Reviewerly/exHarmony huggingface dataset:
MARTINI: The Little Match and Replace Tool for Automatic Application Rewriting with Code Examples

Rewriting code for cleanliness, API changes, and new programming models is a common, yet time-consuming task. Localized or syntax-based changes are often mechanical and can be automated with text-based rewriting tools, like sed. However, non-localized or semantic-based changes require specialized tools that usually come with complex, hard-coded rules that require expertise in compilers. This means techniques for source rewriting are either too simple for complex tasks or too complex to be customized by non-expert users; in either case, developers are often forced to manually update their code instead. This work describes a new approach to code rewriting which exposes complex and semantic-driven rewrite capabilities to users in a simple and natural way. Rewrite rules are expressed as a pair of parameterized “before-and-after” source code snippets, one to describe what to match and one to describe what the replacement looks like. Through this novel and user-friendly interface, programmers can automate and customize complex code changes which require a deep understanding of the language without any knowledge of compiler internals. As our prototype, MARTINI, is built on top of Clang, we can (conceptually) handle all Clang’s input languages, including CUDA, SYCL, and many other C/C++-based interfaces, like Kokkos and OpenMP. To showcase usability, we implemented the clang-tidy rule “modernize-use-nullptr” and a simple instrumentation example. To further illustrate the potential of MARTINI, we reimplemented HIPIFY, which translates CUDA to HIP. Compared to hipify-perl and hipify-clang from HIPIFY, our version is easier to implement, more understandable, and customizable. The latter allows us to outperform HIPIFY-generated code by slightly modifying the translation rules of MARTINI-HIPIFY.
Accelerating Parallel Operation for Compacting Selected Elements on GPUs

Compacting is a common and heavily used operation in different application areas like statistics, database systems, simulations and artificial intelligence. The task of this operation is to produce a smaller output array by writing selected elements of an input array contiguously back to a new output array. The selected elements are usually defined by means of a bit mask. With the always increasing amount of data elements to be processed in the different application areas, better performance becomes a key factor for this operation. Thus, exploiting the parallel capabilities of GPUs to speed up the compacting operation is of great interest. In this paper, we present different optimization approaches for GPUs and evaluate our optimizations (i) on a variety of GPU platforms, (ii) for different sizes of the input array, (iii) for bit distributions of the corresponding bit mask, and (iv) for data types. As we are going to show, we achieve significant speedups compared to the state-of-the-art implementation.
Construction of a Knowledge Map Based on Text CNN Algorithm for Maritime English Subjects

Knowledge map is a new method of knowledge management with the information revolution. This paper is aimed at forming a systematic and standardized huge redundant knowledge structure, which can be used to mine the knowledge structure and the relationship between knowledge and visualize it in a graphical way, in order to obtain more representative information and improve the classification accuracy of text classification model. In this paper, a knowledge map construction method based on the Text CNN algorithm is proposed for the subject of Nautical English. It is of practical significance and academic value to make use of knowledge map to study Chinese Maritime English, which is helpful to the development of Chinese Maritime English and provides guidance. In order to maintain the diversity of particle swarm optimization, the Text CNN algorithm is combined with the construction of Maritime English subject knowledge map, and the network parameters and structure are optimized. Using knowledge map to study China Maritime English has important practical significance and academic value and has certain guiding significance for the development of China Maritime English.
The Effects of Population on Economic Growth In Nigeria

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL The Effects of Population on Economic Growth In Nigeria 22 Pages Posted: 18 Jul 2022 See all articles by Meshach ZhizhiMeshach ZhizhiKogi State University - Kogi State University, studentsRashidat Lamidi OwudaKogi State University Date Written: january 25, 2019 Abstract Population and economic growth commonly entails to the study of the costs of population trends on human welfare, socio-economic development, and the natural environment. Though there is a long ritual of scholarship on the costs of population trends, starting with Malthus to modern researchers. However, modern research on population and its impacts on development lack a cumulative research tradition and dominant paradigm. The main limitation of the literature, which lies largely outside the body of modern demography, embraces a "doomsday scenario" that assumes that population growth pose a grave consequence on human wellbeing and its natural environment. Consequently there are various factors that lead to population increase in Nigeria such as religion and lack of education. However, the outcomes of population increase are identified as environmental degradation, human congestion, and high unemployment rate. This paper concludes that they could be a positive growth in Nigeria if the recommendations outlined here are strictly adhered to such as education of the girl child, strict adherence to family planning and measures should be adopted to check excessive environmental degradation rate in Nigeria. Keywords: Population growth, economic development Suggested Citation: Suggested Citation Zhizhi, Meshach and Owuda, Rashidat Lamidi, The Effects of Population on Economic Growth In Nigeria (january 25, 2019). Available at SSRN: https://ssrn.com/abstract=4146547 Meshach Zhizhi (Contact Author) Kogi State University - Kogi State University, students ( email ) Rashidat Lamidi Owuda Kogi State University ( email ) Km 1. Ankpa RoadAnyigba, KS Dekina 234Nigeria Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 1 Abstract Views 8 PlumX Metrics Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo5 in 0.190 seconds
Extreme climatic events in northern Chile and their impact on the geochemical composition of the Huasco River

The climatic variations observed in the last 10 years in northern Chile have influenced the dispersion of metals and metalloids accumulated in the transverse valleys. This study is a spatio-temporal geochemical analysis of sediment and water samples from the Huasco River, Chile, obtained during April 2007 and January 2008. There were differences in elemental concentrations along the river between the study years, demonstrating that climate change has affected the study area. These climatic variations were related to ENSO-type cycles: during periods of lower rainfall, enrichment of sediments was observed; in times of greater rainfall, thaws, and water flows, the water samples contained higher elemental concentrations. This pattern is consistent with data obtained in 2015–2016. Elemental provenance analysis indicated that the sediments of the Huasco River were markedly enriched in Ca, Co, Cu, Zn, Cd, Bi, Ag, Pb and La. This enrichment is consistent with the lithology of the study area, which is characterized by the presence of numerous cobalt deposits associated with copper, silver, nickel and cadmium. The results obtained for water samples are consistent with those for sediment samples. We conclude that ionic transfer from sediments to water takes place, caused by chemical dissolution of the minerals that make up the sediments.
Grouping Student Awareness on Security Of E-Learning Information Using Fuzzy C-Means Method

Many educational institutions have been forced to adapt how they present the teaching and learning process, including the creation of appropriate learning media due to the current Covid-19 pandemic. This is accomplished through the development of an integrated online learning system known as E-Learning. Aside from all of the benefits and positive outcomes that E-Learning can give, there are also drawbacks to student information security, such as assignment theft, piracy of E-Learning, the misuse of passwords by irresponsible students, and other problems. To anticipate this, the researcher intended to group students' awareness of their respective E-Learning information security by using the Fuzzy C Means method. Fuzzy C Means uses a fuzzy grouping model so that data can be members of all classes or clusters formed with different degrees or levels of membership between 0 to 1. The sample used to represent the population is 20 students of STIKOM PGRI Banyuwangi, Indonesia. The results obtained are to find out how well the grouping of student awareness clusters on E-Learning information security. There are 3 clusters of student E-Learning information security awareness. Cluster 1 consists of students with high awareness, cluster 2 contains categories of students with low awareness, and the third cluster consists of students with moderate awareness.
Evaluation of the Tectonic Boundaries Using Potential Data at Al-Tharthar Lake and Surrounding Area, Middle of Iraq

The gravity and magnetic data of Al-Tharthar lake and surrounding areas in central Iraq were considered to study the tectonic situation in the area. The residual anomalies were separated from regional using the polynomial method with three orders. The Total Horizontal Derivative anomalies of gravity and magnetic are used with the residual anomalies to identify the faults and their trends with the basement rocks. The predominant trends are approximately N45W and N60E. Many faults and tectonic boundaries were detected and traced on the tectonic map of the study area.
A Hybrid Genetic Service Mining Method Based on Trace Clustering Population

Service mining aims to use process mining for the analysis of services, making it possible to discover, analyze, and improve service processes. In the context of Web services, the recording of all kinds of events related to activities is possible, which can be used to extract new information of service processes. However, the distributed nature of the services tends to generate large-scale service event logs, which complicates the discovery and analysis of service processes. To solve this problem, this research focus on the existing large-scale service event logs, a hybrid genetic service mining based on a trace clustering population method (HGSM) is proposed. By using trace clustering, the complex service system is divided into multiple functionally independent components, thereby simplifying the mining environment; And HGSM improves the mining efficiency of the genetic mining algorithm from the aspects of initial population quality improvement and genetic operation improvement, makes it better handle large service event logs. Experimental results demonstrate that compare with existing state-of-the-art mining methods, HGSM has better characteristics to handle large service event logs, in terms of both the mining efficiency and model quality.
Adaptive Modulation and Rectangular Convolutional Network for Stereo Image Super-Resolution

Deep learning based stereo image super-resolution algorithm can make use of the additional information in the stereo image pairs to improve the quality of the reconstructed images. However, extracting the similarity information of one image to another precisely is challenging when partially occluded regions exist in stereo images. In this paper, we propose an adaptive modulation alignment mechanism to modulate the aligned features calculated from the parallax attention mechanism and effectively deal with the inaccuracy caused by occlusion. Furthermore, because disparities of stereo image exist only along the epipolar line, we use rectangular convolution kernel in some convolution layers, to extend the receptive field horizontally. Finally, experimental results demonstrate that our method achieves state-of-the-art performance on the Middlebury, KITTI 2012 and KITTI 2015 stereo benchmarks.
Automatic Complexity Analysis of Integer Programs via Triangular Weakly Non-Linear Loops

Abstract There exist several results on deciding termination and computing runtime bounds for triangular weakly non-linear loops (twn-loops). We show how to use results on such subclasses of programs where complexity bounds are computable within incomplete approaches for complexity analysis of full integer programs. To this end, we present a novel modular approach which computes local runtime bounds for subprograms which can be transformed into twn-loops. These local runtime bounds are then lifted to global runtime bounds for the whole program. The power of our approach is shown by our implementation in the tool $$\textsf {KoAT}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>KoAT</mml:mi> </mml:math> which analyzes complexity of programs where all other state-of-the-art tools fail.
Cyclic Proofs, Hypersequents, and Transitive Closure Logic

Abstract We propose a cut-free cyclic system for Transitive Closure Logic (TCL) based on a form of hypersequents , suitable for automated reasoning via proof search. We show that previously proposed sequent systems are cut-free incomplete for basic validities from Kleene Algebra (KA) and Propositional Dynamic Logic ( $$\mathrm {PDL}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>PDL</mml:mi> </mml:math> ), over standard translations. On the other hand, our system faithfully simulates known cyclic systems for KA and $$\mathrm {PDL}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>PDL</mml:mi> </mml:math> , thereby inheriting their completeness results. A peculiarity of our system is its richer correctness criterion, exhibiting ‘alternating traces’ and necessitating a more intricate soundness argument than for traditional cyclic proofs.
Guiding an Automated Theorem Prover with Neural Rewriting

Abstract Automated theorem provers (ATPs) are today used to attack open problems in several areas of mathematics. An ongoing project by Kinyon and Veroff uses Prover9 to search for the proof of the Abelian Inner Mapping (AIM) Conjecture, one of the top open conjectures in quasigroup theory. In this work, we improve Prover9 on a benchmark of AIM problems by neural synthesis of useful alternative formulations of the goal. In particular, we design the 3SIL (stratified shortest solution imitation learning) method. 3SIL trains a neural predictor through a reinforcement learning (RL) loop to propose correct rewrites of the conjecture that guide the search. 3SIL is first developed on a simpler, Robinson arithmetic rewriting task for which the reward structure is similar to theorem proving. There we show that 3SIL outperforms other RL methods. Next we train 3SIL on the AIM benchmark and show that the final trained network, deciding what actions to take within the equational rewriting environment, proves 70.2% of problems, outperforming Waldmeister (65.5%). When we combine the rewrites suggested by the network with Prover9, we prove 8.3% more theorems than Prover9 in the same time, bringing the performance of the combined system to 90%.
Term Orderings for Non-reachability of (Conditional) Rewriting

Abstract We propose generalizations of reduction pairs, well-established techniques for proving termination of term rewriting, in order to prove unsatisfiability of reachability (infeasibility) in plain and conditional term rewriting. We adapt the weighted path order, a merger of the Knuth–Bendix order and the lexicographic path order, into the proposed framework. The proposed approach is implemented in the termination prover , and the strength of our approach is demonstrated through examples and experiments.
Spherical Tree-Structured SOM and Its Application to Hierarchical Clustering

When analyzing high-dimensional data with many elements, a data visualization that maps the data onto a low-dimensional space is often performed. By visualizing the data, humans can intuitively understand the structure of the data in the high-dimensional space. The self-organizing map (SOM) is one such data visualization method. We propose a spherical tree-structured SOM (S-TS-SOM), which speeds up the search for winner nodes and eliminates the unevenness of learning due to the position of the winner nodes by placing the nodes on a sphere and applying the tree search method. In this paper, we confirm that the S-TS-SOM can achieve the same results as a normal spherical SOM while reducing the learning time. In addition, we confirm the granularity of clustering on the tree structure of the S-TS-SOM.
An Efficient Person Search Method Using Spatio-Temporal Features for Surveillance Videos

Existing person search methods mainly focus on searching for the target person using database images. However, this is different from real-world surveillance videos which involve a temporal relationship between video frames. To solve this problem, we propose an efficient person search method that employs spatio-temporal features in surveillance videos. This method not only considers the spatial features of persons in each frame, but also utilizes the temporal relationship of the same person between adjacent frames. For this purpose, the spatial features are extracted by combining Yolo network with Resnet-50 model, and the temporal relationship is processed by gated recurrent unit. The spatio-temporal features are generated by the following average pooling layer and used to represent persons in the videos. To ensure search efficiency, locality sensitive hashing is used to organize massive spatio-temporal features and calculate the similarity. A surveillance video database is also constructed to evaluate the proposed method, and the experimental results demonstrate that our method improves search accuracy while ensuring search efficiency.
An Area-Optimized and Power-Efficient CBC-PRESENT and HMAC-PHOTON

This paper introduces an area-optimized and power-efficient implementation of the Cipher Block Chaining (CBC) mode for an ultra-lightweight block cipher, PRESENT, and the Keyed-Hash Message Authentication Code (HMAC)-expanded PHOTON by using a feedback path for a single block in the scheme. The proposed scheme is designed, taped out, and integrated as a System-on-a-Chip (SoC) in a 65-nm CMOS process. An experimental analysis and comparison between a conventional implementation of CBC-PRESENT/HMAC-PHOTON with the proposed feedback basis is performed. The proposed CBC-PRESENT/HMAC-PHOTON has 128-bit plaintext/text and a 128-bit secret key, which have a gate count of 5683/20,698 and low power consumption of 1.03/2.62 mW with a throughput of 182.9/14.9 Mbps at the maximum clock frequency of 100 MHz, respectively. The overall improvement in area and power dissipation is 13/50.34% and 14.87/75.28% when compared to a conventional design.
An Ontology Based Multilayer Perceptron for Object Detection

In object detection, spatial knowledge assisted systems are effective.Object detection is a main and challenging issue to analyze object-related information.Several existing object detection techniques were developed to consider the object detection problem as a classification problem to perform feature selection and classification.But these techniques still face, less computational efficiency and high time consumption.This paper resolves the above limitations using the Fuzzy Tversky index Ontology-based Multi-Layer Perception method which improves the accuracy of object detection with minimum time.The proposed method uses a multilayer for finding the similarity score.A fuzzy membership function is used to validate the score for predicting the burned and non-burned zone.Experimental assessment is performed with different factors such as classification rate, time complexity, error rate, space complexity, and precision by using the forest fire dataset.The results show that this novel technique can help to improve the classification rate and reduce the time and space complexity as well as error rate than the conventional methods.
Human and Machine Vision Based Indian Race Classification Using Modified-Convolutional Neural Network

The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue, we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India, referring to human vision.We have created an Automated Human Intelligence System (AHIS) to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient (MCC) of 0.92 and 0.84, respectively, on the validation set, outperforming the traditional Convolutional Neural Network (CNN).The CNN-Contoured Face (CNN-FC) model is developed to train contoured face images to investigate the influence of face shape.Finally, to cross-validate the accuracy of these models, the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%, the Modified-CNN (M-CNN) model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work, and it will be available to the research community.