Reviewerly/exHarmony huggingface dataset:
Cluster-Based Probabilistic Neural Networks for Outlier Detection Via Autoencoder Variants

Outlier detection in wireless sensor networks (WSNs) is an indispensable issue that plays a significant role in confirming sensor data quality and consistent recognition of exciting events. Since the erroneous and defective sensor data might raise false alarms with wrong conclusions, therefore, it is vital to spot outliers in sensor data proficiently and precisely for comprehensive decision-making. This manuscript proposed a cluster-based probabilistic neural network for outlier detection in WSNs via autoencoder variants. For performance analysis, we analyzed the proposed model with other existing models on real-world datasets. Experimental results show that both DAE-PNN and AE with dropout-PNN outperform all other existing approaches in detection rate and F-score.
Eye-Tracking and Usability in (Mobile) ERP Systems

This article covers the development of a specific model for the end-user-based evaluation of the usability of Mobile ERP systems employing eye-tracking technology. Recent studies show that the mobile use of ERP software is significant, even though there is also room for improvement. Simultaneously, it is undisputed that ERP-specific usability models are necessary to meet the specific requirements of ERP systems. Therefore, we examine usability dimensions in the Mobile ERP context in a first, preliminary study. In addition, we investigate how eye-tracking technology can support the usability measurement for each usability dimension. Therefore, after a detailed literature analysis, a usability model for the mobile application of ERP systems (Mobile ERP) is developed. Subsequently, we summarize the results of a first explorative preliminary study with 19 test persons. The results show that the model allows the usability of mobile ERP systems to be classified and that few decisive factors contribute to the perception of good usability in ERP systems. In the third part of this paper (after literature review and usability study), we introduce an overview of eye-tracking metrics for MERP-U to examine which MERP-U dimensions eye-tracking measures.
Binary Classification of Toxic Comments on Imbalanced Datasets Using Recurrent Neural Networks

Toxic (hateful and rude) behaviour is a huge problem in social media websites since toxic comments prevent constructive discussions and discourage people from starting conversations. Since millions of comments are posted daily, there needs to be some automated way to identify comments that are toxic and take action accordingly. In this paper, we aim to create a deep neural network that uses word embeddings for feature extraction and classifies comments into two classes—“Toxic” and “Non-toxic”. We have used a single recurrent layer, either LSTM, GRU, Bi-LSTM or Bi-GRU, in combination with other layers in our deep neural network to determine which architecture best suits this application. To handle class imbalance, we have used random under sampling on the majority class and data augmentation on the minority class. Our model was tested on Wikipedia Comments and Twitter Hate Comments datasets and achieved F1 scores of 0.89 and 0.84, respectively, outperforming the other works in this domain.
RCC: Enabling Receiver-Driven RDMA Congestion Control With Congestion Divide-and-Conquer in Datacenter Networks

The development of datacenter applications leads to the need for end-to-end communication with microsecond latency. As a result, RDMA is becoming prevalent in datacenter networks to mitigate the latency caused by the slow processing speed of the traditional software network stack. However, existing RDMA congestion control mechanisms are either far from optimal in simultaneously achieving high throughput and low latency or in need of additional in-network function support. In this paper, by leveraging the observation that most congestion occurs at the last hop in datacenter networks, we propose RCC, a receiver-driven rapid congestion control mechanism for RDMA networks that combines explicit assignment and iterative window adjustment. Firstly, we propose a network congestion distinguish method to classify congestions into two types, last-hop congestion and in-network congestion. Then, an Explicit Window Assignment mechanism is proposed to solve the last-hop congestion, which enables senders to converge to a proper sending rate in one-RTT. For in-network congestion, a PID-based iterative delay-based window adjustment scheme is proposed to achieve fast convergence and near-zero queuing latency. RCC does not need additional in-network support and is friendly to hardware implementation. In our evaluation, the overall average FCT (Flow Completion Time) of RCC is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4{\sim }79\%$ </tex-math></inline-formula> better than Homa, ExpressPass, DCQCN, TIMELY, and HPCC.
Algorithmic Differentiation Cheat Sheet

Algorithmic Differentiation (AD), also known as automatic differentiation, computes the derivative(s) of computer code. It was pioneered by (Giles and Glasserman, 2006) and produces exact derivatives with low latency. AD is well presented in finance, see (Capriotti, 2010), (NAG, n.d.) and (Savine,2018), where it can be used to compute financial risks such as Swap DV01, see (Burgess, 2022).In this paper we summarize (Burgess, 2022) and provide a cheat sheet of how to perform AD by hand. Firstly we present algorithmic differentiation and how to compute the exact derivative(s) of computer code to machine precision. Secondly we give a summary of AD and illustrate how to perform AD by hand using tangent and adjoint calculation modes. Throughout we gave examples in C++ code, which are available for download.
Method for Adaptive Video Information Estimation in Aerospace Monitoring Systems

The problem of increasing the efficiency of aerospace monitoring and automatic processing of video information under conditions of a priori uncertainty is investigated. To estimate the informative features of objects under observation in the contex of a priori uncertainty about the signal-noise environment, an approach is proposed that implements the procedures for obtaining a binary image and its clustering using adaptive minimax estimation of original multi-zone (grayscale) image elements. A description and testing of a computational algorithm for adaptive video information estimation in the problems of aerospace monitoring of observation objects is given. #CSOC1120.
Distributed Ledger Model for Agriculture Product Supply Chain

Computing technology solutions are increasingly being applied to agricultural product supply chains. Information sharing and analytics, market access, finance access and product tracking are the common applications developed in smart agriculture systems. Normally, agriculture product data is stored in a centralized database managed by government agencies and the integrity of data is not audited properly. In collaborative digital transaction, data validity is the main concern in product supply chain. This article proposes a disruptive approach in managing agriculture product supply chain. The integration of digital information management platform and Blockchain technology is applied in developing a secured distributed ledger for managing the transaction flows of agriculture product chain. The integrated model is presented in structured and formal approach. The Blockchain platform and its distributed ledger framework are discussed in details. With this condition, all information regarding to agriculture product are trusted, low risk of fraud, removes marketing overhead and time saving.
A Design Framework for Social Virtual Reality Experiences: Exploring Social and Cultural Dimensions for Meaningful and Impactful VR

Virtual Reality has often been described as providing a means to “walk a mile in another’s shoes” offering powerful interventions for experiential learning. Such experiences can, for example, provide a safe and controlled means of engaging with often difficult, unsafe or emotionally charged situations. Crucial to this experience is the sense of presence, informed by place and plausibility, of the simulation design. However, past studies often create such experiences through the lens of the developer, and they may therefore lack the authenticity and social nuance of the situations they are attempting to model. Health care students, for example, will often face difficult conversations with seriously ill patients during their placement time when in study. Currently, there is an under-preparedness associated with placement shock when students’ previous assumptions and the reality of patient care do not match. VR would seem well suited to preparing students for this reality but only if the simulations capture the complexity and social nuance. As currently there is little consideration of the social and cultural dimensions for developing social VR experiences, this paper proposes a framework for designing such socially oriented VR applications. We case study the framework by designing a social VR application for health care students to prepare for placement.
Roots and trends in knowledge organization

The chapter focuses on the position of knowledge organization inside the discipline of Library and Information Science. The notion of knowledge organization and its connection with the domain analysis theory is studied. Moreover, knowledge organization processes as indexing and summarizing are analyzed in the context to their application to texts and multimedia documents. Besides, the main news and trends in knowledge organization systems are considered as well.
Self-deduction Training Method Based on Deep Reinforcement Learning and Monte Carlo Tree Search

Aiming at the problem of how to construct the strategy and value function in reinforcement learning through deep neural network, this article first constructs the value function and strategy function under the framework of reinforcement learning through deep neural network, and improves the design of residual block to improve model performance. The feature extraction ability, and finally the Monte Carlo tree search method is introduced, and the decision model is intensively trained on the basis of supervised learning to improve its deduction ability. Simulation analysis proves the effectiveness of this method.
Blockchain Land Transfers: Technology, Promises, and Perils

The blockchain’s apparent immutability has attracted significant interest on whether it may be relied on for registering and transferring land. Proponents of blockchain-based land systems point toward data security, automated transacting, and improved accessibility as key benefits; critics raise concerns over structural vulnerabilities, such as majority attacks, and inconsistencies with existing legal frameworks. The literature, however, tends to conceptualise blockchain as one monolithic data structure invariably built on the same mechanisms powering Bitcoin. This paper seeks to situate the debate on a closer understanding of the range of blockchain implementations possible. To this end, we provide a detailed technological survey of established and emerging blockchain technologies, clarifying that different consensus mechanisms, permissioning schemes, and other use-based customisations, are possible. We then re-evaluate the promises and perils of blockchain land transfers in this light, focusing on the English conveyancing system, and illustrate how different implementations involve different advantages and limitations. However, the features necessary to avoid key vulnerabilities also diminish the marginal advantages of using blockchains over traditional electronic databases. Thus, we conclude that blockchains, even properly understood, remain unsuitable for land transfers.
AI Based Healthcare Chatbot

Now-a-days we are facing major challenges in India related to affordable medical services due to growing population. And even in this Covid-19 situation it is very difficult to provide medical/healthcare facilities. Condition is getting worst day by day because of unavailability of doctors. This can be cause of death as well. Chatbot is a software application where there is interaction between patients over the internet. Chatbots are playing an important role in today's world by supporting and helping patients with their appropriate information. Proposed idea is to create a Medical Chatbot based on Artificial Intelligence using NLP. Through chatbots one can communicate with text or voice interface and get reply back to the user. These bots connect with potential patients visiting the site, books appointments, and also help getting proper treatment. These chatbots are available 24/7, comfortable, efficient, beneficial and very friendly to use.
Testing Burrows' Delta on Ancient Greek Authors

This paper tests the effectiveness of Burrow’s Delta Method on a corpus of selected prose writings in ancient Greek. When tested on a corpus of fourteen and eight authors, the method yields good results with relatively small samples (1000, 3000, and 5000 words) and different word frequency vectors (100, 200, 500 words), but its performance is worse with texts of similar genres (oratory, historical or medical writings). We conclude that it is the generic proximity that influences the results of classification most. However, in cases where confusion is more likely, such as the writings of Demosthenes and Aeschines, the method proves effective for shortlisting potential authors. Shortlists can give an adequate idea of a sample’s nearest neighbors while leaving some freedom for the researcher in interpreting the results.
Improving Adversarial Robustness of Deep Neural Networks via Linear Programming

Adversarial training provides an effective means to improve the robustness of neural networks against adversarial attacks. The nonlinear feature of neural networks makes it difficult to find good adversarial examples where project gradient descent (PGD) based training is reported to perform best. In this paper, we build an iterative training framework to implement effective robust training. It introduces the Least-Squares based linearization to build a set of affine functions to approximate the nonlinear functions calculating the difference of discriminant values between a specific class and the correct class and solves it using LP solvers by simplex methods. The solutions found by LP solvers turn out to be very close to the real optimum so that our method outperforms PGD based adversarial training, as is shown by extensive experiments on the MNIST and CIFAR-10 datasets. Especially, our methods can provide considerable robust networks on CIFAR-10 against the strong strength attacks, where the other methods get stuck and do not converge.
Analysis of Feature Selection Methods for Hand Gestures Recognition Based on Machine Learning and the Leap Motion Controller

Hand gestures are widely used in communication among people or between people and machines. Therefore, hand gesture recognition is a topic of great interest. Hand gesture recognition is closely associated with pattern recognition, where overfitting may occur when dealing with many predictors relative to the size of the training set. Therefore, it is necessary to reduce the dimensionality of the feature vectors through feature selection techniques. This study compares three feature selection methods: maximum relevance and minimum redundancy (mrmr), neighbor’s component analysis (nca), and Relief-F, the nca method is analyzed in two variants. We analyze the behavior of these feature selection methods using the classification and recognition accuracy and time of processing. Feature selection methods were fed through seventeen feature-extraction functions, returning a score proportional to its importance. Next, the functions are ordered according to the score and fed to the machine learning algorithms, such as ANN, SVM, KNN, and DT. This work demonstrates that the classification and recognition accuracy have significant growth using the first three or four feature-extraction functions, then stabilized and finally decreased. Also, this work demonstrate that the processing time presents a linear growth according to the number of feature extraction functions used.
Custom Data Augmentation Technique (A Deeper Insight)

This paper presents a deeper insight into prior research for the classification of NFRs based on deep learning techniques. This classification system incorporates a novel custom data augmentation approach that preserves domain vocabulary. Previously, for DNNs training, the entire corpus was enhanced before being separated into train/validation sets. This paper adopts a contrasting approach to train four deep neural networks with only augmentation on the train set. We compare our method to a baseline (no augmentation) and a state-of-the-art EDA (Easy data augmentation technique) that uses pre-trained word embeddings. Our findings indicate a marginal difference when trained with our proposed approach compared to the EDA under these settings. Additionally, with augmentation on both the train and validation set, the CNN results improved by 10% Overall.
An Audio-Based Fault Detection and Classification Method with Applications in the Idler

The intelligent fault detection and classification method of the idler is proposed based on audio. In order to enhance the recognition of the data features, the Neighborhood Preserving Embedding (NPE) algorithm is improved. The intelligent fault detection and classification method mainly includes the primary extraction of audio relative wavelet energy feature and the Multi-level Neighborhood Preserving Embedding (MNPE) feature extraction. Compared with single projection feature extraction algorithms, MNPE has two projections, which are nonreduced and reduced projection. The nonreduced dimensionality projection can remove the part of tiny features not related to the key features of the data. The reduced dimensionality projection can reduce the dimensionality of the data and improve the visibility of the data. Through this method, the main features of the data can be obtained and the detection and classification rate of fault audio can be improved.
Investigation of Amazigh Speech Recognition Performance Based on G711 and GSM Codecs

In this chapter, we evaluate the Amazigh alphabets speech performance through an interactive voice response (IVR) system based on the losses in the G711 and GSM coding schemes. To investigate the effect of voice coding on the speech recognition rate, our designed system was trained to recognize the 33 Amazigh alphabets by using 3 and 5 HMM states with different Gaussian mixture models (GMMs) and the mel-frequency spectral coefficients (MFCCs) used to extract the feature. The speech corpus contains a total of 15,840 alphabets recorded by 30 (15 male and 15 female) Amazigh native speakers. The proposed Amazigh speech recognition system is based on the Carnegie Mellon University (CMU) Sphinx tools. Our results indicate that the best system performance is found for the G711 codec, 3 HMM, and 16 GMMs.
A Recent Survey on Cybercrime and Its Defensive Mechanism

CybercrimeGarima Bajaj Saurabh Tailwal Anupama Mishra is one of the severe issues in today’s world that is increasing day by day due to unawareness of people about the harm it can cause. The main reason against the augmentation of cybercrime is the lack of education or knowledge about the impact it can lead to. Cybercrime can be done against individuals, society, or any organization whether it is private or government. The aim of the paper is to focus on what cybercrime is, its types, related work, and its defensive mechanism. Defensive mechanism against cybercrime includes the ways or measures that how can any individual or any organization protects them against cybercrime. This paper also includes the related work which includes some points about the work which has been done so far on cybercrime.
In Situ Solutions with CinemaScience

Cinema is a flexible in situ visualization ecosystem that combines data extracts with viewers and analysis capabilities to support in situ, post hoc and hybrid approaches for data processing. With data extracts that include metadata, images, meshes, and other data types, Cinema databases generated in situ are a central component of post hoc analysis workflows. These workflows support visualization and exploration of data, verification and validation tasks, and leverage computer vision and statistical techniques for post hoc analysis. This chapter describes the Cinema approach, its database specification, and demonstrates its use through example workflows.
Graph hierarchical dwell-time attention network for session-based recommendation

Session-based recommendation (SBR) is making item recommendations based on anonymous click behavior. SBR based on graph neural networks has shown great power in recent years. It can enhance the representation of items in a session. The aggregation of items is then used to generate a session vector for the recommendation. However, existing SBR models rarely consider the impact of dwell-time in session data when performing session item aggregation. The dwell-time contains the implicit behavior of anonymous users in the session sequence. In order to obtain a more accurate session embedding and take into account the impact of multiple perspectives, we propose a new model, graph hierarchical dwell-time attention network. This approach uses a modified graph neural network to learn session items by extracting loss information from graph modeling. We also design a hierarchical dwell-time attention module that uses the effect of dwell-time to generate long-term preferences for sessions. Experimental results show that GHDAN outperforms the state-of-the-art session-based recommendation methods.
Does Utilizing Online Social Relations Improve the Diversity of Personalized Recommendations?

Personalized recommendations are widely used to improve customer experience and drive sales. Traditional recommender systems typically focus on using accuracy as the key metric to evaluate the performance of personalized recommendations. However, recent studies suggest that recommending a diverse list of products improves user satisfaction and is positively associated with customer retention rates. In this study, we propose to incorporate the product ratings from users’ online social relations into recommendation model to enhance the diversity of personalized recommendation list. The empirical results indicate that our proposed approach performs well in increasing the recommendation diversity while maintaining comparable level of accuracy. The findings offer practical implications for online businesses to leverage online social relations.
Real-Time Implementation of Caption Generation System Using LSTMS and WEB API

Automatic captioning for images brings together the latest advances in computer vision research. This paper will explore sentence-based unidirectional matching of images and their descriptions. The proposed image captioning model is accomplished by generating novel captions of any image using deep learning techniques. This proposed model is evaluated based on image retrieval and sentence generation. During the image retrieval phase, the objects in the image are detected using a Convolutional Neural Network’s pre-trained model like InceptionV3. After the objects have been detected using CNN, a syntactically and semantically proper caption for the image is generated utilizing a Recurrent Neural Network with Long Short-Term Memory (LSTM) and its attention mechanism in the sentence production phase. The efficiency of the proposed work is assessed using the Flickr8k dataset. The proposed model produces significant results when compared to the existing blue metric algorithm and other state-of-the-art techniques.