Reviewerly/exHarmony huggingface dataset:
Knowledge graph enhanced multi-task learning between reviews and ratings for movie recommendation

In many current recommender systems, online reviews are used to boost the recommendation performance. As historical ratings and reviews are the two main instances of user feedback, and the combination of these two is very important to understand why a user likes or dislikes an item. A general way of learning the correlation between reviews and ratings is to learn their distributions in the same latent semantic space, where each dimension is a set of item features. However, because there are only a few words related to items in reviews, this method may introduce noise and output misunderstandings. In addition, it is difficult to align heterogeneous data using general ways. In this paper, we introduce a knowledge graph enhanced multi-task learning method to learn cross features from ratings and reviews by fusing users and movies with their review knowledge entities in the same graph. The fusion graph structure enables the graph-link prediction task to learn the review entity features that are relevant to target users and movies, which further assists our recommendation task. Experiments on 18 different sparse datasets that were preprocessed from two public datasets demonstrate the effectiveness of our model. Our method also achieves good performance when user-movie interactions are sparse.
Efficient Network Architecture Search Using Hybrid Optimizer

Manually designing a convolutional neural network (CNN) is an important deep learning method for solving the problem of image classification. However, most of the existing CNN structure designs consume a significant amount of time and computing resources. Over the years, the demand for neural architecture search (NAS) methods has been on the rise. Therefore, we propose a novel deep architecture generation model based on Aquila optimization (AO) and a genetic algorithm (GA). The main contributions of this paper are as follows: Firstly, a new encoding strategy representing the CNN coding structure is proposed, so that the evolutionary computing algorithm can be combined with CNN. Secondly, a new mechanism for updating location is proposed, which incorporates three typical operators from GA cleverly into the model we have designed so that the model can find the optimal solution in the limited search space. Thirdly, the proposed method can deal with the variable-length CNN structure by adding skip connections. Fourthly, combining traditional CNN layers and residual blocks and introducing a grouping strategy provides greater possibilities for searching for the optimal CNN structure. Additionally, we use two notable datasets, consisting of the MNIST and CIFAR-10 datasets for model evaluation. The experimental results show that our proposed model has good results in terms of search accuracy and time.
An Evaluation Method of English Teaching Ability Based on Deep Learning

The traditional English teaching ability assessment algorithm has some problems, such as low accuracy and poor effect. Therefore, this paper proposes an English teaching ability assessment algorithm based on deep learning. According to the proposed method, it analyzes the characteristics of the input layer, convolution layer, pooling layer, and fully connected layer in the deep learning technology. And then, it also help to determine the dimensions of the convolution kernel matrix and derive the weight parameters through back propagation. Finally, it introduces the open-minded computing for deep learning assessment of English teaching ability, calculate the best standard data and best vector data ratio, and establish a deep learning model to realize English teaching ability assessment. The experimental results show that this method can improve the accuracy of English teaching ability assessment.
Sketch-Based Modeling in Mechanical Engineering Design: Current Status and Opportunities

Sketch-Based Modeling (SBM) is a field of study that focuses on the automatic creation of 3D models from freehand drawings. Today, two related branches coexist: one aimed at facilitating input for 3D content creation, and the other aimed at routing engineering designs into CAD/CAM/CAE. The latter is the goal of this position paper. Early attempts concentrated on the problem of line-drawing interpretation, but efforts switched toward geometric reconstruction as the “inverse projection” problem became the most challenging step to produce 3D models from 2D line-drawings. The term SBM was popularized when sketches began to displace line-drawings as main input. In the context of engineering design, interest in SBM has somewhat decreased, as studies have shown that current SBM tools are not as usable as paper and pencil sketches, nor do they yet provide any additional value to traditional media. Furthermore, engineers feel reasonably comfortable with current Mechanical CAD (MCAD) paradigms based on parametric modeling, and fail to recognize the potential benefits of interacting with computers via sketches. However, new technological trends such as personal fabrication and the democratization of CAD and manufacturing can significantly benefit from improved SBM tools. In this paper, we conduct a meta-review of the SBM literature which we view as a combination of three elements: geometry (shape), psychology (perception), and engineering (function). We advocate for a new approach to SBM based on reformulating the weights of these elements as an approach for searching the set of intentions in sketches conveyed through cues which, when perceived, reveal regularities and features of the object. Finally, we consider quality of CAD models not just as error-free models, reusable models, or even models that convey design intent, but models that consider these interrelated aspects as a whole. B-Rep models produced by current SBM approaches are “dumb models” without parametrization or procedures required to enable reusability and ensure that the design intent of the sketch is properly conveyed. We advocate for improved analysis approaches aimed at revealing higher-level design information embedded in engineering sketches, as a critical stage to generate richer 3D MCAD models.
ATPL: Mutually enhanced adversarial training and pseudo labeling for unsupervised domain adaptation

Unsupervised domain adaptation aims to transfer knowledge from a labeled source domain to a related but unlabeled target domain. Most existing approaches either adversarially reduce the domain shift or use pseudo-labels to provide category information during adaptation. However, an adversarial training method may sacrifice the discriminability of the target data, since no category information is available. Moreover, a pseudo labeling method is difficult to produce high-confidence samples, since the classifier is often source-trained and there exists the domain discrepancy. Thus, it may have a negative influence on learning target representations. A potential solution is to make them compensate each other to simultaneously guarantee the feature transferability and discriminability, which are the two key criteria of feature representations in domain adaptation. In this paper, we propose a novel method named ATPL, which mutually promotes Adversarial Training and Pseudo Labeling for unsupervised domain adaptation. ATPL can produce high-confidence pseudo-labels by adversarial training. Accordingly, ATPL will use the pseudo-labeled information to improve the adversarial training process, which can guarantee the feature transferability by generating adversarial data to fill in the domain gap. Those pseudo-labels can also boost the feature discriminability. Extensive experiments on real datasets demonstrate that the proposed ATPL method outperforms state-of-the-art unsupervised domain adaptation methods.
Load balancing in cloud environment using enhanced migration and adjustment operator based monarch butterfly optimization

In the decades before the advent of computers, humans tend to make mistakes while calculating and remembering tasks. Distributed computing helped to reduce the workload of each computer by distributing the workload evenly among computers connected in the network. Cloud computing have eradicated most of the problems that occurred in distributed computing but were also prone to different types of issues. Major issues in cloud computing relate to security and load balancing. Load balance of a node relates to two important parameters namely request time and response time. Meta heuristics algorithms can be used to provide proper load balancing techniques in cloud. This paper provides a mechanism namely EMAMBO to ensure that each node is properly load-balanced in cloud. Based on different metrics considered, it could be inferred that the proposed system fares better when compared to different benchmarked existing systems.
Systems View to Designing RF Fingerprinting for Real-World Operations

Great progress has been made recently in radio frequency (RF) machine learning (ML), including RF fingerprinting. Much of this work to date, however, has been limited in scope to proof-of-concept demonstrations or narrowly defined and tested under circumstances that only address part of the actual operational considerations. In this paper we expand this consideration for an RF fingerprinting application. In doing so, we build on our previous work developing our RiftNet#8482; deep learning classifier to consider realistic operational and systems aspects to ensure the solution is robust to real-world signal environments and other tasks of an RF receiver. In particular, we show new results on how to handle difficult cases of signal interference, how to efficiently improve performance with the use of geolocation information, and ways to further reduce computational constraints for edge operation. In summary, this work presents moving beyond initial success in RF ML towards expanding the solution for eventual edge operation in a larger system.
Gray wolf optimizer with bubble-net predation for modeling fluidized catalytic cracking unit main fractionator

Fluidized catalytic cracking unit (FCCU) main fractionator is a complex system with multivariable, nonlinear and uncertainty. Its modeling is a hard nut to crack. Ordinary modeling methods are difficult to estimate its dynamic characteristics accurately. In this work, the gray wolf optimizer with bubble-net predation (GWO_BP) is proposed for solving this complex optimization problem. GWO_BP can effectively balance the detectability and exploitability to find the optimal value faster, and improve the accuracy. The head wolf has the best fitness value in GWO. GWO_BP uses the spiral bubble predation method of whale to replace the surrounding hunting scheme of the head wolf, which enhances the global search ability and speeds up the convergence speed. And Lévy flight is applied to improve the wolf search strategy to update the positions of wolfpack for overcoming the disadvantage of easily falling into local optimum. The experiments of the basic GWO, the particle swarm optimization (PSO) and the GWO_BP are carried out with 12 typical test functions. The experimental results show that GWO_BP has the best optimization accuracy. Then, the GWO_BP is used to solve the parameter estimation problem of FCCU main fractionator model. The simulation results show that the FCCU main fractionator model established by the proposed modeling method can accurately reflect the dynamic characteristics of the real world.
What Can a Robot’s Skin Be? Designing Texture-changing Skin for Human–Robot Social Interaction

Biological skin has numerous functions like protection, sensing, expression, and regulation. On the contrary, a robot’s skin is usually regarded as a passive and static separation between the body and environment. In this article, we explore the design opportunities of a robot’s skin as a socially expressive medium. Inspired by living organisms, we discuss the roles of interactive robotic skin from four perspectives: expression, perception, regulation, and mechanical action. We focus on the expressive function of skin to sketch design concepts and present a flexible technical method for embodiment. The proposed method integrates pneumatically actuated dynamic textures on soft skin, with forms and kinematic patterns generating a variety of visual and haptic expressions. We demonstrate the proposed design space with six texture-changing skin prototypes and discuss their expressive capacities.
Towards Blockchain Based Architecture for Building Information Modelling (BIM)

Building Management Systems (BMS) is a set of software and hardware tools that enables the monitoring and the control of building&#x0027;s mechanical and electrical equipment. These systems take advantage of Internet of Things (IoT) and Information Technology (IT) domains to efficiently enhance the buildings management. Blockchain technology is a new emerging technology that has attracted many researchers this last years. Basically, it has many applications in the field of cryptocur-rency, finance and insurances fields. However, these last years, blockchain has known other applications beyond cryptocurrency domain, such as the IoT. In this paper, we shed the light on one specific application of blockchain technology to assist the design of efficient BMS. We propose an hierarchical architecture composed of IoT, fog and cloud layers where the blockchain is integrated on the different layers to enhance the security of the whole BMS systems. We show the main advantages of applying blockchain technology in BMS systems and how our architecture outperforms the existing architectures in terms of low latency, security and privacy.
On the Use of Morpho-Syntactic Description Tags in Neural Machine Translation with Small and Large Training Corpora

With the transition to neural architectures, machine translation achieves very good quality for several resource-rich languages. However, the results are still much worse for languages with complex morphology, especially if they are low-resource languages. This paper reports the results of a systematic analysis of adding morphological information into neural machine translation system training. Translation systems presented and compared in this research exploit morphological information from corpora in different formats. Some formats join semantic and grammatical information and others separate these two types of information. Semantic information is modeled using lemmas and grammatical information using Morpho-Syntactic Description (MSD) tags. Experiments were performed on corpora of different sizes for the English–Slovene language pair. The conclusions were drawn for a domain-specific translation system and for a translation system for the general domain. With MSD tags, we improved the performance by up to 1.40 and 1.68 BLEU points in the two translation directions. We found that systems with training corpora in different formats improve the performance differently depending on the translation direction and corpora size.
BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image Matching

In stereoscope-based minimally invasive surgeries (MISs), dense stereo matching plays an indispensable role in 3-D shape recovery, AR, VR, and navigation tasks. Although numerous deep neural network (DNN) approaches are proposed, the conventional prior-free approaches are still popular in the industry because of the lack of open-source annotated dataset and the limitation of the task-specific pretrained DNNs. Among the prior-free stereo matching algorithms, there is no successful real-time algorithm in none GPU environment for MIS. This article proposes the first CPU-level real-time prior-free stereo matching algorithm for general MIS tasks. We achieve an average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$14-17$</tex-math></inline-formula> Hz on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$640 \times 480$</tex-math></inline-formula> images with a single-core CPU (i5-9400) for surgical images. Meanwhile, it achieves slightly better accuracy than the popular efficient large-scale stereo matching (ELAS) method. The patch-based fast disparity searching algorithm is adopted for the rectified stereo images. A coarse-to-fine Bayesian probability and a spatial Gaussian mixed model were proposed to evaluate the patch probability at different scales. An optional probability density function estimation algorithm was adopted to quantify the prediction variance. Extensive experiments demonstrated the proposed method's capability to handle ambiguities introduced by the textureless surfaces and the photometric inconsistency from the non-Lambertian reflectance and dark illumination. The estimated probability managed to balance the confidences of the patches for stereo images at different scales. It has similar or higher accuracy and fewer outliers than the baseline ELAS in MIS, while it is 4–5 times faster.
Modelling of Sign Language Smart Glove Based on Bit Equivalent Implementation Using Flex Sensor

Breaks down boundaries and inspires creativity. These are just a few examples of how communication affects the world around us. Many successful people use this tremendous tool of communication to change the world, while others with disabilities in speech, hearing, or having multiple disabilities use sign language to change the world. However, the disadvantage of employing sign language is that not everyone would be able to understand it. To address this issue, a sensor-based system is proposed with five flex sensors put on five fingers as binary bits, yielding a total of 32 options in this study. In addition, this proposal includes two options for broadening the reach of terms. This proposed model eliminates the need for other types of sensors besides flex sensors, lowering production costs significantly. It is more accurate and efficient than vision-based approaches because it is sensor-based. Because of this project, people who do not understand sign language can communicate easily with people who have speech or hearing problems.
Salient object detection with image-level binary supervision

Recent deep learning based salient object detection (SOD) methods have achieved impressive performance. However, while fully-supervised methods require a large amount of labeled data, weakly-supervised methods still require a considerable human effort. To address this problem, we propose a novel weakly-supervised method for salient object detection based on only binary image tags, which are much cheaper to collect. Our basic idea is to construct a dataset of images that are labeled as either salient (with salient objects) or non-salient (without salient objects), and leverage such binary labels as supervision to learn a salient object detector based on existing unsupervised methods. In particular, we propose a target saliency map hallucinator, which can synthesize pseudo ground truth saliency maps for the salient images in the training data solely from binary labels. We can then use the pseudo ground truth labels to train a salient object detector. Experimental results show that our method performs comparably to the state-of-the-art weakly-supervised methods, but requires considerably less human supervision.
Information Technology for Person Identification by Occluded Face Image

This paper describes the research of information technology for person identification by occluded face image. As far as the spread of coronavirus disease (COVID-19) raised the problem of identification by facial image with masks covering the face as a prevention measure, the research of face recognition and identification technologies has become crucial for all of the cybersecurity areas based on the identity verification by digital technologies. The proposed algorithm, that is the cornerstone of the information technology, is based on the methods of anisotropic diffusion for image preprocessing, Gabor wavelet transform, histogram of oriented gradients (HOG) and local binary patterns in 1-dimensional space (1DLBP) for image feature vector extraction, and square Euclidean distance metric for vector classification. Experiments on the proposed technology after applying it on the occluded images from the SCface database provided the result of 85%, increased on 2.5% after image format and resolution conversion.
The effectiveness of AI salesperson vs. human salesperson across the buyer-seller relationship stages

Given the infusion of artificial intelligence (AI) in B2B sales, AI salespeople perform required sales tasks more effectively than human salespeople in some contexts but not in others. To gain insights about the contexts in which the relative effectiveness of AI over human salespeople varies, this conceptual paper develops a framework which systematically organizes contingency factors. Further, by combining the relationship lifecycle theory with AI job replacement theory, this paper provides well-grounded propositions about when AI or human salespeople perform better for buyers in distinct relationship stages with the seller. The paper concludes with implications and future research directions on the adoption of AI in B2B sales. In doing so, this paper provides a solid foundation which future researchers can build upon and enriches the discussion about the contingency factors, the changing roles of human salespeople, and how to structure sales organization with AI and human salespeople.
CoViMask: A Novel Face Mask Type Detector Using Convolutional Neural Networks

The COVID-19 pandemic has led to many lifestyle changes, one of them being the mandatory use of face masks in public settings. Given the importance of masks, there are various types for people to use, such as cloth and N95. A proper mask must be used to protect oneself and others from the spread of the coronavirus. This paper proposes CoViMask, a face mask type detector that detects the type of mask that a person is wearing, and is trained using a custom-made dataset. Accuracy, precision and recall are used to evaluate the proposed method. The paper also mentions the application areas. The results obtained prove that CoViMask is efficient in mask type detection and may aid in controlling the spread of covid.
Forecasting the Energy Consumption of Cloud Data Centers Based on Container Placement with Ant Colony Optimization and Bin Packing

Currently, the energy consumption in the data centers is a major problem due to the increase in the number of servers and services. Physical machines represent huge energy consumers who work continuously to ensure quality in services through virtual instances. The placement of these instances as containers in hosts has an impact on the minimization of energy consumption. Precisely, the guarantee of an efficient placement without wasting material resources of the hosts, makes it possible to minimize the number of active hosts and thus reduce energy consumption. In this article, we will calculate the energy consumed by a data center using CloudSim before and after applying the placement of containers in the hosts by the First Fit Decreasing algorithm (FFD) of Bin Packing and Ant Colony Optimization (ACO). The results show the FFD&#x0027;s superiority over the ACO in minimizing the energy consumed by any type of Cloud system.