Reviewerly/exHarmony huggingface dataset:
SISRSet: Single image super-resolution subjective evaluation test and objective quality assessment

With the outstanding performance of deep learning based single image super-resolution (SISR) methods, some widely used quality evaluation metrics such as peak signal to noise ratio (PSNR) and structure similarity index (SSIM) have become more and more difficult to meet the assessment requirements of SISR methods, especially in terms of their consistency with subjective visual perception. To deal with this super-resolution (SR) image quality assessment (IQA) issue, it calls for a specialized objective evaluation metric based on the visual characteristics of SR images and the human visual system (HVS). Notwithstanding, there is a lack of practical databases for analysis. In this paper, the SISR subjective evaluation tests are conducted to build an SISRSet database, which requires a large number of reconstructed images generated by various SR algorithms. The statistical analyses of SISRSet reveal that high-frequency texture details play an important role in the performance evaluation of SR algorithms. Inspired by the origination selectivity mechanism (OSM) and the internal generative mechanism (IGM) of the HVS, a visual content prediction model (VCPM) is proposed to measure different visible structural contents of SR images, especially the texture details. Finally, a novel SISR quality assessment metric is devised based on the VCPM similarity comparison between the references and SR images. The experimental results demonstrate that the proposed SISR-IQA metric does well in the performance evaluation of SISR methods and well correlates with those by the subjective evaluation.
A topic enhanced approach to detecting multiple standpoints in web texts

Internet has become the most popular platform for people to exchange opinions and express stances. The stances implied in web texts indicate people's fundamental beliefs and viewpoints. Understanding the stances people take is beneficial and critical for many security and business related applications, such as policy design, emergency response and marketing management. Most previous work on stance detection focuses on identifying the supportive or unsupportive attitudes towards a specific target. However, another important type of stance detection, i.e. multiple standpoint detection, has been largely ignored. Multiple standpoint detection aims to identify the distinct standpoints people hold among multiple parties, which reflects people's intrinsic values and judgments. When expressing standpoints, people tend to discuss diverse topics, and the word usage in the topics of different standpoints often varies a lot. As topics can provide latent information for identifying various standpoints, in this paper, we propose a topic-based approach to detecting multiple standpoints in Web texts, by enhancing generative classification model as well as feature representation of texts. In addition, we develop an adaptive process to determine parameter values in our approach automatically. Experimental studies on several real-world datasets verify the effectiveness of our proposed approach in detecting multiple standpoints.
Interactive volumetric segmentation for textile micro‐tomography data using wavelets and nonlocal means

This work addresses segmentation of volumetric images of woven carbon fiber textiles from micro‐tomography data. We propose a semi‐supervised algorithm to classify carbon fibers that requires sparse input as opposed to completely labeled images. The main contributions are: (a) design of effective discriminative classifiers, for three‐dimensional textile samples, trained on wavelet features for segmentation; (b) coupling of previous step with nonlocal means as simple, efficient alternative to the Potts model; and (c) demonstration of reuse of classifier to diverse samples containing similar content. We evaluate our work by curating test sets of voxels in the absence of a complete ground truth mask. The algorithm obtains an average 0.95 F1 score on test sets and average F1 score of 0.93 on new samples. We conclude with discussion of failure cases and propose future directions toward analysis of spatiotemporal high‐resolution micro‐tomography images.
Comparative analysis and application of LBP face image recognition algorithms

Summary As a branch of biometrics, face recognition focuses on face features and approaches to distinguishing different individuals through recognizing their faces by virtue of the uniqueness of each face. Face recognition technology using image set, and single image is the main application technology at present. Although the face recognition technology using a single image is relatively mature, it also has some shortcomings. In this paper, Honda/USCD video database is used to compare and analyze the two technologies, and the recognition accuracy of face recognition method based on local binary pattern (LBP) is tested. The results show that the face recognition technology using image sets has better recognition accuracy and can be better applied intelligent shopping, access control system, vehicle access system, intelligent security, identity recognition.
Integrating the Scrum Framework and Lean Six Sigma

Lean Six Sigma approach has been a successful manufacturing quality improvement tool. In the last decades, it has helped many services and product industries to achieve better performance and higher results. No wonder that the approach has aroused the interest of the software development industry. As result, some software companies have been trying to adopt Lean Six Sigma in their development processes, specifically in conjunction with agile methods. Based on some of these experiences this paper intends to propose the integration in the Scrum method the quality procedures defined on the Lean Six Sigma (LSS). By complementing the current generic quality procedures of Scrum method with more formalized and well-proven quality measures from LSS we intend to improve the efficacy and efficiency of Scrum.
Wide-range holographic interferometer

Computer simulation of the interferometer combining properties of a low-sensitive Talbot interferometer and high-sensitive holographic interferometer is considered. The interferometer has four output channels having different sensitivity. Channel sensibility can be varied by means of spatial filtration. A wide range of sensitivity makes it possible to use the interferometer to study complex phase objects. The base of the interferometer is the holographic Talbot effect. The efficiency of this interferometer was verified by a computer simulation method. We present some results of the computer simulation. These results were compared with results obtained in optical experiments under the same conditions.
A Board-Game for Co-Designing Smart Nature Environments in Workshops with Children

In recent years, the Human Computer Interaction community has increasingly engaged children in the design process of technology for them as co-designers, and recently as protagonists in co-design. It has also recognised the need of “bringing children back to nature”. This paper combines both lines of research, giving children the role of protagonist co-designers of smart nature ecosystems. In order to engage children as co-designers, the research reported in this paper has employed a playful solution: a board game for children for co-designing smart interactive objects for outdoors nature ecosystems. This paper illustrates the genesis and recent evolution of the board-game in two workshops with children of different ages and gender. Its conclusions draw lessons for involving children as protagonist co-designers of smart nature ecosystems.
Two-dimensional maximal repetitions

Maximal repetitions or runs in strings have a wide array of applications and thus have been extensively studied. In this paper, we extend this notion to 2-dimensions, precisely defining a maximal 2D repetition. We provide initial bounds on the number of maximal 2D repetitions that can occur in an n×n array. The main contribution of this paper is the presentation of the first algorithm for locating all maximal 2D repetitions. The algorithm is efficient and straightforward, with runtime O(n2log⁡n+ρ), where n2 is the size of the input array and ρ is the number of maximal 2D repetitions in the output.
Word Usage Similarity Estimation with Sentence Representations and Automatic Substitutes

Usage similarity estimation addresses the semantic proximity of word instances in different contexts.We apply contextualized (ELMo and BERT) word and sentence embeddings to this task, and propose supervised models that leverage these representations for prediction.Our models are further assisted by lexical substitute annotations automatically assigned to word instances by context2vec, a neural model that relies on a bidirectional LSTM.We perform an extensive comparison of existing word and sentence representations on benchmark datasets addressing both graded and binary similarity.The best performing models outperform previous methods in both settings.
A survey on energy aware VM consolidation strategies

The rapid growth of Cloud computing is accompanied by a significant increase in the consumed energy by data centers. This huge increase in energy consumption has become a major concern because of both its costs and environment impact. Consolidating virtual machines (VMs) in minimum numbers of physical machines (PMs) is an effective way to reduce energy consumption. Various algorithms and techniques have been proposed to improve energy efficiency via VM consolidation. However, recent findings of Green Peace showed that energy demands of data centers are continuing to grow annually. This continuous increase queries the area of reducing energy consumption of data centers. As an attempt to figure out the reasons behind the gap between Green Peace recent findings on energy consumption and the proposed VM consolidation techniques and to bridge this gap, we undertake this survey. We discuss these techniques by highlighting their strengths and limitations and we justify the need for taking into consideration the energy overhead created by both the VM migrations and PMs transitions in and out of the low power mode.
A Design Model for Using Virtual Reality in Behavioral Skills Training

This study suggests a design model for developing virtual reality (VR)-based learning environments which can be used for basic behavioral skills training. VR-Based Fire Safety Training Environment (VR-FST) was designed considering the principles of the persuasive technology. Following the suggested model, VR-FST was setup by integrating head-mounted display and joysticks on the Second Life. Evaluations through participants’ perspectives indicated that the VR-FST environment designed through the proposed model framework can provide high presence and the participants who use the environment perceived the VR-FST as realistic. The results indicate that the learning environments based on the VR-FST model can improve fire safety behavioral skills. It is thought that behavioral skills including danger can be safely delivered by employing the behavioral skills training approach, especially for young children. Current limitations and future refinements as well as suggestions for practitioners and researchers are also included.
Self-Specialization of General Robot Plans Based on Experience

For robots to work outside of laboratory settings, their plans should be applicable to a variety of environments, objects, task contexts, and hardware platforms. This requires general-purpose methods that are, at this moment, not sufficiently performant for real-world applications. We propose an approach to specialize such general plans through running them for specific tasks and thereby learning appropriate specializations from experience. We present a system architecture, which collects data during plan execution for making up supervised learning problems and utilizes learned models for specializing the plans in a closed loop. We demonstrate our approach by letting a PR2 robot specialize its general fetch and place plan, whereby learned results are automatically installed into the plan. We show that the specialized plan performs better than the original plan in a statistically significant sense.
Human Detection Aided by Deeply Learned Semantic Masks

Human detection is one of the long-standing computer vision tasks, and it has been a cornerstone for many real-world applications, such as photo album organization, video surveillance, and autonomous driving. Benefiting from deep learning technologies, such as convolutional neural networks and modern object detectors, have been achieving much improved accuracy in generic object detection tasks. In this paper, we aim to improve deep learning-based human detection. Our main idea is to exploit semantic context information for human detection by using deep-learnt semantic features provided by semantic segmentation masks. Segmentation masks play as an attention mechanism and enforce the detectors to focus on the image regions where potential object candidates are likely to appear. Meanwhile, the extra segmentation mask channel can also guide the convolutional kernels to automatically learn more discriminative features which make it easier to distinguish the background and foreground. We implement our methods with two popular detection frameworks, i.e., faster R-CNN and SSD and experimentally analyze the effectiveness of the proposed methods. Evaluation results on the widely used MS-COCO dataset and the very recent CrowdHuman dataset are provided. Our proposed methods outperform the baseline detectors and achieve better performance on highly occluded human detection.
Securing instant messaging based on blockchain with machine learning

Instant Messaging (IM) offers real-time communications between two or more participants on Internet. Nowadays, most IMs take place on mobile applications, such as WhatsApp, WeChat, Viber and Facebook Messenger, which have more users than social networks, such as Twitter and Facebook. Among the applications of IMs, online shopping has become a part of our everyday life, primarily those who are busiest. However, transaction disputes are often occurred online shopping. Since most IMs are centralized and message history is not stored in the center, the messaging between users and owners of online shops are not reliable and traceable. In China, online shopping sales have soared from practically zero in 2003 to nearly 600 hundred million dollars last year, and now top those in the United States. It is very crucial to secure the instant messaging in online shopping in China. We present techniques to exploit blockchain and machine learning algorithms to secure instant messaging. Since the cryptography of Chinese national standard is encouraged to adopt in security applications of China, we propose a blockchain-based IM scheme with the Chinese cryptographic bases. First, we design a message authentication model based on SM2 to avoid the counterfeit attack and replay attack. Second, we design a cryptographic hash mode based on SM3 to verify the integrity of message. Third, we design a message encryption model based on SM4 to protect the privacy of users. Besides, we propose a method based on machine learning algorithms to monitor the activity on blockchain to detect anomaly. To prove and verify the blockchain-based IM scheme, a blockchain-based IM system has been designed on Linux platforms. The implementation result shows that it is a practical and secure IM system, which can be applied to a variety of instant messaging applications directly.
Adding Distributed Decryption and Key Generation to a Ring-LWE Based CCA Encryption Scheme

We show how to build distributed key generation and distributed decryption procedures for the $$\textsf {LIMA} $$ Ring-LWE based post-quantum cryptosystem. Our protocols implement the CCA variants of distributed decryption and are actively secure (with abort) in the case of three parties and honest majority. Our protocols make use of a combination of problem specific MPC protocols, generic garbled circuit based MPC and generic Linear Secret Sharing based MPC. We also, as a by-product, report on the first run-times for the execution of the SHA-3 function in an MPC system.
On the analysis of adaptability in multi-source domain adaptation

In many real-world applications, it may be desirable to benefit from a classifier trained on a given source task from some largely annotated dataset in order to address a different but related target task for which only weakly labeled data are available. Domain adaptation (DA) is the framework which aims at leveraging the statistical similarities between the source and target distributions to learn well. Current theoretical results show that the efficiency of DA algorithms depends on (i) their capacity of minimizing the divergence between the source and target domains and (ii) the existence of a good hypothesis that commits few errors in both domains. While most of the work in DA has focused on new divergence measures, the second aspect, often modeled as the capability term, remains surprisingly under-investigated. In this paper, we show that the problem of the best joint hypothesis estimation can be reformulated using a Wasserstein distance-based error function in the context of multi-source DA. Based on this idea, we provide a theoretical analysis of the capability term and derive inequalities allowing us to estimate it from finite samples. We empirically illustrate the proposed idea on different data sets.
Supporting the Statistical Analysis of Variability Models

Variability models are broadly used to specify the configurable features of highly customizable software.In practice, they can be large, defining thousands of features with their dependencies and conflicts.In such cases, visualization techniques and automated analysis support are crucial for understanding the models.This paper contributes to this line of research by presenting a novel, probabilistic foundation for statistical reasoning about variability models.Our approach not only provides a new way to visualize, describe and interpret variability models, but it also supports the improvement of additional state-of-the-art methods for software product lines; for instance, providing exact computations where only approximations were available before, and increasing the sensitivity of existing analysis operations for variability models.We demonstrate the benefits of our approach using real case studies with up to 17,365 features, and written in two different languages (KConfig and feature models).
Evaluating Architecture Impacts on Deep Imitation Learning Performance for Autonomous Driving

Imitation learning has gained huge popularity due to its promises in different fields such as robotics and autonomous systems. A great deal of past research work in the field of imitation learning has been devoted to developing efficient and effective policies using deep convolutional neural networks (CNNs). The performance of CNN-based control policies intimately depends on the network architecture. Determination of the optimal architecture for CNNs is still a hot research topic for the deep learning community. This study comprehensively investigates and quantifies the impact of CNN architecture on the performance of learned policy for an autonomous vehicle. CNN models with different architectures (number of layers and filters) are fed by visual information from multiple cameras obtained from multiple driving simulations. These networks are trained to precisely find the mapping between visual information and the steering angle. Two ensemble approaches are also introduced to further improve the overall accuracy of steering angle estimations. Obtained results indicate that deeper networks show a better performance than less deep networks during autonomous driving. Also it is observed that best results are achieved by ensemble approaches.
Blockchain-Based Solution to Prevent Postage Stamps Fraud

Counterfeit stamps cause considerable financial damage to the states and companies. The blockchain-based supply chain management system for their market is proposed in the article. It can make stamps circulation transparent and guarantee invariability of stamps volume produced and used. The technical description and performance tests of the proposed system are provided.
A Systematic Mapping Study on Software Architectures Description Based on ISO/IEC/IEEE 42010:2011

Software architecture is considered an important area of Software Engineering, as it is useful for managing the development and maintenance of large scale software-intensive systems. Due to Software Architecture importance, the ISO/IEC/IEEE 42010:2011 standard was published in 2011. In this paper, we present a Systematic Mapping Study (SMS) for describing studies that explicitly used the ISO/IEC/IEEE 42010:2011 standard, and identifying which parts of this standard were most considered in the literature. Through the research, we selected 19 papers published between 2007 and September 2018. One interesting result is that ISO/IEC/IEEE 42010:2011 standard has been used, and its presence in papers has increased since 2016. However, parts of the standard are still not considered in practice. Industry and academia can still benefit from learning and improve the use of ISO/IEC/IEEE 42010:2011. Based on our findings, we suggest further research to analyze all the aspects of the standard as well as an industrial study.
Nirnayam

Classification is a supervised learning process which requires the decision labels available for learning process. Decision classifier is a rule based approach for classification. This paper proposes a new approach for classification by proposing a new approach to build the decision tree. The construction of decision tree is based on the principle of distance computation instead of finding Gini index or Information Gain of split attributes. The distance function is the normalized Lp-norm distance function with p=2. The fundamental idea for obtaining decision tree is based on determining the class based probability vectors. The distance is computed for probability vectors computed for attributes and root node of the decision tree. The minimal distance attribute is chosen as the best choice and the dataset records are split based on the choice made. A working example that demonstrates the proposed approach for building decision tree is discussed for understanding the classification process.
Pulse interference method against PRI sorting

Based on the purpose of proactive anti-reconnaissance, the essential characteristics of several typical pulse repetition interval (PRI) sorting algorithms were analysed and the feasibility of counter-deinterleaving was discussed. A method for disturbing arrival time sequence by using interference pulse was proposed, which changes the statistical properties of the radar PRI to disturb the deinterleaving results. The simulation results show that the pulse jamming method proposed in this paper can influence PRI sorting process effectively, which let signal processer make a misjudgment on PRI pattern of radar.