Reviewerly/exHarmony huggingface dataset:
Automated selection of build configuration based on machine learning

Nowadays, High-Performance Computing (HPC) application codes are becoming larger and more complex. There is an increasing demand for optimizing such codes to reduce execution time and hence achieve high performance. Compilers are used to translate the codes into executable programs. Many optimization techniques have already been incorporated into compilers, and the performance gain by each combination of optimization techniques strongly depends on the code. By selecting a compiler and its option flags, programmers can change the compilation process and its behaviors so that an HPC application can achieve high performance. However, it is not easy to express the selection of a compiler and its option flags as an explicit algorithm. So far, a compiler and its option flags have been selected in a trial-and-error fashion, which is labor-intensive, time-consuming, and potentially leading to in-appropriate selection. In addition, configuring the build process for more complex and heterogeneous HPC systems, in which different kinds of processors such as accelerators are employed, becomes even more challenging. Therefore, we propose an automatic compilation process configuration approach based on machine learning that uses dynamic information obtained by executing the code. A feature selection method is proposed to remove the irrelevant attributes for the machine learning model. A neural network is used to learn useful features from data to predict the appropriate build configuration for individual application codes. The evaluation results show that the proposed method outperforms other existing methods in the literature. The proposed feature selection helps the machine learning model characterize the application codes more accurately. Furthermore, it is demonstrated that the proposed approach can select the optimal configuration for each of different processors.
Keynote Talk 1: Efficient DNN Training at Scale: from Algorithms to Hardware

The recent popularity of deep neural networks (DNNs) has generated a lot of research interest in performing DNN-related computation efficiently. However, the primary focus of systems research is usually quite narrow and limited to inference (i.e., how to efficiently execute already trained models) and image classification networks as the primary benchmark for evaluation. In this talk, we will demonstrate a holistic approach to DNN training acceleration and scalability starting from the algorithm, to software and hardware optimizations, to special development and optimization tools.
GrAPL 2022 Keynote Speaker: GraphBLAS Beyond Simple Graphs

Presents the conference keynote speech.
IPDPS 2022 PhD Forum

The IEEE Computer Society has created the Technical Consortium on High Performance Computing (TCHPC) to advance and coordinate work in the field of high performance computing networking, storage, and analysis concepts, technologies and applications, and to expand the IEEE's role in this interdisciplinary and pervasive field. The Consortium has launched an Education and Outreach Initiative to coordinate activities, information, and best practices around HPC education/outreach across its member technical committees and the broader community. This includes the coordination of student activities across conferences, and the lead chair for this initiative is also chair the IPDPS 2022 PhD Forum.
EDAML 2022 Invited Speaker 3: Scalable ML Architectures for Real-time Energy-efficient Computing

Technological advancements have led to a proliferation machine learning systems to assist humans in a wide range of tasks. However, we are still far from accurate, reliable, and resource-efficient operations for many of these systems. Despite the strengths of convolutional neural networks (CNNs) for object recognition, these discriminative techniques have several shortcomings that leave them vulnerable to exploitation from adversaries. In addition, the computational cost incurred to train these discriminative models can be quite significant. Discriminative-generative approaches offers a promising avenue for robust perception and action. Such methods combine inference by deep learning with sampling and probabilistic inference models to achieve robust and adaptive understanding. In this talk, I will present our work on implementing a scalable, computationally efficient generative inference algorithm in hardware that can achieve real-time results in an energy efficient manner. I will also discuss future directions in designing scalable and efficient ML algorithms in hardware more broadly.
Message from the GrAPL Workshop Chairs

GrAPL 2022: Workshop on Graphs, Architectures, Programming, and Learning, brings together two closely related topics --how the synthesis (representation) and analysis of graphs is supported in hardware and software, and the ways graph algorithms interact with machine learning to learn structured models and representations.Driven by the natural outgrowth of a wide range of methods used in large-scale data analytics workflows, GrAPL's scope is broad.GrAPL'2022 is the fourth edition of the merger between two successful workshop series at IPDPS: GABB and GraML It is our great pleasure to welcome you to GrAPL 2022.GrAPL focuses on early dissemination of research on the theory, model-based analysis, simulation, and analysis of massive, linked data corpora and related machine learning applications.In this workshop we are interested in graphs, how their synthesis (representation) and analysis is supported in hardware and software, and the ways graph algorithms interact with machine learning.The interaction can be one of two wayswith graph algorithms benefiting learning applications or with learning used to improve graph analytic kernels.This year we welcomed a regular contributor, Gabor Szarnyas, to serve as the local chair since the conference was originally scheduled to be in person in Lyon, France.For the third year in a row, however, GrAPL will be a virtual workshop.Hopefully next year we will meet in person.Putting together GrAPL 2022 has been a team effort.We are grateful to the technical program committee for providing high-quality feedback in paper reviews, as well as to the authors for the high-quality submissions.Each paper was reviewed by at least 3 (in some cases, 4) reviewers with conclusive decisions.After a thorough peer review process and rigorous discussions, the program committee selected 12 papers (9 regular and 3 short papers) from 19 submissions.We thank Tim Mattson of the Parallel Computing Lab at Intel Research for agreeing to deliver the keynote for GrAPL'22.In his talk, Tim will review work applying the GraphBLAS to problems that go beyond simple graphs.
AI for Datacenter Optimization (ADOPT'22)

Artificial intelligence (AI) and Machine learning (ML) workloads are an increasingly larger share of the compute workloads in traditional High-Performance Computing (HPC) centers and commercial cloud systems. This has led to a new focus on approaches to optimized resource usage, allocations and deployment of new AI frameworks. With these changes, there is a need to better understand HPC/cloud/datacenter operations with the goal of developing improved scheduling policies, identifying inefficiencies in resource utilization, energy/power consumption, failure prediction, as well as identifying policy violations. Simultaneously, there is a growing interest in addressing the increasing power requirements for AI training and inference operations in HPC/Cloud environments with the goal of minimizing the accompanying effects on climate change. There are several publicly available datasets such as the Argonne Leadership Computing Facility dataset, Blue Waters System Monitoring dataset, Philly traces by Microsoft Inc., the Google Cluster Usage traces and the Atlas cluster trace repository that can enable this research. In addition, the recently released MIT Supercloud Dataset includes monitoring logs from the MIT Supercloud system which include time series of CPU and GPU usage by jobs, memory usage, file system logs, and physical monitoring data. This workshop will focus on AI/ML approaches to datacenter operations, power/energy modeling, scheduling, optimization and monitoring tools. In addition to these topics, this workshop solicits papers that address any of the challenges in enabling large-scale AI in HPC/Cloud environments, including:
Optimization of Compiler-Generated OpenCL CNN Kernels and Runtime for FPGAs

We translate frozen CNN models into OpenCL kernels with the TVM compiler and then use Intel's OpenCL SDK to compile to an FPGA bitstream. We improve the performance of the generated base hardware with optimizations that increase parallelism, reduce memory access latency, and save on-chip resources. We automate these optimizations in TVM and evaluate them by generating accelerators for LeNet-5, MobileNetV1 and ResNet-34 on an Intel Stratix 10SX. The optimizations improve the performance of the generated accelerators by up to 846 × over the base ones. The optimized accelerators are up to 4.57 × faster than TensorFlow on CPU, 3.83 × faster than single-threaded TVM and are only 0.34 × slower than TVM with 56 threads. Our optimized kernels also outperform ones generated by similar approaches that use high-level synthesis, but they underperform ones that utilize hand-optimized designs. Thus, our approach is most useful in environments that benefit from increased performance and fast prototyping, realizing the benefits of FPGAs without hardware design expertise.
4th Workshop on Parallel AI and Systems for the Edge

Applications involving voluminous data require that the computing be performed as close to the data source as possible, due to communication constraints, privacy and sensitivity of data, latency and liveliness requirements, or costs of moving data.Given the growth in such application scenarios and the recent advances in algorithms and techniques, machine learning and inference at the edge are unfolding and growing at a rapid pace.In support of these applications, a wide range of computing platforms (hardware and software) are venturing farther away from the center and enabling computation closer to the physical world, often at the interface to the physical world.Such edge computing platforms greatly vary in computational resources, spanning from intelligent embedded devices like smart cameras, powerful on-premise systems.
Single Image Dehazing Using Local Detail Enhancement

Most existing single image dehazing algorithms require the estimation of atmospheric light using simple procedures based on error-prone assumptions. In this letter, a new dehazing method based on local detail enhancement is proposed to estimate atmospheric light and transmission map by considering local detail enhanced images as quasi-haze-free images. The proposed method is based on the decomposition model that interprets the transmission map as a base layer and the haze-free image as a detail-like layer. From a hazy image, local detail information is extracted using the local detail enhancement approach based on an edge preserving filter. Then, atmospheric lights and transmission maps for each color channel are estimated using Koschmieder’s law. The transmission map is refined using the dark channel prior and a guided filter. The experimental results show the proposed method can remove the layer of haze effectively and produce better performance than existing dehazing methods.
Media Literacy in Greece during the COVID-19 Pandemic

In the digital knowledge economy, especially in times of crisis like the COVID-19 pandemic, media literacy, apart from being known as a key element for a critical analysis of the news and information ecosystem, is hastily upgraded as a viable solution for e-learning, distance education, homeschooling, and home entertainment for children and youth, as well as a tool for work from home, e-business, and e-coaching for adults. From a definitional point of view, media literacy is an umbrella concept that expands cross-thematically to various areas of knowledge, communication, and information. Thus, a preferred policy framework would be a combination of an intersectoral developmental agenda that, like concentric circles –—spinning in different directions simultaneously—–engages social, educational, media, and civic duties. Such duties may be the right to freedom of expression, the right to seek valuable information and content, and the right to access and evaluate it toward UNESCO-led Media and Information Literacy (MIL) societies. This paper focuses on the Greek reaction to the pandemic with regard to media and digital literacy toward setting a sustainable policy infrastructure, inspired by the vast challenges and policies driven on an EU level that favor media literacy.
FIRMS’ PERFORMANCE AND INNOVATION: EVIDENCE FROM SMALL AND MEDIUM ENTERPRISES (SMES) IN NIGERIA

The effect of innovations on the performance of SMEs in Nigeria was studied in this study using firm-level data from the Enterprise Survey Panel Data for the years 2007-200. Meanwhile, to investigate the relationships between innovation outcomes and firms’ performance, we used static panel data models. The Hausman test statistic, which Hausman created, was used to choose between fixed and random effects estimators. The result showed that 52.05 percent of the 1729 companies polled claimed they had developed a new or significantly improved product, and 52.54 percent said they had developed a new or significantly better process. In the last three years, however, less than half of the organizations (41.87 percent) had undergone organizational transformation. Finally, we noticed that there was a transition from one form of innovation to another throughout time. Furthermore, there was a significant and favorable link between innovation outcomes and R&amp;D spending, as well as employee training. The studies also demonstrated a positive and statistically significant link between firm’s productivity and innovation outcomes. As a result, we recommend that the government encourage strategic coordination of both human and non-human capital in SMEs, such as R&amp;D personnel, FDI inflows, technology, and information. Increased access to funds for research and innovation in formal institutional contexts, as well as policy packages that are highly supportive of innovation
Evaluation of different extractors of features at the level of sentiment analysis

Sentiment analysis is the process of recognizing and categorizing the emotions being expressed in a textual source. Tweets are commonly used to generate a large amount of sentiment data after they are analyzed. These feelings data help to learn about people's thoughts on a various range of topics. People are typically attracted for researching positive and negative reviews, which contain dislikes and likes, shared by the consumers concerning the features of a certain service or product. Therefore, the aspects or features of the product/ service play an important role in opinion mining. Furthermore to enough work being carried out in text mining, feature extraction in opinion mining is presently becoming a hot research field. In this paper, we focus on the study of feature extractors because of their importance in classification performance. The feature extraction is the most critical aspect of opinion classification since classification efficiency can be degraded if features are not properly chosen. A few scientific researchers have addressed the issue of feature extraction. And we found in the literature that almost every article deals with one or two feature extractors. For that, we decided in this paper to cover all the most popular feature extractors which are BOW, N-grams, TF-IDF, Word2vec, GloVe and FastText. In general, this paper will discuss the existing feature extractors in the opinion mining domain. Also, it will present the advantages and the inconveniences of each extractor. Moreover, a comparative study is performed for determining the most efficient combination CNN/extractor in terms of accuracy, precision, recall, and F1 measure.
ERROR ANALYSIS ON THE INTRODUCTION OF ENGLISH ACADEMIC WRITING BY CHINESE NON-ENGLISH MAJOR POSTGRADUATES AND WAYS TO IMPROVE THEIR WRITING ABILITIES

The ability to write English academic paper is a basic language skill that postgraduate students should master. This article was intended to analyze the errors in English academic writing made by Chinese non-English major postgraduates and find out ways to improve students’ writing abilities. The author selected one class randomly from the first-year postgraduate students of non-English major at North China Electric Power University (NCEPU). The class consisted 48 students and all of them could participate on the research. This study used quantitative and qualitative research methods in analyzing the data. The instrument of this research was iWrite 2.0 (an online system for automatically correcting English papers) correction and then the author collected the types and frequency of the errors. According to the results of iWrite 2.0, the author collected the error types into the following eight categories: subject-predicate disagreement, run-on sentences, missing conjunctions, incorrect use of nouns in singular and plural forms, verb tense errors, misuse of determiners, punctuation problems, and spelling errors. The results of this research showed that the main reason for the errors was the difference between the Chinese and English ways of thinking. In response, the author proposed three ways to help students improve their academic writing skills, namely, enhancing students’ basic knowledge and application ability of English, improving students’ subject knowledge and professional ability, and giving feedback from teachers.
Comparison of Image Enhancement Algorithms for Improving the Visual Quality in Computer Vision Application

Computer vision has its numerous real-world applications on Visual Object Tracking which includes humancomputer interaction, autonomous vehicles, robotics, motionbased recognition, video indexing, surveillance and security, human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security.The factors affecting the tracking process is due to low illumination, haze and cloudy environment and noisy environment.In this paper, we aim to extensively review the latest trends and advances in adaptive enhancement algorithm and evaluate the performance using Full reference like, SSIM (Structure Similarity Index Measure), MS-SSIM (Multi-scale Structure Similarity Index Measure), ESSIM (Edge Strength Structural Similarity Index), FSIM (Feature Similarity Index Measure), VIF (Visual Information Fidelity), CW-SSIM (complex wavelet structural similarity), UQI (Universal Quality Index), IEF (Image Enhancement Factor), IQI (Image Quality Index), EME (Enhancement Measurement Error), CVSI (Contrast and Visual Salient Information), MCSD (Multiscale contrast similarity deviation), NQM (Noise Quality Measure), Gradient Magnitude Similarity Mean (GMSM), Gradient Magnitude Similarity Deviation (GMSM) and no-reference image quality measures Perception based Image Quality Evaluator (PIQE), Blind/Reference less Image Spatial Quality Evaluator (BRISQUE), Naturalness Image Quality Evaluator (NIQE), Average Gradient (AG), Contrast, Information Entropy (IE), Lightness order Error (LOE).The main purpose of adaptive image enhancement is to smooth the uniform area and sharpen the border of an image to improve its visual quality.In this paper, fourteen image enhancement algorithms were tested on LoL dataset to benchmark the time taken to process them and their output quality was evaluated.Results from this study will give insights to image analysts for selecting image enhancement algorithms which acts as a pre-processing stage for Visual object Tracking.
A Revival of Joseon Poetry and the Literary Significance of Poetry Journal Sigeonseol in the Late 1930s focusing on a coterie of the first issue and the trend of published works

Sigeonseol (1936.11~1940.6) is a professional poetry magazine that published the 8th volume, participated in a total of 87 poets, and published about 269 works in the late 1930s. A recent study discussed the overall outline and main characteristics of the magazine. However, the character and performance of the magazine and its historical value still needed to be studied more abundantly. Therefore, based on the recently published original text, this study summarized basic matters such as determining the list of participating poets and cataloging works. And, I studied the cotrie of the first issue, which had not been discussed before, and discussed the meaning of its composition. And the major trends of works published in Sigeonseol were classified and their literary historical significance was considered. At the start of the magazine, based on Kim Hae-gang’s literary network, Kim Chang-sul, Kim Byeong-ho, and Kim Hae-gang, who were KAPF writers, and others such as Kim Nam-in and Jeong Kang-seo who maintained proletarian literary consciousness were formed. Through this, the magazine tried to have an identity as a progressive poetry magazine.&#x0D; In the socio-cultural situation of Joseon under the influence of Japanese imperialist fascism that led to the Sino-Japanese War (1937) and the Pacific War in the late 1930s, Sigeonseol attempted a new construction of progressive Joseon poetry. The lyrical poems that contained the inner self of the individual, published in the magazine, represent the sentiments of the time, and the creative imagination of the poets looking at the realities of the people’s life expressed the values of rural Joseon and the subjectivity of workers.&#x0D; Reflecting the historical experiences of ‘migration’ and ‘wandering’, the work revealed the plight of the migrants at the border and Manchuria in the late 1930s and the sufferings of living abroad. The works containing the symbols of youth and nature reflected the purpose and direction of Sigeonseol to revive Joseon literature, and clearly showed the response of Korean poetry to overcome the historical situation of the time and obtain a future prospect.&#x0D; In this way, various and dynamic works were published in Sigeonseol, which competed with other media and filled the gaps in the media, producing rich results of Joseon poetry literature in the late 1930s.
Pelatihan Troubleshooting laptop Alumni SMK Se-Lombok Timur

With the rapid development of technology, especially the development of computers, both hardware and software, users must add more knowledge to running computers. Not only operating it but also installing and troubleshooting so that later it will be more accessible when faced with obstacles. SMK alumni mainly contribute to unemployment due to very few industry-standard job opportunities. Additional skills/competencies are needed to equip the alumni, one of which is by providing Troubleshooting knowledge. Troubleshooting is solving problems with damage to Computer/Laptop hardware. This training purpose is to increase the competence of alumni to be independent or prepare for the world of work. The training method applied in this training is the lecture and practice method. The activity took place with 30 registrants and was selected based on terms and conditions; the results were that only 5 participants from SMK 3 Selong and SMK 1 Pringgabaya passed. This activity was initially carried out in June at the Lombok Center IT. The result of this training activity is that participants can assemble and install laptops and install open laptop mainboard components. Participants can measure the signal on the laptop to know the damage