Miracle-dz/UniFAR-FTUs huggingface dataset:
Selecting Generative Models for Networks using Classification with Machine Learning

NICHOLAS A. LARSEN: Model Selection with Machine Learning for Classifying Network Community Structures (Under the direction of Peter J. Mucha, Shankar Bhamidi, and Nicolas Fraiman) By representing data entities as a map of edges and vertices, where each edge encodes a relationship between two vertices, networks have an almost unlimited ability to capture relationships and patterns impossible to see with the human eye. Because these patterns often reflect key aspects of the data, a significant portion of network science is devoted to detecting and distinguishing networks by using these topological features. The use of machine learning for classifying networks is a popular solution; research in this area includes techniques ranging from k-Nearest Neighbors to language modeling-inspired deep learning methods. Another area of interest with respect to networks is model selection, which can provide unique insights into a graph’s topological and probabilistic properties. This thesis combines the two areas of network classification with machine learning and generative model selection by using the popular algorithm known as “random forests” as a potential model selection criterion. First, we perform a series of experiments designed to characterize the discriminatory power of random forests on a wide variety of synthetic graphs generated by dozens of Stochastic Block Models (SBMs). Then, we take advantage of well-known network structural properties and compare the generative model of best fit selected by random forests to the model chosen by a previously established selection criterion known as Integrated Completed Likelihood (ICL). In applying these techniques to selecting Erdős-Rényi mixture models for a macaque brain connectivity dataset and using the model that maximizes the ICL criterion as the “gold standard,” we observed that random forests serves as a comparable model selection method when using topological network statistics as the feature space, selecting the same best-fit model chosen by ICL over 95% of the time.
Evaluating the Perceived Quality of Binaural Technology

This thesis studies binaural sound reproduction from both a technical and a perceptual perspective, with the aim of improving the headphone listening experience for entertainment media audiences. A detailed review is presented of the relevant binaural technology and of the concepts and methods for evaluating perceived quality. A pilot study assesses the application of state-of-the-art binaural rendering systems to existing broadcast programmes, finding no substantial improvements in quality over conventional stereo signals. A second study gives evidence that realistic binaural simulation can be achieved without personalised acoustic calibration, showing promise for the application of binaural technology. Flexible technical apparatus is presented to allow further investigation of rendering techniques and content production processes. Two web-based studies show that appropriate combination of techniques can lead to improved experience for typical audience members, compared to stereo signals, even without personalised rendering or listener head-tracking. Recent developments in spatial audio applications are then discussed. These have made dynamic client-side binaural rendering with listener head-tracking feasible for mass audiences, but also present technical constraints. To limit distribution bandwidth and computational complexity during rendering, loudspeaker virtualisation is widely used. The effects on perceived quality of these techniques are studied in depth for the first time. A descriptive analysis experiment demonstrates that loudspeaker virtualisation during binaural rendering causes degradations to a range of perceptual characteristics and that these vary across other system conditions. A final experiment makes novel use of the check-all-that-apply method to efficiently characterise the quality of seven spatial audio representations and associated dynamic binaural rendering techniques, using single sound sources and complex dramatic scenes. The perceived quality of these different representations varies significantly across a wide range of characteristics and with programme material. These methods and findings can be used to improve the quality of current binaural technology applications.
A Low-Complexity Linear Precoding for Secure Transmission Over MIMOME Wiretap Channels With Finite-Alphabet Inputs

In this paper, we investigate the precoding scheme for multiple-input multiple-output multiple antennas eavesdropper (MIMOME) channels under the constraint of finite-alphabet inputs to ensure secure transmission between the source and the destination in the presence of an eavesdropper. With finite-alphabet inputs, there are no closed-form solutions for mutual information (MI) and its gradient-minimum mean-square error (MMSE) matrix. Therefore traditional precoding schemes based on direct calculation of MI and MMSE matrix through numerical integration involve prohibitive computational burden. To address this problem, this paper first derives asymptotically optimal lower bounds of MI and ergodic MI with which the computational complexity can be significantly reduced compared with the direct computation of MI. With the derived lower bounds, a low-complexity precoding scheme is proposed to maximize the secrecy rate, wherein a gradient descent based algorithm is used to obtain the precoding result. In the low and high signal-to-noise ratio (SNR) regions, we demonstrate that the proposed precoding scheme gives the same result as directly maximizing the secrecy rate, which indicates that the proposed low-complexity precoding scheme is asymptotically optimal.
Image stitching algorithm for description of integrated circuit layouts

The problem of optimum matching of partially overlapped frames of an image is formulated with use of common criteria of matching quality. The image stitching algorithm based on key points matching is proposed for quasi-optimal solution of the problem with the following restrictions: the frames are rectangular and have an identical scale. It was implemented as a software module in the system of integrated circuit layout analyses.
Demographical Changes of Student Subgroups in MOOCs: Towards Predicting At-Risk Students

Past studies have shown that student engagement in Massive Open Online Courses (MOOCs) could be used to identify at-risk students (students with drop-out tendency). Some studies have further considered student diversity by looking into subgroup behavior. Yet, most of them lack consideration of students’ behavioral changes along the course. Towards bridging the gap, this study clusters students based on both their interaction with the system and their characteristics and explores how their cluster membership changes along the course. The result shows that students’ cluster membership changes significantly in the first half of the course and stabilized in the second half of the course. Our findings provide insight into how students may be engaged in learning on MOOC platforms and suggest the improvement of identifying at-risk students based on their temporal data.
Query Expansion and Query Fuzzy with Large-Scale Click-through Data for Microblog Retrieval

 Abstract —Searchers using microblog search often have difficulty in expressing their needs, they often struggle to modify queries by adding or removing terms, and this lead to unsatisfied experience. On the other hand, Microblogs content is limited in size, contain a few words and these will cause a real problem of term mismatch in microblog search. We propose a probabilistic model for query reformulation on click-through data, which can help user auto-complete their search need. We assume that the knowledge of good queries of former searchers can then be used for improving a poor query of following users, by expanding extra meaningful terms or fuzzing trivial query terms. The key idea is to discover terms that can grasp users' click need or carry less meaning from large-scale click-through data. Our results indicate the reformulated query can better describe users's real functional need. In the experiment, we try to reduce query drift and noise in click-through data by using crowd-sourced relevance feedback and smoothing methods. The experimental result is much better than that of searching only the original query, especial on gains of low-frequency and long-tailed query.
Medical Information Extraction Model for User-generated Content

Introduction: The number of social network users is on the rise, and the size of the user-generated contents is increasing as well. Analyzing the generated contents can lead to the attainment of a vast amount of information, such as users’ feelings on specific products or events, or personal information about life events. Aim: The aim of this paper is to describe an model for detecting medical information present in generated contents, such as posts or comments. Results: The proposed model is based on the Unified Medical Language System (UMLS) and is tested on a dataset collected from Twitter and Facebook. The extracted information can be used to aid in the early detection of diseases or to supply commercial benefits to medical companies. Experimental results demonstrate that the proposed model achieves 94.6% accuracy and 87% precision. Conclusion: In this study, we attempted to extract clinical information present in UGC. Using the proposed model should involve a reliable dataset that contains most clinical expressions; the UMLS was a suitable dataset for our model.
IoT Fundamentals: Networking Technologies, Protocols, and Use Cases for the Internet of Things

Today, billions of devices are Internet-connected, IoT standards and protocols are stabilizing, and technical professionals must increasingly solve real problems with IoT technologies. Now, five leading Cisco IoT experts present the first comprehensive, practical reference for making IoT work. IoT Fundamentals brings together knowledge previously available only in white papers, standards documents, and other hard-to-find sourcesor nowhere at all. The authors begin with a high-level overview of IoT and introduce key concepts needed to successfully design IoT solutions. Next, they walk through each key technology, protocol, and technical building block that combine into complete IoT solutions. Building on these essentials, they present several detailed use cases, including manufacturing, energy, utilities, smart+connected cities, transportation, mining, and public safety. Whatever your role or existing infrastructure, youll gain deep insight what IoT applications can do, and what it takes to deliver them. Fully covers the principles and components of next-generation wireless networks built with Cisco IOT solutions such as IEEE 802.11 (Wi-Fi), IEEE 802.15. 4-2015 (Mesh), and LoRaWAN Brings together real-world tips, insights, and best practices for designing and implementing next-generation wireless networks Presents start-to-finish configuration examples for common deployment scenarios Reflects the extensive first-hand experience of Cisco experts
INNOVATIVE INSTRUCTIONAL CLASSROOM PROJECTS/BEST PRACTICES Bridging the gap: A model for interdisciplinary collaboration between entrepreneurship and software engineering students

By framing entrepreneurship courses in the context of interdisciplinary team projects, students build important work-related skills whether they pursue an entrepreneurial path or employment after graduation. However, the traditional classroom environment is not conducive to realistic interdepartmental interactions found in most corporations or startups. The authors describe a model for interdisciplinary collaboration between students in entrepre-neurship and computer science engineering programs to cultivate the development of real software products. The authors outline the partnership and illustrate how sister courses covering lean startup and agile software development methodologies complement each other to foster realistic projects.
Bead-Chain Construction Set and Interlocking Puzzle Inspired by Polyhedranes

We introduce a new type of construction set which can be used to build valence sphere models of any cage-like hydrocarbon, of general formula (CH)2n. Using n linear five-bead chains, one can connect these building units into a three-dimension arrangement that stands for its valence sphere model through n cross-linkings. More interestingly, the assembly processes of certain symmetric molecules, such as three Platonic hydrocarbons, including tetrahedrane, cubane, and dodecahedrane, are similar to solving interlocking puzzles. Hence, a bead chain construction set can also be considered as a new type of take-apart put-together puzzles.
LOS/NLOS Wireless Channel Identification based on Data Mining of UWB Signals

Localisation algorithms based on the estimation of the time-of-arrival of the received signal are particularly interesting when ultra-wide band (UWB) signaling is adopted for high-definition location aware applications. In this context non-line-of-sight (NLOS) propagation condition may drastically degrade the localisation accuracy if not properly recognised. We propose a new NLOS identification technique based on the analysis of UWB signals through supervised and unsupervised machine learning algorithms, which are typically adopted to extract knowledge from data according to the data mining approach. Thanks to these algorithms we can automatically generate a very reliable model that recognises if an UWB received signal has crossed obstacles (NLOS situation). The main advantage of this solution is that it extracts the model for NLOS identification directly from example waveforms gathered in the environment and does not rely on empirical tuning of parameters as required by other NLOS identification algorithms. Moreover experiments show that accurate NLOS classifiers can be extracted from measured signals either pre-classified or unclassified and even from samples algorithmically-generated from statistical models, allowing the application of the method in real scenarios without training it on real data.
Fame and fortune, or just fun? A study on why people create content on video platforms

Purpose The purpose of this paper is to examine the motivations behind online video content creation on services such as YouTube and Twitch. These activities, performed by private individuals online, have become increasingly monetized and professionalised through the accessible tools provided by video sharing services, which has presented a noteworthy manifestation of the increasing merger of the work and leisure within digital environments and the emergence of a hybrid form of work and play, playbour. Design/methodology/approach The data for the study were collected using an online survey of 377 video content creators and it was analysed via structural equation modelling. Findings The findings of the study indicate that although the practice of video content creation is becoming more commercialised and professionalised, the extrinsic motivations, often associated with work (e.g. income, prestige), remain less significant drivers for content creation than intrinsic motivations (e.g. enjoyment, socialisation), which are associated with leisure activities. Originality/value This study offers insight into how the authors have begun to reorganise the position in the new digital labour culture, where monotonous tasks are increasingly automated, allowing room for intrinsically driven playful labour to develop within the leisure activities.
Construction of Girth-8 QC-LDPC Codes Free of Small Trapping Sets

In this letter, we propose a new technique to design quasi-cyclic low-density parity-check (QC-LDPC) codes with girth 8 and variable node degrees 3 and 4. Small trapping sets are avoided in the designed codes by imposing simple constraints on the cycles of length 8 in the code’s Tanner graph. We also derive lower bounds on the block length of the codes that can satisfy such constraints. Simulation results are provided to demonstrate the superiority of the designed codes in comparison with similar codes in the literature.
A Policy-based Middleware for Self-Adaptive Distributed Systems

This paper presents a novel approach to dynamically adapting distributed applications to changes in environmental conditions. e.g., available network resources and users requirements. The key idea behind the approach is to introduce the relocation of software components to define functions between computers, as a basic mechanism for dynamic adaptation on distributed systems. It also introduces application-specific builtin policies for relocating software components to define highlevel adaptation by human-readable declarative policy scripts. It is constructed as a middleware system for Java-based generalpurposed software components. This paper describes the design and implementation of the approach with several applications, e.g., remote information retrieval and distributed media service. Keywords–software component; policy-based; self-adaptive; middleware; Distributed System; architecture-level.
An unmanned aerial vehicle pose estimation system based on SLAM

This paper presents an unmanned aerial vehicle (UAV) pose estimation system based on monocular simultaneous localization and mapping (SLAM) guided by the desired shot. The system enables UAV to automatically adjust the pose to achieve a shot close to the desired shot provided by the user. The SLAM module in the system includes ORB feature-based visual odometry and Levenberg-Marquardt method-based optimizer. To ensure the reliability of the camera pose estimation result, the bag of words model is used to select an image which has enough good matches with the desired shot. The experimental results prove that the system is valid and effective.
What drives subscribing to premium in freemium services? A consumer value‐based view of differences between upgrading to and staying with premium

Fostering the conversion of free users to premium subscribers and retaining those premium users are critical objectives for freemium service providers. Building on consumer value theory, we empirically examine the differences between basic and premium users in terms of the emotional, functional, social, epistemic, and economic values driving basic users' decisions to upgrade to premium subscriptions and premium users' decisions to retain their paid subscriptions. We employ enjoyment, intrusiveness of advertising in the free subscription, ubiquity, social connectivity, the discovery of new content, and the price value of the premium subscription as drivers of intentions and test our model using data from a leading digital content service that employs the freemium model. Our results show that enjoyment and price value of the premium subscription predict the intention to upgrade to premium, whereas the intention to retain the premium subscription is driven by ubiquity and the discovery of new content. Interestingly, social connectivity has no effect on the intention to upgrade but does have a small negative effect on the intention to retain the premium subscription. Contrary to our expectations, intrusiveness of advertising in the free subscription had a negative effect on the price value of the premium subscription. Collectively, our results imply that the intention to retain the premium subscription is influenced by attribute‐level value perceptions such as ubiquity, the discovery of new content, and social connectivity whereas the intention to upgrade is driven by benefits, ie, enjoyment and price value of the premium subscription.
Design and Implementation of Simulation Training System Based on MVC Architecture

With the development of technology of computer, the missile force has been developing towards informatization. A new training method based on simulation training is launched. This article mainly introduced missile simulation training system and the MVC architecture, and proposed the structure of MVC architecture design simulation training system, in the last, implementing the simulation training system for a missile weapon. The usage of MVC frame design achieved a good structure, loosely coupled and high cohesive simulation training system, which effectively improves the development and late maintenance cost of the simulation training system, and realizes that one system can meet the requirements of multiple batches of missile weapons.
Passion Fruit Disease Detection using Image Processing

Fruit diseases are a major problem in economic and production losses in the agricultural industry worldwide. In this paper, an image processing approach is proposed for identifying passion fruit diseases. According to the Sri Lankan context, treatment details are taken by the farmers from the field officers. However, it can take a few days. So, this proposed system can be used to identify passion fruit diseases quickly and automatically. This proposed approach is composed of the following main steps; Image Acquisition, Image Preprocessing, Image Segmentation, Feature Extraction, Dataset Preparation, Training & Testing. Healthy and two types of passion fruit diseases, namely passion fruit scab and woodiness images were used for this approach. This approach was tested according to passion fruit disease type and its' stages, such as mild, moderate and severe. K-Means clustering was used for segmentation. Images were clustered according to k values, such as 2, 4, 6 and 8. Before the segmentation, images were converted to RGB, L*a*b, HSV and Grey colour models, because of find out the most suitable colour model for this approach. Local Binary Pattern was used for feature extraction and Support Vector Machine was used for creating the model. Seventy percent (70%) of each dataset was used to train the SVM and the other thirty percent (30%) was used to test the model. According to this approach, passion fruit diseases can be identified in the average accuracy of 79% and its' stage can be identified in average accuracy 66%.
Toward Optimal Adaptive Online Shortest Path Routing With Acceleration Under Jamming Attack

We consider the online shortest path routing (SPR) of a network with stochastically time varying link states under potential adversarial attacks. Due to the denial of service (DoS) attacks, the distributions of link states could be stochastic (benign) or adversarial at different temporal and spatial locations. Without any a priori, designing an adaptive and optimal DoS-proof SPR protocol to thwart all possible adversarial attacks is a very challenging issue. In this paper, we present the first such integral solution based on the multi-armed bandit (MAB) theory, where jamming is the adversarial strategy. By introducing a novel control parameter into the exploration phase for each link, a martingale inequality is applied in our formulated combinatorial adversarial MAB framework. The proposed algorithm could automatically detect the specific jammed and un-jammed links within a unified framework. As a result, the adaptive online SPR strategies with near-optimal learning performance in all possible regimes are obtained. Moreover, we propose the accelerated algorithms by multi-path route probing and cooperative learning among multiple sources, and study their implementation issues. Comparing to existing works, our algorithm has the respective 30.3% and 87.1% improvements of network delay for oblivious jamming and adaptive jamming given a typical learning period and a 81.5% improvement of learning duration under a specified network delay on average, while it enjoys almost the same performance without jamming. Lastly, the accelerated algorithms can achieve a maximal of 150.2% improvement in network delay and a 431.3% improvement in learning duration.