Reviewerly/exHarmony huggingface dataset:
UMS nursing student awareness on the impact of social media on nursing profession

Social media have been used widely by majority of population around the world and have been proved to be useful for sharing information as well as gain knowledge. For student, media social plays a huge role especially during learning session. Recently, a lot of issues regarding nursing services flooded social media, which gave a negative impression about nurses profession. This study aims to identify the awareness on the impact of social media in regards to nursing services among Faculty of Medicine and Health Sciences (FMHS), UMS nursing students. Methods: Descriptive quantitative cross-sectional study using modified questionnaire for data collection was conducted in FMHS, UMS. A total of 100 nursing student, year 3 (37%), year 2 (40%) and year 1 (23%) participated in the study. Results: Overall, 51% respondents agree and 45% strongly agree that social media is a powerful tool to connect among individuals. 63% agree and 34% strongly agree that social media can make jobs more efficient and innovative. 63% agree and 32% strongly agree that social media can be an agent of providing clinical education to nurses by facilitating conversations with colleagues about best practice and advanced healthcare. 51% agree and 22% strongly that social media can be trace and legal action can be taken towards nurses. 54% agree and 20% strongly agree that social media can be used to reveal malpractice among nurses. 45% agree and 40% strongly agree that social media can be misuse by the non-healthcare provider. Conclusion: Based on this study’s result, the respondents are well aware with the positive and negative impact of social media on nursing profession. There may has a small number of unaware or unsure about the impact among the respondents, with that the recommendation for this study is to ensure the UMS nursing students to abide with the professional and personal etiquettes and use common sense and caution while communicating on social media.
Z-NetMF: A Biased Embedding Method Based on Matrix Factorization

Network embedding represents the graph in low dimensions, improving the processing of big scale tasks. As node2vec can only be modeled as a tensor, and NetMF cannot be generalized to a biased form directly. In this paper, we draw Z-Laplacian, a framework that describes the dynamic process on the graph, into NetMF, so that NetMF can be extends to a biased form. The biased version of NetMF is named as Z-NetMF. We analyze the biased random walk in the view of graph signal processing and prove the effectiveness of Z-NetMF in the node classification task. The result shows that Z-NetMF outperforms the comparison methods, NetMF and node2vec.
Cost-Sensitive Semi-supervised Classification for Fraud Applications

This research explores Cost-Sensitive Learning (CSL) in the fraud detection domain to decrease the fraud class’s incorrect predictions and increase its accuracy. Notably, we concentrate on shill bidding fraud that is challenging to detect because the behavior of shill and legitimate bidders are similar. We investigate CSL within the Semi-Supervised Classification (SSC) framework to address the scarcity of labeled fraud data. Our paper is the first attempt to integrate CSL with SSC for fraud detection. We adopt a meta-CSL approach to manage the costs of misclassification errors, while SSC algorithms are trained with imbalanced data. Using an actual shill bidding dataset, we assess the performance of several hybrid models of CSL and SSC and then compare their misclassification error and accuracy rates statistically. The most efficient CSL+SSC model was able to detect 99% of fraudsters and with the lowest total cost.
Preserving the Shape of Functions by Applying Multidimensional Schoenberg-Type Operators

The paper presents a multidimensional generalization of the Schoenberg operators of higher order. The new operators are powerful tools that can be used for approximation processes in many fields of applied sciences. The construction of these operators uses a symmetry regarding the domain of definition. The degree of approximation by sequences of such operators is given in terms of the first and the second order moduli of continuity. Extending certain results obtained by Marsden in the one-dimensional case, the property of preservation of monotonicity and convexity is proved.
A Novel Encryption Technique to Protect Patient Health Information Electronically Using Playfair Cipher 15 by 14 Matrix

Healthcare data such as patient name, DOB, address, phone number, fax number, E-mail address, etc., need to be kept private to protect patient privacy. It has been found that unauthorized person always try to get this information. With the help of proper encryption technique, we can protect the information. Play fair cipher is a very much well-known encryption technique for text messages. It is an example of a digraph substitution cipher. In this technique, pair of alphabets (digraphs) was encrypted instead of a single alphabet. That is why, it becomes so strong and not easy to break by conventional method. Play fair is fairly fast to apply and calls for no unique tools. It converts a plain text to cipher text message mathematically with the help of a secret key. Likewise, while restoring the original message from encrypted message, the same secret key has been used. However, the traditional play fair cipher supports only 25 uppercase alphabets. Here, in our work, we use 210 characters instead of 25 characters earlier, which make the cipher text stronger. These 210 characters include all the alphanumeric characters as well as 148 special characters supported by MATLAB. Here, we carried out cryptanalysis and found that the cipher is a strong one also it is thus almost impossible to be broken by any cryptanalytic attack. Finally, we have represented the final result with the help of MATLAB with two iteration steps.
Smart Ideological Assessment System Based on Data Visualization and Cluster Computing

Nowadays, the development of society is closely related to the development of education. How to improve the quality of education has become a topic of common concern all over the world. With the progress of science and technology, great changes have taken place in the type of demand for talents in today's society. It is the focus of attention to let students adapt, meet and change the society. From the perspective of online learning, this research work has analyzed various forms of data, such as text data, voice data and so on. At present, the data processing is mainly to display the data in the form of numbers. If there are a large number of data, people will be dazzled and they will not be able to understand the meaning contained in the data. Therefore, these data should be integrated to achieve visual processing. This paper attempts to use clustering algorithm and data visualization technology to realize the construction of intelligent evaluation system.
Data Preprocessing for Learning, Analyzing and Detecting Scene Text Video based on Rotational Gradient

Challenging annotated video datasets are in huge demand for the researchers and embedded industrials to learn and build an artificial intelligence for detecting, localizing and classifying the objects of interest aimed at various applications under pattern recognition and computer vision domain. It is very significant to produce those annotated sets to the respective communal. This paper focuses on text as annotated data in video for detection, localization, tracking and classification to solve several optical character recognition (OCR) based problems. Text is very essential in understanding the nature of the video because of diverse applications which are in renowned today like video retrieval and searching, driverless cars, industrial goods automation, geocoding and many more. Hence, it is important to understand how to create, prepare and load datasets to make ready for the machine to learn and understand. First, we have applied bilateral filter to preserve the edge information. Then, rotational gradient approach is proposed to detect the text in variable viewpoints. Later, the combination of morphology and contours has applied to generate blobs with bounding box around the detected regions by eradicating quasi text areas. The simulation results have shown better performance than traditional techniques with better detection rate on ICDAR Robust Reading Competition on Text in Video 2013-15 datasets.
HaptWarp: Soft Printable and Motion Sensible Game Controller

Many interaction techniques have been introduced for controlling virtual reality contents and navigation.Traditional controllers need to map between two-dimensional inputs and three-dimensional actions while interactive surfaces enable more natural approaches, they still lack tactile feedback.We present real-virtual bend and twist, a tactile feedback technology that delivers the twist and bends effect into a virtual environment.The method uses two inertial measurement unit (IMU) sensors fixed on both sides on the 3D printed soft material bar to acquire object deformation to simulate the virtual deformation based on the changes in the angle exerted on a physical device.People can customize the 3D printed connection shape to apply to different virtual scenarios.
SYMMeTRy: Exploiting MIMO Self-Similarity for Under-Determined Modulation Recognition

Modulation recognition (modrec) seeks to identify the modulation of a transmitter from coresponding spectrum scans. It is an essential functional component of future spectrum sensing with critical applications in dynamic spectrum access and spectrum enforcement. While predominantly studied in single-input single-output (SISO) systems, practical modrec for multiple-input multiple-output (MIMO) communications requires more research attention. Existing MIMO modrec impose stringent requirements of fully- or over-determined sensing front-end, i.e. the number of sensor antennas should exceed that at the transmitter. This poses a prohibitive sensor cost even for simple 2x2 MIMO systems and will severely hamper progress in flexible spectrum access. We design a MIMO modrec framework that enables efficient and cost-effective modulation classification for under-determined settings involving fewer sensor antennas than those used for transmission. Our key idea is to exploit the inherent multi-scale self-similarity of MIMO modulation IQ constellations, which persists in under-determined settings. Our framework, called SYMMeTRy (Self-similaritY for MIMO ModulaTion Recognition), designs domain-aware classification features with high discriminative potential by summarizing regularities of symbol co-location in the MIMO constellation. To this end, we summarize the fractal geometry of observed samples to extract discriminative features for supervised MIMO modrec. We evaluate SYMMeTRy in a realistic simulation and in a small-scale MIMO testbed. We demonstrate that it maintains high and consistent performance across various noise regimes, channel fading conditions and with increasing MIMO transmitter complexity. Our efforts highlight SYMMeTRy's high potential to enable efficient and practical MIMO modrec in spectrum sensing infrastructures with mixed-complexity sensors.
Pedestrian Behavior Anomaly Detection Based on Dynamic Mode Decomposition and One-Class SVM

Anomaly detection of human behavior, which has been widely applied in video supervision in recent years, aims at recognizing the human behaviors that are out of the normal scope in high-level semantic. With the development of deep learning (DL), many DL-based methods for behavior recognition have been developed. However, the lack of negative samples hinders the application of DL-based methods. Thus, this work proposed a new framework based on dynamic mode decomposition (DMD)which is free from the requirements of negative samples. First, key points of the human body in each frame of a video are exacted by the -deep neural networks (DNN)which have been solved in recent studies. Second, the key points are projected into a mode space of much lower dimensions with DMD. At last, the detection results are obtained with One-class SVM, which is a classification method that does not require negative samples. The data set we use in the experiment is the CMU motion data set. Experiment show that the accuracy of DMD combined with support vector classification (SVC) achieve 85% while the accuracy of DMD combined with one-class SVM achieve 81%, and the proposed framework can distinguish anomaly behavior with a small number of samples.
Artificial Intelligence Simulation System in Sports Training Under VR Technology

Virtual reality technology has very unique advantages in sports training. For example, it can effectively monitor, predict and evaluate sports technical movements, allowing virtual athletes to try new movements in advance, reducing the risk of sports training, and improving sports training efficiency. This article aims to study the artificial intelligence simulation system in sports training under VR technology. This article uses virtual scene position mapping technology to realize the movement of the upper limbs of the human body. The movement of the different joints of the upper limbs drives the rotation of the corresponding degrees of freedom of the mechanical arm in the mechanical system, which causes the rotation of the sensor, and then realizes the change of the sensor voltage. This article compares the virtual reality results of diving athletes with the training results through comparative analysis methods, and finds out the gap. Experimental data shows that VR technology is mainly used in diving training to help athletes obtain three-dimensional sports information and perform three-dimensional realistic simulation of athletes, because the technical analysis of athletes is all based on obtaining three-dimensional sports information. The results of the experiment show that 85% of athletes believe that the learning of artificial intelligence simulation systems and technologies is of great help to real-life operations and applications, 10% of athletes think it is helpful, and 5% of athletes think it is not helpful. Through the three-dimensional virtual reality simulation of diving athletes’ movements, different levels of movement are truly displayed. This helps to reduce the risk of athletes’ personal experience in the process of action and technological innovation.
Learning to Teach Global Competencies in a Transforming Digital World

In our globally connected, ever-changing society, the ability to adapt to new environments and technologies can greatly enhance success. In Canada,“21st century skills” are being prioritized in the education system so that young children can develop skills to thrive in our technologically advanced world. However, current teaching practices do not always appear to include 21st century skills in the curriculum. This chapter seeks to examine Canadian university Bachelor of Education programs to gather information about where, and how often, 21st century skill training occurs in pre-service teacher education. A keyword search was conducted on program and course descriptions from 45 Canadian university websites to determine where 21st century skill terminology was present. Next, a more in-depth examination of one specific teacher education program in a consecutive, pre-service program in a mid-sized urban centre in Ontario was conducted. Recommendations are discussed for pre-service education in support of integrating 21st century skills in teacher preparation.
A Center-Based Light and Simple Method for Multi-Object Tracking

Traditionally, multi-object tracking is a technique for tracking points of interest through space and time. With the rise of powerful deep networks, this situation has changed. Nowadays, tracking mainly consists of pipelines that perform object detection and time correlation (also called tracking by detection). However, in order to extract more representative functions, many State-Of-The-Art (SOTA) models use a lot of complex backbones, which hindered the speed of inference. We proposed a new multi-object tracking model named Squeezed-Excited-Center-Based tracking (SECB-tracking), and replace the original IoU loss with the new Complete IoU loss, which can help the model converge faster. We evaluated our proposed method on the MOT17 dataset. Compared with CenterTrack, we have increased MOTA by 7 points (a relative increase of 10.3%) and reduced inference time by 20%.
A Novel Approach for AES Encryption–Decryption Using AngularJS

The goal of this paper is to implement a simple encryption–decryption web application that utilizes the facility provided by AngularJS. The advanced encryption standard (AES) algorithm is implemented using the methods from the CryptoJS library. This algorithm was chosen, as it is a U.S. Federal Information Processing Standard, which was selected after a thorough process, trumping 15 competing solutions. The advantages associated with using AngularJS include the fact that it is quick to learn a language. Furthermore, two-way data-binding capability means that developer intervention is not required for data binding. Developing powerful Web applications is made easier since AngularJS architecture separates data from design. Additionally, the developers must be familiar with the model-view-controller architecture to work efficiently with AngularJS. Plaintext or instances of CryptoJS.lib.word array library may be accepted as inputs. When a string is passed for a certain key, it is considered as the passphrase, which is subsequently used to derive the key. When it comes to the CipherText, strings, or CryptoJS.lib.CipherParams, instances are accepted. All strings passed are eventually converted to a CipherParams object. Coming to the cipher output, the plain text derived after the decryption process is a word array object. The CipherText returned is a CipherParams object, which gives access to the parameters used during encryption. Ample flexibility is provided, allowing various formats to be used. Essentially, a format is nothing but two-method objects—stringify and parse. This help converts CipherParams objects and CipherText strings. The practical implementation of the method involves downloading and installing NodeJS, AngularCLI, and Visual Studio code, the detailed explanation of which is elaborated under the proposed method section. AES algorithm performs well irrespective of the size of data to be encrypted as it has a big-O time complexity which is constant—O(1).
Commodity Image Retrieval Based on Convolutional Neural Network and Late Fusion

Commodity image retrieval has great research value in the field of e-commerce. However, at the same time, the diversity of commodity images makes the effectiveness of commodity retrieval often unsatisfactory. For image retrieval, the question of whether a given query image is the best query result for the underlying framework using a convolutional neural network as a feature extractor is often neglected. Inspired by the superiority of deep learning in image content understanding and powerful image feature extraction, this paper proposes a new deep learning-based framework for commodity image retrieval to make commodity image retrieval have a better retrieval effect. The framework achieves an efficient retrieval of images by combining deep Convolutional Neural Networks for the late fusion of images at the scoring level. A database of 35 categories of commodities containing 3500 images was created for experimental validation in this paper. Experiments comparing the performance between the frameworks using our dataset show that this paper’s proposed framework has higher retrieval accuracy than the base framework.
Towards real-time monocular depth estimation for mobile systems

Nowadays, thanks to the development of new advanced driver-assistance systems (ADAS) able to help drivers in driving tasks, autonomous driving is becoming part of our lives. This massive development is mainly due to the great possibilities of guaranteeing higher safety levels that these systems can offer to vehicles that every day travel on our roads. At the heart of every application in this area, there stands the environment’s perception, guiding the vehicle’s behavior. This counts in autonomous driving field and all the applications characterized by a system that moves in the 3D real-world like robotics, augmented reality, etc. For this purpose, an effective 3D perception system is necessary to accurately localize all the objects that compose the scene and reconstruct it in a 3D model. This issue is often faced using LIDAR sensors, which allow an accurate 3D perception offering high robustness in unfavorable light and weather conditions. But these sensors are generally expensive, and thus do not represent the right choice for low-cost vehicles and robots. Moreover, they need to be used in a particular position that does not permit integrating it on the car changing both the appearance and the aerodynamics. Besides, their output is a point cloud data that, due to its structure is not easily manageable with deep learning models that promise outstanding results in various similar predictive tasks. For these reasons, in some applications, it is better to leverage other sensors like RGB cameras to estimate 3D perception. For this purpose, more classic approaches are based on stereo-cameras, RGB-D cameras, and stereo from motion, which generally can reconstruct the scene with less accuracy than LIDARS, but still produce acceptable results. In recent years, several approaches have been proposed in literature which aim to estimate the depth from a monocular camera leveraging deep learning models. Some of these methods use a supervised approach,1, 2 however they mainly rely on annotated datasets which in practice can be labor-expensive to be collected. Thus, some works3, 4 use, on the contrary, self-supervised training procedure leveraging reprojection error. Notwithstanding their good performance, most of the proposed approaches use very deep neural networks, that are power- and resource- consuming and need high-end GPUs to produce results in real-time. For these reasons, these approaches cannot be used in systems with power and computational limits. In this work, we propose a new approach based on a standard CNN proposed in the literature to deal with the image segmentation problem created not to be highly resource-dependent. For the training, we used the knowledge-distillation method using an out of shell pre-trained network as teacher network. We execute large scale experiments to qualitatively and quantitatively compare our results with those obtained with baselines. Moreover, we propose a deep study of inference times using both general-purpose and mobile architectures.