Reviewerly/exHarmony huggingface dataset:
Mass surveillance in the age of COVID-19

Abstract Epidemiological surveillance programs such as digital contact tracing have been touted as a silver bullet that will free the American public from the strictures of social distancing, enabling a return to school, work, and socializing. This Article assesses whether and under what circumstances the United States ought to embrace such programs. Part I analyzes the constitutionality of programs like digital contact tracing, arguing that the Fourth Amendment’s protection against unreasonable searches and seizures may well regulate the use of location data for epidemiological purposes, but that the legislative and executive branches have significant latitude to develop these programs within the broad constraints of the ``special needs'' doctrine elaborated by the courts in parallel circumstances. Part II cautions that the absence of a firm warrant requirement for digital contact tracing should not serve as a green light for unregulated and mass digital location tracking. In light of substantial risks to privacy, policy makers must ask hard questions about efficacy and the comparative advantages of location tracking versus more traditional means of controlling epidemic contagions, take seriously threats to privacy, tailor programs parsimoniously, establish clear metrics for determining success, and set clear plans for decommissioning surveillance programs.
DeepFaceLab: A simple, flexible and extensible face swapping framework.

DeepFaceLab is an open-source deepfake system created by \textbf{iperov} for face swapping with more than 3,000 forks and 13,000 stars in Github: it provides an imperative and easy-to-use pipeline for people to use with no comprehensive understanding of deep learning framework or with model implementation required, while remains a flexible and loose coupling structure for people who need to strengthen their own pipeline with other features without writing complicated boilerplate code. In this paper, we detail the principles that drive the implementation of DeepFaceLab and introduce the pipeline of it, through which every aspect of the pipeline can be modified painlessly by users to achieve their customization purpose, and it's noteworthy that DeepFaceLab could achieve results with high fidelity and indeed indiscernible by mainstream forgery detection approaches. We demonstrate the advantage of our system through comparing our approach with current prevailing systems. For more information, please visit: this https URL.
Neural Syntactic Preordering for Controlled Paraphrase Generation

Paraphrasing natural language sentences is a multifaceted process: it might involve replacing individual words or short phrases, local rearrangement of content, or high-level restructuring like topicalization or passivization.Past approaches struggle to cover this space of paraphrase possibilities in an interpretable manner.Our work, inspired by pre-ordering literature in machine translation, uses syntactic transformations to softly "reorder" the source sentence and guide our neural paraphrasing model.First, given an input sentence, we derive a set of feasible syntactic rearrangements using an encoder-decoder model.This model operates over a partially lexical, partially syntactic view of the sentence and can reorder big chunks.Next, we use each proposed rearrangement to produce a sequence of position embeddings, which encourages our final encoder-decoder paraphrase model to attend to the source words in a particular order.Our evaluation, both automatic and human, shows that the proposed system retains the quality of the baseline approaches while giving a substantial increase in the diversity of the generated paraphrases. 1
What Does BERT with Vision Look At?

Pre-trained visually grounded language models such as ViLBERT, LXMERT, and UNITER have achieved significant performance improvement on vision-and-language tasks but what they learn during pre-training remains unclear.In this work, we demonstrate that certain attention heads of a visually grounded language model actively ground elements of language to image regions.Specifically, some heads can map entities to image regions, performing the task known as entity grounding.Some heads can even detect the syntactic relations between non-entity words and image regions, tracking, for example, associations between verbs and regions corresponding to their arguments.We denote this ability as syntactic grounding.We verify grounding both quantitatively and qualitatively, using Flickr30K Entities as a testbed.
MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering

While progress has been made on the visual question answering leaderboards, models often utilize spurious correlations and priors in datasets under the i.i.d.setting.As such, evaluation on out-of-distribution (OOD) test samples has emerged as a proxy for generalization.In this paper, we present MUTANT, a training paradigm that exposes the model to perceptually similar, yet semantically distinct mutations of the input, to improve OOD generalization, such as the VQA-CP challenge.Under this paradigm, models utilize a consistency-constrained training objective to understand the effect of semantic changes in input (question-image pair) on the output (answer).Unlike existing methods on VQA-CP, MUTANT does not rely on the knowledge about the nature of train and test answer distributions.MUTANT establishes a new state-ofthe-art accuracy on VQA-CP with a 10.57% improvement.Our work opens up avenues for the use of semantic input mutations for OOD generalization in question answering.
Experimental quantum communication enhancement by superposing trajectories

In quantum communication networks, wires represent well-defined trajectories along which quantum systems are transmitted. In spite of this, trajectories can be used as a quantum control to govern the order of different noisy communication channels, and such a control has been shown to enable the transmission of information even when quantum communication protocols through well-defined trajectories fail. This result has motivated further investigations on the role of the superposition of trajectories in enhancing communication, which revealed that the use of quantum control of parallel communication channels, or of channels in series with quantum-controlled operations, can also lead to communication advantages. Building upon these findings, here we experimentally and numerically compare different ways in which two trajectories through a pair of noisy channels can be superposed. We observe that, within the framework of quantum interferometry, the use of channels in series with quantum-controlled operations generally yields the largest advantages. Our results contribute to clarify the nature of these advantages in experimental quantum-optical scenarios, and showcase the benefit of an extension of the quantum communication paradigm in which both the information exchanged and the trajectory of the information carriers are quantum.4 MoreReceived 14 July 2020Accepted 15 December 2020DOI:https://doi.org/10.1103/PhysRevResearch.3.013093Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum channelsQuantum communicationQuantum controlQuantum information processingQuantum opticsAtomic, Molecular & OpticalQuantum Information
Fake Reviews Detection: A Survey

In e-commerce, user reviews can play a significant role in determining the revenue of an organisation. Online users rely on reviews before making decisions about any product and service. As such, the credibility of online reviews is crucial for businesses and can directly affect companies’ reputation and profitability. That is why some businesses are paying spammers to post fake reviews. These fake reviews exploit consumer purchasing decisions. Consequently, the techniques for detecting fake reviews have extensively been explored in the past twelve years. However, there still lacks a survey that can analyse and summarise the existing approaches. To bridge up the issue, this survey paper details the task of fake review detection, summing up the existing datasets and their collection methods. It analyses the existing feature extraction techniques. It also summarises and analyses the existing techniques critically to identify gaps based on two groups: traditional statistical machine learning and deep learning methods. Further, we conduct a benchmark study to investigate the performance of different neural network models and transformers that have not been used for fake review detection yet. The experimental results on two benchmark datasets show that RoBERTa performs about 7% better than the state-of-the-art methods in a mixed domain for the deception dataset with the highest accuracy of 91.2%, which can be used as a baseline for future studies. Finally, we highlight the current gaps in this research area and the possible future directions.
A Data Driven Trust Mechanism Based on Blockchain in IoT Sensor Networks for Detection and Mitigation of Attacks

Utilization of smart applications in various domains is facilitated pervasively by sensor nodes (SN) that are connected in a wireless manner and a number of smart things. Hazards due to internal and external attacks exist along with the advantages of the smart things and its applications. Security measures are influenced by three main factors namely scalability, latency and network lifespan, without which mitigation of internal attacks is a challenge. The deployment of SN based Internet of things (IoT) is decentralized in nature. However, centralized solutions and security measures are provided by most researchers. A data driven trust mechanism based on blockchain is presented in this paper as a decentralized and energy efficient solution for detection of internal attacks in IoT powered SNs. In grey and black hole attack settings, the message overhead is improved using the proposed model when compared to the existing solutions. In both grey and black hole attacks, the time taken for detection of malicious nodes is also reduced considerably. The network lifetime is improved significantly due to the enhancement of these factors.
Explainable Artificial Intelligence for Human Decision Support System in the Medical Domain

In this paper, we present the potential of Explainable Artificial Intelligence methods for decision support in medical image analysis scenarios. Using three types of explainable methods applied to the same medical image data set, we aimed to improve the comprehensibility of the decisions provided by the Convolutional Neural Network (CNN). In vivo gastral images obtained by a video capsule endoscopy (VCE) were the subject of visual explanations, with the goal of increasing health professionals’ trust in black-box predictions. We implemented two post hoc interpretable machine learning methods, called Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), and an alternative explanation approach, the Contextual Importance and Utility (CIU) method. The produced explanations were assessed by human evaluation. We conducted three user studies based on explanations provided by LIME, SHAP and CIU. Users from different non-medical backgrounds carried out a series of tests in a web-based survey setting and stated their experience and understanding of the given explanations. Three user groups (n = 20, 20, 20) with three distinct forms of explanations were quantitatively analyzed. We found that, as hypothesized, the CIU-explainable method performed better than both LIME and SHAP methods in terms of improving support for human decision-making and being more transparent and thus understandable to users. Additionally, CIU outperformed LIME and SHAP by generating explanations more rapidly. Our findings suggest that there are notable differences in human decision-making between various explanation support settings. In line with that, we present three potential explainable methods that, with future improvements in implementation, can be generalized to different medical data sets and can provide effective decision support to medical experts.
Filter-enhanced MLP is All You Need for Sequential Recommendation

Recently, deep neural networks such as RNN, CNN and Transformer have been applied in the task of sequential recommendation, which aims to capture the dynamic preference characteristics from logged user behavior data for accurate recommendation. However, in online platforms, logged user behavior data is inevitable to contain noise, and deep recommendation models are easy to overfit on these logged data. To tackle this problem, we borrow the idea of filtering algorithms from signal processing that attenuates the noise in the frequency domain. In our empirical experiments, we find that filtering algorithms can substantially improve representative sequential recommendation models, and integrating simple filtering algorithms (e.g., Band-Stop Filter) with an all-MLP architecture can even outperform competitive Transformer-based models. Motivated by it, we propose FMLP-Rec, an all-MLP model with learnable filters for sequential recommendation task. The all-MLP architecture endows our model with lower time complexity, and the learnable filters can adaptively attenuate the noise information in the frequency domain. Extensive experiments conducted on eight real-world datasets demonstrate the superiority of our proposed method over competitive RNN, CNN, GNN and Transformer-based methods. Our code and data are publicly available at the link: https://github.com/RUCAIBox/FMLP-Rec .
BOSA: Binary Orientation Search Algorithm

Optimization algorithms have many applications in solve the optimization problems in various sciences. The Orientation Search Algorithm (OSA) is an optimizer that is simulated the rules of a game called orientation game. In OSA searcher agent are players of orientation game that moves on the playground depending on the direction of the referee. in this study, binary model of OSA which called Binary Orientation Search Algorithm (BOSA) is proposed. BOSA and eight other algorithms (BGA, BPSO, BGSA, BGOA, BBA, BMOA, and BDA) are tested on twenty-three Benchmark test function. Proposed BOSA has a high ability to solve optimization problems compared to other algorithms according to the evaluation results.
Convolutional Neural Network-Based Fractional-Pixel Motion Compensation

Fractional-pixel motion compensation (MC) improves the efficiency of inter prediction and has been utilized extensively in video coding standards. The traditional methods of fractional-pixel MC usually follow the approach of interpolation, i.e., they adopt different kinds of filters, either fixed or adaptive, to interpolate fractional-pixel values from integer-pixel values in a reference picture. Different from the interpolation approach, in this paper, we formulate the fractional-pixel MC as an inter-picture regression problem, which is to predict the pixel values of the current to-be-coded picture from the integer-pixel values of a reference picture, given a fractional-pixel motion vector that relates the two pictures. We then propose to adopt convolutional neural network (CNN) models to approach the regression problem, inspired by the recent advances of CNN. Accordingly, we propose fractional-pixel reference generation CNN (FRCNN) for both uni-directional and bi-directional MC in video coding. We further investigate how to train FRCNN by using encoded video sequences, and empirically study the effect of different training data and different CNN structures. Moreover, we propose to integrate FRCNN into the high efficiency video coding (HEVC) scheme, and perform a comprehensive set of experiments to evaluate the effectiveness of FRCNN. The experimental results show that our proposed FRCNN achieves on average 3.9%, 2.7%, and 1.3% bits saving compared with HEVC, under low-delay P, low-delay B, and random-access configurations, respectively.
Smart vehicle forensics: Challenges and case study

Vehicles are fast becoming another important source of digital evidence in a criminal investigation. Traditionally, when a vehicle is involved in a crime scene (e.g. drink driving) or a terrorist attack, the investigators focus on the acquisition of DNA, fingerprints and other identifying materials that are usually non digital in nature. However, modern-day cars, particularly smart or driverless cars, store a wealth of digital information, such as recent destinations, favorite locations, routes, and personal data (e.g. call logs, contact lists, SMS messages, pictures, and videos). In this paper, we describe some of the challenges associated with vehicle data forensics, which is an understudied area. Next, we present our case studies on forensic acquisition and data analysis of an entertainment system on a Volkswagen car. We also discuss potential hardware and software solutions that can be used to acquire forensic artifacts from such vehicles. Finally, we describe and analyze the mobile data traffic from an Audi car, a VW car and a BMW car.
Probabilistic Models for Spatio-Temporal Photovoltaic Power Forecasting

Photovoltaic (PV) power generation is characterized by significant variability. Accurate PV forecasts are a prerequisite to securely and economically operating electricity networks, especially in the case of large-scale penetration. In this paper, we propose a probabilistic spatio-temporal model for the PV power production that exploits production information from neighboring plants. The model provides the complete future probability density function of PV production for very short-term horizons (0-6 h). The method is based on quantile regression and a L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> penalization technique for automatic selection of the input variables. The proposed modeling chain is simple, making the model fast and scalable to direct on-line application. The performance of the proposed approach is evaluated using a real-world test case, with a high number of geographically distributed PV installations and by comparison with state-of-the-art probabilistic methods.
SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning

Machine learning applications, such as object detection and content recommendation, often require training a single model to predict multiple targets at the same time. Multi-task learning through neural networks became popular recently, because it not only helps improve the accuracy of many prediction tasks when they are related, but also saves computation cost by sharing model architectures and low-level representations. The latter is critical for real-time large-scale machine learning systems.&#x0D; However, classic multi-task neural networks may degenerate significantly in accuracy when tasks are less related. Previous works (Misra et al. 2016; Yang and Hospedales 2016; Ma et al. 2018) showed that having more flexible architectures in multi-task models, either manually-tuned or softparameter-sharing structures like gating networks, helps improve the prediction accuracy. However, manual tuning is not scalable, and the previous soft-parameter sharing models are either not flexible enough or computationally expensive.&#x0D; In this work, we propose a novel framework called SubNetwork Routing (SNR) to achieve more flexible parameter sharing while maintaining the computational advantage of the classic multi-task neural-network model. SNR modularizes the shared low-level hidden layers into multiple layers of subnetworks, and controls the connection of sub-networks with learnable latent variables to achieve flexible parameter sharing. We demonstrate the effectiveness of our approach on a large-scale dataset YouTube8M. We show that the proposed method improves the accuracy of multi-task models while maintaining their computation efficiency.
Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning

In existing visual representation learning tasks, deep convolutional neural networks (CNNs) are often trained on images annotated with single tag, such as ImageNet. However, single tag annotation cannot describe all important contents of one image, and some useful visual information may be wasted during training. In this work, we propose to train CNNs from images annotated with multiple tags, to enhance the quality of visual representation of the trained CNN model. To this end, we build a large-scale multi-label image database with 18M images and 11K categories, dubbed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tencent ML-Images</i> . We efficiently train the ResNet-101 model with multi-label outputs on Tencent ML-Images, taking 90 hours for 60 epochs, based on a large-scale distributed deep learning framework, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , TFplus. The good quality of the visual representation of the Tencent ML-Images checkpoint is verified through three transfer learning tasks, including single-label image classification on ImageNet and Caltech-256, object detection on PASCAL VOC 2007, and semantic segmentation on PASCAL VOC 2012. The Tencent ML-Images database, the checkpoints of ResNet-101, and all the training codes have been released at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/Tencent/tencent-ml-images</uri></i> . It is expected to promote other vision tasks in the research and industry community.
Quantum teleportation using highly coherent emission from telecom C-band quantum dots

Abstract A practical way to link separate nodes in quantum networks is to send photons over the standard telecom fibre network. This requires sub-Poissonian photon sources in the wavelength band around 1550 nm, with photon coherence times sufficient to enable the many interference-based technologies at the heart of quantum networks. Here, we show that droplet epitaxy InAs/InP quantum dots emitting in the telecom C-band can provide photons with coherence times exceeding 1 ns under low power non-resonant excitation, and demonstrate that these coherence times enable near-optimal interference with a C-band polarisation-encoded laser qubit, with visibilities only limited by the quantum dot multiphoton emission. Using entangled photons, we further show teleportation of such qubits in six different bases with average postselected fidelity reaching 88.3 ± 4.0%. Beyond direct applications in long-distance quantum communication, the high degree of coherence in these quantum dots is promising for future spin-based telecom quantum network applications.
nnU-Net: Breaking the Spell on Successful Medical Image Segmentation.

Fueled by the diversity of datasets, semantic segmentation is a popularsubfield in medical image analysis with a vast number of new methods beingproposed each year. This ever-growing jungle of methodologies, however, becomesincreasingly impenetrable. At the same time, many proposed methods fail togeneralize beyond the experiments they were demonstrated on, thus hampering theprocess of developing a segmentation algorithm on a new dataset. Here wepresent nnU-Net ('no-new-Net'), a framework that automatically adapts itself toany given new dataset. While this process was completely human-driven so far,we make a first attempt to automate necessary adaptations such aspreprocessing, the exact patch size, batch size, and inference settings basedon the properties of a given dataset. Remarkably, nnU-Net strips away thearchitectural bells and whistles that are typically proposed in the literatureand relies on just a simple U-Net architecture embedded in a robust trainingscheme. Out of the box, nnU-Net achieves state of the art performance on sixwell-established segmentation challenges. Source code is available athttps://github.com/MIC-DKFZ/nnunet.