Reviewerly/exHarmony huggingface dataset:
Identity-Based Encryption with Security Against the KGC: A Formal Model and Its Instantiation from Lattices

The key escrow problem is one of the main barriers to the widespread real-world use of identity-based encryption (IBE). Specifically, a key generation center (KGC), which generates secret keys for a given identity, has the power to decrypt all ciphertexts. At PKC 2009, Chow defined a notion of security against the KGC, that relies on assuming that it cannot discover the underlying identities behind ciphertexts. However, this is not a realistic assumption since, in practice, the KGC manages an identity list and hence it can easily guess the identities corresponding to given ciphertexts. Chow later closed the gap between theory and practice by introducing a new entity called an identity-certifying authority (ICA) and proposed an anonymous key-issuing protocol. Essentially, this allows the users, KGC, and ICA to interactively generate secret keys without users ever having to reveal their identities to the KGC. Unfortunately, the proposed protocol did not include a concrete security definition, meaning that all of the subsequent works following Chow lack the formal proofs needed to determine whether or not it delivers a secure solution to the key escrow problem. In this paper, based on Chow’s work, we formally define an IBE scheme that resolves the key escrow problem and provide formal definitions of security against corrupted users, KGC, and ICA. Along the way, we observe that if we are allowed to assume a fully trusted ICA, as in Chow’s work, then we can construct a trivial (and meaningless) IBE scheme that is secure against the KGC. Finally, we present a lattice-based construction in our new security model based on the Gentry–Peikert–Vaikuntanathan (GPV) IBE scheme (STOC 2008) and Rückert’s lattice-based blind signature scheme (ASIACRYPT 2010).
CORMORANT

People own and carry an increasing number of ubiquitous mobile devices, such as smartphones, tablets, and notebooks. Being small and mobile, those devices have a high propensity to become lost or stolen. Since mobile devices provide access to their owners' digital lives, strong authentication is vital to protect sensitive information and services against unauthorized access. However, at least one in three devices is unprotected, with inconvenience of traditional authentication being the paramount reason. We present the concept of CORMORANT, an approach to significantly reduce the manual burden of mobile user verification through risk-aware, multi-modal biometric, cross-device authentication. Transparent behavioral and physiological biometrics like gait, voice, face, and keystroke dynamics are used to continuously evaluate the user's identity without explicit interaction. The required level of confidence in the user's identity is dynamically adjusted based on the risk of unauthorized access derived from signals like location, time of day and nearby devices. Authentication results are shared securely with trusted devices to facilitate cross-device authentication for co-located devices. Conducting a large-scale agent-based simulation of 4 000 users based on more than 720 000 days of real-world device usage traces and 6.7 million simulated robberies and thefts sourced from police reports, we found the proposed approach is able to reduce the frequency of password entries required on smartphones by 97.82% whilst simultaneously reducing the risk of unauthorized access in the event of a crime by 97.72%, compared to conventional knowledge-based authentication.
Understanding eWhoring

In this paper, we describe a new type of online fraud, referred to as 'eWhoring' by offenders. This crime script analysis provides an overview of the 'eWhoring' business model, drawing on more than 6,500 posts crawled from an online underground forum. This is an unusual fraud type, in that offenders readily share information about how it is committed in a way that is almost prescriptive. There are economic factors at play here, as providing information about how to make money from 'eWhoring' can increase the demand for the types of images that enable it to happen. We find that sexualised images are typically stolen and shared online. While some images are shared for free, these can quickly become 'saturated', leading to the demand for (and trade in) more exclusive 'packs'. These images are then sold to unwitting customers who believe they have paid for a virtual sexual encounter. A variety of online services are used for carrying out this fraud type, including email, video, dating sites, social media, classified advertisements, and payment platforms. This analysis reveals potential interventions that could be applied to each stage of the crime commission process to prevent and disrupt this crime type.
Sarcasm Detection Using RNN with Relation Vector

Sarcasm detection has been treated as a task that classifies text as sarcastic or non-sarcastic. Sarcasm detection is a significant challenge for sentiment analysis because sarcasm involves a positive expression with a negative meaning. Surface information in text is commonly used as a classification feature. However, the authors must consider both surface and non-surface features. In this article, the authors focus on relation information between pairs of role expressions, such as “boss and staff,” and propose a sarcasm detection method based on surface and relation information. First, the authors extract role pairs from a corpus. Then, the authors construct a relation vector generated from these role pairs and incorporate the relation vector into a recurrent neural network model. The authors evaluated the proposed method by comparing it to previously proposed methods. The results demonstrate the effectiveness of introducing the relation vector to sarcasm detection.
Resizing and cleaning of histopathological images using generative adversarial networks

Bilinear and Bicubic interpolation techniques are frequently used to increase image resolution. These techniques with data modeling approach are replaced by intelligent systems that can learn automatically from data. SRGAN is a modern Generative Adversarial Network developed as an alternative to classical interpolation techniques. His ability to produce images in super resolution has attracted the attention of many researchers. In this study, noise elimination performance of super resolution generative adversarial network (SRGAN) with image magnification was investigated. The results of the noise cleaning were compared with the classical approaches (mean, median, adaptive filters). SSIM, PSNR and FFT_MSE metrics were evaluated in experimental studies using images in the data set Camelyon17. When the results were evaluated, it was observed that SRGAN was superior to the classical approaches not only in increasing the resolution but also in the noise cleaning area.
Reasoning Analytically about Password-Cracking Software

A rich literature has presented efficient techniques for estimating password strength by modeling password-cracking algorithms.Unfortunately, these previous techniques only apply to probabilistic password models, which real attackers seldom use.In this paper, we introduce techniques to reason analytically and efficiently about transformation-based password cracking in software tools like John the Ripper and Hashcat.We define two new operations, rule inversion and guess counting, with which we analyze these tools without needing to enumerate guesses.We implement these techniques and find orders-of-magnitude reductions in the time it takes to estimate password strength.We also present four applications showing how our techniques enable increased scientific rigor in optimizing these attacks' configurations.In particular, we show how our techniques can leverage revealed password data to improve orderings of transformation rules and to identify rules and words potentially missing from an attack configuration.Our work thus introduces some of the first principled mechanisms for reasoning scientifically about the types of password-guessing attacks that occur in practice.
Cornering Trajectory Planning Avoiding Slip for Differential-Wheeled Mobile Robots

In this article, we propose an efficient algorithm for cornering trajectory planning avoiding slip based on dynamics for differential-wheeled mobile robots (DWMRs). In general, slip between tires and the ground increases odometry errors. Hence, to consider not only full dynamics, including actuators, but also slip avoidance, we formulate a complex planning problem, in which the following essential constraints are imposed: longitudinal and lateral force constraints to avoid slip and motor control input bounds for DWMR dynamics, including actuators. Given a single corner, the proposed trajectory is divided into three turning sections, which are composed of multiple intervals. In order to satisfy all constraints, the intervals are then split into several subintervals, which are with constant or time-varying control inputs based on the bang-bang principle or on force constraint bounds for optimal acceleration. We then find two local minimum trajectories by analyzing the time trend w.r.t. the turning radius for cornering. These are the low-velocity decrease trajectory and the shortest distance trajectory. The time-optimal solution is determined by the minimum of the local minima. Both simulations and experiments are conducted to verify the effectiveness of the proposed algorithm.
NES-TL: Network Embedding Similarity-Based Transfer Learning

The transfer learning methodology leverages knowledge from the source domain with abundant training data to the insufficient target domain. Recently, new approaches continue to be developed and used to solve different classification tasks, ranging from public news to videos and to many others. Most transfer learning methods are based on the assumption that both source and target data are in the same feature space or with the same data distribution, which however is not always true in real applications where it would lead to a negative transfer. In order to overcome this hurdle, the multiple-source transfer learning framework is useful. Since many real systems can be represented by networks, how to utilize the structural similarity between different networks so as to increase the transfer effectiveness becomes important. In this paper, the NES specification index is used to quantitatively measure the structural similarity between two networks, based on which a new transfer learning method (named NES-TL) is developed. Experiments on tag popularity prediction in StackExchange Q&A communities verify the effectiveness of the proposed approach, showing that it behaves better than existing baseline methods.
Experimental Research on Deep Reinforcement Learning in Autonomous navigation of Mobile Robot

The paper is concerned with the autonomous navigation of mobile robot from the current position to the desired position only using the current visual observation, without the environment map built beforehand. Under the framework of deep reinforcement learning, the Deep Q Network (DQN) is used to achieve the mapping from the original image to the optimal action of the mobile robot. Reinforcement learning requires a large number of training examples, which is difficult to directly be applied in a real robot navigation scenario. To solve the problem, the DQN is firstly trained in the Gazebo simulation environment, followed by the application of the well-trained DQN in the real mobile robot navigation scenario. Both simulation and real-world experiments have been conducted to validate the proposed approach. The experimental results of mobile robot autonomous navigation in the Gazebo simulation environment show that the trained DQN can approximate the state action value function of the mobile robot and perform accurate mapping from the current original image to the optimal action of the mobile robot. The experimental results in real indoor scenes demonstrate that the DQN trained in the simulated environment can work in the real indoor environment, and the mobile robot can also avoid obstacles and reach the target location even with dynamics and the presence of interference in the environment. It is therefore an effective and environmentally adaptable autonomous navigation method for mobile robots in an unknown environment.
Code Quality Improvement for All: Automated Refactoring for Scratch

Block-based programming has been overwhelmingly successful in revitalizing introductory computing education and in facilitating end-user development. However, poor code quality makes block-based programs hard to understand, modify, and reuse, thus hurting the educational and productivity effectiveness of blocks. There is great potential benefit in empowering programmers in this domain to systematically improve the code quality of their projects. Refactoring--improving code quality while preserving its semantics--has been widely adopted in traditional software development. In this work, we introduce refactoring to Scratch. We define four new Scratch refactorings: Extract Custom Block, Extract Parent Sprite, Extract Constant, and Reduce Variable Scope. To automate the application of these refactorings, we enhance the Scratch programming environment with powerful program analysis and transformation routines. To evaluate the utility of these refactorings, we apply them to remove the code smells detected in a representative dataset of 448 Scratch projects. We also conduct a between-subjects user study with 24 participants to assess how our refactoring tools impact programmers. Our results show that refactoring improves the subjects' code quality metrics, while our refactoring tools help motivate programmers to improve code quality.
Fuzzy logic interpretation of quadratic networks

Over past several years, deep learning has achieved huge successes in various applications. However, such a data-driven approach is often criticized for lack of interpretability. Recently, we proposed artificial quadratic neural networks consisting of quadratic neurons in potentially many layers. In cellular level, a quadratic function is used to replace the inner product in a traditional neuron, and then undergoes a nonlinear activation. With a single quadratic neuron, any fuzzy logic operation, such as XOR, can be implemented. In this sense, any deep network constructed with quadratic neurons can be interpreted as a deep fuzzy logic system. Since traditional neural networks and quadratic counterparts can represent each other and fuzzy logic operations are naturally implemented in quadratic neural networks, it is plausible to explain how a deep neural network works with a quadratic network as the system model. In this paper, we generalize and categorize fuzzy logic operations implementable with individual quadratic neurons, and then perform statistical/information-theoretic analyses of exemplary quadratic neural networks.
Efficient Edge-Based Image Interpolation Method Using Neighboring Slope Information

This paper presents an image interpolation method that provides superior performance with low complexity.Applying a simple linear method achieves time-efficient interpolation, but often produces artifacts; however, applying other complex methods reduce unwanted artifacts at the cost of high computation time.The proposed interpolation scheme is based on the improvement of an algorithm called ''Edge Slope Tracing (EST)'' that predicts the slope based on the information of the adjacent slopes.Predicted slopes are used in slope-based line averaging to perform the interpolation.Slope-based line averaging is followed by post-processing of an interpolated image using two-way interpolation and thin edge correction to avoid the production of unwanted artifacts at the cost of low complexity.The proposed algorithm is very simple and simulation results indicate that it provides better or nearly equal results compared to conventional state-ofthe-art algorithms and that it also reduces complexity to a great extent compared to conventional methods.
A Buyer and Seller’s Protocol via Utilization of Smart Contracts Using Blockchain Technology

In this paper we have proposed and implemented a buyer and seller's protocol on ethereum platform via utilization of smart contracts using blockchain technology. We have discussed the shift towards the digitization of registering lands and the necessity to use Blockchain instead of traditional storage technologies and provide a brief description and working implementation of the proposed model.
Cryptober: A Blockchain-based Secure and Cost-Optimal Car Rental Platform

Blockchain is a growing list of records, stored in blocks, which are linked and secured using cryptography. Blockchain is important because it brings trust to peer-to-peer networks. Various blockchain applications from small to big are focused towards decentralizing different tasks and are trying to empower the masses to act without any intermediary in between. The existing and upcoming blockchain applications are highly promising to increase the level of comfort for everyone. Smart contracts can be thought of as self-executing contracts with the terms of the agreement between buyer and seller directly written into lines of code. In this paper, we present a detailed review of how blockchain and smart contracts can be used to create a platform for car rental services that will be beneficial for both the car owner and the renter. The platform is cost optimal because there will be no intermediary in between. This will also introduce high security, privacy, authentication, and safety in the car rental industry.
From GenderMag to InclusiveMag: An Inclusive Design Meta-Method

How can software practitioners assess whether their software supports diverse users?Although there are empirical processes that can be used to find "inclusivity bugs" piecemeal, what is often needed is a systematic inspection method to assess software's support for diverse populations.To help fill this gap, this paper introduces InclusiveMag, a generalization of Gender-Mag that can be used to generate systematic inclusiveness methods for a particular dimension of diversity.We then present a multicase study covering eight diversity dimensions, of eight teams' experiences applying InclusiveMag to eight under-served populations and their "mainstream" counterparts.
A Value Recognition Algorithm for Pointer Meter Based on Improved Mask-RCNN

The automatic reading recognition technology for pointer meter is widely used in the military, industrial, and aerospace fields. However, the accuracy of recognizing pointer meter is susceptible to some factors during the recognition process, such as uneven illumination, complex background, rotation angle, image blur, shooting angle, scale change and other factors, all of which may result in lower accuracy and usability. In this paper, in order to overcome these difficulties, we make improvements to the existing Mask-RCNN network, and propose a novel deep learning based algorithm using PrRoIPooling instead of RoiAlign to achieve the accurate segmentation to improve the accuracy of meter readings. The main procedure of the algorithm is to first use the Mask-RCNN instance segmentation network to segment the meter dial and the pointer area, and then classify and recognize the meter type while fitting the pointer. Finally, the angle reading method is used to calculate the pointer reading. Experiments show that the algorithm is robust, adaptable and effective to the natural environment.
Explainable AI Planning (XAIP): Overview and the Case of Contrastive Explanation (Extended Abstract)

Model-based approaches to AI are well suited to explainability in principle, given the explicit nature of their world knowledge and of the reasoning performed to take decisions. AI Planning in particular is relevant in this context as a generic approach to action-decision problems. Indeed, explainable AI Planning (XAIP) has received interest since more than a decade, and has been taking up speed recently along with the general trend to explainable AI. In the lecture, we provide an overview, categorizing and illustrating the different kinds of explanation relevant in AI Planning; and we outline recent works on one particular kind of XAIP, contrastive explanation. This extended abstract gives a brief summary of the lecture, with some literature pointers. We emphasize that completeness is neither claimed nor intended; the abstract may serve as a brief primer with literature entry points.
Harmonized Multimodal Learning with Gaussian Process Latent Variable Models

Multimodal learning aims to discover the relationship between multiple modalities. It has become an important research topic due to extensive multimodal applications such as cross-modal retrieval. This paper attempts to address the modality heterogeneity problem based on Gaussian process latent variable models (GPLVMs) to represent multimodal data in a common space. Previous multimodal GPLVM extensions generally adopt individual learning schemes on latent representations and kernel hyperparameters, which ignore their intrinsic relationship. To exploit strong complementarity among different modalities and GPLVM components, we develop a novel learning scheme called Harmonization, where latent representations and kernel hyperparameters are jointly learned from each other. Beyond the correlation fitting or intra-modal structure preservation paradigms widely used in existing studies, the harmonization is derived in a model-driven manner to encourage the agreement between modality-specific GP kernels and the similarity of latent representations. We present a range of multimodal learning models by incorporating the harmonization mechanism into several representative GPLVM-based approaches. Experimental results on four benchmark datasets show that the proposed models outperform the strong baselines for cross-modal retrieval tasks, and that the harmonized multimodal learning method is superior in discovering semantically consistent latent representation.
Dynamic Switch Control of Steering Modes for Four Wheel Independent Steering Rescue Vehicle

For the sake of realizing dynamic switch among three steering modes for the four wheel independent steering (4WIS) rescue vehicle in the non-stop state, the switch control strategy and the optimized wheel switch trajectory are studied in this paper.Aiming at ensuring the constant turning radius of the 4WIS rescue vehicle in the switch process of steering modes, the steering angle relationships between the driving wheel and driven wheels are derived, and the desired driving wheel angle of the target steering mode is calculated.The B-spline curve is proposed to design the switch trajectory of the driving wheel.Two objectives including minimizing the sudden change of vehicle dynamic parameters and minimizing the energy consumption in the switch process are discussed.A multi-objective genetic algorithm (MOGA) based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed to deal with the optimization problem of the wheel switch trajectory.The numerical simulations based on Matlab/Simulink indicate that the presented algorithm can generate the Pareto optimal solutions and is suitable for the optimization of the wheel switch trajectory.In addition, the proposed switch control strategy of steering modes for the 4WIS rescue vehicle in the non-stop state is validated to be reasonable and effective.INDEX TERMS Four wheel independent steering (4WIS) rescue vehicle, dynamic switch of steering modes, constant turning radius, optimal switch trajectory, multi-objective genetic algorithm (MOGA).