Reviewerly/exHarmony huggingface dataset:
Dynamic Connection-Based Social Group Recommendation

Group recommendation has become highly demanded when users communicate in the forms of group activities in online sharing communities. These group activities include student group study, family TV program watching, friends travel decision, etc. Existing group recommendation techniques mainly focus on the small user groups. However, online sharing communities have enabled group activities among thousands of users. Accordingly, recommendation over large groups has become urgent. In this paper, we propose a new framework to accomplish this goal by exploring the group interests and the connections between group users. We first divide a big group into different interest subgroups, each of which contains users closely connected with each other and sharing the similar interests. Then, for each interest subgroup, our framework exploits the connections between group users to collect a comparably compact potential candidate set of media-user pairs, on which the collaborative filtering is performed to generate an interest subgroup-based recommendation list. After that, a novel aggregation function is proposed to integrate the recommended media lists of all interest subgroups as the final group recommendation results. Extensive experiments have been conducted on two real social media datasets to demonstrate the effectiveness and efficiency of our proposed approach.
Distribution theory for hierarchical processes

Hierarchies of discrete probability measures are remarkably popular as nonparametric priors in applications, arguably due to two key properties: (i) they naturally represent multiple heterogeneous populations; (ii) they produce ties across populations, resulting in a shrinkage property often described as "sharing of information."In this paper, we establish a distribution theory for hierarchical random measures that are generated via normalization, thus encompassing both the hierarchical Dirichlet and hierarchical Pitman-Yor processes.These results provide a probabilistic characterization of the induced (partially exchangeable) partition structure, including the distribution and the asymptotics of the number of partition sets, and a complete posterior characterization.They are obtained by representing hierarchical processes in terms of completely random measures, and by applying a novel technique for deriving the associated distributions.Moreover, they also serve as building blocks for new simulation algorithms, and we derive marginal and conditional algorithms for Bayesian inference.
Impact of Lèvy flight on modern meta-heuristic optimizers

In this paper, a variant based on Lèvy flight was proposed to enhance the performance of two recently proposed optimizers. The first optimizer used in the study is Sine-Cosine Algorithm (SCA) while the second is Whale Optimization Algorithm (WOA). Both optimizers are composed of two phases of random walks in each optimization iteration and both have stagnation and premature convergence problems. Lèvy flight is used to replace the walk based on cosine function in the SCA and the spiral motion in the WOA as well. The Lèvy-based search guarantees a fraction of solutions to be generated apart from the current best solution and hence tolerates for optimizer stagnation, premature convergence, and allows for local optima avoidance. A smooth control of the scale of the Lèvy random walk is also proposed to ensure a smooth adaptation of exploration to exploitation switching. The proposed variants, as well as the original algorithms, were benchmarked using a set of unimodal, multimodal, fixed-dimension multimodal and composite benchmark functions. The evaluation is performed using a set of assessment indicators and results prove the capability of the proposed variants to outperform the original optimizers.
Where's My Data? Evaluating Visualizations with Missing Data

Many real-world datasets are incomplete due to factors such as data collection failures or misalignments between fused datasets. Visualizations of incomplete datasets should allow analysts to draw conclusions from their data while effectively reasoning about the quality of the data and resulting conclusions. We conducted a pair of crowdsourced studies to measure how the methods used to impute and visualize missing data may influence analysts' perceptions of data quality and their confidence in their conclusions. Our experiments used different design choices for line graphs and bar charts to estimate averages and trends in incomplete time series datasets. Our results provide preliminary guidance for visualization designers to consider when working with incomplete data in different domains and scenarios.
Comparing traditional conceptual modeling with ontology-driven conceptual modeling: An empirical study

This paper conducts an empirical study that explores the differences between adopting a traditional conceptual modeling (TCM) technique and an ontology-driven conceptual modeling (ODCM) technique with the objective to understand and identify in which modeling situations an ODCM technique can prove beneficial compared to a TCM technique. More specifically, we asked ourselves if there exist any meaningful differences in the resulting conceptual model and the effort spent to create such model between novice modelers trained in an ontology-driven conceptual modeling technique and novice modelers trained in a traditional conceptual modeling technique. To answer this question, we discuss previous empirical research efforts and distill these efforts into two hypotheses. Next, these hypotheses are tested in a rigorously developed experiment, where a total of 100 students from two different Universities participated. The findings of our empirical study confirm that there do exist meaningful differences between adopting the two techniques. We observed that novice modelers applying the ODCM technique arrived at higher quality models compared to novice modelers applying the TCM technique. More specifically, the results of the empirical study demonstrated that it is advantageous to apply an ODCM technique over an TCM when having to model the more challenging and advanced facets of a certain domain or scenario. Moreover, we also did not find any significant difference in effort between applying these two techniques. Finally, we specified our results in three findings that aim to clarify the obtained results.
Discrete Optimal Graph Clustering

Graph-based clustering is one of the major clustering methods. Most of it works in three separate steps: 1) similarity graph construction; 2) clustering label relaxing; and 3) label discretization with k -means (KM). Such common practice has three disadvantages: 1) the predefined similarity graph is often fixed and may not be optimal for the subsequent clustering; 2) the relaxing process of cluster labels may cause significant information loss; and 3) label discretization may deviate from the real clustering result since KM is sensitive to the initialization of cluster centroids. To tackle these problems, in this paper, we propose an effective discrete optimal graph clustering framework. A structured similarity graph that is theoretically optimal for clustering performance is adaptively learned with a guidance of reasonable rank constraints. Besides, to avoid the information loss, we explicitly enforce a discrete transformation on the intermediate continuous label, which derives a tractable optimization problem with a discrete solution. Furthermore, to compensate for the unreliability of the learned labels and enhance the clustering accuracy, we design an adaptive robust module that learns the prediction function for the unseen data based on the learned discrete cluster labels. Finally, an iterative optimization strategy guaranteed with convergence is developed to directly solve the clustering results. Extensive experiments conducted on both real and synthetic datasets demonstrate the superiority of our proposed methods compared with several state-of-the-art clustering approaches.
Improving the Robustness of Adaptive Steganographic Algorithms Based on Transport Channel Matching

Moving steganography and steganalysis from the laboratory into the real world, the robustness of steganography needs to be further considered. In this paper, we propose a robust steganographic algorithm to resist the JPEG compression of transport channel based on transport channel matching. Transport channel matching can adjust images to meet the requirements of transport channel so that the impact of JPEG compression from the channel can be reduced. To improve the robustness of steganography, the embedded message bits will be encoded by the error correction code. Then, the adaptive steganographic algorithms will be used to embed messages. To enhance the coding rate, the error correction capability t of the error correction code is dynamically adjusted according to the images. Experimental results on the local simulation of JPEG compression and social network site demonstrate that the proposed steganographic algorithm has a good performance with respect to both robustness and security.
3D computational simulation-based mineral prospectivity modeling for exploration for concealed Fe–Cu skarn-type mineralization within the Yueshan orefield, Anqing district, Anhui Province, China

The Yueshan orefield is one of the best-known Fe–Cu orefields in China and is located within the central Yangtze metallogenic belt ofin Anhui Province, China. The recent discovery of the skarn-type Zhuchong Fe–Cu deposit at depth within the orefield has highlighted the prospectivity of this area to host deep-seated skarn-type mineralization. However, the effectiveness of traditional geophysical exploration techniques is reduced with increasing depth. This, combined with recent developments in 3D mineral prospectivity modeling has led to the use of a 3D targeting approach for exploration for deep-seated and concealed mineralization in this area. However, to date the usefulness of this approach has been limited by a lack of an approach that could generate more useful 3D predictive maps. This study presents a 3D computational simulation based mineral prospectivity modeling approach that identified several exploration targets for concealed and deep-seated skarn-type mineralization within the Yueshan orefield. These prospective targets include areas of known mineralization as well as a number of new targets for future mineral exploration. In addition, the analysis of the resulting data using a capture-efficiency curve indicates that these 3D computational simulation approaches can provide additional predictive information for mineral exploration, indicating that 3D computational simulation should have a key role in the development and use of future 3D prospectivity modeling techniques during exploration targeting.
The Anti-Social System Properties: Bitcoin Network Data Analysis

Bitcoin is a cryptocurrency and a decentralized semi-anonymous peer-to-peer payment system in which the transactions are verified by network nodes and recorded in a public massively replicated ledger called the blockchain. Bitcoin is currently considered as one of the most disruptive technologies. Bitcoin represents a paradox of opposing forces. On one hand, it is fundamentally social, allowing people to transact in a peer-to-peer manner to create and exchange value. On the other hand, Bitcoin's core design philosophy and user base contain strong anti-social elements and constraints, emphasizing anonymity, privacy, and subversion of traditional centralized financial systems. We believe that the success of Bitcoin, and the financial ecosystem built around it, will likely rely on achieving an optimal balance between these social and anti-social forces. To elucidate the role of these forces, we analyze the evolution of the entire Bitcoin transaction graph from its inception, and quantify the evolution of its key structural properties. We observe that despite its different nature, the Bitcoin transaction graph exhibits many universal dynamics typical of social networks. However, we also find that Bitcoin deviates in important ways due to anonymity-seeking behavioral patterns of its users. As a result, the network exhibits a two-orders-of-magnitude larger diameter, sparse treelike communities, and an overwhelming majority of transitional or intermediate accounts with incoming and outgoing edges but zero cumulative balances. These results illuminate the evolutionary dynamics of the most popular cryptocurrency, and provide us with initial understanding of social networks rooted in and driven by anti-social constraints.
Arabic Natural Language Processing and Machine Learning-Based Systems

Arabic natural language processing (ANLP) consists of developing techniques and tools that can utilize and analyze the Arabic language in both written and spoken contexts.ANLP makes an important contribution to many existing developed systems.It provides Arabic and non-Arabic speakers with helpful and convenient tools that can be used in different domains.Modern ANLP tools are developed using machine learning (ML) techniques.ML algorithms are widely used in NLP because of their high accuracy rate regardless of the robustness of the data that is used and because of the ease with which they can be implemented.On the other hand, the methodology of ANLP applications based on ML involves several distinct phases.It is, therefore, crucial to recognize and understand these phases in detail as well as the most widely used ML algorithms.This survey discusses this concept in detail, shows the involvement of ML techniques in developing such tools, and identifies well-known techniques used in ANLP.Moreover, this survey discusses the characteristics and complexity of the Arabic language in addition to the importance and needs of ANLP.
Large-scale e-learning recommender system based on Spark and Hadoop

The present work is a part of the ESTenLigne project which is the result of several years of experience for developing e-learning in Sidi Mohamed Ben Abdellah University through the implementation of open, online and adaptive learning environment. However, this platform faces many challenges, such as the increasing amount of data, the diversity of pedagogical resources and a large number of learners that makes harder to find what the learners are really looking for. Furthermore, most of the students in this platform are new graduates who have just come to integrate higher education and who need a system to help them to take the relevant courses that take into account the requirements and needs of each learner. In this article, we develop a distributed courses recommender system for the e-learning platform. It aims to discover relationships between student’s activities using association rules method in order to help the student to choose the most appropriate learning materials. We also focus on the analysis of past historical data of the courses enrollments or log data. The article discusses particularly the frequent itemsets concept to determine the interesting rules in the transaction database. Then, we use the extracted rules to find the catalog of more suitable courses according to the learner’s behaviors and preferences. Next, we deploy our recommender system using big data technologies and techniques. Especially, we implement parallel FP-growth algorithm provided by Spark Framework and Hadoop ecosystem. The experimental results show the effectiveness and scalability of the proposed system. Finally, we evaluate the performance of Spark MLlib library compared to traditional machine learning tools including Weka and R.
ReCDroid: Automatically Reproducing Android Application Crashes from Bug Reports

The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). Developers heavily rely on bug reports in issue tracking systems to reproduce failures (e.g., crashes). However, the process of crash reproduction is often manually done by developers, making the resolution of bugs inefficient, especially that bug reports are often written in natural language. To improve the productivity of developers in resolving bug reports, in this paper, we introduce a novel approach, called ReCDroid, that can automatically reproduce crashes from bug reports for Android apps. ReCDroid uses a combination of natural language processing (NLP) and dynamic GUI exploration to synthesize event sequences with the goal of reproducing the reported crash. We have evaluated ReCDroid on 51 original bug reports from 33 Android apps. The results show that ReCDroid successfully reproduced 33 crashes (63.5% success rate) directly from the textual description of bug reports. A user study involving 12 participants demonstrates that ReCDroid can improve the productivity of developers when resolving crash bug reports.
Prediction of Malignant and Benign Breast Cancer: A Data Mining Approach in Healthcare Applications

As much as data science is playing a pivotal role everywhere, health care also finds its prominent application. Breast Cancer is the top-rated type of cancer amongst women; which alone took away 627,000 lives. This high mortality rate due to breast cancer does need attention, for early detection so that prevention can be done in time. As a potential contributor to state-of-the-art technology development, data mining finds a multi-fold application in predicting Brest cancer. This work focuses on different classification techniques implementation for data mining in predicting malignant and benign breast cancer. Breast Cancer Wisconsin data set from the UCI repository has been used as an experimental dataset while attribute clump thickness being used as an evaluation class. The performances of these twelve algorithms: Ada Boost M1, Decision Table, J-Rip, J48, Lazy IBK, Lazy K-star, Logistics Regression, Multiclass Classifier, Multilayer–Perceptron, Naïve Bayes, Random Forest, and Random Tree is analyzed on this data set.
High-Precision Depth Estimation Using Uncalibrated LiDAR and Stereo Fusion

We address the problem of 3D reconstruction from uncalibrated LiDAR point cloud and stereo images. Since the usage of each sensor alone for 3D reconstruction has weaknesses in terms of density and accuracy, we propose a deep sensor fusion framework for high-precision depth estimation. The proposed architecture consists of calibration network and depth fusion network, where both networks are designed considering the trade-off between accuracy and efficiency for mobile devices. The calibration network first corrects an initial extrinsic parameter to align the input sensor coordinate systems. The accuracy of calibration is markedly improved by formulating the calibration in the depth domain. In the depth fusion network, complementary characteristics of sparse LiDAR and dense stereo depth are then encoded in a boosting manner. Since training data for the LiDAR and stereo depth fusion are rather limited, we introduce a simple but effective approach to generate pseudo ground truth labels from the raw KITTI dataset. The experimental evaluation verifies that the proposed method outperforms current state-of-the-art methods on the KITTI benchmark. We also collect data using our proprietary multi-sensor acquisition platform and verify that the proposed method generalizes across different sensor settings and scenes.
Enhanced grey wolf optimizer with a model for dynamically estimating the location of the prey

Grey Wolf Optimizer (GWO) is a new meta-heuristic inspired by the hunting behavior of grey wolves. Our findings reveal that the optimizer has a strong search bias towards the origin of the coordinate system. In this article, a more realistic model is proposed to mimic the leadership hierarchy and group hunting mechanism of grey wolves in nature. In the innovative model, the location of the prey is dynamically estimated by leader wolves and each wolf is directly moving towards the estimated location of the prey. The proposed algorithm is compared with the original grey wolf optimizer and its recent variants on the CEC2017 test suite. The experimental results indicate that the enhanced optimizer significantly outperforms the original version and recent variants in terms of the convergence speed and the quality of solution found. The proposed algorithm also achieves the best solutions in solving two real engineering optimization problems at a lower computation cost.
Blockchain analytics and artificial intelligence

Blockchain records track information about financial payments, movements of products through supply chains, identity verification information, and many other assets. Analytics on this data can provide provenance histories, predictive planning, fraud identification, and regulatory compliance. In this paper, we describe analytics engines connected to blockchains to provide easy-to-use configurable dashboards, predictive models, provenance histories, and compliance checking. We also describe how blockchain data can be combined with external data sources for secure and private analytics, enable artificial intelligence (AI) model creation over geographically dispersed data, and create a history of model creation enabling provenance and lineage tracking for trusted AI.
Motion sickness in automated vehicles with forward and rearward facing seating orientations

Automated vehicles (AV's) offer greater flexibility in cabin design particularly in a future where no physical driving controls are required. One common concept for an automated vehicle is to have both forward and rearward facing seats. However, traveling backwards could lead to an increased likelihood of experiencing motion sickness due to the inability of occupants to anticipate the future motion trajectory. This study aimed to empirically evaluate the impact of seating orientation on the levels of motion sickness within an AV cabin. To this end, a vehicle was modified to replicate the common concept of automated vehicles with forward and rearward facing seats. Two routes were chosen to simulate motorway and urban driving. The participants were instructed to carry out typical office tasks whilst being driven in the vehicle which consisted of conducting a meeting, operating a personal device and taking notes. The participants conducted the test twice to experience both forward and rearward seating orientations in a randomised crossover design. Levels of sickness reported was relatively low with a significant increase in the mean level of sickness recorded when traveling rearwards. As expected, this increase was particularly pronounced under urban driving conditions. It is concluded that rearward travel in automated vehicles will compromise the passenger experience.
A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM

Recently, the demand for human activity recognition has become more and more urgent. It is widely used in indoor positioning, medical monitoring, safe driving, etc. Existing activity recognition approaches require either the location information of the sensors or the specific domain knowledge, which are expensive, intrusive, and inconvenient for pervasive implementation. In this paper, a human activity recognition algorithm based on SDAE (Stacking Denoising Autoencoder) and LightGBM (LGB) is proposed. The SDAE is adopted to sanitize the noise in raw sensor data and extract the most effective characteristic expression with unsupervised learning. The LGB reveals the inherent feature dependencies among categories for accurate human activity recognition. Extensive experiments are conducted on four datasets of distinct sensor combinations collected by different devices in three typical application scenarios, which are human moving modes, current static, and dynamic behaviors of users. The experimental results demonstrate that our proposed algorithm achieves an average accuracy of 95.99%, outperforming other comparative algorithms using XGBoost, CNN (Convolutional Neural Network), CNN + Statistical features, or single SDAE.