Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of duck egg white N-glycoproteome and insight into the course of biological evolution.

Protein glycosylation is a ubiquitous posttranslational modification, which modulates protein properties, thereby influencing bioactivities within a system. Duck egg white (DEW) proteins exhibit diverse biological properties compared with chicken egg white (CEW) counterparts, which might be related to glycosylation. The N-glycoproteome analysis of DEW was conducted, and a total of 231 N-glycosites from 68 N-glycoproteins were identified. Gene Ontology analysis was used to elucidate the biofunctions of DEW N-glycoproteins and compared with those of CEW, which showed that the difference mostly participated in molecular function and biological process. The biological functions of DEW N-glycoproteins were illuminated through bioinformatic analysis for comparing with CEW orthologues, which showed different allergenicities and anti-bacterial abilities. These divergences might be initiated by specific alterations in glycosylation which enhance the proteolysis resistance and protein steric hindrance. These results provide new insights for discovering the effects of N-glycosylation on biofunctions during the divergence of homologue proteins.
Spatial Memory: From Theory to Application

Spatial memory has been investigated across diverse environments and under numerous cognitive constraints. This research has provided the basis for understanding the cognitive and neural underpinnings of remembering places in space. We review these findings with a particular focus on how they may apply to problems of spatial memory posed by technological advances that are fundamentally changing the way people process spatial information. While there are a myriad of applications tied to spatial memory processing, we primarily consider those linked to technological innovations, as these provide exciting new frontiers for exploration. Before beginning our review, we provide an overview of three technologies of particular interest: 1) the widespread use of virtual environments, 2) the implementation of augmented reality, and 3) the widespread use of global positioning systems. Research on spatial memory has traditionally used real-world settings of various orders of magnitude to test participants' spatial abilities. However, these environments pose problems related to controlling elements of experimental design. It may be difficult to find environments that suit the needs of the study, whether because of size, environmental features, availability, or familiarity. In contrast, VEs, such as those found in video games and military training simulators, provide an excellent testing ground for spatial memory. VEs make it possible to generate completely novel worlds that simulate the kinds of real world environmental features that people encounter without the limitations of those environments. Spatial memory results from VEs tend to be very similar to those from real environments, with the only consistent difference being greater underestimation of distance in VEs compared with real environments (Jansen-Osmann & Ber-endt, 2002). Whereas researchers first used VEs for greater experimental control of the environment , the case can now be made that understanding how spatial memory applies to VEs is of inherent interest. This is because VEs are becoming the primary environment we experience in some cases. For example, medical surgery is now guided by VE renditions , as is the piloting of drone planes. Thus, it is important to pay close attention to how VEs are perceived and remembered. Chen
Towards context-aware automated writing evaluation systems

Writing is a crucial skill in our society, which is regularly exerted by students across all disciplines. Automated essay scoring and automatic writing evaluation systems can support professors in the evaluation of written texts and, conversely, help students improving their writing. However, most of those systems fail to consider the context of the writing, such as the targeted audience and the genre. In this paper, we depict our vision towards new-generation AES systems that could evaluate written products while considering their specific context. In education, such tools could support students not only in adapting their written product to their particular context, but also in identifying points for improvement and situational settings where their writing is less proficient.
Semantic Enrichment of Web Data for the Provision of an Unified Data Repository of Brazilian Missing Persons

Communication and data technologies are becoming closely linked to people's lives. Therefore it is natural to make use of all this progress to reduce and solve social problems. In order for government and society to make the most appropriate decisions to deal with civilian disappearance, it's necessary to have a well-structured source of information. In several countries, it's difficult to access government data, since information is dispersed, not connected and poorly structured. So this work presents a framework to gather information on civil disappearance in Brazil through techniques such as Data Scraping and Linked Data. The goal is to make available an automatic data centralization of these individual cases, and to encourage the use of standards for the publication of data that are frequently ignored by organizations, hindering analysis and decision making on data.
Better Android apps using Android vitals

Google provides Android Vitals, a set of reports and tools for Android Developers as part of Google Play Console. Android Vitals can help developers improve their Android apps after an app has been launched by providing information on how their app is performing in key areas such as battery use, performance, and stability (freezes and crashes). Android Vitals also provides various comparisons, including against global bad behavior thresholds, against various peer groups of apps, and across releases of this app. Developers confirm Android Vitals notifies them of relevant problems and they found it valuable even if they also use crash reporting and mobile analytics. The underlying data is used by Google to assess the relative quality of Android apps; and the perceived quality may materially affect the visibility of an app in the Google Play Store. Yet little is known about the tools. This paper outlines various experiences from the developers' perspective of using Android Vitals with several popular Android apps to help open discussions and suggest further research areas. It introduces an open source project, created as part of our work, that enables developers to download pertinent data, particularly crash reports. The data can be analysed both by the development team and others. A particular benefit of this tool is to make the data available outside of the Google platform, which allows developers and (indirectly) researchers to develop additional analysis techniques not currently provided by the platform.
Predicting reliability by severity and priority of defects

Quality of software systems is continuing to be an important investigation of software systems. Assessing and predicting quality attributes of object-oriented design are performed by using software metrics, knowing that a good internal structure of software system influences in a great extent its external quality attributes. This study presents an empirical investigation of software reliability. The goal is to identify the applicability of object-oriented design metrics for reliability prediction. Firstly, an estimation of the reliability is conducted. We proposed a new reliability metric at the class level considering two perspectives related to failures/bugs found, i.e. priority and severity. Later, the estimated reliability value helps us to predict the reliability of other software projects based on their internal structure. The prediction value for reliability can be made earlier in the software development life cycle. The approach’s methodology for prediction is a statistical method, the multiple linear regression considering as dependent variable for our analysis the bugs count for a class (reflected in the newly proposed metric) and as independent variables the values of the Chidamber and Kemerer (CK) metrics. The results indicated that the most influential CK metrics in predicting reliability are WMC (Weighted Methods per Class) and CBO (Coupling Between Object classes), and that the RFC (Response For Class) and LCOM (Lack of Cohesion of Methods) metrics have no impact on the value of reliability. The root mean square error is used to validate our proposed regression equation considering data from the other four projects.
Sequential Sentiment Pattern Mining to Predict Churn in CRM Systems: A Case Study with Telecom Data

Competitive markets have driven companies to engage in customer retention. Therefore, anticipating client churn (i.e., cancellation) became a significant challenge. Sequential pattern detection is one of the most popular approaches to the churn prediction problem. Although promising, initiatives based on this approach do not take into account information that can be useful to detect churn: the underlying sentiment in the interactions between client and company. This study raises the hypothesis that considering such information may improve churn prediction, since they may indicate the client's satisfaction level with services and products. Hence, the present study aims to evaluate the validity of the formulated hypothesis. For this purpose, it applies a method that generates churn prediction models from the combination of sequential pattern detection with sentiment extraction from customer-company interactions. Experimental results from telecom data confirm the adequacy of the generated models and of the raised hypothesis.
Modified Watershed Transform for Automated Brain Segmentation from Magnetic Resonance Images

The segmentation of human brain from Magnetic Resonance Image (MRI) is one of the most important parts of clinical diagnostic. Brains' anatomical structures can be visualized and measured through image segmentation. Especially, while clinical analysis of magnetic resonance images, accurate segmentation is a crucial task for precise subsequent analysis. Watershed transform is a widely used segmentation method in medical image analysis filed. Regarding MRI images, they always contain noise caused by different operating equipment and environmental situation. However, the performance of the watershed transform depends on converges of numerous local minima on the image. Wrong regional minima on the image cause a high rate of over-segmentation of the watershed transform method. To address this problem, in this paper we propose a modified watershed transform method to prevent over-segmentation using k-means clustering method. Our modified watershed transform utilizes the k-means clustering method for region classification to remove wrong regional minima on image and provides a guideline for watershed transform to prevent the over-segmentation problem. Experimental results on brain MRI images evaluations (Dice coefficient: 95.32%) demonstrate that the proposed method can substantially prevent the over-segmentation problem of conventional watershed transform method.
Deciding among Fake, Satirical, Objective and Legitimate news: A multi-label classification system

Currently, the widespread of fake news has raised on the political class and society members in general, increasing concerns about the potential of misinformation that can be propagated, appearing on the center of the debate about election results around the world. On the other hand, satirical news has an entertaining purpose and are mistakenly put on the same boat of objective fake news. In this work, we address the differences between objectivity and legitimacy of news documents, treating each article as having two conceptual classes: objective/satirical and legitimate/fake. Thus, we propose a Decision Support System (DSS) based on a text mining pipeline and a set of novel textual features that uses multi-label methods for classifying news articles on those two domains. For validating the approach, a set of multi-label methods was evaluated with a combination of different base classifiers and then compared to a multi-class approach. Results reported our DSS as proper (0.80 F1-score) in addressing the scenario of misleading news from challenging perspective of multi-label modeling, outperforming the multi-class methods (0.71 F1-score) over a real-life news dataset collected from several portals of news.
Market Prediction in Criptocurrency: A Systematic Literature Mapping

The social media exerts an important role in publishing information and newspaper online. The quality of this information and the sentiment analysis might help predict the price of diverse market asset and cause great gains and losses. In this scenario, many researchers have been studying the diverse aspects that influence this area. Recently, cryptocurrencies have gained a spotlight between financial assets and, one of its characteristics is the fact that its market is strongly influenced by opinions and speculation being a proper area for sentiment analysis and data mining techniques. However, there is not any complete theoretical and technical framework about this subject. Due to its interdisciplinary characteristics involving topics in economics, human behavior, and artificial intelligence, there is a lack of clarity about the techniques and tools used in sentiment analysis in the cryptocurrencies scenario. The goal of this paper is to analyze related research in market prediction based on text mining and other artificial intelligence techniques and generate a systematic mapping about the main research, identifing the possible gaps in this field. This work might help the research community to better structure this emerging area and identify more exactly aspects that require research and are of essential importance.
Human-Interaction-aware Adaptive Functional Safety Processing for Multi-Functional Automotive Cyber-Physical Systems

The functional safety research for automotive cyber-physical systems (ACPS) has been studied in recent years; however, these studies merely consider the change in the exposure of the functional safety classification and assume that the driver’s controllability in the functional safety classification is always fixed and uncontrollable. In fact, the driver’s controllability is variable during the runtime phase, such that the execution process of safety-critical automotive functions is a human-interaction-aware process between the driver and ACPS. To adapt to the changes in the driver’s controllability, this article studies the human-interaction-aware adaptive functional safety processing for multi-functional ACPS in two main phases. In the design phase, where the driver’s controllability is fixed at the highest level (i.e., C3), we obtain the approximate optimal priority sequence of safety-critical functions without exhausting all sequences by proposing the refined exploration method. In the runtime phase, where the driver’s controllability level is variable (i.e., C0, C1, C2, or C3), we propose the human-interaction-aware task remapping method to autonomously respond to the change of the driver’s controllability. Examples and experiments confirm that the proposed adaptive functional safety processing can reduce overall task redundancy of safety-critical automotive functions while meeting their functional safety requirements, shorten the overall response time of safety-critical automotive functions, and increase the slack time for non-safety-critical automotive functions.
LifeReview: A model for monitoring people with anxiety disorder

This article presents LifeReview, a model that aims to facilitate the therapeutic process and the interaction between patients and mental health professionals, helping them in the treatment of anxiety disorder. The model uses as a basis the records of dysfunctional thoughts, a tool widely used by cognitive-behavioral therapy, incorporating it into an application capable of collecting media data and the patient's location. Allied to this, a management panel allows the therapist to visualize and interact with the sent records, besides personalizing the patient's experience through tips and techniques for anxiety control. A prototype was developed following the model specifications and evaluated with specialists in the area through scenarios, denoting a potential treatment aid.
Novel Congestion-estimation and Routability-prediction Methods based on Machine Learning for Modern FPGAs

Effectively estimating and managing congestion during placement can save substantial placement and routing runtime. In this article, we present a machine-learning model for accurately and efficiently estimating congestion during FPGA placement. Compared with the state-of-the-art machine-learning congestion-estimation model, our results show a 25% improvement in prediction accuracy. This makes our model competitive with congestion estimates produced using a global router. However, our model runs, on average, 291× faster than the global router. Overall, we are able to reduce placement runtimes by 17% and router runtimes by 19%. An additional machine-learning model is also presented that uses the output of the first congestion-estimation model to determine whether or not a placement is routable. This second model has an accuracy in the range of 93% to 98%, depending on the classification algorithm used to implement the learning model, and runtimes of a few milliseconds, thus making it suitable for inclusion in any placer with no worry of additional computational overhead.
1 5 Bit Stage 12 Bit Pipeline ADC Design with Foreground Calibration

In this paper, 12-Bit pipeline ADC is to be designed together with caring non-idealities. Pipeline issue in modern computers is quite advantageous for performance. Such structure can be constructed in analog-to-digital converters to make the performance faster. By considering the non-ideal cases for the design, more realistic outputs are expected. There are several non-ideal effects that lower the ADC performance such as gain error and capacitor mismatches. By using calibration techniques like background or foreground calibration, non-ideal effects can be reduced. In this paper, foreground calibration technique is applied and tabloid results are presented at the end. Modelling of the pipeline ADC is constructed on Matlab Simulink environment. Keywords—ADC, data converter, foreground, non-idealities, pipeline, sample and hold
Implementasi Sistem Keamanan Komputer Menggunakan Sistem Terintegrasi Client Server Metode Service Oriented Architecture (Soa)

Pembatasan hak akses dalam suatu sistem komputer yang dibangun merupakan bagian dari keamanan sistem yang dapat dilakukan. Meningkatnya akses melalui internet memberikan peluang cukup besar bagi seseorang untuk mengganggu sistem yang dibangun orang lain yang sifatnya stand alone, jaringan lokal maupun jaringan global. Dalam membangun sistem keamanan atas aplikasi pada penelitian ini adalah menggunakan Service Oriented Architecture (SOA). Dalam SOA sebuah permodelan sistem perangkat lunak dibangun dengan pendekatan service oriented yaitu pendekatan pada pelayanan terhadap keamanan data. Hasil penelitian menunjukkan bahwa dengan metode SOA, sistem aplikasi belajar mengajar dapat digunakan lebih baik dan tepat tanpa perlu khawatir akan ada data yang hilang atau dikendalikan oleh oranglain.
In-Silico Optimisation of Tileable SiPM Based Monolithic Scintillator Detectors for SPECT Applications

Over the last decade one of the most significant technological advances made in the of field radiation detectors for nuclear medicine was the development of Silicon Photomultipler (SiPM) sensors. At present a only small number of SiPM based radiation detectors for Single Photon Emission Computed Tomography (SPECT) applications has been explored, and even fewer experimental prototypes developed. An in-silico investigation into the optimal design of a Philips DPC3200 SiPM photosensor based monolithic scintillator detector for SPECT applications was undertaken using the Monte Carlo radiation transport modelling toolkit Geant4 version 10.5. The performance of the 20 different SPECT radiation detector configurations, 4 scintillator materials (NaI(Tl), GAGG(Ce), CsI(Tl) and LYSO(Ce)) and 5 thicknesses (1 to 5 mm), were determined through the use of six different figures of merit. For SPECT/X-ray CT applications it was determined that GAGG(Ce) was an optimal scintillator material, with crystal thicknesses of 3 mm and 5 mm being ideal for SPECT/X-ray CT systems with pinhole and parallel hole coded apertures respectively. Conversely for SPECT/MR applications it was determine that CsI(Tl) was an optimal scintillator material, with crystal thicknesses of 3 mm and 5 mm again being ideal for SPECT/MR systems with pinhole and parallel hole coded apertures respectively.
Learning elementary structures for 3D shape generation and matching

We propose to represent shapes as the deformation and combination of learnable elementary 3D structures, which are primitives resulting from training over a collection of shape. We demonstrate that the learned elementary 3D structures lead to clear improvements in 3D shape generation and matching. More precisely, we present two complementary approaches for learning elementary structures: (i) patch deformation learning and (ii) point translation learning. Both approaches can be extended to abstract structures of higher dimensions for improved results. We evaluate our method on two tasks: reconstructing ShapeNet objects and estimating dense correspondences between human scans (FAUST inter challenge). We show 16% improvement over surface deformation approaches for shape reconstruction and outperform FAUST inter challenge state of the art by 6%.
Cyclic Oritatami Systems Cannot Fold Infinite Fractal Curves

RNA cotranscriptional folding is the phenomenon in which an RNA transcript folds upon itself while being synthesized out of a gene. The oritatami system (OS) is a computation model of this phenomenon, which lets its sequence (transcript) of beads (abstract molecules) fold cotranscriptionally by the interactions between beads according to the binding ruleset. The OS is an useful computational model for predicting and simulating an RNA folding as well as constructing a biological structure. A fractal is an infinite pattern that is self-similar across different scales, and is an important structure in nature. Therefore, the fractal construction using self-assembly is one of the most important problems. We focus on the problem of generating an infinite fractal instead of a partial finite fractal, which is much more challenging. We use a cyclic OS, which has an infinite periodic transcript, to generate an infinite structure. We prove a negative result that it is impossible to make a Koch curve or a Minkowski curve, both of which are fractals, using a cyclic OS. We then establish sufficient conditions of infinite aperiodic curves that a cyclic OS cannot fold.
Three Branches: Detecting Actions With Richer Features

We present our three branch solutions for International Challenge on Activity Recognition at CVPR2019. This model seeks to fuse richer information of global video clip, short human attention and long-term human activity into a unified model. We have participated in two tasks: Task A, the Kinetics challenge and Task B, spatio-temporal action localization challenge. For Kinetics, we achieve 21.59% error rate. For the AVA challenge, our final model obtains 32.49% mAP on the test sets, which outperforms all submissions to the AVA challenge at CVPR 2018 for more than 10% mAP. As the future work, we will introduce human activity knowledge, which is a new dataset including key information of human activity.