Reviewerly/exHarmony huggingface dataset:
Real-time Wall Identification for Underwater Mapping using Imaging Sonar

Wall and other planar structures are common in environments as harbors, marinas, or dams. In this paper we introduce an algorithm aimed at the identification of these structures through acoustic images retrieved from an imaging sonar, building on an application of the Hough Transform algorithm. We employ a polar-based line parametric model for improved computational efficiency and further adapt the core Hough Transform blocks to the characteristics of acoustic imaging. The developed solution was subjected to experimental tests employing acoustic data acquired in a water tank, from different viewpoints and under different sonar gain configurations.
On heterogeneity or sub-classes aspect in construction of stylometric input datasets

Stylometric analysis of texts relies on learning characteristic traits of writing styles for authors. Once these patterns are discovered, they can be compared to the ones present in other text samples, to recognise their authorship. This recognition can be compromised if input datasets are prepared without taking into consideration possible stratification of the input space, leading to specific grouping of datapoints, or sub-classes within distinguished classes. The paper shows research dedicated to construction of various structures of input datasets, and combinations of such structures between train and test sets. In the research the influence of different stratification forms on the performance of selected popular classification systems was observed. To minimise the number of influencing factors, a task of authorship attribution was performed as binary classification with balanced classes. Stylometric descriptors exploited belonged to lexical and syntactic group, giving frequencies of occurrence for chosen style-markers. It resulted in real-valued attributes and these values were explored without applying discretisation, in order to avoid the possible bias of this procedure on observations.
Modern Stage Performance Simulation Based on Augmented Reality Style

Drawing on the Spring Festival Gala stage, a virtual simulation system based on Unity3D and adapted to contemporary stage effects is developed for the characteristics of modern Augmented Reality (AR) style stage shows. This paper proposes the realization ideas and methods of using the Shader module of Unity3D and the OpenCV plug-in component in Unity3D to simulate the stage effects such as naked-eye 3D, AR technology, and video matting effects. The combination of Unity3D virtual simulation and stage performance art can assist the program team in the design and rehearsal of the stage performance, and promote the transformation of the performance industry and the transformation of the production model.
An Automatic Code Generation Method Based on Sequence Generative Adversarial Network

Automatic code generation has been around for a long time, and the core technologies used are constantly evolving. Around application development work in the demand for high quality code generated rapidly, this paper proposes a automatic code generation method based on sequence generative adversarial network, the method adopts the adversarial learning thought, using LSTM as a generator, CNN as a discriminator, both through against each other to generate higher quality code samples. Experiment results show that this method can generate code automatically at the second level, and is significantly better than the existing methods in terms of generation rate and accuracy, which explores a new way of automatic code generation.
Underwater Image Restoration and Enhancement Based on Machine and Deep Learning Algorithms

Underwater image processing is the key research of the last decade. Due to attenuation and scattering of light into beneath of ocean, it is impossible to find capture image. Therefore image processing keeps into picture. To automate process and find relevant information, there is a need of good quality image. There are many of the applications, where need of processed image due to unclear of original images. In the artificial intelligence the image processing is also one of the important research domains. The machine learning (ML), deep learning (DL) approaches based image processing like image enhancement, denoising, encryption, fusion etc. are emerging trend of the research. Under-water imagery is an important carrier and presentation of under-water information, which plays a vital role in the exploration, exploitation and utilization of marine resources. This paper studied about under-water image restoration and enhancement based on machine and deep learning algorithms, it also suggest an approach to restore and enhance image.
SVFLS: A Secure and Verifiable Federated Learning Training Scheme

Federated learning has received extensive attention in recent years since the clients only need to share their local gradients with the servers without directly sharing their datasets to train the model. However, the existing research shows that the attackers can still reconstruct private information from shared gradients, resulting in privacy leakage. In addition, the aggregated results could be tampered with by servers or attackers. In this paper, we propose a secure and verifiable federated learning training scheme (SVFLS) to protect the privacy of data owners and verify aggregated results. Specifically, we employ threshold paillier encryption to protect the local gradients of data owners and use the bilinear aggregate signature to verify the correctness (or integrity) of aggregated results. Furthermore, our scheme can tolerate data owners dropping out during the training phase. We conduct extensive experiments on real datasets and demonstrate that our scheme is effective and practical.
Deep Partial Updating: Towards Communication Efficient Updating for On-Device Inference

Emerging edge intelligence applications require the server to retrain and update deep neural networks deployed on remote edge nodes to leverage newly collected data samples. Unfortunately, it may be impossible in practice to continuously send fully updated weights to these edge nodes due to the highly constrained communication resource. In this paper, we propose the weight-wise deep partial updating paradigm, which smartly selects a small subset of weights to update in each server-to-edge communication round, while achieving a similar performance compared to full updating. Our method is established through analytically upper-bounding the loss difference between partial updating and full updating, and only updates the weights which make the largest contributions to the upper bound. Extensive experimental results demonstrate the efficacy of our partial updating methodology which achieves a high inference accuracy while updating a rather small number of weights.
Causality for Remote Sensing: An Exploratory Study

Causality is one of the most important topics in a Machine Learning (ML) research, and it gives insights beyond the dependency of data points. Causality is a very vital concept also for investigating the dynamic surface of our living planet. However, there are not many attempts for integrating a causal model in Remote Sensing (RS) methodologies. Hence, in this paper, we propose to use patch-based RS images and to represent each patch-based image by a single variable (e.g. entropy). Then we use a Structural Equation Model (SEM) to study their cause-effect relation. Moreover, the SEM is a simple causal model characterized by a Directed Acyclic Graph (DAG). Its nodes are causal variables, and its edges represent causal relationships among causal variables if and only if causal variables are dependent.
Reversible Image Authentication Scheme with Tampering Reconstruction Based on Very Deep Super Resolution Network

Reversible image authentication (RIA) involves a data hiding process in which watermarks are imperceptibly embedded into an image to protect it against tampering attacks. If a marked image is not tampered, then watermarks can be erased from the marked image and the original cover image is obtained without distortion. Watermarks can be enhanced with recovery information for reconstruction of tampered image regions. However, due to embedding capacity limitations, recovery information must be strategically selected to obtain better image reconstruction quality. To address this issue, a very deep super resolution (VDSR) network is used in the reconstruction phase of the proposed scheme. Sparse signal representations of downscaled image blocks are obtained to apply compressed sensing theory and obtain the reference values used as recovery information. Experimental results indicate that our scheme outperforms previous state-of-the-art methods in terms of reconstructed images quality while reducing time complexity in sparse signal approximation phase as a result of dealing with downscaled image blocks.
Deep Reservoir Computing Based Random Vector Functional Link for Non-sequential Classification

Reservoir Computing (RC) is well-suited for simpler sequential tasks which require inexpensive, rapid training, and the Echo State Network (ESN) plays a significant role in RC. In this article, we proposed variations of the Random Vector Functional Link (RVFL) network based on reservoir computing for non-sequential tasks. To commence, we present a plain echo state-based RVFL (esRVFL) that is distinguished from randomly generated input weights by the fact that esRVFL generates sparse matrices randomly to complete the initialization of the neuron weights. Following that, we extended it to a deep structure and introduced several network topologies. We also follow esRVFL and replace the single layer of echo state with a multi-layer stacked echo state network, where the entire network only needs to compute a set of output weights, which is called deep esRVFL (desRVFL). We evaluated our method on several public datasets and compared it with related methods. Experiments have shown that the proposed method can handle the classification tasks for tabular data and outperform some state-of-the-art randomized neural networks.
Pixel to Binary Embedding Towards Robustness for CNNs

There are several problems with the robustness of Convolutional Neural Networks (CNNs). For example, the prediction of CNNs can be changed by adding a small magnitude of noise to an input, and the performances of CNNs are degraded when the distribution of input is shifted by a transformation never seen during training (e.g., the blur effect). There are approaches to replace pixel values with binary embeddings to tackle the problem of adversarial perturbations, which successfully improve robustness. In this work, we propose Pixel to Binary Embedding (P2BE) to improve the robustness of CNNs. P2BE is a learnable binary embedding method as opposed to previous hand-coded binary embedding methods. P2BE outperforms other binary embedding methods in robustness against adversarial perturbations and visual corruptions that are not shown during training.
On the Generalization of Learned Constraints for ASP Solving in Temporal Domains

The representation of a dynamic problem in ASP usually boils down to using copies of variables and constraints, one for each time stamp, no matter whether it is directly encoded or via an action or temporal language. The multiplication of variables and constraints is commonly done during grounding and the solver is completely ignorant about the temporal relationship among the different instances. On the other hand, a key factor in the performance of today’s ASP solvers is conflict-driven constraint learning. Our question is now whether a constraint learned for particular time steps can be generalized and reused at other time stamps, and ultimately whether this enhances the overall solver performance on dynamic problems. Knowing full well the domain of time, we study conditions under which learned dynamic constraints can be generalized and propose a simple translation of the original logic program such that, for the translated programs, all learned constraints can be generalized to other time points. Last but not least, we empirically evaluate the impact of adding the generalized constraints to an ASP solver.
Optimal Constrained Task Planning as Mixed Integer Programming

For robots to successfully execute tasks as-signed to them, they must be capable of planning the right sequence of actions. These actions must be both optimal with respect to a specified objective and satisfy whatever constraints exist in their world. We propose an approach for robot task planning that is capable of planning the optimal sequence of grounded actions to accomplish a task given a specific objective function while satisfying all specified numerical constraints. Our approach accomplishes this by encoding the entire task planning problem as a single mixed integer convex program, which it then solves using an off-the-shelf Mixed Integer Program-ming solver. We evaluate our approach on several mobile manipulation tasks in both simulation and on a physical humanoid robot. Our approach is able to consistently produce optimal plans while accounting for all specified numerical constraints in the mobile manipulation tasks. Open-source implementations of the components of our approach as well as videos of robots executing planned grounded actions in both simulation and the physical world can be found at this url: https://adubredu.github.io/gtpmip
The construction of University Chinese as a foreign language multimedia material and network resource base based on Big Data Analysis

There are many problems in TCFL, such as relatively simple teaching resources, chaotic teaching system and so on, facing learners from different countries and regions, cultural background and needs all over the world, directly affects the quality of college Chinese as a foreign language. With the development and application of Internet technology, online intelligent teaching has triggered profound changes in educational system. As an important supplement to education, web-based resource banks are of great practical significance to the current innovation and optimization of Chinese as a foreign language systems. Therefore, in the current innovation of University Chinese as a foreign language, we should build a large-scale and knowledge-based network resource base based on Big Data Analysis to help university teaching.
Modeling Interactions among Cyber-Physical Services

Modeling interactions among cyber-physical services is a crucial problem for analyzing behaviors of a composed system, where these cyber-physical services communicate with each other via synchronous and asynchronous messages. In this paper, we present an approach for modeling interactions of cyber-physical systems, which can be automated under the tool support. First, a service model is proposed to represent a cyber-physical service using a labeled transition system, where the order of executing transitions in a cyber-physical service is defined. Second, a synchronous composition is defined to compose a set of cyber-physical services communicating with synchronous messages into a cyber-physical system. Third, an asynchronous composition is defined to compose cyber-physical services interacting asynchronously through FIFO buffers, where three types of interactions are considered. Finally, experimental results show that our approach is automated and effective.
An Implemented review for Intent Creation using different Clustering Techniques

One of the major challenges for any interactive system is understanding the user needs or technically called intent detection. A human interacts with a machine with a same expectation as with another person. The key factor for a machine to make a human like interaction is to know the intent of the user before answering the query. Thus intent detection is one of the crucial and important tasks of any interactive system. Moreover detection of intent prior requires labeled data for training under supervision which can be expensive. Though very few papers have been published showing the methods for intent creation, the main focus of this research is to create the intents before its detection. This paper proposes and implements various techniques like K-means, Agglomerative and LDA for creating the labels. After the creation of the clusters as labels, transfer learning technique called zero shot learning is applied for detecting the intents. Experiment analysis shows that Agglomerative clustering generates better clusters as compared to K-means and LDA.
Analysis of Age Sage Classification for Students’ Social Engagement Using REPTree and Random Forest

Study and analysis of train dataset along with various ML algorithms is used widely in different sectors. The accuracy parameters can be clarified to have prediction of different score levels. This study covers the extension work of Students’ social engagement during covid-19 pandemic. The study was initiated with students’ social connection during the pandemic. We had compared various machine learning algorithms with its performance about the engagement of students in various social network. After studied, analyzed & compared, we derived that the most of students’ social engagement found in WhatsApp, YouTube & Instagram. The current study is foreseeing age wise social media connection. It correlates between student & their social engagement during the pandemic phase. In which age group, which social media is one of the most popular one. This study focuses on age wise classification using Machine Learning. In this paper, the decision-making classification is compared. The Reduced Error Pruning Tree (REPTree) and Random Forest algorithm is implemented on train dataset with diverse nodes. The attributes are focused as age & time spent on social media as per necessity of study. This paper includes the study and analysis of RAE & RMSE along with ML tree approach. The discoveries of this study can lead better classification in regards of students’ age and duration which they have spent on social media for derived social platform.
A Preliminary Cyber-Physical Study of a VR Training Material for Engineering Students to Give a Presentation in English

Due to the rapid globalization of engineering fields and studies, engineering students are required to present their research in English at an international level. However, many engineering students feel anxiety for speaking in English; thus, proper training is necessary for them by providing a simulated conference environment. In this study, the authors develop a prototype of virtual reality (VR) training material for engineering students to practice and experience difficult communication situations in English at an international conference. The effects of the material were investigated by measuring the changes in the hemoglobin (Hb) concentration of the frontal cortex and heart rates of subjects who participated in the VR training and evaluation sessions with an English conversation trainer. The physiological data measurements obtained from the subjects before and after the VR training showed that they were more relaxed when responding to difficult English questions at a mocked question-and-answer (Q&A) session after the training. The result indicates that the VR training material can reduce the anxiety of engineering students who need to give a presentation in English, and this type of learning material is expected to be effectively used from an educational standpoint.
Web and iOS-based Instant Messaging (IM) Application for Educational Institution with Limited Internet Access Regions

The development of communication technology through smartphone has made real time communication (chat) faster, easier, and cheaper for educational institutions, especially between students and lecturers. This study aims to develop an architecture for Web and iOS-based instant messaging (IM) application and implement it into educational institutions in limited internet access regions according to its conditions and needs as a fast, easy, efficient, and effective communication and to support the lecture operations. Data collection method in this study is carried out by distributing questionnaire to lecturers and students. The IM development method is by using the Rational Unified Process (RUP). XMPP protocol is implemented into architecture and IM is developed with features of private chat, class-grouping based chat, broadcast message, scheduled message, and file attachment. This IM can accelerate the delivery of information from educational institutions to or between students and lecturers.
Virtualisation

Hardware virtualisation technologies have improved significantly over the past decades, and have reached a level of maturity today, making them an omnipresent building block in modern cloud centre architectures. Virtualisation is also a core feature in modern satellite ground segment engineering and a key enabling technology to modernise existing infrastructure and deal with obsolete hardware and heritage operating systems. This chapter provides an introduction into the basic concepts of hardware virtualisation and explaines the concept of a hypervisor and a virtual machine (VM). The terminology of a Hyper-Converged Infrastructure (HCI) is introduced which builds on the three elements of computing, networking, and storage virtualisation simultaneously. Advantages of hardware virtualisation for the support of business continuity is outlined and the difference between a high availabilty and fault tolerant VM configuration is explained. Finally, the basic concept of container virtualisation is introduced as a further evolution of virtualisation which today has become an important technology in cloud based computing.
Database Table Modeling Design based on Snowflake Model

In this paper, the snowflake model, a multidimensional data model, is used to model the data required by the armored target vulnerability database from the three subjects of warhead, target and evaluation results, construct the subject domain in turn, divide the hierarchical granularity, and finally determine the tables and models required for the vulnerability database.
COGITO: A Platform for Developing Cognitive Environments

A cognitive environment (CE) is a smart environment having self-learning and self-adaptation capabilities. It is obtained by augmenting a physical environment by using IT equipment and artificial intelligence technologies. The goal is to furnish the environment’s dwellers and the environment itself with advanced services devoted to (i) improving the quality of life of the people, (ii) optimizing the use of shared resources and spaces, (iii) increasing security and safety, (iv) assisting people in daily life activities, (v) extending the lifetime of devices and infrastructures, and (vi) enforcing and actuating policies promoting sustainability, green-aware behaviors, and energy-saving management of the whole system. Realizing a CE is a complex and multidisciplinary task. It requires transversal skills among which those related to distributed systems, IoT and artificial intelligence technologies, and networking. In addition, suitable methodological approaches and platforms could be leveraged in order to deal with the complex process of designing and implementing a CE. In this chapter, as enabling technology, the COGITO platform is introduced. COGITO is an agent-based IoT platform tailored to the development of CEs in a heterogeneous continuum computing environment comprising cloud, fog, and edge resources. The practical use of the platform is demonstrated through some use cases developed at the ICAR-CNR headquarter at Rende (Italy).