Reviewerly/exHarmony huggingface dataset:
Medical Feature Selection Approach Based on Generalized Normal Distribution Algorithm

This paper proposes a new pre-processing technique to separate the most effective features from those that might deteriorate the performance of the machine learning classifiers in terms of computational costs and classification accuracy because of their irrelevance, redundancy, or less information; this pre-processing process is often known as feature selection. This technique is based on adopting a new optimization algorithm known as generalized normal distribution optimization (GNDO) supported by the conversion of the normal distribution to a binary one using the arctangent transfer function to convert the continuous values into binary values. Further, a novel restarting strategy (RS) is proposed to preserve the diversity among the solutions within the population by identifying the solutions that exceed a specific distance from the best-so-far and replace them with the others created using an effective updating scheme. This strategy is integrated with GNDO to propose another binary variant having a high ability to preserve the diversity of the solutions for avoiding becoming stuck in local minima and accelerating convergence, namely improved GNDO (IGNDO). The proposed GNDO and IGNDO algorithms are extensively compared with seven state-of-the-art algorithms to verify their performance on thirteen medical instances taken from the UCI repository. IGNDO is shown to be superior in terms of fitness value and classification accuracy and competitive with the others in terms of the selected features. Since the principal goal in solving the FS problem is to find the appropriate subset of features that maximize classification accuracy, IGNDO is considered the best.
Extreme Learning Machine for Automatic Language Identification Utilizing Emotion Speech Data

The technique used for recognizing a language by utilizing pronounced speech is called spoken Language Identification (LID). This field has a high significance in the interaction between human and computer. Besides, it can be implemented in several applications such as call centers, speaker diarization in multilingual environments, and in translation systems using a speech-to-speech manner. However, most studies that used LID systems are used and focused on neutral speech only. Moreover, the application of emotional speech in LID systems is crucial in real applications. Therefore, this study aims to investigate the performance of Extreme Learning Machine (ELM) in LID system by utilizing emotional speech. The system is evaluated based on two different languages (Germany and English language). This study has used the Berlin Emotional Speech Dataset (BESD) for the Germany language while the Ryerson Audio-Visual Dataset of Emotional Speech and Song (RAVDESS) for the English language. Four different evaluation scenarios (All Dataset (AD), Normal-Speech Dependent (N-SD), Gender-Female Dependent (G-FD), and Gender-Male Dependent (G-MD) scenario) have been conducted in order to evaluate the system. The experiments results have shown that the highest performance was achieved an accuracy of 99.08%, 100.00%, 98.22%, and 99.37% for AD, N-SD, G-FD, and G-MD scenario, respectively.
Empirical Auto-Evaluation of Python Code for Performance Analysis of Transformer Network Using T5 Architecture

The immense real-time applicability of Python coding makes the task of evaluating the code highly intriguing, in the Natural Language Processing (NLP) domain. Evaluation of computer programs induces a challenge of logical and arithmetic understanding. Therefore, it is indeed very relevant to analyze the empirical ability of current state-of-the-art sequence-based neural architectures in evaluating small computer programs. One of the possible applications of such analysis is the auto-evaluation of erroneous Python code. In this context, we focused our work on evaluating small python code blocks with or without error and examined the efficiency of the latest T5 Transformer network model in this task. In terms of accuracy, different Rouge scores, and BLEU scores, the performance measurements has been calculated. Observations reveal that T5 Transformer is able to compute the output for both correct and erroneous python code blocks with more than 65% accuracy.
The moderating effect of abusive supervision on information security policy compliance: Evidence from the hospitality industry

Organizations have recognized the importance of information security and have developed information security policies for their employees. Deterrence is often used to enhance employees’ compliance intention. However, the literature reports mixed results for the effects of deterrence, and we argue that those conflicting findings can be due to different managerial contexts across organizations. To understand how managerial factors influence the effects of deterrence, our study focused on abusive supervision and examined how abusive supervision moderated the relationship between deterrence perception and employee’ intention to comply with information security policies. Two rounds of surveys were conducted to collect data from Chinese hotel employees. The results show that abusive supervision could not enhance the effect of perceived severity and certainty of deterrence, when compliance intention from the second-round survey was used for hypotheses testing. Our study contributes to the literature by taking the first step toward explaining the inconsistent results in the literature on deterrence. Our study also provides important strategic guidelines informing managers that abusive supervision should not be used to enhance employees’ compliance with information security policies.
Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text

We proposed an application and data variations-independent, generic social media Textual Variations Handler (TVH) to deal with a wide range of noise in textual data generated in various social media (SM) applications for enhanced text analysis. The aim is to build an effective hybrid normalization technique that ensures the use of useful information of the noisy text in its intended form instead of filtering them out to analyze SM text better. The proposed TVH performs context-aware text normalization based on intended meaning to avoid the wrong word substitution. We integrate the TVH with state-of-the-art (SOTA) deep-learning-based text analysis methods to enhance their performance for noisy SM text data. The proposed scheme shows promising improvement in the text analysis of informal SM text in terms of precision, recall, accuracy, and F1-score in simulation.
Location Sensitive Network for Human Instance Segmentation

Location is an important distinguishing information for instance segmentation. In this paper, we propose a novel model, called Location Sensitive Network (LSNet), for human instance segmentation. LSNet integrates instance-specific location information into one-stage segmentation framework. Specifically, in the segmentation branch, Pose Attention Module (PAM) encodes the location information into the attention regions through coordinates encoding. Based on the location information provided by PAM, the segmentation branch is able to effectively distinguish instances in feature-level. Moreover, we propose a combination operation named Keypoints Sensitive Combination (KSCom) to utilize the location information from multiple sampling points. These sampling points construct the points representation for instances via human keypoints and random points. Human keypoints provide the spatial locations and semantic information of the instances, and random points expand the receptive fields. Based on the points representation for each instance, KSCom effectively reduces the mis-classified pixels. Our method is validated by the experiments on public datasets. LSNet-5 achieves 56.2 mAP at 18.5 FPS on COCOPersons. Besides, the proposed method is significantly superior to its peers in the case of severe occlusion.
Toward Efficient Academia-Industry Collaboration: A Case Study of Joint VR System Development

Collaborative academia-industry development and evaluation of virtual reality (VR) systems is a mutually beneficial opportunity to investigate VR technology in a real context and conduct user studies with target users. However, such collaboration is rarely performed due to variations in project pace and work methods. In this article, we introduce the process of action research on joint design, development, and evaluation of a collaborative VR system to address industrial needs. The paper further presents employees’ subjective opinions and perceived value of industrial VR applications and reflects on their involvement throughout the process. The article concludes with a process-oriented framework for remote academia-industry collaboration, supported with practical suggestions on how to support this collaboration. Our experiences reveal the methods and advantages of remote collaboration in all phases of the process and signify the efficiency of the remote framework for academia-industry collaboration, especially relevant in the light of the COVID-19 pandemia..
Object-Level Remote Sensing Image Augmentation Using U-Net-Based Generative Adversarial Networks

With the continuous development of deep learning in computer vision, semantic segmentation technology is constantly employed for processing remote sensing images. For instance, it is a key technology to automatically mark important objects such as ships or port land from port area remote sensing images. However, the existing supervised semantic segmentation model based on deep learning requires a large number of training samples. Otherwise, it will not be able to correctly learn the characteristics of the target objects, which results in the poor performance or even failure of semantic segmentation task. Since the target objects such as ships may move from time to time, it is nontrivial to collect enough samples to achieve satisfactory segmentation performance. And this severely hinders the performance improvement of most of existing augmentation methods. To tackle this problem, in this paper, we propose an object-level remote sensing image augmentation approach based on leveraging the U-Net-based generative adversarial networks. Specifically, our proposed approach consists two components including the semantic tag image generator and the U-Net GAN-based translator. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a public dataset HRSC2016. State-of-the-art generative models, DCGAN, WGAN, and CycleGAN, are selected as baselines. According to the experimental results, our proposed approach significantly outperforms the baselines in terms of not only drawing the outlines of target objects but also capturing their meaningful details.
A note on tight projective 2‐designs

Abstract We study tight projective 2‐designs in three different settings. In the complex setting, Zauner's conjecture predicts the existence of a tight projective 2‐design in every dimension. Pandey, Paulsen, Prakash, and Rahaman recently proposed an approach to make quantitative progress on this conjecture in terms of the entanglement breaking rank of a certain quantum channel. We show that this quantity is equal to the size of the smallest weighted projective 2‐design. Next, in the finite field setting, we introduce a notion of projective 2‐designs, we characterize when such projective 2‐designs are tight, and we provide a construction of such objects. Finally, in the quaternionic setting, we show that every tight projective 2‐design for determines an equi‐isoclinic tight fusion frame of subspaces of of dimension 3.
Point2CN: Progressive two-view correspondence learning via information fusion

Finding reliable correspondences between two views is crucial for solving feature matching based tasks. Given initial correspondences of feature points, recent researches propose an end-to-end permutation-equivariant classification network based on the multi-layer perceptron to label each initial correspondence as inlier or outlier and then regress the camera pose. However, they utilize the PointCN block as network backbones, which cannot gather sufficient contextual information for network learning due to the single sequential structure. In this paper, we propose a new modified block, called Point2CN block, via multi-subsets information fusion, for feature matching. Specifically, the Point2CN block exploits a hierarchical residual-like manner to connect feature map subsets, and then fuses their mutual information by the weighted addition operation to improve feature representation capacity. The simple yet effective Point2CN block can significantly improve the performance of current learning-based feature matching methods with the negligible increase of network parameters. The Point2CN block also will not bring expensive computational overheads since it only involves some novel information fusion operations. Extensive experiments over different challenging datasets on the task of outlier removal and camera pose estimation consistently demonstrate that our Point2CN block is able to gain outstanding performance improvements compared with the existing state-of-the-art methods.
Distributional Models of Category Concepts Based on Names of Category Members

In computational linguistics, a large body of work exists on distributed modeling of lexical relations, focussing largely on lexical relations such as hypernymy (scientist -- person) that hold between two categories, as expressed by common nouns. In contrast, computational linguistics has paid little attention to entities denoted by proper nouns (Marie Curie, Mumbai, ...). These have investigated in detail by the Knowledge Representation and Semantic Web communities, but generally not with regard to their linguistic properties. Our paper closes this gap by investigating and modeling the lexical relation of instantiation, which holds between an entity-denoting and a category-denoting expression (Marie Curie -- scientist or Mumbai -- city). We present a new, principled dataset for the task of instantiation detection as well as experiments and analyses on this dataset. We obtain the following results: (a), entities belonging to one category form a region in distributional space, but the embedding for the category word is typically located outside this subspace; (b) it is easy to learn to distinguish entities from categories from distributional evidence, but due to (a), instantiation proper is much harder to learn when using common nouns as representations of categories; (c) this problem can be alleviated by using category representations based on entity rather than category word embeddings.
3D Expression-Invariant Face Verification Based on Transfer Learning and Siamese Network for Small Sample Size

Three-dimensional (3D) face recognition has become a trending research direction in both industry and academia. However, traditional facial recognition methods carry high computational costs and face data storage costs. Deep learning has led to a significant improvement in the recognition rate, but small sample sizes represent a new problem. In this paper, we present an expression-invariant 3D face recognition method based on transfer learning and Siamese networks that can resolve the small sample size issue. First, a landmark detection method utilizing the shape index was employed for facial alignment. Then, a convolutional network (CNN) was constructed with transfer learning and trained with the aligned 3D facial data for face recognition, enabling the CNN to recognize faces regardless of facial expressions. Following that, the weighted trained CNN was shared by a Siamese network to build a 3D facial recognition model that can identify faces even with a small sample size. Our experimental results showed that the proposed method reached a recognition rate of 0.977 on the FRGC database, and the network can be used for facial recognition with a single sample.
ScatSimCLR: self-supervised contrastive learning with pretext task regularization for small-scale datasets

In this paper, we consider a problem of self-supervised learning for small-scale datasets based on contrastive loss between multiple views of the data, which demonstrates the state-of-the-art performance in classification task. Despite the reported results, such factors as the complexity of training requiring complex architectures, the needed number of views produced by data augmentation, and their impact on the classification accuracy are understudied problems. To establish the role of these factors, we consider an architecture of contrastive loss system such as SimCLR, where baseline model is replaced by geometrically invariant "hand-crafted" network ScatNet with small trainable adapter network and argue that the number of parameters of the whole system and the number of views can be considerably reduced while practically preserving the same classification accuracy. In addition, we investigate the impact of regularization strategies using pretext task learning based on an estimation of parameters of augmentation transform such as rotation and jigsaw permutation for both traditional baseline models and ScatNet based models. Finally, we demonstrate that the proposed architecture with pretext task learning regularization achieves the state-of-the-art classification performance with a smaller number of trainable parameters and with reduced number of views. Code: https://github.com/vkinakh/scatsimclr
Vectorized instance segmentation using periodic B-splines based on cascade architecture

Instance segmentation is an essential part of image semantic understanding. In this paper we propose a novel cascade framework for instance segmentation. Unlike some existing methods that only output contour discrete coordinates, our approach obtains a vectorized representation of contours using periodic B-splines. In order to make better use of geometry and appearance information, we consider the global and local features of objects and introduce two types of graph structures, the star graph and circular graph, for feature extraction. Thereby, we develop a neural network, termed the mix network, to better exploit extracted features. Specifically, we first regress the spline control points to an object boundary via the mix network, then perform spline sampling to obtain the initial predictions of contours, and finally deform the predicted contours to the real contours of the objects. In addition, we add a regularization to further constrain the fairness of contour splines. Experiments show that our approach achieves 34.6% in mask mAP, Mean Average Precision, with a ResNet-101-FPN-DCN backbone on the challenging COCO benchmark, which significantly improves the performance of contour-based methods.
Improving Recognition Performance for Low-Resolution Images Using DBPN

Artificial Intelligence (AI) has evolved and been used for many different purposes. Python is one of a well-known language when it comes to AI due to the presence of the libraries like TensorFlow, Keras and Pytorch. The Aim of the project is to recognize faces from low resolution (LR) images, small size or poor-quality images with varying pose, illumination, expression, etc. This issue has received much attention due to the increasing demands for long distance surveillance applications. In this paper, an overview on the problems and the expected progress are presented. A solution based on Deep Back-Propagation Network (DBPN) is proposed to improve accuracy of the face recognition by using super resolution(SR) approach, which include detection / plotting of the facial-features/landmarks in an image, then using this landmark to align the image, training on a state-of-art Neutral Network for SR of LR image, etc. Experimental results shows that the proposed system achieved8.41 % precision and 8.38 % recall rate for a 4425 persons dataset.
Enhancing adversarial defense for medical image analysis systems with pruning and attention mechanism

Deep learning has achieved impressive performance across a variety of tasks, including medical image processing. However, recent research has shown that deep neural networks (DNNs) are susceptible to small adversarial perturbations in the image, which raise safety concerns about the deployment of these systems in clinical settings.To improve the defense of the medical imaging system against adversarial examples, we propose a new model-based defense framework for medical image DNNs model equipped with pruning and attention mechanism module based on the analysis of the reason why existing medical image DNNs models are vulnerable to attacks from adversarial examples is that complex biological texture of medical imaging and overparameterized medical image DNNs model.Three benchmark medical image datasets have verified the effectiveness of our method in improving the robustness of medical image DNNs models. In the chest X-ray datasets, our defending method can even achieve up 77.18% defense rate for projected gradient descent attack and 69.49% defense rate for DeepFool attack. And through ablation experiments on the pruning module and the attention mechanism module, it is verified that the use of pruning and attention mechanism can effectively improve the robustness of the medical image DNNs model.Compared with the existing model-based defense methods proposed for natural images, our defense method is more suitable for medical images. Our method can be a general strategy to approach the design of more explainable and secure medical deep learning systems, and can be widely used in various medical image tasks to improve the robustness of medical models.
Discerning Meaning and Producing Information: Semiosis in Knowing the Past

This article explores how the meaning of information related to things, people, events, and processes in the past is discerned and interpreted to satisfy some current purpose. Starting from the premise that Information about the Past results from a cognitive construction, it considers factors that affect the probability of success in producing Information about the Past. The article analyzes the process, components, and products of learning about the past, building on Constructed Past Theory and applying concepts from semiotics. It identifies characteristic ways in which things in the past are misinterpreted.
City Transit Rider Tweets: Understanding Sentiments and Politeness

With the expanding popularity of Web 2.0, there has been a huge surge in the use of social media, like Twitter, to express user sentiments or opinions. Delays and breakdowns in transit operations can make riders annoyed and irritated, and as a result, they express their anger and frustration via social media posts. Understanding the tipping points of public frustration will help in developing better solutions. This study aims to develop a framework by developing multilevel sentiment analysis and determine the emotion and politeness measures using transit-related tweets from New York (New York City) and California (San Francisco). The popular hashtags associated with the transit systems of New York and California were collected during 2019. The words associated with negative sentiments widely differ in these two states. Moderate levels of differences are seen in the politeness measures for these two states. Additionally, co-occurrence measures associated with negative emotions identified unique issues based on the demographics. This study demonstrates that Twitter provides a great opportunity to understand the public perception of transit, and the findings can help authorities design a more efficient transit system to improve user experience.