Reviewerly/exHarmony huggingface dataset:
MRI denoising via sparse tensors with reweighted regularization

In recent years, image denoising based on sparse tensors has been one promising technique for denoising magnetic resonance images or video processing. This paper aims at developing a new sparse tensor model based on reweighted regularization of factor matrices for magnetic resonance images denoising. An improved Split-Bregman scheme is proposed which is simple in implementation and efficient in computation. Additionally, the convergence of proposed scheme is proved. Experiments show that the proposed algorithm is efficient, and the denoising results are better than the state-of-the-art image denoising methods. The average computational time of our method is slightly longer than the others under the same iteration, except LPGPCA and model in Ruru and Zhixun (2018) [22].
Authenticated Medical Documents Releasing with Privacy Protection and Release Control

In the context of Information Societies, a tremendous amount of information is daily exchanged or released. Among various information-release cases, medical document release has gained significant attention for its potential in improving healthcare service quality and efficacy. However, integrity and origin authentication of released medical documents is the priority in subsequent applications. Moreover, sensitive nature of much of this information also gives rise to a serious privacy threat when medical documents are uncontrollably made available to untrusted third parties. Redactable signatures allow any party to delete pieces of an authenticated document while guaranteeing the origin and integrity authentication of the resulting (released) subdocument. Nevertheless, most of existing redactable signature schemes (RSSs) are vulnerable to dishonest redactors or illegal redaction detection. To address the above issues, we propose two distinct RSSs with flexible release control (RSSs-FRC). We also analyse the performance of our constructions in terms of security, efficiency and functionality. The analysis results show that the performance of our construction has significant advantages over others, from the aspects of security and efficiency.
A novel level set approach for image segmentation with landmark constraints

Level set methods are widely used in image segmentation and shape analysis. However, most of the current research focuses on fast computational algorithms, initial value selection, and practical applications in various areas. To the best of our knowledge, no research has been conducted on segmentation with level set models where the segmentation contours have to pass through some prior landmark points. In this paper, we propose a new variational model for image segmentation based on the classical Chan-Vese model for this new problem. The new model incorporates prior landmarks information as constraints in a formulated optimization problem. Then, we investigate the theoretical solvability of the new model and design a new algorithm based on the Split Bregman algorithm for numerical implementation. Finally, we conduct some segmentation experiments on gray images and compare with the original Chan-Vese model. The obtained results show many advantages of the proposed model with broad applications. Additionally, we give some critical analysis of the proposed algorithm.
Sparse representation based multi‐frame image super‐resolution reconstruction using adaptive weighted features

This study presents a novel sparse-representation based multi-frame super-resolution (SR) technique to reconstruct a high-resolution (HR) frame from multiple noisy low-resolution (LR) frames by using registration in sub-pixel accuracy and adaptive weighted feature operators. First, the registration of multiple frames in sub-pixel level and the mapping of pixels from LR frames to HR grid puts more information into the reconstructed image with respect to the conventional sparse representation based single image SR technique. This improves the overall resolution of the output image. Second, the introduction of adaptive weighted feature operators in the reconstruction process has significantly improved the robustness of the algorithm to noisy input frames. Hence, from the outputs, it can be seen that the proposed method outperforms the recent techniques in terms of noise robustness even in the higher noise level in the input image. The performance of the proposed algorithm is evaluated and quantified through a set of well-defined quality metrics and compared with some recently developed techniques. The results of the proposed technique confirm the claims of the authors.
HappyFeet

In this paper, we discuss our experience in building an automated dance assessment tool with IMU and IoT devices and highlight the major challenges of such an endeavor. In a typical dance classroom scenario, where the students frequently outnumber their instructors, such a system can add an immense value to both parties by providing systematic breakdown of the dance moves, comparing the dance moves between the students and the instructors, and pinpointing the places for improvement in an autonomous way. Along that direction, our prototypical work, HappyFeet [1], showcases our initial attempts of developing such an intelligent Dance Activity Recognition (DAR) system. Our CNN based Body Sensor Network proves more effective (by ≈7% margin at 94.20%) at accurately recognizing the micro-steps of the dance activities than traditional feature engineering approaches. These metrics are derived by purposely evaluating the setup on a dance form known for its gentle, smooth and subtle limb movements. In this paper, we articulate how our proposed DAR framework will be generalizable for diverse dance styles involving very pronounced movements, human body kinematics and energy profiles.
Effect of subject's age and gender on face recognition results

Nowadays, more and more places need authentication. Face recognition is a mature technology for identity verification research. Recognition accuracy is an important indicator for evaluating authentication algorithms. In order to improve the accuracy of identity verification, advanced face is used. Feature recognition algorithm is an effective way, but it is also an effective algorithm to study the factors affecting facial features. Therefore, many researchers study the recognition results based on the poses of the face, light and other factors. This paper is also a study on the factors affecting face recognition, mainly by studying the influence of the age and gender factors on the identity verification results, and using the deep learning method to classify facial features. The simulation results show that the average recognition rate reaches 83.73%. At the same time, this paper analyzes the effect of age and gender on the classification results. The results show that the recognition effect of middle-aged men in male subjects is lower than that of youth and the elderly. Women have little difference in recognition effect with age. Males have higher recognition rates than women.
<i>'Shada Baksho'</i>

Previous Information and Communication Technology and Development (ICTD) research has shown that rural women's access to digital technologies in many Low and Middle Income Countries (LMICs), including Bangladesh, is challenging because of a number of reasons, including Techno-Phobia. With a view to mediating access for rural women, we designed an one-button communication box, and documented rural women's user-experiences with it. Drawing upon our discovery of the set of gender specific and culturally contextual problems, this paper also discusses several design and policy interventions to ensure a better Information and Communication Technology (ICT) access for the rural women in Bangladesh.
The design, implementation and evaluation of a professional development programme to support teachers’ technology integration in a public education centre

The main purpose of this study was to design and implement a professional development programme on technology integration for teachers in a public education centre based on the technological pedagogical content knowledge (TPACK) framework and learning by design approach, and to evaluate the programme’s impact. Ten adult education teachers in a public education centre participated in this case study research. Data sources include teacher-reflective reports, lesson plans and focus group interviews. The results revealed that the programme both enhanced teachers’ TPACK and influenced their teaching practices. The results emphasise the importance of providing information that aligns with teachers’ specific needs in adult education centres, giving teachers an active role, making opportunities available to design technology-enhanced learning artefacts while working in collaborative teams and selecting ICT tools based on typical adult learning characteristics. The study also offers suggestions for future research and practice on the design of professional development programmes.
Clustering in the Presence of Concept Drift

Clustering naturally addresses many of the challenges of data streams and many data stream clustering algorithms (DSCAs) have been proposed. The literature does not, however, provide quantitative descriptions of how these algorithms behave in different circumstances. In this paper we study how the clusterings produced by different DSCAs change, relative to the ground truth, as quantitatively different types of concept drift are encountered. This paper makes two contributions to the literature. First, we propose a method for generating real-valued data streams with precise quantitative concept drift. Second, we conduct an experimental study to provide quantitative analyses of DSCA performance with synthetic real-valued data streams and show how to apply this knowledge to real world data streams. We find that large magnitude and short duration concept drifts are most challenging and that DSCAs with partitioning-based offline clustering methods are generally more robust than those with density-based offline clustering methods. Our results further indicate that increasing the number of classes present in a stream is a more challenging environment than decreasing the number of classes. Code related to this paper is available at: https://doi.org/10.5281/zenodo.1168699 , https://doi.org/10.5281/zenodo.1216189 , https://doi.org/10.5281/zenodo.1213802 , https://doi.org/10.5281/zenodo.1304380 .
New Evolutionary-Based Techniques for Image Registration

The work reported in this paper aims at the development of evolutionary algorithms to register images for signature recognition purposes. We propose and develop several registration methods in order to obtain accurate and fast algorithms. First, we introduce two variants of the firefly method that proved to have excellent accuracy and fair run times. In order to speed up the computation, we propose two variants of Accelerated Particle Swarm Optimization (APSO) method. The resulted algorithms are significantly faster than the firefly-based ones, but the recognition rates are a little bit lower. In order to find a trade-off between the recognition rate and the computational complexity of the algorithms, we developed a hybrid method that combines the ability of auto-adaptive Evolution Strategies (ES) search to discover a global optimum solution with the strong quick convergence ability of APSO. The accuracy and the efficiency of the resulted algorithms have been experimentally proved by conducting a long series of tests on various pairs of signature images. The comparative analysis concerning the quality of the proposed methods together with conclusions and suggestions for further developments are provided in the final part of the paper.
Cyber crime: a portrait of the landscape

Purpose The purpose of this paper is to review current evidence in relation to scale and impacts of cyber crime, including various approaches to defining and measuring the problem. Design/methodology/approach A review and analysis of survey evidence is used to enable an understanding of the scope and scale of the cyber crime problem, and its effect upon those experiencing it. Findings The analysis evidences that cyber crime exists in several dimensions, with costs and harms that can be similarly varied. There is also a sense that, moving forward, the “cyber” label will become somewhat redundant as many crimes have the potential to have a technology component. Research limitations/implications The key evidence in this particular discussion has some geographic limitations, with much of the discussion focussed upon data drawn from the Crime Survey for England and Wales, as well as other UK-based sources. However, many of the broader points still remain more widely relevant. Practical implications This study helps in: better understanding the range and scale of cyber crime threats; understanding how the cyber element fits into the wider context of crime; improving the appreciation of what cyber crime can mean for potential victims; and recognising the cost dimensions, and the implications for protection and response. Social implications The discussion will help businesses and individuals to have a better appreciation of the cyber crime threat, and what ought to be considered in response to it. Originality/value The discussion is based upon recent evidence, and therefore represents a more up-to-date view of the cyber crime landscape than reviews already available in earlier literature.
Quantifying the Information Leakage in Cache Attacks via Symbolic Execution

Cache attacks allow attackers to infer the properties of a secret execution by observing cache hits and misses. But how much information can actually leak through such attacks? For a given program, a cache model, and an input, our CHALICE framework leverages symbolic execution to compute the amount of information that can possibly leak through cache attacks. At the core of CHALICE is a novel approach to quantify information leakage that can highlight critical cache side-channel leakage on arbitrary binary code. In our evaluation on real-world programs from OpenSSL and Linux GDK libraries, CHALICE effectively quantifies information leakage: For an AES-128 implementation on Linux, for instance, CHALICE finds that a cache attack can leak as much as 127 out of 128 bits of the encryption key.
Assessment of solar PV power potential over Asia Pacific region with remote sensing considering meteorological factors

The intensity of solar radiation (SR) is one of the most important required inputs for the estimation of photovoltaic (PV) power station output. Meanwhile, the efficiency of solar PV systems is affected by meteorological factors such as temperature, dust, precipitation, and snow. Meteorological data from satellites provide a viable way for estimating PV potential due to its advantage in spatial coverage and temporal resolution. This paper presents a new approach to adjust SR data from satellites based on the cloud optical thickness (CLOT) before evaluating the solar PV power (PPV) potential, with the effective efficiency of solar cells computed based on temperature, dust, precipitation, and snow. The objective of this study is to evaluate the over-all spatiotemporal solar PV potential in the Asia Pacific region which will holistically include limiting meteorological factors and identify which factor contributes most significantly to the decrease in solar PV potential in selected cities in the region. First, SR and CLOT data from Advanced Himawari Imager 8 and a SKYNET station were processed to derive the correction factor for solar radiation data. Second, satellite data for temperature (MOD11), precipitation (global satellite mapping of precipitation), dust (MOD04), and snow cover (MOD10) were processed to derive the effective solar PV efficiency. Finally, maps showing the seasonal PV power potential over the Asia Pacific region were generated, with selected cities zoomed in for detailed analysis using mean monthly values from March 2016 to February 2017. The results showed that the maximum theoretical PPV in the region was estimated to be 1.9 GW per 17.5 km2 effective pixel area. Moreover, PPV decreased by maximum values of 180 MW, 550 MW, and 225 MW due to temperature, dust, and snow, respectively. For Beijing, Tokyo, and Jakarta, the major contributor to the decrease in PPV is dust, while Khabarovsk is consistently affected by snow effects. Initial validation of the model shows over- and underestimation of solar PV output compared to the actual values by as high as 30%. However, very high values of coefficient of determination (&amp;gt;0.90) show promising results of the model. The contribution of this study is two-fold: regional-scale assessment of PPV potential and investigation of the collective effect and individual contributions of dust, temperature, and snow to the decrease in PPV potential.
Data fusion methods in multimodal human computer dialog

In multimodal human computer dialog, non-verbal channels, such as facial expression, posture, gesture, etc, combined with spoken information, are also important in the procedure of dialogue. Nowadays, in spite of high performance of users' single channel behavior computing, it is still great challenge to understand users' intention accurately from their multimodal behaviors. One reason for this challenge is that we still need to improve multimodal information fusion in theories, methodologies and practical systems. This paper presents a review of data fusion methods in multimodal human computer dialog. We first introduce the cognitive assumption of single channel processing, and then discuss its implementation methods in human computer dialog; for the task of multi-modal information fusion, serval computing models are presented after we introduce the principle description of multiple data fusion. Finally, some practical examples of multimodal information fusion methods are introduced and the possible and important breakthroughs of the data fusion methods in future multimodal human-computer interaction applications are discussed.
Online Handwritten Gurmukhi Words Recognition

Identification of offline and online handwritten words is a challenging and complex task. In comparison to Latin and Oriental scripts, the research and study of handwriting recognition at word level in Indic scripts is at its initial phases. The two main methods of handwriting recognition are global and analytical. The present work introduces a novel analytical approach for online handwritten Gurmukhi word recognition based on a minimal set of words and recognizes an input Gurmukhi word as a sequence of characters. We employed a sequential step-by-step approach to recognize online handwritten Gurmukhi words. Considering the massive variability in online Gurmukhi handwriting, the present work employs the completely linked non-homogeneous hidden Markov model. In the present study, we considered the dependent, major-dependent, and super-dependent nature of strokes to form Gurmukhi characters in words. On test sets of online handwritten Gurmukhi datasets, the word-level accuracy rates are 85.98%, 84.80%, 82.40%, and 82.20% in four different modes. Besides the online Gurmukhi word recognition, the present work also provides Gurmukhi handwriting analysis study for varying writing styles and proposes novel techniques for zone detection and rearrangement of strokes. Our proposed algorithms have been successfully employed to online handwritten Gurmukhi word recognition in dependent and independent modes of handwriting.
Changes in self-perceived willingness to communicate during visits to <i>Second Life</i>: a case study

The present paper presents the results of a study which examined the changes in the levels of willingness to communicate (WTC) in the virtual world of Second Life (SL) reported by a single student of English philology in 19 sessions spanning the period of one semester, and the factors accounting for such fluctuations. Using data gathered by means of a set of collection instruments (i.e. a background questionnaire, Learning Styles Survey, a session log and a semi-structured interview), the study found that the student’s WTC underwent some changes both during her visits to SL and from one session to the next. These ups and downs in WTC levels were affected by a host of positive (e.g. friendly and willing to talk interlocutors, interesting topics, the shielding effect of avatar, confidence in using English) and negative (e.g. the lack of SL users willing to talk, the experience of boredom, group conversations) factors.
A Comprehensive Study of Challenges and Issues in Cloud Computing

Cloud computing is a technology which provides the capability to use storage resources and computing services via Internet. Performance and resource management are a challenging task in cloud computing due to increase in demand of services by the user in a multitenant environment. Also, data privacy in cloud is a major issue because different organizations use various services of cloud. Security of cloud computing refers to a method used to protect data and various applications of cloud computing. In this paper, we have surveyed various issues in cloud computing related to resource management, load balancing, data privacy, energy consumption, data storage, etc. Moreover, the survey has also identified the solution to the existing problems in the literature to counter various issues in cloud environment.
Parsing/Theorem-Proving for Logical Grammar CatLog3

$${ CatLog3}$$ is a 7000 line Prolog parser/theorem-prover for logical categorial grammar. In such logical categorial grammar syntax is universal and grammar is reduced to logic: an expression is grammatical if and only if an associated logical statement is a theorem of a fixed calculus. Since the syntactic component is invariant, being the logic of the calculus, logical categorial grammar is purely lexicalist and a particular language model is defined by just a lexical dictionary. The foundational logic of continuity was established by Lambek (Am Math Mon 65:154–170, 1958) (the Lambek calculus) while a corresponding extension including also logic of discontinuity was established by Morrill and Valentín (Linguist Anal 36(1–4):167–192, 2010) (the displacement calculus). $${ CatLog3}$$ implements a logic including as primitive connectives the continuous (concatenation) and discontinuous (intercalation) connectives of the displacement calculus, additives, 1st order quantifiers, normal modalities, bracket modalities, and universal and existential subexponentials. In this paper we review the rules of inference for these primitive connectives and their linguistic applications, and we survey the principles of Andreoli’s focusing, and of a generalisation of van Benthem’s count-invariance, on the basis of which $${ CatLog3}$$ is implemented.