Miracle-dz/UniFAR-FTUs huggingface dataset:
Braking distance algorithm for autonomous cars using road surface recognition

India is yet to accept semi/fully – autonomous cars and one of the reasons, was loss of control on bad roads. For a better handling on these roads we require advanced braking and that can be done by adapting electronics into the conventional type of braking. In Recent years, the automation in braking system led us to various benefits like traction control system, anti-lock braking system etc. This research work describes and experiments the method for recognizing road surface profile and calculating braking distance. An ultra-sonic surface recognition sensor, mounted underneath the car will send a high frequency wave on to the road surface, which is received by a receiver with in the sensor, it calculates the time taken for the wave to rebound and thus calculates the distance from the point where sensor is mounted. A displacement graph will be plotted based on the output of the sensor. A relationship can be derived between the displacement plot and roughness index through which the friction coefficient can be derived in Matlab for continuous calculation throughout the distance travelled. Since it is a non-contact type of profiling, it is non-destructive. The friction coefficient values received in real-time is used to calculate optimum braking distance. This system, when installed on normal cars can also be used to create a database of road surfaces, especially in cities, which can be shared with other cars. This will help in navigation as well as making the cars more efficient.
Generalized Version of the One-third Law

While in the evolutionary game theory, the deterministic dynamics is mainly assumed, the stochastic dynamics as well as deterministic dynamics has been assumed in the recent several studies. A previous study developed a stochastic model of two-player games and specified the conditions required for natural selection to favor an invader eventually replacing the population consisting of the resident strategies in finite populations (Nowak et al.). It has been shown that the fixation probability of a single mutant in a population of resident strategies can be greater than 1/N, the fixation probability expected in the absence of natural selection, even when the resident strategy is ESS in the traditional sense. And Nowak et al. has derived the simple rule called the one-third law. Kurokawa and Ihara extended the two player games to n-player games and derived the {2/[n(n+1)]}1/(n–1) law (where n is the group size) as a generalized version of the one-third law. However, the {2/[n(n+1)]}1/(n–1) law holds true for some specific case, and when the law ({2/[n(n+1)]}1/(n–1) law) holds true is unexplored. This paper examines when the law ({2/[n(n+1)]}1/(n–1) law) holds true, and in addition, derives another extension of one-third law (1/[1+(n) 1/(n–1)] law).
Computational modelling Procedures for Geometry Optimization, Kinetic and Thermodynamic Calculations using Spartan Software - A Review

Computational chemistry is a technique used only by people that have experience in using tools that are difficult to understand and apply. This reviewed article give insight to simple procedure that can be used to optimize geometry of molecules to obtain geometric parameters such as bond length, bond angles and dihedral angles and also to carry out kinetic and thermo dynamic calculations on unimolecular reactions involving a first order and bimolecular reactions involving second order reactions.
Dynamic Resource Allocation of Relay Routing Nodes and Similarity Measures for different Routing Protocols

Understanding relay nodes and their modes of action is a fundamental challenge in systems medicine. Key to addressing this challenge is the elucidation of nodes targets, an important step in the search for new relay nodes or novel targets for existing relay nodes. Incorporating multiple biological information sources is of essence for improving the accuracy of nodes target prediction. In this article, we introduce a novel framework-Similarity-based Inference of nodes-TARgets (SITAR) -for incorporating multiple nodes-nodes and gene-gene similarity measures for nodes target prediction. The framework consists of a new scoring scheme for nodes-gene associations based on a given pair of nodes-nodes and gene-gene similarity measures, combined with a logistic regression component that integrates the scores of multiple measures to yield the final association score. We apply our framework to predict targets for hundreds of relay nodes using both commonly used and novel nodes-nodes and gene-gene similarity measures and compare our results to existing state of the art methods, markedly outperforming them.
Effective keyword search on large scale graphs in a distributed system

Keyword query is more user-friendly than the structured query (SQL, XQuery, SPARQL) as it requires no pre-knowledge about the underlying schema of the databases. In the recent decade there has been much work focusing on the keyword search in both the structured databases and unstructured databases. Particularly, since the labeled graph model can generally present both the structured and unstructured databases, graph keyword search has been a hot topic in the database community. Furthermore, with the rapid development of semantic web, the graph data has grown to a huge scale and has to be stored and processed in a distributed environment. However, no existing approach can be directly applied to answer the keyword queries in the distributed graphs. The challenges exist in two-fold, the poor locality exists in the keyword query answers may lead to high communication cost for graph exploration and the huge traffic load for shifting the data among different machines. To address these two challenges, in this thesis, we first propose to optimize the index in order to minimal the distributed query processing cost within the space budget on each machine; Then, based on this indexing scheme we introduce an advanced distributed query processing algorithm, which includes an optimal scheduling problem defined to reduce traffic and the time cost, and we propose a greedy algorithm with a factor-2 approximation to the optimal schedule problem; At last, we verify the effectiveness and efficiency of our solution with large scale real datasets. ix
Calculation of 20 kV Distribution Network Energy Losses and Minimizing Effort Using Network Reconfiguration in Region of PT PLN (Persero) UPJ Bantul

Power distribution system is a component of an electric power system to deliver electrical energy from substation to customer location. In power distribution system, there are some power loss was changed as heat. Power distribution losses is a natural occurrence, so one gets to be done only minimize to support global energy efficiency. The way to reduce power loss in the power distribution system is by reconfiguration the existing line. Reconfiguration means a process of operating the switch (NO and NC) and change the topology line. Then, power loss in the power distribution system is computed with “ETAP” simulation software. From computing result of distribution network losses on existing line at PT. PLN UPJ. Bantul BNL 6, BNL 7 and BNL 11 feeders are gotten energy losses as 2,669,328 kWh per year or 1.72 %. Network reconfiguration that involves BNL 6, BNL 7 and BNL 11 feeder gets energy losses decrease by 1.00 % per year.
Fault Prone Analysis of Software Systems Using Rough Fuzzy C- means Clustering

Prediction of fault proneness of modules in software is one of the ways to ensure the achievement of software quality and reliability. Software delivered cannot not always be bug free, the more the number of bug the more the dis-satisfaction among client due to degradation in software quality and reliability. Though we have few models for detecting software fault prone modules, the intend of our work is to increase the reliability of the software by using an approach named Rough Fuzzy c-means (RFCM) clustering algorithm to analyse the fault proneness of the software modules under test. This helps in an efficient analysis of the modules having an ambiguous behaviour using rough set boundary which is not possible using traditional clustering methods. A dataset from PROMISE software engineering repository has been taken for the experimental analysis. The results were promising enough to determine software modules which fall in the boundary region of ambiguity emphasizing the software team to focus on those modules to achieve higher reliable system.
Differential evolution with improved sub-route reversal repair mechanism for multiobjective urban transit routing problem

The urban transit routing problem (UTRP) deals with public transport systems in determining a set of efficient transit routes on existing road networks to meet transit demands. The UTRP is a complex combinatorial optimization problem characterized with a large search space, multi-constraint, and multiobjective nature where the likelihood of generating infeasible route sets is high. In this paper, an improved sub-route reversal repair mechanism is proposed to deal with the infeasibility. A population-based metaheuristic, namely, Differential Evolution (DE) algorithm is then proposed to handle the multiobjective UTRP with the aim of devising an efficient transit route network that optimizes both passengers' and operators' costs. Computational experiments are performed on well-known benchmark instances to evaluate the effectiveness of the proposed repair mechanism and the DE algorithm. The computational results are reported to have better parameter values in most cases when compared to other approaches in the literature.
Review of Methods in TREC from 1992 to 2014

The Text REtrieval Conferences (TRECs) are a series of workshops that encourage research in information retrieval from realistic applications by providing large test collections, uniform scoring procedures, and a forum for organizations interested in comparing results. This paper describes the overview of Text Retrieval conferences (TRECs) from 1992 (TREC-1) to 2014 (TREC-23). The goals the judging procedure of TREC are mentioned for understanding the importance & compounds involved in text retrieval in real time applications. A brief comparative report about the methods, number of tracks and the outcomes are presented in this paper so that researchers working or want to work under this domain get an up to date view regarding Text retrieval process and applications.
Modulation Domain Based Processing for Speech Enhancement

For a long time, the spectral envelope has been accepted as the principal carrier of information important to speech. Therefore much of the work done for speech applications, such as automatic speech recognition and speech enhancement, has aimed to process this envelope. For speech enhancement, given the quasi-stationarity of speech, many approaches have been based on short-time processing of speech in a Fourier analysis-modification-synthesis (AMS) framework. Within this framework, either the magnitude and/or phase spectrum can be modified by a noise suppression or signal estimation approach to achieve enhancement. Most commonly, it is the short-time (acoustic) magnitude spectrum which is modified in order to suppress noise. While there are many methods for enhancement in the literature, it is generally agreed that current methods only achieve in making noise less perceptually annoying while maintaining intelligibility, leaving much room for improvement. In more recent years, the low-frequency temporal modulations of the spectral envelope have received increasing attention. Findings of physiological and psychoacoustic experiments have indicated the importance of these modulations in the human auditory system. This has led to the view that these temporal modulations convey much of the information necessary for speech perception. Many of the efforts to apply modulation processing to the enhancement of speech originated from work in automatic speech recognition, and are based on filtering the trajectories of each acoustic band. However, these filters were typically designed to operate over the entire utterance, without accounting for the properties of speech and noise in the signal. Consequently, processed speech quality is quite poor where corrupting noise types are dissimilar from that used to design the filters.
Data Acquisition, Monitoring and Management of Purchased Items Using Java Programing

The Purchasing department manages all related transaction of items and services of the institution. In Lyceum of the Philippines University – Laguna (LPU-L), the Purchasing department provides services to item purchases of the school (inside or outside the campus). The method of monitoring and storage of data is done manually. Considering that purchasing transaction is becoming plentiful in just a month’s notice because of different colleges requiring items to be bought, the task of in-charge personnel is becoming laborious and tedious. With increasing purchase of items and rendering of related services, it is essential that information will be kept organized and secured. This paper proposes a system for effective data monitoring and management of transactions related to purchasing of items in Netbeans IDE platform using Java programming language with additional use of Apache POI Application Program Interface (API) to control and manipulate Excel files. Item specifications and transactions are to be stored in a Spreadsheet, as it offers easy manipulation of cells, sheets and workbooks. Ergo, it is easy to manage formulas for arithmetic use and it provides visual presentation of data through graphs and charts. The developed system is capable of handling data entered by the user and it will easily store the data in its proper excel directories, which would simplify and speed up the manual method of managing data. The system is also equipped with searching methodology and printing of summary report. This would facilitate tangible monitoring of data and its status. Most importantly, the system could further assist the decision making of the department head.
Effects of Different Camera Perspectives on Preservice Teachers’ Written Reflections

Promoting meaningful reflection from teacher candidates is an ongoing challenge for many teacher preparation programs. Video-based reflection provides an opportunity for candidates to examine their own teaching more closely as they reflect on their continued growth. This study examined the role of different cameras and camera angles in the reflection process for preservice teachers implementing one-on-one reading tutoring sessions. In particular, we were interested in whether using video from head-mounted cameras as a basis for reflection activities would have an influence on the focus and type of statements used in reflections. We were also interested in the advantages and disadvantages of the different cameras from the teacher candidates’ perspectives. Results indicate that camera type did not influence the focus and type of reflective statements. In general, candidates preferred the traditional camera setup, but the head-mounted camera did offer some advantages. Implications for practice and future research are discussed.
Gram-Schmidt Orthogonalisation-based Antenna Selection for Pre-Coding Aided Spatial Modulation

In this paper, we introduce a computationally-efficient antenna selection algorithm for the pre-coding aided spatial modulation (PSM) that is applicable in both the under-determined and over-determined multiple-input multiple-output (MIMO) systems. The proposed algorithm is based on a modified Gram-Schmidt orthogonalisation, where the optimisation function is the sum of scalars that is computed successively. The proposed algorithm does not only select antennas one-by-one with low computations, but it can also remove one or two antennas per iteration, leading to further reduction in the computational complexity. Simulation results show that the proposed algorithm achieves the optimal diversity with tolerable degradation in the bit-error-rate.
Named entity extraction and disambiguation for informal text: the missing link

Social media content represents a large portion of all textual content appearing on the Internet. These streams of user generated content (UGC) provide an opportunity and challenge for media analysts to analyze huge amount of new data and use them to infer and reason with new information. An example of a main sector for social media analysis is the area of customer feedback through social media. With so many feedback channels, organizations can mix and match them to best suit corporate needs and customer preferences. Another beneficial sector is social security. Automatic monitoring and gathering of information posted in social media can be helpful to take actions to prevent violent and destructive behavior. A main challenge of natural language is its ambiguity and vagueness. To automatically resolve ambiguity by computers, the grammatical structure of sentences is used, for instance, which groups of words go together (phrases) and which words are the subject or object of a verb. However, when we move to informal language widely used in social media, the language becomes more ambiguous and thus more challenging for automatic understanding. Information Extraction (IE) is the research field that enables the use of unstructured text in a structured way. Named Entity Extraction (NEE) is a sub task of IE that aims to locate phrases (mentions) in the text that represent names of entities such as persons, organizations or locations regardless of their type. Named Entity Disambiguation (NED) is the task of determining which correct person, place, event, etc. is referred to by a mention. The main goal of this thesis is to mimic the human way of recognition and disambiguation of named entities especially for domains that lack formal sentence structure. The proposed methods open the doors for more sophisticated applications based on users' contributions on social media. We propose a robust combined framework for NEE and NED in semi-formal and informal text. The achieved robustness has been proven to be valid across languages and domains and to be independent of the selected extraction and disambiguation techniques. It is also shown to be robust against shortness in labeled training data and against the informality of the used language. We have discovered a reinforcement effect and exploited it a technique that improves extraction quality by feeding back disambiguation results. We present a method of handling the uncertainty involved in extraction to improve the disambiguation results. A generic approach for NED in tweets for any named entity (not entity oriented) is presented. This approach overcomes the problem of limited coverage of KBs. Mentions are disambiguated by assigning them to either a Wikipedia article or a home page. We also introduce a method to enrich the limited entity context.
Leader-Member Exchange and Local/Expat Promotive and Prohibitive Voice Behaviors

Drawing on self consistency theory, this paper tests a new moderated-mediation model, where employees’ supervisor based self-esteem (SBSE) is positively related to their promotive and prohibitive voice and mediates the positive relationship between leadermember exchange social comparison (LMXSC) and their promotive and prohibitive voice, but that these relationships are weakened for expatriate employees (compared to nationals) working in the Kingdom of Saudi Arabia (KSA). To test hypotheses, multi-source questionnaire data were collected from 341 matched supervisor-supervisee dyads working in a diverse range of companies in the KSA. As predicted, employees’ SBSE is positively related to their promotive and prohibitive voice and mediates the positive relationships between their LMXSC and their promotive and prohibitive voice behaviors, but only for Saudi nationals. This study responds to recent calls for leadership and voice research that explores the potential moderating roles of macro environmental and contextual factors on employees’ constructive voice behaviors.
Physiotherapy student perspectives on synchronous dual-campus learning and teaching

An increasing number of universities offer educational programmes across multiple campuses, as a way of facilitating access to tertiary education and filling the shortage of health professionals in rural and regional settings. Offering an equitable learning experience across all sites has been considered an important aspect in any learning and teaching approach. This qualitative study analysed data from 10 focus group discussions and 11 unit evaluations, to explore student perceptions of synchronous dual-campus delivery of a physiotherapy programme in Central Queensland, Australia. An inductive approach to thematic analysis was used. Three themes emerged: (a) Student location influences learning; (b) Videoconferencing impacts learning and teaching; and (c) Dual-campus delivery determines teaching structures and shapes teaching processes. Difficulties related to cross-campus communication, logistics, and opportunities for interaction and engagement were seen as detrimental to synchronous dual-campus delivery. Skill-based demonstrations added another level of complexity. However, students identified a potential benefit from accessing expertise from both campuses. With careful planning and consideration of the potential barriers and facilitators, synchronous dual-campus learning environments can be an effective delivery option for higher education institutions. This study builds on existing literature and suggests a number of strategies that are specific to this mode of programme delivery.
Identify the Shortest Path in Wireless Sensor Network using of Routing Information Protocols

---A routing algorithm is a method for determining the routing of packets in a node. For each node of a network, the algorithm determines a routing table, which in each destination, matches an output line. The algorithm should lead to a consistent routing, that is to say without loop. This means that you should not route a packet a node to another node that could send back the package. Our contribution in this paper is e3D, diffusion based routing protocol that prolongs the system lifetime, evenly distributes the power dissipation throughout the network, and incurs minimal overhead for synchronizing communication. We compare e3D with other algorithms in terms of system lifetime, power dissipation distribution
IMPROVING STUDENTS’ FACTUAL REPORT WRITING SKILL BY USING CONTENT AND LANGUAGE INTEGRATED LEARNING (CLIL) METHOD

This study investigates Dalton-Puffer theory which state that writing is one of unaffected by CLIL method. Dalton-Puffer (2007) stated that the term Content and Language Integrated Learning (CLIL) refers to educational setting where a language other than the student’s mother tongue is used as a medium of instruction. Here, the researcher focuses on writing skill to know whether or not CLIL method can improve students’ factual report writing skill as well as to know students’ response towards the implementation of this method. This research was conducted in two eleventh-grade social classes of Senior High School 1 Garawangi, in which each class consists of 30 students. CLIL is a new method, so the researcher employed a quasi-experimental design that was supported by statistical analysis. The present research showed two major findings. First, there was a significant difference on the students’ writing skill after receiving the treatment by using CLIL method in experimental group, which means that CLIL is effective in improving students’ writing skill. Second, students showed positive response towards the implementation of CLIL in teaching and learning process. Keywords: content and language integrated learning (CLIL), factual report, writing skill