OpenRaiser/Intern-Atlas huggingface dataset:
AntFuzzer: A Grey-Box Fuzzing Framework for EOSIO Smart Contracts
Venue: arXiv cs.CR 2022
Year: 2022

In the past few years, several attacks against the vulnerabilities of EOSIO smart contracts have caused severe financial losses to this prevalent blockchain platform. As a lightweight test-generation approach, grey-box fuzzing can open up the possibility of improving the security of EOSIO smart contracts. However, developing a practical grey-box fuzzer for EOSIO smart contracts from scratch is time-consuming and requires a deep understanding of EOSIO internals. In this work, we proposed AntFuzzer, the first highly extensible grey-box fuzzing framework for EOSIO smart contracts. AntFuzzer implements a novel approach that interfaces AFL to conduct AFL-style grey-box fuzzing on EOSIO smart contracts. Compared to black-box fuzzing tools, AntFuzzer can effectively trigger those hard-to-cover branches. It achieved an improvement in code coverage on 37.5% of smart contracts in our benchmark dataset. AntFuzzer provides unified interfaces for users to easily develop new detection plugins for continually emerging vulnerabilities. We have implemented 6 detection plugins on AntFuzzer to detect major vulnerabilities of EOSIO smart contracts. In our large-scale fuzzing experiments on 4,616 real-world smart contracts, AntFuzzer successfully detected 741 vulnerabilities. The results demonstrate the effectiveness and efficiency of AntFuzzer and our detection pl
Evaluating Emerging CXL-enabled Memory Pooling for HPC Systems
Venue: arXiv cs.DC 2022
Year: 2022

Current HPC systems provide memory resources that are statically configured and tightly coupled with compute nodes. However, workloads on HPC systems are evolving. Diverse workloads lead to a need for configurable memory resources to achieve high performance and utilization. In this study, we evaluate a memory subsystem design leveraging CXL-enabled memory pooling. Two promising use cases of composable memory subsystems are studied -- fine-grained capacity provisioning and scalable bandwidth provisioning. We developed an emulator to explore the performance impact of various memory compositions. We also provide a profiler to identify the memory usage patterns in applications and their optimization opportunities. Seven scientific and six graph applications are evaluated on various emulated memory configurations. Three out of seven scientific applications had less than 10% performance impact when the pooled memory backed 75% of their memory footprint. The results also show that a dynamically configured high-bandwidth system can effectively support bandwidth-intensive unstructured mesh-based applications like OpenFOAM. Finally, we identify interference through shared memory pools as a practical challenge for adoption on HPC systems.
LightNorm: Area and Energy-Efficient Batch Normalization Hardware for On-Device DNN Training
Venue: arXiv cs.AR 2022
Year: 2022

When training early-stage deep neural networks (DNNs), generating intermediate features via convolution or linear layers occupied most of the execution time. Accordingly, extensive research has been done to reduce the computational burden of the convolution or linear layers. In recent mobile-friendly DNNs, however, the relative number of operations involved in processing these layers has significantly reduced. As a result, the proportion of the execution time of other layers, such as batch normalization layers, has increased. Thus, in this work, we conduct a detailed analysis of the batch normalization layer to efficiently reduce the runtime overhead in the batch normalization process. Backed up by the thorough analysis, we present an extremely efficient batch normalization, named LightNorm, and its associated hardware module. In more detail, we fuse three approximation techniques that are i) low bit-precision, ii) range batch normalization, and iii) block floating point. All these approximate techniques are carefully utilized not only to maintain the statistics of intermediate feature maps, but also to minimize the off-chip memory accesses. By using the proposed LightNorm hardware, we can achieve significant area and energy savings during the DNN training without hurting the training accuracy. This makes the proposed hardware a great candidate for the on-device training.
MalGrid: Visualization Of Binary Features In Large Malware Corpora
Venue: arXiv cs.CR 2022
Year: 2022

The number of malware is constantly on the rise. Though most new malware are modifications of existing ones, their sheer number is quite overwhelming. In this paper, we present a novel system to visualize and map millions of malware to points in a 2-dimensional (2D) spatial grid. This enables visualizing relationships within large malware datasets that can be used to develop triage solutions to screen different malware rapidly and provide situational awareness. Our approach links two visualizations within an interactive display. Our first view is a spatial point-based visualization of similarity among the samples based on a reduced dimensional projection of binary feature representations of malware. Our second spatial grid-based view provides a better insight into similarities and differences between selected malware samples in terms of the binary-based visual representations they share. We also provide a case study where the effect of packing on the malware data is correlated with the complexity of the packing algorithm.
Achieving mouse-level strategic evasion performance using real-time computational planning
Venue: arXiv cs.AI 2022
Year: 2022

Planning is an extraordinary ability in which the brain imagines and then enacts evaluated possible futures. Using traditional planning models, computer scientists have attempted to replicate this capacity with some level of success but ultimately face a reoccurring limitation: as the plan grows in steps, the number of different possible futures makes it intractable to determine the right sequence of actions to reach a goal state. Based on prior theoretical work on how the ecology of an animal governs the value of spatial planning, we developed a more efficient biologically-inspired planning algorithm, TLPPO. This algorithm allows us to achieve mouselevel predator evasion performance with orders of magnitude less computation than a widespread algorithm for planning in the situations of partial observability that typify predator-prey interactions. We compared the performance of a real-time agent using TLPPO against the performance of live mice, all tasked with evading a robot predator. We anticipate these results will be helpful to planning algorithm users and developers, as well as to areas of neuroscience where robot-animal interaction can provide a useful approach to studying the basis of complex behaviors.
Clustering above Exponential Families with Tempered Exponential Measures
Venue: arXiv cs.LG 2022
Year: 2022

The link with exponential families has allowed $k$-means clustering to be generalized to a wide variety of data generating distributions in exponential families and clustering distortions among Bregman divergences. Getting the framework to work above exponential families is important to lift roadblocks like the lack of robustness of some population minimizers carved in their axiomatization. Current generalisations of exponential families like $q$-exponential families or even deformed exponential families fail at achieving the goal. In this paper, we provide a new attempt at getting the complete framework, grounded in a new generalisation of exponential families that we introduce, tempered exponential measures (TEM). TEMs keep the maximum entropy axiomatization framework of $q$-exponential families, but instead of normalizing the measure, normalize a dual called a co-distribution. Numerous interesting properties arise for clustering such as improved and controllable robustness for population minimizers, that keep a simple analytic form.
Forecasting User Interests Through Topic Tag Predictions in Online Health Communities
Venue: arXiv cs.LG 2022
Year: 2022

The increasing reliance on online communities for healthcare information by patients and caregivers has led to the increase in the spread of misinformation, or subjective, anecdotal and inaccurate or non-specific recommendations, which, if acted on, could cause serious harm to the patients. Hence, there is an urgent need to connect users with accurate and tailored health information in a timely manner to prevent such harm. This paper proposes an innovative approach to suggesting reliable information to participants in online communities as they move through different stages in their disease or treatment. We hypothesize that patients with similar histories of disease progression or course of treatment would have similar information needs at comparable stages. Specifically, we pose the problem of predicting topic tags or keywords that describe the future information needs of users based on their profiles, traces of their online interactions within the community (past posts, replies) and the profiles and traces of online interactions of other users with similar profiles and similar traces of past interaction with the target users. The result is a variant of the collaborative information filtering or recommendation system tailored to the needs of users of online health communities. We report results of our experiments on an expert curated data set which demonstrate the superiority of the proposed approach over the state of the art baselines with respect to accurate and timely prediction of topic tags (and hence information sources of interest).
Evaluating Novel Mask-RCNN Architectures for Ear Mask Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

The human ear is generally universal, collectible, distinct, and permanent. Ear-based biometric recognition is a niche and recent approach that is being explored. For any ear-based biometric algorithm to perform well, ear detection and segmentation need to be accurately performed. While significant work has been done in existing literature for bounding boxes, a lack of approaches output a segmentation mask for ears. This paper trains and compares three newer models to the state-of-the-art MaskRCNN (ResNet 101 +FPN) model across four different datasets. The Average Precision (AP) scores reported show that the newer models outperform the state-of-the-art but no one model performs the best over multiple datasets.
Evaluation of Automated Speech Recognition Systems for Conversational Speech: A Linguistic Perspective
Venue: arXiv cs.CL 2022
Year: 2022

Automatic speech recognition (ASR) meets more informal and free-form input data as voice user interfaces and conversational agents such as the voice assistants such as Alexa, Google Home, etc., gain popularity. Conversational speech is both the most difficult and environmentally relevant sort of data for speech recognition. In this paper, we take a linguistic perspective, and take the French language as a case study toward disambiguation of the French homophones. Our contribution aims to provide more insight into human speech transcription accuracy in conditions to reproduce those of state-of-the-art ASR systems, although in a much focused situation. We investigate a case study involving the most common errors encountered in the automatic transcription of French language.
Learning Product Graphs from Spectral Templates
Venue: arXiv cs.LG 2022
Year: 2022

Graph Learning (GL) is at the core of inference and analysis of connections in data mining and machine learning (ML). By observing a dataset of graph signals, and considering specific assumptions, Graph Signal Processing (GSP) tools can provide practical constraints in the GL approach. One applicable constraint can infer a graph with desired frequency signatures, i.e., spectral templates. However, a severe computational burden is a challenging barrier, especially for inference from high-dimensional graph signals. To address this issue and in the case of the underlying graph having graph product structure, we propose learning product (high dimensional) graphs from product spectral templates with significantly reduced complexity rather than learning them directly from high-dimensional graph signals, which, to the best of our knowledge, has not been addressed in the related areas. In contrast to the rare current approaches, our approach can learn all types of product graphs (with more than two graphs) without knowing the type of graph products and has fewer parameters. Experimental results on both the synthetic and real-world data, i.e., brain signal analysis and multi-view object images, illustrate explainable and meaningful factor graphs supported by expert-related research, as well as outperforming the rare current restricted approaches.
Tri-Attention: Explicit Context-Aware Attention Mechanism for Natural Language Processing
Venue: arXiv cs.CL 2022
Year: 2022

In natural language processing (NLP), the context of a word or sentence plays an essential role. Contextual information such as the semantic representation of a passage or historical dialogue forms an essential part of a conversation and a precise understanding of the present phrase or sentence. However, the standard attention mechanisms typically generate weights using query and key but ignore context, forming a Bi-Attention framework, despite their great success in modeling sequence alignment. This Bi-Attention mechanism does not explicitly model the interactions between the contexts, queries and keys of target sequences, missing important contextual information and resulting in poor attention performance. Accordingly, a novel and general triple-attention (Tri-Attention) framework expands the standard Bi-Attention mechanism and explicitly interacts query, key, and context by incorporating context as the third dimension in calculating relevance scores. Four variants of Tri-Attention are generated by expanding the two-dimensional vector-based additive, dot-product, scaled dot-product, and bilinear operations in Bi-Attention to the tensor operations for Tri-Attention. Extensive experiments on three NLP tasks demonstrate that Tri-Attention outperforms about 30 state-of-the-art non-attention, standard Bi-Attention, contextual Bi-Attention approaches and pretrained neural language models1.
VISinger 2: High-Fidelity End-to-End Singing Voice Synthesis Enhanced by Digital Signal Processing Synthesizer
Venue: arXiv cs.SD 2022
Year: 2022

End-to-end singing voice synthesis (SVS) model VISinger can achieve better performance than the typical two-stage model with fewer parameters. However, VISinger has several problems: text-to-phase problem, the end-to-end model learns the meaningless mapping of text-to-phase; glitches problem, the harmonic components corresponding to the periodic signal of the voiced segment occurs a sudden change with audible artefacts; low sampling rate, the sampling rate of 24KHz does not meet the application needs of high-fidelity generation with the full-band rate (44.1KHz or higher). In this paper, we propose VISinger 2 to address these issues by integrating the digital signal processing (DSP) methods with VISinger. Specifically, inspired by recent advances in differentiable digital signal processing (DDSP), we incorporate a DSP synthesizer into the decoder to solve the above issues. The DSP synthesizer consists of a harmonic synthesizer and a noise synthesizer to generate periodic and aperiodic signals, respectively, from the latent representation z in VISinger. It supervises the posterior encoder to extract the latent representation without phase information and avoid the prior encoder modelling text-to-phase mapping. To avoid glitch artefacts, the HiFi-GAN is modified to accept the waveforms generated by the DSP synthesizer as a condition to produce the singing voice. Moreover, with the improved waveform decoder, VISinger 2 manages to generate 44.1kHz singing audio with richer expression and better quality. Experiments on OpenCpop corpus show that VISinger 2 outperforms VISinger, CpopSing and RefineSinger in both subjective and objective metrics.
Cross-Subject Emotion Recognition with Sparsely-Labeled Peripheral Physiological Data Using SHAP-Explained Tree Ensembles
Venue: arXiv cs.HC 2022
Year: 2022

There are still many challenges of emotion recognition using physiological data despite the substantial progress made recently. In this paper, we attempted to address two major challenges. First, in order to deal with the sparsely-labeled physiological data, we first decomposed the raw physiological data using signal spectrum analysis, based on which we extracted both complexity and energy features. Such a procedure helped reduce noise and improve feature extraction effectiveness. Second, in order to improve the explainability of the machine learning models in emotion recognition with physiological data, we proposed Light Gradient Boosting Machine (LightGBM) and SHapley Additive exPlanations (SHAP) for emotion prediction and model explanation, respectively. The LightGBM model outperformed the eXtreme Gradient Boosting (XGBoost) model on the public Database for Emotion Analysis using Physiological signals (DEAP) with f1-scores of 0.814, 0.823, and 0.860 for binary classification of valence, arousal, and liking, respectively, with cross-subject validation using eight peripheral physiological signals. Furthermore, the SHAP model was able to identify the most important features in emotion recognition, and revealed the relationships between the predictor variables and the response variables in terms of their main effects and interaction effects. Therefore, the results of the proposed model not only had good performance using peripheral physiological data, but also gave more insights into the underlying mechanisms in recognizing emotions.
HREyes: Design, Development, and Evaluation of a Novel Method for AUVs to Communicate Information and Gaze Direction
Venue: arXiv cs.HC 2022
Year: 2022

We present the design, development, and evaluation of HREyes: biomimetic communication devices which use light to communicate information and, for the first time, gaze direction from AUVs to humans. First, we introduce two types of information displays using the HREye devices: active lucemes and ocular lucemes. Active lucemes communicate information explicitly through animations, while ocular lucemes communicate gaze direction implicitly by mimicking human eyes. We present a human study in which our system is compared to the use of an embedded digital display that explicitly communicates information to a diver by displaying text. Our results demonstrate accurate recognition of active lucemes for trained interactants, limited intuitive understanding of these lucemes for untrained interactants, and relatively accurate perception of gaze direction for all interactants. The results on active luceme recognition demonstrate more accurate recognition than previous light-based communication systems for AUVs (albeit with different phrase sets). Additionally, the ocular lucemes we introduce in this work represent the first method for communicating gaze direction from an AUV, a critical aspect of nonverbal communication used in collaborative work. With readily available hardware as well as open-source and easily re-configurable programming, HREyes can be easily integrated into any AUV with the physical space for the devices and used to communicate effectively with divers in any underwater environment with appropriate visibility.
Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity?
Venue: arXiv cs.CV 2022
Year: 2022

Skeleton-based Motion Capture (MoCap) systems have been widely used in the game and film industry for mimicking complex human actions for a long time. MoCap data has also proved its effectiveness in human activity recognition tasks. However, it is a quite challenging task for smaller datasets. The lack of such data for industrial activities further adds to the difficulties. In this work, we have proposed an ensemble-based machine learning methodology that is targeted to work better on MoCap datasets. The experiments have been performed on the MoCap data given in the Bento Packaging Activity Recognition Challenge 2021. Bento is a Japanese word that resembles lunch-box. Upon processing the raw MoCap data at first, we have achieved an astonishing accuracy of 98% on 10-fold Cross-Validation and 82% on Leave-One-Out-Cross-Validation by using the proposed ensemble model.
A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting
Venue: arXiv cs.LG 2022
Year: 2022

Time Series Forecasting has been an active area of research due to its many applications ranging from network usage prediction, resource allocation, anomaly detection, and predictive maintenance. Numerous publications published in the last five years have proposed diverse sets of objective loss functions to address cases such as biased data, long-term forecasting, multicollinear features, etc. In this paper, we have summarized 14 well-known regression loss functions commonly used for time series forecasting and listed out the circumstances where their application can aid in faster and better model convergence. We have also demonstrated how certain categories of loss functions perform well across all data sets and can be considered as a baseline objective function in circumstances where the distribution of the data is unknown. Our code is available at GitHub: https://github.com/aryan-jadon/Regression-Loss-Functions-in-Time-Series-Forecasting-Tensorflow.
Generalization and Personalization of Mobile Sensing-Based Mood Inference Models: An Analysis of College Students in Eight Countries
Venue: arXiv cs.HC 2022
Year: 2022

Mood inference with mobile sensing data has been studied in ubicomp literature over the last decade. This inference enables context-aware and personalized user experiences in general mobile apps and valuable feedback and interventions in mobile health apps. However, even though model generalization issues have been highlighted in many studies, the focus has always been on improving the accuracies of models using different sensing modalities and machine learning techniques, with datasets collected in homogeneous populations. In contrast, less attention has been given to studying the performance of mood inference models to assess whether models generalize to new countries. In this study, we collected a mobile sensing dataset with 329K self-reports from 678 participants in eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, UK) to assess the effect of geographical diversity on mood inference models. We define and evaluate country-specific (trained and tested within a country), continent-specific (trained and tested within a continent), country-agnostic (tested on a country not seen on training data), and multi-country (trained and tested with multiple countries) approaches trained on sensor data for two mood inference tasks with population-level (non-personalized) and hybrid (partially personalized) models. We show that partially personalized country-specific models perform the best yielding area under the receiver operating characteristic curve (AUROC) scores of the range 0.78-0.98 for two-class (negative vs. positive valence) and 0.76-0.94 for three-class (negative vs. neutral vs. positive valence) inference. Overall, we uncover generalization issues of mood inference models to new countries and how the geographical similarity of countries might impact mood inference.