Miracle-dz/UniFAR-FTUs huggingface dataset:
An Algorithmic Blockchain Readiness Index

This paper presents the design of a Blockchain Readiness Index (BRI) to be used as a tool for assisting nations to monitor the level of Blockchain maturity according to their suitability on hosting blockchain-based activities, and successfully adopting a blockchain regulatory framework. BRI is a composite index that combines a variety of indicators from a range of sources to a single score. The proposed methodology attempts to fill a knowledge gap by evaluating the relatively unexplored area of blockchain adoption per nation. The index presented in this paper aims to distinguish between the most promising and non-hostile countries, acting as the basis for professional work, decision making and operations of organizations, investors, academics, and other stakeholders within the blockchain space. As the index is updated regularly, all new developments on the fast-changing landscape of blockchain and cryptocurrencies are reflected. In addition, this research aims to go beyond the regulatory environment towards examining several other factors such as local engagement, expertise, investments, and the need for a decentralized provision of services. This paper presents an overview of the landscape of similar attempts on designing such indexes by reviewing and identifying potential gaps and opportunities for improving their methodological design that can lead to more accurate and relevant conclusions. In addition, with this paper we contribute a systematic methodology for building a BRI using techniques from the information retrieval domain to normalize the non-normalized values, and a cosine similarity measure to derive an index ranking consisting of various nations. More specifically the proposed BRI covers a wide range of blockchain readiness indicators which can be organized into the following “pillars”: (1) Government Regulation, (2) Research, (3) Technology, (4) Industry, and (5) User Engagement. An empirical evaluation reports preliminary but promising results of the algorithmic design methodology showing evidence that, the identified indicators are sufficient for developing our index when compared to judgements made by human experts.
CAPS: Coding-Based Adaptive Packet Spraying to Reduce Flow Completion Time in Data Center

Modern data-center applications generate a diverse mix of short and long flows with different performance requirements and weaknesses. The short flows are typically delay-sensitive but to suffer the head-of-line blocking and out-of-order problems. Recent solutions prioritize the short flows to meet their latency requirements, while damaging the throughput-sensitive long flows. To solve these problems, we design a Coding-based Adaptive Packet Spraying (CAPS) that effectively mitigates the negative impact of short and long flows on each other. To exploit the availability of multiple paths and avoid the head-of-line blocking, CAPS spreads the packets of short flows to all paths, while the long flows are limited to a few paths with Equal Cost Multi Path (ECMP). Meanwhile, to resolve the out-of-order problem with low overhead, CAPS encodes the short flows using forward error correction (FEC) technology and adjusts the coding redundancy according to the blocking probability. Moreover, since the coding efficiency decreases when the coding unit is too small or large, we demonstrate how to obtain the optimal size of coding unit. The coding layer is deployed between the TCP and IP layers, without any modifications on the existing TCP/IP protocols. The test results of NS2 simulation and small-scale testbed experiments show that CAPS significantly reduces the average flow completion time of short flows by ~30%–70% over the state-of-the-art multipath transmission schemes and achieves the high throughput for long flows with negligible traffic overhead.
Knowledge-oriented Hierarchical Neural Network for Sentiment Classification

Sentiment classification aims to classify the sentimental polarities of given texts. Lexicon-based approaches utilize lexical resources to explore the opinions according to some specific rules, whose effectiveness strongly depends on the goodness of the lexical resources and the rules. Traditional machine-learning methods tightly rely on feature engineering and external NLP toolkits with unavoidable errors. Deep learning models strongly rely on a large amount of labelled data to train their numerous parameters, which often suffer from overfitting issue since it is difficult to obtain sufficient training data. To address the issues, we design a model that combines Knowledge-oriented Convolutional Neural Network (K-CNN) and bidirectional Gated Recurrent Neural Network (biGRU) in a hierarchical way for sentiment classification. Firstly K-CNN is used to capture the n-gram features in sentences. Sentiment word filters are constructed in the knowledge-oriented channel of K-CNN based on the linguistic knowledge from SentiWv ordNet, which can capture the sentiment lexicons and alleviate overfitting effectively. Then biGRU with attention mechanism is utilized to model the sequential relations between sentences and obtain the document-level representation based on the relevance of each sentence to the final sentiment classification. Experiments on two datasets show that our model outperforms other classical deep neural network models.
Kidney and tumor segmentation using combined Deep learning method

This paper presents our method for automatic segmentation for kidney and tumor as part of the Kidney Tumor Segmentation Challenge (KiTS19). The KiTS19 Challenge had released a dataset of 300 unique kidney cancer patients, with manual annotations done by Climb 4 Kidney Cancer (C4KC). Here we have proposed our new combined cascade deep learning (DL) approach for solving the tasks of the challenge. We used deep learning based detection for localising kidney with the tumor, followed by deep learning based segmentation to create the labels for kidney and tumor locally. Our approach resulted in high re-call (96.13) and high Jacquard score (95.4) on the randomly selected 30 volumes that were picked as the validation set.
Implementing an Iot Approach to Careawareness in the Home.

There is a need for new thinking in care previsions and IOT is likely to play a major role in achieving transformation of care services at lower cost. Such an approach sits at the heart of this work. Here we present the outcome of an ongoing research programme based on sensor and App interventions (trial) at homes of participants who live independently but require care. The results indicate contrasting views of carers and the persons who receive care regarding (a) the uptake and utilisation of IoT, and (b) the potential impact on the quality of life and independence of both groups. In contrast to current telecare solutions that respond to “hard”- acute alerts, our research proposes the introduction of “soft” warnings, based on the continuous evaluation of the occupancy behaviour, care conditions and the home environment. In this context the results suggest that the care needs of a person can be more important determinants of occupancy behaviour than socio-demographic attributes that are typically used in residential occupancy classification. This research under the CareTeam programme, is a non-intrusive IoT approach geared to generate proactive care-awareness to help people who receive care stay independent and active for longer.
XTRA: Towards Portable Transport Layer Functions

XTRA (XFSM for Transport) aims at providing a first attempt towards a “code-once-port-everywhere” platform-agnostic programming abstraction tailored to the deployment of transport layer functions. XTRA’s programming abstraction not only fits SW platforms, but is specifically designed to harness, with no re-coding effort, the offloading opportunities offered by CPU-less HW boards or smart NICs. We demonstrate the viability of XTRA with three completely different implementations of the underlying execution engine (HW proof-of-concept on a NetFPGA board, User-space SW over Linux’ Open Data Plane, and NS3 emulator). Flexibility is shown via a number of example applications, ranging from a variety of congestion control algorithms, to a middlebox-type TCP proxy functionality, up to a customized “Timer-Based” (TB) TCP which leverages the native reliance of XTRA on timers, so as to produce a loss recovery operation which, despite being formalized only via a handful of code lines, performs almost comparable with the highly optimized Linux and FreeBSD implementations.
A Sub-1-V 100-mA OCL-LDO Regulator With Process-Temperature-Aware Design for Transient Sustainability

In this article, an output-capacitorless low-dropout (OCL-LDO) regulator that features low-power, small-transient-spike, and process-temperature (PT)-aware design for transient sustainability is presented. The circuit architecture is based on the improved PT-aware current source for keeping stable bandwidth and the proposed PT-aware transistor biasing network in conjunction of dual fast local feedback (DFLF) loops in a single power transistor stage to yield both enhanced and sustained transient metrics under a sub-1-V supply. Fabricated in 40-nm CMOS technology, the regulator can deliver a full-load current of 100 mA at a 100-pF load under a 0.75-V supply. From the measured results of 12 samples, it consumes an average quiescent power of 19.5 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> and quiescent current of 26 <inline-formula> <tex-math notation="LaTeX">$\mu \text{A}$ </tex-math></inline-formula>. It displays an average settling time of 414 ns for a full-load current of 100 mA at room temperature. The average load transient voltage spike is 23.9 mV and small when compared to the reported works at a similar level of load current. Finally, the process corner simulations at different temperatures together with the 12 measured samples at temperature corners have validated the sustainability of transient metrics.
N.E.A.T. - Novel Emergency Analysis Tool

We present N.E. A.T. - a Visual Analytics approach to the collaborative management of large-scale emergencies. N.E. A.T. unifies the analysis and annotation of heterogeneous, uncertainty-afflicted data sources in a single, adjustable screen. Stakeholders can create individual or shared workspaces providing configurable views tailored to the needs of different emergency responders. Within each workspace, annotated findings are automatically shared in real-time for effective collaboration. We illustrate the functionality of the tool and showcase exemplary findings on the St. Himark incident.
Swarm Routing in Information Centric Networks

The current Internet usage is changing, with an important increase in the consumption of content-based applications. However, the current host-based architecture is not prepared to integrate this amount of information. Therefore, novel proposals for the future Internet architecture are researched, as Information Centric Networks (ICN). ICN tries to shape the Internet architecture to the current Internet challenges by moving from the host-to-host model to a content-centric substitute. The survey on the related work shows how the routing techniques proposed to solve this subject take quite contrasting directions, from a deterministic link-state to a broadcast system. The literature review provides signs suggesting that Name Data Networking, a research project under ICN, might be able to adopt a swarm technique for the routing and forwarding planes due to the similarities between both concepts. Therefore, these techniques are thoroughly studied to present a design proposal implementing them, which applies the features from an antbased system and complies with the NDN architecture. The system that evaluates the design is a simulator, implemented by means of several iterations of design implementation. The gathering of data comes from different experiments over two distinct topologies. The results certify the correct behavior of the system, operating as it is expected, and providing with interesting results. Moreover, the comparison between a flooding system with the ant-based design establishes a more fruitful outcome for the later in terms of content retrieved and path length. The contribution done by this Thesis is to provide an alternative with promising results to the currently employed routing techniques in the NDN field. hopefully, the solution presented may support further work in this line of research.
Binary Image Steganography using Flipping Distortion Technique

: In binary image steganography many methods are used. The flipping distortion of the pixel is important to hide the message in image. The binary steganography image is divided into 3X3 blocks. Binary image encrypted with a private key . In this method non-overlapped blocks is take and scan image from top left corner. To decrypt the message string user need private key. Some techniques like minimizing flipping distortion are used securely data transfer. In steganographic scheme divide the scrambled image into superpixels and generate cover image. In this method, the image divided into blocks of order 3 X 3. Each block is divided diagonally and the number of black and white pixel in both the upper and lower halves of the block is determined. After that connectivity and smoothness test is performed on each 3X3 block to decide whether block is embeddable or not. For extraction the hidden data bits, reverse process is applied on the image. Hence original image is not use for extraction.
Kidney and Kidney Tumor Segmentation Using Two- stage Convolutional Neural Network

Kidney tumor is typically diagnosed using computed tomography (CT) imaging by investigating geometric features of kidney tumor. For a reliable diagnosis and treatment planning, kidney tumor quantification is necessary. However, manual segmentation by human requires time and expertise. In addition, inter/intra variability of segmentation results can lead to suboptimal decision. In this study, we propose the two-stage segmentation method using 2.5D and 3D convolutional neural network for kidney and kidney tumor delineation. The two-stage model was trained with multi-task loss for pixel-wise cross-entropy loss function for segmentation task and mean square error function for regression task. Experimental results confirm that the proposed method effectively segments kidney and kidney tumor.
Mammography maintenance: A longitudinal, population-based study of insured women

JENNIFER MARIE GIERISCH: Mammography Maintenance: A longitudinal population-based study of insured women (Under the direction of Jo Anne Earp, Noel Brewer, Barbara K. Rimer, Celette Sugg Skinner and Catherine Zimmer) Early detection through mammography screening is an effective way to control breast cancer. Rates of mammography screening have increased dramatically over the last decade although recent data suggest that rates may now be declining. To reduce their risk of breast cancer morbidity and mortality, women should maintain regular on-schedule mammography use (i.e., mammography maintenance). Repeat use is a necessary step towards that goal. Many variables have been examined as predictors or correlates of repeat mammography use. However, few studies of mammography maintenance exist. The data for these secondary analyses come from PRISM (Personally Relevant Information on Screening Mammography), a five-year health communication intervention trial. Participants in this study come from PRISM control group (n=1522). Predictors of interest were informed by behavioral theory and previous research on maintenance of health behaviors. Descriptive statistics and survival analysis were used to assess study aims. Mammography use was calculated at the end of each follow-up period (12, 24 and 36 months). Unsustained maintenance was defined as not having consecutive mammograms on schedule (10 to 14 months apart).
Simplified LLR algorithm for m‐QAM demodulation

Here, a simplified algorithm to calculate log-likelihood ratio (LLR) for m-QAM demodulation is proposed. Traditional method calculating LLR for m-QAM demodulation is supposed to do distance computing between any two constellation points so that the high complexity of the hardware implementation is not acceptable. This simplified method is different from the traditional ways that the distance calculation between any two points in a plane can be omitted, indicating that there is an advantage in the amount of computation in this simplified method. Finally, the simplified method is compared with traditional methods from the perspective of complexity and system performance. The simulation results show that the simplified method can greatly reduce the amount of calculation and have little effect on the performance of the system.
Strict Memory Protection for Microcontrollers

Modern desktop systems protect processes from each other with memory protection. Microcontroller systems, which lack support for memory virtualization, typically only uses memory protection for areas that the programmer deems necessary and does not separate processes completely. As a result the application still appears monolithic and only a handful of errors may be detected. This thesis presents a set of solutions for complete separation of processes, unleashing the full potential of the memory protection unit embedded in modern ARM-based microcontrollers. The operating system loads multiple programs from disk into independently protected portions of memory. These programs may then be started, stopped, modified, crashed etc. without affecting other running programs. When loading programs, a new allocation algorithm is used that automatically aligns the memories for use with the protection hardware. A pager is written to satisfy additional run-time demands of applications, and communication primitives for inter-process communication is made available. Since every running process is unable to get access to other running processes, not only reliability but also security is improved. An application may be split so that unsafe or error-prone code is separated from mission-critical code, allowing it to be independently restarted when an error occurs. With executable and writeable memory access rights being mutually exclusive, code injection is made harder to perform. The solution is all transparent to the programmer. All that is required is to split an application into sub-programs that operates largely independently. An added benefit of this modularity is that code may also be developed and tested as independent programs. The standard POSIX API is used as the programming interface, allowing programmers to use existing knowledge and code when designing applications. For programs that do not depend on specific hardware, development may be done and tested on a regular desktop system entirely before running it on a microcontroller.
A New Framework For Helicopter Vibration Suppression; Time-Periodic System Identification and Controller Design

In forward flight, helicopter rotor blades function within a highly complex aerodynamic environment that includes both near-blade and far-blade aerodynamic phenomena. These aerodynamic phenomena cause fluctuating aerodynamic loads on the rotor blades. These loads when coupled with the dynamic characteristics and elastic motion of the blade create excessive amount of vibration. These vibrations degrade helicopter performance, passenger comfort and contributes to high cost maintenance problems. In an effort to suppress helicopter vibration, recent studies have developed active control strategies using active pitch links, flaps, twist actuation and higher harmonic control of the swash plate. In active helicopter vibration control, designing a controller in a computationally efficient way requires accurate reduced-order models of complex helicopter aeroelasticity. In previous studies, controllers were designed using aeroelastic models that were obtained by coupling independently reduced aerodynamic and structural dynamic models. Unfortunately, these controllers could not satisfy stability and performance criteria when implemented in highfidelity computer simulations or real-time experiments. In this thesis, we present a novel approach that provides accurate time-periodic reduced-order models and time-periodic H2 and HQO controllers that satisfy the stability and performance criteria. Computational efficiency and the necessity of using the approach were validated by implementing an actively controlled flap strategy. In this proposed approach, the reduced-order models were directly identified from highfidelity coupled aeroelastic analysis by using the time-periodic subspace identification method Time-periodic H2 and H^ controllers that update the control actuation at every time step were designed. The control synthesis problem was solved using Linear Matrix Inequality and periodic Riccati Equation based formulations, for which an in-house periodic Riccati solver was developed. The results show that first, important helicopter aeroelastic features can only be captured using high-fidelity coupled aeroelastic analysis; ignoring these features through uncoupled analysis leads to closed-loop performance degradation and instabilities. Second, timeperiodic models are necessary to obtain an accurate map between control actuation and helicopter aeroelastic response; time-invariant models fail to provide accurate prediction. iii Third, time-Periodic Hi and B. ^ controllers satisfy the stability and design performance criteria when implemented in high-fidelity aeroelastic analysis. Finally, we propose robust H.2 and H^ controller design strategies that are capable of modeling variable advance ratios.
Automatic 3D Medical Image Segmentation with Densely-Connected Volumetric ConvNets

The Automated medical image segmentation in 3D medical images play an important role in many clinical applications, such as diagnosis of prostatitis, medical image cancer and enlarged medical image. However, it is still a challenging task due to the complex background, lacking of clear boundary and various shape and texture between the slices. In this paper, we propose a novel 3D convolutional neural network with densely-connected layers to automatically segment the medical image. Compared with other methods, our method has three compelling advantages. First, our model can effectively detect the medical image region in a volume-to-volume manner by utilizing the 3D convolution rather than the 2D convolution, which can fully exploit both spatial and region information. Second, the proposed network architecture alleviates the vanishing-gradient problem, strengthens the information propagation between layers, overcomes the problem of overfitting and makes the network deeper by adopting a densely-con-nected manner. Third, besides the densely-connected manner inside each block, we also adopt the long connections strategy between blocks.