Reviewerly/exHarmony huggingface dataset:
Novel Tone Mapping Method via Macro-Micro Modeling of Human Visual System

Tone mapping(TM) aims to adapt high dynamic range (HDR) images to conventional displays with visual information preserved. In this paper, a novel TM method based on macro-micro modeling is proposed, which can address the common problems in existing TM methods, such as exposure imbalance and halo artifact. From a microscopic perspective, multi-layer decomposition and reconstruction are applied to model the properties of brightness, structure, and detail for HDR images, and then different strategies are adopted for each layer by the human visual system (HVS) to reduce the overall brightness contrast and retain as much scene information. From a macroscopic perspective, scene content-based global operator is designed to adaptively adjust the scene brightness so that it is consistent with the subjective perception of human eyes. Both the micro and macro models are processed in parallel, which can ensure the integrity and subjective consistency of scene information. Experiments with numerous HDR images and TM methods are conducted and the results show that the proposed method achieves visually compelling results with little exposure imbalance and halo artifact, and is superior to the current state-of-the-art TM methods in both subjective and objective evaluations.
$$\mathsf {dL}_{\iota }$$: Definite Descriptions in Differential Dynamic Logic

We introduce , which extends differential dynamic logic ( ) for hybrid systems with definite descriptions and tuples, thus enabling its theoretical foundations to catch up with its implementation in the theorem prover . Definite descriptions enable partial, nondifferentiable, and discontinuous terms, which have many examples in applications, such as divisions, nth roots, and absolute values. Tuples enable systems of multiple differential equations, arising in almost every application. Together, definite description and tuples combine to support long-desired features such as vector arithmetic. We overcome the unique challenges posed by extending with these features. Unlike in , definite descriptions enable non-locally-Lipschitz terms, so our differential equation (ODE) axioms now make their continuity requirements explicit. Tuples are simple when considered in isolation, but in the context of hybrid systems they demand that differentials are treated in full generality. The addition of definite descriptions also makes a free logic; we investigate the interaction of free logic and the ODEs of , showing that this combination is sound, and characterize its expressivity. We give an example system that can be defined and verified using these extensions.
New Online/Offline text-dependent Arabic Handwriting dataset for Writer Authentication and Identification

Word-based writer identification and authentication have been under investigation for years. In this paper, we present a new Arabic online/offline handwriting dataset for writer authentication and identification. The created dataset includes two parts: AHWDB1 and AHWDB2 which are made freely available for the research community. Each part of the dataset consists of 2000 (10 trials X 200 writers) samples captured using HUAWEI MediaPad M3. Dynamic information such as pressure, timestamp and the coordinates(X, Y) have been collected and involved for both parts of the dataset. In addition, age, gender and education level have been added to the dataset for future investigation. Several experiments are conducted on the dataset. The preliminary identification results are obtained using K-Nearest Neighbor (KNN) with Dynamic time warping (DTW) and support vector machines (SVM). We recorded 81.35% identification rate as the best result using SVM classifier on AHWDB2.
Advanced Similarity Measures Using Word Embeddings and Siamese Networks in CBR

Automatic fuzzy text processing, context extraction and disambiguation are three challenging research areas with high relevance to complex business domains. Business knowledge can be found in plain text message exchanges, emails, support tickets, internal chat messengers and other volatile means, making the decoding of text-based domain knowledge a challenging task. Traditional natural language processing approaches focus on a comprehensive representation of business knowledge and any relevant mappings. However, such approaches can be highly complex, not cost-effective and of high maintenance, especially in environments that experience frequent changes. This work applies LSTM Siamese Networks to measure text similarities in ambiguous domains. We implement the Manhattan LSTM (MaLSTM) Siamese neural network for semi-automatic knowledge acquisition of business knowledge and decoding of domain-relevant features that enable building similarity measures. Our aim is to minimize the effort from human experts while extracting domain knowledge from rich text, containing context-free abbreviations, grammatically incorrect text and mixed language.
An organization-friendly blockchain system

Transaction privacy protection has always been the subject of interest in blockchain-based transaction system. The one-time public key method commonly used in current system does not perfectly achieve this function. Other technologies such as Mixcoin and zk-SNARKs also confront with some centralization and efficiency drawbacks. In this paper, we propose a new organization-level privacy protection mechanism for blockchain-based transaction system, which realizes the transformation of transactions between users to transactions between organizations, and further achieves a balance between privacy protection and security supervision. Specifically, we formalize the definition of this system architecture and give its generic construction and concrete implementation through the basic cryptography modules such as digital signature, public key encryption, group signature, zero-knowledge proof and range proof of committed value. Analysis and experiment results show that our system satisfies three important properties of protocol security, transaction privacy and identity traceability, which makes it suitable for various trading scenarios, especially for public-to-public transactions.
Verifying an Incremental Theory Solver for Linear Arithmetic in Isabelle/HOL

Dutertre and de Moura developed a simplex-based solver for linear rational arithmetic that has an incremental interface and provides unsatisfiable cores. We present a verification of their algorithm in Isabelle/HOL that significantly extends previous work by Spasić and Marić. Based on the simplex algorithm we further formalize Farkas’ Lemma. With this result we verify that linear rational constraints are satisfiable over $$\mathbb {Q}$$ if and only they are satisfiable over $$\mathbb {R}$$ . Hence, our verified simplex algorithm is also able to decide satisfiability in linear real arithmetic.
Observer manoeuvre control to track multiple targets considering Doppler‐bearing measurements in threat environments

Tracking a target using Doppler-bearing measurements is called the Doppler-bearing target motion analysis (DBTMA) problem. In order to improve the estimation performance of the DBTMA, this study introduces observer manoeuvre controls under the constraint that the turn rate of the observer is bounded above. The proposed approach is unique in considering the following factors: (i) approach a target while localising the target; (ii) track multiple targets simultaneously; (iii) avoid threat environments; (iv) bearing standard deviation varies as ship-relative bearing varies. The authors demonstrate the performance of the proposed approach under MATLAB simulations.
An adaptive probabilistic atlas for anomalous brain segmentation in MR images

Automated segmentation of brain structures (objects) in MR three-dimensional (3D) images for quantitative analysis has been a challenge and probabilistic atlases (PAs) are among the most well-succeeded approaches. However, the existing models do not adapt to possible object anomalies due to the presence of a disease or a surgical procedure. Post-processing operation does not solve the problem, for example, tissue classification to detect and remove such anomalies inside the resulting segmentation mask, because segmentation errors on healthy tissues cannot be fixed. Such anomalies very often alter the shape and texture of the brain structures, making them different from the appearance of the model. In this paper, we present an effective and efficient adaptive probabilistic atlas, named AdaPro, to circumvent the problem and evaluate it on a challenging task - the segmentation of the left hemisphere, right hemisphere, and cerebellum, without pons and medulla, in 3D MR-T1 brain images of Epilepsy patients. This task is challenging due to temporal lobe resections, artifacts, and the absence of contrast in some parts between the structures of interest.In AdaPro, we first build one probabilistic atlas per object of interest from a training set with normal 3D images and the corresponding 3D object masks. Second, we incorporate a texture classifier based on convex optimization which dynamically indicates the regions of the target 3D image where the PAs (shape constraints) should be further adapted. This strategy is mathematically more elegant and avoids problems with post-processing. Third, we add a new object-based delineation algorithm based on combinatorial optimization and diffusion filtering. AdaPro can then be used to locate and delineate the objects in the coordinate space of the atlas or of the test image. We also compare AdaPro with three other state-of-the-art methods: an statistical shape model based on synergistic object search and delineation, and two methods based on multi-atlas label fusion (MALF).We evaluate the methods quantitatively on 3D MR-T1 brain images of 2T and 3T from epilepsy patients, before and after temporal lobe resections, and on the template and native coordinate spaces. The results show that AdaPro is considerably faster and consistently more accurate than the baselines with statistical significance in both coordinate spaces.AdaPro can be used as a fast and effective step for brain tissue segmentation and it can also be easily extended to segment subcortical brain structures. By choice of its components, probabilistic atlas, texture classifier, and delineation algorithm, it can also be extended to other organs and imaging modalities.
Key-Insulated and Privacy-Preserving Signature Scheme with Publicly Derived Public Key

Since the introduction of Bitcoin in 2008, cryptocurrency has been undergoing a quick and explosive development.At the same time, privacy protection, one of the key merits of cryptocurrency, has attracted much attention by the community.A deterministic wallet algorithm and a stealth address algorithm have been widely adopted in the community, due to their virtues on functionality and privacy protection, which come from a key derivation mechanism that an arbitrary number of derived keys can be generated from a master key.However, these algorithms suffer a vulnerability.In particular, when a minor fault happens (say, one derived key is compromised somehow), the damage is not limited to the leaked derived key only, instead, it spreads to the master key and all derived keys are compromised.In this paper, to provide a formal treatment for the problem, we introduce and formalize a new signature variant, called Key-Insulated and Privacy-Preserving Signature Scheme with Publicly Derived Public Key (PDPKS), which forms a convenient and robust cryptographic tool for offering the virtues of deterministic wallet and stealth address, while eliminating the security vulnerabilities.Specifically, PDPKS allows anyone to derive new signature verification keys for a user, say Alice, based on her long-term public key, while only Alice can derive the signing keys corresponding to those verification keys.In terms of privacy, given a derived verification key and valid signatures with respect to it, an adversary is not able to tell which long-term public key, out of a set of known long-term public keys, is the one from which the verification key was derived.A distinguishing security feature of PDPKS, with the above functionality and privacy features, is that the derived keys are independent/insulated from each other, namely, compromising the signing key associated with a verification key does not allow an adversary to forge a valid signature for another verification key, even if both verification keys are derived from the same long-term public key.We formalize the notion of PDPKS and propose a practical and proven secure construction, which could be a convenient and secure cryptographic tool for building privacy-preserving cryptocurrencies and supporting promising use cases in practice, as it can be used to implement secure stealth addresses, and can be used to implement deterministic wallets and the related appealing use cases, without security concerns.
Directive local color transfer based on dynamic look-up table

Color transfer in image processing usually suffers from misleading color mapping and loss of details. This paper presents a novel directive local color transfer method based on dynamic look-up table (D-DLT) to solve these problems in two steps. First, a directive mapping between the source and the reference image is established based on the salient detection and the color clusters to obtain directive color transfer intention. Then, dynamic look-up tables are created according to the color clusters to preserve the details, which can suppress pseudo contours and avoid detail loss. Subjective and objective assessments are presented to verify the feasibility and the availability of the proposed approach. Experimental results demonstrate that our proposed method has better performance on natural color images than classical color transfer algorithms. Furthermore, the reference image can be extended to color blocks instead of images.
Secure Delegation to a Single Malicious Server: Exponentiation in RSA-type Groups

In cloud computing application scenarios involving computationally weak clients, the natural need for applied cryptography solutions requires the delegation of the most expensive cryptography algorithms to a computationally stronger cloud server. Group exponentiation is an important operation used in many public-key cryptosystems and, more generally, cryptographic protocols. Solving the problem of delegating group exponentiation in the case of a single, possibly malicious, server, was left open since early papers in the area. Only recently, we have solved this problem for a large class of cyclic groups, including those commonly used in cryptosystems proved secure under the intractability of the discrete logarithm problem. In this paper we solve this problem for an important class of non-cyclic groups, which includes RSA groups when the modulus is the product of two safe primes, a common setting in applications using RSA-based cryptosystems. We show a delegation protocol for fixed-exponent exponentiation in such groups, satisfying natural correctness, security, privacy and efficiency requirements, where security holds with exponentially small probability. In our protocol, with very limited offline computation and server computation, a client can delegate an exponentiation to an exponent of the same length as a group element by only performing two exponentiations to an exponent of much shorter length (i.e., the length of a statistical parameter). We obtain our protocol by a non-trivial adaptation to the RSA group of our previous protocol for cyclic groups.
Subjective Versus Objective Face Image Quality Evaluation For Face Recognition

The performance of any face recognition system gets affected by the quality of the probe and the reference images. Rejecting or recapturing images with low-quality can improve the overall performance of the biometric system. There are many statistical as well as learning-based methods that provide quality scores given an image for the task of face recognition.
Sharp bounds on the runtime of the (1+1) EA via drift analysis and analytic combinatorial tools

The expected running time of the classical (1+1) EA on the ONEMAX benchmark function has recently been determined by Hwang et al. (2018) up to additive errors of O((log n)/n). The same approach proposed there also leads to a full asymptotic expansion with errors of the form O(n-K log n) for any K > 0. This precise result is obtained by matched asymptotics with rigorous error analysis (or by solving asymptotically the underlying recurrences via inductive approximation arguments), ideas radically different from well-established techniques for the running time analysis of evolutionary computation such as drift analysis. This paper revisits drift analysis for the (1+1) EA on ONE MAX and obtains that the expected running time E (T), starting from [n/2] one-bits, is determined by the sum of inverse drifts up to logarithmic error terms, more precisely
Virtual Reality and Older Hands

Whilst the VR Headset has a sense of commonality with users, VR hand controllers are largely incongruent with each other. This paper examines the common ground between VR developments in the area of hand control devices. This paper looks at VR controllers through the lens of older people, specifically for the purpose of including people from any age in the opportunity to enjoy the benefits of VR experiences. This study uses Fitts' Index of Difficulty to determine areas of concern in terms of dexterity and accessibility. The study recommends a standardized approach to agility and access in VR hand control development.
ReplicaTEE: Enabling Seamless Replication of SGX Enclaves in the Cloud

With the proliferation of Trusted Execution Environments (TEEs) such as Intel SGX, a number of cloud providers will soon introduce TEE capabilities within their offering (e.g., Microsoft Azure). The integration of SGX within the cloud considerably strengthens the threat model for cloud applications. However, cloud deployments depend on the ability of the cloud operator to add and remove application dynamically; this is no longer possible given the current model to deploy and provision enclaves that actively involves the application owner. In this paper, we propose ReplicaTEE, a solution that enables seamless commissioning and decommissioning of TEE-based applications in the cloud. ReplicaTEE leverages an SGX-based provisioning service that interfaces with a Byzantine Fault-Tolerant storage service to securely orchestrate enclave replication in the cloud, without the active intervention of the application owner. Namely, in ReplicaTEE, the application owner entrusts application secret to the provisioning service; the latter handles all enclave commissioning and decommissioning operations throughout the application lifetime. We analyze the security of ReplicaTEE and show that it is secure against attacks by a powerful adversary that can compromise a large fraction of the cloud infrastructure. We implement a prototype of ReplicaTEE in a realistic cloud environment and evaluate its performance. ReplicaTEE moderately increments the TCB by approximately 800 LoC. Our evaluation shows that ReplicaTEE does not add significant overhead to existing SGX-based applications.
A Modern Approach for Sign Language Interpretation Using Convolutional Neural Network

There are nearly 70 million deaf people in the world. A significant portion of them and their families use sign language as a medium for communicating with each other. As automation is being gradually introduced to many parts of everyday life, the ability for machines to understand the act on sign language will be critical to creating an inclusive society. This paper presents multiple convolutional neural network based approaches, suitable for fast classification of hand sign characters. We propose two custom convolutional neural network (CNN) based architectures which are able to generalize 24 static American Sign Language (ASL) signs using only convolutional and fully connected layers. We compare these networks with transfer learning based approaches, where multiple pre-trained models were utilized. Our models have remarkably outperformed all the preceding models by accomplishing $$86.52\%$$ and $$85.88\%$$ accuracy on RGB images of the ASL Finger Spelling dataset.
Proximity semantics for topic-based abstract argumentation

As we engage in a debate with other parties, it is usual that several issues might come under discussion. Many of these issues are interrelated by the topic they address, while others represent a departure from the focus of the discussion. In this work, we propose to extend Dung's abstract argumentation frameworks by decorating arguments with a set of interrelated topics. These topics represent what the arguments are addressing and provide a supporting structure for the analysis of multi-topic argumentation. The introduction of a notion of distance between two topics associated with the arguments permits to consider the proximity of an argument to the focus of the debate, supporting in that manner an enriched semantic analysis.
Significant Feature Based Representation for Template Protection

The security of biometric templates is of paramount importance. Leakage of biometric information may result in loss of private data and can lead to the compromise of the biometric system. Yet, the security of templates is often overlooked in favour of performance. In this paper, we present a plug-and-play framework for creating secure face templates with negligible degradation in the performance of the system. We propose a significant bit based representation which guarantees security in addition to other biometric aspects such as cancelability and reproducibility. In addition to being scalable, the proposed method does not make unrealistic assumptions regarding the pose or illumination of the face images. We provide experimental results on two unconstrained datasets - IJB-A and IJB-C.
Development of Real-Time ADAS Object Detector for Deployment on CPU

In this work, we outline the set of problems, which any neural network for object detection faces when its development comes to the deployment stage and propose methods to deal with such difficulties. We show that these practices allow one to get neural network for object detection, which can recognize two classes: vehicles and pedestrians and achieves more than 60 frames per second inference speed on Core $$^{\mathrm{TM}}$$ i5-6500 CPU. The proposed model is built on top of the popular Single Shot MultiBox Object Detection framework but with substantial improvements, which were inspired by the discovered problems. The network has just 1.96 GMAC (GMAC – billions of multiply-accumulate operations) complexity and less than 7 MB model size. It is publicly available as a part of Intel® OpenVINO $$^{\mathrm{TM}}$$ Toolkit.
Jet transport-based nonlinear state and parameter estimation for geostationary spacecraft

Based on the jet transport technique, this paper proposes a novel nonlinear Kalman filter for simultaneously estimating the spacecraft state vector and uncertain parameters, either physically related with the spacecraft or with the measurement procedure. Two different coordinate representations, including Cartesian and hybrid geostationary orbital elements, are exploited in the new nonlinear estimators. The performance and sensitivity analyses of the proposed jet transport-based nonlinear estimators are assessed by numerical simulations and compared with the classical extended Kalman filter.
Analysis of model parallelism for distributed neural networks

We analyze the performance of model parallelism applied to the training of deep neural networks on clusters. For this study, we elaborate a parameterized analytical performance model that captures the main computational and communication stages in distributed model parallel training. This model is then leveraged to assess the impact on the performance of four representative convolutional neural networks (CNNs) when varying the node throughput in terms of operations per second and memory bandwidth, the number of nodes of the cluster, the bandwidth of the network links, and algorithmic parameters such as the dimension of the batch.