Reviewerly/exHarmony huggingface dataset:
HoAFM: A High-order Attentive Factorization Machine for CTR Prediction

Modeling feature interactions is of crucial importance to predict click-through rate (CTR) in industrial recommender systems. However, manually crafting cross features usually requires extensive domain knowledge and labor-intensive feature engineering to obtain the desired cross features. To alleviate this problem, the factorization machine (FM) is proposed to model feature interactions from raw features automatically. In particular, it embeds each feature in a vector representation and discovers second-order interactions as the product of two feature representations. In order to learn nonlinear and complex patterns, recent works, such as NFM, PIN, and DeepFM, exploited deep learning techniques to capture higher-order feature interactions. These approaches lack guarantees about the effectiveness of high-order pattern as they model feature interactions in a rather implicit way. To address this limitation, xDeepFM is recently proposed to generate high-order interactions of features in an explicit fashion, where multiple interaction networks are stacked. Nevertheless, xDeepFM suffers from rather high complexity which easily leads to overfitting. In this paper, we develop a more expressive but lightweight solution based on FM, named High-order Attentive Factorization Machine (HoAFM), by accounting for the higher-order sparse feature interactions in an explicit manner. Beyond the linearity of FM, we devise a cross interaction layer, which updates a feature’s representation by aggregating the representations of other co-occurred features. In addition, we perform a bit-wise attention mechanism to determine the different importance of co-occurred features on the granularity of dimensions. By stacking multiple cross interaction layers, we can inject high-order feature interactions into feature representation learning, in order to establish expressive and informative cross features. Extensive experiments are performed on two benchmark datasets, Criteo and Avazu, to demonstrate the rationality and effectiveness of HoAFM. Empirical results suggest that HoAFM achieves significant improvement over other state-of-the-art methods, such as NFM and xDeepFM. https://github.com/zltao/HoAFM.
Improvement of Maximum Variance Weight Partitioning Particle Filter in Urban Computing and Intelligence

At present, urban computing and intelligence has become an important topic in the research field of artificial intelligence. On the other hand, computer vision as a crucial bridge between urban world and artificial intelligence is playing a key role in urban computing and intelligence. Conventional particle filter is derived from Karman filter, which theoretically based on Monte Carlo method. Sequential importance resampling (SIR) is implemented in conventional particle filter to avoid the degeneracy problem. In order to overcome the shortcomings of the resampling algorithm in the traditional particle filter, we proposed an optimized particle filter using the maximum variance weight segmentation resampling algorithm in this paper, which improved the performance of particle filter. Compared with the traditional particle filter algorithm, the experimental results show that the proposed scheme outperforms in terms of computational consumption and the accuracy of particle tracking. The final experimental results proved that the quality of the maximum variance weight segmentation method increased the accuracy and stability in motion trajectory tracking tasks.
Fuzzy clustering of mixed data

A fuzzy clustering model for data with mixed features is proposed. The clustering model allows different types of variables, or attributes, to be taken into account. This result is achieved by combining the dissimilarity measures for each attribute by means of a weighting scheme, so as to obtain a distance measure for multiple attributes. The weights are objectively computed during the optimization process. The weights reflect the relevance of each attribute type in the clustering results. Two simulation studies and two empirical applications were carried out that show the effectiveness of the proposed clustering algorithm in finding clusters that would be otherwise hidden if a multi–attributes approach were not pursued.
Toward Highly Secure Yet Efficient KNN Classification Scheme on Outsourced Cloud Data

Nowadays, outsourcing data and machine learning tasks, e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor (KNN) classification, to clouds has become a scalable and cost-effective way for large scale data storage, management, and processing. However, data security and privacy issue have been a serious concern in outsourcing data to clouds. In this article, we propose a privacy-preserving KNN classification scheme on cloud data in a twin-cloud model based on an additively homomorphic cryptosystem and secret sharing. Compared with existing works, we redesign a set of lightweight building blocks, such as secure square Euclidean distance, secure comparison, secure sorting, secure minimum, and maximum number finding, and secure frequency calculating, which achieve the same security level but with higher efficiency. In our scheme, data owners stay offline, which is different from secure-multiparty computation-based solutions which require data owners’ stay online during computation. In addition, query users do not interact with the cloud except sending query data and receiving the query results. Our security analysis shows that the scheme protects outsourced data security and query privacy, and hides access patterns. The experiments on real-world dataset indicate that our scheme is significantly more efficient than existing schemes.
Skills in the area of digital safety as a key component of digital literacy among teachers

The goal of the research was to assess the level of Digital Literacy (DL) among teachers. The study was diagnostic in order to show DL in six selected key areas: the ergonomics of using ICT, assessing the credibility of information, secure online communication, maintaining anonymity in the digital world, safe logging-in, and intellectual property. DL was measured using a knowledge and competence test. The study was conducted in Poland in 2017/2018 among 701 primary school teachers (primary being the second stage of education). Based on the data collected, we have noticed the following: DL is a heterogeneous concept; the respondents possess the lowest level of knowledge in the area of intellectual property law and know the most about ergonomics; gender does not determine the level of knowledge and competencies in the group. Furthermore, the Dunning-Kruger effect is noticeable among the teachers, in the context of evaluation of DL related to digital safety. We also need to emphasise that for teachers, DL is one of the key protective factors in digital safety, viewed holistically, in schools. Thus, diagnosing and facilitating the development of DL has become one of the key challenges faced by schools today.
Do Head-Mounted Display Stereo Deficiencies Affect 3D Pointing Tasks in AR and VR?

Most AR and VR headsets use stereoscopic displays to show virtual objects in 3D. However, the limitations of current stereo display systems affect depth perception through conflicting depth cues, which then also affect virtual hand interaction in peri-personal space, i.e., within arm's reach. We performed a Fitts' law experiment to better understand the impact of stereo display deficiencies of AR and VR headsets on pointing at close-by targets arranged laterally or along the line of sight. According to our results, the movement direction and the corresponding change in target depth affect pointing time and throughput; subjects' movements towards/away from their head were slower and less accurate than their lateral movements (left/right). However, even though subjects moved faster in AR, we did not observe a significant difference for pointing performance between AR and VR headsets, which means that previously identified differences in depth perception between these platforms seem to have no strong effect on interaction. Our results also help 3D user interface designers understand how changes in target depth affect users' performance in different movement directions in AR and VR.
Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization With Medical Applications

Medical applications challenge today's text categorization techniques by demanding both high accuracy and ease-of-interpretation.Although deep learning has provided a leap forward in regard to accuracy, this leap comes at the sacrifice of interpretability.In this paper, we introduce a text categorization approach that leverages the recently introduced Tsetlin Machine to address this accuracy-interpretability challenge.Briefly, we represent the terms of a text as propositional variables.From these variables, we capture categories using simple propositional formulae, such as: IF ''rash'' AND ''reaction'' AND ''penicillin'' THEN Allergy.The Tsetlin Machine learns these formulae from labeled text, utilizing conjunctive clauses to represent the particular facets of each category.Therefore, also the absence of terms (negated features) can be used for categorization purposes.Our empirical comparisons with Naïve Bayes classifiers, decision trees, linear support vector machines (SVMs), random forest, long short-term memory (LSTM) neural networks, and other techniques, are quite conclusive.Using relatively simple propositional formulae, the accuracy of the Tsetlin Machine either outperforms or performs approximately on par with the best evaluated methods on both the 20 Newsgroups and IMDb datasets, as well as on a clinical dataset containing authentic electronic health records (EHRs).On average, the Tsetlin Machine delivers the best recall and precision scores across the datasets.The main merit of the proposed approach is thus its capacity for producing human-interpretable rules, while at the same time achieving acceptable accuracy.We believe that our novel approach can have a significant impact on a wide range of text analysis applications, providing a promising starting point for deeper natural language understanding with the Tsetlin Machine.
Content-Based Light Field Image Compression Method With Gaussian Process Regression

Light field (LF) imaging enables new possibilities for digital imaging, such as digital refocusing, changing of focus plane, changing of viewpoint, scene-depth estimation, and 3D scene reconstruction, by capturing both spatial and angular information of light rays. However, one main problem in dealing with LF data is its sheer volume. In this context, efficient compression methods are needed for such a particular type of content. In this paper, we propose a content-based LF image-compression method with Gaussian process regression to improve the compression efficiency and accelerate the prediction procedure. First, the LF image is fed to the intra-frame codec of HEVC. In the prediction procedure, the prediction units (PUs) are classified as non-homogenous texture units, homogenous texture units, and visually flat units, based on the content property of the LF image. For each category, we design a corresponding Gaussian process regression (GPR)-based prediction method. Moreover, we propose a classification mechanism to exactly decide to which category the current PU belongs, so as to adjust the trade-off between the computational burden and the LF image coding efficiency. Experimental results demonstrate that the proposed LF image compression method is superior to several other state-of-the-art compression methods in terms of different quality metrics. Furthermore, the proposed method can also achieve a good visual quality of views rendered from decoded LF contents.
NaviBoard and NaviChair: Limited Translation Combined with Full Rotation for Efficient Virtual Locomotion

Walking has always been considered as the gold standard for navigation in Virtual Reality research. Though full rotation is no longer a technical challenge, physical translation is still restricted through limited tracked areas. While rotational information has been shown to be important, the benefit of the translational component is still unclear with mixed results in previous work. To address this gap, we conducted a mixed-method experiment to compare four levels of translational cues and control: none (using the trackpad of the HTC Vive controller to translate), upper-body leaning (sitting on a “NaviChair”, leaning the upper-body to locomote), whole-body leaning/stepping (standing on a platform called NaviBoard, leaning the whole body or stepping one foot off the center to navigate), and full translation (physically walking). Results showed that translational cues and control had significant effects on various measures including task performance, task load, and simulator sickness. While participants performed significantly worse when they used a controller with no embodied translational cues, there was no significant difference between the NaviChair, NaviBoard, and actual walking. These results suggest that translational body-based motion cues and control from a low-cost leaning/stepping interface might provide enough sensory information for supporting spatial updating, spatial awareness, and efficient locomotion in VR, although future work will need to investigate how these results might or might not generalize to other tasks and scenarios.
Secure data transmission framework for confidentiality in IoTs

The large-scale data exchange envisioned to occur in distributed IoTs is prone to raise issues regarding privacy, confidentiality and authentication at the cyberspace level. This work focuses to preserve privacy and confidentiality of data in an insecure environment of multimedia exchange between two IoT hops. In order to thwart an adversary, and ensure data confidentiality, we propose a robust multi-level security approach based on information hiding and chaotic theory. Although, some existing block-based robust data hiding schemes based on transform domain offer good results; but the inefficient block and coefficient selection/modification results in poor performance to various commonly occurring cyber-attacks. The proposed scheme is based on Random Coefficient Selection and Mean Modification Approach (RCSMMA). Unlike conventional approaches, which use single coefficient from a block to embed an information bit, RCSMMA uses multiple Discrete Cosine Transform coefficients selected randomly from two non-neighbourhood blocks to ensure information spread over different areas of the cover image. The proposed approach results in very high robustness of the information transferred between two nodes, for all signal processing and the region-oriented attacks, acting simultaneous or standalone. For attacks like rotation, cropping, histogram equalization and sharpening, a very high robustness is achieved with Normalized Cross Correlation close to unity. For other singular/combined attacks average Normalized Cross Correlation is greater than 0.95. The average Peak Signal to Noise Ratio obtained for various images is greater than 40 dB with the payload of 4Kb. The experimental results demonstrate the superiority of the proposed scheme over other state-of-art techniques.
Workflow scheduling in distributed systems under fuzzy environment

A well-managed time-constrained workflow scheduling is needed for improving system performance and end user satisfaction. Meanwhile, the intrinsic uncertainty in dynamic systems increases the difficulties of scheduling problem. Therefore, it is a great challenge to improve performance and optimize several objectives simultaneously. To address these issues, a novel workflow scheduling method for distributed systems based on TOPSIS method with fuzzy set is proposed in this paper. The new method can minimize the makespan of the workflow application under uncertain environment. Finally, a numerical example is provided to demonstrate the efficiency of the proposed method.
What kind of questions do developers ask on Stack Overflow? A comparison of automated approaches to classify posts into question categories

On question and answer sites, such as Stack Overflow (SO), developers use tags to label the content of a post and to support developers in question searching and browsing. However, these tags mainly refer to technological aspects instead of the purpose of the question. Tagging questions with their purpose can add a new dimension to the identification of discussed topics in posts on SO. In this paper, we aim at automating the classification of SO question posts into seven question categories. As a first step, we harmonized existing taxonomies of question categories and then, we manually classified 1,000 SO questions according to our new taxonomy. Additionally to the question category, we marked the phrases that indicate a question category for each of the posts. We then use this data set to automate the classification of posts using two approaches. For the first approach, we manually analyzed the phrases to find patterns. Based on regular expressions, we implemented a classifier, for each of the categories, that determines whether a post belongs to a category. These regular expressions are derived by analyzing patterns in the phrases. In the second approach, we use the curated data set to train classification models of supervised machine learning algorithms (Random Forest and Support Vector Machines). For the machine learning algorithms, we experimented with 1,312 different configurations regarding the preprocessing of the text and the representation of the input data. Then, we compared the performance of the regex approach with the performance of the best configuration that uses machine learning algorithms on a validation set of 110 posts. The results show that using the regular expression approach, we can classify posts into the correct question category with an average precision and recall of 0.90, and an MCC of 0.68. Additionally, we applied the regex approach on all questions of SO that deal with Android app development and investigated the co-occurrence of question categories in posts. We found that the categories API usage, Conceptual, and Discrepancy are the most frequently assigned question categories and that they also occur together frequently. Our approach can be used to support developers in browsing SO discussions or researchers in building recommender systems based on SO.
Distributed Deep Forest and its Application to Automatic Detection of Cash-Out Fraud

Internet companies are facing the need for handling large-scale machine learning applications on a daily basis and distributed implementation of machine learning algorithms which can handle extra-large-scale tasks with great performance is widely needed. Deep forest is a recently proposed deep learning framework which uses tree ensembles as its building blocks and it has achieved highly competitive results on various domains of tasks. However, it has not been tested on extremely large-scale tasks. In this work, based on our parameter server system, we developed the distributed version of deep forest. To meet the need for real-world tasks, many improvements are introduced to the original deep forest model, including MART (Multiple Additive Regression Tree) as base learners for efficiency and effectiveness consideration, the cost-based method for handling prevalent class-imbalanced data, MART based feature selection for high dimension data, and different evaluation metrics for automatically determining the cascade level. We tested the deep forest model on an extra-large-scale task, i.e., automatic detection of cash-out fraud, with more than 100 million training samples. Experimental results showed that the deep forest model has the best performance according to the evaluation metrics from different perspectives even with very little effort for parameter tuning. This model can block fraud transactions in a large amount of money each day. Even compared with the best-deployed model, the deep forest model can additionally bring a significant decrease in economic loss each day.
A novel enhanced cuckoo search algorithm for contrast enhancement of gray scale images

A good contrast image has a significant role in different image processing applications and computer vision algorithms. One of the most common contrast enhancement approaches is histogram equalization (HE) that enhances the contrast of an image globally. However, it gives rise to some over-enhanced regions, loss of detail information, and enhancement of noise. In order to improve the performance of the HE algorithm, local HE and adaptive HE algorithms have been proposed but with limited success. Recently, an evolutionary algorithm named cuckoo search (CS) algorithm has been employed for automatic image contrast enhancement showing promising performance. In this paper, we propose a novel enhanced cuckoo search (ECS) algorithm for image contrast enhancement. In addition, we propose a new range of search space for the parameters of the local/global enhancement (LGE) transformation that need to be optimized. The proposed ECS algorithm is applied to several low contrast test images and its performance is compared with that of the CS algorithm. Next, we compare the performance of the ECS algorithm with artificial bee colony algorithm using the proposed LGE transformation and a global transformation. In the last stage of performance evaluation, the ECS algorithm is compared with several image enhancement algorithms, namely, HE, CLAHE, Particle Swarm Optimization (PSO), CS, modified CS and CS-PSO algorithms. In all cases, we have shown the superiority of the ECS algorithm in terms of several performance measures.
Detection Approach Based on an Improved Faster RCNN for Brace Sleeve Screws in High-Speed Railways

Brace sleeve screws are an important component of catenary support devices in high-speed railways. According to the statistical data, there is a much risk for failure of brace sleeve screws during the operation of the catenary system. In order to obtain better results on the fault diagnosis for brace sleeve screws, it is of vital importance to develop a high-precision detection approach for brace sleeve screws. With the aim of solving the problems that the size of the brace sleeve screw is too small and the employed previous methods are difficult to detect accurately, a novel object detection network based on an improved Faster RCNN is proposed. Through the analysis of the factors that influence the detection accuracy for brace sleeve screws and the characteristics of original Faster RCNN, the concepts of discrimination maps and proposal maps are introduced for the improved Faster RCNN. In order to retain the characteristic information of brace sleeve screws, the high-resolution discrimination maps are adopted to identify the object type and the low-resolution proposal maps are adopted to generate the candidate boxes of region with an appropriate anchor scale configuration. Experimental results show that the proposed method has a higher detection accuracy for brace sleeve screws. In addition, compared with original Faster RCNN implementation based on the VGG-16 and ResNet-101 backbone, the detection accuracy of the improved Faster RCNN is increased by 8%. It also provides a new idea for general object detection of small targets.