Reviewerly/exHarmony huggingface dataset:
Assessing the Impact of Mixed Reality Immersion on Presence and Embodiment

When placed inside an immersive virtual simulation, subjects will tend to experience the feeling of being ’really there’ and to respond realistically to their environment, forgetting that it is not real. This behaviour is observed when subjects experience a high sense of presence, the sensation of being in a real place and that the scenario being depicted to them is real. Here we present an experiment designed to evaluate the impact of different levels of immersion, and of different blending of virtual and real objects and body representations, on participant’s subjective experience. Presence is evaluated with an innovative method combining the random introduction of breaks-in-presence (BiP) with a rapid decision-making test. Results show that the level of immersion impacts both the Sense of Presence (SoP) and the Sense of Embodiment (SoE), that the BiP has a limited impact on the SoE without breaking it, and that the level of confidence in the decision test correlates with both the SoP and the SoE.
Eye Blink Rate Based Detection of Cognitive Impairment Using In-the-wild Data

Investigating automatic methods for the early detection of dementia and related conditions that cause cognitive impairment is an area of growing interest. Video processing could play a role by providing a non-invasive and low-cost alternative to current expensive assessments. For this to be successful it is crucial that approaches are robust to in-the-wild challenges. In this paper, visual cues, related to the eye blink rate (EBR), are investigated to quantify the early phase of neurodegenerative disorder (ND) and mild cognitive impairment (MCI) as well as functional memory disorder (FMD; problems with memory not related to neurodegenerative disorder). This paper aims to improve the detection of ND and MCI by investigating a novel approach to calculating the EBR that is more robust to in-the-wild challenges. An in-house dataset with 18 participants is used. The EBR is calculated from eye landmarks extracted using two libraries (Dlib and Openface). To mitigate issues observed in the noisy, in-the-wild recordings, a multiple threshold approach for EBR detection is proposed. It involves generating multiple thresholds for identifying a blink, where a threshold is used to determine whether an eye is open or closed. Several supervised machine learning approaches are used for automatic classification. The results show that accuracy measures of 89% and 78% are achieved using Dlib and OpenFace data, respectively, when distinguishing between three conditions with ND, MCI and FMD.
MovieDIRec: Drafted-Input-Based Recommendation System for Movies

In a DNN-based recommendation system, the input selection of a model and design of an appropriate input are very important in terms of the accuracy and reflection of complex user preferences. Since the learning of layers by the goal of the model depends on the input, the more closely the input is related to the goal, the less the model needs to learn unnecessary information. In relation to this, the term Drafted-Input, defined in this paper, is input data that have been appropriately selected and processed to meet the goals of the system, and is a subject that is updated while continuously reflecting user preferences along with the learning of model parameters. In this paper, the effects of properly designed and generated inputs on accuracy and usability are verified using the proposed systems. Furthermore, the proposed method and user–item interaction are compared with state-of-the-art systems using simple embedding data as the input, and a model suitable for a practical client–server environment is also proposed.
Image Denoising: Pre-processing for enhanced subsequent CAD Analysis

In the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century when the world is developing at a rapid speed, there is a dire need of having high resolution images as extent of human activities are growing at a rapid pace. However, changes in environmental condition makes a difficult task for making an image completely denoisable for the researchers. With the noise in the image, it is difficult to get a clear image or video quality. Due to this aspect, image denoising plays a vital role as it not only produces appealing images but however recovers the degraded values of pixels. Image denoising is a technique that is used to remove the noises which cause blur image/ distorted image. This research article attempts to study about the image denoising and various algorithms/techniques of image denoising and its application in various domains.
A Certificateless Searchable Public Key Encryption Scheme for Multiple Receivers

Security, efficiency and availability are three key factors that affect the application of searchable encryption schemes in mobile cloud computing environments. In order to meet the above characteristics, this paper proposes a certificateless public key encryption with a keyword search (CLPEKS) scheme. In this scheme, a CLPEKS generation method and a Trapdoor generation method are designed to support multiple receivers to query. Based on the elliptic curve scalar multiplication, the efficiencies of encrypting keywords, generating Trapdoors, and testing are improved. By adding a random number factor to the Trapdoor generation, the scheme can resist the internal keyword guessing attacks. Under the random oracle model, it is proved that the scheme can resist keyword guessing attacks. Theoretical analyses and implementation show that the proposed scheme is more efficient than the existing schemes.
Fusion and Visualization Design of Violence Detection and Geographic Video

The fusion of violence detection and geographic video (GeoVideo) helps to strengthen the comprehensive dynamic perception of the objective area and better maintain social security and stability. To address the problem that the surveillance screen is fragmented from the geographic space after the occurrence of an abnormal situation in the surveillance scene by combining the positions of cameras to detect where it occurs, the violence detection and geographic video is fused to implement the visualization design. Firstly, we adopt the action detection algorithm to detect the violent actions in the video, and output the position information of the subject in the image coordinate system; then we map the position information in the image coordinate system to the world coordinate system to realize the mapping of the dynamic information obtained by the deep learning model to the static geographic space; finally, the position information of the subject is automatically marked in the remote sensing image to complete the visualization design. The results show that the fusion and visualization design of violence detection and geographic video can accurately map the location information in the surveillance screen to geographic space, which helps to grasp the global security situation of the surveillance scene.
Syntax Matters! Syntax-Controlled in Text Style Transfer

Existing text style transfer (TST) methods rely on style classifiers to disentangle the text's content and style attributes for text style transfer.While the style classifier plays a critical role in existing TST methods, there is no known investigation on its effect on the TST methods.In this paper, we conduct an empirical study on the limitations of the style classifiers used in existing TST methods.We demonstrate that the existing style classifiers cannot learn sentence syntax effectively and ultimately worsen existing TST models' performance.To address this issue, we propose a novel Syntax-Aware Controllable Generation (SACG) model, which includes a syntaxaware style classifier that ensures learned style latent representations effectively capture the syntax information for TST.Through extensive experiments on two popular TST tasks, we show that our proposed method significantly outperforms the state-of-the-art methods.Our case studies have also demonstrated SACG's ability to generate fluent target-style sentences that preserved the original content.
Individuation Based Usability of a Customized E-commerce Platform

The usability of interfaces is critical in commerce, as it can lead to increased productivity and revenue. However, employees often have different needs from a system based on their role within the company. Hancock, Hancock, and Warm (2009) suggested that applied scientists should embrace person-specific interactions as the world moves ever more toward individual customization. Magento is an open-source e-commerce platform that can be tailored to the needs of respective companies. In conjunction with a retail plant and flower sales company in Clearwater, Florida, we assessed the usability of a custom Magento interface, focusing on each employee’s individual needs in the organization using heuristic evaluation, usability assessment, and structured interviews.
A PKI-based Framework for Establishing Efficient MPC Channels

The Transport Layer Security (TLS) protocol is a fundamental building block for ensuring security on Internet. It provides an easy to use framework for the purposes of establishing an authenticated and secure channel between two parties that have never physically met. Nevertheless, TLS only provides a simple cryptographic functionality compared to more advanced protocols such as protocols for secure multiparty computation (MPC).
GAN Based Try-On System: Improving CAGAN Towards Commercial Viability

In the era of disruptive innovations where technological leaps in internet-based applications along with advanced Artificial intelligence and Computing are combining to produce consumer-based applications, there is a notable shift of the fashion retail industry towards e-commerce. However, it still falls behind the traditional experience of shopping which offers customers not just a more visual and tactile stimuli, but also assurance of their purchase. This gap in experience can therefore be attributed to the lack of online try on solutions with online retail industry giants. This generates a disconnect between the retailers and consumers, making them hesitant and hence, skeptical about ordering via e-commerce. Further, consumer misjudgment of products results in returns, which are undesirable for online retailers with thin margins. We in this paper propose a prototypical solution to this problem by introducing a photorealistic virtual try-on solution while ensuring optimization and minimalistic use of resources as required by industrial standards. We present a Generative Adversarial Network (GAN) built upon the existing CAGAN architecture, with an improved generator based on a reworked U-Net generator architecture and a combination of instance and batch normalization for generator and discriminator respectively. Further, a combination of ReLu and Leaky ReLu activation functions have been utilized for the generator along with hyperparameter optimization of the network. In our visual analysis of the generated images, comparing with the CAGAN approach on Zalando dataset, we have achieved a superior quality in shorter training duration of 13 hours on a smaller dataset of 10,167 images.
Dual-Target Cross-Domain Bundle Recommendation

The conventional Cross-Domain Recommendation(CDR) approaches are single-target that focus only on improving the recommendation performance of the target domain. To enhance the performance of both source and target domains, dual-target CDR approaches have been proposed. However, existing approaches considered recommending only a single item to users. Besides, they cannot effectively combine the features on both domains. To this end, we propose a novel bundle graphical and attentional model named Dual-Target Cross-Domain Bundle Recommendation(DT-CDBR). Specifically, we first integrate useritem, user-bundle interaction, and bundle-item affiliation into a heterogeneous graph. Then, we assign different weights for both domains via an attention mechanism, and combine the features of common users based on the weights. In this way, our DT-CDBR can dynamically adjust the weights of two domains and further enrich the representation of features. Extensive experiments conducted on real-world datasets demonstrate that our DT-CDBR can improve the recommendation performance on both domains simultaneously.
Categorization of Learning Materials Using Multilabel Classification

Adaptive learning can adjust learning materials based on students' individual abilities. To facilitate the selection of appropriate materials, the categorization of learning materials can be done first. This study aims to categorize learning materials based on topics and subtopics with multilabel classification. Multilabel problem is handled by problem transformation approach. The problem transformation methods used are Binary Relevance, Label Powerset, and Classifier Chain. While the classification algorithms are Naive Bayes, SVM, and Random Forest. The dataset used in this study is 448 learning materials which are science subject materials that include biology, physics, and chemistry for junior high school students. The evaluation results show that the best combination is achieved by Binary Relevance method and SVM algorithm with accuracy value of 0.966 for topics and 0.699 for subtopics.
Adversarial Attack for Deep Learning Based IoT Appliance Classification Techniques

Simultaneous advancements in Internet of Things and Machine Learning have resulted in fascinating interdisciplinary applications, such as classification tasks based on smart device generated data for diverse applications such as resource allocation, security, and activity classification. However, such applications may be susceptible to attacks by adversarial examples. In this paper, we develop a white-box adversarial attack mechanism to generate adversarial examples for data obtained from smart-meters installed in residential houses, and demonstrate that their statistical properties are indistinguishable from those of the true datapoints. The attack mechanism focuses specifically on Deep Learning based models used to perform appliance classification in smart home environments. The statistical indistinguishability of the adversarial datapoints from the true datapoints indicates that non Machine Learning based solutions may not be able to tackle the challenge posed by adversarial examples. The effectiveness of the proposed techniques is demonstrated using the publicly available United Kingdom-Domestic Appliance-Level Electricity smart-meter dataset.
Evaluation of Dyke Rocks as Building Material, Accessing the Properties Using Mat Lab for Quality

Environmental change on fauna and flora, affects water quality and quantity due to indiscriminate mining for societal usage. Illegal sand mining is one of the activities posing serious threat to river systems. With the increase of urban population and non availability of sand for construct on of residential complexes leads too many fold increase in illegal mining. Statistics show that requirement of housing increase by one hundred and thirty three present in the next decade. Construction companies, one of the largest users of river sand, need sand with specific parameters. Some of parameters are high compressive strength, durability, economically viable, and availability of large quantity of sand. The dyke rocks chosen for this study lies in the outskirts of Hyderabad making it available. Detailed geological mapping was carried out and field samples and core samples were collected. Laboratory studies to assess the compressive strength were done. The study proved the dyke rock to be suitable to generate Robo sand and create an eco—friendly environment for a better future.
Construction of Sports Recognition System Based on Sports Visual Image Technology under the Background of Information Technology

Abstract With the development of the times, sports competitions are sought after and favored by more and more people, and the judgment of athletes in the competition is becoming more and more strict and standardized. The further development of Internet technology and information technology has provided great convenience for the effective recognition of athletes’ movements in sports competitions. This article aims to study the background of information technology, through the use of sports visual image technology to design and construct an action recognition system, in order to efficiently and accurately identify and judge the various movements of sports competitions, thereby effectively reducing various manpower and the cost of material resources, while improving the accuracy of the competition results. In the experiment, this article invites 100 volunteers to participate in the system’s behavioral test recognition. The volunteers’ simple exercises such as jumping, raising hands, kicking, turning and squatting are tested, and the result is the exercise designed in this article. The recognition system is able to recognize five sets of actions in the experiment, among which jumping is 97.66%, kicking is 98.13%, squatting is 97.62%, raising hand is 95.24%, and turning is 96.43%. Research shows that the motion recognition system based on sports visual image technology designed and constructed in this paper has high motion recognition accuracy and can recognize athletes’ movements scientifically and effectively.
Investigate the Impact of Resampling Techniques on Imbalanced Datasets: A Case Study in Plant Disease Prediction

In the current circumstances, plant disease prediction is drawing the attention of various scientists and agricultural experts. The prediction of plant diseases is the foundation of the early identification of diseases in plants efficiently using machine-learning algorithms. However, this area of agriculture science faces the challenge of the imbalanced dataset. Imbalanced datasets can bias the results of machine learning models towards the major class containing the largest number of samples of datasets. This problem can be dealt with the use of resampling techniques that balance the dataset to improve the efficiency of machine learning models. Hence, in the current study, the impact of resampling techniques such as Importance Sampling, Random over Sampling, Synthetic Minority Over-sampling Technique, and Random under Sampling has been evaluated on imbalanced plant disease datasets, i.e., Tomato Powdery Mildew Disease and Soybean Large using various machine-learning classifiers, i.e., Random Forest, Naïve Bayes, Multinomial Logistic Regression and Bagged Classification and Regression Tree. The results of this evaluation show that amongst all the resampling techniques Random Over Sampling has performed the best with 99.24% accuracy for Tomato Powdery Mildew Disease dataset for Random Forest Classifier, whereas Synthetic Minority Over-sampling Technique performed the best with 98.53% accuracy for Soybean Large dataset in case of Bagged Classification and Regression Tree Classifier.
Variants on Block Design Based Gradient Codes for Adversarial Stragglers

Gradient coding is a coding theoretic framework to provide robustness against slow or unresponsive machines, known as stragglers, in distributed machine learning applications. Recently, Kadhe et al. proposed a gradient code based on a combinatorial design, called balanced incomplete block design (BIBD), which is shown to outperform many existing gradient codes in worst-case adversarial straggling scenarios. However, parameters for which such BIBD constructions exist are very limited. In this paper, we aim to overcome such limitations and construct gradient codes which exist for a wide range of parameters while retaining the superior performance of BIBD gradient codes. Two such constructions are proposed, one based on a probabilistic construction that relax the stringent BIBD gradient code constraints, and the other based on taking the Kronecker product of existing gradient codes. Theoretical error bounds for worst-case adversarial straggling scenarios are derived. Simulations show that the proposed constructions can outperform existing gradient codes with similar redundancy per data piece.
Design and Implementation of Autoencoder-LSTM Accelerator for Edge Outlier Detection

Sensors are used to monitor various parameters in many real-world applications. Sudden changes in the underlying patterns of the sensors readings may represent events of interest. Therefore, event detection, an important temporal version of outlier detection, is one of the primary motivating applications in sensor networks. This work describes the implementation of a real-time outlier detection that uses an Autoencoder-LSTM neural-network accelerator implemented on the Xilinx PYNQ-Z1 development board. The implemented accelerator consists of a fine-tuned Autoencoder to extract the latent features in sensor data followed by a Long short-term memory (LSTM) network to predict the next step and detect outliers in real-time. The implemented design achieves 2.06 ms minimum latency and 85.9 GOp/s maximum throughput. The low latency and 0.25 W power consumption of the Autoencoder-LSTM outlier detector makes it suitable for resource-constrained computing platforms.
Precise Position Intelligent Matching System of Online Recruitment Platform Based on Data Mining Technology

Abstract With the rapid development of Internet technology and computer technology, network applications have been developed more and more, and have penetrated into all walks of life in society. The emergence of the networking of the talent market has made the scale of online recruitment increase, and the amount of data on the Internet has become larger and larger, and online recruitment has become the main channel for corporate recruitment. Therefore, how to use the massive online recruitment data to quickly and accurately find the corresponding information and explore the hidden knowledge mode is a very valuable research topic. Data mining (DM) is a technology for data analysis for large amounts of data. It can discover hidden, hidden, and potentially useful knowledge hidden in the data from the vague, noisy, and random mass data, and build relevant Model, realize prediction, etc. The characteristics of data mining technology (DMT) are very suitable for the analysis of online recruitment information, research on large amounts of information, and find out the knowledge in it for decision support. This article aims to study the accurate job matching system of the online recruitment platform based on DMT. Based on the analysis of the advantages of online recruitment, related DMT and the design principles of the online recruitment platform system, the data collected by Weka DM tools are analyzed. Analyzing and getting useful job positions is just to provide job seekers and corporate-related recruiters with useful job information. The experimental results show that the online recruitment platform system can complete the collection of online recruitment position information, and can realize the DM function, which has good practical application value.
Automated Atlas-based Segmentation of Single Coronal Mouse Brain Slices using Linear 2D-2D Registration

A significant challenge for brain histological data analysis is to precisely identify anatomical regions in order to perform accurate local quantifications and evaluate therapeutic solutions. Usually, this task is performed manually, becoming therefore tedious and subjective. Another option is to use automatic or semi-automatic methods, among which segmentation using digital atlases co-registration. However, most available atlases are 3D, whereas digitized histological data are 2D. Methods to perform such 2D-3D segmentation from an atlas are required. This paper proposes a strategy to automatically and accurately segment single 2D coronal slices within a 3D volume of atlas, using linear registration. We validated its robustness and performance using an exploratory approach at whole-brain scale.