Miracle-dz/UniFAR-FTUs huggingface dataset:
Accelerating Hyperparameter Optimisation with PyCOMPSs

Machine Learning applications now span across multiple domains due to the increase in computational power of modern systems. There has been a recent surge in Machine Learning applications in High Performance Computing (HPC) in an attempt to speed up training. However, besides training, hyperparameters optimisation(HPO) is one of the most time consuming and resource intensive parts in a Machine Learning Workflow. Numerous algorithms and tools exist to accelerate the process of finding the right parameters for a model. Most of these tools do not utilize the parallelism provided by modern systems and are serial or limited to a single node. The few ones that are offer distributed execution require a serious amount of programming effort. There is, therefore, a need for a tool/scheme that can scale and leverage HPC infrastructures such as supercomputers, with minimum programmers effort and little or no overhead in performance. We present a HPO scheme built on top of PyCOMPSs, a programming model and runtime which aims to ease the development of parallel applications for distributed infrastructures. We show that PyCOMPSs is a powerful framework that can accelerate the process of Hyperparameter Optimisation across multiple devices and computing units. We also show that PyCOMPSs provides easy programmability, seamless distribution and scalability, key features missing in existing tools. Furthermore, we perform a detailed performance analysis showing different configurations to demonstrate the effectiveness our approach.
Cross-Cultural Adaptation Questionnaire of South-Asian Students in China Mainland Universities

A majority of South-Asian students have been enrolled into China mainland universities to earn degrees. This study makes a survey on the cross-cultural adaptation of South Asian students who are from different cultural backgrounds. The questionnaire is designed basing on the cross-cultural theory which is constructed of three dimensions including psychological adaptation, social cultural adaptation and academic adaption. Conclusions are descriptive analysis by SPSS 24.0 and also supported with in-depth interviews. The findings paint a holistic picture of South-Asian students' adaptation in China mainland universities and discuss the problems they encounter, causes and solving strategies. The paper aims to provide a data-based theory and solution-oriented suggestions for educators and institutions.
SimCA*

Self-adaptation provides a principled way to deal with software systems’ uncertainty during operation. Examples of such uncertainties are disturbances in the environment, variations in sensor readings, and changes in user requirements. As more systems with strict goals require self-adaptation, the need for formal guarantees in self-adaptive systems is becoming a high-priority concern. Designing self-adaptive software using principles from control theory has been identified as one of the approaches to provide guarantees. In general, self-adaptation covers a wide range of approaches to maintain system requirements under uncertainty, ranging from dynamic adaptation of system parameters to runtime architectural reconfiguration. Existing control-theoretic approaches have mainly focused on handling requirements in the form of setpoint values or as quantities to be optimized. Furthermore, existing research primarily focuses on handling uncertainty in the execution environment. This article presents SimCA*, which provides two contributions to the state-of-the-art in control-theoretic adaptation: (i) it supports requirements that keep a value above and below a required threshold, in addition to setpoint and optimization requirements; and (ii) it deals with uncertainty in system parameters, component interactions, system requirements, in addition to uncertainty in the environment. SimCA* provides guarantees for the three types of requirements of the system that is subject to different types of uncertainties. We evaluate SimCA* for two systems with strict requirements from different domains: an Unmanned Underwater Vehicle system used for oceanic surveillance and an Internet of Things application for monitoring a geographical area. The test results confirm that SimCA* can satisfy the three types of requirements in the presence of different types of uncertainty.
An Ontology for Human-Human Interactions and Learning Interaction Behavior Policies

Robots are expected to possess similar capabilities that humans exhibit during close proximity dyadic interaction. Humans can easily adapt to each other in a multitude of scenarios, ensuring safe and natural interaction. Even though there have been attempts to mimic human motions for robot control, understanding the motion patterns emerging during dyadic interaction has been neglected. In this work, we analyze close-proximity human-human interaction and derive an ontology that describes a broad range of possible interaction scenarios by abstracting tasks and using insights from attention theory. This ontology enables us to group interaction behaviors into separate cases, each of which can be represented by a particular graph. Using imitation learning, we train unique interaction policies with recurrent neural networks for each case. The ontology offers a unified and generic approach to categorically analyze and learn close-proximity interaction behaviors that can both be utilized as base models for future studies and enhance natural human-robot collaboration.
Change of the Electronic Conductivity of CNTs and Graphene Sheets Caused by a Three-dimensional Strain Field

The prediction of the change in the conductivity of carbon nanotubes (CNTs) and graphene nanoribbons (GNRs) under strain is very important to assure the reliability of the performance of CNT-based devices, such as transistors, sensors, actuators, etc. In this study, the change of the electronic state of CNTs and GNRs caused by their deformation was analyzed. We found that the change of the electronic sate of CNTs was mainly dominated by two factors; (a) distortion of a six-membered ring due to the change in the C-C bond length and (b) increase in the local curvature of the tube. The increase of the curvature caused the orbital hybridization. The electronic state of GNRs also changed drastically when the hybridization was occurred by the curvature deformation. Keywords-carbon nanotube; graphene nanoribbon; strain, conductivity, DFT, tight-binding approach
Ambiguity, Low Risk-Free Rates and Consumption Inequality

Macroeconomists failed to predict the Great Recession, suggesting that the existing macroeconomic models may have been misspecified. Bearing in mind this potential misspecification or ‘model uncertainty’, how do agents’ optimal decisions change? Furthermore, how large are the welfare costs of model misspecification? To shed light on these questions, we develop a tractable continuous-time general equilibrium model to show that a fear of model misspecification reduces both the equilibrium interest rate and the relative inequality of consumption to income, making the model’s predictions closer to the data. Our quantitative analysis shows that the welfare costs of model uncertainty are sizable.
A novel elevation phase elimination method for PSInSAR in urban area with high buildings

The terrain phase elimination is a key step in the Permanent Scatterer Interferometric SAR technique (PSInSAR) for monitoring land deformation. The Shuttle Radar Topography Mission digital elevation model (SRTM DEM) is widely used. In high-rise urban areas, there are main effects that high buildings have on land subsidence where SRTM DEM is applied: after temporal and spatial filtering, a portion of the terrain phase errors propagates in the residual phase, which directly affects the precision of the land subsidence estimation. In this study, we propose a new method to minimize the errors. We have created a digital surface model (DSM) by overlying SRTM DEM with the layer of the building height obtained by SAR tomography. To validate the proposed approach, the land subsidence was calculated using the SRTM DEM and DSM correction methods. Based on the benchmarks used in the comparison, we show that the elevation phase elimination using DSM leads to more precise measurements of land subsidence rates.
Effect of Incorporation of Lungru ( Diplazium esculentum ) on Physico-chemical , Microbiological and Sensory Quality of Chicken Patties

Accepted: 23/05/2015 Abstract The aim of the study was to optimize the level of i ncorporation of Lungru (a naturally grown seasonal vegetable in high hill s of Himalaya) paste in chicken meat patty and evaluate their effe ct on proximate, physicochemical, microbiological and organoleptic quality. Significant difference (p<0.05) were observed in fat, protein, ash, TBARS and juiciness of treated and control patties. No significant differences wer e observed in moisture, pH, cooking yield, total plate count (TPC) and othe r organoleptic parameters. However, overall higher moisture, lower pH, higher cooking yield, lower TPC and higher organoleptic values wer e observed for treated chicken patties compared to control. Therefore, it can be concluded that incorporation of Lungru in chicken patties successfully improved the functional and sensory quality attributes.
D ec 2 01 6 Improved Dropout for Shallow and Deep Learning

Dropout has been witnessed with great success in training de ep n ural networks by independently zeroing out the outputs of neurons at random. It has also received a surge of interest for shallow learning, e.g., logistic reg ression. However, the independent sampling for dropout could be suboptimal for the s ak of convergence. In this paper, we propose to use multinomial sampling for dro pout, i.e., sampling features or neurons according to a multinomial distributio n with different probabilities for different features/neurons. To exhibit the op timal dropout probabilities, we analyze the shallow learning with multinomial dropout an d establish the risk bound for stochastic optimization. By minimizing a samplin g dependent factor in the risk bound, we obtain a distribution-dependent dropout with sampling probabilities dependent on the second order statistics of the dat a is ribution. To tackle the issue of evolving distribution of neurons in deep learni ng, we propose an efficient adaptive dropout (namedvolutional dropout) that computes the sampling probabilities on-the-fly from a mini-batch of examples. Emp irical studies on several benchmark datasets demonstrate that the proposed drop outs achieve not only much faster convergence and but also a smaller testing error than the standard dropout. For example, on the CIFAR-100 data, the evolutiona l dropout achieves relative improvements over 10% on the prediction performan ce and over 50% on the convergence speed compared to the standard dropout.
Urban transport traﬀic network regulation and evaluation using a fuzzy evolutionary approach

- The real-time traffic regulation of an urban collective transport network is a very hard problem, especially in case of appearance of simultaneous disturbances (vehicle’s breakdown, strike, and demonstration….etc). Indeed, regulator (decision-maker) has to carry out difficult tasks that are often inaccessible at the human scale, which involves the assistance of a decision support system (DSS). In this paper, we present the pit of this DSS which is a generator and an evaluator of the decision strategies for a disrupted traffic transport network regulation. Our evaluation proposed module is based on a hybrid approach using a fuzzy evaluation method and an evolutionary algorithm. It treats the regulation problem as an optimization one and provides the regulator with evaluated and classified effective decisions by taking into account his/her preferences.
A Comparative Study of Faster R-CNN Models for Anomaly Detection in 2019 AI City Challenge

Traffic anomaly detection forms an integral part of intelligent traffic monitoring and management system. Timely detection of anomalies is crucial in providing necessary assistance to accident victims. Track 3 of 2019 AI city challenge addresses traffic anomaly detection problem. We propose an unsupervised method to tackle this problem. Proposed system consists of three stages. The first stage is a background extraction stage which isolates the stalled vehicles from moving vehicles. An anomaly detection is the second stage that identifies the stalled vehicles in the background and finally anomaly confirmation module confirms anomaly and determines the start time. We have used faster RCNN (FRCNN) with Inception v2 and ResNet 101 to detect stalled vehicles and confirm possible anomalies. A comparative study shows that FRCNN with Inception v2 gives superior performance.
Effect of Diet Restriction on Serum Levels of Some Adipocytokins in A Rat Model of High Fat Diet Induced Obesity

Numerous adipocytokines are released by both fat cells and macrophages that infiltrate it in obesity.This study was designed to investigate the effect of obesity and diet restriction on serum levels of some adipocytokines by using high fat diet induced obesity rat model. Thirty adult male albino rats were used and randomly divided into three equal groups. Group-1 normal diet control group composition, group-2 obese rats in which obesity was induced by high fat diet (58% fat, 18% protein and 24% carbohydrates) for 16 weeks and group-3 fed on high fat diet (HFD) for 16 weeks then followed by diet restriction by feeding normal diet for 8 weeks. Body weight, abdominal circumference (AC), body Mass Index (BMI), serum levels of glucose, insulin, insulin resistance,lipid profile,adipocytokines [adiponectin, leptin, resistin, interleukin6 (IL-6), tumor necrosis factor alpha (TNF-α) and C-reactive protein (CRP)] were estimated. Obtained results revealed that there was a significant increase in body weight, AC, BMI, glucose, insulin, insulin resistance, total cholesterol, triglyceride, Low density lipoproteins (LDLc), very low density lipoproteins (VLDLc), leptine, resistine, IL-6, TNF-α and CRP and a significant decrease in high density lipoproteins (HDLc) and adiponectin in group-2 when compared with group-1and significant improvement in group-3 compared with group-2. A significant correlation was found between BMI and all of AC, glucose, insulin, insulin resistance,total cholesterol, triglyceride, HDL, LDLc and VLDLc. Also, that there was found a significant correlation between insulin resistance and all of adiponectin, leptine, resistine, IL-6, TNF-α and CRP in obese group. In conclusion: The present study demonstrated that HFD was associated with central obesity, changes in serum levels of adipocytokins together with the presence of the criteria of metabolic syndrome such as dyslipidemia and type-2 diabetes mellitus. All these changes were improved by caloric restriction.
Process-centered Software Engineering Environments

From the Publisher: Process-Centered Software Engineering Environments (PSEEs) represent a new generation of software engineering environments in which the processes used to produce and maintain software products are explicitly modeled in the environment. PSEEs hold the exciting promise of enabling a significant increase in both software productivity and quality. The book presents a comprehensive picture of this emerging technology while highlighting the key concepts and issues. The first chapter introduces some of the basic concepts and developments behind PSEEs and discusses the unifying role it plays in combining project management, software engineering, and process engineering. The second chapter reviews related process modeling and representation concepts, terminology, and issues. Chapter 3 analyzes the features of some example PSEEs and Chapter 4 takes an inside look at the implementation of these features by describing specific design choices made by researchers. The last chapter discusses the evolution of PSEEs to accommodate practical issues in actual work settings and to play a more significant role in the software life cycle. The text is a collection of influential papers that will bring the newcomer quickly up to speed on this fast-moving field. For the researcher, the issues described in the text present a challenge to be conquered and directions to pursue. For the practitioner, they represent benefits that may be gained in the application of PSEEs in the work environment.
Semantic Segmentation of Crop Type in Africa: A Novel Dataset and Analysis of Deep Learning Methods

Automatic, accurate crop type maps can provide unprecedented information for understanding food systems, especially in developing countries where ground surveys are infrequent. However, little work has applied exist-ing methods to these data scarce environments, which also have unique challenges of irregularly shaped ﬁelds, fre-quent cloud coverage, small plots, and a severe lack of training data. To address this gap in the literature, we provide the ﬁrst crop type semantic segmentation dataset of small holder farms, speciﬁcally in Ghana and South Sudan. We are also the ﬁrst to utilize high resolution, high frequency satellite data in segmenting small holder farms. Despite the challenges, we achieve an average F1 score and overall accuracy of 57.3 and 60.9% in Ghana and 69.7 and 85.3% in South Sudan. Additionally, our approach outperforms the state-of-the-art method in a data-rich setting of Germany by over 8 points in F1 and 6 points in accuracy. Code and a link to the dataset are publicly available at https://github.
Case-Specific Random Forests for Big Data Prediction

Some training datasets may be too large for storage on a single computer. Such datasets may be partitioned and stored on separate computers connected in a parallel computing environment. To predict the response associated with a specific target case when training data are partitioned, we propose a method for finding the training cases within each partition that are most relevant for predicting the response of a target case of interest. These most relevant training cases from each partition can be combined into a single dataset, which can be a subset of the entire training dataset that is small enough for storage and analysis in memory on a single computer. To generate a prediction from this selected subset, we use Case-Specific Random Forests, a variation of random forests that replaces the uniform bootstrap sampling used to build a tree in a random forest with unequal weighted bootstrap sampling, where training cases more similar to the target case are given greater weight. We demonstrate our method with an example concrete dataset. Our results show that predictions generated from a small selected subset of a partitioned training dataset can be as accurate as predictions generated in a traditional manner from the entire training dataset.
Class-Adapted Blind Image Deblurring

Over the past few decades, significant progress has been made in solving image deblurring problems; however, most of the developed methods are focused on deblurring of natural images. Specific classes of images, like text, face, fingerprints are found in many important applications, such as document analysis or forensics. State-of-the-art blind image deblurring methods are usually based on edge extraction or on typical statistics of natural images. When there is not much texture in a blurred image (e.g., face images), performance of methods based on edge extraction is limited. On the other hand, methods tailored for natural images do not take into consideration the particular structure of images of a specific class (e.g., text or fingerprints). In this work, we proposed a method with a patch-based class-adapted image prior trained from a dataset which contains clean images of a specific class. Results obtained so far show that the method can be used for at least two specific classes of images: text and face. Additionally, the proposed method uses a week prior on the blurring filter (positivity and support) and, because of that, is able to recover a wide variety of blurring filters.
CHINESE CONCEPT MAP GENERATION BASED ON OPEN INFORMATION EXTRACTION

Creating a concept map automatically from Chinese texts is challenging, because 1) Chinese words require no delimiters in between (like those spaces in English), which makes word, phrase, or concept identification difficult; 2) Chinese is discourse-oriented, meaning there is no strict grammar imposed on a sentence, which makes relation extraction difficult. For example, the three Chinese sentences “ ” (‘Apple nutritious’), “ ” (‘Apple are nutritious’) and “ ” (‘Apple are rich in nutrition’) are semantically synonymous sentences, but the first one, which lacks an overt verb, is used more often than the other two in real-world situations. In this study, we present an Extended Chinese Open Relation Extraction (ECORE) approach that is able to extract entity-relation triples (i.e. a relation that links two concepts) from Chinese free texts based on a series of natural language processing techniques, i.e., word segmentation, POS tagging, syntactic parsing, and linguistic rules. Take the sentence as an example: “ ” (‘Radish is vegetable, and is a rootstock plant.), our ECORE software can correctly extract two triples: < (‘Radish’), (‘is’), (‘vegetable’)> and < (‘Radish’), (‘is’), (‘rootstock plant’)>, although the subject in the second clause is elided. Another example is a paragraph describing the (‘Tilapia’) in a textbook of elementary school. Figure 1 shows the original texts, 8 extracted triples and a generated concept map.
Deep Neural Networks in Embedded Systems

This thesis explores possibilities of running deep neural networks on embedded systems for traffic monitoring. The main goal is to design, implement and test a prototype of the system for vehicle and people detection from video records and camera streams using neural networks. In this work, three different CNN architectures are compared, namely RetinaNet, YOLO and SqueezeDet. Software prototype is designed so that it can be deployed on commercial off-the-shelf devices, e.g. Jetson Xavier module from NVIDIA.
The RedPRL Proof Assistant

RedPRL is an experimental proof assistant based on Cartesian cubical computational type theory, a new type theory for higher-dimensional constructions inspired by homotopy type theory. In the style of Nuprl, RedPRL users employ tactics to establish behavioral properties of cubical functional programs embodying the constructive content of proofs. Notably, RedPRL implements a two-level type theory, allowing an extensional, proof-irrelevant notion of exact equality to coexist with a higherdimensional proof-relevant notion of paths.