Reviewerly/exHarmony huggingface dataset:
On the Security of a Latin-Bit Cube-Based Image Chaotic Encryption Algorithm

In this paper, the security analysis of an image chaotic encryption algorithm based on Latin cubes and bit cubes is given. The proposed algorithm adopts a first-scrambling-diffusion- second-scrambling three-stage encryption scheme. First, a finite field is constructed using chaotic sequences. Then, the Latin cubes are generated from finite field operation and used for image chaotic encryption. In addition, according to the statistical characteristics of the diffusion image in the diffusion stage, the algorithm also uses different Latin cube combinations to scramble the diffusion image for the second time. However, the generation of Latin cubes in this algorithm is independent of plain image, while, in the diffusion stage, when any one bit in the plain image changes, the corresponding number of bits in the cipher image follows the change with obvious regularity. Thus, the equivalent secret keys can be obtained by chosen plaintext attack. Theoretical analysis and experimental results indicate that only a maximum of 2 . 5 × w × h 3 + 6 plain images are needed to crack the cipher image with w × h resolution. The size of equivalent keys deciphered by the method proposed in this paper are much smaller than other general methods of cryptanalysis for similar encryption schemes.
Orthogonal modulated computer-generated moiré profilometry

An orthogonal modulated computer-generated moiré profilometry is proposed. Only one composite grating in which two sinusoidal fringe patterns with a π phase difference are modulated by two other orthogonal sinusoidal fringe patterns with different frequencies respectively is projected on the object. While a single-shot deformed composite pattern is captured, the two sinusoidal deformed patterns with a π phase difference are demodulated from the deformed composite pattern. The AC component of deformed pattern is calculated by subtraction of the two demodulated deformed patterns. If this AC component of deformed pattern is respectively multiplied by the two AC components of fringe patterns of reference plane with a π∕2 phase difference prepared and saved in computer in advance, two computer generated moiré fringes which just respectively stand for sine and cosine of phase modulated by the height of the object relative to the reference plane are extracted. The tangent of the modulated phase is obtained by the ratio of these two moiré fringes. So the three-dimensional (3D) shape of object can be reconstructed using only one deformed composite pattern. Both simulation and experimental results demonstrate the feasibility and validity of the proposed method. It has potential in real-time 3D measurement due to its single-shot feature.
Linkages between Globalisation, Carbon Dioxide Emissions and Governance in Sub-Saharan Africa

This study investigates linkages between environmental degradation, globalisation and governance in 44 countries in Sub-Saharan Africa using data for the period 2000–2012. The Generalised Method of Moments is employed as empirical strategy. Environmental degradation is proxied by carbon dioxide emissions whereas globalisation is appreciated in terms of trade openness and net foreign direct investment inflows. Bundled and unbundled governance indicators are used, namely: political governance (consisting of political stability/no violence and “voice & accountability”), economic governance (encompassing government effectiveness and regulation quality), institutional governance (entailing corruption-control and the rule of law) and general governance (a composite measurement of political governance, economic governance and institutional governance). The following main finding is established. Trade openness modulates carbon dioxide emissions to have positive net effects on political stability, economic governance, the rule of law and general governance.
Toward a Blockchain-Enabled Crowdsourcing Platform

Crowdsourcing has been pursued as a way to leverage the power of the crowd for many different purposes in diverse sectors from collecting information, aggregating funds, and gathering employees to perform tasks of different sizes among other targets. Data integrity and nonrepudiation are of utmost importance in these systems and are currently not guaranteed. Blockchain technology has been proven to improve on these aspects. In this article, we investigate the benefits that the adoption of Blockchain technology can bring in crowdsourcing systems. To this end, we provide examples of real-life crowdsourcing use cases and explore the benefits of using Blockchain, mainly as a database.
Automatic Labeling of Topic Models Using Graph-Based Ranking

Generated topic label, an alternative representation of topics learned by topic model, is widely used to help the user interpret the topics more efficiently.A major challenge now is to label a discovered topic accurately in an objective way.This article introduces a novel graph-based ranking model (TLRank), to find a meaningful topic label with high Relevance, Coverage, and Discrimination.The model applies a specific strategy that suppresses or enhances the matrix transition probability according to the textual similarity between vertices (sentences) and the characteristics of vertices respectively.Moreover, to boost diversity and enhance performance, TLRank scores the candidate sentences and refrains redundancy of topic labels simultaneously in a single labeling process.In our experiments, the evaluation results showed that the TLRank model significantly and consistently outperformed the prevailing state-of-the-art and classic models in topic labeling task.
Open Framework for Mammography-based Breast Cancer Risk Assessment

In recent years, several studies have established a relationship between mammographic parenchymal patterns and breast cancer risk. However, there is a lack of publicly available data and software for objective comparison and clinical validation. This paper presents an open and adaptable implementation (OpenBreast v1.0) of a fully-automatic computerized framework for mammographic image analysis for breast cancer risk assessment. OpenBreast implements mammographic image analysis in four stages: breast segmentation, detection of region-of-interests, feature extraction and risk scoring. For each stage, we provide implementations of several state-of-the-art methods. The pipeline is tested on a set of 305 full-field digital mammography images corresponding to 84 patients (51 cases and 49 controls) from the breast cancer digital repository (BCDR). OpenBreast achieves a competitive AUC of 0.846 in breast cancer risk assessment. In addition, used jointly with widely accepted risk factors such as patient age and breast density, mammographic image analysis using OpenBreast shows a statistically significant improvement in performance with an AUC of 0.876 ( ). Our framework will be made publicly available and it is easy to incorporate new methods.
Multi-stakeholder recommendations

Recommender systems are able to produce a list of recommended items tailored to user preferences, while the end user is the only stakeholder in the system. However, there could be multiple stakeholders in several applications or domains, e.g., e-commerce, advertising, educations, dating, job seeking, and so forth. Recommendations are necessary to be produced by balancing the needs of different stakeholders. This tutorial covers the introductions to multi-stakeholder recommender systems (MSRS), introduces multiple case studies, discusses the corresponding methods and challenges to develop MSRS. Particularly, a demo based on the MOEA framework will be given in the talk by using a speed-dating dataset.
Occluded Face Recognition Based on the Deep Learning

With the aggravation of social public security problems, the face recognition without occluded parts can no longer meet the needs of modern society. All kinds of face recognitions in complex environment need to be realized in the real situations. The paper proposed a new method to recognize the occluded face based on the deep learning. The face recognition model is trained and learned based on convolution neural network of the deep learning, which has strong robustness to illumination difference, facial expression change and facial occlusion. Through a large number of experimental tests and result analysis, the occluded face recognition rate can reach up to 98.6%. Therefore, this method proposed in the paper realizes face recognition with occlusion in complex environment and meets the needs of practical applications.
A generative model for review-based recommendations

User generated reviews is a highly informative source of information, that has recently gained lots of attention in the recommender systems community. In this work we propose a generative latent variable model that explains both observed ratings and textual reviews. This latent variable model allows to combine any traditional collaborative filtering method, together with any deep learning architecture for text processing. Experimental results on four benchmark datasets demonstrate its superiority comparing to all baseline recommender systems. Furthermore, a running time analysis shows that this approach is in order of magnitude faster that relevant baselines. Moreover, underlying our solution there is a general framework that may be further explored.
Joint Tracking and Classification of Extended Targets Using Random Matrix and Bernoulli Filter for Time-Varying Scenarios

In surveillance applications, the extent states and measurements of extended targets received by sensors are time-varying.In this paper, we propose a joint tracking and classification (JTC) method for single extended target under the presence of clutter and detection uncertainty.The extent state is modeled as elliptic shape via random matrix model (RMM), and is used as the feature for target classification.To adapt to the time-varying conditions of an extended target, the RMM proposed by Lan et al. is used.Besides, the RMM is integrated into Bernoulli filter to detect an extended target with clutter and detection uncertainty.The resulting method is called joint tracking and classification Gaussian inverse Wishart Bernoulli (JTC-GIW-Ber) filter, and the closed expressions for JTC-GIW-Ber filter recursions are derived under the necessary assumptions and approximations.Comprehensive simulations are carried out to test the performance, and the results demonstrate that the proposed JTC-GIW-Ber filter not only outperforms the JTC-GIW probability hypothesis density (JTC-GIW-PHD) filter and the GIW-Ber filter in extent state estimation, but also outperforms the JTC-GIW-PHD filter in target classification.
Robust visual tracking based on variational auto-encoding Markov chain Monte Carlo

In this study, we present a novel visual tracker based on the variational auto-encoding Markov chain Monte Carlo (VAE-MCMC) method. A target is tracked over time with the help of multiple geometrically related supporters whose motions correlate with those of the target. Good supporters are obtained using variational auto-encoding techniques that measure the confidence of supporters in terms of marginal probabilities. These probabilities are then used in the MCMC method to search for the best state of the target. We extend the VAE-MCMC method to a variational mixture of posteriors (VampPrior)-MCMC and hierarchical VampPrior-MCMC methods. Experimental results demonstrate that the supporters are useful for robust visual tracking and that the variational auto-encoding can accurately estimate the distribution of supporters' states. Moreover, our proposed VAE-MCMC method quantitatively and qualitatively outperforms recent state-of-the-art tracking methods.
An enhanced image encryption technique combining genetic algorithm and particle swarm optimization with chaotic function

Image encryption is a very common and attractive issue in digital image processing. Here, a combination of genetic algorithm and particle swarm optimization with a chaotic function model is proposed for image encryption. This paper uses the genetic algorithm for the enhanced encryption of pixel value and particle swarm optimization for improving the optimization process. The proposed method is divided into two parts. In the first stage, the plain RGB image is used for the initial population and then the genetic algorithm is applied to encrypt the image. In the next stage, the particle swarm optimization algorithm is applied for deciding the best-encrypted image. Next, the best image is re-encrypted until the best value is found. The double-point crossover is used for encryption. On the other hand, entropy is used as a fitness function for analyzing the fitness value. The obtained average entropy of the image is approximately 7.999, which is very close to the ideal value of entropy.
Enhancing Learning-Enabled Software Systems to Address Environmental Uncertainty

An overarching problem with Learning-Enabled Systems (LES) is determining whether training data is sufficient to ensure the LES is resilient to environmental uncertainty and how to obtain better training data to improve the system's performance when it is not. Automated methods can ease the burden for developers by augmenting real-world data with synthetically generated data. We propose an evolution-based method to assist developers with the assessment of learning-enabled systems in environments not covered by available datasets. We have developed Enki, a tool that can generate various conditions of the environment in order to discover properties that lead to diverse and unique system behaviors. These environmental properties are then used to construct synthetic data for two purposes: (1) to assess a system's performance in an uncertain environment and (2) to improve system resilience in the presence of uncertainty. We show that our technique outperforms a random generation method when assessing the effect of multiple adverse environmental conditions on a Deep Neural Network (DNN) trained for the commonly-used CIFAR-10 benchmark.
Improved Adaptive Median Filtering for Structured Light Image Denoising

In order to overcome the shortcomings of standard median filtering and adaptive median filtering in the denoising of structured light image, an improved adaptive median filtering method is proposed. The method determines the true noise point and the local noise density through two noise detections. Moreover, the size of the filtering window is selected according to the density, and the detected noise points are filtered by using the proposed method. The experimental results show that the improved adaptive median filtering can effectively protect the image details when noise is removed. And the proposed method can provide better filtering performance than standard median filtering and adaptive median filtering.
A Neuromorphic Sparse Coding Defense to Adversarial Images

Adversarial images are a class of images that have been slightly altered by very specific noise to change the way a deep learning neural network classifies the image. In many cases, this particular noise is imperceptible to the human vision system and thus presents a vulnerability of significant concern to the machine learning and artificial intelligence community. Research towards mitigating this type of attack has taken many forms, one of which is to filter or post process the image before classifying the image with a deep neural network. Techniques such as smoothing, filtering, and compression have been used with varying levels of success.
The Effect of Presence and Appearance of Guides in Virtual Reality Exhibitions

Virtual reality (VR) enables users to experience informal learning activities, such as visiting museum exhibitions or attending tours independent of their physical locations. Consequently, VR offers compelling use cases by making informal learning and education accessible to a broader audience and simultaneously reducing the carbon footprint. For many learning activities, the presence of a human guide is essential for participants' experience. The effect of the presence of a guide and its appearance in VR is, however, unclear. In this paper, we compare a real-world guide with a realistic, an abstract, and an audio-only representation of a virtual guide. Participants followed four multimodal presentations while we investigated the effect on comprehension, presence, co-presence and the perception of the guide. Our results show that even a realistic presentation of a guide results in significantly lower co-presence, humanness, and attractiveness compared to a human guide. Qualitative results and participants' feedback indicate that having no visual representation of the guide helps to focus on the content but can reduce the connection with the guide.
Attentive Dual Embedding for Understanding Medical Concepts in Electronic Health Records

Medical concept embedding is to learn a distributed representation for a medical related entity, e.g.diagnosis, treatment procedure, and medicine, which is a code stored in Electronic Health Record (EHR).The distributed representation is expected to preserve the comprehensive relationships among medical concepts rather than one-hot encoding, and it will be the inputs of machine learning based healthcare analytic tasks.Therefore, the performance of the analytic tasks highly depends on the quality of embedding outputs.To fully utilise the information in EHR, this paper proposes a novel attentive dual embedding method, namely MC2Vec, to intensively capture the proximity relationships among medical concepts.In particular, the proposed MC2Vec method uses a two-step optimisation framework to recursively refine the embedding via two components 1) Skip-gram based method to generate the initial embedding of medical concept, and 2) Attentive CBOW based method to fine-tune the code embedding by adding the temporal information of one patient's sequential healthcare activities.The experiment studies on two public EHR datasets demonstrate the effectiveness of the proposed MC2Vec method, which performs superior than other five state-of-the-art embedding methods.
Infinity Walk in VR: Effects of Cognitive Load on Velocity during Continuous Long-Distance Walking

Bipedal walking is generally considered to be the most natural and common locomotion technique in the physical world, for humans, and the most presence-enhancing form of locomotion in virtual reality (VR). However, there are significant differences in the way people walk in VR compared to their walking behaviour in the real world. For instance, previous studies have shown a significant decrease of gait parameters, in particular, velocity and step length in the virtual environment (VE). However, those studies have only considered short periods of walking. In contrast, many VR applications involve extended exposures to the VE and often include additional cognitive tasks such as way-finding. Hence, it remains an open question whether velocity during VR walking will further slowdown over time or if users of VR will eventually speed-up and adapt their velocity to the VE and move with the same speed as in the real world.
Learning From Personal Longitudinal Dialog Data

We explore the use of longitudinal dialog data for two dialog prediction tasks: next message prediction and response time prediction. We show that a neural model using personal data that leverages a combination of message content, style matching, time features, and speaker attributes leads to the best results for both tasks, with error rate reductions of up to 15% compared to a classifier that relies exclusively on message content and to a classifier that does not use personal data.
Towards a Conversational Agent to Support the Software Testing Education

The training of professionals in the field of software testing is increasing its relevance in the past few years and, therefore, efforts in appropriate methodologies for the learning-teaching process in this context have been proposed and appreciated. The emergence of pedagogical models, such as flipped classroom and team-based learning, which demand from the students a previous study of the theory before the lecture, creates a concern: how to support the before class learning? Because of the hybrid nature of these pedagogical models, which means they mix elements from traditional and distance education, it is possible that the support mechanisms used in distance learning platforms, such as conversational agents, can be applied for this matter. At the same time in which the academic work tries carefully to provide a proper software testing formation, there are also many contributions being established regarding the training and non-formal learning. Improvement and personal training courses about criteria, tools, and software testing good practices are being created by teaching institutes and offered in Massive Open Online Courses platforms (MOOCs). However, in this type of course, in the absence of a teacher, the student might be in a situation where there is nobody available to answer their questions about the topic. In this paper, we propose the use of conversational agents in solving the problems and challenges which encompass the learning through MOOCs and hybrid models. A conversational agent, called TOB-SST is proposed to support software testing education. A viability study was conducted to understand the quality of the given answers by TOB-SST and the possibility of it serving as a learning support tool. The results indicate that it is promising to employ a conversational agent to guide student study.
Requirements Engineering and Software Testing in Agile Methodologies

The insertion of agile practices in software development has increased exponentially. Both industry and academic staff constantly face challenges related to requirements specification and testing in this context. In this study, we conducted a systematic mapping of the literature to investigate the practices, strategies, techniques, tools, and challenges met in the association of Requirements Engineering with Software Testing (REST) in the agile context. By searching seven major bibliographic databases, we identified 1.099 papers related to Agile REST. Based on the systematic mapping guidelines, we selected 14 of them for a more specific analysis. In general, the main findings include the fact that weekly meetings should be held to establish frequent communication with stakeholders. Also, most projects adopt use cases as conceptual models and perform use case detailing. Test cases are an important artifact with test case design as a Software Testing practice. For the automation of test cases, fit tables have been recommended as an artifact. Finally, proper project documentation constitutes a critical basis in Agile REST.