Reviewerly/exHarmony huggingface dataset:
Optimizing Performance and Satisfaction in Matching and Movement Tasks in Virtual Reality with Interventions Using the Data Visualization Literacy Framework

Virtual reality (VR) has seen increased use for training and instruction. Designers can enable VR users to gain insights into their own performance by visualizing telemetry data from their actions in VR. Our ability to detect patterns and trends visually suggests the use of data visualization as a tool for users to identify strategies for improved performance. Typical tasks in VR training scenarios are manipulation of 3D objects (e.g., for learning how to maintain a jet engine) and navigation (e.g., to learn the geography of a building or landscape before traveling on-site). In this paper, we present the results of the RUI VR (84 subjects) and Luddy VR studies (68 subjects), where participants were divided into experiment and control cohorts. All subjects performed a series of tasks: 44 cube-matching tasks in RUI VR and 48 navigation tasks through a virtual building in Luddy VR (all divided into two sets). All Luddy VR subjects used VR gear; RUI VR subjects were divided across three setups: 2D Desktop (with laptop and mouse), VR Tabletop (in VR, sitting at a table), and VR Standup (in VR, standing). In an intervention called "Reflective phase," the experiment cohorts were presented with data visualizations, designed with the Data Visualization Literacy Framework (DVL-FW), of the data they generated during the first set of tasks before continuing to the second part of the study. For Luddy VR, we found that experiment users had significantly faster completion times in their second trial (p = 0.014) while scoring higher in a mid-questionnaire about the virtual building (p = 0.009). For RUI VR, we found no significant differences for completion time and accuracy between the two cohorts in the VR setups; however, 2D Desktop subjects in the experiment cohort had significantly higher rotation accuracy as well as satisfaction (p(rotation) = 0.031, p(satisfaction) = 0.040).
Facial Expression Recognition using Machine Learning models in FER2013

Facial expression recognition aims to classify human expression using the designed algorithm. Recently, the popular FER2013 dataset suffered several bottlenecks for learning a good model. In this paper, we aim to design a data augmentation method to solve the issues in FER2013. Furthermore, we compare four different machine learning methods, including SVM, AlexNet, VGG, and ResNet. The experiments on FER2013 show VGG16 model is the best model for facial expression recognition, and our data augmentation achieves a significant performance promotion in FER2013. Our method provides an in-depth research on image classification, which can be widely applied in most of real-world applications.
Training Method and Device of Chemical Industry Chinese Language Model Based on Knowledge Distillation

Recent advances in pretraining language models have obtained state-of-the-art results in various natural language processing tasks. However, these huge pretraining language models are difficult to be used in practical applications, such as mobile devices and embedded devices. Moreover, there is no pretraining language model for the chemical industry. In this work, we propose a method to pretrain a smaller language representation model of the chemical industry domain. First, a huge number of chemical industry texts are used as pretraining corpus, and nontraditional knowledge distillation technology is used to build a simplified model to learn the knowledge in the BERT model. By learning the embedded layer, the middle layer, and the prediction layer at different stages, the simplified model not only learns the probability distribution of the prediction layer but also learns the embedded layer and the middle layer at the same time, to acquire the learning ability of BERT model. Finally, it is applied to the downstream tasks. Experiments show that, compared with the current BERT model distillation method, our method makes full use of the rich feature knowledge in the middle layer of the teacher model while building a student model based on the BiLSTM architecture, which effectively solves the problem that the traditional student model based on the transformer architecture is too large and improves the accuracy of the language model in the chemical domain.
Entangled Intelligent Driving: Relations with Automated Cars

As machines become increasingly intelligent, the HCI community is presented with new challenges regarding methods to capture and understand user experience (UX). In the case of autonomous driving (AD), this involves new scenarios where humans and intelligent vehicles need to act together in real-life traffic situations with other road users. This article responds to this context by 1) outlining a longitudinal design ethnography method whereby participants drove semiautonomous cars in their everyday environments to capture such human-machine relations in real-life settings, 2) demonstrating the complexities of the relations between humans and AD vehicles, 3) engaging theories of socio-materiality and entanglement to understand the human-machine relations of AD cars, and 4) identifying anticipatory experiences that emerge from these relations and their implications for informing UX design.
Secure Real-Time Artificial Intelligence System against Malicious QR Code Links

Recently, hackers intend to reproduce malicious links utilizing several ways to mislead users. They try to control victims’ machines or get their data remotely by gaining access to private information they use via cyberspace. QR codes are two-dimensional barcodes with the capacity to encode various data types and can be viewed by digital devices, such as smartphones. However, there is no approved protocol in QR code generation; therefore, QR codes might be exposed to several questionable attacks. QR code attacks might be perpetrated using barcodes, and there are some security countermeasures. Some of these solutions are restricted to malicious link detection techniques with knowledge of cryptographic methods. Therefore, this study aims to detect malicious links embedded in 1D (linear) and 2D (QR) codes. A cybercrime attack was proposed based on barcode counterfeiting that can be used to perform online attacks. A dataset of 100000 malicious and benign URLs was created via several resources, and their lexical features were obtained. Analyses were conducted to illustrate how different features and users deal with online barcode content. Several artificial intelligence models were implemented. A decision tree classifier was identified as the most suitable model for identifying malicious URLs. Our outcomes suggested that a secure artificial intelligence barcode scanner (BarAI) is recommended to detect malicious barcode links with an accuracy of 90.243%.
Improving Streaming Cryptocurrency Transaction Classification via Biased Sampling and Graph Feedback

We show that knowledge of wallet addresses from the current time state of a blockchain network, such as Bitcoin, increases the performance of illicit activity detection. Based on this finding we introduce two new methods for the sampling of classifier training data so that precedence is given to transaction information from the recent past and the current time state. This sampling enables streaming classification in which a decision on the class of a transaction needs to be made based on data seen to date. Our new approach provides insight into how the dynamics of the blockchain network plays a central role in the detection of illicit transactions, and is independent of the classifier choice. Our proposed sampling methods enable graph convolution network (GCN) and random forest (RF) classifiers to better adapt to changes in the network due to significant events, such as the closure of a large ‘Darknet’ marketplace. We introduce Graphlet spectral correlation analysis for exposing the effect of such network re-organisation due to major events. Finally, based on our analysis, we propose a new two-stage random forest classifier that feeds back intermediate predictions of neighbours to improve the classification decision. Our methodology enables practical streaming classification, even in the scenario of very limited information on the feature space of each transaction.
Augmentation of a Virtual Reality Environment Using Generative Adversarial Networks

Building a dataset to train machine and deep learning models has become a challenging task ever since more complex architectures and deeper neural networks have begun to be utilized with higher frequency, especially across computer vision applications in various fields. This hardship is prompted by the lack of data readiness and the difficulty associated with gathering enough of it, in order to avoid unbalanced datasets and overfitting during training. Occasionally, simulation environments are employed as a result of the unavailability of time and resources to collect real-world data. We present an application of Generative Adversarial Network (GAN) data augmentation in a vision-based planetary rover localisation application. Due to the absence of extra-terrestrial data, the system was trained on several artificially-built lunar terrains inside a Virtual Reality (VR) simulation environment. In order to further enhance our dataset, a GAN was utilized to augment the data retrieved from the simulation, while simultaneously averting a reduction in image quality, which is common with other forms of data augmentation. Despite the fact that GAN was trained with only a small number of images, it was able to recreate areas of the VR environment with high precision. Additionally, training the system with the GAN enhanced data improved the output compared to employing only basic data augmentation techniques.
Statistical modelling of factor analysis to set causals of hybrid learning success during Covid-19 lockdown

Abstract Around the world, Covid-19 outbreak caused a sudden and forced migration from face-to-face education to online education generating an unprecedented phenomenon in the history of education. In Mexico, the most affected Education level was Basic Public Education, the least unprepared while Private Higher Education has experienced by years alternative models using technology. Despite, around the world, new findings arose evidencing that students could require emotional support under the confinement due to the extended lockdown and an intense effort to follow their new educative plans revealing behavioral issues as success factors of that extended online education in the emergent strategy. Based on a statistical model of exploratory factor analysis of data applied to Freshman and Sophomore engineering students, this work presents a roadmap of statistical modelling and testing for the analysis of several dimensions of more effective causal in the success of the forced online education paradigm implementation. Obtaining a Cronbach α value of 0.817, it points to meaningful internal reliability to discriminate and rearrange those causal and dimensions into a more comprehensive education ecosystem.
Evaluating user cognition of network diagrams

Edges crossing and nodes overlapping have a significant effect on the users’ recognition and comprehension of network diagrams. In this study, we propose a visual evaluation method for users’ cognition of network diagrams. First, this method carries out a set of cognitive experiments to collect the user’s cognitive performance that affects the variables, including accuracy and response time. The user’s pupil diameter is measured through an eye tracker to reflect their cognitive load. Second, the significance test points out the visual features as independent variables and then establishes an evaluation regression model. The experimental results show that the number of edges, edge length, node visual interference, and edge occlusion contribute to the evaluation models of response time, and edge occlusion and the number of node connections contribute to the accuracy model. Finally, these evaluation models demonstrate good predictability when assessing users’ cognition of network diagrams and provide practical recommendations for their use.
Hybrid SOM based cross-modal retrieval exploiting Hebbian learning

Lately, cross-modal retrieval has attained plenty of attention due to enormous multi-modal data generation every day in the form of audio, video, image, and text. One vital requirement of cross-modal retrieval is to reduce the heterogeneity gap among various modalities so that one modality's results can be efficiently retrieved from the other. So, a novel unsupervised cross-modal retrieval framework based on associative learning has been proposed in this paper where two traditional SOMs are trained separately for images and collateral text and then they are associated together using the Hebbian learning network to facilitate the cross-modal retrieval process. Experimental outcomes on a popular Wikipedia dataset demonstrate that the presented technique outshines various existing state-of-the-art approaches.
Energy-saving mechanism based on tidal characteristic in computing power network

In the Computing power network, based on the tidal characteristic of computing power nodes, with the goal of reducing the overall network energy consumption, the classification between tidal computing power nodes and non-tidal computing power nodes is proposed. This paper also proposes a new anycast routing algorithm with weighted wakeup routing penalty for tidal computing power nodes in sleep state. The simulation results show that the proposed anycast routing algorithm with tidal node classification and wake-up penalty weighted can effectively reduce energy consumption under the premise of meeting the service delay requirements.
An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks

With rapid advancement in artificial intelligence (AI) and machine learning (ML), automatic modulation classification (AMC) using deep learning (DL) techniques has become very popular. This is even more relevant for Internet of things (IoT)-assisted wireless systems. This paper presents a lightweight, ensemble model with convolution, long short term memory (LSTM), and gated recurrent unit (GRU) layers. The proposed model is termed as deep recurrent convoluted network with additional gated layer (DRCaG). It has been tested on a dataset derived from the RadioML2016(b) and comprises of 8 different modulation types named as BPSK, QPSK, 8-PSK, 16-QAM, 4-PAM, CPFSK, GFSK, and WBFM. The performance of the proposed model has been presented through extensive simulation in terms of training loss, accuracy, and confusion matrix with variable signal to noise ratio (SNR) ranging from -20 dB to +20 dB and it demonstrates the superiority of DRCaG vis-a-vis existing ones.
A Hybrid FPGA-ASIC Delayed Feedback Reservoir System to Enable Spectrum Sensing/Sharing for Low Power IoT Devices ICCAD Special Session Paper

The delayed feedback reservoir (DFR) network is a delay-dynamic architecture that incorporates time in its training and inference. This quality enables DFR networks to proficiently model time series in a scalable architecture with only one nonlinear neuron. Previous studies have highlighted the accuracy and energy efficiency of DFR networks in ASIC implementations; however, these approaches are limited by hardcoded weights and static reservoir architectures. In this work, we introduce a hybrid FPGA-ASIC DFR system that combines the flexibility of a FPGA platform with the energy efficiency of an ASIC. To be specific, the FPGA allows for dynamic reconfiguration and training of the readout weights during runtime, while the ASIC provides an analog activation function for the single neuron. The accuracy and energy consumption of the introduced system is demonstrated for the applications of NARMA10 as well as MIMO spectrum sensing which is a critical component of dynamic spectrum sharing/access for 5G/beyond-5G systems. Results showcase the potential to enable on-board intelligence for future wireless systems, especially for Internet of Things (IoT) devices in low-power environments.
Multi-hierarchy feature extraction and multi-step cost aggregation for stereo matching

Compared with the traditional hand-crafted feature based methods, learning-based stereo matching methods have made great progress in matching accuracy. However, current CNN-based stereo matching methods usually require a lot of time and memory consumption. It is very difficult to achieve the good balance between disparity estimation accuracy and inference speed that is significant to the application in real scenarios. To this end, we propose a accurate and fast stereo matching network (named MMNet), which contains two key modules of M ulti-hierarchy feature extraction and M ulti-step cost aggregation. In order to achieve a good trade-off between better disparity estimation and faster inference speed, a lightweight multi-hierarchy feature extractor is first proposed. This module obtains reliable feature information of different scales through three stable scale hierarchy branches, and outputs multi-step feature flows containing multi-scale fusion information at each step of the highest scale hierarchy branch. Moreover, we also propose a multi-step cost aggregation scheme, which uses shallow features to guide cost aggregation for ensuring a better aggregation effect with a small number of 3D convolutions. The experimental results on SceneFlow, KITTI 2012 and KITTI 2015 datasets show that our proposed network achieves extremely competitive disparity estimation accuracy with fast inference speed.
Understanding and Supporting Self-Tracking App Selection

People often face barriers to selecting self-tracking tools that support their goals and needs, resulting in tools not meeting their expectations and ultimately abandonment. We therefore examine how people approach selecting self-tracking apps and investigate how technology can better support the process. Drawing on past literature on how people select and perceive the features of commercial and research tracking tools, we surface seven attributes people consider during selection, and design a low-fidelity prototype of an app store that highlights these attributes. We then conduct semi-structured interviews with 18 participants to further investigate what people consider during selection, how people select self-tracking apps, and how surfacing tracking-related attributes could better support selection. We find that people often prioritize features related to self-tracking during selection, such as approaches to collecting and reflecting on data, and trial apps to determine whether they would suit their needs. Our results also show potential for technology surfacing how apps support tracking to reduce barriers to selection. We discuss future opportunities for improving self-tracking app selection, such as ways to enhance existing self-tracking app distribution platforms to enable people to filter and search apps by desirable features.
Incentivizing Federated Learning Under Long-Term Energy Constraint via Online Randomized Auctions

Mobile users are often reluctant to participate in federated learning to train models, due to the excessive consumption of the limited resources such as the mobile devices’ energy. We propose an auction-based online incentive mechanism, FLORA, which allows users to submit bids dynamically and repetitively and compensates such bids subject to each user’s long-term battery capacity. We formulate a nonlinear mixed-integer program to capture the social cost minimization in the federated learning system. Then we design multiple polynomial-time online algorithms, including a fractional online algorithm and a randomized rounding algorithm to select winning bids and control training accuracy, as well as a payment allocation algorithm to calculate the remuneration based on the bid-winning probabilities. Maintaining the satisfiable quality of the global model that is trained, our approach works on the fly without relying on the unknown future inputs, and achieves provably a sublinear regret and a sublinear fit over time while attaining the economic properties of truthfulness and individual rationality in expectation. Extensive trace-driven evaluations have confirmed the practical superiority of FLORA over existing alternatives.
A Case Study on Data Classification Approach Using K-Nearest Neighbor

Data mining is the process of obtaining knowledge and information from massive amounts of data. Data mining is mostly used for data analysis. In Data Mining various techniques are used that are association mining, regression, prediction, classification, clustering, etc. Classification is described as the process of identifying a collection of models (or functions) that explain and differentiate data classes and ideas, to use the model for detecting the classes of unknown objects or patterns, whose class designations aren't clear. Classification is a supervised learning problem. That means in machine learning, Classification is the problem of identifying data patterns from the group of patterns according to their characteristics and define which patterns are from which class. Pattern classification can be done by using various classifiers. A classifier is a program that inputs the feature vector of a pattern or data point and assigns it to one of a set of designated classes. The classifiers such as Artificial Neural Network (ANN), k-Nearest Neighbor (k-NN) classifier, Support Vector Machine (SVM), etc. are used for pattern classification purposes. Focusing on the classification technique of data mining, in this research work the accuracy of k-NN using three datasets from the UCI machine learning library is presented. The main goal of this paper is to provide a review to find out the accuracy of the k-NN classification technique using different datasets in data mining. The k-NN classifier is a simple but efficient approach used for classification in research.
A performance tunable CPIR-based privacy protection method for location based service

Location-based service has become one of the essential parts of our daily lives, which raises a problem: how to preserve location privacy. Computational Private Information Retrieval (CPIR) protocol, which does not need a trusted third party, allows users to retrieve data from a service provider without revealing users' query information. However, the CPIR protocol scans the entire data space, which causes high costs and limits the scale of CPIR-based applications. To address this problem, we propose a performance tunable Computational Private Information Retrieval (PTCPIR) model. We divide data space into subspaces and scan a subset of subspaces to achieve better performance while sacrificing some privacy. By tuning the percentage of scanned subspaces, we can achieve a tradeoff between performance and privacy. We design a hierarchical encrypted secure index to support data users to retrieve subspace information safely and efficiently, which facilitates privacy-preserving subspace information retrieval. Furthermore, we propose a novel subspace information retrieval method where the confused query keyword sequence makes servers unable to count query probability. Hence access pattern privacy is protected. Experiments on Sequoia and synthetic datasets demonstrate that our PTCPIR model provides a tunable tradeoff between performance and privacy with high efficiency and good scalability.