Reviewerly/exHarmony huggingface dataset:
Robot Concept Acquisition Based on Interaction Between Probabilistic and Deep Generative Models

We propose a method for multimodal concept formation. In this method, unsupervised multimodal clustering and cross-modal inference, as well as unsupervised representation learning, can be performed by integrating the multimodal latent Dirichlet allocation (MLDA)-based concept formation and variational autoencoder (VAE)-based feature extraction. Multimodal clustering, representation learning, and cross-modal inference are critical for robots to form multimodal concepts from sensory data. Various models have been proposed for concept formation. However, in previous studies, features were extracted using manually designed or pre-trained feature extractors and representation learning was not performed simultaneously. Moreover, the generative probabilities of the features extracted from the sensory data could be predicted, but the sensory data could not be predicted in the cross-modal inference. Therefore, a method that can perform clustering, feature learning, and cross-modal inference among multimodal sensory data is required for concept formation. To realize such a method, we extend the VAE to the multinomial VAE (MNVAE), the latent variables of which follow a multinomial distribution, and construct a model that integrates the MNVAE and MLDA. In the experiments, the multimodal information of the images and words acquired by a robot was classified using the integrated model. The results demonstrated that the integrated model can classify the multimodal information as accurately as the previous model despite the feature extractor learning in an unsupervised manner, suitable image features for clustering can be learned, and cross-modal inference from the words to images is possible.
Neuro-Fuzzy: Artificial Neural Networks &amp; Fuzzy Logic

Abstract: Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches. There are two basic streams of neural network and fuzzy system study. Modelling several elements of the human brain (structure, reasoning, learning, perception, and so on) as well as artificial systems and data: pattern clustering and recognition, function approximation, system parameter estimate, and so on. In general, neural networks and fuzzy logic systems are parameterized nonlinear computing methods for numerical data processing (signals, images, stimuli). These algorithms can be integrated into dedicated hardware or implemented on a general-purpose computer. The network system acquires knowledge through a learning process. Internal parameters are used to store the learned information (weights). Keywords: Artificial Neural Networks (ANNs), Neural Networks (NNs), Fuzzy Logic (FL), Neuro-Fuzzy, Probability Reasoning, Soft Computing, Fuzzification, Defuzzification, Fuzzy Inference Systems, Membership Function.
Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case

Contextualised word embeddings is a powerful tool to detect contextual synonyms.However, most of the current state-of-the-art (SOTA) deep learning concept extraction methods remain supervised and underexploit the potential of the context.In this paper, we propose a self-supervised pre-training approach which is able to detect contextual synonyms of concepts being training on the data created by shallow matching.We apply our methodology in the sparse multi-class setting (over 15,000 concepts) to extract phenotype information from electronic health records.We further investigate data augmentation techniques to address the problem of the class sparsity.Our approach achieves a new SOTA for the unsupervised phenotype concept annotation on clinical text on F1 and Recall outperforming the previous SOTA with a gain of up to 4.5 and 4.0 absolute points, respectively.After fine-tuning with as little as 20% of the labelled data, we also outperform BioBERT and ClinicalBERT.The extrinsic evaluation on three ICU benchmarks also shows the benefit of using the phenotypes annotated by our model as features.
Cyclic Path Planning of Hyper-redundant Manipulator using Whale Optimization Algorithm

This paper develops a path planning algorithm of hyper-redundant manipulators to achieve a cyclic property.The basic idea is based on a geometrical analysis of a 3-link planar series manipulator in which there is an orientation angle boundary of a prescribed path.To achieve the repetitive behavior, for hyper-redundant manipulators consisting of 3-link components, an additional path is chosen in such away so that it is a repetitive curve which has the same curve frequency with the prescribed end-effector path.To solve the redundancy resolution, meta-heuristic optimizations, namely Genetic Algorithm (GA) and Whale Optimization Algorithm (WOA), are applied to search optimal trajectories inside local orientation angle boundaries.Results show that using constant of the local orientation angle trajectories for the 3-link component, the cyclic properties can be achieved.The performance of the WOA shows very promising result where generally it obtains the lowest fitness value as compare with the GA.Depending on the complexity of the path planning, dividing the path into several stages via intermediate points may be necessary to achieve the good posture.The performance of the swarm based meta-heuristic optimization, namely the WOA, shows very promising result where generally it obtains the lowest fitness value as compare with the GA.Using the developed approach, not only the cyclic property is obtained but also the optimal movement of the hyperredundant manipulator is achieved.
Constrained Quantum Optimization for Resource Distribution Management

The cloud computing field suffers from the heavy processing caused by the exponentially increasing data traffic.Therefore, optimizing the network performance and achieving a better quality of service (QoS) became a central goal.In cloud computing, the problem of energy consumption of resource distribution management system (RDMS) is presented as an optimization problem.Most of the existing classical optimization approaches, such as heuristic and metaheuristic have high computational complexity.In this work, we proposed a quantum optimization strategy that executes the tasks exponentially faster and with high accuracy named constrained quantum optimization algorithm (CQOA).We exploit the CQOA in RDMS as a toy example for pointing out the efficiency of the proposed quantum strategy in reducing energy consumption and computational complexity.Following that, we investigate the CQOA's implementation, setup, and computational complexity.Finally, we create a simulation environment to evaluate the efficiency of the suggested implemented constrained quantum strategy.
A Novel Travel Group Recommendation Model Based on User Trust and Social Influence

The interactions between group members often have a significant impact on the results of group recommendations. The traditional group recommendation algorithm does not consider the trust and social influence among users. It involves a low utilization rate of social relationship information, which leads to a low accuracy and satisfaction of group recommendations. Considering these issues, in this study, we propose a travel group recommendation model based on user trust and social influence. Based on the user trust relationship, this model defines the user direct and indirect trust and calculates the user global trust by combining the two trusts. Subsequently, the PageRank algorithm is used to calculate the social influence of users based on their interaction relationship history. Thereafter, a consensus model integrating the intra- and intergroup prediction scores is designed by integrating users’ global trust and social influence to realize group recommendations for tourist attractions. Comparison experiments with several well-known group recommendation models for datasets of different scenic spots in Beijing demonstrate that the proposed model provides a better recommendation performance.
HEVC Information-Hiding Algorithm Based on Intra-Prediction and Matrix Coding

Aiming at the problem that the data hiding algorithm of high efficiency video coding (HEVC) has great influence on the video bit rate and visual quality, an information hiding algorithm based on intra prediction mode and matrix coding is proposed. Firstly, 8 prediction modes are selected from 4×4 luminance blocks in I frame to embed the hidden information. Then, the Least Significant Bit (LSB) algorithm is used to modulate the LSB of the last prediction mode. Finally, the modulated luminance block is re-encoded to embed 4 bits secret information. Experimental results show that the algorithm improves the embedding capacity, guarantees the subjective and objective quality of the video, and the bit rate increases by 1.14% on average.
End-to-End Optimized Multi-Stage Vector Quantization of Spectral Envelopes for Speech and Audio Coding

Spectral envelope modeling is an instrumental part of speech and audio codecs, which can be used to enable efficient entropy coding of spectral components.Overall optimization of codecs, including envelope models, has however been difficult due to the complicated interactions between different modules of the codec.In this paper, we study an end-to-end optimization methodology to optimize all modules in a codec integrally with respect to each other while capturing all these complex interactions with a global loss function.For the quantization of the spectral envelope parameters with a fixed bitrate, we use multistage vector quantization which gives high quality, but yet has a computational complexity which can be realistically applied in embedded devices.The obtained results demonstrate benefits in terms of PESQ and PSNR in comparison to the 3GPP EVS, as well as our recently proposed PyAWNeS codecs.
Hybrid Methodology Based on Extension Neural Network for Fault Diagnosis of Photovoltaic Module

method, photovoltaic (PV) moduleWe propose a human-machine graphic control fault diagnosis system based on an extension neural network (ENN) and the chaos synchronization detection method.A high-frequency signal is injected into a photovoltaic (PV) module to observe the voltage variation under different fault conditions, and the fault type is diagnosed using the proposed algorithm.Firstly, defects are introduced into the PV module and a high-frequency signal is injected by a signal generator.The high-frequency oscilloscope through the high-frequency sensor captures the voltage signal.The feature of the signal is calculated by the chaos synchronization detection method and a chaotic error scatter map is established.The chaos eye coordinates of the scatter diagram are used as eigenvalues for fault diagnosis.Finally, the ENN is used for fault diagnosis of the PV module.Also, from a comparison of analysis results with those of a traditional neural network, the ENN can identify the type of PV module fault rapidly and the recognition accuracy is as high as 87.5%.Small changes in the voltage signal can be detected effectively by using the chaos synchronization detection method, and the preprocessing of big data is reduced.The PV module fault state is identified accurately to demonstrate the applicability of the method to PV module fault diagnosis.
Corporate Social Responsibility and Firm Performance in SMEs: Empirical Evidence from Cameroon

We examined the mediating effects of corporate governance and image on CSR and performance link. We find CSR positive related to firm performance but insignificant. We further established CSR and FP are positive and insignificant without the existence of CI and CG but becomes significant with the inclusion of both mediating variables. Therefore, corporate governance codes and principles should be enhanced in Cameroon, since CSR engagement helps in better governance practice through establishing good internal controls and monitoring that ultimately enhances a firm financial performance. Several policy recommendations were discussed.
You should evaluate your language model on marginal likelihood over tokenisations

Neural language models typically tokenise input text into sub-word units to achieve an open vocabulary.The standard approach is to use a single canonical tokenisation at both train and test time.We suggest that this approach is unsatisfactory and may bottleneck our evaluation of language model performance.Using only the one-best tokenisation ignores tokeniser uncertainty over alternative tokenisations, which may hurt model out-of-domain performance.In this paper, we argue that instead, language models should be evaluated on their marginal likelihood over tokenisations.We compare different estimators for the marginal likelihood based on sampling, and show that it is feasible to estimate the marginal likelihood with a manageable number of samples.We then evaluate pretrained English and German language models on both the one-besttokenisation and marginal perplexities, and show that the marginal perplexity can be significantly better than the one best, especially on out-of-domain data.We link this difference in perplexity to the tokeniser uncertainty as measured by tokeniser entropy.We discuss some implications of our results for language model training and evaluation, particularly with regard to tokenisation robustness.
Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories

Measuring event salience is essential in the understanding of stories.This paper takes a recent unsupervised method for salience detection derived from Barthes Cardinal Functions and theories of surprise and applies it to longer narrative forms.We improve the standard transformer language model by incorporating an external knowledgebase (derived from Retrieval Augmented Generation) and adding a memory mechanism to enhance performance on longer works.We use a novel approach to derive salience annotation using chapteraligned summaries from the Shmoop corpus for classic literary works.Our evaluation against this data demonstrates that our salience detection model improves performance over and above a non-knowledgebase and memory augmented language model, both of which are crucial to this improvement.
PermuteFormer: Efficient Relative Position Encoding for Long Sequences

A recent variation of Transformer, Performer, scales Transformer to longer sequences with a linear attention mechanism.However, it is not compatible with relative position encoding, which has advantages over absolute position encoding.In this paper, we discuss possible ways to add relative position encoding to Performer.Based on the analysis, we propose Per-muteFormer, a Performer-based model with relative position encoding that scales linearly on long sequences.PermuteFormer applies position-dependent transformation on queries and keys to encode positional information into the attention module.This transformation is carefully crafted so that the final output of selfattention is not affected by absolute positions of tokens.PermuteFormer introduces negligible computational overhead by design that it runs as fast as Performer.We evaluate Per-muteFormer on Long-Range Arena, a dataset for long sequences, as well as WikiText-103, a language modeling dataset.The experiments show that PermuteFormer uniformly improves the performance of Performer with almost no computational overhead and outperforms vanilla Transformer on most of the tasks. 1
Adaptive multi-threshold object selection in remote sensing images

Introduction: Detection, selection and analysis of objects of interest in digital images is a major problem for remote sensing and technical vision systems. The known methods of threshold detection and selection of objects avoid using the processing results, therefore not providing a low probability of false alarms, and not keeping the shape of the selected objects well enough. There are only few results from the studies about quantifying the quality of such algorithms on either model or real images. Purpose: Studying the effectiveness of algorithms for detecting, selecting, and localizing objects of interest using their geometric characteristics, when the object properties and background are a priori uncertain, and the shape of the selected objects is kept unchanged. Results: We have obtained and studied the characteristics of algorithms for detecting and selecting objects of interest on test models of monochrome images. These software-implemented algorithms use multi-threshold processing, providing a set of binary slices. This makes it possible to perform morphological processing of objects on each slice in order to analyze their geometric characteristics and then select them according to geometric criteria, taking into account the percolation effect which causes changes in the area, and fragmentation of the objects. As a result of analyzing these changes, an adaptive detection threshold is set for each of the selected objects. The selection allows you to significantly reduce the number of false positives during the detection and to use lower thresholds, increasing the correct detection probability. We present the detection characteristics and the results of test model processing, as well as the results of object selection on a real television and radar image, confirming the effectiveness of the considered algorithms. Practical relevance: The proposed algorithms can more effectively select objects on images of various nature obtained in remote sensing, material research or medical diagnostics systems. Their microprocessor implementation is much simpler than the implementation of universal trainable neural network algorithms.
Static hand gesture recognition of Arabic sign language by using deep CNNs

An Arabic sign language recognition using two concatenated deep convolution neural network models DenseNet121 &amp;amp; VGG16 is presented. The pre-trained models are fed with images, and then the system can automatically recognize the Arabic sign language. To evaluate the performance of concatenated two models in the Arabic sign language recognition, the red-green-blue (RGB) images for various static signs are collected in a dataset. The dataset comprises 220,000 images for 44 categories: 32 letters, 11 numbers (0:10), and 1 for none. For each of the static signs, there are 5000 images collected from different volunteers. The pre-trained models were used and trained on prepared Arabic sign language data. These models were used after some modification. Also, an attempt has been made to adopt two models from the previously trained models, where they are trained in parallel deep feature extractions. Then they are combined and prepared for the classification stage. The results demonstrate the comparison between the performance of the single model and multi-model. It appears that most of the multi-model is better in feature extraction and classification than the single models. And also show that when depending on the total number of incorrect recognize sign image in training, validation and testing dataset, the best convolutional neural networks (CNN) model in feature extraction and classification Arabic sign language is the DenseNet121 for a single model using and DenseNet121 &amp;amp; VGG16 for multi-model using.
Utilizing test items analysis to examine the level of difficulty and discriminating power in a teacher-made test

Apart from teaching, English language teachers need to assess their students by giving a test to know the students� achievements. In general, teachers are barely conducting item analysis on their tests. As a result, they have no idea about the quality of their test distributed to the students. The present study attempts to figure out the levels of difficulty (LD) and the discriminating power (DP) of the multiple-choice (MC) test item constructed by an English teacher in the reading comprehension test utilizing test item analysis. This study employs a qualitative approach. For this purpose, a test of 50-MC test items of reading comprehension was obtained from the students� test results. Thirty-five students of grade eight took part in the MC test try-out. They are both male (15) and female (20) students of junior high school 2 Kempo, in West Nusa Tenggara Province. The findings revealed that16 items out of 50 test items were rejected due to the poor and worst quality level of difficulty and discriminating index. Meanwhile, 12 items need to be reviewed due to their mediocre quality, and 11 items are claimed to have good quality items. Besides, 11 items out of 50 test items were considered as the excellent quality as their DP scores reached around 0.44 through 0.78. The implications of the present study will shed light on the quality of teacher-made test items, especially for the MC test.
Sonic Environments as Systems of Places: A Critical Reading of Husserl’s <i>Thing and Space</i>

Abstract This article offers a thorough and critical reading of Husserl’s Thing and Space . This reading is principally motivated by the effort to methodologically design a phenomenological–topological approach to the research of lived sonic environments. In this book, Husserl lays foundations of phenomenological topology by understanding perceptions as places and defining, consequently, the space as a system of places. The critical reading starts with pointing out the ambiguity of location in Thing and Space , which consists mainly in the insufficient implementation of the distinction between the location and the localization. Further investigations then reveal the roots of this ambiguity in both the preference of visual perception and the omission of subjective aspects of kinesthesia. The article critically examines Husserl’s notion of the appended localization that expresses the marginalization of (among others) acoustic experience. In conclusion, the article utilizes the critical findings to formulate the project of a place-based (phenomenological–topological), medium-centered, and multi-sensory approach to sonic environments.
Design of a Web System to Optimize the Logistics and Costing Processes of a Chocolate Manufacturing Company

The research work is focused on the solution to a problem that a company dedicated to the manufacture of chocolates has.This company does not have a computer system to help it improve its management.The information recorded from the logistics and cost processes is stored locally in Excel files, and the information from the operational processes is stored in bond sheets and then transferred to Excel files.Since information is very valuable for a company, it must be orderly, accessible, and secure.Therefore, the Scrum methodology was implemented for the development of a prototype web system for the company.The prototype of this web system was developed with Adobe XD software because it is easy to use and complete for the design of interfaces for web pages.As a result of the development of the prototype of the web system, there is a record of information in an orderly, interactive, easy, fast, and above all safe way.And it was concluded that the management of the logistics and cost area of the company was optimized and improved.