Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic program analysis for data parallel kernels

It is widely known that GPUs have more computational power and expose a far greater level of parallelism than conventional CPUs. Despite their high potential, GPUs are not yet a popular choice in practice, mainly because of their high programming complexity. The complexity derives from two factors. First, the existing programming models are tied to the underlying GPU architectures. Because of this, GPU programming methodology is different from conventional CPU programming, and forces developers to think in different terms. Second, a lot of architectural details that heavily influence program performance are not exposed to the programmer. The consequence is that in order to get fast programs developers must have a deep understanding of the targeted GPU platform. Moreover, mapping existing sequential programs to GPU is even more difficult. The reason is that not every part of a given program is suitable to be mapped to the GPU. Identifying those suitable parts implies looking for dependencies and recognizing data parallel patterns. This is not an easy task if the program code base is very large. The aim of this thesis is to ease the problem of mapping existing sequential programs to a GPU. To this end, we explore automatic program analyses that enable automatic and guided transformation of sequential programs to data parallel GPU kernels. Our main contributions consist of identification and implementation of key program analyses that enable such transformations. The result is a system that can identify kernel regions in a sequential program, detect GPU specific optimisations and provide additional kernel information that can be used to estimate performance. The work serves as a foundation for an automatic GPU parallelization system.
Why Did I Ask That? The Questions Graduate Teaching Assistants Ask Their Students

This qualitative study examined three graduate teaching assistants (GTAs) and the types of questions used in their teaching. We considered data consisting of video from two class meetings for each GTA. After the first recorded class meetings, each GTA viewed and discussed a video case of college mathematics instruction concerning the concept of slope that illustrated various questioning techniques. Each GTA was then video recorded a second time. The goal of the study was to explore the potential of the video-case professional development activity on GTAs classroom practice in the context of questioning techniques.
Metric Dependent Performance Optimization of Virtual Machine Using Hyper Threading Concept

Accurate estimate of resource demands is crucial for managing and planning resources in various computer systems. Any work done by computer system considered as a path in the potential field of resources. Metrics provide a quantitative basis for the development and validation of models in the development process. Virtual systems and virtualization technology are taking the momentum nowadays in data centers and IT infrastructure models. Performance analysis of such systems is very invaluable for enterprises but yet is not a deterministic process. n this paper we will present an overview of the key requirements and characteristics of virtual systems performance metrics and workload characterization which can be considered one step further in implementing virtual systems benchmark and performance. The benchmark tests are taken to describe the type of system scalability feature being tested, and describe observed behaviour based on the workload. In particular we explore how Hyper-Threading affects the throughput. Keywords: Metric, Virtualization, Hyper-thread, Throughput, Virtual Machine
An Efficient Group Key Management Scheme for Ad Hoc Networks

Ad Hoc networks are characterized by frequently changing network topology. Due to the lack of central authority, forming security association among a group of members in Ad Hoc networks is more challenging than in traditional networks. With the view in mind, group key management plays an important building block of any secure group communication. In this paper, we proposed a group key management based on Identity-based Cryptosystem and Chinese Remainder Theorem. In the proposed scheme, there are no requirements of member serialization and existence of a central entity. Besides this, the scheme the protocol also many highly desirable properties such as contributory and efficient computation of group key, uniform work load for all sensor nodes and efficient support for high dynamics. Compare to other existing group key agreement protocols, the proposed protocol make no assumption about the structure of the underlying wireless network, making it suitable for Ad Hoc networks.
Urban land use mapping with multi-spectral and SAR satellite data using neural networks

Statistical, textural and Gabor features were extracted from integrated multitemporal multispectral TM data and ERS-1 SAR imagery for urban land use mapping. The computed features are first normalised using the SOM algorithm and then a decision tree algorithm is applied for feature selection. The classification procedure was carried out with a multilayer perceptron, trained with the resilient backpropagation algorithm. The authors' results demonstrate the potential of the proposed methodology.
A control allocation approach for energetic swarm control of wheeled mobile robots

In this paper, an energetic swarm controller is developed that controls the swarm temperature, swarm centre position, and swarm potential energy. A sliding control approach is combined with a control allocation process to solve the overactuated control problem. The control allocation problem is solved using nonlinear programming software which allows the optimization problem to be solved with input saturation constraints. Furthermore, a low level trajectory controller based on dynamic feedback linearization is developed in order to improve the trajectory tracking performance of the individual swarm members. Application to a group of wheeled mobile robots is used to demonstrate the approach. Together, these results allow energetic swarm controllers to be implemented on wheeled mobile robot (WMR) systems with uncertainty and input saturation constraints.
Hiding to hedge against information overload

Many workspaces nowadays overload people with information – often with information that is irrelevant to the task at hand. Such information cannot only be distracting but additionally misleading, and can potentially impair the performance in relevant tasks. Here we set out to investigate how higher order cognition is influenced by such irrelevant or misleading information. Specifically we were interested in disentangling distraction effects due to task-irrelevant information from distraction effects due to misleading associations. To this end, we examined the solution rates for Remote Associates Test (RAT) items as a function of the presence of additional irrelevant or misleading word material, presented alongside the RAT items. Solving these kinds of word riddles is considered higher cognition as it is closely related to problem solving in real world scenarios. Additionally we manipulated the expectation of participants towards the nature of additional information across two experiments. In Experiment 1 participants believed that all additional words were irrelevant. In Experiment 2 they thought some of the information might be useful for their task. Alongside other manipulations we hereby ensured an attentional focus on the additional information in Experiment 2. Results showed, participants performed poorer in solving RAT items when irrelevant or misleading words were presented along with the RAT items compared to no additional presentation. Moreover misleading information was additionally interfering, but only if attentively processed. To avoid such distraction and misdirection, future personal information systems like the Semantic Desktop [1-2] can help by detecting and hiding temporarily irrelevant or misleading information.
A Systematic Mapping Study of Software Architectures for Cloud Based Systems

Context: Cloud computing has gained significant attention of researchers and practitioners. This emerging paradigm is being used to provide solutions in multiple domains without huge upfront investment because of its on demand recourseprovisioning model. However, the information about how software systems are constructed for cloud based systems and what architecture approaches are used to build these systems in not available in synthesized form, which makes it hard to find common architecture solutions for building applications for cloud and identify research gaps. Object: The main objective of this study is to systematically identify and analyze the currently published research on the topics related to software architectures for cloudbased systems in order to identify architecture solutions for achieving quality requirements. Method: We decided to carry out a systematic mapping study to find as much peerreviewed literature on the topics related to software architectures for cloud-based systems as possible. This study has been carried out by following the guidelines for conducting systematic literature reviews and systematic mapping studies as reported in the literature. Based on our paper selection criteria defined by the requirements of the study’s objectives and research questions, we have found 86 papers to be included in this study out of initial set of 1491 papers. Results: We have grouped selected papers into different categories of themes including: quality attributes (15%), multi-tenancy (3%), frameworks (3%), workflow based systems (4%), support for hybrid devices (7%), middleware infrastructure for managing services and resources (10%), architecture refactoring considerations for migrating applications to cloud (4%), generic architecture solution (12%), emerging research areas (3%) and cloud application domains (39%). We have described problems being addressed in each category, which architecture solutions have been proposed to solve these problems and technologies that have been used. Conclusions: The selected studies reports challenges and potential solutions related to maintaining underlying infrastructure for supporting large number of users, utilizing cloud computing for making applications available for devices with limited resource, performing sensitive data processing on cloud and achieving service level agreements at different levels or abstractions. Energy optimization, service adaptability, reliability, resource provisioning and service integration are most commonly addressed quality attributes in cloud bases system. Work reported in this study focuses on architectural constructs of cloud-enabled systems reported in literature. We have synthesized architecture approaches proposed in paper into architecture styles
SSCSSC: a tool for the teaching of digital circuits

The authors present a computer program to support the teaching of digital circuits. The program is used to describe, synthesize, and simulate combinational and synchronous sequential logic circuits, and to help students in their first contact with automatic synthesis tools. The program runs on both MS-DOS and UNIX machines, and it can be given freely to students for home use to significantly reduce the crowding in laboratories. Care has been taken to provide a friendly interface, so that the user can immediately concentrate on the study of circuits instead of having to learn a new program. The software described by the authors is currently used as a support tool in the course on Digital Circuits for Electronic Engineers at the University of Pisa. >
Fault-tolerant object on network-wide distributed object-oriented systems for future telecommunications applications

This paper describes a fault-tolerant object using replication of objects in network-wide distributed object-oriented communications systems, and a mechanism for managing multiple objects that execute the target functions in the systems. This mechanism is located in the distributed processing platform that controls the execution of objects. The replication management mechanism combines fault detection using the local timer of each node and a mechanism for maintaining internal state consistency. This mechanism reduces the overhead of replication and at the same time distribute the load. The method for creating the fault-tolerant object and generating its code reduces the cost of design, development, and management of fault-tolerant applications programs by hiding their internal structure. This approach will contribute to flexible construction of multimedia-generation communications software.
Cloud-Resolving Model Simulation and Mosaic Treatment of Subgrid Cloud-Radiation Interaction

Improving the representation of cloud-radiation interaction is a major challenge for the global climate simulation. The development of cloud-resolving models (CRMs) and the extensive Atmospheric Radiation Measurements (ARMs) provide a unique opportunity for shading some lights on this problem. Current general circulation models (GCMs) predict cloud cover fractions and hydrometeor concentrations only in individual model layers, where clouds are assumed to be horizontally homogeneous in a coarse grid. They do not explicitly specify geometric associations or optical variations. Subsequently, clouds within a GCM grid are represented as a single effective volume that impacts radiation using various overlap assumptions (Liang and Wang 1997). On the other hand, the CRMs explicitly resolve convection and mesoscale organization, where cloud microphysical processes and cloud-radiation interactions directly respond to the cloud-scale dynamics (Grabowski et al. 1996, Wu et al. 1999, Wu and Moncrieff 2001). In particular, the fine spatial resolution allows the CRM to more realistically represent the detailed structure of cloud systems, including cloud geometric and radiative properties. The CRM simulations in combination with the ARM measurements thus provide comprehensive datasets, based on which a more realistic GCM parameterization of sub-grid cloud-radiation interactions can be developed.
A Comparison of a Smart City’s Trends in Urban Planning before and after 2016 through Keyword Network Analysis

The aim of this study was to explore the keywords related to smart city concepts, and to understand their flow. This research used a keyword network analysis by collecting keywords from papers published on the web from Scopus, which is an international scholarly papers engine. The data were collected from before and after 2016, and since the amount of data has been growing rapidly after global agreements such as the United Nations’ Sustainable Development Goals (SDGs) in 2015, we attempted to focus on adjacent years of publication. In order to understand the flow of research, we conducted a central analysis, which is widely used in quantitative research relating to social network analysis, and performed cluster analysis to identify relationships with related research. The results of the analysis are represented in the form of network maps, and the role of each keyword was clarified based on these network maps. In addition, the overall flow explained the change of flow through discarded and emerging keywords, and the relationships with related fields were explained through cluster analysis. The findings could serve as a basis for policymakers, urban managers, and researchers seeking a comprehensive understanding of the smart city concept in urban planning areas.
Design techniques for fully differential amplifiers

The authors explore in detail the possible approaches to the design of fully differential amplifiers needed to enhance performance in switched-capacitor circuits. This research has been focused on the behavior of the common-mode feedback circuits. Several structures are evaluated and compared. A strategy is presented that allows the design of differential amplifiers from any given set of specifications. As an illustration a 1-MHz differential amplifier has been designed. <<ETX>>
Parallel stratified and rotating turbulence simulation based on MPIs

We describe a parallel implementation for large-eddy simulation (LES) of the stratified and rotating turbulence based on MPI. The parallelization strategy is specified by eliminating the tridiagonal solver with explicit method and by a domain decomposition for solving the poisson equation. In this simulation we have run on CRAY-T3E under the message passing platform MPI with various domain decompositions. The processing time and speed up with respect to each domain decomposition and the scalability of this parallel code of LES are presented. The results show that we can gain up to 16 times faster speed up on 64 processors with xyz-direction domain decomposition and scalable up to 128/spl times/128/spl times/128 which processing time is almost similar to that of 40/spl times/40/spl times/40 on a single processor machine with a sequential code.
SUPERPIXEL CLASSIFICATION OF HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGE BASED ON MULTI-SCALE CNN AND SCALE PARAMETER ESTIMATION

Abstract. In recent years, considerable attention has been paid to integrate convolutional neural network (CNN) with land cover classification of high spatial resolution remote sensing image. Per-pixel classification method based on CNN (Per-pixel CNN) achieved higher accuracy with the help of high-level features, however, this method still has limitations. Even though per-superpixel classification method based on CNN (Per-superpixel CNN) overcome the limitations of per-pixel CNN, classification accuracy of complex urban is easily influenced by scale effect. To solve this issue, superpixel classification method combining multi-scale CNN (Per-superpixel MCNN) method is proposed. Besides, this paper proposes a novel spatial statistics based method to estimate applicable scale parameter of per-superpixel CNN. Experiments using proposed method were performed on Digital Orthophoto Quarer Quad (DOQQ) images in urban and suburban area. Classification results show that per-superpixel MCNN can effectively avoid misclassification in complex urban area compared with per-superpixel classification method combining single-scale CNN (Per-superpixel SCNN). Series of classification results also show that using the pre-estimated scale parameter can guarantee high classification accuracy, thus arbitrary nature of scale estimation can be avoided to some extent.
AN ANALYSIS OF THE SECOND YEAR STUDENTS’ PROBLEMS AT SMPN 1 PARIAMAN TO UNDERSTAND ARTICLES AND PREPOSITIONS IN SIMPLE SENTENCE

A bstract The purpose of this research was to know the problems of the second year students to understand articles and prepositions of SMPN 1 Pariaman. The design of this research was descriptive research. The population of this research was the second year students of SMPN 1 Pariaman. The number of the population member was 148 students. Thirty of them were taken as sample of this study by using simple cluster random sampling. The instrument used to collect the data in this research was grammar test in the form of completion test. From the result of research, the researcher found that the problems of the second year students of SMPN 1 Pariaman to understand articles and prepositions in simple sentence as follows; there were three problems faced by the students to understand articles. They were article ‘a’ ( 22.7%), article ‘an’ (27.8%), and article ‘the’ (50.7%). There were three problems faced by the students to understand time prepositions. They were time preposition ‘in’ (20%), time preposition ‘on’ (20%), time preposition ‘at’ (60%). And there were three problems faced by the students to understand place prepositions. They were place prepositions ‘in’ (41.3%), place prepositions ‘on’ (33.3%), and place prepositions ‘at’ (45.3%). Based on the results above, it is suggested that the teacher should give more exercises, especially about articles and prepositions to the students. And it is suggested to the students that they should pay more attention to understand articles and prepositions in simple sentence. Key Words: Problem, Structure, Articles and Prepositions
MixFX-SCORE: Heterogeneous Fixed-Point Compilation of Dataflow Computations

Mixed-precision implementation of computation can deliver area, throughput and power improvements for dataflow computations over homogeneous fixed-precision circuits without any loss in accuracy. When designing circuits for reconfigurable hardware, we can exercise independent control over bitwidth selection of each variable in the computation. However, selecting the best precision for each variable is an NP-hard problem. While traditional solutions use automated heuristics like simulated annealing or integer linear programming, they still rely on the manual formulation of resource models, which can be tedious, and potentially inaccurate due to the unpredictable interactions between different stages of the FPGA CAD flow. We develop MixFX-SCORE, an automated tool-flow based on FX-SCORE fixed-point compilation framework and simulated annealing, to address this challenge. We outsource error analysis (Gappa++) and resource model generation (Vivado HLS, Logic Synthesis, Xilinx Place-and-Route) to external tools that offer a more accurate representation of error behavior (backed by proofs) and resource usage (based on actual utilization). We demonstrate 1.1 -- 3.5x LUTs count savings, 1 -- 1.8x DSP count reductions, and 1 -- 3.9x dynamic power improvements while still satisfying the accuracy constraints when compared to homogeneous fixed-point implementations.
Autonomous blimp control using model-free reinforcement learning in a continuous state and action space

In this paper, we present an approach that applies the reinforcement learning principle to the problem of learning height control policies for aerial blimps. In contrast to previous approaches, our method does not require sophisticated hand- tuned models, but rather learns the policy online, which makes the system easily adaptable to changing conditions. The blimp we apply our approach to is a small-scale vehicle equipped with an ultrasound sensor that measures its elevation relative to the ground. The major problem in the context of learning control policies lies in the high-dimensional state-action space that needs to be explored in order to identify the values of all state-action pairs. In this paper, we propose a solution to learning continuous control policies based on the Gaussian process model. In practical experiments carried out on a real robot we demonstrate that the system is able to learn a policy online within a few minutes only.