Reviewerly/exHarmony huggingface dataset:
Fault diagnosis for vehicle air conditioning blower using deep learning neural network

This study presents a fault diagnosis system for vehicle heating, ventilation and air conditioning (HVAC) acoustic signal with various feature extractions in deep learning neural network. Traditionally, sound used for fault diagnosis or signal classification is observed the difference of energy in time or frequency domains. Unfortunately, the frequency smearing effect often arises in some critical conditions. In the present study, discrete wavelet transform (DWT) and wavelet packet transform (WPT) are proposed in fault diagnosis. Meanwhile, when using mechanical learning methods, the data are relatively large, in order to reduce the amount of data, DWT and WPT low-frequency decomposition could be used to improve the performance. Furthermore, the signal characteristics more comprehensive, this study attempts to use the feature extraction method of wavelet packet conversion to improve the signal characteristics. In the experiment process, the operation state of the blade blower in the vehicle air conditioner, four different faults were designed, test database was established through sound to classify, and identify the data using deep neural networks to achieve the purpose of blower fault diagnosis. In data analysis, the original signal is presented through wavelet packet decomposition and discrete packet conversion technology, compared with traditional time and frequency domain signals to explore the identification rate, identification speed and related issues. Experimental results show that using WPT combined with deep neural networks have good fault diagnosis and discrimination capabilities, training, and identification time is shorter than time-frequency domain signals.
Robust control technique in power converter with linear induction motor

Induction motors are widely used in industrial applications as actuators, thanks to their simplicity of construction, which is subsequently reflected in low-cost maintenance. This paper shows the behavior of a three-phase power converter with a linear induction motor (LIM) as load, using a quasi-sliding control technique for output voltage regulation and a new control technique to control chaos. Digital pulse width modulation (DPWM) techniques are widely used to control electronic power converters. The controller proposed in this paper was designed using zero average dynamic (ZAD) and fixed point inducting control (FPIC) techniques. The ZAD-FPIC control strategy was designed and applied to a three-phase converter with linear induction motor load. Since it is not possible to measure the secondary currents, a secondary current observer was included in the system. Finally, bifurcation diagrams are shown as a technique for tuning controller parameters in ZAD-FPIC controllers. For the illustration of numerical results a simulation of the linear induction motor drive controlled was made by MATLAB/Simulink. The designs were tested in a rapid control prototyping (RCP) system based on digital signal processing (DSP) for dSPACE platform, using the 1103 controller card and control desk interface.
ZionimeArt App: Designing Mobile Application as A Medium For Selling Anime T-shirts

Technology and information media is currently experiencing a rapid development, this is seen from the use of technology media as a tool in various aspects of life and one of them is the development of applications as a place to buy and sell a product. The purpose of designing this application is as a place / information system that can help the public in finding and buying reliable Japanese anime T-shirts, in addition this application is also a promotional media with the marketing segment of anime T-shirts. This research used qualitative descriptive analysis method with High fidelity prototyping and SDLC method. The development of ZionimeArt application can make it easier for people especially anime fans to easily find and buy various types of anime T-shirts in one place. In addition, because of the concept of a mobile application, it can facilitate users in accessing the site under any conditions easily and efficiently. ZionimeArt itself has the main purpose as a special application for the sale of various types of anime T-shirts, with a display that presents a wide selection of clothing types up to the purchase process. With this, the purpose and function of this application is to help and overcome the difficulties of the community in finding different types of anime T-shirts. It can be concluded, ZionimeArt application is an intermediary media between the community and anime products that they like, so they can buy it easily.
Scale and Background Aware Asymmetric Bilateral Network for Unconstrained Image Crowd Counting

This paper attacks the two challenging problems of image-based crowd counting, that is, scale variation and complex background. To that end, we present a novel crowd counting method, called the Scale and Background aware Asymmetric Bilateral Network (SBAB-Net), which is able to handle scale variation and background noise in a unified framework. Specifically, the proposed SBAB-Net contains three main components, a pre-trained backbone convolutional neural network (CNN) as the feature extractor and two asymmetric branches to generate a density map. These two asymmetric branches have different structures and use features from different semantic layers. One branch is densely connected stacked dilated convolution (DCSDC) sub-network with different dilation rates, which relies on one deep feature layer and can handle scale variation. The other branch is parameter-free densely connected stacked pooling (DCSP) sub-network with various pooling kernels and strides, which relies on shallow feature and can fuse features with several receptive fields to reduce the impact of background noise. Two sub-networks are fused by the attention mechanism to generate the final density map. Extensive experimental results on three widely-used benchmark datasets have demonstrated the effectiveness and superiority of our proposed method: (1) We achieve competitive counting performance compared to state-of-the-art methods; (2) Compared with baseline, the MAE and MSE are decreased by at least 6.3% and 11.3%, respectively.
Method for detecting and recognizing pointer meter reading based on deep learning

As an instrument widely used in substations, pointer meters are mainly used to detect the working status of substation equipment, so regular calibration is very important. In order to solve the problems of extraction difficulty, large positioning error and poor recognition accuracy in the reading recognition of pointer meters, a method for automatic reading detection and recognition of pointer meters based on deep learning is proposed. Use the improved EAST algorithm to extract character features, and perform module detection on the results. It is concluded that the automatic detection and recognition of analog meters under complex backgrounds has good accuracy and stability, which can meet the application needs of substations.
A Formal Verification Based on Yu-Cao Delayed Chaotic Neural Network

Yu and Cao proposed “Cryptography based on delayed chaotic neural networks” in 2006. However, in 2009, Yang et al. pointed out the Yu-Cao scheme can not against chosen plaintext attack. Liu et al. studies exclusiveor logical operation very well, and provided Boolean algebra proofs in 2012. Ye et al. used Liu et al.’s method to reinterpret and analyze Yu-Cao scheme in 2018. In this paper the authors would like to give a formal verification by Galois field expression on the exclusive-or operation problem again. As this result, it makes more effective insecure to Yu-Cao algorithm.
Event and Query Processing in a Fuzzy Active Graph Database System

There is a lot of uncertain and imprecise information in a real-world scenario. Graph database systems based on the graph model support crisp and precise data. This paper presents a system structure that processes fuzzy quantified queries in the context of a graph database. Fuzzy logic allows decisions to be taken more realistically. A specific form of structural quantified query is designed and demonstrated in this paper, which then can be expressed as an extension to the Neo4j Cypher query language. In addition, this research built an active rule system on top of the graph database that reacts to event occurrences automatically. This paper presents our approach of supporting temporal event handling using fuzzy active rules and fuzzy query processing over Neo4j graph database systems.
The effectiveness of question-and-answer relationship as a remediation technique in improving reading comprehension of grade three pupils at Angono Elementary School for SY 2019-2020

This study aimed to present the effectiveness of QAR Technique, as a remediation tool in improving comprehension of grade three pupils of Angono Elementary School.The respondents of the study were forty-seven (47) grade three pupils who were found out to be non-readers in the conduct of the Philippine Informal Reading Inventory (Phil-IRI).The study used the descriptive research design that utilized Question-Answer-Relationship (QAR) Technique in improving Reading Comprehension.This study used the Pre-Test and Post Test Reading materials from Phil-IRI.The result of such test was used to determine the reading performance of each pupil.The content of the pretest was the same with posttest in such a way that the reading skills were measured.Focus of the evaluation is more of the determination of pupils' comprehension level.The researcher employed the four phases of the Question-and-Answer Relationship namely: Phase 1: Introduction and Model QAR, Phase 2: Phase 2: Model QAR with Subcategories, Phase 3: QAR with Just Right Text, and Phase 4: QAR with Sample Reading Assessment.Action plan crafted based upon the result of the study and from the suggestions and recommendations drawn in the course of implementation.When it was permitted, the researcher personally administered the research instrument to the subject as well as the remediation.After the pretest/posttest,
Auto-Tuning Flink Configurations Based on GBDT

Apache Flink is widely used in distributed computing scenarios, and its real-time performance is more powerful than Spark, so to maximize the performance of Flink, we need to set the relevant configuration parameters reasonably. However, it is quite difficult to select and tune the appropriate parameters from many parameters. To improve the overall efficiency of computation, it is important to study the performance modeling and Auto-Tuning Flink Configurations. In this paper, we analyze the key parameters affecting the execution time of Flink tasks and build a performance model to automatically optimize the Flink parameters based on the performance modeling. By extracting the parameters that have an impact on performance during job execution, we use GBDT to build a performance prediction model for a specific task, and finally use Particle swarm optimization (PSO) to find the optimal combination of parameters based on the performance model. Experimental results show that the tuned execution time is reduced by an average of 30% compared to the default configuration parameters.
A General Framework for Modelling Conditional Reasoning -- Preliminary Report

We introduce and investigate here a formalisation for conditionals that allows the definition of a broad class of reasoning systems. This framework covers the most popular kinds of conditional reasoning in logic-based KR: the semantics we propose is appropriate for a structural analysis of those conditionals that do not satisfy closure properties associated to classical logics.
C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References

We consider the problem of pretraining a two-stage open-domain question answering (QA) system (retriever + reader) with strong transfer capabilities. The key challenge is how to construct a large amount of high-quality question-answer-context triplets without task-specific annotations. Specifically, the triplets should align well with downstream tasks by: (i) covering a wide range of domains (for open-domain applications), (ii) linking a question to its semantically relevant context with supporting evidence (for training the retriever), and (iii) identifying the correct answer in the context (for training the reader). Previous pretraining approaches generally fall short of one or more of these requirements. In this work, we automatically construct a large-scale corpus that meets all three criteria by consulting millions of references cited within Wikipedia. The well-aligned pretraining signals benefit both the retriever and the reader significantly. Our pretrained retriever leads to 2%-10% absolute gains in top-20 accuracy. And with our pretrained reader, the entire system improves by up to 4% in exact match.
Domain Invariant Siamese Attention Mask for Small Object Change Detection via Everyday Indoor Robot Navigation

The problem of image change detection via everyday indoor robot navigation is explored from a novel perspective of the self-attention technique. Detecting semantically non-distinctive and visually small changes remains a key challenge in the robotics community. Intuitively, these small non-distinctive changes may be better handled by the recent paradigm of the attention mechanism, which is the basic idea of this work. However, existing self-attention models require significant retraining cost per domain, so it is not directly applicable to robotics applications. We propose a new self-attention technique with an ability of unsupervised on-the-fly domain adaptation, which introduces an attention mask into the intermediate layer of an image change detection model, without modifying the input and output layers of the model. Experiments, in which an indoor robot aims to detect visually small changes in everyday navigation, demonstrate that our attention technique significantly boosts the state-of-the-art image change detection model.
Evolution of two-mode quantum states under a dissipative environment: which quantum resource survives better, squeezing or entanglement?

We explore the relative robustness of squeezing and entanglement (which are quantum resources interconvertible via passive optics) for two-mode Gaussian states under different dissipative environments. When the individual modes interact with identical local baths, entanglement and squeezing decay at the same rate. However, when only one of the modes interacts with a local bath, the comparative robustness of entanglement and squeezing depends on the initial squeezing of the state. Similarly, when the system interacts with a global bath, the robustness of entanglement and squeezing depends on the initial squeezing. Thus depending on the nature of dissipative environments and the initial squeezing of the state, one can select the more robust form of resource out of squeezing and entanglement to store quantumness. This can be used to effectively enhance the performance of various quantum information processing protocols based on continuous variable Gaussian states.
FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows

Despite recent progress in open-domain dialogue evaluation, how to develop automatic metrics remains an open problem. We explore the potential of dialogue evaluation featuring dialog act information, which was hardly explicitly modeled in previous methods. However, defined at the utterance level in general, dialog act is of coarse granularity, as an utterance can contain multiple segments possessing different functions. Hence, we propose segment act, an extension of dialog act from utterance level to segment level, and crowdsource a large-scale dataset for it. To utilize segment act flows, sequences of segment acts, for evaluation, we develop the first consensus-based dialogue evaluation framework, FlowEval. This framework provides a reference-free approach for dialog evaluation by finding pseudo-references. Extensive experiments against strong baselines on three benchmark datasets demonstrate the effectiveness and other desirable characteristics of our FlowEval, pointing out a potential path for better dialogue evaluation.
Generative modeling with projected entangled-pair states

We argue and demonstrate that projected entangled-pair states (PEPS) outperform matrix product states significantly for the task of generative modeling of datasets with an intrinsic two-dimensional structure such as images. Our approach builds on a recently introduced algorithm for sampling PEPS, which allows for the efficient optimization and sampling of the distributions.
Interpretable Research Replication Prediction via Variational Contextual Consistency Sentence Masking

Research Replication Prediction (RRP) is the task of predicting whether a published research result can be replicated or not.Building an interpretable neural text classifier for RRP promotes the understanding of why a research paper is predicted as replicable or non-replicable and therefore makes its real-world application more reliable and trustworthy.However, the prior works on model interpretation mainly focused on improving the model interpretability at the word/phrase level, which are insufficient especially for long research papers in RRP.Furthermore, the existing methods cannot utilize a large size of unlabeled dataset to further improve the model interpretability.To address these limitations, we aim to build an interpretable neural model which can provide sentence-level explanations and apply weakly supervised approach to further leverage the large corpus of unlabeled datasets to boost the interpretability in addition to improving prediction performance as existing works have done.In this work, we propose the Variational Contextual Consistency Sentence Masking (VCCSM) method to automatically extract key sentences based on the context in the classifier, using both labeled and unlabeled datasets.Results of our experiments on RRP along with European Convention of Human Rights (ECHR) datasets demonstrate that VCCSM is able to improve the model interpretability for the long document classification tasks using the area over the perturbation curve and post-hoc accuracy as evaluation metrics.
KESA: A Knowledge Enhanced Approach For Sentiment Analysis

Though some recent works focus on injecting sentiment knowledge into pre-trained language models, they usually design mask and reconstruction tasks in the post-training phase. In this paper, we aim to benefit from sentiment knowledge in a lighter way. To achieve this goal, we study sentence-level sentiment analysis and, correspondingly, propose two sentiment-aware auxiliary tasks named sentiment word cloze and conditional sentiment prediction. The first task learns to select the correct sentiment words within the input, given the overall sentiment polarity as prior knowledge. On the contrary, the second task predicts the overall sentiment polarity given the sentiment polarity of the word as prior knowledge. In addition, two kinds of label combination methods are investigated to unify multiple types of labels in each task. We argue that more information can promote the models to learn more profound semantic representation. We implement it in a straightforward way to verify this hypothesis. The experimental results demonstrate that our approach consistently outperforms pre-trained models and is additive to existing knowledge-enhanced post-trained models. The code and data are released at https://github.com/lshowway/KESA.
Korean Tokenization for Beam Search Rescoring in Speech Recognition

The performance of automatic speech recognition (ASR) models can be greatly improved by proper beam-search decoding with external language model (LM). There has been an increasing interest in Korean speech recognition, but not many studies have been focused on the decoding procedure. In this paper, we propose a Korean tokenization method for neural network-based LM used for Korean ASR. Although the common approach is to use the same tokenization method for external LM as the ASR model, we show that it may not be the best choice for Korean. We propose a new tokenization method that inserts a special token, SkipTC, when there is no trailing consonant in a Korean syllable. By utilizing the proposed SkipTC token, the input sequence for LM becomes very regularly patterned so that the LM can better learn the linguistic characteristics. Our experiments show that the proposed approach achieves a lower word error rate compared to the same LM model without SkipTC. In addition, we are the first to report the ASR performance for the recently introduced large-scale 7,600h Korean speech dataset.
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks

We explore the utility of information contained within a dropout based Bayesian neural network (BNN) for the task of detecting out of distribution (OOD) data. We first show how previous attempts to leverage the randomized embeddings induced by the intermediate layers of a dropout BNN can fail due to the distance metric used. We introduce an alternative approach to measuring embedding uncertainty, justify its use theoretically, and demonstrate how incorporating embedding uncertainty improves OOD data identification across three tasks: image classification, language classification, and malware detection.
Playable Environments: Video Manipulation in Space and Time

We present Playable Environments - a new representation for interactive video generation and manipulation in space and time. With a single image at inference time, our novel framework allows the user to move objects in 3D while generating a video by providing a sequence of desired actions. The actions are learnt in an unsupervised manner. The camera can be controlled to get the desired viewpoint. Our method builds an environment state for each frame, which can be manipulated by our proposed action module and decoded back to the image space with volumetric rendering. To support diverse appearances of objects, we extend neural radiance fields with style-based modulation. Our method trains on a collection of various monocular videos requiring only the estimated camera parameters and 2D object locations. To set a challenging benchmark, we introduce two large scale video datasets with significant camera movements. As evidenced by our experiments, playable environments enable several creative applications not attainable by prior video synthesis works, including playable 3D video generation, stylization and manipulation. Further details, code and examples are available at https://willi-menapace.github.io/playable-environments-website
A Product Production and Transfer Application with Blockchain

In traditional software systems, user information is kept in databases of the system. Much of this information is critical. Theft of data and getting into the hands of malicious people can cause big problems. The most efficient way to get rid of this is to keep the information encrypted, to decrypt the encrypted information with private keys that are not registered in the system, to never access the encrypted information without the private key, and to use it in platform-independent systems. Blockchain technology offers us this system. Blockchain technology, which can be applied very easily in economic fields or systems with transparent data policy of priority, is also the basic structure of cryptocurrencies. Its importance is increasing day by day in the world and it is increasing its weight in banking-finance, voting, asset management and company-specific software systems. In this study, encryption methods of Blockchain technology, data verification approaches, wallet creation, the roles of public and private keys in wallets in the system, their authorization, the formation of transfers within a certain rule and their verification by a third party after the transfer are discussed theoretically and practically. An application was developed on peer-to-peer transfers of white goods production and products, which were fictionalized as a scenario, and Blockchain technology was applied.