Reviewerly/exHarmony huggingface dataset:
RetinaNet with Harmony Search Algorithm for Dynamic and Static Object Detection and Classification Model

Object detection is becoming a challenging problem in several computer vision related applications. The recently developed deep learning (DL) models enable to design of effective object detection models with enhanced outcomes. But it is difficult to attain many characteristics from the objects identified in real time. To resolve this issue, this study introduces an optimal RetinaNet with harmony search algorithm for dynamic and static object detection (RNHSA-DSOD) model. The proposed RNHSA-DSOD technique aims to identify the dynamic as well as static objects that exist in the input frame. Besides, the RNHSA-DSOD technique derives a RetinaNet based object detection model to recognize multiple objects. Next, harmony search algorithm (HSA) with multilayer perceptron (MLP) is applied for the classification of detected objects into multiple classes. The design of HSA for MLP model shows the novelty of the work. In order to demonstrate the enhanced outcomes of the RNHSA-DSOD technique, a series of simulations were carried out and the results indicated the enhanced outcomes of the RNHSA-DSOD technique over its recent techniques.
An Overview of the 1st International Workshop on eXtended Reality for Industrial and Occupational Supports (XRIOS)

This workshop, the eXtended Reality for Industrial and Occupational Supports (XRIOS), aims to identify the current state of XR research and the gaps in the scope of human factors and ergonomics, mainly related to industrial and occupational tasks, and discuss potential future research directions. XRIOS will build a community that bridges XR developers/practitioners and human factors and ergonomics researchers interested in industrial and occupational applications.
Employing a Dance-Somatic Methodological Approach to VR to Investigate the Sensorial Body across Physical-Virtual Terrains

This article takes a somatic dance standpoint to investigate bodily sensorial encounters with virtual reality technology (VR). Using somatic dance practices as sensory tuning methods, this article investigates the virtuality and corporeality of human and nonhuman bodies across virtual and physical environments and expanded sensory modes of seeing and feeling, using a multiperson interactive VR framework. Predicated on a dance-based sensibility and mode of engagement with VR, workshops crafted for groups of dancer and nondancer participants offer methods for the design and facilitation of VR encounters that expand ideas and expectations of both body and technology. Using autoethnographic writing and participant testimonies, alongside conceptual thinking around participation in VR, I present this methodological approach and demonstrate its potential to address new questions about VR experiences that are centred on the sensorial body.
Discriminative Shape Feature Pooling in Deep Neural Networks

Although deep learning approaches are able to generate generic image features from massive labeled data, discriminative handcrafted features still have advantages in providing explicit domain knowledge and reflecting intuitive visual understanding. Much of the existing research focuses on integrating both handcrafted features and deep networks to leverage the benefits. However, the issues of parameter quality have not been effectively solved in existing applications of handcrafted features in deep networks. In this research, we propose a method that enriches deep network features by utilizing the injected discriminative shape features (generic edge tokens and curve partitioning points) to adjust the network's internal parameter update process. Thus, the modified neural networks are trained under the guidance of specific domain knowledge, and they are able to generate image representations that incorporate the benefits from both handcrafted and deep learned features. The comparative experiments were performed on several benchmark datasets. The experimental results confirmed our method works well on both large and small training datasets. Additionally, compared with existing models using either handcrafted features or deep network representations, our method not only improves the corresponding performance, but also reduces the computational costs.
Workspace Solution and Space Trajectory Planning of 5-DOF Manipulator

Abstract In this paper, a 5-DOF manipulator is designed to carry the pipeline robot. The joint coordinate system and connecting rod parameters of the manipulator are confirmed by D-H method, and the kinematics model of the manipulator is established. The position and direction of the end of the manipulator are deduced by using the homogeneous transformation method. Monte Carlo method is used to solve the workspace of manipulator, and MATLAB is used to draw the workspace cloud map. Set the motion trajectory of manipulator, using joint space trajectory planning, through MATLAB simulation of motion form, get the change of joint. It provides a foundation for trajectory planning, dynamics analysis, motion control and parameter optimization of manipulator.
DeepFake Video Detection through Facial Sparse Optical Flow based Light CNN

Abstract DeepFake detection has become an attractive research topic with tremendous growth of interests recently. However, existing DeepFake detection studies spare no effort to improve accuracy or Area Under Curve metric, regardless of computing costs. In this work, the tradeoff between result accuracy and computing resources is taken into consideration. A facial sparse optical flow method is proposed to extract spatio-temporal features representing facial expression incoherence, which helps to distinguish fake videos and real videos. The features fed into a light CNN model are highly compact and low-dimensional. The proposed method has an amazing small amount of parameters with high training speed and low usage of GPU memory. The low resource requirement makes it possible to port to embedded development platform.
Unified implementation of global high dynamic range image tone-mapping algorithms

High dynamic range (HDR) images and video require tone-mapping for display on low dynamic range (LDR) screens. Many tone-mapping operators have been proposed to convert HDR content to LDR, but almost each has a different implementation structure and requires a different execution time. We propose a unified structure that can represent any global tone-mapping algorithm with an array of just 256 coefficients. These coefficients extracted offline for every HDR image or video frame can be used to convert them to LDR in real time using linear interpolation. The produced LDR images are identical to the images produced by the original implementation of the algorithm. This unified implementation can replicate any global tone-mapping function and requires very low and fixed execution time, which is independent of algorithm and type of content and depends only on image size. Experimental studies are presented to show the accuracy and time efficiency of the proposed implementation.
Blockchain Based Cloud Storage of Patients Health Records

The widespread popularity of digital crypt-currencies has led the foundation of Blockchain, which is fundamentally a public digital ledger to share information in a trustworthy and secure way. The concept and applications of Blockchain have now spread from crypto-currencies to various other domains, including business process management, smart contracts, IoT and so on. Cloud Computing Services are growing every year and also used by various applications. Cloud service usage and monitoring is done by the cloud service provider. Based on the service level agreement, the cloud service is used and accessed by cloud user. This pay per use service model has led the foundation of various business in the technological eco system. The Security issues in cloud are also known and received substantial stability in the cloud computing services. The invention of blockchain for providing security and transparency, increases the trust between the service providers and consumers. The applications of Blockchain in the cloud is becoming the key area of research, where trust plays a major role between the cloud service provider and cloud user. This paper gives an introduction about Blockchain technology. It also discusses about Cloud Computing features for providing transparency and security are with the existing solutions. One of the applications that attracts concerns about the cloud data storage is patient's health record management system. A cloud service of patient's electronic health records is discussed with respect to security by using encryption and sensitivity of data. The application aspects of Blockchain is discussed with respect to patient health record monitoring system that is stored in a cloud computing environment. The electronic health records are stored in the cloud in encrypted form. The level of encryption is used depending on the sensitivity of the data. These encrypted data are accessed and used with the application of Blockchain to keep the track of viewing history. With the inclusion of chain code, the private communication can also be performed. Here a system is proposed and its application aspects is discussed. The analysis of the system shows that a blockchain based cloud storage service can be used as a secure system for patient health records.
Application Analysis of Emotional Learning Model Based on Improved Text in Campus Review and Student Public Opinion Management

In an era of rapid mobile Internet development, students are increasingly expressing their views on specific events through campus comments. In the Web 2.0 era, the concept of public media participation is widely used by students, and it is important to effectively analyze online campus public opinion comments and present the findings in a rationalized form for the management of campus public opinion. In the new era of education governance modernization, the level of public opinion management in universities has become a key indicator to improve the standard of education management in higher education institutions; therefore, this paper focuses on proposing an innovative deep learning-based research method for topic management of university public opinion. Firstly, through the improved LDA module with sentiment discrimination learning capability, the sentiment of the main arguments in the campus commentary is extracted, and then the statistical sentiment intensity of the in-depth learning module is used to analyze the sentiment intensity of the thematic arguments of different events in time series, so as to achieve the long-term tracking of the trend of the sentiment intensity of the whole event.
Big data architecture for connected vehicles: Feedback and application examples from an automotive group

Nowadays, using their onboard built-in sensors and communication devices, connected vehicles (CVs) can perform numerous measurements (speed, temperature, fuel consumption, etc.) and transmit them, in a real-time fashion, to dedicated infrastructure, usually via 4G/5G wireless communications. This raises many opportunities to develop new innovative telematics services, including, among others, driver safety, customer experience, location-based services and infotainment. Indeed, it is expected that there will be roughly 2 billion connected cars by the end of 2025 on the world’s roadways, where each of which can produce up to 30 terabytes of data per day. Managing this big automotive data, in real and batch modes, imposes tight constraints on the underlying data management platform. To contribute to this research area, in this paper, we report on a real, in-production automotive big data platform; specifically, the one deployed by Groupe PSA (a French car manufacturer known also as Peugeot-Citroën). In particular, we present the technologies and open-source products used within the different components of this CV platform to gather, store, process, and leverage big automotive data. The proposed architecture is then assessed through realistic experiments, and the obtained results are reported and analyzed. Finally, we also provide examples of deployed automotive applications and reveal the implementation details of one of them (an eco-driving service).
Research on improved sparrow algorithm based on random walk

Abstract The optimization problem is a hot issue in today’s science and engineering research. The sparrow algorithm has the advantages of simple structure, few control parameters and high solution accuracy, and has been widely used in the research of optimization problems. Purposing at the problem that the sparrow search algorithm (SSA) can’t take into account the global and local optimization, an improved sparrow algorithm based on random walk strategy is proposed. After the sparrow search, the random walk is used to perturb the optimal sparrow to demonstrate its search-ability. At the original of the iteration, the random walk boundary is large, which is favourable to demonstrate the whole search-ability. After several iterations, the walk boundary becomes smaller, which improves the local search-ability of the best location of the algorithm. Taking the convergence speed, algorithm stability and convergence precision as evaluation indicators, the improved Sparrow Algorithm (RWSSA) is verified by 4 unimodal functions and 5 multimodal classical test functions, and compared with the traditional Sparrow algorithm. The experimental results show that the capacity of the improved sparrow algorithm based on random walk is significantly improved. At the same time, RWSSA is put into practice the power prediction problem, which checkouts the feasibility of RWSSA in actual engineering problems.
Multi-class WHMBoost: An ensemble algorithm for multi-class imbalanced data

The imbalanced data problem is widespread in the real world. In the process of training machine learning models, ignoring imbalanced data problems will cause the performance of the model to deteriorate. At present, researchers have proposed many methods to deal with the imbalanced data problems, but these methods mainly focus on the imbalanced data problems in two-class classification tasks. Learning from multi-class imbalanced data sets is still an open problem. In this paper, an ensemble method for classifying multi-class imbalanced data sets is put forward, called multi-class WHMBoost. It is an extension of WHMBoost that we proposed earlier. We do not use the algorithm used in WHMBoost to process the data, but use random balance based on average size so as to balance the data distribution. The weak classifiers we use in the boosting algorithm are support vector machine and decision tree classifier. In the process of training the model, they participate in training with given weights in order to complement each other’s advantages. On 18 multi-class imbalanced data sets, we compared the performance of multi-class WHMBoost with state of the art ensemble algorithms using MAUC, MG-mean and MMCC as evaluation criteria. The results demonstrate that it has obvious advantages compared with state of the art ensemble algorithms and can effectively deal with multi-class imbalanced data sets.
SAGA: Self-Augmentation with Guided Attention for Representation Learning

Self-supervised training that elegantly couples contrastive learning with a wide spectrum of data augmentation techniques has been shown to be a successful paradigm for representation learning. However, current methods implicitly maximize the agreement between differently augmented views of the same sample, which may perform poorly in certain situations. For example, considering an image comprising a boat on the sea, one augmented view is cropped solely from the boat and the other from the sea, whereas linking these two to form a positive pair could be misleading. To resolve this issue, we introduce a Self-Augmentation with Guided Attention (SAGA) strategy, which augments input data based on predictive attention to learn representations rather than simply applying off-the-shelf augmentation schemes. As a result, the proposed self-augmentation framework enables feature learning to enhance the robustness of representation.
Self-Supervised Learning on A Lightweight Low-Light Image Enhancement Model with Curve Refinement

Deep learning networks with deeper layers become a trend for their good performance but lacks the potential for real-time mobile deployment. Another challenge for paired training networks is the limited generalization capacity caused by the sample bias. To overcome these two challenges, we propose a lightweight self-supervised low-light image enhancement method, that trains with low light images only. Specifically, our method consists of a low-resolution dense CNN network stream and a full-resolution guidance stream, responsible for image-to-curve transformation with refinement and spatial guidance fusion, respectively. Then, a new self-supervised loss function is introduced to measure the restored patch-based color deviations among color channels. Experimental results show that our method gives competitive performance to the full-supervised approaches.
Diversity Features Enhanced Prototypical Network for Few-shot Intent Detection

Few-shot Intent Detection (FSID) is a challenging task in dialogue systems due to the scarcity of available annotated utterances. Although existing few-shot learning approaches have made remarkable progress, they fall short in adapting to the Generalized Few-shot Intent Detection (GFSID) task where both seen and unseen classes are present. A core problem of the simultaneous existence of these two tasks is that limited training samples fail to cover the diversity of user expressions. In this paper, we propose an effective Diversity Features Enhanced Prototypical Network (DFEPN) to enhance diversity features for novel intents by fully exploiting the diversity of known intent samples. Specially, DFEPN generates diversity features of samples in the hidden space via a diversity feature generator module and then fuses these features with original support vectors to get a more suitable prototype vector of each class. To evaluate the effectiveness of our model on both FSID and GFSID tasks, we carry out sufficient experiments on two benchmark intent detection datasets. Results demonstrate that our proposed model outperforms existing state-of-the-art methods and keeps stable performance on both two tasks.
Disruptive Technology from an Organizational Management Perspective

Organisations face tremendous pressures and challenges associated with the rapid development of disruptive technologies. To capture these technologies’ benefits and keep up with the competitors, they must pursue large-scale changes in the operating system, business models, and organisation structure. However, today, technology is evolving quickly and dramatically reshaping industries and business environments, and disruptive technologies will remain a dominant theme in the years ahead. Therefore, today’s organisational management is constantly concerned with the notions of disruptive technologies that could create much more value than the existing ones. This paper bridges this gap by systematically synthesising previous related literature. This systematic literature review covered 13 published articles since 2016. The paper argues that organisations can no longer ignore this rapid development and must change through smart integration of digital technologies, processes, and competencies across all levels and functions. Nevertheless, it is not only about coping with the emerging disruptive technologies. Organisations also need to change their strategic thinking and management style. It tackles the need to change leaders’ mindsets and evolve employees to develop a competitive advantage continuously.
Retrieval Enhanced Segment Generation Neural Network for Task-Oriented Dialogue Systems

For task-oriented dialogue systems, Natural Language Generation (NLG) is the last and vital step which aims at generating an appropriate response according to the dialogue act (DA). While end-to-end neural networks have achieved promising performances on this task, the existing models still struggle to avoid slot mistakes. To address this challenge, we propose a novel segmented generation approach in this paper. The proposed method operates by progressively generating text for the span between two adjacent keywords (act type and slots) in semantically ordered DA. This procedure is recursively applied from left to right until a response is completed. Besides, a retrieval mechanism is utilized to better match the diversity and fluency in human language. Experimental results on four datasets demonstrate that our model achieves state-of-the-art slot error rate and also gets competitive performance on BLEU score with all strong baselines.
Multi-Hierarchy Proxy Structure for Deep Metric Learning

Mainstream methods for deep metric learning can be divided into pair-based and proxy-based methods. In recent years, proxy-based methods have attracted wide attention for their low training complexity and fast network convergence. Most proxy-based studies assign only one proxy per class to capture the features of the class, this leads to ignoring the hidden hierarchy and regular aggregation of features within the class. However, these details are meaningful for capturing features of the class. Therefore, we propose a multi-hierarchy proxy (MHP) structure to extract the hierarchical details and regular features hidden in the embedding space. At the same time, we design a layerwise merging similarity operator to reasonably measure the similarity between samples and classes. Our MHP method maintains the low time complexity of the proxy-based method and can be easily integrated into existing proxy-based losses. The effectiveness of our method is evaluated by extensive experiments on three public datasets and compared with state-of-the-art methods. The results show that the proposed MHP method can significantly improve the performance of proxy-based methods, reaching 69.8% on CUB-2002011 and 87.4% on Cars-196 dataset at Recall@1.
Overview of Crow Search Algorithm

Abstract Crow Search Algorithm (CSA) is a swarm intelligence optimization resolving schemes. Its optimization method simulates the behavior of crows tracking each other. The paper elaborates the basic principle of the algorithm, discusses the main parameters affecting the algorithm performance, and analyzes the search mechanism of the algorithm. The improvement strategies of the algorithm are summarized. Examples are given to illustrate the application of the algorithm in 0-1 knapsack problem, Image processing, Scheduling issues, feature selection and parameter optimization. Finally, according to the research and application status of crow search algorithm, the future research and development direction are prospected.