Miracle-dz/UniFAR-FTUs huggingface dataset:
Media Audio Visual Menggunakan Videoscribe Sebagai Penyajian Informasi Pembelajaran Pada Kelas Sistem Operasi

Dalam proses belajar mengajar, diperlukannya konten materi sebagai bahan ajar perkuliahan. Konten materi tersebut tentunya harus jelas dan mudah dipahami supaya mahasiswa dapat mengerti konten materi yang dipelajari itu. Materi yang dijelaskan pun bentuk atau formatnya bisa berupa format slide materi dengan menggunakan power point atau drive serta berupa format media video pembelajaran. Media yang biasa digunakan untuk memperlancar komunikasi proses belajar mengajar disebut dengan media pembelajaran. Namun masih kurang optimalnya dalam proses video pembelajaran tersebut. Dalam melakukan penelitian ini didapatkan 3 permasalahan yang terjadi. Hal yang diterapkan ini merupakan pembuatan video pembelajaran sebagai penyajian informasi serta merupakan inovasi pembelajaran kelas sistem operasi pada program iLearning plus. Serta dibuatnya gaya kerangka pemikiran dalam penelitian yang digunakan untuk model penelitian korelasi. Untuk hal ini dilakukan 10 literature review perihal media audio visual menggunakan videoscribe di setiap universitas, sekolah atau pendidikan lainnya. Diimplementasikan video pembelajaran pada kelas sistem operasi program iLearning Plus. Setelah adanya video pembelajaran berupa media audio visual sebagai penyajian informasi pembelajaran serta mempermudah mahasiswa untuk memahami dan mendalami materi yang telah disampaikan. Kata kunci: Videoscribe, Media Audio Visual, Video Pembelajaran In the process of teaching and learning, the need for creative content as lectures teaching materials. The creative content must be clear and easy to understand so that students can understand the content of the materials studied. The material described any form or format can be in the form of a slide format material using a power point or drive as well as the form of instructional video media formats. Media used to facilitate communication with the teaching and learning process called learning media. But still less than optimal in the process of learning videos. In conducting this study, the three problems that occur. It is applied is an instructional video creation as the presentation of information and increase the effectiveness of classroom learning operating system on program iLearning plus. As well as the style made in the research framework used to model the correlation study. For this was done 10 literature review concerning the audio-visual media using videoscribe in every university, school or othereducation. Implemented instructional video in class operating system program iLearning Plus. After the video lessons in the form of audio-visual media is expected to increase the effectiveness of learning and easier for students to understand and explore the material that was submitted. Keywords: Videoscribe, Media Audio Visual, Video Learning
Analysis of Four Phase Interleaved Boost Converter

This paper deals with analysis of the four phase interleaved DC-DC converter for higher power application. The interleaved topology is widely used due to its advantage as lower input current ripple which means volume reduction of the input capacitor. The current ripple equation of an input current for the boost operation mode and the ripple current in the individual phase of the interleaved converter using uncoupled inductor are shown. The theoretical equations are supplemented by the simulation results using the Spice simulator and by measurement on the interleaved converter.
Energy Optimization through Position Responsive Routing Protocol (PRRP) in Wireless Sensor Network

Wireless Sensor Networks (WSNs) are consist of very tiny nodes structure, with very small batteries without having facility to recharge it. It works under any hard circumstances, hence this type of network is gaining high importance day by day. Maximum focus of researchers is to enhance or optimized its energy efficiency level. Routing protocol is the main energy expensive operation of sensor networks which utilize maximum energy resources of sensor network. Already a good number of routing protocols has been designed for energy efficiency or optimization with different approaches under controlled conditions. With this research we try to propose energy optimization of wireless sensor network through our proposed position response routing protocol (PRRP), under certain controlled parametric consideration. Research results shows a significant energy optimization in the life cycle of WSN. Index Terms—WSN, energy efficient, WSN routing protocol, GPS, PRRP, energy optimization
Distributed Sensor Systems: Practice and Applications

DESCRIPTION This book focuses on the distinct but tightly inter-related areas of development for distributed sensing systems In this book, the authors discuss the technological developments lead by sensor technology, addressing viable new applications to inspire a technological evolution. Under the advanced and visionary approach of distributed intelligence, the authors focus on three distinct but tightly inter-related areas of developments for distributed sensing systems (DSS): firstly, the sensor technology embracing the conversion of the phenomena of interest into desirable form of signal such as electric, secondly, the interaction process between sensing points which requires immense intelligence loosely called networking, and finally, the adoption of useful maturing systems through potential applications for right impacts for a better life and a brighter economy. Furthermore, the book contains a number of case studies and typical applications illustrating the technical details, features and functions of the systems, as well as demonstrating their benefits and limitations.
Egospace Motion Planning Representations for Micro Air Vehicles

Navigation of micro air vehicles (MAVs) in unknown environments is a complex sensing and trajectory generation task, particularly at high velocities. In this work, we introduce an efficient sense-and-avoid pipeline that compactly represents range measurements from multiple sensors, trajectory generation, and motion planning in a 2.5–dimensional projective data structure called an egospace representation. Egospace coordinates generalize depth image obstacle representations and are a particularly convenient choice for configuration flat mobile robots, which are differentially flat in their configuration variables and include a number of commonly used MAV plant models. After characterizing egospace obstacle avoidance for robots with trivial dynamics and establishing limits on applicability and performance, we generalize to motion planning over full configuration flat dynamics using motion primitives expressed directly in egospace coordinates. In comparison to approaches based on world coordinates, egospace uses the natural sensor geometry to combine the benefits of a multi-resolution and multi-sensor representation architecture into a single simple and efficient layer. We also present an experimental implementation, based on perception with stereo vision and an egocylinder obstacle representation, that demonstrates the specialization of our theoretical results to particular mission scenarios. The natural pixel parameterization of the egocylinder is used to quickly identify dynamically feasible maneuvers onto radial paths, expressed directly in egocylinder coordinates, that enable finely detailed planning at extreme ranges within milliseconds. We have implemented our obstacle avoidance pipeline with an Asctec Pelican quadcopter, and demonstrate the efficiency of our approach experimentally with a set of challenging field scenarios. The scalability potential of our system is discussed in terms of sensor horizon, actuation, and computational limitations and the speed limits that each imposes, and its generality to more challenging environments with multiple moving obstacles is developed as an immediate extension to the static framework.
Entity Search Track submission by Yahoo! Research Barcelona

The data has been indexed using the distributed indexing method describedin [3], i.e. implementing distributed indexing for MG4J [2] using Hadoop. Werefer the reader to our paper for the details on the indexing process, the variousalternative index structures we have implemented, the cost of creating theseindices and the size of the resulting indices.As a rst step of processing, we grouped triples about the same subject intovirtual documents using Hadoop. We indexed the data primarily using the ver-tical alternative, but for the Entity Search Track we also added the URI of theobject as an index eld as shown in Figure 2. We have preselected 300 datatypeproperties to be indexed based on the frequency of the properties. (We were re-quired to cap the number of indexed elds due to memory requirements duringindex building.) Object-properties and their values have not been indexed. Wehave segmented literal values into tokens using MG4J’s FastBu eredReader. Wehave blacklisted popular terms and also ignored terms longer than four char-acters that contained only numbers. As the data set contains text in Asianlanguages that would not be queried for and would have required special tok-enization, we also ignored terms with non-ASCII characters. Lastly, we ignoreddocuments longer than 10,000 triples.In addition to the vertical index, we have also used the token eld of ahorizontal index (see Figure 1). Note that the two indices do not necessarilyhave the same contents, because the horizontal index contains the values for alldatatype-properties. We used the horizontal index solely for acquiring globalterm frequency information (see section 3) and document sizes.We have also built a document collection for our own testing using MG4J’sSimpleCompressedDocumentCollection. The building of this collection took con-1
A multi-objective optimization model for minimizing cost, travel time and Co2 emission in an AS/RS

A multi-objective optimization of automated warehouses is discussed and evaluated in the present paper. Since most of the researchers in material handling community had performed optimization of decision variables with single objective function only (usually named with minimum travel time, maximum throughput capacity, minimum cost, maximum energy efficiency, etc.), the multi-objective optimization (cost – travel time – CO2 emission/ energy efficiency) will be presented. For the optimization of decision variables in objective functions, the method with genetic algorithms was used. To find the Pareto optimal solutions, the NSGA II genetic algorithm was used. The main objective of our contribution is to determine the performance of the system according to the multi-objective optimization technique. The results of the proposed model could be useful tool for the warehouse designer in the early stage of warehouse design.
Semantic cluster based Search in UDDI for Health Care Domain

Objective: To improve relevancy and personalization of service discovery process of search engines by introducing intelligence in representation of web service called semantic reputation. Methodology: The discovery and distribution of healthcare biological data through web services is being an important characteristic of today's search engine and this process should be efficiently handled. This paper proposes a semantic cluster based recommendation rules approach called semantic reputation for retrieving tagged biological web services. Textual description of biological web services is retrieved by using the semantic web services clustering approach and Ranking is done by recommendation rule based semantic matching. Findings: Retrieving meaningful information is difficult in today's available search engines. Semantic intelligent representation plays a major role in efficiently retrieving meaningful information intelligently. The proposed approach is implemented and compared with the other similar approaches and obtained the recall value of 97.6% and precision value of 98.5% which is better than the existing approaches comparatively by 10% to 19%. Application/Improvement: Our system supports service requestors to retrieve the services relevantly without deviating them to some other fraudulent web sites which proves to be time consuming and trustworthy. Extended work of this paper is to implement these approaches in extending UDDI for efficient web service discovery.
An analysis of the factors affecting the adoption of cloud consumer relationship management in the machinery industry in Taiwan

In recent years, cloud computing services have developed rapidly and are being widely used, but most applications focus on consumers. However, a few successful cases of enterprise application systems have been constructed and adopted by companies. To understand the factors that affect the adoption by a company of cloud Customer Relationship Management (CRM), which is an enterprise application system, this study used a Technology Organization Environment Framework (TOE) as a theoretical framework, reviewed related literature on the relevant factors affecting the adoption of a cloud service, and then constructed a three-level hierarchical table of the factors. A pairwise comparison questionnaire was then designed and issued to top managers of firms in the machinery industry in Taiwan that have adopted cloud CRM. A fuzzy analytical hierarchy process (FAHP) was then used to calculate the weights of the factors, based on the returned questionnaires. The results show that enterprises in the machinery industry gave priority to the factor, ‘support of senior managers’, followed by the factors ‘organizational policies’ and ‘system information security’. Some implications of the findings are discussed, which can serve as a guide for the successful adoption of cloud CRM in machinery industry if firms allocate their resources appropriately to these important factors. These results can also be used by the service providers of cloud CRM to devise effective marketing strategies.
Tailoring case study research: a strengths approach

This paper presents findings from the process of conducting a qualitative case study of a strengths-based parenting program designed for parents with a mental illness. An overall evaluation of the program was conducted that generated the intended recommendations for enhancing such complex parenting education programs. However, unexpected methodological findings also emerged from the case study method. This article reflects on the strengths-based method used in the research and the understandings gained from using this adaptive process. It was found that using a strengths approach to case study design was not only generative regarding the outcomes of the program but also enabled a sensitive and 'tailored' research experience for the participants. Few targeted parenting support programs exist for parents with a mental illness and even fewer evidence-based research studies exist that offer suggestions on how to conduct strengths-based case study research with participants that are experiencing complex and sensitive needs.
Semi-automatic annotation of semantic relations in a Swiss German sign language lexicon

We propose an approach to semi-automatically obtaining semantic relations in Swiss German Sign Language (Deutschschweizerische Gebardensprache, DSGS). We use a set of keywords including the gloss to represent each sign. We apply GermaNet, a lexicographic reference database for German annotated with semantic relations. The results show that approximately 60% of the semantic relations found for the German keywords associated with 9000 entries of a DSGS lexicon also apply for DSGS. We use the semantic relations to extract sub-types of the same type within the concept of double glossing (Konrad 2011). We were able to extract 53 sub-type pairs.
The Analysis of Compressed Sensing for Total Variation Minimization and Bregman

1) CS introduces a framework for simultaneous sensing and compression of big size vectors that applies in a range of applications including Optical Imaging and Synthetic Aperture Radar. 2) Total variation minimization, split Bregman, linearized Bregman and sparse reconstruction propose extremely efficient methods for solving optimization problems, which transform l1-norm constrained problems into unconstrained problems by adding penalty term. In the paper, the main principles of several algorithms are firstly introduced, then optimization iteration steps for algorithms are presented in detail. 3) Next, to research the performances of the algorithms in terms of the convergence and reconstruction precision, a series of numerical experiments for the above algorithms clearly show visual qualities of reconstructed images.4) we analyze the influence of the parameters u and g on iterative performances as well as the difficulties of controlling parameters, making clear the advantage of The Minimum total variation compared to other algorithms, and the low-complexity of Bregman .
Detection of Assembly Variations for Automatic Program Adaptation in Robotic Welding Systems

This paper proposes a novel approach for detection of assembly variations to adapt the programming of a robotic welding system. A matching process is performed between the different parts of an assembly CAD model and a measured point cloud using an Iterative Closest Point algorithm to determine assembly variations. Experimental validation is performed with an industrial robot equipped with stereo camera and welding gun. A method for accurate calibration of sensor and welding gun is presented as it influences the quality of the welding process. Experimental results show an increased weld seam quality and the robustness of the proposed approach in industrial applications.
Social Network Optimization a New Methaheuristic for General Optimization Problems

In the recent years metaheuristics were studied and developed as powerful technics for hard optimization problems. Some of well-known technics in this field are: Genetic Algorithms, Tabu Search, Simulated Annealing, Ant Colony Optimization, and Swarm Intelligence, which are applied successfully to many complex optimization problems. In this paper, we introduce a new metaheuristic for solving such problems based on social networks concept, named as Social Network Optimization – SNO. We show that a wide range of np-hard optimization problems may be solved by SNO.
Discrete event simulation analysis of a reservation-based, one-way car-sharing system

Abstract We perform a detailed simulation analysis of the operational and financial performance of a hypothetical reservation-based car-sharing system that allows users to make one-way journeys. The system is characterised by a fixed set of stations; a fixed number of cars; a limited number of parking spaces at each station; and an online reservation system that makes vehicle relocations unnecessary except in the case of a cancelled reservation. A discrete event simulation model of the system is developed using C++. The model tracks the state of the online reservation system in continuous time and provides a full animation of vehicle pick-ups, journeys, and returns. Five experiments show how system performance is affected by (i) the total number of vehicles, (ii) the total number of parking spaces, (iii) the allocation of parking spaces among stations, (iv) reservations which include extra parking time, and (v) the distribution of demand among O-D pairs.
Introducing a Gestation Period of Time-Delayed Benefit into an Animat-based Artificial Life Model

Bio-inspired models such as animat agent based artificial life models provide a useful platform for exploring collective and emergent phenomena including decision making, financial markets and resource management systems. Breeding mechanisms whereby new animat agents are introduced into the system provide an important mechanism for the system as a whole to adapt to new or changing circumstances. We explore the consequences of decoupling individual animat behaviours in time from their effect on the species and system as a whole by incorporating a gestation period of time delay between animat breeding and animat birth. The breeding system incorporated in previous models enabled instant births. This allowed prey animats to propagate with no regard to their own safety. This research introduces a gestation period, thus requiring animats to keep themselves alive long enough to successfully reproduce. We discuss the consequences for large scale animat models with bio-inspired selection mechanisms based on predator-prey cycles and a spatial mixing environment.
English-Urdu Religious Parallel Corpus

English-Urdu parallel corpus is a collection of religious texts (Quran, Bible) in English and Urdu language with sentence alignments. The corpus can be used for experiments with statistical machine translation. Our modifications of crawled data include but are not limited to the following: 1- Manually corrected sentence alignment of the corpora. 2- Our data split (training-development-test) so that our published experiments can be reproduced. 3- Tokenization (optional, but needed to reproduce our experiments). 4- Normalization (optional) of e.g. European vs. Urdu numerals, European vs. Urdu punctuation, removal of Urdu diacritics.
Plato: Learning-based Adaptive Streaming of 360-Degree Videos

Interactive applications that come along with 360- degree (or 360) videos have brought immersive experiences to users thanks to the elevated machine computability. In fact, the provision of such high quality of experience (QoE) hinges on the persistent delivery of 360 videos, potentially consuming an excessive need of network bandwidth. To prevent the delivery of entire 360 videos from adversely affecting QoE, tile-based viewport adaptive streaming that divides 360 video chunks into tiles and conveys streams with differentiated quality levels to viewport and non-viewport areas has been regarded as a promising solution. Existing works have been devoted to the design of 1) viewport prediction (VPP) to predict users' viewport orientation due to head movements, and 2) tile bitrate selection (TBS) to determine tile-based bitrates for viewport and non-viewport areas. Despite the heuristic solutions proposed by the existing works, there is lack of knowledge of whether QoE can be enhanced by learning from historical data. In this paper, we propose the system–Plato, to leverage machine learning to tile-based viewport adaptive streaming for 360 videos. In particular, Plato applies long short term memory (LSTM) model to VPP, and uses part of non-viewport areas to help resist prediction errors. In addition, Plato uses real-world traces to train a TBS agent based on reinforcement learning to determine tile bitrates for both viewport and non-viewport areas. Our simulation results show that Plato outperforms existing schemes in various QoE metrics.