Reviewerly/exHarmony huggingface dataset:
Hierarchical Contrast for Unsupervised Skeleton-Based Action Representation Learning

This paper targets unsupervised skeleton-based action representation learning and proposes a new Hierarchical Contrast (HiCo) framework. Different from the existing contrastive-based solutions that typically represent an input skeleton sequence into instance-level features and perform contrast holistically, our proposed HiCo represents the input into multiple-level features and performs contrast in a hierarchical manner. Specifically, given a human skeleton sequence, we represent it into multiple feature vectors of different granularities from both temporal and spatial domains via sequence-to-sequence (S2S) encoders and unified downsampling modules. Besides, the hierarchical contrast is conducted in terms of four levels: instance level, domain level, clip level, and part level. Moreover, HiCo is orthogonal to the S2S encoder, which allows us to flexibly embrace state-of-the-art S2S encoders. Extensive experiments on four datasets, i.e., NTU-60, NTU-120, PKU-I and PKU-II, show that HiCo achieves a new state-of-the-art for unsupervised skeleton-based action representation learning in two downstream tasks including action recognition and retrieval, and its learned action representation is of good transferability. Besides, we also show that our framework is effective for semi-supervised skeleton-based action recognition. Our code is available at https://github.com/HuiGuanLab/HiCo.
Are cryptocurrencies a threat to financial stability and economic growth of India? Evidence from the cointegration approach

The purpose of this paper is to investigate whether the cryptocurrency market affects the financial stability and economic growth of India. The study used time series quarterly data on bitcoin, financial stability, inflation rate, real GDP, economic volatility uncertainty, exchange rate, and market volatility index for the period 2015Q1–2022Q4. The robustness of the findings was confirmed by the fully modified OLS (FMOLS) and canonical cointegration regression (CCR). The study results demonstrated that an increase in cryptocurrency investments will affect the financial stability of India significantly. Each 1% increase in the cryptocurrency would reduce the financial stability by 5% approximately. However, there was a marginal effect of cryptocurrency on economic growth. The results also found that exchange rate volatility and inflationary pressure would also deteriorate the financial stability of the country. Furthermore, the results also identified positive and significant cointegration between economic growth and financial stability. Due to most transactions in the economy being done through the financial system, it is paramount for economic growth. Going forward, aggressive monetary policy tightening, volatility in capital flows and exchange rates, de-anchoring of inflation expectations, faltering in the economic recovery, disruptions due to global supply chains and climate change will be the major risks to the financial stability and economic growth of India.
Mitigation strategies against the phishing attacks: A systematic literature review

Phishing attacks are among the most prevalent attack mechanisms employed by attackers. The consequences of successful phishing include (and are not limited to) financial losses, impact on reputation, and identity theft. The paper presents a systematic literature review featuring 248 articles (from the beginning of 2018 until March 2023) across the main digital libraries to identify, (1) the existing mitigation strategies against phishing attacks, and the underlying technologies considered in the development of these strategies; (2) the most considered phishing vectors in the development of the mitigation strategies; (3) anti-phishing guidelines and recommendations for organizations and end-users respectively; and (4) gaps and open issues that exist in the state of the art. The paper advocates for the need to consider the abilities of human users during the design and development of the mitigation strategies as only technology-centric solutions will not suffice to cater to the challenges posed by phishing attacks.
GRAPE optimization for open quantum systems with time-dependent decoherence rates driven by coherent and incoherent controls

The GRadient Ascent Pulse Engineering (GRAPE) method is widely used for optimization in quantum control. GRAPE is gradient search method based on exact expressions for gradient of the control objective. It has been applied to coherently controlled closed and open quantum systems. In this work, we adopt GRAPE method for optimizing objective functionals for open quantum systems driven by both coherent and incoherent controls. In our case, the tailored or engineered environment acts on the system as control via it time-dependent decoherence rates $\gamma_k(t)$ or, equivalently, via it spectral density of the environment $n_\omega(t)$. To develop GRAPE approach for this problem, we compute gradient of various objectives for general N-level open quantum systems both for piecewise class of control. The case of a single qubit is considered in details and solved analytically. For this case, an explicit analytical expression for evolution and objective gradient is obtained via diagonalization of a $3\times 3$ matrix determining the system's dynamics in the Bloch ball. The diagonalization is obtained by solving a cubic equation via Cardano's method. The efficiency of the algorithm is demonstrated through numerical simulations for the state-to-state transition problem and its complexity is estimated.
SENSITIVITY OF OBTAINING ERRORS IN THE COMBINATION OF FUZZY AND NEURAL NETWORKS FOR CONDUCTING STUDENT ASSESSMENT ON E-LEARNING

Utilization of information technology in the midst of the covid-19 pandemic, including in the field of education, namely by holding On-line Classes. The development of information technology is also growing rapidly, especially in the field of education. Distance learning facilities via the Internet allows maha students undertake independent learning so as to facilitate the learning process. In addition, with the development of the system, it is hoped that this facility will be able to direct the ongoing learning process, so that the learning process that occurs resembles the learning process in the classroom. In conducting an assessment, a lecturer/teacher must pay attention to the rules of assessment. However, because there are no clear rules in conducting the assessment, the assessment process must be designed adaptively to adjust its calculations to the assessment rules of the lecturers / instructors who use the system. In this study, the fuzzy logic method approach and the artificial neural network method were used in calculating the assessment of learning outcomes.
Classification of Barriers to Digital Transformation in Higher Education Institutions: Systematic Literature Review

Digital transformation of higher education institutions (HEIs) is of paramount importance in today’s technology-driven world. However, there are numerous barriers that hinder the successful implementation of digital transformation initiatives in HEIs. In this article, we identify and classify these barriers based on a systematic literature review. The findings reveal twenty distinct barriers that inhibit digital transformation in higher education. These were then organised into six broad categories, namely environmental, strategic, organisational, technological, people-related and cultural. The study provides a comprehensive understanding of the barriers faced, facilitating the development of effective strategies and interventions. Our analysis provides valuable information for higher education institutions, policy makers and stakeholders involved in digital transformation initiatives.
Boosting the Item-Based Collaborative Filtering Model with Novel Similarity Measures

Abstract Collaborative filtering (CF), one of the most widely employed methodologies for recommender systems, has drawn undeniable attention due to its effectiveness and simplicity. Nevertheless, a few papers have been published on the CF-based item-based model using similarity measures than the user-based model due to the model's complexity and the time required to build it. Additionally, the substantial shortcomings in the user-based measurements when the item-based model is taken into account motivated us to create stronger models in this work. Not to mention that the common trickiest challenge is dealing with the cold-start problem, in which users' history of item-buying behavior is missing (i.e., new users) or items for which activity is not provided (i.e., new items). Therefore, our novel five similarity measures, which have the potential to solve sparse data, are developed to alleviate the impact of this important problem. Most importantly, a thorough empirical analysis of how the item-based model affects the CF-based recommendation system’s performance has also been a critical part of this work, which presents a benchmarking study for thirty similarity metrics. The MAE, MSE, and accuracy metrics, together with fivefold cross-validation, are used to properly assess and examine the influence of all considered similarity measures using the Movie-lens 100 K and Film Trust datasets. The findings demonstrate how competitive the proposed similarity measures are in comparison to their alternatives. Surprisingly, some of the top "state-of-the-art" performers (such as SMD and NHSM) have been unable to fiercely compete with our proposed rivals when utilizing the item-based model.
Industrial Automation Debug Message Display Over Modbus RTU Using C#

Purpose: To debug any device needs to display debug message. Seeing the message, we can detect what is the issue in our program. The are several popular debug tools available. The popular is JTAG. This debug interface is not available to debug inside all embedded systems. Instead of it, the Researcher uses a serial debug terminal. However, it has several drawbacks. Its wire length is generally within the table. The message is displayed only in one terminal. Sometimes the connected system is damaged due to high voltage spike injection. Here we demonstrate a procedure to eliminate all the above drawbacks. Instead of a direct serial display, we recommend using a modified serial debugger like Modbus hooked debug display. It is safe, can display multiple arrays simultaneously using broadcast messages, and can transmit long-range data. We developed a terminal program in C#. We provide a sample Embedded side test program. The complete project code is available to download. Design/Methodology/Approach: First, we create a Graphical user interface using C# language. To display the message, we use the “Listbox” control. We added a timer control to fetch the data availability. The timer interval is one millisecond by default. Within this time, it checks whether any data is reached. When the message/data arrives, it starts parsing. At first, it reads the Device ID. If the device ID matches, then it checks the CRC. If CRC is valid, it extracts the data from the received packets and displays it inside the list box. Findings/Result: Various tools are needed to resolve the issue when we debug the system. We connect the debugger and try to uncover the problem. Sometimes we do less concentrate on the hardware connection. So, there are chances of a wrong connection. Further, it can lead to the cause for damage to our PC/laptop. Here is our approach to minimize the damage which is unintentionally created. We isolate the PC/Laptop from the debugging system. It is safe and reliable. The researchers, new to embedded system debugging, can get information on where they can safely debug by creating an isolated environment. Originality/Value: This work provides some efficient view to debug message display using Modbus. It has several advantages over a simple serial debug interface. It can be long-range. The same message can be displayed in multiple terminals for team debugs season. The Researcher working with various team members on the same project can be viewed effectively. The new Researcher can be found some good reference information from this work. Paper Type: Experimental-based Research.
Precise Zero-Shot Dense Retrieval without Relevance Labels

While dense retrieval has been shown to be effective and efficient across tasks and languages, it remains difficult to create effective fully zero-shot dense retrieval systems when no relevance labels are available.In this paper, we recognize the difficulty of zero-shot learning and encoding relevance.Instead, we propose to pivot through Hypothetical Document Embeddings (HyDE).Given a query, HyDE first zero-shot prompts an instruction-following language model (e.g., InstructGPT) to generate a hypothetical document.The document captures relevance patterns but is "fake" and may contain hallucinations.Then, an unsupervised contrastively learned encoder (e.g., Contriever) encodes the document into an embedding vector.This vector identifies a neighborhood in the corpus embedding space, from which similar real documents are retrieved based on vector similarity.This second step grounds the generated document to the actual corpus, with the encoder's dense bottleneck filtering out the hallucinations.Our experiments show that HyDE significantly outperforms the state-ofthe-art unsupervised dense retriever Contriever and shows strong performance comparable to fine-tuned retrievers across various tasks (e.g.web search, QA, fact verification) and in non-English languages (e.g., sw, ko, ja, bn).
Text-Only Training for Image Captioning using Noise-Injected CLIP

We consider the task of image-captioning using only the CLIP model and additional text data at training time, and no additional captioned images.Our approach relies on the fact that CLIP is trained to make visual and textual embeddings similar.Therefore, we only need to learn how to translate CLIP textual embeddings back into text, and we can learn how to do this by learning a decoder for the frozen CLIP text encoder using only text.We argue that this intuition is "almost correct" because of a gap between the embedding spaces, and propose to rectify this via noise injection during training.We demonstrate the effectiveness of our approach by showing SOTA zero-shot image captioning across four benchmarks, including style transfer.Code, data, and models are available at https://github.com/DavidHuji/CapDec.
Memory-assisted prompt editing to improve GPT-3 after deployment

Large LMs such as GPT-3 are powerful, but can commit mistakes that are obvious to humans.For example, GPT-3 would mistakenly interpret "What word is similar to good?" to mean a homophone, while the user intended a synonym.Our goal is to effectively correct such errors via user interactions with the system but without retraining, which will be prohibitively costly.We pair GPT-3 with a growing memory of recorded cases where the model misunderstood the user's intents, along with user feedback for clarification.Such a memory allows our system to produce enhanced prompts for any new query based on the user feedback for error correction on similar cases in the past.On four tasks (two lexical tasks, two advanced ethical reasoning tasks), we show how a (simulated) user can interactively teach a deployed GPT-3, substantially increasing its accuracy over the queries with different kinds of misunderstandings by the GPT-3.Our approach is a step towards the low-cost utility enhancement for very large pre-trained LMs.
RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning

Prompting has shown impressive success in enabling large pre-trained language models (LMs) to perform diverse NLP tasks, especially with only few downstream data.Automatically finding the optimal prompt for each task, however, is challenging.Most existing work resorts to tuning soft prompts (e.g., embeddings) which fall short of interpretability, reusability across LMs, and applicability when gradients are not accessible.Discrete prompts, on the other hand, are difficult to optimize, and are often created by "enumeration (e.g., paraphrasing)-then-selection" heuristics that do not explore the prompt space systematically.This paper proposes RLPROMPT, an efficient discrete prompt optimization approach with reinforcement learning (RL).RLPROMPT formulates a parameter-efficient policy network that generates the optimized discrete prompt after training with reward.To harness the complex and stochastic reward signals from the large LM environment, we incorporate effective reward stabilization that substantially enhances training efficiency.RLPROMPT is flexibly applicable to different types of LMs, such as masked (e.g., BERT) and left-to-right models (e.g., GPTs), for both classification and generation tasks.Experiments on few-shot classification and unsupervised text style transfer show superior performance over a wide range of existing fine-tuning or prompting methods.Interestingly, the resulting optimized prompts are often ungrammatical gibberish text; and surprisingly, those gibberish prompts are transferrable between different LMs to retain significant performance, indicating that LM prompting may not follow human language patterns.
Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs

Recent graph-based models for joint multiple intent detection and slot filling have obtained promising results through modeling the guidance from the prediction of intents to the decoding of slot filling.However, existing methods (1) only model the unidirectional guidance from intent to slot; (2) adopt homogeneous graphs to model the interactions between the slot semantics nodes and intent label nodes, which limit the performance.In this paper, we propose a novel model termed Co-guiding Net, which implements a two-stage framework achieving the mutual guidances between the two tasks.In the first stage, the initial estimated labels of both tasks are produced, and then they are leveraged in the second stage to model the mutual guidances.Specifically, we propose two heterogeneous graph attention networks working on the proposed two heterogeneous semantics-label graphs, which effectively represent the relations among the semantics nodes and label nodes.Experiment results show that our model outperforms existing models by a large margin, obtaining a relative improvement of 19.3% over the previous best model on Mix-ATIS dataset in overall accuracy.
Can language models learn from explanations in context?

Language Models (LMs) can perform new tasks by adapting to a few in-context examples.For humans, explanations that connect examples to task principles can improve learning.We therefore investigate whether explanations of few-shot examples can help LMs.We annotate questions from 40 challenging tasks with answer explanations, and various matched control explanations.We evaluate how different types of explanations, instructions, and controls affect zero-and few-shot performance.We analyze these results using statistical multilevel modeling techniques that account for the nested dependencies among conditions, tasks, prompts, and models.We find that explanations can improve performance-even without tuning.Furthermore, explanations hand-tuned for performance on a small validation set offer substantially larger benefits, and building a prompt by selecting examples and explanations together substantially improves performance over selecting examples alone.Finally, even untuned explanations outperform carefully matched controls, suggesting that the benefits are due to the link between an example and its explanation, rather than lower-level features.However, only large models benefit.In summary, explanations can support the in-context learning of large LMs on challenging tasks.' common_morpheme ' , ' c r a s h _ b l o s s o m ' , ' c r a s s _ a i ' , ' c s _ a l g o r i t h m s / v a l i d _ p a r e n t h e s e s ' , ' d i s a m b i g u a t i o n _ q a ' , ' e m p i r i c a l _ j u d g m e n t s ' , ' e n g l i s h _ p r o v e r b s ' , ' e p i s t e m i c _ r e a s o n i n g ' , ' e v a l u a t i n g _ i n f o r m a t i o n _ e s s e n t i a l i t y ' , ' f a n t a s y _ r e a s o n i n g ' , ' f i g u r e _ o f _ s p e e c h _ d e t e c t i o n ' , ' g o a l _ s t e p _ w i k i h o w / g o a l _ i n f e r e n c e ' , ' g o a l _ s t e p _ w i k i h o w / s t e p _ i n f e r e n c e ' , ' i d e n t i f y _ o d d _ m e t a p h o r ' , ' i m p l i c a t u r e s ' ,
Does artificial intelligence (AI) boost digital banking user satisfaction? Integration of expectation confirmation model and antecedents of artificial intelligence enabled digital banking

In the era disruptive technology the emergence of artificial intelligence has fundamentally improved banking operations. The execution of artificial intelligence is no longer discretionary for financial institutions and now it is considered an essential tool to meet customer expectations. Although artificial intelligence enabled digital banking is faster efficient and effective however user acceptance of digital banking driven by artificial intelligence is in its initial stages. Therefore, current study develops and integrated research framework with expectation confirmation model and examines digital banking user satisfaction and acceptance of AI enabled digital banking. Data were collected from digital banking user through structured questionnaire. Overall, 320 respondents were approached and requested to participate in digital banking survey. In return 251 valid responses were received and analyzed with structural equation modeling. Findings of the structural model indicate that satisfaction is jointly determined by expectation confirmation, perceived performance, trendiness, visual attractiveness, problem solving, customization, communication quality and revealed substantial variance Rˆ2 51.1% in digital banking user satisfaction. Therefore, satisfaction and corporate reputation have shown considerable variance Rˆ2 48.3 in user acceptance of AI enabled digital banking. Moreover, the research framework has revealed substantial predictive power Qˆ2 0.449 to predict digital banking user satisfaction and Qˆ2 0.493 user acceptance of artificial intelligence enabled digital banking. Concerning with hypotheses relationships exogenous factors have shown positive and significant impact user satisfaction except trendiness and customization. Practically, this research has suggested that policy makers should pay attention in improving user expectation confirmation, perceived performance, visual attractiveness, communication quality and corporate reputation which in turn enhance satisfaction and boost digital banking user's confidence to accept artificial intelligence enabled digital banking. This study is original as it integrates expectation confirmation model with the antecedents of artificial intelligence and examines user behavior towards acceptance of artificial intelligence enabled digital banking.