Reviewerly/exHarmony huggingface dataset:
Chaos Based Enhanced RC5 Algorithm for Security and Integrity of Clinical Images in Remote Health Monitoring

The demand for remote health monitoring will significantly increase in the near future due to a decrease in the doctor/patient ratio as the world population grows. In remote health monitoring, one of the major goals is to send patient's data to clinical experts at geographically distant locations. In this scenario, the importance of implementing security on patients' clinical information increases, so that this could not be either changed or read by an unauthorized person. Rivest Cipher (RC5) algorithm is a secure and simple encryption algorithm. Due to its simplicity, fast encryption, and low memory requirements, it is considered as a suitable block cipher for resource constraint environments, such as body area networks (BAN), although it can be broken by various attacks due to poor diffusion. In this paper, we propose a chaotic-based key scheduling algorithm for the RC5 in which round keys are generated based on 2-D chaotic maps and used as a symmetric key during the encryption and decryption process. Cipher feedback mode is adapted to further increase the diffusion property of the cipher. This chaos-based key is very difficult to trace, and the cipher is extremely hard to break. A strict security analysis of the proposed cipher is performed against several attacks. The experimental results have shown that the impact of this approach is significantly better than the conventional security mechanisms in case of BAN when applied to critical clinical images.
CHI4EVIL

The HCI community is experiencing a resurgence of interest in the ethical, social, and political dimensions of HCI research and practice. Despite increased attention to these issues is not always clear that our community has the tools or training to adequately think through some of the complex issues that these commitments raise. In this workshop, we will explore the creative use of HCI methods and concepts such as design fiction or speculative design to help anticipate and reflect on the potential downsides of our technology design, research, and implementation. How can these tools help us to critique some of the assumptions, metaphors, and patterns that drive our field forward? Can we, by intentionally adopting the personas of would-be evil-doers, learn something about how better to accomplish HCI for Good?
Joint blur kernel estimation and CNN for blind image restoration

Convolutional neural networks (CNN) have shown its excellent performance in computer vision fields. Recently, they are successfully applied to image restoration. This paper proposes a joint blur kernel estimation and CNN method for blind image restoration. The blur kernel estimation is based on both blur support parameter estimation and blur type identification. An automatic feature line detection algorithm is presented for blur support parameter estimation and a dictionary learning algorithm is presented for the blur type identification. Once the blur kernel estimate is obtained, we use an effective CNN for iterative non-blind deconvolution, which is able to automatically learn image priors. Compared with current blind image restoration methods, the proposed joint method can obtain restored images under three types of unknown blur kernels. The experimental result shows that the proposed blur kernel estimation algorithm can provide high accuracy results. Furthermore, the proposed joint blur kernel estimation and CNN algorithm is superior to conventional blind image restoration algorithms in terms of restoration quality and computation time.
Encoding the UX: User Interface as a Site of Encounter between Data Journalists and Their Constructed Audiences

This paper examines how data journalism producers encode their audiences' user experience – or how they construct meaning within the parameters of a user interface. We argue that interfaces enact data journalists' perceptual image of their audience, and are therefore a meaningful object of study to explore: (a) the relationship between data reporters and their publics; (b) the tensions that emerge from that relationship; and (c) how those tensions are negotiated. Our combined evaluation of producer and artefact perspectives is based on interviews with 12 data journalism producers in legacy UK newsrooms, and interaction design, and multimodal discourse analysis of 12 stories produced by our informants. Findings suggest that data journalism producers' efforts to create content that resonates with their audiences have resulted in the diversification of data journalism formats, and a more mature view of interactivity, with producers striving to offer experiences in whose meaning negotiation the reader takes centre stage. The reader is perceived as a co-creator of meaning through the expectation of either physical interaction or cognitive immersion. Yet, the paternalistic element, even in the most discovery-/reader-driven stories, remains stronger, manifesting in a carefully crafted experience and pointing to journalism producers' traditional self-perception as gatekeepers of information.
Territorial Functioning in Collaborative Writing

This paper examines territorial functioning in collaborative writing through a mixed methods study involving interviews and analysis of collaboratively authored documents. Our findings have implications for the way we think about collaborative writing as a design problem, in that current conceptualizations of collaborative writing emphasize the work context rather than the work itself, at the cost of understanding interpersonal dynamics that are central to the common process. The findings come from 23 interviews with 32 university researchers and students regarding their experiences with collaborative writing of academic texts. The analysis of these interviews is supplemented with visualizations of the revision histories of documents written by a subset of the study participants. We discuss our findings in terms of fragmented exchanges in common information spaces and consider the shared document as a mediator for the simultaneous accomplishment and negotiation of work.
Uni-Detect

Data errors are ubiquitous in tables. Extensive research in this area has resulted in a rich variety of techniques, each often targeting a specific type of errors, e.g., numeric outliers, constraint violations, etc. While these diverse techniques clearly improve data quality, it places a significant burden on humans to configure these techniques with suitable rules and parameters for each data set. For example, an expert is expected to define suitable functional-dependencies between column pairs, or tune appropriate thresholds for outlier-detection algorithms, all of which are specific to one individual data set. As a result, users today often hire experts to cleanse only their high-value data sets. We propose \sj, a unified framework to automatically detect diverse types of errors. Our approach employs a novel "what-if'' analysis that performs local data perturbations to reason about data abnormality, leveraging classical hypothesis-tests on a large corpus of tables. We test \sj on a wide variety of tables including Wikipedia tables, and make surprising discoveries of thousands of FD violations, numeric outliers, spelling mistakes, etc., with better accuracy than existing algorithms specifically designed for each type of errors. For example, for spelling mistakes, \sj outperforms the state-of-the-art spell-checker from a commercial search engine.
A Novel Sign Language Recognition Framework Using Hierarchical Grassmann Covariance Matrix

Visual sign language recognition is an interesting and challenging problem. To create a discriminative representation, a hierarchical Grassmann covariance matrix (HGCM) model is proposed for sign description. Furthermore, a multi-temporal belief propagation (MTBP) based segmentation approach is presented for continuous sequence spotting. Concretely speaking, a sign is represented by multiple covariance matrices, followed by evaluating and selecting their most significant singular vectors. These covariance matrices are transformed into a more compact and discriminative HGCM, which is formulated on the Grassmann manifold. Continuous sign sequences can be recognized frame by frame using the HGCM model, before being optimized by MTBP, which is a carefully designed graphic model. The proposed method is thoroughly evaluated on isolated and synthetic and real continuous sign datasets as well as on HDM05. Extensive experimental results convincingly show the effectiveness of our proposed framework.
New architecture of deep recursive convolution networks for super-resolution

More existing methods of single image super-resolution (SR) often direct super-resolving the details, but when the upsampling factor is larger, it is challenging to reconstruct high-frequency details. Lately, deep convolution neural networks have made significant progress with regard to SR. However, with an increase in networks width and depth, the information used for reconstruction is becoming increasingly weaker, and the training of neural networks is becoming more difficult. This paper proposes a novel architecture of a deep recursive convolution neural networks used to reconstruct a high-resolution image from an original low-resolution (LR) image in a step-by-step manner. The architecture consists of three parts: an embedding network, cascaded fine extraction blocks, and reconstruction networks. Concretely, a wide convolution is used to extract more features from the original LR images, cascaded fine extraction blocks are employed to extract more useful information through a step-by-step approach and remove redundant information, and a deconvolution operation is utilized to restore the features. The proposed networks adopt a residual-feature learning scheme, and the Caffe framework is chosen for training the networks. The experimental results show that the proposed method exhibits a superior performance compared with various other state-of-the-art methods.
The naive Bayes classifier for functional data

In this article, we extended the approach of Dai et al. (2017) to several populations case and compared various other approaches on simulated and real data. The results show that the naive Bayes classifier for functional data is applicable to multi-category classification problems and has preferable finite-sample performance over competitors.
Hybridization of Chaos and Flower Pollination Algorithm over K-Means for data clustering

Classical clustering algorithms like K-means often converge to local optima and have slow convergence rates for larger datasets. To overcome such situations in clustering, swarm based algorithms have been proposed. Swarm based approaches attempt to achieve the optimal solution for such problems in reasonable time. Many swarm based algorithms such as Flower Pollination Algorithm (FPA), Cuckoo Search Algorithm (CSA), Black Hole Algorithm (BHA), Bat Algorithm (BA) Particle Swarm Optimization (PSO), Firefly Algorithm (FFA), Artificial Bee Colony (ABC) etc have been successfully applied to many non-linear optimization problems. In this paper, an algorithm is proposed which hybridizes Chaos Optimization and Flower Pollination over K-means to improve the efficiency of minimizing the cluster integrity. The proposed algorithm referred as Chaotic FPA (CFPA) is compared with FPA, CSA, BHA, BA, FFA, and PSO over K-Means for data clustering problem. Experiments are conducted on sixteen benchmark datasets. Algorithms are compared on four different performance parameters — cluster integrity, execution time, number of iterations to converge (NIC) and stability. Results obtained are analyzed statistically using Non-parametric Friedman test. If Friedman test rejects the Null hypothesis then pair wise comparison is done using Nemenyi test. Experimental Result demonstrates the following: (a) CFPA and BHA have better performance on the basis of cluster integrity as compared to other algorithms; (b) Prove the superiority of CFPA and CSA over others on the basis of execution time; (c) CFPA and FPA converges earlier than other algorithms to evaluate optimal cluster integrity; (d) CFPA and BHA produce more stable results than other algorithms.
Using hyperspectral imaging to vector towards mineralization at the Canadian Malartic gold deposit, Québec, Canada

Canadian Malartic is a large-tonnage, low-grade Archean gold deposit (16.3 Moz, 1.08 g/t Au) located in the Abitibi region of Québec, Canada. A large part of the mineralization is hosted in the Pontiac Group metasedimentary rocks, which consist of mudstones to greywackes at upper greenschist to amphibolite facies. In exploration and production environments, these lithologies are challenging to characterize by conventional core logging methods, while in a research setting thin section-sized samples (2 cm × 4 cm) do not capture the full extent of mineralogical variability, which can extend from centimeters to meters away from mineralized zones. Here, high-resolution hyperspectral imagery (0.2–1.0 mm/pixel) in both shortwave infrared (SWIR, 1000–2500 nm) and longwave infrared (LWIR, 8000–12000 nm) is acquired for over two thousand meters of drill core, and is used to visualize changes in mineralogy and mineral chemistry related to metamorphism and hydrothermal alteration. Unaltered metasedimentary rocks contain metamorphic white mica with AlVI contents varying between 1.90 and 1.75 apfu (2195 to 2203 nm), depending on metamorphic grade. Hydrothermal alteration is characterized by white mica which becomes progressively more phengitic with increasing alteration intensity, with AlVI contents ranging from 1.70 to 1.50 apfu (2204 to 2212 nm). Phengitic white mica extends from meters to tens of meters away from major mineralized zones, and can be used as a vector towards mineralization in an exploration setting. White mica composition is correlated to Au content, and can be used to discriminate between unmineralized (<0.3 g/t Au, 2195–2204 nm), weakly mineralized (>0.3 g/t Au, 2205–2208 nm), and highly mineralized (>1.0 g/t Au, >2208 nm) samples, which is a simple metric that can directly be applied to sort ore in a production environment. The Mg# (molar Mg/[Mg + Fe]) of biotite, on the other hand, is unaffected by metamorphic grade, and consistently is Mg# 55–60 (2251–2250 nm) in unaltered samples. In mineralized samples, biotite is Mg-rich (Mg# >65, <2249 nm), but rapidly grades into background compositions outside of the ore zones. LWIR hyperspectral data is used in drill core to estimate silicification abundance from the strength of the 9200 nm quartz reflectance peak. Peak strength was generally in good agreement with the silicification abundance estimated by core logging, and is therefore a metric that can directly be used for characterization of this parameter in exploration and production environments. Over 1500 SWIR point measurements were also acquired in a 50 km by 15 km area around the deposit, and were used to delineate metamorphic isograds on regional scales, and a large 12 km by 3 km hydrothermal alteration halo around the Canadian Malartic deposit. Similar measurements, if performed along the over 200 km long Cadillac-Larder Lake Deformation zone, could be used to rapidly delineate other potentially mineralized areas.
Evaluation of Disjunctive Programs in WASP

Answer Set Programming (ASP) is a well-established declarative programming language based on logic. The success of ASP is mainly due to the availability of efficient ASP solvers, therefore their development is still an important research topic. In this paper we report the recent improvements of the well-known ASP solver wasp. The new version of wasp includes several improvements of the main solving strategies and advanced reasoning techniques for computing paracoherent answer sets. Indeed, wasp is the first ASP solver handling paracoherent reasoning under two mainstream semantics, namely semi-stable and semi-equilibrium. However, semi-equilibrium semantics may require the introduction of several disjunctive rules, which are usually considered as a source of inefficiency for modern solvers. Such a drawback is addressed in wasp by implementing ad-hoc techniques to efficiently handle disjunctive logic programs. These techniques are presented and evaluated in this paper.
High-capacity reversible image steganography based on pixel value ordering

In this study, an improved reversible image steganography method based on pixel value ordering (PVO) is proposed to improve the steganographic capacity. Before the steganography process, three continuous and neighboring pixels are regarded as one group for sequencing, where the maximum and minimum values are adopted for difference value calculation and the number of difference values is recorded. The pixels with more steganographic difference values in rows or columns are determined, after which the steganography and ciphertext retrieval steps are performed in the rows or columns of the digital image. The experiment results prove that the method replaces the block structure in frame selection by groups of continuously read pixels, i.e., every three pixels form a pixel group where two bits of confidential information can be hidden, thus improving the steganographic capacity effectively. Comparisons with other PVO hiding schemes confirm the superiority of the proposed scheme, which has a higher capacity and maintains an acceptable peak signal-to-noise ratio (PSNR).
An Approach for Recommending Contextualized Services in e-Tourism

«You take delight not in a city’s seven or seventy wonders, but in the answer it gives to a question of yours». Those are the words used by Italo Calvino in his book “Invisible Cities” to give us a key aspect of a city, and it is from this consideration that this research work starts. In particular, the aim is to study and develop innovative solutions that guarantee value for the territory and for the cultural and landscape assets that insist on it. At the same time, such innovative solutions should be able to make the answers, connected to these resources, “tailored” to the user who requested it. For this reason, an architecture will be described, which allows content generating actors and users to operate by means of a broker-platform. Through this platform, it is possible to search a content within a Knowledge Base and, through the automatic orchestration of services, to activate in a controlled way the access and fruition of the information contained in it.
Comparing machine learning and regression models for mortality prediction based on the Hungarian Myocardial Infarction Registry

The objective of the current study is to compare the relative performance of decision tree, neural network, and logistic regression for predicting 30-day and 1-year mortality in a real-word, unfiltered dataset (n=47,391) of patients hospitalized with acute myocardial infarction. Area under the ROC curve (AUC) was used for evaluating performance of a learning algorithm. For 30-day mortality, we achieved an average of 0.788 for decision tree models, 0.837 for neural net models and 0.836 for regression models on training set (on validation sets: 0.774, 0.835 and 0.834, respectively). For 1-year mortality, the averages were 0.754 for decision tree models, 0.8194 for neural net models and 0.8191 for regression models (on validation sets: 0.743, 0.8179 and 0.8176, respectively). Differences were non-significant between neural network and regression, but both significantly outperformed decision trees. The machine learning methods investigated in the present study could not outperform traditional regression modelling for mortality prediction in myocardial infarction.
A novel approach to steganography based on the properties of Catalan numbers and Dyck words

This paper presents a new method of data hiding using Catalan numbers and Dyck words. The proposed steganographic solution belongs to the category of techniques based on the key generating process, rather than steganographic techniques such as injecting or substituting bits. The complex stego key consists of three sets of values that provide the technique of a hidden message generation completely. Hidden message is generated based on the data carrier and an adequate complex stego key. An important characteristic of the proposed method is that the data carrier retains its original shape, without supplements or modifications. Proposed method is explained in detail through the general scenario and through concrete examples. State of the art steganographic analysis of the proposed solution is presented in this paper, as well as possible suggestions for application in business information systems, authentication and distribution of secret cryptographic keys.
Building Robust Machine Learning Systems

Machine learning, in particular deep learning, is being used in almost all the aspects of life to facilitate humans, specifically in mobile and Internet of Things (IoT)-based applications. Due to its state-of-the-art performance, deep learning is also being employed in safety-critical applications, for instance, autonomous vehicles. Reliability and security are two of the key required characteristics for these applications because of the impact they can have on human's life. Towards this, in this paper, we highlight the current progress, challenges and research opportunities in the domain of robust systems for machine learning-based applications.
Comparing the Performance Potentials of Singleton and Non-singleton Type-1 and Interval Type-2 Fuzzy Systems in Terms of <i>Sculpting the State Space</i>

This paper provides a novel and better understanding of the performance potential of a nonsingleton (NS) fuzzy system over a singleton (S) fuzzy system. It is done by extending sculpting the state space works from S to NS fuzzification and demonstrating uncertainties about measurements, modeled by NS fuzzification: first, fire more rules more often, manifested by a reduction (increase) in the sizes of first-order rule partitions for those partitions associated with the firing of a smaller (larger) number of rules-the coarse sculpting of the state space; second, this may lead to an increase or decrease in the number of type-1 (T1) and interval type-2 (IT2) first-order rule partitions, which now contain rule pairs that can never occur for S fuzzification-a new rule crossover phenomenon-discovered using partition theory; and third, it may lead to a decrease, the same number, or an increase in the number of second-order rule partitions, all of which are system dependent-the fine sculpting of the state space. The authors' conjecture is that it is the additional control of the coarse sculpting of the state space, accomplished by prefiltering and the max-min (or max-product) composition, which provides an NS T1 or IT2 fuzzy system with the potential to outperform an S T1 or IT2 system when measurements are uncertain.
Towards a Blockchain Deployment at UAE University

Blockchain technology initially developed for transactions on cryptocurrencies is now gaining popularity in major domains such as education, healthcare, media, government, smart computing, and business enterprise. Blockchain manages the transactions database in a decentralized way replicated on all the nodes with no single governing authority. In this paper, we present a classification and taxonomy of different blockchain development platforms and simulators along with their consensus protocols, the types of supported blockchain networks and programming languages. We also assess the performance of a blockchain application for deployment at the UAE University (UAEU) versus increasing number of nodes, blocks and block size, on top of experimental network latencies and bandwidth between the nodes. Our results show that a large-scale deployment of blockchain at UAEU is feasible.