Reviewerly/exHarmony huggingface dataset:
Semi-Adaptable Human Hand Motion Prediction Based on Neural Networks and Kalman Filter

Abstract This paper focuses on predicting trajectories of the human hand in order to improve the safety for human-robot interactions. In this work, the position and orientation are represented by two curves in the operation space such that the same algorithm can be used for both position and orientation prediction. The motion prediction is achieved in two steps. Firstly, the neural network (NN) model is applied for offline training to model the human hand motion. Secondly, the Kalman filter is added to adjust the weight coefficients of the NN model’s output layer online when a set of new data is measured, such that the NN model is adaptive to new data. An experiment study has been conducted to validate the effectiveness of the proposed algorithm. The result shows that the proposed algorithm achieves a higher prediction accuracy and requires a smaller amount of data to achieve optimal performance compared with the advanced method.
A Rapid Tour of NLP

In this chapter, we briefly review the NLP methods utilized in this book. Should the readers desire, a number of highly regarded texts have been authored recently (Eisenstein in Adaptive computation and machine learning series. MIT Press (2019) [], Goldberg in Synth Lect Human Lang Technol 10(1):1-309 (2017) []) which provide a thorough and rigorous grounding of NLP. We discuss static and contextual word and document embeddings, and their applications. We then look at polyglot training in static and contextual embeddings.
Frequency aware face hallucination generative adversarial network with semantic structural constraint

In this paper, we address the issue of face hallucination. Most current face hallucination methods rely on two-dimensional facial priors to generate high resolution face images from low resolution face images. These methods are only capable of assimilating global information into the generated image. Still there exist some inherent problems in these methods, such as, local features, subtle structural details and depth information are missing in final output image. This work proposes a generative adversarial network (GAN) based novel progressive face hallucination (FH) network to address these issues present among current methods. The generator of the proposed model comprises of FH network and two sub-networks, assisting FH network to generate high resolution images. The first sub-network leverages on explicitly adding high frequency components into the model. To explicitly encode the high frequency components, an auto encoder is proposed to generate high resolution coefficients of discrete cosine transform (DCT). To add three dimensional parametric information into the network, second sub-network is proposed. This network uses a shape model of 3D morphable models (3DMM) to add structural constraint to the FH network. Extensive experimentation evaluation show the usefulness of proposed architecture in the form of state-of-the-art quantitative results. • The paper presents multi module based GAN face hallucination network. • The first sub-network explicitly learns high frequency components to leverage image texture. • Second sub-network adds three dimensional parametric information. • Experiments on facial benchmark datasets reflect superior performance over state-of-the-art.
Bias Loss for Mobile Neural Networks

Compact convolutional neural networks (CNNs) have witnessed exceptional improvements in performance in recent years. However, they still fail to provide the same predictive power as CNNs with a large number of parameters. The diverse and even abundant features captured by the layers is an important characteristic of these successful CNNs. However, differences in this characteristic between large CNNs and their compact counterparts have rarely been investigated. In compact CNNs, due to the limited number of parameters, abundant features are unlikely to be obtained, and feature diversity becomes an essential characteristic. Diverse features present in the activation maps derived from a data point during model inference may indicate the presence of a set of unique descriptors necessary to distinguish between objects of different classes. In contrast, data points with low feature diversity may not provide a sufficient amount of unique descriptors to make a valid prediction; we refer to them as random predictions. Random predictions can negatively impact the optimization process and harm the final performance. This paper proposes addressing the problem raised by random predictions by reshaping the standard cross-entropy to make it biased toward data points with a limited number of unique descriptive features. Our novel Bias Loss focuses the training on a set of valuable data points and prevents the vast number of samples with poor learning features from misleading the optimization process. Furthermore, to show the importance of diversity, we present a family of SkipNet models whose architectures are brought to boost the number of unique descriptors in the last layers. Our Skipnet-M can achieve 1% higher classification accuracy than MobileNetV3 Large.
Fish aggregating devices as buoyant apparatus for improving safety on traditional fishing vessel

Abstract The availability of standard safety equipment on fishing vessels in Indonesia is still a problem that needs to be addressed. Liferaft, which functions as a rescue raft for the crew when the ship has an accident and sinks, is rarely found on traditional fishing vessel in Indonesia. Traditional fishermen believed that some of the equipment on board can be used as safety equipment, such as the floating part of Fish Aggregating Devices. Proper buoyant apparatus has been designed to fulfil the minimum standard for safety equipment according to FAO/ILO/IMO recommendation. The designed buoyant apparatus has other function as a floating part for FAD. The buoyant apparatus can hold 6 crew in the water, and at the same time can support FAD module that consist of 1 unit 100WP solar panel, 4 unit batteries 12v 18Ah, and 4 light @10watt.
FOSTERING COMMUNICATION SKILL OF PRESCHOOL CHILDREN

Academic and social skills in young children are dependent on effective communication. Children gain the language and communication skills needed to convey their wants, thoughts, and feelings in social interactions during their preschool years, as well as how to respond appropriately to others. They also learn to be socially competent persons through effective communication, developing respectful, good interactions and connections with others. This enables youngsters to gain knowledge about themselves, others, and the world around them. Early childhood teachers need to prioritize to teach communication skills early and strategically, particularly in preschool when aiding children acquire necessary the knowledge and skills to be successful in kindergarten and the primary grades. This article focuses on the importance of communication skills and discusses the way of promoting this competence.
Learning Hierarchical Representations with Spike-and-Slab Inception Network

Recently, deep convolutional neural networks (CNN) with inception modules have attracted much attention due to their excellent performances on diverse domains. Nevertheless, the basic CNN can only capture a univariate feature, which is essentially linear. It leads to a weak ability in feature expression, further resulting in insufficient feature mining. In view of this issue, researchers incessantly deepened the network, bringing parameter redundancy and model over-fitting. Hence, whether we can employ this efficient deep neural network architecture to improve CNN and enhance the capacity of image recognition task still remains unknown. In this paper, we introduce spike-and-slab units to the modified inception module, enabling our model to capture dual latent variables and the average and covariance information. This operation further enhances the robustness of our model to variations of image intensity without increasing the model parameters. The results of several tasks demonstrated that dual variable operations can be well-integrated into inception modules, and excellent results have been achieved.
Investigating the Critical Success Factors of Artificial Intelligence-Driven CRM in J. K. Tyres

AI-powered technologies allow online B2B companies to serve their customers with accurate and relevant information, 24/7. For example, they experience an increase in requests for information from customers on such aspects as product availability, features, or other services. The chapter aims to explore artificial intelligence in B2B business. The study employed qualitative research, and the data was collected through a focus group for data collection. An AI-powered chatbot enhanced with natural language processing and understanding conversationally-worded requests could instantaneously provide this information without a human representative. This is vital as the added uncertainty around the pandemic means business customers seek real answers and ways to adapt and fast. The findings suggest the critical success factors of AI-driven CRM in B2B markets. The limitations of the study include the data collection being restricted to one B2B company. The implications are that further study can be extended for exploring AI-based CRM in B2B markets.
A new projection neural network for linear and convex quadratic second-order cone programming

A new projection neural network approach is presented for the linear and convex quadratic second-order cone programming. In the method, the optimal conditions of the linear and convex second-order cone programming are equivalent to the cone projection equations. A Lyapunov function is given based on the G-norm distance function. Based on the cone projection function, the descent direction of Lyapunov function is used to design the new projection neural network. For the proposed neural network, we give the Lyapunov stability analysis and prove the global convergence. Finally, some numerical examples and two kinds of grasping force optimization problems are used to test the efficiency of the proposed neural network. The simulation results show that the proposed neural network is efficient for solving some linear and convex quadratic second-order cone programming problems. Especially, the proposed neural network can overcome the oscillating trajectory of the exist projection neural network for some linear second-order cone programming examples and the min-max grasping force optimization problem.
Analysis of MIMO detector using artificial neural network and deep neural network

: Next generation cellular wireless communication network should be effective, ultra reliable, low latency and an intelligent Network. By using Artificial Neural Network based algorithms can be preferred to solve current wireless network problems. The most complicated problems in current wireless network possibly effectively formulated and efficiently solved using Artificial Neural Network and powerful Deep Neural Networks. The DNN approach proved to be less complex with better BER performance over traditional MIMO detectors. This paper proposes the Conventional Neural network and Deep Neural Network proceeds for implementation of MIMO detectors. The proposed method exhibits better robustness and better BER performance.
A DBDN with the MAS Ellipsis

This demonstration paper aims to present the multi-agent system (MAS) Ellipsis, which has been enhanced within the joint research project SMART DISTRIBUTION LOGISTICS (SDL) to build a distributed agent-based Big Data network (DBDN) between the cooperating media logistics companies []. The goal was to enable an e-mobility focused TCO (total cost of ownership) analysis on a broad data basis while keeping the original data itself securely within the control of each company. With the help of mobile agents and a BDI concept, agents are able to react independently to unforeseen events. They are able to decide in an autonomous fashion, to travel and to transfer algorithms from one system to another. Thus, the system enables asynchronous communication between the logistics entrepreneur and the agent acting on its behalf in the TCO study. Proactive and distributed systems with autonomous units (acting deputies) on heterogeneous data meet the unbroken demand of companies and stakeholders for the sovereignty of their data and their underlying business models. These business models express themselves in dynamic structures and different types of negotiation.
Constructing VeSNet: Mapping LOD Thesauri onto Princeton WordNet and Polish WordNet

Lexical resources are crucial in many modern applications of Natural Language Processing and Artificial Intelligence. We present VeSNet – a network of lexical resources resulting from the merge of Polish-English WordNet (PEWN) with several existing large electronic thesauri from the Linked Open Data cloud (DBpedia, Wikipedia, GeoWordNet, Agrovoc, Eurovoc, Gemet and MeSH). We describe the procedure of making the resource and depict its elementary properties, as well as, evaluate its quality. The created lexical network is characterised both by great coverage and high precision: nearly 1.3M new exactMatch links were created, including 85K to PEWN, with the estimated precision of 94%.
Multimedia Sensor Network Supported IoT Service

Because of the ability to ubiquitously capture multimedia content from the environment, multimedia sensor networks have great potential for strengthening the traditional wireless sensor networks applications, as well as creating a series of new applications. After introducing three typical service patterns of IoT, information publish service, sensing-controlling service, and IoT search service, this chapter focuses on the IoT search and proposes a progressive search paradigm, which contains three important search strategies: (1) coarse-to-fine search in feature space; (2) near-to-distant search in spatial-temporal space; and (3) low-to-high permission search in the security space. This chapter also proposes a progressive vehicle re-identification framework based on deep neural networks.
Design and Implementation of New Media Resource Search System Based on Cognitive Heuristic

The rapid development of network promotes the development of multimedia teaching resources, which contains a large number of multimedia teaching resources. In order to effectively use these multimedia teaching resources, the importance of retrieval tools is highlighted. This paper designs and implements a teaching resource search system, which is used to search new media teaching resources related to education.
PREFERENCE AGGREGATION MODEL CONSTRUCTION OF GROUP MANAGEMENT DECISION-MAKING BASED ON RELATIVE ENTROPY AND NORM OPERATOR

Due to the complexity of the problem in the actual group management decision, the ambiguity of the information of all parties and the uncertainty of the decision environment, it is often difficult for decision-makers to describe the decision information by quantitative methods, but to give uncertain linguistic variables. Based on the analysis of previous research results, this paper proposes a group management decision-making aggregation model based on relative entropy and norm operator. First, the model uses the operation of [Formula: see text]-norm and new [Formula: see text]-norm operator to make the fuzzy set satisfy the ranking rate and contradiction rate. Then, based on the reliability of relative entropy, the attribute weights of different expert decisions are obtained, and the optimal assembly model of the corresponding group decision is established, respectively. The optimal solution and decision algorithm are given for each optimization model. The last example of the algorithm proves the validity and feasibility of the model, and can effectively avoid the distortion and distortion in the process of group management decision preference aggregation.
Research on intelligent mobile robot full coverage path planning

Aiming at the full coverage path planning problem of intelligent robot, a local chaos evaluation planning method is proposed. Considering the randomness and unpredictability of the mobile robot, a four-dimensional chaotic system with feedback control variables is designed. The path planning model is established by fusing the system with the robot motion model. At the same time, the coupling control parameters are introduced to adjust the system error. According to the initial coordinates and chaotic initial state parameters of the robot, the moving path points can be calculated by differential discretization. Considering the local optimal solution of the path planning, the mobile space of the robot is meshed, and the dynamic mesh activity value is calculated according to the excitation. The mesh activity is used to divide the mobile path planning, and then the local path and angular displacement changes are obtained. At the same time, the corresponding index evaluation is designed for local path planning to correct the planning results. The simulation results show that the proposed local chaos evaluation planning method has a good effect of full path coverage, and obtains lower path repetition rate, moving distance and path planning time, which effectively improves the mobile efficiency and control stability of the robot.
Stenosis Detection of X-Ray Coronary Angiographic Image Sequence

Automatic lesion detection from coronary X-ray angiography images is important for the auxiliary diagnosis of coronary heart diseases. However, the current methods are inefficient, and the detection accuracy cannot meet the criteria of doctors. This paper proposes a two-step method including video phase partition and video stenosis detection to automatically identify coronary stenosis from the complete X-ray angiography (XRA) video. First, convolutional neural network and long short-term memory based spatial–temporal network are used to automatically extract a continuous video segment that is full of contrast agent. Second, a detection network for attention video object is used to accurately and efficiently discern coronary stenosis on the continuous video segment. In the experiment, 166 video data were used for training and testing. The accuracy of video phase partition network can reach 0.838, and the precision and F1 of video stenosis detection results are 0.8 and 0.76 respectively. This performance is the best among all comparison methods. Therefore, we have implemented a complete process for detecting stenoses from coronary XRA sequences.
PENDIDIKAN LITERASI MEDIA GURU SEKOLAH MIS AL-HIDAYAH DALAM MENGHADAPI PENGARUH NEGATIF DUNIA DIGITAL PADA SISWA

In the digital era and current technological developments, there have been changes in human life in various ways, such as communication, especially in obtaining information, so that the negative effects of the digital world cannot be avoided. Therefore, someone needs to have media literacy skills, especially for students so that it can be a tool to reduce the negative influence of the presence of the digital world. Before students can have good media literacy skills, of course, this ability must also be possessed by parents and a teacher. Strengthening literacy skills in a teacher is a very important element because a teacher is a person who will provide learning to students in the world of education. Understanding media literacy is the main foundation in forming a smart and cultured generation in the digital era.
Saudi EFL Learners’ Perceptions of the Frequent Use of Spelling Correction Tools in Social Media

Spelling is an essential skill in EFL writing especially if writing is frequently used in texting through social media and smartphones. The study surveys Arabic EFL learners about their perceptions of using spelling correction tools: spell checkers and auto-correctors. The study focused on the perceived usefulness and ease of use of such tools in social media applications, since they are used frequently by the study’s participants. The questionnaire was sent online to 84 participants at Aljazeerah Academy in Riyadh, Saudi Arabia. The data was analyzed quantitively with descriptive and inferential statistics indicating that spell checkers are mostly perceived to be easier to use than auto-correctors. However, no significant difference was found in terms of learning the misspelled words.
Classification and Detection of Brain Tumor Using Segmentation Techniques on MRI Images

In health care monitoring and observations, medical imaging is playing a vital role. Intelligent machine learning techniques for computer-aided diagnosis for automatic identification and detection of infected tissues, defected organs require accurate image segmentation algorithms. In medical image analysis, brain tumor is of high priority for the better diagnosis to improve the quality of the treatment, which increases the existing rate of the patient. MR (magnetic resonance) scans organs, bones and tissues by using immense magnetic forces and radio waves to generate images, which gives clear comprehension that provides a powerful medical diagnostic tool especially for soft tissues like brain. It is a challenging and time-consuming process to identify brain tumors from MRI scans manually in medical routing. Hence computational image segmentation involves systematic and more simplifying anatomical organs, which gives complete information of three-dimensional brain tissue and provides accurate volumetric measurements and other regions of interest (ROI). In this paper, the methodology of implementation of three techniques namely K-Means clustering, thresholding and region growing segmentation algorithms for a spontaneous segmentation of white matter (WM), cerebrospinal fluid (CSF) and gray matter (GM) are presented. The add-on cranial regions and the existence of tumors and also described for the performance evaluation of the three different segmentation algorithms for accurate and significant quantitative search for classification, identification and detection in biomedical images. The experiments are carried out on MRI brain images, the classification accuracy on both training and test data images demonstrates the comparative investigation with the other existing machine learning techniques.
Automatic Syntax Error Correction

Correcting syntax errors in programming languages is not easy. The presence of false positives in the error report makes it more problematic. We have developed a tool that filters the spurious errors and gives suggestions to correct syntax errors. Our method is language agnostic. We operate on the grammar of the programming language. The algorithm that suggests corrections considers all changes that can be made at the location of the error and tries to produce corrections that prevent subsequent spurious errors and allow minimal change to input as possible. Our tool provides the user with a Graphical User Interface that shows suggested repairs and allows the programmer to apply them.
Quantum Networks with Artificial Atoms in Scalable Photonic Circuits: Architecture Designs to Proof of Concept Systems

The central challenge in building quantum computers and long-range quantum networks is distributing entanglement across multiple individually-controllable quantum memories. While proof-of-concept experiments are possible with today’s optical components, scaling these systems to tens, hundreds, or thousands of individually controllable quantum memories requires very-large-scale integration of electronic and photonic circuits.