Reviewerly/exHarmony huggingface dataset:
AECT: Accurate Energy Efficient Contact Tracing Using Smart Phones for Infectious Disease Detection

Contact tracing is an important technique to reduce the impact of infectious diseases in smart cities. Smart phones equipped proximity sensors can be used to enable contact tracing, however accuracy of detection and energy efficiency is a key challenge. To address this challenge, we propose an accurate energy-efficient contact tracing (AECT) algorithm that detects which users came in contact with an infected user by performing computations at the server-side. Additionally, the AECT algorithm uses the wireless scan method, which calculates proximity based on pseudo-range multilateration and makes relevant comparisons with the matching score (MS) method based on the computation of received signal strength indication (RSSI) metric. Simulation results demonstrate that the scan method (AECT) is highly accurate and outperforms the scan method, highlighting that real distance is a better metric in contact tracing than a proxy for distance such as RSSI. Lastly, simulation results also demonstrate that the scan method (AECT) is 16 times more energy-efficient than the baseline 1 Hz frequency method, and we recommend it as a method of choice for performing contact tracing against infectious diseases such as COVID-19.
Rel Topic : A graph-based semantic relatedness measure in topic ontologies and its applicability for topic labeling of old press articles

Graph-based semantic measures have been used to solve problems in several domains. They tend to compare semantic entities in order to estimate their similarity or relatedness. While semantic similarity is applicable to hierarchies or taxonomies, semantic relatedness is adapted to ontologies. In this work, we propose a novel semantic relatedness measure, named Rel Topic , within topic ontologies for topic labeling purposes. In contrast to traditional measures, which are dependent on textual resources, Rel Topic considers semantic properties of entities in ontologies. Thus, correlations of nodes and weights of nodes and edges are assessed. The pertinence of Rel Topic is evaluated for topic labeling of old press articles. For this purpose, a topic ontology representing the articles, named Topic-OPA, is derived from open knowledge graphs by applying a SPARQL-based automatic approach. A use-case is presented in the context of the old French newspaper Le Matin. The generated topics are evaluated using a dual evaluation approach with the help of human annotators. Our approach shows an agreement quite close to that shown by humans. The entire approach’s reuse is demonstrated for labeling a different context of articles, recent (modern) newspapers.
Three Fish Algorithm: T-Mix Cipher using SHA-256

In every organization, use of online services is increasing. With this the sensitive data is carried over internet on daily basis. Hence, there is a chance of misleading the data by unauthorized parties. So, there is need to provide security for that data and cryptography is the science that helps in providing security. By using cryptography different types of security algorithms have been developed. Three fish is a symmetric-key and tweakable block cipher algorithm designed as a part of the skein hash function. The strength of three fish encryption relies on 128-bit tweak value. The proposed work focuses on strengthening Encryption Process by implementing tweak buffer along with input. Whereas key scheduling is secured by applying SHA-256 algorithm. SHA-256 is a secured hash function which belongs to SHA-2 family. Three Fish is used in providing security on software and hardware. It is also implemented in electronic media such as transactions like banking.
Perfomance Analysis of Text Extraction from Complex Degraded Image Using Fusion of DNN, Steganography, and AGSO

In present time, any complex degraded image consists of very important and confidential information details which is recognized as a non-textual image and textual information. Due to diversity of text style in image, complicated background, and various interference factors makes detection of text (DOT) from complex degraded image as a field of research. The secure and accurate text in a complex degraded image is found useful for the audience to understand the complete situation. So, a fusion of DNN, adaptive galactic swarm optimization (AGSO), and steganography are applied in this proposed technique to securely, efficiently identify information in the form of text and thereafter, to recognize each character from degraded complex images. In general, images are affected by different type of noise such as structured noise, Poisson–Gaussian noise, periodic noise, and impulse valued noise, and to discard it in the initial preprocessing phase, the guided filter (GF) is used. A very important task in the text identification and recognition process is feature extraction, performed by using Gabor and stroke width transform. The extracted features of the image are required during the classification process. Thereafter, text identification and recognition is done by WNBA. Subsequently, performance comparison of various performance parameters such as precision, F1-scores, and recall was tested using the IIIT5K database for proposed algorithm along with other existing techniques.
Twitter Sentiment Analysis Using Machine Learning

Millions of tweets are created every day on diverse issues. Topical variety in substance demands space autonomous responses for analyzing Twitter sentiments. Twitter sentiment analysis offers associations capacity to screen public feelings toward the occasions and product identified with them progressively. The initial step of the sentiment analysis is the pre-handling of Twitter data. Scalability is another issue while overseeing enormous measure of tweets. This paper presents a supervised method for looking at tweet sentiments. Sentiment analysis by means of electronic media, for instance, Twitter has gotten a crucial and testing task. Because of the attributes of such information such as tweet length, spelling mistakes, abbreviations, and special characters, the sentiment analysis task in such an environment requires a non-conventional approach. Twitter posts are generally short and generated constantly by public and very well-suited for opinion mining. These messages can be elegant as containing either positive or negative sentiment on the basis of specific viewpoints with respect to a term-based query. The previous investigations of sentiment classification are not very conclusive about which features and supervised classification algorithms are good for designing precise and efficient sentiment classification system. In this paper, I mostly centered around existing development of Twitter dataset with approaches like sentiment analysis algorithm using machine learning. The challenging arrangement would be analyzing abbreviations, emoticons, hashtags, thus, so on.
A Cross Platform Contact Tracing Mobile Application for COVID-19 Infections using Deep Learning

The COVID-19 pandemic has remained a global health crisis following the declaration by the World Health Organization.As a result, a number of mechanisms to contain the pandemic have been devised.Popular among these are contact tracing to identify contacts and carry out tests on them in order to minimize the spread of the coronavirus.However, manual contact tracing is tedious and time consuming.Therefore, contact tracing based on mobile applications have been proposed in literature.In this paper, a cross platform contact tracing mobile application that uses deep neural networks to determine contacts in proximity is presented.The application uses Bluetooth Low Energy technologies to detect closeness to a Covid-19 positive case.The deep learning model has been evaluated against analytic models and machine learning models.The proposed deep learning model performed better than analytic and traditional machine learning models during testing.
Subspace Clustering Multi-module Self-organizing Maps with Two-Stage Learning

Clustering complexity increases with the number of categories and sub-categories and with data dimensionality. In this case, the distance metrics lose discrimination power with the growth of such dimensionality. Thus, we propose a multiple-module soft subspace clustering algorithm called Subspace Clustering Multi-Module Self-Organizing Maps (SC-MuSOM) that produces a map for each category. Moreover, SC-MuSOM learns a relevance coefficient for each dimension of each cluster handling the dimensionality curse. This fast-training model has a second learning stage in which the cluster prototypes are finely tuned considering the spatial resemblance between cluster centers. We validated the model with data mining sets from UCI Repository and computer vision data. Our experiments suggest that SC-MuSOM is competitive with other state-of-the-art models for the tested problems.
Feature Recalibration Network for Salient Object Detection

Learning-based models have demonstrated the superiority of extracting and aggregating saliency features. However, we observe that most off-the-shelf methods mainly focus on the calibration of decoder features while ignore the recalibration of vital encoder features. Moreover, the fusion between encoder features and decoder features, and the transfer between boundary features and saliency features deserve further study. To address the above issues, we propose a feature recalibration network (FRCNet) which consists of a consistency recalibration module (CRC) and a multi-source feature recalibration module (MSFRC). Specifically, intersection and union mechanisms in CRC are embedded after the decoder unit to recalibrate the consistency of encoder and decoder features. By the aid of the special designed mechanisms, CRC can suppress the useless external superfluous information and enhance the useful internal saliency information. MSFRC is designed to aggregate multi-source features and reduce parameter imbalance between saliency features and boundary features. Compared with previous methods, more layers are applied to generate boundary features, which sufficiently leverage the complementary features between edges and saliency. Besides, it is difficult to predict the pixels around the boundary because of the unbalanced distribution of edges. Consequently, we propose an edge recalibration loss (ERC) to further recalibrate the equivocal boundary features by paying more attention to salient edges. In addition, we also explore a compact network (cFRCNet) that improves the performance without extra parameters. Experimental results on five widely used datasets show that the FRCNet achieves consistently superior performances under various evaluation metrics. Furthermore, FRCNet runs at the speed of around 30 fps on a single GPU.
Design and implementation of heterogeneous data exchange platform based on web technology

In order to solve the data and information resources merging and sharing between different databases, make full use of the databases already used by enterprises and institutions, realize the connection, data exchange and data sharing between different databases, and solve the problem of "information island" in the current information system application to the greatest extent. This paper uses XML data exchange technology to build a data exchange center model of heterogeneous databases, which enables safe, reliable, traceable and efficient information data exchange between different databases of various application systems through the data exchange center; While providing exchange, it can restrict, manage and record the access rights of users.
An Adversarial Multi-task Learning Method for Chinese Text Correction with Semantic Detection

Text correction, especially the semantic correction of more widely used scenes, is strongly required to improve, for the fluency and writing efficiency of the text. An adversarial multi-task learning method is proposed to enhance the modeling and detection ability of character polysemy in Chinese sentence context. Wherein, two models, the masked language model and scoring language model, are introduced as a pair of not only coupled but also adversarial learning tasks. Moreover, the Monte Carlo tree search strategy and a policy network are introduced to accomplish the efficient Chinese text correction task with semantic detection. The experiments are executed on three datasets and five comparable methods, and the experimental results show that our method can obtain good performance in Chinese text correction task for better semantic rationality.
F-Transformer: Point Cloud Fusion Transformer for Cooperative 3D Object Detection

We present a novel cooperative detection framework to fuse multi-view point clouds, for accurately detecting hard samples (e.g., partly or fully occluded, or small objects). Building on a two-step communication scheme to transmit the pillar features between views, it is possible to observe the same object from different viewpoints. We then design a feature fusion scheme based on Transformer to fuse the pillar features by discretizing the point clouds. Considering the sparsity of information, we improve Transformer’s self-attention mechanism, with Re-Scaled Dot-Product Attention, which allows the sparse information to capture valuable information more effectively. We evaluate the performance of our method by generating synthetic cooperative datasets over multiple complex traffic scenarios. The results show that our method surpasses all other cooperative perception methods with significant margins.
Understanding People's Perceptions of Approaches to Semi-Automated Dietary Monitoring

The respective benefits and drawbacks of manual food journaling and automated dietary monitoring (ADM) suggest the value of semi-automated journaling systems combining the approaches. However, the current understanding of how people anticipate strategies for implementing semi-automated food journaling systems is limited. We therefore conduct a speculative survey study with 600 responses, examining how people anticipate approaches to automatic capture and prompting for details. Participants feel the location and detection capability of ADM sensors influences anticipated physical, social, and privacy burdens. People more positively anticipate prompts which contain information relevant to their journaling goals, help them recall what they ate, and are quick to respond to. Our work suggests a tradeoff between ADM systems' detection performance and anticipated acceptability, with sensors on facial areas having higher performance but lower acceptability than sensors in other areas and more usable prompting methods like those containing specific foods being more challenging to produce than manual reminders. We suggest opportunities to improve higher-acceptability, lower-accuracy ADM sensors, select approaches based on individual and practitioner journaling needs, and better describe capabilities to potential users.
Geochemical studies of the Green River Formation in the Piceance Basin, Colorado

Hierarchical cluster analysis (HCA) was applied to a geochemical dataset representing the Eocene Green River Formation in the Piceance Basin of Colorado to identify chemofacies in core and outcrop samples from the basin margin and the basin center. The input dataset consisted of inductively coupled plasma optical emission spectroscopy and mass spectrometry and total organic carbon (TOC) content analyses of 186 basin margin outcrop samples and 190 basin center core samples discussed in Part 1 of this study (this volume). TOC values and twenty-five major and trace elements were used as variables to define statistical clusters of samples for the overall dataset, for the two basin center cores, and for each separate core or outcrop dataset by HCA applying Euclidean distance and Ward’s method algorithms. For each dataset, five cluster-defined chemofacies were identified. The chemofacies for each dataset show chemical affinities with five informally defined rock types– mudstone, marlstone, carbonate-rich mudstone, siliciclastic-rich mudstone/siltstone/sandstone, and Na-rich (saline) mudstone, with each showing variations in TOC content and abundance of redox sensitive minor and trace elements. A close relationship between enrichment of redox sensitive elements, particularly As and Mo, and TOC is identified in the basin center. Whereas enrichment factors (relative to average shale) are relatively low for many Period IV (PIV) transition metals, as discussed in Part 1 of this study, their consistent coherence in enrichment or depletion in HCA-defined chemofacies demonstrates the expected relationship between redox state and organic richness. Enrichment in PIV transition metals also shows a correlation to enrichment in elements with affinity for siliciclastic sediment. Enrichment/depletion among several groups of redox indicators is not everywhere consistent, with some chemofacies showing, for example, enrichment of PIV transition metals and depletion of sulfur and arsenic. Early timing of saline conditions in the basin margin is clearly displayed in the chemofacies log display, consistent with observations based on geochemical interpretations of concentrations and elemental ratios discussed in Part 1. Overall, the chemofacies are consistent with major mineralogical units and lake history stages defined in previous work, but provide more detail on the fluctuations in lake chemistry that occurred during deposition of Green River oil shale in the Piceance Basin.
Continual Learning for Activity Recognition

The recent success of deep neural networks in prediction tasks on wearable sensor data is evident. However, in more practical online learning scenarios, where new data arrive sequentially, neural networks suffer severely from the “catastrophic forgetting” problem. In real-world settings, given a pre-trained model on the old data, when we collect new data, it is practically infeasible to re-train the model on both old and new data because the computational costs will increase dramatically as more and more data arrive in time. However, if we fine-tune the model only with the new data because the new data might be different from the old data, the neural network parameters will change to fit the new data. As a result, the new parameters are no longer suitable for the old data. This phenomenon is known as catastrophic forgetting, and continual learning research aims to overcome this problem with minimal computational costs. While most of the continual learning research focuses on computer vision tasks, implications of catastrophic forgetting in wearable computing research and potential avenues to address this problem have remained unexplored. To address this knowledge gap, we study continual learning for activity recognition using wearable sensor data. We show that the catastrophic forgetting problem is a critical challenge for real-world deployment of machine learning models for wearables. Moreover, we show that the catastrophic forgetting problem can be alleviated by employing various training techniques.
Closed-Form Solution of Principal Line for Camera Calibration Based on Orthogonal Vanishing Points

Vanishing point is an important geometric element in sports video. In this paper, a new calibration algorithm is proposed by using the algebraic and geometric properties of vanishing points, which resolves the three main problems of the traditional camera calibration technology based on vanishing points: (1) calibrating camera with varied focal length; (2) screening out outliers from a set of images; (3) estimation of distortion coefficients. The principal line passes through the principal point, and the algebraic relationship between it and the vanishing points is deduced. Using the geometry feature of the principal line, problems (1) and (2) can be easily solved. The linear relationship between the point and the line is used to estimate the distortion coefficient under the condition of obtaining the principal point. Simulation and real experiments show the validity and robustness of the proposed algorithm, and satisfactory results can be obtained by solving the above three problems.
Rapid COVID-19 Testing and On-site Case Investigation and Contact Tracing in an Underresourced Area of Salt Lake City, Utah, December 2020–April 2021

This case study describes how we paired free SARS-CoV-2 rapid antigen testing with on-site case investigation and contact tracing at a drive-through site in an underresourced area of Salt Lake City. Residents of this area had lower rates of employment and health insurance and higher rates of poverty than in the Utah general population. People were given an option to remain on-site and wait until their test results were ready. If a vehicle occupant received a positive test result, the case investigation occurred on-site; contact tracing with the other vehicle occupants was also initiated. People were provided resources to support isolation and quarantine. Bilingual staff who spoke Spanish were incorporated into the workflow. From December 2020 through April 2021, public health staff administered 39 587 rapid tests; 4094 people received a positive test result and 1133 stayed for on-site case investigation. More than half (60.5%) of people with a positive test result who agreed to stay for on-site case investigation were Hispanic or self-reported belonging to a non-Hispanic racial minority group (American Indian/Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, or other racial identities). Pairing rapid antigen testing with on-site case investigation and contact tracing is feasible and improved the timeliness of case investigation by ≥1 day. On-site vaccination services were later integrated. Future emergency responses might consider assisting underresourced communities with on-site services that provide convenient and accessible public health interventions. By providing dependable and reliable services, we were able to achieve buy-in and become a consistent resource for those in the community.
A Course Outline for Education in Legal and Managerial Aspects of Technology

This is the era of technological innovation, and the integration of technology into all sectors of society is a reality. Technology industry has already indicated the need for technology professionals who work and take decisions led not only by their engineering skills, but also by their legal and managerial skills. In this direction, in 2019 we conducted a survey using a questionnaire that was distributed to graduated students of Technology Institutes in Greece, in order to examine their knowhow on Entrepreneurship and on Patent Law in the direction of supporting a potential technological invention. Considering the above mentioned and the conclusion of the survey, in this paper a course is compiled and is proposed to be integrated in undergraduate curriculum of technical universities. The course regards both legal and managerial aspects of technology that concern a professional of technology. Educating new technological scientists to cope with the Technology Industry is a major issue and a qualification that scientific and industrial communities require. This paper serves this purpose; the proposed course is an innovative tool for the new generation technocratic scientists, who will serve the future technological and industrial society.