OpenRaiser/Intern-Atlas huggingface dataset:
Towards Understanding Analytics in Software Startups
Venue: arXiv cs.SE 2022
Year: 2022

Analytics plays a crucial role in the data-informed decision-making processes of modern businesses. Unlike established software companies, software startups are not seen utilizing the potential of analytics even though a startup process should be primarily data-driven. There has been little understanding in the literature about analytics for software startups. This study set out to address the knowledge gap by exploring how analytics is understood in the context of software startups. To this end, we collected the qualitative data of three analytics platforms that are mostly used by startups from multiple sources. We covered platform documentation as well as experience reports of the software startups using these platforms. The data was analyzed using content analysis techniques. Four high-level concepts were identified that encapsulate the real understanding of software startups on analytics, including instrumentation of analytics, experimentation, diagnostic analysis, and getting insights. The first concept describes how startups set up analytics and the latter three illustrate the usage scenarios of analytics. This study is the first step toward understanding analytics in the software startup context. The identified concepts can guide further investigation of analytics in this context. It also provides some insights for software startups to set up analytics for data-informed decisions. Given the limitation of the data used in the study, the immediate next step is to ground as well as validate the acquired understanding using the primary data, by directly interacting with software startups.
Can instability variations warn developers when open-source projects boost?
Venue: arXiv cs.SE 2022
Year: 2022

Although architecture instability has been studied and measured using a variety of metrics, a deeper analysis of which project parts are less stable and how such instability varies over time is still needed. While having more information on architecture instability is, in general, useful for any software development project, it is especially important in Open Source Software (OSS) projects where the supervision of the development process is more difficult to achieve. In particular, we are interested when OSS projects grow from a small controlled environment (i.e., the cathedral phase) to a community-driven project (i.e., the bazaar phase). In such a transition, the project often explodes in terms of software size and number of contributing developers. Hence, the complexity of the newly added features, and the frequency of the commits and files modified may cause significant variations of the instability of the structure of the classes and packages. Consequently, in this registered report we suggest ways to analyze the instability in OSS projects, especially during that sensitive phase where they become community-driven. We intend to suggest ways to predict the evolution of the instability in several OSS projects. Our preliminary results show that it seems possible to provide meaningful estimations that can be useful for OSS teams before a project grows in excess.
Resource Allocation for Multiuser Edge Inference with Batching and Early Exiting (Extended Version)
Venue: arXiv cs.IT 2022
Year: 2022

The deployment of inference services at the network edge, called edge inference, offloads computation-intensive inference tasks from mobile devices to edge servers, thereby enhancing the former's capabilities and battery lives. In a multiuser system, the joint allocation of communication-and-computation ($\text{C}^\text{2}$) resources (i.e., scheduling and bandwidth allocation) is made challenging by adopting efficient inference techniques, batching and early exiting, and further complicated by the heterogeneity in users' requirements on accuracy and latency. Batching groups multiple tasks into one batch for parallel processing to reduce time-consuming memory access and thereby boosts the throughput (i.e., completed task per second). On the other hand, early exiting allows a task to exit from a deep-neural network without traversing the whole network to support a tradeoff between accuracy and latency. In this work, we study optimal $\text{C}^\text{2}$ resource allocation with batching and early exiting, which is an NP-complete integer programming problem. A set of efficient algorithms are designed under the criterion of maximum throughput by tackling the challenge. Experimental results demonstrate that both optimal and sub-optimal $\text{C}^\text{2}$ resource allocation algorithms can leverage integrated batching and early exiting to double the inference throughput compared with conventional schemes.
Performance analysis of WDM in LoS communications with arbitrary orientation and position
Venue: arXiv cs.IT 2022
Year: 2022

This letter considers the wavenumber-division-multiplexing (WDM) scheme that was recently proposed in [1] for line-of-sight communications between parallel (side-by-side) spatially-continuous electromagnetic segments. Our aim is to analyze the performance of WDM, combined with different digital processing architectures, when the electromagnetic segments have an arbitrary orientation and position. To this end, we first show how the general electromagnetic MIMO (multiple-input multiple-output) model from [1] can be particularized to the case of interest and then use numerical results to evaluate the impact of geometric parameters (e.g., horizontal and vertical distances, azimuth and elevation orientations). It turns out that WDM performs satisfactorily also when the transmit and receive segments are not in boresight direction of each other.
Towards Generalizable Semantic Product Search by Text Similarity Pre-training on Search Click Logs
Venue: arXiv cs.IR 2022
Year: 2022

Recently, semantic search has been successfully applied to e-commerce product search and the learned semantic space(s) for query and product encoding are expected to generalize to unseen queries or products. Yet, whether generalization can conveniently emerge has not been thoroughly studied in the domain thus far. In this paper, we examine several general-domain and domain-specific pre-trained Roberta variants and discover that general-domain fine-tuning does not help generalization, which aligns with the discovery of prior art. Proper domain-specific fine-tuning with clickstream data can lead to better model generalization, based on a bucketed analysis of a publicly available manual annotated query-product pair da
Exploring the Universal Vulnerability of Prompt-based Learning Paradigm
Venue: arXiv cs.CL 2022
Year: 2022

Prompt-based learning paradigm bridges the gap between pre-training and fine-tuning, and works effectively under the few-shot setting. However, we find that this learning paradigm inherits the vulnerability from the pre-training stage, where model predictions can be misled by inserting certain triggers into the text. In this paper, we explore this universal vulnerability by either injecting backdoor triggers or searching for adversarial triggers on pre-trained language models using only plain text. In both scenarios, we demonstrate that our triggers can totally control or severely decrease the performance of prompt-based models fine-tuned on arbitrary downstream tasks, reflecting the universal vulnerability of the prompt-based learning paradigm. Further experiments show that adversarial triggers have good transferability among language models. We also find conventional fine-tuning models are not vulnerable to adversarial triggers constructed from pre-trained language models. We conclude by proposing a potential solution to mitigate our attack methods. Code and data are publicly available at https://github.com/leix28/prompt-universal-vulnerability
Physically Disentangled Representations
Venue: arXiv cs.CV 2022
Year: 2022

State-of-the-art methods in generative representation learning yield semantic disentanglement, but typically do not consider physical scene parameters, such as geometry, albedo, lighting, or camera. We posit that inverse rendering, a way to reverse the rendering process to recover scene parameters from an image, can also be used to learn physically disentangled representations of scenes without supervision. In this paper, we show the utility of inverse rendering in learning representations that yield improved accuracy on downstream clustering, linear classification, and segmentation tasks with the help of our novel Leave-One-Out, Cycle Contrastive loss (LOOCC), which improves disentanglement of scene parameters and robustness to out-of-distribution lighting and viewpoints. We perform a comparison of our method with other generative representation learning methods across a variety of downstream tasks, including face attribute classification, emotion recognition, identification, face segmentation, and car classification. Our physically disentangled representations yield higher accuracy than semantically disentangled alternatives across all tasks and by as much as 18%. We hope that this work will motivate future research in applying advances in inverse rendering and 3D understanding to representation learning.
A Simple Approach to Adversarial Robustness in Few-shot Image Classification
Venue: arXiv cs.CV 2022
Year: 2022

Few-shot image classification, where the goal is to generalize to tasks with limited labeled data, has seen great progress over the years. However, the classifiers are vulnerable to adversarial examples, posing a question regarding their generalization capabilities. Recent works have tried to combine meta-learning approaches with adversarial training to improve the robustness of few-shot classifiers. We show that a simple transfer-learning based approach can be used to train adversarially robust few-shot classifiers. We also present a method for novel classification task based on calibrating the centroid of the few-shot category towards the base classes. We show that standard adversarial training on base categories along with calibrated centroid-based classifier in the novel categories, outperforms or is on-par with state-of-the-art advanced methods on standard benchmarks for few-shot learning. Our method is simple, easy to scale, and with little effort can lead to robust few-shot classifiers. Code is available here: \url{https://github.com/UCDvision/Simple_few_shot.git}
Coevolutionary Pareto Diversity Optimization
Venue: arXiv cs.NE 2022
Year: 2022

Computing diverse sets of high quality solutions for a given optimization problem has become an important topic in recent years. In this paper, we introduce a coevolutionary Pareto Diversity Optimization approach which builds on the success of reformulating a constrained single-objective optimization problem as a bi-objective problem by turning the constraint into an additional objective. Our new Pareto Diversity optimization approach uses this bi-objective formulation to optimize the problem while also maintaining an additional population of high quality solutions for which diversity is optimized with respect to a given diversity measure. We show that our standard co-evolutionary Pareto Diversity Optimization approach outperforms the recently introduced DIVEA algorithm which obtains its initial population by generalized diversifying greedy sampling and improving the diversity of the set of solutions afterwards. Furthermore, we study possible improvements of the Pareto Diversity Optimization approach. In particular, we show that the use of inter-population crossover further improves the diversity of the set of solutions.
Out-Of-Distribution Detection In Unsupervised Continual Learning
Venue: arXiv cs.CV 2022
Year: 2022

Unsupervised continual learning aims to learn new tasks incrementally without requiring human annotations. However, most existing methods, especially those targeted on image classification, only work in a simplified scenario by assuming all new data belong to new tasks, which is not realistic if the class labels are not provided. Therefore, to perform unsupervised continual learning in real life applications, an out-of-distribution detector is required at beginning to identify whether each new data corresponds to a new task or already learned tasks, which still remains under-explored yet. In this work, we formulate the problem for Out-of-distribution Detection in Unsupervised Continual Learning (OOD-UCL) with the corresponding evaluation protocol. In addition, we propose a novel OOD detection method by correcting the output bias at first and then enhancing the output confidence for in-distribution data based on task discriminativeness, which can be applied directly without modifying the learning procedures and objectives of continual learning. Our method is evaluated on CIFAR-100 dataset by following the proposed evaluation protocol and we show improved performance compared with existing OOD detection methods under the unsupervised continual learning scenario.
Heterogeneity in Algorithm-Assisted Decision-Making: A Case Study in Child Abuse Hotline Screening
Venue: arXiv cs.HC 2022
Year: 2022

Algorithmic risk assessment tools are now commonplace in public sector domains such as criminal justice and human services. These tools are intended to aid decision makers in systematically using rich and complex data captured in administrative systems. In this study we investigate sources of heterogeneity in the alignment between worker decisions and algorithmic risk scores in the context of a real world child abuse hotline screening use case. Specifically, we focus on heterogeneity related to worker experience. We find that senior workers are far more likely to screen in referrals for investigation, even after we control for the observed algorithmic risk score and other case characteristics. We also observe that the decisions of less-experienced workers are more closely aligned with algorithmic risk scores than those of senior workers who had decision-making experience prior to the tool being introduced. While screening decisions vary across child race, we do not find evidence of racial differences in the relationship between worker experience and screening decisions. Our findings indicate that it is important for agencies and system designers to consider ways of preserving institutional knowledge when introducing algorithms into high employee turnover settings such as child welfare call screening.
Scalable privacy-preserving cancer type prediction with homomorphic encryption
Venue: arXiv cs.CR 2022
Year: 2022

Machine Learning (ML) alleviates the challenges of high-dimensional data analysis and improves decision making in critical applications like healthcare. Effective cancer type from high-dimensional genetic mutation data can be useful for cancer diagnosis and treatment, if the distinguishable patterns between cancer types are identified. At the same time, analysis of high-dimensional data is computationally expensive and is often outsourced to cloud services. Privacy concerns in outsourced ML, especially in the field of genetics, motivate the use of encrypted computation, like Homomorphic Encryption (HE). But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer detection using a real-world dataset consisting of more than 2 million genetic information for several cancer types. Since the data is inherently high-dimensional, we explore smaller ML models for cancer prediction to enable fast inference in the privacy preserving domain. We develop a solution for privacy preserving cancer inference which first leverages the domain knowledge on somatic mutations to efficiently encode genetic mutations and then uses statistical tests for feature selection. Our logistic regression model, built using our novel encoding scheme, achieves 0.98 micro-average area under curve with 13% higher test accuracy than similar studies. We exhaustively test our model's predictive capabilities by analyzing the genes used by the model. Furthermore, we propose a fast matrix multiplication algorithm that can efficiently handle high-dimensional data. Experimental results show that, even with 40,000 features, our proposed matrix multiplication algorithm can speed up concurrent inference of multiple individuals by approximately 10x and inference of a single individual by approximately 550x, in comparison to standard matrix multiplication.
Position-aware Location Regression Network for Temporal Video Grounding
Venue: arXiv cs.CV 2022
Year: 2022

The key to successful grounding for video surveillance is to understand a semantic phrase corresponding to important actors and objects. Conventional methods ignore comprehensive contexts for the phrase or require heavy computation for multiple phrases. To understand comprehensive contexts with only one semantic phrase, we propose Position-aware Location Regression Network (PLRN) which exploits position-aware features of a query and a video. Specifically, PLRN first encodes both the video and query using positional information of words and video segments. Then, a semantic phrase feature is extracted from an encoded query with attention. The semantic phrase feature and encoded video are merged and made into a context-aware feature by reflecting local and global contexts. Finally, PLRN predicts start, end, center, and width values of a grounding boundary. Our experiments show that PLRN achieves competitive performance over existing methods with less computation time and memory.
SwinNet: Swin Transformer drives edge-aware RGB-D and RGB-T salient object detection
Venue: arXiv cs.CV 2022
Year: 2022

Convolutional neural networks (CNNs) are good at extracting contexture features within certain receptive fields, while transformers can model the global long-range dependency features. By absorbing the advantage of transformer and the merit of CNN, Swin Transformer shows strong feature representation ability. Based on it, we propose a cross-modality fusion model SwinNet for RGB-D and RGB-T salient object detection. It is driven by Swin Transformer to extract the hierarchical features, boosted by attention mechanism to bridge the gap between two modalities, and guided by edge information to sharp the contour of salient object. To be specific, two-stream Swin Transformer encoder first extracts multi-modality features, and then spatial alignment and channel re-calibration module is presented to optimize intra-level cross-modality features. To clarify the fuzzy boundary, edge-guided decoder achieves inter-level cross-modality fusion under the guidance of edge features. The proposed model outperforms the state-of-the-art models on RGB-D and RGB-T datasets, showing that it provides more insight into the cross-modality complementarity task.
ASR in German: A Detailed Error Analysis
Venue: arXiv cs.CL 2022
Year: 2022

The amount of freely available systems for automatic speech recognition (ASR) based on neural networks is growing steadily, with equally increasingly reliable predictions. However, the evaluation of trained models is typically exclusively based on statistical metrics such as WER or CER, which do not provide any insight into the nature or impact of the errors produced when predicting transcripts from speech input. This work presents a selection of ASR model architectures that are pretrained on the German language and evaluates them on a benchmark of diverse test datasets. It identifies cross-architectural prediction errors, classifies those into categories and traces the sources of errors per category back into training data as well as other sources. Finally, it discusses solutions in order to create qualitatively better training datasets and more robust ASR systems.
Beautiful secrets: using aesthetic images to authenticate users
Venue: arXiv cs.HC 2022
Year: 2022

We propose and evaluate an authentication scheme that improves usability and user experience issues in the authentication process due to its reliance on people's aesthetic tastes and preferences. The scheme uses aesthetic images to verify the identity of computer users. It relies on three major premises regarding visual aesthetics: (i) that an individual has different preferences for different aesthetic stimuli; (ii) that these preferences are relatively consistent; and (iii) that aesthetic tastes are subjective and, therefore, there are considerable individual differences in aesthetic preferences. Following a review of the scientific basis for these premises, we describe the concept of the aesthetic evaluation-based authentication (AEbA) method and illustrate an implementation of it. We address AEbA's advantages and disadvantages relative to other related methods and conclude that it is adequate for low-to-medium security domains. It cannot serve as a compulsory method because we suspect that a certain portion of the user population lacks the degree of aesthetic sensitivity required to use the system effectively. On the plus side, the method offers a positive experience. It alleviates the burden of memorizing passwords to a minimum, and relative to other usability-oriented schemes provides better security in terms of shoulder-surfing, phishing, and password space. Finally, we report on a pilot evaluation of the concept and its feasibility that supports the method's main tenets, provides insights about implementation challenges and suggestions for improvements.
Membership-Mappings for Practical Secure Distributed Deep Learning
Venue: arXiv cs.LG 2022
Year: 2022

This study leverages the data representation capability of fuzzy based membership-mappings for practical secure distributed deep learning using fully homomorphic encryption. The impracticality issue of secure machine (deep) learning with fully homomorphic encrypted data, arising from large computational overhead, is addressed via applying fuzzy attributes. Fuzzy attributes are induced by globally convergent and robust variational membership-mappings based local deep models. Fuzzy attributes combine the local deep models in a robust and flexible manner such that the global model can be evaluated homomorphically in an efficient manner using a boolean circuit composed of bootstrapped binary gates. The proposed method, while preserving privacy in a distributed learning scenario, remains accurate, practical, and scalable. The method is evaluated through numerous experiments including demonstrations through MNIST dataset and Freiburg Groceries Dataset. Further, a biomedical application related to mental stress detection on individuals is considered.
A Note on the Blockchain Trilemma for Decentralized Identity: Learning from Experiments with Hyperledger Indy
Venue: arXiv cs.CR 2022
Year: 2022

The challenge to establish and verify human identity over the Internet in a secure and privacy-respecting way is long-standing. In this paper, we explore the blockchain trilemma of scalability, security, and decentralization in the context of the Trust Registry: a root of trust for a decentralized identity scheme that enables read and write access to shared records and is tamper-resistant. We make a case study of Hyperledger Indy -- an open-source technology bespoke for decentralized identity -- and conduct two empirical experiments to measure the latency of more than 45,000 transactions in the naturalistic environment of Amazon Web Services. We conclude that issues of Trust Registry scalability have multiple facets. While Hyperledger Indy captures data useful to underpin a decentralized identity scheme, the knock-on effect of its scalability limitations may indeed place constraints on properties of security and decentralization. The current credential verification process relies on transaction processing by a ledger with transaction processing bottlenecks, which may constrain the ideal of non-repudiation.