Reviewerly/exHarmony huggingface dataset:
Large Language Models Effectively Leverage Document-level Context for Literary Translation, but Critical Errors Persist

Large language models (LLMs) are competitive with the state of the art on a wide range of sentence-level translation datasets. However, their ability to translate paragraphs and documents remains unexplored because evaluation in these settings is costly and difficult. We show through a rigorous human evaluation that asking the GPT-3.5 (text-davinci-003) LLM to translate an entire literary paragraph (e.g., from a novel) at once results in higher-quality translations than standard sentence-by-sentence translation across 18 linguistically-diverse language pairs (e.g., translating into and out of Japanese, Polish, and English). Our evaluation, which took approximately 350 hours of effort for annotation and analysis, is conducted by hiring translators fluent in both the source and target language and asking them to provide both span-level error annotations as well as preference judgments of which system’s translations are better. We observe that discourse-level LLM translators commit fewer mistranslations, grammar errors, and stylistic inconsistencies than sentence-level approaches. With that said, critical errors still abound, including occasional content omissions, and a human translator’s intervention remains necessary to ensure that the author’s voice remains intact. We publicly release our dataset and error annotations to spur future research on the evaluation of document-level literary translation.
SummIt: Iterative Text Summarization via ChatGPT

Existing text summarization systems have made significant progress in recent years, but typically generate summaries in a single step. The one-shot summarization setting is sometimes inadequate, however, as the generated summary may contain hallucinations or overlook important details related to the reader’s interests. In this paper, we address this limitation by proposing SummIt, an iterative text summarization framework based on large language models like ChatGPT. Our framework enables the model to refine the generated summary iteratively through self-evaluation and feedback, closely resembling the iterative process humans undertake when drafting and revising summaries. Furthermore, we explore the potential benefits of integrating knowledge and topic extractors into the framework to enhance summary faithfulness and controllability. We evaluate the performance of our framework on three benchmark summarization datasets through empirical and qualitative analyses. We also conduct a human evaluation to validate the effectiveness of the model’s refinements and find a potential issue of over-correction.
MEAL: Stable and Active Learning for Few-Shot Prompting

Few-shot classification has made great strides due to foundation models that, through priming and prompting, are highly effective few-shot learners. However, this approach has high variance both across different sets of few shots (*data selection*) and across different finetuning runs (*run variability*). This is problematic not only because it impedes the fair comparison of different approaches, but especially because it makes few-shot learning too unreliable for many real-world applications. To alleviate these issues, we make two contributions for more stable and effective few-shot learning: First, we propose novel ensembling methods and show that they substantially reduce *run variability*. Second, we introduce a new active learning (AL) criterion for *data selection* and present the first AL-based approach specifically tailored towards prompt-based learning. In our experiments, we show that our combined method, MEAL (**M**ultiprompt finetuning and prediction **E**nsembling with **A**ctive **L**earning), improves overall performance of prompt-based finetuning by 2.3 points on five diverse tasks. We publicly share our code and data splits in https://github.com/akoksal/MEAL.
Addressing Bio-Cybersecurity Workforce Employee Shortages in Biotechnology and Health Science Sectors in the U.S.

Abstract The ever-increasing demand for employees with cybersecurity and artificial intelligence expertise in healthcare technology roles has resulted in an unprecedented skills shortage. Historically Black Colleges and Universities (HBCUs) and Historically Women’s Colleges (HWCs) can significantly develop under-represented and diverse talent in this field. As bio-cybersecurity concerns widen, there is an urgent need for individuals with skillsets related to the security of biological payloads and systems. In addition, the inclusion of traditionally under-represented groups into the cybersecurity workforce is necessary to ensure the diversity of skills and security knowledge needed to combat cyberattacks. This paper will discuss how HBCUs and HWCs can contribute to diversifying the cybersecurity workforce, their role in developing artificial intelligence (AI) and bio-cybersecurity talent, and how they can provide an educational platform for new and under-represented talent in the field. It will also address the need for experienced artificial intelligence experts, bio-cybersecurity professionals, educators, and policymakers to facilitate the integration of cybersecurity experts into the traditional biological workforce. Ultimately, this paper will demonstrate how the inclusion of diverse and traditionally under-represented individuals in bio-cybersecurity and artificial intelligence (AI) will benefit the security infrastructure of biological systems.
SCRIMP: Scalable Communication for Reinforcement- and Imitation-Learning-Based Multi-Agent Pathfinding

Trading off performance guarantees in favor of scalability, the Multi-Agent Path Finding (MAPF) community has recently started to embrace Multi-Agent Reinforcement Learning (MARL), where agents learn to collaboratively generate individual, collision-free (but often suboptimal) paths. Scalability is usually achieved by assuming a local field of view (FOV) around the agents, helping scale to arbitrary world sizes. However, this assumption significantly limits the amount of information available to the agents, making it difficult for them to enact the type of joint maneuvers needed in denser MAPF tasks. In this paper, we propose SCRIMP, where agents learn individual policies from even very small (down to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\mathrm{x}3$</tex> ) FOVs, by relying on a highly-scalable global communication mechanism based on a modified transformer. We further equip agents with a state-value-based tie-breaking strategy to further improve performance in symmetric situations, and introduce intrinsic rewards to encourage exploration while mitigating the long-term credit assignment problem. Empirical evaluations on a set of experiments indicate that SCRIMP can achieve higher performance with improved scalability compared to other state-of-the-art learning-based MAPF planners with larger FOVs, and even yields similar performance as a classical centralized planner in many cases. Ablation studies further validate the effectiveness of our proposed techniques. Finally, we show that our trained model can be directly implemented on real robots for online MAPF through high-fidelity simulations in gazebo.
Performance Characterization of Using Quantization for DNN Inference on Edge Devices

Quantization is a popular technique used in Deep Neural Networks (DNN) inference to reduce the size of models and improve the overall numerical performance by exploiting native hardware. This paper attempts to conduct an elaborate performance characterization of the benefits of using quantization techniques-mainly FP16/INT8 variants with static and dynamic schemes-using the MLPerf Edge Inference benchmarking methodology. The study is conducted on Intel x86 processors and Raspberry Pi device with ARM processor. The paper uses a number of DNN inference frameworks, including OpenVINO (for Intel CPUs only), TensorFlow Lite (TFLite), ONNX, and PyTorch with MobileNetV2, VGG-19, and DenseNet-121. The single-stream, multi-stream, and offline scenarios of the MLPerf Edge Inference benchmarks are used for measuring latency and throughout in our experiments. Our evaluation reveals that OpenVINO and TFLite are the most optimized frameworks for Intel CPUs and Raspberry Pi device, respectively. We observe benefits of using quantization for these optimized frameworks with no loss in accuracy except for the static quantization techniques. For example, INT8-based quantized models deliver 3.3x and 3.7x better performance over FP32 using OpenVINO on Intel CPU and TFLite on Raspberry Pi device, respectively, for the MLPerf offline scenario. To the best of our knowledge, this paper is the first one that presents a unique characterization study characterizing the impact of quantization for a range of DNN inference frameworks-including OpenVINO, TFLite, PyTorch, and ONNX-on Intel x86 processors and Raspberry Pi device with ARM processor using the MLPerf Edge Inference benchmark methodology,
Integration of Computer Vision with Analogical Reasoning for Characterizing Unknowns

Current state-of-the-art artificial intelligence struggles with accurate interpretation of out-of-library (OOL) objects. One method proposed remedy is analogical reasoning (AR), which utilizes abductive reasoning to draw inferences on an unfamiliar scenario given knowledge about a similar familiar scenario. Currently, applications of visual AR gravitate toward analogy-formatted image problems rather than to computer vision data sets. The Image Recognition Through Analogical Reasoning Algorithm (IRTARA) approach described herein shows how AR can be leveraged to improve computer vision in OOL situations. IRTARA produces a word-based term frequency list that characterizes the OOL object of interest. To evaluate the quality of the results of IRTARA, both quantitative and qualitative assessments are used, including a baseline to compare the automated methods with human-generated results. Fifteen OOL objects were tested using IRTARA, which showed consistent results across all three evaluation methods on the objects that performed exceptionally well or poorly overall.
Evaluating Effects of the Payment Ecosystem on Central Bank Digital Currency Adoption and Design

CBDC has become a discussion topic with worldwide economic and societal relevance. Payment system efficiency is a top driver for digital currency adoption. We examine literature gaps for payment ecosystem impact on CBDC adoption and design, and conduct test for 65 World Bank countries. We measure payment alternatives and preferences impacts and estimate least squares and limited dependent variables regressions. CBDC adoption is driven by payment ecosystem, alternative characteristics, e-money usage, perceived safety, and infrastructure. This signals that user familiarity with digital payment methods, trust in authorities, and structural capabilities support CBDC adoption. Adoption is inversely related to alternatives that dampen the need for CBDC. Transaction volume and trust in authorities are linked to choosing a central database, not a digital ledger technology (DLT) infrastructure. Deviations occur from ambiguous effects of cash and DLT usage on CBDC adoption and industry concentration, architecture, and linkages.
Random Projection with Filtering for Nearly Duplicate Search

High dimensional nearest neighbor search is a fundamental problem and has found applications in many domains. Although many hashing based approaches have been proposed for approximate nearest neighbor search in high dimensional space, one main drawback is that they often return many false positives that need to be filtered out by a post procedure. We propose a novel method to address this limitation in this paper. The key idea is to introduce a filtering procedure within the search algorithm, based on the compressed sensing theory, that effectively removes the false positive answers. We first obtain a sparse representation for each data point by the landmark based approach, after which we solve the nearly duplicate search that the difference between the query and its nearest neighbors forms a sparse vector living in a small ℓp ball, where p ≤ 1. Our empirical study on real-world datasets demonstrates the effectiveness of the proposed approach compared to thestate-of-the-art hashing methods.
A Neural-Network-Based Eye Pupil Reaction Model for Use with Television Programs

If the effects of variations in brightness can be controlled, it is possible to use changes in pupil size for the evaluation of educational TV programs. This paper describes an improved, neural-network-based method for studying pupil reactions and the feasibility of evaluating TV programs by investigating changes in pupil size. This neural-network pupil reaction model uses M-sequences and Markov-sequences to register changes in pupil size based upon responses to changes in brightness, and this paper shows it to be more effective in removing the effects of brightness on pupil size than other previously reported methods. The authors also discuss methods for removing the influence of brightness from pupil response, as well as methods for evaluating changes in pupil size. It is also shown that this new method permits the objective evaluation of TV programs through the measurement of changes in pupil size while viewing TV programs.
Discovering Constraints for Inductive Process Modeling

Scientists use two forms of knowledge in the construction ofexplanatory models: generalized entities and processes that relatethem; and constraints that specify acceptable combinations of thesecomponents. Previous research on inductive process modeling, whichconstructs models from knowledge and time-series data, has relied onhandcrafted constraints. In this paper, we report an approach todiscovering such constraints from a set of models that have beenranked according to their error on observations. Our approach adaptsinductive techniques for supervised learning to identify processcombinations that characterize accurate models. We evaluate themethod's ability to reconstruct known constraints and to generalizewell to other modeling tasks in the same domain. Experiments with synthetic data indicate that the approach can successfully reconstructknown modeling constraints. Another study using natural data suggests that transferring constraints acquired from one modeling scenario to another within the same domain considerably reduces the amount of search for candidate model structures while retaining the most accurate ones.
A Software Process Engineering Course

In these areas, he developed and applied technologies including distributed, component-based software architectures, software and systems engineering process models, intelligent control, the semantic web, and real-time artificial intelligence.In
Energy Efficient Resource Allocation in Cloud Computing

Cloud service provider in cloud environment will provide or provision resource based on demand from the user. The cloud service provider (CSP) will provide resources as and when required or demanded by the user for execution of the job on the cloud environment. The CSP will perform this in a static and dynamic manner. The CSP should also consider various other factors in order to provide the resources to the user, the prime among that will be the Service Level Agreement (SLA), which is normally signed by the user and cloud service provider during the inception phase of service. There are many algorithm which are used in order to allocate resources to the user in cloud environment. The algorithm which is proposed will be used to reduce the amount of energy utilized in performing various job execution in cloud environment. Here the energy utilized for execution of various jobs are taken into account by increasing the number of virtual machines that are used on a single physical host system. There is no thumb rule to calculate the number of virtual machines to be executed on a single host. The same can be derived by calculating the amount of space, speed required along with the time to execute the job on a virtual machine. Based up on this we can derive the number of Virtual machine on a single host system. There can be 10 virtual machines on a single system or even 20 number of virtual machines on single physical system. But if the same is calculated by the equation then the result will be exactly matching with the threshold capacity of the physical system[1]. If more number of physical systems are used to execute fewer virtual machines on each then the amount of energy consumed will be very high. So in order to reduce the energy consumption , the algorithm can be used will not only will help to calculate the number of virtual machines on single physical system , but also will help to reduce the energy as less number of physical systems will be in need[2].
Distance Learning in Agent-Centered Heuristic Search

Real-time agent-centric algorithms have been used for learning and solving problems since the introduction of the LRTA* algorithm in 1990. In this time period, numerous variants have been produced, however, they have generally followed the same approach in varying parameters to learn a heuristic which estimates the remaining cost to arrive at a goal state. This short paper discusses the history and implications of learning g-costs, both alone and in conjunction with learning h-costs as an introduction to the new f-LRTA* algorithm which learns both.
HUGE2: a Highly Untangled Generative-model Engine for Edge-computing

As a type of prominent studies in deep learning, generative models have been widely investigated in research recently. Two research branches of the deep learning models, the Generative Networks (GANs, VAE) and the Semantic Segmentation, rely highly on the upsampling operations, especially the transposed convolution and the dilated convolution. However, these two types of convolutions are intrinsically different from standard convolution regarding the insertion of zeros in input feature maps or in kernels respectively. This distinct nature severely degrades the performance of the existing deep learning engine or frameworks, such as Darknet, Tensorflow, and PyTorch, which are mainly developed for the standard convolution. Another trend in deep learning realm is to deploy the model onto edge/ embedded devices, in which the memory resource is scarce. In this work, we propose a Highly Untangled Generative-model Engine for Edge-computing or HUGE2 for accelerating these two special convolutions on the edge-computing platform by decomposing the kernels and untangling these smaller convolutions by performing basic matrix multiplications. The methods we propose use much smaller memory footprint, hence much fewer memory accesses, and the data access patterns also dramatically increase the reusability of the data already fetched in caches, hence increasing the localities of caches. Our engine achieves a speedup of nearly 5x on embedded CPUs, and around 10x on embedded GPUs, and more than 50% reduction of memory access.
Analisis dan Perancangan Aplikasi Berbasis Web untuk Pengelolaan Harga Pokok Produksi Menggunakan Metode Harga Pokok Proses

Manufacturing of products incurred raw material costs, direct labor costs, and factory overhead costs, which could be used to determine the price of the product sold. On the contrary, the company, which manufactured thousands of toys and dolls monthly, did not have proper cost record. Production cost calculated by the average of total cost divided with a total of confirmed product. Hence, the production cost for each product could not be defined precisely. Based on the problems described, a web application was developed to manage the recording of transaction and automatically calculated the cost of production. This application's functionalities were able to manage purchasing activities, handing over raw materials to production, confirming finished goods, calculating production costs, posting journals and ledgers, and reporting of the cost of production. This application could help companies to calculate the unit cost. Based on a comparison between manual and application testing, it could be concluded that output in the application displayed an exact match from the manual. Thus, this web-based application was ready to be implemented in the company.
THE POSTER AS A FORM OF ACTIVE PARTICIPATION OF STUDENTS IN THE SCIENTIFIC MOVEMENT

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 10630-10633ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.2703Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Towards a General-Purpose Linguistic Annotation Backend

Language documentation is inherently a time-intensive process; transcription, glossing, and corpus management consume a significant portion of documentary linguists’ work. Advances in natural language processing can help to accelerate this work, using the linguists’ past decisions as training material, but questions remain about how to prioritize human involvement. In this extended abstract, we describe the beginnings of a new project that will attempt to ease this language documentation process through the use of natural language processing (NLP) technology. It is based on (1) methods to adapt NLP tools to new languages, based on recent advances in massively multilingual neural networks, and (2) backend APIs and interfaces that allow linguists to upload their data (§2). We then describe our current progress on two fronts: automatic phoneme transcription, and glossing (§3). Finally, we briefly describe our future directions (§4).
DEVELOPMENT OF DIGITAL COMPETENCIES FOR PROFESSIONAL PERFORMANCE IN UNIVERSITY STUDENTS

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 7605-7609ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.1838Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Assessment Genres on Ukrainian Language Lessons in 10 ‒ 11 Grades of Lyceum

Publication of the article is motivated by reforming processes of general secondary education in Ukraine. One of the important tasks of modernization is to review the content component of each school subject in order to remove everything that overloads schoolchildren but is not vital. In accordance with such tasks, a new Ukrainian language program for&nbsp; 10‒11 grade students (standard level) has been developed. The authors considered it expedient to delete the section "Development of speech" and replace it with elements of practical rhetoric. Experimental research of Ukrainian language teaching staff at the Institute of Pedagogy of the National Academy of Science of Ukraine has shown that interest in Ukrainian language as a school subject and a social phenomenon has diminished.The article justified introduction of speech genres into a Ukrainian language school program for 10-11 grades (standard level). Gaps in Ukrainian linguodidactics related to the problem of teaching school rhetoric are determined. The essence and character of assessment genres are determined, features of the structure and semantics of terms are determined, genres of criticism and praise are described. An emphasis is put on importance and meaningfulness of these genres, on value aspect in preparation of speeches of assessment genres. It is emphasized on the importance of information about values - universal, for which there are no geographical boundaries, nationality and age, national (characteristic of a single nation), state, cultural, family, personal. The purpose of assessment genres is outlined, objects and subjects are defined. An algorithm for organizing work with genres on Ukrainian language lessons, examples of exercises and tasks is offered.
EYE TRACKING THE BEHAVIOR OF LEARNERS WITH DIFFERENT LEVEL OF EXPERTISE IN A SIMULATION-BASED LEARNING ENVIRONMENT

EYE TRACKING THE BEHAVIOR OF LEARNERS WITH DIFFERENT LEVEL OF EXPERTISE IN A SIMULATION-BASED LEARNING ENVIRONMENT