 2021
Year: 2021

Condition-based maintenance (CBM) strategies exploit machine learning models to assess the health status of systems based on the collected data from the physical environment, while machine learning models are vulnerable to adversarial attacks. A malicious adversary can manipulate the collected data to deceive the machine learning model and affect the CBM system's performance. Adversarial machine learning techniques introduced in the computer vision domain can be used to make stealthy attacks on CBM systems by adding perturbation to data to confuse trained models. The stealthy nature causes difficulty and delay in detection of the attacks. In this paper, adversarial machine learning in the domain of CBM is introduced. A case study shows how adversarial machine learning can be used to attack CBM capabilities. Adversarial samples are crafted using the Fast Gradient Sign method, and the performance of a CBM system under attack is investigated. The obtained results reveal that CBM systems are vulnerable to adversarial machine learning attacks and defense strategies need to be considered.
WallStreetBets: Positions or Ban
Venue: arXiv cs.HC 2021
Year: 2021

r/wallstreetbets (WallStreetBets or WSB) is a subreddit devoted to irreverent memes and high-risk options trading. As of March 30, 2020, the subreddit boasts a usership of nearly 1.1 millions subscribers and self-describes as "if 4chan found a Bloomberg terminal." This paper will utilize Amy Jo Kim's community design principles along with social psychology theory as frameworks to understand how this chaotic, oftentimes offensive community has developed one of the largest and most loyal user bases on the platform. We will further argue that humor plays a vital role in promoting in-group cohesion and in providing an unconventional third place for traders (and thinly veiled gamblers) to seek support from each other in the form of vulgar, yet good-humored taunting.
Theory of heterogeneous circuits with stochastic memristive devices
Venue: arXiv cs.ET 2021
Year: 2021

We introduce an approach based on the Chapman-Kolmogorov equation to model heterogeneous stochastic circuits, namely, the circuits combining binary or multi-state stochastic memristive devices and continuum reactive components (capacitors and/or inductors). Such circuits are described in terms of occupation probabilities of memristive states that are functions of reactive variables. As an illustrative example, the series circuit of a binary memristor and capacitor is considered in detail. Some analytical solutions are found. Our work offers a novel analytical/numerical tool for modeling complex stochastic networks, which may find a broad range of applications.
Improved Rate-Energy Trade-off For SWIPT Using Chordal Distance Decomposition In Interference Alignment Networks
Venue: arXiv cs.IT 2021
Year: 2021

This paper investigates the simultaneous wireless information and power transfer (SWIPT) precoding scheme for K-user multiple-input-multiple-output (MIMO) interference channels (IC), for which interference alignment (IA) schemes provide optimal precoders to achieve full degrees-of-freedom (DoF) gain. However, harvesting RF energy simultaneously reduces the achievable DoFs. To study a trade-off between harvested energy and sum rate, the transceiver design problem is suboptimally formulated in literature via convex relaxations, which is still computationally intensive, especially for battery limited nodes running on harvested energy. In this paper, we propose a systematic method using chordal distance (CD) decomposition to obtain the balanced precoding, which improves the trade-off. Analysis shows that given the nonnegative value of CD, the achieved harvested energy for the proposed precoder is higher than that for perfect IA precoder. Moreover, energy constraints can be achieved, while maintaining a constant rate loss without losing DoFs via tuning the CD value and splitting factor. Simulation results verify the analysis and add that the IA schemes based on max-SINR or mean-squared error are better suited for SWIPT maximization than subspace or leakage minimization methods.
CML-COVID: A Large-Scale COVID-19 Twitter Dataset with Latent Topics, Sentiment and Location Information
Venue: arXiv cs.SI 2021
Year: 2021

As a platform, Twitter has been a significant public space for discussion related to the COVID-19 pandemic. Public social media platforms such as Twitter represent important sites of engagement regarding the pandemic and these data can be used by research teams for social, health, and other research. Understanding public opinion about COVID-19 and how information diffuses in social media is important for governments and research institutions. Twitter is a ubiquitous public platform and, as such, has tremendous utility for understanding public perceptions, behavior, and attitudes related to COVID-19. In this research, we present CML-COVID, a COVID-19 Twitter data set of 19,298,967 million tweets from 5,977,653 unique individuals and summarize some of the attributes of these data. These tweets were collected between March 2020 and July 2020 using the query terms coronavirus, covid and mask related to COVID-19. We use topic modeling, sentiment analysis, and descriptive statistics to describe the tweets related to COVID-19 we collected and the geographical location of tweets, where available. We provide information on how to access our tweet dataset (archived using twarc).
Probabilistic Data with Continuous Distributions
Venue: arXiv cs.DB 2021
Year: 2021

Statistical models of real world data typically involve continuous probability distributions such as normal, Laplace, or exponential distributions. Such distributions are supported by many probabilistic modelling formalisms, including probabilistic database systems. Yet, the traditional theoretical framework of probabilistic databases focusses entirely on finite probabilistic databases. Only recently, we set out to develop the mathematical theory of infinite probabilistic databases. The present paper is an exposition of two recent papers which are cornerstones of this theory. In (Grohe, Lindner; ICDT 2020) we propose a very general framework for probabilistic databases, possibly involving continuous probability distributions, and show that queries have a well-defined semantics in this framework. In (Grohe, Kaminski, Katoen, Lindner; PODS 2020) we extend the declarative probabilistic programming language Generative Datalog, proposed by (Bárány et al.~2017) for discrete probability distributions, to continuous probability distributions and show that such programs yield generative models of continuous probabilistic databases.
Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent
Venue: arXiv cs.DC 2021
Year: 2021

We consider the problem of Byzantine fault-tolerance in the peer-to-peer (P2P) distributed gradient-descent method -- a prominent algorithm for distributed optimization in a P2P system. In this problem, the system comprises of multiple agents, and each agent has a local cost function. In the fault-free case, when all the agents are honest, the P2P distributed gradient-descent method allows all the agents to reach a consensus on a solution that minimizes their aggregate cost. However, we consider a scenario where a certain number of agents may be Byzantine faulty. Such faulty agents may not follow an algorithm correctly, and may share arbitrary incorrect information to prevent other non-faulty agents from solving the optimization problem. In the presence of Byzantine faulty agents, a more reasonable goal is to allow all the non-faulty agents to reach a consensus on a solution that minimizes the aggregate cost of all the non-faulty agents. We refer to this fault-tolerance goal as $f$-resilience where $f$ is the maximum number of Byzantine faulty agents in a system of $n$ agents, with $f < n$. Most prior work on fault-tolerance in P2P distributed optimization only consider approximate fault-tolerance wherein, unlike $f$-resilience, all the non-faulty agents' compute a minimum point of a non-uniformly weighted aggregate of their cost functions. We propose a fault-tolerance mechanism that confers provable $f$-resilience to the P2P distributed gradient-descent method, provided the non-faulty agents satisfy the necessary condition of $2f$-redundancy, defined later in the paper. Moreover, compared to prior work, our algorithm is applicable to a larger class of high-dimensional convex distributed optimization problems.
Atomic Swaps between Bitcoin and Monero
Venue: arXiv cs.CR 2021
Year: 2021

Due to the evergrowing blockchain ecosystem, interoperability has become a matter of great importance. Atomic swaps allow connecting otherwise isolated blockchains while adhering to the core principles of censorship resistance and permissionlessnes. Up until recently, atomic swap protocols have mostly relied on complex script support, excluding certain types of blockchains. With advances in cryptography, it is now possible to build a bridge between almost any two blockchains. In this work, we give an explanation of one such protocol which applies adaptor signatures on Bitcoin to procure atomic swaps between Monero and Bitcoin. We dive into the cryptographic details, discuss its limitations and give an outlook on our current work where we use adaptor signatures on the Monero signature scheme.
Are Top School Students More Critical of Their Professors? Mining Comments on RateMyProfessor.com
Venue: arXiv cs.IR 2021
Year: 2021

Student reviews and comments on RateMyProfessor.com reflect realistic learning experiences of students. Such information provides a large-scale data source to examine the teaching quality of the lecturers. In this paper, we propose an in-depth analysis of these comments. First, we partition our data into different comparison groups. Next, we perform exploratory data analysis to delve into the data. Furthermore, we employ Latent Dirichlet Allocation and sentiment analysis to extract topics and understand the sentiments associated with the comments. We uncover interesting insights about the characteristics of both college students and professors. Our study proves that student reviews and comments contain crucial information and can serve as essential references for enrollment in courses and universities.
DNN-Life: An Energy-Efficient Aging Mitigation Framework for Improving the Lifetime of On-Chip Weight Memories in Deep Neural Network Hardware Architectures
Venue: arXiv cs.AR 2021
Year: 2021

Negative Biased Temperature Instability (NBTI)-induced aging is one of the critical reliability threats in nano-scale devices. This paper makes the first attempt to study the NBTI aging in the on-chip weight memories of deep neural network (DNN) hardware accelerators, subjected to complex DNN workloads. We propose DNN-Life, a specialized aging analysis and mitigation framework for DNNs, which jointly exploits hardware- and software-level knowledge to improve the lifetime of a DNN weight memory with reduced energy overhead. At the software-level, we analyze the effects of different DNN quantization methods on the distribution of the bits of weight values. Based on the insights gained from this analysis, we propose a micro-architecture that employs low-cost memory-write (and read) transducers to achieve an optimal duty-cycle at run time in the weight memory cells, thereby balancing their aging. As a result, our DNN-Life framework enables efficient aging mitigation of weight memory of the given DNN hardware at minimal energy overhead during the inference process.
Fairness for Whom? Understanding the Reader's Perception of Fairness in Text Summarization
Venue: arXiv cs.IR 2021
Year: 2021

With the surge in user-generated textual information, there has been a recent increase in the use of summarization algorithms for providing an overview of the extensive content. Traditional metrics for evaluation of these algorithms (e.g. ROUGE scores) rely on matching algorithmic summaries to human-generated ones. However, it has been shown that when the textual contents are heterogeneous, e.g., when they come from different socially salient groups, most existing summarization algorithms represent the social groups very differently compared to their distribution in the original data. To mitigate such adverse impacts, some fairness-preserving summarization algorithms have also been proposed. All of these studies have considered normative notions of fairness from the perspective of writers of the contents, neglecting the readers' perceptions of the underlying fairness notions. To bridge this gap, in this work, we study the interplay between the fairness notions and how readers perceive them in textual summaries. Through our experiments, we show that reader's perception of fairness is often context-sensitive. Moreover, standard ROUGE evaluation metrics are unable to quantify the perceived (un)fairness of the summaries. To this end, we propose a human-in-the-loop metric and an automated graph-based methodology to quantify the perceived bias in textual summaries. We demonstrate their utility by quantifying the (un)fairness of several summaries of heterogeneous socio-political microblog datasets.
Few-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learning
Venue: arXiv cs.CL 2021
Year: 2021

Most existing Grammatical Error Correction (GEC) methods based on sequence-to-sequence mainly focus on how to generate more pseudo data to obtain better performance. Few work addresses few-shot GEC domain adaptation. In this paper, we treat different GEC domains as different GEC tasks and propose to extend meta-learning to few-shot GEC domain adaptation without using any pseudo data. We exploit a set of data-rich source domains to learn the initialization of model parameters that facilitates fast adaptation on new resource-poor target domains. We adapt GEC model to the first language (L1) of the second language learner. To evaluate the proposed method, we use nine L1s as source domains and five L1s as target domains. Experiment results on the L1 GEC domain adaptation dataset demonstrate that the proposed approach outperforms the multi-task transfer learning baseline by 0.50 $F_{0.5}$ score on average and enables us to effectively adapt to a new L1 domain with only 200 parallel sentences.
Zero-Error Communication over Adversarial MACs
Venue: arXiv cs.IT 2021
Year: 2021

We consider zero-error communication over a two-transmitter deterministic adversarial multiple access channel (MAC) governed by an adversary who has access to the transmissions of both senders (hence called omniscient) and aims to maliciously corrupt the communication. None of the encoders, jammer and decoder is allowed to randomize using private or public randomness. This enforces a combinatorial nature of the problem. Our model covers a large family of channels studied in the literature, including all deterministic discrete memoryless noisy or noiseless MACs. In this work, given an arbitrary two-transmitter deterministic omniscient adversarial MAC, we characterize when the capacity region 1) has nonempty interior (in particular, is two-dimensional); 2) consists of two line segments (in particular, has empty interior); 3) consists of one line segment (in particular, is one-dimensional); 4) or only contains $ (0,0) $ (in particular, is zero-dimensional). This extends a recent result by Wang, Budkuley, Bogdanov and Jaggi (2019) from the point-to-point setting to the multiple access setting. Indeed, our converse arguments build upon their generalized Plotkin bound and involve delicate case analysis. One of the technical challenges is to take care of both "joint confusability" and "marginal confusability". In particular, the treatment of marginal confusability does not follow from the point-to-point results by Wang et al. Our achievability results follow from random coding with expurgation.
Peeler: Profiling Kernel-Level Events to Detect Ransomware
Venue: arXiv cs.CR 2021
Year: 2021

Ransomware is a growing threat that typically operates by either encrypting a victim's files or locking a victim's computer until the victim pays a ransom. However, it is still challenging to detect such malware timely with existing traditional malware detection techniques. In this paper, we present a novel ransomware detection system, called "Peeler" (Profiling kErnEl -Level Events to detect Ransomware). Peeler deviates from signatures for individual ransomware samples and relies on common and generic characteristics of ransomware depicted at the kernel-level. Analyzing diverse ransomware families, we observed ransomware's inherent behavioral characteristics such as stealth operations performed before the attack, file I/O request patterns, process spawning, and correlations among kernel-level events. Based on those characteristics, we develop Peeler that continuously monitors a target system's kernel events and detects ransomware attacks on the system. Our experimental results show that Peeler achieves more than 99\% detection rate with 0.58\% false-positive rate against 43 distinct ransomware families, containing samples from both crypto and screen-locker types of ransomware. For crypto ransomware, Peeler detects them promptly after only one file is lost (within 115 milliseconds on average). Peeler utilizes around 4.9\% of CPU time with only 9.8 MB memory under the normal workload condition. Our analysis demonstrates that Peeler can efficiently detect diverse malware families by monitoring their kernel-level events.
Synthesizing Monolingual Data for Neural Machine Translation
Venue: arXiv cs.CL 2021
Year: 2021

In neural machine translation (NMT), monolingual data in the target language are usually exploited through a method so-called "back-translation" to synthesize additional training parallel data. The synthetic data have been shown helpful to train better NMT, especially for low-resource language pairs and domains. Nonetheless, large monolingual data in the target domains or languages are not always available to generate large synthetic parallel data. In this work, we propose a new method to generate large synthetic parallel data leveraging very small monolingual data in a specific domain. We fine-tune a pre-trained GPT-2 model on such small in-domain monolingual data and use the resulting model to generate a large amount of synthetic in-domain monolingual data. Then, we perform back-translation, or forward translation, to generate synthetic in-domain parallel data. Our preliminary experiments on three language pairs and five domains show the effectiveness of our method to generate fully synthetic but useful in-domain parallel data for improving NMT in all configurations. We also show promising results in extreme adaptation for personalized NMT.