Reviewerly/exHarmony huggingface dataset:
Sampling-invariant fully metric learning for few-shot object detection

Few-shot object detection (FSOD) aims to learn models to detect unseen objects with a few annotated exemplars. Despite great success in FSOD, existing metric-based methods heavily rely on class prototypes extracted from limited training data and thus suffer severely from data shift, which can learn inductive bias and produce inconsistent performance across multiple runs. According to statistic law, data shift happens more frequently under low-shot scenarios than that in many-shot scenarios. However, there is insufficient research about utilizing intrinsic and robust properties of limited data for data-stable results instead of blindly achieving high performance on a specific dataset. This inspires us to model intrinsic properties of limited data for building a set of robust class prototypes. To this end, we propose a novel Sampling-Invariant Fully metric learning Network (SIF-Net) that enhances class prototypes in three aspects. Specifically, our model includes three novel modules: (1) a multi-scale context matching (MSCM) module that aggregates more accurate class concepts in a scale-matching manner, (2) a semantic drift correlation (SDC) module that recovers distorted class prototypes through object context, and (3) a fully metric learning (FML) module that encodes both class and spatial priors into class prototypes, which is fundamentally different from previous metric-based approaches. As a result, our SIF-Net is robust to various settings of few-shot data quality. Extensive experiments show that our fully metric-based framework is superior to other metric-based approaches and achieves state-of-the-art performance on PASCAL VOC and MS COCO datasets.
Combining conversational speech with read speech to improve prosody in Text-to-Speech synthesis

For isolated utterances, speech synthesis quality has improved immensely thanks to the use of sequence-to-sequence models.However, these models are generally trained on read speech and fail to generalise to unseen speaking styles.Recently, more research is focused on the synthesis of expressive and conversational speech.Conversational speech contains many prosodic phenomena that are not present in read speech.We would like to learn these prosodic patterns from data, but unfortunately, many large conversational corpora are unsuitable for speech synthesis due to low audio quality.We investigate whether a data mixing strategy can improve conversational prosody for a target voice based on monologue data from audiobooks by adding real conversational data from podcasts.We filter the podcast data to create a set of 26k question and answer pairs.We evaluate two FastPitch models: one trained on 20 hours of monologue speech from a single speaker, and another trained on 5 hours of monologue speech from that speaker plus 15 hours of questions and answers spoken by nearly 15k speakers.Results from three listening tests show that the second model generates more preferred question prosody.
RETRACTED: Phishing attack detection using Machine Learning

Phishing is a type of digital assault, which adversely affects people where the client is coordinated to counterfeit sites and hoodwinked to screen their touchy and private data which integrates watchwords of records, monetary data, ATM pin-card data, etc. Recently safeguarding touchy records, it's fragile to cover yourself from malware or web phishing. AI is an investigation of information examination and logical investigation of calculations has demonstrated outcomes. Contradicting phishing sprinters with remarkable perception and felonious outcomes comparable as care shops, and custom against phishing approaches. This paper examines the association of Machine Literacy routes in identifying phishing assaults and records their advantages and drawbacks. There are countless Machine Learning calculations that have been dug to proclaim the relevant decision that act as against phishing apparatuses. We made a phishing section framework that extracts capacities that are expected to descry phishing. We likewise utilize numeric outline, as well as an overall investigation of customary Machine Learning methodologies comparable as Decision Tree, Random Forest, Multi-layer Perceptron’s, XG Boost Classifier, SVM, Light BGM Classifier, Cat Boost Classifier, and covering grounded highlights choice, which contains the metadata of URLs and assists with deciding if a site is licit or not.
Is honesty the best policy for mismatched partners? Aligning multi-modal affordances of a social robot: An opinion paper

OPINION article Front. Virtual Real., 16 September 2022Sec. Virtual Reality and Human Behaviour Volume 3 - 2022 | https://doi.org/10.3389/frvir.2022.1020169
Gaussian Processes for radiation dose prediction in nuclear power plant reactors

In nuclear power plants, there are high-exposure jobs, like refuelling and maintenance, that require getting close to the reactor between operation cycles. Therefore, reducing radiation dose during these periods is of paramount importance regarding safety regulations. While there are some manipulable variables, like levels of certain corrosion products, that can influence the final level of radiation dose, there is no way to determine it in a principled way. In this work, we propose to use Machine Learning to predict the radiation dose in the reactor at the cycle end based on information available during the cycle operation. In particular, we use a Gaussian Process to model the relation between cobalt radioisotopes (a certain kind of corrosion product) and radiation dose levels. Gaussian Processes acknowledge the uncertainty on their predictions, a desirable property considering the high-risk nature of the present application. We report experiments on real data gathered from five different power plants in Spain. Results show that these models can be used to estimate the future values of radiation dose in a data-driven way. Moreover, there are tools based on these models currently in development for their application in power plants.
Design and Implementation of NPC AI based on Genetic Algorithm and BP Neural Network

In real game scenes, there may be a problem that it is impossible to collect a large amount of data to train NPC's correct behavior. This paper implements a method of training the neural networks to control game NPC behavior based on an improved genetic algorithm. This method optimizes the weights of the fixed network structure through the genetic algorithm, realizes the self-evolution of the neural network, and improves the fitness function and the selection and crossover mutation method in the traditional genetic algorithm, so as to be suitable for game production. Testing in two real game scenes shows that game NPC can acquire intelligent behavior capabilities using this algorithm.
CredTrust: Credential Based Issuer Management for Trust in Self-Sovereign Identity

Self Sovereign Identity (SSI) facilitates secure issuance and verification of digitised credentials without having to rely on a centralised authority. Most SSI implementations rely on blockchain to take advantage of decentralisation and cryptographic security over the credentials. Given that verifying the authenticity of credentials such as identification papers or proof of qualifications are critical to the system, current SSI solutions, assume the issuers to be “official” entities (e.g., government agencies) who must follow a stringent process to vet their credentials. However, there is no systematic support for directing the same level of trust agencies for individual users who may issue credentials (e.g., delegation of access, consent letter) in the context of business processes. A verifier who relies on user-issued credentials to complete a business process (e.g., a postal worker handing over parcel to someone other than the addressee) bears the risk of accepting these credentials without reliance on a trust agency. This paper proposes CredTrust, a blockchain based SSI framework that allows individual users to be “onboarded” to the platform as a verifiable issuer via the establishment of a trust registry in the blockchain. Our approach utilises the existing SSI credential generation and verification processes to create a “chain of trust” as a trust propagation method. Our evaluation results show that CredTrust is feasible and exhibits minimal overheads when compared with a conventional SSI based credential management system.
Fast Chaotic Image Encryption Algorithm Using a Novel Divide and Conquer Diffusion Strategy

To protect image privacy in real-time transmission, a fast chaotic image encryption algorithm using a novel divide and conquer diffusion strategy is proposed in this paper.Firstly, a fast pseudorandom sequence generator is constructed using 2D hyperchaotic systems.And by performing bitwise XOR operation between two integer sequences obtained from different systems, the final key stream used for diffusion will have better randomness and unpredictability.Secondly, to achieve divide and conquer diffusion, the plain image is divided into three parts, then a parallel CBC-based diffusion method can simultaneously act on upper and bottom parts (or left and right parts), which achieves a time complexity of O(W + H ).Moreover, within the diffusion process, the mechanism of information interaction on the central row or column further enhances avalanche effect to resist differential attack.Thirdly, the session key of the proposed algorithm is a mixture of the plain image's hash value and a true random sequence, which not only improve plaintext sensitivity but also realize one-time pad.Finally, experimental results indicate the superiority of our algorithm to resist statistical, chosen-plaintext, entropy, and other common attacks.Furthermore, by comparison with previous works, our algorithm preforms much faster execution speed with only average of 0.08s to encrypt images of size 512 × 512, while it provide the same or even higher level of security.Therefore, the proposed algorithm can meet security and efficiency requirements of real-time communications of image data.1718 INDEX TERMS Image encryption, hyperchaotic system, fast encryption, divide and conquer diffusion, one
The Influencers: Van Gogh Immersive Experiences and the Attention-Experience Economy

Van Gogh immersive exhibitions—multi-sited, branded multimedia environments inspired by the artist’s life and paintings—are seemingly ubiquitous in 2022. These itinerant digital spectacles bundle reproductions of Vincent Van Gogh’s most recognizable artistic motifs with tropes of fin-de-siècle madness, bathing their visitors in an artistic wonderland of projected images and soundscapes spread throughout cavernous exhibition venues. The popularity of these commercial juggernauts is unmatched. At present, at least five different companies are staging competing versions of digital Van Gogh art exhibitions in dozens of cities worldwide, with a particular emphasis at present on sites throughout North America. How are we as art critics to make sense of these exhibitions as well as their influence within the institutional context of the visual arts? Taking the digital Van Gogh phenomenon as its central case study, this article investigates the emerging art-themed immersive exhibition model and explores the specific mode of spectatorship it promotes. Situating these projects within the broader framework of the contemporaneous attention and experience economies, and with an eye toward the crucial role of social media, I propose that art-themed immersive exhibitions such as the Van Gogh immersive experiences exemplify habits of digitally-mediated, 24/7 immersive attention and consumption in art and in everyday life.
Quantum Blockchain Based on Dimensional Lifting Generalized Gram-Schmidt Procedure

The advancement of quantum computers undermines the security of classical blockchain, necessitating either a post-quantum upgrade of the existing architecture or creation of an inherently quantum blockchain. Here we propose a practically realizable model of a fully quantum blockchain based on a generalized Gram-Schmidt procedure utilizing dimensional lifting. In this model, information of transactions stored in a multi-qubit state are subsequently encoded using the generalized Gram-Schmidt process. The chain is generated as a result of the reliance of orthogonalized state on the sequence of states preceding it. Various forking scenarios and their countermeasures are considered for the proposed model. It is shown to be secure even against quantum computing attacks using the no-cloning theorem and non-democratic nature of Generalized Gram-Schmidt orthogonalization. Finally, we outline a framework for a quantum token built on the same architecture as our blockchain.
AI-SPedia: a novel ontology to evaluate the impact of research in the field of artificial intelligence

Sharing knowledge such as resources, research results, and scholarly documents, is of key importance to improving collaboration between researchers worldwide. Research results from the field of artificial intelligence (AI) are vital to share because of the extensive applicability of AI to several other fields of research. This has led to a significant increase in the number of AI publications over the past decade. The metadata of AI publications, including bibliometrics and altmetrics indicators, can be accessed by searching familiar bibliographical databases such as Web of Science (WoS), which enables the impact of research to be evaluated and identify rising researchers and trending topics in the field of AI.In general, bibliographical databases have two limitations in terms of the type and form of metadata we aim to improve. First, most bibliographical databases, such as WoS, are more concerned with bibliometric indicators and do not offer a wide range of altmetric indicators to complement traditional bibliometric indicators. Second, the traditional format in which data is downloaded from bibliographical databases limits users to keyword-based searches without considering the semantics of the data.To overcome these limitations, we developed a repository, named AI-SPedia. The repository contains semantic knowledge of scientific publications concerned with AI and considers both the bibliometric and altmetric indicators. Moreover, it uses semantic web technology to produce and store data to enable semantic-based searches. Furthermore, we devised related competency questions to be answered by posing smart queries against the AI-SPedia datasets.The results revealed that AI-SPedia can evaluate the impact of AI research by exploiting knowledge that is not explicitly mentioned but extracted using the power of semantics. Moreover, a simple analysis was performed based on the answered questions to help make research policy decisions in the AI domain. The end product, AI-SPedia, is considered the first attempt to evaluate the impacts of AI scientific publications using both bibliometric and altmetric indicators and the power of semantic web technology.
A Song of (Dis)agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language Processing

There has been significant debate in the NLP community about whether or not attention weights can be used as an explanation – a mechanism for interpreting how important each input token is for a particular prediction. The validity of “attention as explanation” has so far been evaluated by computing the rank correlation between attention-based explanations and existing feature attribution explanations using LSTM-based models. In our work, we (i) compare the rank correlation between five more recent feature attribution methods and two attention-based methods, on two types of NLP tasks, and (ii) extend this analysis to also include transformer-based models. We find that attention-based explanations do not correlate strongly with any recent feature attribution methods, regardless of the model or task. Furthermore, we find that none of the tested explanations correlate strongly with one another for the transformer-based model, leading us to question the underlying assumption that we should measure the validity of attention-based explanations based on how well they correlate with existing feature attribution explanation methods. After conducting experiments on five datasets using two different models, we argue that the community should stop using rank correlation as an evaluation metric for attention-based explanations. We suggest that researchers and practitioners should instead test various explanation methods and employ a human-in-the-loop process to determine if the explanations align with human intuition for the particular use case at hand.
Hidden malware ratchets up cybersecurity risks

Cybercriminals could be hiding malware payloads in places where commercial cybersecurity software is unable to detect it.
Academic writing centers and the teaching of academic writing at colleges: Literature review

Although colleges have offered academic writing courses, many students still struggle with this writing ability. Their poor writing skills are also associated with a lack of teacher guidance The tasks given by teachers are sometimes beyond their capabilities. Students are not accustomed to writing long academic papers. As a result, they do not enjoy writing essays, and they are not confident in their writing skills. This research is intended to collect and analyze articles related to Academic Writing Courses and the teaching of academic writing at colleges. The design used is a literature review, articles are collected using search engines such as JStore, Academic One file, Sciencedirect, and Proquest. The criteria for the articles used are those published in 2008-2021. Based on the articles collected the result is that academic writing centers (AWC) does not guarantee better grades for the students. Instead, it is time to help them fulfill their needs: i) Becoming self-directed; ii) Independent writers; iii) Building the confidence of writers; and iv) Helping writers with advanced writing tasks.
Service Recovery Performance of Frontline Officer in the Public Sector Organization in Jordan

This study considers that management practices have the ability to turn dissatisfied customers to loyal customers through effective service recovery encounter. This study investigates the potential impact of selected management practices on service recovery performance and its outcomes of front-line officers by adapting Boshoff and Allen, (2000) research model. There were front line officers who involve with electronic government were analyzed about their perceptions about public sector organisation management practices. Quantitative approach using multivariate data analysis such as descriptive analysis, reliability analysis and multiple regressions were used to estimate the predictors and its consequences of service recovery performance in the public sector organization (PSO) in Jordan. Results shows that ‘affective organizational commitment’ exert a strong positive relationship compared with customer service training, empowerment and supervisor support. The variable role ambiguity shows a weak non-significant relationship as opposed to the proposed effect. Results also suggest that successful service recovery performance leads to increased job satisfaction and decreased turnover intentions. This study could be generalized as a main guideline for public sector organization in Jordan. Practically, the findings indicate the organizational behavior variables that the manager has to take care to achieve front line service recovery excellence.
Learning Deep Learning

As a consequence of its capability of creating high level abstractions from data, deep learning has been effectively employed in a wide range of applications, including physics. Though deep learning can be, at first and simplistically understood in terms of very large neural networks, it also encompasses new concepts and methods. In order to understand and apply deep learning, it is important to become familiarized with the respective basic concepts. In this text, after briefly revising some works relating to physics and deep learning, we introduce and discuss some of the main principles of deep learning as well as some of its principal models. More specifically, we describe the main elements, their use, as well as several of the possible network architectures. A companion tutorial in Python has been prepared in order to complement our approach.
Feature fusion based on joint sparse representations and wavelets for multiview classification

Abstract Feature-level-based fusion has attracted much interest. Generally, a dataset can be created in different views, features, or modalities. To improve the classification rate, local information is shared among different views by various fusion methods. However, almost all the methods use the views without considering their common aspects. In this paper, wavelet transform is considered to extract high and low frequencies of the views as common aspects to improve the classification rate. The fusion method for the decomposed parts is based on joint sparse representation in which a number of scenarios can be considered. The presented approach is tested on three datasets. The results obtained by this method prove competitive performance in terms of the datasets compared to the state-of-the-art results.
Time-frequency analysis-based deep interference classification for frequency hopping system

It is known that, interference classification plays an important role in protecting the authorized communication system and avoiding its performance degradation in the hostile environment. In this paper, the interference classification problem for the frequency hopping communication system is discussed. Considering the possibility of presence multiple interferences in the frequency hopping system, in order to fully extract effective features of the interferences from the received signals, the linear and bilinear transform based composite time-frequency analysis method is adopted. Then the time-frequency spectrograms obtained from the time-frequency analysis are constructed as matching pairs and input to the deep neural network for classification. In particular, the Siamese neural network is used as the classifier, where the paired spectrograms are input into the two sub-networks of the deep networks, and these two sub-networks extract the features of the paired spectrograms for interference type classification. The simulation results confirm that the proposed algorithm can obtain higher classification accuracy than both traditional single time-frequency representation based approach and the AlexNet transfer learning or convolutional neural network based methods.
Using Machine Learning and Natural Language Processing to Analyze Library Chat Reference Transcripts

The use of artificial intelligence and machine learning has rapidly become a standard technology across all industries and businesses for gaining insight and predicting the future. In recent years, the library community has begun looking at ways to improve library services by applying AI and machine learning techniques to library data. Chat reference in libraries generates a large amount of data in the form of transcripts. This study uses machine learning and natural language processing methods to analyze one academic library’s chat transcripts over a period of eight years. The built machine learning model tries to classify chat questions into a category of reference or nonreference questions. The purpose is to predict the category of future questions by the model with the hope that incoming questions can be channeled to appropriate library departments or staff.