pwc-archive/papers-with-abstracts huggingface dataset:
DISC: a Dataset for Integrated Sensing and Communication in mmWave Systems
Authors: Jacopo Pegoraro, Pablo Saucedo, Jesus Omar Lacruz, Michele Rossi, Joerg Widmer
Date: 2023-06-15 00:00:00
Tasks: Activity Recognition, Benchmarking, Human Activity Recognition, Integrated sensing and communication

This paper presents DISC, a dataset of millimeter-wave channel impulse response measurements for integrated human activity sensing and communication. This is the first dataset collected with a software-defined radio testbed that transmits 60 GHz IEEE 802-11ay-compliant packets and estimates the channel response including scattered signals off the moving body parts of subjects moving in an indoor environment. The provided data consists of three parts, for more than 2 hours of channel measurements with high temporal resolution (0.27 ms inter-packet time). DISC contains the contribution of 7 subjects performing 5 different activities, and includes data collected from two distinct environments. Unlike available radar-based millimeter-wave sensing datasets, our measurements are collected using uniform packet transmission times and sparse traffic patterns from real Wi-Fi deployments. We develop, train, and release open-source baseline algorithms based on DISC to perform human sensing tasks. Our results demonstrate that DISC can serve as a multi-purpose benchmarking tool for machine learning-based human activity recognition, radio frequency gait analysis, and sparse sensing algorithms for next-generation integrated sensing and communications.
The Core & Periphery Hypothesis: A Conceptual Basis for Generality in Cell and Developmental Biology
Authors: Elisa Gallo, Stefano De Renzis, James Sharpe, Roberto Mayor, Jonas Hartmann
Date: 2023-06-15 00:00:00
Tasks: Diversity

The discovery of general principles underlying the complexity and diversity of cellular and developmental systems is a central and long-standing aim of biology. Whilst new technologies collect data at an ever-accelerating rate, there is growing concern that conceptual progress is not keeping pace. We contend that this is due to a paucity of appropriate conceptual frameworks to serve as a basis for general theories of mesoscale biological phenomena. In exploring this issue, we have developed a foundation for one such framework, termed the Core and Periphery (C&P) hypothesis, which reveals hidden generality across the diverse and complex behaviors exhibited by cells and tissues. Here, we present the C&P concept, provide examples of its applicability across multiple scales, argue its consistency with evolution, and discuss key implications and open questions. We propose that the C&P hypothesis could unlock new avenues of conceptual progress in cell and developmental biology.
Evolutionary Algorithms in the Light of SGD: Limit Equivalence, Minima Flatness, and Transfer Learning
Authors: Andrei Kucharavy, Rachid Guerraoui, Ljiljana Dolamic
Date: 2023-05-20 00:00:00
Tasks: Evolutionary Algorithms, Transfer Learning

Whenever applicable, the Stochastic Gradient Descent (SGD) has shown itself to be unreasonably effective. Instead of underperforming and getting trapped in local minima due to the batch noise, SGD leverages it to learn to generalize better and find minima that are good enough for the entire dataset. This led to numerous theoretical and experimental investigations, especially in the context of Artificial Neural Networks (ANNs), leading to better machine learning algorithms. However, SGD is not applicable in a non-differentiable setting, leaving all that prior research off the table. In this paper, we show that a class of evolutionary algorithms (EAs) inspired by the Gillespie-Orr Mutational Landscapes model for natural evolution is formally equivalent to SGD in certain settings and, in practice, is well adapted to large ANNs. We refer to such EAs as Gillespie-Orr EA class (GO-EAs) and empirically show how an insight transfer from SGD can work for them. We then show that for ANNs trained to near-optimality or in the transfer learning setting, the equivalence also allows transferring the insights from the Mutational Landscapes model to SGD. We then leverage this equivalence to experimentally show how SGD and GO-EAs can provide mutual insight through examples of minima flatness, transfer learning, and mixing of individuals in EAs applied to large models.
Using a one-dimensional convolutional neural network with a conditional generative adversarial network to classify plant electrical signals
Authors: Xiao-Huang Qin, Zi-Yang Wang, Jie-Peng Yao, Qiao Zhou, Peng-Fei Zhao, Zhong-Yi Wang, Lan Huang
Date: 2020-07-01 00:00:00
Proceeding: Computers and Electronics in Agriculture 2020 7
Tasks: Data Augmentation, Generative Adversarial Network

Identification of salt tolerance of crops usually requires long-term observation of morphology, or physiological
and biochemical experiments, which are time-consuming and laborious tasks. This paper proposes a model,
based on a one-dimensional convolutional neural network (1D-CNN) with a conditional generative adversarial
network (CGAN), which can quickly and effectively identify the salt tolerance of the seedlings using plant
electrical signals at the early seedling stage. To address the problem of the small-scale dataset, the improved
CGAN was used for sample augmentation of plant electrical signals under salt stress. The 1D-CNN can extract
features efficiently and automatically and distinguish between salt-tolerant and salt-sensitive varieties.
Furthermore, the 1D-CNN was trained using real samples and a training set augmented with generated samples,
separately. After data augmentation by the improved CGAN, the accuracy of the CNN increased to 92.31%, and
the classification performance was better than that of the traditional method. In conclusion, this method is useful
and promising for identifying the salt tolerance of plants at the early seedling stage. It is also applicable to other
1D signals with small-scale datasets, and to other types of crops.
A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance
Authors: Jie-Peng Yao, Zi-Yang Wang, Ricardo Ferraz de Oliveira, Zhong-Yi Wang, Lan Huang
Date: 2021-11-01 00:00:00
Proceeding: Computers and Electronics in Agriculture 2021 11

Screening salt-tolerant crops at different salt concentrations is of great significance but time-consuming. It remains a major challenge to automatically find the appropriate stress concentration to identify the salt tolerance 
of crops by using the recorded electrical signals of plants over long periods of time. To solve this problem, we 
designed a data-driven signal dynamics prediction model (SLSTM-TCNN), based on a one-dimensional convolutional neural network (1D-CNN) combined with a long short-term memory neural network. Furthermore, we 
developed a quantitative model, named the NaCl stress concentration discrimination model (SCDM), to investigate the relationship between the electrical signals, NaCl stress concentration, and time dependence, and used a 
salt tolerance classification model (STCM) to discover the most appropriate NaCl stress concentration for distinguishing the salt tolerance of wheat. These methods perform the time-consuming task of selecting salt-tolerant 
varieties of plants under different NaCl concentrations. The results show that the SLSTM-TCNN could quickly 
predict the signal dynamics of wheat leaves DeKang961 (salt-tolerant) and Langdon (salt-sensitive) under the 
ongoing stress of different NaCl concentrations. The accuracy of SCDM for differentiating NaCl stress concentrations increased to 88% and 83%, and that of STCM for classifying salt-tolerant and salt-sensitive varieties 
reached 92.36%. Finally, it was found that the salt tolerance of the two varieties (DeKang961 and Langdon) was 
higher when the NaCl concentration was in the range of 50–200 mM. In the future, the method will be a 
potentially useful tool for identifying the salt tolerance of other crops at the seedling stage.
What Can We Learn Privately?
Authors: Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, Adam Smith
Date: 2008-03-06 00:00:00

Learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real-life data sets. We ask: what concept classes can be learned privately, namely, by an algorithm whose output does not depend too heavily on any one input or specific training example? More precisely, we investigate learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in contexts where aggregate information is released about a database containing sensitive information about individuals. We demonstrate that, ignoring computational constraints, it is possible to privately agnostically learn any concept class using a sample size approximately logarithmic in the cardinality of the concept class. Therefore, almost anything learnable is learnable privately: specifically, if a concept class is learnable by a (non-private) algorithm with polynomial sample complexity and output size, then it can be learned privately using a polynomial number of samples. We also present a computationally efficient private PAC learner for the class of parity functions. Local (or randomized response) algorithms are a practical class of private algorithms that have received extensive investigation. We provide a precise characterization of local private learning algorithms. We show that a concept class is learnable by a local algorithm if and only if it is learnable in the statistical query (SQ) model. Finally, we present a separation between the power of interactive and noninteractive local learning algorithms.
ProteinBERT: a universal deep-learning model of protein sequence and function
Authors: Nadav Brandes, Dan Ofer, Yam Peleg, Nadav Rappoport, Michal Linial
Date: 2022-02-10 00:00:00
Proceeding: Bioinformatics, Volume 38, Issue 8 2022 2
Tasks: Language Modeling, Language Modelling, Protein Secondary Structure Prediction, Protein Structure Prediction

Self-supervised deep language modeling has shown unprecedented success across natural language tasks, and has recently been repurposed to biological sequences. However, existing models and pretraining methods are designed and optimized for text analysis. We introduce ProteinBERT, a deep language model specifically designed for proteins. Our pretraining scheme combines language modeling with a novel task of Gene Ontology (GO) annotation prediction. We introduce novel architectural elements that make the model highly efficient and flexible to long sequences. The architecture of ProteinBERT consists of both local and global representations, allowing end-to-end processing of these types of inputs and outputs. ProteinBERT obtains near state-of-the-art performance, and sometimes exceeds it, on multiple benchmarks covering diverse protein properties (including protein structure, post-translational modifications and biophysical attributes), despite using a far smaller and faster model than competing deep-learning methods. Overall, ProteinBERT provides an efficient framework for rapidly training protein predictors, even with limited labeled data.
Privacy-Enhancing Face Obfuscation Guided by Semantic-Aware Attribution Maps
Authors: Jingzhi Li, Hua Zhang, Siyuan Liang, Pengwen Dai, Xiaochun Cao
Date: 2023-06-02 00:00:00
Proceeding: IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2023 6
Tasks: Face Recognition

Face recognition technology is increasingly being integrated into our daily life, e.g. Face ID. With the advancement of machine learning algorithms, the personal information such as age, gender, and race can be easily deduced from the recorded face images in these applications. This poses a serious privacy threat to individuals who do not want to be profiled, as face images are collected for biometric purposes. Existing methods mostly focus on adding the invisible adversarial perturbations into the images to make automatic inference infeasible. However, the application scenarios of these methods are limited due to the perturbations depending on the specific model. In this paper, we introduce a novel face privacy-enhancing framework by obfuscating the stored faces, which could maintain the data utility (face identity) while protecting the privacy of users (facial attributes). Specifically, we first develop a feature attribution module to discover the identity-related facial parts. Within this module, we introduce a pixel importance estimation model based on Shapley value to obtain a pixel-level attribution map, and then each pixel on the attribution map is aggregated into semantic facial parts, which are used to quantify the importance of different facial parts. Next, we design a privacy-enhancing module to generate the high-quality obfuscated images, which can modify the privacy semantic content and preserve the identity-related information. Using the proposed method, users can choose the single or multiple attributes to be obfuscated without affecting identity matching. Extensive experiments conducted on CelebA-HQ and VGGFace2-HQ benchmarks demonstrate the effectiveness and generalization ability of our method.
Improved Sigma Filter for Speckle Filtering of SAR Imagery
Authors: Lee, J. S., Wen, J. H., Ainsworth, T. L., Chen, K. S., & Chen, A. J
Date: 2008-11-25 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2008 11

Abstract—The Lee sigma filter was developed in 1983 based on the simple concept of two-sigma probability, and it was reasonably effective in speckle filtering. However, deficiencies were discovered in producing biased estimation and in blurring and depressing strong reflected targets. The advancement of synthetic aperture radar (SAR) technology with high-resolution data of large dimensions demands better and efficient speckle filtering algorithms. In this paper, we extend and improve the Lee sigma filter by eliminating these deficiencies. The bias problem is solved by redefining the sigma range based on the speckle probability density functions. To mitigate the problems of blurring and depressing strong reflective scatterers, a target signature preservation technique is developed. In addition, we incorporate the minimum-mean-square-error estimator for adaptive speckle reduction. Simulated SAR data are used to quantitatively evaluate the characteristics of this improved sigma filter and to validate its effectiveness. The proposed algorithm is applied to spaceborne and airborne SAR data to demonstrate its overall speckle filtering characteristics as compared with other algorithms. This improved sigma filter remains simple in concept and is computationally efficient but without the deficiencies of the original Lee sigma filter.
Shale oil production prediction and fracturing optimization based on machine learning
Authors: Chunhua Lu, Hanqiao Jiang, Jinlong Yang, Zhiqiang Wang, Miao Zhang, Junjian Li *
Date: 2022-10-01 00:00:00
Proceeding: Journal of Petroleum Science and Engineering 2022 10

With the advancement of horizontal well and hydraulic fracturing technology, the development of unconven­
tional reservoirs such as shale oil has become a hot issue in the energy field. However, production tests have
demonstrated that production is non-uniformly between stages due to reservoir heterogeneity and fracturing
design limitations. Therefore, it makes sense to improve fracturing design to enhance shale oil development. In
this paper, we propose a computational framework for shale oil production prediction and fracturing parameters
optimization that couples machine learning and particle swarm optimization (PSO). Firstly, we construct a deep
neural network (DNN) model database with 841 numerical simulation data as training set and validation set, and
87 field data as test set. Secondly, the hyperparameter optimized DNN are performed to predict the production
performance. And the predictive performance compares to the random forest (RF) and support vector machine
(SVM). Thirdly, coupled with DNN, PSO is performed to optimize fracturing parameters. Finally, conducting
rapid fracturing design based on PSO optimization results and reservoir sweet spot distribution. The results
reveal that DNN exhibit best production prediction accuracy compared to RF and SVM. The generalization ability
of DNN is verified by accurate prediction performance of 4 cases with extreme parameters. Optimized fracturing
parameters using PSO in an actual well resulted in 2969 m3 increases in cumulative oil and 68*104 USD increases
in NPV. According to the optimization results of PSO in four extreme cases, the reasonable fracturing parameters
for different reservoir quality are obtained, including the number of fracturing stages and clusters, the volume of
single stage fracturing fluid and proppant. Our work guides engineers in rapid fracturing design while improving
shale oil development effect.
CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling Task
Authors: Nguyen Minh Phuong, Tung Le, Nguyen Le Minh
Date: 2022-09-21 00:00:00
Proceeding: Advances in Computational Collective Intelligence 2022 9
Tasks: intent-classification, Intent Classification, Intent Classification and Slot Filling, Intent Detection, NER, Semantic Parsing, slot-filling, Slot Filling, Spoken Language Understanding

Spoken Language Understanding (SLU) task is a wide application task in Natural Language Processing. In the success of the pre-trained BERT model, NLU is addressed by Intent Classification and Slot Filling task with significant improvement performance. However, classed imbalance problem in NLU has not been carefully investigated, while this problem in Semantic Parsing datasets is frequent. Therefore, this work focuses on diminishing this problem. We proposed a BERT-based architecture named JointBERT Classify Anonymous Entity (JointBERT-CAE) that improves the performance of the system on three Semantic Parsing datasets ATIS, Snips, ATIS Vietnamese, and a well-known Named Entity Recognize (NER) dataset CoNLL2003. In JointBERT-CAE architecture, we use multitask joint-learning to split conventional Slot Filling task into two sub-task, detect Anonymous Entity by Sequence tagging and Classify recognized anonymous entities tasks. The experimental results show the solid improvement of JointBERT-CAE when compared with BERT on all datasets, as well as the wide applicable capacity to other NLP tasks using the Sequence Tagging technique.
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
Authors: Yiyuan Yang, Chaoli Zhang, Tian Zhou, Qingsong Wen, Liang Sun
Date: 2023-06-17 00:00:00
Tasks: Anomaly Detection, Contrastive Learning, Representation Learning, Time Series, Time Series Anomaly Detection

Time series anomaly detection is critical for a wide range of applications. It aims to identify deviant samples from the normal sample distribution in time series. The most fundamental challenge for this task is to learn a representation map that enables effective discrimination of anomalies. Reconstruction-based methods still dominate, but the representation learning with anomalies might hurt the performance with its large abnormal loss. On the other hand, contrastive learning aims to find a representation that can clearly distinguish any instance from the others, which can bring a more natural and promising representation for time series anomaly detection. In this paper, we propose DCdetector, a multi-scale dual attention contrastive representation learning model. DCdetector utilizes a novel dual attention asymmetric design to create the permutated environment and pure contrastive loss to guide the learning process, thus learning a permutation invariant representation with superior discrimination abilities. Extensive experiments show that DCdetector achieves state-of-the-art results on multiple time series anomaly detection benchmark datasets. Code is publicly available at https://github.com/DAMO-DI-ML/KDD2023-DCdetector.
Understanding Revision Behavior in Adaptive Writing Support Systems for Education
Authors: Luca Mouchel, Thiemo Wambsganss, Paola Mejia-Domenzain, Tanja Käser
Date: 2023-06-17 00:00:00

Revision behavior in adaptive writing support systems is an important and relatively new area of research that can improve the design and effectiveness of these tools, and promote students' self-regulated learning (SRL). Understanding how these tools are used is key to improving them to better support learners in their writing and learning processes. In this paper, we present a novel pipeline with insights into the revision behavior of students at scale. We leverage a data set of two groups using an adaptive writing support tool in an educational setting. With our novel pipeline, we show that the tool was effective in promoting revision among the learners. Depending on the writing feedback, we were able to analyze different strategies of learners when revising their texts, we found that users of the exemplary case improved over time and that females tend to be more efficient. Our research contributes a pipeline for measuring SRL behaviors at scale in writing tasks (i.e., engagement or revision behavior) and informs the design of future adaptive writing support systems for education, with the goal of enhancing their effectiveness in supporting student writing. The source code is available at https://github.com/lucamouchel/Understanding-Revision-Behavior.
Structured Thoughts Automaton: First Formalized Execution Model for Auto-Regressive Language Models
Authors: Tristan Vanderbruggen, Chunhua Liao, Peter Pirkelbauer, Pei-Hung Lin
Date: 2023-06-16 00:00:00

In recent months, Language Models (LMs) have become a part of daily discourse, with focus on OpenAI and the potential of Artificial General Intelligence (AGI). Furthermore, the leaking of LLama's weights to the public has led to an influx of innovations demonstrating the impressive capabilities of generative LMs. While we believe that AGI is still a distant goal, we recognize the potential of LMs in solving tasks such as searching complex documents, compiling reports with basic analysis, and providing assistance in problem-solving. In this paper, we propose formalizing the execution model of language models. We investigate current execution models, to find that this formalism has received little attention, and present our contribution: the first formalized execution model for LMs. We introduce a new algorithm for sampling the predictions of LMs, which we use to build a reliable and inspectable execution model. We introduce a low-level language to write "cognitive program" for this execution model. We hope to shed light on the need for execution models for LMs and encourage further research in this area.