Reviewerly/exHarmony huggingface dataset:
Face Manipulation Detection via Auxiliary Supervision

The rapid progress of face manipulation technology has attracted people's attention. At present, a reliable edit detection algorithm is urgently needed to identify real and fake faces to ensure social credibility. Previous deep learning approaches formulate face manipulation detection as a binary classification problem. Many works struggle to focus on specific artifacts and generalize poorly. In this paper, we design reasonable auxiliary supervision to guide the network to learn discriminative and generalizable cues. A multi-scale framework is proposed to estimate the manipulation probability with texture map and blending boundary as auxiliary supervisions. These supervisions will guide the network to focus on the underlying texture information and blending boundary, making the learned features more generalized. Experiments on FaceForensics and FaceForensics++ datasets have demonstrated the effectiveness and generalization of our method.
Changes in public preferences for technologically enhanced surveillance following the COVID-19 pandemic: a discrete choice experiment

As governments attempt to navigate a path out of COVID-19 restrictions, robust evidence is essential to inform requirements for public acceptance of technologically enhanced communicable disease surveillance systems. We examined the value of core surveillance system attributes to the Australian public, before and during the early stages of the current pandemic.A discrete choice experiment was conducted in Australia with a representative group of respondents, before and after the WHO declared COVID-19 a Public Health Emergency of International Concern. We identified and investigated the relative importance of seven attributes associated with technologically enhanced disease surveillance: respect for personal autonomy; privacy/confidentiality; data certainty/confidence; data security; infectious disease mortality prevention; infectious disease morbidity prevention; and attribution of (causal) responsibility. Specifically, we explored how the onset of the COVID-19 outbreak influenced participant responses.2008 Australians (general public) completed the experiment: 793 before COVID-19 outbreak onset (mean age 45.9 years, 50.2% male) and 1215 after onset (mean age 47.2 years, 49% male).All seven attributes significantly influenced respondents' preferences for communicable disease surveillance systems. After onset, participants demonstrated greater preference for a surveillance system that could prevent a higher number of illnesses and deaths, and were less concerned about their personal autonomy. However, they also increased their preference for a system with high data security.Public acceptance of technology-based communicable disease surveillance is situation dependent. During an epidemic, there is likely to be greater tolerance of technologically enhanced disease surveillance systems that result in restrictions on personal activity if such systems can prevent high morbidity and mortality. However, this acceptance of lower personal autonomy comes with an increased requirement to ensure data security. These findings merit further research as the pandemic unfolds and strategies are put in place that enable individuals and societies to live with SARS-CoV-2 endemicity.
Ping-pong quantum key distribution with trusted noise: non-Markovian advantage

The ping-pong protocol adapted for quantum key distribution is studied in the trusted quantum noise scenario, wherein the legitimate parties can add noise locally. For a well-studied attack model, we show how non-unital quantum non-Markovianity of the added noise can improve the key rate. We also point out that this noise-induced advantage cannot be obtained by Alice and Bob by adding local classical noise to their post-measurement data.
Realistic rate–distance limit of continuous-variable quantum key distribution

Continuous variable quantum key distribution (CV QKD) is a promising candidate for the deployment of quantum cryptography. At present, the longest distance is limited to ∼100 km at fiber-based quantum channel. We investigated in depth the realistic rate-distance limit (RDL) of CV QKD, considering reconciliation efficiency, finite-size effect, and realistic excess noise under collective attack. It is shown that the excess noise generated on Bob's side degrades significantly the transmission distance and we verify it in experiment. The improvement in RDL by reconciliation efficiency depends on the excess noise level, considerable increase of RDL by improving the reconciliation efficiency occurs only for relative large excess noises. A convergence modulation variance, useful in calculation simplification, is found. Furthermore, we restudy the finite-size analysis and eliminates a loophole arising from the Holevo-bound information monotonicity and a safe RDL is guaranteed. Based on the revised finite-size analysis, the optimum ratio determining the amount of data used for parameter estimation is analyzed.
Battery size determination for photovoltaic capacity firming using deep learning irradiance forecasts

Large photovoltaic (PV) power plants benefit from the introduction of batteries to increase their dispatchability. Among other services, batteries enable PV plants to firm their hourly energy production and avoid in this way the economic penalties associated with deviations between the contracted commitment made by the renewable generator to the grid and the final energy delivered. Due to the increase in the cost of the plant derived from the storage integration, the size of these batteries must be minimized. This work analyses the minimum battery capacity required for such a service when using a new deep-learning irradiance forecasting methodology. The low prediction error of the developed forecasting tool supports the optimized operation of large PV plants under different European intraday electricity markets with no deviations and reduced battery sizes. Results obtained for a whole year analysis using actual data at three different locations with varying irradiance patterns prove that 1-hour capacity batteries grant PV capacity firming in most intraday continuous market structures regardless of their lead times.
Feature subset selection via an improved discretization-based particle swarm optimization

High-dimensional data analysis has attracted increasingly attention in machine learning or data mining tasks. Due to the existence of irrelevant and redundant features, classification accuracy is often degraded seriously. Feature selection (FS), which aims to improve the predictive accuracy by selecting a subset of features, plays a very important role. In this paper, we proposed an improved discretization-based particle swarm optimization (PSO) for FS. In our method, we applied a moderate pre-screening process to obtain a reduced size of features at first. Then, a ranking-based cut-point table that stores multiple cut-points sorted by an entropy-based cut-point priority for each feature was obtained. To find the optimal combination of the cut-points that could best distinguish the data samples, a simple yet efficient encoding and decoding approach in PSO was used to select a flexible number of cut-points. Moreover, a probability-guided local search strategy was applied to search for better combination of cut-points to achieve promising feature subset. Comprehensive simulation results on 19 benchmark datasets demonstrate the effectiveness of several improved strategies in our proposed method and the advantages of our proposed method over some state-of-the-art PSO-based competitors.
DB-Scan Algorithm based Colon Cancer Detection And Stratification Analysis

Histopathological examination of tissue models is basic for the conclusion and reviewing of colon malignancy. In any case, the technique is subjective and prompts imperative intra/bury spectator distinction in the examination as it predominantly relies upon the graphical evaluation of histopathologists. Thus, a tried and true PC supported technique, which can naturally group harmful and ordinary colon tests are required; however, automating this strategy is demanding because of the nearness of exceptions. In this paper, a productive technique for identifying colon disease from biopsy tests which comprise of four imperative stages. DB-SCAN estimation to distinguish colon tumor from biopsy tests is presented in this paper. In the proposed approach, from the outset, the colon biopsy tests are preprocessed using DB-SCAN configuration to make a set of redundant localities in which groups or clusters are formed. At that point, the exceptions inside the bunched areas are created as a tree structure in light of the choice tree in which the anomalies are hubs, and the connection between hubs are delivered based on data about exceptions. At that point, entropy-based exception score calculation will be done on every hub of the tree. The Information picks up technique is utilized to figure the score for the exceptions. At long last, score based grouping is accomplished to order the ordinary or harmful cells. Experimental trials exhibit, the proposed strategy has better outcomes contrasted to existing strategies. It furthermore acclaims that the proposed procedure is adequate for the colon tumor identification process. The proposed strategy is executed on Matlab working platform and the investigations exhibit that the proposed technique has high accomplished high grouping precision contrasted and different strategies.
Compositional Information Flow Analysis for WebAssembly Programs

WebAssembly is a new W3C standard, providing a portable target for compilation for various languages.All major browsers can run WebAssembly programs, and its use extends beyond the web: there is interest in compiling cross-platform desktop applications, server applications, IoT and embedded applications to WebAssembly because of the performance and security guarantees it aims to provide.Indeed, WebAssembly has been carefully designed with security in mind.In particular, WebAssembly applications are sandboxed from their host environment.However, recent works have brought to light several limitations that expose WebAssembly to traditional attack vectors.Visitors of websites using WebAssembly have been exposed to malicious code as a result.In this paper, we propose an automated static program analysis to address these security concerns.Our analysis is focused on information flow and is compositional.For every WebAssembly function, it first computes a summary that describes in a sound manner where the information from its parameters and the global program state can flow to.These summaries can then be applied during the subsequent analysis of function calls.Through a classical fixed-point formulation, one obtains an approximation of the information flow in the WebAssembly program.This results in the first compositional static analysis for WebAssembly.On a set of 34 benchmark programs spanning 196kLOC of WebAssembly, we compute at least 64% of the function summaries precisely in less than a minute in total.
Performance analysis of hyperledger fabric 2.0 blockchain platform

Hyperledger Fabric is currently one of the most popular business Blockchain platforms. With its included functionality of executing custom smart contracts, Fabric has become one of the most widely used frameworks, e.g. for industry 4.0 applications. Though, most applications require a previously known data throughput. For a potentially interested developer, it is not trivial to decide, whether a given Fabric network configuration will meet the required expectations in regards to performance. Thus this work shows a performance analysis for Hyperledger Fabric v2.0 and focusses on the evaluation of throughput, latency and error rate, together with the overall scalability of the Fabric Blockchain platform. The results show, that Fabric v2.0 outperforms previous versions in almost any regard in addition to an improved smart contract lifecycle.
Source Printer Classification Using Printer Specific Local Texture Descriptor

The knowledge of source printer can help in printed text document authentication, copyright ownership, and provide important clues about the author of a fraudulent document along with his/her potential means and motives. Development of automated systems for classifying printed documents based on their source printer, using image processing techniques, is gaining a lot of attention in multimedia forensics. Currently, state-of-the-art systems require that the font of letters present in test documents of unknown origin must be available in those used for training the classifier. In this work, we attempt to take the first step towards overcoming this limitation. Specifically, we introduce a novel printer specific local texture descriptor. The highlight of our technique is the use of encoding and regrouping strategy based on small linear-shaped structures composed of pixels having similar intensity and gradient. The results of experiments performed on two separate datasets show that: 1) on a publicly available dataset, the proposed method outperforms state-of-the-art algorithms for characters printed in the same font, and 2) on another dataset\footnote{Code and dataset will be made publicly available with published version of this paper.} having documents printed in four different fonts, the proposed method correctly classifies all test samples when sufficient training data is available in same font setup. In addition, it outperforms state-of-the-art methods for cross font experiments. Moreover, it reduces the confusion between the printers of same brand and model.
High-Threshold Code for Modular Hardware With Asymmetric Noise

We consider an approach to fault tolerant quantum computing based on a simple error detecting code operating as the substrate for a conventional surface code. We develop a customised decoder to process the information about the likely location of errors, obtained from the error detect stage, with an advanced variant of the minimum weight perfect matching algorithm. A threshold gate-level error rate of 1.42% is found for the concatenated code given highly asymmetric noise. This is superior to the standard surface code and remains so as we introduce a significant component of depolarising noise; specifically, until the latter is 70% the strength of the former. Moreover, given the asymmetric noise case, the threshold rises to 6.24% if we additionally assume that local operations have 20 times higher fidelity than long range gates. Thus for systems that are both modular and prone to asymmetric noise our code structure can be very advantageous.
Toward automatic verification of quantum programs

This paper summarises the results obtained by the author and his collaborators in a program logic approach to the verification of quantum programs, including quantum Hoare logic, invariant generation and termination analysis for quantum programs. It also introduces the notion of proof outline and several auxiliary rules for more conveniently reasoning about quantum programs. Some problems for future research are proposed at the end of the paper.
Time Series Encodings with Temporal Convolutional Networks

The training of anomaly detection models usually requires labeled data. We present in this paper a novel approach for anomaly detection in time series which trains unsupervised using a convolutional approach coupled to an autoencoder framework. After training, only a small amount of labeled data is needed to adjust the anomaly threshold. We show that our new approach outperforms several other state-of-the-art anomaly detection algorithms on a Mackey-Glass (MG) anomaly benchmark. At the same time our autoencoder is capable of learning interesting representations in latent space. Our new MG anomaly benchmark allows to create an unlimited amount of anomaly benchmark data with steerable difficulty. In this benchmark, the anomalies are well-defined, yet difficult to spot for the human eye.
Discriminative Partial Domain Adversarial Network

Domain adaptation (DA) has been a fundamental building block for Transfer Learning (TL) which assumes that source and target domain share the same label space. A more general and realistic setting is that the label space of target domain is a subset of the source domain, as termed by Partial domain adaptation (PDA). Previous methods typically match the whole source domain to target domain, which causes negative transfer due to the source-negative classes in source domain that does not exist in target domain. In this paper, a novel Discriminative Partial Domain Adversarial Network (DPDAN) is developed. We first propose to use hard binary weighting to differentiate the source-positive and source-negative samples in the source domain. The source-positive samples are those with labels shared by two domains, while the rest in the source domain are treated as source-negative samples. Based on the above binary relabeling strategy, our algorithm maximizes the distribution divergence between source-negative samples and all the others (source-positive and target samples), meanwhile minimizes domain shift between source-positive samples and target domain to obtain discriminative domain-invariant features. We empirically verify DPDAN can effectively reduce the negative transfer caused by source-negative classes, and also theoretically show it decreases negative transfer caused by domain shift. Experiments on four benchmark domain adaptation datasets show DPDAN consistently outperforms state-of-the-art methods.
Speeding up adiabatic passage with an optimal modified Roland–Cerf protocol

In this article we propose a novel method to accelerate adiabatic passage in a two-level system with only longitudinal field (detuning) control, while the transverse field is kept constant. The suggested method is a modification of the Roland-Cerf protocol, during which the parameter quantifying local adiabaticity is held constant. Here, we show that with a simple ``on-off" modulation of this local adiabaticity parameter, a perfect adiabatic passage can be obtained for every duration larger than the lower bound $\pi/\Omega$, where $\Omega$ is the constant transverse field. For a fixed maximum amplitude of the local adiabaticity parameter, the timings of the ``on-off" pulse-sequence which achieves perfect fidelity in minimum time are obtained using optimal control theory. The corresponding detuning control is continuous and monotonic, a significant advantage compared to the detuning variation at the quantum speed limit which includes non-monotonic jumps. The proposed methodology can be applied in several important core tasks in quantum computing, for example to the design of a high fidelity controlled-phase gate, which can be mapped to the adiabatic quantum control of such a qubit. Additionally, it is expected to find applications across all Physics disciplines which exploit the adiabatic control of such a two-level system.
Multimode Fock states with large photon number: effective descriptions and applications in quantum metrology

Abstract We develop general tools to characterise and efficiently compute relevant observables of multimode N -photon states generated in nonlinear decays in one-dimensional waveguides. We then consider optical interferometry in a Mach–Zender interferometer where a d -mode photonic state enters in each arm of the interferometer. We derive a simple expression for the quantum Fisher information in terms of the average photon number in each mode, and show that it can be saturated by number-resolved photon measurements that do not distinguish between the different d modes.
ProxyBNN: Learning Binarized Neural Networks via Proxy Matrices

Training Binarized Neural Networks (BNNs) is challenging due to the discreteness. In order to efficiently optimize BNNs through backward propagations, real-valued auxiliary variables are commonly used to accumulate gradient updates. Those auxiliary variables are then directly quantized to binary weights in the forward pass, which brings about large quantization errors. In this paper, by introducing an appropriate proxy matrix, we reduce the weights quantization error while circumventing explicit binary regularizations on the full-precision auxiliary variables. Specifically, we regard pre-binarization weights as a linear combination of the basis vectors. The matrix composed of basis vectors is referred to as the proxy matrix, and auxiliary variables serve as the coefficients of this linear combination. We are the first to empirically identify and study the effectiveness of learning both basis and coefficients to construct the pre-binarization weights. This new proxy learning contributes to new leading performances on benchmark datasets.
Single View Metrology in the Wild

Most 3D reconstruction methods may only recover scene properties up to a global scale ambiguity. We present a novel approach to single view metrology that can recover the absolute scale of a scene represented by 3D heights of objects or camera height above the ground as well as camera parameters of orientation and field of view, using just a monocular image acquired in unconstrained condition. Our method relies on data-driven priors learned by a deep network specifically designed to imbibe weakly supervised constraints from the interplay of the unknown camera with 3D entities such as object heights, through estimation of bounding box projections. We leverage categorical priors for objects such as humans or cars that commonly occur in natural images, as references for scale estimation. We demonstrate state-of-the-art qualitative and quantitative results on several datasets as well as applications including virtual object insertion. Furthermore, the perceptual quality of our outputs is validated by a user study.
Air-Writing Recognition using Deep Convolutional and Recurrent Neural Network Architectures

In this paper, we explore deep learning architectures applied to the air-writing recognition problem where a person writes text freely in the three dimensional space. We focus on handwritten digits, namely from 0 to 9, which are structured as multidimensional time-series acquired from a Leap Motion Controller (LMC) sensor. We examine both dynamic and static approaches to model the motion trajectory. We train and compare several state-of-the-art convolutional and recurrent architectures. Specifically, we employed a Long Short-Term Memory (LSTM) network and also its bidirectional counterpart (BLSTM) in order to map the input sequence to a vector of fixed dimensionality, which is subsequently passed to a dense layer for classification among the targeted air-handwritten classes. In the second architecture we adopt 1D Convolutional Neural Networks (CNNs) to encode the input features before feeding them to an LSTM neural network (CNN-LSTM). The third architecture is a Temporal Convolutional Network (TCN) that uses dilated causal convolutions. Finally, a deep CNN architecture for automating the feature learning and classification from raw input data is presented. The performance evaluation has been carried out on a dataset of 10 participants, who wrote each digit at least 10 times, resulting in almost 1200 examples.