Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive Sculpting of Digital Faces Using an Anatomical Modeling Paradigm

Digitally sculpting 3D human faces is a very challenging task. It typically requires either 1) highly‐skilled artists using complex software packages for high quality results, or 2) highly‐constrained simple interfaces for consumer‐level avatar creation, such as in game engines. We propose a novel interactive method for the creation of digital faces that is simple and intuitive to use, even for novice users, while consistently producing plausible 3D face geometry, and allowing editing freedom beyond traditional video game avatar creation. At the core of our system lies a specialized anatomical local face model (ALM), which is constructed from a dataset of several hundred 3D face scans. User edits are propagated to constraints for an optimization of our data‐driven ALM model, ensuring the resulting face remains plausible even for simple edits like clicking and dragging surface points. We show how several natural interaction methods can be implemented in our framework, including direct control of the surface, indirect control of semantic features like age, ethnicity, gender, and BMI, as well as indirect control through manipulating the underlying bony structures. The result is a simple new method for creating digital human faces, for artists and novice users alike. Our method is attractive for low‐budget VFX and animation productions, and our anatomical modeling paradigm can complement traditional game engine avatar design packages.
Toward a Generic AutoML-Based Assistant for Contracts Negotiation

Contracts are the cornerstone of legal agreements in the industry. As essential legal documents, contracts are subject to negotiations, demanding extensive analysis and evaluation efforts. Although the emergence of machine learning has enabled assistance tools for text analysis tasks, the speciﬁcity and constraints of each business context remain obstacles for the automation of contracts evaluation. In this paper, we propose an AutoML based approach for automated negotiation assistance that uses expert annotated contracts and the business-speciﬁc knowledge of acceptance policies. Driven by policies rules, our approach generates a classiﬁcation process composed of hierarchies of complementary classiﬁers, each being automatically prepared according, but not limited to, feature extraction, learning model and data granularity. Experiments conducted on real-world service contracts have yielded promising results.
Black-Box Adversarial Attacks Against Deep Learning Based Malware Binaries Detection with GAN

For efﬁcient malware detection, there are more and more deep learning methods based on raw software binaries. Recent studies show that deep learning models can easily be fooled to make a wrong decision by introducing subtle perturbations to inputs, which attracts a large inﬂux of work in adversarial attacks. However, most of the existing attack methods are based on manual features (e.g., API calls) or in the white-box setting, making the attacks impractical in current real-world scenarios. In this work, we propose a novel attack framework called GAPGAN, which generates adversarial pay-loads (padding bytes) with generative adversarial networks (GANs). To the best of our knowledge, it is the ﬁrst work that performs end-to-end black-box attacks at the byte-level against deep learning based malware binaries detection. In our attack framework, we map input discrete malware binaries to continuous space, then feed it to the generator of GAPGAN to generate adversarial payloads. We append payloads to the original binaries to craft an adversarial sample while preserving its functionality. We propose to use a dynamic threshold for reducing the loss of the effectiveness of the payloads when mapping it from continuous format back to the original discrete format. For balancing the attention of the generator to the payloads and the adversarial samples, we use an automatic weight tuning strategy. We train GAPGAN with both malicious and benign software. Once the training is ﬁnished, the generator can generate an adversarial sample with only the input malware in less than twenty milliseconds. We apply GAPGAN to attack the state-of-the-art detector MalConv and achieve 100% attack success rate with only appending payloads of 2.5% of the total length of the data for detection. We also attack deep learning models with different structures under different defense methods. The experiments show that GAPGAN outperforms other state-of-the-art attack models in efﬁciency and effectiveness
A Variant of Recurrent Entity Networks for Targeted Aspect-Based Sentiment Analysis

Deep neural network models have achieved promising results on targeted aspect-based sentiment analysis. However, previous models did not effectively match long-distance ﬁne-grained sentiment polarity with the associated target and aspects, and the interdependence among the speciﬁc target, corresponding aspects, and the context is always ignored. This work proposes a novel recurrent entity memory network that employs word-level information and sentence-level hidden memory to entity state tracking. In addition, the entity state is utilized to ﬁne-tune the target embedding and aspect embedding. The experimental results showed that the proposed model outperformed previous models.
Asymptotic normality of the test statistics for the unified relative dispersion and relative variation indexes

Dispersion indexes with respect to the Poisson and binomial distributions are widely used to assess the conformity of the underlying distribution from an observed sample of the count with one or the other of these theoretical distributions. Recently, the exponential variation index has been proposed as an extension to nonnegative continuous data. This paper aims to gather to study the unified definition of these indexes with respect to the relative variability of a nonnegative natural exponential family of distributions through its variance function. We establish the strong consistency of the plug-in estimators of the indexes as well as their asymptotic normalities. Since the exact distributions of the estimators are not available in closed form, we consider the test of hypothesis relying on these estimators as test statistics with their asymptotic distributions. Simulation studies globally suggest good behaviours of these tests of hypothesis procedures. Applicable examples are analysed, including the lesser-known references such as negative binomial and inverse Gaussian, and improving the very usual case of the Poisson dispersion index. Concluding remarks are made with suggestions of possible extensions.
Approximating isosurfaces by guaranteed‐quality triangular meshes

We describe a new method for approximating an implicit surface F by a piecewise‐flat triangulated surface whose triangles are as close as possible to equilateral. The main advantage is improved mesh quality which is guaranteed for smooth surfaces. The GradNormal algorithm generates a triangular mesh that gives a piecewise‐differentiable approximation of F, with angles between 35.2 and 101.5 degrees. As the mesh size approaches 0, the mesh converges to F through surfaces that are isotopic to F.
Give Inconsistency a Chance: Semantics for Ontology-Mediated Verification

Recently, we have introduced ontologized programs as a formalism to link description logic ontologies with programs speciﬁed in the guarded command language, the de-facto standard input formalism for probabilistic model checking tools such as Prism , to allow for an ontology-mediated veriﬁcation of stochastic systems. Central to our approach is a complete separation of the two formalisms involved: guarded command language to describe the dynamics of the stochastic system and description logics are used to model background knowledge about the system in an ontology. In ontologized programs, these two languages are loosely coupled by an interface that mediates between these two worlds. Under the original semantics deﬁned for ontologized programs, a program may enter a state that is inconsistent with the ontology, which limits the capabilities of the description logic component. We approach this issue in diﬀerent ways by introducing consistency notions, and discuss two alternative semantics for ontologized programs. Furthermore, we present complexity results for checking whether a program is consistent under the diﬀerent semantics.
A Study on the Flow Characteristics of Supersonic Microjets

Supersonic microjets acquire considerable research interest from a fundamental fluid dynamics perspective, in part because the combination of highly compressible flow at low-to-moderate Reynolds number is not very common, and in part due to the complex nature of the flow itself. In addition, microjets have a great variety engineering applications such as micro-propulsion, MEMS (Micro-Electro Mechanical Systems) components, microjet actuators and fine particle deposition and removal. Numerical simulations have been carried out at moderate nozzle pressure ratios and for different nozzle exit diameters to investigate and to understand in-depth of aerodynamic characteristics of supersonic microjets.
How rare are power-law networks really?

The putative scale-free nature of real-world networks has generated a lot of interest in the past 20 years: if networks from many different fields share a common structure, then perhaps this suggests some underlying ‘network law’. Testing the degree distribution of networks for power-law tails has been a topic of considerable discussion. Ad hoc statistical methodology has been used both to discredit power-laws as well as to support them. This paper proposes a statistical testing procedure that considers the complex issues in testing degree distributions in networks that result from observing a finite network, having dependent degree sequences and suffering from insufficient power. We focus on testing whether the tail of the empirical degrees behaves like the tail of a de Solla Price model, a two-parameter power-law distribution. We modify the well-known Kolmogorov–Smirnov test to achieve even sensitivity along the tail, considering the dependence between the empirical degrees under the null distribution, while guaranteeing sufficient power of the test. We apply the method to many empirical degree distributions. Our results show that power-law network degree distributions are not rare, classifying almost 65% of the tested networks as having a power-law tail with at least 80% power.
Fabricable Unobtrusive 3D‐QR‐Codes with Directional Light

QR code is a 2D matrix barcode widely used for product tracking, identification, document management and general marketing. Recently, there have been various attempts to utilize QR codes in 3D manufacturing by carving QR codes on the surface of the printed 3D shape. Nevertheless, significant shape editing and modulation may be required to allow readability of the embedded 3D‐QR‐codes with good decoding accuracy. In this paper, we introduce a novel QR code 3D fabrication framework aimed at unobtrusive embedding of 3D‐QR‐codes in the shape hence introducing minimal shape modulation. Essentially, our method computes bi‐directional carvings in the 3D shape surface to obtain the black‐and‐white QR pattern. By using a directional light source, the black‐and‐white QR pattern emerges as lighted and shadow casted blocks on the shape respectively. To account for minimal modulation and elusiveness, we optimize the QR code carving w.r. t. shape geometry, visual disparity and light source position. Our technique employs a simulation of lighting phenomena through carved modules on the shape to ensure adequate contrast of the printed 3D‐QR‐code.
A Shared-Word Sensitive Sequence-to-Sequence Features Extractor for Sentences Matching

Sentences matching is a basic task in Natural Language Processing (NLP). Interaction-based methods, which employ interactions between words of two sentences and construct word-level matching features to classify, are generally used due to their ﬁne-grained features. However, they have many invalid interactions that may affect matching precision. In this paper, we limit the objects of interacting to shared words 4 of two sentences. On the one hand, they can reduce invalid interactions. On the other hand, because of the different context semantics, the representation of the same word may be quite different, conversely, the representation difference can also be used to reﬂect the semantic difference of different contexts. To better extract global features of shared words, we introduce a sequence-to-sequence features extractor to force decoder to learn more contextual information from encoder. We implement the method based on Transformer[28], with syntactic parsing as additional knowledge. Our proposed method achieved better performance than strong baselines and the experiment results also demonstrate the efﬁciency of sequence-to-sequence features extractor and signiﬁcance of the shared words.
Rapidly Finding the Best Arm Using Variance

We address the problem of identifying the best arm in a pure-exploration multi-armed bandit problem. In this setting, the agent repeatedly pulls arms in order to identify the one associated with the maximum expected reward. We focus on the ﬁxed-budget version of the problem in which the agent tries to ﬁnd the best arm given a ﬁxed number of arm pulls. We propose a novel sequential elimination method exploiting the empirical variance of the arms. We detail and analyse the overall approach providing theoretical and empirical results. The experimental evaluation shows the advantage of our variance-based rejection method in heterogeneous test settings, considering both identiﬁcation accuracy and execution time
MobiSenseUs: Inferring Aggregate Objective and Subjective Well-Being from Mobile Data

Assessing the well-being of a population is of utmost importance for policy and decision makers so they can design appropriate policies and interventions to improve the quality of life of their citizens. Traditional methods to determine aggregate well-being consist of surveys which are expensive to obtain and difﬁcult to scale. Thanks to the availability of large-scale human behavioral data, new methods to assess well-being might be possible. In this paper we describe one of such methods: MobiSenseUs, a machine-learning based system to automatically estimate geographically aggregated objec-tive and subjective well-being measures in the UK from mobile data. We propose a comprehensive battery of features that capture different aspects of human behavior – i.e. communication patterns, mobile app usage and spatial mobility – from two sources of pseudonymized mobile data of more than one million smartphone users. We are the ﬁrst to build machine-learning models to predict both objective (IMD) and subjective (SWB) indicators in the UK from these mobile features. We ﬁnd that the IMD can be predicted more accurately than SWB, reaching 99% and 78% average accuracies in a binary clas-siﬁcation task for the IMD and SWB, respectively. We analyze the most predictive features and derive implications for the design of data-driven machine-learning public health policy systems.
Classification Rules in Relaxed Logical Form

Machine learning algorithms that produce rule-based predictions in Conjunctive Normal form (CNF) or in Disjunctive Normal form (DNF) are arguably some of the most interpretable ones. For example, decision set is an interpretable model in practice, that represents the decision function in the form of DNF. In this paper, we consider relaxed deﬁnitions of standard OR/AND operators which allow exceptions in the construction of a clause and also in the selection of clauses in a rule. Building on these relaxed deﬁnition, we introduce relaxed-CNF rules, which are motivated by the popular usage of checklists in the medical domain and generalizes the widely employed rule representations including CNF, DNF, and decision sets. While the combinatorial structure of relaxed-CNF rules offers exponential succinctness, the naive learning techniques are computationally expensive. To this end, we propose a novel incremental mini-batch learning procedure, called CRR , that employs advances in the Integer Linear Programming (ILP) solvers to efﬁ-ciently learn relaxed-CNF rules. Our experimental analysis demonstrates that CRR can generate relaxed-CNF rules, which are more accurate and sparser compared to the alternative rule-based models.
Common and Discriminative Semantic Pursuit for Multi-Modal Multi-Label Learning

Multi-modal multi-label (MMML) learning provides an important framework to learn complex objects with diverse representations and annotations. Most existing multi-modal multi-label learning approaches focus on exploiting shared information of all modalities, but neglect speciﬁc information of each modality. Besides, how to effectively utilize relationship among modalities is also a challenging issue. In this paper, we propose a novel MMML learning approach called Common and Discriminative Semantic Pursuit (CoDiSP), which learns low-dimensional common representation with all modalities, and extracts discriminative information of each modality by enforcing orthogonal constraint. Meanwhile, the common representation is used as a new modality and added to the speciﬁc modal sequence. Furthermore, CoDiSP learns deep models with adaptive depth and exploits label correlations simultaneously based on the extracted modal sequence. Finally, extensive experiments on several benchmark MMML datasets show superior performance of CoDiSP compared with other state-of-the-art approaches.
A Compact Rack Model for Data Center CFD Modeling

We propose a compact rack model which provides the capabilities of an explicitly-detailed model and the computational efficiency of a black-box model. The proposed model idealizes internal-rack airflows as conforming to a well-defined flow network topology. It predicts IT-equipment inlet temperatures (which depend on internal-rack recirculations) and pressure-driven leakage airflows. Consequently, it can model, for example, contained and uncontained architectures, the effects of under-rack cable cutouts, and ceiling-ducted racks (with a sealed rear door). The model is efficient and robust because it eliminates the need to explicitly model small-scale features in large-scale data-center CFD simulations while not appreciably increasing computational cost relative to the simplest black-box models. It has the potential for high accuracy as internal-rack flow resistances are taken directly from experimental measurements and inputs may be tailored to any rack population or application.
A Novel Fully Integrated ULV SC DC-DC Converter for Indoor Light Energy Harvesting

Indoor energy harvesting using small photovoltaic (PV) cells is still not widely exploited. It can be a solution to supply ultra-low power (ULP) devices for Internet of Things (IoT) applications. However, a PV cell may suffer shading caused by walls, furniture, and human movements. This requires an efficient conversion system that can adapt to voltage variations, demanding a specific adjustable DC-DC converter. The present work aims to propose a fully integrated conversion system for indoor photovoltaic energy harvesting capable to provide a range of voltage conversion ratios (VCRs) suitable for ULP and ultra-low voltage (ULV) applications. The proposed converter is based on a reconfigurable switched-capacitor (SC) topology. A systematic simulation-based methodology for designing the DC-DC converter is presented, including switches and capacitors sizing and operating frequency optimization. Results show that the proposed circuit can obtain 19 different VCRs with an estimated peak efficiency of 94.26%. This is an important step towards a practical self-powered indoor system for IoT applications.
Observability in Target Motion Analysis from the Sum or the Difference of Ranges with Two Stationary Sensors

We address in this paper the problem of observability in target motion analyses (TMA), when the measurements are a sum or a difference of ranges between a target and two motionless observers. Necessary and sufficient conditions of observability are given in time difference of arrival (TDOA) situation and in a bistatic situation.
Josephson Arbitrary Waveform Synthesizer as a Reference Standard for the Calibration of Lock-in Amplifiers

We have extended the voltage range of the Josephson arbitrary waveform synthesizer from 1 mV down to $1\ \mu\mathrm{V}$ at frequencies from 60 Hz to 1 kHz to calibrate precision lock-in amplifiers. Experimental results show that the system's uncertainty is dominated by the resolution of the lock-in amplifier. We anticipate that the Josephson arbitrary waveform synthesizer-based system will extend the lower voltage and frequency range of ac voltage metrology and improve the uncertainties by one order of magnitude compared to conventional techniques.
A Machine Learning Framework for Pulse Detection During Out-of-Hospital Cardiac Arrest

The availability of an automatic pulse detection during out-of-hospital cardiac arrest (OHCA) would allow the rapid identification of cardiac arrest and the prompt detection of return of spontaneous circulation. The aim of this study was to develop a reliable pulse detection algorithm using the electrocardiogram (ECG) and thoracic impedance (TI), the signals available in most defibrilators. The dataset used in the study consisted of 1140 ECG and TI segments from 187 OHCA patients, whereof 792 were labelled as pulse-generating rhythm (PR) and 348 as pulseless electrical activity (PEA) by a pool of experts in OHCA. First, an adaptive filtering scheme was used to extract the impedance circulation component and its first derivative from the TI. Then, the wavelet decomposition of the ECG was carried out to obtain the different subband components and the denoised ECG. Pulse/no-pulse (PR/PEA) discrimination features were extracted from those signals and fed into a support vector machine (SVM) classifier that made the pulse/no-pulse decision. A quasi-stratified and patient wise nested cross validation procedure was used to select the best feature subset and to tune the SVM hyperparameters. This procedure was repeated 50 times to estimate the statistical distributions of the performance metrics of the method. The optimal solution consisted in a five feature classifier that yielded a mean (standard deviation) sensitivity, specificity, balanced accuracy and total accuracy of 92.4% (0.7), 93.0% (0.8), 92.7% (0.5) and 92.6% (0.5), respectively. When compared to available methods, our solution presented an improvement in balanced accuracy of at least 2.5 points. A reliable pulse detection algorithm for OHCA using the signals available in defibrillators was acomplished.