Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Patterns: Infrastructure and Examples

Modern interactive systems can be incredibly complex, with a variety of screens, menus, widgets, etc. available to the user. Due to this, modelling these interactive systems can also be incredibly complex and while there are techniques to help overcome this, it can often lead to radically different models depending on the modeller. This thesis explores the use of two design patterns created in order to help simplify modelling interactive systems. In the process of doing this, we first explore μ-Charts and its semantics, which are defined in Z, in order to understand its capabilities. We then discover a feature we name the Return feature, which we break down into two parts, Return Home and Return Back, and create patterns in order to concisely model them. Finally, we test these patterns and evaluate the tools we used to create the μ-charts, translate them into their Z semantics and test them.
Remote inspection with multi-copters, radiological sensors and SLAM techniques

Activated material can be found in different scenarios, such as in nuclear reactor facilities or medical facilities (e.g. in positron emission tomography commonly known as PET scanning). In addition, there are unexpected scenarios resulting from possible accidents, or where dangerous material is hidden for terrorism attacks using nuclear weapons. Thus, a technological solution is important to cope with fast and reliable remote inspection. The multi-copter is a common type of Unmanned Aerial Vehicle (UAV) that provides the ability to perform a first radiological inspection in the described scenarios. The paper proposes a solution with a multi-copter equipped with on-board sensors to perform a 3D reconstruction and a radiological mapping of the scenario. A depth camera and a Geiger-Muler counter are the used sensors. The inspection is performed in two steps: i) a 3D reconstruction of the environment and ii) radiation activity inference to localise and quantify sources of radiation. Experimental results were achieved with real 3D data and simulated radiation activity. Experimental tests with real sources of radiation are planned in the next iteration of the work.
Ergonomics Design on Bottom Curve Shape of Shoe-Last Based on Experimental Contacting Pressure Data

Comfortable shoes design was depended on the bottom shape of shoes and its relative last. Mostly, the curve of bottom last plays important roles in shoes design research. To date, it was known that the morphology of shoe last bottom had a great influence to user’s health and comfort. In this paper, key characteristic parameters were used to control the 3-D digital model of the shoe last curves, and optimized design was determined through analyzing the dynamic contacting pressure experimental data between the shoe and the foot, the reported comfort degree of the user’s psychological evaluations was also acquired subsequently. Finally, the experimental results show the effectiveness of the proposed method while compared to the traditional methodologies. Comfort shoes design will have potential benefits for human health.
Latency-Tolerant Distributed Shared Memory For Data-Intensive Applications

Latency-Tolerant Distributed Shared Memory For Data-Intensive Applications Jacob Eric Nelson Co-Chairs of the Supervisory Committee: Professor Luis Ceze Computer Science and Engineering Professor Mark Oskin Computer Science and Engineering Grappa is a modern take on software distributed shared memory (DSM) for in-memory dataintensive applications. Grappa enables users to program a cluster as if it were a single, large, non-uniform memory access (NUMA) machine. Performance scales up even for applications that have poor locality and input-dependent load distribution. Grappa addresses deficiencies of previous DSM systems by exploiting application parallelism, trading off latency for throughput. We evaluate Grappa with an in-memory map/reduce framework (10× faster than Spark [117]); a vertex-centric framework inspired by GraphLab (1.33× faster than native GraphLab [68]); and a relational query execution engine (12.5× faster than Shark [39]). All these frameworks required only 60-690 lines of Grappa code.
A Survey On Interference In Wireless Ad-Hoc Networks

Energy utilization in general and interference in particular being among the most critical issues in wireless ad-hoc networks. The Interference reduction is through the topology control, which seeks to establish a strong network while still keeping the interference at a minimum. It conserves energy by either reducing the transmission power for each node or preserving energy-efficient routes for the entire network. There is a tradeoff between energy efficiency of the nodes and routes in the topology. In addition, it may consume considerable energy to maintain the topology due to node mobility. In this paper describes the plain definition of interference and compare the model for interference in wireless ad-hoc networks
Being an E-Teacher: Preparing the ESL Teacher to Teach English with Technology

In response to the extensive use of technology in the field of education particularly in English language teaching (ELT) context, both the pre-service and in-service ESL teachers are expected to use ICT tools in their teaching and learning environment. It is important to have a closer look at the use of ICT tools and the ease of access to the internet to see the opportunities to harness the technology resources for effective blended learning. Recently, although many studies revealed merely to promote the student’s self-directed learning and the successful of CALL, it is the teacher as a key role to blend such technologies into an effective teaching strategy. This study strives to develop a conceptual framework for teaching English with technology. The framework delineates six perennial aspects of e-teacher development; they are; (1) teacher technology competency, (2) professional use of ICT tools, (3) professional use of the Internet, (4) web-based technology skills, (5) ESL teacher’s digital literacy, and (6) ESL materials and resources development. This concept will bring many contributions to the way ESL teacher exploit the technology resources for an adaptive and interactive teaching strategy. This paper also provides the implications for the practice, teacher, and student
Classification of non-chronic and chronic kidney disease using SVM neural networks

Chronic kidney disease (CKD) refers to the failure of the renal functionalities that leads to the deposition of wastes, electrolytes and other fluids in the body. It is very important to recognize the symptoms that cause the CKD and pathological blood and urine test indicates the key attributes. It is well fact that one has to undergo dialysis due to renal failure. The severity level of disease can be predicted as well as classified using appropriate computer aided quantitative tools. This specific study discusses the classification of chronic and non-chronic kidney disease NCKD using support vector machine (SVM) neural networks. The simulation study makes use of UCI repository CKD datasets with n=400. In order to train to train the attributes of kidney dialysis four cases were considered by including the nominal and numerical values. A radical basis kernel function was employed to train SVM. The performance of the proposed scheme is evaluated in terms of the sensitivity, specificity and classification accuracy. Results reveal an overall classification accuracy of 94.44% was obtained by combining 6 attributes. It can be concluded that the SVM based approach found to be a potential candidate for classification of CKD and NCKD.
Robot-trifle: A robotic system for automated combinatorial growth phenotyping

This project, robot-trifle, is an analysis pipeline for streamlining the automated construction of minimal chemically-defined media for microorganisms known to grow in culture. The first part of the pipeline analyzes known growth data to produce media elimination experiments that are planned and executed by the second and third sections. This software package aims to streamline metabolic profiling by automating the extensive bookkeeping and manual labor required to perform dozens of independent growth experiments. The metabolic profile of an organism can help inform metabolic reconstructions, community metabolism analysis, and genetic metabolism inference.
An Adaptive Scheme for Learning the Probability Threshold in Pattern Recognition

The statistical minimum risk pattern recognition problem, when the classification costs are random variables of unknown statistics, is considered. Using medical diagnosis as a possible application, the problem of learning the optimal decision scheme is studied for a two-class twoaction case, as a first step. This reduces to the problem of learning the optimum threshold (for taking appropriate action) on the a posteriori probability of one class. A recursive procedure for updating an estimate of the threshold is proposed. The estimation procedure does not require the knowledge of actual class labels of the sample patterns in the design set. The adaptive scheme of using the present threshold estimate for taking action on the next sample is shown to converge, in probability, to the optimum. The results of a computer simulation study of three learning schemes demonstrate the theoretically predictable salient features of the adaptive scheme.
Data confidentiality

When releasing data to the public, data disseminators typically are required to protect the confidentiality of survey respondents' identities and attribute values. Removing direct identifiers such as names and addresses generally is not sufficient to eliminate disclosure risks, so that data must be altered before release to limit the risks of unintended disclosures. When intense data alteration is needed to ensure protection, the quality of the released data can be seriously degraded. This article reviews a disclosure limitation approach called synthetic data, in which values of confidential data are replaced with simulations from statistical models. Theoretical and empirical investigations have shown that synthetic data approaches have the potential to result in higher data quality than other disclosure limitation procedures, particularly when intense data alteration is necessary. The article discusses the main variants of synthetic data approaches, namely full synthesis and partial synthesis. It includes discussions of synthetic data generation and disclosure risk assessment. WIREs Comp Stat 2011 3 450–456 DOI: 10.1002/wics.174
Rancang Bangun Sistem Kendali Laju Alir Fluida Pada Dual Reservoir Berbasis Arduino-LabView

Sistem kendali ketinggian level air pada sebuah tangki banyak digunakan pada industri. Perancangan dan pembuatan simulasi sistem pengatur ketinggian air pada dua tangki telah dilakukan menggunakan mikrokontroler arduino uno R3, relay, pompa air, sensor aliran YF-S201dan program LABVIEW . Sistem ini menggunakan sensor laju alir YF-S201untuk mengatur debit air yang masuk ke dalam tangki dan sensor ultra sonik untuk mengukur ketinggian level air. Karena sistem ini dibuat sebagai alat simulasi, maka data kecepatan laju alir dan ketinggian level air dapat dipantau menggunakan komputer. Pengambilan data dilakukan dengan membaca sensor-sensor oleh mikrokontroler yang kemudian dikirim secara serial ke komputer menggunakan program LabVIEW. Komunikasi antara mikrokontroler dan komputer menggunakan mode firmata yang sudah terintegrasi dengan LabVIEW, sehingga pembuatan program hanya dilakukan pada sisi komputer saja. Sedangkan pada sisi mikrokontroler hanya perlu menambahkan sub program firmata yang sudah dibuat oleh arduino. Perhitungan kecepatan laju alir, debit, ketinggian level air dan penampilan data serta kontrol ketinggian level air dilakukan pada program LabVIEW. Sensor water flow YF-S201 dapat digunakan untuk mengukur kecepatan laju alir air (fluida) pada drain system dan operation reservoir terimplementasikan dengan menunjukkan kestabilan pembacaan dengan rata-rata tegangan (2,96V) dan flow rate (13,40 L/det) pada drain system. Rata-rata tegangan (2,80V) dan flow rate (12,70 L/det) pada operation reservoir
T2LD - An automatic framework for extracting, interpreting and representing tables as Linked Data

We present an automatic framework for extracting, interpreting and generating linked data from tables. In the process of representing tables as linked data, we assign every column header a class label from an appropriate ontology, link table cells (if appropriate) to an entity from the Linked Open Data cloud and identify relations between various columns in the table, which helps us to build an overall interpretation of the table. Using the limited evidence provided by a table in the form of table headers and table data in rows and columns, we adopt a novel approach of querying existing knowledge bases such as Wikitology, DBpedia etc. to figure the class labels for table headers. In the process of entity linking, besides querying knowledgebases, we use machine learning algorithms like support vector machine and algorithms which can learn to rank entities within a given set to link a table cell to entity. We further use the class labels, linked entities and information from the knowledge bases to identify relations between columns. We prototyped a system to evaluate our approach against tables obtained from Google Squared, Wikipedia and set of tables obtained from a dataset which Google shared with us.
Frequency Domain vs. Time Domain Filter Design of RRC Pulse Shaper for Spectral Confinement in High Speed Optical Communications

In this work, we present two design methods of the root raised cosine (RRC) pulse shaper in the transmitter of a Nyquist wavelength division multiplexing (WDM) system. Spectral shaping is one of the techniques needed to meet the requirement for high spectral efficiency in high bandwidth applications. A frequency domain (FD) design and a time domain (TD) design of the RRC filter are given and compared based on their length and their spectral shape. The spectral shape of a frequency domain RRC filter desgined with double degrees of freedom as the time domain finite impulse response (FIR) RRC filter has side lobes with less power. Additionally, the root sum squared values of the intersymbol interference (ISI) with an FD design is lower than with a TD FIR design.
Implementation of UML2.0 Based Change Proneness Prediction in OO Software through Dependency

Predicting change prone class in software is a difficult software engineering process. In order to keep up with the pace of modern day expansion, change in any software is inevitable Software enhancement and defects are two main reasons for software change . In the earlier research, of predicting change prone classes the stress was given only on static characteristics rather than dynamic characteristics.In this paper we aim to predict change prone classes using execution time of methods, trace events and dependencies between classes. Software prediction models based on these results can help us identify change prone classes of software which would lead to more rigorous testing and better results. We evaluate dependency between classes using UML2.0 sequence and class diagrams. Further, the results are obtained using a hospital application and implementing in java
Algorithmes parallèles à grain adaptatif - Application à la parallélisation de gzip

Nous proposons un schéma algorithmique générique original qui exploite un ordonnancement glouton des processus réalisé par le système sous-jascent pour contrôler la granularité du parallélisme en cours d’exécution. Le schéma est basé sur le couplage de deux algorithmes, l’un séquentiel, l’autre parallèle à grain fin. La génération de parallélisme n’est effectuée qu’en cas d’inactivité d’un processeur. Lors de l’exécution sur un nombre restreint voire variable de ressources, ce schéma permet ainsi de limiter le surcoût lié à la génération de parallélisme, sans limiter le degré de parallélisme potentiel. Il est particulièrement adapté à la résolution de problèmes pour lesquels la parallélisation entraîne malgré un gain de temps une pénalité en nombre d’opérations ou en performance ; nous appliquons ce schéma à la parallélisation du code séquentiel gzip qui implémente la méthode de compression de Lempel-Ziv, problème -complet considéré difficilement parallélisable.
Distributed skip list in fine-grain message passing interface : implementation and analysis of a dictionary data structure that supports range queries

With scalability in mind, we have implemented a pure message-passing distributed data structure ideal for range queries. The structure is a distributed skip list that takes full advantage of Fine-Grain MPI to execute on a variety of scales ranging from a single multicore to multiple machines across clusters. The skip list data structure provides parallel implementation of range queries. Our implementation architecture is based on several services layered on top of each other that simplifies the effort required in building distributed code. Unlike concurrent data structures the distributed skip list operations are deterministic and atomic. The layered service architecture of our implementation exposes several control parameters that make it easy to distribute load, have operation flow control, vary granularity at different layers and tune performance to the underlying machine and network. Range-queries are implemented in a way that parallelizes the operation and takes advantage of the recursive properties of the skip list structure. We investigate a shortcut mechanism that alleviates the bottleneck at the head and introduces semantic trade offs between performance and consistency. We report on the performance of the skip list on a medium size cluster of two hundred cores with twenty thousand processes.
The Knowledge Graph for End-to-End Learning on Heterogeneous Knowledge

In modern machine learning,raw data is the preferred input for our models. Where a decade ago data scientists were still engineering features, manually picking out the details we thought salient, they now prefer the data in their raw form. As long as we can assume that all relevant and irrelevant information is present in the input data, we can design deep models that build up intermediate representations to sift out relevant features. However, these models are often domain specific and tailored to the task at hand, and therefore unsuited for learning on heterogeneous knowledge: information of different types and from different domains. If we can develop methods that operate on this form of knowledge, we can dispense with a great deal more ad-hoc feature engineering and train deep models end-to-end in many more domains. To accomplish this, we first need a data model capable of expressing heterogeneous knowledge naturally in various domains, in as usable a form as possible, and satisfying as many use cases as possible. We argue that the knowledge graph is a suitable candidate for this data model. We further discuss some of the promises and challenges of this approach, and how we are currently broadening our efforts to multi-modal knowledge graphs.
A Game Theoretic Approach to Demand Side Management in Smart Grid with Multiple Energy Sources and Storage

A smart grid is an advancement in electrical grid which includes a variety of operational and energy measures. To utilize energy distribution in an eﬃcient manner, demand side management has become the fore-runner. In our research paper, we use game theory as a tool to model our system as a Stackelberg game. We make use of diﬀerent energy sources like solar energy, energy from battery and energy from the provider to run appliances of a subscriber. We consider the scenario where the subscriber can give excess energy that is generated, back to the grid, thereby reducing the load on the grid during peak hours. We design a pricing scheme to calculate the utilities of the subscriber and the provider and show how our model maximizes the utility of the entire system, thereby showing the existence of a Nash equilibrium.
Shape detection of object behind thin medium using ultrasonic sensors

This research aims to develop a novel method for recognizing the shape of an object located behind thin medium utilizing combination of ultrasonic sensor signal and edge detection technique. This system is extensively designed so that it can be easily mounted on a surveillance autonomous ground vehicle and is robust for operating in a hazardous environment. Once the autonomous ground vehicle reaches near the hidden objects, the proposed ultrasonic sensor system will identify shapes of the objects. Generally, the number of different sensors mounted on the robot and the number of tasks related to navigation, exploration and detection operation make the design of the autonomous ground vehicle control system very complex. The proposed ultrasonic sensing system for an autonomous ground vehicle provides two-dimensional shape information with low computational cost and will not add significant computational burden to the autonomous ground vehicle’s overall control system. At first, a mathematical model for the ultrasonic sensor is created to find the relationships between the attenuation coefficient, the transmission coefficient, the thickness of the medium and ultrasonic wavelength. The mathematical model is followed by numerical simulation to establish several design parameters. Finally, a prototype for the proposed shape detection system is developed using the kHz range off-the-shelf ultrasonic sensors and edge detection technique. Experimental results confirmed that the accuracy of the proposed mathematical model and shape of an object located behind a thin medium could be estimated successfully. It was concluded from the results that the developed novel method was useful for identifying objects located behind thin media and this will help in conducting the further development of research for surveillance and rescue operation using the autonomous ground vehicle.
Exploring contact points of interactive media in context of fashion market: A qualitative study

The emergence of evolving path to purchase leads to gaining a clearer understanding of revolutionized digital channels. Customer engagement in today’s electronic word is happening through interactive media contact points. This study investigates various interactive media contact points in context of fashion market. This research was based on qualitative approach using sequential incident technique to understand all contact points and episodes encountered during the customer journey. The study presents detailed analysis of semistructured interviews. The findings uncovered forty interactive media contact points. The results highlight areas of online customer journey and provide comprehensive understanding of relationship between different stages of an online customer journey. The findings contribute to the growing area of interactive media marketing research. The study provides direction for scholars and practitioners to design interactive media contact points map for fashion brands. Corresponding author: Ridhima Bhanot Sharma Email addresses for corresponding author: ridhima1511@gmail.com First submission received: 30th August 2017 Revised submission received: 30th October 2017 Accepted: 18th December 2017 1.