Reviewerly/exHarmony huggingface dataset:
Multi-modal Knowledge Graphs for Recommender Systems

Recommender systems have shown great potential to solve the information explosion problem and enhance user experience in various online applications. To tackle data sparsity and cold start problems in recommender systems, researchers propose knowledge graphs (KGs) based recommendations by leveraging valuable external knowledge as auxiliary information. However, most of these works ignore the variety of data types (e.g., texts and images) in multi-modal knowledge graphs (MMKGs). In this paper, we propose Multi-modal Knowledge Graph Attention Network (MKGAT) to better enhance recommender systems by leveraging multi-modal knowledge. Specifically, we propose a multi-modal graph attention technique to conduct information propagation over MMKGs, and then use the resulting aggregated embedding representation for recommendation. To the best of our knowledge, this is the first work that incorporates multi-modal knowledge graph into recommender systems. We conduct extensive experiments on two real datasets from different domains, results of which demonstrate that our model MKGAT can successfully employ MMKGs to improve the quality of recommendation system.
A Survey on Adaptive 360° Video Streaming: Solutions, Challenges and Opportunities

Omnidirectional or 360° video is increasingly being used, mostly due to the latest advancements in immersive Virtual Reality (VR) technology. However, its wide adoption is hindered by the higher bandwidth and lower latency requirements than associated with traditional video content delivery. Diverse researchers propose and design solutions that help support an immersive visual experience of 360° video, primarily when delivered over a dynamic network environment. This paper presents the state-of-the-art on adaptive 360° video delivery solutions considering end-to-end video streaming in general and then specifically of 360° video delivery. Current and emerging solutions for adaptive 360° video streaming, including viewport-independent, viewport-dependent, and tile-based schemes are presented. Next, solutions for network-assisted unicast and multicast streaming of 360° video content are discussed. Different research challenges for both on-demand and live 360° video streaming are also analyzed. Several proposed standards and technologies and top international research projects are then presented. We demonstrate the ongoing standardization efforts for 360° media services that ensure interoperability and immersive media deployment on a massive scale. Finally, the paper concludes with a discussion about future research opportunities enabled by 360° video.
Reference-Based Sketch Image Colorization Using Augmented-Self Reference and Dense Semantic Correspondence

This paper tackles the automatic colorization task of a sketch image given an already-colored reference image. Colorizing a sketch image is in high demand in comics, animation, and other content creation applications, but it suffers from information scarcity of a sketch image. To address this, a reference image can render the colorization process in a reliable and user-driven manner. However, it is difficult to prepare for a training data set that has a sufficient amount of semantically meaningful pairs of images as well as the ground truth for a colored image reflecting a given reference (e.g., coloring a sketch of an originally blue car given a reference green car). To tackle this challenge, we propose to utilize the identical image with geometric distortion as a virtual reference, which makes it possible to secure the ground truth for a colored output image. Furthermore, it naturally provides the ground truth for dense semantic correspondence, which we utilize in our internal attention mechanism for color transfer from reference to sketch input. We demonstrate the effectiveness of our approach in various types of sketch image colorization via quantitative as well as qualitative evaluation against existing methods.
Innovation and intellectual capital as intermediary variables among transformational leadership, transactional leadership, and organizational performance

Purpose The purpose of this paper is to examine the mediating effect of intellectual capital and innovation on the relationship between transformational and transactional leadership and organizational performance in Jordanian banks located in Irbid city. Design/methodology/approach A questionnaire that targeted 350 respondents resulted in 298 usable ones with a response rate of 85.14 percent. To test the research hypotheses, a structural equation model was conducted, in addition to descriptive statistics that provided background on the respondents. Findings The findings indicate that transformational and transactional leadership relate positively to organizational performance. The results also support the argument that intellectual capital and innovation played mediating roles in transformational and transactional leadership and organizational performance. Practical implications The present study provides managers with empirical proof that possessing strong intellectual capital in its three dimensions seems to help the banking sector in Jordan to reinforce their ability to generate both radical and incremental innovation. Also, applying an effective leadership style will motivate and lead to superior performance. Originality/value Although papers have shown that leadership style is an important factor influencing employees' performance and outcomes, this is one of the few studies that investigates the interrelationships between leadership styles, intellectual capital, and innovation on organization performance. Furthermore, it is the first to test the model on the banking sector in Jordan.
SCRATCH: A Scalable Discrete Matrix Factorization Hashing Framework for Cross-Modal Retrieval

In this paper, we present a novel supervised cross-modal hashing framework, namely Scalable disCRete mATrix faCtorization Hashing (SCRATCH). First, it utilizes collective matrix factorization on original features together with label semantic embedding, to learn the latent representations in a shared latent space. Thereafter, it generates binary hash codes based on the latent representations. During optimization, it avoids using a large n × n similarity matrix and generates hash codes discretely. Besides, based on different objective functions, learning strategy, and features, we further present three models in this framework, i.e., SCRATCH-o, SCRATCH-t, and SCRATCH-d. The first one is a one-step method, learning the hash functions and the binary codes in the same optimization problem. The second is a two-step method, which first generates the binary codes and then learns the hash functions based on the learned hash codes. The third one is a deep version of SCRATCH-t, which utilizes deep neural networks as hash functions. The extensive experiments on two widely used benchmark datasets demonstrate that SCRATCH-o and SCRATCH-t outperform some state-of-the-art shallow hashing methods for cross-modal retrieval. The SCRATCH-d also outperforms some state-of-the-art deep hashing models.
Group-Wise Learning for Weakly Supervised Semantic Segmentation

Acquiring sufficient ground-truth supervision to train deep visual models has been a bottleneck over the years due to the data-hungry nature of deep learning. This is exacerbated in some structured prediction tasks, such as semantic segmentation, which require pixel-level annotations. This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation. To achieve this, we propose, for the first time, a novel group-wise learning framework for WSSS. The framework explicitly encodes semantic dependencies in a group of images to discover rich semantic context for estimating more reliable pseudo ground-truths, which are subsequently employed to train more effective segmentation models. In particular, we solve the group-wise learning within a graph neural network (GNN), wherein input images are represented as graph nodes, and the underlying relations between a pair of images are characterized by graph edges. We then formulate semantic mining as an iterative reasoning process which propagates the common semantics shared by a group of images to enrich node representations. Moreover, in order to prevent the model from paying excessive attention to common semantics, we further propose a graph dropout layer to encourage the graph model to capture more accurate and complete object responses. With the above efforts, our model lays the foundation for more sophisticated and flexible group-wise semantic mining. We conduct comprehensive experiments on the popular PASCAL VOC 2012 and COCO benchmarks, and our model yields state-of-the-art performance. In addition, our model shows promising performance in weakly supervised object localization (WSOL) on the CUB-200-2011 dataset, demonstrating strong generalizability. Our code is available at: https://github.com/Lixy1997/Group-WSSS.
Control Synthesis of Hidden Semi-Markov Uncertain Fuzzy Systems via Observations of Hidden Modes

This paper investigates the stability analysis and fuzzy control problems for a class of discrete-time fuzzy systems with hidden semi-Markov stochastic uncertainties. The nonlinear plant is described via the Takagi-Sugeno (T-S) fuzzy model, and the parameter uncertainties are represented by a hidden semi-Markov chain. Owing to the semi-Markov kernel (SMK), the probability density functions (PDFs) of sojourn time for different modes in describing the stochastic uncertainties can address different types of distributions according to different target modes. A novel Lyapunov function that depends on the hidden mode and the observed mode with the elapsed time is proposed to analyze the stability and the H∞ performance of the fuzzy system. Then, the sufficient criteria for the elapsed-time-dependent and observed-mode-dependent fuzzy controller are achieved by exploiting the observations of hidden modes, ensuring that the closed-loop system is σ -error mean square stable with guaranteed H∞ performance. A cart-pendulum system is used to demonstrate the effectiveness and applicability of the proposed theoretical results.
Two-Shot Spatially-Varying BRDF and Shape Estimation

Capturing the shape and spatially-varying appearance (SVBRDF) of an object from images is a challenging task that has applications in both computer vision and graphics. Traditional optimization-based approaches often need a large number of images taken from multiple views in a controlled environment. Newer deep learning-based approaches require only a few input images, but the reconstruction quality is not on par with optimization techniques. We propose a novel deep learning architecture with a stage-wise estimation of shape and SVBRDF. The previous predictions guide each estimation, and a joint refinement network later refines both SVBRDF and shape. We follow a practical mobile image capture setting and use unaligned two-shot flash and no-flash images as input. Both our two-shot image capture and network inference can run on mobile hardware. We also create a large-scale synthetic training dataset with domain-randomized geometry and realistic materials. Extensive experiments on both synthetic and real-world datasets show that our network trained on a synthetic dataset can generalize well to real-world images. Comparisons with recent approaches demonstrate the superior performance of the proposed approach.
Variance-based differential evolution algorithm with an optional crossover for data clustering

The differential evolution optimization-based clustering techniques are powerful, robust and more sophisticated than the conventional clustering methods due to their stochastic and heuristic characteristics. Unfortunately, these algorithms suffer from several drawbacks such as the tendency to be trapped or stagnated into local optima and slow convergence rates. These drawbacks are consequences of the difficulty in balancing the exploitation and exploration processes which directly affects the final quality of the clustering solutions. Hence, a variance-based differential evolution algorithm with an optional crossover for data clustering is presented in this paper to further enhance the quality of the clustering solutions along with the convergence speed. The proposed algorithm considers the balance between the exploitation and exploration processes by introducing (i) a single-based solution representation, (ii) a switchable mutation scheme, (iii) a vector-based estimation of the mutation factor, and (iv) an optional crossover strategy. The performance of the proposed algorithm is compared with current state-of-the-art differential evolution-based clustering techniques on 15 benchmark datasets from the UCI repository. The experimental results are also thoroughly evaluated and verified via non-parametric statistical analysis. Based on the obtained experimental results, the proposed algorithm achieves an average enhancement up to 11.98% of classification accuracy and obtains a significant improvement in terms of cluster compactness over the competing algorithms. Moreover, the proposed algorithm outperforms its peers in terms of the convergence speed and provides repeatable clustering results over 50 independent runs.
Problems with Shapley-value-based explanations as feature importance measures

Game-theoretic formulations of feature importance have become popular as a way to explain machine learning models. These methods define a cooperative game between the features of a model and distribute influence among these input elements using some form of the game's unique Shapley values. Justification for these methods rests on two pillars: their desirable mathematical properties, and their applicability to specific motivations for explanations. We show that mathematical problems arise when Shapley values are used for feature importance and that the solutions to mitigate these necessarily induce further complexity, such as the need for causal reasoning. We also draw on additional literature to argue that Shapley values do not provide explanations which suit human-centric goals of explainability.
Microlearning: A New Learning Model

Changes in the current workplace are creating changes in traditional processes. One such process is the way that the millennial and subsequent generations joining the workplace consume learning. The hospitality industry is an ever-changing service industry in which organizations need to continue to adapt to stay relevant. Trained employees providing excellent guest experiences are a competitive advantage. Some barriers to training include lack of time, lack of resources, and changing learning expectations. The aim of this article was to propose a new learning model that integrates performance workflow and microlearning. Microlearning is an approach that focuses on a single concept, utilizing multisensory and multimodality in a focused short amount of time. It can be easily incorporated to the job workflow. The model, future research, and implications are discussed.
KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment

Blind super-resolution (SR) methods aim to generate a high quality high resolution image from a low resolution image containing unknown degradations. However, natural images contain various types and amounts of blur: some may be due to the inherent degradation characteristics of the camera, but some may even be intentional, for aesthetic purposes (e.g. Bokeh effect). In the case of the latter, it becomes highly difficult for SR methods to disentangle the blur to remove, and that to leave as is. In this paper, we propose a novel blind SR framework based on kernel-oriented adaptive local adjustment (KOALA) of SR features, called KOALAnet, which jointly learns spatially-variant degradation and restoration kernels in order to adapt to the spatially-variant blur characteristics in real images. Our KOALAnet outperforms recent blind SR methods for synthesized LR images obtained with randomized degradations, and we further show that the proposed KOALAnet produces the most natural results for artistic photographs with intentional blur, which are not over-sharpened, by effectively handling images mixed with in-focus and out-of-focus areas.
A Blockchain-Based Mutual Authentication Scheme for Collaborative Edge Computing

With the ever-increasing requirements of delay-sensitive and mission-critical applications, it becomes a popular research trend to incorporate edge computing in the Internet of Things (IoT) to mitigate the pressure of traditional cloud-based IoT architecture. Edge computing delivers real-time computations and communications for IoT devices by leveraging edge servers deployed close to users, which creates a collaborative edge computing (CEC) paradigm. The capacity of edge servers is beneficial but risky, as vulnerable servers can be exploited to conduct surveillance or perform other nefarious activities. Besides, fake IoT devices would bring security threats and compromise the IoT system. This highlights the necessity of designing a secure and efficient mutual authentication scheme for CEC. In this direction, related works have proposed various authentication mechanisms, but most of them are found unfit due to the absence of decentralization, anonymity, and mobility. Motivated by this fact, we propose a blockchain-based mutual authentication scheme that bridges these gaps. Specifically, blockchain, certificateless cryptography, elliptic curve cryptography, and pseudonym-based cryptography are integrated into our scheme to provide mutual authentication between edge servers and IoT devices. Except for static conditions, both intraedge and interedge authentication are considered. Besides, we elaborate on the key generation procedures and design a session key negotiation mechanism. Extensive experiments and security analyses have been conducted to show the feasibility of the proposed scheme.
A Critical Historical and Scientific Overview of all Industrial Revolutions

Over the course of history, mankind has perfected its industry by not only relying on technical evolution but also by reinventing it as new resources have created new technical means. TECIS Conferences have been advocating and rightfully so, that control, automation theories and practices can contribute in making the world a more stable place for everyone. Industry has benefited from qualitative advancements which have sometimes been so ingrained in a certain time period and have had such an overwhelming impact that we have dubbed them “revolutions”. Industrialization changed the economy, transportation, health and medicine and led to many inventions and firsts in history. The first four industrial revolutions as historically been developed are review from a critical point of view. The main technological development for each one is briefly discussed. Their social impacts are highlighted. The numbering of the industrial revolutions has brought new controvercies into the world. The last 10 years many difficult problems have come upon the world. The COVID-19 pandemic has created a new difficult situation for the whole planet. The TECIS vision is of a civilization in which our technologies, systems and processes are in service to all humanity. This is called "human-machine symbiosis" and is a vision of the application of control and automation technologies and systems which benefit all life. A new sustainable economic model for the meta COVID-19 period is needed. A new concept for the humanized revolution is proposed, the INDUSTRY 6.0.
Imbalanced learning based on adaptive weighting and Gaussian function synthesizing with an application on Android malware detection

The existence of imbalanced classes can considerably degrade the performance of most standard learning algorithms. This paper presents a new imbalanced learning method called AWGSENN to address this problem on data level. In AWGSENN, each minority instance is firstly weighted based on the number of majority class neighbors and the distance of the minority instance to its neighbors. Then, a Gaussian distribution probability density function is designed to generate new instances nonlinearly. Finally, the edited nearest neighbor rule is used as a data cleaning technique to remove overlapping and noisy instances. The proposed method is evaluated in extensive experiments by comparing it with five over-sampling and two hybrid sampling methods on 37 data sets from the KEEL data repository. Empirical study results show that our approach can achieve significant performance improvement on G-mean and the area under curve metrics. Wilcoxon signed-rank test results show that our approach is superior to other resampling approaches. We apply the proposed method for Android malware detection, and the results further demonstrate the promising performance of our approach.
Offline Persian Handwriting Recognition with CNN and RNN-CTC

Offline Persian handwriting recognition is a challenging task due to the cursive nature of the Persian scripts and similarity among the Persian alphabet letters. This paper presents a Persian handwritten word recognizer based on a sequence labeling method with deep convolutional neural networks (CNN) and recurrent neural networks (RNN). In addition, a connectionist temporal classification (CTC) loss function is utilized in order to eliminate the segmentation step required in conventional methods. The CNN layers are employed to extract the sequence of features from the word image. Altogether, the RNN layer with CTC function is used for labeling the input sequence. We showed that this combination is a robust recognizer for the Persian language as it was for other fields of application such as scene text recognition. The method is tested on the popular Persian and Arabic datasets including IFN/ENIT. It also is compared with novel methods and promising results have been obtained particularly in comparison with the conventional approaches including HMM and other machine learning-based methods.
Application improvement of A* algorithm in intelligent vehicle trajectory planning

Trajectory planning is one of the key technologies for autonomous driving. A* algorithm is a classical trajectory planning algorithm that has good results in the field of robot path planning. However, there are still some practical problems to be solved when the algorithm is applied to vehicles, such as the algorithm fails to consider the vehicle contours, the planned path is not smooth, and it lacks speed planning. In order to solve these problems, this paper proposes a path processing method and a path tracking method for the A* algorithm. First, the method of configuring safe redundancy space is given considering the vehicle contour, then, the path is generated based on A* algorithm and smoothed using Bessel curve, and the speed is planned based on the curvature of the path. The trajectory tracking algorithm in this paper is based on an expert system and pure tracking theory. In terms of speed tracking, an expert system for the acceleration characteristics of the vehicle is constructed and used as a priori information for speed control, and good results are obtained. In terms of path tracking, the required steering wheel angle is calculated based on pure tracking theory, and the influence factor of speed on steering is obtained from test data, based on which the steering wheel angle is corrected and the accuracy of path tracking is improved. In addition, this paper proposes a target point selection method for the pure tracking algorithm to improve the stability of vehicle directional control. Finally, a simulation analysis of the proposed method is performed. The results show that the method can improve the applicability of the A* algorithm in automated vehicle planning.