Reviewerly/exHarmony huggingface dataset:
Sentimental Analysis, Technology and Application

Sentiment analysis, is often called opinion mining. It is the process of studying people's feelings or opinions based on text data. Sentiment analysis plays an important role in the research area of text mining. It identifies the sentiment or opinion of each person with respect to specific events. For this process, we need to pass a document or text which can be analysed and generates a system or model which represents a summarized form of opinion of a given document. Sentiment analysis is useful for review of movies, products, customer services, opinion about any event etc. This helps us to make decisions regarding a specific item or service, whether it is bad or good or preferred or not preferred. It can be useful in order to identify the opinion of an individual or about any event or person and also finds polarity of text whether positive, negative or neutral. In this, we have discussed the various methods and techniques of sentimental analysis and the areas which are using this technique based on some specific data.
Self-Surpervised Tableware Grasp Learning from Visual Information and Grasp Pose Selection Considering Grasp Redundancy

In the research of object grasping, systems that output consistent results from recognition to grasp motion have been actively studied. Usually, a single grasp point is determined even though an object such as tableware has redundancy to be grasped. In addition, it is difficult to reflect the input constraints due to the robot’s hardware or the surrounding environment. In this study, we propose a neural network that modifies the grasp pose around the initial pose from visual information and the actual trial. Our system can autonomously collect supervised data so that the robot can learn by itself. Since the search points are narrowed down to the edge points of the object, the real robot can efficiently acquire the grasp ability in fewer trials. As a result, it can grasp unknown objects, and flexibly change its grasp position because the input can be easily constrained.
Neural Network Pruning via Genetic Wavelet Channel Search

Neural network pruning has been commonly adopted for alleviating the computational cost of resource-limited devices. Neural architecture search (NAS) is an efficient approach to facilitate neural network compression. However, most existing NAS-based algorithms focus on searching the best sub-network architecture at the layer level, while ignoring the channels that contain richer information. Meanwhile, fixing one pruning rate could not be suitable for all the layers with various features. In this paper, we present a novel NAS-based network pruning strategy called genetic wavelet channel search (GWCS) model, which automatically prunes the network at the channel level with two-fold ideas: (a) Each channel in the whole network is gene coded and pruned adaptively in multiple stages with dynamic genetic selection, and (b) the fitness function of channels is carefully designed by calculating the similarity between the pre-trained network and pruned network using wavelet decomposition to discover the most representative and discriminative channels. Extensive experiments are conducted on CIFAR-10 and CIFAR-100 using ResNet series networks. The proposed GWCS method outperforms other existing pruning approaches in terms of accuracy with a higher compression rate. Our source code is freely available at https://github.com/MGongsj/Network-pruning.git.
Mutual entity authentication protocol for mobile cloud computing

Mobile cloud computing offers a combination of both mobile computing and cloud computing services. Mobile cloud computing has many advantages like high mobility, high availability, high accessibility, adaptability to different types of devices of various configurations and screen sizes. Despite these advantages, MCC has its disadvantages. The disadvantages include connectivity issues, performance issues, and security threats to the data stored in the cloud. [5] Mobile cloud computing provides data transmission through an insecure channel with the help of the internet. So there is a possible risk of security threats or attacks like man-in-the-middle attack, impersonation, masquerading, etc. For providing a solution to these problems, there is a need for an authentication protocol that ensures secure data transmission. A protocol named Mutual Entity Authentication Protocol is defined, which will help us overcome the limitations possessed by Mobile Cloud Computing. This protocol uses an OTP to authenticate the user. [4] The OTP is formed by concatenating the username, password, and IMEI number of our mobile phone. [6] The OTP generated is encrypted by Triple Data Encryption Standard Algorithm in which a randomly generated key given to the algorithm is helpful for encryption. This protocol also uses Data Encryption Standard Algorithm to encrypt the files uploaded in the cloud. Not only for the 3DES algorithm, the key generated for the DES algorithm is also generated randomly. The protocol proposed is implemented and tested using Java EE to show its efficiency in authenticating the user and providing security during the data transmission. Compared with the existing protocols, the proposed protocol results demonstrates that it delivers better security for user's authentication and data transmission.
Sustainable solar power from wall mounted photovoltaics

The plausibility of wall-mounting of photovoltaics in inaccessible or restricted rooftops to generate power necessitated this study. Meeting energy consumption demands is an infrastructural challenge in several developing economies. Power generation could leverage on the photoelectric effect from intense diffuse radiation and intermittent direct solar radiation abundantly available in tropical Africa, near the equator. A test-bed was developed and an investigation was conducted into the energy consumption needs of small and medium scale buildings; namely, offices and small homesteads. The prototype test-bed was found to provide all the power requirements of an average consumer utilizing less than 5000 Watts daily load from the Sun's conventional daily migration from east to west. Increase in generated and consumed Wattage has been observed to increase with scalability of photovoltaics participating in the east, front and west wings of test-bed. The spatial analysis of the trajectory of solar energy for both directed and diffused intense solar radiation was also carried out in this work. The need of blocking power leaks to dormant photovoltaics due to conduction through less resistance pathway by active photovoltaics, via diode compensation or relay blocking was also discovered to increase the overall power generated that was available to the interconnected loads.
Using machine learning approach to predict short-term mortality risk of acute myocardial infarction after emergency admission

The short-term mortality risk of Acute Myocardial Infarction (AMI) remains high all over the world. Accurately predicting risk of death may assist physicians’ decisions on medical treatments and advance care planning for patients. The Taiwan National Health Insurance Research Database (NHIRD) was applied to construct a model to predict whether a diagnosed AMI patient could turn into a severe condition within a short interval. We used IQR and propensity score matching to select control group subjects, and Kaplan Meier method to define a duration boundary for distinguishing two patient groups. Both chi-square test and p-value were applied to evaluate and screen statistical significance of each feature. An XGBoost model was applied to construct a prediction model and compare prediction results at different stages. System performance was optimized by tuning parameters based on increasing recall rate as much as possible. The proposed prediction system can determine mortality risk of patients immediately and prevent delays in treatment. The results showed that although various attempts were tried to optimize the system with different feature combinations, the prediction performance was yet unsatisfied due to NHIRD providing limited longitudinal diagnosed records and medicine information only. No biometric feature was collected in the NHIRD and it only achieved a low recall rate of 56%. It is firmly believed that features obtained from biometric screening test could provide important factors for a better prediction system and precision treatment in practical clinical applications.
Mini UAV Orientation Control based on Face Tracking Algorithm

This research proposes an orientation control algorithm for unmanned aerial vehicle (UAV) type quadcopter for entertainment uses such as photoshoot and video vlogging. The algorithm consists of face detection, feature extraction, face recognition, and face tracking. There are two experiments designed in this study. The first experiment is used to determine the adaptability of the face recognition algorithm. The second experiment is used to measure the difference between the desired orientation and the actual orientation. The experimental result shows that the proposed algorithm is adaptable. However, there are still several improvements needed such as the recognition performance and orientation accuracy. In conclusion, the current constructed algorithm is promising for further development.
Implementation and Optimization of Image Processing on the Map of SABRE i.MX_6

Vision relieves humans to understand the environmental deviations over a period. These deviations are seen by capturing the images. The digital image plays a dynamic role in everyday life. One of the processes of optimizing the details of an image whilst removing the random noise is image denoising. It is a well-explored research topic in the field of image processing. In the past, the progress made in image denoising has advanced from the improved modeling of digital images. Hence, the major challenges of the image process denoising algorithm is to advance the visual appearance whilst preserving the other details of the real image. Significant research today focuses on wavelet-based denoising methods. This research paper presents a new approach to understand the Sobel imaging process algorithm on the Linux platform and develop an effective algorithm by using different optimization techniques on SABRE i.MX_6. Our work concentrated more on the image process algorithm optimization. By using the OpenCV environment, this paper is intended to simulate a Salt and Pepper noisy phenomenon and remove the noisy pixels by using Median Filter Algorithm. The Sobel convolution method included and used in the design of a Sobel Filter and then process the image following the median filter, to achieve an effective edge detection result. Finally, this paper optimizes the algorithm on SABRE i.MX_6 Linux environment. By using algorithmic optimization (lower complexity algorithm in the mathematical sense, using appropriate data structures), optimization for RISC (loop unrolling) processors, including optimization for efficient use of hardware resources (access to data, cache management and multi-thread), this paper analyzed the different response parameters of the system with varied inputs, different compiler options (O1, O2, or O3), and different doping degrees. The proposed denoising algorithm shows the meaningful addition of the visual quality of the images and the algorithmic optimization assessment.
Accommodation Finder: An Augmented Reality Based Mobile Application Integrated with Smart Contracts

Accommodation is one of the basic needs for travelers, tourists, students, and employees. Accommodations range from low-budget lodges to world-class luxury hotels, but finding the preferable accommodation is undoubtedly a tedious task. And due to the COVID-19 pandemic, it has become problematic state to visit each accommodation property to check whether it's suitable for the accommodation seeker, considering the location, environment, and to check if the property matches the user’s preferences. There have been incidents reported where thousands of people have been victimized because of contract breaches in the accommodation and real estate sectors, recurring from contract alterations. Considering these problems, we have proposed a system to provide solutions using Natural Language Processing (NLP), Automatic Speech Recognition (ASR), Augmented Reality (AR), Block-chain, and K-Nearest Neighbor (KNN). This system provides an efficient approach to viewing the exterior and interior of an accommodation using 360-degree views, providing recommendations to the user based on user preferences using KNN and cosine similarity, providing security in a digital agreement using blockchain technology, and a map navigation system using ASR. With the aid of the previously mentioned techniques, a mobile application prototype is created with the possibility of future testing and implementation.
Evaluation Model of English–Chinese Cross-Language Initiation Oral Teaching Based on SOFMNN

With the continuous deepening and development of the current higher education teaching reform, the research on the quality of education has attracted more and more attention. Therefore, the establishment and improvement of the teaching quality evaluation (TE) system is of great significance to education management. The evaluation of English–Chinese cross-language initiation of oral English teaching quality is a multi-dimensional and huge system. In the actual evaluation, there are evaluation deviations caused by the evaluator’s subjective factors. At the same time, most of the existing evaluation methods can only carry out quantitative evaluation on the score, but not the qualitative classification and evaluation of teachers’ classroom teaching effect. Based on this, this article starts with the study of the English–Chinese cross-language oral TE model based on the SOFM neural network (SOFMNN). This paper proposes a method to evaluate the quality of oral English teaching using SOFMNN constructs a TE model based on SOFMNN, and trains and tests the generalization ability of the model. The research results show that the applicable evaluation model can classify the teaching quality of teachers according to the position of the winning neuron in the competition. The network has the characteristics of fast convergence, high classification accuracy, and strong generalization.
Assistive Technology-Based Solution for Hearing Impairment Using Smartphones

Developing a system for sign language recognition becomes essential for the deaf as well as a mute person. The recognition system acts as a translator between a disabled and an able person. This eliminates the hindrances in the exchange of ideas. Most of the existing systems are very poorly designed with limited support for the needs of their day to day facilities. The proposed system embedded with gesture recognition capability has been introduced here which extracts signs from a video sequence and displays them on screen. On the other hand, a speech to text as well as text to speech system is also introduced to further facilitate the grieved people. To get the best out of a human-computer relationship, the proposed solution consists of various cutting-edge technologies and Machine Learning based sign recognition models that have been trained by using TensorFlow and Keras library. The proposed architecture works better than several gesture recognition techniques like background elimination and conversion to HSV
Benchmarking Multi-instance Learning for Multivariate Time Series Analysis

Successful incorporation of Electronic Health Records to the data mining tools created new frontiers in digital clinical data analysis. One of the well-known applications of clinical data analysis is the mortality prediction of patients in intensive care units (ICUs). One important aspect of mortality prediction is the analysis of multivariate time series of observations after 24 or 48 h of ICU admission. Recent mortality prediction models for ICU patients are based on either recurrent neural networks or traditional machine learning algorithms using statistical summaries of timestamped observations. Instead of using complex neural network architectures and statistical summaries, we transform multivariate time series into multi-instance representation by keeping the expressiveness of the original observations. We then perform mortality prediction using multi-instance machine learning algorithms. Our empirical study shows that multi-instance representation achieves comparable or better (in some configurations) performance in various experiments.
A Novel Hyperchaos-Based Image Encryption Algorithm with Two Scrambling-Diffusion Operations

This paper proposes a hyperchaos-based image encryption algorithm, which consists of two scrambling-diffusion operations and one scrambling operation. In the proposed algorithm, the initial conditions of a hyperchaotic Chen system are first generated using Message Digest 5 (MD5) value of digital images and giving initial values, and then the images will be encrypted using the key stream generated by the system. Since the initial values of hyperchaotic Chen systems are related to plaintext and the encryption process is related to the images, this algorithm is able to effectively protect images against selective plaintext attacks. Simulation results demonstrated that the present algorithm offers enhanced encryption performance, high security, and strong resistance to known attacks. Therefore, it may find wide application in image encryption transmission.
Incremental Learning for Radio Frequency Fingerprint Identification

With the rapid development of Internet of Things technology, wireless communication become an essential part in every field, which also bring about many wireless communication security problems. Traditional solutions to wireless communication security problems are mostly at the software level and protocol level, ignoring the physical characteristics of the device itself. Radio frequency fingerprint (RFF) can distinguish different devices in the physical level. Most of the existing incremental learning based radio frequency fingerprint identification (RFFI) are need a large amount of old data. In this paper, we review lots of RFFI method based on ML, DL or IL, and summarize a generic framework for RFFI, and propose our method to efficiently reduce the needed amount of old data in IL based RFFI, which saves training time and storage space.
An Empirical Study of Evaluating the Correlation between Class Stability and Bad Smells

Several quality attributes are used in designing and developing object-oriented software. Some of these quality attributes have high influence on developing desirable high-quality software (i.e. stable software) by reducing the maintenance cost and efforts. One of these quality attributes is stability. Software quality attributes can be affected by many factors. One of these factors is bad smells. The main objective of this empirical study is to investigate the correlations between bad smells and the stability on the level of class. Proper software metrics such as Class Stability Metric (CSM) will be used to measure class stability. In addition, different bad smells such as Fowler bad smells are collected and correlated with CSM. The results show that there is a negative correlation between bad smells and class stability.
A Study on Image Generation Methods for the Pose Estimation of Surgical Tools

Using nasal endoscopic surgery as a case study, we are investigating the pose estimation of surgical tools using endoscopic images. Current state-of-the-art algorithms are well known to require a large amount of training data. Our group has been investigating the generation of synthetic images with realistic appearance to consistently address this difficulty using photorealistic rendering. In this work, we preliminarily investigate the use of generative-adversarial networks to further improve the rendered images' appearance. Our current implementation reveals some difficulties in this strategy and highlights directions for future work.
Extreme learning machine with confidence interval based bias initialization

The extreme learning machine (ELM) neural network has got noticeable momentum in the computational intelligence and machine learning communities. However, the random initialization of the input weights and biases of classical ELM increases its sensibility to input perturbations and results in poor network stability. In this work, we propose a novel approach, named confidence random bias ELM (CRB-ELM), that inherits the randomness of the ELM for bias tuning based on confidence interval and confidence level. The experimental comparison of CRB-ELM to the classical ELM and the base projection vector machine reports that CRB-ELM achieves higher performance in classification and regression problems, being more stable over several benchmark datasets.
Achieving a Security Culture

A security culture can be a competitive advantage when employees uphold strong values for the protection of information and exhibit behavior that is in compliance with policies, thereby introducing minimal incidents and breaches. The security culture in an organization might, though, not be similar among departments, job levels, or even generation groups. It can pose a risk when it is not conducive to the protection of information and when security incidents and breaches occur due to employee error or negligence. This chapter aims to give organizations an overview of the concept of security culture, the factors that could influence it, an approach to assess the security culture, and to prioritize and tailor interventions for high-risk areas. The outcome of the security culture assessment can be used as input to define security awareness, training, and education programs aiding employees to exhibit behavior that is in compliance with security policies.
Hybrid Approach for Fall Detection Based on Machine Learning

The number of older people living independently in their own homes is growing worldwide due to the high cost of health care services. Accordingly, it is critical to developing an accurate system with the intelligence to detect human falls during daily activities. In this study, an intelligent system is suggested to effectively detect the fall from daily life activities and to reduce the false alarm. This system employed Machine Learning (ML) techniques because of its classification capabilities. The proposed method is tested and evaluated based on a publicly available University of Rzeszow Fall Detection dataset (URFD). This dataset contains two different signals, namely depth maps from Kinect camera and tri-axial data from ADXL345 accelerometer.
Research on the Design of Mobile Guiding in Art Museum Based on Augmented Reality

According to the current application of augmented reality and art appreciation, this study is to design a set of guide tool with mobile augmented reality in painting appreciation, and compare the learning performance of students using the augmented reality guide with those using the audio guide and without any assistance devices. This research was adopted the quasi-experimental design with nonequivalent pretest-post-test design, and the participants were 135 undergraduates. The independent variables were three painting guide ways; the dependent variables included learning outcomes, flow experience and learners’ attitude on the use and acceptance of guide systems.The results showed that: (a) the augmented reality guide promoted the learning performance in painting appreciation; (b) the augmented reality guide promoted the flow experience; (c) some of the learners had positive attitudes on the use and acceptance of the mobile augmented reality guide system; (d) overview of the above factors, we could summarize standardized services of the mobile augmented reality guide system.