Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis from Depression-Related User-Generated Contents from Social Media

In this paper, we try to detect the sentiment levels such as positive, negative and neutral sentiments from depression related posts and comments generated in social media platforms. Social media platforms such as Facebook, Twitter are not only used for communication or building networks among connections, but also are getting useful for supporting needy peoples who are on special need or care in terms of mental support. In Facebook, there are several depression support groups, which are very much effective to provide mental support to the victims. In this paper, we try to formalize the depression-related posts and comments into a concise lexicon database and detect the sentiment levels form each instance. We have segmented the total work into two parts: sentiment detection and applying machine learning algorithms to analyze the ability to detect sentiment from such special category of texts. We have utilized python textblob package to detect the sentiment levels and applied traditional machine learning algorithms such as Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Sequential Minimal Optimization (SMO), Logistic Regression (LR), Adaboost (AB), Bagging (Bg), Stacking (St) and Multilayer Perceptron (MP) on the linguistic features. We have determined the precision, recall, F-measure, accuracy, ROC values for each of the classifiers. Among the classifiers Random Forest has outperformed others showing 60.54% correctly classified instance. We believe such sentiment analysis on special category of texts may lead to further investigation in natural language understandings.
4D mixed chaotic system and its application to RGB image encryption using substitution-diffusion

Low order chaotic maps has many distinct physiognomies, such as strong sensitiveness to initial conditions, parameters, better unpredictability, distinct random like complex temporal behavior and so on. This results the demand of new generators for chaotic systems with complicated dynamics but simple designs. In this work, a 4D mixed chaotic map with hyper-chaotic properties and high randomness behavior has been constructed by using the 1D Sine map and the 2D Thinker bell map. Furthermore, by using the proposed chaotic system and symmetric group S8 permutation, a novel image encryption scheme is introduced to explore the applications of the proposed chaotic system. The action of symmetric group S8 increases the key size by a factor of (8!)65536 for an image of size 256 × 256 which results a strong encryption structure to resists the well-known attacks. Simulation results on different images and extensive security analysis demonstrate that the proposed approach has adequate properties in image encryption.
A Study of Cloud Computing Adoption in Universities as a Guideline to Cloud Migration

Universities face many challenges such as budget costs, licensing, and software and hardware management in their activities related to information and communications technology (ICT). Through overcoming these challenges, universities may deliver smart, secure, and fluent services for their instructors, students, researchers, information technologies (IT) staff, and administrators. Cloud computing can be a solution to these challenges. The transition to cloud computing in universities is an important step in terms of online education, economic crisis, globalization, and high and constantly changing requirements, especially in the COVID-19 period. Cloud computing can play a very important role in quickly solving the problems faced by universities during this coronavirus period. The purpose of this research was to determine the position of universities in Turkey within the context of cloud computing and to present an abstract hybrid cloud framework for these universities. Descriptive method and survey technique were used in the research. SPSS program was used to analyze the data. Percentage, frequency, and chi-square statistics were used in the analysis process. As a result of the research, the existing conditions and problems in the use of the cloud service model in universities were tried to be identified and a road map for solving these problems was drawn up. In this context, a hybrid framework for adopting cloud computing in universities for them to overcome their identified challenges was proposed. The results are primarily intended to provide a guideline to universities in cloud computing adoption.
An efficient encoder–decoder model for portrait depth estimation from single images trained on pixel-accurate synthetic data

Depth estimation from a single image frame is a fundamental challenge in computer vision, with many applications such as augmented reality, action recognition, image understanding, and autonomous driving. Large and diverse training sets are required for accurate depth estimation from a single image frame. Due to challenges in obtaining dense ground-truth depth, a new 3D pipeline of 100 synthetic virtual human models is presented to generate multiple 2D facial images and corresponding ground truth depth data, allowing complete control over image variations. To validate the synthetic facial depth data, we propose an evaluation of state-of-the-art depth estimation algorithms based on single image frames on the generated synthetic dataset. Furthermore, an improved encoder–decoder based neural network is presented. This network is computationally efficient and shows better performance than current state-of-the-art when tested and evaluated across 4 public datasets. Our training methodology relies on the use of synthetic data samples which provides a more reliable ground truth for depth estimation. Additionally, using a combination of appropriate loss functions leads to improved performance than the current state-of-the-art network performances. Our approach clearly outperforms competing methods across different test datasets, setting a new state-of-the-art for facial depth estimation from synthetic data.
RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning

Despite the remarkable successes of convolutional neural networks (CNNs) in computer vision, it is time-consuming and error-prone to manually design a CNN. Among various neural architecture search (NAS) methods that are motivated to automate designs of high-performance CNNs, the differentiable NAS and population-based NAS are attracting increasing interests due to their unique characters. To benefit from the merits while overcoming the deficiencies of both, this work proposes a novel NAS method, RelativeNAS. As the key to efficient search, RelativeNAS performs joint learning between fast learners (i.e., decoded networks with relatively lower loss value) and slow learners in a pairwise manner. Moreover, since RelativeNAS only requires low-fidelity performance estimation to distinguish each pair of fast learner and slow learner, it saves certain computation costs for training the candidate architectures. The proposed RelativeNAS brings several unique advantages: 1) it achieves state-of-the-art performances on ImageNet with top-1 error rate of 24.88%, that is, outperforming DARTS and AmoebaNet-B by 1.82% and 1.12%, respectively; 2) it spends only 9 h with a single 1080Ti GPU to obtain the discovered cells, that is, 3.75× and 7875× faster than DARTS and AmoebaNet, respectively; and 3) it provides that the discovered cells obtained on CIFAR-10 can be directly transferred to object detection, semantic segmentation, and keypoint detection, yielding competitive results of 73.1% mAP on PASCAL VOC, 78.7% mIoU on Cityscapes, and 68.5% AP on MSCOCO, respectively. The implementation of RelativeNAS is available at https://github.com/EMI-Group/RelativeNAS.
Weighted clustering: Towards solving the user’s dilemma

This paper makes a major step towards addressing a long-standing challenge in cluster analysis, known as the user’s dilemma, which is the problem of selecting an appropriate clustering algorithm for a specific task. A formal approach for addressing this challenge relies on the identification of succinct, user-friendly properties that capture formal differences amongst clustering techniques. While helpful for gaining insight into the nature of clustering paradigms, there is a theory-practice gap that has so far limited the utility of this approach: Formal properties typically highlight advantages of classical linkage-based algorithms, while practical experience shows that center-based methods are preferable for many applications. We present simple new properties that delineate core differences between common clustering paradigms and overcome this theory-practice gap. The properties we present give a formal understanding of the advantages of center-based approaches for some applications and insight into when different clustering paradigms should be used. These properties address how sensitive algorithms are to changes in element frequencies, which we capture in a generalized setting where every element is associated with a real-valued weight. To complement extensive formal analysis, we discuss how these properties can be applied in practice.
Trusted audit with untrusted auditors: A decentralized data integrity Crowdauditing approach based on blockchain

Edge computing emerges as an alternative to cloud computing in the scenarios where the end devices require lower latency and faster access speeds. Edge nodes are deployed at the proximity of the end devices to reduce response time. On the other hand, the edge nodes are usually owned by small organizations that have limited operations and maintenance capabilities. Data on the edge may be easily damaged, due to external attacks or internal hardware failures. Therefore, it is essential to verify data integrity in edge computing. However, edge environment requires a different trust model compared with other computing and storage paradigm. Besides, compared with cloud storage, edge storage is decentralized and storage service participants may pose greater internal and external threats. This paper proposes a blockchain-based intelligent crowdsourcing audit approach (Crowdauditing) to achieve on-chain and off-chain credibility of audit results. The model relies on an untrusted auditor committee from the crowd to audit data integrity and uses smart contracts as the core of the intelligent system to ensure the reliability of result submission, the accuracy of the result judgment, and reasonable punishments and rewards. Specifically, an unbiased selection algorithm is proposed to achieve fairness during the auditor committee construction. An innovative two-stage submission strategy is proposed to ensure that the auditor committee can reach a consensus on the off-chain audit results. An incentive mechanism is carefully designed to force auditors providing audit services honestly to maximize their own rewards. Moreover, we modeled that as a game of n players, which proves the reliability of the result. Finally, we implement a prototype of Crowdauditing based on smart contracts. The extensive experimental results demonstrate the effectiveness of Crowdauditing.
Can Workplace Tracking Ever Empower? Collective Sensemaking for the Responsible Use of Sensor Data at Work

People are increasingly subject to the tracking of data about them at their workplaces. Sensor tracking is used by organizations to generate data on the movement and interaction of their employees to monitor and manage workers, and yet this data also poses significant risks to individual employees who may face harms from such data, and from data errors, to their job security or pay as a result of such analyses. Working with a large hospital, we developed a set of intervention strategies to enable what we call "collective sensemaking" describing worker contestation of sensor tracking data. We did this by participating in the sensor data science team, analyzing data on badges that employees wore over a two-week period, and then bringing the results back to the employees through a series of participatory workshops. We found three key aspects of collective sensemaking important for understanding data from the perspectives of stakeholders: 1) data shadows for tempering possibilities for design with the realities of data tracking; 2) data transducers for converting our assumptions about sensor tracking, and 3) data power for eliciting worker inclusivity and participation. We argue that researchers face what Dourish (2019) called the "legitimacy trap" when designing with large datasets and that research about work should commit to complementing data-driven studies with in-depth insights to make them useful for all stakeholders as a corrective to the underlying power imbalance that tracked workers face.
Adversarial Camera Alignment Network for Unsupervised Cross-Camera Person Re-Identification

In person re-identification (Re-ID), supervised methods usually need a large amount of expensive label information, while unsupervised ones are still unable to deliver satisfactory identification performance. In this paper, we introduce a novel person Re-ID task called unsupervised cross-camera person Re-ID, which only needs the within-camera (intra-camera) label information but not cross-camera (inter-camera) labels which are more expensive to obtain. In real-world applications, the intra-camera label information can be easily captured by tracking algorithms and few manual annotations. In this situation, the main challenge becomes the distribution discrepancy across different camera views, caused by the various body pose, occlusion, image resolution, illumination conditions, and background noises in different cameras. To address this situation, we propose a novel Adversarial Camera Alignment Network (ACAN) for unsupervised cross-camera person Re-ID. It consists of the camera-alignment task and the supervised within-camera learning task. To achieve the camera alignment, we develop a Multi-Camera Adversarial Learning (MCAL) to map images of different cameras into a shared subspace. Particularly, we investigate two different schemes, including the existing GRL (i.e., gradient reversal layer) scheme and the proposed scheme called &#x201C;other camera equiprobability&#x201D; (OCE), to conduct the multi-camera adversarial task. Based on this shared subspace, we then leverage the within-camera labels to train the network. Extensive experiments on five large-scale datasets demonstrate the superiority of ACAN over multiple state-of-the-art unsupervised methods that take advantage of labeled source domains and generated images by GAN-based models. In particular, we verify that the proposed multi-camera adversarial task does contribute to the significant improvement.
Improving user verification in human-robot interaction from audio or image inputs through sample quality assessment

In this paper, we tackle the task of improving biometric verification in the context of Human-Robot Interaction (HRI).A robot that wants to identify a specific person to provide a service can do so by either image verification or, if light conditions are not favourable, through voice verification.In our approach, we will take advantage of the possibility a robot has of recovering further data until it is sure of the identity of the person.The key contribution is that we select from both image and audio signals the parts that are of higher confidence.For images we use a system that looks at the face of each person and selects frames in which the confidence is high while keeping those frames separate in time to avoid using very similar facial appearance.For audio our approach tries to find the parts of the signal that contain a person talking, avoiding those in which noise is present by segmenting the signal.Once the parts of interest are found, each input is described with an independent deep learning architecture that obtains a descriptor for each kind of input (face/voice).We also present in this paper fusion methods that improve performance by combining the features from both face and voice, results to validate this are shown for each independent input and for the fusion methods.
GGM Knockoff Filter: False Discovery Rate Control for Gaussian Graphical Models

Abstract We propose a new method to learn the structure of a Gaussian graphical model with finite sample false discovery rate control. Our method builds on the knockoff framework of Barber and Candès for linear models. We extend their approach to the graphical model setting by using a local (node-based) and a global (graph-based) step: we construct knockoffs and feature statistics for each node locally, and then solve a global optimization problem to determine a threshold for each node. We then estimate the neighbourhood of each node, by comparing its feature statistics to its threshold, resulting in our graph estimate. Our proposed method is very flexible, in the sense that there is freedom in the choice of knockoffs, feature statistics and the way in which the final graph estimate is obtained. For any given data set, it is not clear a priori what choices of these hyperparameters are optimal. We therefore use a sample-splitting-recycling procedure that first uses half of the samples to select the hyperparameters, and then learns the graph using all samples, in such a way that the finite sample FDR control still holds. We compare our method to several competitors in simulations and on a real data set.
C_CART: An instance confidence-based decision tree algorithm for classification

In classification, a decision tree is a common model due to its simple structure and easy understanding. Most of decision tree algorithms assume all instances in a dataset have the same degree of confidence, so they use the same generation and pruning strategies for all training instances. In fact, the instances with greater degree of confidence are more useful than the ones with lower degree of confidence in the same dataset. Therefore, the instances should be treated discriminately according to their corresponding confidence degrees when training classifiers. In this paper, we investigate the impact and significance of degree of confidence of instances on the classification performance of decision tree algorithms, taking the classification and regression tree (CART) algorithm as an example. First, the degree of confidence of instances is quantified from a statistical perspective. Then, a developed CART algorithm named C_CART is proposed by introducing the confidence of instances into the generation and pruning processes of CART algorithm. Finally, we conduct experiments to evaluate the performance of C_CART algorithm. The experimental results show that our C_CART algorithm can significantly improve the generalization performance as well as avoiding the over-fitting problem to a certain extend.
MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking

Many RGB-T trackers attempt to attain robust feature representation by utilizing an adaptive weighting scheme (or attention mechanism). Different from these works, we propose a new dynamic modality-aware filter generation module (named MFGNet) to boost the message communication between visible and thermal data by adaptively adjusting the convolutional kernels for various input images in practical tracking. Given the image pairs as input, we first encode their features with the backbone network. Then, we concatenate these feature maps and generate dynamic modality-aware filters with two independent networks. The visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively. Inspired by residual connection, both the generated visible and thermal feature maps will be summarized with input feature maps. The augmented feature maps will be fed into the RoI align module to generate instance-level features for subsequent classification. To address issues caused by heavy occlusion, fast motion and out-of-view, we propose to conduct a joint local and global search by exploiting a new direction-aware target driven attention mechanism. The spatial and temporal recurrent neural network is used to capture the direction-aware context for accurate global attention prediction. Extensive experiments on three large-scale RGB-T tracking benchmark datasets validated the effectiveness of our proposed algorithm.
Hybrid deep convolutional neural network with one-versus-one approach for solar flare prediction

ABSTRACT We propose a novel hybrid Convolutional Neural Network (CNN) model with one-versus-one approach to forecast solar flare occurrence with the outputs of four classes (No-flare, C, M, and X) within 24 h. We train and test our model using the same data sets as in Zheng, Li &amp; Wang, and then compare our results with previous models using the true skill statistic (TSS) as primary metric. The main results are as follows. (1) This is the first time that the CNN model in conjunction with one-versus-one approach is used in solar physics to make multiclass flare prediction. (2) In the four-class flare prediction, our model achieves quite high mean scores of TSS = 0.703, 0.489, 0.432, and 0.436 for No-flare, C, M, and X class, respectively, which are much better than or comparable to those of previous studies. In addition, our model obtains TSS scores of 0.703 ± 0.070 for ≥C-class and 0.739 ± 0.109 for ≥M-class predictions. (3) This is the first attempt to open the black-box CNN model to study the visualization of feature maps for interpreting the prediction model. Furthermore, the visualization results indicate that our model pays attention to the regions with strong gradient, strong intensity, high total intensity, and large range of the intensity in high-level feature maps. The median gradient and intensity, the total intensity, and the range of the intensity for high-level feature maps increase approximately with the increase of flare level.
Ontology-Based Resume Searching System for Job Applicants in Information Technology

In recruitment industry nowadays, the selection of best curriculum vitae (CV) for a particular job description (JD) among thousands of CVs is really challenging. The goal of this paper is to optimize job recruitment process by automatically matching skill graph extracted from CV and JD. Ontologies for contents of CVs and JDs are proposed to represent information of them. The screening system is worked based on built ontologies and solve matching problems between them. Some problems for matching the content of CVs and JDs are used by using natural language processing and machine learning techniques. The system has been tested on job domains about information technology (IT). Dataset is collected from TopCV, Dice and Indeed which are large IT labor markets on internet. Through testing results, the proposed method is effective to search CVs being appropriate with a determined JD in IT domain.
Prevalence of Prejudice-Denoting Words in News Media Discourse: A Chronological Analysis

This work analyzes the prevalence of words denoting prejudice in 27 million news and opinion articles written between 1970 and 2019 and published in 47 of the most popular news media outlets in the United States. Our results show that the frequency of words that denote specific prejudice types related to ethnicity, gender, sexual, and religious orientation has markedly increased within the 2010–2019 decade across most news media outlets. This phenomenon starts prior to, but appears to accelerate after, 2015. The frequency of prejudice-denoting words in news articles is not synchronous across all outlets, with the yearly prevalence of such words in some influential news media outlets being predictive of those words’ usage frequency in other outlets the following year. Increasing prevalence of prejudice-denoting words in news media discourse is often substantially correlated with U.S. public opinion survey data on growing perceptions of minorities’ mistreatment. Granger tests suggest that the prevalence of prejudice-denoting terms in news outlets might be predictive of shifts in public perceptions of prejudice severity in society for some, but not all, types of prejudice.