Reviewerly/exHarmony huggingface dataset:
Probing the Purview of Neural Networks via Gradient Analysis

We analyze the data-dependent capacity of neural networks and assess anomalies in inputs from the perspective of networks during inference. The notion of data-dependent capacity allows for analyzing the knowledge base of a model populated by learned features from training data. We define purview as the additional capacity necessary to characterize inference samples that differ from the training data. To probe the purview of a network, we utilize gradients to measure the amount of change required for the model to characterize the given inputs more accurately. To eliminate the dependency on ground-truth labels in generating gradients, we introduce confounding labels that are formulated by combining multiple categorical labels. We demonstrate that our gradient-based approach can effectively differentiate inputs that cannot be accurately represented with learned features. We utilize our approach in applications of detecting anomalous inputs, including out-of-distribution, adversarial, and corrupted samples. Our approach requires no hyperparameter tuning or additional data processing and outperforms state-of-the-art methods by up to 2.7%, 19.8%, and 35.6% of AUROC scores, respectively.
Concurrent Gaussian Elimination

Working in a semi-constructive logical system that supports the extraction of concurrent programs, we extract a program inverting non-singular real valued matrices from a constructive proof based on Gaussian elimination. Concurrency is used for efficient pivoting, that is, for finding an entry that is apart from zero in a non-null vector of real numbers.
An Image Recommendation Algorithm Based on Target Alternating Attention and User Affiliation Network

Currently, how to exploit the deep features of images in image recommender systems to achieve image enhancement still needs further research. In addition, little research has explored the implicit and increasing preferences of users by using the affiliation generated by indirect users and virtual users of the main users, which leads to the phenomenon of information cocoon. An Image Recommendation Algorithm Based on Target Alternating Attention and User Affiliation Network (TAUA) is proposed in this paper that addresses the problems of inadequate extraction of semantic features in an image and information cocoon in image recommender systems. First, to complete the multi-dimensional description of the image, we extract the category, color, and style features of the image through a multi-channel convolutional neural network (MCNN), and we then perform migration and integration on these features. Then, to enhance the pixel-level representation ability of the image and achieve image feature enhancement, we propose target alternating attention to capture the information of surrounding pixels alternately from inside to outside. Finally, a user affiliation network, including indirect users and virtual users, is established according to the user behavior and transaction record, and the users’ increasing preferences and affiliated users are mined through the implicit interaction relationship of users. Experimental results show that compared with baselines on the Amazon dataset, the results of F@10, NDCG@10, and AUC of the proposed algorithm are 4.02%, 5.00%, and 2.14% higher than those of ACF, and 5.76%, 0.86% and 1.16% higher than those of VPOI. On the Flickr dataset, our algorithm outperforms ACF by 5.74%, 5.12%, and 3.68% in F@10, NDCG@10, and AUC, respectively, and outperforms VPOI by 0.45%, 0.47%, and 0.49%. TAUA has better recommendation performance and can significantly improve the recommendation effect.
The Metaverse as Influencer Marketing Platform: Influencer-Brand Collaborations of Parıs Hilton with ‘Superplastic’, ‘Bohoo’, and ‘Levi’s’

The Metaverse can be defined as a collection of fictional and virtual worlds that offer different experiences in all areas of life to users represented by avatars with Web 3.0 infrastructure on the basis of artificial intelligence, blockchain, and extended reality technologies. From an economics perspective, the Metaverse turns out to offer a virtual economy, new business models, new products, as well as a new marketing and advertising channel. Taking this into account, brands have started to carry out their influencer marketing activities in the Metaverse environment. With the opportunities offered by Metaverse, brands operate in a market area that tends to expand and become more interactive. In such a market, the interaction between influencers and their followers takes place within the framework of a new business model. In this study, in order to evaluate the influencer marketing activities in Metaverse, the brand and influencer collaborations of Paris Hilton with Superplastic, Boohoo, and Levi’s, were examined with the case study and the semiotic analysis method of Roland Barthes. The analysis results showed that influencers direct their followers from social media to Metaverse platforms. It is also noteworthy that the features such as an individual style and lifestyle that influencers manifest on social media appear to be quite similar in their Metaverse posts. In terms of influencer marketing, Metaverse seems to be more creative and flexible with higher interaction due to the vast opportunities offered as a consequence of using the virtual worlds and avatars on different platforms. When it comes to users, it is observed that followers are given a real-time interaction opportunity in online virtual environments, that content forms are created in a way which is experienced through gamification; and that further opportunities are offered to develop a closer interaction between the user and the influencer avatars, and other possibilities such as purchasing digital assets and winning awards. In the Metaverse, brands appear to benefit from such opportunities as developing parallel marketing strategies between the physical world and the virtual world, preparing effective advertising campaigns with the creative potential of the virtual world, coming up with solutions to difficulties such as payment problems and counterfeiting in influencer marketing by relying on blockchain technology on the basis of Web 3.0 infrastructure, apart from selling digital products and NFTs.
Calibrated reconstruction based adversarial autoencoder model for novelty detection

Novelty detection detects outliers located at any location, such as abnormalities (i.e., far distance outliers) and novel/unobserved patterns (i.e., close distance outliers). While many novelty detection approaches have been proposed in the literature, they generally focus on detecting one specific type of outlier, e.g., Multi-Class Open Set Recognition (MCOSR) and One-Class Novelty Detection (OCND) approaches are applied for far and close distance outlier detection, respectively. However, in practice, it is difficult to measure in advance whether the distance between outliers and inliers is far or close. Recent work on outlier detection at any location with a unified model has yielded mixed performance. In this paper, we propose a new unified model, named Calibrated Reconstruction Based Adversarial AutoEncoder (CRAAE), for location agnostic outlier detection. The key idea is to integrate implicit and explicit confidence calibration strategies into a reconstruction based model for building a more accurate decision boundary. We leverage the category information disentangled from feature space to calibrate the decision metric (i.e., reconstruction error) constructed in the original data space. CRAAE also adds Uniform or Dirichlet noise into the artificial outlier generation process to represent various outliers. Experimental results show that CRAAE can outperform state-of-the-art unified models (e.g., GPND) and achieve similar performance with OCND and MCOSR methods in close and far distance outlier detection, respectively.
Out-of-Distribution Detection as Support for Autonomous Driving Safety Lifecycle

[Context and Motivation] The automotive industry is moving towards increased automation, where features such as automated driving systems typically include machine learning (ML), e.g. in the perception system. [Question/Problem] Ensuring safety for systems partly relying on ML is challenging. Different approaches and frameworks have been proposed, typically where the developer must define quantitative and/or qualitative acceptance criteria, and ensure the criteria are fulfilled using different methods to improve e.g., design, robustness and error detection. However, there is still a knowledge gap between quality methods and metrics employed in the ML domain and how such methods can contribute to satisfying the vehicle level safety requirements. [Principal Ideas/Results] In this paper, we argue the need for connecting available ML quality methods and metrics to the safety lifecycle and explicitly show their contribution to safety. In particular, we analyse Out-of-Distribution (OoD) detection, e.g., the frequency of novelty detection, and show its potential for multiple safety-related purposes. I.e., as (a) an acceptance criterion contributing to the decision if the software fulfills the safety requirements and hence is ready-for-release, (b) in operational design domain selection and expansion by including novelty samples into the training/development loop, and (c) as a run-time measure, e.g., if there is a sequence of novel samples, the vehicle should consider reaching a minimal risk condition. [Contribution] This paper describes the possibility to use OoD detection as a safety measure, and the potential contributions in different stages of the safety lifecycle.
Explainable for Trustworthy AI

Black-box Artificial Intelligence (AI) systems for automated decision making are often based on over (big) human data, map a user’s features into a class or a score without exposing why. This is problematic for the lack of transparency and possible biases inherited by the algorithms from human prejudices and collection artefacts hidden in the training data, leading to unfair or wrong decisions. The future of AI lies in enabling people to collaborate with machines to solve complex problems. This requires good communication, trust, clarity, and understanding, like any efficient collaboration. Explainable AI (XAI) addresses such challenges, and for years different AI communities have studied such topics, leading to different definitions, evaluation protocols, motivations, and results. This chapter provides a reasoned introduction to the work of Explainable AI to date and surveys the literature focusing on symbolic AI-related approaches. We motivate the needs of XAI in real-world and large-scale applications while presenting state-of-the-art techniques and best practices and discussing the many open challenges.
Vibration-assisted multiphoton resonance and multi-ion excitation

We investigate the multiphoton resonance and multi-ion excitation in a single-mode cavity with identical vibrating ionqubits, which enables the tripartite interaction among the internal states of ions, the cavity mode and the ions' vibrational motion. Under particular resonant conditions, we derive effective Hamiltonians for the three-photon and the three-excitation cases, respectively, and find that the magnitude of the effective coupling energy can be tuned through the vibration mode, allowing for manipulations of ion-photon coupling in experiments. Furthermore, we analyze the system dynamics of our proposed setups and demonstrate the Rabi oscillation behaviors in these systems with dissipation effects. We propose our system as a versatile platform for the exploration of entangled multiqubit physics.
A Comparative Analysis of the Computation Cost and Energy Consumption of Relevant Curves of ECC Presented in Literature

With the Internet of Things (IoT) becoming expo-nentially more prevalent, the need for lightweight cryptography functions increases simultaneously. Namely, IoT end devices are mostly limited by their resource-constrained capabilities and therefore cannot rely on heavyweight cryptographic algorithms such as Rivest-Shamir-Adleman (RSA) or Diffie-Hellman (DH) for security. Elliptic Curve Cryptography (ECC) offers a more lightweight alternative by being based on a mathematical prob-lem named the Elliptic Curve Discrete Logarithm Problem (ECDLP) which is not known to be solvable in sub-exponential time. Within the field of ECC, many different curve types exist following various standards for this protocol. In this paper, the energy consumption and time consumption for key generation, encryption, and decryption are measured within the ElGamal protocol using ECC for the various curves. To measure this, a Raspberry Pi 4B and a Personal Computer are used to conclude the disproval of the hypothesis that the Twisted Edwards curve performs most efficient to achieve its security strength. Namely, Brainpool curves function most efficient within this benchmark, after which Short Weierstrass curves follow. Moreover, it is concluded the performance pattern for both data processors and data providers is equal to each other for all message sizes.
Students’ Attitudes towards Mobile Learning: A Case Study in Higher Education in Vietnam

The use of mobile learning has been investigated in many educational settings to support the effectiveness of mobile devices in teaching and learning. The study aims to explore students’ attitudes towards the use of mobile technology in higher education. To gain insight into students’ attitudes towards it, both quantitative and qualitative methods were employed from a 5-point Likert scale questionnaire and a semi-structured interview with the participation of 118 students in a university. The results showed that students expressed highly positive attitudes towards the use of mobile technology in learning. The easy access to resources and course materials was most beneficial to students accompanied by enhanced communication with peers and instructors to help students achieve better academic scores. Besides, students could develop various skills through mobile learning such as computer skills, analytical skills, and note-taking skills. It is to suggest the implication of using mobile devices to support teaching and learning to improve the quality of education in higher education as a whole.
Information Transfer Technique to Improve Beginner Level Students’ Listening Skill and Classroom Participation

The lack of English vocabulary makes listening the most challenging skill for most of EFL students. Some techniques have been applied to help students overcome their difficulties in learning English. This research aimed at improving non-English department students’ listening skill and engagement by using information transfer technique. The design used was classroom action research that consisted of planning, implementation, observation and reflection. The data were taken from the pre-test and post-test results. The instruments used were listening task, observation, and questionnaire. The findings showed that by using transformation transfer of reading text, the EFL students got much better listening comprehension and were more involved in their classroom activities. Thus, more techniques to improve students’ language skill should be implemented.
Role of E-governance in Higher Education in Jammu and Kashmir

Education is one of the most important factors in achieving any country's development objectives. Education has grown dramatically in Jammu and Kashmir in recent years as a result of the use of e-government in this sector. Initiatives in information and communication technology aid in the provision of world-class education. The use of egovernance in the education sector has spawned a slew of new ideas. Among the social sectors, Higher Education is one of the most important sectors for developmentgovernment facilities for colleges and universities are implemented and maintained by the Department of Higher Education. E-Government is regarded as critical to improved governance. Institutions around the world are launching E-governance initiatives in order to provide their constituents with more transparent, efficient, and cost-effective services. Kashmir's higher education has conceptualised e-governance projects and service initiatives in the educational system as time has passed. This paper provides an overview of the e-governance services, initiatives, and benefits reaped by establishing these services for stakeholders in Jammu and Kashmir's higher education.
A Quantum Evolutionary Learning Tracker for Video

Video object tracking has been a popular area in the field of computer vision. As video data evolves, more special perspectives and challenging video data are constantly kept up to date. This poses challenges for object tracking tasks and places higher demands on the generalization capabilities of the models. In this paper, we propose a novel quantum evolutionary learning tracker for video. The model combines quantum evolution with deep networks for tracking video objects. The model uses a quantum evolutionary learning tracker to generate a reliable population of candidate regions and a deep network for classification. In particular, the quantum evolutionary predictor predicts the object motion state through rotation operator and trajectory inference, and provides motion state information for the tracker. The predictor can incorporate object history contextual information and can provide stable candidate estimation populations for the model in case of failure of appearance features. Both quantum evolution and deep networks are combined to form an end-to-end online video object tracker. In addition, we propose a new video object tracking evaluation algorithm, Balanced Intersection over Union. The evaluation algorithm uses aspect ratios to balance the share of overlap and distance. Finally, we test the model on the OTB 2015 dataset for natural video and on the SV248A10-SOT dataset for satellite video. The performance of the proposed model is also analyzed and validated by comparing it with more than twenty classical tracker models. The experimental results show that our model has high generalization ability and robustness.
NL-Augmenter 🦎 → 🐍 A Framework for Task-Sensitive Natural Language Augmentation

Data augmentation is an important method for evaluating the robustness of and enhancing the diversity of training data for natural language processing (NLP) models. In this paper, we present NL-Augmenter, a new participatory Python-based natural language (NL) augmentation framework which supports the creation of transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of NL tasks annotated with noisy descriptive tags. The transformations incorporate noise, intentional and accidental human mistakes, socio-linguistic variation, semantically-valid style, syntax changes, as well as artificial constructs that are unambiguous to humans. We demonstrate the efficacy of NL-Augmenter by using its transformations to analyze the robustness of popular language models. We find different models to be differently challenged on different tasks, with quasi-systematic score decreases. The infrastructure, datacards, and robustness evaluation results are publicly available on GitHub for the benefit of researchers working on paraphrase generation, robustness analysis, and low-resource NLP. El aumento de datos es un método importante para evaluar la solidez y mejorar la diversidad del entrenamiento datos para modelos de procesamiento de lenguaje natural (NLP). इस लेख में, हम एनएल-ऑगमेंटर का प्रस्ताव करते हैं - एक नया भागी- दारी पूर्वक, पायथन में बनाया गया, लैंग्वेज (एनएल) ऑग्मेंटेशन फ्रेमवर्क जो ट्रांसफॉर्मेशन (डेटा में बदलाव करना) और फीलटर (फीचर्स के अनुसार डेटा का भाग करना) के नीरमान का समर्थन करता है।. 我们描述了NL-Augmenter框架及其初步包含的117种转换和23个过滤器，并 大致标注分类了一系列可适配的自然语言任务. این دگرگونی ها شامل نویز، اشتباهات عمدی و تصادفی انسانی، تنوع اجتماعی-زبانی، سبک معنایی معتبر، تغییرات نحوی و همچنین ساختارهای مصنوعی است که برای انسان ها مبهم است. NL-Augmenterpa allin kaynintam qawachiyku, tikrakuyninku- nata servichikuspayku, chaywanmi qawariyku modelos de lenguaje popular nisqapa allin takyasqa kayninta. Kami menemukan model yang berbeda ditantang secara berbeda pada tugas yang berbeda, dengan penurunan skor kuasi-sistematis. Infrastruktur, kartu data, dan hasil evaluasi ketahanan dipublikasikan tersedia secara gratis di GitHub untuk kepentingan para peneliti yang mengerjakan pembuatan parafrase, analisis ketahanan, dan NLP sumber daya rendah.
Experiential Learning In Kindergarten

The most natural and fundamental way of learning is learning from experiences.A child firstly starts learning through different cognitive, social, emotional and physical experiences.It is very important to encourage the child's needs to explore, to test new things.Before starting the school, a child is learning through a range of practical activities, iniatives and self-evaluation.The findings suggest to be paid a lot of attention for authentic practices, encouraging exploration and personalization.It is not easy for a teacher to find out different ways of teaching efficiently and keeping the children focused.The purpose of the experiential learning is to determine the child to take initiative, to take decisions and to be responsible for his actions.Engaging children in free play provides relevant experiences and meaningful learning that then become sources of their learning process to build new concepts and knowledge.Then, the development of the scheme provides the basic foundation for cognitive, physical, emotional, social and moral development among preschool children.It is not easy for a teacher to learn different ways to teach effectively and keep children focused.Experiential learning involves exploration and experimentation and is used when children have the opportunity to observe various phenomena and objects, so that, based on the information gathered, they must arrive at the formulation of generalizations.
Determinants of Cryptocurrency: An Analysis of Volatility and Risk-Return Trade-Off

As an investor, volatility plays an important role in decision making. It is defined as the rate at which a security’s price increases or decreases, i.e., shows pricing behavior during a definite span of time. A high volatility will lead to high risk. Thus, it becomes critical to determine the volatility and the risk-return trade-off among investments. This paper tries to document the volatility and risk-return trade-off of four prominent crypto-currencies (Bitcoin, Ethereum, Binance and Ripple), based on market-capitalization. For analysis, closing prices of cryptocurrencies has been accumulated through secondary method for 365 days, starting from 1st March 2022 and ending on 28th February 2023. Standard Deviation and Kurtosis, used together for volatility and risk assessment, documented that Bitcoin has the highest volatility and risk associated with expected returns. Regression, for assessing the impact of volatility in BTC price on others, derived that ETH has a strong, but not very strong, bivariate relationship with BTC, among all the pairs. Durbin Watson (DW) test concluded that there was no auto-correlation in the prices of crypto-currencies, i.e., previous day’s price does not play significant role in today’s price. For risk-return trade-off, Coefficient of Variation (CoV) has been applied. It determined that Ethereum has the highest ratio indicating its non-suitability to a conservative investor because of having the lowest returns as compared to risks involved; while Binance has the lowest Coefficient of Variation (CoV) depicting lower risk and maximum return among all.