Reviewerly/exHarmony huggingface dataset:
Rapid Image Super Resolution

Image Super-Resolution (ISR) is a long-established challenge that finds extensive usage in the field of medical imaging, media consumption, drone surveillance, etc. Recent advancements in deep learning and improved GPU hardware have enabled researchers to create sophisticated research work. Earlier approaches focused on improving the Peak-Signal-to-Noise-Ratio of SR images, but it led to the loss of finer details. Recently GAN-based architectures like SRGAN and ESRGAN have been introduced which improves the human-perceived quality of the generated SR images. However, these architectures have high computational costs, not suitable for low-end or mobile devices. We propose a lighter, faster, and optimized GAN-based super-resolution architecture, Rapid-SR, using depthwise convolutional layers. It produces similar results as state-of-the-art approaches while reducing the model parameters and the time taken to produce SR images substantially. We also use a novel training strategy for Rapid-SR which incorporates the measure of the perceived similarity in the training loss by using Learned perceptual image patch similarity (LPIPS). The results are analyzed and compared using PSNR/SSIM, LPIPS, and Mean Opinion Scoring.
Intelligent Negotiation Bot using Machine Learning Techniques

E-commerce, in today’s day and age is becoming increasingly competitive, and there’s an emerging need for new technological advancements in order to stand in this competitive market. The employment of a negotiation bot is a new AI technology that has a growing appeal in E-commerce. The goal of a negotiation is not just to get the most money for yourself, but also to persuade your opponent that the deal is good for him. Businesses can use negotiation bots to personalize rates for each consumer and offer them discounts. The majority of people lack bargaining skills when it comes to trading, which causes them to miss out on a fair and lucrative offer. This paper proposes to develop an intelligent chatbot that can act as sellers in bargaining conversations with buyers.
Simulation of Vending Machine Design using Verilog HDL

Today, due to advancement in the field of electronic industry vending machine use is increasing rapidly. Food items, newspaper, beverages, Toys, tickets, and lot more consumer products are sold. Due to better quality product and fast processing speed users of vending machines are increasing in metropolitan cities. This paper aims to design an FSM based vending machine for dispensing products of different types and cost and verify its behavioral specification, logical operation and to generate RTL netlist of the proposed design. The proposed algorithm is implemented using Verilog HDL in Xilinx Vivado 2018.3 tool and simulated using Vivado simulator.
Efficient Face Mask Detection For Different Surveillance Applications Using Python

In this paper, the application of face mask detection for tracking has been explored. This model can be used for data collection and its interpretation. The proposed scheme has been designed to detect between a masked and a non-masked person with the help of python. This proposed system requires an inbuilt webcam and operating system of Windows 10 version or higher. Furthermore, the scheme may find applications in the video surveillance system. The model is validated by TensorFlow, MobileNet, and the Transfer Learning platform.
Mapping of ferric (Fe<sup>3+</sup>) and ferrous (Fe<sup>2+</sup>) iron oxides distribution using ASTER and Landsat 8 OLI data, in Negash Lateritic iron deposit, Northern Ethiopia

Iron plays an important role in industrial and engineering fields development of a country and as such there is an enormous demand for iron in Ethiopia. However, a search for this valuable primary mineral resource exploration remains challenging and costly. Therefore, this study aims to map iron oxide minerals using Landsat-8/operational land imager (OLI) and advanced space-borne thermal emission and reflection (ASTER) satellite imagery in Negash Lateritic iron deposit, Northern Ethiopia to ease the costs and reduce the time. Different image processing techniques such as band ratio, selective principal component analysis, linear spectral unmixing, and mixture-tuned matched filter were used to produce iron oxide maps. Minimum noise fraction (MNF), pixel purity index (PPI), and N-dimensional visualizer were also applied to extract endmembers in the automated spectral hourglass wizard. In addition to this, the enhanced image thresholding and scatter plot were used to map the potential areas. Ferric iron oxide band ratio of ASTER mapped maximum area of 62.1 km2 followed by a laterite band ratio of ASTER covering 57.8 km2. The result was validated using existing iron oxide polygons and the outcome obtained from selective PCA shows a strong match with the existing iron oxide polygons. The sub-pixel mapping techniques show poor accuracy in mapping goethite and hematite relative to the pixel level. Thus, it is evident from the results that ASTER mapped better than Landsat 8 OLI for band ratios of selective PCA, unmixing, MTMF, and mineralized areas while characterizing with limited fieldwork.
LIMU-BERT

Deep learning greatly empowers Inertial Measurement Unit (IMU) sensors for a wide range of sensing applications. Most existing works require substantial amounts of wellcurated labeled data to train IMU-based sensing models, which incurs high annotation and training costs. Compared with labeled data, unlabeled IMU data are abundant and easily accessible. This article presents a novel representation learning model that can make use of unlabeled IMU data and extract generalized rather than task-specific features. With the representations learned via our model, task-specific models trained with limited labeled samples can achieve superior performances in typical IMU sensing applications, such as Human Activity Recognition (HAR).
A Voice Cloning Method Based on the Improved HiFi-GAN Model

With the aim of adapting a source Text to Speech (TTS) model to synthesize a personal voice by using a few speech samples from the target speaker, voice cloning provides a specific TTS service. Although the Tacotron 2-based multi-speaker TTS system can implement voice cloning by introducing a d-vector into the speaker encoder, the speaker characteristics described by the d-vector cannot allow for the voice information of the entire utterance. This affects the similarity of voice cloning. As a vocoder, WaveNet sacrifices speech generation speed. To balance the relationship between model parameters, inference speed, and voice quality, a voice cloning method based on improved HiFi-GAN has been proposed in this paper. (1) To improve the feature representation ability of the speaker encoder, the x-vector is used as the embedding vector that can characterize the target speaker. (2) To improve the performance of the HiFi-GAN vocoder, the input Mel spectrum is processed by a competitive multiscale convolution strategy. (3) The one-dimensional depth-wise separable convolution is used to replace all standard one-dimensional convolutions, significantly reducing the model parameters and increasing the inference speed. The improved HiFi-GAN model remarkably reduces the number of vocoder model parameters by about 68.58% and boosts the model's inference speed. The inference speed on the GPU and CPU has increased by 11.84% and 30.99%, respectively. Voice quality has also been marginally improved as MOS increased by 0.13 and PESQ increased by 0.11. The improved HiFi-GAN model exhibits outstanding performance and remarkable compatibility in the voice cloning task. Combined with the x-vector embedding, the proposed model achieves the highest score of all the models and test sets.
Prediction and Management of Regional Economic Scale Based on Machine Learning Model

The country is developing rapidly and the economy is taking off. For the country, the economic development of each region is very important, and the economy is the key to measuring the development of a region. To this end, the machine learning model is combined with regional economic scale prediction and management, and the two are studied together to obtain the following: (1) The key to measuring the regional economy is the growth rate of GDP and GDP. For the economic comparison between region A and region B, the economic scale is predicted by machine learning model. The regional economic development is better, which has an important basis for the comparison of the following methods. (2) There are many ways to improve the regional economy. Among them, the development of the industrial economy has the highest degree of regional economic indicators GDP and GDP growth rate. (3) The machine learning model has many benefits in the development of regional economic forecasting. Among them, reducing errors has the greatest impact on the regional economy. By comparison, it can be known that the accuracy of the regional economic forecasting under the machine learning model and the required forecasting time have been greatly optimized.
Collaborative analysis for energy usage monitoring and management on a large university campus

This describes a successful ongoing collaboration between the Department of Statistics and the Facilities Operations at the University of Connecticut (UConn). The collaboration stemmed from facilities reaching out to the productive Statistical Consulting Services (SCS) in the Department of Statistics for help in understanding and optimizing energy usage across the large university campus. Starting on a small scale with one faculty leader and two graduate assistants in 2016, the continued high level of deliverables resulted in the project growing in personnel and scope to involve four statistics faculty collaborators, four engineers and operations staff from the facilities unit, one staff from Office of Budget and Planning, one postdoc, ten PhD graduate assistants, four MS graduate students, five UConn undergraduate students, and two international visiting undergraduate researchers. This paper highlights this journey and explains how the concerted effort and team work continues to pave the way for useful implementation, many‐tiered mentoring, interesting research projects, and team satisfaction of a job well done.
A New Design Paradigm for Enabling Smart Headphonse

Headphones continue to grow more intelligent as new functions (e.g., touch-based gesture control) appear. These functions usually rely on auxiliary sensors (e.g., accelerometer and gyroscope) that are available in smart headphones. However, for those headphones that do not have such sensors, supporting these functions becomes a daunting task. This paper presents HeadFi, a new design paradigm for bringing intelligence to all headphones. Instead of adding auxiliary sensors into headphones, HeadFi turns the pair of drivers that are readily available inside all headphones into a versatile sensor to enable new applications, spanning across mobile health, user-interface, and context-awareness. HeadFi works as a plug-in peripheral connecting the headphones and the pairing device (e.g., a smartphone). The simplicity (can be as simple as just two resistors) and small form factor of this design lend itself to be embedded into the pairing device as an integrated circuit. We envision that HeadFi can serve as a vital supplementary solution to existing smart headphone design by directly transforming large amounts of existing "dumb" headphones into intelligent ones.
‘It’s Not a Race, It’s a Religion’: Denial of Anti-Muslim Racism in Online Discourses

This article investigates the denial of anti-Muslim racism in online discourses. It does so by examining Facebook posts responding to a bystander anti-racism video about a Muslim woman. Particularly salient on social media is the lack of rules, etiquette or social taboos around racism controlling what people post and how they express themselves. This allows comments that are blatantly racist and antagonistic rather than concealed and subtle as is more socially acceptable in offline spaces. Using critical discourse analysis, the article will delve into the rhetorical and linguistic strategies the posters use to deny that racism toward Muslims can exist. It will expose how the denial of anti-Muslim racism is used in attempts to silence Muslims and anti-racists as well as to convince the general public that Muslims deserve the ill treatment they receive. However, exposing the strategies of racism deniers gives us a better understanding of how to resist such discourses.
Designing the Metaverse: A Study of Design Research and Creative Practice from Speculative Fictions to Functioning Prototypes

As ubiquitous computing evolves towards greater adoption of Virtual, Augmented, and Mixed (VAM) Reality design paradigms, designers, engineers, and artists must develop new creative strategies to advance both the technical innovations necessary for new technology to emerge, as well as a critical framework to examine the consequences that these new technologies may have on society and culture. In this paper we present design research that explores these themes through a creative process of iterative prototyping and critique, from speculative fictions to functioning prototypes. These strategies draw heavily on literature, performance, as well as other areas in the humanities not usually considered within the domain of engineering. The prototypes presented in this paper demonstrate innovations for the design of VAM Reality within the emerging metaverse, as they give insight into the creative process from which they developed.
Cybercrime and Intellectual Property Theft: An Analysis of Modern Digital Forensics

Technology use has become ubiquitous, and with thousands of cybercrimes happening each day, there is a high demand for those who know how to identify these crimes and investigate them safely and appropriately. Since new and revolutionary ideas and technologies occur almost daily, so does the risk of intellectual property theft to acquire trade secrets and proprietary products. Theft of intellectual property has also become a growing threat to our national security. By refusing to recognize these actions as criminal, other countries shield many of the individuals responsible for these crimes. This research aims to examine how countries, companies, and agencies deal with intellectual property theft and to provide a breakdown of how intellectual property theft affects today’s industries and the damage it can cause. When intellectual property thefts do occur, it is important to understand the necessary steps to handle the situation and how professionals go about collecting evidence from these crimes.
The Awareness of Internet of Things (IoT) Privacy Risk: A Survey Study

The last years have witnessed the rapid development and deployment of the Internet of Things (IoT) in societies. Along with the growth of IoT, new security and privacy issues arise and become more urgent. in this paper, we tried to study people's awareness of this new technology and how do they deal with the risk to their privacy. In this paper, we explore the understanding and awareness of privacy in the field of IoT. We conducted a survey study for some IoT users in the Kingdom of Saudi Arabia. The main findings of this study shows that the users of IoT devices are about 65%. In addition, the results found that the users believe that IoT devices affect their level of privacy and they think that privacy is an obsession during the use of the IoT in Saudi Arabia. Moreover, we found that about 75% of the participants are dependant on their IoT devices some or most of their time.
Sentiment Analysis and Topic Modeling for Identifying Key Public Concerns of Water Quality/Issues

A Slovakian proverb says, “Pure water is the World’s first and foremost medicine”. Public opinion on water quality can play a critical role in the policy process, primarily because water quality determines the quality of health. The goal of the paper is to analyze Twitter data to extract feelings and opinions into unstructured text. We perform the topic extraction that discovers the keywords in sentiments that capture the text’s recurring theme and is widely used to analyze large sets of sentiments to identify the most common topics quickly and efficiently. The main purpose of the article is to collect public opinion on Twitter to understand critical issues related to water quality. We used a few weeks of Twitter data and applied the principle of latent semantic analysis and decomposition of singular values to group key water quality questions that impact people’s lives. People do realise that bad water deteriorates the quality of life, particularly for children. Also, there is a looming fear about imminent water, which bothers them a lot. They are hopeful that some of the measures like the Swatch Bharat Mission, Rally for River, Water Kiosk and applying Machine Learning and Artificial Intelligence solutions to provide safe and clean water could solve the crisis.
Multioutput Image Classification to Support Postearthquake Reconnaissance

After hazard events, large numbers of images are collected by reconnaissance teams to document the post-event state of structures, and to assess their performance and improve design procedures and codes. The majority of these data are captured as images and manually labeled. This highly repetitive task requires considerable domain expertise and time. Advances in deep learning have enabled researchers to rapidly classify reconnaissance images. Thus far, these classification methods are limited to a simple classification schema in which the classes are all either mutually exclusive or independent. To date, an efficient classification system of a complex schema containing many classes arranged in a multi-level hierarchical structure is not available to support earthquake reconnaissance. To address this gap, this paper introduces a comprehensive classification schema and a multi-output deep convolutional neural network (DCNN) model for rapid postearthquake image classification. In contrast to past work, herein a single multi-output DCNN classification model with a hierarchy-aware prediction was trained to enable the rapid organization of images. The performance of the proposed multi-output model was validated through comparisons with multi-label and multi-class models using an F1-score. As result, the multi-output model outperformed other models. Then, the multi-output model was deployed to a web-based platform called the Automated Reconnaissance Image Organizer, which can be used to easily organize earthquake reconnaissance images.
Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches

The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS, SLAMSA, (p, k)-Angelization, and (p, l)-Angelization, but these were found to be insufficient in terms of robust privacy and performance. (p, l)-Angelization was successful against different privacy disclosures, but it was not efficient. To the best of our knowledge, no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple records. In this paper, we suggest an improved version of (p, l)-Angelization based on a hybrid AI approach and privacy-preserving approach like Generalization. Fuzz-classification (p, l)-Angel uses artificial intelligence based fuzzy logic for classification, a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive attributes. We demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri Nets. The results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility.
BLS-identification: A device fingerprint classification mechanism based on broad learning for internet of things

The popularity of the Internet of Things (IoT) has enabled a large number of vulnerable devices to connect to the Internet, bringing huge security risks. As a network-level security authentication method, device fingerprint based on machine learning has attracted considerable attention because it can detect vulnerable devices in complex and heterogeneous access phases. However, flexible and diversified IoT devices with limited resources increase difficulty of the device fingerprint authentication method executed in IoT, because it needs to retrain the model network to deal with incremental features or types. To address this problem, a device fingerprinting mechanism based on a Broad Learning System (BLS) is proposed in this paper. The mechanism firstly characterizes IoT devices by traffic analysis based on the identifiable differences of the traffic data of IoT devices, and extracts feature parameters of the traffic packets. A hierarchical hybrid sampling method is designed at the preprocessing phase to improve the imbalanced data distribution and reconstruct the fingerprint dataset. The complexity of the dataset is reduced using Principal Component Analysis (PCA) and the device type is identified by training weights using BLS. The experimental results show that the proposed method can achieve state-of-the-art accuracy and spend less training time than other existing methods.
Surface Realization Architecture for Low-resourced African Languages

There has been growing interest in building surface realization systems to support the automatic generation of text in African languages. Such tools focus on converting abstract representations of meaning to a text. Since African languages are low-resourced, economical use of resources and general maintainability are key considerations. However, there is no existing surface realizer architecture that possesses most of the maintainability characteristics (e.g., modularity, reusability, and analyzability) that will lead to maintainable software that can be used for the languages. Moreover, there is no consensus surface realization architecture created for other languages that can be adapted for the languages in question. In this work, we solve this by creating a novel surface realizer architecture suitable for low-resourced African languages that abides by the features of maintainable software. Its design comes after a granular analysis, classification, and comparison of the architectures used by 77 existing NLG systems. We compare our architecture to existing architectures and show that it supports the most features of a maintainable software product.