Reviewerly/exHarmony huggingface dataset:
A Novel Image Falsification Detection Using Vision Transformer (Vi-T) Neural Network

Image classification and image recognition are significantly impacted by the creation of Vision Transformers. It is no wonder that CNN architectures utilize multiple layers and a considerable number of hyper-parameters are necessary for training, as they are much more complex. While training and implementing, CNN uses a considerable number of resources. Alternately, Vision Transformers are the innovative architecture of neural networks that allows it to take in an image and break it into little pieces, then use an attention mechanism to search for correlations in the pieces. For localization of the attention mechanism, the transformers are well trained on small visual patches. With the introduction of image manipulation tools, much of the picture data available on the Internet today are designed to fool consumers into thinking the image is genuine. To authenticate photographs, we must utilize many different neural networks. The main purpose of this research was to detect forgery and locate the tampered image utilizing the transformers and attention mechanism concept. Using the RTX-3080 graphic card, the study effort is implemented on two benchmark datasets. These datasets are used to get the evaluation results, and they are compared to the state-of-the-art techniques. During the training and validation process, training accuracy of 98% and validation accuracy of 97% are achieved on benchmark datasets.
Joyful Jaccard: An Analysis of Jaccard-Based Similarity Measures in Collaborative Recommendations

Recommender systems (RSs) are utilized by various e-commerce giants such as Amazon, YouTube and Netflix for providing a personalized experience to the individual users. For this reason, it has become very important to develop and use efficient techniques to provide recommendations. Neighborhood-based collaborative filtering approaches are traditional techniques for recommendations and are very popular due to their simplicity and efficiency. Neighborhood-based recommender systems use numerous kinds of similarity measures for finding similar users or items. Further, defining novel ways to model the notion of similarity is an active thread of research among RS researchers (by using rating data available in the user-item matrix). This work compares and analyzes the performance of various Jaccard-based similarity measures. Primarily, various Jaccard-based similarity measures are examined with their authoritative definitions. We have conducted various experiments using standardized benchmark datasets (MovieLens-100 K, MovieLens-1 M, and Yahoo music) for assessing the performance of these measures. Empirically obtained results demonstrate that the New Weighted Similarity Measure (NWSM) provides better predictive accuracy among all measures.
Capacitive Eye Tracker Made of Fractured Carbon Nanotube-Paper Composites for Wearable Applications

The uniqueness of eyes, facial geometry, and gaze direction makes eye tracking a very challenging technological pursuit. Although camera-based eye-tracking systems are popular, the obtrusiveness of their bulky equipment along with their high computational cost and power consumption is considered problematic for wearable applications. Noncontact gaze monitoring using capacitive sensing technique has been attempted but failed due to low sensitivity and parasitic capacitance. Here, we study the interaction between a novel capacitive sensor and eye movement for wearable eye-tracking. The capacitive sensors are made of a pair of asymmetric electrodes; one comprising carbon nanotube-paper composite fibers (CPC) and the other being a rectangular metal electrode. The interaction between the asymmetric sensor and a spherical object mimicking an eyeball is analyzed numerically. Using a face simulator, both single- and differential capacitive measurements are characterized with respect to proximity, geometry, and human body charge. Using a Â prototype eye tracker, multiple sensor locations are studied to determine the optimal configurations. The capacitive responses to vertical and horizontal gaze directions are analyzed in comparison to those of a commercial eye tracking system. The performance is demonstrated for sensitive eye-movement tracking, closed-eye monitoring, and human-machine interface. This research has important implications for the development of capacitive, wearable eye trackers, which can facilitates the fields of human-machine interface, cognitive monitoring, neuroscience research, and rehabilitation.
Recommendation of Student Information Management System Based on Meta-algorithm Django Architecture

With the development of education, improving school management efficiency and service quality has become an urgent problem to be solved. Therefore, this paper proposes a student information management system, the purpose is to design a related system to effectively manage student information. This paper mainly uses the experimental method and quantitative analysis method to test and analyze the performance of the student information management system under the meta-algorithm Django architecture. The experimental data show that the average response time of the system in this paper is within 1s when the number of people reaches 1000. This data is enough to show that the system is fast enough to meet basic needs.
Directionality Information Aware Encoding for Facial Expression Recognition

Facial expression recognition has gained a huge research interest in the field of computer vision. However, the main issue is designing an appropriate face descriptor that can capture the facial variations for different expressions. Toward such an objective, in this paper, we propose a new face descriptor based on facial muscle movements. We employed the Robinson compass mask to capture these movements and derived the edge responses in eight directions. Next, we propose a new texture encoding scheme based on a local binary pattern called direction aware binary pattern (DABP), which encodes the directional and magnitude of edge responses. Finally, the face is described through a histogram descriptor and then fed to the KNN algorithm for classification. For experimental validation, we simulated the proposed approach over FER-2013 dataset, and the performance is measured through recognition accuracy.
Invention Concept Latent Spaces for Analogical Ideation

Analogy is a powerful form of ideation and therefore an automated or semiautomated analogical method is a potentially useful way to develop, or at least inspire, new and possibly patentable ideas. The last few years have shown significant developments in the training and use of latent spaces for text generation using Variational Autoencoders (VAE), though many problems remain including preventing ‘collapse’ of the latent space during its training and successfully disentangling the latent variables, including the syntax from the semantics. A hierarchical sentence and document variational denoising autoencoder architecture is presented, in which the encoded sentence vectors are first generated and then an encoding and decoding is performed of the sequence (in the document) of these sentence vectors. The latent vectors for both sentences and documents are structured into ‘syntactic’ and ‘semantic’ subsections based on their use in auxiliary training tasks. A large dataset of patent titles and abstracts, along with their IPC6 codes, is used to train the VAE networks. The resulting document latent space is used to perform analogy transforms to seek to generate/inspire useful and potentially novel patent concepts.
Deriving and Using Synthetic Consequences for Privacy Risk Modeling

Highly contextual socio-technical systems demand highly contextual risk models for relevant system properties, including privacy. Many privacy risk models revolve around adverse consequences, i.e., typologies of bad privacy outcomes. The attendant mirror issues of insufficient contextual nuance (too few/general consequences) versus problematic analytical manageability (too many/granular consequences) can be addressed through the development of synthetic consequences as part of privacy engineering risk management. The objective is to categorize the total set of consequences so as to produce a small number of categories that nonetheless manage to capture the full range of meaning of the original consequences. Those categories can then be tailored to better reflect the context of the socio-technical system being analyzed. Relevant theoretical foundations for the construction of synthetic consequences are discussed and a step-by-step methodology for deriving them is described. This methodology is then applied to the example of a smart TV.
Registration of Geospatial Information Elements

Geospatial information elements are the fundamental constituents of geographic information. A dataset of geospatial information includes the geographic features, their attributes, and the relationships between these features together with the associated metadata and spatial elements. Most of the suite of geospatial standards define templates for describing these features, attributes, relations, and metadata. The actual feature, attribute, and metadata information element definitions are application dependent; for example, a road may be described differently on a transportation logistic map than on a cadastral map, and more detailed attributes about trees may exist on a forestry land cover map than on a political jurisdiction map. Registers may be used to hold these definitions so that they may be used in common in similar applications. In fact, the largest part of the standardization of geographic information, with respect to the management of detail, is the standardization of feature and attribute dictionaries and common code lists that may be used to ISO 19135support attributes and metadata. This chapter describes how ISO standards 19135-1 and 19135-2 provide for the management of registers of geographic information elements, including the complex relationships between multiple registers. Registers allow for the management of diversity.
A Comparative Study of PSO, PSO Variants, and Random Scheduling in Solving Workflow Scheduling Problem in Cloud Computing Environment

To obtain an optimal solution for an optimization problem, the most important and, hence, the crucial step is to make the right choice of the optimization algorithm from a diverse range of algorithms available. Cloud computing is a methodology that dynamically as well as simultaneously provides services and allocates resources to remotely residing users through Internet-based tools using the pay-for-use model. The unlimited storage, ease of use, backup and amp, recovery, and security are some of the features of a cloud computing environment which make its high demand even higher. This demand is the reason that calls for better cloud optimization which minimizes the processing cost using scheduling policies and algorithms. The objective here is to minimize the total cloudlet processing cost using an optimal scheduling algorithm on virtual machines. In this paper, the optimization heuristics named particle swarm optimization, its variants, and random scheduling are compared. The comparison of the results shows that different variants of the PSO heuristic perform better in comparison with random scheduling. Among different variants of PSO used for task scheduling purposes, PSO using constriction factor and PSO variant using both linear decreasing inertia weight (LDIW) and amp; constriction factor is found to perform better than others.
Predicting Instances Demand and Occupancy Toward Efficient VMs Rightsizing and Resources Allocation Strategies: Amazon Case Study

As cloud traffic never stops growing in minutes, hours and daily basis, proactive cloud resources orchestration becomes a prerequisite. In this paper, we investigate an Amazon case study, in which we intend to compare respectively univariate and multivariate predictions of multimodal AWS instances demand and their related instances resources occupancies. For this purpose, we implemented four nonlinear deep neural network (DNNs) models, namely: LSTM, GRU with their bidirectional variants BiLSTM and BiGRU. Experimentation test scenarios demonstrated the performance of BiGRU models above other candidate models, achieving until (0.71, 0.11, 0.26, 0.97) of RMSE values, respectively while predicting four instances families’ future demands. Adopting an extended BiGRU version, we further demonstrate how multivariate predictions remain much less accurate than univariate forecasting scenarios.
Attention Weight is Indispensable in Joint Entity and Relation Extraction

Joint entity and relation extraction (JERE) is an important foundation for unstructured knowledge extraction in natural language processing (NLP). Thus, designing efficient algorithms for it has become a vital task. Although existing methods can efficiently extract entities and relations, their performance should be improved. In this paper, we propose a novel model called Attention and Span-based Entity and Relation Transformer (ASpERT) for JERE. First, differing from the traditional approach that only considers the last hidden layer as the feature embedding, ASpERT concatenates the attention head information of each layer with the information of the last hidden layer by using an attentional contribution degree algorithm, so as to remain the key information of the original sentence in a deep transferring of the pre-trained model. Second, considering the unstable performance of the linear span classification and width embedding structure of the SpERT, ASpERT uses a multilayer perceptron (MLP) and softmax-based span classification structure. Ablation experiments on the feature embedding and span classification structures both show better performances than SpERT’s. Moreover, the proposed model achieved desired results on three widely-used domain datasets (SciERC, CoNLL04, and ADE) and outperforms the current state-of-the-art model on SciERC. Specifically, the F1 score on SciERC is 52.30%, that on CoNLL04 is 71.66%, and that on ADE is 82.76%.
Improving distributed collaboration at Porsche Engineering Services GmbH through the application of the EDiT Method

To support development teams with their individual challenges of distributed collaboration in product development, the EDiT method is being developed. This paper contributes to the continuous validation of the EDiT method by a field study at the process team of Porsche Engineering Services GmbH to support the successful transfer into practice. The validation is based on validation by application, validation by evaluation of the contribution to success, and validation by comparison of the requirements. The contribution of the EDiT method could be proven by different measurement criteria, e.g. reducing the average number of days to finish a task by a remarkable amount.
Robust and High Accuracy Algorithm for Detection of Pupil Images

Recently, many researchers have tried to develop a robust, fast, and accurate algorithm. This algorithm is for eye-tracking and detecting pupil position in many applications such as head-mounted eye tracking, gaze-based human-computer interaction, medical applications (such as deaf and diabetes patients), and attention analysis. Many real-world conditions challenge the eye appearance, such as illumination, reflections, and occasions. On the other hand, individual differences in eye physiology and other sources of noise, such as contact lenses or make-up. The present work introduces a robust pupil detection algorithm with and higher accuracy than the previous attempts for real-time analytics applications. The proposed circular hough transform with morphing canny edge detection for Pupillometery (CHMCEP) algorithm can detect even the blurred or noisy images by using different filtering methods in the pre-processing or start phase to remove the blur and noise and finally the second filtering process before the circular Hough transform for the center fitting to make sure better accuracy. The performance of the proposed CHMCEP algorithm was tested against recent pupil detection methods. Simulations and results show that the proposed CHMCEP algorithm achieved detection rates of 87.11, 78.54, 58, and 78 according to Świrski, ExCuSe, Else, and labeled pupils in the wild (LPW) data sets, respectively. These results show that the proposed approach performs better than the other pupil detection methods by a large margin by providing exact and robust pupil positions on challenging ordinary eye pictures.
Proposal of “micro:bit PC” Powered by Numeric Key Programming for both Visually Impaired and Sighted Elementary School Students

In the informational society, elementary school students are expected to learn programming. The BBC micro:bit, which has many sensors, is employed as one of the programming materials. However, MakeCode, which is a block programming editor of the micro:bit, is not accessible for visually impaired yet. Regarding this problem, we have proposed a new and easy programming environment for a mobile robot kit involving the micro:bit. The environment does not require any PC or tablet so visually impaired students can use it. Therefore, visually impaired students were able to obtain the programming skill within 15 min at maximum from their first touch of the robot. In this paper, we explain on the details of the command set of our robot and how to use the sensors of the micro:bit with our programming environment for the robot kit.
Bitcoin Price Predictor using Deep Learning

Bitcoin is a decentralized digital currency created in January 2009..We will go deep into the datasets, do an EDA, feature extraction and predict the price of bitcoin using Stochastic, Machine Learning and Deep Learning models. In this particular work we will be visualising the Time Series data, handling the missing values with various imputation techniques. Feature extraction is performed before model building, the focus of study will be these four models ARIMA, Facebook prophet, XG boost and LSTM. Comparing the models and their evaluation metrics to see how each model have performed.
An Improved Reversible Data Hiding Technique For Encrypted Images Using Difference Error Expansion

Digital data hiding exists in many forms, one of its most useful form is reversible data hiding which recovers the secret data and extracts the cover image exactly as these were at the time of embedding. In last few years, encryption and reversible data hiding have been combined to explore a new area of research, seeking researchers’ interest in very short span of time. To enhance the payload, this paper presents a novel reversible data hiding technique for encrypted images using bilinear interpolation and difference error expansion. Difference error expansion is a technique used to embed watermark in the images, by exploiting the spatial redundancy which exists in digital images. In difference expansion schemes, correlation of adjacent pixels is exploited to create the space for embedding of secret data. This paper aims to elevate the embedding capacity and reduce the distortion effect caused by embedding with the help of difference error expansion-based reversible data hiding method. The proposed scheme investigates the use of bilinear interpolation by utilizing the pixels located at even rows and even columns for the prediction of neighbouring pixels. Notably, good results are obtained when proposed work is compared with the existing ones on the basis of embedding capacity and PSNR.
Intercultural Communication in Teaching Strategies for a Polyethnic Environment in Modern University (Online and Digital Forms)

The intercultural competence of students in the context of the value approach helps to harmonize the psychological climate in the ingtergroup throughout the entire period of study, which helps to improve the academic performance of students and prevents interethnic conflicts both in online and digital forms. Modern education in higher educational institutions, the leadership of which was forced to largely revise the principles of interaction between students and teachers due to the introduced self-isolation regime and lockdown measures during the Covid-19 pandemic around the world, today includes contact and distant forms of communication, implemented through online and offline formats. At the same time, an increase in the number of situations in which it is necessary to interact in writing and orally is due to the introduction of social networks and instant messengers into the educational process, in which intercultural competence allows expressing one's thoughts more correctly and better understanding the foreign recipient. Tasks: (1) identify the main predictors of misunderstanding that may arise in the process of intercultural communication; (2) identify the factors contributing to the establishment of intercultural communication; (3) analyze the specifics of intercultural communication in digital and offline formats.
The Collision Detection Technology in Virtual Environment

In-depth analysis of the development and advantages of virtual manufacturing technology, detailed discussion of current hot issues in virtual manufacturing technology, collision detection simulation status, overview of the development of collision detection algorithms, specific analysis of the current virtual manufacturing environment collision detection technology For existing bottlenecks, a parallel detection technology for optimized calculations is proposed, including: fast parallel collision detection based on graphics hardware and a divide-and-conquer strategy and pipeline technology to complete collision detection. Finally, the efficiency and superiority of the algorithm are demonstrated through experimental data.
The Design and Development of A Text Mining Software for Multiple Digital Platforms

A text mining software is specifically developed to process the information contained in the texts, electronically stored on the computer. Text mining software analyses huge amount of data, faster than any technical tool, and extracts patterns from structured and unstructured data. It is a very useful tool for knowledge driven organizations. In a fast digitizing world, the digital tools have become essential for research and development in every field. The availability of a text mining tool for a language opens up many possibilities for cutting edge research in that language. Indian languages, in general, are digitally low-resource languages. They need technically sophisticated digital tools that can perform on different digital platforms. This paper describes the development of a multi-platform enabled text-mining software for Hindi language.
Process for Model Development

The main objective of this book is to develop a quality model for cloud gaming services that can be used for network planning and quality monitoring purposes. In order to build a quality model predicting gaming QoE, a framework is developed that keeps the different types of impairment separately. This separation of impairments, which is inspired by the E-Model (ITU-T Recommendation G.107, The E-model: a Computational Model for Use in Transmission Planning. Geneva: International Telecommunication Union, 2015), helps to be flexible in the replacement of impairment models with a new model without change of the core model structure. Figure 3.1 illustrates the proposed structure of the framework. The framework is developed based on two quality features of Video Quality (sub-feature of output quality, see Sect. 2.3 ) and Input Quality. The structure of the model is discussed in the next section.