Reviewerly/exHarmony huggingface dataset:
Future of Online Signatures: The One-Sign Feature

Have you ever messed up trying to sign a document digitally? If you did, then you know how difficult it is to use your index finger as a god-given stylus; To draw between those tiny exact spaces and redo it, again and again, in case the screen does not capture your finger drawn signature properly. Shouldn’t there be a better convenient, hassle-free way to do this? Something that ensures authenticity, accountability, security, and at the same time doesn’t feel like ‘draw-wrestling’? Here we present our solution in the form of a software system as a hypothesis going by the name ‘the One sign feature’. The feature is a crossplatform, profile-based, Sign Insertion method, which works when a password is successfully accepted, thereby inserting the sign on the document where selected. It is proposed as a solution to the "Small Screen, Too Much Work, Less Authenticity and Less Accountability" problem statement, which are common limitations of all the current solution offerings. The paper does a brief comparative study of all the popular technologies available in the industry used to digitally sign a document; discussing their drawbacks and drastic impacts of their limitations in terms of loss of money, productivity, and time. All this with the motive to prove that the 'Smart era' of Online Signatures is yet to come to its breakthrough and that there is still a wide opportunity gap for a system that could increase Data Privacy, overcome the limitations of the current systems, and eliminate loopholes.
Satellite Navigation Multipath Channel Sparse Reconstruction Scheme Applied in Performance Evaluation of Constellation Channel Emulation

In this paper, we study how to decrease the computational complexity of constellation channel emulation and keep the identical results, when employing a channel model in the evaluation of satellite navigation receivers for zero on site testing. We propose a sparse reconstruction scheme for the satellite navigation channel model. In this scheme, we explore how to create a channel impulse response subset via K-medoids technique, which can retain the multipath-error effect of the original channel. The obtained sparse channel can be used for realistic emulation on the satellite navigation channel model without huge hardware resources. This allows a fast and cost-efficient implementable emulation system. The evaluation results indicate that the proposed scheme and approach provides good result under the preservation of the auto-correlation function criterion.
Third International Workshop on Algorithmic Bias in Search and Recommendation (BIAS@ECIR2022)

Creating search and recommendation algorithms that are efficient and effective has been the main goal for the industry and the academia for years. However, recent research has shown that these algorithms lead to models, trained on historical data, that might exacerbate existing biases and generate potentially negative outcomes. Defining, assessing and mitigating these biases throughout experimental pipelines is hence a core step for devising search and recommendation algorithms that can be responsibly deployed in real-world applications. The Bias 2022 workshop aims to collect novel contributions in this field and offer a common ground for interested researchers and practitioners. The workshop website is available at https://biasinrecsys.github.io/ecir2022/ .
Data Fusion Method of Data News Report Based on Relative Difference Coefficient

The traditional data fusion methods of data news reports have the problems of poor data fusion accuracy and poor data fusion effect. A data fusion method of data news reports based on relative difference coefficient is proposed. By analyzing the functional requirements of data fusion, collect news report data, preprocess the collected data based on the relative difference coefficient, and fuse the data and news report data according to the processing results. Comparative experiments are used to verify that the data fusion accuracy of this method is higher, the data fusion effect is better, and it is of great promotion value.
Stereo Image Inpainting for UGVs Based on a Multistage Feature Alignment and Refinement Network

The binocular vision systems provide a wealth of visual information for UGVs. However, the dust, soil and water droplets in the environment can easily contaminate the camera lens, which will cause errors in the robot's visual perception system. However, the current GAN-based image inapinting methods usually generate wrong textures, which are quite different from the real scenes. To address this problem, we propose an inpainting method that uses stereo image information as prior knowledge. In this work, we first use a multistage feature alignment module to align the left and right feature maps, and use the dynamic fusion module to fuse the two features after each alignment stage. After that, we use the feature refinement module to refine the texture information of the feature maps to generate more refined details. Experiments show that our method surpasses previous work, and achieves a higher PSNR and SSIM evaluation metrics while running over 25 fps on single NVIDIA GeForce RTX 2080 Ti, which provides effective self-inpainting capabilities for the binocular vision system of UGVs to keep driving safety.
Fusion of Deep Features for Classification of Breast Cancer Using Multi-Deep CNNs

Breast cancer remains a deadly disease that frightens women in several parts of the world. At the same time, deep learning becomes the widely used and fast-growing area of traditional machine learning. The work experiments a newer computer-aided diagnosis (CAD) tool that comprises of feature extrication and classification through deep learning for assisting radiologists in breast cancer classification in mammogram images. And this is done by three different experimentations for determining the optimum way of robust classification. Herein, the first one makes use of pretrained Deep CNNs namely AlexNet, GoogleNet, Res-Net50, and DenseNet121. The second one is based on the experimentation of extracting features using the Deep CNNs and applied to a support vector machine (SVM) model. The last one is based on fusing the deep features for designing a robust classification framework. All these experimentations are evaluated using MIAS database. And finally, the results reveal that the fusing of deep features enhanced the classification performance of SVM, i.e., this deep feature fusion (feature set_3) with SVM provides a maximum classification accuracy of 96.739% than other approach.
A Too Thin True Future: The Problem of Grounding Within Presentist TRL Semantics

In this paper, we discuss the coherence and the stability of three rather plausible philosophical intuitions: the idea that all that exists is present (Presentism); the idea that there exists a true future, although it is just a contingent future (Thin Red Line); and the idea that a proposition depends on (or is grounded in) a truthmaker (Truthmaking). We will not show that assuming these three ideas together is logically incoherent; however, their combination seems to be very difficult to hold and, ultimately, it should be discarded. As a consequence, some of these assumptions must be rejected. We will analyze in detail one of the most promising strategies that can be pursed to reconcile Truthmaking and Presentism: adopting a liberalized version of Truthmaking, for which not only what exists but also what will exist and what existed can ground the truth of a proposition. However, as for the future, this strategy works only if the future is historically closed. If there are future contingents, this line of defense is flawed.
Cloud Services: An Ultimate Tool for Business Process Execution or Not

Cloud Computing is a technology that provides computing services like network, storage, servers, software, computing resources, database and applications over the internet on demand. Cloud computing uses technologies like virtualization, utility computing, service- oriented architecture and many more to provide these cloud services to the customers. In this paper, we will focus on the cloud computing delivery models, technologies used in cloud computing and at last we will discuss whether cloud services are an appropriate option to use in business process execution or not based on the advantages and concerns about cloud services.
The Customized Visual Geometry Group Deep Learning Architecture for Facial Emotion Recognition

Facial emotion recognition (FER) detects a user’s facial expression with the camera sensors and behaves according to the user’s emotion. The FER can apply to entertainment, security, and traffic safety. The FER system requires a highly accurate and efficient algorithm to classify the driver’s emotions. The-state-of-art architectures for FER, such as visual geometry group (VGG), Inception-V1, ResNet, and Xception, have some level of performance for classification. Nevertheless, the original VGG architectures suffer from the vanishing gradient, limited improvement performance, and expensive computational cost. In this paper, we propose the customized visual geometry group-19 (CVGG-19), which adopts the designs of the VGG, Inception-v1, ResNet, and Xception. Our proposed CVGG-19 architecture outperforms the conventional VGG-19 architecture by 59.29%, reducing the computational cost by 89.5%. Moreover, the CVGG-19 architecture’s F1-score, which represents the real-time classifying performance, displays superior to the Inception-V1, ResNet50, and Xception architectures by 3.86% on average.
A Smart Expiry Date Reminder System For Medicines

In this paper, an expiry date reminder system is proposed which will notify the users about the expiry dates of the medicines, along with few additional details via wireless communication. The entire model was set up on the Raspberry Pi which hosted the consumer database gathered through scan app. It also aims to help the pharmacists to keep track of what medicines are going to be expired soon. A database (made in SQLite) that keeps track of all of the medicines bought by a consumer along with their expiry dates and additional information is maintained. The maintained database is regularly monitored to check what medicine is approaching its expiry. A text message is sent via Twilio API services to the consumer notifying them about the approaching expiry date of the medicine along with some details about the medicine. This system does not require the tedious manual entry of each product name and expiration date into the smartphone app by the consumer.
Optimization of Classification Rules and Voting Strategies for Random Forest

As an efficient learning method, random forest is widely used in data mining, machine learning, artificial intelligence and other fields. It has excellent capabilities in specific practice. However, the decision tree model used in the classification process for random forest traverses all attribute values to find the split points, which leads to over-fitting and reduction of algorithm efficiency. In addition, the meta-base models of random forests vote with the same weight, which may result in decreasing algorithm accuracy. In this paper we accomplish the following two optimization tasks. Firstly, the continuous attributes are discretized based on the boundary theorem of Fayyad and Irani. Secondly, Gaussian mixture model is used to adjust the weight of the meta-base models in optimized random forest according to the similarity between the subsets and the training sets. Finally, the optimized algorithm is applied to the student information data set and the terrain types data set. The experiment results show that the optimized algorithm can effectively improve the classification efficiency and prediction accuracy.
Making Licensing of Content and Data Explicit with Semantics and Blockchain

Creation and reuse of content and data are on the rise. Tracing of who, when and how has created and modified content and data in an explicit manner becomes paramount for transparent, explainable, efficient and fair digital ecosystems. We specifically address a challenge that there is currently no uniform method to link data and content with licenses explicitly, immutably and in a traceable way (with authentication). Thus we have created a blockchain-based method which makes it possible to attach semantic licenses with data and content. Ethereum and the Data Licenses and Clearance Center - “DALICC”, a software framework for automated clearance of rights, are used as a bases for our prototype implementation for the license annotation (see: https://github.com/kilianhnt/dalicc-license-annotator ). Particular care is taken to ensure that the solution remains generic. Its practical feasibility, such as compliance with requirements, particularly, authentication, affordable deployment and usage costs, is positively evaluated.
Evaluation of Accuracy of Leap Motion Controller Device

As Human-Computer Interaction has continued to advance with technology, Augmented Virtuality (AV) systems have become increasingly useful and improve our interaction with technology. This article addresses the effectiveness of the Leap Motion Controller at capturing this interaction, as well as present new ways to improve the experience. First, we will present and discuss data from test trials with human input to show how accurately the Leap Motion Controller represents the user’s hand motion. This will provide the background information needed to understand our proposal for the potential changes and modifications to the device that would improve the Human-Computer Interaction. This will also provide insight into how implementing deep learning could improve the accuracy of this device. Improving the accuracy of the Leap Motion Controller could lead to an increased usage of the device in games and it could also potentially be used for educational purposes. While it has a long way to go, the Leap Motion Controller could potentially become an incredible source for virtual environments in academia, the world of rehabilitation, and for recreational use.
Manufacturing improvement capabilities of machine learning algorithms: Evaluation using an electrode-separator compound handling demonstrator

Machine learning based analyses of the production data promise direct improvements of manufacturing conditions and offer a way to justify involved investment costs for the data provision. In the current literature, explanatory and predictive capabilities of machine learning algorithms are described. The explanatory capabilities focus on root cause and structural analyses, while the predictive capabilities concentrate on real-time predictions. The aim of this contribution is to evaluate the different capabilities of data analyses using a handling device responsible for positioning and transporting electrodes for the lithium-ion cell assembly. The evaluation shows that the root cause analyses offer tools to improve especially simple use cases. The real-time prediction promises a reduction of production costs by classifying the final position of the electrodes and enables an early outlier detection. Additionally, an approach is presented to detect relevant production parameters of the resulting quality of produced cells.
CypherQA: Question-answering method based on Attribute Knowledge Graph

In knowledge-based question answering(KBQA), most research adopts the question template matching, which faces with challenges such as unclear entity boundaries and difficult path inference when solving complex questions. In this paper, we propose a KBQA solution based on attribute graph. It extracts the mentions in text to recognize relations and entities, and transforms it into a slot-filling Cypher statement to query the answer. Meanwhile, we design a two-layer network based on a structural attention mechanism to optimize entity boundary identification. The solution provides new ideas of relation recognition for answering complex questions over attribute knowledge graph. Experimental results show that the proposed approach achieves promising performance on both CCKS2019 public dataset and the self-built vertical domain dataset.
User Interface Design With Data Visualization Technique

User experience designers have to put in tremendous effort to convey complex information such that it can be easily understood and be visually appealing to the users. In today's world of big data, visualization techniques are essential to analyse massive amounts of complex data and make data-driven decisions. To support better decision making, visualization technologies enable users to uncover hidden patterns. During the COVID-19 pandemic, these techniques have been used as user interface in order to communicate the impact of the pandemic on the public. Considerable effort has been devoted to monitor the spread of the disease across the world and understand the various aspects of the pandemic. This chapter emphasizes the role of the data visualization technique as an effective user interface. Visualization of COVID-19 data is performed in the form of interactive dashboards, which can be beneficial to healthcare users and policy makers to plan the resource allocation and implement strategies to mitigate the effects of the pandemic.
Effective Personalized Taxonomy Pruning for Identification of Relevant Knowledge Domain for Knowledge Support of User Interests

Although living in the information era, users are still more likely to have partial information rather than comprehensive understanding on some concepts due to limited knowledge. In order to provide user with knowledge support of his interests, we propose an efficient personalized pruning model called TP-Alpha for the pruning of taxonomy graph while quantifying the relation between connected entities based on introduced concept called as relevancy degree. TP-Alpha model is a variant of approximation algorithm for acyclic directed Steiner tree problem. Compared to state-of-art pruning models, TP-Alpha model can preserve most of the user-given interests and entities with relevant knowledge having maximum average of relevancy degree inferred by user-given threshold value, and the least of other entities for maintaining connectivity without the constraint of the location of user-interested entities in the taxonomy graph. We evaluate our model on the benchmark datasets from a SemEval challenge. Experimental results verify the effectiveness of our TP-Alpha model in achieving relevant knowledge domain with more comprehensive knowledge support of user interests while maintaining less unnecessary cost.
Real-time Face Mask Detection at Gateway Using OpenCV and IoT

To combat the pandemic spread of coronavirus, this research work has suggested a face mask recognition system to prevent people from accessing public areas such as malls and theaters unless they are wearing a face mask. The manual inspection of a person's face mask can be automated by installing this technology in public areas. The proposed system is connected to a Raspberry Pi camera, which records video at the main entrance. Face detection is performed first from the recorded video. This research work utilizes a Caffe model, which is a DNN face detector in OpenCV to detect the faces. Only when a face is detected, a mask is detected. The MobileNet architecture is used to create a model that determines whether or not a person is wearing a face mask. The gate's operation is determined by the classification result. If the classification result indicates that the person is wearing a face mask, the gate will open and the individual will be able to access the public area; otherwise, the gate will stay closed. IoT is used to regulate the gate's operation depending on the classification result.
Image restoration via Picard's and Mountain-pass Theorems

&lt;abstract&gt;&lt;p&gt;In this work, we present existence results for some problems which arise in image processing namely image restoration. Our essential tools are Picard's fixed point theorem for a strict contraction and Mountain-pass Theorem for critical point.&lt;/p&gt;&lt;/abstract&gt;
Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks

In the modern age of social media and networks, graph representations of real-world phenomena have become an incredibly useful source to mine insights. Often, we are interested in understanding how entities in a graph are interconnected. The Graph Neural Network (GNN) has proven to be a very useful tool in a variety of graph learning tasks including node classification, link prediction, and edge classification. However, in most of these tasks, the graph data we are working with may be noisy and may contain spurious edges. That is, there is a lot of uncertainty associated with the underlying graph structure. Recent approaches to modeling uncertainty have been to use a Bayesian framework and view the graph as a random variable with probabilities associated with model parameters. Introducing the Bayesian paradigm to graph-based models, specifically for semi-supervised node classification, has been shown to yield higher classification accuracies. However, the method of graph inference proposed in recent work does not take into account the structure of the graph. In this paper, we propose a novel algorithm called Bayesian Graph Convolutional Network using Neighborhood Random Walk Sampling (BGCN-NRWS), which uses a Markov Chain Monte Carlo (MCMC) based graph sampling algorithm utilizing graph structure, reduces overfitting by using a variational inference layer, and yields consistently competitive classification results compared to the state-of-the-art in semi-supervised node classification.
Tagnorm: Tagalog Text Normalization Withslang Word Detection And Classification Of Type Of Slang Word Using Support Vector Machine

TagNorm: Tagalog Text Normalization with Slang Word Detection and Classification of Type of Slang Word using Support Vector Machine was designed to normalize informal word and transform it to their canonical forms. The study aimed to improve text normalization in the Filipino domain in which the informal texts such as Alphanumeric Abbreviation and Phonetic, Stylistic variation, Combined Words, Misspelled words/ typographical errors (except rumbled letters), Omission of Vowels and Slang Words can decrease the performance of Natural Language Processing. Using the Facebook data the accuracy of the system in terms of informal text types the researchers got an accuracy of 91.23% in Alphanumeric Abbreviation and Phonetic, 77.61% in Repetition of Characters, 85.19% in Stylistic variation, 69.64% in Combined Words, 72.41% in Misspelled words/ typographical errors, 90.7S% in Omission of Vowels and 93.89% in Slang Words, while the data from previous study obtained an accuracy of 86.96% in Alphanumeric Abbreviation and Phonetic, 66.67% in Repetition of Characters, 57.14% in Stylistic variation, 80% in Combined Words, 85.29% in Misspelled words/ typographical errors, 93.15 in Omission of Vowels and 75% in Slang Words..