Reviewerly/exHarmony huggingface dataset:
An Efficient Convolutional Neural Network Learning Based on Radon Space for Face Recognition Application

Unconstrained artifacts as low resolution, change in pose, occlusion present challenges for face recognition. This paper presents an efficient approach for robust face learning using Radon space and convolutional neural network. Face image is mean normalized, converted to grayscale and cropped using Voila Jones algorithm. Further Radon space of image is derived for various number of projections. Radon space effectively represents face attributes and is used as input to CNN for face learning. Radon space retains genuine facial features with minimum dimensions; results into efficient face learning of CNN. This also minimizes overall network size and convergence time. Due to Radon space representation of face, this approach suits for face learning under occlusion conditions / partial faces. Algorithm is evaluated and compared with few reported methods using FERET and Extended Yale B databases to showcase the overall improvement in face recognition application. Reduced network size results in minimized computational complexity.
Joint Natural Awareness and Spatial-Frequency Attention Lightweight Network for Super-Resolution Image Quality Assessment

No-reference super-resolution image quality assessment (NR-SRIQA) is still a challenging task due to complicated degradation factors and the lack of reference high-resolution (HR) images. However, either traditional hand-crafted perceptual features or deep learning-based features are not sufficient to quantify the degradation of SR images. In this paper, we propose a novel NR-SRIQA framework called JNSFANet that integrates natural scene statistics (NNS) features and spatial-frequency mixed domain attention (SFMDA) lightweight network to jointly predict the quality of SR images. The NSS features are extracted by the BRISQUE algorithm through fitting generalized Gaussian distribution (GGD) and asymmetric generalized Gaussian distribution (AGGD) with mean subtracted contrast normalized (MSCN) coefficients. While the spatial-frequency domain attention lightweight network employs MobileNet-V2 as the backbone to reveal non-local authentic distortion features that NSS-based features cannot represent well. By integrating the NNS-based perceptual features and deep learning-based features, the proposed NR-SRIQA model facilitates more compelling quality prediction but requires obviously less computational cost in contrast to compared deep networks for NR-SRIQA. Experimental results verify the superior performance of our method on two benchmark databases in terms of both quantitative evaluations and thorough analysis on the model deployment.
Personalized Recommendation System Based on Social Relationships and Historical Behaviors

Previous studies show that recommendation algorithms based on historical behaviors of users can provide satisfactory recommendation performance. Many of these algorithms pay attention to the interest of users, while ignore the influence of social relationships on user behaviors. Social relationships not only carry intrinsic information of similar consumption tastes or behaviors, but also imply the influence of individual to its neighbors. In this paper, we assume that social relationships and historical behaviors of users are related to the same factors. Based on this assumption, we propose an algorithm to focus on social relationships useful for recommendation systems through mutual constraints from both types of information. We test the performance of our algorithm on four types of users, including all users, active users, inactive users and cold-start users. Results show that the proposed algorithm outperforms benchmarks in four types of scenarios subject to recommendation accuracy and diversity metrics. We further design a randomization model to explore the contribution of social relationships to recommendation performance, and the result shows that the contribution of social relationships in the proposed algorithm depends on the coupling strength of social relationships and historical behaviors.
A Fast Decoding Algorithm for Codes Raised by Subgraph of the Edge Space of Complete Graph

In this paper, we give a new method to construct the error correcting codes obtained from the subgraph of in the edge space of complete graph. These codes are easy to construct. All constructed codes show better parameters than cycle codes and some of them matches perfectly with best known codes. Further, we present a new and fast decoding algorithm with complexity O(n) with n is the length of the codes. The main advantage of our algorithm is based on graph properties of complete graph, which will not only reduce the decoding time buy also has high correct capability.
Salt and Pepper Noise Removal Method Based on Graph Signal Reconstruction

Salt and Pepper (SAP) noise is a very common type of impulse noise which frequently arises in image acquisition and transmission. Removing SAP noise is an important task in image restoration. In this paper, we model the SAP removal problem as a graph signal reconstruction problem, so that the information of noisy pixels can also participate in the estimation of their gray values. In the proposed model, the regularization term depends on the Power Spectral Density (PSD) of original signals, which makes the reconstructed signal follows the frequency content of the potential original signal well. Further, an algorithm for estimating the local PSD of an image block is designed and corresponding theoretical analysis is given. Finally, a block SAP noise removal algorithm is proposed. The computational complexity is also discussed. Experimental results on ten commonly used test images demonstrate that the proposed algorithm outperforms the state-of-the-art methods.
A Rapid and Accurate Gamma Compensation Method Based on Double Response Curve Fitting for High-Quality Fringe Pattern Generation

The gamma effect of structured light illumination devices and camera will yield undesired nonlinear phase errors both in fringe projection system and fringe reflection system. The existing nonlinear correction methods can be classified into two categories: actively modifying the fringe patterns before their projection, or passively compensating the phase error after the fringe patterns are captured. The former needs to capture lots of fringe patterns in advance, while the latter occupies measurement time. In this paper, a rapid and robust active gamma compensation method based on double response curve fitting is proposed for the first time. In the proposed method, the appropriate pre-coding gamma value is determined jointly by the two constructed response curves, so the correction results will not get worse when the number of projected grayscale images decreases and no more than 4 frames of grayscale images are required. It is fastest in the existing active compensation methods. Experimental results verified that this method is fast, accurate and easy to implement. With the proposed technique, high accuracy measurement can be achieved with the conventional 3-step phase-shifting algorithm.
A Tiny Target and Edge Feature Extraction Algorithm for Semantic Segmentation

Semantic segmentation is a fundamental computer vision task which is still struggled with tiny targets. One of the important reasons is that existing semantic segmentation algorithms insufficiently gained the detail information from tiny targets and semantic edges. To handle this issue, we present a tiny target and edge feature extraction (T 2 E 2 ) algorithm for semantic segmentation. Especially, we can obtain spatial semantic information at pixel-level by constructing a pixel attention mechanism (PAM) on each decoder layer, which provide high-level feature as a guidance of low-level feature. The Edge Extraction Module (EEM) and the Tiny Target Extraction Module (TTE) are designed to get the feature information from the edges and the tiny target. Both the tiny target feature and the edges are supervised by specific loss functions. Extensive experiments are conducted on Cityscapes, Camvid, VOC2012 and ADE20K which demonstrate the effectiveness of the proposed algorithm. T 2 E 2 Â well embeds the tiny target and edge information, avoids inner contour noise, and surpasses state-of-the-art algorithms by 2%.
The Role of Agile Pockets in Agile Transformation

Agility spread from the bottom of organizations, carried by software professional acting as grassroots in promoting and integration of agility, until the practices and approaches got foothold in the organizations. This is unique in comparison with other major transformation of the industry such as that of the capability maturity model (CMM). This paper aims at understanding this agile phenomenon and the underlying informal structure and dynamics. The paper suggests to theorizes this as agile pockets of professionals playing an important role in the agile transformation. Especially four arch types of agile pockets are suggested to be crucial in pivotal situations of the transition. The theory and arguments are based in the well-known theory Communities of practice by Etienne Wenger [21, 22], but needs more development and empirical validation.
Traits: Correctness-by-Construction for Free

We demonstrate that traits are a natural way to support correctness-by-construction (CbC) in an existing programming language in the presence of traditional post-hoc verification (PhV). With Correctness-by-Construction, programs are constructed incrementally along with a specification that is inherently guaranteed to be satisfied. CbC is complex to use without specialized tool support, since it needs a set of refinement rules of fixed granularity which are additional rules on top of the programming language. In this work, we propose TraitCbC, an incremental program construction procedure that implements correctness-by-construction on the basis of PhV by using traits. TraitCbC enables program construction by trait composition instead of refinement rules. It provides a programming guideline, which similar to CbC should lead to well-structured programs, and allows flexible reuse of verified program building blocks. We introduce TraitCbC formally and prove the soundness of our verification strategy. Additionally, we implement TraitCbC as a proof of concept.
Production Data

A speech sample was elicited from the participants with Down Syndrome (DS) who took part in the comprehension tests reported in Chap. 3 , with the aid of a wordless picture book. The participants produced a range of complex syntactic structures, the most frequent being conjoined sentences, post-posed reason/purpose clauses and infinitival complements to V. Lexical variation was found for many of the structures (for example, infinitival complements to V occurred with seven different main verbs). The range of structures produced developed with the age of the participants, with the oldest group (over 25 years) producing a total of 13 different structures. We make a comparison between two individuals whose scores on standardized tests were similar, but whose behaviour in the production task was notably dissimilar in terms of number of complex structures produced, cohesiveness and length of narrative.
Encodability Criteria for Quantum Based Systems

Quantum based systems are a relatively new research area for that different modelling languages including process calculi are currently under development. Encodings are often used to compare process calculi. Quality criteria are used then to rule out trivial or meaningless encodings. In this new context of quantum based systems, it is necessary to analyse the applicability of these quality criteria and to potentially extend or adapt them. As a first step, we test the suitability of classical criteria for encodings between quantum based languages and discuss new criteria. Concretely, we present an encoding, from a sublanguage of CQP into qCCS. We show that this encoding satisfies compositionality, name invariance (for channel and qubit names), operational correspondence, divergence reflection, success sensitiveness, and that it preserves the size of quantum registers. Then we show that there is no encoding from qCCS into CQP (or its sublanguage) that is compositional, operationally corresponding, and success sensitive.
360° Video Summarization: Research Scope and Trends

Video summarization provides short and precise representation of the original video clips preserving important information. Video summarization assists us in navigating through a long video sequence and retrieving the most important segments. Output summary can be static containing some key frames or dynamic comprising of short video clips. The aim of video summarization is representing the original video for easy comprehension of the visual information. There are various applications of video summarization which includes video surveillance, egocentric videos, news, movies, and sports videos. Watching a lengthy 360-degree video is an exhausting task for a human annotator and to identify the key events, especially when there are unlimited field of views (FoVs). 360-degree video summarization is a less-explored area. In this article, existing methods for 360-degree video summarization are reviewed and analyzed based on algorithms, key features, merits, and demerits.
Visual Words based Static Indian Sign Language Alphabet Recognition using KAZE Descriptors

Sign language is a powerful tool used by differently-abled people as a means of communication. It facilitates and simplifies the interaction of the differently abled population with the general public. Sign language (SL) recognition can narrow down the communication gap that occur due to the lack of knowledge of sign language. This paper proposes a static sign language alphabet recognition based on KAZE descriptors and the bag of visual words (BoVW) model. KAZE is a 2D feature recognition and description algorithm that works entirely in nonlinear scale space. An efficient skin segmentation method based on hybrid HSV-YCbCr color space is also proposed in the paper to obtain skin mask without noise. Linear discriminant analysis is used for dimensionality reduction of the features extracted and multilayer perceptron neural network is used as classifier in this method. Results demonstrate that the proposed method obtained an accuracy of 99.23% with KAZE descriptor and MLP classifier.
Bias-Correction Feature Learner for Semi-Supervised Instance Segmentation

Instance segmentation is heavily reliant on large-scale annotated datasets to yield an ideal accuracy. However, annotated data are difficult to collect. To expand the annotated data, a straightforward idea is to introduce semi-supervised learning, which uses a trained model to obtain initial proposals on unlabeled images and then use initial proposals to generate pseudo labels. However, existing methods inevitably introduce the bias for the model learning, i.e., the foreground in initial low-confident proposals (low-confident foreground) is arbitrarily assigned as background. This bias makes the foreground and background closer in the feature space, which degenerates the model accuracy. To address this issue, this paper discards incorrect supervision and designs a bias-correction feature learner. Specifically, on the one hand, low-confident foreground does not participate in supervised learning. On the other hand, we extract possible foreground regions from all initial proposals to construct high-quality positive pairs which depict objects of the same category in contrastive learning. Then, positive pairs are pulled closer in the feature space. This helps models extract closely clustered foreground features. Experimental results demonstrate the effectiveness of our method on the public datasets (i.e., COCO, Cityscapes and Pascal VOC).
Balanced Nonadaptive Redundancy Scheduling

Distributed computing systems implement redundancy to reduce the job completion time and variability. Despite a large body of work about computing redundancy, the analytical performance evaluation of redundancy techniques in queuing systems is still an open problem. In this work, we take one step forward to analyze the performance of scheduling policies in systems with redundancy. In particular, we study the pattern of shared servers among replicas of different jobs. To this end, we employ combinatorics and graph theory and define and derive performance indicators using the statistics of the overlaps. We consider two classical nonadaptive scheduling policies: random and round-robin. We then propose a scheduling policy based on combinatorial block designs. Compared with conventional scheduling, the proposed scheduling improves the performance indicators. We study the expansion property of the graphs associated with round-robin and block design-based policies. It turns out the superior performance of the block design-based policy results from better expansion properties of its associated graph. As indicated by the performance indicators, the simulation results show that the block design-based policy outperforms random and round-robin scheduling in different scenarios. Specifically, it reduces the average waiting time in the queue to up to 25% compared to the random policy and up to 100% compared to the round-robin policy.
A Framework for Analyzing and Comparing Software Projects in Academia and Industry

The increasingly significant and indispensable role of software in society calls for ever more attention on its education. It is common for students graduating from computer science, software engineering, or similar programs to seek employment in industry. To be adequately prepared for a normal onboarding and transition from academia to industry, it is important for students to not only have theoretical as well as practical knowledge of software engineering state-of-the-art, but also an appreciation of and the necessary skills to successfully manage the technical as well as non-technical realities of the respective industry’s continually evolving heterogeneous ecosystem. The software projects provide means, in space as well as time, for such a preparation. In that regard, this chapter presents a brief history and evolution of software projects in software engineering education; based on conceptual modeling, professional experience, and international standards, provides an understanding of software projects in academia; using a detailed criteria, compares software projects in academia to those in industry, highlighting essential similarities as well as differences; presents a survey of undergraduate and graduate students’ experience in software projects; and provides recommendations in the direction of positive as well as negative learning for teachers and trainers involved in software projects.
Efficiency and Correlation Analysis of Intelligent Interconnected System in Blockchain Application Scenario

Under the background that blockchain and intelligent interconnection system are developing steadily, at the same time, our operational requirements for intelligent interconnected systems are becoming higher and higher. While urgently requiring intelligent interconnected systems to work more comprehensively, we add blockchain technology that can bring more possibilities to optimize the functions of intelligent interconnected systems and help solve the shortcomings of intelligent interconnected systems. Our optimization methods for intelligent interconnection system under blockchain include virtual blockchain tie line method, blockchain tearing method, simulation model construction method, data transformation control, TG algorithm, and PI model algorithm. The advantages are as follows: (1) we can divide the blockchain intelligent interconnection system into multiple areas and multiple tie lines by using the virtual blockchain tie line method, and finally greatly improve the information receiving efficiency and analysis efficiency of the intelligent interconnection system by numbering the whole area. (2) Using blockchain tearing method and simulation model building method, we can divide the blockchain system with large nodes into multiple areas, adjust the amount of data that can be processed in the blockchain data output capacity allocation area according to this value, and improve the functional control performance of each part. (3) We use the data transformation control method and TG algorithm to accurately obtain the global optimal solution of the path data of the intelligent interconnected system, and improve the accuracy while computing resources and time faster, and complete the optimization process of the blockchain for intelligent connection at a faster speed, which makes the optimization decision of the intelligent interconnected system of the blockchain more suitable.
From the Workplace to the Classroom: How Ideas from Industry Can Inspire New Kinds of Learning

The relationships between education and industry are many and varied. These relationships can be very direct, for example where students do some of their learning in the workplace, or practitioners engage with schools and universities by bringing their knowledge of the workplace into the classroom. There are, however, many more creative relationships that involve concepts and ideas from industry being adopted and repurposed in new ways for the educational context. We begin this chapter with an overview of the more traditional relationships between education and industry, including areas such as internships and externships, before exploring a range of ideas that have come from repurposing ideas from industry for the very different context of the classroom. These include agile methods from the software industry, lean thinking from the motor industry, creative community spaces for product development, design thinking, crowdsourcing, entrepreneurship, and industry-based models of leading change. We outline several ways in which these ideas from industry can inspire new kinds of student learning, not just to prepare them for the contemporary workplace but to provide them with essential higher-level skills that can help them to prepare for an unpredictable future.
Re: Place: Participatory and Discursive Place Making in Augmented Reality (AR) Public Art

Building on the concepts of discursive places and relational site-specificity, this chapter analyzes augmented reality (AR) public art and heritage projects that recover lost or repressed narratives and reembed them in material space. Accordingly, these projects enable an expanded sense of place that demonstrates their potential to reframe places as inclusive sites, which emerge from the combination of multiple histories. Although AR has been used as a surveillance technology in platforms like Pokémon Go, this chapter reconceptualizes AR as a relational technology by recognizing that its output emerges through integrating digital content within material environments. Accordingly, I argue that AR does not simply “augment” reality; rather, AR can be used to (re)program immediate landscapes by facilitating different relationships between virtual content and actual space, which can diversify our experiences of places and cultural narratives. I demonstrate this by analyzing projects like Border Memorial (2012/2017) and Ward One (2019), which seek to reimagine landscapes in light of the histories of racialized communities. These participatory engagements emphasize how digital technologies produce embodied, multisensorial experiences and a mixed sense of reality. Accordingly, they reimagine sites as discursive and relational places, thus exposing the social constructions embedded in them.