Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient information dissemination in distributed network architectures

In this dissertation I cover two distinct networking topics in three thesis groups: dissemination of emergency messages among vehicles, and an analysis of peer-to-peer Video-on-Demand. Inter-Vehicle Communication is expected to be used in the near future to improve road safety, and to optimize vehicle traffic. The problem I worked on was propagating emergency messages among vehicles as a locality-based multicasting. This requires rapid forwarding, and low signaling overhead due to the high speed of the vehicles. My solution is to use random drops to restrict the messages into a finite area around the initiator vehicle – this is a radically new approach to the well-known gossiping algorithm. I complement this with constructing a stateless mechanism for setting the rebroadcast probability on each hop, without the use of any signaling messages. I show the feasibility of this approach analytically for the simple cases, and I use simulations to demonstrate that my message dissemination protocol, CLoUD, works in realistic environments as well. Video-on-Demand systems are rapidly replacing conventional television due to their flexibility, and content richness. To increase availability, and decrease the generated network traffic VoD systems are utilizing distributed architectures, including peer-to-peer technologies. Conventional P2P solutions are impractical for streaming, because they can’t guarantee timely arrival of the video segments for the playback; usually this is fixed by introducing a P2P download window. In the second thesis group I use my VoD simulator to analyze the performance of a VoD system assisted by P2P, and caching. I show that the P2P system is hard to misconfigure, because it is insensitive to its window settings, and I demonstrate how P2P, and caching can be effectively used in conjunction. In the third thesis group I construct a mathematical model for the P2P download window of the VoD client. From that I derive closed formulas for the parameters that describe the state of the P2P window, and the download initiation probability required for the P2P system to be self-sufficient. I also examine the effect of limited client downlink capacities, and show that uplink, and downlink limitations are not
Characteristics of Students’ Cognitive Conflict in Solving a Problem Based on Information Processing Theory

The purpose of this study was to describe the characteristics of students’ cognitive conflict in solving problems based on information processing theory. This research was descriptive qualitative, and data were collected through direct observation, semi-structured in-depth interviews, and tests. Tests were in the form of story questions with distance material that had been previously studied by students. Participants in this study were grade 6 elementary school students. Out of 32 sixth-grade students, two were selected as participants in the study. The results of the study show that there are two characteristics of students’ cognitive conflict in solving problems: error—cognitive conflict—equilibrium—solving the problem and error—cognitive conflict—equilibrium—can’t solve the problem. Participants experienced difficulties in accessing information contained in long-term memory, so participants were constrained in connecting the newly received information to their short-term memories. The difficulty in accessing this information resulted in participants’ experiencing cognitive conflict. Information was not directly connected between what had been given and what was already available in the cognitive structure. These results indicate that cognitive conflict occurs during information processing if the information received by sensory memory and transferred to short-term memory cannot be directly linked to information in long-term memory. https://doi.org/10.26803/ijlter.18. 2.6
A new interpretation of the progressive hedging algorithm for multistage stochastic minimization problems

The progressive hedging algorithm of Rockafellar and Wets for multistage stochastic programming problems could be viewed as a two-block alternating direction method of multipliers. This correspondence brings in some useful results. In particular, it provides a new proof for the convergence of the progressive hedging algorithm with a flexibility in the selection of primal and dual step lengths and it helps to develop a new progressive hedging algorithm for solving risk averse stochastic optimization problems with cross constraints.
Electrically Small Huygens Antenna-Based Fully-Integrated Wireless Power Transfer and Communication System

This paper introduces the first reported electrically small Huygens dual-functional wireless power transfer (WPT) and communication system operating in the 915-MHz ISM band. It is realized by the seamless combination of a Huygens linearly polarized (HLP) antenna and a highly efficient HLP rectenna. The configuration consists of two orthogonally oriented HLP subsystems. Each one intrinsically combines two pairs of metamaterial-inspired near-field resonant parasitic elements, i.e., an Egyptian axe dipole (EAD) and a capacitively loaded loop (CLL). Through the development of a very tightly coupled feed subsystem that includes the WPT mode’s rectifier circuit and the communications mode’s feedline while preserving their isolation, the independent operation of both functions is facilitated in an electrically small volume ( $ka < 0.77$ ). The measured results of its fabricated prototype agree well with their simulated values. The communications mode antenna resonates at 910 MHz and radiates a cardioid-shaped Huygens pattern with the peak gain of 2.7 dBi. The Huygens-based WPT rectenna achieves an 87.2% peak ac-to-dc conversion efficiency at 907 MHz. The dual-functional system is an ideal candidate for many emerging Internet-of-Things (IoT) wireless applications that require simultaneous wireless information and power transfer (SWIPT) and wirelessly powered communications (WPC).
MEV: Visual Analytics for Medication Error Detection

To detect harmful medication errors and inform regulatory actions, the U.S. Food & Drug Administration uses the FAERS spontaneous reporting system to collect medication error reports. Drug safety analysts, however, review the submitted report narratives one by one to pinpoint critical medication errors. Based on a formative study of the review process requirements, we design an interactive visual analytics prototype called Medication Error Visual analytics (MEV), to facilitate the medication error review process. MEV visualizes distributions of the reports over multiple data attributes such as products, types of error, etc., to guide analysts towards most concerning medication errors. MEV supports interactive filtering on key data attributes that aim to help analysts hone in on the set of evidential reports. A multi-layer treemap visualizes the count and severity of the errors conveyed in the underlying reports, while the interaction between these layers aid in the analysis of the corresponding data attributes and their relationships. The results of a user study conducted with analysts at the FDA suggests that participants are able to perform the essential screening and review tasks more quickly with MEV and perceive tasks as being easier with MEV than with their existing tool set. Post-study qualitative interviews illustrates analysts’ interest in the use of visual analytics for FAERS reports analysis operations, opportunities for improving the capabilities of MEV, and new directions for analyzing critical spontaneous reports at scale.
Realization and Evaluation of Marketplace Functionalities Using Ethereum Blockchain

Usage of IoT marketplaces as central components in distributed systems have become common, as they allow for easy exploitation of other services and data sources. When relying on a marketplace to be available in order for a system to be operational, it is critical that the marketplace operation is highly robust. Marketplaces are typically centralized components which means that single point of failure is a possible issue. Also users of the marketplace must trust the operator to be fair and follow a common set of rules. This work proposes to utilize blockchain technology to realize a distributed marketplace where both functionalities and storage are distributed and thereby increasing availability to users, while removing the need for a central operator. The blockchain also makes the operational rules transparent to the users of the marketplace. In this relation two core marketplace functionalities, offering creation and discovery, are realized using smart contracts on a private Ethereum blockchain and evaluated using an experimental testbed. The results demonstrate the feasibility of a blockchain based marketplace implementation.
Optimization Model for Cross-Domain Network Slices in 5G Networks

Network Slicing (NS) is a key enabler of the upcoming 5G and beyond system, leveraging on both Network Function Virtualization (NFV) and Software Defined Networking (SDN), NS will enable a flexible deployment of Network Functions (NFs) belonging to multiple Service Function Chains (SFC) over various administrative and technological domains. Our novel architecture addresses the complexities and heterogeneities of verticals targeted by 5G systems, whereby each slice consists of a set of SFCs, and each SFC handles specific traffic within the slice. In this paper, we propose and evaluate a MILP optimization model to solve the complexities that arise from this new environment. Our proposed model enables a cost-optimal deployment of network slices allowing a mobile network operator to efficiently allocate the underlying layer resources according to its users’ requirements. We also design a greedy-based heuristic to investigate the possible trade-offs between execution runtime and network slice deployment. For each network slice, the proposed solution guarantees the required delay and the bandwidth, while efficiently handling the use of both the VNF nodes and the physical nodes, reducing the service provider's Operating Expenditure (OPEX).
Automated prostate segmentation of volumetric CT images using 3D deeply supervised dilated FCN

Segmentation of the prostate in 3D CT images is a crucial step in treatment planning and procedure guidance such as brachytherapy and radiotherapy. However, manual segmentation of the prostate is very time-consuming and depends on the experience of the clinician. On the contrary, automated prostate segmentation is more helpful in practice, whereas the task is very challenging due to low soft-tissue contrast in CT images. In this paper, we propose a 3D deeply supervised fully-convolutional-network (FCN) with dilated convolution kernel to automatically segment prostate in CT images. A deep supervision strategy could acquire more powerful discriminative capability and accelerate the optimization convergence in training stage, while concatenating the dilated convolution enlarges the receptive field to extract more global contextual information for accurate prostate segmentation. The presented method was evaluated using 15 prostate CT images and obtained a mean Dice similarity coefficient (DSC) of 0.85±0.04 and mean surface distance (MSD) of 1.92±0.46 mm. The experimental results show that our approach yields accurate CT prostate segmentation, which can be employed for the prostate-cancer treatment planning of brachytherapy and external beam radiotherapy.
Newsvendor Model for Multi-Inputs and -Outputs with Random Yield: Applications to Agricultural Processing Industries

Consider a newsvendor model, which we extend to include both multiple inputs and outputs. Different input types possess different levels of quality, and are purchased at different prices by a processing firm. Each type of input is processed into multiple outputs, which are sold at different prices. The yield for each output type is random and depends on the input type. We need to determine the purchase quantities of different types of input, before demands of different types of output are known. In our analytical results, we show that the expected total profit is jointly concave in the purchasing quantities and derive the optimality condition. Our multi-input and -output newsvendor model is suitable for processing industries in agriculture supply chain. In our numerical example, we apply our model to the rice milling industry, whose primary output is head rice and byproducts are broken rice, bran and husk. Our model can help the rice mill to decide which paddy types to procure and how much, in order to maximize the total expected profit from all outputs. We also show that the expected profit can be significantly better than using the standard newsvendor model.
Design of gesture recognition system based on Deep Learning

The rapid development of computer vision has had a significant impact on all areas of life. Real-time image capture using computer images for small movements has strong utility in some areas of life. Based on the EgoHands dataset, this paper uses the SSD detection method and the MobileNet neural network to establish a recognition model for students’ gestures. In the end, the model adopted in this paper achieved a score of 96.86% for mAP.
Clustering Outlier Detection Algorithm

Outlier detection and clustering technologies are an important branch of data mining, such as combining the two technologies can improve the mining significance. In this paper, both clustering and outlier detection can be the starting point, proposed a DBSCAN-LOF algorithm is the core idea is to use k_ neighbors thought, DBSCAN redefine the core of the object, making the only non-core objects LOF The operation, thereby reducing the original LOF algorithm is computing the number of global objects, and makes no DBSCAN algorithm input parameters Eps. Real and simulated data sets by experimental results confirm that the algorithm to improve the operating efficiency and the LOF algorithm DBSCAN clustering effect, and while producing clustering and outlier detection results.
AHE Detection With a Hybrid Intelligence Model in Smart Healthcare

Acute hypotensive episode (AHE) is one of the postoperative complications with the high sudden death rate in the intensive care unit (ICU), which threatens patients’ life safety seriously. Therefore, it is meaningful, valuable, and essential for the analysis including the prediction of AHE before its actual occurrence. In this paper, a novel methodology combining with deep network and multiple-gene expression programming is presented to predict the AHE automatically which will improve the efficiency of prevention and lighten the burden of diagnosis for ICU doctors. The experimental data (time series data) are obtained from multi-intelligent monitoring in intensive care II (MIMICII). The sliding window (SW) is used to generate the data stream segments from the original sources. A decomposition method (ensemble empirical mode decomposition (EEMD)) for highly complex and the nonlinear signal process is used to decompose each data stream segments into a group of intrinsic mode functions (IMFs). A novel three-layer auto-encoder network is then presented for the learning and extracting of features automatically in the mean arterial pressure (MAP) time series integrated with the SW, EEMD, and deep learning (DL) techniques. At last, the feature sets are used to construct classification models using multiple gene expression programming (GEP) classifier. The result shows that the proposed method has higher prediction accuracy of 86.16% and robustness in the prediction of the AHE.
Gavel: A Fast and Easy-to-Use Plain Data Representation for Software-Defined Networks

In software-defined networking (SDN), high-level abstractions typically offer a useful means to avoid writing network applications and policies on lower levels. However, abstractions are typically developed for a specific use case, which in turn results in an abundance of existing abstractions for different networking tasks. As a consequence, orchestrating these abstractions to implement a common network policy becomes an arduous task. To address this challenge, plain data representations of the network and its control infrastructure have been proposed recently, which offer programmable ad-hoc abstractions to administrators. However, these frameworks suffer from quite complex programming requirements and impractical performance in terms of latency, as they are based on relational database engines. In this paper, we address these shortcomings by introducing Gavel, an SDN controller that at its heart facilitates a plain data representation based on a graph database. By exploiting the native graph support of the database engine, Gavel significantly eases application and policy writing. Additionally, we show by experimental evaluation of several typical applications on multiple different topologies that Gavel offers significant performance improvements over the state-of-the-art solutions.
Screenomics: A Framework to Capture and Analyze Personal Life Experiences and the Ways that Technology Shapes Them

Digital experiences capture an increasingly large part of life, making them a preferred, if not required, method to describe and theorize about human behavior. Digital media also shape behavior by enabling people to switch between different content easily, and create unique threads of experiences that pass quickly through numerous information categories. Current methods of recording digital experiences provide only partial reconstructions of digital lives that weave – often within seconds – among multiple applications, locations, functions, and media. We describe an end-to-end system for capturing and analyzing the “screenome” of life in media, i.e., the record of individual experiences represented as a sequence of screens that people view and interact with over time. The system includes software that collects screenshots, extracts text and images, and allows searching of a screenshot database. We discuss how the system can be used to elaborate current theories about psychological processing of technology, and suggest new theoretical questions that are enabled by multiple timescale analyses. Capabilities of the system are highlighted with eight research examples that analyze screens from adults who have generated data within the system. We end with a discussion of future uses, limitations, theory, and privacy.
Estimating the Recreation Use Values of National Parks

A comprehensive application of the travel cost method (TCM) to the estimation of the recreation use values of Dorrigo and Gibraltar Range National Parks is outlined. As well as providing value estimates that are useful for the development of park acquisition and management policy, the application also provides some important lessons for the future use of the TCM. So long as some key assumptions relating to the value of travel time and site congestion are shown to hold, then a stream-lined version of the TCM can be used. The questionnaire required to implement the abridged version is brief, thus encouraging a better response rate. Survey and analysis costs are therefore likely to be lower, making the TCM a more cost-effective decision making tool.
Challenges in On-Chip Antenna Design and Integration With RF Receiver Front-End Circuitry in Nanoscale CMOS for 5G Communication Systems

This paper investigates design considerations and challenges of integrating on-chip antennas in nanoscale CMOS technology at millimeter-wave (mm-wave) to achieve a compact front-end receiver for 5G communication systems. Solutions to overcome these challenges are offered and realized in digital 28-nm CMOS. A monolithic on-chip antenna is designed and optimized in the presence of rigorous metal density rules and other back-end-of-the-line (BEoL) challenges of the nanoscale technology. The proposed antenna structure further exploits ground metallization on a PCB board acting as a reflector to increase its radiation efficiency and power gain by 37.3% and 9.8 dB, respectively, while decreasing the silicon area up to 30% compared to the previous works. The antenna is directly matched to a two-stage low noise amplifier (LNA) in a synergetic way as to give rise to an active integrated antenna (AIA) in order to avoid additional matching or interconnect losses. The LNA is followed by a double-balanced folded Gilbert cell mixer, which produces a lower intermediate frequency (IF) such that no probing is required for measurements. The measured total gain of the AIA is 14 dBi. Its total core area is 0.83 mm2 while the total chip area, including the pad frame, is $1.55 \times 0.85$ mm2.
Image Retrieval Based on Deep Belief Networks

According to the local and global feature of image, matching the image from a lot image library, this is the image retrieval task; however, the image retrieval need to search the information in the database, we need to find a method for efficient information retrieval. Deep belief network according to the characteristic of the initiative, through the method of training a multilayer neural network to process large amounts of data, and it is very efficient, in this article, as to the characteristics of image local features and global features, it gives a deep belief network image retrieval algorithm, the experiment verify the effectiveness of the algorithm.
Design and Fabrication of Unmanned Terrain Vehicle

-Rocker bogie is important for conducting in-situ scientific analysis of objectives that are separated by many meters to tens of kilometres. Current mobility designs are complex, using many wheels or legs. They are open to mechanical failure caused by the harsh environment on Mars. A four wheeled rover capable of traversing rough terrain using an efficient high degree of mobility suspension system. The primary mechanical feature of the rocker bogie design is its drive train simplicity, which is accomplished by using only two motors for mobility. Both motors are located inside the body where thermal variation is kept to a minimum, increasing reliability and efficiency. Four wheels are used because there are few obstacles on natural terrain that require both front wheels of the rover to climb simultaneously. A series of mobility experiments in the agriculture land, rough roads, inclined, stairs and obstacles surfaces concluded that rocker bogie can achieve some distance traverses on field.
Development of a neuromuscular driver model with an estimation of steering torque feedback in vehicle steer-by-wire systems

In this paper, a neuromuscular driver model for sensing torque feedback or haptic interaction between the vehicle equipped with steer-by-wire (SBW) system and the driver has been developed. The proposed driver model consists of a preview model and a neuromuscular model. The preview driver model calculates the desired angle of the steering-wheel to follow the path, and the neuromuscular driver model, with the ability of perceiving real-time torque feedback, determines the real angle of the steering-wheel angle according to muscular system transfer functions to follow the desired steering-wheel angle. In order to calculate torques on the steering-wheel, the lateral tyre-road forces are estimated by Kalman filter designed using a linear 2-DOF vehicle model. So, the design of the neuromuscular driver model combined with torque feedback estimation is the main contribution of this paper. The simulation results from TruckSim and Simulink software indicate that the novel designed driver model with torque feedback estimation has an important role in the controlling and steering vehicle to follow the desired paths.