Reviewerly/exHarmony huggingface dataset:
Appearance-Based Gaze Estimator for Natural Interaction Control of Surgical Robots

Robots are playing an increasingly important role in modern surgery. However, conventional human-computer interaction methods, such as joystick control and sound control, have some shortcomings, and medical personnel are required to specifically practice operating the robot. We propose a human-computer interaction model based on eye movement with which medical staff can conveniently use their eye movements to control the robot. Our algorithm requires only an RGB camera to perform tasks without requiring expensive eye-tracking devices. Two kinds of eye control modes are designed in this paper. The first type is the pick and place movement, with which the user uses eye gaze to specify the point where the robotic arm is required to move. The second type is user command movement, with which the user can use eye gaze to select the direction in which the user desires the robot to move. The experimental results demonstrate the feasibility and convenience of these two modes of movement.
Descriptive and Predictive Modeling of Student Achievement, Satisfaction, and Mental Health for Data-Driven Smart Connected Campus Life Service

Yonsei University in Korea launched an educational innovation project entitled "Data-Driven Smart-Connected Campus Life Service", for which student-related data have been accumulated at university level since spring of 2015, and descriptive, predictive and prescriptive modeling have been conducted to offer innovative education service to students. The dataset covers not only conventional student information, student questionnaire survey, and university administrative data, but also unconventional data sets such as student location data and learning management system (LMS) log data. Based on the datasets, with respect to 4,000+ freshman students at residential college, we conducted preliminary implementation of descriptive and predictive modeling for student achievement, satisfaction, and mental health. The results were overall promising. First, descriptive and predictive modeling of GPA for student achievement presented a list of significant predictive variables from student locations and LMS activities. Second, descriptive modeling of student satisfaction revealed influential variables such as "improvement of creativity" and "ability of cooperation". Third, similar descriptive modeling was applied to students' mental health changes by semesters, and the study uncovered influential factors such as "difficulty with relationship" and "time spent with friends increased' as key determinants of student mental health. Although the educational innovation project is still in its early stages, we have three strategies of the future modelling efforts: They are: (1) step-by-step improvement from descriptive, predictive, to prescriptive modelling; (2) full use of recurring data acquisition; (3) different level of segmentation.
Confidence-weighted safe semi-supervised clustering

In this paper, we propose confidence-weighted safe semi-supervised clustering where prior knowledge is given in the form of class labels. In some applications, some samples may be wrongly labeled by the users. Therefore, our basic idea is that different samples should have different impacts or confidences on the clustering performance. In our algorithm, we firstly use unsupervised clustering to perform the dataset partition and compute the normalized confusion matrix Nc. Nc is used to estimate the safe confidence of each labeled sample based on the assumption that a correctly clustered sample should have a high confidence. Then we construct a local graph to model the relationship between the labeled and its nearest unlabeled samples through the clustering results. Finally, a confidence-weighted fidelity term and a graph-based regularization term are incorporated into the objective function of unsupervised clustering. In this case, on the one hand, the outputs of the labeled samples with high confidences are restricted to be the given prior labels. On the other hand, the outputs of the labeled ones with low confidences are forced to approach those of the local homogeneous unlabeled neighbors modeled by the local graph. Hence, the labeled samples are expected to be safely exploited which is the goal of safe semi-supervised clustering. To verify the effectiveness of our algorithm, we carry out some experiments over several datasets by comparison to the unsupervised and semi-supervised clustering methods and achieve the promising results.
Distributed Self-Adjusting Tree Networks

We consider the problem of designing dynamic network topologies that self-adjust to the (possibly changing) traffic pattern they serve. Such demand-aware networks currently receive much attention, especially in the context of datacenters, due to emerging technologies supporting the fast reconfiguration of the physical topology. We present the first fully distributed, provably efficient self-adjusting network. Our network called DiSptayNet relies on algorithms that perform decentralized and concurrent topological adjustments to account for changes in the demand. We present a rigorous formal analysis of the correctness and performance of DiSptayNet, which can be seen as an interesting generalization of analyses known from sequential self-adjusting datastructures. We also report on results from extensive trace-driven simulations.
Simplified hybrid fireworks algorithm

As a relatively new swarm intelligence algorithm, the fireworks algorithm (FWA) has been applied to solve lots of real-world optimization problem. However, there are still some shortcomings in the FWA algorithms. The search equation of FWA is relatively simple. Since the search mechanism of FWA mainly relies on the explosion sparks, the exploration and exploitation abilities of the algorithm are limited. In order to improve the performance of FWA, a simplified hybrid fireworks algorithm (SHFWA) is proposed in this paper. In SHFWA, to enhance the exploitation ability, a modified search formula is designed for core firework swarm. To enhance the exploration ability, for each firework swarm, another way of generating sparks–harmony spark is designed. In the conventional fireworks algorithm, the calculation of the number of sparks generated by each firework and the calculation of amplitude of explosion for each firework are very complex. In SHFWA, a simplified method is employed to compute these two variables. By introducing these methods, SHFWA is easy to implement and is good at exploration and exploitation. The proposed algorithm is tested on 40 benchmark functions. The experimental results demonstrate that SHFWA performs effectively and competitively when compared with several reported algorithms.
Cords and Chords: Exploring the Role of E-Textiles in Computational Audio

Electronic textiles (e-textiles) have played a significant role in computational audio ranging from wearable interfaces for creative expression to more utilitarian purposes such as acoustic monitoring for military applications. This article looks at e-textiles within computational audio from three perspectives: the historical developments of the field; the core enabling technologies; and the primary application areas. It closes with a discussion of what role e-textiles may play in future computational audio systems.
Large scale anomaly detection in mixed numerical and categorical input spaces

This work presents the ADMNC method, designed to tackle anomaly detection for large-scale problems with a mixture of categorical and numerical input variables. A flexible parametric probability measure is adjusted to input data, allowing low likelihood values to be tracked as anomalies. The main contribution of this method is that, to cope with the variable nature of the variables, we factorize the joint probability measure into two parts, namely, the marginal density of the continuous variables and the conditional probability of the categorical variables given the continuous part of the feature vector. The result is a model trained through a maximum likelihood objective function optimized with stochastic gradient descent that yields an effective and scalable algorithm. Compared with other well-known anomaly detection algorithms over several datasets, ADMNC is observed to both offer top level accuracy in datasets that are out of reach for the most effective existing methods and to scale up well to processing very large datasets. This makes it a powerful tool for solving a problem growing in popularity that currently lacks suitable scalable algorithms.
User Review-Based Change File Localization for Mobile Applications

In the current mobile app development, novel and emerging DevOps practices (e.g., Continuous Delivery, Integration, and user feedback analysis) and tools are becoming more widespread. For instance, the integration of user feedback (provided in the form of user reviews) in the software release cycle represents a valuable asset for the maintenance and evolution of mobile apps. To fully make use of these assets, it is highly desirable for developers to establish semantic links between the user reviews and the software artefacts to be changed (e.g., source code and documentation), and thus to localize the potential files to change for addressing the user feedback. In this paper, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RISING</small> ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> eview <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> ntegration via cla <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> sification, cluster <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> ng, and linki <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NG</b> ), an automated approach to support the continuous integration of user feedback via classification, clustering, and linking of user reviews. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RISING</small> leverages domain-specific constraint information and semi-supervised learning to group user reviews into multiple fine-grained clusters concerning similar users’ requests. Then, by combining the textual information from both commit messages and source code, it automatically localizes potential change files to accommodate the users’ requests. Our empirical studies demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results.
Live Migration Ate My VM: Recovering a Virtual Machine after Failure of Post-Copy Live Migration

Post-copy is one of the two key techniques (besides pre-copy) for live migration of virtual machines in data centers. Post-copy provides deterministic total migration time and low downtime for write-intensive VMs. However, if post-copy migration fails for any reason, the migrating VM is lost because the VM's latest consistent state is split between the source and destination nodes during migration. In this paper, we present PostCopyFT, a new approach to recover a VM after a destination or network failure during post-copy live migration using an efficient reverse incremental checkpointing mechanism. We have implemented and evaluated our approach in the KVM/QEMU platform. Our experimental results show that the total migration time of post-copy remains unchanged while maintaining low failover time, downtime, and application performance overhead.
Adapting YOLO Network for Ball and Player Detection

In this paper, we consider the task of detecting the players and sports balls in real-world handball images, as a building block for action recognition. Detecting the ball is still a challenge because it is a very small object that takes only a few pixels in the image but carries a lot of information relevant to the interpretation of scenes. Balls can vary greatly regarding color and appearance due to various distances to the camera and motion blur. Occlusion is also present, especially as handball players carry the ball in their hands during the game and it is understood that the player with the ball is a key player for the current action. Handball players are located at different distances from the camera, often occluded and have a posture that differs from ordinary activities for which most object detectors are commonly learned. We compare the performance of 6 models based on the YOLOv2 object detector, trained on an image dataset of publicly available sports images and images from custom handball recordings. The performance of a person and ball detection is measured on the whole dataset and the custom part regarding mean average precision metric.
Detection of Blockchain Transactions Used in Blockchain Mixer of Coin Join Type

This paper is devoted to both the detection of transactions that can participate in money laundering schemes and the possibility of modification of payment systems based on blockchain technology in the interests of AML/CFT. In the process of work, the research of transaction anonymization method with the use of a decentralized approach based on Coin Join transactions is performed. Also, methods of tracking such transactions, the possibility of their deanonymization and the ability to determine the relationships between users and their transactions are examined. The results show that tracking Coin Join transactions is a feasible task that allows to determine the fact of user participation in the creation of a transaction, which gives additional advantages in the process of detecting transactions involved in money laundering schemes.
Image Shadow Removal Using End-to-End Deep Convolutional Neural Networks

Image degradation caused by shadows is likely to cause technological issues in image segmentation and target recognition. In view of the existing shadow removal methods, there are problems such as small and trivial shadow processing, the scarcity of end-to-end automatic methods, the neglecting of light, and high-level semantic information such as materials. An end-to-end deep convolutional neural network is proposed to further improve the image shadow removal effect. The network mainly consists of two network models, an encoder–decoder network and a small refinement network. The former predicts the alpha shadow scale factor, and the latter refines to obtain sharper edge information. In addition, a new image database (remove shadow database, RSDB) is constructed; and qualitative and quantitative evaluations are made on databases such as UIUC, UCF and newly-created databases (RSDB) with various real images. Using the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) for quantitative analysis, the algorithm has a big improvement on the PSNR and the SSIM as opposed to other methods. In terms of qualitative comparisons, the network shadow has a clearer and shadow-free image that is consistent with the original image color and texture, and the detail processing effect is much better. The experimental results show that the proposed algorithm is superior to other algorithms, and it is more robust in subjective vision and objective quantization.
An Automated Player Detection and Tracking in Basketball Game

Vision-based player recognition is critical in sports applications. Accuracy, efficiency, and Low memory utilization is alluring for ongoing errands, for example, astute communicates and occasion classification. We developed an algorithm that tracks the movements of different players from a video of a basketball game. With their position tracked, we then proceed to map the position of these players onto an image of a basketball court. The purpose of tracking player is to provide the maximum amount of information to basketball coaches and organizations, so that they can better design mechanisms of defence and attack. Overall, our model has a high degree of identification and tracking of the players in the court. We directed investigations on soccer, basketball, ice hockey and pedestrian datasets. The trial comes about an exhibit that our technique can precisely recognize players under testing conditions. Contrasted and CNNs that are adjusted from general question identification systems, for example, Faster-RCNN, our approach accomplishes cutting edge exactness on three sorts of recreations (basketball, soccer and ice hockey) with 1000×fewer parameters. The all-inclusive statement of our technique is additionally shown on a standard passer-by recognition dataset in which our strategy accomplishes aggressive execution contrasted and cutting-edge methods.
Deep residual dense U-Net for resolution enhancement in accelerated MRI acquisition

Typical Magnetic Resonance Imaging (MRI) scan may take 20 to 60 minutes. Reducing MRI scan time is beneficial for both patient experience and cost considerations. Accelerated MRI scan may be achieved by acquiring less amount of k-space data (down-sampling in the k-space). However, this leads to lower resolution and aliasing artifacts for the reconstructed images. There are many existing approaches for attempting to reconstruct high-quality images from down-sampled k-space data, with varying complexity and performance. In recent years, deep-learning approaches have been proposed for this task, and promising results have been reported. Still, the problem remains challenging especially because of the high fidelity requirement in most medical applications employing reconstructed MRI images. In this work, we propose a deep-learning approach, aiming at reconstructing high-quality images from accelerated MRI acquisition. Specifically, we use Convolutional Neural Network (CNN) to learn the differences between the aliased images and the original images, employing a U-Net-like architecture. Further, a micro-architecture termed Residual Dense Block (RDB) is introduced for learning a better feature representation than the plain U-Net. Considering the peculiarity of the downsampled k-space data, we introduce a new term to the loss function in learning, which effectively employs the given k-space data during training to provide additional regularization on the update of the network weights. To evaluate the proposed approach, we compare it with other state-of-the-art methods. In both visual inspection and evaluation using standard metrics, the proposed approach is able to deliver improved performance, demonstrating its potential for providing an effective solution.
Augmenting Recurrent Neural Networks Resilience by Dropout

This brief discusses the simple idea that dropout regularization can be used to efficiently induce resiliency to missing inputs at prediction time in a generic neural network. We show how the approach can be effective on tasks where imputation strategies often fail, namely, involving recurrent neural networks and scenarios where whole sequences of input observations are missing. The experimental analysis provides an assessment of the accuracy-resiliency tradeoff in multiple recurrent models, including reservoir computing methods, and comprising real-world ambient intelligence and biomedical time series.
An Unsupervised Embedding Learning Feature Representation Scheme for Network Big Data Analysis

With the arrival of the big data era, data come frequently with increasing volume and high dimensionality, which imposes a considerable challenge on data compression in network representation and analysis. How to learn an effective low-dimensional representation has a dramatic influence on performance of specific network learning tasks. In this paper, we propose an unsupervised embedding learning feature representation scheme by deep Siamese neural networks, aiming to learn an efficient low-dimensional feature subspace. Unsupervised embedding learning is one tough but interesting task in which its searching strategy is performed without the guidance of class label information. Siamese network is a neural network that can learn an efficient feature subspace in a supervised mode. It trains two networks with shared weights simultaneously, and feeds them with random sampling from the same dataset. As a result, the feature space is projected onto a low-dimensional subspace such that the similar samples are with small values close to zero, whereas the dissimilar ones come with big values greater than a predefined margin. We further discuss deep Siamese neural network in an unsupervised mode and its applications to embedding learning. The proposed method can be also used to address semi-supervised feature representation problems. Finally, the learned unsupervised embedding is validated on eight publicly available databases including images, voices, and text documents. Extensive experiments demonstrate the superiority of the proposed method against the compared existing state-of-the-art embedding approaches.
Real-time high-dynamic-range fringe acquisition for 3D shape measurement with a RGB camera

This paper introduces a novel real-time, high-dynamic-range (HDR) three-dimensional (3D) shape measurement method using a RGB camera. The proposed method takes advantage of two effects to realize HDR 3D fringe acquisition for 3D shape measurements: (1) the different responses of a RGB camera's three channels to a single color light source can be utilized to produce fringe images with different intensity levels; (2) the projector's dark time can be utilized to produce bright versus dark intensity contrast if bit-wise binary patterns are used. Experiments demonstrate the real-time capabilities of the proposed method by dynamic 3D shape measurements (with a maximum camera frame rate of 166 Hz). Given that a bit-wise defocused binary pattern projection is adopted, the proposed HDR method has the potential for high-speed applications if a high-speed color camera is present.
Reference Coupling: An exploration of inter-project technical dependencies and their characteristics within large software ecosystems

Software projects often depend on other projects or are developed in tandem with other projects. Within such software ecosystems, knowledge of cross-project technical dependencies is important for (1) practitioners understanding of the impact of their code change and coordination needs within the ecosystem and (2) researchers in exploring properties of software ecosystems based on these technical dependencies. However, identifying technical dependencies at the ecosystem level can be challenging. In this paper, we describe Reference Coupling, a new method that uses solely the information in developers online interactions to detect technical dependencies between projects. The method establishes dependencies through user-specified cross-references between projects. We then use the output of this method to explore the properties of large software ecosystems. We validate our method on two datasets — one from open-source projects hosted on GitHub and one commercial dataset of IBM projects. We manually analyze the identified dependencies, categorize them, and compare them to dependencies specified by the development team. We examine the types of projects involved in the identified ecosystems, the structure of the identified ecosystems, and how the ecosystems structure compares with the social behavior of project contributors and owners. We find that our Reference Coupling method often identifies technical dependencies between projects that are untracked by developers. We describe empirical insights about the characteristics of large software ecosystems. We find that most ecosystems are centered around one project and are interconnected with other ecosystems. By exploring the socio-technical alignment within the GitHub ecosystems, we also found that the project owners social behavior aligns well with the technical dependencies within the ecosystem, but the project contributors social behavior does not align with these dependencies. We conclude with a discussion on future research that is enabled by our Reference Coupling method.