OpenRaiser/Intern-Atlas huggingface dataset:
CGEMs: A Metric Model for Automatic Code Generation using GPT-3
Venue: arXiv cs.AI 2021
Year: 2021

Today, AI technology is showing its strengths in almost every industry and walks of life. From text generation, text summarization, chatbots, NLP is being used widely. One such paradigm is automatic code generation. An AI could be generating anything; hence the output space is unconstrained. A self-driving car is driven for 100 million miles to validate its safety, but tests cannot be written to monitor and cover an unconstrained space. One of the solutions to validate AI-generated content is to constrain the problem and convert it from abstract to realistic, and this can be accomplished by either validating the unconstrained algorithm using theoretical proofs or by using Monte-Carlo simulation methods. In this case, we use the latter approach to test/validate a statistically significant number of samples. This hypothesis of validating the AI-generated code is the main motive of this work and to know if AI-generated code is reliable, a metric model CGEMs is proposed. This is an extremely challenging task as programs can have different logic with different naming conventions, but the metrics must capture the structure and logic of the program. This is similar to the importance grammar carries in AI-based text generation, Q&A, translations, etc. The various metrics that are garnered in this work to support the evaluation of generated code are as follows: Compilation, NL description to logic conversion, number of edits needed, some of the commonly used static-code metrics and NLP metrics. These metrics are applied to 80 codes generated using OpenAI's GPT-3. Post which a Neural network is designed for binary classification (acceptable/not acceptable quality of the generated code). The inputs to this network are the values of the features obtained from the metrics. The model achieves a classification accuracy of 76.92% and an F1 score of 55.56%. XAI is augmented for model interpretability.
ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories
Venue: arXiv cs.AR 2021
Year: 2021

Spiking neural networks (SNNs) have shown a potential for having low energy with unsupervised learning capabilities due to their biologically-inspired computation. However, they may suffer from accuracy degradation if their processing is performed under the presence of hardware-induced faults in memories, which can come from manufacturing defects or voltage-induced approximation errors. Since recent works still focus on the fault-modeling and random fault injection in SNNs, the impact of memory faults in SNN hardware architectures on accuracy and the respective fault-mitigation techniques are not thoroughly explored. Toward this, we propose ReSpawn, a novel framework for mitigating the negative impacts of faults in both the off-chip and on-chip memories for resilient and energy-efficient SNNs. The key mechanisms of ReSpawn are: (1) analyzing the fault tolerance of SNNs; and (2) improving the SNN fault tolerance through (a) fault-aware mapping (FAM) in memories, and (b) fault-aware training-and-mapping (FATM). If the training dataset is not fully available, FAM is employed through efficient bit-shuffling techniques that place the significant bits on the non-faulty memory cells and the insignificant bits on the faulty ones, while minimizing the memory access energy. Meanwhile, if the training dataset is fully available, FATM is employed by considering the faulty memory cells in the data mapping and training processes. The experimental results show that, compared to the baseline SNN without fault-mitigation techniques, ReSpawn with a fault-aware mapping scheme improves the accuracy by up to 70% for a network with 900 neurons without retraining.
Collect & Infer -- a fresh look at data-efficient Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

This position paper proposes a fresh look at Reinforcement Learning (RL) from the perspective of data-efficiency. Data-efficient RL has gone through three major stages: pure on-line RL where every data-point is considered only once, RL with a replay buffer where additional learning is done on a portion of the experience, and finally transition memory based RL, where, conceptually, all transitions are stored and re-used in every update step. While inferring knowledge from all explicitly stored experience has lead to a tremendous gain in data-efficiency, the question of how this data is collected has been vastly understudied. We argue that data-efficiency can only be achieved through careful consideration of both aspects. We propose to make this insight explicit via a paradigm that we call 'Collect and Infer', which explicitly models RL as two separate but interconnected processes, concerned with data collection and knowledge inference respectively. We discuss implications of the paradigm, how its ideas are reflected in the literature, and how it can guide future research into data efficient RL.
PW-MAD: Pixel-wise Supervision for Generalized Face Morphing Attack Detection
Venue: arXiv cs.CV 2021
Year: 2021

A face morphing attack image can be verified to multiple identities, making this attack a major vulnerability to processes based on identity verification, such as border checks. Various methods have been proposed to detect face morphing attacks, however, with low generalizability to unexpected post-morphing processes. A major post-morphing process is the print and scan operation performed in many countries when issuing a passport or identity document. In this work, we address this generalization problem by adapting a pixel-wise supervision approach where we train a network to classify each pixel of the image into an attack or not, rather than only having one label for the whole image. Our pixel-wise morphing attack detection (PW-MAD) solution proved to perform more accurately than a set of established baselines. More importantly, PW-MAD shows high generalizability in comparison to related works, when evaluated on unknown re-digitized attacks. Additionally to our PW-MAD approach, we create a new face morphing attack dataset with digital and re-digitized samples, namely the LMA-DRD dataset that is publicly available for research purposes upon request.
C5T5: Controllable Generation of Organic Molecules with Transformers
Venue: arXiv cs.LG 2021
Year: 2021

Methods for designing organic materials with desired properties have high potential impact across fields such as medicine, renewable energy, petrochemical engineering, and agriculture. However, using generative modeling to design substances with desired properties is difficult because candidate compounds must satisfy multiple constraints, including synthetic accessibility and other metrics that are intuitive to domain experts but challenging to quantify. We propose C5T5, a novel self-supervised pretraining method that enables transformers to make zero-shot select-and-replace edits, altering organic substances towards desired property values. C5T5 operates on IUPAC names -- a standardized molecular representation that intuitively encodes rich structural information for organic chemists but that has been largely ignored by the ML community. Our technique requires no edited molecule pairs to train and only a rough estimate of molecular properties, and it has the potential to model long-range dependencies and symmetric molecular structures more easily than graph-based methods. C5T5 also provides a powerful interface to domain experts: it grants users fine-grained control over the generative process by selecting and replacing IUPAC name fragments, which enables experts to leverage their intuitions about structure-activity relationships. We demonstrate C5T5's effectiveness on four physical properties relevant for drug discovery, showing that it learns successful and chemically intuitive strategies for altering molecules towards desired property values.
Issuing Green Bonds on the Algorand Blockchain
Venue: arXiv cs.CR 2021
Year: 2021

Green bonds have been shown to be effective tool for sustainability however market growth is impeded by high issuance and transaction costs. The lack of appropriate standardisation and frameworks raise fear of greenwashing. In this paper, we propose a platform for green bond issuance on the Algorand blockchain. It offers "Green Bonds as a Service", increasing accessibility through automation. The solution has minimal associated costs and supports fractional asset ownership, both of which will help adoption especially in developing countries. A financial regulator must preapprove an investor and can freeze assets in the case of financial irregularities. Green bonds can be bought directly from an issuer or in the secondary market. We also introduce a novel mechanism whereby an issuer can upload proof of impact reports. A green verifier uses these to submit a green rating; poor green ratings result in reputational damage and economic penalties.
Approximation and parameterized algorithms to find balanced connected partitions of graphs
Venue: arXiv cs.DS 2021
Year: 2021

Partitioning a connected graph into $k$~vertex-disjoint connected subgraphs of similar (or given) orders is a classical problem that has been intensively investigated since late seventies. Given a connected graph $G=(V,E)$ and a weight function $w : V \to \mathbb{Q}_\geq$, a connected $k$-partition of $G$ is a partition of $V$ such that each class induces a connected subgraph. The balanced connected $k$-partition problem consists in finding a connected $k$-partition in which every class has roughly the same weight. To model this concept of balance, one may seek connected $k$-partitions that either maximize the weight of a lightest class $(\text{max-min BCP}_k)$ or minimize the weight of a heaviest class $(\text{min-max BCP}_k)$. Such problems are equivalent when $k=2$, but they are different when $k\geq 3$. In this work, we propose a simple pseudo-polynomial $\frac{k}{2}$-approximation algorithm for $\text{min-max BCP}_k$ which runs in time $\mathcal{O}(W|V||E|)$, where $W = \sum_{v \in V} w(v)$. Based on this algorithm and using a scaling technique, we design a (polynomial) $(\frac{k}{2} +\varepsilon)$-approximation for the same problem with running-time $\mathcal{O}(|V|^3|E|/\varepsilon)$, for any fixed $\varepsilon>0$. Additionally, we propose a fixed-parameter tractable algorithm based on integer linear programming for the unweighted $\text{max-min BCP}_k$ parameterized by the size of a vertex cover.
Evaluating Fairness in Argument Retrieval
Venue: arXiv cs.IR 2021
Year: 2021

Existing commercial search engines often struggle to represent different perspectives of a search query. Argument retrieval systems address this limitation of search engines and provide both positive (PRO) and negative (CON) perspectives about a user's information need on a controversial topic (e.g., climate change). The effectiveness of such argument retrieval systems is typically evaluated based on topical relevance and argument quality, without taking into account the often differing number of documents shown for the argument stances (PRO or CON). Therefore, systems may retrieve relevant passages, but with a biased exposure of arguments. In this work, we analyze a range of non-stochastic fairness-aware ranking and diversity metrics to evaluate the extent to which argument stances are fairly exposed in argument retrieval systems. Using the official runs of the argument retrieval task Touché at CLEF 2020, as well as synthetic data to control the amount and order of argument stances in the rankings, we show that systems with the best effectiveness in terms of topical relevance are not necessarily the most fair or the most diverse in terms of argument stance. The relationships we found between (un)fairness and diversity metrics shed light on how to evaluate group fairness -- in addition to topical relevance -- in argument retrieval settings.
The Case for Task Sampling based Learning for Cluster Job Scheduling
Venue: arXiv cs.DC 2021
Year: 2021

The ability to accurately estimate job runtime properties allows a scheduler to effectively schedule jobs. State-of-the-art online cluster job schedulers use history-based learning, which uses past job execution information to estimate the runtime properties of newly arrived jobs. However, with fast-paced development in cluster technology (in both hardware and software) and changing user inputs, job runtime properties can change over time, which lead to inaccurate predictions. In this paper, we explore the potential and limitation of real-time learning of job runtime properties, by proactively sampling and scheduling a small fraction of the tasks of each job. Such a task-sampling-based approach exploits the similarity among runtime properties of the tasks of the same job and is inherently immune to changing job behavior. Our study focuses on two key questions in comparing task-sampling-based learning (learning in space) and history-based learning (learning in time): (1) Can learning in space be more accurate than learning in time? (2) If so, can delaying scheduling the remaining tasks of a job till the completion of sampled tasks be more than compensated by the improved accuracy and result in improved job performance? Our analytical and experimental analysis of 3 production traces with different skew and job distribution shows that learning in space can be substantially more accurate. Our simulation and testbed evaluation on Azure of the two learning approaches anchored in a generic job scheduler using 3 production cluster job traces shows that despite its online overhead, learning in space reduces the average Job Completion Time (JCT) by 1.28x, 1.56x, and 1.32x compared to the prior-art history-based predictor.
A Game Characterization for Contrasimilarity
Venue: arXiv cs.GT 2021
Year: 2021

We present the first game characterization of contrasimilarity, the weakest form of bisimilarity. The game is finite for finite-state processes and can thus be used for contrasimulation equivalence checking, of which no tool has been capable to date. A machine-checked Isabelle/HOL formalization backs our work and enables further use of contrasimilarity in verification contexts.
Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues
Venue: arXiv cs.MM 2021
Year: 2021

Recently, fake news with text and images have achieved more effective diffusion than text-only fake news, raising a severe issue of multimodal fake news detection. Current studies on this issue have made significant contributions to developing multimodal models, but they are defective in modeling the multimodal content sufficiently. Most of them only preliminarily model the basic semantics of the images as a supplement to the text, which limits their performance on detection. In this paper, we find three valuable text-image correlations in multimodal fake news: entity inconsistency, mutual enhancement, and text complementation. To effectively capture these multimodal clues, we innovatively extract visual entities (such as celebrities and landmarks) to understand the news-related high-level semantics of images, and then model the multimodal entity inconsistency and mutual enhancement with the help of visual entities. Moreover, we extract the embedded text in images as the complementation of the original text. All things considered, we propose a novel entity-enhanced multimodal fusion framework, which simultaneously models three cross-modal correlations to detect diverse multimodal fake news. Extensive experiments demonstrate the superiority of our model compared to the state of the art.
StyleAugment: Learning Texture De-biased Representations by Style Augmentation without Pre-defined Textures
Venue: arXiv cs.CV 2021
Year: 2021

Recent powerful vision classifiers are biased towards textures, while shape information is overlooked by the models. A simple attempt by augmenting training images using the artistic style transfer method, called Stylized ImageNet, can reduce the texture bias. However, Stylized ImageNet approach has two drawbacks in fidelity and diversity. First, the generated images show low image quality due to the significant semantic gap betweeen natural images and artistic paintings. Also, Stylized ImageNet training samples are pre-computed before training, resulting in showing the lack of diversity for each sample. We propose a StyleAugment by augmenting styles from the mini-batch. StyleAugment does not rely on the pre-defined style references, but generates augmented images on-the-fly by natural images in the mini-batch for the references. Hence, StyleAugment let the model observe abundant confounding cues for each image by on-the-fly the augmentation strategy, while the augmented images are more realistic than artistic style transferred images. We validate the effectiveness of StyleAugment in the ImageNet dataset with robustness benchmarks, such as texture de-biased accuracy, corruption robustness, natural adversarial samples, and occlusion robustness. StyleAugment shows better generalization performances than previous unsupervised de-biasing methods and state-of-the-art data augmentation methods in our experiments.
Spectral Efficiency Analysis of Cell-free Distributed Massive MIMO Systems with Imperfect Covariance Matrix
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, the impacts of imperfect channel covariance matrix on the spectral efficiency (SE) of cell-free distributed massive multiple-input multiple-output (MIMO) systems are analyzed. We propose to estimate the channel covariance matrix by alternately using the assigned pilots and their phase-shifted pilots in different coherent blocks, which improves the accuracy of channel estimation with imperfect covariance matrix and reduces pilot overhead. Under this scheme, the closed-form expressions of SE with maximum ratio combination (MRC) and zero-forcing (ZF) receivers are derived, which enables us to select key parameters for better system performance. Simulation results verify the effectiveness of the proposed channel estimation method and the accuracy of the derived closed-form expressions. When more coherent blocks are used to estimate the covariance matrix, we can get better system performance. Moreover, some insightful conclusions are arrived at from the SE comparisons between different receiving schemes (ZF and MRC) and different pilot allocation schemes (orthogonal pilot and pilot reuse).
Weakly Supervised Cross-platform Teenager Detection with Adversarial BERT
Venue: arXiv cs.CL 2021
Year: 2021

Teenager detection is an important case of the age detection task in social media, which aims to detect teenage users to protect them from negative influences. The teenager detection task suffers from the scarcity of labelled data, which exacerbates the ability to perform well across social media platforms. To further research in teenager detection in settings where no labelled data is available for a platform, we propose a novel cross-platform framework based on Adversarial BERT. Our framework can operate with a limited amount of labelled instances from the source platform and with no labelled data from the target platform, transferring knowledge from the source to the target social media. We experiment on four publicly available datasets, obtaining results demonstrating that our framework can significantly improve over competitive baseline models on the cross-platform teenager detection task.
Context-aware Telco Outdoor Localization
Venue: arXiv cs.NI 2021
Year: 2021

Recent years have witnessed the fast growth in telecommunication (Telco) techniques from 2G to upcoming 5G. Precise outdoor localization is important for Telco operators to manage, operate and optimize Telco networks. Differing from GPS, Telco localization is a technique employed by Telco operators to localize outdoor mobile devices by using measurement report (MR) data. When given MR samples containing noisy signals (e.g., caused by Telco signal interference and attenuation), Telco localization often suffers from high errors. To this end, the main focus of this paper is how to improve Telco localization accuracy via the algorithms to detect and repair outlier positions with high errors. Specifically, we propose a context-aware Telco localization technique, namely RLoc, which consists of three main components: a machine-learning-based localization algorithm, a detection algorithm to find flawed samples, and a repair algorithm to replace outlier localization results by better ones (ideally ground truth positions). Unlike most existing works to detect and repair every flawed MR sample independently, we instead take into account spatio-temporal locality of MR locations and exploit trajectory context to detect and repair flawed positions. Our experiments on the real MR data sets from 2G GSM and 4G LTE Telco networks verify that our work RLoc can greatly improve Telco location accuracy. For example, RLoc on a large 4G MR data set can achieve 32.2 meters of median errors, around 17.4% better than state-of-the-art.
Data Aggregation for Reducing Training Data in Symbolic Regression
Venue: arXiv cs.LG 2021
Year: 2021

The growing volume of data makes the use of computationally intense machine learning techniques such as symbolic regression with genetic programming more and more impractical. This work discusses methods to reduce the training data and thereby also the runtime of genetic programming. The data is aggregated in a preprocessing step before running the actual machine learning algorithm. K-means clustering and data binning is used for data aggregation and compared with random sampling as the simplest data reduction method. We analyze the achieved speed-up in training and the effects on the trained models test accuracy for every method on four real-world data sets. The performance of genetic programming is compared with random forests and linear regression. It is shown, that k-means and random sampling lead to very small loss in test accuracy when the data is reduced down to only 30% of the original data, while the speed-up is proportional to the size of the data set. Binning on the contrary, leads to models with very high test error.
Efficacy of BERT embeddings on predicting disaster from Twitter data
Venue: arXiv cs.CL 2021
Year: 2021

Social media like Twitter provide a common platform to share and communicate personal experiences with other people. People often post their life experiences, local news, and events on social media to inform others. Many rescue agencies monitor this type of data regularly to identify disasters and reduce the risk of lives. However, it is impossible for humans to manually check the mass amount of data and identify disasters in real-time. For this purpose, many research works have been proposed to present words in machine-understandable representations and apply machine learning methods on the word representations to identify the sentiment of a text. The previous research methods provide a single representation or embedding of a word from a given document. However, the recent advanced contextual embedding method (BERT) constructs different vectors for the same word in different contexts. BERT embeddings have been successfully used in different natural language processing (NLP) tasks, yet there is no concrete analysis of how these representations are helpful in disaster-type tweet analysis. In this research work, we explore the efficacy of BERT embeddings on predicting disaster from Twitter data and compare these to traditional context-free word embedding methods (GloVe, Skip-gram, and FastText). We use both traditional machine learning methods and deep learning methods for this purpose. We provide both quantitative and qualitative results for this study. The results show that the BERT embeddings have the best results in disaster prediction task than the traditional word embeddings. Our codes are made freely accessible to the research community.