Reviewerly/exHarmony huggingface dataset:
An ontology-based learning approach for automatically classifying security requirements

Although academia has recognized the importance of explicitly specifying security requirements in early stages of system developments for years, in reality, many projects mix security requirements with other types of requirements. Thus, there is a strong need for precisely and efficiently classifying such security requirements from other requirements in requirement specifications. Existing studies leverage lexical evidence to build probabilistic classifiers, which are domain-dependent by design and cannot effectively classify security requirements from different application domains. In this paper, we propose an ontology-driven learning approach to automatically classify security requirements. Our approach consists of a conceptual layer and a linguistic layer, which understands security requirements based on not only lexical evidence but also conceptual domain knowledge. In particular, we apply a systematic approach to identify linguistic features of security requirements based on an extended security requirements ontology and linguistic knowledge, connecting the conceptual layer with the linguistic layer. Such linguistic features are then used to train domain-independent security requirements classifiers by using machine learning techniques. We have carried out a series of experiments to evaluate the performance and generalization ability of our proposal against existing approaches. The results of the experiments show that the proposed approach outperforms existing approaches with a significant increase of F1 score (0.63 VS. 0.44) when the training dataset and the testing dataset come from different application domains, i.e., the classifiers trained by our approach can be generalized to classify security requirements from different domains.
Study on real-time wearable sport health device based on body sensor networks

As a branch of the wireless sensor network and an important part of the Internet of Things (IoT), Body Sensor Network (BSN) integrates biomedical engineering technology and wireless sensor network. It has high precision, small monitoring range, miniaturization and low power. The congruent feature can monitor the physiological information and motion information of the human body in real time, and provide the tester with many important quantitative information, to realize early monitoring, early prevention and early intervention of some common chronic diseases. This technology can be widely applied in biomedical, sports training, daily activity monitoring and other fields. In addition, the human sensor network is also an implementation of home health care, providing a new way of thinking for telemedicine and home health care. In order to realize the human motion monitoring system based on human body sensor network. This research aims to design a wearable sport health monitoring system with low cost, low power consumption, high modularity, high reliability, and high precision. The human body motion monitoring system of the axis wireless sensor platform enables real-time monitoring of sports health.
H3-hybrid multilayered hyper chaotic hyper elliptic curve based image encryption system

Excessive need for digitized images in all domains have necessitated the need for protecting the images against adversaries for secured transmission in the network. Sensitive images processed in medical, crime investigations are presumed to be highly due its sensitive nature. As per the state of art encryption methodologies are not exclusively designed for color images and simply extended from the one established for gray images. A multilayered hybrid model has been proposed and named as Hybrid multilayered Hyper-chaotic Hyper-elliptic curve based image encryption (HHH) scheme to handle color images used in medical and forensic domains. In the first layer, hyper chaotic based DNA encoding is implemented to generate chaotic sequences. Substitution permutation increases confusion diffusion level to increase the security strength of crypto system. In the second layer, Genus-2 Hyper Elliptic Curve Cryptography over GF(p) that emphasis spatial domain for encryption has been employed for its mathematical strength. The proposed HHH system is hybrid in nature as it combines symmetric and asymmetric cryptosystem. All sort of signal processing mechanisms that include susceptible colored images can be benefited with this approach as the experimental results endorse ideal measure for PSNR, entropy, NPCR, UACI and MSE values tested with standard bench mark images.
Weakly Supervised Reinforced Multi-Operator Image Retargeting

Image retargeting aims to adjust the resolution and aspect ratio to an arbitrary size while preserving important content of the image. Usually multi-operator image retargeting demonstrates better generalization than single operator scheme due to heterogeneous characteristics of different regions in the image. Most existing multi-operator retargeting methods search the optimal operator at each step with exponential complexity and with the possibility of falling into local optimum. Therefore, in order to produce better results with lower computational costs, we formulate the multi-operator retargeting as a Markov decision-making process and apply Reinforcement Learning (RL) to achieve global optimum. Instead of using traditional image-level measures, we design a high-level semantic and aesthetic reward function to better match human visual perception. With the priori in reward, we further propose a weakly supervised Semantics and Aesthetics aware Multi-operator Image Retargeting (SAMIR) framework. Particularly, the semantic part of the reward helps to constrain the severe deformations that may occur during retargeting process, while the aesthetic part guarantees the sensory quality, which can effectively measure the perceptual effects of different operators on various image content. The operator of each step is learned in an end-to-end manner. In addition, retargeting can be performed in arbitrary target size, step size, and direction. The experiment results on both representative aesthetic datasets and retargeting datasets consistently show that our model outperforms the state-of-the-art methods.
Multitask Learning and Reinforcement Learning for Personalized Dialog Generation: An Empirical Study

Open-domain dialog generation, which is a crucial component of artificial intelligence, is an essential and challenging problem. In this article, we present a personalized dialog system, which leverages the advantages of multitask learning and reinforcement learning for personalized dialogue generation (MRPDG). Specifically, MRPDG consists of two subtasks: 1) an author profiling module that recognizes user characteristics from the input sentence (auxiliary task) and 2) a personalized dialog generation system that generates informative, grammatical, and coherent responses with reinforcement learning algorithms (primary task). Three kinds of rewards are proposed to generate high-quality conversations. We investigate the effectiveness of three widely used reinforcement learning methods [i.e., Q-learning, policy gradient, and actor-critic (AC) algorithm] in a personalized dialog generation system and demonstrate that the AC algorithm achieves the best results on the underlying framework. Comprehensive experiments are conducted to evaluate the performance of the proposed model on two real-life data sets. Experimental results illustrate that MRPDG is able to produce high-quality personalized dialogs for users with different characteristics. Quantitatively, the proposed model can achieve better performance than the compared methods across different evaluation metrics, such as the human evaluation, BiLingual Evaluation Understudy (BLEU), and perplexity.
unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata

Abstract In recent years, scholarly data sets have been used for various purposes, such as paper recommendation, citation recommendation, citation context analysis, and citation context-based document summarization. The evaluation of approaches to such tasks and their applicability in real-world scenarios heavily depend on the used data set. However, existing scholarly data sets are limited in several regards. In this paper, we propose a new data set based on all publications from all scientific disciplines available on arXiv.org. Apart from providing the papers’ plain text, in-text citations were annotated via global identifiers. Furthermore, citing and cited publications were linked to the Microsoft Academic Graph, providing access to rich metadata. Our data set consists of over one million documents and 29.2 million citation contexts. The data set, which is made freely available for research purposes, not only can enhance the future evaluation of research paper-based and citation context-based approaches, but also serve as a basis for new ways to analyze in-text citations, as we show prototypically in this article.
SIF: Self-Inspirited Feature Learning for Person Re-Identification

The re-identification (ReID) task has received increasing studies in recent years and its performance has gained significant improvement. The progress mainly comes from searching for new network structures to learn person representations. Most of these networks are trained using the classic stochastic gradient descent optimizer. However, limited efforts have been made to explore potential performance of existing ReID networks directly by better training scheme, which leaves a large space for ReID research. In this paper, we propose a Self-Inspirited Feature Learning (SIF) method to enhance performance of given ReID networks from the viewpoint of optimization. We design a simple adversarial learning scheme to encourage a network to learn more discriminative person representation. In our method, an auxiliary branch is added into the network only in the training stage, while the structure of the original network stays unchanged during the testing stage. In summary, SIF has three aspects of advantages: (1) it is designed under general setting; (2) it is compatible with many existing feature learning networks on the ReID task; (3) it is easy to implement and has steady performance. We evaluate the performance of SIF on three public ReID datasets: Market1501, DuckMTMC-reID, and CUHK03(both labeled and detected). The results demonstrate significant improvement in performance brought by SIF. We also apply SIF to obtain state-of-the-art results on all the three datasets. Specifically, mAP / Rank-1 accuracy are: 87.6% / 95.2% (without re-rank) on Market1501, 79.4% / 89.8% on DuckMTMC-reID, 77.0% / 79.5% on CUHK03 (labeled) and 73.9% / 76.6% on CUHK03 (detected), respectively. The code of SIF will be available soon.
Jumps in Geopolitical Risk and the Cryptocurrency Market: The Singularity of Bitcoin

Are price discontinuities in cryptocurrencies jointly related to large swings in geopolitical risk? This is a relevant question to answer given recent news from the press that Bitcoin's price jumps are driven by jumps in the level of geopolitical risk index. To answer this question, we examine first the jump incidence of daily returns for Bitcoin and other leading cryptocurrencies and then study the co-jumps between cryptocurrencies and the geopolitical risk index using logistic regressions. Our dataset is at the daily frequency and covers the period 30 April 2013 to 31 October 2019. The results show that the price behaviour of all cryptocurrencies under study is jumpy but only Bitcoin jumps are dependent on jumps in the geopolitical risk index. This revealed evidence of significant co-jumps for the case of Bitcoin only nicely complements previous studies arguing that Bitcoin is a hedge against geopolitical risk.
An algebraic framework for swarm and evolutionary algorithms in combinatorial optimization

A popular trend in evolutionary computation is to adapt numerical algorithms to combinatorial optimization problems. For instance, this is the case of a variety of Particle Swarm Optimization and Differential Evolution implementations for both binary and permutation-based optimization problems. In this paper, after highlighting the main drawbacks of the approaches in literature, we provide an algebraic framework which allows to derive fully discrete variants of a large class of numerical evolutionary algorithms to tackle many combinatorial problems. The strong mathematical foundations upon which the framework is built allow to redefine numerical evolutionary operators in such a way that their original movements in the continuous space are simulated in the discrete space. Algebraic implementations of Differential Evolution and Particle Swarm Optimization are then proposed. Experiments have been held to compare the algebraic algorithms to the most popular schemes in literature and to the state-of-the-art results for the tackled problems. Experimental results clearly show that algebraic algorithms outperform the competitors and are competitive with the state-of-the-art results.
Animal Detection in Man-made Environments

Automatic detection of animals that have strayed into human inhabited areas has important security and road safety applications. This paper attempts to solve this problem using deep learning techniques from a variety of computer vision fields including object detection, segmentation, tracking and edge detection. Several interesting insights into transfer learning are elicited while adapting models trained on benchmark datasets for real world deployment. Empirical evidence is presented to demonstrate the inability of detectors to generalize from training images of animals in their natural habitats to deployment scenarios of man-made environments. A solution is also proposed using semi-automated synthetic data generation for domain specific training. Code and data used in the experiments are made available to facilitate further work in this domain.
Component Attention Guided Face Super-Resolution Network: CAGFace

To make the best use of the underlying structure of faces, the collective information through face datasets and the intermediate estimates during the upsampling process, here we introduce a fully convolutional multi-stage neural network for 4× super-resolution for face images. We implicitly impose facial component-wise attention maps using a segmentation network to allow our network to focus on face-inherent patterns. Each stage of our network is composed of a stem layer, a residual backbone, and spatial upsampling layers. We recurrently apply stages to reconstruct an intermediate image, and then reuse its space-to-depth converted versions to bootstrap and enhance image quality progressively. Our experiments show that our face super-resolution method achieves quantitatively superior and perceptually pleasing results in comparison to state of the art.
A GAN-based Tunable Image Compression System

The method of importance map has been widely adopted in DNN-based lossy image compression to achieve bit allocation according to the importance of image contents. However, insufficient allocation of bits in non-important regions often leads to severe distortion at low bpp (bits per pixel), which hampers the development of efficient content-weighted image compression systems. This paper rethinks content-based compression by using Generative Adversarial Network (GAN) to reconstruct the non-important regions. Moreover, multiscale pyramid decomposition is applied to both the encoder and the discriminator to achieve global compression of high-resolution images. A tunable compression scheme is also proposed in this paper to compress an image to any specific compression ratio without retraining the model. The experimental results show that our proposed method improves MS-SSIM by more than 10.3% compared to the recently reported GAN-based method [3] to achieve the same low bpp (0.05) on the Kodak dataset.
Hard and Soft Skills for Scrum Global Software Development Teams

Scrum is considered as one of the solutions to overcome problems encountered in global software development (GSD). The skills of a Scrum team largely determine the successful implementation of Scrum in a GSD project. The present study aims to identify and rank the essential skills for Scrum GSD teams. First, we identified five hard skills and five soft skills by conducting a literature study and depth interviews with a software engineering expert. Second, these skills were arranged into a questionnaire, which was then distributed to thirty undergraduate students taking software engineering subjects. The students were considered as Scrum beginners in development teams. The questionnaire data were subsequently analyzed by ranking the importance of the skills. Next, the rankings were validated in a focus group discussion (FGD) with four practitioners, two academics, one policymaker, and one representative of a relevant professional association. All members of the FGD had at least five years of experience in Scrum GSD. In the following stage, frequency analysis was employed to achieve a consensus among the experts. After that, a round table discussion was conducted to confirm the consensus. The results show that programming is the most important hard skill, while interpersonal and communication are the most important soft skills. Database expertise and leadership were identified as the least important hard and soft skills, respectively. Furthermore, the rank of these skills by importance is aligned with the nature of Scrum and GSD. Our findings can be used as a foundation to construct competencies in Scrum GSD teams.
TandemTrack: Shaping Consistent Exercise Experience by Complementing a Mobile App with a Smart Speaker

Smart speakers such as Amazon Echo present promising opportunities for exploring voice interaction in the domain of in-home exercise tracking. In this work, we examine if and how voice interaction complements and augments a mobile app in promoting consistent exercise. We designed and developed TandemTrack, which combines a mobile app and an Alexa skill to support exercise regimen, data capture, feedback, and reminder. We then conducted a four-week between-subjects study deploying TandemTrack to 22 participants who were instructed to follow a short daily exercise regimen: one group used only the mobile app and the other group used both the app and the skill. We collected rich data on individuals' exercise adherence and performance, and their use of voice and visual interactions, while examining how TandemTrack as a whole influenced their exercise experience. Reflecting on these data, we discuss the benefits and challenges of incorporating voice interaction to assist daily exercise, and implications for designing effective multimodal systems to support self-tracking and promote consistent exercise.
Design and Optimization of Energy-Accuracy Tradeoff Networks for Mobile Platforms via Pretrained Deep Models

Many real-world edge applications including object detection, robotics, and smart health are enabled by deploying deep neural networks (DNNs) on energy-constrained mobile platforms. In this article, we propose a novel approach to trade off energy and accuracy of inference at runtime using a design space called Learning Energy Accuracy Tradeoff Networks (LEANets). The key idea behind LEANets is to design classifiers of increasing complexity using pretrained DNNs to perform input-specific adaptive inference. The accuracy and energy consumption of the adaptive inference scheme depends on a set of thresholds, one for each classifier. To determine the set of threshold vectors to achieve different energy and accuracy tradeoffs, we propose a novel multiobjective optimization approach. We can select the appropriate threshold vector at runtime based on the desired tradeoff. We perform experiments on multiple pretrained DNNs including ConvNet, VGG-16, and MobileNet using diverse image classification datasets. Our results show that we get up to a 50% gain in energy for negligible loss in accuracy, and optimized LEANets achieve significantly better energy and accuracy tradeoff when compared to a state-of-the-art method referred to as Slimmable neural networks.
HYPNER: A Hybrid Approach for Personalized News Recommendation

A personalised news recommendation system extracts news set from multiple press releases and presents the recommended news to the user. In an effort to build a better recommender system with high accuracy, this paper proposes a personalised news recommendation framework named Hybrid Personalised NEws Recommendation (HYPNER). HYPNER combines both collaborative filtering-based and content-based filtering methods. The proposed framework aims at improving the accuracy of news recommendation by resolving the issues of scalability due to large news corpus, enriching the user's profile, representing the exact properties and characteristics of news items, and recommending diverse set of news items. Validation experiments showed that HYPNER achieved 81.56% improvement in F1 -score and 5.33% in diversity as compared to an existing recommender system, SCENE.
Heterogeneous Coded Distributed Computing: Joint Design of File Allocation and Function Assignment

This paper studies the computation-communication tradeoff in a heterogeneous MapReduce computing system where each distributed node is equipped with different computation capability. We first obtain an achievable communication load for any given computation load and any given function assignment at each node. The proposed file allocation strategy has two steps: first, the input files are partitioned into disjoint batches, each with possibly different size and computed by a distinct node; then, each node computes additional files from its non-computed files according to its redundant computation capability. In the Shuffle phase, coded multicasting opportunities are exploited thanks to the repetitive file allocation among different nodes. Based on this scheme, we further propose the computation-aware and the shuffle-aware function assignments. We prove that, by using proper function assignments, our achievable communication load for any given computation load is within a constant multiplicative gap to the optimum in an equivalent homogeneous system with the same average computation load. Numerical results show that our scheme with shuffle-aware function assignment achieves better computation- communication tradeoff than existing works in some cases.
Advantages of using digital image correlation techniques in uniaxial compression tests

This paper discusses the advantages of using stereo vision imaging along with Digital Image Correlation techniques as an alternate data acquisition procedure in uniaxial compression testing of specimens that may include undetected internal defects before testing. The discussed techniques were implemented during an extensive experimental investigation at the University of South Carolina related to concrete material characterization in order to investigate inconsistencies in test measurements acquired through conventional instrumentation. The use of the proposed system in compression tests of concrete and rock specimens facilitated the accurate and efficient measurement of the modulus of elasticity of the materials based on full field strain measurements. Most importantly, however, the proposed system facilitated the assessment of the quality of the specimen and measurements, as these may be affected by internal material defects that do not show any evidence on the surface of the specimen before any testing takes place, or the deviation of the shape and size of the specimen from the specifications.