Miracle-dz/UniFAR-FTUs huggingface dataset:
Structured and Unstructured Data Integration with Electronic Medical Records

— Medicine is a field with high volatility of changes. Everyday new discoveries and procedures are tested with the sole goal of providing a better-quality life to patients. With the evolution of computer science, multiple fields saw an increase of productivity and solutions that could be implemented. More specifically, in medicine new techniques started being tested in order to under-stand how the systems and practices used can reach higher performances, while maintaining the predefined high standards of quality. For many years data generated in hospitals was collected and stored yet few tools were implemented to extract knowledge or any type of advantage. One of the areas that successfully implemented in medicine was the usage of data processing tools and techniques to further extract information regarding the high abundance of data generated in a daily basis, in this field of work. This data can be stored in different ways which leads to multiple approaches on how to deals with it. The sole purpose of this paper is to give an overview of some case studies where structured and un-structured data was used, joint and separately and the value of it.
Degree of approximation of functions in the H~ider metric by (e, c) means

G DAS, T U L I K A G H O S H and B K RAY + Department of Mathematics, Utkal University, Bhubaneswar 751 004, Orissa, India + Department of Mathematics, BJB Morning College, Bhubaneswar 751014, Orissa, India MS received 15 January 1994; revised 5 December 1994 Abstract. Degree of approximation of functions by the (e, c) means ofits Fourier series in the Hrlder metric is studied.
Feature and Algorithm Selection for Capacitated Vehicle Routing Problems

Many exact, heuristic, and metaheuristic algorithms have been proposed to eﬀectively produce high quality solutions to vehicle routing problems. However, it remains an open question which algorithm is the most appropriate for solving a given problem instance, mostly because the diﬀerent strengths and weaknesses of algorithms are still not well understood. We propose an extensive feature set for describing capacitated vehicle routing problem instances and illustrate how it can be used in al-gorithm selection, and how diﬀerent feature selection approaches can be used to recognize the most relevant features for this task
THE PRELIMINARY STUDY OF ACCOUNTING PPROFESSIONAL’S PERCEPTIONS TOWARDS IFRS IMPLEMENTATION IN INDONESIA

Indonesian Institute of Accountants (Ikatan Akuntan Indonesia or IAI) has announced the plan to converge Indonesian GAAP (Pernyataan Standar Akuntansi Keuangan or PSAK) with IFRS as of 1 January 2009 starting to be effective on 1 January 2012. Indonesia as one of member of the Group of Twenty (G-20) should strive to achieve IFRS compliance as it is one of G-20 goal and commitment in enhancing economic cooperation around the world. This study aims to examine Indonesian accounting professionals‟ perceptions regarding IFRS convergence in 2012. This study is based on a survey which focuses on accounting professionals‟ perceptions in Indonesia The total population in this study was 275 accountants and auditors who attended the four accounting events in Jakarta held by IAI and IAPI. There was a total sample of 143 respondents, 76 accountants from companies and 67 auditors from public accounting firms. The research revealed many interesting findings. Language issue in understanding PSAK (a translation from IFRS) is perceived as a problem more to the accountants works in company rather than auditors. However both types of accountants sees IFRS training to staffs was viewed as an important issue for IFRS implementation in Indonesia. With regard to the advantages of IFRS implementation in Indonesia, the foreign-affiliated institutions voiced higher agreement that IFRS will result in greater comparability of financial performance. They are also confident that Indonesia is ready for IFRS convergence by 2012 in contrast with non-affiliated institutions. And lastly accountants with 0-5 years experience group expressed greater concerns towards Indonesia‟s readiness for IFRS implementation as compared to accountants with more than 15 years experience. However overall perception shows that accounting professionals surveyed has positive perceptions towards IFRS convergence in Indonesia
Polarity Detection for Twitter Users

In this project, we study the problem of political polarity detection, which aims to predict a person’s political leaning according to his/her behaviors on social network. We designed a conditional random ﬁeld model that can classify a person into two partisans: democratic and republican, conducted experiment on a politicians’ twitter dataset released in 2016 and compared with existing baselines, and also collected a new twitter dataset with more complete information. Our experiment results indicate our model is eﬀective.
Blind-spot network for image anomaly detection: A new approach to diabetic retinopathy screening

The development of computer-aided screening (CAS) systems is motivated by the high prevalence and severity of the target disease along with the time taken to manually assess each case. This is the case with diabetic retinopathy screening, that is based on the manual grading of retinography images. The development of CAS systems, however, usually involves data-driven approaches that require extensive and usually scarce manually labeled datasets. With this in mind, we propose the use of un-supervised anomaly detection methods for screening that can take advantage of the large amount of healthy cases available. Concretely, we focus on reconstruction-based anomaly detection methods, which are usually approached with autoencoders. We propose a new network architecture, the Blind-Spot Network, that, according to the presented experiments, improves the performance of autoencoders in this setting.
Agricultural Production Inputs Support Programme As A Socioeconomic Development Strategy

This paper presents the impact of the Agricultural Production Inputs Support Programme on crop yield, and reflects challenges and opportunities for improving agricultural production to ensure socio-economic development and sustainability. Policy-makers assume that farmers who receive subsidised agricultural inputs produce high yields, generate income to sustain production, and reduce poverty. A structured interview schedule was used to collect data from horticultural crop farmers who received agricultural production inputs from the provincial agricultural department during the 2014/15 season in Mopani District. Findings indicate no significant linear relationship between agricultural production inputs received and crop yield. In addition, there was no significant linear relationship between agricultural production practices and horticultural crop yield. However, the study observed that production practices such as soil testing for nutrient content, fertilisation, irrigation scheduling, use of hybrid seeds, and nursery propagated seedlings are critical for farmers to experience high crop yield. This highlights the importance and need to strengthen extension services to improve production practices. It is therefore concluded that provision of production inputs is a necessary strategy to ensure improved agricultural production. This study thus recommends a careful selection of recipients with skills to farm, sound farming experience, improved level of education, and capacity building of the farmers.
Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model

There is great interest in data-driven modelling for the forecasting of building energy consumption while using machine learning (ML) modelling. However, little research considers classification-based ML models. This paper compares the regression and classification ML models for daily electricity and thermal load modelling in a large, mixed-use, university building. The independent feature variables of the model include outdoor temperature, historical energy consumption data sets, and several types of ‘agent schedules’ that provide proxy information that is based on broad classes of activity undertaken by the building’s inhabitants. The case study compares four different ML models testing three different feature sets with a genetic algorithm (GA) used to optimize the feature sets for those ML models without an embedded feature selection process. The results show that the regression models perform significantly better than classification models for the prediction of electricity demand and slightly better for the prediction of heat demand. The GA feature selection improves the performance of all models and demonstrates that historical heat demand, temperature, and the ‘agent schedules’, which derive from large occupancy fluctuations in the building, are the main factors influencing the heat demand prediction. For electricity demand prediction, feature selection picks almost all ‘agent schedule’ features that are available and the historical electricity demand. Historical heat demand is not picked as a feature for electricity demand prediction by the GA feature selection and vice versa. However, the exclusion of historical heat/electricity demand from the selected features significantly reduces the performance of the demand prediction.
PPKWS: An Efficient Framework for Keyword Search on Public-Private Networks

Due to the unstructuredness and the lack of schemas of graphs, such as knowledge graphs, social networks and RDF graphs, keyword search has been proposed for querying such graphs/networks. In many applications (e.g., social networks), users may prefer to hide parts or all of her/his data graphs (e.g., private friendships) from the public. This leads to a recent graph model, namely the public-private network model, in which each user has his/her own network. While there have been studies on public-private network analysis, keyword search on public- private networks has not yet been studied. For example, query answers on private networks and on a combination of private and public networks can be different. In this paper, we propose a new keyword search framework, called public-private keyword search (PPKWS). PPKWS consists of three major steps: partial evaluation, answer refinement, and answer completion. Since there have been plenty of keyword search semantics, we select three representative ones and show that they can be implemented on the model with minor modifications. We propose indexes and optimizations for PPKWS. We have verified through experiments that, on average, the algorithms implemented on top of PPKWS run 113 times faster than the original algorithms directly running on the public network attached to the private network for retrieving answers that spans through them.
Knowledge Graphs Embeddings to Support Knowledge Exploration

Knowledge Graphs (KGs) have become a standard model for knowledge representation used both in research and industry. In a KG, real world entities are represented and connected by relations. The huge amount of information that is contained inside KGs requires the development of techniques for the exploration of data. One problem with KGs is that they provide a sparse representation that is computationally engaging to manage and that hides some of the global knowledge of both entities and relations. To solve this problem, high dimensional graphs can be embedded in a lower dimensional vector space in which both local and global properties of KG’s elements are more visible. The information that is contained inside the embeddings can be also used for KGs exploration. In this work we introduce the problem and the state of the art. We also present some of the initial results and our next steps.
Toward the extraction of production rules for solving logic proofs

: In building intelligent tutoring systems, it is critical to be able to understand and diagnose student responses in interactive problem solving. However, building this understanding into the tutor is a time-intensive process usually conducted by subject experts. Much of this time is spent in building production rules that model all the ways a student might solve a problem. We propose a novel application of Markov decision processes (MDPs), a reinforcement learning technique, to automatically extract production rules for an intelligent tutor that learns. We demonstrate the feasibility of this approach by extracting MDPs from student solutions in a logic proof tutor, and using these to analyze and visualize student work. Our results indicate that extracted MDPs contain many production rules generated by domain experts and reveal errors that experts do not always predict. These MDPs also help us identify areas for improvement in the tutor.
Optimizing JPEG Quantization for Classification Networks

Deep learning for computer vision depends on lossy image compression: it reduces the storage required for training and test data and lowers transfer costs in deployment. Mainstream datasets and imaging pipelines all rely on standard JPEG compression. In JPEG, the degree of quantization of frequency coefficients controls the lossiness: an 8 by 8 quantization table (Q-table) decides both the quality of the encoded image and the compression ratio. While a long history of work has sought better Q-tables, existing work either seeks to minimize image distortion or to optimize for models of the human visual system. This work asks whether JPEG Q-tables exist that are "better" for specific vision networks and can offer better quality--size trade-offs than ones designed for human perception or minimal distortion. We reconstruct an ImageNet test set with higher resolution to explore the effect of JPEG compression under novel Q-tables. We attempt several approaches to tune a Q-table for a vision task. We find that a simple sorted random sampling method can exceed the performance of the standard JPEG Q-table. We also use hyper-parameter tuning techniques including bounded random search, Bayesian optimization, and composite heuristic optimization methods. The new Q-tables we obtained can improve the compression rate by 10% to 200% when the accuracy is fixed, or improve accuracy up to $2\%$ at the same compression rate.
Liveness Verification of Stateful Network Functions

Network veriﬁcation tools focus almost exclusively on various safety properties such as “reachability” invariants, e.g., is there a path from host A to host B ? Thus, they are inappli-cable to providing strong correctness guarantees for modern programmable networks that increasingly rely on stateful network functions . Correct operations of such networks depend on the validity of a larger set of properties, in particular liveness properties. For instance, a stateful ﬁrewall that only allows solicited external trafﬁc works correctly if it eventually detects and blocks malicious connections, e.g., if it eventually blocks an external host E that tries to reach the internal host I before receiving a request from I . Alas, verifying liveness properties is computationally expensive and, in some cases, undecidable. Existing veriﬁcation techniques do not scale to verify such properties. In this work, we provide a compositional programming abstraction, model the programs expressed in this abstraction using compact Boolean formulas, and show that veriﬁcation of complex properties is fast on these formulas, e.g., for a 100-host network, these formulas result in 8 ⇥ speedup in the veriﬁcation of key properties of a UDP ﬂood mitigation function compared to a naive baseline. We also provide a compiler that translates the programs written using our abstraction to P4 programs.
Proposal for a M2 Research Internship 2019: Automated Algorithm Conﬁguration

General Information: The student will be primarily hosted by LIP6, on the Jussieu campus of Sorbonne University, research visits to Manchester are advisable, but not mandatory; the travel cost will be reimbursed. We aim to achieve results that are publishable in high-quality international conferences and journals. We particularly welcome applications from students interested in continuing their internship with a PhD under our supervision.
The SentiME System at the SSA Challenge

We describe SentiME, an ensemble system composed of 5 state-of-the-art sentiment classiﬁers that have proved to perform well on short-texts. Sen-tiME ﬁrst trains the different classiﬁers using the Bootstrap Aggregating Algorithm. The generated models are used by the classiﬁers separately, while the re-sults are then aggregated using a linear function averaging the different classiﬁ-cation distributions. SentiME has been initially tested over the SemEval2015 test set, properly trained with the SemEval2015 train test. It would outperform the best ranked system of the challenge. It has also showed robust performance over the SSA Task 1 training set in a 4 cross-fold experimental setup.
D-Lib Magazine

This article describes an approach to publishing metadata on the Semantic Web from an Open Access repository to foster interoperability with distributed data. The article describes two significant movements which led to the application of Semantic Web principles to repository content: the development of Open Access repositories and software systems implementing them, and the Semantic Web movement, especially within libraries and related institutions. The article provides both conceptual and practical insight into the process of converting a legacy relational dataset to machine understandable semantic statements, a.k. a. 'triplification', and gives an overview of current software frameworks that can fulfill this goal. One important outcome of this effort and proof-of-concept is that a repository's content can be straightforwardly published as Linked Open Data. Another result is the ability to link to valuable external datasets, enabling a repository's data to become more contextualized and 'meaningful'. Finally, SPARQL querying shows the potential of triplifications to surpass the querying and use of local data. From both practical and software engineering points of view, the article describes an approach towards publishing a repository's content as Linked Open Data, which is of interest to librarians, repository managers and software developers who work for libraries.
IVABRADINE HYDROCHLORIDE DIRECTLY COMPRESSED TABLETS USING NANO-CELLULOSE EXTRACTED FROM ARACHIS HYPOGAEA SHELLS

Methods: IVH and colloidal silicon dioxide are gift samples from Mylan Laboratory, Hyderabad, and Karnataka Antibiotics and Pharmaceuticals Ltd., Bengaluru, respectively. Nanocellulose was extracted from A. hypogaea shells by alkaline treatment followed by acid hydrolysis and it was characterized by particle size analysis by zeta sizer, melting point determination, differential scanning calorimetry (DSC), and Fourier transform infrared (FTIR) analysis. Compatibility between IVH and nanocellulose was confirmed by FTIR and DSC analysis. Then, fast-release tablets of containing 5 mg of IVH were prepared by direct compression using various compositions containing nanocellulose, starch, and colloidal silicon dioxide and evaluated.
HOM cluster decomposition in APi-TOF mass spectrometers

<p>Recent developments in mass spectrometry have brought huge advancements to the field of atmospheric science. For example, mass spectrometers are now able to detect ppq-level (10<sup>-15</sup>) concentrations of both clusters and precursor vapours in atmospheric samples (Junninen et al., 2010; Jokinen et al., 2012), as well as directly explore the chemistry of new particle formation (NPF) in the atmosphere (Kulmala et al., 2014; Bianchi et al., 2016; Ehn et al., 2014). One of the most common mass spectrometers used to measure online cluster composition and concentration in the atmosphere is the Atmospheric Pressure interface Time Of Flight Mass Spectrometer (APi-TOF MS). <br>Identification of atmospheric molecular clusters and measurement of their concentrations by APi-TOF may be affected by systematic error due to possible decomposition of clusters inside the instrument. Indeed, the detection process in the APi-TOF involves energetic interactions between the carrier gas and the clusters, possibly leading to their decomposition, and thus altering the measurement results. <br>Here we use a theoretical model to study in detail the decomposition of clusters involving so-called Highly-Oxygenated organic Molecules (HOM), which have recently been identified as a key contributor to NPF (Bianchi et al., 2019). HOM are molecules formed in the atmosphere from Volatile Organic Compounds (VOC). Some VOC with suitable functional groups can undergo an autoxidation process involving peroxy radicals, generating polyfunctional low-volatility vapors (i.e. HOM) that subsequently condense onto pre-existing particles. &#160;<br>Our study involves a specific kind of representative HOM (C<sub>10</sub>H<sub>16</sub>O<sub>8</sub>) in the APi. This elemental composition corresponds to one of the most common mass peaks observed in experiments on ozone-initiated autoxidation of &#945;-pinene, which also fulfills the &#8220;HOM&#8221; definition of Bianchi et al. (2019). The precise molecular structure was adopted from Kurte&#769;n et al. (2016), and corresponds to the lowest-volatility structural isomer of the three C<sub>10</sub>H<sub>16</sub>O<sub>8</sub> compounds investigated in that study. <br>The main scope of this work is to determine to what extent we are able to perform measurements of atmospheric cluster concentrations using APi-TOF mass spectrometers. More specifically, we want to determine whether decomposition can possibly be responsible for the lack of observations of some HOM-containing clusters in an APi-TOF. Here, we predict both an upper bound for decomposition energy necessary for decomposition in the APi-TOF, and a lower bound for new-particle formation in the atmosphere given realistic vapor concentrations. <br>Our results show that decomposition is highly unlikely for the considered clusters, provided their bonding energy is large enough to allow formation in the atmosphere in the first place. </p>
Robotic technology towards industry 4.0: Automatic object sorting robot arm using kinect sensor

As one of the most commonly employed robots in manufacturing business, robot arm has to develop into a technology capable of successing the industry 4.0 revolution. A robot arm that can interact well with humans to solve a problem becomes one of the encouraging research topics. It requires the ability to behave like humans which causes a robot arm should be utilizing intelligent peripherals. This study aims to create a robot arm design that uses a Kinect sensor as a smart peripheral to approach the human ability to see an object. As a case study, a color-based object sorting simulation was conducted. The automatic movement that the robot used is based on inverse kinematics. The experimental test shows that average percentage errors for end-effector’s position in x, y and z coordinates are 5.83%, 5.89% and 8.59%, respectively. This result has led the robot arm equipped with the Kinect sensor managed well to sort and move objects based on the color.
A Simple and Agile Cloud Infrastructure to Support Cybersecurity Oriented Machine Learning Workflows

Generating up to date, well labeled datasets for machine learning (ML) security models is a unique engineering challenge, as large data volumes, complexity of labeling, and constant concept drift makes it difficult to generate effective training datasets. Here we describe a simple, resilient cloud infrastructure for generating ML training and testing datasets, that has enhanced the speed at which our team is able to research and keep in production a multitude of security ML models.