Reviewerly/exHarmony huggingface dataset:
Type-1 and type-2 fuzzy logic-based space vector modulation for two-level inverter fed induction motor

Two-level inverter control with type-1 and type-2 fuzzy logic-based space vector pulse-width modulation (PWM) method for induction motor drive (IMD) is presented in this paper. A new sampling time independent strategy with type-1 and type-2 fuzzy based methods are used in generating three phase duty ratios which are directly obtained without mathematical equations. The conventional method of space vector modulation (SVM) produces the duty ratios for the inverter which are sampling time dependent. However, in type-1 and type-2 fuzzy based space vector PWM algorithms, the three phases duty ratios generated are sampling time independent and with a new integrated dead-time insertion in SVM itself, can be implemented practically for any switching frequency. As the rule-base for Mamdani non-singleton interval type-1 and type-2 fuzzy inference systems are designed manually, with the expert knowledge of conventional space vector PWM, the duty ratios of the inverter are generated such that the performance of the IMD is improved. The simulation studies for aforementioned cases is performed in matrix laboratory (MATLAB) and experimental validation for the proposed space vector PWM algorithms is validated using real-time sacraetheologiae magister (STM) hardware with STM32F429I Cortex M4 processor for a 1 hp IMD.
Hybrid Moth-Flame Optimization Algorithm with Slime Mold Algorithm for Global Optimization

The Moth-Flame Optimization (MFO) algorithm is a one-of-a-kind metaheuristic that is based on natural phenomena. The primary moth navigation mechanism called transversal orientation is the major motivation of this program. Moths fly throughout the night at a constant angle toward the moon, a mechanism allowing long distance travels in a straight line. These beautiful insects, however, are confined around artificial light in a spiral route. The enhanced version of MFO algorithms based on the Slime Mold Algorithm (SMA) approach, which is called the hMFOSMA, is being presented for the phenomena of delayed convergence and low accuracy of MFO algorithms. The SMA mechanism is able to enhance population diversification against premature convergence and enhance the algorithm’s efficiency in the local optimum. This technique can assist in making the hMFOSMA quicker and more robust than MFO and SMA, thereby obtaining a more effective balance between MFO exploitation and exploration capacity. In 56 unconstrained benchmarking functions and 2 unconstrained design problems using SMA, MFO, Particle Swarm Optimization, Differential Evolution, Sine Cosine Algorithm, Artificial Bee Colony, Butterfly Optimization Algorithm, Cuckoo Search, and Bat Algorithm, comparability has also been examined. These findings show that hMFOSMA performs better.
An Economic Feasibility Study on Solar Installation for University Campus: A Case of Universiti Utara Malaysia

Transforming towards renewable energy consumption has become the main agenda for the country due to the drawback of consuming conventional resources. Climate change, energy security, and price fluctuation are the main factors contributing towards the penetration of renewable energy. Solar energy seems to be the most attractive due to unlimited source of energy, and fundamentally, its availability will never run out. This form of energy can also be used as an alternative in producing electricity. Malaysia has potential in developing solar energy for daily usage due to its location in the equatorial area and acquiring approximately 400–600 MJ/ m–2 of solar radiation every month. Universiti Utara Malaysia (UUM) is a public higher learning institution located in Sintok, Kedah. Sintok is a potential area for the installation of this solar system because the area has a longer peak sun hour rate than other areas. The aims of this study are: (1) to analyse the factors involved in solar installation, and (2) to examine the economic feasibility of solar installation at a university campus. This study used a qualitative approach, whereby the data were taken from interviews with the Development and Maintenance Department of UUM and Detrolic Solar Company. The solar implementation in UUM could reduce the cost of energy usage annually. Furthermore, UUM could gain revenue from the Net Energy Metering (NEM) 3.0 Government Ministries and Entities (GoMEn) incentive. Solar installation in the campus could hasten the effort of UUM moving towards a green and eco-friendly university campus. It is best believed that this effort could be a success if UUM is ready to move towards sustainable energy with solar consumption.
Designing a Framework for Metaverse Architecture

Metaverse, which was first interpreted as an isolated virtual universe, is now evolving into a multiverse, where virtual worlds superimpose on the real one.Spaces, people, and activities in both physical and virtual formats are going to be seamlessly integrated.Architecture is seen as a container of spaces, people, and activities.Architectural elements are arranged to define spaces guiding people to conduct required activities.The emerging duality of the metaverse will change not only the architectural requirements but also the nature of architecture in terms of form and function.While holograms are becoming a new kind of architectural material, envisioning a potential framework to guide future exploration of metaverse architecture is the goal.In this paper, we conduct a qualitative user study to collect users' needs, identify architectural requirements, and build a preliminary framework for metaverse architecture.This framework provides strategies for mixing holographic and physical architectural elements to fulfill the users' needs of the metaverse.It could further lead to a potential way of reducing the production and consumption of carbon to contribute to the post-carbon framework.
Dance with Shadows - Capturing tacit knowledge with smart device augmented reality (SDAR)

Tacit knowledge has been notified with its involvement in the creative and innovative process of design.However, it has been an elusive subject due to its difficulty to be articulated, recorded, and communicated.Augmented Reality (AR) is introduced as an affordable, accessible, and collaborative way to revisit tacit knowledge in the design process.In this paper, a computational design approach with Smart Device Augmented Reality (SDAR) is proposed for a real-time fenestration design in a targeted room.In comparison to standard methods of showcasing daylighting metrics, the use of Smart Device Augmented Reality (SDAR) is an alternative method as it delivers a dynamic experience by combining both the real and digital environments, enabling the visualization of the design in its intended site context with real-time feedback.The implementation of the proposed approach is explained and the design process with SDAR is also demonstrated in this paper.
Analisis Faktor-Faktor yang Mempengaruhi Rendahnya Kemampuan Menulis Peserta Didik Kelas Redah di Sekolah Dasar

This study aims to analyze the factors that cause the low writing ability of Elementary School grade 1 students at Nurul Huda Islamic Elementary School after two years of students participating in online learning activities. The research used a qualitative approach with the type of case study research. First-graders, first-grade teachers, and the principal of SD Islam Nurul Huda were the subjects. In-depth interviews, documentation, field notes, and observation were the data gathering methods used in this study. The data analysis method applied was the Miles and Huberman analysis method, which entails three concurrent streams of activity: data reduction, data presentation, and conclusion drawing or verification. The result shows that student learning capacity has been negatively impacted by the Covid-19 epidemic, notably regarding their writing abilities. The causes are as follows: (1) the neglectful and uninvolved parental role at home, (2) family environmental factors, and (3) the student environment.
Differentiable Predictions for Large Scale Structure with SHAMNet

Neural nets have become popular to accelerate parameter inferences, especially for the upcoming generation of galaxy surveys in cosmology.As neural nets are approximative by nature, a recurrent question has been how to propagate the neural net's approximation error, in order to avoid biases in the parameter inference.We present a Bayesian solution to propagating a neural net's approximation error and thereby debiasing parameter inference.We exploit that a neural net reports its approximation errors during the validation phase.We capture the thus reported approximation errors via the highest-order summary statistics, allowing us to eliminate the neural net's bias during inference, and propagating its uncertainties.We demonstrate that our method is quickly implemented and successfully infers parameters even for strongly biased neural nets.In summary, our method provides the missing element to judge the accuracy of a posterior if it cannot be computed based on an infinitely accurately theory code.
How to Approximate any Objective Function via Quadratic Unconstrained Binary Optimization

Quadratic unconstrained binary optimization (QUBO) has become the standard format for optimization using quantum computers, i.e., for both the quantum approximate optimization algorithm (QAOA) and quantum annealing (QA). We present a toolkit of methods to transform almost arbitrary problems to QUBO by (i) approximating them as a polynomial and then (ii) translating any polynomial to QUBO. We showcase the usage of our approaches on two example problems (ratio cut and logistic regression).
DIComP: Lightweight Data-Driven Inference of Binary Compiler Provenance with High Accuracy

Binary analysis is pervasively utilized to assess software security and test vulnerabilities without accessing source codes. The analysis validity is heavily influenced by the inferring ability of information related to the code compilation. Among the compilation information, compiler type and optimization level, as the key factors determining how binaries look like, are still difficult to be inferred efficiently with existing tools. In this paper, we conduct a thorough empirical study on the binary&#x0027;s appearance under various compilation settings and propose a lightweight binary analysis tool based on the simplest machine learning method, called DIComP to infer the compiler and optimization level via most relevant features according to the observation. Our comprehensive evaluations demonstrate that DIComP can fully recognize the compiler provenance, and it is effective in inferring the optimization levels with up to 90&#x0025; accuracy. Also, it is efficient to infer thousands of binaries at a millisecond level with our lightweight machine learning model (1MB).
Broadening Horizons of Design Ethics? Importing concepts from applied anthropology

This paper is a thought experiment: we explore how certain ethical considerations of applied anthropology might contribute to the evolving body of work on design ethics.To begin to consider ethical analogies between these two fields, we first align them on a conceptual level by scrutinizing how they both change relationships.Further, we introduce three central concepts and related debates of applied anthropology that could supplement discussions on contemporary design ethics: beneficence, collaborative approach and advocacy.The authors are specialized in (design) anthropology, architecture and human-computer interaction (HCI); in this paper, we draw from our respective fields and backgrounds.
The Problem of Methodological Training of Future Teachers in the Digital Environment

Changes in modern socio-economic conditions and modernization of the global and Ukrainian education system impose new requirements for education policy, which should meet the progressive needs of the state and society. Given the changing paradigm of education, society and the state urgently need to prepare applicants for higher education - future teaching specialists for professional activities in the educational digital environment. The study aims to identify the factors affecting the quality of methodological training of future elementary school teachers in the modern educational environment. Methodology. To identify the factors influencing the effectiveness of methodological training of future elementary school teachers in the digital environment, the method of theoretical analysis was applied.To determine the advantages and disadvantages of distance learning, the method of comparative analysis of the elements of classroom and distance learning was used. Results. The theoretical and methodological analysis of the problems of teacher training in the conditions of digitalization allowed to identify the factors and obstacles affecting the effectiveness of methodological training of future elementary school teachers. Conclusions. The study of the problem of methodological training of future teachers in the conditions of digitalization in Ukraine and abroad allowed to identify gaps in the professional training of elementary school teachers today. The results of this theoretical study can serve as the basis for further research (theoretical and empirical) in the field of professional training of future teachers.
LiST: Lite Prompted Self-training Makes Parameter-efficient Few-shot Learners

We present a new method LiST for efficient fine-tuning of large pre-trained language models (PLMs) in few-shot learning settings. LiST improves over recent methods that adopt prompt-based fine-tuning (FN) using two key techniques. The first is the use of self-training to leverage large amounts of unlabeled data for prompt-based FN in few-shot settings. We use self-training in conjunction with meta-learning for re-weighting noisy pseudo-prompt labels. Traditionally, self-training is expensive as it requires updating all the model parameters repetitively. Therefore, we use a second technique for light-weight fine-tuning where we introduce a small number of task-specific parameters that are fine-tuned during self-training while keeping the PLM encoder frozen. Our experiments show that LiST can effectively leverage unlabeled data to improve the model performance for few-shot learning. Additionally, the finetuning process is efficient as it only updates a small percentage of the parameters and the overall model footprint is reduced since several tasks can share a common PLM encoder as backbone. We present a comprehensive study on six NLU tasks to validate the effectiveness of LiST. The results show that LiST improves by 35% over classic fine-tuning methods and 6% over prompt-based FN with 96% reduction in number of trainable parameters when fine-tuned with no more than 30 labeled examples from each task. With only 14M tunable parameters, LiST outperforms GPT-3 in-context learning by 33% on few-shot NLU tasks
Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model

Knowledge distillation (KD) is a successful approach for deep neural network acceleration, with which a compact network (student) is trained by mimicking the softmax output of a pre-trained high-capacity network (teacher). In tradition, KD usually relies on access to the training samples and the parameters of the white-box teacher to acquire the transferred knowledge. However, these prerequisites are not always realistic due to storage costs or privacy issues in real-world applications. Here we propose the concept of decision-based black-box (DB3) knowledge distillation, with which the student is trained by distilling the knowledge from a black-box teacher (parameters are not accessible) that only returns classes rather than softmax outputs. We start with the scenario when the training set is accessible. We represent a sample's robustness against other classes by computing its distances to the teacher's decision boundaries and use it to construct the soft label for each training sample. After that, the student can be trained via standard KD. We then extend this approach to a more challenging scenario in which even accessing the training data is not feasible. We propose to generate pseudo samples distinguished by the teacher's decision boundaries to the largest extent and construct soft labels for them, which are used as the transfer set. We evaluate our approaches on various benchmark networks and datasets and experiment results demonstrate their effectiveness. Codes are available at: https://github.com/zwang84/zsdb3kd.
Transitional Learning: Exploring the Transition States of Degradation for Blind Super-Resolution

Being extremely dependent on iterative estimation of the degradation prior or optimization of the model from scratch, the existing blind super-resolution (SR) methods are generally time-consuming and less effective, as the estimation of degradation proceeds from a blind initialization and lacks interpretable representation of degradations. To address it, this paper proposes a transitional learning method for blind SR using an end-to-end network without any additional iterations in inference, and explores an effective representation for unknown degradation. To begin with, we analyze and demonstrate the transitionality of degradations as interpretable prior information to indirectly infer the unknown degradation model, including the widely used additive and convolutive degradations. We then propose a novel Transitional Learning method for blind Super-Resolution (TLSR), by adaptively inferring a transitional transformation function to solve the unknown degradations without any iterative operations in inference. Specifically, the end-to-end TLSR network consists of a degree of transitionality (DoT) estimation network, a homogeneous feature extraction network, and a transitional learning module. Quantitative and qualitative evaluations on blind SR tasks demonstrate that the proposed TLSR achieves superior performances and costs fewer complexities against the state-of-the-art blind SR methods. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">github.com/YuanfeiHuang/TLSR</uri> .
LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification

Multi-label text classification (MLTC) is an attractive and challenging task in natural language processing (NLP).Compared with single-label text classification, MLTC has a wider range of applications in practice.In this paper, we propose a label-interpretable graph convolutional network model to solve the MLTC problem by modeling tokens and labels as nodes in a heterogeneous graph.In this way, we are able to take into account multiple relationships including token-level relationships.Besides, the model allows better interpretability for predicted labels as the token-label edges are exposed.We evaluate our method on four real-world datasets and it achieves competitive scores against selected baseline methods.Specifically, this model achieves a gain of 0.14 on the F1 score in the small label set MLTC, and 0.07 in the large label set scenario.
An Epistemic Separation Logic with Action Models

Abstract In this paper we present an extension of (bunched) separation logic, Boolean BI, with epistemic and dynamic epistemic modalities. This logic, called action model separation logic ( $$\mathrm {AMSL}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>AMSL</mml:mi> </mml:math> ), can be seen as a generalization of public announcement separation logic in which we replace public announcements with action models. Then we not only model public information change (public announcements) but also non-public forms of information change, such as private announcements. In this context the semantics for the connectives $$*$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow /> <mml:mo>∗</mml:mo> </mml:mrow> </mml:math> and $$\mathrel {-*}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>-</mml:mo> <mml:mrow /> <mml:mo>∗</mml:mo> </mml:mrow> </mml:math> from separation logic are epistemic versions of their usual semantics. This is because formulas are interpreted in states, not in resources, and agents may be uncertain between different states representing the same resource. We present the logic $$\mathrm {AMSL}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>AMSL</mml:mi> </mml:math> and its semantics, with a detailed case study that highlights its interest for modeling. We also prove the elimination of the dynamics modalities and discuss some alternative epistemic semantics for the separation connectives.
M6: A Chinese Multimodal Pretrainer

In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1.9TB images and 292GB texts that cover a wide range of domains. We propose a cross-modal pretraining method called M6, referring to Multi-Modality to Multi-Modality Multitask Mega-transformer, for unified pretraining on the data of single modality and multiple modalities. We scale the model size up to 10 billion and 100 billion parameters, and build the largest pretrained model in Chinese. We apply the model to a series of downstream applications, and demonstrate its outstanding performance in comparison with strong baselines. Furthermore, we specifically design a downstream task of text-guided image generation, and show that the finetuned M6 can create high-quality images with high resolution and abundant details.
LightXML: Transformer with Dynamic Negative Sampling for  High-Performance Extreme Multi-label Text Classification

Extreme Multi-label text Classification (XMC) is a task of finding the most relevant labels from a large label set. Nowadays deep learning-based methods have shown significant success in XMC. However, the existing methods (e.g., AttentionXML and X-Transformer etc) still suffer from 1) combining several models to train and predict for one dataset, and 2) sampling negative labels statically during the process of training label ranking model, which reduces both the efficiency and accuracy of the model. To address the above problems, we proposed LightXML, which adopts end-to-end training and dynamic negative labels sampling. In LightXML, we use generative cooperative networks to recall and rank labels, in which label recalling part generates negative and positive labels, and label ranking part distinguishes positive labels from these labels. Through these networks, negative labels are sampled dynamically during label ranking part training by feeding with the same text representation. Extensive experiments show that LightXML outperforms state-of-the-art methods in five extreme multi-label datasets with much smaller model size and lower computational complexity. In particular, on the Amazon dataset with 670K labels, LightXML can reduce the model size up to 72% compared to AttentionXML.