OpenRaiser/Intern-Atlas huggingface dataset:
WebtoonMe: A Data-Centric Approach for Full-Body Portrait Stylization
Venue: arXiv cs.CV 2022
Year: 2022

Full-body portrait stylization, which aims to translate portrait photography into a cartoon style, has drawn attention recently. However, most methods have focused only on converting face regions, restraining the feasibility of use in real-world applications. A recently proposed two-stage method expands the rendering area to full bodies, but the outputs are less plausible and fail to achieve quality robustness of non-face regions. Furthermore, they cannot reflect diverse skin tones. In this study, we propose a data-centric solution to build a production-level full-body portrait stylization system. Based on the two-stage scheme, we construct a novel and advanced dataset preparation paradigm that can effectively resolve the aforementioned problems. Experiments reveal that with our pipeline, high-quality portrait stylization can be achieved without additional losses or architectural changes.
Comparison of Varied 2D Mapping Approaches by Using Practice-Oriented Evaluation Criteria
Venue: arXiv cs.RO 2022
Year: 2022

A key aspect of the precision of a mobile robots localization is the quality and aptness of the map it is using. A variety of mapping approaches are available that can be employed to create such maps with varying degrees of effort, hardware requirements and quality of the resulting maps. To create a better understanding of the applicability of these different approaches to specific applications, this paper evaluates and compares three different mapping approaches based on simultaneous localization and mapping, terrestrial laser scanning as well as publicly accessible building contours.
Using deep convolutional neural networks to classify poisonous and edible mushrooms found in China
Venue: arXiv cs.CV 2022
Year: 2022

Because of their abundance of amino acids, polysaccharides, and many other nutrients that benefit human beings, mushrooms are deservedly popular as dietary cuisine both worldwide and in China. However, if people eat poisonous fungi by mistake, they may suffer from nausea, vomiting, mental disorder, acute anemia, or even death. Each year in China, there are around 8000 people became sick, and 70 died as a result of eating toxic mushrooms by mistake. It is counted that there are thousands of kinds of mushrooms among which only around 900 types are edible, thus without specialized knowledge, the probability of eating toxic mushrooms by mistake is very high. Most people deem that the only characteristic of poisonous mushrooms is a bright colour, however, some kinds of them do not correspond to this trait. In order to prevent people from eating these poisonous mushrooms, we propose to use deep learning methods to indicate whether a mushroom is toxic through analyzing hundreds of edible and toxic mushrooms smartphone pictures. We crowdsource a mushroom image dataset that contains 250 images of poisonous mushrooms and 200 images of edible mushrooms. The Convolutional Neural Network (CNN) is a specialized type of artificial neural networks that use a mathematical operation called convolution in place of general matrix multiplication in at least one of their layers, which can generate a relatively precise result by analyzing a huge amount of images, and thus is very suitable for our research. The experimental results demonstrate that the proposed model has high credibility and can provide a decision-making basis for the selection of edible fungi, so as to reduce the morbidity and mortality caused by eating poisonous mushrooms. We also open source our hand collected mushroom image dataset so that peer researchers can also deploy their own model to advance poisonous mushroom identification.
Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Face Recognition (FR) is increasingly used in critical verification decisions and thus, there is a need for assessing the trustworthiness of such decisions. The confidence of a decision is often based on the overall performance of the model or on the image quality. We propose to propagate model uncertainties to scores and decisions in an effort to increase the transparency of verification decisions. This work presents two contributions. First, we propose an approach to estimate the uncertainty of face comparison scores. Second, we introduce a confidence measure of the system's decision to provide insights into the verification decision. The suitability of the comparison scores uncertainties and the verification decision confidences have been experimentally proven on three face recognition models on two datasets.
Adaptive Neural Network Ensemble Using Frequency Distribution
Venue: arXiv cs.LG 2022
Year: 2022

Neural network (NN) ensembles can reduce large prediction variance of NN and improve prediction accuracy. For highly nonlinear problems with insufficient data set, the prediction accuracy of NN models becomes unstable, resulting in a decrease in the accuracy of ensembles. Therefore, this study proposes a frequency distribution-based ensemble that identifies core prediction values, which are expected to be concentrated near the true prediction value. The frequency distribution-based ensemble classifies core prediction values supported by multiple prediction values by conducting statistical analysis with a frequency distribution, which is based on various prediction values obtained from a given prediction point. The frequency distribution-based ensemble can improve predictive performance by excluding prediction values with low accuracy and coping with the uncertainty of the most frequent value. An adaptive sampling strategy that sequentially adds samples based on the core prediction variance calculated as the variance of the core prediction values is proposed to improve the predictive performance of the frequency distribution-based ensemble efficiently. Results of various case studies show that the prediction accuracy of the frequency distribution-based ensemble is higher than that of Kriging and other existing ensemble methods. In addition, the proposed adaptive sampling strategy effectively improves the predictive performance of the frequency distribution-based ensemble compared with the previously developed space-filling and prediction variance-based strategies.
A sensor-to-pattern calibration framework for multi-modal industrial collaborative cells
Venue: arXiv cs.RO 2022
Year: 2022

Collaborative robotic industrial cells are workspaces where robots collaborate with human operators. In this context, safety is paramount, and for that a complete perception of the space where the collaborative robot is inserted is necessary. To ensure this, collaborative cells are equipped with a large set of sensors of multiple modalities, covering the entire work volume. However, the fusion of information from all these sensors requires an accurate extrinsic calibration. The calibration of such complex systems is challenging, due to the number of sensors and modalities, and also due to the small overlapping fields of view between the sensors, which are positioned to capture different viewpoints of the cell. This paper proposes a sensor to pattern methodology that can calibrate a complex system such as a collaborative cell in a single optimization procedure. Our methodology can tackle RGB and Depth cameras, as well as LiDARs. Results show that our methodology is able to accurately calibrate a collaborative cell containing three RGB cameras, a depth camera and three 3D LiDARs.
Virtual learning environment is pleasure or pressure
Venue: arXiv cs.CY 2022
Year: 2022

The primary aim of this study intends the perception of students towards online learning in the covid-19 pandemic period. The pandemic has changed the traditional concepts of the education system and broken the functions of the educational institutions. But, they give it an opportunity to change pedagogy. The research paper discussed the students opinions on online learning and virtual classroom learning. This study applied a qualitative approach and prepared a systematic questionnaire for data collection. The researcher collected the data from 258 students from different places in India and also, the disproportionate sampling used for data collection. The research mainly focused on the students perception, the comfort and discomfort of e-learning, using electronic devices for communication, the virtual learning is a pleasure or pressure to the students, the digital skills of the students and their active performance. The study revealed that over 50 percent of the students are having excellent knowledge of digital skills. The students are attending online classes through their personal computers or laptops and phones. The teachers are allowing the students to ask questions and clear the doubt of the students. The study found that the students are losing social interaction with teachers, friends and cannot access the library because of online classes. Finally, the students felt that online learning is a pressure instead of pleasure.
Segmentation-free Direct Iris Localization Networks
Venue: arXiv cs.CV 2022
Year: 2022

This paper proposes an efficient iris localization method without using iris segmentation and circle fitting. Conventional iris localization methods first extract iris regions by using semantic segmentation methods such as U-Net. Afterward, the inner and outer iris circles are localized using the traditional circle fitting algorithm. However, this approach requires high-resolution encoder-decoder networks for iris segmentation, so it causes computational costs to be high. In addition, traditional circle fitting tends to be sensitive to noise in input images and fitting parameters, causing the iris recognition performance to be poor. To solve these problems, we propose an iris localization network (ILN), that can directly localize pupil and iris circles with eyelid points from a low-resolution iris image. We also introduce a pupil refinement network (PRN) to improve the accuracy of pupil localization. Experimental results show that the combination of ILN and PRN works in 34.5 ms for one iris image on a CPU, and its localization performance outperforms conventional iris segmentation methods. In addition, generalized evaluation results show that the proposed method has higher robustness for datasets in different domain than other segmentation methods. Furthermore, we also confirm that the proposed ILN and PRN improve the iris recognition accuracy.
Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction
Venue: arXiv cs.CL 2022
Year: 2022

Chinese Grammatical Error Correction (CGEC) is both a challenging NLP task and a common application in human daily life. Recently, many data-driven approaches are proposed for the development of CGEC research. However, there are two major limitations in the CGEC field: First, the lack of high-quality annotated training corpora prevents the performance of existing CGEC models from being significantly improved. Second, the grammatical errors in widely used test sets are not made by native Chinese speakers, resulting in a significant gap between the CGEC models and the real application. In this paper, we propose a linguistic rules-based approach to construct large-scale CGEC training corpora with automatically generated grammatical errors. Additionally, we present a challenging CGEC benchmark derived entirely from errors made by native Chinese speakers in real-world scenarios. Extensive experiments and detailed analyses not only demonstrate that the training data constructed by our method effectively improves the performance of CGEC models, but also reflect that our benchmark is an excellent resource for further development of the CGEC field.
Miners in the Cloud: Measuring and Analyzing Cryptocurrency Mining in Public Clouds
Venue: arXiv cs.CR 2022
Year: 2022

Cryptocurrencies, arguably the most prominent application of blockchains, have been on the rise with a wide mainstream acceptance. A central concept in cryptocurrencies is "mining pools", groups of cooperating cryptocurrency miners who agree to share block rewards in proportion to their contributed mining power. Despite many promised benefits of cryptocurrencies, they are equally utilized for malicious activities; e.g., ransomware payments, stealthy command, control, etc. Thus, understanding the interplay between cryptocurrencies, particularly the mining pools, and other essential infrastructure for profiling and modeling is important. In this paper, we study the interplay between mining pools and public clouds by analyzing their communication association through passive domain name system (pDNS) traces. We observe that 24 cloud providers have some association with mining pools as observed from the pDNS query traces, where popular public cloud providers, namely Amazon and Google, have almost 48% of such an association. Moreover, we found that the cloud provider presence and cloud provider-to-mining pool association both exhibit a heavy-tailed distribution, emphasizing an intrinsic preferential attachment model with both mining pools and cloud providers. We measure the security risk and exposure of the cloud providers, as that might aid in understanding the intent of the mining: among the top two cloud providers, we found almost 35% and 30% of their associated endpoints are positively detected to be associated with malicious activities, per the virustotal.com scan. Finally, we found that the mining pools presented in our dataset are predominantly used for mining Metaverse currencies, highlighting a shift in cryptocurrency use, and demonstrating the prevalence of mining using public clouds.
A Segment-Wise Gaussian Process-Based Ground Segmentation With Local Smoothness Estimation
Venue: arXiv cs.LG 2022
Year: 2022

Both in terrestrial and extraterrestrial environments, the precise and informative model of the ground and the surface ahead is crucial for navigation and obstacle avoidance. The ground surface is not always flat and it may be sloped, bumpy and rough specially in off-road terrestrial scenes. In bumpy and rough scenes the functional relationship of the surface-related features may vary in different areas of the ground, as the structure of the ground surface may vary suddenly and further the measured point cloud of the ground does not bear smoothness. Thus, the ground-related features must be obtained based on local estimates or even point estimates. To tackle this problem, the segment-wise GP-based ground segmentation method with local smoothness estimation is proposed. This method is an extension to our previous method in which a realistic measurement of the length-scale values were provided for the covariance kernel in each line-segment to give precise estimation of the ground for sloped terrains. In this extension, the value of the length-scale is estimated locally for each data point which makes it much more precise for the rough scenes while being not computationally complex and more robust to under-segmentation, sparsity and under-represent-ability. The segment-wise task is performed to estimate a partial continuous model of the ground for each radial range segment. Simulation results show the effectiveness of the proposed method to give a continuous and precise estimation of the ground surface in rough and bumpy scenes while being fast enough for real-world applications.
Hope of Delivery: Extracting User Locations From Mobile Instant Messengers
Venue: arXiv cs.CR 2022
Year: 2022

Mobile instant messengers such as WhatsApp use delivery status notifications in order to inform users if a sent message has successfully reached its destination. This is useful and important information for the sender due to the often asynchronous use of the messenger service. However, as we demonstrate in this paper, this standard feature opens up a timing side channel with unexpected consequences for user location privacy. We investigate this threat conceptually and experimentally for three widely spread instant messengers. We validate that this information leak even exists in privacy-friendly messengers such as Signal and Threema. Our results show that, after a training phase, a messenger user can distinguish different locations of the message receiver. Our analyses involving multiple rounds of measurements and evaluations show that the timing side channel persists independent of distances between receiver locations -- the attack works both for receivers in different countries as well as at small scale in one city. For instance, out of three locations within the same city, the sender can determine the correct one with more than 80% accuracy. Thus, messenger users can secretly spy on each others' whereabouts when sending instant messages. As our countermeasure evaluation shows, messenger providers could effectively disable the timing side channel by randomly delaying delivery confirmations within the range of a few seconds. For users themselves, the threat is harder to prevent since there is no option to turn off delivery confirmations.
NET-TEN: a silicon neuromorphic network for low-latency detection of seizures in local field potentials
Venue: arXiv cs.HC 2022
Year: 2022

Therapeutic intervention in neurological disorders still relies heavily on pharmacological solutions, while the treatment of patients with drug resistance remains an open challenge. This is particularly true for patients with epilepsy, 30% of whom are refractory to medications. Implantable devices for chronic recording and electrical modulation of brain activity have proved a viable alternative in such cases. To operate, the device should detect the relevant electrographic biomarkers from Local Field Potentials (LFPs) and determine the right time for stimulation. To enable timely interventions, the ideal device should attain biomarker detection with low latency while operating under low power consumption to prolong the battery life. Neuromorphic networks have progressively gained reputation as low-latency low-power computing systems, which makes them a promising candidate as processing core of next-generation implantable neural interfaces. Here we introduce a fully-analog neuromorphic device implemented in CMOS technology for analyzing LFP signals in an in vitro model of acute ictogenesis. We show that the system can detect ictal and interictal events with ms-latency and with high precision, consuming on average 3.50 nW during the task. Our work paves the way to a new generation of brain implantable devices for personalized closed-loop stimulation for epilepsy treatment.
What matters in the new field of machine learning and satellite imagery-based poverty predictions? A review with relevance for potential downstream applications and development research
Venue: arXiv cs.CY 2022
Year: 2022

This paper reviews the state of the art in satellite and machine learning based poverty estimates and finds some interesting results. The most important factors correlated to the predictive power of welfare in the reviewed studies are the number of pre-processing steps employed, the number of datasets used, the type of welfare indicator targeted, and the choice of AI model. As expected, studies that used hard indicators as targets achieved better performance in predicting welfare than those that targeted soft ones. Also expected was the number of pre-processing steps and datasets used having a positive and statistically significant relationship with welfare estimation performance. Even more important, we find that the combination of ML and DL significantly increases predictive power by as much as 15 percentage points compared to using either alone. Surprisingly, we find that the spatial resolution of the satellite imagery used is important but not critical to the performance as the relationship is positive but not statistically significant. The finding of no evidence indicating that predictive performance of a statistically significant effect occurs over time was also unexpected. These findings have important implications for future research in this domain. For example, the level of effort and resources devoted to acquiring more expensive, higher resolution SI will have to be reconsidered given that medium resolutions ones seem to achieve similar results. The increasingly popular approach of combining ML, DL, and TL, either in a concurrent or iterative manner, might become a standard approach to achieving better results.
Enhanced vectors for top-k document retrieval in Question Answering
Venue: arXiv cs.IR 2022
Year: 2022

Modern day applications, especially information retrieval webapps that involve "search" as their use cases are gradually moving towards "answering" modules. Conversational chatbots which have been proved to be more engaging to users, use Question Answering as their core. Since, precise answering is computationally expensive, several approaches have been developed to prefetch the most relevant documents/passages from the database that contain the answer. We propose a different approach that retrieves the evidence documents efficiently and accurately, making sure that the relevant document for a given user query is not missed. We do so by assigning each document (or passage in our case), a unique identifier and using them to create dense vectors which can be efficiently indexed. More precisely, we use the identifier to predict randomly sampled context window words of the relevant question corresponding to the passage along with the words of passage itself. This naturally embeds the passage identifier into the vector space in such a way that the embedding is closer to the question without compromising he information content. This approach enables efficient creation of real-time query vectors in ~4 milliseconds.
DIAMBRA Arena: a New Reinforcement Learning Platform for Research and Experimentation
Venue: arXiv cs.LG 2022
Year: 2022

The recent advances in reinforcement learning have led to effective methods able to obtain above human-level performances in very complex environments. However, once solved, these environments become less valuable, and new challenges with different or more complex scenarios are needed to support research advances. This work presents DIAMBRA Arena, a new platform for reinforcement learning research and experimentation, featuring a collection of high-quality environments exposing a Python API fully compliant with OpenAI Gym standard. They are episodic tasks with discrete actions and observations composed by raw pixels plus additional numerical values, all supporting both single player and two players mode, allowing to work on standard reinforcement learning, competitive multi-agent, human-agent competition, self-play, human-in-the-loop training and imitation learning. Software capabilities are demonstrated by successfully training multiple deep reinforcement learning agents with proximal policy optimization obtaining human-like behavior. Results confirm the utility of DIAMBRA Arena as a reinforcement learning research tool, providing environments designed to study some of the most challenging topics in the field.
CLEAR: Causal Explanations from Attention in Neural Recommenders
Venue: arXiv cs.IR 2022
Year: 2022

We present CLEAR, a method for learning session-specific causal graphs, in the possible presence of latent confounders, from attention in pre-trained attention-based recommenders. These causal graphs describe user behavior, within the context captured by attention, and can provide a counterfactual explanation for a recommendation. In essence, these causal graphs allow answering "why" questions uniquely for any specific session. Using empirical evaluations we show that, compared to naively using attention weights to explain input-output relations, counterfactual explanations found by CLEAR are shorter and an alternative recommendation is ranked higher in the original top-k recommendations.