Reviewerly/exHarmony huggingface dataset:
Coefficient quantization effects on new filters based on Chebyshev fourth-kind polynomials

The aim of this paper is to construct non-recursive filters, extensively used type of digital filters in digital signal processing applications, based on Chebyshev orthogonal polynomials. The paper proposes the use of the fourth-kind Chebyshev polynomials as functions in generating new filters. In this kind, low-pass filters with linear phase responses are obtained. Comprenhansive study of the frequency response characteristics of the generated filter functions is presented. The effects of coefficient quantization as one type of quantization that influences a filter characteristic are investigated here also. The quantized-coefficient errors are considered based on the number of bits and the implementation algorithms.
Teaching NLP with Bracelets and Restaurant Menus: An Interactive Workshop for Italian Students

Although Natural Language Processing (NLP) is at the core of many tools young people use in their everyday life, high school curricula (in Italy) do not include any computational linguistics education.This lack of exposure makes the use of such tools less responsible than it could be and makes choosing computational linguistics as a university degree unlikely.To raise awareness, curiosity, and longer-term interest in young people, we have developed an interactive workshop designed to illustrate the basic principles of NLP and computational linguistics to high school Italian students aged between 13 and 18 years.The workshop takes the form of a game in which participants play the role of machines needing to solve some of the most common problems a computer faces in understanding language: from voice recognition to Markov chains to syntactic parsing.Participants are guided through the workshop with the help of instructors, who present the activities and explain core concepts from computational linguistics.The workshop was presented at numerous outlets in Italy between 2019 and 2021, both face-to-face and online.
ICT Diffusion, Foreign Direct Investment and Inclusive Growth in Sub-Saharan Africa

This study examines the joint effects of ICT diffusion (composed of access, usage and skills), and foreign direct investment (FDI) on inclusive growth in sub-Saharan Africa (SSA). The study draws on data from the World Bank’s World Development Indicators, and the Global Consumption and Income Project for the period 1980–2019 for the analysis. The study provides evidence robust to several specifications from ordinary least squares and dynamic system GMM estimation techniques to show that: (1) FDI and ICT diffusion and corresponding components (ICT access, usage, skills) induce inclusive growth in SSA; (2) compared to its direct effect, FDI is remarkable in fostering shared growth in SSA in the presence of greater ICT diffusion, and (3) compared to ICT access and usage, ICT skills are more effective in driving inclusive growth in SSA. Overall FDI modulates ICT dynamics to engender positive synergy effects on inclusive growth. Policy recommendations are provided in line with the implementation of the African Continental Free Trade Area (AfCFTA) Agreement and the projected rise in FDI in SSA from 2022.
Memory-optimized distributed utility mining for big data

In recent days, social media, online services, smartphones, and the Internet of Things (IoT) produces large quantities of data every second. The generated data is structured, unstructured, or semi-structured and available in various formats. Therefore, traditional approaches are not sufficient to handle such kind of data effectively. High utility pattern mining is a famous study area of data analytics that incorporates utility measures to consider user-based constraints such as number of units and benefit, in addition to frequency statistics of datasets. It is also essential to make effective decisions, and their demand is increasing in the last decade. Several techniques have been suggested for utility-based frequent pattern extraction. However, these approaches are limited to data size and operate on standalone systems. We have proposed a parallel approach named distributed memory-optimized utility mining (DMOUM) for high utility-based frequent pattern mining to address this issue. A new pruning technique is implemented that greatly reduces processing time and memory use. The proposed approach can handle huge amounts of data, i.e., big data. DMOUM approach is implemented in cluster-node architecture using Spark. The results are validated on various real-time datasets and found proposed approach has better performance than cutting edge approaches in terms of run-time, memory consumption, and scalability measures. The proposed work will provide solutions to real-time issues such as health care, education, e-commerce, and so on.
Beyond Paragraphs: NLP for Long Sequences

Iz Beltagy, Arman Cohan, Hannaneh Hajishirzi, Sewon Min, Matthew E. Peters. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorials. 2021.
Facial Recognition and Emotional Expressions Over Video Conferencing Based on Web Real Time Communication and Artificial Intelligence

Since the emergence of the Coronavirus pandemic, relying on video conferencing skyrocketed. The stay home and stay safe slogan increased the need of using Video Conferences in—for example but not limited to—education, work meeting, business deals ,.. etc providing a safer yet effective communication channel. On other hand, online meetings are lacking interaction. Emotional expressions provide a method to determine the Audience’s impressions about the meeting. Demonstrated in this paper, an application for video conferencing capable of detecting the facial emotion like surprise, happy, sad or angry by reading the particepants’ facial expressions also it can detect age and gender. The application is based on WebRTC and TensorFlow.js technologies. WebRTC, Web Real Time Communication, is a collection of standards and protocols which are supported in most of the modern browsers. Using web applications and platforms based on WebRTC technology helps in establishing a peer to peer communication and streaming, transmitting and receiving video, audio and data in real-time. In other hand, TensorFlow.js is opensource models in JavaScript which applied the concept of machine learning. Both WebRTC and TensorFlow.js can be used as regular JavaScript APIs which provide optimization in the performance and simplicity of the applications because the most of processing will be over the client-side and that is the core of our application. In this paper, it’s used the OpenVidu as a videoconference platform based on WebRTC and integrated it to Faceapi.js which is built based on tensorflow.js to can recognition face emotions and expressions over videoconference. We will use four classifications of servers in our application. Hosted Server, signaling server, NAT traversal server, and media server are used for WebRTC to handle the streaming across various types of networks.
Ethical Management and Implementation of COVID-19 Immunity Passports and Vaccination Certificates: Lawfulness, Fairness, and Transparency

This paper analyzes the outcomes of an exploratory review of the current research on the ethical management and implementation of COVID-19 immunity passports and vaccination certificates. The data used for this study was obtained and replicated from previous research conducted by Ada Lovelace Institute, Ipsos, SchengenVisaInfo.com, The Serco Institute, Survation, and Verywell. We performed analyses and made estimates regarding whether COVID-19 immunity passports would risk exacerbating discrimination in law. Data collected from 4,800 respondents are tested against the research model. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.
Design, FPGA-based Implementation and Performance of a Pseudo Random Number Generator of Chaotic Sequences

Pseudo-Random Number Generator of Chaotic Sequences (PRNG-CS) has caught the attention in various security applications, especially for stream and block ciphering, steganography, and d ...
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices

The goal of meta-reinforcement learning (meta-RL) is to build agents that can quickly learn new tasks by leveraging prior experience on related tasks. Learning a new task often requires both exploring to gather task-relevant information and exploiting this information to solve the task. In principle, optimal exploration and exploitation can be learned end-to-end by simply maximizing task performance. However, such meta-RL approaches struggle with local optima due to a chicken-and-egg problem: learning to explore requires good exploitation to gauge the exploration's utility, but learning to exploit requires information gathered via exploration. Optimizing separate objectives for exploration and exploitation can avoid this problem, but prior meta-RL exploration objectives yield suboptimal policies that gather information irrelevant to the task. We alleviate both concerns by constructing an exploitation objective that automatically identifies task-relevant information and an exploration objective to recover only this information. This avoids local optima in end-to-end training, without sacrificing optimal exploration. Empirically, DREAM substantially outperforms existing approaches on complex meta-RL problems, such as sparse-reward 3D visual navigation.
T-Hy-Demosaicing: Hyperspectral Reconstruction Via Tensor Subspace Representation Under Orthogonal Transformation

This article aims to solve the problem of the hyperspectral imagery (HSI) demosaicing under a novel subsampling hyperspectral sensing strategy. The existing method utilizes the periodic structure of subsampling to estimate a fixed subspace in matrix form from the measurement result, which reduces the representation ability of the subspace in iterations and destroys the intrinsic structure of the tensor. To overcome these drawbacks, we propose a tensor-based HSI demosaicing (T-Hy-demosaicing) model with tensor subspace representation, which takes the low-tubal-rankness and the nonlocal self-similarity into account. In particular, we suggest a tensor singular value decomposition based on orthogonal transformation (Tran-based t-SVD) to learn the tensor subspace that possesses a more powerful representation ability. In addition, we develop an effective algorithm to solve the proposed nonconvex model under the framework of the proximal alternating minimization algorithm. Experiments conducted on simulated datasets illustrate that the proposed method outperforms other comparative methods in both visual and quantitative terms.
Comparative studies of multiscale edge detection using different edge detectors for MRI thigh

Edge detection plays an important role in computer vision to extract object boundary. Multiscale edge detection method provides a variety of image features by different resolution at multiscale of edges. The method extracts coarse and fine structure edges simultaneously in an image. Due to this, the multiscale method enables more reliable edges are detected. Most of the multiscale methods are not translation invariant due to the decimated process. They mostly depend on the corresponding transform coefficients. These methods need more computation and a larger storage space. This study proposes a multiscale method that uses an average filter to smooth image at three different scales. Three different classical edge detectors namely Prewitt, Sobel and Laplacian were used to extract the edges from the smooth images. The edges extracted from the different scales of smooth images were then combined to form the multiscale edge detection. The performances of the multiscale images extracted from the three classical edge detectors were then compared and discussed.
X-Ray Images Dataset Augmentation with Progressively Growing Generative Adversarial Network

This paper proposes a deep learning based architecture to mitigate the problem of imbalance medical dataset due to non-availability of adequate data and privacy policy of medical organizations. A modified Progressively Growing Generative Adversarial Network (PGGAN) is used to create good quality of artificial X-ray images to augment the original dataset. A dataset having 2,000 X-ray images per class representing four different pathologies is used by the modified PGGAN for data augmentation. Transfer learning is used to adopt standard classification models which are fine tuned to suit this application. Fine tuning is done by re-training the classification models with the newly created augmented and balanced dataset (original X-ray images and artificially created X-ray images). The augmented dataset containing a total of 56,000 X-ray images are used to train and 4,000 original images are used to test the classification models. The experimental results show that the use of modified PGGAN for data augmentation and transfer learning for classification models increases the accuracy of predicting Chest pathologies.
On the Use of Context for Predicting Citation Worthiness of Sentences in Scholarly Articles

Rakesh Gosangi, Ravneet Arora, Mohsen Gheisarieha, Debanjan Mahata, Haimin Zhang. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.
One-Shot M-Array Pattern Based on Coded Structured Light for Three-Dimensional Object Reconstruction

Pattern encoding and decoding are two challenging problems in a three-dimensional (3D) reconstruction system using coded structured light (CSL). In this paper, a one-shot pattern is designed as an M-array with eight embedded geometric shapes, in which each 2 × 2 subwindow appears only once. A robust pattern decoding method for reconstructing objects from a one-shot pattern is then proposed. The decoding approach relies on the robust pattern element tracking algorithm (PETA) and generic features of pattern elements to segment and cluster the projected structured light pattern from a single captured image. A deep convolution neural network (DCNN) and chain sequence features are used to accurately classify pattern elements and key points (KPs), respectively. Meanwhile, a training dataset is established, which contains many pattern elements with various blur levels and distortions. Experimental results show that the proposed approach can be used to reconstruct 3D objects.
The Communication of Hit Quality Using Natural History Visualizations (NHVs)

High-throughput screening (HTS) often yields a list of compounds that requires prioritization before further work is performed. Prioritization criteria typically include activity, selectivity, physicochemical properties, and other absolute or calculated measurements of compound "value." One critical method of compound prioritization is often not discussed in published accounts of HTS. We have referred to this oft-overlooked metric as "compound natural history." These natural histories are observational evaluations of how a compound has been reported in the historical literature or compound databases. The purpose of this work was to develop a useful natural history visualization (NHV) that could form a standard, important part of hit reporting and evaluation. In this case report, we propose an efficient and effective NHV that will assist in the prioritization of active compounds and demonstrate its utility using a retrospective analysis of reported hits. We propose that this method of compound natural history evaluation be adopted in HTS triage and become an integral component of published reports of HTS outcomes.
Ensemble Modeling on Job Scam Detection

Abstract The new advancement of online enrollment and occupation enlistment systems has made another media for web fraudsters. The improvement of the update of ongoing innovation has restricted a few organizations to move into electronic board structures. The benefits of such electronic edges are liberal. From one perspective, they are the most ideal approach to lead numerous organizations’ applicants, and afterward once more, the contenders while following a task will be more involved. Community with bogus motives research these structures for sensitive information to be optimized by techniques for fake counteractive action advancements. In this article we are utilizing open informational indexes and AI computations to arrange phony or real posts. The target of this audit is to fuse both content, put together information and meta-information with respect to the situations in the work. The informational collection can be utilized to make characterization models that can acclimate themselves with the deceptive arrangements of duties. Gathering information models utilizing request computations like Logistic Regression, K Nearest Neighbors and RandomForest.
Design of Manifold with Pressure Controller for Automatic Exchange of Oxygen Gas Cylinders in Hospital

The regulation and supply of oxygen as one of the medical gases in the hospital is important to ensure the availability of these gases for the survival of patients. The regulation of oxygen gas in hospitals usually uses a piping system with manifolds. The manifold will monitor the oxygen gas pressure on each tube. Manifold systems that are widely used in general can only monitor pressure but cannot perform an automatic exchange on gas cylinders if the pressure is under the permissible conditions. The manifold system design developed is equipped with pressure monitoring for automatic exchange of oxygen gas cylinders using pressure sensors and microprocessors. The test results of the system using regulator and barometer comparisons showed the percentage value of sensor pressure accuracy of 96.92 percent and 97.16 percent. At pressure below the limit of 285 KPa manifold can perform the exchange of active gas cylinders automatically. These results show the manifold design built can work quite well.
RETRACTED: Research on the Opportunities and Challenges Faced by Educational Leadership Based on Artificial Intelligence in the Education Field

Abstract Artificial intelligence technology is the advanced computer technology. It has brought earth-shaking changes to the education field. This paper analyzes the integration and transfer of artificial intelligence technology in the education field, and it also elaborates the challenges and development trends of artificial intelligence.
IDEF Models And Innovative System For Search Data In Stochastic Information Environment

IDEF models of the method of forming a semantic connection through calculations of the relevance on the basis of the area with the zone and attributes, and the calculation of the most cost-effective value of g, the method of calculating the weight of the data frequency and the frequency with inverse data, based on the model of intellectual semantic search, the creation of the ISIDR in the SIS and for software.
Instabilities of Offline RL with Pre-Trained Neural Representation

In offline reinforcement learning (RL), we seek to utilize offline data to evaluate (or learn) policies in scenarios where the data are collected from a distribution that substantially differs from that of the target policy to be evaluated. Recent theoretical advances have shown that such sample-efficient offline RL is indeed possible provided certain strong representational conditions hold, else there are lower bounds exhibiting exponential error amplification (in the problem horizon) unless the data collection distribution has only a mild distribution shift relative to the target policy. This work studies these issues from an empirical perspective to gauge how stable offline RL methods are. In particular, our methodology explores these ideas when using features from pre-trained neural networks, in the hope that these representations are powerful enough to permit sample efficient offline RL. Through extensive experiments on a range of tasks, we see that substantial error amplification does occur even when using such pre-trained representations (trained on the same task itself); we find offline RL is stable only under extremely mild distribution shift. The implications of these results, both from a theoretical and an empirical perspective, are that successful offline RL (where we seek to go beyond the low distribution shift regime) requires substantially stronger conditions beyond those which suffice for successful supervised learning.
Quantile regression modeling to predict extreme precipitation

Abstract Quantile regression is an extension of the median regression that analyzes various quantile values. This method is used to predict the relationship between the response variable (Y) and the predictor variable (X) on the conditional quantile function. Quantile regression can be used to detect extreme conditions, either extreme dry (quantile 5) or extreme wet (quantile 95). Extreme precipitation often occurs in Indonesian territory because the area is surrounded by oceans. High frequency of extreme precipitation may trigger disasters, one of which is flooding. In 2017, there were floods in Sidoarjo area with a loss of up to 2 billion. In an effort to anticipate the adverse effects of extreme precipitation, forecast information abaut extreme precipitation is needed, one of which is using quantile regression. The objectives of this research was to determine the best quantile regression model for predict extreme precitipation. The data of this study were secondary data obtained from BMKG with a data length of 30 years. Modeling of extreme precipitation in Sidoarjo area involved variables of humidity, temperature and air pressure for model 1. Whereas model 2 involved 3 variables in model 1 plus the month and month square variables. The addition of the month variable and the month quadratic variable in model 2 was based on the precipitation data plot which formed a quadratic trend. Based on the Pseudo R 2 value, it can be concluded that the best model to predict extreme precipitation in Sidoarjo is Model 2.
Binary Image Encryption Based on Chaotic and DNA Encoding

This paper proposed method for encrypting images depending on the chaotic system and the DNA coding. The encrypting operation is done with several procedures. In the first procedure, the image is divided into non-overlapped blocks of n × n pixels. In the second procedure, the divide blocks are scrambled according to the desired key. In the third procedure, the quadratic chaotic system is used for generating a mask in order to scramble the pixels of the scrambled blocks. In the final procedure, the encrypting process based on DNA rules is used to encoding the scrambled image. Also, the decryption operation is done with several procedures. In the first procedure, the encrypted image is decrypted based on DNA rules. In the second procedure, the pixels of scrambled blocks are descrambled via quadratic chaotic system. Finally, the scrambled block is descrambled in order to get the decrypted image. More details about the proposed method are shown in the sections of the paper. The experimental results show that the proposed method satisfied the requirements of encryption.
Indoor Intelligent Decoration System based on BIM + VR Technology

Abstract In the interior decoration industry, disputes between design and actual effects, waste of materials, and poor construction quality often occur. In order to improve customer satisfaction, reduce waste of construction resources, and realize smart decoration in the interior decoration industry, BIM + VR technology has begun to take the stage. BIM + VR interior decoration technology is the use of BIM technology to build a three-dimensional model to realize the three-dimensional visual display of the decoration design plan. Users can really feel the effect of interior decoration through VR technology, and modify the unsatisfactory parts of the design before construction, which can more accurately meet the needs of users. And finally achieve the smart decoration function.