Reviewerly/exHarmony huggingface dataset:
Aligning software engineering education with industrial needs: A meta-analysis

According to various reports, many software engineering (SE) graduates often face difficulties when beginning their careers, which is mainly due to misalignment of the skills learned in university education with what is needed in the software industry. Our objective is to perform a meta-analysis to aggregate the results of the studies published in this area to provide a consolidated view on how to align SE education with industry needs, to identify the most important skills and also existing knowledge gaps. To synthesize the body of knowledge, we performed a systematic literature review (SLR), in which we systematically selected a pool of 35 studies and then conducted a meta-analysis using data extracted from those studies. Via a meta-analysis and using data from 13 countries and over 4,000 data points, highlights of the SLR include: (1) software requirements, design, and testing are the most important skills; and (2) the greatest knowledge gaps are in configuration management, SE models and methods, SE process, design (and architecture), as well as in testing. This paper provides implications for both educators and hiring managers by listing the most important SE skills and the knowledge gaps in the industry.
Virtual reality in medicine: a brief overview and future research directions

Virtual reality (VR), also called phantomatics, is an image of artificial reality created entirely in three-dimensional graphics with the use of information technology.VR makes it possible to experience the imaginary world as if it was real, where sensory data are delivered to the brain through a specialized system.The purpose of virtual medicine is to minimize direct contact and impact on human body during treatment.Taking into consideration the increasing accessibility of high quality electronic devices, their immense computing powers, and the continuously developing Internet infrastructure, the advancement in this area is only a matter of time.Therefore, the aim of the study was to identify the applications of VR in medicine, focusing on the areas of psychiatry and rehabilitation and considering guidelines for future research.
Multiview Clustering by Joint Latent Representation and Similarity Learning

Subspace learning-based multiview clustering has achieved impressive experimental results. However, the similarity matrix, which is learned by most existing methods, cannot well characterize both the intrinsic geometric structure of data and the neighbor relationship between data. To consider the fact that original data space does not well characterize the intrinsic geometric structure, we learn the latent representation of data, which is shared by different views, from the latent subspace rather than the original data space by linear transformation. Thus, the learned latent representation has a low-rank structure without solving the nuclear-norm. This reduces the computational complexity. Then, the similarity matrix is adaptively learned from the learned latent representation by manifold learning which well characterizes the local intrinsic geometric structure and neighbor relationship between data. Finally, we integrate clustering, manifold learning, and latent representation into a unified framework and develop a novel subspace learning-based multiview clustering method. Extensive experiments on benchmark datasets demonstrate the superiority of our method.
Self-Attention-Based BiLSTM Model for Short Text Fine-Grained Sentiment Classification

Fine-grained sentiment polarity classification for short texts has been an important and challenging task in natural language processing until these years.The short texts may contain multiple aspect-terms, opinion terms expressing different sentiments for different aspect-terms.The polarity of the whole sentence is highly correlated with the aspect-terms and opinion terms.Besides, there are two challenges, which are how to effectively use the contextual information and the semantic features, and how to model the correlations between aspect-terms and context words including opinion terms.To solve these problems, a Self-Attention-Based BiLSTM model with aspect-term information is proposed for the fine-grained sentiment polarity classification for short texts.The proposed model can effectively use contextual information and semantic features, and especially model the correlations between aspect-terms and context words.The model mainly consists of a word-encode layer, a BiLSTM layer, a self-attention layer and a softmax layer.Among them, the BiLSTM layer sums up the information from two opposite directions of a sentence through two independent LSTMs.The self-attention layer captures the more important parts of a sentence when different aspect-terms are input.Between the BiLSTM layer and the self-attention layer, the hidden vector and the aspect-term vector are fused by adding, which reduces the computational complexity caused by the vector splicing directly.The experiments on public Restaurant and Laptop corpus from the SemEval 2014 Task 4, and Twitter corpus from the ACL 14.The Friedman and Nemenyi tests are used in the comparison study.Compared with existing methods, experimental results demonstrate that the proposed model is feasible and efficient.
Planar Prior Assisted PatchMatch Multi-View Stereo

The completeness of 3D models is still a challenging problem in multi-view stereo (MVS) due to the unreliable photometric consistency in low-textured areas. Since low-textured areas usually exhibit strong planarity, planar models are advantageous to the depth estimation of low-textured areas. On the other hand, PatchMatch multi-view stereo is very efficient for its sampling and propagation scheme. By taking advantage of planar models and PatchMatch multi-view stereo, we propose a planar prior assisted PatchMatch multi-view stereo framework in this paper. In detail, we utilize a probabilistic graphical model to embed planar models into PatchMatch multi-view stereo and contribute a novel multi-view aggregated matching cost. This novel cost takes both photometric consistency and planar compatibility into consideration, making it suited for the depth estimation of both non-planar and planar regions. Experimental results demonstrate that our method can efficiently recover the depth information of extremely low-textured areas, thus obtaining high complete 3D models and achieving state-of-the-art performance.
Zhorai: Designing a Conversational Agent for Children to Explore Machine Learning Concepts

Understanding how machines learn is critical for children to develop useful mental models for exploring artificial intelligence (AI) and smart devices that they now frequently interact with. Although children are very familiar with having conversations with conversational agents like Siri and Alexa, children often have limited knowledge about AI and machine learning. We leverage their existing familiarity and present Zhorai, a conversational platform and curriculum designed to help young children understand how machines learn. Children ages eight to eleven train an agent through conversation and understand how the knowledge is represented using visualizations. This paper describes how we designed the curriculum and evaluated its effectiveness with 14 children in small groups. We found that the conversational aspect of the platform increased engagement during learning and the novel visualizations helped make machine knowledge understandable. As a result, we make recommendations for future iterations of Zhorai and approaches for teaching AI to children.
Why Contact Tracing Efforts Have Failed to Curb Coronavirus Disease 2019 (COVID-19) Transmission in Much of the United States

Abstract By late April 2020, public discourse in the United States had shifted toward the idea of using more targeted case-based mitigation tactics (eg, contact tracing) to combat coronavirus disease 2019 (COVID-19) transmission while allowing for the safe “reopening” of society, in an effort to reduce the social, economic, and political ramifications associated with stricter approaches. Expanded tracing-testing efforts were touted as a key solution that would allow for a precision approach, thus preventing economies from having to shut down again. However, it is now clear that many regions of the United States were unable to mount robust enough testing-tracing programs to prevent major resurgences of disease. This viewpoint offers a discussion of why testing-tracing efforts failed to sufficiently mitigate COVID-19 across much of the nation, with the hope that such deliberation will help the US public health community better plan for the future.
Investigating Smart Home Security: Is Blockchain the Answer?

Smart Home automation is increasingly gaining popularity among current applications of Internet of Things (IoT) due to the convenience and facilities it provides to the home owners.Sensors are employed within the home appliances via wireless connectivity to be accessible remotely by home owners to operate these devices.With the exponential increase of smart home IoT devices in the marketplace such as door locks, light bulbs, power switches etc, numerous security concerns are arising due to limited storage and processing power of such devices, making these devices vulnerable to several attacks.Due to this reason, security implementations in the deployment of these devices has gained popularity among researchers as a critical research area.Moreover, the adoption of traditional security schemes has failed to address the unique security concerns associated with these devices.Blockchain, a decentralised database based on cryptographic techniques, is gaining enormous attention to assure security of IoT systems.The blockchain framework within an IoT system is a fascinating substitute to the traditional centralised models, which has some significant concerns in fulfilling the demand of smart homes security.In this article, we aim to examine the security of smart homes by instigating the adoption of blockchain and exploring some of the currently proposed smart home architectures using blockchain technology.To present our findings, we describe a simple secure smart home framework based on a refined version of blockchain called Consortium blockchain.We highlight the limitations and opportunities of adopting such an architecture.We further evaluate our model and conclude with the results by designing an experimental testbed using a few household IoT devices commonly available in the marketplace.
Human face recognition based on convolutional neural network and augmented dataset

(2021). Human face recognition based on convolutional neural network and augmented dataset. Systems Science & Control Engineering: Vol. 9, Recent Developments on Complexity Science; Guest editors: Zhijian Ji, Yang Liu and Lei Zou, pp. 29-37.
Checking For Identity-Based Remote Data Integrity Cloud Storage with Perfect Data Privacy

Abstract The protection of the data maintained on a database system has the benefit that the cloud service creates a dynamic high-order and often a management issue comparable to the security operating costs. Multiple Online Compensation Monitoring Protocols have been developed to date that will pose a key problem to protect the privacy of cloud-based details. The much-needed role of the data servers is to render the data available to the consumer as necessary in the heavy traffic scenario. In this article, we planned privacy security for unavailable data held by the user, and promoted identification by using encryption strategies such as sha1 and sha2 algorithms to improve user data safety. We use zero general knowledge tests against third force Settlers which in effect improve the user data confidentiality. Under-reaching is achieved in our architecture by the use of file cabinet signature tune generating servers that operate in compliance with wirework traffic and attempt to access the less significant traffic wire work with the necessary details. Our strategy has proved to be safe in the face of threats of abuse, supplying the high-security department with identity-based consumer data deposit and improving efficiency by settling well on the server over cloud vehicle traffic. Implies being more directly linked to defensive shell, which often implies being protected which realistic.
Classification of white blood cells using weighted optimized deformable convolutional neural networks

Machine learning (ML) algorithms have been widely used in the classification of white blood cells (WBCs). However, the performance of ML algorithms still needs to be addressed for being short of gold standard data sets, and even the implementation of the proposed algorithms.In this study, the method of two-module weighted optimized deformable convolutional neural networks (TWO-DCNN) was proposed for WBC classification. Our algorithm is characterized as two-module transfer learning and deformable convolutional (DC) layers for the betterment of robustness. To validate the performance, our method was compared with classical MLs of VGG16, VGG19, Inception-V3, ResNet-50, support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT) and random forest (RF) on our undisclosed WBC data set and public BCCD data set.TWO-DCNN achieved the best performance with the precisions (PREs) of 95.7%, 94.5% and 91.6%, recalls (RECs) of 95.7%, 94.5% and 91.6%, F1-scores (F1s) of 95.7%, 94.5% and 91.6%, area under curves (AUCs) of 0.98, 0.97 and 0.95 for low-resolution and noisy undisclosed data sets, BCCD data set, respectively.With accurate feature extraction and optimized network weights, the proposed TWO-DCNN showed the best performance in WBC classification for low-resolution and noisy data sets. It could be used as an alternative method for clinical applications.
Contrastive Learning with Stronger Augmentations

Representation learning has significantly been developed with the advance of contrastive learning methods. Most of those methods are benefited from various data augmentations that are carefully designated to maintain their identities so that the images transformed from the same instance can still be retrieved. However, those carefully designed transformations limited us to further explore the novel patterns exposed by other transformations. Meanwhile, as shown in our experiments, direct contrastive learning for stronger augmented images can not learn representations effectively. Thus, we propose a general framework called Contrastive Learning with Stronger Augmentations (CLSA) to complement current contrastive learning approaches. Here, the distribution divergence between the weakly and strongly augmented images over the representation bank is adopted to supervise the retrieval of strongly augmented queries from a pool of instances. Experiments on the ImageNet dataset and downstream datasets showed the information from the strongly augmented images can significantly boost the performance. For example, CLSA achieves top-1 accuracy of 76.2% on ImageNet with a standard ResNet-50 architecture with a single-layer classifier fine-tuned, which is almost the same level as 76.5% of supervised results. The code and pre-trained models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/maple-research-lab/CLSA</uri> .
Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook

Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It entails estimating the trajectory of the target in an image sequence, given only its initial location, and segmentation, or its rough approximation in the form of a bounding box. Discriminative Correlation Filters (DCFs) and deep Siamese Networks (SNs) have emerged as dominating tracking paradigms, which have led to significant progress. Following the rapid evolution of visual object tracking in the last decade, this survey presents a systematic and thorough review of more than 90 DCFs and Siamese trackers, based on results in nine tracking benchmarks. First, we present the background theory of both the DCF and Siamese tracking core formulations. Then, we distinguish and comprehensively review the shared as well as specific open research challenges in both these tracking paradigms. Furthermore, we thoroughly analyze the performance of DCF and Siamese trackers on nine benchmarks, covering different experimental aspects of visual tracking: datasets, evaluation metrics, performance, and speed comparisons. We finish the survey by presenting recommendations and suggestions for distinguished open challenges based on our analysis.
Local Differential Private Data Aggregation for Discrete Distribution Estimation

For the purpose of improving the quality of services, softwares or online services are collecting various of user data, such as personal information and locations. Such data facilitates mining statistical knowledge of users, but threatens users' privacy as it may reveal sensitive information (e.g., identities and activities) about individuals. This work considers distribution estimation over user-contributed data meanwhile providing rigid protection of their data with local ε-differential privacy (ε-LDP), which sanitizes each user's data on the client's side (e.g, on the user's mobile device). Our privacy protection covers both qualitative data (e.g., categorical data) and discrete quantitative data (e.g., location data). Specifically, for categorical data, we derive an optimal ε-LDP mechanism (termed as k-subset mechanism) from mutual information perspective, and further show its optimality over existing approaches within the context of discrete distribution estimation; for discrete quantitative data that have arbitrary distance metric, we provide an efficient extension of k-subset mechanism by proposing a variant of the popular Exponential Mechanism (EM) to tackle the asymmetry issue on the data domain. Experiments on real-world datasets and simulated scenarios show that our mechanism is highly efficient and reduces nearly a fraction of exp(- ε/2) error for distribution estimation when compared to existing approaches.
Deep Attention-Based Classification Network for Robust Depth Prediction

In this paper, we present our deep attention-based classification (DABC) network for robust single image depth prediction, in the context of the Robust Vision Challenge 2018 (ROB 2018) ( http://www.robustvision.net/index.php ). Unlike conventional depth prediction, our goal is to design a model that can perform well in both indoor and outdoor scenes with a single parameter set. However, robust depth prediction suffers from two challenging problems: (a) How to extract more discriminative features for different scenes (compared to a single scene)? (b) How to handle the large differences of depth ranges between indoor and outdoor datasets? To address these two problems, we first formulate depth prediction as a multi-class classification task and apply a softmax classifier to classify the depth label of each pixel. We then introduce a global pooling layer and a channel-wise attention mechanism to adaptively select the discriminative channels of features and to update the original features by assigning important channels with higher weights. Further, to reduce the influence of quantization errors, we employ a soft-weighted sum inference strategy for the final prediction. Experimental results on both indoor and outdoor datasets demonstrate the effectiveness of our method. It is worth mentioning that we won the 2-nd place in single image depth prediction entry of ROB 2018, in conjunction with IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018.
Controllable Generative Adversarial Network

Recently introduced generative adversarial networks (GANs) have been shown numerous promising results to generate realistic samples.In the last couple of years, it has been studied to control features in synthetic samples generated by the GAN.Auxiliary classifier GAN (ACGAN), a conventional method to generate conditional samples, employs a classification layer in discriminator to solve the problem.However, in this paper, we demonstrate that the auxiliary classifier can hardly provide good guidance for training of the generator, where the classifier suffers from overfitting.Since the generator learns from classification loss, such a problem has a chance to hinder the training.To overcome this limitation, here, we propose a controllable GAN (ControlGAN) structure.By separating a feature classifier from the discriminator, the classifier can be trained with data augmentation technique, which can support to make a fine classifier.Evaluated with the CIFAR-10 dataset, ControlGAN outperforms AC-WGAN-GP which is an improved version of the ACGAN, where Inception score of the ControlGAN is 8.61 ± 0.10.Furthermore, we demonstrate that the ControlGAN can generate intermediate features and opposite features for interpolated input and extrapolated input labels that are not used in the training process.It implies that the ControlGAN can significantly contribute to the variety of generated samples.
Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word Representations

Contextualized word representations are able to give different representations for the same word in different contexts, and they have been shown to be effective in downstream natural language processing tasks, such as question answering, named entity recognition, and sentiment analysis.However, evaluation on word sense disambiguation (WSD) in prior work shows that using contextualized word representations does not outperform the stateof-the-art approach that makes use of noncontextualized word embeddings.In this paper, we explore different strategies of integrating pre-trained contextualized word representations and our best strategy achieves accuracies exceeding the best prior published accuracies by significant margins on multiple benchmark WSD datasets.
A convolutional neural network-based linguistic steganalysis for synonym substitution steganography

In this paper, a linguistic steganalysis method based on two-level cascaded convolutional neural networks (CNNs) is proposed to improve the system's ability to detect stego texts, which are generated via synonym substitutions. The first-level network, sentence-level CNN, consists of one convolutional layer with multiple convolutional kernels in different window sizes, one pooling layer to deal with variable sentence lengths, and one fully connected layer with dropout as well as a softmax output, such that two final steganographic features are obtained for each sentence. The unmodified and modified sentences, along with their words, are represented in the form of pre-trained dense word embeddings, which serve as the input of the network. Sentence-level CNN provides the representation of a sentence, and can thus be utilized to predict whether a sentence is unmodified or has been modified by synonym substitutions. In the second level, a text-level CNN exploits the predicted representations of sentences obtained from the sentence-level CNN to determine whether the detected text is a stego text or cover text. Experimental results indicate that the proposed sentence-level CNN can effectively extract sentence features for sentence-level steganalysis tasks and reaches an average accuracy of 82.245%. Moreover, the proposed steganalysis method achieves greatly improved detection performance when distinguishing stego texts from cover texts.