Reviewerly/exHarmony huggingface dataset:
Connecting Everyday Objects with the Metaverse: A Unified Recognition Framework

The recent Facebook rebranding to Meta has drawn renewed attention to the metaverse. Technology giants, amongst others, are increasingly embracing the vision and opportunities of a hybrid social experience that mixes physical and virtual interactions. As the metaverse gains in traction, it is expected that everyday objects may soon connect more closely with virtual elements. However, discovering this “hidden” virtual world will be a crucial first step to interacting with it in this new augmented world. In this paper, we address the problem of connecting phys-ical objects with their virtual counterparts, especially through connections built upon visual markers. We propose a unified recognition framework that guides approaches to the metaverse access points. We illustrate the use of the framework through experimental studies under different conditions, in which an interactive and visually attractive decoration pattern, an Artcode, is used as the approach to enable the connection. This paper will be of interest to, amongst others, researchers working in Interaction Design or Augmented Reality who are seeking techniques or guidelines for augmenting physical objects in an unobtrusive, complementary manner.
Error bound characterization for reservoir computing-based OFDM symbol detection

In this paper, we derive an upper bound on the generalization error of reservoir computing (RC), a special category of recurrent neural networks (RNNs). The particular RC implementation considered in this paper is the echo state network (ESN), and the generalization bound is derived via the empirical Rademacher complexity (ERC) approach. While recent work in deriving risk bounds for RC frameworks makes use of a non-standard empirical Rademacher Complexity (ERC) measure and a direct application of its definition, our work uses the standard ERC measure and tools which allow easier extension to deep RC structures and a fair comparison with other RNN structures including the vanilla RNNs. Finally, we train an ESN for the task of symbol detection, a key component in the receive processing of 5G systems and show with an example how the derived generalization bound can guide the underlying system design. To be specific, we utilize the derived generalization error together with the characterized training loss to analytically identify the optimum number of neurons in the reservoir for the symbol detection task. Simulation results using 3GPP specified channels corroborate our theoretical findings, illustrating the significance and practical relevance of our work.
A Correlation-based Real-time Segmentation Scheme for Multi-user Collaborative Activities

Activity Segmentation, dividing a continuous sensor stream into a set of activity segments, is a crucial pre-process in Human Activity Recognition (HAR) and it is required to be done in real-time for real-world smart services. Existing single-user activity segmentation schemes fail to correctly detect transition points due to concurrent and overlapping events from multiple users in case of Multi-user Collaborative Activity Recognition (MCAR). In this paper, we propose a novel scheme for activity segmentation for MCAR that expresses complex events and the correlations between them. For this, the proposed scheme first creates an event stream from a sensor stream and defines event sets in terms of time windows. For each time window, two types of correlations for every event pair are calculated: duration correlation and history correlation. After calculating event correlation, the change score of a time window is measured by comparing the calculated correlation values with those of the preceding windows. Then, the proposed scheme elects as an activity transition point a time window whose change score exceeds the transition threshold. We evaluate the proposed method on two multi-user collaborative activity datasets and experiment results show that the proposed scheme achieves better segmentation performance than existing approaches.
Blockchain Sharding Strategy for Collaborative Computing Internet of Things Combining Dynamic Clustering and Deep Reinforcement Learning

Immutability, decentralization, and linear promoted scalability make sharded blockchain a promising solution, which can effectively address the trust issue in the large-scale Internet of Things (IoT). However, currently, the throughput of sharded blockchains is still limited when it comes to high proportions of cross-shard transactions (CST). On the other hand, assemblage characteristics of collaborative computing in IoT have not been received attention. Therefore, in this paper, we present a clustering-based sharded blockchain strategy for collaborative computing in the IoT, where the sharding of the blockchain system is implemented in two steps: k-means clustering-based user grouping and the assignment of consensus nodes. In this framework, how to reasonably group the IoT users while simultaneously guaranteeing the system performance is the key point. Specifically, we describe the data transactions among IoT devices by data transaction flow graph (DTFG) based on a dynamic stochastic block model. Then, formed as a Markov decision process (MDP), the optimization of the cluster number (shard number) and the adjustment of consensus parameters are jointly trained by deep reinforcement learning (DRL). Simulation results show that the proposed scheme improves the scalability of the sharded blockchain in the IoT application.
Optimizing feature-aset in image classification using a hybrid approach

Classification is categorizing different input dataset to respective class labels. Classification has been an important area of research. Image classification deals with the categorization of images. There are many problems those needed to be addressed in this area. Feature subset selection is a significant stage for improving the complexity. A hybrid method / approach constituting of feature subset selection and classification is used for obtaining results. Feature subset selection (FSS) is attained by performing GA and classification is performed by neural networks. Cervical cancer behavior risk dataset is a standard dataset taken from UCI repository. Feature subset selection is performed to obtain an optimized feature set. Classification is implemented on this reduced feature set using neural network.
Moneo: Non-intrusive Fine-grained Monitor for AI Infrastructure

Cloud-based AI infrastructure is increasingly important, especially on large-scale distributed training. To improve its efficiency and serviceability, real-time monitoring of the infrastructure and profiling the workload are proved to be the effective approach empirically. However, cloud environment poses great challenges as service providers cannot interfere with their tenants' workloads or touch user data, thus previous instrumentation-based monitoring approach cannot be applied, nor does the workload trace collection.We propose Moneo, a non-intrusive cloud-friendly monitoring system for AI infrastructure. Moneo is capable of intelligently collecting the key architecture-level metrics at finer granularity in real-time without instrumenting or tracing the workloads, which has been deployed in real production cloud, Azure. We analyze the results reported by Moneo for typical large-scale distributed AI workloads from real deployment. Results demonstrate that Moneo can effectively help service providers understand the real resource usage patterns of various AI workloads and real networking requirements, so as to get valuable findings help improve the efficiency of cloud infrastructure and optimize the software stack with the consideration of the characteristic resource usage requirements for different AI workloads.
Research on multi baseline large depth 3D imaging

The acquisition of 3D information about targets of interest in real scenes and 3D scene restoration is receiving more and more attention in the field of machine vision. Binocular stereo vision techniques are already relatively mature for this purpose. Traditional fixed-baseline binocular stereo imaging techniques are more accurate in 3D imaging of targets at close range. However, in the face of large depth scenes, the 3D imaging effect in terms of imaging accuracy and imaging distance can no longer meet the demand. In this paper, we use the multi-level and multi-view characteristics of multi-baseline 3D imaging to obtain 3D information of targets of interest at different depths in the range of 5m-15m depth of field. The error of different depth-of-field targets in large depth scenes is maintained at a controllable level, thus reducing the overall error of 3D imaging of targets in large depth scenes.
Compromised IoT Devices Detection in Smart Home via Semantic Information

The safety and security of IoT devices in smart home systems is attracting booming attention, due to the cascading threat introduced by the interoperability of IoT devices. It is observed that semantic information of behaviors could be utilized to identify anomalies of IoT devices, and some prior works have attempted to detect single abnormal behavior based on the mined semantic patterns. However, the performance of these methods could be usually affected by some noisy data (e.g., user activities), suffering from false alarms of detection. In this work, we propose a semantic-aware framework of compromised IoT devices detection, which extracts the Mutual Information feature from the semantic information of IoT devices to eliminate interference of the noise. The proposed framework includes three modules: semantic analysis to generate event correlations, feature extraction to construct the feature vector, and detection model to train binary classifiers for detection. To collect real-world data for evaluation, we construct three testbeds of the following scenes: bedroom, living room and kitchen. The performance on the collected dataset shows that our method achieves high accuracy (the average is over 90.0%) on the compromised devices detection.
Adaptive Navigation for Lunar Surface Operations Using Deep Learning and Holographic Telepresence

Current solutions for navigation and guidance on the lunar South pole have limited visual spatial cues to help guide astronaut crewmembers to mission objectives. Challenges include low solar elevation angles that create areas of high contrast, permanently shadowed regions (PSR), and a lack of radionavigation systems such as the Global Positioning System (GPS) on Earth. This paper proposes a novel and adaptive navigation system through the Space Communications, Operations, and User Telepresence (SCOUT) Augmented Reality (AR) assistant to support astronaut navigation on the Moon. SCOUT is an artificial intelligence (AI) system that can be integrated into an astronaut&#x0027;s visor to collaboratively identify points of interest, detect hazards, and guide a group of crewmembers towards mission waypoints using hologram overlays. SCOUT uses deep learning to iteratively improve its under-standing of the nearby environment, and can be trained using data from robotic systems deployed on the Moon before astro-nauts arrive. To assist an astronaut&#x0027;s navigation capability in extreme environments, SCOUT displays 3D spline holograms towards waypoints and objectives. For example, SCOUT can display a clear label during an extra-vehicular activity (EVA), such as a switch that should be flipped, or which direction they should walk. To effectively navigate through and explore any region of the lunar surface, SCOUT has been trained on a multitude of randomly generated Moon-like virtual environ-ments. Through this process, this AI system can quickly identify features that it has seen before as well as adapt to new situations it has never experienced before. Results show that SCOUT can map out an optimal path towards a waypoint, even without having previous data samples from that specific environment. SCOUT is designed to benefit the user as much as possible, through automated guidance and continuous learning of the surrounding environment.
A Wisdom of Crowds approach to Supervised Learning Ensembles using FPGAs

In his 2004 book The Wisdom of Crowds, author James Surowiecki details the successes and failures of collective wisdom in Business, Economics, Societies and Nations. The implications of the intelligence of a collective decision of independent judgements are far reaching and include such important things as the success of democracy. Furthermore, the Wisdom of Crowds also finds application in Machine Learning where the collective performance of a group of independent weak learners can outperform individual expert learners. In this paper, we consider a quantitative look at a Wisdom of Crowds approach to create successful supervised learning ensembles. Specifically, this paper considers four (4) characteristics of learners that create a wise ensemble such as diversity of learner, learner independence, decentralization and aggregation of learner results. Design data and equations are provided to scale up a wise ensemble. Equally important, this systems level look at the ensemble provides clarity with respect to when ensemble performance fails. This paper also considers the challenging task of improving the performance of an ensemble of independent learners. This paper also considers implementation of such a supervised learning ensemble using Field Programmable Gate Arrays (FPGAs). The approach described in this paper applies to high performance, high throughput and scalable implementations. Design data is provided for FPGA implementation of a wise ensemble. Finally, an example is provided based on the Xilinx Adaptive Compute Acceleration Platform (ACAP) using the Versal Premium Series device to illustrate the concepts in this paper.
IoT for Real-time Accessibility Ontology Population to Context-awareness Adapt User Interfaces

With the evolution of mobile devices such as laptops, tablets, smartphones, etc., the number of users has also increased, but it is difficult for some people due to their limited abilities. The Internet of Things (IoT) can make people's lives more convenient, and especially important for people with disabilities. This paper presents an approach to semantic representation using ontology. This approach is based on IoT to facilitate the real-time populating of the ontology in the context of adapting User Interfaces (UI) to people with disabilities such as sensory impairments (hearing and vision). IoT technology offers a great service to gain more autonomy and independence for people with disabilities.
Parallel implementation of CRYSTALS-Dilithium for effective signing and verification in autonomous driving environment

In the autonomous driving environment, each vehicle performs numerous signing and verification while sending and receiving BSMs (Basic Security Messages) in real-time. We present an optimized CRYSTALS-Dilithium software which can quickly process each signing and verification in parallel using embedded Graphic Processing Unit. For efficiency, we propose several optimization techniques such as dummy operation-based warp divergence reducing technique, parallel implementation NTT (Number Theoretic Transform)-based polynomial multiplication, optimization of rejection sampling process using rejection sequence table, and so on. The proposed CRYSTALS-Dilithium software provides a performance improvement of up to 19.41 times compared to the Dilithium Software on CPUs on NVIDIA Jetson AGX Xavier which is an off-the-shelf autonomous vehicle OBU (On-Board Unit).
Corpus-Based Hashing Count Frequency Vectorization of Sentiment Analysis of Movie Reviews

Sentiment analysis is textual context—specific extraction that identifies and extracts qualitative information from various information, assisting businesses in understanding the social emotion of their model, product, brand, or service while measuring online conversations. With the advancement in entertainment scenario, the analysis of customer real-time pulse while watching movie is essentially needed for rating the movie and to analyze the success of movie toward marketing industry. The machine learning can be used for predicting the sentiment of the movie review given by the customers. The movie review dataset from KAGGLE repository with ‘117211’ movie reviews is used for analyzing the type of movie based on the review comments. The movie review dataset is preprocessed by removing the stop words and unwanted tokens from the dataset. The tokens are extracted from the text using NGram method. The emotional labels are substituted for the tokens, and the machine is trained to identify the sentiment emotions during learning phase. The sentimental emotional labels are converted into features to form corpus text for predicting the emotions in the movie reviews. The corpus is splitted to form training and testing dataset and fitted to Count Vectorizer, Hash Vectorizer, TFID Transformer, and TFIDVectorizer to extract the important features from the text conversation. The extracted features from the movie reviews are subjected to all the classifiers to analyze the performance of the emotion prediction. The scripting is written in Python and implemented with Spyder in Anaconda Navigator IDE, and the experimental results show that the decision tree classifier with Hash Vectorizer is exhibiting 98.7% of accuracy toward predicting the sentiment from the movie reviews.
Cyberbullying Detection in Social Media Using Supervised ML and NLP Techniques

With the increase in the number of social media applications, cyberbullying is growing day by day. Though individuals are expressively attached in out of community Internet platform, these chances stance a major threat, such as cyberattacks, including cyberbullying. In the existing system, we have more machine learning models to overcome the cyberattacks, even though it does not hold all the security metrics. To overcome this problem, we have developed a machine learning model to identify abusive and non-abusive tweets with the source of Twitter data. Consequently, we also established a model that automatically detects bullying on the social media application. There are many ways to detect bullying on social media, but many are text-based and give minimal accuracy on the Twitter dataset. In this system, we have explored about 20,001 unique tweets from Twitter. Finally, in the proposed model, the data is given into two classifiers such as SVM and Naive Bayes which are used to train and test destructive social media content. Experimental results are achieved in a better manner to detect the unwanted tweets from the application. The proposed model is a better model that gives good quality precision, and thus, this model can be used for future works.
A Dynamic Programming Bayesian Network Structure Learning Algorithm Based on Mutual Information

Existing Bayesian network (BN) structure learning algorithms based on dynamic programming have high computational complexity and are difficult to apply to large-scale networks. Therefore, this paper proposes a Dynamic Programming BN structure learning algorithm based on Mutual Information, the MIDP (Dynamic Programming Based on Mutual Information) algorithm. The algorithm uses mutual information to build the maximum spanning tree and [Formula: see text]-order matrix, and introduces a penalty coefficient d based on the matrix-based node removal strategy, so as to reduce the number of scoring calculations and time consumption of the algorithm. Simulation results show that, compared with DP, SMDP and MEDP algorithms, the MIDP algorithm can reduce the calculation times and time consumption of algorithm scores while maintaining the accuracy of the algorithm when selecting the appropriate [Formula: see text] value.
And She Be like ‘Tenemos Frijoles en la Casa’: Code-Switching and Identity Construction on YouTube

This empirical case study explores the (co-)construction and negotiation of identities through code-switching (CS) as found on the video-sharing platform YouTube, disentangling the complexities of social practice anchored in a discursive online environment. Drawing on a YouTube comment corpus and paying special attention to the socio-technical affordances of the platform, the study examines users’ positioning practices and metapragmatic replies in response to a culturally themed video priming discussion about LatinX family stereotypes. More specifically, it analyses how users discursively position themselves vis-à-vis the video and which linguistic strategies they exploit to (co-)construct and negotiate their cultural identity. Focusing on interrelated positioning devices such as code-choice, identity labels and quoting, this contribution proposes a multi-level model of analysis to account for the dynamic interplay between CS practices and identity construction in a heterogeneous online space. Following a social-constructivist approach to identity, CS is shown to reinforce in-group solidarity rooted in the shared experience and discussion of LatinX culture and provides evidence of a sense of togetherness in an emerging community of practice.
Innovation of French Interpretation Teaching Mode in the New Liberal Arts Environment

The status of French as a language has improved to some extent in recent years as a result of the exchange and blending of cultures from various nations. Each language, including French, must undergo a protracted process in order to become more widely spoken. It is essential to develop top-notch French interpreters in order to lower the barriers preventing cross-border communication. The teaching of French interpretation has recently received more attention in many colleges, but the issue of a single teaching method has long persisted. In recent years, a new approach to teaching the liberal arts has emerged. Its main goal is to implement comprehensive interdisciplinary teaching by fusing contemporary information technology with traditional liberal arts instruction. This paper conducted a cutting-edge study on the teaching method of French interpretation against the backdrop of the new liberal arts in order to alter the teaching strategy and increase teaching effectiveness. In this paper, an intelligent interpretation teaching system was constructed using information technology. Through text classification algorithms, it incorporated and optimized teaching resources. According to the experimental findings, the teaching process was optimized, and the teaching efficiency increased by 7.94% when French interpretation was taught against a backdrop of new liberal arts.
Research on Organization Design of College Chinese Teaching under Big Data Environment

The pivotal and most important technology point of the application of big data in modern network teaching is the establishment of teaching platform. Based on Hadoop big data technology, this paper establishes a big data platform designed by Chinese teaching institutions in colleges and universities. The teaching system not only realizes the diversified development of network teaching but also realizes the discovery of teaching platform users' needs and the push of resources. In addition, it is found that a small number of people do not have a high understanding of the teaching mode of big data through extensive studies, which also shows that traditional teaching is still lagging behind in big data education. However, on the whole, the Chinese teaching model in universities based on the big data environment deserves further promotion and application in universities.