OpenRaiser/Intern-Atlas huggingface dataset:
A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets
Venue: arXiv cs.CL 2022
Year: 2022

Health mention classification deals with the disease detection in a given text containing disease words. However, non-health and figurative use of disease words adds challenges to the task. Recently, adversarial training acting as a means of regularization has gained popularity in many NLP tasks. In this paper, we propose a novel approach to train language models for health mention classification of tweets that involves adversarial training. We generate adversarial examples by adding perturbation to the representations of transformer models for tweet examples at various levels using Gaussian noise. Further, we employ contrastive loss as an additional objective function. We evaluate the proposed method on the PHM2017 dataset extended version. Results show that our proposed approach improves the performance of classifier significantly over the baseline methods. Moreover, our analysis shows that adding noise at earlier layers improves models' performance whereas adding noise at intermediate layers deteriorates models' performance. Finally, adding noise towards the final layers performs better than the middle layers noise addition.
Mitigating Bias in Facial Analysis Systems by Incorporating Label Diversity
Venue: arXiv cs.CV 2022
Year: 2022

Facial analysis models are increasingly applied in real-world applications that have significant impact on peoples' lives. However, as literature has shown, models that automatically classify facial attributes might exhibit algorithmic discrimination behavior with respect to protected groups, potentially posing negative impacts on individuals and society. It is therefore critical to develop techniques that can mitigate unintended biases in facial classifiers. Hence, in this work, we introduce a novel learning method that combines both subjective human-based labels and objective annotations based on mathematical definitions of facial traits. Specifically, we generate new objective annotations from two large-scale human-annotated dataset, each capturing a different perspective of the analyzed facial trait. We then propose an ensemble learning method, which combines individual models trained on different types of annotations. We provide an in-depth analysis of the annotation procedure as well as the datasets distribution. Moreover, we empirically demonstrate that, by incorporating label diversity, our method successfully mitigates unintended biases, while maintaining significant accuracy on the downstream tasks.
CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection
Venue: arXiv cs.CL 2022
Year: 2022

The last decade has witnessed a surge in the interaction of people through social networking platforms. While there are several positive aspects of these social platforms, the proliferation has led them to become the breeding ground for cyber-bullying and hate speech. Recent advances in NLP have often been used to mitigate the spread of such hateful content. Since the task of hate speech detection is usually applicable in the context of social networks, we introduce CRUSH, a framework for hate speech detection using user-anchored self-supervision and contextual regularization. Our proposed approach secures ~ 1-12% improvement in test set metrics over best performing previous approaches on two types of tasks and multiple popular english social media datasets.
Population Diversity Leads to Short Running Times of Lexicase Selection
Venue: arXiv cs.NE 2022
Year: 2022

In this paper we investigate why the running time of lexicase parent selection is empirically much lower than its worst-case bound of O(N*C). We define a measure of population diversity and prove that high diversity leads to low running times O(N + C) of lexicase selection. We then show empirically that genetic programming populations evolved under lexicase selection are diverse for several program synthesis problems, and explore the resulting differences in running time bounds.
Clinical trial site matching with improved diversity using fair policy learning
Venue: arXiv cs.LG 2022
Year: 2022

The ongoing pandemic has highlighted the importance of reliable and efficient clinical trials in healthcare. Trial sites, where the trials are conducted, are chosen mainly based on feasibility in terms of medical expertise and access to a large group of patients. More recently, the issue of diversity and inclusion in clinical trials is gaining importance. Different patient groups may experience the effects of a medical drug/ treatment differently and hence need to be included in the clinical trials. These groups could be based on ethnicity, co-morbidities, age, or economic factors. Thus, designing a method for trial site selection that accounts for both feasibility and diversity is a crucial and urgent goal. In this paper, we formulate this problem as a ranking problem with fairness constraints. Using principles of fairness in machine learning, we learn a model that maps a clinical trial description to a ranked list of potential trial sites. Unlike existing fairness frameworks, the group membership of each trial site is non-binary: each trial site may have access to patients from multiple groups. We propose fairness criteria based on demographic parity to address such a multi-group membership scenario. We test our method on 480 real-world clinical trials and show that our model results in a list of potential trial sites that provides access to a diverse set of patients while also ensuing a high number of enrolled patients.
Safer Autonomous Driving in a Stochastic, Partially-Observable Environment by Hierarchical Contingency Planning
Venue: arXiv cs.LG 2022
Year: 2022

When learning to act in a stochastic, partially observable environment, an intelligent agent should be prepared to anticipate a change in its belief of the environment state, and be capable of adapting its actions on-the-fly to changing conditions. As humans, we are able to form contingency plans when learning a task with the explicit aim of being able to correct errors in the initial control, and hence prove useful if ever there is a sudden change in our perception of the environment which requires immediate corrective action. This is especially the case for autonomous vehicles (AVs) navigating real-world situations where safety is paramount, and a strong ability to react to a changing belief about the environment is truly needed. In this paper we explore an end-to-end approach, from training to execution, for learning robust contingency plans and combining them with a hierarchical planner to obtain a robust agent policy in an autonomous navigation task where other vehicles' behaviours are unknown, and the agent's belief about these behaviours is subject to sudden, last-second change. We show that our approach results in robust, safe behaviour in a partially observable, stochastic environment, generalizing well over environment dynamics not seen during training.
On Extremal Rates of Secure Storage over Graphs
Venue: arXiv cs.IT 2022
Year: 2022

A secure storage code maps $K$ source symbols, each of $L_w$ bits, to $N$ coded symbols, each of $L_v$ bits, such that each coded symbol is stored in a node of a graph. Each edge of the graph is either associated with $D$ of the $K$ source symbols such that from the pair of nodes connected by the edge, we can decode the $D$ source symbols and learn no information about the remaining $K-D$ source symbols; or the edge is associated with no source symbols such that from the pair of nodes connected by the edge, nothing about the $K$ source symbols is revealed. The ratio $L_w/L_v$ is called the symbol rate of a secure storage code and the highest possible symbol rate is called the capacity. We characterize all graphs over which the capacity of a secure storage code is equal to $1$, when $D = 1$. This result is generalized to $D> 1$, i.e., we characterize all graphs over which the capacity of a secure storage code is equal to $1/D$ under a mild condition that for any node, the source symbols associated with each of its connected edges do not include a common element. Further, we characterize all graphs over which the capacity of a secure storage code is equal to $2/D$.
Sentiment Analysis of Political Tweets for Israel using Machine Learning
Venue: arXiv cs.IR 2022
Year: 2022

Sentiment Analysis is a vital research topic in the field of Computer Science. With the accelerated development of Information Technology and social networks, a massive amount of data related to comment texts has been generated on web applications or social media platforms like Twitter. Due to this, people have actively started proliferating general information and the information related to political opinions, which becomes an important reason for analyzing public reactions. Most researchers have used social media specifics or contents to analyze and predict public opinion concerning political events. This research proposes an analytical study using Israeli political Twitter data to interpret public opinion towards the Palestinian-Israeli conflict. The attitudes of ethnic groups and opinion leaders in the form of tweets are analyzed using Machine Learning algorithms like Support Vector Classifier (SVC), Decision Tree (DT), and Naive Bayes (NB). Finally, a comparative analysis is done based on experimental results from different models.
Predicting score distribution to improve non-intrusive speech quality estimation
Venue: arXiv cs.SD 2022
Year: 2022

Deep noise suppressors (DNS) have become an attractive solution to remove background noise, reverberation, and distortions from speech and are widely used in telephony/voice applications. They are also occasionally prone to introducing artifacts and lowering the perceptual quality of the speech. Subjective listening tests that use multiple human judges to derive a mean opinion score (MOS) are a popular way to measure these models' performance. Deep neural network based non-intrusive MOS estimation models have recently emerged as a popular cost-efficient alternative to these tests. These models are trained with only the MOS labels, often discarding the secondary statistics of the opinion scores. In this paper, we investigate several ways to integrate the distribution of opinion scores (e.g. variance, histogram information) to improve the MOS estimation performance. Our model is trained on a corpus of 419K denoised samples by 320 different DNS models and model variations and evaluated on 18K test samples from DNSMOS. We show that with very minor modification of a single task MOS estimation pipeline, these freely available labels can provide up to a 0.016 RMSE and 1% SRCC improvement.
SkiQL: A Unified Schema Query Language
Venue: arXiv cs.DB 2022
Year: 2022

Most NoSQL systems are schema-on-read: data can be stored without first having to declare a Schema that imposes a structure. This schemaless feature offers flexibility to evolve data-intensive applications when data frequently change. However, freeing from declaring schemas does not mean their absence, but rather that they are implicit in data and code. Therefore, diagramming tools similar to those available for relational systems are also needed to help developers and administrators understanding NoSQL schemas. Visualizing diagrams is not practical if schemas contain hundreds of database entities, and exploration or query facilities are then needed. In schemaless NoSQL stores, data of the same entity can be stored with different structure which can increase the difficulty of having readable diagrams. NoSQL schema management tools should therefore have three main components: schema extraction, schema visualization, and schema query. Since that there exist four main NoSQL data models, it is convenient that such tools can be built on a generic data model that provide platform-independence to query and visualize schemas. With the aim of favoring the creation of generic database tools, the authors of this paper defined the U-Schema unified data model that integrates the four main NoSQL data models and the relational model. This paper is focused on querying NoSQL and relational schemas which are represented as U-Schema models. We present the SkiQL language designed on U-Schema to achieve a platform-independent schema query service. SkiQL provides two constructs: schema-query and relationship-query. The former allows to obtain information of entity or relationship types, and the latter that of the aggregations or references (relations among types). We will show how SkiQL was evaluated by calculating well-known metrics for languages and using a survey.
SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide Association Study
Venue: arXiv cs.LG 2022
Year: 2022

Self-supervised pre-training methods have brought remarkable breakthroughs in the understanding of text, image, and speech. Recent developments in genomics has also adopted these pre-training methods for genome understanding. However, they focus only on understanding haploid sequences, which hinders their applicability towards understanding genetic variations, also known as single nucleotide polymorphisms (SNPs), which is crucial for genome-wide association study. In this paper, we introduce SNP2Vec, a scalable self-supervised pre-training approach for understanding SNP. We apply SNP2Vec to perform long-sequence genomics modeling, and we evaluate the effectiveness of our approach on predicting Alzheimer's disease risk in a Chinese cohort. Our approach significantly outperforms existing polygenic risk score methods and all other baselines, including the model that is trained entirely with haploid sequences. We release our code and dataset on https://github.com/HLTCHKUST/snp2vec.
Gallery D.C.: Auto-created GUI Component Gallery for Design Search and Knowledge Discovery
Venue: arXiv cs.SE 2022
Year: 2022

GUI design is an integral part of software development. The process of designing a mobile application typically starts with the ideation and inspiration search from existing designs. However, existing information-retrieval based, and database-query based methods cannot efficiently gain inspirations in three requirements: design practicality, design granularity and design knowledge discovery. In this paper we propose a web application, called \tool that aims to facilitate the process of user interface design through real world GUI component search. Gallery D.C. indexes GUI component designs using reverse engineering and deep learning based computer vision techniques on millions of real world applications. To perform an advanced design search and knowledge discovery, our approach extracts information about size, color, component type, and text information to help designers explore multi-faceted design space and distill higher-order of design knowledge. Gallery D.C. is well received via an informal evaluation with 7 professional designers. Web Link: http://mui-collection.herokuapp.com/. Demo Video Link: https://youtu.be/zVmsz_wY5OQ.
Normalisation for Some Infectious Logics and Their Relatives
Venue: arXiv cs.LO 2022
Year: 2022

We consider certain infectious logics (Sfde, dSfde, K3w, and PWK) and several their non-infectious modifications, including two new logics, reformulate previously constructed natural deduction systems for them (or present such systems from scratch for the case of new logics) in way such that the proof of normalisation theorem becomes possible for these logics. We present such a proof and establish the negation subformula property for the logics in question.
Learning Task-Aware Energy Disaggregation: a Federated Approach
Venue: arXiv cs.LG 2022
Year: 2022

We consider the problem of learning the energy disaggregation signals for residential load data. Such task is referred as non-intrusive load monitoring (NILM), and in order to find individual devices' power consumption profiles based on aggregated meter measurements, a machine learning model is usually trained based on large amount of training data coming from a number of residential homes. Yet collecting such residential load datasets require both huge efforts and customers' approval on sharing metering data, while load data coming from different regions or electricity users may exhibit heterogeneous usage patterns. Both practical concerns make training a single, centralized NILM model challenging. In this paper, we propose a decentralized and task-adaptive learning scheme for NILM tasks, where nested meta learning and federated learning steps are designed for learning task-specific models collectively. Simulation results on benchmark dataset validate proposed algorithm's performance on efficiently inferring appliance-level consumption for a variety of homes and appliances.
Environment Imitation: Data-Driven Environment Model Generation Using Imitation Learning for Efficient CPS Goal Verification
Venue: arXiv cs.SE 2022
Year: 2022

Cyber-Physical Systems (CPS) continuously interact with their physical environments through software controllers that observe the environments and determine actions. Engineers can verify to what extent the CPS under analysis can achieve given goals by analyzing its Field Operational Test (FOT) logs. However, it is challenging to repeat many FOTs to obtain statistically significant results due to its cost and risk in practice. To address this challenge, simulation-based verification can be a good alternative for efficient CPS goal verification, but it requires an accurate virtual environment model that can replace the real environment that interacts with the CPS in a closed loop. This paper proposes a novel data-driven approach that automatically generates the virtual environment model from a small amount of FOT logs. We formally define the environment model generation problem and solve it using Imitation Learning (IL) algorithms. In addition, we propose three specific use cases of our approach in the evolutionary CPS development. To validate our approach, we conduct a case study using a simplified autonomous vehicle with a lane-keeping system. The case study results show that our approach can generate accurate virtual environment models for CPS goal verification at a low cost through simulations.
deep-significance - Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks
Venue: arXiv cs.LG 2022
Year: 2022

A lot of Machine Learning (ML) and Deep Learning (DL) research is of an empirical nature. Nevertheless, statistical significance testing (SST) is still not widely used. This endangers true progress, as seeming improvements over a baseline might be statistical flukes, leading follow-up research astray while wasting human and computational resources. Here, we provide an easy-to-use package containing different significance tests and utility functions specifically tailored towards research needs and usability.
Justice in interaction design: preventing manipulation in interfaces
Venue: arXiv cs.HC 2022
Year: 2022

Designers incorporate values in the design process that raise risks for vulnerable groups. Persuasion in user interfaces can quickly turn into manipulation and become potentially harmful for those groups in the realm of intellectual disabilities, class, or health, requiring proactive responsibility approaches in design. Here we introduce the Capability Sensitive Design Approach and explain how it can be used proactively to inform designers' decisions when it comes to evaluating justice in their designs preventing the risk of manipulation.
(Fractional) Online Stochastic Matching via Fine-Grained Offline Statistics
Venue: arXiv cs.DS 2022
Year: 2022

Motivated by display advertising on the internet, the online stochastic matching problem is proposed by Feldman, Mehta, Mirrokni, and Muthukrishnan (FOCS 2009). Consider a stochastic bipartite graph with offline vertices on one side and with i.i.d. online vertices on the other side. The algorithm knows the offline vertices and the distribution of the online vertices in advance. Upon the arrival of each online vertex, its type is realized and the algorithm immediately and irrevocably decides how to match it. In the vertex-weighted version of the problem, each offline vertex is associated with a weight and the goal is to maximize the total weight of the matching. In this paper, we generalize the model to allow non-identical online vertices and focus on the fractional version of the vertex-weighted stochastic matching. We design fractional algorithms that are $0.718$-competitive and $0.731$-competitive for non i.i.d. arrivals and i.i.d. arrivals respectively. We also prove that no fractional algorithm can achieve a competitive ratio better than $0.75$ for non i.i.d. arrivals. Furthermore, we round our fractional algorithms by applying the recently developed multiway online correlated selection by Gao et al. (FOCS 2021) and achieve $0.666$-competitive and $0.704$-competitive integral algorithms for non i.i.d. arrivals and i.i.d. arrivals. Our results for non i.i.d. arrivals are the first algorithms beating the $1-1/e \approx 0.632$ barrier of the classical adversarial setting. Our $0.704$-competitive integral algorithm for i.i.d. arrivals slightly improves the state-of-the-art $0.701$-competitive ratio by Huang and Shu (STOC 2021).
Distributed Over-the-air Computing for Fast Distributed Optimization: Beamforming Design and Convergence Analysis
Venue: arXiv cs.IT 2022
Year: 2022

Distributed optimization concerns the optimization of a common function in a distributed network, which finds a wide range of applications ranging from machine learning to vehicle platooning. Its key operation is to aggregate all local state information (LSI) at devices to update their states. The required extensive message exchange and many iterations cause a communication bottleneck when the LSI is high dimensional or at high mobility. To overcome the bottleneck, we propose in this work the framework of distributed over-the-air computing (AirComp) to realize a one-step aggregation for distributed optimization by exploiting simultaneous multicast beamforming of all devices and the property of analog waveform superposition of a multi-access channel. We consider two design criteria. The first one is to minimize the sum AirComp error (i.e., sum mean-squared error (MSE)) with respect to the desired average-functional values. An efficient solution approach is proposed by transforming the non-convex beamforming problem into an equivalent concave-convex fractional program and solving it by nesting convex programming into a bisection search. The second criterion, called zero-forcing (ZF) multicast beamforming, is to force the received over-the-air aggregated signals at devices to be equal to the desired functional values. In this case, the optimal beamforming admits closed form. Both the MMSE and ZF beamforming exhibit a centroid structure resulting from averaging columns of conventional MMSE/ZF precoding. Last, the convergence of a classic distributed optimization algorithm is analyzed. The distributed AirComp is found to accelerate convergence by dramatically reducing communication latency. Another key finding is that the ZF beamforming outperforms the MMSE design as the latter is shown to cause bias in subgradient estimation.
Challenges for Open-domain Targeted Sentiment Analysis
Venue: arXiv cs.CL 2022
Year: 2022

Since previous studies on open-domain targeted sentiment analysis are limited in dataset domain variety and sentence level, we propose a novel dataset consisting of 6,013 human-labeled data to extend the data domains in topics of interest and document level. Furthermore, we offer a nested target annotation schema to extract the complete sentiment information in documents, boosting the practicality and effectiveness of open-domain targeted sentiment analysis. Moreover, we leverage the pre-trained model BART in a sequence-to-sequence generation method for the task. Benchmark results show that there exists large room for improvement of open-domain targeted sentiment analysis. Meanwhile, experiments have shown that challenges remain in the effective use of open-domain data, long documents, the complexity of target structure, and domain variances.