pwc-archive/papers-with-abstracts huggingface dataset:
Optical Script Identification for multi-lingual Indic-script
Authors: Sidhantha Poddar, Rohan Gupta
Date: 2023-08-10 00:00:00
Tasks: Survey

Script identification and text recognition are some of the major domains in the application of Artificial Intelligence. In this era of digitalization, the use of digital note-taking has become a common practice. Still, conventional methods of using pen and paper is a prominent way of writing. This leads to the classification of scripts based on the method they are obtained. A survey on the current methodologies and state-of-art methods used for processing and identification would prove beneficial for researchers. The aim of this article is to discuss the advancement in the techniques for script pre-processing and text recognition. In India there are twelve prominent Indic scripts, unlike the English language, these scripts have layers of characteristics. Complex characteristics such as similarity in text shape make them difficult to recognize and analyze, thus this requires advance preprocessing methods for their accurate recognition. A sincere attempt is made in this survey to provide a comparison between all algorithms. We hope that this survey would provide insight to a researcher working not only on Indic scripts but also other languages.
Deep learning for spike detection in deep brain stimulation surgery
Authors: Arkadiusz Nowacki, Ewelina Kołpa, Mateusz Szychiewicz, Konrad Ciecierski
Date: 2023-08-04 00:00:00
Tasks: Deep Learning

Deep brain stimulation (DBS) is a neurosurgical procedure successfully used to treat conditions such as Parkinson's disease. Electrostimulation, carried out by implanting electrodes into an identified focus in the brain, makes it possible to reduce the symptoms of the disease significantly. In this paper, a method for analyzing recordings of neuronal activity acquired during DBS neurosurgery using deep learning is presented. We tested using a convolutional neural network (CNN) for this purpose. Based on the time window, the classifier assesses whether neuronal activity (spike) is present. The maximum accuracy value for the classifier was 98.98%, and the area under the receiver operating characteristic curve (AUC) was 0.9898. The method made it possible to obtain a classification without using data preprocessing.
Classification Method of Road Surface Condition and Type with LiDAR Using Spatiotemporal Information
Authors: Ju Won Seo, Jin Sung Kim, Chung Choo Chung
Date: 2023-08-11 00:00:00

This paper proposes a spatiotemporal architecture with a deep neural network (DNN) for road surface conditions and types classification using LiDAR. It is known that LiDAR provides information on the reflectivity and number of point clouds depending on a road surface. Thus, this paper utilizes the information to classify the road surface. We divided the front road area into four subregions. First, we constructed feature vectors using each subregion's reflectivity, number of point clouds, and in-vehicle information. Second, the DNN classifies road surface conditions and types for each subregion. Finally, the output of the DNN feeds into the spatiotemporal process to make the final classification reflecting vehicle speed and probability given by the outcomes of softmax functions of the DNN output layer. To validate the effectiveness of the proposed method, we performed a comparative study with five other algorithms. With the proposed DNN, we obtained the highest accuracy of 98.0\% and 98.6\% for two subregions near the vehicle. In addition, we implemented the proposed method on the Jetson TX2 board to confirm that it is applicable in real-time.
Exploring Predicate Visual Context in Detecting Human-Object Interactions
Authors: Frederic Z. Zhang, Yuhui Yuan, Dylan Campbell, Zhuoyao Zhong, Stephen Gould
Date: 2023-08-11 00:00:00
Proceeding: ICCV 2023 1
Tasks: Human-Object Interaction Detection, Object

Recently, the DETR framework has emerged as the dominant approach for human--object interaction (HOI) research. In particular, two-stage transformer-based HOI detectors are amongst the most performant and training-efficient approaches. However, these often condition HOI classification on object features that lack fine-grained contextual information, eschewing pose and orientation information in favour of visual cues about object identity and box extremities. This naturally hinders the recognition of complex or ambiguous interactions. In this work, we study these issues through visualisations and carefully designed experiments. Accordingly, we investigate how best to re-introduce image features via cross-attention. With an improved query design, extensive exploration of keys and values, and box pair positional embeddings as spatial guidance, our model with enhanced predicate visual context (PViC) outperforms state-of-the-art methods on the HICO-DET and V-COCO benchmarks, while maintaining low training cost.
SVoice: Enabling Voice Communication in Silence via Acoustic Sensing on Commodity Devices
Authors: Yongjian Fu, Shuning Wang, Linghui Zhong, Lili Chen, Ju Ren, Yaoxue Zhang
Date: 2022-11-09 00:00:00
Proceeding: SenSys 2022 11

Silent Speech Interface (SSI) has been proposed as a means of reconstructing audible speech from silent articulatory gestures for covert
voice communication in public and voice assistance for the aphasic.
Prior arts of SSI, either relying on wearable devices or cameras, may
lead to extended contact requirements or privacy leakage risks. The
recent advances in acoustic sensing have brought new opportunities
for sensing gestures, but their original intention is to infer speech
content for classification instead of audible speech reconstruction,
resulting in the loss of some important speech information (e.g.,
speech rate, intonation, and emotion). In this paper, we propose , the
first system that supports accurate audible speech reconstruction
by analyzing the disturbance of tiny articulatory gestures on the
reflected ultrasound signal. The design of introduces a new model
that provides the unique mapping relationship between ultrasound
and speech signals, so that the audible speech can be successfully
reconstructed from the silent speech. However, establishing the
mapping relationship depends on plenty of training data. Instead of
the time-consuming collection of massive amounts of data for training, we construct an inverse task that constitutes a dual form with
the original task to generate virtual gestures from widely available
audio (e.g., phone calls) for facilitating model training. Furthermore,
we introduce a fine-tuning mechanism using unlabeled data for user
adaptation. We implement using a portable smartphone and evaluate it in various environments. The evaluation results show that
can reconstruct speech with a (Character Error Rate) CER as low
as 7.62%, and decrease the CER from 82.77% to 9.42% on new users
with only 1 hour of ultrasound signals provided, which outperforms
state-of-the-art acoustic-based approaches while preserving rich
speech information.
Enhancing Network Management Using Code Generated by Large Language Models
Authors: Sathiya Kumaran Mani, Yajie Zhou, Kevin Hsieh, Santiago Segarra, Ranveer Chandra, Srikanth Kandula
Date: 2023-08-11 00:00:00
Tasks: Management, Natural Language Queries, Program Synthesis

Analyzing network topologies and communication graphs plays a crucial role in contemporary network management. However, the absence of a cohesive approach leads to a challenging learning curve, heightened errors, and inefficiencies. In this paper, we introduce a novel approach to facilitate a natural-language-based network management experience, utilizing large language models (LLMs) to generate task-specific code from natural language queries. This method tackles the challenges of explainability, scalability, and privacy by allowing network operators to inspect the generated code, eliminating the need to share network data with LLMs, and concentrating on application-specific requests combined with general program synthesis techniques. We design and evaluate a prototype system using benchmark applications, showcasing high accuracy, cost-effectiveness, and the potential for further enhancements using complementary program synthesis techniques.
ChatGPT-based Investment Portfolio Selection
Authors: Oleksandr Romanko, Akhilesh Narayan, Roy H. Kwon
Date: 2023-08-11 00:00:00
Tasks: Decision Making, Portfolio Optimization

In this paper, we explore potential uses of generative AI models, such as ChatGPT, for investment portfolio selection. Trusting investment advice from Generative Pre-Trained Transformer (GPT) models is a challenge due to model "hallucinations", necessitating careful verification and validation of the output. Therefore, we take an alternative approach. We use ChatGPT to obtain a universe of stocks from S&P500 market index that are potentially attractive for investing. Subsequently, we compared various portfolio optimization strategies that utilized this AI-generated trading universe, evaluating those against quantitative portfolio optimization models as well as comparing to some of the popular investment funds. Our findings indicate that ChatGPT is effective in stock selection but may not perform as well in assigning optimal weights to stocks within the portfolio. But when stocks selection by ChatGPT is combined with established portfolio optimization models, we achieve even better results. By blending strengths of AI-generated stock selection with advanced quantitative optimization techniques, we observed the potential for more robust and favorable investment outcomes, suggesting a hybrid approach for more effective and reliable investment decision-making in the future.
Automated Sizing and Training of Efficient Deep Autoencoders using Second Order Algorithms
Authors: Kanishka Tyagi, Chinmay Rane, Michael Manry
Date: 2023-08-11 00:00:00
Tasks: Philosophy

We propose a multi-step training method for designing generalized linear classifiers. First, an initial multi-class linear classifier is found through regression. Then validation error is minimized by pruning of unnecessary inputs. Simultaneously, desired outputs are improved via a method similar to the Ho-Kashyap rule. Next, the output discriminants are scaled to be net functions of sigmoidal output units in a generalized linear classifier. We then develop a family of batch training algorithm for the multi layer perceptron that optimizes its hidden layer size and number of training epochs. Next, we combine pruning with a growing approach. Later, the input units are scaled to be the net function of the sigmoidal output units that are then feed into as input to the MLP. We then propose resulting improvements in each of the deep learning blocks thereby improving the overall performance of the deep architecture. We discuss the principles and formulation regarding learning algorithms for deep autoencoders. We investigate several problems in deep autoencoders networks including training issues, the theoretical, mathematical and experimental justification that the networks are linear, optimizing the number of hidden units in each layer and determining the depth of the deep learning model. A direct implication of the current work is the ability to construct fast deep learning models using desktop level computational resources. This, in our opinion, promotes our design philosophy of building small but powerful algorithms. Performance gains are demonstrated at each step. Using widely available datasets, the final network's ten fold testing error is shown to be less than that of several other linear, generalized linear classifiers, multi layer perceptron and deep learners reported in the literature.
Safety in Traffic Management Systems: A Comprehensive Survey
Authors: Wenlu Du, Ankan Dash, Jing Li, Hua Wei, Guiling Wang
Date: 2023-08-11 00:00:00
Tasks: Management, Survey

Traffic management systems play a vital role in ensuring safe and efficient transportation on roads. However, the use of advanced technologies in traffic management systems has introduced new safety challenges. Therefore, it is important to ensure the safety of these systems to prevent accidents and minimize their impact on road users. In this survey, we provide a comprehensive review of the literature on safety in traffic management systems. Specifically, we discuss the different safety issues that arise in traffic management systems, the current state of research on safety in these systems, and the techniques and methods proposed to ensure the safety of these systems. We also identify the limitations of the existing research and suggest future research directions.
Towards a Causal Probabilistic Framework for Prediction, Action-Selection & Explanations for Robot Block-Stacking Tasks
Authors: Ricardo Cannizzaro, Jonathan Routley, Lars Kunze
Date: 2023-08-11 00:00:00
Tasks: Causal Inference, counterfactual

Uncertainties in the real world mean that is impossible for system designers to anticipate and explicitly design for all scenarios that a robot might encounter. Thus, robots designed like this are fragile and fail outside of highly-controlled environments. Causal models provide a principled framework to encode formal knowledge of the causal relationships that govern the robot's interaction with its environment, in addition to probabilistic representations of noise and uncertainty typically encountered by real-world robots. Combined with causal inference, these models permit an autonomous agent to understand, reason about, and explain its environment. In this work, we focus on the problem of a robot block-stacking task due to the fundamental perception and manipulation capabilities it demonstrates, required by many applications including warehouse logistics and domestic human support robotics. We propose a novel causal probabilistic framework to embed a physics simulation capability into a structural causal model to permit robots to perceive and assess the current state of a block-stacking task, reason about the next-best action from placement candidates, and generate post-hoc counterfactual explanations. We provide exemplar next-best action selection results and outline planned experimentation in simulated and real-world robot block-stacking tasks.
Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic Features
Authors: Angus Maiden, Bahareh Nakisa
Date: 2023-08-11 00:00:00
Tasks: Continual Learning, Emotion Recognition, Facial Expression Recognition, Few-Shot Learning, Knowledge Distillation

Complex emotion recognition is a cognitive task that has so far eluded the same excellent performance of other tasks that are at or above the level of human cognition. Emotion recognition through facial expressions is particularly difficult due to the complexity of emotions expressed by the human face. For a machine to approach the same level of performance in complex facial expression recognition as a human, it may need to synthesise knowledge and understand new concepts in real-time, as humans do. Humans are able to learn new concepts using only few examples by distilling important information from memories. Inspired by human cognition and learning, we propose a novel continual learning method for complex facial expression recognition that can accurately recognise new compound expression classes using few training samples, by building on and retaining its knowledge of basic expression classes. In this work, we also use GradCAM visualisations to demonstrate the relationship between basic and compound facial expressions. Our method leverages this relationship through knowledge distillation and a novel Predictive Sorting Memory Replay, to achieve the current state-of-the-art in continual learning for complex facial expression recognition, with 74.28% Overall Accuracy on new classes. We also demonstrate that using continual learning for complex facial expression recognition achieves far better performance than non-continual learning methods, improving on state-of-the-art non-continual learning methods by 13.95%. Our work is also the first to apply few-shot learning to complex facial expression recognition, achieving the state-of-the-art with 100% accuracy using only a single training sample per class.
Software Doping Analysis for Human Oversight
Authors: Sebastian Biewer, Kevin Baum, Sarah Sterz, Holger Hermanns, Sven Hetmank, Markus Langer, Anne Lauber-Rönsberg, Franz Lehr
Date: 2023-08-11 00:00:00
Tasks: Decision Making

This article introduces a framework that is meant to assist in mitigating societal risks that software can pose. Concretely, this encompasses facets of software doping as well as unfairness and discrimination in high-risk decision-making systems. The term software doping refers to software that contains surreptitiously added functionality that is against the interest of the user. A prominent example of software doping are the tampered emission cleaning systems that were found in millions of cars around the world when the diesel emissions scandal surfaced. The first part of this article combines the formal foundations of software doping analysis with established probabilistic falsification techniques to arrive at a black-box analysis technique for identifying undesired effects of software. We apply this technique to emission cleaning systems in diesel cars but also to high-risk systems that evaluate humans in a possibly unfair or discriminating way. We demonstrate how our approach can assist humans-in-the-loop to make better informed and more responsible decisions. This is to promote effective human oversight, which will be a central requirement enforced by the European Union's upcoming AI Act. We complement our technical contribution with a juridically, philosophically, and psychologically informed perspective on the potential problems caused by such systems.
Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook
Authors: Amira Guesmi, Muhammad Abdullah Hanif, Bassem Ouni, Muhammed Shafique
Date: 2023-08-11 00:00:00
Tasks: Adversarial Attack, Depth Estimation, Face Recognition, Semantic Segmentation, Survey

In this paper, we present a comprehensive survey of the current trends focusing specifically on physical adversarial attacks. We aim to provide a thorough understanding of the concept of physical adversarial attacks, analyzing their key characteristics and distinguishing features. Furthermore, we explore the specific requirements and challenges associated with executing attacks in the physical world. Our article delves into various physical adversarial attack methods, categorized according to their target tasks in different applications, including classification, detection, face recognition, semantic segmentation and depth estimation. We assess the performance of these attack methods in terms of their effectiveness, stealthiness, and robustness. We examine how each technique strives to ensure the successful manipulation of DNNs while mitigating the risk of detection and withstanding real-world distortions. Lastly, we discuss the current challenges and outline potential future research directions in the field of physical adversarial attacks. We highlight the need for enhanced defense mechanisms, the exploration of novel attack strategies, the evaluation of attacks in different application domains, and the establishment of standardized benchmarks and evaluation criteria for physical adversarial attacks. Through this comprehensive survey, we aim to provide a valuable resource for researchers, practitioners, and policymakers to gain a holistic understanding of physical adversarial attacks in computer vision and facilitate the development of robust and secure DNN-based systems.
Phased Deep Spatio-temporal Learning for Highway Traffic Volume Prediction
Authors: Weilong Ding, Tianpu Zhang, Zhe Wang
Date: 2023-08-11 00:00:00

Inter-city highway transportation is significant for citizens' modern urban life and generates heterogeneous sensory data with spatio-temporal characteristics. As a routine analysis in transportation domain, daily traffic volume estimation faces challenges for highway toll stations including lacking of exploration of correlative spatio-temporal features from a long-term perspective and effective means to deal with data imbalance which always deteriorates the predictive performance. In this paper, a deep spatio-temporal learning method is proposed to predict daily traffic volume in three phases. In feature pre-processing phase, data is normalized elaborately according to latent long-tail distribution. In spatio-temporal learning phase, a hybrid model is employed combining fully convolution network (FCN) and long short-term memory (LSTM), which considers time, space, meteorology, and calendar from heterogeneous data. In decision phase, traffic volumes on a coming day at network-wide toll stations would be achieved effectively, which is especially calibrated for vital few highway stations. Using real-world data from one Chinese provincial highway, extensive experiments show our method has distinct improvement for predictive accuracy than various traditional models, reaching 5.269 and 0.997 in MPAE and R-squre metrics, respectively.
Application of Artificial Neural Networks for Investigation of Pressure Filtration Performance, a Zinc Leaching Filter Cake Moisture Modeling
Authors: Masoume Kazemi, Davood Moradkhani, Alireza A. Alipour
Date: 2023-08-11 00:00:00

Machine Learning (ML) is a powerful tool for material science applications. Artificial Neural Network (ANN) is a machine learning technique that can provide high prediction accuracy. This study aimed to develop an ANN model to predict the cake moisture of the pressure filtration process of zinc production. The cake moisture was influenced by seven parameters: temperature (35 and 65 Celsius), solid concentration (0.2 and 0.38 g/L), pH (2, 3.5, and 5), air-blow time (2, 10, and 15 min), cake thickness (14, 20, 26, and 34 mm), pressure, and filtration time. The study conducted 288 tests using two types of fabrics: polypropylene (S1) and polyester (S2). The ANN model was evaluated by the Coefficient of determination (R2), the Mean Square Error (MSE), and the Mean Absolute Error (MAE) metrics for both datasets. The results showed R2 values of 0.88 and 0.83, MSE values of 6.243x10-07 and 1.086x10-06, and MAE values of 0.00056 and 0.00088 for S1 and S2, respectively. These results indicated that the ANN model could predict the cake moisture of pressure filtration in the zinc leaching process with high accuracy.
A Game-Theoretic Framework for Joint Forecasting and Planning
Authors: Kushal Kedia, Prithwish Dan, Sanjiban Choudhury
Date: 2023-08-11 00:00:00

Planning safe robot motions in the presence of humans requires reliable forecasts of future human motion. However, simply predicting the most likely motion from prior interactions does not guarantee safety. Such forecasts fail to model the long tail of possible events, which are rarely observed in limited datasets. On the other hand, planning for worst-case motions leads to overtly conservative behavior and a "frozen robot". Instead, we aim to learn forecasts that predict counterfactuals that humans guard against. We propose a novel game-theoretic framework for joint planning and forecasting with the payoff being the performance of the planner against the demonstrator, and present practical algorithms to train models in an end-to-end fashion. We demonstrate that our proposed algorithm results in safer plans in a crowd navigation simulator and real-world datasets of pedestrian motion. We release our code at https://github.com/portal-cornell/Game-Theoretic-Forecasting-Planning.