Reviewerly/exHarmony huggingface dataset:
Positive tone and initial coin offering

Abstract An initial coin offering (ICO) has become a popular venue for crowdfunding at an early stage in a blockchain project. In the ICO process, a whitepaper may serve as an effective marketing tool to influence investor perception and investor trading behaviour. In this paper, we investigate whether management sentiment, as captured by the disclosure tone of the ICO whitepaper, is seen as a credible signal and capable of affecting price behaviour of the ICO on its first trading day. We document a positive association between management’s net positive tone in the whitepaper and ICO first‐day return, indicating that management sentiment has a significant impact on investor behaviour. Our findings show, however, that such association is contingent on the presence of causal argument, suggesting that disclosure tone needs a significant extent of causal reasoning support to back up its credibility and make it persuasive. These findings provide incremental evidence on the relationship between management tone and relative asset returns during an ICO process in the cryptocurrency markets. While management may benefit from soft information disclosure in the whitepaper, investors and regulators should be aware of self‐serving incentives in whitepaper disclosures.
Automated Artifact Retouching in Morphed Images With Attention Maps

Morphing attack is an important security threat for automatic face recognition systems.Highquality morphed images, i.e. images without significant visual artifacts such as ghosts, noise, and blurring, exhibit higher chances of success, being able to fool both human examiners and commercial face verification algorithms.Therefore, the availability of large sets of high-quality morphs is fundamental for training and testing robust morphing attack detection algorithms.However, producing a high-quality morphed image is an expensive and time-consuming task since manual post-processing is generally required to remove the typical artifacts generated by landmark-based morphing techniques.This work describes an approach based on the Conditional Generative Adversarial Network paradigm for automated morphing artifact retouching and the use of Attention Maps to guide the generation process and limit the retouch to specific areas.In order to work with high-resolution images, the framework is applied on different facial crops, which, once processed and retouched, are accurately blended to reconstruct the whole morphed face.Specifically, we focus on four different squared face regions, i.e. the right and left eyes, the nose, and the mouth, that are frequently affected by artifacts.Several qualitative and quantitative experimental evaluations have been conducted to confirm the effectiveness of the proposal in terms of, among the others, pixel-wise metrics, identity preservation, and human observer analysis.Results confirm the feasibility and the accuracy of the proposed framework.
Contrastive Attention Maps for Self-supervised Co-localization

The goal of unsupervised co-localization is to locate the object in a scene under the assumptions that 1) the dataset consists of only one superclass, e.g., birds, and 2) there are no human-annotated labels in the dataset. The most recent method achieves impressive co-localization performance by employing self-supervised representation learning approaches such as predicting rotation. In this paper, we introduce a new contrastive objective directly on the attention maps to enhance co-localization performance. Our contrastive loss function exploits rich information of location, which induces the model to activate the extent of the object effectively. In addition, we propose a pixel-wise attention pooling that selectively aggregates the feature map regarding their magnitudes across channels. Our methods are simple and shown effective by extensive qualitative and quantitative evaluation, achieving state-of-the-art co-localization performances by large margins on four datasets: CUB-200-2011, Stanford Cars, FGVC-Aircraft, and Stanford Dogs. Our code will be publicly available online for the research community.
S3-Net: A Fast Scene Understanding Network by Single-Shot Segmentation for Autonomous Driving

Real-time segmentation and understanding of driving scenes are crucial in autonomous driving. Traditional pixel-wise approaches extract scene information by segmenting all pixels in a frame, and hence are inefficient and slow. Proposal-wise approaches only learn from the proposed object candidates, but still require multiple steps on the expensive proposal methods. Instead, this work presents a fast single-shot segmentation strategy for video scene understanding. The proposed net, called S3-Net, quickly locates and segments target sub-scenes , and meanwhile extracts attention-aware time-series sub-scene features ( ats-features ) as inputs to an attention-aware spatio-temporal model (ASM) . Utilizing tensorization and quantization techniques, S3-Net is intended to be lightweight for edge computing. Experiments results on CityScapes, UCF11, HMDB51, and MOMENTS datasets demonstrate that the proposed S3-Net achieves an accuracy improvement of 8.1% versus the 3D-CNN based approach on UCF11, a storage reduction of 6.9× and an inference speed of 22.8 FPS on CityScapes with a GTX1080Ti GPU.
CNN Based Approach for Traffic Sign Recognition System

Machine Learning (ML) involves making a machine able to learn and take decisions on real-life problems by working with an efficient set of algorithms. The generated ML models find application in different areas of research and management. One such field, automotive technology, employs ML enabled commercialized advanced driver assistance systems (ADAS) which include traffic sign recognition as a part. With the increasing demand for the intelligence of vehicles, and the advent of self-driving cars, it is extremely necessary to detect and recognize traffic signs automatically through computer technology. For this, neural networks can be applied for analyzing images of traffic signs for cognitive decision making by autonomous vehicles. Neural networks are the computing systems which act as a means of performing ML. In this work, a convolutional neural network (CNN) based ML model is built for recognition of traffic signs accurately for decision making, when installed in driverless vehicles.
Systematic Analysis of Effectiveness of Using Mobile Technologies (MT) in Teaching and Learning Foreign Language

Although Mobile-Assisted Language Learning (MALL) is rapidly evolving, there is still a lack of research synthesis and systematic meta-analysis. The effectiveness of mobile devices for language learning under various conditions remains unclear. A limited study is carried out on how the latest smartphone devices effectively teach and learn. Mobile technology is the most Information and Communication Technology (ICT) tool used in our daily life for different purposes, including educational purposes. For instance, Mobile Technology (MT) has been significantly used for learning languages because today's students tend to use this technology frequently and almost every hour. This research aimed to explore the effects of mobile devices in foreign language learning and present the sample studies conducted between 2014 and 2021; 100 articles were chosen from Google Scholar, ResearchGate and Science Direct database through using different search keywords. The results of the research revealed that the most examined studies were published in Indonesia and Saudi Arabia. The university students were selected as sample groups in these articles, and that most articles used the experimental research design. The findings also showed that English was the most foreign language examined in these articles, and WhatsApp is the most mobile application used in these articles.
A comparative evaluation of the wearable augmented reality-based data presentation interface and traditional methods for data entry tasks

Data entry is a ubiquitous task performed in today's offices. Persistent data entry is linked with high workload and fatigue due to poor ergonomic workplace design and poor posture. This study aims to alleviate data entry operators' workload and improve data entry performance by applying wearable augmented reality (AR) technology to data entry tasks. An AR-based interface was developed and used to present data to the participants, who entered the data in web-based data entry forms. A total of eighteen participants performed data entry tasks to evaluate the AR interface with traditional methods – extra desktop monitor and paper-based data presentation methods. Each method's performance was judged on the task completion time, typing error rate, workload, and usability. The usability and overall perceived workload while using an AR interface for data entry were similar to the traditional way of using paper, despite the additional burden due to the weight of the AR headset. AR interface did not perform better than the extra desktop monitor interface for usability and overall perceived workload. The results from this study can be utilized to design AR devices that are suited for data entry tasks.
Application of a Fast RCNN Based on Upper and Lower Layers in Face Recognition

With the development of society, deep learning has been widely used in object detection, face recognition, speech recognition, and other fields. Among them, object detection is a popular direction in computer vision and digital image processing, and face detection is a focus of this hot direction. Although face detection technology has gone through a long research stage, it is still considered as one of the more difficult subjects in human feature detection technology. In addition, the face detection technology itself has two sides, imperceptibility and complexity of the environment, and other defects cause the existing technology to be unable to accurately recognize faces of different proportions, obscured and different postures. Therefore, this paper adopts an advanced deep learning method based on machine vision to detect human faces automatically. In order to accurately detect a variety of human faces, a multiscale fast RCNN method based on upper and lower layers (UPL-RCNN) is proposed. The network is composed of spatial affine transformation components and feature region components (ROI). This method plays a vital role in face detection. First of all, multiscale information can be grouped in detection, so as to deal with small areas of the face. Then, the method can use the inspiration of the human visual system to perform contextual reasoning and spatial transformation, including zooming, cutting, and rotating. Through comparative experiments, the analysis results show that this method can not only accurately detect human faces but also has better performance than fast RCNN. Compared with some advanced methods, this method has the advantages of high accuracy, less time consumption, and no correlation mark.
Antifragile Philosophy in R&amp;D Projects: Applying Q Methodology and the Possibility of Open Innovation

Antifragile philosophy can be the key to improving the management of organizations that base their activity on research and development (R&D) projects. These are types of projects with the greatest uncertainty in all aspects, and the application of antifragile philosophy can result in streamlining their management and development. In this article, the Q methodology is used to investigate whether organizations in R&D environments have antifragile characteristics. To this end, 15 innovation experts from research institutes located in Northern Spain were interviewed about their position regarding project management behaviors that are related to antifragile philosophy. As a result, it was verified that the characteristics of an ideal system of a research institute with antifragile philosophy are multidisciplinary and autonomous teams with a capacity for rapid response and adaptation to the environment.
Anomaly detection for sensor data of semiconductor manufacturing equipment using a GAN

In semiconductor manufacturing, it is required to detect anomalies which cause expensive defects. In recent years, Generative Adversarial Networks (GANs) have played a big role in anomaly detection. This study aims to detect anomalies by analyzing sensor data using a GAN when multivariate time series of sensor data are given. Our GAN could detect anomalies which cannot be detected visually. Experimental results indicated that an attention mechanism could tell us important sensors in detecting anomalies.
Py_FS: A Python Package for Feature Selection Using Meta-Heuristic Optimization Algorithms

Guha, Ritam Chatterjee, Bitanu Khalid Hassan, S. K. Ahmed, Shameem Bhattacharyya, Trinav Sarkar, RamIn today's data-driven world, every workforce is relentlessly exploiting the power of data to get that extra edge in order to triumph over the others. However, there is a saying that goes like, "Work smarter, not harder." Studies have shown that the amount of data people actually use is way smaller than the data being generated. This very fact gives rise to an important research topic, called dimension reduction, which is one of the smartest (not hardest) strategies for retrieving useful information (here, features) from a given high-dimensional dataset. Feature selection (FS) is among the best dimension reduction tactics available in the literature. To this end, in this paper, we have made an effort to introduce an FS framework called Py_FS that we have developed to simplify the task for the researchers. Py_FS currently provides an interesting combination of 12 wrapper- and 4 filter-based FS techniques along with various evaluation metrics. For converting the continuous search space to a binary search space, three transfer functions have been used. The algorithms have been experimented on two Microarray and four UCI datasets. To the best of our knowledge, it is the first ever framework to provide wrappers, filters, and evaluation metrics under one structure. The framework is highly flexible and can easily cater to the needs of the FS researchers. It is publicly hosted at the following link: Py_FS: A Python Framework for FS .
A Novel Rank Learning Based No-Reference Image Quality Assessment Method

Recently, applying deep learning to no-reference image quality assessment (NR-IQA) has received significant attention. Especially in the last five years, an increasing interest has been drawn to the studies of rank learning since it can help mitigate the problem of small IQA datasets. However, on one hand, existing rank learning is not suitable for the authentically distorted images due to the lack of generated rank samples. On the other hand, the output of existing rank loss functions is uncontrollable, resulting in reduced performance. Motivated by these two limitations, we propose a novel rank learning based NR-IQA method, termed controllable list-wise ranking IQA (CLRIQA) in this paper. To be specific, we first present an imaging-heuristic approach, in which the over- and under-exposure is formulated as an inverse of the Weber-Fechner law, and fusion strategy and compression are adopted, to simulate the authentic distortion and generate the rank image samples. These samples are label-free yet associated with quality ranking information. Then we design a controllable list-wise ranking (CLR) loss function by setting an upper and lower bound of rank range and introducing an adaptive margin to tune rank interval. Finally, both the generated rank samples and proposed CLR are used to pre-train a convolutional neural network. Moreover, to obtain a more accurate prediction model, we take advantage of the IQA datasets to fine-tune the pre-trained network further. Various experiments are conducted on the IQA benchmark datasets, and experimental results demonstrate the effectiveness of the proposed CLRIQA method. The source code and network model can be downloaded at the following web address: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com</uri> <inline-formula><tex-math notation="LaTeX">$/$</tex-math></inline-formula> GZHU-DVL <inline-formula><tex-math notation="LaTeX">$/$</tex-math></inline-formula> CLRIQA</i> .
Energy Efficiency of Inference Algorithms for Clinical Laboratory Data Sets: Green Artificial Intelligence Study

The use of artificial intelligence (AI) in the medical domain has attracted considerable research interest. Inference applications in the medical domain require energy-efficient AI models. In contrast to other types of data in visual AI, data from medical laboratories usually comprise features with strong signals. Numerous energy optimization techniques have been developed to relieve the burden on the hardware required to deploy a complex learning model. However, the energy efficiency levels of different AI models used for medical applications have not been studied.The aim of this study was to explore and compare the energy efficiency levels of commonly used machine learning algorithms-logistic regression (LR), k-nearest neighbor, support vector machine, random forest (RF), and extreme gradient boosting (XGB) algorithms, as well as four different variants of neural network (NN) algorithms-when applied to clinical laboratory datasets.We applied the aforementioned algorithms to two distinct clinical laboratory data sets: a mass spectrometry data set regarding Staphylococcus aureus for predicting methicillin resistance (3338 cases; 268 features) and a urinalysis data set for predicting Trichomonas vaginalis infection (839,164 cases; 9 features). We compared the performance of the nine inference algorithms in terms of accuracy, area under the receiver operating characteristic curve (AUROC), time consumption, and power consumption. The time and power consumption levels were determined using performance counter data from Intel Power Gadget 3.5.The experimental results indicated that the RF and XGB algorithms achieved the two highest AUROC values for both data sets (84.7% and 83.9%, respectively, for the mass spectrometry data set; 91.1% and 91.4%, respectively, for the urinalysis data set). The XGB and LR algorithms exhibited the shortest inference time for both data sets (0.47 milliseconds for both in the mass spectrometry data set; 0.39 and 0.47 milliseconds, respectively, for the urinalysis data set). Compared with the RF algorithm, the XGB and LR algorithms exhibited a 45% and 53%-60% reduction in inference time for the mass spectrometry and urinalysis data sets, respectively. In terms of energy efficiency, the XGB algorithm exhibited the lowest power consumption for the mass spectrometry data set (9.42 Watts) and the LR algorithm exhibited the lowest power consumption for the urinalysis data set (9.98 Watts). Compared with a five-hidden-layer NN, the XGB and LR algorithms achieved 16%-24% and 9%-13% lower power consumption levels for the mass spectrometry and urinalysis data sets, respectively. In all experiments, the XGB algorithm exhibited the best performance in terms of accuracy, run time, and energy efficiency.The XGB algorithm achieved balanced performance levels in terms of AUROC, run time, and energy efficiency for the two clinical laboratory data sets. Considering the energy constraints in real-world scenarios, the XGB algorithm is ideal for medical AI applications.
Learning from Substitutable and Complementary Relations for Graph-based Sequential Product Recommendation

Sequential product recommendation, aiming at predicting the products that a target user will interact with soon, has become a hotspot topic. Most of the sequential recommendation models focus on learning from users’ interacted product sequences in a purely data-driven manner. However, they largely overlook the knowledgeable substitutable and complementary relations between products. To address this issue, we propose a novel Substitutable and Complementary Graph-based Sequential Product Recommendation model, namely, SCG-SPRe. The innovations of SCG-SPRe lie in its two main modules: (1) The module of interactive graph neural networks jointly encodes the high-order product correlations in the substitutable graph and the complementary graph into two types of relation-specific product representations. (2) The module of kernel-enhanced transformer networks adaptively fuses multiple temporal kernels to characterize the unique temporal patterns between a candidate product to be recommended and any interacted product in a target behavior sequence. Thanks to the seamless integration of the two modules, SCG-SPRe obtains candidate-dependent user representations for different candidate products to compute the corresponding ranking scores. We conduct extensive experiments on three public datasets, demonstrating SCG-SPRe is superior to competitive sequential recommendation baselines and validating the benefits of explicitly modeling the product-product relations.
Advancement of artificial intelligence techniques based lexicon emotion analysis for vaccine of COVID-19

Emotions are a vital and fundamental part of life.Everything we do, say, or do not say, somehow reflects some of our feelings, perhaps not immediately.To analyze a human's most fundamental behavior, we must examine these feelings using emotional data, also known as affect data.Text, voice, and other types of data can be used.Affective Computing, which uses this emotional data to analyze emotions, is a scientific fields.Emotion computation is a difficult task; significant progress has been made, but there is still scope for improvement.With the introduction of social networking sites, it is now possible to connect with people from all over the world.Many people are attracted to examining the text available on these various social websites.Analyzing this data through the Internet means we're exploring the entire continent, taking in all of the communities and cultures along the way.This paper analyze text emotion of Iraqi people about COVID-19 using data collected from twitter, People's opinions can be classified based on lexicon into different separate classifications of feelings (anticipation, anger, trust, fear, sadness, surprise, disgust, and joy) as well as two distinct emotions (positive and negative), which can then be visualized using charts to find the most prevalent emotion using lexicon-based analysis.
Bimodal Approach for Noise Figures of Merit Evaluation in Quantum-Limited Josephson Traveling Wave Parametric Amplifiers

The advent of ultra-low noise microwave amplifiers revolutionized several research fields demanding quantum-limited technologies. Exploiting a theoretical bimodal description of a linear phase-preserving amplifier, in this contribution we analyze some of the intrinsic properties of a model architecture (i.e., an rf-SQUID based Josephson Traveling Wave Parametric Amplifier) in terms of amplification and noise generation for key case study input states (Fock and coherents). Furthermore, we present an analysis of the output signals generated by the parametric amplification mechanism when thermal noise fluctuations feed the device.