Reviewerly/exHarmony huggingface dataset:
An Efficient Manifold Density Estimator for All Recommendation Systems

Most current neural recommender systems for session-based data cast recommendations as a sequential or graph traversal problem, applying recurrent networks (LSTM/GRU) or graph neural networks (GNN). This makes the systems increasingly elaborate in order to model complex user/item connection networks and results in poor scalability to large item spaces and long item view/click sequences. Instead on focusing on the sequential nature of session-based recommendation, we propose to cast it as a density estimation problem on item sets. We introduce EMDE (Efficient Manifold Density Estimator) - a method utilizing arbitrary vector representations with the property of local similarity to succinctly represent smooth probability densities on Riemannian manifolds using compressed representations we call sketches. Within EMDE, session behaviors are represented with weighted item sets, largely simplifying the sequential aspect of the problem. Applying EMDE to both top-k and session-based recommendation settings, we establish new state-of-the-art results on multiple open datasets in both uni-modal and multi-modal settings. EMDE has also been applied to many other tasks and areas in top machine learning competitions involving recommendations and graph processing, taking the podium in KDD Cup 2021, WSDM Challenge 2021, and SIGIR eCom Challenge 2020. We release the code at https://github.com/emde-conf/emde-session-rec.
Augmented Reality Research and Applications in Education

Augmented reality is defined as the technology in which virtual objects are blended with the real world and also interact with each other. Although augmented reality applications are used in many areas, the most important of these areas is the field of education. AR technology allows the combination of real objects and virtual information in order to increase students’ interaction with physical environments and facilitate their learning. Developing technology enables students to learn complex topics in a fun and easy way through virtual reality devices. Students interact with objects in the virtual environment and can learn more about it. For example; by organizing digital tours to a museum or zoo in a completely different country, lessons can be taught in the company of a teacher as if they were there at that moment. In the light of all these, this study is a compilation study. In this context, augmented reality technologies were introduced and attention was drawn to their use in different fields of education with their examples. As a suggestion at the end of the study, it was emphasized that the prepared sections should be carefully read by the educators and put into practice in their lessons. In addition it was also pointed out that it should be preferred in order to communicate effectively with students by interacting in real time, especially during the pandemic process.
Clustering

Clustering is an unsupervised data science technique where the records in a dataset are organized into different logical groupings. The data are grouped in such a way that records inside the same group are more similar than records outside the group. Clustering has a wide variety of applications ranging from market segmentation to customer segmentation, electoral grouping, web analytics, and outlier detection. Clustering is also used as a data compression technique and data preprocessing technique for supervised tasks. Many different data science approaches are available to cluster the data and are developed based on proximity between the records, density in the dataset, or novel application of neural networks. k-Means clustering, density clustering, and self-organizing map techniques are reviewed in the chapter along with implementations using RapidMiner.
A novel clustering-based purity and distance imputation for handling medical data with missing values

Nowadays, people pay increasing attention to health, and the integrity of medical records has been put into focus. Recently, medical data imputation has become a very active field because medical data usually have missing values. Many imputation methods have been proposed, but many model-based imputation methods such as expectation–maximization and regression-based imputation based on the variables data have a multivariate normal distribution, which assumption can lead to biased results. Sometimes, this becomes a bottleneck, such as computationally more complex than model-free methods. Furthermore, directly removing instances with missing values has several problems, and it is possible to lose the important data, produce ineffective research samples, and cause research deviations. Therefore, this study proposes a novel clustering-based purity and distance imputation method to improve the handling of missing values. In the experiment, we collected eight different medical datasets to compare the proposed imputation methods with the listed imputation methods with regard to the results of different situations. In imputation measures, the area under the curve (AUC) is used to evaluate the performance of the imbalanced class datasets in MAR and MCAR experiments, and accuracy is applied to measure its performance of the balanced class in MNAR experiment. Finally, the root-mean-square error (RMSE) is also used to compare the proposed and the listing imputation methods. In addition, this study utilized the elbow method and the average silhouette method to find the optimal number of clusters for all datasets. Results showed that the proposed imputation method could improve imputation performance in the accuracy, AUC, and RMSE of different missing degrees and missing types.
Escaping unsustainable digital interactions: Toward “more meaningful” and “moderate” online experiences

Growing and even excessive use of digital technology has unquestionably fuelled demand for digital devices and online services leading to a wide range of societal and environmental impacts. In sustainability terms, ICT as a whole is estimated to produce up to nearly 4% of global greenhouse gas emissions. As presumed responsible innovators, the HCI community should now consider design strategies that will reduce use and demand for digital technology for the good of both its users and the planet—strategies perhaps even seen as retrogressive in an era where digital technology is constantly implicated in innovation and economic growth. Prior work has noted the potential to design “more moderate” interactions for sustainability, simultaneously addressing negative societal impacts on users’ wellbeing, relationships, productivity at work, and privacy. In this paper, we explore how we may design intentionally moderate digital interactions that retain our participants’ “more meaningful” experiences. We report on the outcomes of two design workshops to uncover experiences of meaningful device and service use, to inform practical designs for ‘moderate and meaningful’ interaction. From this, we offer design recommendations that aim to address the multiple negative impacts that digital technology can create, and discuss the possible barriers to these designs.
Data Cleaning and AutoML: Would an Optimizer Choose to Clean?

Abstract Data cleaning is widely acknowledged as an important yet tedious task when dealing with large amounts of data. Thus, there is always a cost-benefit trade-off to consider. In particular, it is important to assess this trade-off when not every data point and data error is equally important for a task. This is often the case when statistical analysis or machine learning (ML) models derive knowledge about data. If we only care about maximizing the utility score of the applications, such as accuracy or F1 scores, many tasks can afford some degree of data quality problems. Recent studies analyzed the impact of various data error types on vanilla ML tasks, showing that missing values and outliers significantly impact the outcome of such models. In this paper, we expand the setting to one where data cleaning is not considered in isolation but as an equal parameter among many other hyper-parameters that influence feature selection, regularization, and model selection. In particular, we use state-of-the-art AutoML frameworks to automatically learn the parameters that benefit a particular ML binary classification task. In our study, we see that specific cleaning routines still play a significant role but can also be entirely avoided if the choice of a specific model or the filtering of specific features diminishes the overall impact.
Text proposals with location-awareness-attention network for arbitrarily shaped scene text detection and recognition

Unlike existing models that aim to address the challenge of scene text detection and recognition separately, the proposed work aims to address both text detection and recognition using a single architecture to deal with arbitrarily oriented/shaped text. Towards this aim, a novel Text Proposal with Location-Awareness-Attention Network (TPLAANet) for arbitrarily oriented/shaped text detection and recognition is proposed. For text detection, the proposed method explores central mask prediction for locating text instances, bounding box regression branch for tight bounding boxes, and mask branch for accurate positions of arbitrarily oriented/shaped text instances. For text recognition, the proposed method explores character information using a Location-Awareness-Attention Network (LAAN), which learns a two-dimensional attention weight and improves the recognition performance. To test the efficacy of the proposed model, we consider the commonly used horizontal and multi-oriented natural scene text datasets, namely, ICDAR2013, ICDAR2015, and the arbitrarily shaped scene text datasets, namely, Total-Text and CTW1500 for experimentation. Experimental results are provided to validate the effectiveness of the proposed method. The code is available at: https://codeocean.com/capsule/5666319/tree/v1.
Proof by Earnestness (PoE) to determine the authenticity of subjective information in blockchains - application in supply chain risk management

Blockchain is being used in various global supply chains with its ever-increasing maturity and popularity. However, in the presence of subjective information that does not have a digital footprint, blockchain application is a grey area. This is due to the difficulty in confirming the authenticity or legitimacy of information before achieving consensus on it using existing mechanisms such as Proof of Work (PoW), Proof of Authority (PoA) or Proof of Stake (PoS). In this paper, we attempt to address this issue. Specifically, we propose the Proof by Earnestness (PoE) consensus mechanism that determines the subjective information’s truthfulness before further processing and formalising in blockchains. We consider supply chain risk management (SCRM) as our application area due to the vast amount of available subjective information.
Adaptive local exposure based region determination for non-uniform illumination and low contrast images

Non-uniform illumination and low contrast is an issue major issue for critical and sensitive images. Recently, researchers have shown an interest in solving this issue by proposing local contrast image enhancement methods to improve the contrast of certain regions of the non-uniform illumination and low contrast images. However, most techniques concentrate on developing a specific algorithm to separately enhance only two main regions of the image such as over-exposed and under-exposed regions. Those techniques faced several issues i.e., the pixels are wrongly classified and thus make the enhancement inefficient to solve non-uniform illumination issues as well as poor contrast. These techniques are not robust, and they are specifically designed to solve a specific problem at one time. Also, these techniques have the limitation of measuring the region determination accuracy. These limitations have motivated this study to propose a new technique to solve the above-mentioned problems. An Adaptive Local Exposure Based Region Determination (ALEBRD) method is proposed to determine the image into three specific regions based on the contrast distribution, namely under-exposed, over-exposed, and well-exposed regions. The results of the proposed ALEBRD method produced better region determination performance than the other state-of-the-art methods. This research aims to determine and separate each region individually as a pre-processing stage. The prominent result from this study can be essential for the enhancement process also it can be extended to be applied in further processing such as segmentation and feature extraction. This research also proposed a new novel method to measure the region determination accuracy named Region Determination Accuracy Measurement System (RDAMS).
Effect of financial knowledge and information behavior on sustainable investments: evidence from India

This study aims to explore the relationship between information behavior and sustainable investments in the Indian context. First, a conceptual model is developed and the relationship between information access and subjective financial knowledge and information behavior of investors. Then, after checking the psychometric properties of the survey instrument, data collected from 301 investors in the southern part of India was analyzed using structural equation modeling (Smart PLS software). The results indicate that (i) information access and subjective financial knowledge are positively associated with information behavior, (ii) information behavior is positively related to risk propensity, (iii) risk propensity and cognitive biases are positively related to sustainable investments, and (iv) risk propensity mediates the relationship between information behavior and sustainable investments. Finally, the implications for investors and practicing managers are discussed.
Secure and effective color image tampering detection and self restoration using a dual watermarking approach✩

Image watermarking represents a powerful tools for addressing multimedia security and digital rights issues. In this work, a secure color image watermarking scheme is proposed for image authentication and tamper detection applications (denoted as RFW-AT). This scheme utilizes two watermarks: a) a robust watermark (RW) that is embedded into different DWT subbands to confuse attackers and make the watermark resistant to popular image processing attacks, and, b) a fragile watermark (FW), that is embedded using the 2-LSBs scenario with incorporating inpainting as well as bilateral filtration as a post-processing stage for noise suppression and edge content preservation. The experimental results have shown that the RFW-AT scheme exhibited high performance, in terms of the robustness, tamper detection and content recovery.
Three-dimensional shape and deformation measurement on complex structure parts

Stereo digital image correlation technique (stereo-DIC or 3D-DIC) has been widely used in three-dimensional (3D) shape and deformation measurement due to its high accuracy and flexibility. But it is a tough task for it to deal with complex structure components because of the severe perspective distortion in two views. This paper seeks to resolve this issue using a single-camera system based on DIC-assisted fringe projection profilometry (FPP). A pixel-wise and complete 3D geometry of complex structures can be reconstructed using the robust and efficient Gray-coded method based on a FPP system. And then, DIC is just used to perform the temporal matching and complete full-field pixel-to-pixel tracking. The in- and out-of-plane deformation are obtained at the same time by directly comparing the accurate and complete 3D data of each corresponding pixel. Speckle pattern design and fringe denoising methods are carefully compared and chosen to simultaneously guarantee the measuring accuracy of 3D shape and deformation. Experimental results demonstrate the proposed method is an effective means to achieve full-field 3D shape and deformation measurement on complex parts, such as honeycomb structure and braided composite tube, which are challenging and even impossible for the traditional stereo-DIC method.
AI framework with computational box counting and Integer programming removes quantization error in fractal dimension analysis of optical images

• A novel AI framework for systematic fractal dimension value estimation. • Integer programming-based box counting method that eliminates quantization error. • Incorporation of image preprocessing, optimization, and regression analysis. • Box size combination selection that yields the best regression metrics. • Application to fields such as neuroscience, plant biology, and interfacial science. Because many Chemical Engineering phenomena and processes involve distinctive shapes and structures, fractal dimension (FD) analysis is of great interest to the Chemical Engineering community due to its utility in providing a statistical index of shape complexity, which not only quantifies image structure but also helps explain functional properties. Past box counting (BC) methods for estimation of FDs are inconsistent due to quantization error (QE) introduced from image rotation and translation. In this work, we propose a systematic and automatic artificial intelligence (AI) framework that consistently estimates FDs of relevance to different fields of Chemical Engineering without QE by integrating image preprocessing, set-covering optimization-based BC, and regression analysis. As a result of the deterministic optimization technique, FD estimations remain consistent for all images regardless of image rotation and translation. The results of image datasets obtained from fields such as interfacial science, biomedical engineering, human anatomy, among others, demonstrate efficient box size specification to maximize regression effectiveness and FD calculation accuracy for a variety of images. Overall, the proposed AI framework offers a new means of estimating FD accurately and efficiently for optical images of interest to the Chemical Engineering community.
Evaluating Automatic Body Orientation Detection for Indoor Location from Skeleton Tracking Data to Detect Socially Occupied Spaces Using the Kinect v2, Azure Kinect and Zed 2i

Analysing the dynamics in social interactions in indoor spaces entails evaluating spatial-temporal variables from the event, such as location and time. Additionally, social interactions include invisible spaces that we unconsciously acknowledge due to social constraints, e.g., space between people having a conversation with each other. Nevertheless, current sensor arrays focus on detecting the physically occupied spaces from social interactions, i.e., areas inhabited by physically measurable objects. Our goal is to detect the socially occupied spaces, i.e., spaces not physically occupied by subjects and objects but inhabited by the interaction they sustain. We evaluate the social representation of the space structure between two or more active participants, so-called F-Formation for small gatherings. We propose calculating body orientation and location from skeleton joint data sets by integrating depth cameras. The body orientation is derived by integrating the shoulders and spine joint data with head/face rotation data and spatial-temporal information from trajectories. From the physically occupied measurements, we can detect socially occupied spaces. In our user study implementing the system, we compared the capabilities and skeleton tracking datasets from three depth camera sensors, the Kinect v2, Azure Kinect, and Zed 2i. We collected 32 walking patterns for individual and dyad configurations and evaluated the system's accuracy regarding the intended and socially accepted orientations. Experimental results show accuracy above 90% for the Kinect v2, 96% for the Azure Kinect, and 89% for the Zed 2i for assessing socially relevant body orientation. Our algorithm contributes to the anonymous and automated assessment of socially occupied spaces. The depth sensor system is promising in detecting more complex social structures. These findings impact research areas that study group interactions within complex indoor settings.
Decision making for large-scale multi-armed bandit problems using bias control of chaotic temporal waveforms in semiconductor lasers

Decision making using photonic technologies has been intensively researched for solving the multi-armed bandit problem, which is fundamental to reinforcement learning. However, these technologies are yet to be extended to large-scale multi-armed bandit problems. In this study, we conduct a numerical investigation of decision making to solve large-scale multi-armed bandit problems by controlling the biases of chaotic temporal waveforms generated in semiconductor lasers with optical feedback. We generate chaotic temporal waveforms using the semiconductor lasers, and each waveform is assigned to a slot machine (or choice) in the multi-armed bandit problem. The biases in the amplitudes of the chaotic waveforms are adjusted based on rewards using the tug-of-war method. Subsequently, the slot machine that yields the maximum-amplitude chaotic temporal waveform with bias is selected. The scaling properties of the correct decision-making process are examined by increasing the number of slot machines to 1024, and the scaling exponent of the power-law distribution is 0.97. We demonstrate that the proposed method outperforms existing software algorithms in terms of the scaling exponent. This result paves the way for photonic decision making in large-scale multi-armed bandit problems using photonic accelerators.
Transformer Network Intelligent Flight Situation Awareness Assessment Based on Pilot Visual Gaze and Operation Behavior Data

Situational awareness is the ability of pilots to master flight status, which is of great significance to aviation flight safety and flight effect. According to the information processing model, the pilot’s main steps of processing information are feeling, perception and execution. There are many problems in situation awareness analysis guided by visual gaze, such as large analysis deviation and high delay due to various influencing factors and complex characteristics. In order to solve this problem, this paper proposes a situation awareness assessment method based on artificial intelligence neural network and integrating visual gaze and flight control. First, this paper carries out simulated flight training experiments for flight cadets, and collects the data of eye movement, line of sight tracking, flight control and flight parameters of pilot cadets. Then, aiming at the flight subjects, a situation awareness analysis method based on events is established, and the situation awareness state in the experiment is evaluated and analyzed through the flight parameter data. Then, the visual gaze and flight control data are sliced in the unit of situational awareness events, and the data set is constructed. Finally, this paper designs a multi-channel sequence data classification and analysis model based on transformer, in which the situation awareness characteristics of visual gaze and operation behavior are analyzed through the attention mechanism. The experimental results show that the accuracy of situation awareness classification of the designed neural network model to the experimental data set is 96%, and can classify and evaluate the pilot’s situation awareness state in 5[Formula: see text]s.