Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical modeling of wastage using the beta distribution

This work attempts to fit a set of observed industrial wastage to the beta distribution using the Chisquare goodness of fit test and it was found to fit the beta distribution at 1% significance level. The mean and the variance of the wastage proportion were found to be 0.429 and 0.0395, respectively. The parameters of the beta distribution were α = 2.25 and β = 2.97. The skewness of the beta distribution is 0.286 confirming the claim that the distribution is skewed to the right whenever β > α and the distribution is a unimodal distribution since α and β is greater than 1.
Reduced dopamine receptor sensitivity as an intermediate phenotype in alcohol dependence and the role of the COMT Val158Met and DRD2 Taq1A genotypes.

CONTEXT Alcohol dependence is a common neuropsychiatric disorder with high heritability. However, genetic association studies on alcohol dependence are often troubled by nonreplication. The use of intermediate phenotypes may help make clear the mode of action of various candidate genes and improve the reproducibility of genetic association studies. OBJECTIVE To test central dopamine receptor sensitivity as an intermediate phenotype for alcohol dependence, specifically evaluating the hypothesis that the dopaminergic genes COMT Val158Met and DRD2 Taq1A affect dopamine receptor sensitivity. DESIGN Case-control pharmacogenetic challenge study. SETTING Patients with alcohol dependence admitted for detoxification were compared with healthy control subjects matched for age and level of education. PARTICIPANTS Patients (n = 110) were a consecutive sample, whereas controls (n = 99) were recruited through advertisements in regional newspapers. INTERVENTION A dopamine challenge test was subcutaneously administered using the dopamine agonist apomorphine hydrochloride (0.005 mg/kg). MAIN OUTCOME MEASURES Outcome measures were plasma growth hormone levels and results of a continuous performance task. RESULTS Central dopamine receptor sensitivity is reduced in alcohol dependence, and this is modulated by dopaminergic genes. Specifically, DRD2 Taq1A genotype affected dopamine receptor sensitivity as measured by plasma growth hormone levels, and COMT Val158Met genotype affected dopamine receptor sensitivity as measured by performance on a continuous performance task. In a logistic regression analysis, reduced dopamine receptor sensitivity on both measures predicted alcohol dependence, without an additive effect of the COMT Val158Met and DRD2 Taq1A genotypes. CONCLUSIONS COMT Val158Met and DRD2 Taq1A may affect the intermediate phenotype of central dopamine receptor sensitivity. COMT Val158Met and DRD2 Taq1A may confer their risk of alcohol dependence through reduced dopamine receptor sensitivity in the prefrontal cortex and hindbrain, respectively.
Changing people’s hair color in images

Many popular apps provide functionality for changing the hair color of people in images. We design and implement a variation of the cycleGAN architecture that can learn this transformation from a dataset of real images with hair color labels, without being shown input-output pairs. In contrast to cycleGAN, our architecture uses only one generator and one discriminator, and both of them take hair color labels as input in addition to images.
Model of Dynamic Smart Contract for Permissioned Blockchains

The blockchain technology and smart contract capabilities encourage scholars and retailers on investigating the new conceptual modeling and technological opportunities for redesigning the current and future business processes. The emerge of blockchain technology indicates the new era in designing and developing the business process mainly by using smart contracts. Along with these opportunities, different challenges are presented in the current state of blockchain technology and smart contracts. Among them, the emphases cases are the alignment of the new changes on the requirements from the business process, to the smart contracts. We present a new way of modeling a smart contract that impacts on the maintainability of the enterprise blockchain-based solution. This paper shows a new approach that allows smart contracts acting dynamically over lift time changes on the business process logic specific to the use case.
SHAPE MEMORY POLYMERS AND SHAPE MEMORY ALLOYS: USE IN SMART TEXTILES

Smart textiles can be defined as materials that perceive and respond to an external stimulus. Interest in smart textiles has increased significantly in the academic area in recent years. As shape memory textiles respond to external stimuli (heat, light, electricity etc.), they are examined as a branch of smart textiles. Shape memory polymers (SMP) and alloys (SMA) can be used in various textile applications. In this study, general properties of shape memory alloys and polymers as well as the differences between these two materials have been mentioned. In the last part, the use of shape memory alloys and polymers in textiles has been examined. As a result of the study, it has been found out that Shape memory polymers have a wider application in textile applications and that polymers are more advantageous than alloys in terms of their ease of use, aesthetics and price.
Knee Injury Detection using MRI with Efficiently-Layered Network (ELNet)

Magnetic Resonance Imaging (MRI) is a widely-accepted imaging technique for knee injury analysis. Its advantage of capturing knee structure in three dimensions makes it the ideal tool for radiologists to locate potential tears in the knee. In order to better confront the ever growing workload of musculoskeletal (MSK) radiologists, automated tools for patients' triage are becoming a real need, reducing delays in the reading of pathological cases. In this work, we present the Efficiently-Layered Network (ELNet), a convolutional neural network (CNN) architecture optimized for the task of initial knee MRI diagnosis for triage. Unlike past approaches, we train ELNet from scratch instead of using a transfer-learning approach. The proposed method is validated quantitatively and qualitatively, and compares favorably against state-of-the-art MRNet while using a single imaging stack (axial or coronal) as input. Additionally, we demonstrate our model's capability to locate tears in the knee despite the absence of localization information during training. Lastly, the proposed model is extremely lightweight ($<$ 1MB) and therefore easy to train and deploy in real clinical settings.
Collaborative human-humanoid carrying using vision and haptic sensing

We propose a framework for combining vision and haptic information in human-robot joint actions. It consists of a hybrid controller that uses both visual servoing and impedance controllers. This can be applied to tasks that cannot be done with vision or haptic information alone. In this framework, the state of the task can be obtained from visual information while haptic information is crucial for safe physical interaction with the human partner. The approach is validated on the task of jointly carrying a flat surface (e.g. a table) and then preventing an object (e.g. a ball) on top from falling off. The results show that this task can be successfully achieved. Furthermore, the framework presented allows for a more collaborative setup, by imparting task knowledge to the robot as opposed to a passive follower.
Enriching Large-Scale Eventuality Knowledge Graph with Entailment Relations

Computational and cognitive studies suggest that the abstraction of eventualities (activities, states, and events) is crucial for humans to understand daily eventualities. In this paper, we propose a scalable approach to model the entailment relations between eventualities ("eat an apple'' entails ''eat fruit''). As a result, we construct a large-scale eventuality entailment graph (EEG), which has 10 million eventuality nodes and 103 million entailment edges. Detailed experiments and analysis demonstrate the effectiveness of the proposed approach and quality of the resulting knowledge graph. Our datasets and code are available at this https URL.
On the isomorphism of sign, logic and language : a novel framework for language modelling

We argue that a connective element between the three domains, signs, logic and language, could be knowledge representation. This paper is an attempt to show that knowledge can be based on signs, that how knowledge emerges, that has a logical meaning, and that language is a representation of knowledge. domains of knowl- edge, such as propositional logic, syntax, morphology etc. We hold that such representation may be shown to be derived from the principles of cognition which in turn are based on 'real' world phenomena, and that moreover, such representation shows a strong anity with Peirce's classication of signs which forms the basis of his semiotics (Peirce, 1931). The novelty of our approach consists in a specic interpretation and combination of these theses. In this paper, knowledge is understood as 'belief' (other aspects of knowledge, such as, for instance, reasoning are left out of consideration), while logic is restricted to propositional logic.
Camera Tracking in Lighting Adaptable Maps of Indoor Environments

Tracking the pose of a camera is at the core of visual localization methods used in many applications. As the observations of a camera are inherently affected by lighting, it has always been a challenge for these methods to cope with varying lighting conditions. Thus far, this issue has mainly been approached with the intent to increase robustness by choosing lighting invariant map representations. In contrast, our work aims at explicitly exploiting lighting effects for camera tracking. To achieve this, we propose a lighting adaptable map representation for indoor environments that allows real-time rendering of the scene illuminated by an arbitrary subset of the lamps contained in the model. Our method for estimating the light setting from the current camera observation enables us to adapt the model according to the lighting conditions present in the scene. As a result, lighting effects like cast shadows do no longer act as disturbances that demand robustness but rather as beneficial features when matching observations against the map. We leverage these capabilities in a direct dense camera tracking approach and demonstrate its performance in realworld experiments in scenes with varying lighting conditions.
Induction agents for rapid sequence intubation in adults outside the operating room-UpToDate

INTRODUCTION — The first task of any clinician managing an acutely unstable patient is to secure the airway. In most circumstances, emergency clinicians use rapid sequence intubation (RSI) to accomplish this task. RSI incorporates a rapidly acting sedative (ie, induction) agent, in addition to a neuromuscular blocking (ie, paralytic) agent, to create optimal intubating conditions. Selection of the sedative agent and dose most appropriate for the clinical scenario is an important component of RSI.
RenderGAN: GAN Based Texture Rendering

In this project, we’ve attempted to replace 3D texture rendering with generative texture transferring. We have trained Pix2Pix GAN on various 3D plane models with different hyperparameters to tune the networks with different performance characteristics. Our experiment results show merely increasing the number of models or the number of captured images of them do not give out optimal results.
A Modular Interface for Multimodal Data Annotation and Visualization with Applications to Conversational AI and Commonsense Grounding

Artificial Intelligence (AI) research, including machine learning, computer vision, and natural language processing, requires large amounts of annotated data. The current research and development (R&D) pipeline involves each group collecting their own datasets using an annotation tool tailored specifically to their needs, followed by a series of engineering efforts in loading other external datasets and developing their own interfaces, often mimicking some components of existing annotation tools. We present a modular annotation, visualization, and inference software framework for computational language and vision research. Our framework enables researchers to set up a web interface for efficiently annotating language and vision datasets, visualizing the predictions made by a machine learning model, and interacting with an intelligent system. In addition, the tool accommodates many of the standard and popular visual annotations such as bounding boxes, segmentation, landmark points, temporal annotation and attributes, as well as textual annotations such as tagging and free form entry. These annotations are directly represented as nodes and edges as part of the graph module, which allow linking visual and textual information. Extensible and customizable as required by individual projects, the framework has been successfully applied to a number of research efforts in human-AI collaboration, including commonsense grounding of language and vision, conversational AI, and explainable AI.
Generalized Humbert Schemes and Intersections of Humbert Surfaces

In 1899 Humbert[Hu] attached to a principally polarized abelian surface (A, λ) which satisfies a certain “singular relation” an invariant ∆ which is now called a Humbert invariant of (A, λ). This invariant gives rise to Humbert surfaces as follows. Let A2 denote the moduli space of principally polarized abelian surfaces (A, λ). For a positive integer n > 0, let Hn ⊂ A2 denote the subset consisting of those surfaces (A, λ) ∈ A2 which have a singular relation with Humbert invariant ∆ = n. It then follows from Humbert’s work that Hn is a closed analytic surface in A2, provided that n ≡ 0, 1 (mod 4). The surfaces Hn are now called Humbert surfaces ; cf. van der Geer[vdG], Ch. IX. Here we want to study the components of the intersectionHn ∩Hm of two Humbert surfaces. This problem was briefly addressed on p. 214 of [vdG], but the results obtained there (via analytic techniques) are somewhat incomplete; cf. Remark 11 below. Here give a different approach to this problem by using the refined Humbert invariant which was introduced in [K1]. This approach is completely algebraic and hence has the advantage that it also works over an arbitrary algebraically closed field K instead of C. Using this invariant, we introduce in Section 2 (closed) subsets H(q) ⊂ A2(K) which are indexed by positive integral quadratic forms q in r ≥ 1 variables. If r = 1, then these are precisely the Humbert surfaces, and so we will refer to the H(q)’s as generalized Humbert schemes. From the definition of the H(q)’s it follows easily that if m 6= n, then
Compost biomass, pH value and C, N, K contents of some local wild plant species: Case of Panicum maximum L., Trypsa- cum laxum L. and Pueraria javanica (Benth.) Benth, in the Region of Kisangani, 
Democratic Republic of Congo

Soil fertility transience and weed management are often among decried impediments undermining agricultural productivity in many tropical countries. A study aiming at transforming baffling problems of abundant weed biomasses especially that of Panicum maximum (T1), Tryps-acum laxum (T2) and Pueraria javanica (T3), often cause of crop yield loss into advantage to enhance soil fertility, increase crop productivity as well as help tackle weed issue was initiated in the region of Kisangani. To do this, a 90-day composting experiment involving a mono factorial randomized complete block design including three replications and three treatments of above mentioned weed biomasses was conducted from 23rd March to 23rdJune 2013. The results of analysis of variance showed significant differences among treatments for pH H2O (p≤0.013) and potassium content (p≤0.001). The three composts demonstrated high nutrient content level in total organic carbon, nitrogen and potassium able to restore soil fertility. Moreover, the study revealed that the pH values of all the composts were closer to neutral on average and can be used to fix the recurrent issue of soil acidity in the region. Finally, the results showed that the species were able to produce sufficient compost mass with 41.5, 36.3 and 30.9% of initial mass loss respectively for T1, T2 andT3 in a relatively short time. Key words: Soil fertility transience, compost, weed management, agricultural productivity, DR Congo.
Humanlike Combat Behavior via Multiobjective Neuroevolution

Although evolution has proven to be a powerful search method for discovering effective behavior for sequential decision-making problems, it seems unlikely that evolving for raw performance could result in behavior that is distinctly humanlike. This chapter demonstrates how humanlike behavior can be evolved by restricting a bot’s actions in a way consistent with human limitations and predilec-tions. This approach evolves good behavior, but assures that it is consistent with how humans behave. The approach is demonstrated in the UTˆ2 bot for the commercial ﬁrst-person shooter videogame Unreal Tournament 2004. UTˆ2 ’s human-like qualities allowed it to take 2 nd place in BotPrize 2010, a competition to develop humanlike bots for Unreal Tournament 2004. This chapter analyzes UTˆ2 , explains how it achieved its current level of humanness, and discusses insights gained from the competition results that should lead to improved humanlike bot performance in future competitions and in videogames in general.
Effect of President’s tweets on the S&P 500 index

Social media platforms like Twitter enables national leaders to communicate directly with the public. The information released potentially drives the movements in the stock market in real time. Here we build machine learning models that predict the likely movement of the S&P 500 index-tracker SPY price in response to a tweet released by President Trump based on word encoding with LSTM and sentence encoding with neural network. We train the models on President Trump tweet record from 2017 to November 2019 and ﬁnd that the trained models offer no signiﬁcant difference in the prediction error compared to a baseline model that always predicts no change. The lack of accuracy improvement from the machine learning models likely stems from the weakness or non-existence of any direct relationship between tweet text and SPY movement.
Application of the digital image correlation algorithms for evaluation of two-phase flow in the heat exchanger

The aim of this research work is application of digital image analysis for working out the method, which will allow to evaluate irregularity rate of two-phase flow across various geometry of tube bundle in aspect of the shell – and – tube heat exchanger optimization. Visualization of liquid flow in the shell – side enables analysis of flow parameters by the used image processing and analysis methods. Images of two-phase flow were obtained with the use of digital high speed CCD camera, then were analysed, to obtain information about hydrodynamics of flow with respect to tube bundle arrangement. Optical techniques of measurement, based on correlation algorithms, allow accurate determination of stabilization of velocity fields for the whole field of flow in shell-side, based on the area size criterion of “still zones” behind rows of tubes. The aim of work was also estimation of the influence of geometrical parameters (tube arrangements, the tube spacings, number of rows) on the homogeneity of two-phase flow in the heat exchangers along with complex analysis of flow area, especially the “still zones”.
Picture Based System to Resist Surfing Attack

: Confirmation taking into account passwords is used to an unconfined degree in applications for PC security and insurance. Nevertheless, human exercises, for instance, picking disgusting passwords and contributing passwords in an unsteady manner are seen as statement the most fragile association quote in the confirmation chain. Rather than optional alphanumeric strings, customers tend to pick passwords either short or noteworthy for basic recognition. With web applications and flexible applications loading up, people can find a workable pace at whatever point and wherever with variegated contraptions. This particulars brings no-go stroll up in like manner extends the probability of introducing passwords to withstand surfing ambushes. Aggressors can observe explicitly or use outside account devices to accumulate customer's capabilities. To overcome this issue, we proposed a novel confirmation structure Pass Matrix, considering graphical passwords to restrict withstand surfing attacks. With a one-time real login marker and circle level and vertical bars tent the entire degree of pass-pictures, Pass Matrix offers no sign for aggressors to comprehend or confine the mystery word plane they lead variegated camera-based ambushes. We in like manner executed a Pass Matrix model on Android and finished real vendee tests to survey its memor ability and convenience. From the preliminary outcome, the proposed structure achieves biggest impenetrability to withstand surfing attacks while looking without usability.
Generative Adversarial Network for Stock Market price Prediction

This project addresses the problem of predicting stock price movement using ﬁnancial data. Although the extensive exploration with GAN, we found that the relative performance of GAN model with respect traditional deep learning models such as LSTM has not been assessed. Our speciﬁc goal was to predict whether the price would increase one day after our sample period. We trained a baseline ARIMA model, a long short-term memory (LSTM) model, a deep LSTM model, and a generative adversarial network (GAN) model to develop this task. All our models predicted near or above 60% accuracy. The best performer were the Shallow LSTM 74.16% and the GAN 72.68 % and the Deep LSTM 62.85% followed by ARIMA 59.57% .
MathBot – A Deep Learning based Elementary School Math Word Problem Solver

We built a Deep Neural Network architecture based framework to make the MathBot learn to convert English language based math word problems into equations involving few unknowns and arithmetic quantities, and solve the equations thus generated. There have been many semantic parser and rule based math word problem solvers, but application of any learning al-gorithm to reduce natural language based math problems into equations is a topic of recent research. In this work, We show that the use of deep learning based natural language processing techniques, such as, Recurrent Neural Networks and Transformers, can help build such a learning system. Our work primarily focused on the use of transformers to predict the equation. We also added an equation solver to get the ﬁnal result from the equation. In addition to traditional BLEU score we used an ingenious solution accuracy metric to evaluate our models. To improve solution accuracy, we introduced number mapping for word embedding as a novel technique to achieve high accuracy. With our transformer architecture and solver, we achieved a BLEU score of 33.4% and solution accuracy of 62.07% on our biggest dataset.
BERT’s Handwriting Testing BERT Word Prediction Effect on Accuracy in Handwritten Text Recognition

In this paper I demonstrate that the addition of BERT to handwritten text recognition does not improve accuracy in transcription. My model is trained on the well-developed IAM dataset and can accept new input from a user in the form of sentences. The model ﬁrst recognizes the words in a given handwritten sentence and then feeds the predicted text to BERT with low-certainty words masked out. The result is no signiﬁcant change in character or word error rate for texts decoded with BERT vs. those without.