Miracle-dz/UniFAR-FTUs huggingface dataset:
Overcoming the reality gap: Studying synthetic image modalities for convolutional neural network training

Nowadays, convolutional neural networks (CNNs) are the first choice for a variety of computer vision tasks such as object recognition, 6-degree-of-freedom (DoF) pose estimation, navigation, and others. Despite the success, they come with a challenge: CNNs require training data, and acquiring this data is a laborious and time-intensive task. A solution is training with synthetic images: synthetic images means one uses rendered content stacked on top of a random background photo. Unfortunately, research results following this approach show that CNNs trained with synthetic images underperform when exposed to recorded images, a discrepancy which is known as the reality gap. However, the results are limited to training using solely synthetic RGB images. The objective of this research is to study whether additional image modalities (normal vectors, depth maps) bridge the reality gap for 6-DoF pose estimation. The approach is experimental. A CNN architecture learns to estimate the pose using synthetic domain randomized datasets only; each dataset implements a different sensory modality (RGB, RGB-D[epth], RGB-N[ormal], RGB-DN). Once trained, the network is exposed to one synthetic and one recorded evaluation dataset, and the pose accuracy difference shows whether a different modality overcomes the reality gap. Experimental results on a synthetic test set shows that adding normal and depth modalities to the RGB modality increases the 5-degree accuracy by 4% and 6% respectively for the POLY dataset. The combination of all RGB, depth and normal modalities yielded promising results on both, the POLY and POSE datasets. More innovative fusion strategies can be explored to fully utilize the unique characteristics of each data modality. Multiple explorations on real recorded dataset revealed the necessity to generate synthetic training datasets which scene context matches real life data.
Joint Beamforming and Relay Selection in AF Two-Way Relay Networks With Energy Transfer

In this paper, we investigate a joint beamforming and relay selection design problem in amplify-and-forward two-way relay networks with energy transfer. In the relay network, multiple relays are responsible for not only information exchange of two sources, but also energy transfer to two sources. Two sources use the power-splitting scheme to harvest energy. The relay selection constraint, included in the aforementioned problem, causes it to be nonconvex. We prove that the relay selection constraint can be replaced by a convex constraint and a concave constraint. Furthermore, we propose to use a sequential parametric convex approximation-based algorithm to solve the problem. Simulation results demonstrate the effectiveness of our proposed scheme.
CONCISENET: AN END TO END ABSTRACTIVE MODEL FOR TOPIC GENERATION

Recent approaches in Title generation using neural approaches have relied on an end to end deep learning system based on the sequence to sequence model. Such approaches have yielded good results but remain constricted in use due to a fixed size input which is often very small compared to the text being used or might take huge compute power to train and use if input size is increased. Our approach amalgamates an extractive and abstractive approach to get the best of both worlds using aive approach to get the best of both worlds using a textrank algorithm for the extractive part and a reasonably small seq2seq architecture as the abstractive part. Testing on the Amazon Fine Food Review dataset, our approach gives good results using less compute power.We utilize the prevailing metrics of ROUGE and Cosine Similarity. Manual checking shows that the majority of our generated topics are grammatically correct.
A Fair Blockchain Based on Proof of Credit

Proof of work and proof of stake (PoS) are commonly used in the current permissionless blockchains. These consensus protocols can be abstracted into a random process of selecting a node for accounting in a blockchain ledger. However, they are generally faced with resource consumption and vulnerability issues. We present proof of credit (PoC), a fair blockchain protocol based on the PoC blockchain protocol. It is a special PoS protocol where the credit is a special kind of stake quantifying whether the node’s activity is beneficial to the system. Any nodes cannot change their credits arbitrarily. We demonstrate that our PoC protocol satisfies the security properties, including common prefix, chain quality, and chain growth, under the assumption that the total credit the honest held is majority. In addition, we propose a self-audit mechanism and a hybrid incentive mechanism to enhance the security and stability. Finally, we explain the method by which the PoC protocol resists the double-spending attacks and the selfish mining attacks.
Design and Analysis of Numerical Schemes with Characteristic Methods on Generic Grids for Flows in Porous Media

This thesis studies characteristic-based numerical schemes for a mathematical model of miscible fluid flow in porous media, a coupled and advection-dominated system of partial differential equations describing an oil recovery process. In particular, we analyse characteristic-based schemes on generic polygonal meshes, with diffusive component discretised using modern finite volume methods. We also develop a novel algorithm for reconstructing velocity fields suitable for characteristic tracking. The proposed scheme mixes two characteristic-based discretisations, and implements an original post-processing algorithm to ensure local and global mass conservation. Extensive numerical tests are provided, on various polygonal meshes. A rigorous convergence analysis is also performed.
Biomechanical Evaluation of Isotropic and Shell-Core Composite Meniscal Implants for Total Meniscus Replacement: A Nonlinear Finite Element Study

Loss of meniscal function due to symptomatic meniscal tears or meniscectomy leads to biomechanical instability and articular cartilage degeneration. Synthetic meniscal implants ought to ideally restore normal joint contact mechanics and thus forfending the overlying cartilage from degeneration. The purpose of this study was to quantify the contact stresses in both tibiofemoral compartments and joint kinematics during a gait cycle after implantation of a synthetic meniscal implant. Anatomically-detailed finite element model of the knee joint was developed from magnetic resonance images of a healthy female volunteer. Gait analysis was conducted using a three-dimensional motion capture system and computed the knee joint forces and moments and quadriceps muscle forces for a complete walking cycle. The effects of a synthetic meniscal implant on joint mechanics during gait were studied by conducting finite element simulations for the following meniscus conditions: (i) intact meniscus, (ii) meniscus with complete radial posterior root tear, (iii) total meniscectomy, (iv) isotropic meniscal implant, and (v) shell-core composite meniscal implant. Posterior root tear and total meniscectomy caused substantially increased contact stresses in both tibiofemoral compartments and altered tibial kinematics. Compared to posterior root tear and total meniscectomy, the isotropic and composite meniscal implants reduced the peak contact stresses in both compartments and reduced the cartilage nodes with higher contact stresses by disseminating the load over a large surface area. The shell-core composite meniscal implant resulted in lower contact stresses in the medial compartment relative to the other meniscus conditions. This study demonstrated that posterior root tear and total meniscectomy leads to detrimental changes in joint mechanics. Superseding the injured meniscus with a synthetic meniscal implant restored the joint mechanics close to the intact meniscal state. This novel synthetic meniscal implantation approach appears to be a promising strategy for treating patients with severe meniscal injuries.
Novel Ternary Logic Gates Design in Nanoelectronics

In this paper, standard ternary logic gates are initially designed to considerably reduce static power consumption. This study proposes novel ternary gates based on two supply voltages in which the direct current is eliminated and the leakage current is reduced considerably. In addition, ST-OR and ST-AND are generated directly instead of ST-NAND and ST-NOR. The proposed gates have a high noise margin near V_(DD)/4. The simulation results indicated that the power consumption and PDP underwent a~sharp decrease and noise margin showed a considerable increase in comparison to both one supply and two supply based designs in previous works. PDP is improved in the proposed OR, as compared to one supply and two supply based previous works about 83% and 63%, respectively. Also, a memory cell is designed using the proposed STI logic gate, which has a considerably lower static power to store logic ‘1’ and the static noise margin, as compared to other designs.
Reputation-Based Approach Toward Web Content Credibility Analysis

Web content credibility implies finding credible and correct information on the web. Recent studies have shown there is an increasing trend of users turning towards the web for searching information related to a variety of topics including health, stocks, education, politics to name few. Information credibility is a critical factor in these domains for the decision makers. There is no limitation on the authorship of those articles and content. One criterion for evaluating credibility is to check the authority or source of information. However, there are situations when wrong information flows from credible sources. There are various approaches towards credibility assessment, broadly categorized into human-based and computational approaches. Computational approaches utilizing machine learning based techniques are computationally expensive. Reputation based approaches overcome this, however the latest work fails to take into account issue of negative referrals and utilizes simple summation as the calculation structure making it more resilient to attacks. This paper put forth verified hypothesis of direct relationship of credibility to the expertise of entity. Authors proposed a Bayesian based approach using feedback in the form of interaction among the entities to compute their expertise level, thereby showing improved results in terms of Precision, Correlation and Mean Average Error. The experiments are performed on two different datasets, one of the dataset is developed from a survey as the part of the research study. The results from the two experiments show that the reputation ranks are independent of the pattern of ratings and density of data, unlike previous techniques whose results were limited by these factors. The proposed technique gives 27% and 18% more precise results for the two experiments respectively compared to the baseline. The correlation results are also significant in both experiments for the proposed technique with significant values of 0.39 and 0.87 showing a linear relationship between predicted and original data. The paper also discusses the reputation attacks and proposes counter measures to tackle these attacks through simulation results.
Household Energy Conservation Intervention: a Game versus Dashboard Comparison

This paper presents the results of a study, comparing a game versus a dashboard with respect to energy conservation in the household. In a pretest-posttest design, an empirical study tested whether change in attitude, knowledge, engagement and behaviour with respect to energy conservation in the household was different for participants playing Powersaver Game compared to a control condition where participants used an energy dashboard with the same content, but excluding game features. The aim of this game (developed using an iterative user-centered game design methodology) is to influence household energy consumption by means of electricity and gas usage in the long-term. The intervention time was at least 5 weeks and pre and post measures were based on 21 days intervals. All energy conservation activities that the application provides (e.g. washing clothes on low temperatures) take place in the real world and feedback is based on real time energy consumption. This real data into the game approach aims to optimize the transfer between the game world and the real world. Energy consumption significantly changed in the game condition compared to the control condition, and the difference between both conditions is more than 33% after the intervention. In the game condition, knowledge about energy conservation was significantly increased, although no significant differences in increase of attitude and engagement were found. We conclude that Powersaver Game is effective in transfer of energy conservation knowledge, which leads to energy saving behaviour on the long term. However, it cannot be concluded that playing the game leads to a greater change in attitude, probably because attitude scores of the participants were high from the start.
Types and Timing of Scaffolding to Promote Meaningful Peer Interaction and Increase Learning Performance in Computer-Supported Collaborative Learning Environments

This study was designed to examine the effects of negotiation scaffolding for solving complex problems, focusing on peer interaction and learning performance in a computer-supported collaborative learning environment. For this research, 38 undergraduate students performed complex tasks using a collaborative discussion tool that implemented scaffolding strategies including the fading of meaning-negotiation scaffolding (MS) and the provision of position-negotiation scaffolding (PS). The participants were assigned to one of the following conditions: (a) MS is not faded and PS is not provided (Group N), (b) MS is faded and PS is not provided (Group F), (c) MS is not faded and PS is provided (Group P), and (d) MS is faded and PS is provided (Group FP). All groups worked in pairs to complete a comprehension task and a lesson-planning task. The results revealed that Group FP outperformed the others, showing the highest learning performance and a significant increase in meaningful peer interactions, such as divergent and reflective types of discussion.
User Involvement in the Implementation of the Enterprise Resource Planning Systems in Public Universities.

In a close examination of previous studies on whether the implementation of ERP systems in universities has been successful or a failure, most of the studies revealed that it was a success while a few indicated that the implementation was below average. This study sought to evaluate user involvement in implementing ERP systems in public universities. This research was guided by the Information Systems Success Model and the Diffusion of Innovation Theory. The researcher adopted a survey research design and the area of the study was at Kisii University. The target population comprised of 930 respondents. The researcher used questionnaires for data collection from the sample size of 76 respondents who were picked randomly and 64 questionnaires were returned. The collected data was analyzed using descriptive statistics methods of mean and standard deviation and inferential statistics methods of correlation and regression analysis. Then the results were presented in the form of tables. The overall result showed that the extent of user involvement was to a large extent achieved (M=2.09; SD=.802) in the implementation of the ERP systems in public universities in Kenya. The study concluded that the extent of user involvement had a statistically significant influence on the ERP system implementations in public universities in Kenya. The study recommended that there was a need for improved quality user involvement and commitment level on the ERP system implementations because they lead to quality information systems.
Credit Valuation Adjustment Compression by Genetic Optimization

Since the 2008–2009 financial crisis, banks have introduced a family of X-valuation adjustments (XVAs) to quantify the cost of counterparty risk and of its capital and funding implications. XVAs represent a switch of paradigm in derivative management, from hedging to balance sheet optimization. They reflect market inefficiencies that should be compressed as much as possible. In this work, we present a genetic algorithm applied to the compression of credit valuation adjustment (CVA), the expected cost of client defaults to a bank. The design of the algorithm is fine-tuned to the hybrid structure, both discrete and continuous parameter, of the corresponding high-dimensional and nonconvex optimization problem. To make intensive trade incremental XVA computations practical in real-time as required for XVA compression purposes, we propose an approach that circumvents portfolio revaluation at the cost of disk memory, storing the portfolio exposure of the night so that the exposure of the portfolio augmented by a new deal can be obtained at the cost of computing the exposure of the new deal only. This is illustrated by a CVA compression case study on real swap portfolios.
Energy Efficiency Maximization for Wirelessly Powered Sensor Networks With Energy Beamforming

This letter studies a wireless powered sensor network (WPSN) where the senor nodes harvest energy from the power beacons by wireless power transfer (WPT). In order to improve the energy transfer efficiency of WPT, a WPT scheme with energy beamforming is presented to power the sensor network. Moreover, for the sake of maximizing energy efficiency (EE), we present a resource assignment optimization problem for this scheme, which is nonconvex because of the fractional form of the objective function. So we develop a low complexity algorithm to resolve this problem. Compared to other traditional algorithms (e.g., Dinkelbach [1]) for solving this type of problem, whose objective function is of fractional form, it works with lower time complexity. Experimental results indicate the superiority of the proposed scheme.
DSM: A Deep Supervised Multi-Scale Network Learning for Skin Cancer Segmentation

The automatic segmentation of the skin lesion on dermoscopy images is an important step for diagnosing the melanoma. However, the skin lesion segmentation is still a challenging task due to the blur lesion border, low contrast between the skin cancer region and normal tissue background, and various sizes of cancer regions. In this paper, we propose a deep supervised multi-scale network (DSM-Network), which achieves satisfied skin cancer segmentation result by utilizing the side-output layers of the network to aggregate information from shallow&deep layers, and designing a multi-scale connection block to handle a variety of cancer sizes’ changes. Moreover, a post-processing of the contour refinement strategy is adopted by a conditional random field (CRF) model to further improve the segmentation results. Extensive experiments on two public datasets: ISBI 2017 and PH2 have demonstrated that our designed DSM-Network has gained competitive performance compared with other state-of-the-art methods.
Planning for future performance: implications for long term training.

: The contribution of cardio-respiratory endurance and strength to elite performance in swimming has been quantitated in a unit of training termed the Training Impulse (TRIMP). The Training Impulse has been used to construct a model for predicting performance in swimming based upon the degree of balance between fitness and fatigue that training engenders.
A Survey on FPGA Support for the Feasible Execution of Virtualized Network Functions

Network Functions Virtualization (NFV) has received considerable attention in the past few years, both from industry and academia, due to its potential for reducing capital and operational expenditures, thus enabling faster innovation in networks. NFV proposes decoupling network functions from fixed hardware platforms and implementing them as virtual machines on off-the-shelf servers. Although potentially able to provide the mentioned benefits, software-based implementations of compute-intensive network functions still struggle to perform at the desired speed, especially when requiring line-rate processing for ever-faster communication links. To address this, hardware acceleration can be used to improve the throughput and latency of virtualized network functions (VNFs). In order to provide the advantages foreseen for NFV, however, such accelerators cannot be fixed and application-specific hardware, since they need to cope with new VNFs as well as fluctuations in demand. In this context, Field-Programmable Gate Arrays (FPGAs) are particularly suitable, since they are able to provide high-performance implementations of network functions and they are completely reprogrammable, thereby being able to implement different VNFs even after deployment. There have been many recent efforts to enable the use of FPGAs in the NFV context, including efficient implementations of network functions on FPGAs, platforms to manage the integration and coexistence of multiple VNFs on an FPGA, and high-level synthesis tools especially tailored to ease the programming of VNFs for FPGAs. In this work we survey previous work covering these aspects, and discuss the main open research challenges that must be addressed before FPGA adoption in NFV infrastructures becomes effectively seamless and efficient.