OpenRaiser/Intern-Atlas huggingface dataset:
Introducing Information Retrieval for Biomedical Informatics Students
Venue: arXiv cs.CL 2021
Year: 2021

Introducing biomedical informatics (BMI) students to natural language processing (NLP) requires balancing technical depth with practical know-how to address application-focused needs. We developed a set of three activities introducing introductory BMI students to information retrieval with NLP, covering document representation strategies and language models from TF-IDF to BERT. These activities provide students with hands-on experience targeted towards common use cases, and introduce fundamental components of NLP workflows for a wide variety of applications.
A Novel Multi-scale Dilated 3D CNN for Epileptic Seizure Prediction
Venue: arXiv cs.LG 2021
Year: 2021

Accurate prediction of epileptic seizures allows patients to take preventive measures in advance to avoid possible injuries. In this work, a novel convolutional neural network (CNN) is proposed to analyze time, frequency, and channel information of electroencephalography (EEG) signals. The model uses three-dimensional (3D) kernels to facilitate the feature extraction over the three dimensions. The application of multiscale dilated convolution enables the 3D kernel to have more flexible receptive fields. The proposed CNN model is evaluated with the CHB-MIT EEG database, the experimental results indicate that our model outperforms the existing state-of-the-art, achieves 80.5% accuracy, 85.8% sensitivity and 75.1% specificity.
SmartScan: An approach to detect Denial of Service Vulnerability in Ethereum Smart Contracts
Venue: arXiv cs.CR 2021
Year: 2021

Blockchain technology (BT) Ethereum Smart Contracts allows programmable transactions that involve the transfer of monetary assets among peers on a BT network independent of a central authorizing agency. Ethereum Smart Contracts are programs that are deployed as decentralized applications, having the building blocks of the blockchain consensus protocol. This technology enables consumers to make agreements in a transparent and conflict-free environment. However, the security vulnerabilities within these smart contracts are a potential threat to the applications and their consumers and have shown in the past to cause huge financial losses. In this paper, we propose a framework that combines static and dynamic analysis to detect Denial of Service (DoS) vulnerability due to an unexpected revert in Ethereum Smart Contracts. Our framework, SmartScan, statically scans smart contracts under test (SCUTs) to identify patterns that are potentially vulnerable in these SCUTs and then uses dynamic analysis to precisely confirm their exploitability of the DoS-Unexpected Revert vulnerability, thus achieving increased performance and more precise results. We evaluated SmartScan on a set of 500 smart contracts collected from the Etherscan. Our approach shows an improvement in precision and recall when compared to available state-of-the-art techniques.
Hashing Modulo Alpha-Equivalence
Venue: arXiv cs.PL 2021
Year: 2021

In many applications one wants to identify identical subtrees of a program syntax tree. This identification should ideally be robust to alpha-renaming of the program, but no existing technique has been shown to achieve this with good efficiency (better than $\mathcal{O}(n^2)$ in expression size). We present a new, asymptotically efficient way to hash modulo alpha-equivalence. A key insight of our method is to use a weak (commutative) hash combiner at exactly one point in the construction, which admits an algorithm with $\mathcal{O}(n (\log n)^2)$ time complexity. We prove that the use of the commutative combiner nevertheless yields a strong hash with low collision probability. Numerical benchmarks attest to the asymptotic behaviour of the method.
Coded Gradient Aggregation: A Tradeoff Between Communication Costs at Edge Nodes and at Helper Nodes
Venue: arXiv cs.IT 2021
Year: 2021

The increasing amount of data generated at the edge/client nodes and the privacy concerns have resulted in learning at the edge, in which the computations are performed at edge devices and are communicated to a central node for updating the model. The edge nodes have low bandwidth and may be available only intermittently. There are helper nodes present in the network that aid the edge nodes in the communication to the server. The edge nodes communicate the local gradient to helper nodes which relay these messages to the central node after possible aggregation. Recently, schemes using repetition codes and maximum-distance-separable (MDS) codes, respectively known as Aligned MDS Coding (AMC) scheme and Aligend Repetition Coding (ARC) scheme, were proposed. It was observed that in AMC scheme the communication between edge nodes and helper nodes is optimal but with an increased cost of communication between helper and master. An upper bound on the communication cost between helpers and master was obtained. In this paper, a tradeoff between communication costs at edge nodes and helper nodes is established with the help of pyramid codes, a well-known class of locally repairable codes. The communication costs at both the helper nodes and edge nodes are exactly characterized. Using the developed technique, the exact communication cost at helper nodes can be computed for the scheme using MDS codes. In the end, we provide two improved aggregation strategies for the existing AMC and ARC schemes, yielding significant reduction in communication cost at helpers, without changing any of the code parameters.
SkyCam: A Dataset of Sky Images and their Irradiance values
Venue: arXiv cs.CV 2021
Year: 2021

Recent advances in Computer Vision and Deep Learning have enabled astonishing results in a variety of fields and applications. Motivated by this success, the SkyCam Dataset aims to enable image-based Deep Learning solutions for short-term, precise prediction of solar radiation on a local level. For the span of a year, three different cameras in three topographically different locations in Switzerland are acquiring images of the sky every 10 seconds. Thirteen high resolution images with different exposure times are captured and used to create an additional HDR image. The images are paired with highly precise irradiance values gathered from a high-accuracy pyranometer.
Download time analysis for distributed storage systems with node failures
Venue: arXiv cs.PF 2021
Year: 2021

We consider a distributed storage system which stores several hot (popular) and cold (less popular) data files across multiple nodes or servers. Hot files are stored using repetition codes while cold files are stored using erasure codes. The nodes are prone to failure and hence at any given time, we assume that only a fraction of the nodes are available. Using a cavity process based mean field framework, we analyze the download time for users accessing hot or cold data in the presence of failed nodes. Our work also illustrates the impact of the choice of the storage code on the download time performance of users in the system.
Understanding Catastrophic Overfitting in Adversarial Training
Venue: arXiv cs.LG 2021
Year: 2021

Recently, FGSM adversarial training is found to be able to train a robust model which is comparable to the one trained by PGD but an order of magnitude faster. However, there is a failure mode called catastrophic overfitting (CO) that the classifier loses its robustness suddenly during the training and hardly recovers by itself. In this paper, we find CO is not only limited to FGSM, but also happens in $\mbox{DF}^{\infty}$-1 adversarial training. Then, we analyze the geometric properties for both FGSM and $\mbox{DF}^{\infty}$-1 and find they have totally different decision boundaries after CO. For FGSM, a new decision boundary is generated along the direction of perturbation and makes the small perturbation more effective than the large one. While for $\mbox{DF}^{\infty}$-1, there is no new decision boundary generated along the direction of perturbation, instead the perturbation generated by $\mbox{DF}^{\infty}$-1 becomes smaller after CO and thus loses its effectiveness. We also experimentally analyze three hypotheses on potential factors causing CO. And then based on the empirical analysis, we modify the RS-FGSM by not projecting perturbation back to the $l_\infty$ ball. By this small modification, we could achieve $47.56 \pm 0.37\% $ PGD-50-10 accuracy on CIFAR10 with $ε=8/255$ in contrast to $43.57 \pm 0.30\% $ by RS-FGSM and also further extend the working range of $ε$ from 8/255 to 11/255 on CIFAR10 without CO occurring.
Recognition of handwritten MNIST digits on low-memory 2 Kb RAM Arduino board using LogNNet reservoir neural network
Venue: arXiv cs.LG 2021
Year: 2021

The presented compact algorithm for recognizing handwritten digits of the MNIST database, created on the LogNNet reservoir neural network, reaches the recognition accuracy of 82%. The algorithm was tested on a low-memory Arduino board with 2 Kb static RAM low-power microcontroller. The dependences of the accuracy and time of image recognition on the number of neurons in the reservoir have been investigated. The memory allocation demonstrates that the algorithm stores all the necessary information in RAM without using additional data storage, and operates with original images without preliminary processing. The simple structure of the algorithm, with appropriate training, can be adapted for wide practical application, for example, for creating mobile biosensors for early diagnosis of adverse events in medicine. The study results are important for the implementation of artificial intelligence on peripheral constrained IoT devices and for edge computing.
Reconstruction of Convex Polytope Compositions from 3D Point-clouds
Venue: arXiv cs.CV 2021
Year: 2021

Reconstructing a composition (union) of convex polytopes that perfectly fits the corresponding input point-cloud is a hard optimization problem with interesting applications in reverse engineering and rigid body dynamics simulations. We propose a pipeline that first extracts a set of planes, then partitions the input point-cloud into weakly convex clusters and finally generates a set of convex polytopes as the intersection of fitted planes for each partition. Finding the best-fitting convex polytopes is formulated as a combinatorial optimization problem over the set of fitted planes and is solved using an Evolutionary Algorithm. For convex clustering, we employ two different methods and detail their strengths and weaknesses in a thorough evaluation based on multiple input data-sets.
Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping
Venue: arXiv cs.CV 2021
Year: 2021

The availability of massive earth observing satellite data provide huge opportunities for land use and land cover mapping. However, such mapping effort is challenging due to the existence of various land cover classes, noisy data, and the lack of proper labels. Also, each land cover class typically has its own unique temporal pattern and can be identified only during certain periods. In this article, we introduce a novel architecture that incorporates the UNet structure with Bidirectional LSTM and Attention mechanism to jointly exploit the spatial and temporal nature of satellite data and to better identify the unique temporal patterns of each land cover. We evaluate this method for mapping crops in multiple regions over the world. We compare our method with other state-of-the-art methods both quantitatively and qualitatively on two real-world datasets which involve multiple land cover classes. We also visualise the attention weights to study its effectiveness in mitigating noise and identifying discriminative time period.
RIS-Aided Cell-Free Massive MIMO: Performance Analysis and Competitiveness
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we consider and study a cell-free massive MIMO (CF-mMIMO) system aided with reconfigurable intelligent surfaces (RISs), where a large number of access points (APs) cooperate to serve a smaller number of users with the help of RIS technology. We consider imperfect channel state information (CSI), where each AP uses the local channel estimates obtained from the uplink pilots and applies conjugate beamforming for downlink data transmission. Additionally, we consider random beamforming at the RIS during both training and data transmission phases. This allows us to eliminate the need of estimating each RIS assisted link, which has been proven to be a challenging task in literature. We then derive a closed-form expression for the achievable rate and use it to evaluate the system's performance supported with numerical results. We show that the RIS provided array gain improves the system's coverage, and provides nearly a 2-fold increase in the minimum rate and a 1.5-fold increase in the per-user throughput. We also use the results to provide preliminary insights on the number of RISs that need to be used to replace an AP, while achieving similar performance as a typical CF-mMIMO system with dense AP deployment.
A Decision Procedure for a Theory of Finite Sets with Finite Integer Intervals
Venue: arXiv cs.LO 2021
Year: 2021

In this paper we extend a decision procedure for the Boolean algebra of finite sets with cardinality constraints ($\mathcal{L}_{\lvert\cdot\rvert}$) to a decision procedure for $\mathcal{L}_{\lvert\cdot\rvert}$ extended with set terms denoting finite integer intervals ($\mathcal{L}_{[\,]}$). In $\mathcal{L}_{[\,]}$ interval limits can be integer linear terms including \emph{unbounded variables}. These intervals are a useful extension because they allow to express non-trivial set operators such as the minimum and maximum of a set, still in a quantifier-free logic. Hence, by providing a decision procedure for $\mathcal{L}_{[\,]}$ it is possible to automatically reason about a new class of quantifier-free formulas. The decision procedure is implemented as part of the $\{log\}$ tool. The paper includes a case study based on the elevator algorithm showing that $\{log\}$ can automatically discharge all its invariance lemmas some of which involve intervals.
Autonomic Management of Power Consumption with IoT and Fog Computing
Venue: arXiv cs.DC 2021
Year: 2021

We introduce a system for Autonomic Management of Power Consumption in setups that involve Internet of Things (IoT) and Fog Computing. The Central IoT (CIoT) is a Fog Computing based solution to provide advanced orchestration mechanisms to manage dynamic duty cycles for extra energy savings. The solution works by adjusting Home (H) and Away (A) cycles based on contextual information, like environmental conditions, user behavior, behavior variation, regulations on energy and network resources utilization, among others. Performance analysis through a proof of concept present average energy savings of 58.4%, reaching up to 61.51% when augmenting with a scheduling system and variable long sleep cycles (LS). However, there is no linear relation increasing LS time and more savings. The significance of this research is to promote autonomic management as a solution to develop more energy efficient buildings and smarter cities, towards sustainable goals.
Imitation Learning via Simultaneous Optimization of Policies and Auxiliary Trajectories
Venue: arXiv cs.RO 2021
Year: 2021

Imitation learning (IL) is a frequently used approach for data-efficient policy learning. Many IL methods, such as Dataset Aggregation (DAgger), combat challenges like distributional shift by interacting with oracular experts. Unfortunately, assuming access to oracular experts is often unrealistic in practice; data used in IL frequently comes from offline processes such as lead-through or teleoperation. In this paper, we present a novel imitation learning technique called Collocation for Demonstration Encoding (CoDE) that operates on only a fixed set of trajectory demonstrations. We circumvent challenges with methods like back-propagation-through-time by introducing an auxiliary trajectory network, which takes inspiration from collocation techniques in optimal control. Our method generalizes well and more accurately reproduces the demonstrated behavior with fewer guiding trajectories when compared to standard behavioral cloning methods. We present simulation results on a 7-degree-of-freedom (DoF) robotic manipulator that learns to exhibit lifting, target-reaching, and obstacle avoidance behaviors.
Improved Approximation for Longest Common Subsequence over Small Alphabets
Venue: arXiv cs.DS 2021
Year: 2021

This paper investigates the approximability of the Longest Common Subsequence (LCS) problem. The fastest algorithm for solving the LCS problem exactly runs in essentially quadratic time in the length of the input, and it is known that under the Strong Exponential Time Hypothesis the quadratic running time cannot be beaten. There are no such limitations for the approximate computation of the LCS however, except in some limited scenarios. There is also a scarcity of approximation algorithms. When the two given strings are over an alphabet of size $k$, returning the subsequence formed by the most frequent symbol occurring in both strings achieves a $1/k$ approximation for the LCS. It is an open problem whether a better than $1/k$ approximation can be achieved in truly subquadratic time ($O(n^{2-δ})$ time for constant $δ>0$). A recent result [Rubinstein and Song SODA'2020] showed that a $1/2+ε$ approximation for the LCS over a binary alphabet is possible in truly subquadratic time, provided the input strings have the same length. In this paper we show that if a $1/2+ε$ approximation (for $ε>0$) is achievable for binary LCS in truly subquadratic time when the input strings can be unequal, then for every constant $k$, there is a truly subquadratic time algorithm that achieves a $1/k+δ$ approximation for $k$-ary alphabet LCS for some $δ>0$. Thus the binary case is the hardest. We also show that for every constant $k$, if one is given two strings of \emph{equal} length over a $k$-ary alphabet, one can obtain a $1/k+ε$ approximation for some constant $ε>0$ in truly subquadratic time, thus extending the Rubinstein and Song result to all alphabets of constant size.
Leakage-Resilient Secret Sharing with Constant Share Size
Venue: arXiv cs.IT 2021
Year: 2021

We consider the leakage resilience of AG code-based ramp secret sharing schemes extending the leakage resilience of linear threshold secret sharing schemes over prime fields done by Benhamouda et al. Since there is not any explicit efficient construction of AG codes over prime fields, we consider constructions over prime fields with the help of concatenation method and those over field extensions. Extending the Fourier analysis done by Benhamouda et al., concatenated algebraic geometric codes over prime fields do produce some nice leakage-resilient secret sharing schemes. One natural and curious question is whether AG codes over extension fields produce better leakage-resilient secret sharing schemes than the construction based on concatenated AG codes. Such construction provides several advantages compared to the construction over prime fields using concatenation method. First, AG codes over extension fields give secret sharing schemes with smaller reconstruction for a fixed privacy parameter t. Second, concatenated AG codes do not enjoy strong multiplicity and hence they are not applicable to secure MPC schemes. It is also confirmed that indeed AG codes over extension fields have stronger leakage-resilience under some reasonable assumptions. These three advantages strongly motivate the study of secret sharing schemes from AG codes over extension fields. The current paper has two main contributions: 1, we obtain leakage-resilient secret sharing schemes with constant share sizes and unbounded numbers of players. Like Shamir secret scheme, our schemes enjoy multiplicity and hence can be applied to MPC. 2, via a sophisticated Fourier Analysis, we analyze the leakage-resilience of secret sharing schemes from codes over extension fields. This is of its own theoretical interest independent of its application to secret sharing schemes from algebraic geometric codes over extension fields.
On interpolation-based decoding of a class of maximum rank distance codes
Venue: arXiv cs.IT 2021
Year: 2021

In this paper we present an interpolation-based decoding algorithm to decode a family of maximum rank distance codes proposed recently by Trombetti and Zhou. We employ the properties of the Dickson matrix associated with a linearized polynomial with a given rank and the modified Berlekamp-Massey algorithm in decoding. When the rank of the error vector attains the unique decoding radius, the problem is converted to solving a quadratic polynomial, which ensures that the proposed decoding algorithm has polynomial-time complexity.
AI in (and for) Games
Venue: arXiv cs.AI 2021
Year: 2021

This chapter outlines the relation between artificial intelligence (AI) / machine learning (ML) algorithms and digital games. This relation is two-fold: on one hand, AI/ML researchers can generate large, in-the-wild datasets of human affective activity, player behaviour (i.e. actions within the game world), commercial behaviour, interaction with graphical user interface elements or messaging with other players, while games can utilise intelligent algorithms to automate testing of game levels, generate content, develop intelligent and responsive non-player characters (NPCs) or predict and respond player behaviour across a wide variety of player cultures. In this work, we discuss some of the most common and widely accepted uses of AI/ML in games and how intelligent systems can benefit from those, elaborating on estimating player experience based on expressivity and performance, and on generating proper and interesting content for a language learning game.
AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset
Venue: arXiv cs.CL 2021
Year: 2021

Along with the COVID-19 pandemic, an "infodemic" of false and misleading information has emerged and has complicated the COVID-19 response efforts. Social networking sites such as Facebook and Twitter have contributed largely to the spread of rumors, conspiracy theories, hate, xenophobia, racism, and prejudice. To combat the spread of fake news, researchers around the world have and are still making considerable efforts to build and share COVID-19 related research articles, models, and datasets. This paper releases "AraCOVID19-MFH" a manually annotated multi-label Arabic COVID-19 fake news and hate speech detection dataset. Our dataset contains 10,828 Arabic tweets annotated with 10 different labels. The labels have been designed to consider some aspects relevant to the fact-checking task, such as the tweet's check worthiness, positivity/negativity, and factuality. To confirm our annotated dataset's practical utility, we used it to train and evaluate several classification models and reported the obtained results. Though the dataset is mainly designed for fake news detection, it can also be used for hate speech detection, opinion/news classification, dialect identification, and many other tasks.