Reviewerly/exHarmony huggingface dataset:
CoreNap: Energy Efficient Core Allocation for Latency-Critical Workloads

In data-center servers, the dynamic core allocation for Latency-Critical (LC) applications can play a crucial role in improving energy efficiency under Service Level Objective (SLO) constraints, allowing cores to enter idle states (i.e., C-states) that consume less power by turning off a part of hardware components of a processor. However, prior studies focus on the core allocation for application threads while not considering cores involved in network packet processing, even though packet processing affects not only response latency but also energy consumption considerably. In this paper, we first investigate the impacts of the explicit core allocation for network packet processing on the tail response latency and energy consumption while running LC applications. We observe that co-adjusting the number of cores for network packet processing along with the number of cores for LC application threads can improve energy efficiency substantially, compared with adjusting the number of cores only for application threads, as prior studies do. In addition, we propose a dynamic core allocation, called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoreNap</monospace> , which allocates/de-allocates cores for both LC application threads and packet processing. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoreNap</monospace> measures the CPU-utilization by application threads and packet processing individually, and predicts response latency and power consumption when the combination of core allocation is enforced via a lightweight prediction model. Based on the prediction, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoreNap</monospace> chooses/enforces the energy-efficient combination of core allocation. Our experimental results show that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoreNap</monospace> reduces energy consumption by up to 18.6% compared with state-of-the-art study that adjusts cores only for LC application in parallel packet processing environments.
Preface

“Game AI” usually brings to mind control of opponents and other characters. We are interested in a different way that AI can intersect games: during the design process. How can retrieval, inference, knowledge representation, learning, and search loosen the bottlenecks in the game design process? How can AI be put to use in ideation, prototyping, feedback, visualization, synthesis and verification of designed artifacts (puzzles, missions, maps, mechanics, stories ...)? How can AI provide assistance to game designers and/or share the creative responsibilities in design?
The graduate program in acoustics at Penn State

The Graduate Program in Acoustics at Penn State offers graduate degrees (M.Eng., M.S., Ph.D.) in Acoustics, with courses and research opportunities in a wide variety of subfields. Our 820 alumni are employed around the world in a wide variety of military and government labs, academic institutions, consulting firms, and consumer audio and related industries. Our 40+ faculty from several disciplines conduct research and teach courses in structural acoustics, nonlinear acoustics, architectural acoustics, signal processing, aeroacoustics, biomedical ultrasound, transducers, computational acoustics, noise and vibration control, acoustic metamaterials, psychoacoustics, and underwater acoustics. Course offerings include fundamentals of acoustics and vibration, electroacoustic transducers, signal processing, acoustics in fluid media, sound and structure interaction, digital signal processing, experimental techniques, acoustic measurements and data analysis, ocean acoustics, architectural acoustics, noise control engineering, nonlinear acoustics, outdoor sound propagation, computational acoustics, biomedical ultrasound, flow induced noise, spatial sound and three-dimensional audio, and the acoustics of musical instruments. This poster highlights faculty research areas, laboratory facilities, student demographics, successful graduates, and recent enrollment and employment trends for the Graduate Program in Acoustics at Penn State.
Evaluation of automatically generated tongue surfaces from three-dimensional/four-dimensional ultrasound recordings of sustained vowels

Three-dimensional/four-dimensional (3-D/4-D) ultrasound is capable of imaging almost the entire tongue surface during the production of speech sounds, including sustained vowels. Manual tongue surface segmentation is slow and laborious, but typically yields good results. Automatic tongue surface segmentation is much faster and objective but can result in errors. In this presentation, we quantify the errors in a set of tongue surfaces that were automatically segmented using the previously published 3D-SLURP algorithm implemented in the WASL toolbox for MATLAB. The ultrasound recordings were of 20 children and 40 adults producing the sustained vowel [o:] with and without a narrow straw semi-occlusion in the mouth. Twenty of the 40 adults produced phonation at threshold phonation. Automatic and manual error detection and quantification methods will be compared to evaluate the accuracy of the automated algorithm when applied to the sustained vowel [o:]. [Work supported in part by NSF.]
LOSSLESS COMPRESSION TECHNIQUES: REVIEW, COMPARISON AND ANALYSIS

In the past few years, there has been a transformation in our daily communication method.The transformation is sparked by the rapidly growing communication tools, which is the Internet.The internet has vastly improved the way we communicate as it allows people from various parts of the world to connect with each other on the same medium.Due to the massive amount of data that is being uploaded daily, data compression has become crucial in order to support the limited bandwidth and efficiently shared data over the network.In this paper, we will be discussing various algorithms used in lossless compression.The aim is to discover the most favorable compression technique among these 4: Huffman encoding, Lempel-Ziv-Welch algorithm, Shannon Fano and Run Length Encoding.The experiment will be conducted by using a personal dataset.The algorithms will be measured by their compression ratio, compression speed and saving percentage.The results proved that the Shannon Fano algorithm yields the most optimum results as it has the highest compression speed and one of the highest of compression ratio and saving percentage.
Handwriting Generation and Synthesis using Recurrent Neural Networks

Handwritten text has been a vital mode of communication and a cornerstone of our culture and education for decades, and it is frequently regarded as an art form. It’s been found to help in tasks like taking notes and reading while writing, as well as improving short and long-term memory. The goal of handwriting synthesis is to create artificial text that closely resembles the user’s writing style. Handwriting synthesis not only adds a personal touch or preserves a user’s style, but it also has a number of other applications, such as improving text recognition systems, personalizing fonts, identifying writers, and spreading as the technology gets more popular. This paper presents a deep learning model for synthesizing handwriting text from computer text input. The goal is to create an AI tool that can generate handwriting while keeping a person’s style, which could potentially help people suffering with Dysgraphia. In addition, this paper compares our model to Alex Graves’ existing handwriting model.
Switching DNN for Autonomous Driving System

In an autonomous driving system, building a rigorous object detection model unaffected by conditions, such as weather or time-of-day, is essential for safety. However, as deep learning models are often limited in generalizability, training over the entire data collection can be suboptimal, e.g., daytime training instances hinder the training for nighttime prediction. We call this the curse of multitasking (CoM), which was first observed in multilingual training, where training a multilingual model can be suboptimal, compared to multiple monolingual models. Our contribution is observing CoM in autonomous driving, overcoming the problem by building multiple mono-task models, or specialized experts for each task, then switching models according to the input condition, enhancing the overall effectiveness of the detection model. We show the effectiveness of using the proposed strategy in both YOLOv3 and RetinaNet models on BDD dataset.
Layerable Apps: Comparing Concurrent and Exclusive Display of Augmented Reality Applications

Current augmented reality (AR) interfaces are often designed for interacting with one application at a time, significantly limiting a user’s ability to concurrently interact with and switch between multiple applications or modalities that could run in parallel. In this work, we introduce an application model called Layerable Apps, which supports a variety of AR application types while enabling multitasking through concurrent execution, fast application switching, and the ability to layer application views to adjust the degree of augmentation to the user’s preference. We evaluated Layerable Apps through a within-subjects user study (n=44), compared against a traditional single-focus application model on a split-information task involving the simultaneous use of multiple applications. We report the results of our study, where we found differences in quantitative task performance, favoring Layerable mode. We also analyzed app usage patterns, spatial awareness, and overall preferences between both modes as well as between experienced and novice AR users.
Studi Literatur Tingkat Keanekaragaman Gastropoda sebagai Sumber Belajar Biologi SMA Kelas X pada Materi Keanekaragaman Hayati

Learning resources are one of the important things in improving the learning process. One of the material for high school class X that requires learning resources that come from the environment is the material on biodiversity. Learning practices in schools generally still use conventional learning resources, and have not used the environment as a learning resource. The purpose of the study was to analyze the requirements of biology learning resources based on research results from literature studies. This research is a literature study research, which is looking for relevant references to research on the Literature Study of Gastropod Diversity Level as a Biology Learning Source for Class X High School on Biodiversity Material. Data analysis used is descriptive qualitative analysis with narrative review method. This study uses 10 scientific articles on the diversity of Gastropods. The results of the study through a literature study found that the Gastropod diversity index had a diversity index of high category H' = 3.0468 and 3.0075. Medium category gastropod diversity index H' = 1.9915; 1,695; 1.5858-1.18742; 2,412; 2.232; 2,059; 1.4786-1.9382; 1,226; 1,251; 2,236; 1.22; and 1.39. Low category gastropod diversity index H' = 0.296; 0.180; 0.972; 0.113; and 0.668. The results of the analysis of the potential requirements for learning resources that have been carried out show that the results of research on gastropod diversity meet the requirements as a source for learning biology in high school class X on the subject of biodiversity.
APLIKASI PENDAFTARAN SISWA BARU SEBAGAI SISTEM INFORMASI MANAJEMAN BUKU INDUK SISWA SMK AL QOLAM KOTAAGUNG

Admission of new students is a process found in educational institutions in public or private schools. The process of accepting new students is the beginning of the administrative management of student profiles in schools. In an effort to improve the school administration of SMK Al-Qolam Kotaagung, Tanggamus Regency, trying to use Mobile technology as an effort to improve administration and electronic archives by implementing new Student Admissions with Mobile Web. The research method uses the waterfall stage in application development, in collecting data using the interviews, interviews, and library studies. The result of this research is the creation of a Mobile-based Al-Qolam Kotaagung Vocational New Student Admission application that helps prospective students in online registration with the Mobile application. With this application, prospective new students can fill out the registration form for the registration of new students so that it will be easier to recap the data of prospective students, as well as improve data accuracy. The recorded data will be a reference for re-registration and will then become the main book data for each student, thus providing convenience in the administration of Administrative Management and Academic Management at SMK Al-Qolam Kotaagung.
Integrating with open archive via OAI-PMH provider

The author examines usability of OAI-PMH technology for metadata exchange between the e-libraries and open archives. Research and educational organizations may maintain both e-libraries and e-archives which main contents are new publications and materials in digital formats. However, in most cases, organizational websites do not enable the integrated search simultaneously against both sources. Using OAI-PMH protocol provides several advantages in distributing and promoting information as it offers additional opportunities for indexing by Internet-based search engines, along with extended metadata exchange options. The RNPLS&amp;T has designed the solution that enables to use ALIS as the universal platform for the two technologies, i.e. e-library portal and provider of open archive data, based on OAI-PMH protocol. The key benefit of the solution is the compatibility of the metadata transfer formats with the popular Dspace platform, which enables to use IRBIS64-pluggable databases as open archives. In this case, the IRBIS-based data become available to other open archive systems interactively.
Multi-Document Abstractive Summarization for PPT Generation

In this paper, we aim to generate abstractive summaries of extensive news articles and a scientific corpus and present them in the form of a PowerPoint presentation. We use a pre-trained model run on a dataset comprising Wikipedia articles. This model is then fine-tuned by running it on a variety of datasets made public by the authors—Multi-News, Multi Science. Additionally, we employ the use of the popular Document Understanding Conference (DUC) (NIST Documents Understanding Conference, 2002) dataset as well. Our experiments also involve training the datasets from the ground up. We also compare our results to several publicly available baselines. Finally, the summaries generated through these datasets are passed through a paragraph separating algorithm to generate a PowerPoint presentation.
A Probe into the Guidance of Cultural Programs on Youth Culture—Taking "Classic Chanting" as an Example

Traditional culture has become a hotspot in education in recent years, and cultural programs are the main entertainment channel for the dissemination of traditional culture."Classic Chanting" spreads poetry culture through "harmonious poems and songs".The unique and innovative model has attracted widespread attention from all walks of life and inspired young people.Enthusiasm for Chinese traditional culture, the educational form of entertaining and entertaining enables young people to gain mental inspiration while watching the program, and enhance their sense of experience of traditional culture."Classic Chanting" has played a role in social media education in a media-integrated environment, helping young people to have a deep understanding of traditional culture and bringing many inspirations to young people's education.
The use of computer mathematics systems to teach numerical methods to students at the university

The article is devoted to the use of computer mathematics systems in teaching the discipline "Numerical Methods" to students of all forms of training in the course of Bachelor's Degrees Programs: "Applied Informatics". The features of organizing the educational process are described. A review of domestic and foreign literature on numerical methods with the usage of computer mathematics systems is presented. The functional capabilities of computer mathematics systems and the advantages of their use in the educational process are described. The article presents the results of the pedagogical experiment. Integration of traditional teaching classes and systems of computer mathematics made the teaching process more interesting and productive. It helped to form good knowledge, abilities and skills of the students. The combined use of computer mathematics systems with the LMS Moodle distance learning system has shown great advantages compared with the traditional approach of teaching the discipline "Numerical Methods". This allowed teaching the discipline at higher quality level.
Car, Cyclist and Pedestrian Object Detection Based on YOLOv5

The target detection of car, cyclist and pedestrian is of great significance to the realization of automatic monitoring and artificial intelligence assisted driving system.In order to rapidly detect car, cyclist and pedestrian, You Only Look Once (YOLO) model is applied to car, cyclist and pedestrian detection tasks in this paper.Experimental result shows that among the four versions of YOLOv5, the mean average precision (MAP) is more than 91.7%, and YOLOv5x has the best recognition ability, which reaches 92.6%.Among the three categories, car recognition accuracy is the highest, followed by cyclist and pedestrian.
A rating system for the knowledge assessment within a university graduate competence formation

Assessment of educational achievements of university students on the basis of rating and competence-based approaches is an urgent issue of pedagogy, psychology and methodology and requires research of various approaches and the search for effective methods of such an assessment. The main emphasis in the article is made on the study of the existing problems of the formation of a competence model of a university graduate, the formation of key competencies, the development of professional skills and their assessment. The author proposes a competence model and a method for assessing the level of competence formation of university students based on the apparatus of fuzzy logic, the author proves the expediency of its use, describes the features of analyzing and processing data, identifies further areas of research on this issue.
How to Strengthen the Connection between Universities and Middle Schools in the Context of “Double Reduction”

This paper introduces the current situation of the "Double Reduction" policy, and points out that the key to education lies in internal drive."Double Reduction" is not only a matter for primary and secondary schools in the stage of compulsory education, but also a social change involving the whole education system and affecting family behavior and industry development.Universities should play a more important role in educational reform.From several aspects, this paper discusses how to strengthen the connection between universities and middle schools, and how to make full use of university resources in the "Double Reduction" reform.
A method to solve unbalanced multi-label text classification in practical projects by introducing association information between classes

Training with more data has always been an effective and convenient way of improving performance in the deep learning era. Therefore, in practical engineering projects, data is generally collected as much as possible. However, as data volume and class size increase, maintaining a balanced dataset across a number of classes is challenging because data are always long-tailed in practical engineering projects. As a consequence, the key of the practical deep learning application is to design solutions to solve the long tail distribution problem according to the characteristics of specific projects. At present, many existing methods are often not designed according to actual engineering problems, and there is a certain gap with the actual application scenarios. In this paper, we will design a strategy based on the actual application scenarios and solve the imbalance distribution problem in practical engineering projects by adjusting the classifier according to the data.
Two-Stream Communication-Efficient Federated Pruning Network

Federated learning is a distributed machine learning framework which enables different parties to collaboratively train a model while protecting data privacy and security. This form of privacy-preserving collaborative learning comes at the cost of a significant communication overhead during training. Another key challenge in federated learning is to handle the heterogeneity of local data distribution across parties. To solve these problems, we proposed a novel two-stream communication-efficient federated pruning network (FedPrune), which consists of two parts: in the downstream stage, deep reinforcement learning is used to adaptively prune each layer of global model to reduce downstream communication costs; in the upstream stage, a pruning method based on the proximal operator is proposed to reduce the upstream communication costs as well as limit the drift of the local update, which is robust to non-IID client data. FedPrune is tested on three DNN models and publicly available datasets. The results demonstrate that it can well control the training overhead while still guaranteeing the learning performance.
TOPOLOGICAL FILTER STRUCTURE USING GEOMETRICAL NOISE PREDICTION

New techniques for noise reduction based on geometrical distribution of color image topology is presented. Triangular distribution supported by Monte Carlo simulation for prediction of impulse noise location on distorted images is used. The Geometrical prediction method is applied to the test images to predict the noise location. The distorted portions in the images predicted by this structure are improved by using a proper nonlinear filter. The nonlinear median filter is showing a suitable way for replacement of degraded pixel. The filter is not applied to the not-detected portions of the image to keep it unchanged and avoid the distortion of fine image details and important edges. Experimental results improved this structure comparing with classical noise reduction structures.
Using Atomic Bounds to Get Sub-modular Approximations

Set functions and inferences over them play an important role in many AI-related problems. They are commonly used in kernel-based methods, game theory, uncertainty representation and preference modelling. Of particular importance are additive set-functions and their associated expectation operator, as well as sub-modular functions. However, specifying precisely and completely such set functions can be a daunting task as well as a lot of data and knowledge. When time or information is missing, it may be handy to have approximating tools of such set-functions, that require less information and possibly enjoy nice mathematical properties. In this paper, we show that if the only information we have are atomic bounds of such functions, we can build conservative approximations that are either sub- or super-modular. We then illustrate the potential use of our approximation on a convolution-based signal processing problem.