Reviewerly/exHarmony huggingface dataset:
Architecture design of a reinforcement environment for learning sign languages

Different fields such as linguistics, teaching, and computing have demonstrated special interest in the study of sign languages (SL). However, the processes of teaching and learning these languages turn complex since it is unusual to find people teaching these languages that are fluent in both SL and the native language of the students. The teachings from deaf individuals become unique. Nonetheless, it is important for the student to lean on supportive mechanisms while being in the process of learning an SL. Bidirectional communication between deaf and hearing people through SL is a hot topic to achieve a higher level of inclusion. However, all the processes that convey teaching and learning SL turn difficult and complex since it is unusual to find SL teachers that are fluent also in the native language of the students, making it harder to provide computer teaching tools for different SL. Moreover, the main aspects that a second language learner of an SL finds difficult are phonology, non-manual components, and the use of space (the latter two are specific to SL, not to spoken languages). This proposal appears to be the first of the kind to favor the Costa Rican Sign Language (LESCO, for its Spanish acronym), as well as any other SL. Our research focus stands on reinforcing the learning process of final-user hearing people through a modular architectural design of a learning environment, relying on the concept of phonological proximity within a graphical tool with a high degree of usability. The aim of incorporating phonological proximity is to assist individuals in learning signs with similar handshapes. This architecture separates the logic and processing aspects from those associated with the access and generation of data, which makes it portable to other SL in the future. The methodology used consisted of defining 26 phonological parameters (13 for each hand), thus characterizing each sign appropriately. Then, a similarity formula was applied to compare each pair of signs. With these pre-calculations, the tool displays each sign and its top ten most similar signs. A SUS usability test and an open qualitative question were applied, as well as a numerical evaluation to a group of learners, to validate the proposal. In order to reach our research aims, we have analyzed previous work on proposals for teaching tools meant for the student to practice SL, as well as previous work on the importance of phonological proximity in this teaching process. This previous work justifies the necessity of our proposal, whose benefits have been proved through the experimentation conducted by different users on the usability and usefulness of the tool. To meet these needs, homonymous words (signs with the same starting handshape) and paronyms (signs with highly similar handshape), have been included to explore their impact on learning. It allows the possibility to apply the same perspective of our existing line of research to other SL in the future.
Relational Navigation Learning in Continuous Action Space among Crowds

In this paper, a novel navigation learning method in continuous action space among crowds based on relational graph is proposed which can be directly deployed on differential-drive mobile robots without any change. More specifically, in order to increase generalization ability in crowd sizes, Graph Convolutional Network (GCN) is at first adopted to extract the relationships between robot and pedestrians. Then the relation features are further utilized as the inputs of the pedestrian state prediction network, the actor network, and the critic network. To efficiently and safely learn the navigation policy, all networks are pretrained through imitating ORCA which is a state-of-the-art algorithm in crowd navigation, and then a model-based reinforcement learning (RL) method which combines the model prediction and the clipped advantage-weighted regression is proposed to finetune the networks. Finally, simulation experiments are performed and it’s verified that the proposed learning method performs significantly better than ORCA and the other state-of-the-art RL methods.
An Authentication Method for Ubiquitous Electric Internet of Things Communication Terminal Based on Coordinated Filtering

The current terminal authentication is mainly based on the identification of user markers, which can not be accurately judged according to the user behavior. It is not only complex to calculate the hardware requirements, but also inefficient when processing a large number of requests. In order to optimize the above defects, an identity authentication method for ubiquitous power Internet of Things communication terminals based on coordination filtering is proposed. After establishing the user identity characteristic information matrix of the communication terminal, the identity authentication protocol is designed by using the public key. Trust degree is introduced to improve coordination filtering, and the improved algorithm is used to process user identity information, so as to realize identity authentication. The design of comparative experiment verifies that the average certification time of the research method is less than 6ms, which can effectively carry out safety protection and is feasible.
Automatic Sign Language Identification Using Convolutional Neural Network

Mukherjee, Himadri Dhar, Ankita Obaidullah, S. K. Md. Phadikar, Santanu Roy, KaushikSign language is the mode of communication for such people who are not blessed with the gift of hearing and speech. It involves the use of hands and facial expressions, Understanding sign language is difficult for people with no prior knowledge. This leads to a communication gap for such people. An automated sign language identification system can aid in reducing this gap. Here, a system is presented to automatically identify sign language. Experiments were performed on the publicly available sign language MNIST dataset. The highest accuracy of 99.98% was obtained in this study with a convolutional neural network-based approach.
PhD Forum Abstract: Efficient Computing and Communication Paradigms for Federated Learning Data Streams

In this work, we proposed an integration of Federated Learning with Apache Kafka, an open-source framework that enables the management of continuous data streams with fault tolerance, low latency, and horizontal scalability. Our main focus is to evaluate the impact of learning delays and network overhead when hundred of users are sending their model updates for the aggregation to improve the global model in Federated Learning.
Data augmentation in network intrusion detection based on S-DCGAN

Aiming at the low detection accuracy problem of small samples in network intrusion detection, this paper researches the data expansion method based on the generative adversarial network algorithm. The data sample is generated to supplement the small data set sample, which is helpful to improve the accuracy of network intrusion detection. Firstly, the non-numerical data in the original data set is converted into numerical data by one-hot coding, and reconstructed them into two-dimensional data. Secondly, this paper proposes an S-DCGAN algorithm to generate data. Initially, we improve the network structure of deep convolutional generative adversarial networks (DCGAN). S_ReLU function is used instead of the ReLU function as the activation function after each convolutional layer of the adversarial network, which makes the quality of the generated data better and the effect of the generated data more stable. Furthermore, we improve the loss function of generation model and discriminant model of DCGAN. A simplified total variation loss function is added to the loss function of the generated model to reduce the noise of the generated data. The gradient penalty term is added to the loss function of the discriminant model to alleviate the problem of gradient explosion or gradient disappearance and improve the stability of network training. In order to verify its effectiveness, various classifiers are used to evaluate the performance of the proposed data augmentation method. The results show the accuracies for classifiers are increased after employing the S-DCGAN-based data augmentation techniques.
Single-sided Multi-layer Electric Circuit by Hot Stamping with 3D Printer

The spread of personal computer-aided fabrication has made it possible for individuals to create things that meet their personal needs. However, it is still not easy for an individual to prototype electronic circuits. Several methods have been proposed for quickly prototyping electronic circuits. Our solution is combining a 3D printer with transfer foil. This paper expands our original circuit printing method so as to fabricate single-sided multi-layer boards. We show that the proposed technique allows a 3D printer and two types of transfer foil to create single-sided multi-layer circuits.
How Much Do We Perceive Geometric Features, Personalities and Emotions in Avatars?

The goal of this paper is to evaluate the human perception regarding geometric features, personalities and emotions in avatars. To achieve this, we used a dataset that contains pedestrian tracking files captured in spontaneous videos which are visualized as identical virtual human beings. The main objective is to focus on individuals motion, not having the distraction of other features. In addition to tracking files containing pedestrian positions, the dataset also contains emotion and personality data for each pedestrian, detected through computer vision and pattern recognition techniques. We are interested in evaluating whether participants can perceive geometric features such as density levels, distances, angular variations and speeds, as well as cultural features (emotions and personality traits) in short video sequences (scenes), when pedestrians are represented by avatars. With this aim in mind, we propose two questions to be answered through this analysis: i) “Can people perceive geometric features in avatars?"; and ii) “Can people perceive differences regarding personalities and emotions in virtual humans without body and facial expressions?". Regarding the participants, 73 people volunteered for the experiment in order to answer the two mentioned questions. Results indicate that, even without explaining to the participants the concepts of cultural features and how they were calculated (considering the geometric features), in most cases the participants perceived the personality and emotion expressed by avatars, even without faces and body expressions.
Social Augmented Reality: A Multiperspective Survey

We introduce research on social augmented reality with an emphasis on social face-to-face interaction. In social interaction interactants employ knowledge about their conversational partner and have their verbal interaction supported by nonverbal interaction cues. We survey the issues that arise when we pursue social interaction in augmented reality. Since handhelds and bulky head-mounted devices hardly allow unobtrusive interaction we pay extensive attention to developments in the field of smart glasses and smart contact lenses. The focus is on the use of smart augmented reality glasses during social interactions. Acceptance issues and disruption of social interaction due to the use of these devices are also touched upon.
A Novel Path Planning Method Based on Regional Index Algorithm for Hyper-redundant Manipulator

Given a large amount of calculation in the traditional path planning algorithm and the inability to control the distance between the path and the obstacle, a path planning method based on a regional index is proposed in this paper. First, by comparing the three methods of measuring distance, the characteristics of the regional index method are outlined, and how to divide regions and the corresponding regional indicators are outlined. Secondly, the potential energy distribution maps by use of the traditional method and the regional index method respectively are analyzed, and the regional indicators method can be used to plan the path artificially. Finally, through simulation, four paths are obtained applying two artificial potential field methods and two regional index methods. By comparing the characteristics of these four paths, it is found that the regional index method can guarantee the distance between the path and the obstacle very well, which verifies the effectiveness of the method.
Complexity-Reduced Super Resolution for Foveation-Based Driving Head Mounted Displays

In this paper, we propose a foveation-based super resolution (SR) algorithm to create high resolution images from low resolution inputs for virtual reality head mounted displays. Because the proposed SR algorithm is integrated in the previous foveation-based driving technology to cover the small area around the foveation point that requires high rendering quality, the overall computational complexity is substantially reduced, compared to the whole area SR. The target display has 4 times as high resolution as the input image, therefore, the proposed SR algorithm generates 4× as well as 2× SR images at the same time. To support two SR output images, small area SR, and small number of weights, we employ cropping as well as progressive and recursive framework used in the previous MS-LapSRN. We reduce the number of neurons by placing the deconvolutional layer after convolutional layers, compared to the MS-LapSRN. PSNR and SSIM performances of the proposed SR for the 4× scale are estimated as 31.152 dB and 0.935 for Set5, 26.656 dB and 0.858 for Set14, 27.138 dB and 0.830 for BSD100, and 25.078 dB and 0.836 for Urban100. For the target 8K display of 7,680×4,320, the proposed FovSR-integrated driving technology achieves the substantial reductions by 76.7 % and 99.02 % on the number of lines from 7,680 to 1,788 and the number of neurons from 24,518,246,400 to 239,541,184, respectively.
Specifics of VR implementation in structuring of learning processes

Today there are many devices that allow people to perform everyday tasks faster, more reliably and more efficiently, such as informal communication, business correspondence, planning the daily routine, calculations, keeping records, including training. New ways of acquiring knowledge appeared, such as webinars, forums, knowledge bases, e-courses and distance learning. Nevertheless, the devices themselves are just a tool that can be used to increase the functionality of certain software tools. Annually the functionality of the programs is increasing, together with the improvement of the amount of code, resources and data in applications. In order to optimize their products, to reduce the size of applications and the resources they use, to support multiplatform, as well as to accelerate the development and implementation of new functionality and less labor-intensive support developers began to use various approaches.
AICoacher

There is a growing demand for online fitness due to the impact of the epidemic. This paper presents a real-time online fitness system framework called AICoacher, which offers different online coaches. The framework constructs an extensible AI-based architecture that supports a variety of fitness movements. Firstly, key frames of motion are extracted automatically, and the feature vectors are calculated with the body pose points. Secondly, the state transition matrix can effectively identify fitness actions and capture their time-continuous characteristics. Finally, AICoacher can accurately provide the number of repetitions and correction tips of fitness movements. Currently, the AICoacher has a number of fitness courses supported by online coaches and has been tested on hundreds of fitness movements. The code can be downloaded from https://github.com/liutiel/AICoacher.
Robust weighted measurement fusion Kalman filter for uncertain multi-sensor descriptor system with colored measurement noises

For uncertain multi-sensor descriptor stochastic linear system with colored measurement noises, by applying the singular value decomposition, the original system can be transformed into two reduced-order subsystems with correlated noises by means of difference transformation of the measurement equation. Then the problem of weighted measurement fusion Kalman filtering is transformed into prediction problem for subsystems. The robust weighted measurement fusion time-varying Kalman predictor is presented for the first subsystem. Based on the relationship between subsystems and original system, the robust weighted measurement fusion Kalman filter of the original multi-sensor descriptor system is presented. The effectiveness of the proposed algorithm is verified by a numerical simulation example.
Disentangled Representation Learning and Enhancement Network for Single Image De-Raining

In this paper, we present a disentangled representation learning and enhancement network (DRLE-Net) to address the challenging single image de-raining problems, i.e., raindrop and rain streak removal. Specifically, the DRLE-Net is formulated as a multi-task learning framework, and an elegant knowledge transfer strategy is designed to train the encoder of DRLE-Net to embed a rainy image into two separated latent spaces representing the task (clean image reconstruction in this paper) relevant and irrelevant variations respectively, such that only the essential task-relevant factors will be used by the decoder of DRLE-Net to generate high-quality de-raining results. Furthermore, visual attention information is modeled and fed into the disentangled representation learning network to enhance the task-relevant factor learning. To facilitate the optimization of the hierarchical network, a new adversarial loss formulation is proposed and used together with the reconstruction loss to train the proposed DRLE-Net. Extensive experiments are carried out for removing raindrops or rainstreaks from both synthetic and real rainy images, and DRLE-Net is demonstrated to produce significantly better results than state-of-the-art models.
Attention-based Multimodal Feature Fusion for Dance Motion Generation

Recent advances in deep learning have enabled the extraction of high-level skeletal features from raw images and video sequences, paving the way for new possibilities in a variety of artificial intelligence tasks, including automatically synthesized human motion sequences. In this paper we present a system that combines 2D skeletal data and musical information to generate skeletal dancing sequences. The architecture is implemented solely with convolutional operations and trained by following a teacher-force supervised learning approach, while the synthesis of novel motion sequences follows an autoregressive process. Additionally, by employing an attention mechanism we fuse the latent representations of past music and motion information in order to condition the generation process. For assessing the system performance, we generated 900 sequences and evaluated the perceived realism, motion diversity and multimodality of the generated sequences based on various diversity metrics.
Using Annotated Portfolios to Understand Webtext Author's Experiences and Design Practices

This poster presentation addresses an important question to communication design scholars across a variety of fields: What decisions do webtext authors make when composing? How do they decide how to balance writing with meta-discourse, linearity with flexibility? Using an annotated portfolio method, the authors studied how webtext designers approached conventions and innovative practices in webtext composition.
Ground Moving Target Tracking Filter Considering Terrain and Kinematics

This paper addresses ground target tracking (GTT) for airborne radar. Digital terrain elevation data (DTED) are widely used for GTT as prior information under the premise that ground targets are constrained on terrain. Existing works fuse DTED to a tracking filter in a way that adopts only the assumption that the position of the target is constrained on the terrain. However, by kinematics, it is natural that the velocity of the moving ground target is constrained as well. Furthermore, DTED provides neither continuous nor accurate measurement of terrain elevation. To overcome such limitations, we propose a novel soft terrain constraint and a constraint-aided particle filter. To resolve the difficulties in applying the DTED to the GTT, first, we reconstruct the ground-truth terrain elevation using a Gaussian process and treat DTED as a noisy observation of it. Then, terrain constraint is formulated as joint soft constraints of position and velocity. Finally, we derive a Soft Terrain Constrained Particle Filter (STC-PF) that propagates particles while approximately satisfying the terrain constraint in the prediction step. In the numerical simulations, STC-PF outperforms the Smoothly Constrained Kalman Filter (SCKF) in terms of tracking performance because SCKF can only incorporate hard constraints.
Game Transforms of Usual Designations of Males as a Means of Representing the Category of Masculinity in Informal Internet Communication

The aim of the research is to identify the mechanisms and to determine the functions of a language game with usual designations of males in Internet texts. The scientific originality of the research lies in the fact that it considers game transforms as a means of representing the category of masculinity and verbalizing gender notions of ordinary Russian speakers for the first time. As a result of the research, it has been determined that informal Internet communication is characterized by reconsideration of morphological category of gender, ludic distortion of phonetic and orthographic word image, as well as pseudo-restoration of its internal form; the expansion of the functions of a language game in gender Internet discourse has been proved.
Systematic Mapping of Literature on Applicable Patterns in Conceptual Modelling of Information Systems

Context: The addition of patterns to the Conceptual Model of Information Systems would allow to optimize the quality level of computer systems. In spite of the fact that there are several studies on the application of patterns in the different phases of software development, since the beginning, these studies have focused on programming rather than analysis and design. Objective: The aim of this study is to carry out a systematic mapping of the field of Software Engineering in an attempt to characterize state-of-the-art patterns that can be applied to Conceptual Modelling, identifying gaps and opportunities for future research. Method: A systematic mapping of existing literature on the topic was performed in order to identify the most important studies on the field. These were then classified and analyzed according to different criteria. We also carried out a subjective assessment highlighting the most important ideas and experiences identified. Results: As a result of systematic mapping, there were found 26 primary studies of interest published between January 1995 and December 2019. In general, these studies are associated with software modelling methodologies which provide empirical evidence of the impact of the use of patterns. Conclusions: In this preliminary study, it was possible to identify the most relevant pattern types that were applied in the Conceptual Modelling of Information Systems. Integrating these types of patterns with existing analysis and design tools is also an important challenge that needs to be addressed in this area.