Reviewerly/exHarmony huggingface dataset:
Revisiting the Product Configuration Systems Development Procedure for Scrum Compliance: An i* Driven Process Fragment

Product Configuration Systems (PCS) are software applications supporting the design of products tailored to the individual desiderata of customers. PCS development does not follow the same procedure as traditional software: indeed, due to its nature, specific knowledge needs to be collected, a set of custom engineering stages have thus been built-up. Within these stages, special requirements representation and design artifacts are used notably to deal with features inter-dependencies. More specifically, the Product Variant Master (PVM) has been specifically created for PCS knowledge representation while Class-Responsibility-Collaboration (CRC) cards and a UML Class Diagram are often indispensable for PCS object-oriented design. PCS development projects have gradually started to use agile methods like the Scrum. This paper presents a process fragment for conducting PCS development projects with Scrum; it overviews how the development team of a specific organization adapted the agile process to the PCS context. This process fragment has indeed been built on the basis of practitioners knowledge collected through 5 qualitative interviews (inductive approach) and exhaustively depict the activities performed by the team on PCS development projects of various size and context. Because of the possibility to represent social (role) dependencies, the fragment is visually represented using an i* Strategic Rationale Diagram. The main contribution of the paper is the fragment itself, it is intended to be dynamically used as an initial guidance for PCS development teams willing to conduct projects using Scrum; it can be tailored to any project/sprint and enriched at will.
Distributional properties and estimation in spatial image clustering

Clusters of different objects are of great interest in many fields, such as agriculture and ecology. One kind of clustering methods is very different from the traditional statistical clustering analysis, which is based on discrete data points. This method of clustering defines clusters as the connected areas where a well-defined spatial random field is above certain threshold. The statistical properties, especially the distributional properties, of the defined clusters are vital for the studies of related fields. However, the available statistical techniques for analyzing clustering models are not applicable to these problems. We study the distribution properties of the clusters by defining a distribution function of the clusters rigorously and providing methods to estimate the spatial distribution function. Our results are illustrated by numerical experiments and an application to a real world problem.
Technical Perspective

We all structure information in our brains: without structure, we would not be able to deal with the huge quantities of highly heterogenous information we process. However, each of us structures this information slightly differently, often leading to misunderstandings or requiring additional rounds of dialog to clarify. Database schema are also designed by humans. The structure imposed on the information by the schema reflects the human designers' perspective on the world, even if mediated through formal design techniques or computer software. Therefore, the structured data querying task can be viewed as having a schema mapping problem at its core: mapping between the "schema" the human has in her brain and the schema used to organize the database.
Identifying Ambiguity in Semantic Resources

In many Information Extraction tasks, dictionaries and lexica are powerful building blocks for sophisticated extractions. The success of the Semantic Web in the last 10 years has produced an unprecedented quantity of available structured data that can be leveraged to produce dictionaries on countless concepts in many domains. While being an invaluable resource, these automatically built dictionaries may contain "problematic" items, such as spurious words, which have been included by mistake, or ambiguous words, which appear with multiple different meanings in the target corpus and therefore necessitating an expensive disambiguation task. In this paper, we propose a simple and effective method to identify problematic terms in a given dictionary, which are ambiguous or spurious with respect to a given corpus, with the aim to facilitate subsequent Information Extraction tasks. We prove the effectiveness of the method with a systematic experiment on publicly available concept dictionaries, using a very large Web corpus as target, with an average precision in identifying a problem term above 85%.
LLDM: Locally linear distance maps for robot motion planning: Extended Abstract

This paper presents a data structure that summarizes distances between configurations across a robot configuration space, using a binary space partition whose cells contain parameters used for a locally linear approximation of the distance function. Querying the data structure is extremely fast, particularly when compared to graph search required for querying Probabilistic Roadmaps, and memory requirements are promising. The paper explores the use of the data structure constructed for a single robot to provide a heuristic for challenging multi-robot motion planning problems. Potential applications also include the use of remote computation to analyze the space of robot motions, which then might be transmitted on-demand to robots with fewer computational resources.
Prediction-Based Scale Adaptive Correlation Filter Tracker

Although correlation filter-based trackers have demonstrated excellent performance for visual object tracking, there remain several challenges to be addressed. In this work, we propose a novel tracker based on the correlation filter framework. Traditional trackers face difficulty in accurately adapting to changes in the scale of the target when the target moves quickly. To address this, we suggest a scale adaptive scheme based on prediction scales. We also incorporate a speed-based adaptive model update method to further improve overall tracking performance. Experiments with samples from the OTB100 and KITTI datasets demonstrate that our method outperforms existing state-of-the-art tracking algorithms in fast motion scenes.
Mixing Hetero- and Homogeneous Models in Weighted Ensembles

The effectiveness of ensembling for improving classification performance is well documented. Broadly speaking, ensemble design can be expressed as a spectrum where at one end a set of heterogeneous classifiers model the same data, and at the other homogeneous models derived from the same classification algorithm are diversified through data manipulation. The cross-validation accuracy weighted probabilistic ensemble is a heterogeneous weighted ensemble scheme that needs reliable estimates of error from its base classifiers. It estimates error through a cross-validation process, and raises the estimates to a power to accentuate differences. We study the effects of maintaining all models trained during cross-validation on the final ensemble's predictive performance, and the base model's and resulting ensembles' variance and robustness across datasets and resamples. We find that augmenting the ensemble through the retention of all models trained provides a consistent and significant improvement, despite reductions in the reliability of the base models' performance estimates.
Toward faster reinforcement learning for robotics applications by using Gaussian processes

Standard robotic control works perfectly in case of ordinary conditions, but in case a change in the conditions (e.g. damaging of one of the motors), the robot won’t achieve its task anymore. We need an algorithm that provide the robot with the ability of adaption to unforeseen situations. Reinforcement learning provide a framework corresponds with that requirements, but it needs big data sets to learn robotic tasks, which is impractical. We propose using Gaussian processes to improve the efficiency of the Reinforcement learning, where GP will learn a state transition model from robot (interaction) phase, and after that we use the GP to simulate trajectories and optimize the robot’s controller in a (simulation) phase. We tested the algorithm in Cart-pole task, and it gave interesting results, where a working controller was learned after just 28 seconds of (interaction) on the real robot, and the whole training time was 21 minuts, considering the training in the (simulation).
A Heuristic Scout Search Mechanism for Artificial Bee Colony Algorithm

Artificial bee colony (ABC) algorithm simulates the foraging behavior of honey bees, which has shown good performance in many application problems and large scale optimization problems. However, the scout foraging behavior in the ABC algorithm is completely random, which would sometimes make it consume more search efforts to discover some promising area and hamper the convergent speed of the ABC algorithm, especially for large scale optimization. To overcome this drawback, this paper proposes a heuristic scout search (HSS) mechanism based on the information obtained during running to guide the scout search. The ABC algorithm with HSS mechanism (HSSABC) has been tested on a set of test functions. Experimental results show that the HSS mechanism can greatly speed up the convergence of the ABC algorithm. After the use of HSS, the performance of the ABC algorithm is significantly improved for both rotated problems and large scale problems.
Cultural Activities and Cultural Needs

The article shows that the need for cultural activities is aimed directly at the development and self-realization of the individual. The activities of people related to the satisfaction of their cultural needs are not always identical to the concept of ‘activities that develop the personality’. Often, the use of one or another of its types is not an indicator of the development of the relevant needs.
Star2vec: From Subspace Embedding to Whole-Space Embedding for Intelligent Recommendation System (Extended Abstract)

Recommendation systems are powerful tools that can alleviate system overload problems by recommending the most relevant items (contents) to users. Recommendation systems allow users to find useful, interesting items from a significantly large space and also enhance the user’s browsing experience. Relevant items are determined by predicting user’s ratings on different items. Two traditional techniques used in recommendation system are Content-Based filtering and Collaborative-Filtering. Content-Based filtering uses content of the items that the user has involved in the past to discover items that the user might be interested in. On the other hands, Collaborative-Filtering determine the similarity between users and recommends items chosen by similar users
Impediments to Gauging ICT-Based Informal English Learning Outcomes

This chapter is a product of the impediments to measuring ICT-based informal learning in the Saudi EFL context. It unravels difficulties of measuring the outcomes of informal English learning with a particular reference to informal technology-based activities. It succinctly discusses why it is difficult to design, develop, implement, and sustain a comprehensive approach to assessing ICT-based informal English learning outcomes. The major problems surfaced from this discussion included absence of theoretical background of this evolving mode of learning, unawareness of recognizing linguistic, and inability to control the informal ICT-based activities for they are arbitrary and difficult to expect and measure by ordinary tests. Thus, there is a need to adopt alternative assessment tools such as portfolio, progress tests, and self-reporting within a new paradigm shift towards learner-centeredness.
Enhanced Scrambled Prime Key Encryption Using Chaos Theory and Steganography

In this paper we have analyzed the strength and weakness of scrambled prime key encryption proposed by Haidar et al. [] for data encryption using key elements which are prime numbers. However, this scheme exhibits its vulnerabilities in the face of chosen plaintext attack. Hence we, in this paper, extended the algorithm by introducing chaos theory and steganography, which offers better security and robustness. We have taken the partially encrypted text from [] and embedded the same in a carrier image. In this process, we have chosen a novel chaos generator to select the pixels into which the text is to be embedded.
Visualizing Algorithmic Selection in Social Media

Social media sites such as Facebook and Twitter use algorithms to filter information in order to reduce overload and selectively pick content for users. These algorithms create unique, individual, and isolated bubbles of information that users are not always aware of. We recommend that algorithmic awareness should be the first step in addressing the pitfalls of the filter bubble effect. We conducted an experimental study to investigate how simple visualizations can be used to achieve algorithmic awareness and to understand how it might influence users' behavior. The visualizations did not lead to increased understanding of the algorithm per se, but its presence created interesting effects that will inform future studies.
BENEFITS OF DISTANCE LEARNING

One of the most promising ways to get an education in our time is distance learning, with the help of opportunities provided by modern telecommunication technologies and, in particular, the Internet.Thus, distance learning today can be called a rapidly developing form of education, but not as massive and pervasive as the modern computerized community would like.Distance learning is closely related to Internet education, but not identical to it from the point of view of concepts, the second is the specific difference of the first, which more strictly regulates the technical and technological specifics of education using global resources.
Efficient Information Retrieval of Encrypted Cloud Data with Ranked Retrieval

Over the past decade, there have been massive developments in technology such as self-driving cars, crypto currencies, streaming services, voice assistants etc. In each of the listed breakthroughs, Cloud Computing was involved. Cloud computing has offered a tremendous breakthrough in enterprise and business transformation bringing with it a previously unknown agility. Projects and databases hosted on the cloud have enabled users to work in unison without any hassle. Users who use cloud computing can upload their data to the cloud from anywhere and get access to the best possible service architecture and applications from a connected network of computer resources. However to ensure that the data of the users are stored safely on the cloud, the data has to be encrypted to protect privacy before outsourcing. Encryption of the user's data leads to poor efficiency of data utilization due to the possibility of a large number of outsourced files. This amounts to a lack of control and access of the out-sourced data from the user's position. Due to lack of efficient and practical cloud computing, the user's lack of control is further magnified. To address this aspect of helping the user reign in control over their encrypted data when out-sourced on a cloud, we have developed a scheme to efficiently search over the encrypted data and retrieve ranked results.
Learnings from the Gawler Craton airborne survey quality control

SummaryThe Geological Survey of South Australia (GSSA) designed the Gawler Craton Airborne Survey (GCAS) to provide high resolution magnetic, gamma-ray and elevation data covering the northern portion of the Gawler Craton. In total, 1.66 million line km were planned over an area of 295,000 km2, covering approximately 30% of the state of South Australia.The survey design of 200 m spaced lines at a ground clearance of 60 m can be compared with the design of existing regional surveys which generally employed 400 m line spacing and a ground clearance of 80 m. The new survey design results in ~2 x the data coverage and ~25% closer to the ground when compared to previous standards for regional surveys in South Australia.Due to the enormous scale of the survey, the data were acquired using four contractors who employed ten systems to fly the sixteen blocks.To standardise the data from the multitude of systems, Geoscience Australia (GA) employed a comprehensive set of technical specifications. As part of these specifications the contractors were required to fly each of the ten systems over a series of test lines termed the “Whyalla Test Lines” (Whyalla).The final GCAS data provide truly impressive high resolution regional scale products. These will allow more detailed geological interpretation of the prospective Gawler Craton.A laser altimeter was added to the list of required survey equipment. Deficiencies in the technical specifications relating to laser altimeters were identified. Standards and procedures specific to laser characteristics will need to be considered on future surveys.Analyses show that weaknesses in current standards and procedures are still evident. The weaknesses identified allow room for improvements to be made for future surveys.Gamma-ray processing results raised the most serious concerns, with repeatability not achieved. Changes to standard procedures may need to be considered.
Image Denoising Algorithm based on Improved NCSR Model

Abstract The Nonlocally Centralized Sparse Representation (NCSR) algorithm aims to reduce sparse coding noise and utilizes the Nonlocal Self-similarity of the image to improve the effectiveness and accuracy of the image denoising algorithm, which has achieved good denoising effect. However, in the local dictionary learning and sparse coefficient estimation, the algorithm directly uses the Euclidean distance of the noise image to measure the similarity, which will have a certain impact on the final denoising effect. Aiming at this deficiency, it is proposed to use the Gaussian Mixed Model (GMM) to train nonlocal self-similar image patch of the external clean image in the local dictionary learning, and use the learned GMM to guide the internal image clustering; when performing the sparse coefficient estimation, the structural similarity index(SSIM) is introduced into the Euclidean distance and the improved Euclidean distance is introduced into the weight calculation. The experimental results show that the improved algorithm proposed in this paper has improved the PSNR and the visual effect of the image.
Iterative Discriminative Domain Adaptation

A popular formulation of domain adaptation (DA) is to simultaneously minimize the source risk and the cross-domain discrepancy between the source domain $$\mathcal {D}_s$$ and target domain $$\mathcal {D}_t$$ . However, this is believed to be suboptimal since the shared feature, which is indistinguishable by a domain classifier, could be far from optimum for the purpose of classification. In this paper, we propose an iterative DA framework for directly optimizing the classification error, which provides DA solutions to both unsupervised and semi-supervised scenarios. Instead of directly attacking $$\mathcal {D}_s \rightarrow \mathcal {D}_t$$ , we employ an iterative self-training approach of $$\mathcal {D}_s + \mathcal {D}_t^{l-1} \rightarrow \mathcal {D}_t^l$$ for progressively-labelling of $$\mathcal {D}_t$$ with the aim of $$\lim _{l\rightarrow \infty }\mathcal {D}_t^l \approx \mathcal {D}_t$$ . For unsupervised DA, it performs comparable to the state-of-the-art DA methods. In particular, it performs the best among various unsupervised DA methods for the very difficult task MNIST $$\rightarrow $$ SVHN. By employing a few labeled samples in the target domain, we show that it can achieve significantly improved performance. For MNIST $$\rightarrow $$ SVHN, the use of 60 labeled samples from SVHN is able to improve the accuracy margin about $$+10\%$$ over the state-or-the-art unsupervised DA method. For a comparison with semi-supervised learning methods, it achieves the accuracy margin about $$+30\%$$ over Mean Teacher with 60 labeled samples in SVHN.
Identity-Based Parallel Key-Insulated Proxy Signature Without Random Oracles

Abstract Exposure of private keys is the dangerous attack in the public key cryptosystems. We put forward an identity-based parallel key-insulated proxy signature (IBPKIPS) scheme to deal with the problem of key exposure in the identity-based proxy signature system. Our IBPKIPS scheme is secure without random oracles. In the IBPKIPS scheme, we use two different helper keys to renew the temporary private keys. Our IBPKIPS scheme is securer and more flexible than the identity-based proxy signature scheme.
An efficient mobile-edge collaborative system for video photorealistic style transfer

In the past decade, convolutional neural networks (CNNs) have achieved great practical success in image transformation tasks, including style transfer, semantic segmentation, etc. CNN-based style transfer, which denotes transforming an image into a desired output image according to a user-specified style image, is one of the most popular techniques in image transformation. It has led to to many successful industrial applications with significant commercial impacts, such as Prisma and DeepArt. Figure 1 shows the general workflow of the CNN-based style transfer. Given a content image and a user-specified style image, the content features and style features can be extracted using a pre-trained CNN, and then be merged to generate the stylized image. The CNN model is trained for generating a stylized image that has similar content features as the content image's and similar style features as the style image's. In this example, we can see the content image is captured at a lake in the daytime, while the style image is another similar scene captured at dusk. After performing style transfer, the content image is successfully transformed to the dusky scene while keeping the content unchanged as the content image.
Good basis vs bad basis: On the ability of Babai’s Round-off Method for solving the Closest Vector Problem

Abstract A lattice basis is consisting of linearly independent basis vectors that span the lattice. A lattice in dimension of 2 and above have infinitely many bases. These bases have different quality in terms of the length (norm) and orthogonality of basis vectors. A lattice basis with reasonably short and orthogonal basis vectors is normally referred to as a ‘good basis’. On the contrary, a lattice basis with long and non-orthogonal basis vectors is normally referred to as a ‘bad basis’. The most establish hard problems in lattice are the Shortest Vector Problem (SVP) and the Closest Vector Problem (CVP). The solutions of these NP-hard problems can be categorized as exact and approximation solutions. Since the hardness of these problems significantly grows once the lattice dimension increases, approximation methods for solving these problems are preferable in practice. Babai’s Round-off Method is an approximation method for solving the CVP. Executing this method by using different bases of the same lattice return outputs with different distance to the target vector. When the method is executed using a good basis, it works effectively for returning a lattice vector that is located close to the target vector. On the contrary, the method works ineffectively when it is executed using a bad basis where the returned lattice vector is located far from the target vector. In this study, we investigated the reasons behind this occurrence. For that purpose, we solved a CVP instance by executing the Babai’s Round-off Method using a good basis and a bad basis. As a result, we discovered how the norms and orthogonality of basis vectors play their role for influencing the quality of the output returned by the method.