Reviewerly/exHarmony huggingface dataset:
Hand Gesture-based Sign Alphabet Recognition and Sentence Interpretation using a Convolutional Neural Network

Sign language (SL) recognition is intended to connect deaf people with the general population via a variety of perspectives, experiences, and skills that serve as a basis for the development of human-computer interaction. Hand gesture-based SL recognition encompasses a wide range of human capabilities and perspectives. The efficiency of hand gesture performance is still challenging due to the complexity of varying levels of illumination, diversity, multiple aspects, self-identifying parts, different shapes, sizes, and complex backgrounds. In this context, we present an American Sign Language alphabet recognition system that translates sign gestures into text and creates a meaningful sentence from continuously performed gestures. We propose a segmentation technique for hand gestures and present a convolutional neural network (CNN) based on the fusion of features. The input image is captured directly from a video via a low-cost device such as a webcam and is pre-processed by a filtering and segmentation technique, for example the Otsu method. Following this, a CNN is used to extract the features, which are then fused in a fully connected layer. To classify and recognize the sign gestures, a well-known classifier such as Softmax is used. A dataset is proposed for this work that contains only static images of hand gestures, which were collected in a laboratory environment. An analysis of the results shows that our proposed system achieves better recognition accuracy than other state-of-the-art systems.
Design and implementation of DA FIR filter for bio-inspired computing architecture

This paper elucidates the system construct of DA-FIR filter optimized for design of distributed arithmetic (DA) finite impulse response (FIR) filter and is based on architecture with tightly coupled co-processor based data processing units. With a series of look-up-table (LUT) accesses in order to emulate multiply and accumulate operations the constructed DA based FIR filter is implemented on FPGA. The very high speed integrated circuit hardware description language (VHDL) is used implement the proposed filter and the design is verified using simulation. This paper discusses two optimization algorithms and resulting optimizations are incorporated into LUT layer and architecture extractions. The proposed method offers an optimized design in the form of offers average miminimizations of the number of LUT, reduction in populated slices and gate minimization for DA-finite impulse response filter. This research paves a direction towards development of bio inspired computing architectures developed without logically intensive operations, obtaining the desired specifications with respect to performance, timing, and reliability.
Classification of Digital Teaching Resources Based on Data Mining

This paper mainly explores the classification of text digital teaching resources.Considering the features of the educational field, the weight calculation method of term frequencyinverse document frequency (TF-IDF) algorithm and the k-nearest neighbor (KNN) algorithm were separately improved, and combined into a novel classification method for digital teaching resources.In the improved TF-IDF algorithm, the intra-class or inter-class distribution of the occurrence frequency of feature terms is determined, making the weight calculation more accurate.By contrast, the classification accuracy of traditional TF-IDF algorithm is limited, because the algorithm only considers term frequency (TF) and inverse document frequency (IDF).The improved KNN algorithm is grounded on density cutting of intra-class and boundary regions, thereby solving the defects of the traditional KNN facing the uneven distribution of sample density of digital teaching resources.The density cutting ensures the uniform distribution of samples and reduce the time required for classification.Experimental results show that the KNN-based digital teaching resource classification method is feasible and effective.
Tracking of Moving Objects Based on Extended Kalman Filter

The paper deals with the moving object tracking in dynamic environments, which is one of the most important problems in the field of computer vision. Over the last decade, an intensive work has been extensively done to create smart, autonomous vehicles that provide very precise and fast algorithms for the object detection and tracking. Our paper elaborates and demonstrates how it can be possible to monitor the trajectory of moving objects with high precision using sensor data, where the detection has been previously done. The standard Kalman Filter is described as an introduction to the Extended Kalman Filter (EKF) which was used for the algorithm implementation. Therefore, a problem of choosing model equations is also described, as well as the KITTI dataset used for the object detection. The main contribution of this paper includes an algorithm for the trajectory tracking that is capable to predict the position of moving objects. This algorithm is verified by experiments using realistic dataset.
Alignment Method of an Axis Based on Camera Calibration in a Rotating Optical Measurement System

The alignment problem of a rotating optical measurement system composed of a charge-coupled device (CCD) camera and a turntable is discussed. The motion trajectory model of the optical center (or projection center in the computer vision) of a camera rotating with the rotating device is established. A method based on camera calibration with a two-dimensional target is proposed to calculate the positions of the optical center when the camera is rotated by the turntable. An auxiliary coordinate system is introduced to adjust the external parameter matrix of the camera to map the optical centers on a special fictitious plane. The center of the turntable and the distance between the optical center and the rotation center can be accurately calculated by the least square planar circle fitting method. Lastly, the coordinates of the rotation center and the optical centers are used to provide guidance for the installation of a camera in a rotation measurement system. Simulations and experiments verify the feasibility of the proposed method.
Shallow, Deep, Ensemble models for Network Device Workload Forecasting

Reliable prediction of workload-related characteristics of monitored devices is important and helpful for management of infrastructure capacity.This paper presents 3 machine learning models (shallow, deep, ensemble) with different complexity for network device workload forecasting.The performance of these models have been compared using the data provided in FedCSIS'20 Challenge.The R 2 scores achieved from the cascade Support Vector Regression (SVR) based shallow model, Long short-term memory (LSTM) based deep model, and hierarchical linear weighted ensemble model are 0.2506, 0.2831, and 0.3059, respectively, and was ranked 3 rd place in the preliminary stage of the challenges.
DeCoCo: Blockchain-based Decentralized Compensation of Digital Content Purchases

Current centralized e-commerce platforms have lock-in effects. These platform-binding effects are constructed towards the content creator and the consumer in the process of purchasing digital content. The lock-in effects originate from the situation in which a centralized platform offers a variety of value propositions towards content creators and consumers. In this article, we propose an approach to decentralize the value propositions and distribute them among different actors. Therefore, we develop a decentralized application based on smart contracts and the blockchain technology. With that application as a base, we offer an alternative to the current centralized marketplaces without lock-in effects.
Understanding robot acceptance/rejection: the SAR model

By shifting the focus from the future and robots’ potentiality to real opportunities, this paper investigates key factors of acceptance/rejection of robots’ service provision. A qualitative method was adopted due to the novelty of the topic, with several steps of investigation, to deal with the socially constructed meanings related to the agents’ acceptance and use of robots. By building on the service robot acceptance model, the paper addresses the role of value-in-context and context congruence to explain the contribution of conversational agents and service robots to value creation. An extended model – the SARM - is presented and highlights the crucial role of value-in-context, representing a novel element to be added to social-emotional, functional, and relational elements. The SARM offers a more complete understanding of users’ perception regarding robots by including context-specific reasons serving as important linkages between functional, emotional, and relational features regarding robot acceptance or rejection.
Design of a Sustainable Blockchain-Oriented Software for Building Workers Management

The building workers sector is one of the most challenging for Human Resources (HR) management. In this work we propose a solution relying on the Blockchain technology and present the design of a Blockchain-Oriented Software system conceived for managing the building workers sector with a focus on workers' safety and guided by sustainable and Agile Methodologies in software design. The proposed approach takes advantage of different features of the Blockchain technology and provides transparency for labour inspectors, grants data integrity and immutability, relies in tamper proof time stamps for any recorded activity, allows the implementation of Smart Contracts where clauses are automatically respected without the need of a trusted control authority, acknowledges the legal requirements in the field, including the possibility of creating the Operational Safety Plans, which construction companies have to provide and finally implements the creation of vacant job positions which workers can find and apply to. In order to achieve these goals we adopt the Blockchain-Oriented Software Engineering (BOSE) methodology to design Blockchain software applications and apply an Agile methodology centered on Blockchain Software development, (called ABCDE) for the design and development of the decentralized application. Such methodology allows to center the software development around the actors of the system in the specific domain, such as Building Workers, Construction Companies, Labour Inspectors and so on. In addition we rely on the software sustainability analysis, based on the five dimensions of sustainability, to evaluate the approach and to avoid mistakes in the system development. We design system elements with specific diagrams and we divided our system in the on-chain and the out-of-chain components. The implementation of the system, done by using Ethereum and the ERC721 standard, allows us to improve some aspect of the design, to know the deployment and usage costs, and to evaluate the effect of the user interface. Finally, we discuss about the effects of our system and its sustainability, and provide a comparison of our system with a similar per aims but centralized system.
Tutorial-Articles: The Importance of Data and Code Sharing

ABSTRACT Context: this document is designed to be along with those that are in the first edition of the new section of the Journal of Contemporary Administration (RAC): the tutorial-articles section. Objective: the purpose is to present the new section and discuss relevant topics of tutorial-articles. Method: I divide the document into three main parts. First, I provide a summary of the state of the art in open data and open code at the current date that, jointly, create the context for tutorial-articles. Second, I provide some guidance to the future of the section on tutorial-articles, providing a structure and some insights that can be developed in the future. Third, I offer a short R script to show examples of open data that, I believe, can be used in the future in tutorial-articles, but also in innovative empirical studies. Conclusion: finally, I provide a short description of the first tutorial-articles accepted for publication in this current RAC’s edition.
Gender Classification of Thai Facebook Usernames

This paper presents an application of machine learning to classify Facebook users' gender based on their username alone.User profile information on social networks is important in many studies, but occasionally no information is publicly available online, such as age or gender.Most studies only use textual information from the web page.Instead, we opted to study gender classification by username, in which the gender is inferred from the users first name and alias name.We focused only on Thai names which may have certain patterns that reveal the owner's gender.A combination of different models is proposed to classify gender based on Thai Facebook usernames.Each model was trained using a supervised learning approach.Furthermore, all the classification results were combined into a final model.Using this method, the model achieved 91.75% level of accuracy.
A Combination of Block-Based Chaos with Dynamic Iteration Pattern and Stream Cipher for Color Image Encryption

This research proposes an encryption method on images using a combination of chaotic methods, streams, and hash functions. SHA-1 is used as a hash function to encrypt key inputs to be more secure and can produce more dynamic keys at chaotic and stream encryption stages. Chaos encryption is done by dividing the image into small blocks where each encrypted block differs based on a dynamic key pattern based on chaotic keys. At the last stage, all blocks are made as whole images again to be encrypted by the stream method. Tests carried out on standard RGB images and Indonesian batik images. Encryption quality measurements using entropy, histogram analysis, UACI, NPCR, SSIM, PSNR, and the avalanche effect. Based on the results of trials the proposed method is proven to be resistant to various attacks such as statistics as evidenced by the average entropy value of 7.9996, avalanche effect value of 50.0366 and a relatively uniform histogram, while differential attack as evidenced by the value of UACI 33.5716 and NPCR 99.6082 where this value is very close to ideal. Also visually the results of the encryption look very chaotic and very different from the original image, which is evidenced by the value of PSNR 8.0191 and SSIM 0.0081. The decryption process can also be done perfectly wherein the resulting infinity value on PSNR and value 1 on SSIM.
Ease of Doing Business and Capital Market Development in a Demand Following Hypothesis: Evidence from ECOWAS

Abstract Capabilities of African businesses in a transformative role in solving the continent‘s challenges are underestimated and misunderstood. This study examined ease of doing business and financial development from a demand following hypothesis in the West African sub-region, employed Structural Equation Model covering the period of 2004 - 2017. Ten ease of doing business indicators and five distinct financial (capital market) development variables from the World Bank database were used. Findings indicate weak demand following the hypothesis of capital market development: positive and negative, depending on the measure of capital market development from the ease of doing business for West African countries majorly because of inadequate electricity. The indirect effect of the construction permit, property registration, access to credit, minority investors‘ protection and cross-border trading are indirectly significant to capital market development while starting a business, tax-paying, contract enforcement and settling insolvency are insignificant. The study recommended roadshow by West African capital markets to improve the listing of companies and the government should improve on the electricity supply.
Word Error Rate Estimation Without ASR Output: e-WER2

Measuring the performance of automatic speech recognition (ASR) systems requires manually transcribed data in order to compute the word error rate (WER), which is often time-consuming and expensive. In this paper, we continue our effort in estimating WER using acoustic, lexical and phonotactic features. Our novel approach to estimate the WER uses a multistream end-to-end architecture. We report results for systems using internal speech decoder features (glass-box), systems without speech decoder features (black-box), and for systems without having access to the ASR system (no-box). The no-box system learns joint acoustic-lexical representation from phoneme recognition results along with MFCC acoustic features to estimate WER. Considering WER per sentence, our no-box system achieves 0.56 Pearson correlation with the reference evaluation and 0.24 root mean square error (RMSE) across 1,400 sentences. The estimated overall WER by e-WER2 is 30.9% for a three hours test set, while the WER computed using the reference transcriptions was 28.5%.
High-Resolution Hyperspectral Mineral Mapping: Case Studies in the Edwards Limestone, Texas, USA and Sulfide-Rich Quartz Veins from the Ladakh Batholith, Northern Pakistan

The study of hand samples is a significant aspect of geoscience. This work showcases a technique for relatively quick and inexpensive mineral characterization, applied to a Cretaceous limestone formation and for sulfide-rich quartz vein samples from Northern Pakistan. Spectral feature parameters are derived from mineral mixtures of known abundance and are used for mineral mapping. Additionally, three well-known classification techniques—Spectral Angle Mapper (SAM), Support Vector Machine (SVM), and Neural Network—are compared. Point counting results from petrographic thin sections are used for validation the limestone samples, and QEMSCAN mineral maps for the sulfide samples. For classifying the carbonates, the SVM classifier produced results that are closest to the training set—with 84.4% accuracy and a kappa coefficient of 0.8. For classifying sulfides, SAM produced mineral abundances that were closest to the validation data, possibly due to the low reflectance of sulfides throughout the short-wave infrared spectrum with some differences in the overall spectral shape.
Bi-level Score Matching for Learning Energy-based Latent Variable Models

Score matching (SM) provides a compelling approach to learn energy-based models (EBMs) by avoiding the calculation of partition function. However, it remains largely open to learn energy-based latent variable models (EBLVMs), except some special cases. This paper presents a bi-level score matching (BiSM) method to learn EBLVMs with general structures by reformulating SM as a bi-level optimization problem. The higher level introduces a variational posterior of the latent variables and optimizes a modified SM objective, and the lower level optimizes the variational posterior to fit the true posterior. To solve BiSM efficiently, we develop a stochastic optimization algorithm with gradient unrolling. Theoretically, we analyze the consistency of BiSM and the convergence of the stochastic algorithm. Empirically, we show the promise of BiSM in Gaussian restricted Boltzmann machines and highly nonstructural EBLVMs parameterized by deep convolutional neural networks. BiSM is comparable to the widely adopted contrastive divergence and SM methods when they are applicable; and can learn complex EBLVMs with intractable posteriors to generate natural images.
On Computing the Discrete Hirschman Transform

The Discrete Hirschman Transform (DHT) is a generalization of the entropy-based Hirschman Transform that canonically represents digital signals with sparse basis functions. The DHT is computationally attractive in providing the flexibility in hardware implementations since it has a sparse structure that is similar to that of the Discrete Fourier Transform (DFT) basis. This Hirschman-based algorithm has been widely applied in many digital signal processing algorithms such as denoising, filtering, and linear convolution. In this paper, we have developed two fast algorithms: the radix-2 and radix-4 DHT algorithms. These efficient algorithms significantly reduce the number of nontrivial real computations when compared to the original DHT algorithm and other popular Fast Fourier Transforms (FFTs). We show that it is feasible to realize our new algorithms in hardware through the use of a repetitive application of butterfly computations in a structure that will be familiar to those already working with the similar FFT algorithms. Our proposed algorithms reduce the number of nontrivial real computations while efficiently using available hardware resources.
Rank and run-time aware compression of NLP Applications

Sequence model based NLP applications can be large.Yet, many applications that benefit from them run on small devices with very limited compute and storage capabilities, while still having run-time constraints.As a result, there is a need for a compression technique that can achieve significant compression without negatively impacting inference run-time and task accuracy.This paper proposes a new compression technique called Hybrid Matrix Factorization that achieves this dual objective.HLF improves low-rank matrix factorization (LMF) techniques by doubling the rank of the matrix using an intelligent hybrid-structure leading to better accuracy than LMF.Further, by preserving dense matrices, it leads to faster inference run-time than pruning or structure matrix based compression technique.We evaluate the impact of this technique on 5 NLP benchmarks across multiple tasks (Translation, Intent Detection, Language Modeling) and show that for similar accuracy values and compression factors, HLF can achieve more than 2.32× faster inference run-time than pruning and 16.77% better accuracy than LMF.
Raising Awareness about the Consequences of Human Activities on Natural Environments through Multisensory Augmented Reality: Amazon Rainforest and Coral Reef Interactive Experiences

Reverse engineering is of great interests for computer aided verification of freeform shapes.Accurate model is crucial to assure the validity of geometry assessment.Freeform reconstruction is especially challenging when the parts are thin and/or self-occlusion.An integrated measuring system was proposed by incorporating laser scanner into hardware mechanism on reconstructing model of complex shape.Mathematical model mapping the transformation relationship between system components and cylinder-based algorithm for mechanism calibration was presented.The proposed system was validated on blade measurement.Experimental results showed that the proposed system and calibration method were able to provide a reliable measurement on freeform shape.