Reviewerly/exHarmony huggingface dataset:
Gemini: A Novel and Universal Heterogeneous Graph Information Fusing Framework for Online Recommendations

Recently, network embedding has been successfully used in recommendation systems. Researchers have made efforts to utilize additional auxiliary information (e.g., social relations of users) to improve performance. However, such auxiliary information lacks compatibility for all recommendation scenarios, thus it is difficult to apply in some industrial scenarios where generality is required. Moreover, the heterogeneous nature between users and items aggravates the difficulty in network information fusion. Many works tried to transform user-item heterogeneous network to two homogeneous graphs (i.e., user-user and item-item), and then fuse information separately. This may limit the representation power of learned embedding due to ignoring the adjacent relationship in the original graph. In addition, the sparsity of user-item interactions is an urgent problem need to be solved. To solve the above problems, we propose a universal and effective framework named Gemini, which only relies on the common interaction logs, avoiding the dependence on auxiliary information and ensuring a better generality. For the purpose of keeping original adjacent relationship, Gemini transforms the original user-item heterogeneous graph into two semi homogeneous graphs from the perspective of users and items respectively. The transformed graphs consist of two types of nodes: network nodes coming from homogeneous nodes and attribute nodes coming from heterogeneous node. Then, the node representation is learned in a homogeneous way, with considering edge embedding at the same time. Simultaneously, the interaction sparsity problem is solved to some extent as the transformed graphs contain the original second-order neighbors. For training efficiently, we also propose an iterative training algorithm to reduce computational complexity. Experimental results on the five datasets and online A/B tests in recommendations of DiDiChuXing show that Gemini outperforms state-of-the-art algorithms.
Fast and Effective Active Clustering Ensemble Based on Density Peak

Semisupervised clustering methods improve performance by randomly selecting pairwise constraints, which may lead to redundancy and instability. In this context, active clustering is proposed to maximize the efficacy of annotations by effectively using pairwise constraints. However, existing methods lack an overall consideration of the querying criteria and repeatedly run semisupervised clustering to update labels. In this work, we first propose an active density peak (ADP) clustering algorithm that considers both representativeness and informativeness. Representative instances are selected to capture data patterns, while informative instances are queried to reduce the uncertainty of clustering results. Meanwhile, we design a fast-update-strategy to update labels efficiently. In addition, we propose an active clustering ensemble framework that combines local and global uncertainties to query the most ambiguous instances for better separation between the clusters. A weighted voting consensus method is introduced for better integration of clustering results. We conducted experiments by comparing our methods with state-of-the-art methods on real-world data sets. Experimental results demonstrate the effectiveness of our methods.
Predictive and Explainable Machine Learning for Industrial Internet of Things Applications

Predictive Analytics and Machine Learning (ML) are at the heart of some of the most popular Industry 4.0 applications such as condition-based monitoring, predictive maintenance, and quality management. To support the implementation of such use cases, various ML models have been proposed and validated in the research literature. This paper introduces a novel set of machine learning algorithms for Industry4.0 use cases, namely the QARMA algorithms, which are capable of mining of quantitative rules. QARMA models present several advantages when compared to conventional ML and Deep Learning mechanisms, including computational performance, predictive accuracy and "explainability". In the scope of this paper, we discuss these advantages based on practical experiences from the field deployment and validation of QARMA models in two different production lines. The deployment has been supported by a state-of-the-art Industrial Internet of Things platform, which is also presented in the paper.
Empirical comparison and evaluation of Artificial Immune Systems in inter-release software fault prediction

Artificial immune systems are bio-inspired machine learning algorithms based on the mammalian immune paradigms. One of the possible uses of these methods is Software Fault Prediction, which consists of classifying the modules of an application as being fault-prone or not, thus allowing a developer to better target the modules during the test phase leading to a high-quality software with lower cost. Despite the high number of works in the field, only five studies included Artificial Immune Systems in their approaches and exclusively focused on the intra-project fault prediction scheme. In this study, our objective is to appraise 8 immunological systems on the rarely treated inter-project software defect prediction scenario over three different benchmarks, hence, we selected 41 datasets corresponding to 11 java projects from the PROMISE data repository. According to the Friedman and Nemenyi Post-hoc test results, none of the performance of the studied algorithms was better than Immunos-1 and Immunos-99 in terms of the Recall measure. Furthermore, the outcomes of the Wilcoxon test suggest that the researches addressing the intra-projects defect prediction problems should also evaluate their models on inter-release scenarios.
Multi-label learning with label-specific features via weighting and label entropy guided clustering ensemble

Multi-label learning has attracted more and more researchers' attention. It deals with the problem where each instance is associated with multiple labels simultaneously. Some methods improve the performance by constructing label-specific features. Specifically, the LIFTACE method constructs label-specific features by clustering ensemble techniques, which ignores the importance of label vectors and does not explore label correlations when constructing the classification model. In this paper, we propose a multi-label learning method called LF-LELC, which considers the importance of label vectors and constructs the classification model by considering label correlations. Firstly, it performs clustering on the positive instances and negative instances respectively. The number of clusters is set by the information contained in the label vectors. After that, it employs clustering ensemble techniques that consider label correlations to make the clustering results more stable and effective. Then, it constructs label-specific features for each label. Finally, it builds the classification model by exploring label correlations. The label set for each test example is predicted by the classification model. Experiments show that LF-LELC can achieve better performance by considering the importance of label vectors and the correlations among labels.
Systematic Linear Word String Recognition and Evaluation Technique

As a proverb goes, “A Picture speaks more than a word”. The influence of a picture on any given information is predominant and has extra mileage in representing the information. In this paper, a linear model of word recognition is proposed using the image datasets. The proposed model is evaluated on primary datasets of UCL repository, the results are processed using MATLAB and the recognition ratio is effective compared to the previous approaches. Typically, the proposed technique is an optimized solution for word string extraction from a given image data-samples.
Designing a Fog-Cloud Architecture using Blockchain and Analyzing Security Improvements

Ethereum is a blockchain-based distributed network and has been a successful platform to host decentralized applications. On the other hand, fog computing has emerged as one of the most demanding field for research as it brings the services of cloud to the edge of the network, i.e. closer to the IoT devices. To satisfy the QoS requirement of latency sensitive applications, the concept of fog computing is introduced in order to implement the idea of smart home, city etc. Though the fog nodes can support latency sensitive applications, but their computational capacity is low which leads to more vulnerability against known attacks. But Ethereum has paved the way to develop smart contracts to implement any logic written in it upon calling. The usage of smart contracts in securing the fog node can be effective as it runs in a blockchain enabled network which makes the smart contract immutable and secured against modification. This creates a scope of using smart contract's logic to ensure the security of fog nodes and IoT devices which they earnestly require. This also eliminates the necessity of public-private key pair and other authentication systems used in fog-cloud architecture. In our work, we modified the conventional fog-cloud architecture by introducing blockchain-based fog layer to ensure better security and privacy for both fog layer and IoT devices.
A novel Collaborative Filtering recommendation approach based on Soft Co-Clustering

Collaborative Filtering (CF) recommendation algorithm has been widely applied into recommender systems. Many CF algorithms associate a user/an item with one of subgroups by explicit or implicit features. However, considering that users may have multiple personalities and items may have diverse attributes, it is more reasonable to associate a user/an item with more than one group. In this paper, we propose the Soft K-indicators Alternative Projection (SKAP) algorithm, which can efficiently resolve soft clustering problem with high dimensions, to generate a sparse partition matrix and further a Top-N recommendation list is given. Unlike fuzzy C-means clustering, the SKAP algorithm is independent on the selection of initial values. In addition to that, we integrate the item type information into recommender systems to improve recommendation accuracy. Experimental results show that the proposed approach behaves superior performance in Top-N recommendation in terms of classical metrics and further show that multi-label classification framework is a better description than classical Co-Clustering framework.
Improving Imbalanced Classification by Anomaly Detection

Although the anomaly detection problem can be considered as an extreme case of class imbalance problem, very few studies consider improving class imbalance classification with anomaly detection ideas. Most data-level approaches in the imbalanced learning domain aim to introduce more information to the original dataset by generating synthetic samples. However, in this paper, we gain additional information in another way, by introducing additional attributes. We propose to introduce the outlier score and four types of samples (safe, borderline, rare, outlier) as additional attributes in order to gain more information on the data characteristics and improve the classification performance. According to our experimental results, introducing additional attributes can improve the imbalanced classification performance in most cases (6 out of 7 datasets). Further study shows that this performance improvement is mainly contributed by a more accurate classification in the overlapping region of the two classes (majority and minority classes). The proposed idea of introducing additional attributes is simple to implement and can be combined with resampling techniques and other algorithmic-level approaches in the imbalanced learning domain.
The Dynamics of Business, Cybersecurity and Cyber-Victimization: Foregrounding the Internal Guardian in Prevention

Private organizations suffer great losses due to cybersecurity incidents, and they invest increasing resources to prevent attacks, but little is known about the effectiveness of cybersecurity measures for prevention. Based on the framework of Routine Activity Theory, this paper analyzes the impact of companies’ online activities and cybersecurity measures on victimization. Our analysis of the UK Cybersecurity Breaches Survey shows that the most promising ways to minimize cyber-attacks and their impacts is to invest in in-house cybersecurity human resources and enhance the employees’ online self-protection by providing cybersecurity training, rather than just basic software protection and guidance about strong passwords.
A Preliminary Study on Full-Body Haptic Stimulation on Modulating Self-motion Perception in Virtual Reality

We introduce a novel experimental system to explore the role of vibrotactile haptic feedback in Virtual Reality (VR) to induce the self-motion illusion. Self-motion (also called vection) has been mostly studied through visual and auditory stimuli and a little is known how the illusion can be modulated by the addition of vibrotactile feedback. Our study focuses on whole-body haptic feedback in which the vibration is dynamically generated from the sound signal of the Virtual Environment (VE). We performed a preliminary study and found that audio and haptic modalities generally increase the intensity of vection over a visual only stimulus. We observe higher ratings of self-motion intensity when the vibrotactile stimulus is added to the virtual scene. We also analyzed data obtained with the igroup presence questionnaire (IPQ) which shows that haptic feedback has a general positive effect of presence in the virtual environment and a qualitative survey that revealed interesting and often overlooked aspects such as the implications of using a joystick to collect data in perception studies and in the concept of vection in relation to people's experience and cognitive interpretation of self-motion.
Learnable pooling weights for facial expression recognition

Pooling layers are spatial down-sampling layers used in convolutional neural networks (CNN) to gradually downscale the feature map, increase the receptive field size and reduce the number of the parameters in the model. The use of pooling layers leads to less computing complexity and memory consumption reduction but also introduces invariance to certain filter distortions which may induce subtle detail loss. This behaviour is undesired for some fine-grained recognition tasks such as facial expression recognition (FER) which highly relies on specific regional distortion detection. In this paper, we introduce a more filter distortion aware pooling layer based on kernel functions. The proposed pooling reduces the feature map dimensions while keeping track of the majority of the information fed to the next layer instead of ignoring part of them. The experiments on RAF, FER2013 and ExpW databases demonstrate the benefits of such layer and show that our model achieves competitive results with respect to the state-of-the-art approaches.
Multi-scale structural kernel representation for object detection

Existing high-performance object detection methods greatly benefit from the powerful representation ability of deep convolutional neural networks (CNNs). Recent researches show that integration of high-order statistics remarkably improves the representation ability of deep CNNs. However, high-order statistics for object detection lie in two challenges. Firstly, previous methods insert high-order statistics into deep CNNs as global representations, which lose spatial information of inputs, and so are not applicable to object detection. Furthermore, high-order statistics have special structures, which should be considered for proper use of high-order statistics. To overcome above challenges, this paper proposes a Multi-scale Structural Kernel Representation (MSKR) for improving performance of object detection. Our MSKR is developed based on the polynomial kernel approximation, which does not only draw into high-order statistics but also preserve the spatial information of input. To consider geometry structures of high-order representations, a feature power normalization method is introduced before computation of kernel representation. Comparing with the most commonly used first-order statistics in existing CNN-based detectors, our MSKR can generate more discriminative representations, and so be flexibly integrated into deep CNNs for improving performance of object detection. By adopting the proposed MSKR to existing object detection methods (i.e., Faster R-CNN, FPN, Mask R-CNN and RetinaNet), it achieves clear improvement on three widely used benchmarks, while obtaining very competitive performance with state-of-the-art methods.
Principles of KLM-style Defeasible Description Logics

The past 25 years have seen many attempts to introduce defeasible-reasoning capabilities into a description logic setting. Many, if not most, of these attempts are based on preferential extensions of description logics, with a significant number of these, in turn, following the so-called KLM approach to defeasible reasoning initially advocated for propositional logic by Kraus, Lehmann, and Magidor. Each of these attempts has its own aim of investigating particular constructions and variants of the (KLM-style) preferential approach. Here our aim is to provide a comprehensive study of the formal foundations of preferential defeasible reasoning for description logics in the KLM tradition. We start by investigating a notion of defeasible subsumption in the spirit of defeasible conditionals as studied by Kraus, Lehmann, and Magidor in the propositional case. In particular, we consider a natural and intuitive semantics for defeasible subsumption, and we investigate KLM-style syntactic properties for both preferential and rational subsumption. Our contribution includes two representation results linking our semantic constructions to the set of preferential and rational properties considered. Besides showing that our semantics is appropriate, these results pave the way for more effective decision procedures for defeasible reasoning in description logics. Indeed, we also analyse the problem of non-monotonic reasoning in description logics at the level of entailment and present an algorithm for the computation of rational closure of a defeasible knowledge base. Importantly, our algorithm relies completely on classical entailment and shows that the computational complexity of reasoning over defeasible knowledge bases is no worse than that of reasoning in the underlying classical DL ALC .
Multi-robot exploration using Dynamic Fuzzy Cognitive Maps and Ant Colony Optimization

An application field of Multi-Robot Systems (MRS) is within victim rescue operations. The main challenge faced by disaster rescue teams is response time. The chances of finding survivors decrease significantly over time and dramatically decrease after 48 hours. In this context, the motivation of this work is to present an MRS inspired by the concepts of swarm robotics to rescue victims in unknown environments. In this case, the robots are unaware of the search area boundaries and obstacles, knowing the number of victims to be rescued as a stopping criterion for the simulations made in Matlab®. Therefore, three approaches inheriting the main aspects of fuzzy logic are used based on previous works: a fuzzy logic controller (FLC), a dynamic fuzzy cognitive map (DFCM) controller, and a DFCM inspired by the ant colony optimization metaheuristic (DFCM- ACO). The proposed task simulates real life disaster rescue operations, or even humans lost in unfamiliar environments such as forests. The simulations were performed in three environments in order to test the overall robustness against unpredictable situations, autonomy, explored area and processing time for both approaches using a subsumption-based architecture. In general, the results suggest that the DFCM-based MRS approaches are able to complete the tasks consuming less processing time, with robots travelling shorter distances to explore a similar environment to the FLC approach and with the DFCM-ACO presenting balanced results between the other techniques. Finally, future works are outlined.
TUHAD: Taekwondo Unit Technique Human Action Dataset with Key Frame-Based CNN Action Recognition

In taekwondo, poomsae (i.e., form) competitions have no quantitative scoring standards, unlike gyeorugi (i.e., full-contact sparring) in the Olympics. Consequently, there are diverse fairness issues regarding poomsae evaluation, and the demand for quantitative evaluation tools is increasing. Action recognition is a promising approach, but the extreme and rapid actions of taekwondo complicate its application. This study established the Taekwondo Unit technique Human Action Dataset (TUHAD), which consists of multimodal image sequences of poomsae actions. TUHAD contains 1936 action samples of eight unit techniques performed by 10 experts and captured by two camera views. A key frame-based convolutional neural network architecture was developed for taekwondo action recognition, and its accuracy was validated for various input configurations. A correlation analysis of the input configuration and accuracy demonstrated that the proposed model achieved a recognition accuracy of up to 95.833% (lowest accuracy of 74.49%). This study contributes to the research and development of taekwondo action recognition.
Impact-Learning

The ultimate goal of this research paper is to introduce a robust machine learning algorithm called Impact-Learning, which is being used widely to achieve more advanced results on many machine-learning related challenges. Impact learning is a supervised machine learning algorithm for resolving classification and linear or polynomial regression knowledge from examples. It also contributes to analyzing systems for competitive data. This algorithm is unique for being capable of learning from a competition, which is the impact of independent features. In other words, it is trained by the impacts of the features from the intrinsic rate of natural increase (RNI). The input to the Impact Learning is a training set of numerical data. In this work, we used six datasets related to regressions and classifications as the experiment of the Impact Learning, and the comparison indicates that at outperforms other standard machine learning regressions and classifications algorithms such as Random forest tree, SVM, Naive Bayes, Logistic regression and so forth.
Stereo matching algorithm based on deep learning: A survey

The development of stereo matching algorithm is still one of the challenging problems, especially in ill-posed regions. Hence, this article presents a survey on the algorithm frameworks related to the stereo matching algorithm. Based on the early survey that had been conducted, two major frameworks available in current stereo matching algorithm development, they are traditional and artificial intelligence (AI) frameworks. Most of the traditional methods are very low accuracy compared to the AI-based approach. This can be observed in the standard benchmarking dataset, such as from the KITTI and the Middlebury, where AI methods rank at the top of the accuracy list. Additionally, the trend for solving computer vision problems uses AI or machine learning tools that become more apparent in recent years. Thus, this paper is focusing on the survey between the deep learning frameworks, which is one of the machine learning tools related to the convolutional neural network (CNN). Several mixed approaches between CNN based method and traditional handcraft method, as well as the end to end CNN method also discussed in this paper.