Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel data preprocessing solution for large scale digital forensics investigation on big data

As the rapid development of high-technology, more and more novel and interesting applications and systems emerge. For example, people are willing to share their life any time any where just by accessing their Facebook accounts. In the same time, the popularity of mobility offices and fault-tolerance working platforms are becoming more and more hot than ever. For example, Dropbox is an popular cloud storage services among the world in recently. In addition, Google collaboration platform is one of the most successful business application for global users to work together in any time, even if they are not in the same office geologically. It is not difficult to find that more similar examples regarding to this concern. However, Nothing is prefect forever. Technology is a double-edged sword, especially in the information technology field. It pops up a lot of challenges. Consequently, digital forensics investigators pop up a significant question of how to implement large scale digital forensics investigation on big data effectively. It is impossible to handle those cases manually. However, some advanced techniques have been developed by research communities. For example, machine learning techniques are one of the most suitable candidate solutions to handle these big data cases. The significant merit for applying machine learning techniques is not only to introduce an automatic way of working, but also to process those complicated cases with higher precision than other means. Machine learning techniques consist of these stages, input gathering, data preprocessing, algorithm designing & deploying and output evaluation. The data preprocessing is an inevitable step for achieving better performance from machine learning techniques. However, research societies pay a lot of effort on advanced machine learning algorithm development and performance optimization. The crucial step of data preprocessing seems to be regarded by the same significance. This is the motivation for us to conduct this piece of work in this field. In this paper, we are going to address how to facilitate the implementation of largescale digital forensics investigation on big data set with the help of our data preprocessing solution. The methodology introduced in this paper is a hybrid solution based the stochastic theory, Grubbs’ criterion and the machine learning method, K Nearest Neighbour (KNN) algorithm. The complete technique contains two round of preprocessing work. While, the performance study on experiment results reflects a considerable achievement by our solution.
Mesoscopic quantum thermo-mechanics: A new frontier of experimental physics

In the last decade, experimentalists have demonstrated their impressive ability to control mechanical modes within mesoscopic objects down to the quantum level: it is now possible to create mechanical Fock states, to entangle mechanical modes from distinct objects, and to store quantum information or transfer it from one quantum bit to another, among the many possibilities found in today's literature. Indeed, mechanics is quantum, very much like spins or electromagnetic degrees of freedom; and all of this is, in particular, referred to as a new engineering resource for quantum technologies. However, there is also much more beyond this utilitarian aspect: invoking the original discussions of Braginsky and Caves, where a quantum oscillator is thought of as a quantum detector for a classical field, namely, a gravitational wave, which is also a unique sensing capability for quantum fields. The subject of study is then the baths to which the mechanical mode is coupled to, let them be known or unknown in nature. This Perspective is about this new potentiality that addresses stochastic thermodynamics, potentially down to its quantum version, the search for a fundamental underlying (random) field postulated in recent theories that can be affiliated to the class of the wave-function collapse models, and more generally open questions of condensed matter like the actual nature of the elusive (and ubiquitous) two-level systems present within all mechanical objects. However, such research turns out to be much more demanding than the use of a few quantum mechanical modes: all the known baths have to be identified, experiments have to be conducted in-equilibrium, and the word “mechanics” needs to be justified by a real ability to move substantially the center-of-mass when a proper drive tone is applied to the system.
Test suite effectiveness metric evaluation: what do we know and what should we do?

Comparing test suite effectiveness metrics has always been a research hotspot. However, prior studies have different conclusions or even contradict each other for comparing different test suite effectiveness metrics. The problem we found most troubling to our community is that researchers tend to oversimplify the description of the ground truth they use. For example, a common expression is that “we studied the correlation between real faults and the metric to evaluate (MTE)”. However, the meaning of "real faults" is not clear-cut. As a result, there is a need to scrutinize the meaning of “real faults”. Without this, it will be half-knowledgeable with the conclusions. To tackle this challenge, we propose a framework ASSENT (evAluating teSt Suite EffectiveNess meTrics) to guide the follow-up research. In nature, ASSENT consists of three fundamental components: ground truth, benchmark test suites, and agreement indicator. First, materialize the ground truth for determining the real order in effectiveness among test suites. Second, generate a set of benchmark test suites and derive their ground truth order in effectiveness. Third, for the benchmark test suites, generate the MTE order in effectiveness by the metric to evaluate (MTE). Finally, calculate the agreement indicator between the two orders. Under ASSENT, we are able to compare the accuracy of different test suite effectiveness metrics. We apply ASSENT to evaluate representative test suite effectiveness metrics, including mutation score metrics and code coverage metrics. Our results show that, based on the real faults, mutation score and subsuming mutation score are the best metrics to quantify test suite effectiveness. Meanwhile, by using mutants instead of real faults, MTEs will be overestimated by more than 20% in values.
SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Deep Vein Thrombosis (DVT) prediction

Eﬀective learning from electronic health records (EHR) data for prediction of clinical outcomes is often challenging because of features recorded at irregular timesteps and loss to follow-up as well as competing events such as death or disease progression. To that end, we propose a generative time-to-event model, SurvLatent ODE, which adopts an Ordinary Diﬀerential Equation-based Recurrent Neural Networks (ODE-RNN) as an encoder to eﬀectively parameterize a latent representation under irregularly sampled data. Our model then utilizes the latent representation to ﬂexibly estimate survival times for multiple competing events without specifying shapes of event-speciﬁc hazard function. We demonstrate competitive performance of our model on MIMIC-III, a freely-available longitudinal dataset collected from critical care units, on predicting hospital mortality as well as the data from the Dana-Farber Cancer Institute (DFCI) on predicting onset of Deep Vein Thrombosis (DVT), a life-threatening complication for patients with cancer, with death as a competing event. SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying DVT risk groups. we multi-task learning our multiple cause-speciﬁc decoder each
Efficient Progressive High Dynamic Range Image Restoration via Attention and Alignment Network

HDR is an important part of computational photography technology. In this paper, we propose a lightweight neural network called Efficient Attention-and-alignment-guided Progressive Network (EAPNet) for the challenge NTIRE 2022 HDR Track 1 and Track 2. We introduce a multi-scale lightweight encoding module to extract features. Besides, we propose Progressive Dilated U-shape Block (PDUB) which is a progressive plug-and-play module for dynamically tuning MAccs and PSNR. Finally, we use fast and low-power feature-alignment module to deal with misalignment problem in place of the time-consuming Deformable Convolutional Network (DCN). The experiments show that our method achieves about 20× compression on MAccs with better PSNR-µ and PSNR compared to the state-of-the-art method. We got the 2nd place of both two tracks during the testing phase. Fig. 1 shows the visualized result of NTIRE 2022 HDR challenge.
Evidence for Parametric Decay Instability in the Lower Solar Atmosphere

We find evidence for the first observation of the parametric decay instability (PDI) in the lower solar atmosphere. In particular, we find that the power spectrum of density fluctuations near the solar transition region resembles the power spectrum of the velocity fluctuations but with the frequency axis scaled up by about a factor of 2. These results are from an analysis of the Si iv lines observed by the Interface Region Imaging Spectrometer in the transition region of a polar coronal hole. We also find that the density fluctuations have radial velocity of about 75 km s−1 and that the velocity fluctuations are much faster with an estimated speed of 250 km s−1, as is expected for sound waves and Alfvén waves, respectively, in the transition region. Theoretical calculations show that this frequency relationship is consistent with those expected from PDI for the plasma conditions of the observed region. These measurements suggest an interaction between sound waves and Alfvén waves in the transition region, which is evidence for the parametric decay instability.
Federated Learning for Distributed Energy-Efficient Resource Allocation

In cellular networks, resource allocation is performed in a centralized way, which brings huge computation complexity to the base station (BS) and high transmission overhead. This paper investigates the distributed resource allocation scheme for cellular networks to maximize the energy efficiency of the system in the uplink transmission, while guaranteeing the quality of service (QoS) for cellular users. Particularly, to cope the fast varying channels in wireless communication environment, we propose a robust federated reinforcement learning (FRL_suc) framework to enable local users perform distributed resource allocation in items of transmit power and channel assignment by the guidance of the the local neural network trained at each user. Analysis and numerical results show that the proposed FRL_suc framework can lower the transmission overhead and offload the computation from the central server to the local users, while outperforming the conventional multi-agent reinforcement learning algorithm in terms of EE, and is more robust to channel variations.
Refinement on Spectral Turán's Theorem

A well-known result in extremal spectral graph theory, due to Nosal and Nikiforov, states that if $G$ is a triangle-free graph on $n$ vertices, then $\lambda (G) \le \lambda (K_{\lfloor \frac{n}{2}\rfloor, \lceil \frac{n}{2} \rceil })$, equality holds if and only if $G=K_{\lfloor \frac{n}{2}\rfloor, \lceil \frac{n}{2} \rceil }$. Nikiforov [Linear Algebra Appl. 427 (2007)] extended this result to $K_{r+1}$-free graphs for every integer $r\ge 2$. This is known as the spectral Tur\'{a}n theorem. Recently, Lin, Ning and Wu [Combin. Probab. Comput. 30 (2021)] proved a refinement on this result for non-bipartite triangle-free graphs. In this paper, we provide alternative proofs for the result of Nikiforov and the result of Lin, Ning and Wu. Our proof can allow us to extend the later result to non-$r$-partite $K_{r+1}$-free graphs. Our result refines the theorem of Nikiforov and it also can be viewed as a spectral version of a theorem of Brouwer.
BugListener: Identifying and Synthesizing Bug Reports from Collaborative Live Chats

In community-based software development, developers frequently rely on live-chatting to discuss emergent bugs/errors they encounter in daily development tasks. However, it remains a challenging task to accurately record such knowledge due to the noisy nature of interleaved dialogs in live chat data. In this paper, we first formulate the task of identifying and synthesizing bug reports from commu-nity live chats, and propose a novel approach, named BugListener, to address the challenges. Specifically, BugListener automates three sub-tasks: 1) Disentangle the dialogs from massive chat logs by using a Feed-Forward neural network; 2) Identify the bug-report dialogs from separated dialogs by leveraging the Graph neural net-work to learn the contextual information; 3) Synthesize the bug reports by utilizing Transfer Learning techniques to classify the sentences into: observed behaviors (OB), expected behaviors (EB), and steps to reproduce the bug (SR). BugListener is evaluated on six open source projects. The results show that: for bug report identification, BugListener achieves the average Fl of 77.74%, im-proving the best baseline by 12.96%; and for bug report synthesis task, BugListener could classify the OB, EB, and SR sentences with the F1 of 84.62%, 71.46%, and 73.13%, improving the best baselines by 9.32%,12.21%,10.91%, respectively. A human evaluation study also confirms the effectiveness of Bug Listener in generating relevant and accurate bug reports. These demonstrate the significant potential of applying BugListener in community-based software development, for promoting bug discovery and quality improvement.
SDGCCA: Supervised Deep Generalized Canonical Correlation Analysis for Multi-omics Integration

Integration of multi-omics data provides opportunities for revealing biological mechanisms related to certain phenotypes. We propose a novel method of multi-omics integration called supervised deep generalized canonical correlation analysis (SDGCCA) for modeling correlation structures between nonlinear multi-omics manifolds that aims at improving the classification of phenotypes and revealing the biomarkers related to phenotypes. SDGCCA addresses the limitations of other canonical correlation analysis (CCA)-based models (such as deep CCA, deep generalized CCA) by considering complex/nonlinear cross-data correlations between multiple (≥2) modalities. Although there are a few methods to learn nonlinear CCA projections for classifying phenotypes, they only consider two views. Methods extended to multiple views either do not perform classification or do not provide feature ranking. In contrast, SDGCCA is a nonlinear multi-view CCA projection method that performs classification and ranks features. When we applied SDGCCA in predicting patients with Alzheimer's disease (AD) and discrimination of early- and late-stage cancers, it outperformed other CCA-based and other supervised methods. In addition, we demonstrate that SDGCCA can be applied for feature selection to identify important multi-omics biomarkers. On applying AD data, SDGCCA identified clusters of genes in multi-omics data, well known to be associated with AD.
A Survey on Multi-hop Question Answering and Generation

The problem of Question Answering (QA) has attracted significant research interest for long. Its relevance to language understanding and knowledge retrieval tasks, along with the simple setting makes the task of QA crucial for strong AI systems. Recent success on simple QA tasks has shifted the focus to more complex settings. Among these, Multi-Hop QA (MHQA) is one of the most researched tasks over the recent years. The ability to answer multi-hop questions and perform multi step reasoning can significantly improve the utility of NLP systems. Consequently, the field has seen a sudden surge with high quality datasets, models and evaluation strategies. The notion of `multiple hops' is somewhat abstract which results in a large variety of tasks that require multi-hop reasoning. This implies that different datasets and models differ significantly which makes the field challenging to generalize and survey. This work aims to provide a general and formal definition of MHQA task, and organize and summarize existing MHQA frameworks. We also outline the best methods to create MHQA datasets. The paper provides a systematic and thorough introduction as well as the structuring of the existing attempts to this highly interesting, yet quite challenging task.
Self-supervised Learning for Sonar Image Classification

Self-supervised learning has proved to be a powerful approach to learn image representations without the need of large labeled datasets. For underwater robotics, it is of great interest to design computer vision algorithms to improve perception capabilities such as sonar image classification. Due to the confidential nature of sonar imaging and the difficulty to interpret sonar images, it is challenging to create public large labeled sonar datasets to train supervised learning algorithms. In this work, we investigate the potential of three self-supervised learning methods (RotNet, Denoising Autoencoders, and Jigsaw) to learn high-quality sonar image representation without the need of human labels. We present pre-training and transfer learning results on real-life sonar image datasets. Our results indicate that self-supervised pre-training yields classification performance comparable to supervised pre-training in a few-shot transfer learning setup across all three methods. Code and self-supervised pre-trained models are be available at agrija9/ssl-sonar-images.
PICT@DravidianLangTech-ACL2022: Neural Machine Translation On Dravidian Languages

This paper presents a summary of the findings that we obtained based on the shared task on machine translation of Dravidian languages. As a part of this shared task, we carried out neural machine translations for the following five language pairs: Kannada to Tamil, Kannada to Telugu, Kannada to Malayalam, Kannada to Sanskrit, and Kannada to Tulu. The datasets for each of the five language pairs were used to train various translation models, including Seq2Seq models such as LSTM, bidirectional LSTM, Conv Seq2Seq, and training state-of-the-art as transformers from scratch, and fine-tuning already pre-trained models. For some models involving monolingual corpora, we implemented backtranslation as well. These models’ accuracy was later tested with a part of the same dataset using BLEU score as an evaluation metric.
A Unified Approach for Multi-Scale Synchronous Correlation Search in Big Time Series - Full Version

—The wide deployment of IoT sensors has enabled the collection of very big time series across different domains, from which advanced analytics can be performed to ﬁnd unknown relationships, most importantly the correlations between them. However, current approaches for correlation search on time series are limited to only a single temporal scale and simple types of relations, and cannot handle noise effectively. This paper presents the integrated SYnchronous COrrelation Search (iSYCOS) framework to ﬁnd multi-scale correlations in big time series. Speciﬁcally, iSYCOS integrates top-down and bottom-up approaches into a single auto-conﬁgured framework capable of efﬁciently extracting complex window-based correlations from big time series using mutual information (MI) . Moreover, iSYCOS includes a novel MI-based theory to identify noise in the data, and is used to perform pruning to improve iSYCOS performance. Besides, we design a distributed version of iSYCOS that can scale out in a Spark cluster to handle big time series. Our extensive experimental evaluation on synthetic and real-world datasets shows that iSYCOS can auto-conﬁgure on a given dataset to ﬁnd complex multi-scale correlations . The pruning and optimizations can improve iSYCOS performance up to an order of magnitude , and the distributed iSYCOS can scale out linearly on a computing cluster.
Optimizing Tensor Network Contraction Using Reinforcement Learning

Quantum Computing (QC) stands to revolutionize computing, but is currently still limited. To develop and test quantum algorithms today, quantum circuits are often simulated on classical computers. Simulating a complex quantum circuit requires computing the contraction of a large network of tensors. The order (path) of contraction can have a drastic effect on the computing cost, but finding an efficient order is a challenging combinatorial optimization problem. We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem. The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment. We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges and obtain significant improvements over state-of-the-art techniques in three varieties of circuits, including the largest scale networks used in contemporary QC.
A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automation

Artificial intelligence (AI) and machine learning (ML) techniques have been increasingly used in several fields to improve performance and the level of automation. In recent years, this use has exponentially increased due to the advancement of high-performance computing and the ever increasing size of data. One of such fields is that of hardware design—specifically the design of digital and analog integrated circuits, where AI/ ML techniques have been extensively used to address ever-increasing design complexity, aggressive time to market, and the growing number of ubiquitous interconnected devices. However, the security concerns and issues related to integrated circuit design have been highly overlooked. In this article, we summarize the state-of-the-art in AI/ML for circuit design/optimization, security and engineering challenges, research in security-aware computer-aided design/electronic design automation, and future research directions and needs for using AI/ML for security-aware circuit design.
Nonlinear Shallow-Water Waves with Vertical Odd Viscosity

The breaking of detailed balance in fluids through Coriolis forces or odd-viscous stresses has profound effects on the dynamics of surface waves. Here we explore both weakly and strongly non-linear waves in a three-dimensional fluid with vertical odd viscosity. Our model describes the free surface of a shallow fluid composed of nearly vertical vortex filaments, which all stand perpendicular to the surface. We find that the odd viscosity in this configuration induces previously unexplored non-linear effects in shallow-water waves, arising from both stresses on the surface and stress gradients in the bulk. By assuming weak nonlinearity, we find reduced equations including Korteweg-de Vries (KdV), Ostrovsky, and Kadomtsev-Petviashvilli (KP) equations with modified coefficients. At sufficiently large odd viscosity, the dispersion changes sign, allowing for compact two-dimensional solitary waves. We show that odd viscosity and surface tension have the same effect on the free surface, but distinct signatures in the fluid flow. Our results describe the collective dynamics of many-vortex systems, which can also occur in oceanic and atmospheric geophysics.