Reviewerly/exHarmony huggingface dataset:
Learning User Preference from Heterogeneous Information for Store-Type Recommendation

Online stores are capable analyzing user preference from click logs and transaction records, while retailers of physical stores still lack effective methods to understand user preference. Traditional ways are predominantly field surveys, which are not effective as they need labor-intensive survey thus limit to small populations. As mobile devices and social media are becoming more and more pervasive, user-generated heterogeneous information (e.g., check-in activities and textual reviews) from these platforms are providing rich information to in-depth understand user preference. In this paper, we present a recommendation model for physical stores by learning user's preference from user-generated heterogeneous information. Specifically, the proposed model consists of two phases: 1) offline modeling multi-relation among users, stores and aspects; 2) online graph-based recommendation. The offline modeling phase is designed to learn two kinds of relations: User-Store relation and Store-Aspect relation, while the online recommendation phase automatically produces top-k recommended stores based on the learnt relations with a graph-based model. To demonstrate the utility of our proposed model, we have performed a comprehensive experimental evaluation on two real-world datasets, which are collected by more than 120,000 users during 12 months. Experimental results show our method outperforms all baselines significantly in terms of recommendation effectiveness.
You Think, Therefore You Are: Transparent Authentication System with Brainwave-Oriented Bio-Features for IoT Networks

The Internet-of-Things (IoT) is an evolutionary paradigm seamlessly integrating an enormous number of smart objects within the Internet. Recently, with the rapid growth and universality of wearable technology, novel security threats are emerging at the system level as well as at edge nodes in IoT-based networks. In this study, we envision a future IoT scenario in which end-users are with smart wearable objects related to human brainwave retrieval. A novel transparent authentication system using brainwaves as bio-features for IoT-based networks is proposed. In brief, this study first provides a comprehensive review of transparent authentication in recent years and presents the state of the art of this important research field. Second, we investigate the feasibility of extracting long-term memory ability from users' brainwaves. Third, we conduct the bio-features identified in the brainwaves of users as authentication tokens in the proposed authentication system which transparently performs continuous (or real-time) entity verification in the background without the need for direct input from the user. Experiment results demonstrate the efficacy of the proposed authentication system in achieving high verification accuracy.
Writer identification approach based on bag of words with OBI features

Handwriter identification aims to simplify the task of forensic experts by providing them with semi-automated tools in order to enable them to narrow down the search to determine the final identification of an unknown handwritten sample. An identification algorithm aims to produce a list of predicted writers of the unknown handwritten sample ranked in terms of confidence measure metrics for use by the forensic expert will make the final decision. Most existing handwriter identification systems use either statistical or model-based approaches. To further improve the performances this paper proposes to deploy a combination of both approaches using Oriented Basic Image features and the concept of graphemes codebook. To reduce the resulting high dimensionality of the feature vector a Kernel Principal Component Analysis has been used. To gauge the effectiveness of the proposed method a performance analysis, using IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting, has been carried out. The results obtained achieved an accuracy of 96% thus demonstrating its superiority when compared against similar techniques.
Parallel extraction of association rules from genomics data

High-throughput experimental platforms like microarrays produce massive amounts of omics data for each analyzed sample. As an example, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform can discover Single Nucleotide Polymorphisms (SNPs) from 225 human genes involved in absorption, distribution, metabolism, and excretion (ADME) of drugs, enabling large pharmacogenomics studies. Moreover, the application of such platforms to large populations of subjects is further increasing the size of experimental datasets produced in clinical studies. Thus, the production of big omics datasets is a first reason to use parallel computing in bioinformatics. Such omics datasets are usually analyzed with classical statistical analysis and, more recently, by using data mining methods that can extract knowledge hidden in the data, e.g. by highlighting multiple associations among features of the data. However, the use of standard off-the-shelf data mining algorithms to large omic datasets, especially when considering association rule mining, poses two main issues: (i) huge requests of central memory that may prevent the execution of data mining software on personal/desktop computers; and (ii) very long response time, that may increase the time requested for completing extensive pharmacogenomics studies. To overcome the limits of standard association rule mining algorithms when applied to omics datasets, we propose PARES (Parallel Association Rules Extractor from SNPs), a novel parallel algorithm for the efficient extraction of association rules from omics datasets. PARES is implemented as a multi-thread version of an optimized version of the Frequent Pattern Growth (FP-Growth) algorithm. Moreover, it includes a customized SNPs datasets preprocessing strategy based on a Fisher’s Test Filter to discard the trivial transactions from the input dataset, reducing the search space from which to build many independent FP-Trees. The experimental results show that PARES has a good speedup and a high memory management efficiency, with respect to several association rule mining algorithms implemented in main off-the-shelf data mining platforms.
Feature-Based Image Patch Classification for Moving Shadow Detection

The presence of shadows in images significantly affects the performance of many computer vision tasks and visual processing applications, such as object tracking, object classification, and behavior recognition. Most methods have been designed to detect shadows in specific situations, but they often fail to distinguish shadow points from the foreground object in many problematic situations, such as chromatic shadows, non-textured and dark surfaces, and foreground-background camouflage. In this paper, we propose a new feature-based image patch approximation and multi-independent sparse representation technique to tackle these environmental problems. In this method, two illumination-invariant features-binary patterns of local color constancy and light-based gradient matching-are introduced, along with the intensity-reduction histogram. These features are extracted from image patches and are used to construct two over-complete dictionaries for objects and shadows, respectively. Given a new image patch, its best approximation for a number of iterations is found from each dictionary. For each iteration, an independent class assignment is performed by finding its distances from the reference dictionaries. The patch is then assigned to a class based on its probability of occurrence. The proposed framework is evaluated on common shadow detection data sets, and it shows improved performance in terms of the shadow detection rate and discrimination rate compared with the state-of-the-art methods.
Requirements Quality Is Quality in Use

Creating a requirements engineering artifact is rarely an end in itself; it is a means to understand and reach the project's goals. So such an artifact's purpose is to support the stakeholders in whatever activities they're performing in the project.
Experimentation of a smart learning system for law based on knowledge discovery and cognitive computing

This work presents a Smart Learning system based on Knowledge Discovery and Cognitive Computing techniques aimed at citizens, legal students and experts alike, providing them with the possibility of submitting legal cases expressed in natural language and obtaining legal insight and advice in return. Advanced features implemented within the system include the automatic conceptualization and classification of textual legal cases via natural language processing, the generation of learning paths by relying upon legal ontologies, and additional features for managing legal knowledge bases, including editing, versioning, integration and enrichment. The system has been experimented on a diversified user-base and succeeded in obtaining a positive evaluation with respect to the aspects that were subject of the investigation, including effectiveness, efficiency and usability, thus paving the way to make the system a successful cognitive learning platform for future law professionals and knowledgeable citizens.
Synchronisation of partial multi-matchings via non-negative factorisations

In this work we study permutation synchronisation for the challenging case of partial permutations, which plays an important role for the problem of matching multiple objects (e.g. images or shapes). The term synchronisation refers to the property that the set of pairwise matchings is cycle-consistent, i.e. in the full matching case all compositions of pairwise matchings over cycles must be equal to the identity. Motivated by clustering and matrix factorisation perspectives of cycle-consistency, we derive an algorithm to tackle the permutation synchronisation problem based on non-negative factorisations. In order to deal with the inherent non-convexity of the permutation synchronisation problem, we use an initialisation procedure based on a novel rotation scheme applied to the solution of the spectral relaxation. Moreover, this rotation scheme facilitates a convenient Euclidean projection to obtain a binary solution after solving our relaxed problem. In contrast to state-of-the-art methods, our approach is guaranteed to produce cycle-consistent results. We experimentally demonstrate the efficacy of our method and show that it achieves better results compared to existing methods.
Reflectance spectroscopy and ASTER based mapping of rock-phosphate in parts of Paleoproterozoic sequences of Aravalli group of rocks, Rajasthan, India

This paper applies reflectance spectroscopy and Advanced Space borne thermal emission reflection radiometer (ASTER) data processing to delineate dolomite-hosted rock phosphate around Udaipur City, Western India, using visible-near infrared (VNIR) and short wave infrared (SWIR) bands of the ASTER sensor. A number of ASTER image spectra and field spectra (resampled to ASTER bands) of pure dolomite and rock phosphate are compared and found to be consistent in terms of the presence of absorption minima at 2.3 µm for dolomite and the absence of the same feature in the spectra of the rock-phosphate. Before attempting spectral mapping, ASTER SWIR band data are clipped using the spatial extent of host rock; which has been prepared from the reference geological map and consequently updated using VNIR-SWIR derived image enhanced products at selected part of the study areas. Two sub-pixel spectral methods namely adaptive coherence estimator (ACE) and matched filtering (MF) methods are applied to ASTER SWIR bands to delineate rock-phosphate-rich zones within dolomite. The output images of the above algorithms are integrated in a principal component (PC) image to delineate phosphate pixels. A principal component; which has similar sign of Eigen vector loading for ACE and MF abundance image is used to delineate rock phosphate within dolomite. The results are validated in the field using a qualitative colorimetric method and also by comparing image spectra of spectrally anomalous zone with the ASTER resampled laboratory spectra of rock phosphate samples. A number of sporadic but important patches of phosphate rich zones in addition to the main surface exposures of the Jhamar Kotra Mine are confirmed in the field. The proposed method is a novel application of spectral mapping of phosphate deposits in dolomitic and carbonatic rocks and present approach of ASTER data processing can be used to delineate phosphate rich zones occurring within similar geological provinces in any parts of the world.
Objectivity and Subjectivity in Aesthetic Quality Assessment of Digital Photographs

Automatic prediction of the aesthetic quality of a photograph has been an important research problem in image processing and computer vision. While assessing the aesthetic quality of a photograph by human is highly subjective, most existing studies have considered only objective (or general) opinion of multiple viewers. In this paper, we provide a comprehensive investigation of the issue of subjectivity in aesthetic quality assessment using a large-scale database containing photos, user ratings, and user comments. First, we analyze how the mean aesthetic quality level and the level of subjectivity have evolved over time. Second, we examine the feasibility of automatic prediction of the level of subjectivity based on visual features, and identify which features are effective for the prediction. Third, we analyze the users' comments given to photos to understand the sources of subjectivity of aesthetic quality rating. Our results show that several factors are simultaneously involved in determining the level of subjectivity of a photo, but it can be predicted with reasonable accuracy. We believe that our results provide insight toward personalized aesthetic photo applications.
(Tightly) QCCA-Secure Key-Encapsulation Mechanism in the Quantum Random Oracle Model

This paper studies indistinguishability against quantum chosen-ciphertext attacks (IND-qCCA security) of key-encapsulation mechanisms (KEMs) in quantum random oracle model (QROM). We show that the SXY conversion proposed by Saito, Yamakawa, and Xagawa (EUROCRYPT 2018) and the HU conversion proposed by Jiang, Zhang, and Ma (PKC 2019) turn a weakly-secure deterministic public-key encryption scheme into an IND-qCCA-secure KEM scheme in the QROM. The proofs are very similar to that for the IND-CCA security in the QROM, easy to understand, and as tight as the original proofs.
Towards Green Cloud Computing: Demand Allocation and Pricing Policies for Cloud Service Brokerage

Functioning as an intermediary between tenants and cloud providers, cloud service brokerages (CSBs) can bring about great benefits to the cloud market. As energy costs of cloud computing have been increasing rapidly, there is a need for cloud providers to optimize energy efficiency while maintain high service level performance to tenants, not only for their own benefit but also for social welfares. Thus, for green cloud companies, two questions have arisen: 1) under what pricing policies from the cloud providers to the CSB, a profit-driven CSB is willing to minimize the total energy cost while satisfy tenant demands and 2) how should a CSB distribute tenants demands to achieve this objective? To address question 1), we find a pricing policy for cloud providers such that maximizing CSBs profit is equivalent to minimizing cloud providers energy cost. To address question 2), we first devise a greedy solution, and then propose an approximation algorithm and a decomposition-based solution with a constant approximation ratio. Both simulation and real-world Amazon EC2 experimental results demonstrate the effectiveness of our pricing policy to incentivize CSBs to save energy and the superior performance of our algorithms in energy efficiency and resource utilization over the previous algorithms.
Emulating Human Play in a Leading Mobile Card Game

Monte Carlo Tree Search (MCTS) has become a popular solution for game AI, capable of creating strong game playing opponents.However, the emergent playstyle of agents using MCTS is not necessarily human-like, believable or enjoyable.AI Factory Spades, currently the top rated Spades game in the Google Play store, uses a variant of MCTS to control AI allies and opponents.In collaboration with the developers, we showed in a previous study that the playstyle of human players significantly differed from that of the AI players [1].This article presents a method for player modelling using gameplay data and neural networks that does not require domain knowledge, and a method of biasing MCTS with such a player model to create Spades playing agents that emulate human play whilst maintaining strong, competitive performance.The methods of player modelling and biasing MCTS presented in this study are applied to the commercial codebase of AI Factory Spades, and are transferable to MCTS implementations for discrete-action games where relevant gameplay data is available.
Automatic question generation with various difficulty levels based on knowledge ontology using a query template

Ontology is a concepts and relationships that can be used to support the question-generation process. However, until now, the ontology models and question templates commonly used to support the question-generation process have remained domain-specific, allowing three weaknesses to persist. First, the role of experts is dominant in the process of ontology generation. Second, the process needs adjustment if it is to be used for other domains. Third, question templates are formed based on the vocabulary of ontology, so they cannot be used to generate questions in other domains. In response to these problems, this research focused on forming an ontology generation model and a template model for generating questions that are not domain-specific. We used a combination of two types of ontology — namely, taxonomy ontology and sentence ontology to form ontology models and question templates that were not domain-specific. We labeled this combination as “knowledge ontology”. We used template queries to retrieve information on the ontology and then translated the results of the query template into questions in natural language. The ratios from our experiments demonstrated that the proposed method was effective for generating questions. Moreover, the method produced good question quality, as evidenced by its high accuracy rate of 90.71%. This research can be applied to help e-learning developers represent information in the form of ontology without involving experts. Furthermore, this research can also help teachers to generate questions automatically with consistent question quality.
M2FaaS: Transparent and fault tolerant FaaSification of Node.js monolith code blocks

Porting existing monoliths to the Function-as-a-Service (FaaS) (FaaSification) can be very challenging for software developers due to different architectural styles. For a successful porting, developers need to resolve various dependencies, such as method invocations of external packages or user-defined codes, as well as global and local variables used in and after the code block that should be faasified. To bridge the gap and automatize FaaSification, this paper introduces M2FaaS, a FaaSifier that automatically converts a Node.js monolith into a hybrid by faasifying annotated code blocks as serverless functions on multiple FaaS providers. M2FaaS is a novel FaaSifier that resolves many challenges for the resulting monolith to work properly after the FaaSification. Developers may annotate all dependencies that need to be resolved for the generated functions to run properly and specify variables that should be returned by the function to the monolith because they are used later in the monolith. Moreover, M2FaaS is the first FaaSifier that faasifies arbitrary code blocks. The current M2FaaS prototype supports FaaSification of individual functions on two FaaS providers, AWS Lambda and IBM Cloud Functions. Finally, M2FaaS introduces an optional annotation for alternative functions to be invoked in case the primary faasified function fails. The resulting hybrid application invokes the automatically deployed serverless functions, while the original code remains executable. Experiments with four complementary monoliths demonstrate that M2FaaS outperforms state-of-the-art FaaSifiers in terms of development effort by up to 73.3%. Moreover, with the fault tolerance support, M2FaaS finishes all submitted functions, thereby achieving by 18.5% higher throughput than the other FaaSifiers.
Multilevel estimation of normalization constants using ensemble Kalman–Bucy filters

In this article we consider the application of multilevel Monte Carlo, for the estimation of normalizing constants. In particular we will make use of the filtering algorithm, the ensemble Kalman–Bucy filter (EnKBF), which is an N-particle representation of the Kalman–Bucy filter (KBF). The EnKBF is of interest as it coincides with the optimal filter in the continuous-linear setting, i.e. the KBF. This motivates our particular setup in the linear setting. The resulting methodology we will use is the multilevel ensemble Kalman–Bucy filter (MLEnKBF). We provide an analysis based on deriving $${\mathbb {L}}_q$$ -bounds for the normalizing constants using both the single-level, and the multilevel algorithms, which is largely based on previous work deriving the MLEnKBF Chada et al. (2022). Our results will be highlighted through numerical results, where we firstly demonstrate the error-to-cost rates of the MLEnKBFs comparing it to the EnKBF on a linear Gaussian model. Our analysis will be specific to one variant of the MLEnKBF, whereas the numerics will be tested on different variants. We also exploit this methodology for parameter estimation, where we test this on the models arising in atmospheric sciences, such as the stochastic Lorenz 63 and 96 model.
On r-primitive k-normal elements over finite fields

Let r, n be positive integers, k be a non-negative integer and q be any prime power such that r|qn−1. An element α of the finite field Fqn is called an r-primitive element, if its multiplicative order is (qn−1)/r and it is called a k-normal element over Fq, if the degree of the greatest common divisor of the polynomials mα(x)=∑i=1nαqi−1xn−i and xn−1 is k. In this article, we discuss the existence of an element α∈Fqn which is both r-primitive and k-normal over Fq. In particular, we show that there exists an element α∈Fqn, which is both 2-primitive and 2-normal over Fq if and only if q is an odd prime power and either n≥5 and gcd(q3−q,n)≠1 or n=4 and q≡1(mod4).
Tumor Segmentation in Breast MRI Using Deep Learning

An uncontrolled growth of tissues in a body part and forming a mass that can either be benign or malignant is referred to as a tumor. Early screening and detection of such a mass are crucial for the best prognosis. In the past five years, 7.8 million women have survived breast cancer, making it the most prevalent cancer in females. A medical imaging method such as MRI or mammography is the first step in a clinical diagnosis. Afterward, a biopsy is the next step if the imaging test shows tissue changes. Consequently, a patient's prognosis is determined by the analysis of the MRI. RIDER Breast MRI data collection is collected hosted on The Cancer Imaging Archive. Our aim is to develop a deep learning method to segment the masses in 2D breast MR images, axial view. Furthermore, an in-depth investigation into the importance of TL (Transfer Learning) will be conducted. A goal of the project is to build and train the U-net model from scratch on the dataset, then test the dataset on the U-net model using TL. When TL is used, the models like Inception and VGG without the last dense layers are used as the encoder part of the network for feature extraction.