pwc-archive/papers-with-abstracts huggingface dataset:
Development of fully intuitionistic fuzzy data envelopment analysis model with missing data: an application to Indian police sector
Authors: Anjali Sonkariya, Awadh Pratap Singh, Shiv Prasad Yadav
Date: 2022-07-27 00:00:00
Tasks: Decision Making, Missing Values

Data Envelopment Analysis (DEA) is a technique used to measure the efficiency of decision-making units (DMUs). In order to measure the efficiency of DMUs, the essential requirement is input-output data. Data is usually collected by humans, machines, or both. Due to human/machine errors, there are chances of having some missing values or inaccuracy, such as vagueness/uncertainty/hesitation in the collected data. In this situation, it will be difficult to measure the efficiencies of DMUs accurately. To overcome these shortcomings, a method is presented that can deal with missing values and inaccuracy in the data. To measure the performance efficiencies of DMUs, an input minimization BCC (IMBCC) model in a fully intuitionistic fuzzy (IF) environment is proposed. To validate the efficacy of the proposed fully intuitionistic fuzzy input minimization BCC (FIFIMBCC) model and the technique to deal with missing values in the data, a real-life application to measure the performance efficiencies of Indian police stations is presented.
Adversarial Attacks on ASR Systems: An Overview
Authors: Xiao Zhang, Hao Tan, Xuan Huang, Denghui Zhang, Keke Tang, Zhaoquan Gu
Date: 2022-08-03 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

With the development of hardware and algorithms, ASR(Automatic Speech Recognition) systems evolve a lot. As The models get simpler, the difficulty of development and deployment become easier, ASR systems are getting closer to our life. On the one hand, we often use APPs or APIs of ASR to generate subtitles and record meetings. On the other hand, smart speaker and self-driving car rely on ASR systems to control AIoT devices. In past few years, there are a lot of works on adversarial examples attacks against ASR systems. By adding a small perturbation to the waveforms, the recognition results make a big difference. In this paper, we describe the development of ASR system, different assumptions of attacks, and how to evaluate these attacks. Next, we introduce the current works on adversarial examples attacks from two attack assumptions: white-box attack and black-box attack. Different from other surveys, we pay more attention to which layer they perturb waveforms in ASR system, the relationship between these attacks, and their implementation methods. We focus on the effect of their works.
Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State Tracking
Authors: Ruolin Su, Ting-Wei Wu, Biing-Hwang Juang
Date: 2022-08-04 00:00:00
Tasks: Dialogue State Tracking, Machine Reading Comprehension, Multi-domain Dialogue State Tracking, Reading Comprehension, Task-Oriented Dialogue Systems

As an essential component in task-oriented dialogue systems, dialogue state tracking (DST) aims to track human-machine interactions and generate state representations for managing the dialogue. Representations of dialogue states are dependent on the domain ontology and the user's goals. In several task-oriented dialogues with a limited scope of objectives, dialogue states can be represented as a set of slot-value pairs. As the capabilities of dialogue systems expand to support increasing naturalness in communication, incorporating dialogue act processing into dialogue model design becomes essential. The lack of such consideration limits the scalability of dialogue state tracking models for dialogues having specific objectives and ontology. To address this issue, we formulate and incorporate dialogue acts, and leverage recent advances in machine reading comprehension to predict both categorical and non-categorical types of slots for multi-domain dialogue state tracking. Experimental results show that our models can improve the overall accuracy of dialogue state tracking on the MultiWOZ 2.1 dataset, and demonstrate that incorporating dialogue acts can guide dialogue state design for future task-oriented dialogue systems.
TunaOil: A Tuning Algorithm Strategy for Reservoir Simulation Workloads
Authors: Felipe Albuquerque Portella, David Buchaca Prats, José Roberto Pereira Rodrigues, Josep Lluís Berral
Date: 2022-08-04 00:00:00
Tasks: Seismic Imaging

Reservoir simulations for petroleum fields and seismic imaging are known as the most demanding workloads for high-performance computing (HPC) in the oil and gas (O&G) industry. The optimization of the simulator numerical parameters plays a vital role as it could save considerable computational efforts. State-of-the-art optimization techniques are based on running numerous simulations, specific for that purpose, to find good parameter candidates. However, using such an approach is highly costly in terms of time and computing resources. This work presents TunaOil, a new methodology to enhance the search for optimal numerical parameters of reservoir flow simulations using a performance model. In the O&G industry, it is common to use ensembles of models in different workflows to reduce the uncertainty associated with forecasting O&G production. We leverage the runs of those ensembles in such workflows to extract information from each simulation and optimize the numerical parameters in their subsequent runs. To validate the methodology, we implemented it in a history matching (HM) process that uses a Kalman filter algorithm to adjust an ensemble of reservoir models to match the observed data from the real field. We mine past execution logs from many simulations with different numerical configurations and build a machine learning model based on extracted features from the data. These features include properties of the reservoir models themselves, such as the number of active cells, to statistics of the simulation's behavior, such as the number of iterations of the linear solver. A sampling technique is used to query the oracle to find the numerical parameters that can reduce the elapsed time without significantly impacting the quality of the results. Our experiments show that the predictions can improve the overall HM workflow runtime on average by 31%.
Proceedings 38th International Conference on Logic Programming
Authors: Yuliya Lierler, Jose F. Morales, Carmine Dodaro, Veronica Dahl, Martin Gebser, Tuncay Tekle
Date: 2022-08-04 00:00:00
Tasks: Automated Theorem Proving, Data Integration, Management, Probabilistic Programming

ICLP is the premier international event for presenting research in logic programming. Contributions to ICLP 2022 were sought in all areas of logic programming, including but not limited to: Foundations: Semantics, Formalisms, Nonmonotonic reasoning, Knowledge representation. Languages issues: Concurrency, Objects, Coordination, Mobility, Higher order, Types, Modes, Assertions, Modules, Meta-programming, Logic-based domain-specific languages, Programming techniques. Programming support: Program analysis, Transformation, Validation, Verification, Debugging, Profiling, Testing, Execution visualization. Implementation: Compilation, Virtual machines, Memory management, Parallel and Distributed execution, Constraint handling rules, Tabling, Foreign interfaces, User interfaces. Related Paradigms and Synergies: Inductive and coinductive logic programming, Constraint logic programming, Answer set programming, Interaction with SAT, SMT and CSP solvers, Theorem proving, Argumentation, Probabilistic programming, Machine learning. Applications: Databases, Big data, Data integration and federation, Software engineering, Natural language processing, Web and semantic web, Agents, Artificial intelligence, Computational life sciences, Cyber-security, Robotics, Education.
Integrating Knowledge Graph embedding and pretrained Language Models in Hypercomplex Spaces
Authors: Mojtaba Nayyeri, ZiHao Wang, Mst. Mahfuja Akter, Mirza Mohtashim Alam, Md Rashad Al Hasan Rony, Jens Lehmann, Steffen Staab
Date: 2022-08-04 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction, Sentence

Knowledge Graphs, such as Wikidata, comprise structural and textual knowledge in order to represent knowledge. For each of the two modalities dedicated approaches for graph embedding and language models learn patterns that allow for predicting novel structural knowledge. Few approaches have integrated learning and inference with both modalities and these existing ones could only partially exploit the interaction of structural and textual knowledge. In our approach, we build on existing strong representations of single modalities and we use hypercomplex algebra to represent both, (i), single-modality embedding as well as, (ii), the interaction between different modalities and their complementary means of knowledge representation. More specifically, we suggest Dihedron and Quaternion representations of 4D hypercomplex numbers to integrate four modalities namely structural knowledge graph embedding, word-level representations (e.g.\ Word2vec, Fasttext), sentence-level representations (Sentence transformer), and document-level representations (sentence transformer, Doc2vec). Our unified vector representation scores the plausibility of labelled edges via Hamilton and Dihedron products, thus modeling pairwise interactions between different modalities. Extensive experimental evaluation on standard benchmark datasets shows the superiority of our two new models using abundant textual information besides sparse structural knowledge to enhance performance in link prediction tasks.
Keyword Spotting System and Evaluation of Pruning and Quantization Methods on Low-power Edge Microcontrollers
Authors: Jingyi Wang, Shengchen Li
Date: 2022-08-04 00:00:00
Tasks: Edge-computing, Keyword Spotting, Quantization

Keyword spotting (KWS) is beneficial for voice-based user interactions with low-power devices at the edge. The edge devices are usually always-on, so edge computing brings bandwidth savings and privacy protection. The devices typically have limited memory spaces, computational performances, power and costs, for example, Cortex-M based microcontrollers. The challenge is to meet the high computation and low-latency requirements of deep learning on these devices. This paper firstly shows our small-footprint KWS system running on STM32F7 microcontroller with Cortex-M7 core @216MHz and 512KB static RAM. Our selected convolutional neural network (CNN) architecture has simplified number of operations for KWS to meet the constraint of edge devices. Our baseline system generates classification results for each 37ms including real-time audio feature extraction part. This paper further evaluates the actual performance for different pruning and quantization methods on microcontroller, including different granularity of sparsity, skipping zero weights, weight-prioritized loop order, and SIMD instruction. The result shows that for microcontrollers, there are considerable challenges for accelerate unstructured pruned models, and the structured pruning is more friendly than unstructured pruning. The result also verified that the performance improvement for quantization and SIMD instruction.
InitialGAN: A Language GAN with Completely Random Initialization
Authors: Da Ren, Qing Li
Date: 2022-08-04 00:00:00

Text generative models trained via Maximum Likelihood Estimation (MLE) suffer from the notorious exposure bias problem, and Generative Adversarial Networks (GANs) are shown to have potential to tackle this problem. Existing language GANs adopt estimators like REINFORCE or continuous relaxations to model word probabilities. The inherent limitations of such estimators lead current models to rely on pre-training techniques (MLE pre-training or pre-trained embeddings). Representation modeling methods which are free from those limitations, however, are seldomly explored because of their poor performance in previous attempts. Our analyses reveal that invalid sampling methods and unhealthy gradients are the main contributors to such unsatisfactory performance. In this work, we present two techniques to tackle these problems: dropout sampling and fully normalized LSTM. Based on these two techniques, we propose InitialGAN whose parameters are randomly initialized in full. Besides, we introduce a new evaluation metric, Least Coverage Rate, to better evaluate the quality of generated samples. The experimental results demonstrate that InitialGAN outperforms both MLE and other compared models. To the best of our knowledge, it is the first time a language GAN can outperform MLE without using any pre-training techniques.
Prompt Tuning for Generative Multimodal Pretrained Models
Authors: Hao Yang, Junyang Lin, An Yang, Peng Wang, Chang Zhou, Hongxia Yang
Date: 2022-08-04 00:00:00
Tasks: Image Captioning, Visual Entailment, Visual Question Answering (VQA)

Prompt tuning has become a new paradigm for model tuning and it has demonstrated success in natural language pretraining and even vision pretraining. In this work, we explore the transfer of prompt tuning to multimodal pretraining, with a focus on generative multimodal pretrained models, instead of contrastive ones. Specifically, we implement prompt tuning on the unified sequence-to-sequence pretrained model adaptive to both understanding and generation tasks. Experimental results demonstrate that the light-weight prompt tuning can achieve comparable performance with finetuning and surpass other light-weight tuning methods. Besides, in comparison with finetuned models, the prompt-tuned models demonstrate improved robustness against adversarial attacks. We further figure out that experimental factors, including the prompt length, prompt depth, and reparameteratization, have great impacts on the model performance, and thus we empirically provide a recommendation for the setups of prompt tuning. Despite the observed advantages, we still find some limitations in prompt tuning, and we correspondingly point out the directions for future studies. Codes are available at \url{https://github.com/OFA-Sys/OFA}
Vocabulary Transfer for Biomedical Texts: Add Tokens if You Can Not Add Data
Authors: Priyanka Singh, Vladislav D. Mosin, Ivan P. Yamshchikov
Date: 2022-08-04 00:00:00
Tasks: Transfer Learning

Working within specific NLP subdomains presents significant challenges, primarily due to a persistent deficit of data. Stringent privacy concerns and limited data accessibility often drive this shortage. Additionally, the medical domain demands high accuracy, where even marginal improvements in model performance can have profound impacts. In this study, we investigate the potential of vocabulary transfer to enhance model performance in biomedical NLP tasks. Specifically, we focus on vocabulary extension, a technique that involves expanding the target vocabulary to incorporate domain-specific biomedical terms. Our findings demonstrate that vocabulary extension, leads to measurable improvements in both downstream model performance and inference time.
N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models
Authors: Shiki Sato, Reina Akama, Hiroki Ouchi, Ryoko Tokuhisa, Jun Suzuki, Kentaro Inui
Date: 2022-08-04 00:00:00
Proceeding: SIGDIAL (ACL) 2022 9
Tasks: Response Generation

Avoiding the generation of responses that contradict the preceding context is a significant challenge in dialogue response generation. One feasible method is post-processing, such as filtering out contradicting responses from a resulting n-best response list. In this scenario, the quality of the n-best list considerably affects the occurrence of contradictions because the final response is chosen from this n-best list. This study quantitatively analyzes the contextual contradiction-awareness of neural response generation models using the consistency of the n-best lists. Particularly, we used polar questions as stimulus inputs for concise and quantitative analyses. Our tests illustrate the contradiction-awareness of recent neural response generation models and methodologies, followed by a discussion of their properties and limitations.
ATP: A holistic attention integrated approach to enhance ABSA
Authors: Ashish Kumar, Vasundhra Dahiya, Aditi Sharan
Date: 2022-08-04 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Dependency Parsing, Position, Sentence, Sentiment Analysis

Aspect based sentiment analysis (ABSA) deals with the identification of the sentiment polarity of a review sentence towards a given aspect. Deep Learning sequential models like RNN, LSTM, and GRU are current state-of-the-art methods for inferring the sentiment polarity. These methods work well to capture the contextual relationship between the words of a review sentence. However, these methods are insignificant in capturing long-term dependencies. Attention mechanism plays a significant role by focusing only on the most crucial part of the sentence. In the case of ABSA, aspect position plays a vital role. Words near to aspect contribute more while determining the sentiment towards the aspect. Therefore, we propose a method that captures the position based information using dependency parsing tree and helps attention mechanism. Using this type of position information over a simple word-distance-based position enhances the deep learning model's performance. We performed the experiments on SemEval'14 dataset to demonstrate the effect of dependency parsing relation-based attention for ABSA.
"Yeah, it does have a...Windows `98 Vibe'': Usability Study of Security Features in Programmable Logic Controllers
Authors: Karen Li, Kopo M. Ramokapane, Awais Rashid
Date: 2022-08-04 00:00:00

Programmable Logic Controllers (PLCs) drive industrial processes critical to society, e.g., water treatment and distribution, electricity and fuel networks. Search engines (e.g., Shodan) have highlighted that Programmable Logic Controllers (PLCs) are often left exposed to the Internet, one of the main reasons being the misconfigurations of security settings. This leads to the question -- why do these misconfigurations occur and, specifically, whether usability of security controls plays a part? To date, the usability of configuring PLC security mechanisms has not been studied. We present the first investigation through a task-based study and subsequent semi-structured interviews (N=19). We explore the usability of PLC connection configurations and two key security mechanisms (i.e., access levels and user administration). We find that the use of unfamiliar labels, layouts and misleading terminology exacerbates an already complex process of configuring security mechanisms. Our results uncover various (mis-) perceptions about the security controls and how design constraints, e.g., safety and lack of regular updates (due to long term nature of such systems), provide significant challenges to realization of modern HCI and usability principles. Based on these findings, we provide design recommendations to bring usable security in industrial settings at par with its IT counterpart.
Mathematical Modeling Analysis and Optimization of Fungal Diversity Growth
Authors: Tongyue Shi, Haining Wang
Date: 2022-08-04 00:00:00
Tasks: Diversity

This paper studied the relationship between the decomposition rate of fungi and temperature, humidity, fungus elongation, moisture tolerance and fungus density in a given volume in the presence of a variety of fungi, and established a series of models to describe the decomposition of fungi in different states. Since the volume of soil was given in this case, the latter two characteristics could be attributed to the influence of the number of fungal population on the decomposition rate. Based on the Logistic model, the relationship between the number of population and time was established, and finally the number of fungi in the steady state was obtained The interaction between different species of fungi was analyzed by Lotka-Volterra model, and the decomposition rate of various fungal combinations in different environments was obtained. After studying the one and two cases, we can extrapher from one to the other, and the community consisting of n fungal populations will be similar to the community consisting of n+1 fungal populations. After the study, we substituted the collected data into the model and found that the fungal community composed of two kinds of fungi had a lower decomposition rate of ground decomposition or wooden fiber than that of a single kind of fungus for the same kind of substance. We found that the fungus in warm and humid environment of decomposition rate is highest, the change of the atmospheric cause some fungal population growth rate decreases, there are also some will increase, which is associated with the nature of fungi.We analyzed the influence of environmental factors, namely temperature and humidity, on the model.
Learning-based Practical Smartphone Eavesdropping with Built-in Accelerometer
Authors: Zhongjie Ba, Tianhang Zheng, Xinyu Zhang, Zhan Qin, Baochun Li, Xue Liu and Kui Ren
Date: 2020-02-26 00:00:00
Proceeding: Network and Distributed Systems Security (NDSS) Symposium 2020 2
Tasks: Common Sense Reasoning

Motion sensors on current smartphones have been exploited for audio eavesdropping due to their sensitivity to vibrations. However, this threat is considered low-risk because of two widely acknowledged limitations: First, unlike microphones, motion sensors can only pick up speech signals traveling through a solid medium. Thus, the only feasible setup reported previously is to use a smartphone gyroscope to eavesdrop on a loudspeaker placed on the same table. The second limitation comes from a common sense that these sensors can only pick up a narrow band (85-100Hz) of speech signals due to a sampling ceiling of 200Hz. In this paper, we revisit the threat of motion sensors to speech privacy and propose AccelEve, a new side-channel attack that employs a smartphone’s accelerometer to eavesdrop on the speaker in the same smartphone. Specifically, it utilizes the accelerometer measurements to recognize the speech emitted by the speaker and to reconstruct the corresponding audio signals. In contrast to previous works, our setup allows the speech signals to always produce strong responses in accelerometer measurements through the shared motherboard, which successfully addresses the first limitation and allows this kind of attacks to penetrate into real-life scenarios. Regarding the sampling rate limitation, contrary to the widely-held belief, we observe up to 500Hz sampling rates in recent smartphones, which almost covers the entire fundamental frequency band (85-255Hz) of adult speech. On top of these pivotal observations, we propose a novel deep learning based system that learns to recognize and reconstruct speech information from the spectrogram representation of ac- celeration signals. This system employs adaptive optimization on deep neural networks with skip connections using robust and gen- eralizable losses to achieve robust recognition and reconstruction performance. Extensive evaluations demonstrate the effectiveness and high accuracy of our attack under various settings.