Reviewerly/exHarmony huggingface dataset:
Relations of logical consequence in logics of partial predicates with composition of predicate complement

In this paper we study software-oriented logics of partial predicates with new special non-monotonic operation (composition) of the predicate complement. We denote these logics by LC and composition of the predicate complement by . Such operations are used in various versions of the Floyd-Hoare program logic with partial pre- and post-conditions. We describe first order composition algebras and LC languages. For LC, a number of logical consequence relations ( Pc |= T , Pc |= F , Rc |= T , Rc |= F , Pc |= TF , Rc |= TF , P с |= IR ) and logical consequence relations under the conditions of undefinedness ( P |= T ^ , P |= F ^ , R |= T ^ , R |= F ^ , P |= TF ^ , R |= TF ^ ) are specified. Properties of the defined relations are investigated, differences and the relationship between them are given. For the introduced relations, we describe the conditions for their guaranteed presence, the decomposition conditions for formulas and the properties of quantifier elimination. The theorem of elimination of the conditions of undefinedness for the relations |= T ^ and |= F is proved. Thus, the relations P |= T ^ , P |= F ^ , R |= T ^ and R |= F ^ can be expressed by Pc |= T , Pc |= F , Rc |= T and Rc |= F respectively. However, it is shown that |= IR ^ cannot be expressed by P с |= IR . Moreover, it is impossible to define correctly the decomposition conditions for formulas for P с |= IR . Properties of decomposition conditions for formulas are different for the relations |= T and |= F , therefore properties of decomposition and equivalent transformations must be specified indirectly through the corresponding properties of |= T and |= F . First order sequent calculi for the introduced logical consequence relations for LC and logical consequence relations under the conditions of undefinedness will be constructed in in the forthcoming articles. Problems in programming 2019; 3: 11-27
Deflecting Adversarial Attacks

There has been an ongoing cycle where stronger defenses against adversarial attacks are subsequently broken by a more advanced defense-aware attack. We present a new approach towards ending this cycle where we "deflect'' adversarial attacks by causing the attacker to produce an input that semantically resembles the attack's target class. To this end, we first propose a stronger defense based on Capsule Networks that combines three detection mechanisms to achieve state-of-the-art detection performance on both standard and defense-aware attacks. We then show that undetected attacks against our defense often perceptually resemble the adversarial target class by performing a human study where participants are asked to label images produced by the attack. These attack images can no longer be called "adversarial'' because our network classifies them the same way as humans do.
Attention GAN-Based Method for Designing Intelligent Making System

Design of the intelligent making system (iMAS) poses a big technical challenge to recognize both printed and handwriting characters from the scanned images. In this paper, we propose an iMAS method based on the attention generative adversarial network (GAN), which innovatively applies the idea of removing raindrops through attention GAN to remove the printed text first, and then generates the image of handwriting text. The proposed method introduces a classical attention model in the visual field and generates the attention maps which focus only on the important areas through recurrent neural network (RNN), and adopts you only look once at object detection (YOLOv3) method to recognize the characters. Experimental results show that the structural similarity (SSIM) of the image generated by attention GAN is 0.89 and the accuracy of recognition is 91.34%.
Generation of Adaptive Mobile Applications Based on Design Patterns for User Interfaces

Generating mobile apps represents a big challenge in several areas, such as considering audience needs, adapting their user interfaces to such needs, dealing with design constraints or using different development technologies. The present work seeks to examine how design patterns can help to support the generation of this kind of adaptive mobile application. In particular, design patterns related to user interfaces are reviewed, and an ontology-based framework is proposed to manage their pattern descriptions and associated rules. Such a framework enables a more versatile and powerful organization of mobile interface items, as well as their adaptation to context changes and user requirements in specific scenarios. An example of adaptive mobile application has been developed to show the suitability of the proposed framework, and the application usability has been evaluated in terms of satisfaction, learnability, and efficiency.
PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement

In autonomous driving pipelines, perception modules provide a visual understanding of the surrounding road scene. Among the perception tasks, vehicle detection is of paramount importance for a safe driving as it identifies the position of other agents sharing the road. In our work, we propose PointRGCN: a graph-based 3D object detection pipeline based on graph convolutional networks (GCNs) which operates exclusively on 3D LiDAR point clouds. To perform more accurate 3D object detection, we leverage a graph representation that performs proposal feature and context aggregation. We integrate residual GCNs in a two-stage 3D object detection pipeline, where 3D object proposals are refined using a novel graph representation. In particular, R-GCN is a residual GCN that classifies and regresses 3D proposals, and C-GCN is a contextual GCN that further refines proposals by sharing contextual information between multiple proposals. We integrate our refinement modules into a novel 3D detection pipeline, PointRGCN, and achieve state-of-the-art performance on the easy difficulty for the bird eye view detection task.
Domain-invariant Stereo Matching Networks

State-of-the-art stereo matching networks have difficulties in generalizing to new unseen environments due to significant domain differences, such as color, illumination, contrast, and texture. In this paper, we aim at designing a domain-invariant stereo matching network (DSMNet) that generalizes well to unseen scenes. To achieve this goal, we propose i) a novel "domain normalization" approach that regularizes the distribution of learned representations to allow them to be invariant to domain differences, and ii) a trainable non-local graph-based filter for extracting robust structural and geometric representations that can further enhance domain-invariant generalizations. When trained on synthetic data and generalized to real test sets, our model performs significantly better than all state-of-the-art models. It even outperforms some deep learning models (e.g. MC-CNN) fine-tuned with test-domain data.
Private Hospital Workflow Optimization via Secure k-Means Clustering

Optimizing the workflow of a complex organization such as a hospital is a difficult task. An accurate option is to use a real-time locating system to track locations of both patients and staff. However, privacy regulations forbid hospital management to assess location data of their staff members. In this exploratory work, we propose a secure solution to analyze the joined location data of patients and staff, by means of an innovative cryptographic technique called Secure Multi-Party Computation, in which an additional entity that the staff members can trust, such as a labour union, takes care of the staff data. The hospital, owning location data of patients, and the labour union perform a two-party protocol, in which they securely cluster the staff members by means of the frequency of their patient facing times. We describe the secure solution in detail, and evaluate the performance of our proof-of-concept. This work thus demonstrates the feasibility of secure multi-party clustering in this setting.
Summary of: A Framework for Quantitative Modeling and Analysis of Highly (re)configurable Systems

This short paper summarises the contributions published in [4], where we introduce QFLan, a framework for quantitative modeling and analysis of highly (re)configurable systems, like software product lines. We define a rich domain specific language (DSL) for systems with variability in terms of features, which can be dynamically installed, removed or replaced, capable of modeling probabilistic behavior, possibly subject to quantitative feature constraints. High-level DSL specifications are automatically encoded in a process algebra whose operational behavior interacts with a store of constraints, which allows to separate a system’s configuration from its behavior. The resulting probabilistic configurations and behavior converge seamlessly in a semantics based on discrete-time Markov chains, thus enabling quantitative analysis. An accompanying Eclipse-based tool offers a modern integrated development environment to specify such systems and to perform analyses that range from the likelihood of specific behavior to the expected average cost, in terms of feature attributes, of specific system variants. Based on a seamless integration with the statistical model checker MultiVeStA, QFLan allows to scale to larger models with respect to precise probabilistic analysis techniques. We provide a number of case studies that have driven and validated the development of the QFLan framework. In particular, we show the versatility of the QFLan framework with an application to risk analysis of a safe lock system from the security domain.
Las infografías como innovación en los artículos científicos: valoración de la comunidad científica

The emergence of new technologies, the universalization of internet use and the creation of increasingly intuitive software has meant a revolution whose consequences are still difficult to predict. In the case of education and science, traditional models have been displaced by new ways of teaching, learning, research and communication. One of the new tools that has gained strength in recent years has been infographics. An infographic is in its most basic concept a series of images that supported in a text intended to facilitate the transmission of information. The objective of this study is to know the degree of acceptance of infographics as a complement to the scientific article. Through the ad hoc design of a Likert scale survey, the opinion of 43 researchers who have published in the journal NAER Journal, which offers among its services the visualization of the article in its infographic format, has been collected. The main results of the investigation confirm that the respondents value this service very positively and are satisfied with the infographic they have obtained. However, the lack of technical competence and time of the subjects causes that this resource is not used. This problem makes us think about the suitability of the creation of graphic departments by the universities and scientific journals that facilitate this type of service to the researcher as those that are currently part of the newspapers.
Achieving Efficient and Privacy-Preserving Multi-Keyword Conjunctive Query Over Cloud

With the explosive growth of data, it has become increasingly popular to deploy the powerful cloud to manage data. Meanwhile, as the cloud is not always fully trusted, personal and sensitive data have to be encrypted before being outsourced to the cloud. Naturally, this brings a serious challenge for the cloud to provide secure and efficient query services over huge volumes of data. Although existing works have proposed some solutions to solve the above challenge, most of them just focus on the single keyword query and cannot directly support multi-keyword query. Even though some works have discussed solutions for the multi-keyword query, they cannot well balance the efficiency and privacy. Therefore, in this paper, we propose a novel multi-keyword conjunctive query scheme over cloud, which can achieve high query efficiency with small privacy leakage. In specific, we first design a tree-based index to support the multi-keyword conjunctive query and employ Boneh-Goh-Nissim (BGN) homomorphic encryption technique to protect its privacy. Then, based on the tree-based index, we propose a wildcard search algorithm to improve its query efficiency. Finally, the detailed security analysis shows that the proposed scheme is really privacy-preserving, and extensive simulation results also demonstrate its efficient.
Download and Fly: An Online Solution for the UAV 3D Trajectory Planning Problem in Smart Cities

Unmanned Aerial Vehicles (UAVs) represent an important asset in the increasing automation required in Smart Cities. However, their short flight autonomy and limited computational resources raise a set of issues to unravel for their full exploitation. In this paper, some of these issues are addressed by leveraging the deployment of Training and Recharging Areas (TRA) in the Smart City. While moving among different Mission Areas (MA), the UAVs pass through dedicated TRA, where Energy and Data Dispensers (EDD) are scattered to allow UAV battery recharge and software update. The tridimensional trajectory planning problem to let the UAVs efficiently and swiftly move through the EDDs, towards the exit of the TRA, is investigated. A mathematical formulation of the problem is presented, and an online approach is designed to achieve a feasible implementation in a realistic scenario.
Accurate Light Field Depth Estimation Using Multi-Orientation Partial Angular Coherence

Occlusion is a critical issue that affects the accuracy for light field depth estimation. In the presence of occlusions, the photo-consistency property is broken, making ambiguity near occlusion areas. In this paper, we proposed using multi-orientation partial angular coherence to achieve accurate depth estimation by which the occlusions are explicitly treated. Unlike previous approaches, the correspondence is partially measured along several lines with different directions. We show that depending on the occlusion edge orientation, the pixels along the occlusion orientation still preserve photo-consistency. By computing the partial angular coherence in four potential directions, we can obtain a sharp initial depth map. Since occlusions are precisely tackled, the outliers can be mostly removed by fast guided filtering on the cost volume. As a result, the depth accuracy at occlusion boundaries is greatly improved. The proposed method can obtain sharp transition edges at occlusion boundaries and has no requirement for extra edge information. Experimental results on a recent benchmark demonstrate that the proposed method outperforms the state-of-the-art algorithms in the accuracy metrics. Further experiments on real-world scenes also show the superior performance of the proposed method.
Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation

Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup but at the cost of inferior translation accuracy compared to autoregressive translation (AT) models. Considering that AT models have higher accuracy and are easier to train than NAT models, and both of them share the same model configurations, a natural idea to improve the accuracy of NAT models is to transfer a well-trained AT model to an NAT model through fine-tuning. However, since AT and NAT models differ greatly in training strategy, straightforward fine-tuning does not work well. In this work, we introduce curriculum learning into fine-tuning for NAT. Specifically, we design a curriculum in the fine-tuning process to progressively switch the training from autoregressive generation to non-autoregressive generation. Experiments on four benchmark translation datasets show that the proposed method achieves good improvement (more than $1$ BLEU score) over previous NAT baselines in terms of translation accuracy, and greatly speed up (more than $10$ times) the inference process over AT baselines.
Designing User Interaction using Gesture and Speech for Mixed Reality Interface

Mixed Reality (MR) is the next evolution of humans interacting with computer as MR can combine the physical environment and digital environment and making them coexist with each other [1]. Interaction is still a valid research area in MR, and this paper focuses on interaction rather than other research areas such as tracking, calibration, and display [2] because the current interaction technique still not intuitive enough to let the user interact with the computer. This paper explores the user interaction using gesture and speech interaction for 3D object manipulation in mixed reality environment. The paper explains the design stage that involves interaction using gesture and speech inputs to enhance user experience in MR workspace. After acquiring gesture input and speech commands, MR prototype is proposed to integrate the interaction technique using gesture and speech. The paper concludes with results and discussion.
The implementation of ”Kuznyechik” encryption algorithm using NVIDIA CUDA technology

In this paper, we discuss various options for implementing the ”Kuznyechik” block encryption algorithm using the NVIDIA CUDA technology. We use lookup tables as a basis for the implementation. In experiments, we study the influence of the size of the block of threads and the location of lookup tables on the encryption speed. We show that the best results are obtained when the lookup tables are stored in the global memory. The peak encryption speed reaches 30.83 Gbps on the NVIDIA GeForce GTX 1070 graphics processor .
Characterization of Visual Acuity and Contrast Sensitivity using Head-Mounted Displays in a Virtual Environment: A Pilot Study

Virtual reality (VR) and personal head-mounted displays (HMDs) can be a viable tool for the presentation of scientifically accurate and valid demonstrative data in the courtroom. However, the capabilities and limitations of the technology need to be fully characterized. The current pilot study evaluated visual acuity and contrast sensitivity using two commercially available HMDs (Oculus Rift and HTC Vive Pro). Preliminary findings indicated that visual acuity and contrast sensitivity experienced in VR may be less than what is experienced in real-world scenarios. The current pilot study provides a quantitative approach for characterizing the limitations of VR with respect to visual acuity and contrast sensitivity, and provides recommendations for the appropriate use of this technology when performing forensic investigations and developing visualization tools.
Quranic Reciter Recognition: A Machine Learning Approach

Recitation and listening of the Holy Quran with Tajweed is an essential activity as a Muslim and is a part of the faith.In this article, we use a machine learning approach for the Quran Reciter recognition.We use the database of Twelve Qari who recites the last Ten Surah of Quran.The twelve Qari thus represents the 12-class problem.Two approaches are used for audio representation, firstly, the audio is analyzed in the frequency domain, and secondly, the audio is treated as images through Spectrogram.The Mel Frequency Cepstral Coefficients (MFCC) and Pitch are used as the features for model learning in the first case.In the second case of audio as images, Auto-correlograms are used to extract features.In both cases, the features are learned with the classical machine learning which includes the Naïve Bayes, J48, and the Random Forest.These classifiers are selected due to their overall good performance in the state-of-the-art.It is observed that classifiers can efficiently learn the separation between classes, when the audio is represented by the MFCC, and the Pitch features.In such a case, we get 88% recognition accuracy with the Naïve Bayes and the Random Forest showing that Qari can be effectively recognized from the recitation of the Quranic verses.
Representations about the subject of future activities by students in the classroom profile training

The article analyzes the problem of profile education in the modern education system and its role in the pre-profile training of students at the senior level of secondary school. The forms of profile training organization are considered. It is shown that the training of high school students in specialized classes promotes conscious professional choice based on holistic, complete and adequate ideas about the future profession, ideas about their own abilities and professional preferences, contributes to the successful building of an individual educational route. The respondents were senior pupils of specialized and non-specialized classes of secondary schools in the number of 115 people. The following methods were used: “the Questionnaire aimed at studying the ideas about the object of activity (E. I. Rogova), the test method of J. Golland to determine the type of professional orientation of the individual. Methods of mathematical and statistical processing. The results of empirical research of professional representations of senior pupils of educational organizations are presented.
SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection

The state-of-the-art object detection method is complicated with various modules such as backbone, feature fusion neck, RPN and RCNN head, where each module may have different designs and structures. How to leverage the computational cost and accuracy trade-off for the structural combination as well as the modular selection of multiple modules? Neural architecture search (NAS) has shown great potential in finding an optimal solution. Existing NAS works for object detection only focus on searching better design of a single module such as backbone or feature fusion neck, while neglecting the balance of the whole system. In this paper, we present a two-stage coarse-to-fine searching strategy named Structural-to-Modular NAS (SM-NAS) for searching a GPU-friendly design of both an efficient combination of modules and better modular-level architecture for object detection. Specifically, Structural-level searching stage first aims to find an efficient combination of different modules; Modular-level searching stage then evolves each specific module and pushes the Pareto front forward to a faster task-specific network. We consider a multi-objective search where the search space covers many popular designs of detection methods. We directly search a detection backbone without pre-trained models or any proxy task by exploring a fast training from scratch strategy. The resulting architectures dominate state-of-the-art object detection systems in both inference time and accuracy and demonstrate the effectiveness on multiple detection datasets, e.g. halving the inference time with additional 1% mAP improvement compared to FPN and reaching 46% mAP with the similar inference time of MaskRCNN.
A qualitative study of Exemplar based Image Inpainting

Image inpainting is exploring the way of filling the missing or left out regions in an image by only having knowledge of surrounding pixel values. One of the block matching algorithms use for image inpainting is exemplar based technique. Exemplar based image inpainting method search the best matching patch for the target region in the image and when it is obtained, the pixel values are copied from the source region and pasted to the target region. The selections of different patch matching criteria, patch size and filling order have a great affect on the results. This paper describes some of the variations in traditional exemplar method which include change in patch selection, priority, patch size and multi-resolution inpainting. The advantages and drawbacks for the modified methods are also discussed here.