Reviewerly/exHarmony huggingface dataset:
RETRACTED ARTICLE: Tensor based stacked fuzzy neural network for efficient data regression

Random vector functional link and extreme learning machine have been extended by the type-2 fuzzy sets with vector stacked methods, this extension leads to a new way to use tensor to construct learning structure for the type-2 fuzzy sets-based learning framework. In this paper, type-2 fuzzy sets-based random vector functional link, type-2 fuzzy sets-based extreme learning machine and Tikhonov-regularized extreme learning machine are fused into one network, a tensor way of stacking data is used to incorporate the nonlinear mappings when using type-2 fuzzy sets. In this way, the network could learn the sub-structure by three sub-structures’ algorithms, which are merged into one tensor structure via the type-2 fuzzy mapping results. To the stacked single fuzzy neural network, the consequent part parameters learning is implemented by unfolding tensor-based matrix regression. The newly proposed stacked single fuzzy neural network shows a new way to design the hybrid fuzzy neural network with the higher order fuzzy sets and higher order data structure. The effective of the proposed stacked single fuzzy neural network are verified by the classical testing benchmarks and several statistical testing methods.
CU-Net: LiDAR Depth-Only Completion With Coupled U-Net

LiDAR depth-only completion is a challenging task to estimate dense depth maps only from sparse measurement points obtained by LiDAR. Even though the depth-only methods have been widely developed, there is still a significant performance gap with the RGB-guided methods that utilize extra color images. We find that existing depth-only methods can obtain satisfactory results in the areas where the measurement points are almost accurate and evenly distributed (denoted as normal areas), while the performance is limited in the areas where the foreground and background points are overlapped due to occlusion (denoted as overlap areas) and the areas where there are no measurement points around (denoted as blank areas) since the methods have no reliable input information in these areas. Building upon these observations, we propose an effective Coupled U-Net (CU-Net) architecture for depth-only completion. Instead of directly using a large network for regression, we employ the local U-Net to estimate accurate values in the normal areas and provide the global U-Net with reliable initial values in the overlap and blank areas. The depth maps predicted by the two coupled U-Nets are fused by learned confidence maps to obtain final results. In addition, we propose a confidence-based outlier removal module, which removes outliers using simple judgment conditions. Our proposed method boosts the final results with fewer parameters and achieves state-of-the-art results on the KITTI benchmark. Moreover, it owns a powerful generalization ability under various depth densities, varying lighting, and weather conditions.
High Efficient 3D Convolution Feature Compression

In this paper, a high efficient 3D convolution feature compression method is proposed. This method is mainly used to compress the three-dimensional convolution deep features extracted by video analysis. Gather the compressed features to the cloud server instead of all video features. This method can solve the problem that the data aggregation of video big data analysis requires a large amount of network bandwidth. Quantization is a common method for feature compression, but the existing quantization-based methods often carry out model training and quantization in stages, which makes the robustness of quantization results poor. To solve this problem, the method proposed in this paper is to apply the feature quantization operation directly to the network and train it with the analysis task, and use the parameter iterative optimization method to solve the non-differentiable problem of quantization operation. Different from the deep features extracted from images or single objects, the 3D convolution features extracted from video clips have high time-domain redundancy. In this paper, by serializing the three-dimensional convolution features, the time-domain prediction coding method is used to remove the time-domain redundancy of the three-dimensional convolution features, to improve the feature compression ratio. The experimental results show that this method can only use 1 bit to represent the elements in the three-dimensional convolution deep feature. When the analysis accuracy loss is no more than 1%, the feature compression ratio can reach 4500 times compared with the original feature data, and the data transmission can be reduced by 96%.
An Improved Technique to Identify Fake News on Social Media Network using Supervised Machine Learning Concepts

This paper presents an improved social media news separation system called unstructured Fake News Detection (UFND) system and it aims to identify the unstructured social media news data that belongs into fake or real class based on probability and improved naive bayes techniques. The proposed UFND consists of four phases likely pre-processing, training, matching and validation respectively. The technique identifies matching phrases over each individual data element based on predetermined key words model and ignore the irrelevant words in the respective document. In the later phase, the proposed system has train the pre-processed data set through the process of separating the data set into two classes namely fake and real based on probability technique. Further the system has identified the given test news document belongs to existing class label over the training data set based on improved Naive Bayes technique. The UFND system evaluates the performance over the result of previous stage. The UFNDS experimental results have demonstrated an outperforming results in segregating and identifying the fake news pattern over the unstructured social media news data set with good accuracy based on supervised probability methods.
Health Care Chatbot using Natural Language Processing with SGD and ADAM Optimizer Parameter Optimization

In today’s world, everyone is not quite sure about the medicine that the users used in a similar situation or critical situation where any medical emergency has come and as all know that the ratio of patients and doctors are very high so, there is a requirement of such kind of applications to help in case of emergency. This paper proposed a novel approach for medical needs, as well as the suggested chatbot that will be useful in the pandemic circumstances. Natural Language Processing (NLP) based applications are proposed to provide help to the patient. In some situations, the patient home member just used it to type their query and if the patient situation is not so serious, so they get proper medicinal information from this application. The proposed methodology takes an input sentence then its tokenization, removal of stop words, feature extraction, and word corpus are used to find the sentence similarity, and the chatbot predicts the accurate sentence. In this work, the Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (ADAM) optimizer optimized parameter values are determined with 86 and 93 percent accuracy respectively. The optimized Lr_value 0.0099 and Decay value 1e-10 for SGD and optimized Learning_rate 0.0099 for ADAM are obtained.
Encoder-decoder based multi-label emoji prediction for Code-Mixed Language (Hindi+English)

Emojis enjoy an important place in digital communication. They can express feelings and emotions in contexts when words cannot. In other words, they add emotions to a piece of text. Emojis are rising concurrently with the increased use of social media platforms for communication and have become a language in itself. Single emoji prediction systems are no longer adequate because multiple emojis are being grouped to convey emotions these days. The multi-label emoji prediction system for code-mixed language has not yet been explored to the best of our knowledge. It explores multi-label emoji prediction in Hinglish, one of the most commonly used code-mixed languages. This paper presents a framework for Hinglish multi-label emoji prediction. The proposed Encoder-decoder based Emoji Prediction model for Hinglish (EDEPHi) model outperforms other baseline models and is far more diverse in terms of predicted emojis.
Exploration of UAV Rope Handling and Flight in Narrow Space Strategies, using a Three-arm Aerial Manipulator System

Aerial robots are well suited for a variety of tasks. Especially in remote locations they can make use of their fast speed, independence of the ground condition and stable hovering capabilities. In uncontrolled environments, such as disaster sites, robots require flexibility to cope with a variety of challenging work environments. This work serves as the continuation of our research on a three-arm aerial manipulator system, developed in our lab. In this work, the manipulator system is used to show the realization of advanced aerial tasks. A method for rope fastening and removal tasks which can be completed by using the aerial robot, as well as a method for flying through narrow spaces using the robotic arms as tools to prevent accidental impact is proposed. Grippers suitable to conduct these tasks are developed and the concept of the proposed tasks is verified through several experiments.
Blended selection in Ant Colony Optimization for solving Travelling Salesman Problem

TSP is one of the most well-known combinatorial optimization problems. Ant Colony optimization is highly recommended to solve discrete optimization problems whereas the selection strategy plays a crucial role in the performance of ACO while solving Travelling Salesman Problem (TSP). There are many selection strategies in ACO to solve TSP, such as roulette wheel selection, ranking selection and annealing selection etc. In ACO, the roulette wheel selection is primarily concerned with exploitation, whereas rank selection is influenced by exploration. Therefore, in this paper, a blend of both roulette wheel and ranking selection is proposed as a new selection strategy in ACO. The proposed selection method is tested over 12 standard TSP instances collected from TSP library TSPLIB. The best results obtained from the above mentioned selection method has been recorded and compared with other three selection methods. The experimental results show that the proposed selection method outperformed with other considered selection methods.
Self-Augmentation and Consumer Experiences: An Exploratory Study: An Abstract

Today, digital content is dominated by interactive and immersive technologies (Mckinesy 2019). Brands are rapidly adopting technologies such as Augmented Reality (AR) for virtual product experiences (Pantano 2015). AR refers to the superimposing of the digital content on consumers' real-world contexts, thereby bridging the gap between physical and virtual purchase situations (Mealy 2018). Two popular AR-based applications are Ikea place (furniture) and Youcam Makeup (cosmetics). At present, AR is on a growth trajectory, as immersive technology growth (pre-COVID-19) was pegged to exceed $55 billion by the year 2021(Research and Markets 2019). Existing research on AR focuses on user experiences, customer engagement, brand attitudes, and purchase intentions (Hilken et al. 2017; Kim and Hall 2019; Park and Yoo 2020; Scholz and Smith 2016). As technology increasingly intertwines with consumers' lives and decisions, it is essential to continue expanding the research into the lived experience of AR-mediated shopping (Chylinski et al. 2020). Hence this paper focuses on the self-augmentation experiences through the virtual product try-on apps. The virtual try-on apps let consumers try out virtual replicas of actual products (cosmetics, apparel, jewelry, etc.) on their real-time images, captured via tablet or smartphone-based selfie cameras. The context of the study is AR virtual try-on apps in the cosmetics industry, owing to their popularity and adoption by consumers. Based on the concepts of extended self, consumption experiences, self-referencing theory, and mental imagery (Belk 1988; Holbrook and Hirschman 1982; Lutz and Lutz 1978; Rogers et al. 1977), this study follows an exploratory approach using netnography and in-depth interviews. The initial findings suggest that using AR try-on tools helps consumers choose the products that enable them to craft unique self-expression rather than conforming to societal stereotypes. Further, AR facilitates experimentation with new consumption patterns, irrespective of age and gender, giving opportunities for consumers to embrace their unique interests. Consumers save their self-augmented images to share among connections via social media, indicating digital expression before actual purchase and use. Thus AR provides an opportunity for brands to be a part of consumers' virtual selves before actual consumption through digital expression. The study's findings open up new avenues for brands to be a meaningful part of consumer extended selves and the role of AR in enabling consumers to express their “true selves” which may be seen as more of an aspirational self given enhancements of face shape and eye size commonly employed in these apps.
Faster 3D Reconstruction by Fusing 2D Object Detection and Self-Supervised Monocular Depth Estimation

In recent years, real-time 3D reconstruction has gained popularity among many researchers due to its increasing applications. 3D reconstruction methods based on depth estimation have a large number of invalid areas for reconstruction in autonomous driving. To further improve the real-time performance of 3D reconstruction, we propose a novel approach to reduce the consumption of computational resources by extracting significant regions of depth maps. First, binary masks that extract significant regions are generated by a 2D object detection model. Then, we design the TRDE module to extract target regions in generated depth maps. Finally, qualitative and quantitative results on KITTI dataset demonstrate that our approach can perform depth maps optimization and reduce computational resources consumed during the 3D reconstruction. As a result, our method achieves faster 3D reconstruction by fusing 2D object detection and self-supervised monocular depth estimation.
Tour to Model-View-Template Architecture and Object–relational mapping with Services

Services’ is an online website to help the Consumer to choose the peoples professionalized in various fields&amp; services that one needs on daily basis. The platform provides a path of communication between the peoples like house help, taylor, plumbers, educational counselors or painters, etc. and its users. This website is designed for simplicity &amp; ease for non-technical person to operate it.The primary purpose of this web- application is to connect Service Provider (The Person Providing Desired Service) to the Consumer (The Person Willing to Purchase the Service). Basically, it is an Employment website for a Small Self- Employed Person (like-Plumbers &amp; Electricians) or Small Sellers. The ‘Services’ is a web-application connecting community of professionals providing their services to whomever it may concern i.e., Consumer. It also fixes the deal between the Consumer and Provider agreeing at the same price so unnecessary clashes is being avoided. So, a better relation between the Consumer and Provider is ensured. This Application is easy to understand and at a same time safe and secure to use. Also focuses on the features like- User Friendliness, Accountability, Security, Easy Search, Simple Login &amp; Registration, etc.
Intention-Aware Motion Modeling Using GP Priors With Conditional Kernels

For the challenging task of modeling actual complex motions, we propose a new class of Gaussian process (GP) models that are data-driven and also take into account prior knowledge of the motion intention. As a theoretical basis, we show that the GP regression is mathematically equivalent to regularized least-squares estimation for random functions with known prior means. Compared with the popular GP models in machine learning literature, the proposed GP motion model priors with conditional kernels have at least two advantages: 1) they are nonstationary and more applicable to represent complex motions by integrating the basic kinematic principles; 2) conditional kernels are further devised by incorporating the motion intent so that the resultant GP models are more versatile and would expectedly entail more accurate trajectory prediction. A superior property of the GP models with conditional kernels is found to improve the computational efficiency. Finally, illustrative examples are provided to show the superiority of the proposed motion models and to verify the theoretical results given in the paper.
Localization of Forgery on Audio Clips Using GLCM Features and Mel Spectograms

With the rapid development of powerful audio editing software makes the forgery of the digital audio easy. Researchers have proposed methods to cope with audio authentication in recent years. In this study, we investigated forgery traces on Mel spectrogram with visual techniques for the first time in the literature. For this purpose, the method divides the Mel spectrogram image into overlapping sub-blocks and extracts features via the Gray-level Co-Occurrence Matrix from these blocks for each RGB color channel. The feature vectors are lexicographically sorted to make the similar vectors closer. Similarity among blocks gives a clue about forgery. Experiment results show that the proposed method gives better results for the detection of audio copy-move forgery compared to other studies in the literature.
Exploring Feature Extraction to Vulnerability Prediction Problem

The growing use of technology makes the development of secure applications essential. In contrast, the secure software development cycle is a costly task, considering the human effort required to review application code for finding vulnerabilities. In order to minimize this cost (human effort), Vulnerability Prediction Models (VPMs) can be used by software development teams during inspection tasks. The VPM low precision makes its application unfeasible, because it indicates the waste of human effort during the inspection. One of the obstacles in the construction of efficient VPMs (i.e., high precision) is modeling meaningful features related to the vulnerabilities, specially in the initial training stages. In this work we compare a promising feature, extracted through another domain (i.e., defect prediction) techniques. We evaluated the feature within an active learning-based VPM through a simulation on real open source projects. Our results indicates that the feature looks promising in cost saving when applied to vulnerability inspection tasks.
Creativity in Science as a Social Phenomenon

The philosophical understanding of scientific creativity cannot be limited to the analysis of cognitive abilities or ways of solving problems. It is always anthropologically-laden, based on a historically specific image image of a human being that acquires knowledge. The problem of creativity also articulates a well-known paradox of novelty: the new does not arise from the old, since it is significantly different from it, but it cannot arise from nothing, because then it remains incomprehensible. Paul Feyerabend criticizes such a “mysterianic” concept of scientific creativity, which emphasizes its emergent character, i.e. its suddenness and inexplicability. The distance between subject and object, man and nature, knowledge and reality in this case remains insurmountable. To solve this problem, it is necessary to place creativity in the space between the uniqueness of the creative personality and the mechanisms of social recognition. This, in turn, requires rethinking of the ontological content of creativity, as well as the concept of “labor” with reference to its treatment by Karl Marx’ rethinking of it as the production of not only material goods, but also of the person itself. Moreover, creativity does not just form a person: it is a way of cultivating the entire social space. An unexpected move is provided by an appeal to a new social group – the precariat, which in science gives a lesson in openness to novelty and theoretical freedom. As a result, a deeper understanding of the social base of creativity arises, and a new strategy for the interaction of the scientific community with extra-paradigm groups and marginal individuals is being built.
Hotel room personalization via ontology and rule-based reasoning

Purpose The increasingly competitive hotel industry and emerging customer trends where guests are more discerning and want a personalized experience has led to the need of innovative applications. Personalization is much more important for hotels, especially now in the post-COVID lockdown era, as it challenges their business model. However, personalization is difficult to design and realize due to the variety of factors and requirements to be considered. Differences are both in the offer (hotels and their rooms) and demand (customers’ profiles and needs) in the accommodation domain. As for the implementation, critical issues are in hardware-dependent and vendor-specific Internet of Things devices which are difficult to program. Additionally, there is complexity in realizing applications that consider varying customer needs and context via existing personalization options. This paper aims to propose an ontological framework to enhance the capabilities of hotels in offering their accommodation and personalization options based on a guest’s characteristics, activities and needs. Design/methodology/approach A research approach combining both quantitative and qualitative methods was used to develop a hotel room personalization framework. The core of the framework is a hotel room ontology (HoROnt) that supports well-defined machine-readable descriptions of hotel rooms and guest profiles. Hotel guest profiles are modeled via logical rules into an inference engine exploiting reasoning functionalities used to recommend hotel room services and features. Findings Both the ontology and the inference engine module have been validated with promising results which demonstrate high accuracy. The framework leverages user characteristics, and dynamic contextual data to satisfy guests’ needs for personalized service provision. The semantic rules provide recommendations to both new and returning guests, thereby also addressing the cold start issue. Originality/value This paper extends HoROnt in two ways, to be able to add: instances of the concepts (room characteristics and services; guest profiles), i.e. to create a knowledge base, and logical rules into an inference engine, to model guests’ profiles and to be used to offer personalized hotel rooms. Thanks to the standards adopted to implement personalization, this framework can be integrated into existing reservation systems. It can also be adapted for any type of accommodation since it is broad-based and personalizes varying features and amenities in the rooms.
Regularized Convolutional Neural Network for Highly Effective Parallel Processing

Convolutional neural network (CNN) has been adopted in various areas. Using graphics processing unit (GPU), speed improvement can be achieved on CNN, and many studies have proposed such acceleration methods. However, parallelizing the CNN on GPU is not straightforward because there are irregular characteristics in generating output feature maps in typical CNN models. In this paper, we propose a method that maximizes the utilization of GPU by modifying convolution combinations of a well-known CNN network, LeNet-5. Our regularized implementation on a heterogeneous system has achieved an improvement of up to 37.26 times in convolution and sub-sampling layers. Further, an energy consumption reduction of up to 26.40 times is achieved.