Reviewerly/exHarmony huggingface dataset:
A Reconfigurable Accelerator for Generative Adversarial Network Training Based on FPGA

In recent years, generative adversarial networks (GANs) have been widely applied in various tasks, demonstrating outstanding performance, such as image generation, style transfer, and video generation. However, due to their high computation complexity and large amount of intermediate data to be stored, the on-device learning that trains GANs on embedded platforms remains a very challenging problem. In this work, we propose an FPGA-based reconfigurable accelerator for efficient GAN training. Firstly, the cascaded fast FIR algorithm (CFFA) is opti-mized towards GAN training, and a fast convolution processing element (FCPE) based on the optimized algorithm is introduced to support various computation patterns during GAN training. Secondly, a well optimized architecture on the basis of FCPEs is presented, which is flexible to support forward, backward, and weight gradient phases of GAN training. Finally, training of a prevailing network (DCGAN) is implemented on Xilinx VCU108 platform with our methods. Experimental results show that our design achieves 315.18 GOPS and 83.87 GOPS/W in terms of throughput and energy efficiency, respectively. Our accelerator achieves 4.0&#x00D7; improvement over the state-of-the-art design in energy efficiency.
Potential of beacons to enhance teaching and learning in stationary education.

Dynamic technological progress is actively influencing the shape of modern education. Inventions that are constantly appearing on the market are often adapted to the needs of schools. One of the technologies that shows special potential in didactics is beacons. Beacons are small devices that can be used for communication, navigation and researching the recipient’s behavior. These functionalities are used in the education process, enabling the creation of completely new learning experiences. According to research, they can be successfully used in the classroom, during the formal learning process, as well as in various institutions, such as in informal transfer of knowledge, for example in cultural institutions. These sensors can contribute to the creation of a dynamic space for the exchange of knowledge and experiences, which will automatically respond to the individual needs of the recipient. The main goal of this article is to present the possibility of using beacons in the context of stationary education. The considerations were based on a review of the available literature. The work draws attention not only to the problems of the sensors themselves, but also to the possibility of their cooperation with other technologies. The collaboration of technical tools may contribute to the creation of more comprehensive solutions and, as a result, an increase in the quality and efficiency of the education process. However, as the analysis shows – beacons can be a promising technology in education, but their use is niche. Perhaps in future years, there will be more such solutions that will enable a more accurate assessment of these sensors. The author intends to continue her research to support the adoption of the described technology in Polish educational institutions.
Unpaired Remote Sensing Image Super-Resolution with Multi-Stage Aggregation Networks

Mainstream image super-resolution (SR) methods are generally based on paired training samples. As the high-resolution (HR) remote sensing images are difficult to collect with a limited imaging device, most of the existing remote sensing super-resolution methods try to down-sample the collected original images to generate an auxiliary low-resolution (LR) image and form a paired pseudo HR-LR dataset for training. However, the distribution of the generated LR images is generally inconsistent with the real images due to the limitation of remote sensing imaging devices. In this paper, we propose a perceptually unpaired super-resolution method by constructing a multi-stage aggregation network (MSAN). The optimization of the network depends on consistency losses. In particular, the first phase is to preserve the contents of the super-resolved results, by constraining the content consistency between the down-scaled SR results and the low-quality low-resolution inputs. The second stage minimizes perceptual feature loss between the current result and LR input to constrain perceptual-content consistency. The final phase employs the generative adversarial network (GAN) to adding photo-realistic textures by constraining perceptual-distribution consistency. Numerous experiments on synthetic remote sensing datasets and real remote sensing images show that our method obtains more plausible results than other SR methods quantitatively and qualitatively. The PSNR of our network is 0.06dB higher than the SOTA method—HAN on the UC Merced test set with complex degradation.
A study on applying interactive art to the active aging of the elderly – Example by the work “Yuechun”

The aging of the population is an important issue facing the world that the issue of active aging can't ignore. Sensory stimulation and concentration training can help the elderly retrieving self-confidence and increasing happiness. In this study, we will combine interactive art and technology to promote the healthy development of the elderly both physically and mentally. Based on the life background of the elderly, three aspects of design are discussed, such as the needs of the elderly, interactive art, and human-computer interaction. Digital technology will bring more meaningful activity to the elderly so as to understand the feasibility of interactive art in the elderly interactive technology.In this study, we first explore the physical, mental functions, and cognitive functions of the elderly to understand the needs of the elderly, then analyzing the trends and kinds of interactive art and human-computer interaction, and summarizing the application cases related to the elderly. Finally, establishing the design principles to develop the work “Yuechun” interactive installation. “Yuechun” is an interactive projection system that uses a scanning rangefinder to track the hand position and provides sensory feedback via graphic operations to create an artistic situation. The expected results of this study are as follows. (1) The operation mode in the human-computer interaction interface can promote physical exercise. (2) The interactive art environment will bring pleasure to the elderly and attract their attention. (3) Interactive art installation can train the cognitive abilities of the elderly via visual, auditory, and tactile sensory stimulation.
Analysis of sentiments conveyed through Twitter concerning COVID-19

Due to the social and economic fallout from the COVID-19 pandemic, we sought to gauge the attitudes of social network users, in this case, Twitter, towards the topic using a sentiment analysis approach. We collected 178,683 tweets using the Twitter API based on queries for the high-frequency hashtag #covid19. After the preprocessing step, we classified them in a binary way (positive and negative) and according to their intensity (valence) using the VADER model and then the NRCLex dictionary, which allows us to classify feelings according to their affective class. The results suggest that overall, the feelings detected through the tweets are positive. In addition, users seem to be interestedin the pandemic as a trend rather than as a topic related to other social or economic aspects.
Fine-Tuning of Patterns Assignment to Subnetworks Increases the Capacity of an Attractor Network Ensemble

It is known that dividing an attractor network into a set of subnetworks whose connectivity is equivalent to the attractor network from which they come, and therefore with the same computational cost, increases the system's recovery capacity. This opens the possibility of optimizing the assignment of pattern subsets to the ensemble modules. The patterns subsets assignment to the network modules can be considered as a combinatorial optimization problem, where varied strategies (i.e. random vs. heuristic assignments) can be tested. In this work, we present a possible heuristic strategy driven by an overlap minimization in the subsets for assigning the patterns input to the modules of the ensemble attractor neural network. In terms of system pattern storage capacity, the assignment driven by the overlap minimization in each subset/module proved to be better than no specific assignment, i.e. distribution of patterns to modules randomly.
Semi-Supervised 3D Hand-Object Pose Estimation Via Pose Dictionary Learning

3D hand-object pose estimation is an important issue to understand the interaction between human and environment. Current hand-object pose estimation methods require detailed 3D labels, which are expensive and labor-intensive. To tackle the problem of data collection, we propose a semi-supervised 3D hand-object pose estimation method with two key techniques: pose dictionary learning and an object-oriented coordinate system. The proposed pose dictionary learning module can distinguish infeasible poses by reconstruction error, enabling unlabeled data to provide supervision signals. The proposed object-oriented coordinate system can make 3D estimations equivariant to the camera perspective. Experiments are conducted on FPHA and HO-3D datasets. Our method reduces estimation error by 19.5% / 24.9% for hands/objects compared to straightforward use of labeled data on FPHA and outperforms several baseline methods. Extensive experiments also validate the robustness of the proposed method.
High performing sentiment analysis based on fast Fourier transform over temporal intuitionistic fuzzy value

Sentiment analysis or opinion mining has an extensive area in the field of research. Today we consider the huge amount of structured and unstructured data available in the web for a particular subject to get an opinion. The surplus data handling termed as big data requires some new technology to deal with. This paper considers the requirement of sentiment analysis of such huge data for fast processing. Based on fast Fourier transform on temporal intuitionistic fuzzy set generated from text, this algorithm (FFT–TIFS) expedites the sentiment classification. Fourier analysis converts a signal from its time domain to its representation in frequency domain. Such frequency domain algorithm on temporal intuitionistic fuzzy set is used in sentiment analysis for the first time. This algorithm is useful for short Twitter text, document-level as well as sentence-level binary sentiment classification. It is tested on aclImdb, Polarity, MR, Sentiment 140 and CR dataset which gives an average of 80% accuracy. The proposed method shows significant improvement in required time complexity where the method achieves 17 times faster processing in comparison with sequential fuzzy C-means (FCM) method, and again, it is at least 7 times faster than distributed FCM method present in the literature. The method presented in this paper has a novel approach towards fastest processing time and suitability of various sizes of the text sentiment analysis.
Hierarchical Variational Autoencoders For Visual Counterfactuals

Conditional Variational Auto Encoders (VAE) are gathering significant attention as an Explainable Artificial Intelligence (XAI) tool. The codes in the latent space provide a theoretically sound way to produce counterfactuals, i.e. alterations resulting from an intervention on a targeted semantic feature. To be applied on real images more complex models are needed, such as Hierarchical CVAE. This comes with a challenge as the naive conditioning is no longer effective. In this paper we show how relaxing the effect of the posterior leads to successful counterfactuals and we introduce VAEX <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> an Hierarchical VAE designed for this approach that can visually audit a classifier in applications.
Systematic Analysis of Joint Entity and Relation Extraction Models in Identifying Overlapping Relations

Named entity recognition and relation extraction are two fundamental tasks in the domain of natural language processing. Joint entity and relation extraction models have attracted more and more attention due to the performance advantage. However, there are difficulties in identifying overlapping relations among the models. To investigate the differences of structures and performances of joint extraction models, this paper implements a list of state-of-the-art joint extraction models and compares their difference in identifying overlapping relations. Experiment results show that the models by separating entity features and relation features work better than the models with feature fusion in identifying overlapping relations on three publicly available datasets.
Evaluation of the Fuzzy Cognitive Model of Professional Competences

The problem of a specialist training level optimization is formulated as a multi-criteria nonlinear discrete programming problem, where the objective function in the form of a weighted power mean provides the required proportionality degree of the individual competences assessments, given by their reference (target) values.
Dynamic Dual Sampling Module For Fine-Grained Semantic Segmentation

Representation of semantic context and local details is the essential issue for building modern semantic segmentation models. However, the interrelationship between semantic context and local details is not well explored in previous works. In this paper, we propose a Dynamic Dual Sampling Module (DDSM) to conduct dynamic affinity modeling and propagate semantic context to local details, which yields a more discriminative representation. Specifically, a dynamic sampling strategy is used to sparsely sample representative pixels and channels in the higher layer, forming adaptive compact support for each pixel and channel in the lower layer. The sampled features with high semantics are aggregated according to the affinities and then propagated to detailed lower-layer features, leading to a fine-grained segmentation result with well-preserved boundaries. Experiment results on both Cityscapes and Camvid datasets validate the effectiveness and efficiency of the proposed approach. Code and models will be available at https://github.com/Fantasticarl/DDSM.
Adaptive Multi-Feature Fusion for Robust Object Tracking

In this paper, in order to better describe the object, an adaptive multi-feature fusion method is proposed, which makes full use of the advantages of various features. Firstly, hierarchical convolution features and two hand-crafted features are fused linearly, and the weights of different features are adjusted adaptively to obtain the optimal object representation in the tracking process. Secondly, a translation filter and a scale filter are adopted to estimate the object’s exact position and scale, respectively. Finally, in the model update stage, an efficient adaptive model update strategy is used to improve the performance, which can significantly alleviate the model noises. Extensive experimental results on well-known benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art tracking methods.
Vehicle Taillamp Intention Recognition for Intelligent and Connected Vehicles Based on YOLOv4

In order to achieve fast and accurate vehicle taillamp intention recognition for intelligent and connected vehicles, a taillamp intention recognition method based on the YOLOv4 algorithm was proposed. Firstly, the images of the vehicles in different lamping conditions during driving were collected, and the corresponding dataset was established for training and testing the model. Then the input parameter was modified, and the cosine annealing learning rate algorithm was applied to the YOLOv4 framework. Experimental result showed that this method had an accuracy of 90.27% for vehicle taillamp intention recognition and a detection rate of 20fps. This method can realize the intention recognition of vehicle taillamps, and provide a basis for driving decision-making of intelligent and connected vehicles.
Simple construction of a toroidal distribution from independent circular distributions

In this paper, we propose a new method for constructing a toroidal distribution from independent circular distributions by specifying marginal distributions. The constructed probability density function is expressed without additional normalizing constants, and its conditional distributions belong to the family of sine-skewed circular distributions. The simple expressions for trigonometric moments, especially for correlation measures, help characterize the proposed model. Maximum likelihood estimation is computationally tractable, and the correlation parameter is orthogonal to the location and concentration parameters when the marginal distributions are symmetric. Two illustrative applications are shown comparing genome structures between paired bacteria.
Javardeye: Gaze Input for Cursor Control in a Structured Editor

editing cursor 1. eye gaze 2. activation key 3. cursor switch latent cursor Figure 1: Latent Cursor: a secondary gaze-driven cursor locates the next editing point to be activated with a key.
The Digital Economy

The digital economy has emerged as an integral part of the world economy. This chapter defines the concept of digital economy and provides examples illustrating the extent and versatility of this branch of economic theory. The existence of the digital economy depends entirely on the evolution of the information and communication technology (ICT) and has created some of the largest companies in the world. This technological evolution started in the 1950s and continues until today. Key evolutions include the user adoption of the Internet and mobile technologies. The academic field of digital economics has many sub-categories, such as the platform economy and the attention economy.
Algorithm of Filling Region Generation Based on Implicit Surface in Complex Surface

research-article Algorithm of Filling Region Generation Based on Implicit Surface in Complex Surface Share on Authors: Jingxian Xu Chang'an University, China Chang'an University, ChinaView Profile , Yangfan Chen Chang'an University, China Chang'an University, ChinaView Profile , Hao Yuan Chang'an University, China Chang'an University, ChinaView Profile , Hengzhi Gu Chang'an University, China Chang'an University, ChinaView Profile , Zhen Xu Chang'an University, China Chang'an University, ChinaView Profile , Yi Han Chang'an University, China Chang'an University, ChinaView Profile Authors Info & Claims ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information SystemsMay 2021 Article No.: 251Pages 1–8https://doi.org/10.1145/3469213.3470684Online:18 August 2021Publication History 0citation10DownloadsMetricsTotal Citations0Total Downloads10Last 12 Months10Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Gender Recognition Using Deep Leering Convolutional Neural Network

Gender recognition is one of the challenges employed in computer vision. The current paper has proposed a Real-time system for gender recognition based on a Convolutional neural network. A convolutional neural network is defined as a deep learning algorithm used for image recognition and image classification. A real-time gender recognition system has the capability to recognize either the face of the person, whether is a male or female, even with having complex face images of variations. Many fields need this system to be utilized for security and identification purposes. The gender recognition model is built from scratch and trained on 10182 face images, out of which are 5027 Males and 5155 Females. The model has achieved 97.84% accuracy on validation data and 98.59% accuracy on training data. The advantage of this model can detect a gender in real-time with high performance.
Sentiment Analysis for Diagnostic Purposes

The natural language processing has been the subject of numerous studies over the last decade. They have focused on the various stages of text processing, from the prepared text, through vectorization, to the final understanding. One of the greatest challenges nowadays is to understand the emotions that are express in written text. This is all the more difficult because sometimes people are also unable to recognize the sentiment of the text. This work focuses on the analysis of emotional attitudes in the texts that are the statements of centuries old computer science at the Technical University of Lodz. Due to the free style of expression, the created application is a good basis for automatic analyses under the diagnostic angle, as an aid for psychologists, educators or sociologists. The bag-of-words and n-gram methods were used to vectorize the text, while for the classification of sentiments, k-nn and NBC were used.
Lexicalised Locality: Local Domains and Non-Local Dependencies in a Lexicalised Tree Adjoining Grammar

Contemporary generative grammar assumes that syntactic structure is best described in terms of sets, and that locality conditions, as well as cross-linguistic variation, is determined at the level of designated functional heads. Syntactic operations (merge, MERGE, etc.) build a structure by deriving sets from lexical atoms and recursively (and monotonically) yielding sets of sets. Additional restrictions over the format of structural descriptions limit the number of elements involved in each operation to two at each derivational step, a head and a non-head. In this paper, we will explore an alternative direction for minimalist inquiry based on previous work, e.g., Frank (2002, 2006), albeit under novel assumptions. We propose a view of syntactic structure as a specification of relations in graphs, which correspond to the extended projection of lexical heads; these are elementary trees in Tree Adjoining Grammars. We present empirical motivation for a lexicalised approach to structure building, where the units of the grammar are elementary trees. Our proposal will be based on cross-linguistic evidence; we will consider the structure of elementary trees in Spanish, English and German. We will also explore the consequences of assuming that nodes in elementary trees are addresses for purposes of tree composition operations, substitution and adjunction.
Gaussian Pseudo-Random Number Generator using LFSR's Rotation and Split

This paper proposes Gaussian pseudo-random number generator using linear feedback shift registers (LSFR). The proposed design combines cyclic rotation and bit splitting to increase the number of samples for applying the Central Limit Theorem (CLT). The proposed generator generates 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sup> +1 times longer sequence for the same N-bit random number and maintain a constant random number stream. Our proposed design has a low implementation cost and a better auto-correlation factor for k > 20 compared to previous related work, where k is the number of delays. The generator is useful for many applications in the fields of security, communication, and computing.
Smart Contracts: Some Practical Considerations

Contracts in which computerized protocols execute the terms—referred to as smart contracts1 —are being increasingly used. Especially, recent advancements in a blockchain have fueled the rapid growth of such contracts.2 Indeed, one of the most high-profile uses of a blockchain has been used in coding and processing smart contracts.