Miracle-dz/UniFAR-FTUs huggingface dataset:
Building a Connection Between Decision Maker and Data-Driven Decision Process

It is quite common that most of companies’ decisions are made based on feelings, intuitions or personal experiences. The reasons for such patterns have organizational, technical and process oriented backgrounds. For instance, there is no structured way to deal with the analytical results on both sides simultaneously – organizational and technical. Usually, in case of analytics the ones doing analysis (e.g. data scientists) and the ones using results of analytics (e.g. decision makers) are different persons. As a result, such a structure leads to ambiguity and misunderstanding between the involved parties. In order to bridge the existing gap between data scientists and decision makers, we introduced the Data Product Profile which links both data science and data-driven decision processes.
Incorporating Textual Information on User Behavior for Personality Prediction

Several recent studies have shown that textual information of user posts and user behaviors such as liking and sharing the specific posts are useful for predicting the personality of social media users. However, less attention has been paid to the textual information derived from the user behaviors. In this paper, we investigate the effect of textual information on user behaviors for personality prediction. Our experiments on the personality prediction of Twitter users show that the textual information of user behaviors is more useful than the co-occurrence information of the user behaviors. They also show that taking user behaviors into account is crucial for predicting the personality of users who do not post frequently.
Statistical range estimation for optical time-of-flight 3D imaging

During the last decades, 3D range imaging has become a subject of extensive research with many applications being developed in various fields such as security, gaming, computer interfaces, robotics, industrial production and quality control. For acquiring the 3D-data, several optical acquisition systems rely on the time-of-flight principle. This principle is based on the time, needed by a photon to travel from a light source to a target and back to a sensor. For this work, we utilize the SwissRanger 3D camera, which continuously modulates the optical-signal power and delivers four optical signal samples for the range measurement. These samples are acquired by sampling the optical signal four times per modulation period. The key performance indicator of time-of-flight systems is the measured-range accuracy. Due to various noise sources, the measured range exhibits random fluctuations, and so a measurement uncertainty. This uncertainty must be minimized in order to obtain time-of-flight systems that deliver a range with the highest possible accuracy. In this work, we present several novel techniques and algorithms, which minimize the random fluctuations in order to increase the measured-range accuracy. In previous works, two kinds of signals are extensively used for modulating the opticalsignal power: the sine waves and the binary m-sequences. In a single-camera environment, where the measured-range accuracy is physically limited by the Poisson shot noise, the sine wave modulation achieves the best range accuracy. However, in a multi-camera environment, where the noise is mainly due to the interference generated by the other camera signals, the m-sequence modulation yields the best range accuracy. However, in general, both the Poisson shot noise and the multi-camera interference have to be taken into account. In these cases, new modulation signals, based on Markov chains, lead to the highest measured-range accuracy. In low-light conditions with two cameras simultaneously operating, the range accuracy is up to 60% higher than for the other modulation schemes. Two additional techniques are also suggested to limit the Poisson shot noise for the msequence and Markov-sequence modulations, in a single-camera environment. First, a new modulation scheme, based on a m-sequences combination, is investigated. Compared to the standard m-sequence modulation, this new modulation scheme yields a higher measuredrange accuracy. Under certain conditions, the accuracy is increased by more than 30%. The second investigated technique is about the optical-signal sampling. Usually, two samples from a correlation function, and separated in time by the duration of one sequence
Robust methods for multiple model discovery in structured and unstructured data

OF THE DISSERTATION Robust Methods for Multiple Model Discovery in Structured and Unstructured Data by Saket Anand Dissertation Director: Professor Peter Meer Humans excel at identifying and locating multiple instances of objects or persons in a scene, despite large variations in lighting conditions, pose or scale. In order to achieve this level of robustness, humans naturally use high-level semantic information. The notion of semantics is hierarchical in nature, for example, a house is constituted of walls, floor and ceiling. When certain entities in this hierarchy can be modeled using a functional form known a priori, we refer to the data as structured, while when the functional form is unknown, the data is unstructured. In this thesis, we address the problem of discovering multiple instances of models in structured and unstructured data. The first part of this thesis deals with structured data. We identify planar regions in an indoor scene by using a single depth image from a Microsoft Kinect sensor. The clutter in the indoor scenes, the depth dependent measurement noise and the unknown number of planar regions pose serious challenges in model discovery. We propose a scalable bottom-up approach that leverages from a heteroscedastic, i.e., point dependent model of the measurement noise. The second part of the thesis addresses multiple model discovery in unstructured data in a semi-supervised setup. We develop a framework for using mean shift clustering
Mobility management schemes for supporting multimedia services in all-IP network infrastructures

The paper argues that, in next-generation, all-IP networks, no existing mobility management protocol is suitable for every type of application, wireless environment or mobility pattern. Two composite mobility management schemes are proposed for supporting multimedia services in a seamless fashion. Both schemes i) follow a domain-based approach, isolating the wireless access from the core network, and ii) use an IP-based micro-mobility solution to handle the local mobility and support fast handoffs and paging. Two different approaches are considered for macro-mobility. The first uses SIP (session initiation protocol) in combination with IP encapsulation mechanisms to support mobility for all types of communications. The second approach performs separation of traffic; SIP signaling in combination with NAT (network address translation) is used to support mobility for real-time communications, while mobility for non-real-time communications - mainly TCP-based applications - is supported by Mobile IP. Both approaches are compared against existing mobility management protocols.
A novel multiprocessor system for road traffic applications

In this paper we describe a novel image processing approach for road traffic analysis. We use combined background differencing and edge detection techniques to detect vehicles and measure various traffic parameters such as the vehicle count and queue parameters. Since the computational requirement for the vision based road traffic algorithms is beyond the capacity of conventional computers, we designed a RISC based array processor system to execute the image processing algorithms in real-time. To expand the performance of the array processor system, we chose a combinatorial architecture for the processing elements; i.e., we add some functional units to a traditional RISC architecture. The image processing algorithms for traffic analysis have been implemented on the array processor system and the results are reported online.
A visual inspector for Boogie programs

Design-by-Contract is an approach that allows a programmer to specify the expected behavior of a component by means of preconditions, postconditions and invariants. These annotations (or logical assertions that complement the code) can be seen as a form of enriched software documentation and they can be used to verify that a program is correct with respect to its contracts. Boogie is an intermediate verification language that is being used by more and more software verification tools as a target language. Actually, several annotation languages are nowadays translated to Boogie language. Despite of its efficiency and popularity, Boogie, that is also a program verifier, does not contain visual information for the user. So, understanding how it works is a difficult task. In this paper, we will discuss a visual tool that we developed to help in comprehending Boogie programs.
Proposal for Xml Schema V 1.1 as a Basis for Xmlrepository.Org

Abstract XML schema was published on May 2, 2001, as a Recommendation of the W3-Consortium (W3C). XML schema forms the basis for modern electronic data processing. However, the presence of some restrictions within XML schema is now recognized, and its inclusion in further standards is discussed. The XML Schema Working Group is currently working to develop a set of requirements for XML Schema 1.1, which is intended to be mostly compatible with XML Schema 1.0. This proposal is additionally extending the scope of Schema 1.1 in a way that is more compatible with URI names. What is the background for this extension? XML schema offers the functionality to import other schemas. xmlrepository.org, presented here, is a portal and name space for testing and publishing XML schema modules. Consequently, xmlrepository.org ought to be a library of basic schemas from all fields, university and scientific as well as economic and industrial ones. Each entry in the library is catalogued in accordance with the catalogue at W3C, built upon year, month, and variable name. Year and month (as numbers) are currently no names according to XML Schema 1.0. Second, the variable—initially "unknown"—name could not be mapped into an XML schema. This article tries to extend the current XML schema model to a more flexible way of naming tags and also to allow variable tags according to XPath expressions.
A new bandwidth efficient transmit antenna modulation diversity scheme for linear digital modulation

A system is proposed and analyzed, which uses several transmit antennas to achieve diversity in a flat fading mobile radio environment. The signals at different antennas carry the same digital information, but have different modulation parameters (modulation diversity). In contrast to other proposals, no bandwidth expansion is required. Modulation diversity for quadrature amplitude modulation (QAM) type modulation is achieved by using different partial response type base pulses. Simulation results confirm the expected gain. <<ETX>>
A Novel Control-Performance-Oriented Data-Driven Fault Classification Approach

This paper selected nine novel feature quantities that can reflect control performance in the closed-loop. Combined with support vector machine (SVM) and k-nearest neighbor (KNN), they are used to perform accurate fault diagnosis. Stability margin describes the degree of stability of the transfer function matrix of system, and thus can be used as an indicator to reflect system’s control performance. Similarly to stability margin, the reciprocals of $\boldsymbol {H_{\infty }}$-norms of eight subsystems related to stability margin can also be selected as the other eight features describing the control performance. To get the data-driven realization of nine feature quantities, stable image representation and stable kernel representation are constructed by data-driven method without system identification in closed-loop system. Through trained SVM and KNN, different types of faults can be accurately categorized by these feature quantities and the correctness of the entire algorithm is testified and demonstrated by a dc motor benchmark system.
Partisi Data Secara Vertikal Untuk Menentukan Aturan Asosiasi Item Set Data Cuaca

Abstract This paper discussed about association rule mining among item sets of weather records, where observation results are distributed from data source and partitioned in order to create an optimal rule pattern. We use decision tree classifiers as method for data partitioning, which each item set has several attributes and these item sets are used to identify the valid global association rule, but did not disclose the items set individual transaction data . The final results of this study was to partition the data to generate a frequency associated items set weather data with the minimal level of support without revealing the value of the item set of individuals. Frequency value associated items set the partition of this data can be used for weather prediction simulations whether there will be rain or no rain. Keywords: association rule mining, item set, weather records, partition, Â Â Â Â Â Â Â Â Â Â Â decision tree classifiers Abstrak Makalah ini membahas aturan penambangan asosiasi (association rule mining) antar item set data cuaca dimana data hasil pemantauan didistribusikan dari sumber data dan dipartisi untuk memperoleh pola aturan yang optimal. Metoda yang akan digunakan untuk partisi data adalah pengklasifikasian pohon keputusan (decision tree classifiers) yaitu setiap item set memegang beberapa atribut dan item set tersebut mengidentifikasi aturan asosiasi global yang valid, namun item set tidak mengungkapkan data transaksi individu. Hasil akhir dari penelitian ini adalah partisi data untuk menghasilkan frekuensi asosiasi item set data cuaca dengan tingkat dukungan minimal tanpa mengungkapkan nilai item set individu. Nilai frekuensi asosiasi item set hasil partisi data ini dapat digunakan untuk simulasi prediksi cuaca apakah akan terjadi hujan atau tidak hujan. Kata-kuci : aturan penambangan asosiasi, item set, data cuaca, Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â partisi, pengklasifikasian pohon
Research on Text Mining of Railway Safety Supervisors Performance Based on BiLSTM and CRF

In the era of big data with unstructured data, the potential value of unstructured data mining is an effective way to solve the industry problem. Based on a large number of data recorded from the actual text of the performance plan of railway security supervisors, this paper proposes a text similarity calculation method based on text mining technology to calculate whether the personnel performance plan matches the reality. First, the text Named Entity Recognition(NER) is realized by using the combination of Bi-directional Long Short Term Memory (BiLSTM) and the Conditional Random Fields (CRF). Then based on the concept similarity calculation of HowNet, we designed the method of calculating the similarity between the same named entity. After that, different weights are given according to the importance of named entity, and a method of computing similarity between named entities is designed. Finally, according to the given threshold, to obtain whether planning and realistic matching, and the actual personnel working situation, provide the basis for personnel assessment for the management personnel.
Performance Evaluation of Survivability Approaches in Optical Networks

The chapter outlines the different survivability approaches for mesh networks under static and dynamic traffic environments. It describes the different solution options and their implementations. Also included are detailed performance analyses and evaluations for the difference survivability approaches under both traffic environments. Finally, we present a performance comparison between the different survivability approaches and end the chapter with some concluding remarks. DOI: 10.4018/978-1-61350-426-0.ch006
Quantifying Complex Systems via Computational Fly Swarms

Complexity is prevalent both in natural and in human-made systems, yet is not well understood quantitatively. Qualitatively, complexity describes a phenomena in which a system composed of individual pieces, each having simple interactions with one another, results in interesting bulk properties that would otherwise not exist. One example of a complex biological system is the bird flock, in particular, a starling murmuration. Starlings are known to move in the direction of their neighbors and avoid collisions with fellow starlings, but as a result of these simple movement choices, the flock as a whole tends to exhibit fluid-like movements and form interesting structures. To understand complexity, we chose fly swarms as the system to model. To do this, we utilized stochastic modeling to simulate the movements of individuals, giving them different guiding rules based on both laboratory observation and other models to best produce a realistic model. We hope to compare values of key properties both with other research groups, as well as under varying conditions within our model to find if there is a property that can qualitatively describe if the system is complex or not.
Data Reduction in Smart Grid

The advancement of the electric grid has led to tremendous growth in data generated from the installed sensors. With this huge amount of data, the electric grid faces a big data problem. Therefore, it is necessary to implement a data reduction technique that would not only curtail the exponential data growth, but also be able to make smart decisions with the reduced data.This thesis deals with two data reduction techniques; data compression and dimensionality reduction. Some of the state of the art compression algorithms exploit the characteristics of load profile data, where consecutive data samples have very small differences. However, performance of these algorithms deteriorate with large value differences. In this thesis, we propose a modification that improves the compression ratio when there are large value differences. The algorithm is evaluated on smart meter load profile data at different data resolution. We show that our proposed changes improve performance by 2 20 % for different data resolution. Also, this algorithm can be extended to other domains. Furthermore, we propose a lossless compression algorithm that uses
Hangup in Phase-Lock Loops

A phase-lock loop occasionally will take a long time to settle to equilibrium. Phase dwells at a large error for a prolonged interval. This phenomenon has been dubbed "hangup. " The periodic nature of phase detectors is responsible for hangup, which occurs near the reverse-slope, unstable null. Restoring force is small in the vicinity of the reverse null, and noise causes the loop to equivocate about the null. Hangup is very troublesome when fast acquistion is needed with high reliability. One example is synchronization of digital communications. Hangups can be avoided if a large restoring force is applied for large phase errors and if equivocation is prevented. An implementation of an antihangup circuit is proposed.
Modelling and 3D Printing of Differential Gear Box

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/ by/4.0) ABSTRACT The main aim of the project is to focus on the modeling and 3D printing of differential gear box. Differential permit each of the driving wheels to rotate at different speeds, while for most vehicles supplying equal torque to each of them. A vehicle's wheels rotate at divergent speeds, mainly when turning corners. The differential is designed to operate a pair of wheels with same torque while permit them to rotate at different speeds. Differential gear box is designed in CATIA V5 software and manufactured using additive manufacturing (3D printing). The function of gear is to protect and provide a platform for gear transmission. It also provides supports for moving parts and protection from outside environmental condition.
Finding the just-in-time service location and path in a ubiquitous service network

Just-in-time (JIT) is an important topic of lean manufacturing and services, focusing on the reduction of the waiting time, as well as the enhancement of timeliness. In this study, the JIT concept is applied to the application of mobile commerce, ambient intelligence, and ubiquitous computing, which has rarely been explored in the past. As a result, a ubiquitous JIT service network is constructed based on the application of a mobile phone. In this network, a user uploads a request using a mobile phone. The request is then met at the appropriate service location in the network. For the JIT service network, an important issue is how to determine the JIT service location and path. In response to this issue, this study proposes a mathematical programming model to identify the JIT path. Subsequently, a systematic procedure is established to determine the JIT service location. The applicability of the proposed methodology is illustrative with an example.
A Unified Approach to Feature-Centric Analysis of Object-Oriented Software

Feature-centric comprehension of software is a prerequisite to incorporating modifications requested by users during software evolution and maintenance. However, featurecentric understanding of large object-oriented programs is difficult to achieve due to size, complexity and implicit character of mappings between features and source code. In this paper, we address these issues through our unified approach to feature-centric analysis of object-oriented software. Our approach supports discovery of feature-code traceability links and their analysis from three perspectives and at three levels of abstraction. We further improve scalability of analysis by partitioning features into canonical groups. To demonstrate feasibility our approach, we use our NetBeansintegrated tool Featureous for conducting a case study of feature-centric analysis of the JHotDraw project. Lastly, we discuss how Featureous supports program comprehension by means of concrete cognitive design elements.
Predicting the reliability of electronic subsystems and 'commercial-off-the-shelf' microprocessors on low-cost small satellites

This thesis presents an investigation into methods for predicting and improving the reliability of electronic subsystems on low cost small satellites [LCSS]. Current methods for predicting the reliability of electronic systems were identified and the feasibility of these methods for predicting the reliability of electronic systems on LCSS were analysed. Trends in satellite failures and satellite design methodologies were studied. It was identified that, highly capable, LCSS have became possible due to the existence of advanced commercial-off-the-shelf (COTS) very large scale integrated circuits (VLSI) components. Typically, a large number of these components are employed on these satellites and the effect of space radiation on these components is a significant source of risk to their reliability. This research program therefore concentrated on addressing the effects of space radiation on the reliability of COTS VLSI components. In particular, traditional methods for predicting the rate and effects of radiation induced spontaneous logic state changes (Single Event Upsets (SEUs)) in satellite on-board microprocessors were critically examined. Problems associated with these methods were identified, and two new models to enable more accurate predictions were proposed, developed and tested. One of the models, a duty cycle prediction model, enables a better prediction of the "observable SEU induced error rate" in satellite microprocessor systems; and the other model, a SEU simulation model, enables the nature of SEU induced errors observed at microprocessor system level to be studied. The practical applications of both models were demonstrated by analysing a real satellite application system. These models will be particularly useful to engineers in the selection and application of reliable COTS microprocessors for satellite onboard computer intensive tasks without jeopardising reliability.