Miracle-dz/UniFAR-FTUs huggingface dataset:
HPPT-NoC: A Dark-Silicon Inspired Hierarchical TDM NoC with Efficient Power-Performance Trading

Networks-on-chip (NoCs) acquired substantial advancements as the typical solution for a modular, flexible and high performance communication infrastructure coping with the scalable Multi-/Manycores technology. However, the increasing chip complexity heading towards thousand cores, together with the approaching dark-silicon era, puts energy efficiency as an integral design key for future NoC-based multicores, where NoCs are significantly contributing to the total chip power. In this paper, we propose HPPT-NoC, a dark-silicon inspired energy-efficient hierarchical TDM NoC with online distributed setup-scheme. The proposed network makes use of the dim silicon parts of the chip to hierarchically connect quad-routers units. Normal routers operate at full-chip-frequency at high supply level, and hierarchical routers operate at half-chip-frequency and lower supply voltage with adequate synchronization. Routers follow a proposed TDM architecture that separates the datapath from the control-setup planes. This allows separate clocking and operating supplies between data and control and to keep the control-setup as a single-slot-cycle design independent of the datapath slot size. The proposed NoC architecture is evaluated versus a base NoC from the state-of-the-art in terms of performance and hardware results using Synopsys VCS and Synopsys Design Compiler for SAED90nm and SAED32nm technologies. The obtained results highlight the power-frequency-trading feature supported by the proposed hierarchical NoC through the configurable data-control clock relation and maintained over the different technology nodes. With the same power budget of the base NoC, the proposed architecture provides up to 74% setup latency enhancement, 32% increased NoC saturation load, and 21% higher success rates, offering up to 78% improved power delay product. On the other hand, with 38% power savings, the proposed NoC provides up to 37% enhanced latency and 15% higher success rates, with 72% enhanced power delay product. The proposed design consumes almost double the area of the base NoC, however with an average of 56% under-clocked (dim) silicon area operating at half to quarter the maximum chip frequency. This results in reduced power density as a main concern in the dark-silicon era down to 24% of the base NoC.
Sentiment Analysis in Facebook using Machine Learning Techniques

Abstract: Sentiment analysis is the process of computationally identifying and categorizing opinions expressed in text to determine the users’ attitude towards a particular product is positive, negative or neutral. In today’s world, opinions and reviews accessible to us are one of the most critical factors in formulating our views and influencing the success of a brand, product or service. With the advent and growth of social media in the world, stakeholders often take to expressing their opinions on popular social media, namely facebook. While Facebook data is extremely informative, it presents a challenge for analysis because of its humongous and disorganized nature. This paper is a thorough effort to dive into the novel domain of performing sentiment analysis about people’s opinion about Google and apple productions. For performance analysis machine learning approaches such as n-gram technique is used. Hybrid approach is implemented by combining lexicon-based and machine learning algorithms. The result obtained through this approach is feasible to perform sentiment analysis in facebook with high accuracy.
Leveraging zero-knowledge succinct arguments of knowledge for efficient verification of outsourced training of artificial neural networks

Incremental innovations have led to practical implementations of both Artificial Neural Networks and Succinct Non-interactive ARGuments, this thesis explores the practicality of using these SNARGs to verify outsourced training of an ANN. This training algorithm presents a particular case of Verifiable Computation as it relies on large quanti- ties of input data, floating point arithmetic and parallel computation. Decomposition of the core concepts ANNs and SNARGs to their elemen- tary building blocks allows the identification of imposed constraints mapped to the case of outsourced training and existing practical implementations of proof systems. The verdict separates the two com- putations and postulates how the Linear Probabilistically Checkable Proofs fit the inference computation and how the Interactive Oracle Proofs, specifically STARKs, fit the training computation. This work builds on the the works of Chabenne et al. and Ghodsi et al. that studied the verification of the inference computation and the work of Wu et al. that studies a similar training algorithm in the context of DIstributed Zero-Knowledge.
RAT Association for Autonomic IoT Systems

The next generation of wireless communications consists of heterogeneous networks that operate over different technologies and standards. Co-existence of multiple technologies aims to enhance the network capacity in order to satisfy the increasing number of connected devices. However, in order to meet high performance, it is important to assign the right device to the right RAT. In this article, we propose a novel distributed approach to solve the RAT selection problem, in a self-organized IoT context. Our scheme is mainly based on tools from MG theory. We show that a stable association between connected IoT devices and available RATs can be met while using a fully distributed algorithm that respects connected devices requirements and RAT constraints as well. The proposed MG assignment allows IoT devices, with limited energy budget, to improve their energy efficiency and to reduce the data transmission cost over the serving RAT at very light signaling overhead. Moreover, extensive simulations show that the MG based approach outperforms the conventional throughput maximization association in terms of energy efficiency and throughput. Our scheme also exhibits some nice load balancing features.
INSTRUMENTATION AND INERTIAL NAVIGATION SYSTEMS DESIGN FOR TENSEGRITY ROBOT IMPLEMENTATIONS

Instrumentation and Inertial Navigation Systems Design for Tensegrity Robot Implementations by Scott E. Harper One of the major challenges faced when developing missions for the exploration of planetary bodies is the risk these terrains pose on the science platform when using a traditional lander or wheeled rover. One means of developing platforms to traverse these harsh terrains is to utilize mobility systems comprised of tensegrity structures. These structures have the capacity to distribute loads across a network of axially loaded members such that they can be constructed in a very lightweight manner and morph their geometries when required. In literature, there has been significant progress in simulated environments to utilize tensegrity structures as mobility platforms, but the leap to real hardware has been a slow transition due to constrains in actuation design and state estimation. This thesis outlines a method of aiding an Inertial Navigation System deployed on a single strut of a tensegrity structure using zero velocity updates. The methods are implemented on a static icosahedran tensegrity structure to test the viability of the ZUPT-aided INS, how it can be improved, and other instrumentation configurations that can improve the results in future works. I dedicate this thesis to my late grandfather, Noel Smith Justice.
Cost-effectiveness analysis with informative missing data: tools and strategies

Cost effectiveness analysis (CEA) of randomised trials are an important source of evidence for informing policy makers on how to best allocate limited resources. Missing data are a common issue in trial-based CEA, and methods such as multiple imputation are now commonly used to account for the missing values, assuming the data are ‘missing at random’ (MAR). This implies that the reasons for the missing data can be explained by the observed data. However, the missingness is often related to unobserved values, that is data are ‘missing not at random’ (MNAR, or ‘informative’). For example, patients whose health status is relatively poor may be less likely to return health questionnaires, even conditional on their observed characteristics. In these settings, methodological guidance recommends assessing whether conclusions are sensitive to departures from the MAR assumption. Sensitivity analysis strategies for handling MNAR is an area of rapid development in medical statistics, but this form of uncertainty has not yet been appropriately addressed in health economics. This PhD thesis aims to develop practical, accessible sensitivity analysis strategies and software tools to handle MNAR data in trial-based CEA. The thesis critically assessed the statistical methods for handling MNAR data in CEA practice, and identified barriers to more widespread use of these methods, via a systematic review and stakeholder focus groups. The research then focused on two strategies to conduct sensitivity analysis under MNAR assumptions: pattern-mixture models, which involve imputing the data assuming MAR, then modifying the imputed values to reflect possible departures from that assumption; and reference-based imputation, where the data are imputed assuming a distribution borrowed from a ‘reference group’. These approaches were illustrated in CEAs of 10TT and CoBalT trials, which evaluated weight loss and depression interventions. Software code and practical guidance are provided to facilitate implementation in practice.
A Computation Offloading and Resource Allocation Mechanism Based on Minimizing Devices Energy Consumption and System Delay

To support the execution of computation-intensive, delay-sensitive computing task by moving down the computing and processing capability in mobile edge computing becomes the current trend. However, when serving a large number of mobile users, how to effectively use the edge nodes with limited computing resources to ensure Quality of service (QoS) of end-user has become a key issue. To solve this problem, the edge cloud and remote cloud are combined to build a layered edge cloud computing architecture. Based on this architecture, with the goal of minimizing mobile device energy consumption and task execution time, the problem which is proved to be convex is formulated to minimize the weight sum of energy and delay. A computation offloading and resource allocation mechanism based on multiplier method is proposed. Simulations are conducted to evaluate the proposed mechanism. Compared with local computing and computation offloading mechanism, the proposed mechanism can effectively reduce the energy consumption of mobile device and the delay of system by up to 60% and 10%, respectively.
Key Concepts of Group Pattern Discovery Algorithms from Spatio-Temporal Trajectories

Over the years, the increasing development of location acquisition devices have generated a significant amount of spatio-temporal data. This data can be further analysed in search for some interesting patterns, new information, or to construct predictive models such as next location prediction. The goal of this paper is to contribute to the future research and development of group pattern discovery algorithms from spatio-temporal data by providing an insight into algorithms design in this research area which is based on a comprehensive classification of state-of-the-art models. This work includes static, big data as well as data stream processing models which to the best of authors' knowledge is the first attempt of presenting them in this context. Furthermore, currently available surveys and taxonomies in this research area do not focus on group pattern mining algorithms nor include the state-of-the-art models. The authors conclude with the proposal of a conceptual model of Universal, Streaming, Distributed and Parameter-light (USDP) algorithm that addresses current challenges in this research area.
SEMANTIC SEGMENTATION OF MANMADE LANDSCAPE STRUCTURES IN DIGITAL TERRAIN MODELS

Abstract. We explore the use of semantic segmentation in Digital Terrain Models (DTMS) for detecting manmade landscape structures in archaeological sites. DTM data are stored and processed as large matrices of depth 1 as opposed to depth 3 in RGB images. The matrices usually contain continuous real-valued information upper bound of which is not fixed, such as distance or height from a reference surface. This is different from RGB images that contain integer values in a fixed range of 0 to 255. Additionally, RGB images are usually stored in smaller multidimensional matrices, and are more suitable as inputs for a neural network while the large DTMs are necessary to be split into smaller sub-matrices to be used by neural networks. Thus, while the spatial information of pixels in RGB images are important only locally within a single image, for DTM data, they are important locally, within a single sub-matrix processed for neural network, and also globally, in relation to the neighboring sub-matrices. To cope with the two differences, we apply min-max normalization to each input matrix fed to the neural network, and use a slightly modified version of DeepLabv3+ model for semantic segmentation. We show that with the architecture change, and the preprocessing, better results are achieved.
Multi Camera System Analysis for Autonomous Navigation using End-to-End Deep Learning

This work aims to present an autonomous vehicle navigation system, based on an End-to-End Deep Learning approach, and to study the impact of different image input conﬁgurations to the system performance. The proposed methodology in this work was to adoptand test different conﬁgurations of RGB and Depth images captured from a Kinect device. We adopted a multi-camera system, composed by 3 cameras, with different RGB and/or Depth input conﬁgurations. Two main systems were developed in order to study and validade de different input conﬁgurations: the ﬁrst one based on a realistic simulator and the second one based on a mini-car (small scale vehicle). Starting with the simulations, it was possible to choose the best camera/input conﬁguration, then we validated that using the real vehicle (mini-car) with real sensors/cameras. The experimental results demonstrated that a multi-camera solution, based on 3 cameras, allow us to obtain better autonomous navigation control results in a End-to-End Deep Learning based approch, with a very small ﬁnal error when using the proposed camera conﬁgurations.
A Review- Image Segmentation using Soft Computing Techniques

: Digital image processing (DIP) receiving organized reinforce strong examine program districts of image upgrade, image division & picture base precedent statement. These all methods are reasonable for some medicinal picture applications. These procedures can be utilized for item acknowledgment and recognition. In beneficial pictures these can be utilized to differentiate disease and in satellite pictures these can be utilized to identify streets and extensions. This review gave a thorough investigation of the grouping calculations proposed in the writing. So as to uncover future bearings for growing new calculations and to manage the determination of calculations for enormous information, a sorting system to arrange various bunching calculations. Because of the need of picture division in numerous applications, it has a difficult future. To portion the pictures, from division procedures edge location, thresholding, district developing and bunching are taken for this examination. This research work centers on various strategies that are generally used to portion photo utilizing bunching systems.
Deep Learning Approach to Track Reconstruction in the upgraded VELO

The LHCb experiment will undergo a major upgrade for LHC Run III, scheduled to start taking data in 2021. In order to exploit the full physics potential of the upgraded detector, LHCb will employ a high level event filter entirely implemented in software. The event filter has to operate in real time at the 40 MHz LHC bunch crossing rate. The LHCb collaboration is currently exploring a variety of new new computing paradigms in order to cope with the challenges posed by the high data taking rates. One contribution to this effort is the application of Machine Learning (ML) techniques to particle track reconstruction. We explore an ML approach to the track reconstruction in the upgraded LHCb Vertex Locator (VELO). The reconstruction algorithm is evaluated with fully simulated Minimum-Bias data that reflects the input to the real time event filter. We have achieved a reasonable track reconstruction efficiency and low ghost rate with our first approach and are currently exploring several methods to further quality of the track reconstruction.
Designing A Protective Relay for Differential Protection of Power Transformers Using Clarks Transform and S Transform

Given that power transformers are one of the most important components of each network, their protection is an important part that the power transformer errors must be accurately identified and distinguished from each other. Therefore, identification and differentiation of transient phenomena of power transformers, including internal and external errors and magnetic inrush current are essential. In this research, Clarke transform and S transform were used to distinguish between these phenomena that the proposed algorithm is very suitable in terms of three characteristics of accuracy, speed and computational cost. Initially, the simulation of internal, external errors and magnetic inrush current of the transformer was performed for different transformer scenarios. For this purpose, 1060 signal tests were performed under different conditions. Subsequently, the signals of differential current obtained by Clarke transformation and S transformation were analyzed and appropriate criteria were extracted for detecting the current of internal errors from external errors and inrush current. The simulated internal and external errors include three- phase, three-phase to ground, two- phase, two- phase to ground and phase to ground error. Simulations were performed using PSCAD software and implementation of the proposed algorithm in MATLAB environment. The results of this study prevent the unwanted performance of differential protection to prevent undesirable electrifying. It is clear that the description of transient phenomena is the first step towards improving new ideas and criteria for protection with the greater reliability of power transformer which can be controlled better such unusual conditions that are currently used in equipment and relay.
Detecting unseen targets during inference in infrared imaging

Performing reliable target recognition in infrared imagery is a challenging problem due to the variation of the signatures of the targets caused by changes in the environment, the viewpoint or the state of the targets. Due to their state-of-the-art performance on several computer vision problems, Convolutional Neural Networks (CNNs) are particularly appealing in this context. However, CNNs may provide wrong classification results with high confidence. Robustness to disturbed inputs can be mitigated through the implementation of specific training strategies to improve classification performances. But they would generally require retraining or fine-tuning the CNN to face new forms of disturbed inputs. Besides, such strategies do not necessarily tackle novelty detection without training an auxiliary classifier. In this paper we propose two solutions to give the ability of a trained CNN to deal with both adversarial examples and novelty detection during inference. The first approach is based on one-class support vector machines (SVM) and the second one relies on the Local Outlier Factor (LOF) algorithm for example detection. We benchmark our contributions on SENSIAC database for a pre-trained network and evaluate how they may help mitigate false classifications on outliers and adversarial inputs.
Assessment of the Disaster Recovery Progress through Mathematical Modelling

Natural disasters, especially major earthquakes, cause widespread devastation in the built environment. Hence, the major component of the recovery in its aftermath constitutes a chain of projects starting at different times, having different costs and durations. In this study, the post disaster recovery curve modelled through a mathematical approach taking into account these properties of the projects. The approach followed is based on the project S-curve concept that provides the opportunity to simulate the progress by outlining the project spending. Well-known mathematical functions are adapted to model the project spending and the handover processes. Monte Carlo simulation is performed to evaluate the general behavior of the recovery curve using the model developed. Weibull distribution is used to generate the model’s parameters. Results of the Monte Carlo simulation demonstrate that the recovery process exhibits an S-shape, the duration of initial portion and the slope of the bulk portion being significantly governed by the level of preparedness of the community.
Human Detection and Motion Analysis from a Quadrotor UAV

This work focuses on detecting humans and estimating their pose and trajectory from an umnanned aerial vehicle (UAV). In our framework, a human detection model is trained using a Region-based Convolutional Neural Network (R-CNN). Each video frame is corrected for perspective using projective transformation. Using Histogram Oriented Gradients (HOG) of the silhouettes as features, the detected human figures are then classified for their pose. A dynamic classifier is developed to estimate forward walking and a turning gait sequence. The estimated poses are used to estimate the shape of the trajectory traversed by the human subject. An average precision of 98% has been achieved for the detector. Experiments conducted on aerial videos confirm our solution can achieve accurate pose and trajectory estimation for different kinds of perspective-distorted videos. For example, for a video recorded at 40m above ground, the perspective correction improves accuracy by 37.1% and 17.8% in pose and viewpoint estimation respectively.
NADPH oxidases (version 2019.4) in the IUPHAR/BPS Guide to Pharmacology Database

The two DUOX enzymes were originally identified as participating in the production of hydrogen peroxide as a pre-requisite for thyroid hormone biosynthesis in the thyroid gland [6]. NOX enzymes function to catalyse the reduction of molecular oxygen to superoxide and various other reactive oxygen species (ROS). They are subunits of the NADPH oxidase complex.
A Survey Paper on Fingerprint Recognition and Cross Matching

: As common as largely used various technical methods, identification of fingerprint is presently growing as a very popular biometric recognition approach, primarily due to its uniqueness. Process of performing a sharp observation and thereby finding out the most appropriate fingerprint match from the given set of data is what actually meant by the term fingerprint recognition. For getting the proper match output using fingerprint recognition systems, a wide variety of theoretical methods and algorithms were proposed and are quite common for last long years. Each system use minutia feature as the key feature for similarity identification because of its accuracy. Some researchers used features like ridge map, core point feature other than the minutia to get better performance and accuracy. Nowadays cross matching of fingerprint from different sensors are also a research topic due to the emergence of new contactless sensors. This survey paper includes the discussion about fingerprint recognition and cross matching techniques. Here different approaches are studied in terms of accuracy and performance.