Reviewerly/exHarmony huggingface dataset:
Digital Literacy within the Context of an Innovation Economy

The article examines the specifics of digital literacy in the context of an innovative economy. A brief historical overview of the emergence of the concept of “digital literacy” and its spread over the past 20–25 years is given. Digital literacy is defined as a set of technical and cognitive skills necessary for navigating in a digital environment, equally essential for both the modern economy and scientific and technological development. The important role of digital literacy in an economy based on innovation is outlined. The article offers a concise comparative analysis of the situation with the development of digital literacy in three countries (USA, India, and Australia) and a comparison of foreign experience with Russian. A general analysis of the challenges in the field of digital literacy for Russia in the context of the problems of higher education and science policies is carried out.
Network Analysis of Chebyshev Polynomial in a Fixed-precision Digital Domain

Recently, the dynamical systems generated by iterating a polynomial on a finite field are widely used in cryptography, physics and so on. Especially, Chebyshev polynomial plays a significant role in such application. This paper depicts the variation rules of Chebyshev integer sequence for every initial value in a ring. As for most of the initial states, the period of the corresponding sequence increase with the implementation precision. In addition, from the perspective of associate state-mapping networks, the circle structure of the Chebyshev polynomial implemented in a fixed-point arithmetic domain is investigated. It is found that the number of circles of different length remains unchanged with respect to the precision. The results can be used to understand the dynamic characteristics of Chebyshev polynomial and evaluate its cryptographic applications.
Keep and Select: Improving Hierarchical Context Modeling for Multi-Turn Response Generation

Hierarchical context modeling plays an important role in the response generation for multi-turn conversational systems. Previous methods mainly model context as multiple independent utterances and rely on attention mechanisms to obtain the context representation. They tend to ignore the explicit responds-to relationships between adjacent utterances and the special role that the user’s latest utterance (the query) plays in determining the success of a conversation. To deal with this, we propose a multi-turn response generation model named KS-CQ, which contains two crucial components, the Keep and the Select modules, to produce a neighbor-aware context representation and a context-enriched query representation. The Keep module recodes each utterance of context by attentively introducing semantics from its prior and posterior neighboring utterances. The Select module treats the context as background information and selectively uses it to enrich the query representing process. Extensive experiments on two benchmark multi-turn conversation datasets demonstrate the effectiveness of our proposal compared with the state-of-the-art baselines in terms of both automatic and human evaluations.
ONTOLOGY-BASED APPROACH FOR FEATURE LEVEL SENTIMENT ANALYSIS

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Parameters Related to Earthquake-early-warning System for Some Seismically Active Areas in the Balkan Peninsula Region

Summary This study investigates the possibility of early warning procedures for some seismically active areas in the Balkan Peninsula using freely available seismic data. Specifically, signals from seismic events in some regions near the stations of the Virtual Seismological Network of Sofia University (VSNSU) are evaluated. A number of characteristics are estimated, including signal-to-noise ratio of P- and S-waves, amplitudes, and durations of the first trains of P- and S-waves, the difference in the arrival time of P- and S-waves, etc. Additionally, the direct relation between magnitude estimation and maximal P-wave amplitude is compiled in order to calculate the magnitude of the detected events as early as possible. Three-component records for some seismic areas are tested to localize events using the method of Golitsyn. Since most of the seismic areas in the Balkans are crustal faults, a first evaluation of the distance range between different parts of the specific fault and the location of the used station was made. Detected parameters may be used in the development of regional early warning systems in the near future.
Key Frame Extraction Techniques: A Survey

With the development of web 2.0 technologies, information based on social media video data has been growing day by day. Scrutinizing of such massive information is a very time-consuming process. Also each video contains vital information. So there is requirement of some technique which not only reduces the extraneous data in a video, but also retains the useful information in it. Hence there arises a demand for accessing such information. This demand leads to the evolution of the video key frame extraction, also known as video summarization. The key frame extraction provides a comprehensive analysis of video by eliminating redundant content from a video. Video summarization also helps in reducing the video content to be processed and better management of video data. It can be performed using audiovisual, textual and key frames. In this paper, the focus is on video summarization using key frame extraction. Extracted key frames have applications in the field of video browsing, indexing, management of video data, etc. In this paper, a review of major techniques in the field of video key frame extraction is presented.
Informed Attentive Predictors: A Generalisable Architecture for Prior Knowledge-Based Assisted Diagnosis of Cancers

Due to the high mortality of many cancers and their related diseases, the prediction and prognosis techniques of cancers are being extensively studied to assist doctors in making diagnoses. Many machine-learning-based cancer predictors have been put forward, but many of them have failed to become widely utilised due to some crucial problems. For example, most methods require too much training data, which is not always applicable to institutes, and the complicated genetic mutual effects of cancers are generally ignored in many proposed methods. Moreover, a majority of these assist models are actually not safe to use, as they are generally built on black-box machine learners that lack references from related field knowledge. We observe that few machine-learning-based cancer predictors are capable of employing prior knowledge (PrK) to mitigate these issues. Therefore, in this paper, we propose a generalisable informed machine learning architecture named the Informed Attentive Predictor (IAP) to make PrK available to the predictor’s decision-making phases and apply it to the field of cancer prediction. Specifically, we make several implementations of the IAP and evaluate its performance on six TCGA datasets to demonstrate the effectiveness of our architecture as an assist system framework for actual clinical usage. The experimental results show a noticeable improvement in IAP models on accuracies, f1-scores and recall rates compared to their non-IAP counterparts (i.e., basic predictors).
Cost effective and Accurate Vehicle Make/Model Recognition method Using YoloV5

Automatic meta-data extraction from images from highway cameras is a necessary component for intelligent transportation and smart city. Meta-data can include detailed information on vehicles, such as car make/model, car registration plate and drivers&#x2019; behaviour, etc.. This paper focuses on real-time car make/model information extraction from highway cameras. As we have very limited access to the real world data due to data privacy and protection, we use open-source data (e.g. car selling websites) and transfer learning on open-source pre-trained models to build a model which is generic enough to be applied directly to similar data sets from other sources, (e.g. real-world highway cameras) without losing much accuracy. To achieve this, we propose applying the object detection method &#x2018;You Only Look Once&#x2019; (Yolo) for classification problem of car make/model. The proposed method and trained model achieve an accuracy of 95.6% when applied directly to real-world highway cameras without using their data for training.
A UHD MIPI CSI-2 image acquisition system based on FPGA

With the development of information technology and the ultra high definition(UHD) video industry, the video’s resolution is continuously improving, from the standard definition(SD) era to the high definition(HD) era, and the UHD era. In the field of biomedical engineering, the requirement of medical device endoscope for image resolution is higher and higher. Compared with the traditional HD endoscope system, the UHD endoscope image acquisition system designed in this article uses FPGA(Field Programmable Gate Array) as the processing center. The image resolution is increased from 1920*1080 to 3840*2160. Our system designed a physical layer and protocol layer of MIPI CSI-2 interface that can support 30 frame per second. It proves that our image acquisition system is practical and stable for medical endoscope image acquisition systems and also suitable for ultra-high-definition image systems in other fields.
Per-Pixel Water Detection on Surfaces with Unknown Reflectance

Water detection is important for machine vision applications such as visual inspection and robot motion planning. In this paper, we propose an approach to per-pixel water detection on unknown surfaces with a hyperspectral image. Our proposed method is based on the water spectral characteristics: water is transparent for visible light but translucent/opaque for near-infrared light and therefore the apparent near-infrared spectral reflectance of a surface is smaller than the original one when water is present on it. Specifically, we use a linear combination of a small number of basis vector to approximate the spectral reflectance and estimate the original near-infrared reflectance from the visible reflectance (which does not depend on the presence or absence of water) to detect water. We conducted a number of experiments using real images and show that our method, which estimates near-infrared spectral reflectance based on the visible spectral reflectance, has better performance than existing techniques.
A Two-Stage Registration Method for Dental Volumetric Data and Mesh Data

Dental computed tomography (CT) images and optical surface scan data are widely used in dental computer-aided design systems. Registration is essential if they are used in software systems. Existing automatic registration methods are either time-consuming or rough, and interactive registration methods are experience-dependent and tedious because of a great deal of purely manual interactions. For overcoming these disadvantages, a two-stage registration method is proposed. In the rough registration stage, the rough translation and rotation matrices are obtained by applying unit quaternion based method on the points interactively selected from the two types of data. In the precise registration stage, the stridden sampling is used to reduce computational complexity and the proposed registration algorithm with scale transformation is used for precise registration. The proposed method offers a good trade-off between precision and time cost. The experimental results demonstrate that the proposed method provides faster convergence and smaller registration errors than existing methods.
Optimization of Quantum Key Distribution Parameters Based on Random Forest

For the actual quantum key distribution (QKD) system, parameter optimization, the selection of the corresponding signal strength and transmission probability are the keys to obtain the best performance, especially when performing low-latency communication. Combining the random forest algorithm with data preprocessing, according to the relative performance parameters of each decision tree, the decision trees with poor performance is eliminated, so that the random forest algorithm is improved. This method is used to predict the best parameters of the QKD system directly, instead of searching for the best parameters. We tested the random forest algorithms on hardware equipment. We find that, compared with the traditional random forest algorithm, we have made great progress and achieved a relatively high optimal key rate. It has broad application prospects in the field of QKD.
SPEC: Seeing People in the Wild With an Estimated Camera

Due to the lack of camera parameter information for in-the-wild images, existing 3D human pose and shape (HPS) estimation methods make several simplifying assumptions: weak-perspective projection, large constant focal length, and zero camera rotation. These assumptions often do not hold and we show, quantitatively and qualitatively, that they cause errors in the reconstructed 3D shape and pose. To address this, we introduce SPEC, the first in-the-wild 3D HPS method that estimates the perspective camera from a single image and employs this to reconstruct 3D human bodies more accurately. %regress 3D human bodies. First, we train a neural network to estimate the field of view, camera pitch, and roll given an input image. We employ novel losses that improve the calibration accuracy over previous work. We then train a novel network that concatenates the camera calibration to the image features and uses these together to regress 3D body shape and pose. SPEC is more accurate than the prior art on the standard benchmark (3DPW) as well as two new datasets with more challenging camera views and varying focal lengths. Specifically, we create a new photorealistic synthetic dataset (SPEC-SYN) with ground truth 3D bodies and a novel in-the-wild dataset (SPEC-MTP) with calibration and high-quality reference bodies. Both qualitative and quantitative analysis confirm that knowing camera parameters during inference regresses better human bodies. Code and datasets are available for research purposes at https://spec.is.tue.mpg.de.
Improved stereo matching algorithm based on multi-scale fusion

Aiming at the low matching accuracy of local stereo matching algorithm in weak texture or discontinuous disparity areas, a stereo matching algorithm combining multi-scale fusion of convolutional neural network (CNN) and feature pyramid structure (FPN) is proposed. The feature pyramid is applied on the basis of the convolutional neural network to realize the multi-scale feature extraction and fusion of the image, which improves the matching similarity of the image blocks. The guide graph filter is used to quickly and effectively complete the cost aggregation. The disparity selection stage adapts the improvement dynamic programming algorithm to obtain the initial disparity map. The initial disparity map is refined so as to obtain the final disparity map. The algorithm is trained and tested on the image provided by Middlebury data set, and the result shows that the disparity map obtained by the algorithm has good effect.
THUNDR: Transformer-Based 3D Human Reconstruction With Markers

We present THUNDR, a transformer-based deep neural network methodology to reconstruct the 3d pose and shape of people, given monocular RGB images. Key to our methodology is an intermediate 3d marker representation, where we aim to combine the predictive power of model-free-output architectures and the regularizing, anthropometrically-preserving properties of a statistical human surface model like GHUM -- a recently introduced, expressive full body statistical 3d human model, trained end-to-end. Our novel transformer-based prediction pipeline can focus on image regions relevant to the task, supports self-supervised regimes, and ensures that solutions are consistent with human anthropometry. We show state-of-the-art results on Human3.6M and 3DPW, for both the fully-supervised and the self-supervised models, for the task of inferring 3d human shape, joint positions, and global translation. Moreover, we observe very solid 3d reconstruction performance for difficult human poses collected in the wild.
Chasing Sparsity in Vision Transformers:An End-to-End Exploration

Vision transformers (ViTs) have recently received explosive popularity, but their enormous model sizes and training costs remain daunting. Conventional post-training pruning often incurs higher training budgets. In contrast, this paper aims to trim down both the training memory overhead and the inference complexity, without sacrificing the achievable accuracy. We launch and report the first-of-its-kind comprehensive exploration, on taking a unified approach of integrating sparsity in ViTs end to end. Specifically, instead of training full ViTs, we dynamically extract and train sparse subnetworks, while sticking to a fixed small parameter budget. Our approach jointly optimizes model parameters and explores connectivity throughout training, ending up with one sparse network as the final output. The approach is seamlessly extended from unstructured to structured sparsity, the latter by considering to guide the prune-and-grow of self-attention heads inside ViTs. For additional efficiency gains, we further co-explore data and architecture sparsity, by plugging in a novel learnable token selector to adaptively determine the currently most vital patches. Extensive results on ImageNet with diverse ViT backbones validate the effectiveness of our proposals which obtain significantly reduced computational cost and almost unimpaired generalization. Perhaps most surprisingly, we find that the proposed sparse (co-)training can even improve the ViT accuracy rather than compromising it, making sparsity a tantalizing free lunch. For example, our sparsified DeiT-Small at (5%, 50%) sparsity for (data, architecture), improves 0.28% top-1 accuracy, and meanwhile enjoys 49.32% FLOPs and 4.40% running time savings. Our codes are available at this https URL.
Nose Tracking Assistive Robot Control for the People with Motor Dysfunctions

Research Objectives To Implement Human Computer Interface (HCI) with digital image processing for a new approach to control assistive robots. Design Experimental Study. Setting In UWM BioRobotics Lab. Participants Healthy participants. Interventions Assistive robot control for daily living activities(Eating, Picking/Placing Objects), Nose tracking cursor control, User interface. Main Outcome Measures #Control the computer cursor with nose movements. #App Interface is developed to control the robotic arm. #Control the interface using nose and teeth (showing teeth triggered the click events) to control the assistive robotic arm. Results Result contains the demonstration of the control of a robotic arm with nose tracking control through an user interface. External webcam was used to get the real-time video for tracking the nose for cursor movement. We conducted experiments on healthy subjects. The feedbacks are recorded in Tables. We compared the results with other two techniques which are eye tracking and camera mouse for cursor movement and clicking events in terms of error pixels and other factors. Conclusions The nose tracking cursor control is mainly for the disabled people who are struggling with or unable to do their basic day to day tasks for their motor dysfunctions. This method will help them to access and control over a computer as well as a robotic assistive arm to ease their lives. Author(s) Disclosures No Conflict To Implement Human Computer Interface (HCI) with digital image processing for a new approach to control assistive robots. Experimental Study. In UWM BioRobotics Lab. Healthy participants. Assistive robot control for daily living activities(Eating, Picking/Placing Objects), Nose tracking cursor control, User interface. #Control the computer cursor with nose movements. #App Interface is developed to control the robotic arm. #Control the interface using nose and teeth (showing teeth triggered the click events) to control the assistive robotic arm. Result contains the demonstration of the control of a robotic arm with nose tracking control through an user interface. External webcam was used to get the real-time video for tracking the nose for cursor movement. We conducted experiments on healthy subjects. The feedbacks are recorded in Tables. We compared the results with other two techniques which are eye tracking and camera mouse for cursor movement and clicking events in terms of error pixels and other factors. The nose tracking cursor control is mainly for the disabled people who are struggling with or unable to do their basic day to day tasks for their motor dysfunctions. This method will help them to access and control over a computer as well as a robotic assistive arm to ease their lives.
15.3 WHAT THE LIVES OF MASS SHOOTERS AND GANG MEMBERS CAN TEACH US ABOUT PREVENTING ONLINE HATE

This session aims to apply lessons learned from the study of mass shooters and gang members to online hate groups and violent extremism. A mixed-methods study of mass shooters in the United States was conducted, including interviews with perpetrators and their families, and ethnographic research with gangs and gang members in the United States and Europe, including content analyses of gang-related social media artifacts. Social media has incentivized the performance of hate and violent extremism. People select hate groups when searching for something to make sense of their lives. Social media provides social proof of hate, facilitating it for some and enhancing it for others. The social media economy is partly responsible for a rise in hate and violent extremism, but research with mass shooters and gang members shows that prevention is possible.
Fuzzy Natural Logic for Sentiment Analysis: A Proposal

Fuzzy Natural Logic (FNL) is introduced as a model that could be useful in the area of sentiment analysis. FNL is a formal theory of human reasoning that includes mathematical models of the semantics of natural language expressions with regard to the vagueness phenomenon. The most elaborated constituent of FNL is the theory of evaluative linguistic expressions. To capture their semantics, it uses a single scale for computing extension of any evaluative expression that might be relevant for sentiment analysis. Therefore, it provides a more fine-grained classification of opinion and sentiments than dichotomous models which only distinguish between ‘positive’ and ‘negative’ values.
Currency Union and Regional Trade Integrations in West Africa: The Role of Institutional Quality

This study investigates the effect of currency union on intra-regional trade integrations in West Africa. The empirical analysis also examines the interaction effect of institutional quality on currency union-trade flows nexus in the West African region over the period of 1996 - 2019.The study employs a Driscoll-Kraay standard error estimator technique on data obtained from the IMF direction of trade statistics and World Bank governance indicators. The study findings suggest that all the gravity variables (Gross domestic product, population, and distance) are in line with a priori expectations; currency union dummy produced a positive association with intra-regional trade integrations; real effective exchange rate appreciation boosts trade performance, and the interaction of currency union and institutional quality produced a positive impact on intra-regional trade flows among the countries in the West African region. Among the disaggregated institutional quality index, the rule of law and political stability have a significant impact on trade flows. The rule of law influenced trade flows positively, while political stability influenced trade flows negatively. The study concludes that the Rose effect of a common currency on trade exists in West Africa and that the level of institutional quality also subscribes to the currency union trade flows relationship.