Reviewerly/exHarmony huggingface dataset:
Wasf-Vec

Word clustering is a serious challenge in low-resource languages. Since words that share semantics are expected to be clustered together, it is common to use a feature vector representation generated from a distributional theory-based word embedding method. The goal of this work is to utilize Modern Standard Arabic (MSA) for better clustering performance of the low-resource Iraqi vocabulary. We began with a new Dialect Fast Stemming Algorithm (DFSA) that utilizes the MSA data. The proposed algorithm achieved 0.85 accuracy measured by the F1 score. Then, the distributional theory-based word embedding method and a new simple, yet effective, feature vector named Wasf-Vec word embedding are tested. Wasf-Vec word representation utilizes a word’s topology features. The difference between Wasf-Vec and distributional theory-based word embedding is that Wasf-Vec captures relations that are not contextually based. The embedding is followed by an analysis of how the dialect words are clustered within other MSA words. The analysis is based on the word semantic relations that are well supported by solid linguistic theories to shed light on the strong and weak word relation representations identified by each embedding method. The analysis is handled by visualizing the feature vector in two-dimensional (2D) space. The feature vectors of the distributional theory-based word embedding method are plotted in 2D space using the t-sne algorithm, while the Wasf-Vec feature vectors are plotted directly in 2D space. A word’s nearest neighbors and the distance-histograms of the plotted words are examined. For validation purpose of the word classification used in this article, the produced classes are employed in Class-based Language Modeling (CBLM). Wasf-Vec CBLM achieved a 7% lower perplexity (pp) than the distributional theory-based word embedding method CBLM. This result is significant when working with low-resource languages.
Sociocultural Aspects of Agility

Culture has long been perceived as a vital aspect of describing behavioral issues among individuals. Additionally, demand to study the cultural aspects has been grown in recent years as business has become people centric chiefly when there’s a need for a business to interface with individuals, either as clients, representatives, providers, or stakeholders. It has turned out to be a regular practice to have software development teams in different locations. There are numerous reasons for this like cost reduction, gathering talent pool, and investment prerequisites enforced by the overseas government. The issue with cultural incompatibility increases when projects are distributed among multicultural society. At the point when teams from various societies engaged among themselves, the multifaceted nature of work connections can result in additional difficulties. A rising pattern of culturally varied project team makes it hard to share a typical “social standards.” When culture is inspected from the nationality point of view, sources of differences between nations are individuality, values, and institutions. The individuality here implies language and religion. Institutions form the principles, associations, and rules associated with them. In dissimilarity, the values are being unseen and implicit, while the impact of these is on the visible institution. Egalitarian control teams/organizations are participating units of individuals, which increase gradually and are more productive in contrast to those who are less participating as team units. While there may arise some conflicts between agile exercises and cultural content, a solution has to be devised that may differ depending upon local context and cultural rules at play, a broad and more workable action which creates the balance between agile practices and behaviors that are present in the local can be implemented.
Quantitative Comparison of Affine Feature Detectors Based on Quadcopter Images

Affine correspondences are in the focus of work in many research groups nowadays. Components of projective geometry, e.g. homography or fundamental matrix, can be recovered more accurately when not only point but affine correspondences are exploited. This paper quantitatively compares state-of-the-art affine covariant feature detectors based-on outdoor images taken by a quadcopter-mounted camera. Accurate Ground Truth (GT) data can be calculated from the restricted flight path of the quadcopter. The GT data consist of not only affine transformation but feature locations as well. Quantitative comparison and in-depth analysis of the affine covariant feature detectors are also presented.
Automated classification of ganglions and spiculated masses: A case study, Mexico's National Institute of Cancerology

The proposed technique is based on Mask R-CNN and was evaluated using INCAN data set. We obtained a ganglion and spiculated mass average precision rate of 43% and pixel-wise segmentation performances with 69% recall, 99% specificity, 64 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> precision, 99% accuracy, and 75% F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score. The data set is validated by 5 experts: oncologist and specialist in mammary pathology, three radiologists and specialists in mammary pathology, and a surgeon mastologist. This approach allows us to employ deep learning techniques to provide assistance to health-care professionals in the medical diagnosis of breast cancer.
Dynamic Multi-swarm Particle Swarm Optimization Based on Elite Learning

This paper presents a dynamic multi-swarm particle swarm optimization based on elite learning (DMS-PSO-EL) that consists of two kinds of sub-swarms to trade-off between exploitation and exploration capabilities. In DMS-PSO-EL, the whole population is divided into several DMS sub-swarms and one following sub-swarm on the basis of the fitness value rankings. In the evolution process, these DMS sub-swarms provide the exploration ability through dynamic regrouping strategy, while following sub-swarm enhances the exploitation ability by learning elite particles from DMS sub-swarms. Besides, randomly regrouping schedule regroups the entire population in each regrouping period aiming to avoid premature convergence and enhance inferior particles' searching ability. Comparing DMSPSO-EL with other 8 peer algorithms on CEC2013 benchmark functions, the results suggest that DMS-PSO-EL demonstrates superior performance for solving different types of functions. Besides that, the massive experiments show the superiority of the proposed strategy used in DMS-PSO-EL.
An Effective Encryption Scheme Based on Chaotic Maps Applied to Digital Image

In this research work, we aim to present a simple and effective encryption method based on chaotic maps for the digital grayscale images. The developed approach is based on the combination of Fibonacci sequence with the chaotic logistic map to create the secret key in order to improve encryption quality. Numerical results using a set of image benchmarks have shown the capability of the proposed encryption model to produce a better image security compared to results given by some other recently literature works.
3DoF+ video compression algorithm based on distortion minimization interpolation with adaptive reference view selection

The emerging 3DoF+ video offers more interactivity and deeper immersion. However, the bulky size of the 3DoF+ video imposes new challenges in its storage and processing. Thus, compressing 3DoF+ video efficiently is vitally important. Due to the geometric relationship between viewpoints, overlaps exist in views and this redundancy is available for further compression. However, existing compression algorithms either utilize partial information of the scene or still retain a lot of redundancy, which results in low quality reconstructed views. To address these problems, our study proposes to select reference views adaptively, which utilizes as few bits as possible to contain the necessary visual information of the scene. Besides, in order to reduce the residues between the reconstructed and original views, the distortion minimization interpolation (DMI) method is then proposed for further refining the synthesized views. The experimental results show that our method achieves on average 52.1% BD-rate reduction than direct coding with High Efficiency Video Coding (HEVC).
The Enlightenment of the Development of MOOC in the United States on China’s Higher Education

This paper mainly through the analysis of the development and operation management of MOOC in American higher education, through comparative analysis, combined with the problems of imperfect development mechanism and poor teaching effect of MOOC in Chinese universities, drawing lessons from the experience of developing MOOC in the United States, proposes to guarantee the teaching effect of MOOC by establishing operation mechanism of MOOC platform and University alliance, etc. in order to promote the benign development of MOOC in China’s higher education.
A Multi-Group Signature Scheme from Lattices

Group signature allows group members to sign on behalf of the group anonymously, and incorporate some tracing mechanism to identify the actual signer. Multi-group signature (MGS), introduced by Ateniese and Tsudik (FC’99), is a proper generalization of group signature. It allows signers to sign messages anonymously on behalf of multiple groups and has extensive applications in electronic commerce. However, all existing MGS schemes are from classical assumptions and will be insecure once quantum computers come true. In this paper, we propose the first MGS scheme in the lattice setting which is also the first quantum-resistant proposal. The keystone of our work is a zero-knowledge argument of knowledge ( $$ \mathsf {ZKAoK} $$ ) system of different syndromes of the same vector based on the work by Libert et al. (Asiacrypt’16) and Ling et al. (PKC’18). With additional signing and encryption layers, our $$ \mathsf {ZKAoK} $$ allows the signer to prove memberships in multiple groups simultaneously, which is the key issue on the MGS construction, and it can be of independent interest. For security proofs, we formalize the MGS model in the framework of Bellare et al. (CT-RSA’05).
Conclusions and Future Recommendations

Nowadays computer networks play a vital role and demand secured data transfer. Cryptography (Sarmah and Bajpai in Int J Comput Appl 8(9):7–10, 2010 [16]) and Steganography (Sarmah and Bajpai in Int J Comput Appl 8(9):7–10, 2010 [16]) are the two important sciences work in this area. There is always a possibility of attack by cryptanalyst (Ahmad et al. in Emerging ICT for Bridging the Future, Advances in Intelligent Systems and Computing, Proceedings of the 49th Annual Convention of the Computer Society of India CSI, Springer, Berlin, pp. 381–388 (2015) [1]) and/or steganalyst (Bohme in Inf Secur Cryptogr 0:11–77 (2010) [4]) or any unintended receivers on the sensitive information, which creates a need to enhance the growth of algorithms in the domain of information security.
Feasibility Study of Solar Energy System at the University of North Dakota

This paper investigates the feasibility of using a solar photovoltaic system to partially supply the University of North Dakota's campus with electricity. A detailed analysis on the rooftop area for panel use and the costs for installation, operation, and maintenance is carried out using local average estimates. The historical power consumption and cost data from the university is used to create a forecasting model using Holt-Winters method to project future electricity usage and costs from electric companies. These forecasts have provided superior analysis over a longer timeframe. The cost benefits, or lack thereof, have been interpreted by comparing the total solar panel system expenses to savings attributed to a reduction in external power demand.
Study on the reduction of computational complexity and sound analysis of the modified Gabor wavelet

We have reported that the computational complexity of Continuous wavelet transform can be theoretically reduced by using wavelet functions that are well designed and support a compact frequency domain. In this study, we apply this theory to Gabor wavelet, which has the highest analysis accuracy among many mother wavelets, and we try to apply it to actual signals.
Towards a Flexible System Architecture for Automated Knowledge Base Construction Frameworks

Although knowledge bases play an important role in many domains (including in archives, where they are sometimes used for entity extraction and semantic annotation tasks), it is challenging to build knowledge bases by hand. This is owing to a number of factors: Knowledge bases must be accurate, up-to-date, comprehensive, and as flexible and as efficient as possible. These requirements mean a large undertaking, in the form of extensive work by subject matter experts (such as scientists, programmers, archivists, and other information professionals). Even when successfully engineered, manually built knowledge bases are typically one-off, use-case-specific, non-standardized, hard-to-maintain solutions.Recent advances in the field of automated knowledge base construction (AKBC) offer a promising alternative. A knowledge base construction framework takes as input source documents (such as journal articles containing text, figures, and tables) and produces as output a database of the extracted information.An important motivation behind these frameworks is to relieve domain experts from having to worry about the complexity of building knowledge bases. Unfortunately, such frameworks fall short when it comes to scalability (ingesting and extracting information at scale), extensibility (ability to add or modify functionality), and usability (ability to easily specify information extraction rules). This is partly because these frameworks are often constructed with relatively limited consideration for architectural design, compared to the attention given to algorithmic performance and low-level optimizations.As knowledge bases will be increasingly relevant to many domains, we present a scalable, flexible, and extensible architecture for knowledge base construction frameworks. As a work in progress, we extend a specific framework to address some of its design limitations. The contributions presented in this short paper can shed a light on the suitability of using AKBC frameworks for computational use cases in this domain and provide future directions for building improved AKBC frameworks.
Classification of Space Objects Using Machine Learning Methods

In the last decade, the number of space object has skyrocketed. Collecting and analyzing data about these objects is essential in maintaining security of space assets. Classifying unknown objects into satellites, rocket bodies and debris represents a significant milestone in the analysis process. In this context, we investigate the effectiveness of several machine learning methods in classifying real-world light curves of space objects. The light curves are represented with a set of features extracted using the feets (feATURE eXTRACTOR FOR tIME sERIES) public tool. To address the problem of class imbalance, the synthetic minority over-sampling technique (SMOTE) is applied. We also investigate the use of Principal Component Analysis (PCA) in reducing the dimensionality of the feature space, prior to classification. In the case of the original feature set, the top performing classifier is the feedforward neural network with an accuracy of 73.6%. When SMOTE is used, an improvement in accuracy of approximately 15% is observed, with the use of SVM. However, PCA-based feature transformation leads to a slight degradation in performance of around 3%, in the case of the original feature representation, and a considerable degradation of 10%-30%, when SMOTE is used.
Imagination and Design

The central thesis of this book is that imagination is important to HCI and where better to begin this discussion than with the design of a digital artefact? When we design a new piece of technology (or engage in any design-related activity) there is an expectation that our imaginations will be at hand to generate ideas with respect to what the technology will do, and how it will look and how it will be experienced. This is imagination as mental time travel and creativity; and imagination as storytelling and as facilitating of social interaction. Having set ourselves what is a very broad target—the design process in all its stripes, and all four aspects of imagination, we will narrow our focus on how we use things (“design tools”) to crystallize our imaginations. Thus, the topic of this chapter may be characterised as variations on “thinking with things”. Firstly, consistent with current cognitive science (e.g. Clark in Being there: putting brain, body, and world together again. MIT press, 1998), we don’t just think with our brains as we routinely employ with all kinds of external representations and artefacts. These different forms of “thinking with things” have been, in turn, studied in a variety of ways, for example, through the use of props in make-believe. This is the major theme of not only this chapter but also for Chap. 4 . Instead of (say) toys, imagine thinking with sketches and prototypes. Secondly, there is also evidence from the study of external cognition (Scaife and Rogers 1996) which is the “interaction between internal and external representations when performing cognitive tasks”. In general, they describe external cognition as a form of computational offloading, which is the extent to which external representations can reduce the amount of cognitive effort required to solve a problem or complete a task. Here we are specifically concerned with capturing our imaginations with the use of sketches, external representations and then thinking with these. In each of these instances we will see how something (whether it be psychological, material or cultural—or a mixture) serves to make or help make material what we imagine.
Extracting Grammatical Error Corrections from Wikipedia Revision History

This paper describes the process of extracting and filtering Wikipedia revision history as a resource for grammatical error correction (GEC). Edits in Wikipedia revision history vary widely, including grammatical error corrections, information supplements, format amendments, and even vandalism. To extract only GEC-related revisions, we use an automated error annotation toolkit, ERRANT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , and extend it to process large data in parallel efficiently. With error-type analysis, we can then identify GEC-related edits and omit other unrelated edits (i.e., only the correction parts are reserved). The resulting corpus is - to our knowledge - the largest publicly available corpus of parallel possibly erroneous and correct sentences with error type labels.
Memory Efficient High-Performance Rotational Image Encryption

Image encryption is an essential part of a Visual Cryptography. Existing traditional sequential encryption techniques are infeasible to real-time applications. High-performance reformulations of such methods are increasingly growing over the last decade. These reformulations proved better performances over their sequential counterparts. A rotational encryption scheme encrypts the images in such a way that the decryption is possible with the rotated encrypted images. A parallel rotational encryption technique makes use of a high-performance device. But it less-leverages the optimizations offered by them. We propose a rotational image encryption technique which makes use of memory coalescing provided by the Compute Unified Device Architecture (CUDA). The proposed scheme achieves improved global memory utilization and increased efficiency.
Using digital technologies to train new generation employees against the background of problems accompanying the formation of an innovative educational environment

The young generation's ability to perceive and process information boosts the use of digital technologies in personnel training. While stressing the importance of digital technologies and their comprehensive penetration into daily living activities, we find it a must to identify and measure their contribution into teaching methods and forms. Present-day economic problems impact the staffing; therefore, we need the triggers that may improve the production efficiency. The present-day pace of life features an intensive renewal of social, economic, and political processes, and such distinctive features, typical for the young generation, as mobility and intensive learning capabilities, help young people to get accustomed to the present-day environment, to get integrated into the structure of social relations and organizational changes. Digital technologies become vital for the educational environment as they help to adjust to these transformations and to boost this breakthrough by fulfilling the functions of reproduction, innovation, and transmission.These issues were brought up within the framework of a sociological survey, launched at several Moscow institutions of higher education. According to its findings, many young respondents use digital technologies to communicate and to study, although they believe that no complete virtualization of their education is expedient.
Finding Stable Clustering for Noisy Data via Structure-Aware Representation

Clustering is one of the most prominent topics in machine learning. A multitude of clustering methods have been proposed, among which the spectral clustering has attracted much attention. However, in practice, spectral clustering is highly sensitive to noise data and a post-processing step (e.g., k-means for eigenvectors) is often required to obtain clustering indicators, which may be not optimal. Also, it does not scale well to large-scale data due to its eigen-decomposition procedures.Here we propose a structure-aware clustering model to address those issues. To achieve our goal, a high-quality affinity matrix is extracted from the original noisy data by a sparse additive decomposition, which is used to approximate the ideal clustering structure. We then jointly learn the high-quality affinity matrix as well as the spectral embedding in a unified model- thus, being robust to noise and obtaining the optimal clustering indicators without any post-processing steps. We further improve the clustering stability by considering the Laplacian eigengap of the affinity matrix. We show that the larger the Laplacian eigengap, the more stable the clustering results. We introduce a speedup strategy to effectively compute eigenvectors of large matrices. Experimental results demonstrate that the proposed model outperforms existing approaches for noisy data.
New Class of Codes, Correcting Errors in the Lee Metric

A new class of Q-ary codes that correct errors in the Lee metric is described. For the received codes there is no restriction on the number of correctable errors associated with the size of the code alphabet. In addition, the resulting codes have less redundancy compared to known codes.
Motion-less super-resolution under blind condition using sparse optimization

The purpose of this paper is two-fold. First, we develop a regularized and accelerated version of Ahmed's multi-channel blind deconvolution algorithm based on low-rank matrix recovery. Second, we apply the developed algorithm to motion-less super-resolution problem, which aims at recovering a high-resolution image from a set of differently blurred low-resolution images. We demonstrate performances of the proposed method by simulation studies and real-image experiments.