Reviewerly/exHarmony huggingface dataset:
Parallax Engine: Head Controlled Motion Parallax Using Notebooks’ RGB Camera

Research on the Fish Tank Virtual Reality (FTVR) technique commonly uses specific sensors (e.g. infrared cameras and LEDs on glasses) to estimate user’s eye position. However, estimating the face position with an RGB camera is becoming more accessible. In this work, we explore community available face characteristics detection software to implement the FTVR technique for everyday uses of 3D-enabled applications on consumer notebooks without requiring extra devices. We introduce the Parallax Engine solution that can be added with ease to any Unity game engine application. The solution supports two parallax-related visualization options: 1) a monoscopic FTVR mode (FishTank), which locks the virtual camera of the 3D environment to the laptop’s screen 2) and a 2D parallax mode (Parallax2DoF), which allows horizontal and vertical displacement of 3D scene camera. Regarding face characteristics detection techniques, the Parallax Engine uses a standardized interface that can receive input from different methods and currently supports three options: Google’s MediaPipe, dlib, and PoseNet. We evaluated the proposed solution with five users, performing tasks using different options for viewing and face characteristics detection, aiming to understand how suitable it is for end-users. Besides some detection failures from dlib, results showed an overall good acceptance for both the FishTank and Parallax2DoF visualization options.
Encryption Management of Accounting Data Based on DES Algorithm of Wireless Sensor Network

The emergence of wireless sensor networks connects the physical world with the information world and changes the way humans interact with nature. With the rapid development of modern information technology, accounting information systems (AIS) have emerged at the historic moment. Under the information environment, accounting data exists in paper or paperless form. The use of information technology not only brings convenient and efficient services to enterprises but also has a huge impact on the internal control of the enterprise. Because the network is open and unstable, the system is vulnerable to illegal intrusion and viruses. Based on the above background, the research content of this article is to use DES algorithm to encrypt accounting data. DES (Data Encryption Standard) encryption algorithm is a symmetric password encryption method. It has the advantages of fast encryption speed, simple and practical algorithm, and consideration of both security and efficiency requirements. This paper discusses the application of DES encryption technology to accounting data processing. To achieve data security management goals. Therefore, this paper proposes a DES algorithm based on the logistic chaotic system. Through experimental simulation, the results show that the chaotic discrete model has initial value sensitivity and iterative nonrepetition. The resulting key space is independent and random. In the application, you can perform random key input according to the performance of software and hardware, which is flexible; there is only one “1186828” in the initial DES algorithm encryption process, but each set of plain text in the improved DES algorithm corresponds to a corresponding set of keys and independence. The test results show that they are maintained between 5 and 6.6. It is proved that using the initial value sensitivity of the logistic system and using the initial value as the key can realize the secure management of accounting data on the premise of ensuring efficiency.
Learning-Based Template Matching for Robot Arm Grasping

When applying template matching to the robot arm grasping of rotated objects with high aspect ratios, the accuracy of the matching process is often degraded by the occurrence of high similarity scores with pixels or patches located in neighboring objects. Accordingly, we propose a learning-based template matching (LbTM) algorithm in which the accuracy of the matching results is improved by clearing the matching scores of these confused pixels or patches to zero using a spatial clustering process. This algorithm consists of two modules: First, the translation matching module uses the learning-based pairwise similarity matrix. Having determined the center coordinate of the target object, the second module is applied to estimate the target rotation angle by using a Siamese network. The effectiveness of the proposed algorithm is evaluated for 600 template-rotated target pairs. It is shown that the area under curve (AUC) performance of the proposed algorithm (0.678) is higher than that of three other template matching algorithms (DDIS, CoTM, and QATM). Moreover, our algorithm achieves a minimum success rate of 80% in practical grasping trials performed even using high-aspect objects with various rotational angles and positions.
A Human in the Loop Based Robotic System by Using Soft Actor Critic with Discrete Actions

In this paper, the possibility of extending the Soft Actor Critic (SAC) with human control feedback is proposed and implemented for mobile robot navigation using Turtlebot3 Burger. The Human In The Loop (HITL) technique is integrated into the reinforcement learning algorithm for obstacle avoidance-based navigation. The algorithm is usually presented in the continuous action domain and for this work, the SAC with discrete action settings is proposed to suit the minimal discrete space. The human feedback assisting the robot with the discrete action settings has proved to be better in path planning and navigation when compared to the standard algorithms with the episode number.
Hyperspectral Imaging in Document Forgery

Document forgery is a crucial problem for this day and age due to the rampant utilisation of paper-based documents. The best type of technology must be utilised to the same. Hyperspectral imaging is a non-invasive and powerful tool used to capture images with several and contiguous bands. This imaging can create unique spectral signatures for each substance. The integration of the application of hyperspectral imaging with document forgery showcases tremendous results. Therefore, this work aims to present a literature review about the modern techniques employed to detect multiple types of forgeries in documents using hyperspectral imaging. This survey organised the various methodologies into various categories based on its central aim namely: writer detection, ink detection and miscellaneous. These methods were tabulated to display their similarities and contrasts. This paper aims to summarise recent advancements in the hyperspectral imaging field in regard to the document forgery application.
Analysis on the Risk and the Categorization on Test Automation in Sri Lankan Software Industry

Delivering quality software to customer is the key objective of software industry. One of the essential fragment of life cycle of software is software testing. In software testing test automation is playing a major role. If test automation cannot be practiced in proper way the delivery of the software quality would impact directly and leads to loss of customer, which is a failure of business. Test automation has several problems which needs to address in each stage. Test automation cause several issues when execute test automation in a company. All these issues need to be handled by different people, therefore initially issues need to be identified and classified and then solve properly. This research is to identify the improvements to categorize the problems automatically and find the solution for the problem in test automation process and hence to practice the test automation in healthier way in order to achieve better software quality. Test automation issue are analyzed and the solutions are proposed. On which stage, the test automation is causing problems and how to solve them are recommend in this research, Test automation issues are categorized and under relevant category therefore issues can be solved speedily. The issues are passed as sentence and they are categorized under the relevant category to fix them quickly. The sentences are preprocessed and conducted feature selection using filter methods and predict under appropriate category. The issue has been cleaned in preprocess stage. Implemented LSTM base algorithm using filter method to categorize the issues. In this research an implementation to categorize test automation problems are formed. Recommendation and solutions are proposed on test automation which would aid to practice test automation in better way and that would leads to better software quality delivery.
Using an Imbalanced Classification Algorithm and Floating Car Data for Predicting Real-Time Traffic Crash Risk on Expressways

Real-time traffic crash risk prediction plays a vital role in active traﬃc management (ATM) systems by identifying the hazardous traffic conditions that tend to precede a crash in the short interval prior to the crash. Recent advancements in traffic sensing and detection technologies have made real-time analysis possible, but few prior studies have examined crash occurrence using real-time floating car data (FCD) collected from expressways. Moreover, real-time crash risk prediction models have mostly been developed from regenerated balanced datasets, which may be inadequate for the large and continuous real-time traﬃc data environment. Therefore, in this study, a comprehensive imbalanced classiﬁcation algorithm, the adaptive boosting algorithm for convolutional neural networks (AdaBoost-CNN), has been used to build a practical real-time traffic crash risk prediction model. This study primarily aims to (1) investigate the feasibility of using FCD to predict real-time crash risk on expressways, and (2) explore the efficiency of the AdaBoost-CNN algorithm to solve the imbalanced data classiﬁcation problem in predicting real-time crash risk. Two models are compared to the proposed AdaBoost-CNN. First, AdaBoost with CNN base classifiers is compared to the proposed model to investigate the influence of transfer learning on prediction accuracy. Second, a one-dimensional conventional neural network (1-DCNN) is developed with balanced data to examine how well AdaBoost-CNN can handle the large imbalanced distribution of crash and non-crash datasets. These experiments demonstrated that AdaBoost-CNN is accurate enough to be useful in predicting crash and non-crash cases using large amounts of imbalanced continuous real-time floating car data, as measured by sensitivity, false alarm rate, and area under the curve scores. Third, a map-matching approach was introduced to analyze FCD, which showed high accuracy in transforming FCD into specific traffic characteristics for the corresponding segments. The results confirm the feasibility of the proposed model for predicting real-time crash risk on expressways.
Addressing the cognitive and noncognitive needs of struggling students through an E7 approach in a virtual laboratory environment to improve their motivation and learning achievement

Abstract In this study that aims to motivate struggling students and helping them to improve their learning achievement, we designed and tested an E7-based approach in a virtual laboratory environment. We rely on cognitive load theory and self-determination theory to enable our approach to meet the cognitive and noncognitive needs of struggling students. This study adopts the pre-experimental method with the design of the pre and post-test, an experimental group and a control group. We evaluated the effect of the intervention on motivation and learning achievements by using a motivation test and the students’ assessment by their teachers. One of the results of this study is the success of our approach in motivating struggling students and helping them to improve their learning achievements.
Advances in AI and Machine Learning for Education Research

There is a growing need for assessment and learning tools that capture a broad range of learner behavior necessary for the evaluation of skills such as problem solving, communication and collaboration. In these real-world applications student data is captured with a high degree of granularity, variety of temporal scales and in a multitude of modalities. Unfortunately such complex, noisy and unstructured data limit the applicability of traditional models of measurement and psychometrics designed to extract evidence of competency from item response data. In this chapter, we present recent advances in AI and Machine Learning that can be utilized for measurement of a variety of complex constructs and competencies. These models include frameworks such as deep neural networks and adversarial generative networks that enable us to harness concept hierarchies and the latent structure within data to learn increasingly complex representations and make powerful predictions.
Transformer Combination Weighting Evaluation Model Based on BP Neural Network

The structure of the power transformer is complex, and there are many indicators that characterize the state. It is necessary to integrate multi-dimensional information indicators to get a comprehensive and scientific evaluation conclusion. The analytic hierarchy process is one of the main methods to solve multi-factor decision-making. It uses a hierarchical way to describe more complex problems, which can effectively mine the information between the transformer's multi-dimensional indicators, and combine quantitative and qualitative analysis to ensure The integrity of the data. The importance of power transformer indexes is quite different. In order to obtain more accurate index weights, the influence of expert experience and data distribution must be considered. This paper establishes an analytic hierarchy model that integrates transformer multi-dimensional information, uses fuzzy analytic hierarchy process to obtain subjective weights, uses improved entropy weight method to calculate objective weights, combines subjective and objective weights, and uses BP neural network to learn the nonlinear mapping of state evaluation Relationship, established a transformer state evaluation model based on the combination weighting method.
An Efficient Temporal Model for Small-Footprint Keyword Spotting

Keyword spotting (KWS), as an essential part of human-computer interaction, is widely used in mobile device terminals. However, the hardware resources of these devices are usually limited, so running on these devices requires a small memory footprint. However, previous works still need massive parameters to achieve high performance. In this work, we propose a context-dependent and compact network for small-footprint KWS. Firstly, to reduce the running time, we apply a sub-sampling technique in which hidden activation values are calculated in a few time steps based on time delay neural network (TDNN). Secondly, to take full advantage of the global context information of the feature maps, we utilize a squeeze-and-excitation block to emphasize the most discriminating area and distinguish the speech and non-speech regions. Finally, we conduct extensive experiments with the publicly available Google Speech Commands dataset and the private Biaobei Chinese Speech Commands dataset. The experimental results on the public dataset verify that the classification error rate of our method reaches 3.56% with only 11K parameters and 322K multiplications, which achieves state-of-the-art performance with the fewest parameters and multiplications.
Optimal data warehouse design with data marts and data cube aggregation

Almost all Data Mining techniques and algorithms suffer from the high time complexity due to the huge amount of data and the algorithms nature. In general it can be concluded that the time complexity of different mining algorithms is a function of number of records in the dataset, number of features and number of distinct values in these features in addition to some other factors. At the same time data privacy, ownership and security represent a big challenge to the warehouse projects. The work in this paper tends to improve such complexity and reduce the required accessing time for different queries and improve privacy, security, data ownership throughout a combination of warehouse design with many fact tables (Galaxy model) and using data cube approach containing both detailed and highly summarized data. Cause of death free dataset available on the internet that include more than 14 million records was used to study the effectiveness of the proposed approach. The results obtained showed high improvement of the mentioned criteria for the proposed approach compared with the traditional way of dealing with such data.
Understanding the Relation between System Usability and End User Performance

Web applications have been spreading everywhere. However, this may have a number of drawbacks mostly related to usability issues. Therefore, the investigation of usability problems continues to improve these applications. In this study, usability metrics are used for web application interface evaluation. The relation between web application usability and end user performance is investigated. Many studies have evaluated interface usability in a number of sectors; however, studies on oil and gas web applications have been limited. In this study, two sessions with twelve participants were designed to obtain data directly from end users. The sessions were observation and user feedback sessions. The findings indicate a potential relation between system usability and end user performance in terms of effectiveness and satisfaction.
Kernelized Transfer Feature Learning on Manifolds

In the past few years in computer vision and machine learning, transfer learning has become an emerging research for leveraging richly labeled data in the source domain to construct a robust and accurate classifier for the target domain. Recent work on transfer learning has focused on learning shared feature representations by minimizing marginal and conditional distributions between domains for linear data sets only. However, they produce poor results if they deal with non-linear data sets. Therefore, in this paper, we put forward a novel framework called Kernelized Transfer Feature Learning on Manifold (KTFLM). KTFLM aims to align statistical differences and preserve the intrinsic geometric structure between the labeled source domain data and unlabeled target domain data. More specifically, we consider Maximum Mean Discrepancy for statistical alignment and Laplacian Regularization term for incorporating manifold structure. We experimented using benchmark data sets such as the PIE Face Recognition and the Office-Caltech (DeCAF features) object recognition dataset to discourse the limitations of the existing classical machine learning and domain adaptation methods. The performance comparison indicates that our model gave splendid accuracy of 79.41% and 91.97% for PIE and Office-Caltech data sets using linear and Gaussian kernels, respectively.
Analyzing the Influence of the Arabic Handwriting Types on a Writer Identification

Writer identification/recognition from off-line Arabic handwriting on sentence-level is still a tough task. In this paper, we start by investigating the performance of textural extractors for writer identification of divergent writing types. Taking into account their strengths and limits, we propose a new method that keeps the main features of the writing and handles the sensitivity of systems towards the available samples of text at the pre-processing phase. We also analyze the influence of the handwriting types on the efficiency of the writer identification process. In this regard, we perform a comparative study between handcrafted and automated features. Under multiple classifiers (RF, XGB, KNN and SVM). We find that writers with good and well clear handwriting have fewer similarities, thus, provides enhanced experimental identification rates. However, Bad handwriting presents more similarities between the writers, which explains the reduction in the identification rate.
Cognitive workload level estimation based on eye tracking: A machine learning approach

Cognitive workload is a critical feature in related psychology, ergonomics, and human factors for understanding performance. However, it still is difficult to describe and thus, to measure it. Since there is no single sensor that can give a full understanding of workload, extended research has been conducted in order to present robust biomarkers. During the last years, machine learning techniques have been used to predict cognitive workload based on various features. Gaze extracted features, such as pupil size, blink activity and saccadic measures, have been used as predictors. The aim of this study is to use gaze extracted features as the only predictors of cognitive workload. Two factors were investigated: time pressure and multi tasking. The findings of this study showed that eye and gaze features are useful indicators of cognitive workload levels, reaching up to 88% accuracy.
An Efficient Trust-Based Supply Chain Management Framework Utilizing the Internet of Things and Blockchain Technology

Every stage of a supply chain is monitored by Internet of Things (IoT) devices. The use of blockchain technology helps the efficient management of product supply and payment of suppliers based on smart contracts. An efficient trust-based supply chain management system is built using these technologies. It also performs an expert audit to find the real-time status of the product at different stages. This chapter describes a computational model that has been developed to determine a product's exact status; decisions are taken based on the computed output of the model.
An Implementation of Robust User Authentication Technique for Big Data Platform

Hadoop framework has been developed to manage Big Data on the Cloud. The main components of this framework are MapReduce and Hadoop Distributed File System (HDFS), but Hadoop does not provide any mechanism for robust authentication. The existing authentication protocols are vulnerable to many security threats. Therefore, we suggest an authentication model based on “Inverse Hyperbolic Functions” in this paper. This model is followed by a mechanism that verifies the identity of the user and determines whether the user is permitted for access or not. The security study takes into account both functionality and security needs. Furthermore, (AVISPA) is used to verify the proposed system. Finally, the suggested scheme defends against a variety of attacks, including replay, MITM, DOS, password guessing, insider attack, and phishing.
ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE

Introduction. Over the past few decades, thanks to advances in algorithm development, the introduction of available computing power, and the management of large data sets, machine learning methods have become active in various fields of life. Among them, deep learning possesses a special place, which is used in many spheres of health care and is an integral part and prerequisite for the development of digital pathology. Objectives. The purpose of the review was to gather the data on existing image analysis technologies and machine learning tools developed for the whole-slide digital images in pathology. Methods: Analysis of the literature on machine learning methods used in pathology, staps of automated image analysis, types of neural networks, their application and capabilities in digital pathology was performed. Results. To date, a wide range of deep learning strategies have been developed, which are actively used in digital pathology, and demonstrated excellent diagnostic accuracy. In addition to diagnostic solutions, the integration of artificial intelligence into the practice of pathomorphological laboratory provides new tools for assessing the prognosis and prediction of sensitivity to different treatments. Conclusions: The synergy of artificial intelligence and digital pathology is a key tool to improve the accuracy of diagnostics, prognostication and personalized medicine facilitation