Reviewerly/exHarmony huggingface dataset:
Automatic Generation of Acceptance Test Cases From Use Case Specifications: An NLP-Based Approach

Acceptance testing is a validation activity performed to ensure the conformance of software systems with respect to their functional requirements. In safety critical systems, it plays a crucial role since it is enforced by software standards, which mandate that each requirement be validated by such testing in a clearly traceable manner. Test engineers need to identify all the representative test execution scenarios from requirements, determine the runtime conditions that trigger these scenarios, and finally provide the input data that satisfy these conditions. Given that requirements specifications are typically large and often provided in natural language (e.g., use case specifications), the generation of acceptance test cases tends to be expensive and error-prone. In this paper, we present Use Case Modeling for System-level, Acceptance Tests Generation (UMTG), an approach that supports the generation of executable, system-level, acceptance test cases from requirements specifications in natural language, with the goal of reducing the manual effort required to generate test cases and ensuring requirements coverage. More specifically, UMTG automates the generation of acceptance test cases based on use case specifications and a domain model for the system under test, which are commonly produced in many development environments. Unlike existing approaches, it does not impose strong restrictions on the expressiveness of use case specifications. We rely on recent advances in natural language processing to automatically identify test scenarios and to generate formal constraints that capture conditions triggering the execution of the scenarios, thus enabling the generation of test data. In two industrial case studies, UMTG automatically and correctly translated 95 percent of the use case specification steps into formal constraints required for test data generation; furthermore, it generated test cases that exercise not only all the test scenarios manually implemented by experts, but also some critical scenarios not previously considered.
FuturePong: Real-time Table Tennis Trajectory Forecasting using Pose Prediction Network

In most sports, the ability to forecast motions and trajectories is among the highest priority, which can be only earned from experience. How to predict the motion from image and visualize for training is a challenging topic for computer vision. In this paper, we present a real-time table tennis forecasting system using a long short-term pose prediction network. Our system can predict the landing point of a serve before the pingpong ball is even hit using the previous and present motions of a player, which is captured only using a single RGB camera. In the precision evaluation, our system shows an acceptable accuracy and the max difference is 8.9 cm. From another pilot study, we know that our system could help the amateur to return an expert's serve. As training application, this system can be either used for training beginner's prediction skill, or used for practitioners to train how to hide their serve from being predicted.
Perceptual Image Hashing With Texture and Invariant Vector Distance for Copy Detection

Content-based image copy detection has become one of the important technologies in copyright protection, where two major processes, content-based feature extraction and matching are included. However, it is certainly true that enough storage space is required to establish feature database for matching, which greatly increases time and storage consumption, as well as lacks flexibility. Fortunately, perceptual image hashing is a good strategy to address these problems, in which content-based features are extracted and further encoded to hash codes. On the one hand, content-based features provide and ensure higher copy detection accuracy, while on the other hand, hash codes instead of feature database reduce storage space and improve time efficiency. Meanwhile, a better balance between robustness and discrimination is one of the most objectives of image hashing, which is conducive to its application in multimedia management and security. Consequently, we present an effective image hashing method for copy detection. Specifically, to obtain perceptual robustness against to copy attacks, we extract the global statistical characteristics in gray-level co-occurrence matrix (GLCM) to reveal texture changes. Then, to make up the discrimination limitation, we leverage the local dominant DCT coefficients from the first row/column in each sub-image to calculate vector distance. Finally, two kinds of complementary information (global feature via texture and local feature via vector distance) are simultaneously preserved to generate hash codes. Various experiments performed on benchmark database indicate that our proposed perceptual image hashing provides higher detection accuracy and better balance between robustness and discrimination than the state-of-the-art algorithms.
Computing Optimal Block Size for Blockchain based Applications with Contradictory Objectives

Distributed ledger technology is the driving force behind the blockchain technology and is proving its usefulness in various types of transaction processing systems. Fast, secure, reliable and efficient transactions are the key features of the blockchain based applications. A suitable or optimal size of block used by an application is dependent on the number of transactions in each block. Block size optimization is an important issue for any blockchain based application as it directly affects the performance of the application as scalability bottlenecks could prevent higher throughput and cause congestion. A larger block size will require higher transmission time compared to the smaller block size. A smaller block is more efficient but building too small a block will require higher block composition time to clear all the transactions. Both performance factors are contradictory to each other. An efficient blockchain network requires a suitable block size that demands lesser transmission time and block composition time. This paper proposes meta-heuristic algorithm based techniques for finding the suitable block size. It uses meta-heuristic algorithms to find the optimal number of transaction in each block. These algorithms are multi-objective particle swarm optimization and strength Pareto evolutionary algorithm. Experimental results reveal that the suitable block size is 213 transactions per block. Since size of a transaction is taken as 1.2 Kb, hence the results show that an optimal block size is of 255 Kbytes when network bandwidth of miners varies from 250 kbps to 1200 kbps. This shall achieve lower block transmission time and block composition time.
A novel mobile robot navigation method based on deep reinforcement learning

At present, the application of mobile robots is more and more extensive, and the movement of mobile robots cannot be separated from effective navigation, especially path exploration. Aiming at navigation problems, this article proposes a method based on deep reinforcement learning and recurrent neural network, which combines double net and recurrent neural network modules with reinforcement learning ideas. At the same time, this article designed the corresponding parameter function to improve the performance of the model. In order to test the effectiveness of this method, based on the grid map model, this paper trains in a two-dimensional simulation environment, a three-dimensional TurtleBot simulation environment, and a physical robot environment, and obtains relevant data for peer-to-peer analysis. The experimental results show that the proposed algorithm has a good improvement in path finding efficiency and path length.
Differential Detection of Facial Retouching: A Multi-Biometric Approach

Facial retouching apps have become common tools which are frequently applied to improve one's facial appearance, e.g. before sharing face images via social media. Beautification induced by retouching has the ability to substantially alter the appearance of face images and hence might represent a challenge for face recognition. Towards deploying secure face recognition as well as enforcing anti-photoshop legislations, a robust and reliable detection of retouched face image is needed. Published approaches consider a single image-based (no-reference) scenario where a potentially retouched face image serves as sole input to the retouching detector. However, in many cases a trusted unaltered face image of a subject examined is available which enables an image pair-based (differential) detection scheme. In this work, ICAO-compliant subsets of the FERET and FRGCv2 face databases are used to automatically create a database containing 9,078 retouched face images together with unconstrained probe images. In evaluations employing the commercial Cognitec FaceVACS and the open-source ArcFace face recognition system, it is shown that facial retouching can negatively impact face recognition performance. Further, a differential facial retouching detection system is proposed which processes pairs of a potentially retouched reference image and corresponding unaltered probe image of single subjects. Estimated differences in feature vectors obtained from texture descriptors, facial landmarks, and deep face representations are leveraged by machine learning-based classifiers of which the detection scores are fused to distinguish between retouched and unaltered face images. The proposed scheme is evaluated in a cross-database scenario where training and testing are performed on the FERET and FRGCv2 databases and vice versa. In the scenario where the used retouching algorithm is known by the detection algorithm, a competitive average D-EER of approximately 2% is achieved. Further, the scenario in which the employed retouching algorithm is not known by the detection algorithm is evaluated. In the latter scenario, the proposed approach obtains an average D-EER below 10% and is shown to outperform several state-of-the-art single image-based detection schemes.
A New CNN-RNN Framework For Remote Sensing Image Captioning

Remote sensing (RS) image captioning has been recently attracting the attention of the community as it provides more semantic information with respect to the traditional tasks such as scene classification. Image captioning aims to generate a coherent and comprehensive description that summarizes the content of an image. The description can be obtained directly from the ground truth descriptions of similar images (retrieval based image captioning) or can be generated through the encoder-decoder framework. The former has the limitation of not generating new descriptions. The latter may be affected by misrecognition of scenes or semantic objects. In this paper we try to address these issues by proposing a new framework which is a combination of generation and retrieval based image captioning. First a CNN-RNN framework combined with beam-search generates multiple captions for a target image. Then the best caption is selected on the basis of its lexical similarity with the reference captions of most similar images. Experimental results on RSCID dataset are reported and discussed.
Hybrid Quantum Protocols for Secure Multiparty Summation and Multiplication

Abstract The summation and multiplication are two basic operations for secure multiparty quantum computation. The existing secure multiparty quantum summation and multiplication protocols have ( n , n ) threshold approach and their computation type is bit-by-bit, where n is total number of players. In this paper, we propose two hybrid ( t , n ) threshold quantum protocols for secure multiparty summation and multiplication based on the Shamir’s secret sharing, SUM gate, quantum fourier transform, and generalized Pauli operator, where t is a threshold number of players that can perform the summation and multiplication. Their computation type is secret-by-secret with modulo d , where d , n ≤ d ≤ 2 n , is a prime. The proposed protocols can resist the intercept-resend, entangle-measure, collusion, collective, and coherent quantum attacks. They have better computation as well as communication costs and no player can get other player’s private input.
Tokenization of sukuk: Ethereum case study

Sukuk is a financial instrument that provides returns similar to conventional bonds. It has served to cater to the capital requirements of big corporations and governments, while circumventing interest to adhere to the Shariah law. Sukuk can be touted as Shariah-compliant bonds that rank among the most successful and the fastest growing financial instrument in the Islamic economy. The sukuk research area is marked by a dearth of quantitative literature, compared to qualitative academic work. This paper seeks to fill this existing gap, and introduces a novel, exploratory analysis of sukuk tokenization based on a case study. The funding needs of small and medium enterprises remains largely unmet through sukuk on account of the high costs involved, among other reasons. As we show in this paper, blockchains can aid to lower the cost incurred through the tokenization of sukuk. We highlight some of the key challenges involved in the issuance of sukuk and discuss their resolution using blockchain. We also provide a taxonomy of blockchain applications in finance, with a particular focus on Islamic finance. Our paper reviews different blockchain architectures to assess their viability for tokenization. We conduct a novel case study on sukuk tokenization by implementing a basic smart contract for Sukuk al-Murabaha on Ethereum. The paper concludes by a conceptual analysis of feasibility concerns, based on a comparison of the conducted cost-benefit analysis of conventional sukuk issuance with tokenization.
The effect of voice and humour on users’ perceptions of personal intelligent agents

Personal Intelligent Agents (PIAs) like Siri and Alexa are becoming more popular among users. In this paper, we investigate the effect of humour and voice of personal intelligent agents, their impact on users' perceptions of intelligence and anthropomorphism, and the relationship of these perceptions with cognitive- and emotion-based trust. The results from an online experiment show that humour and voice significantly and positively influence users' perceptions of anthropomorphism. These perceptions positively impact users' emotion-based trust, which increases their intention to use the PIA. We also find that perceptions of intelligence shape users' cognitive-based trust in the PIA. Our model is novel because it examines two key design characteristics of PIAs and articulates their effects on user perceptions. The effect of human-like characteristics, specifically humour and voice, on perceptions of intelligence and anthropomorphism and the potential impact on users' cognitive- and emotion-based trust in PIAs have not been explored in an IS context.
Noise Robust Generative Adversarial Networks

Generative adversarial networks (GANs) are neural networks that learn data distributions through adversarial training. In intensive studies, recent GANs have shown promising results for reproducing training images. However, in spite of noise, they reproduce images with fidelity. As an alternative, we propose a novel family of GANs called noise robust GANs (NR-GANs), which can learn a clean image generator even when training images are noisy. In particular, NR-GANs can solve this problem without having complete noise information (e.g., the noise distribution type, noise amount, or signal-noise relationship). To achieve this, we introduce a noise generator and train it along with a clean image generator. However, without any constraints, there is no incentive to generate an image and noise separately. Therefore, we propose distribution and transformation constraints that encourage the noise generator to capture only the noise-specific components. In particular, considering such constraints under different assumptions, we devise two variants of NR-GANs for signal-independent noise and three variants of NR-GANs for signal-dependent noise. On three benchmark datasets, we demonstrate the effectiveness of NR-GANs in noise robust image generation. Furthermore, we show the applicability of NR-GANs in image denoising. Our code is available at https://github.com/takuhirok/NR-GAN/.
Auxiliary Training: Towards Accurate and Robust Models

Training process is crucial for the deployment of the network in applications which have two strict requirements on both accuracy and robustness. However, most existing approaches are in a dilemma, i.e. model accuracy and robustness form an embarrassing tradeoff - the improvement of one leads to the drop of the other. The challenge remains as for we try to improve the accuracy and robustness simultaneously. In this paper, we propose a novel training method via introducing the auxiliary classifiers for training on corrupted samples, while the clean samples are normally trained with the primary classifier. In the training stage, a novel distillation method named input-aware self distillation is proposed to facilitate the primary classifier to learn the robust information from auxiliary classifiers. Along with it, a new normalization method - selective batch normalization is proposed to prevent the model from the negative influence of corrupted images. At the end of training period, a L2-norm penalty is applied to the weights of primary and auxiliary classifiers such that their weights are asymptotically identical. In the stage of inference, only the primary classifier is used and thus no extra computation and storage are needed. Extensive experiments on CIFAR10, CIFAR100 and ImageNet show that noticeable improvements on both accuracy and robustness can be observed by the proposed auxiliary training. On average, auxiliary training achieves 2.21% accuracy and 21.64% robustness (measured by corruption error) improvements over traditional training methods on CIFAR100. Codes has been released on github.
Constant-factor approximation of near-linear edit distance in near-linear time

We show that the edit distance between two strings of length n can be computed via a randomized algorithm within a factor of f(є) in n 1+є time as long as the edit distance is at least n 1−δ for some δ(є) > 0.
Joint Texture and Geometry Optimization for RGB-D Reconstruction

Due to inevitable noises and quantization error, the reconstructed 3D models via RGB-D sensors always accompany geometric error and camera drifting, which consequently lead to blurring and unnatural texture mapping results. Most of the 3D reconstruction methods focus on either geometry refinement or texture improvement respectively, which subjectively decouples the inter-relationship between geometry and texture. In this paper, we propose a novel approach that can jointly optimize the camera poses, texture and geometry of the reconstructed model, and color consistency between the key-frames. Instead of computing Shape-From-Shading (SFS) expensively, our method directly optimizes the reconstructed mesh according to color and geometric consistency and high-boost normal cues, which can effectively overcome the texture-copy problem generated by SFS and achieve more detailed shape reconstruction. As the joint optimization involves multiple correlated terms, therefore, we further introduce an iterative framework to interleave the optimal state. The experiments demonstrate that our method can recover not only fine-scale geometry but also high-fidelity texture.
Runtime Analysis for Self-adaptive Mutation Rates

We propose and analyze a self-adaptive version of the $$(1,\lambda )$$ evolutionary algorithm in which the current mutation rate is encoded within the individual and thus also subject to mutation. A rigorous runtime analysis on the OneMax benchmark function reveals that a simple local mutation scheme for the rate leads to an expected optimization time (number of fitness evaluations) of $$O(n\lambda /\log \lambda +n\log n)$$ when $$\lambda$$ is at least $$C \ln n$$ for some constant $$C > 0$$ . For all values of $$\lambda \ge C \ln n$$ , this performance is asymptotically best possible among all $$\lambda$$ -parallel mutation-based unbiased black-box algorithms. Our result rigorously proves for the first time that self-adaptation in evolutionary computation can find complex optimal parameter settings on the fly. In particular, it gives asymptotically the same performance as the relatively complicated self-adjusting scheme for the mutation rate proposed by Doerr, Gießen, Witt, and Yang (Algorithmica 2019). On the technical side, the paper contributes new tools for the analysis of two-dimensional drift processes arising in the analysis of dynamic parameter choices in EAs, including bounds on occupation probabilities in processes with non-constant drift.
Progressive Mirror Detection

The mirror detection problem is important as mirrors can affect the performances of many vision tasks. It is a difficult problem as it requires an understanding of global scene semantics. Recently, a method was proposed to detect mirrors by learning multi-level contextual contrasts between inside and outside of mirrors, which helps locate mirror edges implicitly. We observe that the content of a mirror reflects the content of its surrounding, separated by the edge of the mirror. Hence, we propose a model in this paper to progressively learn the content similarity between the inside and outside of the mirror while explicitly detecting the mirror edges. Our work has two main contributions. First, we propose a new relational contextual contrasted local (RCCL) module to extract and compare the mirror features with its corresponding context features, and an edge detection and fusion (EDF) module to learn the features of mirror edges in complex scenes via explicit supervision. Second, we construct a challenging benchmark dataset of 6,461 mirror images. Unlike the existing MSD dataset, which has limited diversity, our dataset covers a variety of scenes and is much larger in scale. Experimental results show that our model outperforms relevant state-of-the-art methods.
Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations

Recent research has made the surprising finding that state-of-the-art deep learning models sometimes fail to generalize to small variations of the input. Adversarial training has been shown to be an effective approach to overcome this problem. However, its application has been limited to enforcing invariance to analytically defined transformations like lp-norm bounded perturbations. Such perturbations do not necessarily cover plausible real-world variations that preserve the semantics of the input (such as a change in lighting conditions). In this paper, we propose a novel approach to express and formalize robustness to these kinds of real-world transformations of the input. The two key ideas underlying our formulation are (1) leveraging disentangled representations of the input to define different factors of variations, and (2) generating new input images by adversarially composing the representations of different images. We use a StyleGAN model to demonstrate the efficacy of this framework. Specifically, we leverage the disentangled latent representations computed by a StyleGAN model to generate perturbations of an image that are similar to real-world variations (like adding make-up, or changing the skin-tone of a person) and train models to be invariant to these perturbations. Extensive experiments show that our method improves generalization and reduces the effect of spurious correlations (reducing the error rate of a "smile" detector by 21% for example).