Reviewerly/exHarmony huggingface dataset:
Qualifying Software Engineers Undergraduates in DevOps - Challenges of Introducing Technical and Non-technical Concepts in a Project-oriented Course

he constant changes in the software industry, practices, and methodologies impose challenges to teaching and learning current software engineering concepts and skills. DevOps is particularly challenging because it covers technical concepts, such as pipeline automation, and non-technical ones, such as team roles and project management. The present study investigates a course setup to introduce these concepts to software engineering undergraduates. We designed the course by employing coding to associate DevOps concepts to Agile, Lean, and Open source practices and tools. We present the main aspects of this project-oriented DevOps course, with 240 students enrolled it since its first offering in 2016. We conducted an empirical study, with both a quantitative and qualitative analysis, to evaluate this project-oriented course setup. We collected the data from the projects repository and students' perceptions from a questionnaire. We mined 148 repositories (corresponding to 72 projects) and obtained 86 valid responses to the questionnaire. We also mapped the concepts which are more challenging to students learn from experience. The results evidence that first-hand experience facilitates the comprehension of DevOps concepts and enriches classes discussions. we present a set of lessons learned, which may help professors better design and conduct project-oriented courses to cover DevOps concepts.
How Few Annotations are Needed for Segmentation Using a Multi-planar U-Net?

U-Net architectures are an extremely powerful tool for segmenting 3D volumes, and the recently proposed multi-planar U-Net has reduced the computational requirement for using the U-Net architecture on three-dimensional isotropic data to a subset of two-dimensional planes. While multi-planar sampling considerably reduces the amount of training data needed, providing the required manually annotated data can still be a daunting task. In this article, we investigate the multi-planar U-Net's ability to learn three-dimensional structures in isotropic sampled images from sparsely annotated training samples. We extend the multi-planar U-Net with random annotations, and we present our empirical findings on two public domains, fully annotated by an expert. Surprisingly we find that the multi-planar U-Net on average outperforms the 3D U-Net in most cases in terms of dice, sensitivity, and specificity and that similar performance from the multi-planar unit can be obtained from half the number of annotations by doubling the number of automatically generated training planes. Thus, sometimes less is more!
Hybrid Imitation Learning Framework for Robotic Manipulation Tasks

This study proposes a novel hybrid imitation learning (HIL) framework in which behavior cloning (BC) and state cloning (SC) methods are combined in a mutually complementary manner to enhance the efficiency of robotic manipulation task learning. The proposed HIL framework efficiently combines BC and SC losses using an adaptive loss mixing method. It uses pretrained dynamics networks to enhance SC efficiency and performs stochastic state recovery to ensure stable learning of policy networks by transforming the learner’s task state into a demo state on the demo task trajectory during SC. The training efficiency and policy flexibility of the proposed HIL framework are demonstrated in a series of experiments conducted to perform major robotic manipulation tasks (pick-up, pick-and-place, and stack tasks). In the experiments, the HIL framework showed about a 2.6 times higher performance improvement than the pure BC and about a four times faster training time than the pure SC imitation learning method. In addition, the HIL framework also showed about a 1.6 times higher performance improvement and about a 2.2 times faster training time than the other hybrid learning method combining BC and reinforcement learning (BC + RL) in the experiments.
Bidirectional Shadow Rendering for Interactive Mixed 360° Videos

In this paper, we provide a bidirectional shadow rendering method to render shadows between real and virtual objects in the 360° videos in real time. We construct a 3D scene approximation from the current output viewpoint to approximate the real scene geometry nearby in the video. Then, we propose a ray casting based algorithm to determine the shadow regions on the virtual objects cast by the real objects. After that, we introduce an object-aware shadow mapping method to cast shadows from virtual objects to real objects. Finally, we use a shadow intensity estimation algorithm to determine the shadow intensity of virtual objects and real objects to obtain shadows consistent with the input 360° video. The experiment results prove the effectiveness of our bidirectional shadow rendering method for mixed 360° videos. Our method can generate visually realistic shadows for virtual objects and real objects in 360° video in realtime, and make virtual objects more natural to integrate with real scenes in 360° videos of the mixed reality applications.
Multi-Features-Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine

Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi-features and support vector machines was proposed for breast tumor diagnosis. Multi-features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi-features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors.
Self and Channel Attention Network for Person Re-Identification

Recent research has shown promising results for person re-identification by focusing on several trends. One is designing efficient metric learning loss functions such as triplet loss family to learn the most discriminative representations. The other is learning local features by designing part based architectures to form an informative descriptor from semantically coherent parts. Some efforts adjust distant outliers to their most similar positions by using soft attention and learn the relationship between distant similar features. However, only a few prior efforts focus on channel-wise dependencies and learn non-local sharp similar part features directly for the degraded data in the person re-identification task. In this paper, we propose a novel Self and Channel Attention Network (SCAN) to model long-range dependencies between channels and feature maps. We add multiple classifiers to learn discriminative global features by using classification loss. Self Attention (SA) module and Channel Attention (CA) module are introduced to model non-local and channel-wise dependencies in the learned features. Spectral normalization is applied to the whole network to stabilize the training process. Experimental results on the person re-identification benchmarks show the proposed components achieve significant improvement with respect to the baseline.
Visualizing Local Minima in Multi-robot Motion Planning Using Multilevel Morse Theory

Multi-robot motion planning problems often have many local minima. It is essential to visualize those local minima such that we can better understand, debug and interact with multi-robot systems. Towards this goal, we present the multi-robot motion explorer, an algorithm which extends previous results on multilevel Morse theory by introducing a component-based framework, where we reduce multi-robot configuration spaces by reducing each robots component space using fiber bundles. Our algorithm exploits this component structure to search for and visualize local minima. A user of the algorithm can specify a multilevel abstraction and an optimization algorithm. We use this information to incrementally build a local minima tree for a given problem. We demonstrate this algorithm on several multi-robot systems of up to 20 degrees of freedom.
Conservation-Law-Based Global Bounds to Quantum Optimal Control

Active control of quantum systems enables diverse applications ranging from quantum computation to manipulation of molecular processes. Maximum speeds and related bounds have been identified from uncertainty principles and related inequalities, but such bounds utilize only coarse system information, and loosen significantly in the presence of constraints and complex interaction dynamics. We show that an integral-equation-based formulation of conservation laws in quantum dynamics leads to a systematic framework for identifying fundamental limits to any quantum control scenario. We demonstrate the utility of our bounds in three scenarios -- three-level driving, decoherence suppression, and maximum-fidelity gate implementations -- and show that in each case our bounds are tight or nearly so. Global bounds complement local-optimization-based designs, illuminating performance levels that may be possible as well as those that cannot be surpassed.
ParsBERT Post-Training for Sentiment Analysis of Tweets Concerning Stock Market

Social media has become a playground for users to share their ideas freely. Analyzing these data has become of special interest to authorities and consulting firms. They seek to choose right policies based on the insight acquired. Hence, sentiment analysis of data spread in social media has gained significant importance. There are two major approaches for sentiment analysis including lexicon-based and supervised methods. Among supervised methods, deep models have proven to be a better fit for the sentiment analysis task. Since, they are domain free and able to handle large volumes of data effectively. In particular, BERT's state of the art performance on various natural language processing tasks has encouraged us to use this network architecture for sentiment analysis. In this research, over 12000 Persian tweets including the stock market keyword have been crawled from twitter. They are labeled manually in three different categories of positive, neutral and negative. Then a pre-trained ParsBERT model has been fine-tuned on these data. Our model is evaluated on the test dataset and compared to its counterpart, lexicon-based method using Polyglot as its lexicon. Accuracy of 82 percent has been achieved by our proposed model surpassing its lexicon-based contender.
A study on training fine-tuning of convolutional neural networks

As a subdiscipline of deep learning, Convolutional Neural Network (CNN) has been increasingly concerned about by people recently and widely applied in various fields related to computer vision. Although various algorithms that can improve CNN model have been proposed continuously, there is no consensus on utilizing which techniques to obtain better results for a specific task. This paper studies the fundamental deep CNN model's training fine-tuning, which can be classified as follows: network depth, network width, nonlinear activation function, pooling method, parameter initialization method, and learning rate strategy. According to each fine-tuning class's different schemes, the relevant comparison CNN model is designed by controlling variables. We trained and tested the models on the benchmark data set CIFAR-10. After comparing the advantages and disadvantages of the different fine-tuning schemes, we investigated their characteristics, explored the reasons, and discussed the general rules among them.
Understanding Blockchain Adoption in Italian Firms

This study investigates individuals' blockchain adoption behavior focusing on the Italian setting, and gathering perceptions from information systems practitioners and entrepreneurs. The aim of the paper is to understand what are the factors that push organizational actors to use the blockchain. To this aim we embrace the second version of the unified theory of acceptance and use of technology (UTAUT). The model was estimated using the structural equation modeling with partial least square estimation (PLS-SEM). Our results show that performance expectancy and social influence are factors that have a strong positive effect on people intention to adopt blockchain. Surprisingly, the findings unveil that experience has a negative effect on blockchain use intention. This allows us to argue that the technology under scrutiny has such a disruptive nature that individuals with previous experience look at it with skepticisms as its implementation involves a full re-think of all routines and practices.
A Malicious URL Detection Model Based on Convolutional Neural Network

With the development of Internet technology, network security is facing great challenges. Malicious URL detection can defend against attacks such as phishing, spams, and malware implantation. However there are some problems on current malicious URL detection, for example the methods used to extract features are inefficient and hard to adapt to the current complex network environment. To solve these problems, this paper uses the word embedding method based on character embedding as the way of vector embedding to improve the deep convolutional neural network, and designs a malicious URL detection system. Finally, we carry out experiments with the system, the results prove the effectiveness of our system.
Multi-Scale Speaker Diarization with Neural Affinity Score Fusion

Predicting the speaker’s identity of short speech segments in human dialogue has been considered one of the most challenging problems in speech signal processing. Speaker representations of short speech segments tend to be unreliable, resulting in poor fidelity of speaker representations in tasks requiring speaker recognition. In this paper, we propose an unconventional method that tackles the trade-off between temporal resolution and the quality of the speaker representations. To find a set of weights that balance the scores from multiple temporal scales of segments, a neural affinity score fusion model is presented. Using the CALLHOME dataset, we show that our proposed multi-scale segmentation and integration approach can achieve a state-of-the-art diarization performance.
FastSal: a Computationally Efficient Network for Visual Saliency Prediction

This paper focuses on the problem of visual saliency prediction, predicting regions of an image that tend to attract human visual attention, under a constrained computational budget. We modify and test various recent efficient convolutional neural network architectures like EfficientNet and MobileNetV2 and compare them with existing state-of-the-art saliency models such as SalGAN and DeepGaze II both in terms of standard accuracy metrics like Area Under Curve (AUC) and Normalized Scanpath Saliency (NSS), and in terms of the computational complexity and model size. We find that MobileNetV2 makes an excellent backbone for a visual saliency model and can be effective even without a complex decoder. We also show that knowledge transfer from a more computationally expensive model like DeepGaze II can be achieved via pseudo-labelling an unlabelled dataset, and that this approach gives result on-par with many state-of-the-art algorithms with a fraction of the computational cost and model size.
Improving Visual Relation Detection using Depth Maps

Visual relation detection methods rely on object information extracted from RGB images such as 2D bounding boxes, feature maps, and predicted class probabilities. We argue that depth maps can additionally provide valuable information on object relations, e.g. helping to detect not only spatial relations, such as standing behind, but also non-spatial relations, such as holding. In this work, we study the effect of using different object features with a focus on depth maps. To enable this study, we release a new synthetic dataset of depth maps, VG-Depth, as an extension to Visual Genome (VG). We also note that given the highly imbalanced distribution of relations in VG, typical evaluation metrics for visual relation detection cannot reveal improvements of under-represented relations. To address this problem, we propose using an additional metric, calling it Macro Recall@K, and demonstrate its remarkable performance on VG. Finally, our experiments confirm that by effective utilization of depth maps within a simple, yet competitive framework, the performance of visual relation detection can be improved by a margin of up to 8%.
Trust Based Fake Node Identification in Social Networking Sites

Abstract In recent years Social Networking is very commonly used for communications. It is the platform where users are free to join and share their data between groups of people. Due to the openness of this network any one can use multiple fake identifications and fraud the people into its group. These types of fake profiles are required to be identified and wipe out from the network. Some of the Social Networking Sites (SNS) that are commonly used are Twitter, Facebook, Instagram, WhatsApp, WeChat etc. These SNSs are already have some mechanism to identify fake profiles and prevent them from such type of malicious activities but are not sufficiently efficient. This paper represents comprehensive literature review for identification mechanism of fake account on Facebook and proposes a new mechanism to stop this type of harmful activities by using graph based activity diagram.
BackSwipe: Back-of-device Word-Gesture Interaction on Smartphones

Back-of-device interaction is a promising approach to interacting on smartphones. In this paper, we create a back-of-device command and text input technique called BackSwipe, which allows a user to hold a smartphone with one hand, and use the index finger of the same hand to draw a word-gesture anywhere at the back of the smartphone to enter commands and text. To support BackSwipe, we propose a back-of-device word-gesture decoding algorithm which infers the keyboard location from back-of-device gestures, and adjusts the keyboard size to suit the gesture scales; the inferred keyboard is then fed back into the system for decoding. Our user study shows BackSwipe is feasible and a promising input method, especially for command input in the one-hand holding posture: users can enter commands at an average accuracy of 92% with a speed of 5.32 seconds/command. The text entry performance varies across users. The average speed is 9.58 WPM with some users at 18.83 WPM; the average word error rate is 11.04% with some users at 2.85%. Overall, BackSwipe complements the extant smartphone interaction by leveraging the back of the device as a gestural input surface.
An Activity Theory Analysis of Search &amp; Rescue Collective Sensemaking and Planning Practices

Search and rescue (SAR), a disaster response activity performed to locate and save victims, primarily involves collective sensemaking and planning. SAR responders learn to search and navigate the environment, process information about buildings, and collaboratively plan with maps. We synthesize data from five sources, including field observations and interviews, to understand the informational components of SAR and how information is recorded and communicated. We apply activity theory, uncovering unforeseen factors that are relevant to the design of collaboration systems and training solutions. Through our analysis, we derive design implications to support collaborative information technology and training systems: mixing physical and digital mapping; mixing individual and collective mapping; building for different levels and sources of information; and building for different rules, roles, and activities.
An Automated Method for Identification of Key frames in Bharatanatyam Dance Videos

Identifying key frames is the first and necessary step before solving the variety of other Bharatanatyam problems.The paper aims to partition the momentarily stationary frames (key frames) from this dance video's motion frames.The proposed key frames (KFs) localization is novel, simple, and effective compared to the existing dance video analysis methods.It is distinctive from standard KFs detection algorithms as used in other human motion videos.In the dance's basic structure, the occurrence of KFs during performances is often not completely stationary and varies with the dance form and the performer.Hence, it is not easy to decide a global threshold (on the quantum of motion) to work across dancers and performances.The earlier approaches try to compute the threshold iteratively.However, the novelty of the paper is: (a) formulating an adaptive threshold, (b) adopting Machine Learning (ML) approach and, (c) generating the effective feature by combining three frame differencing and bit-plane technique for the KF detection.In ML, we use Support Vector Machine (SVM) and Convolutional Neural Network (CNN) as the classifiers.The proposed approaches are also compared and analyzed with the earlier approaches.Finally, the proposed ML techniques emerge as a winner with around 90% accuracy.
Multi-Speaker Emotional Speech Synthesis with Fine-Grained Prosody Modeling

We present an end-to-end system for multi-speaker emotional speech synthesis. In particular, our system learns emotion classes from just two speakers then generalizes these classes to other speakers from whom no emotional data was seen. We address the problem by integrating disentangled, fine-grained prosody features with global, sentence-level emotion embedding. These fine-grained features learn to represent local prosodic variations disentangled from speaker, tone and global emotion label. Compared to systems that model emotions at sentence level only, our method achieves higher ratings in naturalness and expressiveness, while retaining comparable speaker similarity ratings.
A Dense 3-D Point Cloud Measurement Based on 1-D Background-Normalized Fourier Transform

The 3-D surface measurement plays a significant role in the industrial area. With the advantages of high resolution and acquisition rate, dual-line-scan camera systems have been gradually studied for dynamic 3-D measurement. However, the partial overexposure and the limited measurement depth range of dual-line-scan cameras are the two main elements affecting the quality of the point cloud. Both of these problems are caused by incorrect pixel matching. Therefore, this article presents a matching strategy for dual-line-scan cameras based on the 1-D background-normalized Fourier transform (1DBNFT) to expand the depth range of measurement and deal with the partial overexposure. The wrapped phases extracted by 1DBNFT, the unwrapped phase extracted by projection distance minimization (PDM), and the matching principle are described in detail. We also analyze the possible errors in the system from three aspects: noncoplanar error, installation error, and motion error. Finally, the comparative experiments and accuracy verification experiments demonstrate the effectiveness of our algorithm.