Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanowire Peel-Off for Tandem Solar Cell Applications

Nanowires provide interesting features for photovoltaic applications. Materials with desirable band gaps can be combined into heterostructures with very little strain and with a fraction of the material consumption seen in conventional solar devices. Moreover, nanowires show outstanding optical absorption and InP and GaAs nanowire solar cells have been shown to reach efficiencies of 13.8 % [1] and 15.3 % [2]. This project has been done as a step towards building tandem solar cell structures with a top cell consisting of nanowires. A method in which arrays of nanowires with lengths around 2 μm can be peeled off from their native substrate when embedded in a polymer while keeping their array structure and their vertical orientation has been investigated. Moreover, nanowire gold tips were successfully localized using a conductive AFM and imaging of pin-doped InPnanowires with this method was performed.
Evaluation of aerobic capacity and muscle function in a case series of patients with relapsing polychondritis

Background: To evaluate aerobic capacity and muscle function in patients with Relapsing Polychondritis (RP). Methods: A cross-sectional single center study evaluated seven women with RP (McAdam criteria) that were compared to seven healthy control individuals matched for age, gender, and body mass index. Both groups performed a treadmill-graded maximal exercise. Aerobic capacity was assessed by peak oxygen uptake (VO2 peak), time at anaerobic Ventilatory Threshold (VAT) and Respiratory Compensation Point (RCP) and time-to-exhaustion. Muscle function was evaluated through the sit-to-stand and the timed up-and-go test.
Fat Pad Cages for Facial Posing

We introduce Fat Pad cages for posing facial meshes. It combines cage representation and facial anatomical elements, and enables users with no artistic skill to quickly sketch realistic facial expressions. The model relies on one or several cage(s) that deform(s) the mesh following the human fat pads map. We propose a new function to filter Green Coordinates using geodesic distances preventing global deformation while ensuring smooth deformations at the borders. Lips, nostrils and eyelids are processed slightly differently to allow folding up and opening. Cages are automatically created and fit any new unknown facial mesh. To validate our approach, we present a user study comparing our Fat Pad cages to regular Green Coordinates. Results show that Fat Pad cages bring a significant improvement in reproducing existing facial expressions.
to Doodle Generator Using a GAN

Many papers and projects have applied deep learning to various problems where the input is a human sketch or doodle. For example retrieving an image based on a human sketch ​[1]​, recognizing/classifying the object drawn in a sketch ​[3]​, or taking a sketch and creating a realistic image from it ​[5]​. However, to our knowledge it seems that very few people, if any, have focused on applying deep learning to go in the other direction, that is taking an image and creating a sketch similar to what a human would create. Certainly algorithms exist for creating drawings from images, but these generally use contours to create much more accurate sketches than what the average person could do. Our early attempts at applying style transfer to these images yielded unappealing results that can easily be distinguished from a human made drawing (below).
Counting Actor Screen Time in Movies

We present an end-to-end system for counting actor screen time in movies through detecting and clustering faces in Avengers trailers. We break the problem apart into two parts: face segmentation and facial recognition. Our data consists of trailers that we divide into different frames (images). Then, we solve the ﬁrst part of the problem using state-of-the-art pre-trained models in order to detect bounding boxes around where the Avengers’ faces are. Given this output, we feed the information into the second model which detects which Avenger character it is. We present results the show our quality predictions with different models
Application of the CIE color spaces for the digital image quality assessment

The digital image quality assessment is one of the most relevant aspects of contemporary digital image processing. A rapid development of some modern quality assessment techniques in recent years has caused the introduction of some new metrics, much better correlated with the Human Visual System (HVS) than the traditional ones such as the Mean Squared Error (MSE) or PSNR (Peak Signal to Noise Ratio). One of the most popular modern image quality assessment techniques is the usage of the Structural Similarity index (SSIM) defined in 2004. Unfortunately, even some modern image quality metrics are usually defined for the grayscale images so the colour information is often ignored. A typical classical approach to the quality assessment of the color images is the use of the Normalized Color Difference calculated in the CIE L*a*b* ob CIE Lu'v' colour space but its correlation with the human assessment is rather poor. In the paper the analysis of the influence of using the color spaces recommended by the CIE on the results of the digital image quality assessment using some modern metrics is performed. All the results have been calculated for the widely known LIVE database (release 2) containing the Differential Mean Opinion Scores (DMOS) for nearly 1000 color images with five types of distortions: JPEG compression, JPEG2000 compression, Gaussian blur, white noise and transmission over the simulated fast fading Rayleigh channel typical of wireless transmissions. As the final result the comparison of the correlations between the DMOS values and the SSIM metric calculated for various colour spaces recommended by the CIE is presented.
Household Animals Classiﬁcation Using Deep Learning

Using deep learning to study household animals’ demeanor and body language, we can ﬁnd out if they are sick or not and provide necessary help in time. In order to achieve this goal, we need to start with animal species classiﬁcation. In this project I will train one of the deep learning models, VGG, to distinguish between images of cats and dogs. After using the Transfer Learning from VGG-16, the accuracy can increase from 80% to over 95%. Then two ways of visualizing the model outputs have been successfully demonstrated for more insight of the VGG model.
Audit Committee Meeting, Expertise and Financial Reporting Quality of Listed Deposit Money Banks in Nigeria

This study examines the effect of audit committee meeting and expertise on financial reporting quality of listed deposit money banks (DMB’s) in Nigeria. The study uses panel data obtained from the Nigerian Stock Exchange fact books and the financial statements of fifteen (15) listed deposit money banks over a period of ten years (2007-2016). The study uses cross sectional and time series research design. The modified Jones (1991) model was adopted to measure financial reporting quality. The data was analyzed using STATA. The study reveals and concludes that the effect of audit committee meeting on the financial reporting quality of listed deposit money banks in Nigeria in model 1 is positive and insignificant while it is negative and insignificant in model 2. Also, the study reveals that the effect of audit committee expertise on the financial reporting quality of listed deposit money banks in Nigeria is negative and insignificant in model 1 while in model 2 it is negative and insignificant. Based on the conclusion, the study recommends that the management of DMB’s should ensure audit committee members are encouraged to attend meetings regularly. It is evident that in some instances meetings are sparsely attended by members which has tendency of affecting the quality of contributions that would have been made if most or all members were to be in attendance. Management of DMB’s should consider the regulation on audit committee expertise to ensure reliable financial reporting of high quality. This will increase the overall credibility of the accounting profession as well.
Data Transmission with 1.55 μm Wavelength InGaAlAs VCSELs

Vertical-cavity surface-emitting lasers emitting at 850 nm wavelength are currently used as transmitters for serial and parallel optical links over multimode fibers [1]. The advantages of VCSELs are their high beam quality, longitudinal and transverse single-mode operation, low power consumption and the possibility of array arrangements. Additionally easy on-wafer testing leads to low production and packaging costs. At 1. 55μm wavelength standard single-mode fibers (SSMF) with high bandwidth length products and low optical dispersion and attenuation can be used. Taking into account that the eye safety restrictions at an emission wavelength of 1. 55μm are relaxed by a factor of about 100 compared to 850 nm wavelength [2], the maximum interconnect length can be up 80 km of SSMF [3, 4]. The technological challenges of monolithically grown 1. 55μm wavelength VCSELs are the poor thermal conductivity and low reflectivities of InP-based Bragg mirrors. Additionally a natural oxidation process for current confinement is not yet known. As an alternative to Bragg mirrors active regions on GaAs, metamorphic growth or wafer bonding techniques are conceivable. Using a substrate removal technique and bottom dielectric mirrors in combination with a buried tunnel junction (BTJ) for current confinement, a significant improvement with respect to single-mode power and operation temperature has been demonstrated [5]. In this paper we present the static and significantly improved dynamic characteristics of InGaAlAs-based BTJ-VCSELs emitting at 1. 55μm wavelength. For the first time we demonstrate 5 Gbit/s data transmission over 20.5 km SSMF as well as 10 Gbit/s for back-to-back (BTB) operation. This shows the excellent performance of 1. 55μm VCSELs for Metropolitan Area Network applications.
An argument on the security of LRBC, a recently proposed lightweight block cipher

LRBC is a new lightweight block cipher that has been proposed for resource-constrained IoT devices. The cipher is claimed to be secure against diﬀerential cryptanalysis and linear cryptanalysis. However, beside short state length which is only 16-bits, the structures of the cipher only use the linear operations, the its s-boxes, and this is a reason why the cipher is completely insecure against the mentioned attacks. we present a few examples to show that. Also, we show that the round function of LRBC has some structural problem and even if we ﬁx them the cipher does not provide complete diﬀusion. Hence, even with replacement of the cipher s-boxes with proper s-boxes, the problem will not be ﬁxed and it is possible to provide deterministic distinguisher for any number of round of the cipher. In addition, we show that for any ﬁxed key, it is possible to create a full code book for the cipher with the complexity of 2 n/ 2 , which should be compared with 2 n for any secure n -bit block cipher.
Pothole Classiﬁcation Using CNNs

Potholes are one of the extreme classes of road damage which can not only cause misalignment of the vehicles from their intended path but also damage vehicle structure and components. Both of these can lead to accidents. The damage caused is also dependent on the speed of the vehicle. Therefore it becomes necessary to avoid them which calls for their detection. With the onset of autonomous vehicles employment in passenger travel, the accurate detection is important for taking evasive measures to prevent potential accidents and reduced vehicle life by preventing subjection to unecessary high stresses. It can also be conducive to road maintenance body in cities.
AffordIt!: A Tool for Authoring Object Component Behavior in Virtual Reality

In this paper we present AffordIt! , a tool for adding affordances to the component parts of a virtual object. Following 3D scene reconstruction and segmentation procedures, users find themselves with complete virtual objects, but no intrinsic behaviors have been assigned, forcing them to use unfamiliar Desktop-based 3D editing tools. AffordIt! offers an intuitive solution that allows a user to select a region of interest for the mesh cutter tool, assign an intrinsic behavior and view an animation preview of their work. To evaluate the usability and workload of AffordIt! we ran an exploratory study to gather feedback. In the study we utilize two mesh cutter shapes that select a region of interest and two movement behaviors that a user then assigns to a common household object. The results show high usability with low workload ratings, demonstrating the feasibility of AffordIt! as a valuable 3D authoring tool. Based on these initial results we also present a road-map of future work that will improve the tool in future iterations.
Learning Power Flow Mappings for Power Grid Simulation

Power system simulation engines are essential tools for power grid operation and planning by utility companies and academic researchers. However, simulations can be computationally expensive given the wide range of timescales of grid operation. Additionally, the accuracy of utility distribution system models has traditionally been limited, although operational data is now becoming more abundant. The objective of this project is to develop a deep learning framework for approximating the outputs from a power flow simulation, and evaluate performance for a variety of power network characteristics. This model could be used for: (1) accelerating simulation speed when trained on the outputs from a traditional power flow solver, or (2) improving network model accuracy when trained on real operational data. These methods would be particularly useful in applications requiring real-time simulation or optimization. The purpose of a power flow solver is to perform the inverse power flow mapping. That is, given the real and reactive power injections at each bus (node) in the power network and the admittance matrix of the system, solve for the magnitude and phase of the voltage at each bus in the network. For a deep learning model, the output is a vector of the voltage magnitudes for every bus and phase, and the input is a feature vector composed of the real and reactive power injections at nominal voltage for each bus and phase. We also investigate how incorporating other types of network information improves model accuracy.
Deep Vein Thrombosis Detection from CT Scans

Patients who get CT scans for any concerns are generally discharged before radiologists evaluate the scans. For patients with deep vein thrombosis (blood clots), it is critical for them to get on medication as soon as clots are discovered from the CT scan [1]. An automated system to detect clots in veins would help early diagnosis at the point of testing and would prevent fatalities from delayed radiologist involvement. We explored solutions to this problem ranging from classical computer vision like SIFT to deep neural networks like DenseNet-161 and ﬁnally multitask segmentation and classiﬁcation neural networks. Our results show that the multitask model which takes into account more local information is best suited for tackling this problem. Our results are currently the best reported numbers on the Stanford Medicine deep vein thrombosis dataset.
Stamp detection in scanned documents

The article presents current challenges in stamp detection problem. It is a crucial topic these days since more and more traditional paper documents are being scanned in order to be archived, sent through the net or just printed. Moreover, an electronic version of paper document stored on a hard drive can be taken as forensic evidence of possible crime. The main purpose of the method presented in the paper is to detect, localize and segment stamps (imprints) from the scanned document. The problem is not trivial since there is no such thing like "stamp standard". There are many variations in size, shape, complexity and ink color. It should be remembered that the scanned document may be degraded in quality and the stamp can be placed on a relatively complicated background. The algorithm consists of several steps: color segmentation and pixel classification, regular shapes detection, candidates segmentation and verification. The paper includes also the initial results of selected experiments on real documents having different types of stamps.
Feasibility in the implementation of 4.0 technologies in the intralogistic activities of Logistic Operators of the Department of Atlántico, a look towards the organizational efficiency

The intralogistic activities of the logistics operators are defined in the grouping of all the processes linked to the reception, storage and distribution of raw materials and final products. Therefore, the study of intralogistic processes as an activity is part of the value chain of a company and, through its management, the result of the operation and an adequate level of customer service will depend on it. The project proposes to evaluate the feasibility of the use of technologies of the industry 4.0 in the intralogistic activities that allow the integration of processes of automation and control for the continuous improvement of the decision making in the processes of storage, load and unload of the logistic operators of the department of the Atlantic Colombia for the improvement of the enterprise efficiency. Therefore, a review of the literature is carried out framed in three categories: intralogistics, Industry and Technology 4.0, and the application of an instrument to measure the absorption of technologies 4.0 in the company under study, which was validated as a result the importance of Simulation, Automation and load sensing, Collaborative Robotics, Big Data powered by M2M (Machine to Machine communication) and Predictive maintenance predictive analysis and M2M communication as a solution to the needs that are represented in the environment of the Logistics Operators of the Department of Atlántico Colombia, based on the challenges and projections imposed by the dynamics of the international market and especially the free trade agreements that the New Granada nation has signed and framed in recent years with the intention of making its economy and especially the organizations of its different regions more efficient.
Development and Validation of a precise single stability indicating HPLC method for determinations of Metformin hydrochloride and Fenofibrate, in Pure form and in Pharmaceutical tablets

A selective, precise, isocratic and accurate stability indicating reverse phase high performance liquid chromatography method have been developed for the simultaneous determination of Metformin hydrochloride and Fenofibrate present in multicomponent dosage forms. The HPLC method was carried out on Inertsil octadecylsilane C18 (250 mm x 4.6 mm i.d., 5 μm particle size) column. A mobile phase composed of acetonitrile water (adjusted to pH 3 using orthophosphoric acid) in proportion of 70:30 v/v, at flow rate of 1 ml/min was used for the separation. Detection was carried out at 250nm. Method was validated statistically and recovery studies were carried out. The proposed method have been applied successfully to the analysis of cited drugs either in pure form or in pharmaceutical formulations with good accuracy and precision. The method herein described can be employed for quality control and routine analysis of drugs in pharmaceutical formulations.
Reducing the Cost of Probabilistic Knowledge Compilation

Bayesian networks (BN) are a popular representation for reasoning under uncertainty. The computational complexity of inference, however, hinders its applicability to many real-world domains that in principle can be modeled by BNs. Inference methods based on Weighted Model Counting (WMC) reduce the cost of inference by exploiting patterns exhibited by the probabilities associated with BN nodes. However, these methods require a computationally intensive compilation step in search of these patterns, limiting the number of BNs that are eligible based on their size. In this paper, we aim to extend WMC methods in general by proposing a scalable, compilation framework that is language agnostic, which solves this problem by partitioning BNs and compiling them as a set of smaller sub-problems. This reduces the cost of compilation and allows state-of-the-art innovations in WMC to be applied to a much larger range of Bayesian networks.
Multi-contact stances planning for multiple agents

We propose a generalized framework together with an algorithm to plan a discrete sequence of multi-contact stances that brings a set of collaborating robots and manipulated objects from a specified initial configuration to a desired goal through non-gaited acyclic contacts with their environment or among each other. The broad range of applications of this generic algorithm includes legged locomotion planning, whole-body manipulation planning, dexterous manipulation planning, as well as any multi-contact-based motion planning problem that might combine several of these sub-problems. We demonstrate the versatility of our planner through example scenarios taken from the aforementioned classes of problems in virtual environments.
Modeling the Dichotomies of Organizational Change: a State-based Capability Typology

Modern digital businesses are facing a constant challenge in adapting to dynamic environments. Therefore, change has become a significant element of business analysis. Capability thinking, when applied to business management, is associated to design and analysis of supporting information systems and is in-extricably linked to strategy and change. This results in the need to monitor and analyze how and when the organization’s capabilities need to change. Capability and change dimensions have been explored in the literature in order to identify dimensions relevant to organizational change. The identified capability dimensions are purpose, potentiality and ownership while the relevant change dimensions are control, scope, stride, frequency, desire and tempo. The two sets of dimensions have been combined forming a typology and visualized in a StateMa-chine diagram. The contribution of this task lies in the conceptualization of the dimensions, including the negative aspect of capabilities, which can provide a starting point for an Enterprise Modeling method optimized for identifying the need for capability change and guiding the transition.
Deep Learning Assisted Image guided Interventions: a feasibility study

We evaluate feasibility of deep learning based tracking for image guided interventions (IGI). Guidewire navigation during real-time IGI currently requires time-consuming manual slice repositioning. Deep learning may increase intervention procedure eﬃciency by automating this slice repositioning step. We developed a deep learning based pipeline for automated guidewire tracking and steering an MRI scanner. A slice repositioning experiment was successfully conducted on an anthropomorphic blood vessel phantom. We show tracking of a guidewire displacement of 140mm in real-time (121ms) within, on average, a 7mm margin. We conclude that deep learning assisted image guided interventions is feasible, though not yet ready for clinical practice. Further research on model robustness and uncertainty is therefore encouraged in order to push deep learning assisted image guided interventions towards clinical practice.
Genetic Improvement of Genetic Programming

GISMOE BNF grammar based GI is applied to optimise run time of the tree interpreter in the fastest single computer floating point genetic programming system, GPavx. Up to two fold speed up is obtained. Performance varies with tree size. The GI version of Singleton’s C++ GPquick is demonstrated on random trees of up to 79 million opcodes on Intel AVX512 SIMD parallel compute servers.