Reviewerly/exHarmony huggingface dataset:
Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads

With the rise of AI in recent years and the increase in complexity of the models, the growing demand in computational resources is starting to pose a significant challenge. The need for higher compute power is being met with increasingly more potent accelerators and the use of large compute clusters. However, the gain in prediction accuracy from large models trained on distributed and accelerated systems comes at the price of a substantial increase in energy demand, and researchers have started questioning the environmental friendliness of such AI methods at scale. Consequently, energy efficiency plays an important role for AI model developers and infrastructure operators alike. The energy consumption of AI workloads depends on the model implementation and the utilized hardware. Therefore, accurate measurements of the power draw of AI workflows on different types of compute nodes is key to algorithmic improvements and the design of future compute clusters and hardware. To this end, we present measurements of the energy consumption of two typical applications of deep learning models on different types of compute nodes. Our results indicate that 1. deriving energy consumption directly from runtime is not accurate, but the consumption of the compute node needs to be considered regarding its composition; 2. neglecting accelerator hardware on mixed nodes results in overproportional inefficiency regarding energy consumption; 3. energy consumption of model training and inference should be considered separately - while training on GPUs outperforms all other node types regarding both runtime and energy consumption, inference on CPU nodes can be comparably efficient. One advantage of our approach is that the information on energy consumption is available to all users of the supercomputer, enabling an easy transfer to other workloads alongside a raise in user-awareness of energy consumption.
An LSTM model for Twitter Sentiment Analysis

Sentiment analysis on social media such as Twitter provides organizations and individuals an effective way to monitor public emotions towards them and their competitors. As a result, sentiment analysis has become an important and challenging task. In this work, we have collected seven publicly available and manually annotated twitter sentiment datasets. We create a new training and testing dataset from the collected datasets. We develop an LSTM model to classify sentiment of a tweet and evaluate the model with the new dataset.
Block Selection Method for Using Feature Norm in Out-of-distribution Detection

Detecting out-of-distribution (OOD) inputs during the inference stage is crucial for deploying neural networks in the real world. Previous methods commonly relied on the output of a network derived from the highly activated feature map. In this study, we first revealed that a norm of the feature map obtained from the other block than the last block can be a better indicator of OOD detection. Motivated by this, we propose a simple framework consisting of FeatureNorm: a norm of the feature map and NormRatio: a ratio of FeatureNorm for ID and OOD to measure the OOD detection performance of each block. In particular, to select the block that provides the largest difference between FeatureNorm of ID and FeatureNorm of OOD, we create Jigsaw puzzle images as pseudo OOD from ID training samples and calculate NormRatio, and the block with the largest value is selected. After the suitable block is selected, OOD detection with the FeatureNorm outperforms other OOD detection methods by reducing FPR95 by up to 52.77% on CIFAR10 benchmark and by up to 48.53% on ImageNet benchmark. We demonstrate that our framework can generalize to various architectures and the importance of block selection, which can improve previous OOD detection methods as well.
Leroy and Blazy were right: their memory model soundness proof is automatable (Extended Version)

Xavier Leroy and Sandrine Blazy in 2007 conducted a formal verification, using the Coq proof assistant, of a memory model for low-level imperative languages such as C. Considering their formalization was performed essentially in first-order logic, one question left open by the authors was whether their proofs could be automated using a verification framework for first-order logic. We took the challenge and automated their formalization using Why3, significantly reducing the proof effort. We systematically followed the Coq proofs and realized that in many cases at around one third of the way Why3 was able to discharge all VCs. Furthermore, the proofs still requiring interactions (e.g. induction, witnesses for existential proofs, assertions) were factorized isolating auxiliary results that we stated explicitly. In this way, we achieved an almost-automatic soundness and safety proof of the memory model. Nonetheless, our development allows an extraction of a correct-by-construction concrete memory model, going thus further than the preliminary Why version of Leroy and Blazy.
The Impact of Digital Currency on Accounting and Management under the Blockchain Architecture

In recent years, the digital economy has developed vigorously relying on Internet technology, and blockchain technology has played an important role in promoting high-quality information processing. The closely connected digital currency system is still in the rising stage, with defects in all aspects and hidden risks in the market that cannot be ignored. In order to ensure the legitimate rights and interests of investors, this paper deeply analyzes the current global economic development trend, from the development of blockchain technology to digital currency, analyzes the accounting measurement and relevant accounting standards, and puts forward suggestions on improving the overall regulatory efficiency of the government, strengthening the accounting treatment of enterprise entities, strengthening the construction of blockchain networks, etc, It is expected to promote the construction of domestic information technology and better coordinate the development of digital economy and society with digital currency.
Complex Support System for Visually Impaired Individuals

It is very difficult for visually impaired individuals to avoid obstacles, to notice or recognize obstacles in distance, to notice and follow the special paths made for them. They continue their lives by touching these situations or finding solutions with the help of a walking stick in their hands. Due to these safety problems, it is difficult for visually impaired individuals to move freely and these situations affect individuals negatively in terms of social and health. In order to find solutions to these problems, a support system has been proposed for visually impaired individuals. The vision support system includes an embedded system with a camera with an audio warning system so that the visually impaired individual can identify the objects in front of him, and a circuit with an ultrasonic sensor so that he can detect the obstacles in front of him early and take precautions. The object recognition system is realized with convolutional neural networks. The Faster R-CNN model was used and in addition to this, a model that we created, which can recognize 25 kinds of products, was used. With the help of the dataset we created and the network trained with this dataset, the visually impaired individual will be able to identify some market products. In addition to these, auxiliary elements were added to the walking sticks they used. This system consists of a camera system that enables the visually impaired individual to notice the lines made for the visually impaired in the environment, and a tracking circuit placed at the tip of the cane so that they can easily follow these lines and move more easily. Each system has been designed separately so that the warnings can be delivered to the visually impaired person quickly without delay. In this way, the error rate caused by the processing load has been tried to be reduced. The system we have created is designed to be wearable, easy to use and low-cost to be accessible to everyone.
Path Planning of Mission-driven USVs Based on Stochastic Schemata Exploiter

In recent years, the research on unmanned surface vessels (USVs) has become a new hot spot in the development of intelligent applications in the shipping industry of various countries. With the gradual maturity and popularization of modern information technologies such as computer technology and artificial intelligence, the research on USVs has scientific and technological support, and is developing from theoretical research to application practice. Among them, the autonomous assignment and decision-making coordination of multi-target tasks is one of the important research topics for autonomous navigation of USVs. At the same time, the path planning of UAVs also plays a crucial role in autonomous assignment and navigation. In view of the above points, this paper proposes a heuristic optimization algorithm based on stochastic schemata exploiter (SSE). It is used to solve the path optimization problem of USVs for mission requirements. In the experiment, two combinations of two USVs are considered, and the paths are optimized in the two cases of jointly completing the task and cooperatively completing the task. The algorithm combines the heuristic information of the distribution structure of static obstacles in the environment, expands the obstacles and optimizes the node selection of the USV, so that the USV can avoid the obstacles with a higher probability and find an excellent feasible path, and improve the global search ability. Compared with the genetic algorithm (GA) through simulation experiments, the proposed algorithm presents a significant improvement in timeliness and path optimization.
Avaliação do conhecimento da equipe de enfermagem em terapia nutricional: teste antes e depois

Objetivo: avaliar conhecimento da equipe de enfermagem após capacitação em terapia nutricional. Método: estudo observacional, transversal, descritivo, de natureza quantitativa. Foram convidados a participar do estudo todos os profissionais de enfermagem que atuavam no hospital e foram parte da amostra, aqueles que concordaram. Aplicou-se um questionário estruturado, auto formulado pelos pesquisadores, com base nas últimas evidências na temática e de acordo com protocolos institucionais, antes e após curso de capacitação em serviço, do qual participaram 46 profissionais. Resultados: a maioria foi constituída por técnicos de enfermagem (65,2%), do sexo feminino (69,6%) e idade entre 31 e 40 anos (56,4%) que afirmou conhecer a atuação da equipe multidisciplinar de terapia nutricional (95,7%). Em relação à lavagem e à fixação do cateter nasoentérico, observa-se que melhorou a porcentagem de acertos após a capacitação (15%). Sobre a desobstrução do cateter alcançou 100% de acertos. Houve piora do escore de acertos sobre o posicionamento do cateter após a capacitação (78,3% para 52,2%). Em relação a nutrição parenteral, o percentual de acertos foi de 78,25% para 92,4%. Conclusão: Foi possível observar melhora no percentual de acertos na maioria dos tópicos avaliados. Abstract Objective: to evaluate the nursing team's knowledge after training in nutritional therapy. Method: observational, cross-sectional, descriptive and quantitative study. All nursing professionals working at the hospital were invited to participate in the study, and those who agreed were part of the sample. A structured questionnaire was applied, self-formulated by the researchers, based on the latest evidence on the subject and according to institutional protocols, before and after in-service training course, of which 46 professionals participated. Results: the majority were nursing technicians (65.2%), female gender (69.6%) and age 31-40 years (56.4%). They claimed to know the multidisciplinary nutrition therapy team (95.7%). Regarding nasoenteric catheter washing and fixation, the percentage of correct answers improved after the training (15%), and regarding the catheter unblocking, it reached 100% of correct answers. There was a worsening of the score on catheter positioning after training (78.3% to 52.2%). About parenteral nutrition, the percentage of correct answers went from 78.25% to 92.4% after training. Conclusion: it was possible to observe an improvement in the percentage of correct answers in most of the topics assessed.
Curriculum Reform of The Internet of Things Application System Based on Online and Offline Hybrid Mode

According to the characteristics of the Internet of Things application system course, this paper proposes a mixed online and offline teaching mode, the main content includes the characteristics of online and offline mixed teaching mode, online and offline mixed teaching mode online resource construction and the construction of online and offline mixed teaching mode. The construction and implementation of this teaching mode closely integrate offline teaching with online learning, which can dynamically adjust the teaching process and highlight the autonomy and subjectivity of students' learning.
Union-Set Multi-source Model Adaptation for Semantic Segmentation

This paper solves a generalized version of the problem of multi-source model adaptation for semantic segmentation. Model adaptation is proposed as a new domain adaptation problem which requires access to a pre-trained model instead of data for the source domain. A general multi-source setting of model adaptation assumes strictly that each source domain shares a common label space with the target domain. As a relaxation, we allow the label space of each source domain to be a subset of that of the target domain and require the union of the source-domain label spaces to be equal to the target-domain label space. For the new setting named union-set multi-source model adaptation, we propose a method with a novel learning strategy named model-invariant feature learning, which takes full advantage of the diverse characteristics of the source-domain models, thereby improving the generalization in the target domain. We conduct extensive experiments in various adaptation settings to show the superiority of our method.
Neural Machine Translation with Contrastive Translation Memories

Retrieval-augmented Neural Machine Translation models have been successful in many translation scenarios. Different from previous works that make use of mutually similar but redundant translation memories~(TMs), we propose a new retrieval-augmented NMT to model contrastively retrieved translation memories that are holistically similar to the source sentence while individually contrastive to each other providing maximal information gains in three phases. First, in TM retrieval phase, we adopt a contrastive retrieval algorithm to avoid redundancy and uninformativeness of similar translation pieces. Second, in memory encoding stage, given a set of TMs we propose a novel Hierarchical Group Attention module to gather both local context of each TM and global context of the whole TM set. Finally, in training phase, a Multi-TM contrastive learning objective is introduced to learn salient feature of each TM with respect to target sentence. Experimental results show that our framework obtains improvements over strong baselines on the benchmark datasets.
A Type System for Effect Handlers and Dynamic Labels

Abstract We consider a simple yet expressive $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>λ</mml:mi> </mml:math> -calculus equipped with references, effect handlers, and dynamic allocation of effect labels, and whose operational semantics does not involve coercions or rely on type information. We equip this language with a type system that supports type and effect polymorphism, allows reordering row entries and extending a row with new entries, and supports (but is not restricted to) lexically scoped handlers. This requires addressing the issue of potential aliasing between effect names. Our original solution is to interpret a row not only as a permission to perform certain effects but also as a disjointness requirement bearing on effect names. The type system guarantees strong type soundness: a well-typed program cannot crash or perform an unhandled effect. We prove this fact by encoding the type system into a novel Separation Logic for effect handlers, which we build on top of Iris. Our results are formalized in Coq.
Anticorrelated Noise Injection for Improved Generalization

Injecting artificial noise into gradient descent (GD) is commonly employed to improve the performance of machine learning models. Usually, uncorrelated noise is used in such perturbed gradient descent (PGD) methods. It is, however, not known if this is optimal or whether other types of noise could provide better generalization performance. In this paper, we zoom in on the problem of correlating the perturbations of consecutive PGD steps. We consider a variety of objective functions for which we find that GD with anticorrelated perturbations ("Anti-PGD") generalizes significantly better than GD and standard (uncorrelated) PGD. To support these experimental findings, we also derive a theoretical analysis that demonstrates that Anti-PGD moves to wider minima, while GD and PGD remain stuck in suboptimal regions or even diverge. This new connection between anticorrelated noise and generalization opens the field to novel ways to exploit noise for training machine learning models.
Pengembangan E-book Berbasis Discovery Learning Terintegrasi Keterampilan 4C Untuk Pembelajaran Fisika SMA

The problem found in the field is that the e-books available in schools do not contain the 4C skills that students should have in the 21st century. In addition, the existing e-books have not shown any learning model syntax that is in accordance with the 2013 curriculum. The e-books used have not been able to involve students effectively. directly in finding the concept of learning. So, it takes a discovery learning-based e-book that is integrated with 4C skills. The purpose of this research is to produce an e-book based on discovery learning integrated 4C skills in physics learning with valid, practical, and effective categories. This type of research is research and development. The e-book development uses the ADDIE development model which consists of the analysis, design, development, implementation, and evaluation stages. Research data in the form of preliminary study data, validation, practicality, and effectiveness. The research instrument consisted of a preliminary questionnaire, validation instrument, practicality instrument, and effectiveness instrument. Data analysis was carried out using the ADDIE development method which consisted of analysis, design, development, implementation and evaluation, including aspect of knowledge, attitudes, and 4C skills; critical thinking skills, creative thinking skills, and communication skills. Based on the research conducted, it can be concluded that the physics e-book based on discovery learning integrated 4C skills is valid, practical and effective. so that it can be used to improve student learning outcomes.
Lecturers’ Opinions and Preferences: Printed Book or E-Book for Science Teaching

Even while printed books and electronic books frequently contain the same material, they differ greatly from one another and each format has its own benefits and drawbacks. This study's purpose is to acquire a thorough grasp of the opinions and preferences of the lecturers in employing book formats for science teaching. The findings of the study would assist other lecturers in thinking through and selecting implementation strategies for employing those two types of book formats. Then, this study identifies the advantages and disadvantages of digital versus print books as well as the reasons why lecturers favor one over the other through this research. This study used a qualitative phenomenological methodology. The stages of the research are selecting research informants, collecting the data and analysis to drawing conclusions. The instrument used in this study is in depth interview. Data collection was carried out through interviews with lecturers who have used both books’ formats. Based on the results of the study it was found that there were 35% lecturers who preferred to use printed books, 10% lecturers who preferred to uses e-books, and 55% lecturers who preferred to use both printed and e-books. They preferences were based on the situational of teaching and learning process.
Parameterization of cloud transmittance for expeditious assessment and forecasting of all-sky DNI

Radiative transfer models require vast computing resources to solve cloud transmittance and reflectance from the radiative transfer equation. As a result, models offering precise simulation in operations often acquire individual cloud transmittance or reflectance from a lookup table precomputed for practicable scenarios. To further expedite the computation of global horizontal irradiance and to reduce the storage requirements, the Fast All-sky Radiation Model for Solar applications (FARMS) parameterized the lookup table using elementary functions with specified coefficients. This study extends FARMS direct normal irradiance (DNI) computation by utilizing hyperbolic tangent functions and various polynomial functions to parameterize the cloud transmittance for scattered solar radiation in the circumsolar region. The parameterization is implemented in FARMS with DNI (FARMS-DNI) and accounts for the circumsolar radiation when assessing or forecasting DNI. The evaluation, with long-term observations at the National Renewable Energy Laboratory's, Solar Radiation Research Laboratory, and the Atmospheric Radiation Measurement, Southern Great Plains, Central Facility, shows that the parameterized DNIs are virtually identical with those computed by coupling FARMS-DNI to a lookup table of cloud transmittance. This parameterization has diverse applications in radiative transfer models and numerical weather prediction models used to assess or forecast direct solar radiation.
JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset

JamPatoisNLI provides the first dataset for natural language inference in a creole language, Jamaican Patois. Many of the most-spoken low-resource languages are creoles. These languages commonly have a lexicon derived from a major world language and a distinctive grammar reflecting the languages of the original speakers and the process of language birth by creolization. This gives them a distinctive place in exploring the effectiveness of transfer from large monolingual or multilingual pretrained models. While our work, along with previous work, shows that transfer from these models to low-resource languages that are unrelated to languages in their training set is not very effective, we would expect stronger results from transfer to creoles. Indeed, our experiments show considerably better results from few-shot learning of JamPatoisNLI than for such unrelated languages, and help us begin to understand how the unique relationship between creoles and their high-resource base languages affect cross-lingual transfer. JamPatoisNLI, which consists of naturally-occurring premises and expert-written hypotheses, is a step towards steering research into a traditionally underserved language and a useful benchmark for understanding cross-lingual NLP.