Reviewerly/exHarmony huggingface dataset:
Differentiating Regularization Weights -- A Simple Mechanism to Alleviate Cold Start in Recommender Systems

Matrix factorization (MF) and its extended methodologies have been studied extensively in the community of recommender systems in the last decade. Essentially, MF attempts to search for low-ranked matrices that can (1) best approximate the known rating scores, and (2) maintain low Frobenius norm for the low-ranked matrices to prevent overfitting. Since the two objectives conflict with each other, the common practice is to assign the relative importance weights as the hyper-parameters to these objectives. The two low-ranked matrices returned by MF are often interpreted as the latent factors of a user and the latent factors of an item that would affect the rating of the user on the item. As a result, it is typical that, in the loss function, we assign a regularization weight λ p on the norms of the latent factors for all users, and another regularization weight λ q on the norms of the latent factors for all the items. We argue that such a methodology probably over-simplifies the scenario. Alternatively, we probably should assign lower constraints to the latent factors associated with the items or users that reveal more information, and set higher constraints to the others. In this article, we systematically study this topic. We found that such a simple technique can improve the prediction results of the MF-based approaches based on several public datasets. Specifically, we applied the proposed methodology on three baseline models -- SVD, SVD++, and the NMF models. We found that this technique improves the prediction accuracy for all these baseline models. Perhaps more importantly, this technique better predicts the ratings on the long-tail items, i.e., the items that were rated/viewed/purchased by few users. This suggests that this approach may partially remedy the cold-start issue. The proposed method is very general and can be easily applied on various recommendation models, such as Factorization Machines, Field-aware Factorization Machines, Factorizing Personalized Markov Chains, Prod2Vec, Behavior2Vec, and so on. We release the code for reproducibility. We implemented a Python package that integrates the proposed regularization technique with the SVD, SVD++, and the NMF model. The package can be accessed at https://github.com/ncu-dart/rdf.
Structural rewriting in XOR-majority graphs

In this paper, we present a structural rewriting method for a recently proposed XOR-Majority graph (XMG), which has exclusive-OR (XOR), majority-of-three (MAJ), and inverters as primitives. XMGs are an extension of Majority-Inverter Graphs (MIGs). Previous work presented an axiomatic system, Ω, and its derived transformation rules for manipulation of MIGs. By additionally introducing XOR primitive, the identities of MAJ-XOR operations should be exploited to enable powerful logic rewriting in XMGs. We first proposed two MAJ-XOR identities and exploit its potential optimization opportunities during structural rewriting. Then, we discuss the rewriting rules that can be used for different operations. Finally, we also address structural XOR detection problem in MIG. The experimental results on EPFL benchmark suites show that the proposed method can optimize the size/depth product of XMGs and its mapped look-up tables (LUTs), which in turn benefits the quantum circuit synthesis that using XMG as the underlying logic representations.
Joint medical image compression–encryption in the cloud using multiscale transform-based image compression encoding techniques

The recent years have witnessed rapid strides in the use of cloud computing and its countless applications. A cloud can contain massive volumes of multimedia data in the form of images, video and audio. Cloud computing platforms confront challenges in terms of data confidentiality, message integrity, user authentication and compression. Multimedia data needs plenty of storage capacity. Consequently, there is a need for multimedia data compression to reduce data size. Compression techniques are quite reliable, offering benefits to organizations dealing with metasized data in the cloud. Compressing large quanta of data leads to superior utilization of cloud storage. Compression techniques can compress data used for storage and transmission, yet compression alone is inadequate because multimedia data shared should, of necessity, be secure. Therefore, both multimedia compression and security are mandatory in the cloud. The chief goal of this paper is to propose a new framework, comprising multiscale transforms, public key cryptography and appropriate encoding techniques, that performs joint medical image compression and image encryption in the cloud. Multiscale transforms play a lead role in image compression, and the ones discussed in this paper include wavelet, bandelet, curvelet, ridgelet and contourlet transforms. Wavelet transforms offer robust localization both in terms of time and frequency domains. Bandelet transforms offer natural images geometric regularity to help improve the efficiency of representation. Curvelet transforms handle curve discontinuities well, with ridgelet transforms being the core idea behind curvelets. Contourlet transforms capture smooth contours and edges at any orientation. The Rivest-Shamir-Adleman (RSA) algorithm is used to encrypt images to provide maximum security when they are being transferred. Encoding techniques involved in this paper comprise the Embedded Zerotree Wavelet (EZW), Set Partitioning in Hierarchical Trees (SPIHT), Wavelet Difference Reduction (WDR), and Adaptively Scanned Wavelet Difference Reduction (ASWDR). Performance parameters such as peak signal to noise ratio (PSNR), mean square error (MSE), image quality index and structural similarity index (SSIM) are used for evaluation. It is justified that the proposed framework compresses images securely in the cloud.
Multi-stage precipitation and redistribution of gold, and its collection by lead-bismuth and lead immiscible liquids in a reduced-intrusion related gold system (RIRGS); Dublin Gulch, western Canada

Dublin Gulch reduced intrusion-related gold system (RIRGS), located in the Selwyn Basin area of western Canada, represents one of the best examples of RIRGS mineralization globally and hence can be studied to unravel genesis and evolution of these types of deposits. Based on textural relationships, mineralogy, and trace element mineral chemistry, three auriferous vein stages were identified. The paragenetic sequence for the auriferous vein stages are: 1) Eagle Style (ES), quartz-albite, low sulfide and sulfosalt content (<5% vol.), As-Fe-Mo-W-Pb-Bi-Au-Ag veins; 2) Potato Hills Style-1 (PHS-1), quartz, high sulfide and sulfosalt content (>30% vol.), As-Fe-W-Pb-Bi-Au-Ag veins; and, 3) Potato Hills Style-2 (PHS-2), high sulfide and sulfosalt content (>30% vol.), Fe-Pb-Sb-Zn-Cu-Au-Ag veins. In the ES and PHS-1 veins, Au is present both as native gold (Au, Ag) and as invisible gold in arsenopyrite, whilst in the PHS-2 veins, Au is present as invisible gold in pyrite. Native gold micrograins (individual grains, 1–100's µm in size) are observed associated with Pb minerals [in anhedral-to-globular cosalite (Pb2Bi2S5) in ES veins, or galena (PbS) in the PHS-1 veins]. Native gold is also observed as micrograins along arsenopyrite margins and in quartz fractures. We suggest a variation on the hydrothermal Bi melt collector model to explain the Au-Pb ± Bi association. The Au, Ag, Pb, and Bi are interpreted to have been locally remobilized from arsenopyrite, which shows textures and trace element distribution patterns consistent with fluid-and-deformation assisted recrystallization. We suggest Pb and Bi were mobilized either as immiscible nanodroplets that coalesced to form larger Pb ± Bi liquid accumulations or into the hydrothermal fluid and subsequently exsolved to form immiscible Pb ± Bi liquids. We propose that remobilized Au and Ag were collected by these Pb+/-Bi immiscible liquids. Subsequent retrograde alteration (sulfidation) is interpreted to have converted the Au-Ag-Pb ± Bi alloys to native gold and cosalite (ES), and native gold and galena mineral assemblages (PHS-1). The similarity of the Au/Ag ratios in native gold and arsenopyrite supports a local source for the native gold micrograins. Temperatures required to attain liquid Pb0.5Bi0.5 (>145.2 °C) and Pb (>327.5 °C), are consistent with arsenopyrite geothermometry (ES 345–405 °C; PHS-1 ≈ 380 °C). These suggested new variations (Pb-Bi and Pb) on the hydrothermal melt (Bi) collector model are important, given the common association of native gold with Pb ± Bi sulfosalts in many gold deposits, and the relatively low temperatures required to maintain these liquids and collect Au from the hydrothermal fluid.
The Handwritten Chinese Character Recognition Uses Convolutional Neural Networks with the GoogLeNet

With the outstanding performance in 2014 at the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14), an effective convolutional neural network (CNN) model named GoogLeNet has drawn the attention of the mainstream machine learning field. In this paper we plan to take an insight into the application of the GoogLeNet in the Handwritten Chinese Character Recognition (HCCR) on the database HCL2000 and CASIA-HWDB with several necessary adjustments and also state-of-the-art improvement methods for this end-to-end approach. Through the experiments we have found that the application of the GoogLeNet for the Handwritten Chinese Character Recognition (HCCR) results into significant high accuracy, to be specific more than 99% for the final version, which is encouraging for us to further research.
pysubgroup: Easy-to-Use Subgroup Discovery in Python

This paper introduces the pysubgroup package for subgroup discovery in Python. Subgroup discovery is a well-established data mining task that aims at identifying describable subsets in the data that show an interesting distribution with respect to a certain target concept. The presented package provides an easy-to-use, compact and extensible implementation of state-of-the-art mining algorithms, interestingness measures, and visualizations. Since it builds directly on the established pandas data analysis library—a de-facto standard for data science in Python—it seamlessly integrates into preprocessing and exploratory data analysis steps. Code related to this paper is available at: http://florian.lemmerich.net/pysubgroup .
Self-boosting first-order autonomous learning neuro-fuzzy systems

In this paper, a detailed mathematical analysis of the optimality of the premise and consequent parts of the recently introduced first-order Autonomous Learning Multi-Model (ALMMo) neuro-fuzzy system is conducted. A novel self-boosting algorithm for structure- and parameter-optimization is, then, introduced to the ALMMo, which results in the self-boosting ALMMo (SBALMMo) neuro-fuzzy system. By minimizing the objective functions with the previously collected data, the SBALMMo is able to optimize its system structure and parameters in few iterations. Numerical examples based benchmark datasets and real-world problems demonstrate the effectiveness and validity of the SBALMMo, and show the strong potential of the proposed approach for real applications.
GPU Acceleration of Clustered DPCM for Lossless Compression of Hyperspectral Images

With the development of remote sensing technology, spatial and spectral resolutions of hyperspectral images have become increasingly dense. In order to overcome difficulties in the storage, transmission, and manipulation of hyperspectral images, an effective compression algorithm is requisite. The clustered differential pulse code modulation (C-DPCM), which is a prediction-based hyperspectral image lossless compression algorithm, can achieve a relatively high compression ratio, but its efficiency still requires improvement. This paper presents a parallel implementation of the C-DPCM algorithm on graphics processing units (GPUs) with the compute unified device architecture, which is a parallel computing platform and programming model developed by NVIDIA. Three optimization strategies are utilized to implement the C-DPCM algorithm in parallel, including a version that uses shared memory and registers, a version that employs multistream, and a version that uses multi-GPU. In addition, we studied how to assign all classes to each GPU to minimize the processing time. Finally, we reduced the compression time from approximately half an hour to an hour to several seconds, with almost no loss in accuracy.
Efficient Algorithms for High Utility Itemset Mining Without Candidate Generation

High utility itemsets are sets of items having a high utility or profit in a database. Efficiently discovering high utility itemsets plays a crucial role in real-life applications such as market analysis. Traditional high utility itemset mining algorithms generate candidate itemsets and subsequently compute the exact utilities of these candidates. These algorithms have the drawback of generating numerous candidates most of which are discarded for having a low utility. In this paper, we propose two algorithms, called HUI-Miner (High Utility Itemset Miner) and HUI-Miner*, for high utility itemset mining. HUI-Miner uses a novel utility-list structure to store both utility information about itemsets and heuristic information for search space pruning. The utility-list of items allows to directly derives the utility-lists of other itemsets and calculate their utilities without scanning the database. By avoiding candidate generation, HUI-Miner can efficiently mine high utility itemsets. To further speed up the construction of utility-lists, HUI-Miner* introduces an improved structure called utility-list* and an horizontal method to construct utility-lists*. Experimental results show that the proposed algorithms are several orders of magnitude faster than the state-of-the-art algorithms, reduce memory consumption, and that HUI-Miner* outperforms HUI-Miner especially for sparse databases.
Information experience in personally meaningful activities

Information behavior in activities that are freely chosen has been little explored. This article conceptualizes personally meaningful activities as a site for information behavior research. Personal meaning is discussed as a necessity for human beings. In the information age, there is an ethical directive for developers of information technology to promote and afford personally meaningful activities. This article builds on discussions of the pleasurable and profound in information science conceptually and empirically. First, it argues for the necessity of phenomenology in these discussions, which heretofore has been mostly absent. Next, it presents results from a qualitative, empirical study on information in personally meaningful activities. The empirical study uses interpretative phenomenological analysis to examine information experience in three domains of personal meaning: Bible reading, ultramarathon running, and art‐making. The following themes emerge and are discussed: identity, central practice, curiosity, and presence. Opportunities for technological development and further research are outlined.
Program Synthesis with Equivalence Reduction

We introduce program synthesis with equivalence reduction, a synthesis methodology that utilizes relational specifications over components of a given synthesis domain to reduce the search space. Leveraging a blend of classic and modern techniques from term rewriting, we use relational specifications to discover a canonical representative per equivalence class of programs. We show how to design synthesis procedures that only consider programs in normal form, thus pruning the search space. We discuss how to implement equivalence reduction using efficient data structures, and demonstrate the significant reductions it can achieve in synthesis time.
Digital and Physical Fabrication as Multimodal Learning

This article proposes and explores the kinds of computational thinking, creative practices, design activities, and inclusive learning opportunities provided to diverse high school youth when designing integrated systems through simultaneously physically and digitally responsive wearable games and systems. Previous work in this area, conducted by Richard, coined the term “bidirectionally responsive design” (BRD) to describe the design of dual-feedback systems using multiple digital and physical interfaces. BRD also emphasizes using simplified fabrication tools, media and coding platforms, and microcontrollers common in youth content creation communities and makerspaces. This study provides a framework to analyze computational concepts, practices, and perspectives that leverage an integrated systems and multimodal learning approach, such as BRD, adding to, building on, and integrating previous analytic approaches to looking at Scratch coding, media design, physical computing and e-textiles. Using a detailed case study of one team during one of the early workshop iterations, we conduct a multimodal analysis of bidirectionally responsive making activities and discuss the ways that they present novel understanding of integrating diverse interests and encouraging collaborative and distributed computational thinking. We further examine how BRD operationalizes and extends multimodal learning theory by adding tangible and integrative dimensions as additional modalities learners can leverage to facilitate meaning making, metacognition, and agency. We also discuss how designing integrated systems, as facilitated through BRD, provides an opportunity to engage in authentic practices around the design of complex systems.
Reducing the price of resource provisioning using EC2 spot instances with prediction models

The increasing demand of computing resources has boosted the use of cloud computing providers. This has raised a new dimension in which the connections between resource usage and costs have to be considered from an organizational perspective. As a part of its EC2 service, Amazon introduced spot instances (SI) as a cheap public infrastructure, but at the price of not ensuring reliability of the service. On the Amazon SI model, hired instances can be abruptly terminated by the service provider when necessary. The interface for managing SI is based on a bidding strategy that depends on non-public Amazon pricing strategies, which makes complicated for users to apply any scheduling or resource provisioning strategy based on such (cheaper) resources. Although it is believed that the use of the EC2 SIs infrastructure can reduce costs for final users, a deep review of literature concludes that their characteristics and possibilities have not yet been deeply explored. In this work we present a framework for the analysis of the EC2 SIs infrastructure that uses the price history of such resources in order to classify the SI availability zones and then generate price prediction models adapted to each class. The proposed models are validated through a formal experimentation process. As a result, these models are applied to generate resource provisioning plans that get the optimal price when using the SI infrastructure in a real scenario. Finally, the recent changes that Amazon has introduced in the SI model and how this work can adapt to these changes is discussed.
One-way local distinguishability of generalized Bell states in arbitrary dimension

In this paper, we study the distinguishability of arbitrary dimensional generalized Bell states under one-way local operations and classical communication (LOCC). We introduce an admissible solutions set and a nonadmissible solutions set for each set of generalized Bell states. Any element that exists in the admissible solution set but not in the nonadmissible one implies the one-way LOCC distinguishablity of a given set. This criterion is very convenient in the following two aspects: both solution sets can be calculated easily and, once the criterion can be applied successfully for a given set, it immediately presents an explicit distinguishing strategy. Furthermore, Fan's results in Phys. Rev. Lett. 92, 177905 (2004) can also be deduced as special cases of our results. For the case no such element exists, we disprove a conjecture proposed by Yang et al. in Quantum Inf. Process. 17, 29 (2018) by presenting a counterexample.
Simulating Collaborative Robots in a Massive Multi-agent Game Environment (SCRIMMAGE)

Testing mobile robotic systems in the field is a costly and risky task. Unfortunately, there is a gap between the existing simulation capabilities and those required to simulate large numbers of aerial vehicles. Many multi-agent robotics simulators have been restricted to the 2D plane, which limits their usefulness for aerial robotic platforms. While high-fidelity 3D robotics simulators exist, simulating large numbers of agents in these simulators can result in slower-than-real-time performance. SCRIMMAGE provides a 3D robotics environment that can simulate varying levels of collision detection, sensor modeling, communications modeling, and motion modeling fidelity due to its flexible plugin interface. This allows a robotics researcher to simulate hundreds of aircraft with low-fidelity motion models or tens of aircraft with high-fidelity motion models on single computer. SCRIMMAGE provides tools for batch simulation runs, varying initial conditions, and deployment to a cluster.
A survey on the 3D printing technology readiness to use

3D printing technology redesigns the production chain and contributes to the production of customised products, or products with specific characteristics. The evolution of this production technology and its various applications improve our daily life and lead to important changes in economic and social level. The aim of this dissertation is to examine the attitude of people towards 3D printing technological innovation and their intention to use it at their workplace. A new conceptual model has been designed and empirically tested using primary data collected using a structured questionnaire. The sample consists of 258 respondents from a number of European countries. The findings highlight the importance of perceived usefulness, output usability, compatibility and attitude towards understanding respondents’ intention to use 3D printing technology.
A learning-based ontology alignment approach using inductive logic programming

Ontologies are key concepts in the semantic web and have an impressive role which comprise the biggest and the most prominent part of the infrastructure in this realm of web research. By fast growth of the semantic web and also, the variety of its applications, ontology mapping (ontology alignment) has been transformed into a crucial issue in the realm of computer science. Several approaches are introduced for ontology alignment during these last years, but developing more accurate and efficient algorithms and finding new effective techniques and algorithms for this problem is an interesting research area since real-world applications with respect to their more complicated concepts need more efficient algorithms. In this paper, we illustrated a new ontology mapping method based on learning using Inductive Logic Programming (ILP), and show how the ILP can be used to solve the ontology mapping problem. As a matter of fact, in this approach, an ontology which is described in OWL format is interpreted to first-order logic. Then, with the use of learning based on inductive logic, the existing hidden rules and relationships between concepts are discovered and presented. Since the inductive logic has high flexibility in solving problems such as discovering relationships between concepts and links, it also can be performed effectively in solving the ontology alignment problem. Our experimental results show that this technique yield to more accurate results comparing to other matching algorithms and systems, achieving an F-measure of 95.6% and 91% on two well-known reference datasets the Anatomy and the Library, respectively.