Reviewerly/exHarmony huggingface dataset:
Estimating the minimum SNR necessary for object detection in the projection domain

The Rose criterion, stating that an object is detectable if it is five standard deviations above background, has been used as a rule of thumb for decades but its applicability is limited in computed tomography. Recent denoising algorithms, powered by convolutional neural networks, promise to reveal objects that were previously obscured by noise, but any denoising algorithm is fundamentally limited by the statistics of the sinogram. In this work, we estimate the minimum SNR necessary for detecting one of a set of objects in the projection domain. We assume there is a set of objects O for which detection is desired, and we study an ideal observer that sequentially compares each member of O to the null hypothesis. This comparison can be reduced to the classic one-dimensional signal detection problem between two Gaussians with different mean values, and from this we define a quantity, the projection SNR. We use simulations to estimate the minimum projection SNR necessary to achieve a sensitivity of 80% and specificity of 80%. We find that when we model a search task of a circular 6 mm lesion in a region of interest that is 60 mm by 60 mm by 10 slices, the minimum projection SNR is 5.1. This required SNR is reminiscent of the Rose criterion but is derived with entirely different assumptions, including the application of the ideal observer in the projection domain.
Spatial-Temporal Graph Convolutional Framework for Yoga Action Recognition and Grading

The rapid development of the Internet has changed our lives. Many people gradually like online video yoga teaching. However, yoga beginners cannot master the standard yoga poses just by learning through videos, and high yoga poses can bring great damage or even disability to the body if they are not standard. To address this problem, we propose a yoga action recognition and grading system based on spatial-temporal graph convolutional neural network. Firstly, we capture yoga movement data using a depth camera. Then we label the yoga exercise videos frame by frame using long short-term memory network; then we extract the skeletal joint point features sequentially using graph convolution; then we arrange each video frame from spatial-temporal dimension and correlate the joint points in each frame and neighboring frames with spatial-temporal information to obtain the connection between joints. Finally, the identified yoga movements are predicted and graded. Experiment proves that our method can accurately identify and classify yoga poses; it also can identify whether yoga poses are standard or not and give feedback to yogis in time to prevent body damage caused by nonstandard poses.
New Approaches in Machine-based Image Analysis for Medical Oncology

Oncology research needs a huge quantity of images and test reports for diagnosing. In vitro methods like computed tomography and magnetic resonance imaging, invivo methods like a biopsy are adopted to diagnose cancer patients. This medical proof is the basis for cancer treatment. But this test proof is accompanied by more than one trouble in exploiting and finding information for predicting sickness dynamics. Due to this, almost 5% of outpatients acquire the incorrect diagnosis each year. One in every three of these misdiagnoses causes substantial patient loss. To combat this, researchers have focused significantly on classical mathematical analysis to discover statistical correlations in physiological and pathological data associated with a cancer diagnosis. Even though mathematical models provide a perfect summary of a realistic context, including its underlying system dynamics, they may not always allow for precise and measurable forecasts. Modern Machine Learning (ML) method has a high level of assurance that, with enough training, it can provide significant forecasting ability on the patient in real-time. ML methodology enables experts to examine enormous amounts of data in a structured manner and deduce underlying knowledge of complex systems. Modern ML methods are adopted to forecast the growth of tumors which is under study and helps to detect new malignancies. Machine learning-based approach in oncology created an impression among clinicians in selecting perfect, target treatment, and clinical outcome measures. To improve the outcome accuracy, the researcher should shed more light to upgrade the machine learning algorithm without compromising the clinician's needs. This chapter discusses the different strategies adopted by the researchers to enhance the result accuracy in classifying cancer in patients.
Impact of Remote Working Environment on Employee Motivation, Engagement, and Job Satisfaction: A Study of Service Sector from UAE

With the advancement in technologies and the outbreak of COVID-19 pandemic, significant changes have begun to take place in businesses, causing jobs to move from workplaces to homes indefinitely. For some, the transition from office to work was simple, while for others, juggling family and work has become a roller-coaster ride of emotions. Not all remote employees can sustain their remote working due to reduction in employee productivity and engagement. Targeting this aspect, the study focuses on examining the impact of remote working on employee motivation, engagement, and job satisfaction in case of UAE service sector. The empirical examination of 316 employee’s perception shows that remote working has a positive as well as negative impact on employee motivation, engagement, and job satisfaction. To sustain in a competitive environment, it is essential to mitigate the shortcomings and create a motivating working environment for employees.
A White-Box Sociolinguistic Model for Gender Detection

Within the area of Natural Language Processing, we approached the Author Profiling task as a text classification problem. Based on the author’s writing style, sociodemographic information, such as the author’s gender, age, or native language can be predicted. The exponential growth of user-generated data and the development of Machine-Learning techniques have led to significant advances in automatic gender detection. Unfortunately, gender detection models often become black-boxes in terms of interpretability. In this paper, we propose a tree-based computational model for gender detection made up of 198 features. Unlike the previous works on gender detection, we organized the features from a linguistic perspective into six categories: orthographic, morphological, lexical, syntactic, digital, and pragmatics-discursive. We implemented a Decision-Tree classifier to evaluate the performance of all feature combinations, and the experiments revealed that, on average, the classification accuracy increased up to 3.25% with the addition of feature sets. The maximum classification accuracy was reached by a three-level model that combined lexical, syntactic, and digital features. We present the most relevant features for gender detection according to the trees generated by the classifier and contextualize the significance of the computational results with the linguistic patterns defined by previous research in relation to gender.
Examining Services Gaps and Competitive Analysis of Online Food Ordering Apps

Abstract: The online food ordering system is one of the latest services. With this method, food is ordered online and delivered to the customer. This is possible through the use of electronic payment system. Customers pay with their credit cards, cash online banking or many other ways of payment. Due to great increase in the awnerness of internet and the technologies associated with it, serval opportunities are coming up on the web, so many business of the internet introduced is an online food ordering system. In today’s age of fast food and take out, many restaurants have chosen to focus on quick preparation and speedy delivery of orders rather then offering a rich dining experience. Until recently most of this delivery orders were placed over the phone but there are many disadvantage to this system. It is possible for anybody to order any goods via the internet and have the goods delivered at his/her daorsteps. But while trying to discuss the transfer method of the goods and services, attention is focused on the payment mode. The most of people are prefer cash payment at the time of delivery. What I propose is an online food ordering system originally designed for use in college cafeteris, but just as applicable in any food delivery industry. The main advantage of this system is that it greatly simplifles the ordering process for both the customer and the restaurants. Once an order is palced on the webpage that will be designed, it is placed into the database and them retrieved, in pretty much real-time, by a desktop application on the restaurants end. Within this applications, all items in the order are displayed along with their corresponding options and deliverd details. This allows the restaurants employes quickly go through the orders as they are placed and produce the necessary ithems with minimal delay and confusion. The greatest advantage of this system is its flexibility.
Resource-Efficient Multi-Task Deep Learning Using a Multi-Path Network

Multi-task learning (MTL) improves learning efficiency compared to the single-task counterpart in that it performs multiple tasks at the same time. Due to the nature, it can achieve generalized performance as well as alleviate overfitting. However, it does not efficiently perform resource-aware inference from a single trained architecture. To address the issue, we aim to build a learning framework that minimizes the cost to infer tasks under different memory budgets. To this end, we propose a multi-path network with a self-auxiliary learning strategy. The multi-path structure contains task-specific paths in a backbone network, where a lower-level path predicts earlier with a smaller number of parameters. To alleviate the performance degradation from earlier predictions, a self-auxiliary learning strategy is presented. The self-auxiliary tasks convey task-specific knowledge to the main tasks to compensate for the performance leak. We evaluate the proposed method on an extensive set of multi-task learning scenarios, including multiple tasks learning, hierarchical learning, and curriculum learning. The proposed method outperforms existing multi-task learning competitors for most scenarios about by a margin of 1% ~ 2% accuracy on average while consuming 30% ~ 60% smaller computational cost.
Development of a Mobile e-Learning Platform on Physics Using Augmented Reality Technology

With the constant increase of computing power in mobile devices (tablets, smartphones), new prospects have opened up for their application, in particular, in trade, industry, medicine, education, etc. In the field of education, mobile devices have a high potential, which, if properly integrated, can form a new effective learning model. This article is devoted to the development of a mobile application for a multilingual (Kazakh, Russian, English) educational platform for distance learning of physics in higher and secondary educational institutions using augmented reality technology.
Face Recognition via Compact Second-Order Image Gradient Orientations

Conventional subspace learning approaches based on image gradient orientations only employ first-order gradient information, which may ignore second-order or higher-order gradient information. Moreover, recent researches on the human vision system (HVS) have uncovered that the neural image is a landscape or a surface whose geometric properties can be captured through second-order gradient information. The second-order image gradient orientations (SOIGO) can mitigate the adverse effect of noise in face images. To reduce the redundancy of SOIGO, we propose compact SOIGO (CSOIGO) by applying linear complex principal component analysis (PCA) in SOIGO. To be more specific, the SOIGO of training data are firstly obtained. Then, linear complex PCA is applied to obtain features of reduced dimensionality. Combined with collaborative-representation-based classification (CRC) algorithm, the classification performance of CSOIGO is further enhanced. CSOIGO is evaluated under real-world disguise, synthesized occlusion, and mixed variations. Under the real disguise scenario, CSOIGO makes 2.67% and 1.09% improvement regarding accuracy when one and two neutral face images per subject are used as training samples, respectively. For the mixed variations, CSOIGO achieves a 0.86% improvement in terms of accuracy. These results indicate that the proposed method is superior to its competing approaches with few training samples, and even outperforms some prevailing deep-neural-network-based approaches.
Iterative and Semi-Supervised Design of Chatbots Using Interactive Clustering

Chatbots represent a promising tool to automate the processing of requests in a business context. However, despite major progress in natural language processing technologies, constructing a dataset deemed relevant by business experts is a manual, iterative and error-prone process. To assist these experts during modelling and labelling, the authors propose an active learning methodology coined Interactive Clustering. It relies on interactions between computer-guided segmentation of data in intents, and response-driven human annotations imposing constraints on clusters to improve relevance.This article applies Interactive Clustering on a realistic dataset, and measures the optimal settings required for relevant segmentation in a minimal number of annotations. The usability of the method is discussed in terms of computation time, and the achieved compromise between business relevance and classification performance during training.In this context, Interactive Clustering appears as a suitable methodology combining human and computer initiatives to efficiently develop a useable chatbot.
On-Premise Server Monitoring with Prometheus and Telegram Bot

On-premise Servers can be classified as a crucial backbone of a company. Sensitive data like Financial Records, User Information are stored on on-premise servers. This makes it necessary to be kept in a separate room with supervision and security checks, making it difficult to monitor the server periodically. This paper researches on-premise servers which lack remote monitoring and introduces a user-friendly solution. By making monitoring data available remotely via a Telegram bot, monitoring the servers becomes easy on-site or off-site and only requires the administrators to physically inspect the server for initial configuration, periodic checks, and any physical repairs. Monitoring software like Prometheus can be used to collect server metrics at set intervals. Telegram provides an API to create and control bots that can send messages to its user. By storing the metrics collected by Prometheus in a cloud database, the Telegram Bot can be requested to fetch these data which can be viewed by the server administrator remotely. Key Words: Server Monitoring, Telegram Bot, Python Service, Prometheus.
Improved Lossless Data Hiding for JPEG Images Based on Optimized VLC Mapping

Most reversible data hiding (RDH) schemes embed data into JPEG images through DCT coefficients modification, but they will cause unavoidable visual distortion in the marked images. Lossless data hiding (LDH) can not only restore the cover JPEG images in a lossless way after extraction of the embedded data but also generate marked images without any visual distortion. For LDH schemes based on variable length code (VLC) mapping, the performance depends mainly on the mapping mode. In this paper, a new LDH scheme for JPEG images based on optimized VLC mapping is proposed. First, for a given mapping mode, a mathematical model of file size change is established. Then, based on the Run/Size frequency of a cover image and the estimation of frequency change caused by simulated data embedding, an iterative method is used to find the optimal mapping mode that leads to the smallest file size increments. Lastly, data is embedded in the cover image using the optimal VLC mapping. Experimental results show that the proposed LDH scheme based on optimized VLC mapping can achieve small increase in the file size of marked images.
Congestion Control of Multipath Parallel Transmission of Data for Blockchain Applications

The control of congestion is very important in the parallel transmission of blockchain-based data over high-speed networks. There are drawbacks to the conventional methods, such as interference between different transmission paths, the collision of data, and channel allocation policies to address these problems, there is a need to investigate newer methods for controlling the congestion on multipath channels for parallel transmission of data. The conventional methods cannot reduce the data packet queuing time, the packet loss rate, and latency time of packets. In parallel data transfer, especially in blockchain-based transactions, congestion control is critical. This research proposes and implements a data congestion control approach for multipath parallel transmission on IoT networks. The multipath parallel transmission path is established in this research work to enable the source node to sense the path congestion information in a real-time environment. Finally, the low congestion path is selected to avoid congestion for parallel data transmission and to achieve high throughput over the network channels. The simulation results show that the strategy provided in this paper minimizes data packet queuing time efficiently, boosts network channel throughput, and decreases data packet loss rate.
Multi-Designated Receiver Authentication-Codes with Information-Theoretic Security

A multi-designated receiver authentication code (MDRA-code) with information-theoretic security is proposed as an extension of the traditional multi-receiver authentication code. The purpose of the MDRA-code is to securely transmit a message via a broadcast channel from a single sender to an arbitrary subset of multiple receivers that have been designated by the sender, and only the receivers in the subset (i.e., not all receivers) should accept the message if an adversary is absent. This paper proposes a model and security formalization of MDRA-codes, and provides constructions of MDRA-codes.
Transportation of Payload Using Multiple Quadrotors via Rigid Connection

Due to the limited payload capability of an aerial robot, multiple quadrotors can be used to manipulate payloads in aerial transportation, construction, and assembly tasks. This paper focuses on the cooperative transportation of a payload rigidly attached to multiple quadrotor bodies. These quadrotors may have different orientations. The dynamics equation of a rigid body in 3-D space is derived to describe the motion of such a transportation system. Robust position and attitude controllers are designed to drive the system to the desired pose. To assign control signals for each quadrotor, the control command allocation method compatible with the case that partial or all quadrotors are in parallel planes is developed. Finally, experimental results are presented to validate the effectiveness of the proposed controllers and control command allocation methods. Different from classical works in this field, this paper can solve the dynamics modeling, controller design, and control command allocation problems for the transportation of a rigidly connected payload using a team of quadrotors with different orientations.
Logical Inference for Counting on Semi-structured Tables

Recently, the Natural Language Inference (NLI) task has been studied for semistructured tables that do not have a strict format.Although neural approaches have achieved high performance in various types of NLI, including NLI between semi-structured tables and texts, they still have difficulty in performing a numerical type of inference, such as counting.To handle a numerical type of inference, we propose a logical inference system for reasoning between semi-structured tables and texts.We use logical representations as meaning representations for tables and texts and use model checking to handle a numerical type of inference between texts and tables.To evaluate the extent to which our system can perform inference with numerical comparatives, we make an evaluation protocol that focuses on numerical understanding between semi-structured tables and texts in English.We show that our system can more robustly perform inference between tables and texts that requires numerical understanding compared with current neural approaches.
Impact of Assessment as Learning on Attitudes towards Mathematics of Senior High School Students in Ahanta West and Mpohor Districts

Students’ attitude towards learning of mathematics has been of interest to both mathematics educators and other stakeholders. This is because students’ achievement and attitude have been described to like the siemese twin; almost impossible to separate. The concerns have been on how to improve students’ attitude towards mathematics. The study examined the impact of assessment as learning on attitudes on mathematics among Senior High School students in Ahanta West and Mpohor Districts. Quasi-experimental pre-test/ post-test control group design was adopted for the study. Multi-stage sampling techniques were applied to generate a sample of 81 participants comprising 32 in the experimental group and 49 in the control group. A questionnaire was mainly used for the data collection. A questionnaire on attitudes was administered students to determine classes with no significant difference in attitudes towards Mathematics. Then a training package with assessment as learning and a similar package with the traditional assessment methods were administered to the Experimental group and control group respectively. Finally, post intervention questionnaire on attitudes were administered to both groups. The results revealed SHS students in the experimental group have positive attitudes towards mathematics than those in the control group. Also gender has no significant effect on attitude towards mathematics in the face of assessment as learning.
Human Health Activity Recognition Algorithm in Wireless Sensor Networks Based on Metric Learning

Wireless sensor network is an ad hoc network with sensing capability. Usually, a large number of sensor nodes are randomly deployed in an unreachable environment or complex area for data collection and transmission, which can realize the perception and monitoring of the target area or specific objects and transmit the obtained data to the remote end of the system. Human health activity recognition algorithm is a hot topic in the field of computer. Based on the small sample problem and the linear indivisibility of real samples encountered in metric learning, this paper proposes a human activity recognition algorithm for wireless sensor networks. Human activity recognition algorithm for wireless sensor networks uses human activity recognition algorithm to solve the singularity of intraclass divergence matrix, so as to reduce the impact of small sample problem. The algorithm maps two different feature spaces to the high-dimensional linearly separable kernel space through the corresponding kernel function, calculates the distance between samples in the two projected feature subspaces to obtain two distance measurement functions, and finally linearly combines them with weights to obtain the final distance measurement function.