Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning Based Approaches to Adaptive Context Caching in Distributed Context Management Systems

Real-time applications increasingly rely on context information to provide relevant and dependable features. Context queries require large-scale retrieval, inferencing, aggregation, and delivery of context using only limited computing resources, especially in a distributed environment. If this is slow, inconsistent, and too expensive to access context information, the dependability and relevancy of real-time applications may fail to exist. This paper argues, transiency of context (i.e., the limited validity period), variations in the features of context query loads (e.g., the request rate, different Quality of Service (QoS), and Quality of Context (QoC) requirements), and lack of prior knowledge about context to make near real-time adaptations as fundamental challenges that need to be addressed to overcome these shortcomings. Hence, we propose a performance metric driven reinforcement learning based adaptive context caching approach aiming to maximize both cost- and performance-efficiency for middleware-based Context Management Systems (CMSs). Although context-aware caching has been thoroughly investigated in the literature, our approach is novel because existing techniques are not fully applicable to caching context due to (i) the underlying fundamental challenges and (ii) not addressing the limitations hindering dependability and consistency of context. Unlike previously tested modes of CMS operations and traditional data caching techniques, our approach can provide real-time pervasive applications with lower cost, faster, and fresher high quality context information. Compared to existing context-aware data caching algorithms, our technique is bespoken for caching context information, which is different from traditional data. We also show that our full-cycle context lifecycle-based approach can maximize both cost- and performance-efficiency while maintaining adequate QoC solely based on real-time performance metrics and our heuristic techniques without depending on any previous knowledge about the context, variations in query features, or quality demands, unlike any previous work. We demonstrate using a real world inspired scenario and a prototype middleware based CMS integrated with our adaptive context caching approach that we have implemented, how realtime applications that are 85% faster can be more relevant and dependable to users, while costing 60.22% less than using existing techniques to access context information. Our model is also at least twice as fast and more flexible to adapt compared to existing benchmarks even under uncertainty and lack of prior knowledge about context, transiency, and variable context query loads.
Unmasking the Power of Play Through TUI Designs

Research on the potential benefits of technology for autistic children is an emergent field in Human-Computer Interaction (HCI), especially within the Child-Computer Interaction Community. At the same time, there are concerns about what these interventions and technologies are for and who benefits. We present a research and design approach for Tangible User Interfaces (TUIs) for minimally verbal to nonverbal autistic children following a neurodiversity narrative through three field studies developed and evaluated with three groups of children within a semi-structured scholastic environment between 2018 and 2021 in the UK. We discuss our insights for research and TUI designs in the context of social play for nonverbal autistic children and critically reflect on the methods and approaches we used. We do this to disrupt the normalisation agenda that subtly permeates the field of HCI and to direct designers’ attention toward supporting autistic ways of being in the world.
The aesthetics of deconstruction: neural synthesis of transformation matrices using GANs on multichannel polyphonic MIDI data

Deconstruction, a concept that originated from French philosopher Jacques Derrida's post-structuralist theories, has significantly influenced architecture and graphic design. It has evolved into a rhizomatic network of styles and concepts ranging from new architectural geometry to AI. Despite its limited explicit application in music, this paper explores deconstruction within musical aesthetics, especially in the post-1945 serial school. Computer-Aided Design (CAD) methods have become an important part in the deconstructivist architectural design process; similarly, in music, Computer-Aided Composition (CAC) techniques have become important from generative algorithms to AI techniques. In graphic design, such systems (i.e. GANs, VAEs, CNNs, RNNs, Stable Diffusion, Transformers, etc.) have been experimented with. Google's Imagen or OpenAI's DALL-E 2, which automatically generate images from text prompts given by users, use, for example, diffusion models. Also, Google LM is a music equivalent to these. This paper delves into the novel aesthetics brought about by the application of GANs on multichannel polyphonic MIDI data in music, discussing the conceptual grounding of such usage and overcoming the challenges associated with it. Effectively, using our concept of distance matrices rather than, for example, Hidden Markov Models, is more suitable for GAN generation.
Asymptotics in the Bradley-Terry model for networks with a differentially private degree sequence

The Bradley-Terry model is a common model for analyzing paired comparison data. Under differential private mechanism, there is a lack of asymptotic properties for the parameter estimator of parameters in this model. In this article, we show that the moment estimators of the parameters based on the differential private degree sequence with Laplace noise is uniformly consistent and asymptotically normal. Simulations are provided to illustrate asymptotic results.
ReFID: Reciprocal Frequency-aware Generalizable Person Re-identification via Decomposition and Filtering

Domain generalization (DG) of person re-identification (ReID) aims to conduct testing across domains that have not been previously encountered, without utilizing target domain data during the training stage. As the number of source domains increases, the relationships between training samples become more complex. This can lead to domain-invariant features that include certain instance-level spurious correlations, which can impact the model’s ability to generalize further. To overcome this limitation, the Reciprocal Frequency-aware Generalizable Person Re-identification (ReFID) method is proposed in this paper, which aims to utilize spectral feature correlation learning to transmit frequency component information and generate more discriminative hybrid features. A module called Bilateral Frequency Component-guided Attention (BFCA) is developed to help the network understand high-level semantic and texture information from various frequency features. Furthermore, to reduce the impact of noise from the frequency domain, this paper proposes an innovative module called Fourier Noise Masquerade Filtering (FNMF). This module enhances the portability of frequency domain components while simultaneously suppressing elements that do not contribute to generalization. Extensive experimental results on various datasets demonstrate that our method is effective and superior to the state-of-the-art methods.
Origins, Theoretical Foundations, and Economic Implications of Cryptocurrencies 

The theoretical foundations and indeed implications related to the emergence of digital assets, particularly cryptocurrencies, remain a scarcely explored area in recent academic literature. This chapter aims to analyze the presumed influences of various economic schools of thought on the genesis of Bitcoin, the monetary policies underlying several digital assets, and the evolution of their market capitalization since 2009. The study also addresses the case of several cryptocurrency exchange platforms, commonly known as crypto exchanges, and focuses on industrial entities, such as Ledger and Bitmain. An overview of use cases involving “smart contracts” and Blockchain technology is provided. This chapter seeks to revisit the emergence of different approaches to national regulation on a global scale and to establish international comparisons. The examples of Japan, long trapped in a deflationary spiral, and Argentina, facing hyperinflation, are particularly enlightening for understanding the legal treatment of digital assets and their perception by local authorities. Two central questions guide this exploration: what are the economic and ideological foundations underlying the conception of Bitcoin, and to what extent does the current development of the digital asset ecosystem remain true to the initial ideas of Satoshi Nakamoto?
Web-based simulation and motion planning for human-robot and multi-robot applications

This paper introduces the human-robot and multi-robot programming and simulation capabilities of a web-based, open-source robot programming and simulation tool called Assembly. The tool supports scenarios with up to three robots and provides support for simulating human arms working closely with a robot. To facilitate the simulation of scenarios involving human-robot and robot-robot collaboration, the paper proposes an improved version of the neural network-based Adaptive Simplex Motion Planner, which is capable of performing motion planning at a rate of over 3500 Hz. The improved toolpath planner uses an obstacle avoidance approach that leverages ray casting and shadow volume teachniques. The new planner is evaluated on the basis of two human-robot and multi-robot simulation scenarios and the results are compared to those of the original algorithm.
Enhancing Facial Recognition in Surveillance Systems through Embedded Super-resolution

This document details the implementation of a sub-pixel convolutional neural network designed to enhance the resolution of face images. The model uses a series of filters to progressively increase the number of pixels, estimating the necessary information for new pixels from the original image and training derived from 22000 synthetic images produced by adversarial neural networks. Within the context of surveillance and related applications, the trained convolutional network exhibits beneficial characteristics. For instance, it can be deployed within a device to achieve higher-resolution images than those the physical camera can produce. This research underscores the feasibility of such a device through the implementation and evaluation of the network on the NVIDIA Jetson TX2 embedded system. The findings demonstrate the model's practicality for real-time surveillance applications and its ability to produce superior-quality images compared to several interpolation methods, as determined by an exhaustive testing process measuring various attributes of the generated images.
A Study to Know Impact of AI on CRM

This research paper delves into the dynamic intersection of Artificial Intelligence (AI) and Customer Relationship Management (CRM), exploring the profound effects of AI technologies on modern business practices. As AI continues to evolve, it reshapes how organizations manage and nurture their relationships with customers, presenting new opportunities and challenges. The study investigates the role of AI in personalizing customer experiences, emphasizing the utilization of advanced algorithms to analyze vast datasets and derive actionable insights. AI's influence on data management, analysis, and the subsequent enhancement of customer insights are scrutinized, providing a comprehensive understanding of its impact on informed decision-making. Furthermore, the paper examines the integration of AI-powered chatbots and virtual assistants in CRM systems, evaluating their effectiveness in providing real-time support, streamlining interactions, and improving overall customer satisfaction. The automation of repetitive CRM tasks through AI technologies is also explored, highlighting the resulting efficiencies and the potential for human resources to engage in more strategic aspects of customer relationship management. Sales forecasting, customer segmentation, and sentiment analysis emerge as key focal points, illustrating how AI contributes to more accurate predictions, targeted marketing strategies, and proactive reputation management. The impact of AI on cross-selling, upselling, and customer retention strategies is scrutinized, offering insights into how businesses can leverage AI to optimize revenue and foster enduring customer loyalty. As businesses navigate the rapidly evolving landscape of AI in CRM, this research paper aims to provide a comprehensive overview of the transformative dynamics at play. By understanding the nuanced influence of AI on customer relationships, organizations can adapt their strategies to align with the evolving expectations and demands of the contemporary market.
Curriculum in the digital age: Intensifying the work of teachers, the remix

This research focuses on the Teacher-to-Teacher Online Marketplace of Ideas (TOMI), the popular ecosystem of participatory online platforms for teachers seeking to share and/or curate classroom materials and curricular ideas. Teachers engage with online platforms for curricular planning through multiple, complex labor practices. In this manuscript, interview data is used to describe the process teachers engage in when using the TOMI: Identifying, Remixing and Teaching, Reflecting, and Reviewing. We then consider how these new labor practices constitute a new type of work intensification that is both richly complex and concerning due to their market orientation. We trouble these findings, considering teachers' labor practices as examples of unpaid, casual digital labor in light of efforts to defund public education. We argue for structural shifts in public education that allow teachers more time and space for productive, creative, and fulfilling work remixing their curriculum and collaborating with colleagues in the digital space.
Survey Paper on Hospital Management System using Blockchain

Electronic health records are health information of patients that are saved digitally in a network. The information of the patient are stored in the blockchain and these details are stored in the block chain as a blocks of data. The data is encrypted by the algorithm known as AES which is used to encrypt all the data of the patients. A Blockchain network is used in the healthcare system to exchange patient data improve the performance, security, and transparency of sharing medical data in the health care system. The three main feature of blockchain technology – Security, Decentralization Transparency make any application secure and not accessible by unauthorized parties. Key Words: Health records, Blockchain, Encryption of data, Decryption of data, Security.
Active Learning for Discrete Latent Variable Models

Active learning seeks to reduce the amount of data required to fit the parameters of a model, thus forming an important class of techniques in modern machine learning. However, past work on active learning has largely overlooked latent variable models, which play a vital role in neuroscience, psychology, and a variety of other engineering and scientific disciplines. Here we address this gap by proposing a novel framework for maximum-mutual-information input selection for discrete latent variable regression models. We first apply our method to a class of models known as mixtures of linear regressions (MLR). While it is well known that active learning confers no advantage for linear-gaussian regression models, we use Fisher information to show analytically that active learning can nevertheless achieve large gains for mixtures of such models, and we validate this improvement using both simulations and real-world data. We then consider a powerful class of temporally structured latent variable models given by a hidden Markov model (HMM) with generalized linear model (GLM) observations, which has recently been used to identify discrete states from animal decision-making data. We show that our method substantially reduces the amount of data needed to fit GLM-HMMs and outperforms a variety of approximate methods based on variational and amortized inference. Infomax learning for latent variable models thus offers a powerful approach for characterizing temporally structured latent states, with a wide variety of applications in neuroscience and beyond.
Criminal Investigation with the Help of Face Recognition

The Criminal Investigation and Face Recognition System (CIFRS) streamlines investigations with an intuitive interface and automated face recognition. Investigators effortlessly manage cases, set parameters, and upload facial images for analysis against a database. Real-time notifications alert investigators to potential matches, contributing to investigative efficiency. CIFRS supports diverse law enforcement management systems, allowing the download of case reports and data in various formats. The system also offers practice scenarios to familiarize users with face recognition technology. Keywords – Criminal Investigation, Face Recognition, Database Management, Law Enforcement.
Aspect-based sentiment analysis with metaphorical information

Aspect-Based Sentiment Analysis (ABSA) has been the focus of increasing study in recent years. Previous research has demonstrated that incorporating syntactic information, such as dependency trees, can enhance ABSA performance. Despite the widespread use of metaphors in daily life to express emotions more vividly, few studies have integrated this literary device into ABSA. In this paper, we propose a novel ABSA model that utilizes Metaphor Identification Procedure (MIP) to encode both the sentence and aspect word as a single unit, thereby overcoming these limitations. Our experimental results demonstrate that our model achieves competitive performance in ABSA.
Integrating learning factors and Bayesian network for interpretable knowledge tracing

Knowledge tracing aims to model learners’ knowledge mastery based on their historical interaction records and predict their future performance. Due to its great potential in enabling personalized learning in intelligent tutoring systems, it has received extensive attention. However, most deep learning-based knowledge tracing methods have significant predictive performance. It is difficult to extract meaningful interpretations from the thousands of parameters in neural networks. The interpretability of knowledge tracing refers to the ability of learners to easily understand the predicted results.To address this problem, based on learning factors that influence the learner’s exercise performance, this paper proposes a novel knowledge tracing model which is named Integrating Learning factors and Bayesian network for interpretable Knowledge Tracing (LBKT). Firstly, meaningful learning factors, including knowledge mastery, learning ability, and exercise difficulty, are calculated from learners’ historical interaction records using deep learning and statistical methods. Then, Bayesian network is constructed to capture the causal relation between the three learning factors and exercise response. Finally, the Bayesian network is generated through structure and parameter learning to obtain interpretable prediction of future exercise performance. The proposed model named LBKT is evaluated on three public real-world educational datasets. The experiment results demonstrate that our approach achieves better predictive performance compared to baseline knowledge tracing methods, while also exhibiting significant superiority in model interpretability.
Movie Recommendation System: Real-time Evaluation

In today's e-commerce time, it has become very troublesome task to find any content of online movies, songs, books , restaurants etc. A practical recommendation scheme now very helpful and important. Nowadays, movie recommendation system are also available( Netflix, Movie lens, Amazon movie recommendation system etc.) But using a method of combination of content based and collaborative filtering, a recommender system worked and finding a customized output to user. Key Words: Content-based, Collaborative Filtering, Recommender System, Machine Learning.
Suppression of chaos in a partially driven recurrent neural network

The dynamics of recurrent neural networks (RNNs), and particularly their response to inputs, play a critical role in information processing. In many applications of RNNs, only a specific subset of the neurons generally receive inputs. However, it remains to be theoretically clarified how the restriction of the input to a specific subset of neurons affects the network dynamics. Considering RNNs with such restricted input, we investigate how the proportion, $p$, of the neurons receiving inputs (the ``input neurons'') and the strength of the input signals affect the dynamics by analytically deriving the conditional maximum Lyapunov exponent. Our results show that for sufficiently large $p$, the maximum Lyapunov exponent decreases monotonically as a function of the input strength, indicating the suppression of chaos, but if $p$ is smaller than a critical threshold, ${p}_{c}$, even significantly amplified inputs cannot suppress spontaneous chaotic dynamics. Furthermore, although the value of ${p}_{c}$ is seemingly dependent on several model parameters, such as the sparseness and strength of recurrent connections, it is proved to be intrinsically determined solely by the strength of chaos in spontaneous activity of the RNN. This is to say, despite changes in these model parameters, it is possible to represent the value of ${p}_{c}$ as a common invariant function by appropriately scaling these parameters to yield the same strength of spontaneous chaos. Our study suggests that if $p$ is above ${p}_{c}$, we can bring the neural network to the edge of chaos, thereby maximizing its information processing capacity, by amplifying inputs.
Optimizing feature selection and parameter tuning for breast cancer detection using hybrid GAHBA-DNN framework

Breast cancer has been life-threatening for many years as it is the common cause of fatality among women. The challenges of screening such tumors through manual approaches can be overcome by computer-aided diagnosis, which aids radiologists in making precise decisions. The selection of significant features is crucial for the estimation of prediction accuracy. This work proposes a hybrid Genetic Algorithm (GA) and Honey Badger Algorithm (HBA) based Deep Neural Network (DNN), HGAHBA-DNN for the concurrent optimal features selection and parameter optimization; further, the optimal features and parameters extracted are fed into the DNN for the prediction of the breast cancer. It fuses the benefits of HBA with parallel processing and efficient feedback with GA’s excellent global convergent rate during the processing stages. The aforementioned method is evaluated on the Wisconsin Original Breast Cancer (WOBC), Wisconsin Diagnostic Breast Cancer (WDBC), and the Surveillance, Epidemiology, and End Results (SEER) datasets. Subsequently, the performance is validated using several metrics like accuracy, precision, Recall, and F1-score. The experimental result shows that HGAHBA-DNN obtains accuracy of 99.42%, 99.84%, and 92.44% for the WOBC, WDBC, and SEER datasets respectively, which is much superior to the other state-of-the-art methods.