Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning

Modern services consist of interconnected components, e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose DeepCoord, a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm on how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on real-world network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available.
: TOWARDS

What is currently known in the literature concerning the relationship between the Ranking and the Weighting problem corresponding to a finite set of data is that a solution to the former provides a solution to the latter, as stated in Lemma 1 repeated below. In this subsection, I review the classical proof of this result offered in Prince and Smolensky (2004) and Keller (2000, 2005). Lemma 1. If a finite set D of underlying/winner/loser form triplets is OT-compatible, then it is also HG-compatible. More precisely, let be a ranking OT-compatible with D. Without loss of generality, assume that it is (101a), with Cn ranked at the top, Cn−1 below it and so on, until the bottom ranked C1.
Chhattisgarh Tourism Review Generator using GPT-2

– The enormous growth and potential of online consumer reviews of tourism are playing an important role in consumer attitude and buying or renting behaviors. In fact, user reviews have become a very popular tool that strongly influence the buying decision of consumers. By having a lot of reviews online, people will be able to get a feel for your business. Many online travel businesses offer quantitative ratings, textual reviews or a combination of both. Our group sought to replicate different tourist’s online reviews using the Open AI’s NLP technique based GPT-2 model and transfer learning. We propose a domain specific language model that operates solely based on text input such as tourist place. We used GPT-2 to extrapolate from a given context, allowing to generate any type of reviews like positive, negative or neutral.
Customer Satisfaction and Sharia Service Quality at Surya Mart Ponorogo: Case Study from Indonesia

This study aims to evaluate the level of satisfaction and service quality through CSI and IPA. The type of this study is quantitative approach under case study at Surya Mart Ponorogo Indonesia. The sample was 125 respondents. The variables of this study used sharia service quality and customer satisfaction composed of six dimensions: (1) compliance with Islamic law, (2) assurance, (3) reliability, (4) tangibility, (5) empathy, and (6) responsiveness, and each dimension has three statement attributes. Data were analyzed using Importance Performance Analysis (IPA), Cartesius Diagram, and Customer Satisfaction Index (CSI). The findings of this study are, firstly, the results of the IPA. The second finding is that the top priority attribute (indicated in Quadrant A of the Cartesian diagram) is sharia service quality. The third is a 75.2% value for customer satisfaction with the quality of sharia services at Surya Mart Supermarket Ponorogo, Indonesia, under CSI analysis, indicating that customers do feel satisfied enough.
Rate Adjustment Based Congestion Control Protocols in Wireless Sensor Networks

— Wireless sensor networks are mainly used for event detection having huge number of sensor nodes distributed geographically with one or more base stations. Event detection results into the congestion among sensors due to limited memory, limited processing power and shared transfer medium. Congestion leads to packet drop, power consumption and reduces reliability; hence congestion control is a necessity in wireless sensor networks. In this paper, firstly a description of the various aspects of congestion control in wireless sensor networks is presented. Then various existing congestion control transport layer protocols based on rate adjustment technique in wireless sensor networks are presented. Finally a comparative analysis of these protocols based on various parameters like congestion detection, congestion notification, rate adjustment, hop-by-hop/end-to-end, loss recovery, application type, traffic direction, evaluation parameters, bandwidth allocation, evaluation type, topology, packet size, number of sensors, buffer size, coverage area, simulation time, traffic load and simulation environment is presented.
Tracing Isomanifolds in R d in Time Polynomial in d Using Coxeter-Freudenthal-Kuhn Triangulations

Isomanifolds are the generalization of isosurfaces to arbitrary dimension and codimension, i.e. submanifolds of R d defined as the zero set of some multivariate multivalued smooth function f : R d → R d − n , where n is the intrinsic dimension of the manifold. A natural way to approximate a smooth isomanifold M is to consider its Piecewise-Linear (PL) approximation ˆ M based on a triangulation T of the ambient space R d . In this paper, we describe a simple algorithm to trace isomanifolds from a given starting point. The algorithm works for arbitrary dimensions n and d , and any precision D . Our main result is that, when f (or M ) has bounded complexity, the complexity of the algorithm is polynomial in d and δ = 1 /D (and unavoidably exponential in n ). Since it is known that for δ = Ω( d 2 . 5 ), ˆ M is O ( D 2 )-close and isotopic to M , our algorithm produces a faithful PL-approximation of isomanifolds of bounded complexity in time polynomial in d . Combining this algorithm with dimensionality reduction techniques, the dependency on d in the size of ˆ M can be completely removed with high probability. We also show that the algorithm can handle isomanifolds with boundary and, more generally, isostratifolds. The algorithm for isomanifolds with boundary has been implemented and experimental results are reported, showing that it is practical and can handle cases that are far ahead of the state-of-the-art.
A Novel Methodology for Error-Resilient Circuits in Near-Threshold Computing

The main goal of designing VLSI system is high performance with low energy consumption. Actually, to realize the human-related techniques, such as internet of things (IoTs) and wearable devices, efficient power management techniques are required. Near threshold computing (NTC) is one of the most well-known techniques which is proposed for the trade-off between energy consumption and performance improvement. With this technique, the solution would be selected by the lowest energy with highest performance. However, NTC suffers a significant performance degradation, which is prone to timing errors. However, main goal of Integrated Circuit (IC) design is making the circuit to always operate correctly though worst-case condition. But, in order to make the circuit always work correctly, considerable area and power overheads may occur. As an alternative, better-than-worst-case (BTWC) design paradigm has been proposed. One of the main design of BTWC design includes error-resilient circuits which detect and correct timing errors, though they cause area and power overheads. In this thesis, we propose various design methodologies which provide an optimal implementation of error-resilient circuits. Slack-based, sensitivity-based methodology and modified Quine-McCluskey (Q-M) algorithm have been exploited to earn the minimum set of error-resilient circuits without any loss of detection ability. From sensitivity-based methodology, benchmark results show that the optimal designs reduces up to 46% monitoring area without compromising error detection ability of the initial error-resilient design. From the Quine-McCluskey (Q-M) algorithm, benchmark results show that optimal design reduces up to 72% of flip-flops which are required to be changed to error-resilient circuits without compromising an error detection ability. In addition, more power and area reduction can be possible when reasonable underestimation of error detection ability is accepted. Monte-Carlo analysis validates that our proposed method is tolerant to process variation.
Comparison of Benchmarks for Machine Learning Cloud Infrastructures

Training of neural networks requires often high computational power and large memory on Graphics Processing Unit (GPU) hardware. Many cloud providers such as Ama­ zon, Azure, Google, Siemens, etc, provide such infrastructure. However, should one choose a cloud infrastructure or an on­ premise system for a neural network application, how can these systems be compared with one another? This paper investigates seven prominent Machine Learning benchmarks, which are MLPerf, DAWNBench, DeepBench, DLBS, TBD, AIBench, and ADABench. The recent popularity and widespread use of Deep Learning in various applications have created a need for bench­ marking in this field. This paper shows that these application domains need slightly different resources and argue that there is no standard benchmark suite available that addresses these different application needs. We compare these benchmarks and summarize benchmark­related datasets, domains, and metrics. Finally, a concept of an ideal benchmark is sketched.
Text Ads Generation Using Deep Neural Network

Automatic text summarization has been a well-researched NLP topic in recent years. It is possible to build machine learning models that are capable of distilling crucial information from a larger piece of text and condensing it to a smaller one. Text summarization using deep neural networks has become an effective approach and there are many use cases for that technique. One possible use case is text ads generation in online search advertising. Many advertisers are publishing text ads with ad titles that are not effective. Less effective ad titles will result in a lower chance of user conversion (clicks), which is harmful to the advertisers, and also a waste of hosting resources to the ads marketplace. It is meaningful to build a model that could rewrite the adtitle by summarizing the ad content. In this paper, we will study and leverage several state-of-the-art text summarization models, compare their performance and limitations, and finally, propose our own solution that could outperform the existing ones.
Automatic Brain Tumor Segmentation using Dense-Net

- Gliomas are commonly prevalent brain tumors that drastically decrease the life expectancy of the patient in their highest grade. Magnetic resonance imaging, used to gauge them, produces copious amounts of data that makes it very difficult to manually segment them in reasonable (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a practical amount of time. Reliable, automatic segmentation methods have become a necessity. But the large spatial and structural inconsistencies among glioma MRI scans make automatic segmentation a challenging problem. In this project, we propose an automatic segmentation method based on the Dense-Net architecture of Convolutional Neural Networks (CNN). The Dense-Net architecture has been proposed in recent years, and work on standard datasets has shown it to be substantially deeper, more accurate and efficient than most architectures. Its dense interconnections between layers is proposed to encourage feature reuse. But there are very few instances of this architecture being applied to medical automatic segmentation applications. So, in this project, we will test the performance of the Dense-Net architecture against that of U-Net, and draw analyses regarding its application to the BRATS dataset as compared to existing models.
Linear and nonlinear stability analyses of double-diffusive convection in a porous medium layer saturated in a Maxwell nanofluid with variable viscosity and conductivity

The double diffusive convection in a horizontal layer of a porous medium saturated with a Maxwell nanofluid is studied using linear and non-linear stability analyses. The modified Darcy-Maxwell model is used for the momentum equation. The model used for the Maxwell nanofluid incorporates the effects of Brownian motion and thermophoresis. The thermal energy equations include the diffusion and cross diffusion terms. The linear theory depends on normal mode technique and the onset criterion for stationary and oscillatory convection is derived analytically. The non-linear theory based on the representation of Fourier series method is used to find the transient behavior of heat and mass transports. We observe that the transient Nusselt numbers show oscillatory motion when time is small. However, when time becomes very large all the three transient Nusselt values approaches to their steady-state values. © 2015 Elixir All rights reserved. ARTICLE INF O Articl e h istory: Received: 22 December 2014; Received in revised form: 20 January 2015; Accepted: 12 February 2015;
Meta-learning with few-shot models Analysis Final Project

This project focuses on understanding the various elements of Meta-learning and few-shot models and the effectiveness of the different detailed implementation approaches. Using the default RobustQA project as a baseline, we explored the different implementations of the Meta-learning algorithm, LEOPARD[1], and evaluate the impact on performance of the prediction accuracy. We have also experimented with the eval-every parameter to understand how fast each implementation can learn when presented with the out of domain questions initially. We found that the multiple datasets implementation of the Leopard algorithm yields the best few-shot result. On the first evaluation at step O (after 1 batch of data for learning) this implementation already achieving a result of a EM score of 34.55 (on the validation set) compared to the 32 EM scores that the other implementation and the baseline are getting. However, after the model is trained for a longer time, we found that the baseline can actually achieve a better EM score overall with 42.202 on the test set. Although, the difference in the overall accuracy of the test set score are very small for different implementations, we found the more simple implementation yields better accuracy in the long run. Our key finding is that the design of a few-shot learning algorithm or model is actually a trade off between few-shot accuracy and the overall highest achievable accuracy.
Domain-Adversarial Training For Robust Question-Answering

In this project, we are building question-answering systems that can be robustly extended to answer questions on domains outside the training question-answering datasets. We used domain-adversarial training methods to improve upon the baseline of a pretrained DistiIBERT system by creating a domain classifier that attempts to classify the domain of the input based on the last hidden layer of the DistilBERT model. This accomplished some improvement on the baseline model, with a score of EM = 41.353 and F1 = 59.803 on the test set, and a score of EM = 31.675 and F1 = 48.836 on the validation set.
Probability-Mixing: Semi-Supervised Learning in Question-Answering with Data Augmentation

Although back-translation has been effective in text data augmentation, the neural translation system needs to be trained with expensive parallel corpus and can generate noisy samples. Therefore, this paper proposes four simple yet effective text data augmentation without contributing non-negligible label noise. Meanwhile, this paper also addresses false information generation of question answering system and proposes Probability-Mixing, a semi-supervised learning approach that mixes augumented text data. This method improves F1(TP) from 83.58 to 87.51 and reduces the FPR of answerable questions from 0.3 to 0.18. It also improves the EM score from 64.40 to 67.08. Ensembling all method achieves an EM score of 69.96 and an F1 score of 72.40. The best model achieves 64.95/67.79 for EM/F1 score on the test set.
Scaling Large Production Clusters with Partitioned Synchronization

The scale of computer clusters has grown signiﬁcantly in recent years. Today, a cluster may have 100 thousand machines and execute billions of tasks, especially short tasks, each day. As a result, the scheduler, which manages resource utilization in a cluster, also needs to be upgraded to work at a much larger scale. However, upgrading the scheduler — a central system component — in a large production cluster is a daunting task as we need to ensure the cluster’s stability and robustness, e.g., user transparency should be guaranteed, and other cluster components and the existing scheduling policies need to remain unchanged. We investigated existing scheduler designs and found that most cannot handle the scale of our production clusters or may endanger their robustness. We analyzed one most suitable design that follows a shared-state architecture, and its limitations led us to a ﬁne-grained staleness-aware state sharing design, called partitioned synchronization (ParSync). ParSync features the simplicity required for maintaining the robustness of a production cluster, while achieving high scheduling efﬁciency and quality in scaling. ParSync has been deployed and is running stably in our production clusters.
ParaDist-HMM: A Parallel Distributed Implementation of Hidden Markov Model for Big Data Analytics using Spark

Big Data is an extremely massive amount of heterogeneous and multisource data which often requires fast processing and real time analysis. Solving big data analytics problems needs powerful platforms to handle this enormous mass of data and efficient machine learning algorithms to allow the use of big data full potential. Hidden Markov models are statistical models, rich and widely used in various fields especially for time varying data sequences modeling and analysis. They owe their success to the existence of many efficient and reliable algorithms. In this paper, we present ParaDist-HMM, a parallel distributed implementation of hidden Markov model for modeling and solving big data analytics problems. We describe the development and the implementation of the improved algorithms and we propose a Spark-based approach consisting in a parallel distributed big data architecture in cloud computing environment, to put the proposed algorithms into practice. We evaluated the model on synthetic and real financial data in terms of running time, speedup and prediction quality which is measured by using the accuracy and the root mean square error. Experimental results demonstrate that ParaDist-HMM algorithms outperforms other implementations of hidden Markov models in terms of processing speed, accuracy and therefore in efficiency and effectiveness. Keywords—Big data; machine learning; Hidden Markov model; forward; backward; baum-welch; parallel distributed computing; spark; cloud computing; ParaDist-HMM
Synthesis and surface activity of novel glucose ester surfactants containing carbohydrate and hydrocarbon chain

ABSTRACT A series of glucosyl esters surfactants were synthesized based on glucose molecule by enzymatic catalysis. It could reach the highest esterification yield of 83.4% at the optimal condition, molar ratio of D-glucose and fatty acyl amino acid as 3:2 using 11% (w/w) enzyme catalyst Lipozyme 435 as catalyst in t-butanol at 40°C. The surface activities were studied, such as the critical micelle concentration (CMC), surface tension (γcmc), maximum excess concentration (Γmax), minimum surface area/molecule (Amin), and the adsorption efficiency (pC20); values of these were obtained by surface tension test. The results show that the longer the hydrophobic chain length, the lower the CMC and γcmc. The CMCs of novel glucosyl esters were between 4.4 and 1.5 mM. Further, the micellization physiochemical parameters, including Gibbs free energy of micellization (ΔG), standard enthalpy change (ΔH), and standard entropy change (ΔS) were calculated. It was indicated the micellization of glucosyl esters 9–16 was driven by entropy and deduced at different temperature. GRAPHICAL ABSTRACT
Extended QA System on SQuAD 2.0

Our motivation is to build a Question Answering (QA) system that gives answers as specific and as accurate to queries, which is in itself an art but based on the science of Natural Language Processing (NLP). Some efforts have been made to either increase the scale of the dataset (semi-annotation, SQUAD), or to improve the NLP structure (Statistical QA, Neural QA). The main goal of our project is to produce a QA system that works well on SQUAD 2.0 dataset that performs better than the baseline Bidirectional Attention Flow (BiDAF) model. To better capture the context from a more expressive set of answers and understand the interactions between the question and the document, we utilized the coattention mechanism by encoding the two-way attention outputs together through a bidirectional reccurrent neural network (RNN). For our main finding, we aim to implement the mechanisms that would improve upon the baseline method under the Exact Match (EM) and the F1 scores. In the experiment, our best performing single model obtained a F1 score of 63.40 and an EM score of 59.87 which both achieved better results than the baseline. To further improve the performance of our model, we built ensemble model that achieved EM of 60.81 and F1 of 64.21.
Using Recurrent Convolutional Neural Network (RCNN) to Predict S&P 500 Movements

Financial news releases and time series are important data sources to predict market directions and movements. The existing forecasting methods mostly work with each input independently. With increasing computational capabilities and artificial intelligence techniques nowadays, we could combine the predicting power of both sentiment analysis and quantitative modelling. In this project, we utilize information from financial news headlines and technical indicators to extend the ability of predicting short-term equity market movements, achieving higher accuracies than a single source system, obtaining comparable results to the state-of-the-art recurrent-convolutional models that are designed for such tasks, further coming up with a novel approach in refining the baseline model architecture