Reviewerly/exHarmony huggingface dataset:
The Performance Evaluation of Big Data-Driven Modulation Classification in Complex Environment

With the proliferation of frequency-using devices and the advent of the era of big data, spectrum management and control are faced with challenges of effectiveness and accuracy.Modulation classification technology is the foundation and key part of spectrum management and control.Therefore, the effectiveness of modulation classification technology in big data scenario is very important.In this paper, we consider not only the validity of the classification model under the background of big data, but also the dynamics of noise in the complex electromagnetic environment.So we construct a big dataset containing different signals under different MSNR, and use the big data to drive the deep learning model, and finally get the classification result.The proposed method can realize modulation classification only by training one model, which avoids the redundancy of model training in previous algorithms.The simulation results demonstrate the effectiveness and reliability of the proposed method.
Multi-contextual spammer detection for online social networks

With prevalent usage of internetworking, online social platforms have greatly revolutionized the traditional exchange of information. Social networking sites, including Facebook, Twitter, LinkedIn, etc., are being extensively used by the masses to create social as well as professional content and connections. However, due to wide transparency in information spread, the user profiles often remain vulnerable to spammers. Such threats occur in terms of harmful links being posted, involving eavesdropping attack and making fake identities of other people. The proposed model performs detection of spammers on the basis of real-time tweets extracted from Twitter. Several features have been taken into consideration while detecting the spammed Twitter profiles. The social network features include replies, mentions, web links, trending tags and profile reputation. Another feature has been introduced in the content-based feature to analyze sentiment of each tweet with consideration of the emoticons. Further, machine learning algorithms have been applied to conduct performance evaluation with respect to accuracy and spam accounts detection.
Teamwork and Project Success in Agile Software Development Methods: A Case Study in Higher Education

Teamwork is vital for agile projects' success from project inception to delivery. Despite the importance of teamwork within agile development teams, little is known about how teamwork affects agile teams within the higher education environment. This research studied the case of honors students in Information Systems from the University of Cape Town by testing the relationship between teamwork, team members' success, and team performance. Simple linear regression analysis was followed to determine teamwork's influence on team members' success and team performance. The results show that teamwork possesses a strong positive relationship with team members' success and a medium positive relationship with team performance. These findings confirm the vital role of teamwork in agile teams to achieve successful team performance and team members' success. This research contributes to the body of knowledge regarding the influence of teamwork in agile software methods in the higher education environment. Understanding such a relationship will help students recognize the importance of teamwork in delivering a good product that will benefit their grades. Moreover, it would contribute to a better design of curriculums for the Information Systems discipline.
Toward blind joint demosaicing and denoising of raw color filter array data

Raw color-filter-array (CFA) data collected in the real world are often noisy and signal-dependent, which makes it difficult to recover the full-resolution noise-free color image. Denoising and demosaicing are two popular tools developed for noisy CFA data in modern color imaging pipeline. However, most existing works on joint demosaicing and denoising (JDD) are based on ad hoc assumptions about image degradation process; while in practice little is known about noise statistics (e.g., noise level) and processing pipeline (e.g., gamma correction). We advocate a blind formulation of joint demosaicing and denoising (bJDD) problem in this paper and present a novel divide-and-conquer approach toward blind reconstruction from noisy raw CFA data. Instead of making over-simplified assumptions about noise statistics, we propose to develop a more realistic Poisson-Gaussian noise model for simulating noisy raw CFA data in the real world. We also introduce a sub-network to adaptively estimate the noise level map from the noisy input, which will provide supplementary information to the deep model for non-blind JDD. Finally, we have adopted a generative adversarial network (GAN) based network for further perceptual optimization. Our extensive experimental results have shown convincingly improved performance over existing state-of-the-art methods in terms of both subjective and objective quality metrics.
Preventing Fake Accounts on Social Media Using Face Recognition Based on Convolutional Neural Network

In today's world, most people are intensely dependent on online social networks (OSN). People use social sites to find and make friends, to associate with people who share comparable intrigue, trade news, organize the event, exploring passion. According to a Facebook review, 5% of monthly active users had fake accounts, and in the last six months, Facebook has deleted 3 billion accounts. According to the Washington Post, Twitter has suspended over 1 billion suspect accounts over a day in recent months. Detection of a fake profile is one of the critical issues these days as people hold fake accounts to slander image, spread fake news, promote sarcasm that has attracted cybercriminals in. There are numerous machine learning methodologies, such as supervised learning and SVM-NN, and they are produced for the effective detection of a fake profile. In this paper, convolutional neural networks are proposed with many artificial neural network algorithms like face recognition, prediction, classification, and clustering for the efficient identification of account being real or fake and elimination of fake profile account. Furthermore, the study is grounded on the fact of the face recognizing of the user and performing feature detection and time series prediction. If the user account detected fake, it would not be created.
WASim

WebAssembly is a new programming language built for better performance in web applications. It defines a binary code format and a text representation for the code. At first glance, WebAssembly files are not easily understandable to human readers, regardless of the experience level. As a result, distributed third-party WebAssembly modules need to be implicitly trusted by developers as verifying the functionality requires significant effort. To this end, we develop an automated classification tool WASim for identifying the purpose of WebAssembly programs by analyzing features at the module-level. It assigns purpose labels to a module in order to assist developers in understanding the binary module. The code for WASim is available at https://github.com/WASimilarity/WASim and a video demo is available at https://youtu.be/usfYFIeTy0U.
Semantic Annotation of IoT Resource with ontology orchestration

With the proliferation of multiple heterogeneous IoT devices in the ecosystem, discovering the best IoT resource has become a challenge. Integration of semantic technologies can help to manage these diverse IoT resources. In these research directions, a novel framework for mapping IoT devices vendor specifications to semantic annotations of IoT resources augmented by IoT Resource Ontology is proposed. Automatic inference of the service capabilities of the IoT resources from the specifications is performed. Word embedding augmented multi label classification of the device specification terminologies is carried out. Experiment studies and comparative analysis in terms of precision, recall, f-measure is presented to showcase the effectiveness of the proposed framework.
Path planning and task assignment for multiple UAVs in dynamic environments

This chapter presents an effective approach to path planning combined with task assignments for a group of unmanned aerial vehicles (UAVs). Path planning for a UAV is a challenging task. Handling multiple UAVs in dynamic environments makes planning more complicated. On the other hand, coordination and cooperation is very significant for multi-UAV planning. To find a collision-free optimal cost path, a particle swarm optimization-based algorithm is used here. The proposed path planning algorithm has the capability to replan the path to avoid collision with static and dynamic obstacles and with other UAVs as well. One of the main contributions of the proposed approach is that it can efficiently solve the task assignment problem for multiple UAVs in a distributed manner. A nearest neighbor search model is applied to decompose/divide a set of tasks into several subsets of tasks and distribute them among UAVs. Following this approach, UAVs can effectively perform their mission in complex environments. The simulation results, using different obstacle configurations and different task distributions, are presented and discussed to validate the proposed approach. The proposed approach is tested with multiple UAVs under various reality factors such as breakdown of UAVs and task switching between UAVs. The result shows that, even in the presence of such uncertainties, UAVs are able to successfully complete all of the given tasks without any collision with other UAVs or obstacles.
Evaluating Readability Scores of Treatment Summaries and Cancer Survivorship Care Plans

Treatment Summaries and Survivorship Care Plans (TS/SCPs) may be difficult for patients to comprehend because of readability, magnitude of information, and complex medical verbiage.Readability scores were calculated for TS/SCP templates including ASCO, Oncolink, Journey Forward, and the authors' institution. The Simple Measure of Gobbledygook (SMOG) index, Flesch-Kincaid reading grade level, Coleman-Liau Index, and Gunning Fog index were used to assess readability.The Flesch-Kincaid reading ease scores for the blank ASCO templates ranged from 47.4 to 53.3, requiring a reading grade level of 10-12. Coleman-Liau and Gunning Fog scores showed that an 11th grade reading level is essential, and SMOG required a college education to comprehend the ASCO templates. For the colorectal case exemplar, Oncolink's template resulted in the lowest SMOG score (11.3; 11th grade), Flesch-Kincaid reading grade level (11; 11th grade), and Coleman-Liau score (12; 12th grade). Journey Forward's TS/SCP template scored the highest on the SMOG (21.2; college graduate), Flesch-Kincaid reading grade level (18.3; college graduate), and Gunning-Fog index (25.8; college graduate) compared with other TS/SCPs.The existing TS/SCP templates used by US cancer centers are written at a grade level beyond the comprehension of most adults. Cancer care teams should assess TS/SCP content for readability and use of plain language and reduce medical jargon.
User-based Collaborative Filtering Algorithm Design and Implementation

Abstract With the rapid development of social humanities and technology, especially the development of the Internet, the current Internet era, the amount of information is increasing rapidly with explosive growth rate, the information overload problem is particularly obvious, on the accurate acquisition of information from the massive amount of information users want, from which the personalized recommendation technology was born. In order to solve the problem of acquiring the information users want, this paper researches and discusses a kind of personalized recommendation algorithm - a user-based collaborative filtering algorithm, analyzing the user behavior, comparing the advantages and disadvantages of other related algorithms, using the UserCF algorithm, and optimizing the sparse matrix to reduce the time and complexity of the operation. The algorithm is implemented by software to generate recommendation results. The results of the experimental data in this paper show that the algorithm is effective in recommending projects to users.
Testing Piecewise Structural Equations Models in the Presence of Latent Variables and Including Correlated Errors

Path models, expressed as Directed Acyclic Graphs (DAGs), and the testing of such DAGs via a d-sep test, have become popular because they can incorporate complicated data structures that are difficult or impossible to accommodate in classical structural equation modeling. However, d-sep tests cannot accommodate DAGs that include unmeasured (latent) variables. We describe (i) how to convert a DAG with latent variables into an observationally equivalent graph without latents (a Mixed Acyclic Graph, MAG), (ii) how this MAG identifies which latents can/cannot be ignored without changing the causal meaning of the original DAG, and (iii) how to perform the MAG equivalent of a d-sep test.
Intelligent pig‐raising knowledge question‐answering system based on neural network schemes

Abstract The most important parts of Taiwan's agriculture are animal husbandry and the pig industry, of which the output value reached NT$75.6 billion in 2017. Taiwan has a high technical level of pig raising. However, practical pig‐raising skills rely mainly on the inheritance of mentoring experience. The livestock and pig breeding industry in Taiwan has no relevant pig breeding knowledge management information system and no intelligent knowledge question‐answering system. Therefore, this study designs and implements an intelligent knowledge question‐and‐answer system for pig farming. To identify intelligent questions and answers for raising livestock pigs, this study addresses the following issues: (a) to determine the semantic meaning of a sentence, the system needs to accurately interpret the meaning of a question and to identify the expression of a knowledge entity. Therefore, this study applies the lattice long‐short‐term memory (LSTM) and structured perceptron methods to parse sentences accurately and correctly perform word segmentation. A set of stop words for pig raising in Taiwan is initially established to ensure accurate sentence parsing; (b) to understand the intent of each sentence, the bidirectional gated recurrent init (bi‐GRU) method is adopted to realize the knowledge extraction of the livestock in the question and complete the intent detection and slot filling. The bi‐GRU extracts the correct livestock knowledge and classifies it into suitable topics; (c) the wide range of knowledge sources in questions often leads to unrelated vocabulary, repeated words, and structural loss in potential answers. To establish an effective knowledge search graph, the method infers implicit knowledge from the existing knowledge and conducts related knowledge retrieval based on the inference results. The knowledge data model is defined in the open standard resource description framework Knowledge filtering and knowledge reasoning strategies are presented to produce candidate answers to the livestock problem; (d) to obtain the final answer, most related works search established knowledge graphs, often producing multiple ambiguous candidate answers. The Siamese neural network (SNN) is adopted to obtain accurate answers. An SNN compares candidate answers to a livestock problem as two input values, previously trained by the bidirectional LSTM neural network. The final answer is determined from the cosine similarity value, which represents the highest relevance to the question. Finally, this study implements the intelligent pig‐raising knowledge question‐answering system based on the proposed methodology. The evaluation results reveal that the proposed system is accurate and practicable.
A Generic Analogy-Centered Software Cost Estimation Based on Differential Evolution Exploration Process

Abstract Software cost estimation is the prediction of development effort and calendar time required to develop any software project. It is considered to be the very fundamental task for successful execution of an on-going project as well as budgetary requirements of futuristic projects. As accuracy in software cost estimation is very hard because of the availability of vague information at the time of inception of the software project, it prompted many researchers to explore in this domain from past decades. Their pioneer works suggest a bulk of techniques for this purpose. However, because of the availability of large number of estimation techniques, it becomes hard for any software practitioners to select an appropriate one. To help the industry practitioners in these situations, a novel analogy-centered model based on differential evolution exploration process is proposed in this research study. The proposed model has been assessed on 676 projects from 5 different data sets and the results achieved are significantly better when compared with other benchmark analogy-based estimation studies. Furthermore, being the very less computational cost of the proposed model, it is suggested that the proposed model be considered as the preliminary stage of any analogy-based software estimation technique.
StuckInSpace: Exploring the Difference Between Two Different Mediums of Play in a Multi-Modal Virtual Reality Game

With the rising popularity of Virtual Reality (VR), there is also a rising interest in co-located multiplayer experiences, as people want to play VR games together with their friends. As having multiple VR headsets is out of reach to the average consumer, we need to look into different possible ways of including multiple people in this play space. We have created a multi-modal co-located multiplayer VR game, Stuck in Space, that introduces a second player in two ways - one with a PC (the baseline that a lot of current games do), as well as a tracked Phone that can be used as a `window into the virtual world'. We have conducted a user study (n = 24) where we explore the difference in immersion and co-presence between the two versions using two questionnaires (IPQ and NMMoSP), as well as a thematic analysis of the subsequent interview data, from which 5 themes emerged. Surprisingly, we found no significant difference in co-presence or immersion based on the quantitative data. However, the qualitative analysis helps reveal one of the main reasons why that is - maintaining a mental model of the real world while also being in the virtual world makes it harder for the person wearing the headset to immerse themselves and feel co-present. From these themes and sub-themes we theorize that each of the two versions has positives and negatives that cancel each other out in the quantitative data, and for there to be a difference we would need to accentuate or change certain elements of the game. The results show that introducing a second player through a Phone is not detrimental in terms of co-presence and immersion and that it is a viable way of doing so, although certain design considerations would have to be taken into account to minimize the negatives.
Automatic Visual Gun Detection Carried by A Moving Person

Few works are reported in firearm detection, even though, it has an important application in both the field of video forensic analysis and surveillance. Security of different crowded areas such as around airport, marketplace, places of worships, shopping mall, ceremonies events such as marriage, parties etc. always have a requirement of automatic detection of moving person carrying illegal weapon. In the present scope of the work, we propose a gun detection technique based on template matching which is invariant to scale & rotation. Template matching is a simple and traditional method used for object recognition with a disadvantage of high time complexity. We propose a innovative yet simple way to reduce the time complexity by employing a background subtraction methodology. Background subtraction algorithm handles other challenges too, such as sudden change of illumination. Experimental results illustrate that the proposed method outperforms efficiently in gun detection in video sequences with lesser number of True negatives in comparison with state-of-the-art template matching algorithms.
Visualizing One Pixel Attack Using Adversarial Maps

One pixel attack is one of the most puzzling adversarial attacks, in which the position of the attack plays an important role. However, little research has been conducted on the distributions of one pixel attack. In this context, a technique called adversarial maps is proposed, which helps visualize the distributions of one pixel attack for the first time. Adversarial maps consist of pixel adversarial maps and probability adversarial maps, which record the pixel changes and the confidence of the target class in successful attack cases, respectively. Leveraging this technique, one pixel attack distributions and why the position of one pixel attack impacts success rate is explored. Adversarial maps reveal that successful attacks always group as regions and the high saliency areas of saliency maps are more likely to be attacked successfully. Moreover, these observations are further corroborated by a mathematical analysis, demonstrating that adversarial attacks are disturbances in the saliency maps.
Opposition Based Joint Grey Wolf-Whale Optimization Algorithm Based Attribute Based Encryption in Secure Wireless Communication

At present times, medical image security becomes a hot research topic in the healthcare sector. This paper presents an efficient lightweight image encryption model based on the Dynamic key generating Attribute based encryption (ABE) method with Opposition based joint Grey Wolf-Whale Optimization Algorithm (OjGW-WOA). The proposed encryption method undergoes certain pre-encryption steps like rotation and random column addition steps. Once the pre-encryption steps are done, ABE with OjGW-WOA is incorporated, where the optimal key is generated based on entropy value. In addition, the oppositional based learning concept is introduced to enhance the convergence rate and searching process of GWO and WOA algorithms. Next, the proposed encryption method is designed with a dynamic key generating model that generates updated keys during every time period. Therefore, during decryption, two-level key verification is done. At the first decryption stage, the key corresponding to that particular time period is required, then the original key is generated from that key and then employed for decrypting the original data. The proposed method is simulated using MATLAB tool and a detailed comparative results analysis is carried out. The performance of the proposed work is validated with the aid of performance metrics like Peak Signal to Noise Ratio (PSNR), number of changing pixel rate (NPCR) and unified averaged changed intensity (UACI). The experimental results stated that the presented model has resulted to a higher PSNR of 62.29 dB, NPCR of 99.23%, and UACI of 23.67%.
A Survey of Transfer Learning in Breast Cancer Image Classification

Worldwide, breast cancer is one of the leading cancer for female, especially in low and middle income countries. Breast cancer's classification in the early stages plays a pivotal role to improve survival rate. But the training dataset which should be large-scaled and well-annotated is hard to collect because of the limitation of data acquisition and annotation. Transfer learning can transfer the knowledge from target domain to source domain to improve model performance. Thus, the dataset needed for training in target domain can be degraded. For the feature mentioned above, it has become the hot topic for research in machine learning. This survey tries to systematize the current research of transfer learning techniques in the area of breast cancer image classification. And also, highlight several findings for the researchers in future.