Miracle-dz/UniFAR-FTUs huggingface dataset:
Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers

We consider the problem of uncertainty estimation in the context of (non-Bayesian) deep neural classification. In this context, all known methods are based on extracting uncertainty signals from a trained network optimized to solve the classification problem at hand. We demonstrate that such techniques tend to introduce biased estimates for instances whose predictions are supposed to be highly confident. We argue that this deficiency is an artifact of the dynamics of training with SGD-like optimizers, and it has some properties similar to overfitting. Based on this observation, we develop an uncertainty estimation algorithm that selectively estimates the uncertainty of highly confident points, using earlier snapshots of the trained model, before their estimates are jittered (and way before they are ready for actual classification). We present extensive experiments indicating that the proposed algorithm provides uncertainty estimates that are consistently better than all known methods.
Performance Comparison of Bitcoin Prediction in Big Data Environment

In today’s world, everything is transforming to digital forms. These yield large amount of data. A good analysis of these data can lead to new knowledge about the present situation as well as the future insight. While many advantages could be obtained from these large data, the issue on how to run the Machine Learning on a large dataset as effective and efficient as possible remains an open problem. In this paper, data processing simulation using machine learning algorithm ofLinear Regression is conducted to learn from Bitcoin trading dataset. The simulation is carried out in Apache Spark cluster architecture and GPU. The running time and error of the algorithm implementation in both architectures are compared with each other. The simulation results show similar error performance between Apache Spark cluster and GPU. Yet, Apache Spark can run the simulation faster than GPU.
Hazardous Drinking among Students over a Decade of University Policy Change: Controlled Before-and-After Evaluation

Background: Responding to high levels of alcohol-related harm among students, a New Zealand university deployed a security and liaison service, strengthened the Student Code of Conduct, increased its input on the operation of alcohol outlets near campus, and banned alcohol advertising on campus. We estimated the change in the prevalence of alcohol consumption patterns among students at the university compared with other universities. Methods: We conducted a controlled before-and-after study with surveys in residential colleges at the target university in 2004 and 2014, and in random samples of students at the target university and three control universities in 2005 and 2013. The primary outcome was the prevalence of recent intoxication, while we analysed drinking per se and drinking in selected locations to investigate mechanisms of change. Results: The 7-day prevalence of intoxication decreased from 45% in 2004 to 33% in 2014 (absolute difference: 12%; 95% CI: 7% to 17%) among students living in residential colleges, and from 40% in 2005 to 26% in 2013 (absolute difference: 14%; 95% CI: 8% to 20%) in the wider student body of the intervention university. The intervention effect estimate, representing the change at the intervention university adjusted for change at other universities (aOR = 1.30; 95% CI: 0.89 to 1.90), was consistent with a benefit of intervention but was not statistically significant (p = 0.17). Conclusion: In this period of alcohol policy reform, drinking to intoxication decreased substantially in the targeted student population. Policy reforms and coincidental environmental changes may each have contributed to these reductions.
Unsupervised Smoke Detection in Normally Smoking Environments

The problem of smoke detection through visual analytics is an open challenging problem. The existing literature has addressed the problem by mainly working on the best feature representation and by exploiting supervised solutions which consider the problem of smoke detection as a binary classification one. Differently from such works, we consider the possibility that in some contexts sensing smokes is a common situation, but want to detect when there are significative fluctuations within this normal situation. In light of such a consideration, we propose an unsupervised solution that leverages on the concept of anomaly detection. Different visual representations have been used together with a temporal smoothing function reduce the effects of noisy measurement. Such temporally smoothed representations are then exploited to learn a robust "normality" model by means of a One-Class Support Vector Machine. A real prototype has been developed and exploited to collect a new dataset which has been considered to evaluate the proposed solution.
Toward Mass Production of CVD Graphene Films

Chemical vapor deposition (CVD) is considered to be an efficient method for fabricating large‐area and high‐quality graphene films due to its excellent controllability and scalability. Great efforts have been made to control the growth of graphene to achieve large domain sizes, uniform layers, fast growth, and low synthesis temperatures. Some attempts have been made by both the scientific community and startup companies to mass produce graphene films; however, there is a large difference in the quality of graphene synthesized on a laboratory scale and an industrial scale. Here, recent progress toward the mass production of CVD graphene films is summarized, including the manufacturing process, equipment, and critical process parameters. Moreover, the large‐scale homogeneity of graphene films and fast characterization methods are also discussed, which are crucial for quality control in mass production.
Data Analyst Platform Using Open Source Based Deep Learning Engine

In this paper, we propose a platform for analyzing data using open source deep learning engine. As the demand for deep learning technology grows, research is needed on a deep learning platform that can process and analyze various learning data. We use a distributed TensorFlow and a TensorFlow serving to provide a deep learning platform for analyzing various data. In order to process large capacity learning data, it preprocesses data in size and type that can be processed by the platform, and provides the function to distribute learning data appropriately according to the processing capability of each node. In the future, we will implement a deep learning platform based on the platform design method, and then we will develop a deep learning model using actual large capacity learning data, and optimize the platform function.
A configurable transport layer for CAF

The message-driven nature of actors lays a foundation for developing scalable and distributed software. While the actor itself has been thoroughly modeled, the message passing layer lacks a common definition. Properties and guarantees of message exchange often shift with implementations and contexts. This adds complexity to the development process, limits portability, and removes transparency from distributed actor systems. In this work, we examine actor communication, focusing on the implementation and runtime costs of reliable and ordered delivery. Both guarantees are often based on TCP for remote messaging, which mixes network transport with the semantics of messaging. However, the choice of transport may follow different constraints and is often governed by deployment. As a first step towards re-architecting actor-to-actor communication, we decouple the messaging guarantees from the transport protocol. We validate our approach by redesigning the network stack of the C++ Actor Framework (CAF) so that it allows to combine an arbitrary transport protocol with additional functions for remote messaging. An evaluation quantifies the cost of composability and the impact of individual layers on the entire stack.
Optimal Scheduling of Generation and Demand Response Resources with Considering Load Uncertainty

In this paper, a new method is proposed for optimal scheduling a micro-grid system consist of generation units and demand response resources in order to minimize operating cost by independent system operators. In the presented mathematical model with new formulation, the load uncertainty is considered. The optimization problem is composed of Mixed Integer Nonlinear Programing (MINLP), which is analyzed with commercial software GAMS. The numerical studies are done on a 6-buses network. Modeling and analyzing are done in two states, with certain data and in a stochastic environment. The impact of demand response with different level of participation is discussed. The results show considerable effect of uncertainties in parameters and costumers participation.
Supporting Student Engagement Through Explorable Visual Narratives

This paper introduces VisEN, a novel visual narrative framework that has been shown to facilitate, support, and enhance student engagement in an adaptive Online Learning Environment (OLE). VisEN provides explorable visual narratives personalized to students in order to support them in engaging with course content. The evaluation of VisEN showed that the explorable visual narratives encouraged the majority of ‘improving engagement students’, that completed the Information Management and Data Engineering module as part of their undergraduate degree, to engage with assigned activities, and subsequently these learners enhanced their engagement levels. Visualizations have been used in OLEs to support students by presenting student data. Information Visualization research has demonstrated the value of visual narratives in communicating a message, by highlighting facts and making the message more memorable. In addition, visual data exploration can support users in understandings the message. However, in OLEs, explorable visual narratives have not been used to date to support student engagement or to guide learners through a message that they could explore and understand. This paper evaluates the impact that explorable visual narratives had on student course engagement during two successive academic years.
Efficient 16 nm SRAM Design for FPGA’s

Static Random Access Memories (SRAM) based configuration bits is used to store data in Look Up Tables (LUTs) and routing through switch matrix is accomplished using the SRAM contents. Date write operation takes place only when the FPGA needs to be configured or reconfigured and hence a major source of power consumption comes from the SRAM cell leakage current during idle phase. Low power SRAM cell design forms an important requirement in today’s FPGAs because it is one of the critical components in FPGAs and consumes a large fraction of the total power. Hence a novel SRAM 6T structure is proposed where cell power supply is raised to higher VDD only during read or write operation and supply line is pulled down to lower voltage during idle phase.
Prevalence of diabetic retinopathy and cataract in adult patients with type 1 and type 2 diabetes in Russia.

AIM The aim of the study was to identify the prevalence of diabetic retinopathy (DR) and diabetic cataract (DC) in type 1 and type 2 diabetic patients within the Russian Federation. Also, the stage of DR at the time of its identification and the proportion of new cases diagnosed with DR or DC were to be determined. METHODS A random sample of 7,186 adult patients with diabetes was screened for DR and DC using fundoscopy and fundus photography. Levels of HbA1c, total cholesterol, triglycerides, creatinine and urinary albumin excretion rate were assessed. RESULTS In diabetic patients, the prevalence of DR and DC was 45.9% and 30.6%, respectively. These complications appeared significantly more frequently in patients with type 1 diabetes than in type 2 diabetes. The prevalence of background, preproliferative and proliferative DR among diabetic patients was 28.1%, 8.1%, and 6.7%, respectively. Patients with DR were older, had a longer duration of diabetes, higher HbA1c, elevated plasma total cholesterol, increased triglicerides, and higher systolic BP, compared with patients without DR. Microalbuminuria and proteinuria were more prevalent among patients with DR compared with non-DR patients. CONCLUSIONS The results showed that diabetic retinopathy and cataract are wide-spread complications among diabetic patients in Russia. However, the disease course is more aggressive and accelerated in patients with type 1 diabetes than in those having type 2 diabetes. Therefore, it is important to prevent DR by identifying diabetes and signs of retinopathy at the earliest possible stage of progression for timely and adequate retina laser coagulation or surgical treatment, compensation of carbohydrate and lipid metabolism, and normalization of blood glucose and pressure.
Exploring the Performance Boundaries of NB-IoT

Narrow band-Internet of Things (NB-IoT) has just joined the low power wide area network (LPWAN) community. Unlike most of its competitors, NB-IoT did not emerge from a blank slate. Indeed, it is closely linked to Long Term Evolution (LTE), from which it inherits many of the features that undoubtedly determine its behavior. In this paper, we empirically explore the boundaries of this technology, analyzing from a user’s point of view critical characteristics such as energy consumption, reliability, and delays. The results show that its performance in terms of energy is comparable and even outperforms, in some cases, an LPWAN reference technology like LoRa, with the added benefit of guaranteeing delivery. However, the high variability observed in both energy expenditure and network delays call into question its suitability for some applications, especially those subject to service-level agreements.
Top-k User-Defined Vertex Scoring Queries in Edge-Labeled Graph Databases

We consider identifying highly ranked vertices in large graph databases such as social networks or the Semantic Web where there are edge labels. There are many applications where users express scoring queries against such databases that involve two elements: (i) a set of patterns describing relationships that a vertex of interest to the user must satisfy and (ii) a scoring mechanism in which the user may use properties of the vertex to assign a score to that vertex. We define the concept of a partial pattern map query (partial PM-query), which intuitively allows us to prune partial matchings, and show that finding an optimal partial PM-query is NP-hard. We then propose two algorithms, PScore_LP and PScore_NWST, to find the answer to a scoring (top-k) query. In PScore_LP, the optimal partial PM-query is found using a list-oriented pruning method. PScore_NWST leverages node-weighted Steiner trees to quickly compute slightly sub-optimal solutions. We conduct detailed experiments comparing our algorithms with (i) an algorithm (PScore_Base) that computes all answers to the query, evaluates them according to the scoring method, and chooses the top-k, and (ii) two Semantic Web query processing systems (Jena and GraphDB). Our algorithms show better performance than PScore_Base and the Semantic Web query processing systems—moreover, PScore_NWST outperforms PScore_LP on large queries and on queries with a tree structure.
Rice Pest and Disease Detection Using Convolutional Neural Network

Detection of rice pest and diseases, and proper management and control of pest infested rice fields may result to a higher rice crop production. According to the International Rice Research Institute, farmers lose an average of 37% of their rice crops due to pest and diseases, yearly. Using modern technologies, like smart phones, farmers can be aided in detecting and identifying the type of pests and diseases found in their rice fields. This study proposed an application that will help farmers in detecting rice insect pests and diseases using Convolutional Neural Network(CNN) and image processing. It looked into the different pests that attack rice fields; information on how they can be controlled and managed was considered; farmers' knowledge in different rice pests and diseases, and how they control these pests was regarded in this study; the study also looked into the reporting mechanism of farmers to government agencies. Using CNN and image processing, the application that detects rice pests and diseases was developed. The searching and comparison of captured images to a stack of rice pest images was implemented using a model based on CNN. Collected images were pre-processed and were used in training the model. The model was able to achieve a final training accuracy of 90.9 percent. Cross-entropy was low, which implies that the trained model can perform prediction or can classify images with low percentage of error. Through the developed application, farmers were provided with information and procedures on how to control and manage rice pest infestation. Future researchers may look into multiple pest comparison to a stack of images for faster retrieval of information.
Redox-dependent rearrangements of the NiFeS cluster of carbon monoxide dehydrogenase

The C-cluster of the enzyme carbon monoxide dehydrogenase (CODH) is a structurally distinctive Ni-Fe-S cluster employed to catalyze the reduction of CO2 to CO as part of the Wood-Ljungdahl carbon fixation pathway. Using X-ray crystallography, we have observed unprecedented conformational dynamics in the C-cluster of the CODH from Desulfovibrio vulgaris, providing the first view of an oxidized state of the cluster. Combined with supporting spectroscopic data, our structures reveal that this novel, oxidized cluster arrangement plays a role in avoiding irreversible oxidative degradation at the C-cluster. Furthermore, mutagenesis of a conserved cysteine residue that binds the C-cluster in the oxidized state but not in the reduced state suggests that the oxidized conformation could be important for proper cluster assembly, in particular Ni incorporation. Together, these results lay a foundation for future investigations of C-cluster activation and assembly, and contribute to an emerging paradigm of metallocluster plasticity.
Relocalización de la industria manufacturera en México en la apertura comercial, 1980-2014

El proposito de este articulo es analizar los cambios en la localizacion y distribucion del sector manufacturero en Mexico. El estudio incluye una revision del contexto de la industria manufacturera y la aplicacion de tecnicas de analisis regional tomando como ano base a 1980 y ano final al 2014. Se concluye que ha existido un fenomeno de relocalizacion manufacturera en Mexico, los estados que conforman a la region del Bajio se constituyen como los mas ganadores en materia manufacturera, asi mismo se ha generado una reestructuracion dentro del sector manufacturero, el subsector alimentario ha perdido gran peso en el total del sector, mientras que la maquinaria y el equipo se posiciona como la principal especializacion manufacturera en las regiones del Bajio y del Norte de Mexico.
A range free localization method for overlapped optical attocells using neighbor’s information

In this paper we present a novel range free method based on connectivity and network architecture information. Optical attocells deployed within an indoor scenario send periodically its own position information as well as neighbor’s information. The mobile node (MN) uses this information in order to self-determine its position. A square 5 × 5(m) room with 9 LED lights placed equidistant in the ceiling is modeled as the proposed scenario. Our method is compared with two traditionally used algorithms in terms of accuracy and error dispersion. Numerical results show that the proposed method outperforms the traditional methods in the both proposed metrics.
Legible Normativity November 2018.pdf

It has become commonplace to assert that autonomous agents will have to be built to follow human rules of behavior–social norms and laws. But human laws and norms are complex and culturally varied systems; in many cases agents will have to learn the rules. This requires autonomous agents to have models of how human rule systems work so that they can make reliable predictions about rules. In this paper we contribute to the building of such models by analyzing an over-looked distinction between important rules and what we call silly rules —rules with no discernible direct impact on welfare. We show that silly rules render a normative system both more robust and more adaptable in response to shocks to perceived stability. They make normativity more legible for humans, and can increase legibility for AI systems as well. For AI systems to integrate into human normative systems, we suggest, it may be important for them to have models that include representations of silly rules.
Mammal communities are larger and more diverse in moderately developed areas

Developed areas are thought to have low species diversity, low animal abundance, few native predators, and thus low resilience and ecological function. Working with citizen scientist volunteers to survey mammals at 1427 sites across two development gradients (wild-rural-exurban-suburban-urban) and four plot types (large forests, small forest fragments, open areas and residential yards) in the eastern US, we show that developed areas actually had significantly higher or statistically similar mammalian occupancy, relative abundance, richness and diversity compared to wild areas. However, although some animals can thrive in suburbia, conservation of wild areas and preservation of green space within cities are needed to protect sensitive species and to give all species the chance to adapt and persist in the Anthropocene.
Enhancing Multiple Jets in Electrospinning: The Role of Auxiliary Electrode

An auxiliary electrode introduced in traditional spinneret electrospinning is an effective and powerful technique to improve the production rate of nanofibers. In this work, the effects of the arrangement of auxiliary electrode, applied voltage, injection speed, and the distance between the electrode tip and the spinneret tip (ESD) on the jet number and the morphology of polyvinyl alcohol (PVA) nanofibers were investigated systematically. The results showed that the number of jets firstly increased and then decreased with the increase of applied voltage and ESD, respectively, while increasing with the injection speed in both the auxiliary electrode in the vertical position and parallel position. The average nanofiber diameter decreased with increasing of applied voltage and injection speed, but decreasing in ESD in these two positions. The numerical simulation results revealed that the auxiliary electrode primarily influenced the electric field intensity in the spinning area. This work provides a deep understanding of multiple jets in electrospinning.