Reviewerly/exHarmony huggingface dataset:
Inducing Embeddings for Rare Words through Morphological Decomposition, Stemming and Bidirectional Translation

Words in a natural language corpus obey a Zipfian distribution, which means only a minority percentage of the total are frequent but the vast majority are rare. Word embedding techniques heavily rely on the frequencies of words in the corpus and cannot provide reliable representations for words appearing infrequently during training. To address this problem, we propose a novel algorithm to induce embeddings for rare words by leveraging morphological decomposition, stemming and bidirectional translation. Compared to existing approaches, our algorithm maintains a relatively lightweight model but generates qualified representations for a wider range of vocabulary from the same corpus. We have performed evaluations on multiple general domain datasets and specific domain datasets, and the experimental results show that our algorithm achieves better performance than the other state-of-the-art techniques.
An MRF Optimisation Framework for Full 3D Reconstruction of Scenes with Complex Reflectance

The ability to digitise real objects is fundamental in applications such as film post-production, cultural heritage preservation and video game development. While many existing modelling techniques achieve impressive results, they are often reliant on assumptions such as prior knowledge of the scene’s surface reflectance. This considerably restricts the range of scenes that can be reconstructed, as these assumptions are often violated in practice. One technique that allows surface reconstruction regardless of the scene’s reflectance model is Helmholtz Stereopsis (HS). However, to date, research on HS has mostly been limited to 2.5D scene reconstruction. In this paper, a framework is introduced to perform full 3D HS using Markov Random Field (MRF) optimisation for the first time. The paper introduces two complementary techniques. The first approach computes multiple 2.5D reconstructions from a small number of viewpoints and fuses these together to obtain a complete model, while the second approach directly reasons in the 3D domain by performing a volumetric MRF optimisation. Both approaches are based on optimising an energy function combining an HS confidence measure and normal consistency across the reconstructed surface. The two approaches are evaluated on both synthetic and real scenes, measuring the accuracy and completeness obtained. Further, the effect of noise on modelling accuracy is experimentally evaluated on the synthetic dataset. Both techniques achieve sub-millimetre accuracy and exhibit robustness to noise. In particular, the method based on full 3D optimisation is shown to significantly outperform the other approach.
Enhanced Alternate Action Recommender System Using Recurrent Patterns and Fault Detection System for Smart Home Users

We present a fault tolerant alternate action recommender system for smart home Internet of Things (IoT) users to enrich the user experience with uninterrupted routines and various methods to achieve the regular routines in the smart home system. Our system takes events data from the smart home IoT devices as input, performs preprocessing using the big data handling techniques to transform it to be applicable to our system, applies our custom pattern-mining algorithm to derive the highly probable and active recurrent patterns of an individual user, ensures those frequently used devices are up and running using our fault detection monitoring system, and then finally recommends the alternate possibilities of achieving the deviated actions. Our custom fault detection system is based on various parameters of the IoT devices and context of the smart home users wherein the alternate recommendations given to the user are practical and useful in real time. We validated our system using user trial methods and various validation techniques.
Design of Microstrip Bandpass Filter Based on Genetic Optimization

In this paper, Structural optimization based microstrip bandpass filter design is discussed. Conventional optimization mainly focuses on the dimensional optimization of the filter. Here, the focus is to design a bandpass filter using rectangular patch-based resonator. The obtained result is then optimized using Genetic Algorithm which is part of Evolutionary algorithm. In order to obtain an optimized result, we define a fitness function in terms of S11 (dB) and S21 (dB) parameters. The final design of the filter will possess maximum transmission co-efficient and minimum reflection co-efficient.
A Hidden Feature Selection Method based on l<sub>2,0</sub>-Norm Regularization for Training Single-hidden-layer Neural Networks

Feature selection is an important data preprocessing for machine learning. It can improve the performance of machine learning algorithms by removing redundant and noisy features. Among all the methods, those based on l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norms or l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norms have received considerable attention due to their good performance. However, these methods cannot produce exact row sparsity to the weight matrix, so the number of selected features cannot be determined automatically without using a threshold. To this end, this paper proposes a feature selection method incorporating the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,0</sub> -norm, which can guarantee exact row sparsity of weight matrix. A method based on iterative hard thresholding (IHT) algorithm is also proposed to solve the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,0</sub> - norm regularized least square problem. For fully using the role of row-sparsity induced by the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,0</sub> -norm, this method acts as network pruning for single-hidden-layer neural networks. This method is conducted on the hidden features and it can achieve node-level pruning rather than the connection-level pruning. The experimental results in several public data sets and three image recognition data sets have shown that this method can not only effectively prune the useless hidden nodes, but also obtain better performance.
Structural Dictionary Learning based on Non-convex Surrogate of ℓ₂,₁ Norm for Classification

Recently, group sparse representation which is based on a hypothesis about correlation of coefficient variables has attracted much attention due to its effectiveness and robustness in dictionary learning. Traditional group sparse representation methods use ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm to enforce the estimation of models with joint sparsity patterns, which often leads to over-punishment phenomenon. To solve this issue, we replace ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> with non-convex surrogate of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , and give a general solver for the corresponding optimization algorithm. Experimental results confirm the effectiveness of our proposed method.
Improving Variational Auto-Encoder with Self-Attention and Mutual Information for Image Generation

When Variational Auto-Encoder (VAE) is applied to the image generation task, it tends to generate images with blurred borders. To solve this problem, we use mutual information to maximize the similarity between the data feature generated by the encoder and the shallow feature, and use self-attention to weight the features of different dimensions to increase the correlation of similar features. Our experiments show that our Self-Attention AE can generate higher quality samples than VAE and Wasserstein Auto-Encoder (WAE), as measured by the Freshet Inception Distance (FID) score.
The 1-Bit Instrument

The 1-bit sonic environment (perhaps most famously musically employed on the ZX Spectrum) is defined by extreme limitation. Yet, belying these restrictions, there is a surprisingly expressive instrumental versatility. This article explores the theory behind the primary, idiosyncratically 1-bit techniques available to the composer-programmer, those that are essential when designing “instruments” in 1-bit environments. These techniques include pulse width modulation for timbral manipulation and means of generating virtual polyphony in software, such as the pin pulse and pulse interleaving techniques. These methodologies are considered in respect to their compositional implications and instrumental applications.
Low Complexity Channel Prediction Using TFOS-ELM Method for Massive MIMO Systems

Multiple-input multiple-output (MIMO) technology can potentially help to achieve high data rates for multiuser communication. To achieve better performance, the channel state information (CSI) is estimated by the pilot. However, the estimated CSI cannot be used in downlinks when the mobile speed is very high, since it becomes outdated due to the rapid channel variation. In a massive MIMO system, the issue of outdated CSI is serious when using traditional techniques. Therefore, in order to obtain accurate CSI, the prediction of future CSI is required. In this paper, a low complexity online extreme learning machine (ELM) is proposed for the online prediction of the fast fading channel. First, we introduce the structure of the online sequential extreme learning machine (OS-ELM) and combine the training process of the OS-ELM with a forgetting mechanism (FM) to predict fast changing channels. Second, we use the truncated polynomial expansion (TPE) to reduce the computational complexity of the OS-ELM with the FM (FOS-ELM). In addition, the performance of the proposed algorithm is verified through simulation results, and we apply channel prediction in the precoding process. It is shown that the communication quality is improved by our channel prediction algorithm.
A State-of-Affairs-Semantic Solution to the Problem of Extensionality in Free Logic

If one takes seriously the idea that a scientific language must be extensional, and accepts Quine’s notion of truth-value-related extensionality, and also recognizes that a scientific language must allow for singular terms that do not refer to existing objects, then there is a problem, since this combination of assumptions must be inconsistent. I will argue for a particular solution to the problem, namely, changing what is meant by the word ‘extensionality’, so that it would not be the truth-value that had to be preserved under the substitution of co-extensional expressions, but the state of affairs that the sentence described. The question is whether or not elementary sentences containing empty singular terms, such as ‘Vulcan rotates’, are extensional in the substitutivity sense. Five conditions are specified under which extensionality in the substitutivity sense of such sentences can be secured. It is demonstrated that such sentences are state-of-affairs-as-extension-related extensional. This implies (in accordance with the basic idea of state-of-affairs semantics) that such sentences are also truth-value-related extensional in Quine’s sense, but not truth-value-as-extension-related extensional.
A Modified Block Based Biometric Fragile Watermark Technique

In the era of digital communication data sent through networks has increased. Providing security to data which is in the form of image, text, video, audio has been big challenge. For copyright information and authentication of the data, biometrics authentication is used. At the same time protection of biometrics data is also essential parameter. To increase the security level of biometric data watermarking technique is used. In this, paper we proposed block based biometric fragile watermarking technique which detects slight changes in the watermarked data. Fragile watermark technique generates watermark depending on the embed watermark. In this proposed method authentication data inserted into the least significant bit (LSB) of cover image. The experimental result shows the quality of the image by using conventional LSB technique, with use of key.
Performance Comparison of Particle Swarm Optimization and Genetic Algorithm for Feature Subset Selection in Keystroke Dynamics

In recent years there has been increased focus on use of keystroke dynamics based authentication for mobile phones. The accuracy of the keystroke dynamics usually increases with an increase in features. But due to the limited processing power of mobile phones, it becomes essential to keep the feature subset minimal and at the same time keeping the accuracy unaffected. Thus, optimization algorithms play a vital role for feature subset selection. We compared the performance of Particle Swarm Optimization and Genetic Algorithm in reducing the number of features. It was observed that the feature set reduced from 49 to a range between 17-26 and the accuracy of the system increased to a maximum of 92.58%.
Development of an Automated Laboratory Assets Inventory Control With Security System

This study is the development of an automated laboratory asset with an inventory system and security features using passive Radio Frequency ID (RFID). The RFID, in combination with bar code, is used to log-in and log- out borrowed equipment during laboratory and non-laboratory classes or even during research class. With the automated system, the dispensing process is reduced. A real-time inventory of equipment, as well as utilization reports, is easily generated and accessed by the authorized personnel. From the manual system with a cycle time process of 15.45 minutes for dispensing of equipment, the dispensing time was reduced to 2.92 minutes, and 5.17 minutes of returning of equipment to 4.54 minutes, which are 81.10% and 12.19% improvement respectively. The inventory report that requires a day to prepare generated in a minute. A user-interface survey was deployed to the students to determine the acceptability of the system. Sixty respondents had high acceptability in terms of user-interface, perceived usefulness, and perceived ease of use. The system has been tested for three semesters now in the Engineering Laboratory.
DeepManner: Automatically Determining Manner of Death

Medical examiners are trained medical professional who are responsible for determining cause and manner of death (natural, accident, suicide, homicide, and undetermined). Their jurisdiction includes unexpec ted, violent, and unexplained deaths. The final conclusions determined by the medical examiners can have significant consequences, therefore it is important every case is handled in a consistent and unbiased fashion. We will show how modern natural language processing techniques can be used to automatically determine the manner of death from the textual descriptions of injuries, demographic info, scene description, and the microscopy results found in autopsy reports.
Semantic Correlations Loss: Improving Model Interpretability for Multi-class Classification

Despite that convolutional neural networks (CNNs) have recently demonstrated high-quality object classification, the trained models suffer from their extreme unexplainability. In this paper, we propose a general method, named as semantic correlation loss, for introducing common-sense knowledge to CNN architectures. In contrast to traditional cross-entropy loss which only considers the ground-truth class, we exploit to be aware of the accuracy of all classes. By adding this simple add-on, current multi-class classification models are able to improve on the ability of “making mistakes reasonably”. In addition, a slight performance gain is also achieved. Experimental results on CUB-200-2011, CIFAR -10 and 100 are provided to demonstrate the efficacy of our proposed method. Moreover, this novel loss is able to be applied in any setting as long as the labels of training data are included in the common sense knowledge base.
Real Time Feature Point Detectors for Determining Ego Motions

Ego motion determination is an important application to recognize the motion of an observer in real time. Here we have implemented feature point detector using FFME for ego-motion determination. The main objective of this work is to detect the motions of any objects such as humans, animals, vehicles, missiles and so on. The camera plays a vital role in surveillance of isolated and or restricted area. To improve the intelligence of camera some efficient video processing algorithms are needed. In this process, the detection of unwanted features were derived and developed for some video processing applications. There are various types of feature detectors like edge, corner and points which can be detected from a video frame. To detect those features, feature point detectors like SIFT, ORB, FAST, BRIEF and FFME are more reliable with good efficiency, less memory size and high processing speed. These feature point detectors are compared and evaluated under parameters of speed and number of feature point matches. Although, FFME algorithm has high speed when compared to other feature point detection algorithms, it is prone to more errors caused by illumination differences, as it detects the feature points using Gradient magnitude of the pixel. To overcome this, we have introduced a novel work that uses FFME with gradient phase technique. The resultant output exhibits high processing speed with less error when compared to other feature point detectors and even FFME with gradient magnitude.
A Representative Fragment Method of Analyzing Complex Systems of Smart Contracts

The paper presents the use of states of explosionproof method for analyzing the behavior of systems that provide smart contract technology.The selected example system is ShadowEth, whose main task is to ensure sufficient confidentiality of information stored in the Ethereum blockchain currency.The Petri network model for the ShadowEth system has been presented.The system properties according to the specifications have been defined.Properties described in a certain extension of the TCTL logic and verification have been carried out.
A Multi-population Helper and Equivalent Objective Differential Evolution Algorithm

The helper and equivalent objective method is to convert a constrained optimisation problem into an optimisation problem consisting of helper and equivalent objectives with only boundary constraints, then to solve the converted problem by a decomposition-based evolutionary algorithm. Based on this method, an algorithm called HECO-DE was designed which outperformed the state-of-art evolutionary algorithms in IEEE CEC 2018 constrained optimisation competition. However, it is observed that its computation time increases quickly as the dimension of the decision space. A potential solution to this problem is to divide a population into several subpopulations and run them separately for parallel processing. This paper considers a multi-population implementation of HECO-DE called HECOD-Em, which splits a population into three sub-populations. Objective decomposition in each subpopulation is assigned with different weights. This leads to different searches directions. A process of regularly sorting all individuals is employed for individual exchange among subpopulations. HECO-DEm is compared with HECO-DE and algorithms in IEEE CEC 2018 constrained optimisation competition. Experiment results show that its overall performance is the same as HECO-DE and better than other algorithms. Compared with HECO-DE, the multi-population version performs slightly better on 50 and 100 dimensional functions. Furthermore, HECO-DEm has great potential in parallel processing.
Least Squares Twin Support Vector Machine Based on Manifold-based Within-class Scatter

Although twin support vector machine (TWSVM) is widely used in practice, it only takes the empirical risk minimization principle into consideration while neglects the data distribution information, which seriously limits the classification performance. In view of this, robust minimum class variance twin support vector machine (RMCV-TWSVM) is proposed. Compared with TWSVM, RMCV-TWSVM has better generalization ability because it takes both the empirical risk minimization principle and data distribution characteristics into consideration. While TWSVM and RMCV-TWSVM always neglect the local characteristics of each class sample. Based on the above analysis, this paper presents least squares twin support vector machine based on manifold-based within-class scatter (MWCS-LSTSVM), which not only focuses on the empirical risk minimization principle and data distribution characteristics, but also preserves the local manifold structure of each class sample. At the same time, the solutions of MWCS-LSTSVM comes directly from solving a pair of systems of linear equations rather than solving two quadratic programming problems. Thus, MWCS-LSTSVM has low time complexity. The experimental results show the significant advantages of proposed approach in both classification accuracy and computational time.
ABR-HIC: Attention Based Bidirectional RNN for Hierarchical Industry Classification

Accurate industry classification of national economic activities as an important component in the construction of economic structure and as the basis of the formulation of economic policies and management of national economic activities has been gaining increasing attention. However, owing to the rapid growth in the number of industries, it is become increasingly difficult for tax bureaus to classify the registered taxpayers' industries. Conventional industrial classification methods only focus on the text features, which can not be analyzed and judged comprehensively according to the registration information, and can only carry on single-label classification since they neglect the primary and secondary relationships between the main and subsidiary industries, which can not meet application requirements. To better address these challenges, this paper proposes a model known as attention based bidirectional RNN for hierarchical industry classification (ABR-HIC), which is the first approach, to the best of our knowledge, to simultaneously address comprehensive registration information utilization and multi-label classification for the main and subsidiary industries. Our architecture establishes a bidirectional RNN using a word-attention mechanism, which is able to capture and fully utilize the text and non-text registration information for feature representation. By separating the taxpayer's primary and secondary multi-label classification problem corresponding to the main and subsidiary industries, respectively, into two subtasks and through multi-task learning, our model can provide comprehensive primary and secondary multi-industrial labels. Experiments were conducted on real tax data-sets of the Shaanxi Province, China and the results demonstrate the outstanding performance of our architecture in terms of both the classification effect and training time compared with those of state-of-the-art approaches.