OpenRaiser/Intern-Atlas huggingface dataset:
Combining resampling and reweighting for faithful stochastic optimization
Venue: arXiv cs.LG 2021
Year: 2021

Many machine learning and data science tasks require solving non-convex optimization problems. When the loss function is a sum of multiple terms, a popular method is the stochastic gradient descent. Viewed as a process for sampling the loss function landscape, the stochastic gradient descent is known to prefer flat minima. Though this is desired for certain optimization problems such as in deep learning, it causes issues when the goal is to find the global minimum, especially if the global minimum resides in a sharp valley. Illustrated with a simple motivating example, we show that the fundamental reason is that the difference in the Lipschitz constants of multiple terms in the loss function causes stochastic gradient descent to experience different variances at different minima. In order to mitigate this effect and perform faithful optimization, we propose a combined resampling-reweighting scheme to balance the variance at local minima and extend to general loss functions. We explain from the numerical stability perspective how the proposed scheme is more likely to select the true global minimum, and the local convergence analysis perspective how it converges to a minimum faster when compared with the vanilla stochastic gradient descent. Experiments from robust statistics and computational chemistry are provided to demonstrate the theoretical findings.
Emergence and algorithmic information dynamics of systems and observers
Venue: arXiv cs.IT 2021
Year: 2021

Previous work has shown that perturbation analysis in software space can produce candidate computable generative models and uncover possible causal properties from the finite description of an object or system quantifying the algorithmic contribution of each of its elements relative to the whole. One of the challenges for defining emergence is that one observer's prior knowledge may cause a phenomenon to present itself to such observer as emergent while for another as reducible. By formalising the act of observing as mutual perturbations between dynamical systems, we demonstrate that emergence of algorithmic information do depend on the observer's formal knowledge, while robust to other subjective factors, particularly: the choice of the programming language and the measurement method; errors or distortions during the information acquisition; and the informational cost of processing. This is called observer-dependent emergence (ODE). In addition, we demonstrate that the unbounded and fast increase of emergent algorithmic information implies asymptotically observer-independent emergence (AOIE). Unlike ODE, AOIE is a type of emergence for which emergent phenomena will remain considered to be emergent for every formal theory that any observer might devise. We demonstrate the existence of an evolutionary model that displays the diachronic variant of AOIE and a network model that displays the holistic variant of AOIE. Our results show that, restricted to the context of finite discrete deterministic dynamical systems, computable systems, and irreducible information content measures, AOIE is the strongest form of emergence that formal theories can attain.
Multi-Scale Temporal Convolution Network for Classroom Voice Detection
Venue: arXiv cs.SD 2021
Year: 2021

Teaching with the cooperation of expert teacher and assistant teacher, which is the so-called "double-teachers classroom", i.e., the course is giving by the expert online and presented through projection screen at the classroom, and the teacher at the classroom performs as an assistant for guiding the students in learning, is becoming more prevalent in today's teaching method for K-12 education. For monitoring the teaching quality, a microphone clipped on the assistant's neckline is always used for voice recording, then fed to the downstream tasks of automatic speech recognition (ASR) and neural language processing (NLP). However, besides its voice, there would be some other interfering voices, including the expert's one and the student's one. Here, we propose to extract the assistant' voices from the perspective of sound event detection, i.e., the voices are classified into four categories, namely the expert, the teacher, the mixture of them, and the background. To make frame-level identification, which is important for grabbing sensitive words for the downstream tasks, a multi-scale temporal convolution neural network is constructed with stacked dilated convolutions for considering both local and global properties. These features are concatenated and fed to a classification network constructed by three linear layers. The framework is evaluated on simulated data and real-world recordings, giving considerable performance in terms of precision and recall, compared with some classical classification methods.
Transfer Learning as an Enhancement for Reconfiguration Management of Cyber-Physical Production Systems
Venue: arXiv cs.LG 2021
Year: 2021

Reconfiguration demand is increasing due to frequent requirement changes for manufacturing systems. Recent approaches aim at investigating feasible configuration alternatives from which they select the optimal one. This relies on processes whose behavior is not reliant on e.g. the production sequence. However, when machine learning is used, components' behavior depends on the process' specifics, requiring additional concepts to successfully conduct reconfiguration management. Therefore, we propose the enhancement of the comprehensive reconfiguration management with transfer learning. This provides the ability to assess the machine learning dependent behavior of the different CPPS configurations with reduced effort and further assists the recommissioning of the chosen one. A real cyber-physical production system from the discrete manufacturing domain is utilized to demonstrate the aforementioned proposal.
Gillian: A Multi-Language Platform for Unified Symbolic Analysis
Venue: arXiv cs.PL 2021
Year: 2021

This is an evolving document describing the meta-theory, the implementation, and the instantiations of Gillian, a multi-language symbolic analysis platform.
LIIR at SemEval-2021 task 6: Detection of Persuasion Techniques In Texts and Images using CLIP features
Venue: arXiv cs.CL 2021
Year: 2021

We describe our approach for SemEval-2021 task 6 on detection of persuasion techniques in multimodal content (memes). Our system combines pretrained multimodal models (CLIP) and chained classifiers. Also, we propose to enrich the data by a data augmentation technique. Our submission achieves a rank of 8/16 in terms of F1-micro and 9/16 with F1-macro on the test set.
Voice of Your Brain: Cognitive Representations of Imagined Speech,Overt Speech, and Speech Perception Based on EEG
Venue: arXiv cs.HC 2021
Year: 2021

Every people has their own voice, likewise, brain signals dis-play distinct neural representations for each individual. Al-though recent studies have revealed the robustness of speech-related paradigms for efficient brain-computer interface, the dis-tinction on their cognitive representations with practical usabil-ity still remains to be discovered. Herein, we investigate the dis-tinct brain patterns from electroencephalography (EEG) duringimagined speech, overt speech, and speech perception in termsof subject variations with its practical use of speaker identifica-tion from single channel EEG. We performed classification ofnine subjects using deep neural network that captures temporal-spectral-spatial features from EEG of imagined speech, overtspeech, and speech perception. Furthermore, we demonstratedthe underlying neural features of individual subjects while per-forming imagined speech by comparing the functional connec-tivity and the EEG envelope features. Our results demonstratethe possibility of subject identification from single channel EEGof imagined speech and overt speech. Also, the comparison ofthe three speech-related paradigms will provide valuable infor-mation for the practical use of speech-related brain signals inthe further studies.
Does the Venue of Scientific Conferences Leverage their Impact? A Large Scale study on Computer Science Conferences
Venue: arXiv cs.DL 2021
Year: 2021

Background: Conferences bring scientists together and provide one of the most timely means for disseminating new ideas and cutting-edge works.The importance of conferences in scientific areas is testified by quantitative indicators. In Computer Science, for instance, almost two out of three papers published on Scopus are conference papers. Objective/Purpose: The main goal of this paper is to investigate a novel research question: is there any correlation between the impact of a scientific conference and the venue where it took place? Approach: In order to measure the impact of conferences we conducted a large scale analysis on the bibliographic data extracted from 3,838 Computer Science conference series and over 2.5 million papers spanning more than 30 years of research. To quantify the "touristicity" of a venue we exploited some indicators such as the size of the Wikipedia page for the city hosting the venue and other indexes from reports of the World Economic Forum. Results/Findings: We found out that the two aspects are related, and the correlation with conference impact is stronger when considering country-wide touristic indicators, such as the Travel&Tourism Competitiveness Index. More-over the almost linear correlation with the Tourist Service Infrastructure index attests the specific importance of tourist/accommodation facilities in a given country. Conclusions: This is the first attempt to focus on the relationship of venue characteristics to conference papers. The results open up new possibilities, such as allowing conference organizers and authors to estimate in advance the impact of conferences, thus supporting them in their decisions.
With Great Freedom Comes Great Opportunity: Rethinking Resource Allocation for Serverless Functions
Venue: arXiv cs.DC 2021
Year: 2021

Current serverless offerings give users a limited degree of flexibility for configuring the resources allocated to their function invocations by either coupling memory and CPU resources together or providing no knobs at all. These configuration choices simplify resource allocation decisions on behalf of users, but at the same time, create deployments that are resource inefficient. In this paper, we take a principled approach to the problem of resource allocation for serverless functions, allowing this choice to be made in an automatic way that leads to the best combination of performance and cost. In particular, we systematically explore the opportunities that come with decoupling memory and CPU resource allocations and also enabling the use of different VM types. We find a rich trade-off space between performance and cost. The provider can use this in a number of ways: from exposing all these parameters to the user, to eliciting preferences for performance and cost from users, or by simply offering the same performance with lower cost. This flexibility can also enable the provider to optimize its resource utilization and enable a cost-effective service with predictable performance. Our results show that, by decoupling memory and CPU allocation, there is potential to have up to 40% lower execution cost than the preset coupled configurations that are the norm in current serverless offerings. Similarly, making the correct choice of VM instance type can provide up to 50% better execution time. Furthermore, we demonstrate that providers can utilize different instance types for the same functions to maximize resource utilization while providing performance within 10-20% of the best resource configuration for each respective function.
Accurate and Efficient Time Series Matching by Season- and Trend-aware Symbolic Approximation -- Extended Version Including Additional Evaluation and Proofs
Venue: arXiv cs.DB 2021
Year: 2021

Processing and analyzing time series data\-sets have become a central issue in many domains requiring data management systems to support time series as a native data type. A crucial prerequisite of these systems is time series matching, which still is a challenging problem. A time series is a high-dimensional data type, its representation is storage-, and its comparison is time-consuming. Among the representation techniques that tackle these challenges, the symbolic aggregate approximation (SAX) is the current state of the art. This technique reduces a time series to a low-dimensional space by segmenting it and discretizing each segment into a small symbolic alphabet. However, SAX ignores the deterministic behavior of time series such as cyclical repeating patterns or trend component affecting all segments and leading to a distortion of the symbolic distribution. In this paper, we present a season- and a trend-aware symbolic approximation. We show that this improves the symbolic distribution and increase the representation accuracy without increasing its memory footprint. Most importantly, this enables a more efficient time series matching by providing a match up to three orders of magnitude faster than SAX.
BiasRV: Uncovering Biased Sentiment Predictions at Runtime
Venue: arXiv cs.SE 2021
Year: 2021

Sentiment analysis (SA) systems, though widely applied in many domains, have been demonstrated to produce biased results. Some research works have been done in automatically generating test cases to reveal unfairness in SA systems, but the community still lacks tools that can monitor and uncover biased predictions at runtime. This paper fills this gap by proposing BiasRV, the first tool to raise an alarm when a deployed SA system makes a biased prediction on a given input text. To implement this feature, BiasRV dynamically extracts a template from an input text and from the template generates gender-discriminatory mutants (semantically-equivalent texts that only differ in gender information). Based on popular metrics used to evaluate the overall fairness of an SA system, we define distributional fairness property for an individual prediction of an SA system. This property specifies a requirement that for one piece of text, mutants from different gender classes should be treated similarly as a whole. Verifying the distributional fairness property causes much overhead to the running system. To run more efficiently, BiasRV adopts a two-step heuristic: (1) sampling several mutants from each gender and checking if the system predicts them as of the same sentiment, (2) checking distributional fairness only when sampled mutants have conflicting results. Experiments show that compared to directly checking the distributional fairness property for each input text, our two-step heuristic can decrease overhead used for analyzing mutants by 73.81% while only resulting in 6.7% of biased predictions being missed. Besides, BiasRV can be used conveniently without knowing the implementation of SA systems. Future researchers can easily extend BiasRV to detect more types of bias, e.g. race and occupation.
CrossASR++: A Modular Differential Testing Framework for Automatic Speech Recognition
Venue: arXiv cs.SE 2021
Year: 2021

Developers need to perform adequate testing to ensure the quality of Automatic Speech Recognition (ASR) systems. However, manually collecting required test cases is tedious and time-consuming. Our recent work proposes CrossASR, a differential testing method for ASR systems. This method first utilizes Text-to-Speech (TTS) to generate audios from texts automatically and then feed these audios into different ASR systems for cross-referencing to uncover failed test cases. It also leverages a failure estimator to find failing test cases more efficiently. Such a method is inherently self-improvable: the performance can increase by leveraging more advanced TTS and ASR systems. So in this accompanying tool demo paper, we devote more engineering and propose CrossASR++, an easy-to-use ASR testing tool that can be conveniently extended to incorporate different TTS and ASR systems, and failure estimators. We also make CrossASR++ chunk texts from a given corpus dynamically and enable the estimator to work in a more effective and flexible way. We demonstrate that the new features can help CrossASR++ discover more failed test cases. Using the same TTS and ASR systems, CrossASR++ can uncover 26.2% more failed test cases for 4 ASRs than the original tool. Moreover, by simply adding one more ASR for cross-referencing, we can increase the number of failed test cases uncovered for each of the 4 ASR systems by 25.07%, 39.63%, 20.9\% and 8.17% respectively. We also extend CrossASR++ with 5 additional failure estimators. Compared to worst estimator, the best one can discover 10.41% more failed test cases within the same amount of time.
An Exploratory Analysis of the Relation Between Offensive Language and Mental Health
Venue: arXiv cs.CL 2021
Year: 2021

In this paper, we analyze the interplay between the use of offensive language and mental health. We acquired publicly available datasets created for offensive language identification and depression detection and we train computational models to compare the use of offensive language in social media posts written by groups of individuals with and without self-reported depression diagnosis. We also look at samples written by groups of individuals whose posts show signs of depression according to recent related studies. Our analysis indicates that offensive language is more frequently used in the samples written by individuals with self-reported depression as well as individuals showing signs of depression. The results discussed here open new avenues in research in politeness/offensiveness and mental health.
Rawlsian Fair Adaptation of Deep Learning Classifiers
Venue: arXiv cs.LG 2021
Year: 2021

Group-fairness in classification aims for equality of a predictive utility across different sensitive sub-populations, e.g., race or gender. Equality or near-equality constraints in group-fairness often worsen not only the aggregate utility but also the utility for the least advantaged sub-population. In this paper, we apply the principles of Pareto-efficiency and least-difference to the utility being accuracy, as an illustrative example, and arrive at the Rawls classifier that minimizes the error rate on the worst-off sensitive sub-population. Our mathematical characterization shows that the Rawls classifier uniformly applies a threshold to an ideal score of features, in the spirit of fair equality of opportunity. In practice, such a score or a feature representation is often computed by a black-box model that has been useful but unfair. Our second contribution is practical Rawlsian fair adaptation of any given black-box deep learning model, without changing the score or feature representation it computes. Given any score function or feature representation and only its second-order statistics on the sensitive sub-populations, we seek a threshold classifier on the given score or a linear threshold classifier on the given feature representation that achieves the Rawls error rate restricted to this hypothesis class. Our technical contribution is to formulate the above problems using ambiguous chance constraints, and to provide efficient algorithms for Rawlsian fair adaptation, along with provable upper bounds on the Rawls error rate. Our empirical results show significant improvement over state-of-the-art group-fair algorithms, even without retraining for fairness.
Generating Interesting Song-to-Song Segues With Dave
Venue: arXiv cs.IR 2021
Year: 2021

We introduce a novel domain-independent algorithm for generating interesting item-to-item textual connections, or segues. Pivotal to our contribution is the introduction of a scoring function for segues, based on their "interestingness". We provide an implementation of our algorithm in the music domain. We refer to our implementation as Dave. Dave is able to generate 1553 different types of segues, that can be broadly categorized as either informative or funny. We evaluate Dave by comparing it against a curated source of song-to-song segues, called The Chain. In the case of informative segues, we find that Dave can produce segues of the same quality, if not better, than those to be found in The Chain. And, we report positive correlation between the values produced by our scoring function and human perceptions of segue quality. The results highlight the validity of our method, and open future directions in the application of segues to recommender systems research.
A New Transmit Antenna Selection Technique for Physical Layer Security with Strong Eavesdropping
Venue: arXiv cs.IT 2021
Year: 2021

We propose a new transmit antenna selection (TAS) technique that can be beneficial for physical layer security purposes. Specifically, we show that the conventional TAS criterion based on the legitimate channel state information (CSI) is not recommended when the average signal-to-noise ratio for the illegitimate user becomes comparable or superior to that of the legitimate user. We illustrate that an eavesdropper's based antenna selection technique outperforms conventional TAS, without explicit knowledge of the eavesdropper's instantaneous CSI. Analytical expressions and simulation results to support this comparison are given, showing how this new TAS scheme is a better choice in scenarios with a strong eavesdropper.
Incomplete Information VCG Contracts for Common Agency
Venue: arXiv cs.GT 2021
Year: 2021

We study contract design for welfare maximization in the well known "common agency" model of [Bernheim and Whinston, 1986]. This model combines the challenges of coordinating multiple principals with the fundamental challenge of contract design: that principals have incomplete information of the agent's choice of action. Motivated by the significant social inefficiency of standard contracts for such settings (which we formally quantify using a price of anarchy/stability analysis), we investigate whether and how a recent toolbox developed for the first set of challenges under a complete-information assumption, VCG contracts [Lavi and Shamash, 2019], can be extended to incomplete information. We define and characterize the class of "incomplete information VCG contracts (IIVCG)", and show it is the unique class guaranteeing truthfulness of the principals and welfare maximization by the agent. Our results reveal an inherent tradeoff between two important properties required to ensure participation in the contract: individual rationality (for the principals) and limited liability (for the agent). We design a polynomial-time algorithm for determining whether a setting has an IIVCG contract with both properties. As our main result we design a polynomial-time "algorithmic IIVCG" contract: given valuation reports from the principals it returns, if possible for the setting, a payment scheme for the agent that constitutes an IIVCG contract with all desired properties. We also give a sufficient graph-theoretic condition on the population of principals that ensures the existence of such an IIVCG contract.