Reviewerly/exHarmony huggingface dataset:
During and After Study Abroad Period: Perception and Attitudes on English Development and Maintenance

&lt;em&gt;This study examines the development and maintenance of English skills from the perspective of higher education alumni during and after studying abroad. The researcher consider attitudes, more specifically how, what, and to what extent attitudes could encourage development and maintenance in the context of study abroad from the 120 respondents’ perspective. This study deploys quantitative data collection in form of a survey with both multiple choice items and open-ended questions. The numeric items were analyzed with tabulation and the open-ended questions responses were analyzed with the stages of Thematic Analysis. Foremost, the findings reveal that the respondents perceive oral production and fluency as the major skills that improved during the study abroad period, followed by an increase in vocabulary knowledge and listening skills. Most respondents regarded the improvement as an influence of high exposure to spoken English. Secondly, the majority of respondents conceive that their English keeps improving after the study abroad period. The most frequent reasons for using English are social life and entertainment purposes, which are mostly individual preferences, followed by academic purposes such as joining a course or working on journal articles. Work purpose, surprisingly, is not the main reason of using English after study abroad period. The conclusion is that study abroad benefits the respondents in developing their English and that attitude on affective domain (how we perceive a language) and conative (behaviour toward a language) help to maintain the cognitive domain (knowledge on a language) and its maintenance.&lt;/em&gt;
Finding Efficiency in Approach Selection in Software Development and Project Management in Bangladesh IT Industry

Here we have tried to find out suitable methods for IT companies of different ages. Some subject companies were chosen and given a survey form from which we got some data about different aged IT companies using different development methods. The models were between agile, agile process model, and other models. Through the survey, we found some data which can help different aged IT companies find the right model for their project development. The data analysis shows the suitable project method for different age companies. The subject companies also listed why they use their method and how long have been they using it. This survey result will help companies that are struggling to grow or companies which are about to start their project method. A good project method can bring success to a company.
Mapping Multimodel Scrolling Techniques for Foot-based Interaction Depending on the Cursor Position

Foot-based input provides an alternative interaction mechanism in the situation where the hands are occupied or unavailable. Our research has implemented and evaluated 5 different mathematical models, i.e., absolute value function, quadratic function, whitey's square-root function, full continuous function, and modified square root function along with Kobayashi and Igarashi [10] equation for foot-based scrolling techniques. We have considered mapping strategies, mapping techniques, input parameters, and gestural mapping in our design. The proposed techniques were evaluated by 24 participants to analyze the efficiency and accuracy of foot-based scrolling tasks to interact with 2D radiological images. The experimental result shows that there were no significant differences among mapping techniques. However, the users preferred whitney's square root mapping technique in respect of precision, faster and low fatigues in comparison to other proposed scrolling techniques.
Machine Learning Method Based Industrial Risk Analysis and Prediction

IoT-based technologies growing all over the world. After the industrial revolution, machines and robots gradually replaced human effort. In the absence of the human brain-machine and robots makes an error. In this paper, a plan was developed to get out of this situation that works not only efficiently but also thinks like humans. In this system, the machine will learn based on the situation that has been made by any occurrence. In this work Raspberry Pi-based system helps to make a proper analysis of the machines. Voltage, current, gas value, and temperate values are taken as input parameters. Machine learning matches/compares these real-time sensor data with training data (which is used to train the system). As a result, The machine learning module provides some statistics graphs of sensor data. Machine performance can analyze by observing these graphs. Also, determine the efficiency and predict the possibility of upcoming threats or risks.
High Cursive Complex Character Recognition using GAN External Classifier

Handwritten characters can be trickier to classify with the increase of their complex and cursive nature in comparison with the characters which are simple and non-cursive. We present an external classifier along with a Generative Adversarial Network that has the capability of classifying high cursive and complex characters. The generator network will generate fake handwritten character images, and these generated images are used to augment training data after adding some adversarially perturbed noise and getting confidence metric above the threshold value with the discriminator network. This result shows that the accuracy of the convolutional neural networks degrades with the increase of complexity of the character images, but our proposed model i.e. ADA-GAN is more robust and sophisticated with high cursive as well as complex characters.
Banking Management System Architecture Using AI &amp; Blockchain

The digital revolution has changed many elements within the online banking industries. Even with many state of the art advancements, there are technologies to help secure the banking system on their digital platforms. So therefore it is crucial to have a complete secure system architecture, where various security features aid the digitized platform. In this paper, a banking management system architecture is proposed where the system is devised with the interaction of three modern technologies: Artificial Intelligence, Blockchain, and Hyperledger. All these three technologies are working together as one part of a multi-layer security and management system, as well as, it improves the security of the bank. In order to analyze the implementation of these systems, each of the architectures is defined separately with comparison-based evaluation to derive the fact how these technologies within our system architecture works efficiently.
AR Lab/Practical Simulation Book for Physics Chemistry &amp; Computer Science

Practical / Lab AR Simulation book for physics, chemistry and computer science is an important part for practical simulation learning. For the development of the system/framework/Application on AR Simulation book, the Public University/Private University practical lab manuals, NCTB registered practical book as the case study for this study because there is often difficult to understand as well as trouble to interpret when viewing a Practical Simulation book's images. To overcome this problem there is a need to be further study how to reinterpret this problem with a better understanding using AR system/framework/Application. Students always have a problem to interact with the practical knowledge in their text-book. As a developing country, education system (for both public and private) and the quality of education in Bangladesh is limited.[24] The practical simulation is very costly in terms of buying the correct equipment. Sometimes the limited number of equipment is insufficient, as an example, the numbers of students in School/College/University with respect to the teachers or faculties. The ratio is not maintained. This study reviewed previous approaches of Simulation based AR system in education to identify drawbacks and strengths in Education base practical simulation. From the review of this study to engage students in more interactive way to learn their practical simulation in their domain of study. The goal is to implementing an Augmented Reality based simulation book where the proposed AR system will reduce the difficulties during practical work that most of the students are facing. This study proposes to design an Augmented Reality based framework for Simulation Book and thus the development of proposed AR system using the framework where the students can utilized the AR system with simulation book for their practical work effectively. This research/study is focused on the development of 3D/2D objects, audio-visual and interaction in Augmented Reality that allows students to understand the scientific practical solution with the proposed AR framework. This framework been conducted by a group of users through a survey. The study shows evaluation of how effective AR Practical simulation book in practical learning education. The evaluation on Practical/Lab AR simulation book is very much effective (overlays more information using AR) for a better understanding of practical lab simulation in comparison with the manual simulation books.
An Expedition to Intelligent Diagnosis of Bone Cancer and It's Direction from Capsule Network

Although Early diagnosis is the lifesaving strategy and superfluous appurtenant to bone cancer. Pathologists, however, find it incommodious to detect bone cancer early due to cellular heterogeneity in Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) images. In this regard, many image processing techniques can assist doctors in classifying cancer and non-cancer samples intelligently. Among various image processing and data mining techniques, Deep Learning outperforms other techniques in image classification applications. However, popular Deep Learning architectures (i.e. AlexNet, VGG-16, Inception-Net, ResNet etc.) still have some serious drawbacks where higher level layers combine lower level features and then construct result regardless of order and spatial relations among the image features. Moreover, it exhibits less accuracy with small dataset in bone cancer classification. This research aims to classify bone cancer from MRI and CT images with high accuracy and less training data as there is scarcity of real bone cancer data. Therefore, we propose an approach combining with Capsule Network which satisfies the research aim. The qualitative and numerical analysis exhibits that, proposed framework provides upper horizontal accuracy than other Deep Learning techniques with the percentage of 95.26 while detecting the bone cancer.
Machine Learning With Gaussian Process Regression For Time-Varying Channel Estimation

The minimum mean-squared error (MMSE) estimator is recognized as the best estimator for measuring transmission channel distortion in orthogonal frequency division multiplexing (OFDM) using pilot-symbol assisted modulation (PSAM) in the presence of noise. In practice, however, the estimator suffers from high complexity and relies on the estimation of second-order statistics which may change rapidly within small-scale fading environments in a high-mobility wireless transmission system. We propose using machine learning (ML) with Gaussian Process Regression (GPR) to adaptively learn the hyperparameters of a channel model, which then can be used to calculate the MMSE estimates. Moreover, GPR can be used to more accurately interpolate the channel estimates in between pilot symbols compared to linear interpolation techniques. After describing the learning process and its equivalency to MMSE, we derive the BER for a receiver using GPR for time-domain interpolation, then use BER to find a practical bound on the number of training points needed to achieve best performance. We show that the performance of GPR-based ML is comparable to that of more complex neural network-based ML.
Joint Scheduling and Power Control for Efficient Consensus Transmission in Wireless Blockchain Systems

This paper proposes to implement the wireless blockchain in a cellular system and focuses on the communication demanding consensus processes. The consensus processes within the cellular network based on three typical consensus protocols, i.e., PoW, Raft and PBFT, are first analyzed. The delay optimal transmission problems subject to consensus traffic demands and power constraints are formulated for uplinks and downlinks respectively. The goal is to minimize the transmission airtime by joint user scheduling and power control of the multi-cell network. We adopt the column generation (CG) and fractional programming (FP) methods to improve transmission efficiency in consensus process. The original problem is first decomposed by the CG method into a restricted master problem and a pricing problem. Then, the FP method is adaptively adapted to optimize the pricing problem for potential improvement of the current solution. The two sub-problems are iteratively processed until the optimal solution is obtained. Numerical results demonstrate that the proposed CGFP algorithm efficiently mitigates the inter-user interference, attributing to the joint spatial-temporal optimization, and notably improves the transmission delay performance.
Efficient Privacy Preserving Nearest Neighboring Classification from Tree Structures and Secret Sharing

The k-nearest neighbor (kNN) algorithm is a very simple manner in the area of machine learning. It is a supervised method to classify according to the distance between different instances and is also widely used in solving some classification problems. It is expected to obtain better training with a larger dataset. However, how to perform kNN algorithm efficiently is an issue with privacy-preserving. In this paper, we proposed a privacy-preserving k -nearest neighboring scheme by secret sharing and improve the kNN classification by preprocessing with tree structures. Finally, the applicability of our method is shown by experiments with real datasets.
SIoT Selection, Clustering, and Routing for Federated Learning with Privacy-Preservation

With the advances in Social Internet of Things (SIoT) and Federated learning (FL), smart devices are now able to cooperatively and locally perform learning tasks to protect sensitive data by Differential Privacy (DP). On the other hand, Hierarchical FL (HFL) clusters SIoTs into multiple local training groups to reduce communication overheads by local aggregation. In this paper, we explore SIoT Training Group Construction (STGC) for HFL to minimize the total SIoT computation, communication and hiring costs, and the privacy cost for exploiting DP. We prove that STGC is NP-hard and inapproximable within any factor unless P = NP. Then, we design an algorithm with the ideas of Coverage Efficiency Indicator, Data Balance-aware Dual Adjustment, and Privacy-Aware Rerouting to choose and cluster SIoTs and to determine the aggregator for local training and SIoT routing in each cluster. Simulation results manifest that the proposed algorithm outperforms state-of-the-arts regarding the total cost, model accuracy, and convergence time.
On Improving Automated Detection of Cyber-Bully in Social Networks with Constrained Datasets: A Hierarchical Deep Learning Approach

During the recent years, online users, particularly in social networks, have witnessed an upsurge in racism, sexism, and other types of aggressive and cyberbully content, which are often manifested through offensive, abusive, or hateful speech and harassment. This can lead to severe physical and psychological stress in young children and adolescents, leading to even suicides and negatively affecting social policies. Therefore, there is a significant need to identify and regulate harassing content posted on the Internet in a smart, automated, and accurate manner. With this aim, in this paper, we design and develop a hierarchical framework comprising machine learning algorithms in order of higher computational complexity to adaptatively switch among them for efficiently detecting hateful and abusive content. We combine simple machine learning models such as Naive Bayes/Logistic Regression classifiers with customized calibration and Expectation-Maximization (EM) algorithms, and compare them with the much stronger deep learning techniques. Our proposed hierarchical framework demonstrates a significant improvement of the automated detection of abusive contents in social networks with a relatively small twitter dataset in contrast with the deep learning-based counterpart, namely the Bidirectional Encoder Representations from Transformers (BERT) model, training of which typically requires a much higher volume of labeled documents to detect abusive comments.
Improving Image-recognition Edge Caches with a Generative Adversarial Network

Image recognition is an essential task in several mobile applications. For instance, a smartphone can process a landmark photo to gather more information about its location. If the device does not have enough computational resources available, it offloads the processing task to a cloud infrastructure. Although this approach solves resource shortages, it introduces a communication delay. Image-recognition caches on the Internet’s edge can mitigate this problem. These caches run on servers close to mobile devices and stores information about previously recognized images. If the server receives a request with a photo stored in its cache, it replies to the device, avoiding cloud offloading. The main challenge for this cache is to verify if the received image matches a stored one. Furthermore, for outdoor photos, it is difficult to compare them if one was taken in the daytime and the other at nighttime. In that case, the cache might wrongly infer that they refer to different places, offloading the processing to the cloud. This work shows that a well-known generative adversarial network, called ToDayGAN, can solve this problem by generating daytime images using nighttime ones. We can thus use this translation to populate a cache with synthetic photos that can help image matching. We show that our solution reduces cloud offloading and, therefore, the application’s latency.
THE PERCEPTION ON USING DARK JOKES IN STUDENT DAILY CONVERSATION

In this modern era, people can easily get information from anywhere and anytime. People can also express their opinions and thoughts about a topic, for example about jokes. There are so many genres or types of jokes, one of them is dark jokes. Dark joke is a form of humor involving a twist or joke making the joke seen as offensive, harsh, horrid . Because of that, some people argue that dark jokes should not be used in daily life. The aim of this research is to analyze the students’ perception about using dark jokes in daily conversation English Department of Universitas Bandar Lampung and analyze some aspects of perception that affect students’ perception about using dark jokes in daily conversation. This research used case study method. The research was done at English Education Study Program Faculty of Teacher Training and Education Universitas Bandar Lampung in all semester. Data gathering was done by doing questionnaire and interview to nine students. The data was analyzed by combining the result from the questionnaire and interview. The results of this research elucidated that they have mix perception towards dark joke and using it in daily conversation. Some of them concur and some of them disagree that dark joke can be used in daily conversation.
Light: A Component Enhances Faster and More Accurate Traffic Measurement

The greatest challenge when designing an online sketch method for data flow measurement is to reduce the storage cost of sketches with little loss of accuracy and obtain a higher bandwidth. To address this issue, we proposed Light component. By storing elephant and mice flows separately, the accuracy of the Light-enhanced sketches is substantially improved, and the processing speed is significantly faster due to a significant reduction in the required computational overhead. We implement Light-enhanced sketches alongside several existing sketch methods on CPU and compare their performance. Experiments show that, under the same storage conditions, Light-enhanced sketches can greatly reduce the Average Relative Error by 1.80 to 5.07 times, as well as increase the average processing speed of each packet by 4.96 to 9.05 times compared with their original structure. Our approach also achieves a stable performance in the measurement of traffic in different traffic distributions. More importantly, Light component can be deployed to different sketch methods, which demonstrates its reusability.
Active Learning Augmented Folded Gaussian Model for Anomaly Detection in Smart Transportation

Smart transportation networks have become instrumental in smart city applications with the potential to enhance road safety, improve the traffic management system and driving experience. A Traffic Message Channel (TMC) is an IoT device that records the data collected from the vehicles and forwards it to the Road Side Units (RSUs). This data is further processed and shared with the vehicles to inquire the fastest route and incidents that can cause significant delays. The failure of the TMC sensors can have adverse effects on the transportation network. In this paper, we propose a Gaussian distribution based trust scoring model to identify anomalous TMC devices. Then we propose a semi-supervised active learning approach that reduces the manual labeling cost to determine the threshold to classify the honest and malicious devices. Extensive simulation results using real-world vehicular data from Nashville are provided to verify the accuracy of the proposed method.
Over-the-Air Computing Aided Federated Learning and Analytics via Belief Propagation Based Stochastic Bandits

This paper considers a mobile edge cloud (MEC) system where several distributed users collaboratively learn a global model by exploiting the over-the-air computing (AirComp) aided federated learning (FL) mechanism. Previous works focus on the FL training process, ignoring the generalization ability of the trained global model. To overcome this, we propose a novel AirComp-aided FL and federated analytics (FL&FA) framework to improve the generalization ability by making full use of user data and opinions. We first formulate this problem as an online user selection problem. Then, we further model it as a stochastic multi-armed bandit (SMAB) framework, where arms are the decentralized users and rewards are user opinions in FA. To tackle the decentralized feature among users, we put forth a belief propagation-based upper confidence bound (BP-UCB) algorithm to solve this SMAB problem. In addition, we derive an upper regret bound for the BP-UCB algorithm, which increases logarithmically over time. Simulation results demonstrate that the proposed algorithm is close to the optimal solution by less than 3.0% and has a fast convergence rate among existing methods.