Reviewerly/exHarmony huggingface dataset:
Joint detection and tracking in videos with identification features

Recent works have shown that combining object detection and tracking tasks, in the case of video data, results in higher performance for both tasks, but they require a high frame-rate as a strict requirement for performance. This is assumption is often violated in real-world applications, when models run on embedded devices, often at only a few frames per second. Videos at low frame-rate suffer from large object displacements. Here re-identification features may support to match large-displaced object detections, but current joint detection and re-identification formulations degrade the detector performance, as these two are contrasting tasks. In the real-world application having separate detector and re-id models is often not feasible, as both the memory and runtime effectively double. Towards robust long-term tracking applicable to reduced-computational-power devices, we propose the first joint optimization of detection, tracking and re-identification features for videos. Notably, our joint optimization maintains the detector performance, a typical multi-task challenge. At inference time, we leverage detections for tracking (tracking-by-detection) when the objects are visible, detectable and slowly moving in the image. We leverage instead re-identification features to match objects which disappeared (e.g. due to occlusion) for several frames or were not tracked due to fast motion (or low-frame-rate videos). Our proposed method reaches the state-of-the-art on MOT, it ranks 1st in the UA-DETRAC'18 tracking challenge among online trackers, and 3rd overall.
Convolutional 3D to 2D Patch Conversion for Pixel-Wise Glioma Segmentation in MRI Scans

Structural magnetic resonance imaging (MRI) has been widely utilized for analysis and diagnosis of brain diseases. Automatic segmentation of brain tumors is a challenging task for computer-aided diagnosis due to low-tissue contrast in the tumor subregions. To overcome this, we devise a novel pixel-wise segmentation framework through a convolutional 3D to 2D MR patch conversion model to predict class labels of the central pixel in the input sliding patches. Precisely, we first extract 3D patches from each modality to calibrate slices through the squeeze and excitation (SE) block. Then, the output of the SE block is fed directly into subsequent bottleneck layers to reduce the number of channels. Finally, the calibrated 2D slices are concatenated to obtain multimodal features through a 2D convolutional neural network (CNN) for prediction of the central pixel. In our architecture, both local inter-slice and global intra-slice features are jointly exploited to predict class label of the central voxel in a given patch through the 2D CNN classifier. We implicitly apply all modalities through trainable parameters to assign weights to the contributions of each sequence for segmentation. Experimental results on the segmentation of brain tumors in multimodal MRI scans (BraTS’19) demonstrate that our proposed method can efficiently segment the tumor regions.
Multifeature Fusion-Based Earthquake Event Classification Using Transfer Learning

This letter proposes a multifeature fusion model using deep convolution neural networks and transfer learning approach for earthquake event classification. There are several feature representations for seismic analysis, such as the time domain, the frequency domain, and the time–frequency domain. To successfully classify various earthquake events, we propose a novel model that combines these features hierarchically. In addition, we apply a transfer learning to mitigate overfitting problem of deep learning model while achieving high classification performance. To evaluate our approach, we conduct experiments with the Korean peninsula earthquake database from 2016 to 2018 and a large earthquake database on the Circum-Pacific belt in 2019. The experimental results show that the proposed method outperforms over the compared state-of-the-art methods.
Mixed Reality Guidance System for Motherboard Assembly Using Tangible Augmented Reality

We developed a mixed reality guidance system (MRGS) for motherboard assembly with purpose of exploring the usability and viability of using mixed reality and tangible augmented reality (TAR) for simulating hands-on manual assembly tasks. TAR was used to remove the need for real-world parts as well as to provide a natural interaction medium for our system. To evaluate our system, we conducted two usability studies involving 25 (10 experienced and 15 naive) participants. For the first study, participants were tasked to rate only the proposed interaction technique. Both experienced and naive participants gave acceptable scores, with experienced users giving significantly higher ratings. In the second study, participants were tasked to partially assemble the motherboard using the MRGS. Participants who utilized the MRGS were able to properly determine the correct orientation and location of the motherboard parts, in contrast to the control group. Observations of users while performing the tasks as well as user feedback through survey questionnaires and interviews are presented and discussed in this paper.
Circular Anchors for the Detection of Hematopoietic Cells Using Retinanet

Analysis of the blood cell distribution in bone marrow is necessary for a detailed diagnosis of many hematopoietic diseases, such as leukemia. While this task is performed manually on microscope images in clinical routine, automating it could improve reliability and objectivity. Cell detection tasks in medical imaging have successfully been solved using deep learning, in particular with RetinaNet, a powerful network architecture that yields good detection results in this scenario. It utilizes axis-parallel, rectangular bounding boxes to describe an object's position and size. However, since cells are mostly circular, this is suboptimal. We replace RetinaNet's anchors with more suitable Circular Anchors, which cover the shape of cells more precisely. We further introduce an extension to the Non-maximum Suppression algorithm that copes with predictions that differ in size. Experiments on hematopoietic cells in bone marrow images show that these methods reduce the number of false positive predictions and increase detection accuracy.
Chinese semantic document classification based on strategies of semantic similarity computation and correlation analysis

Document classification has become an indispensable technology to realize intelligent information services. This technique is often applied to the tasks such as document organization, analysis, and archiving or implemented as a submodule to support high-level applications. It has been shown that semantic analysis can improve the performance of document classification. Although this has been incorporated in previous automatic document classification methods, with an increase in the number of documents stored online, the use of semantic information for document classification has attracted greater attention as it can greatly reduce human effort. In this present paper, we propose two semantic document classification strategies for two types of semantic problems: (1) a novel semantic similarity computation (SSC) method to solve the polysemy problem and (2) a strong correlation analysis method (SCM) to solve the synonym problem. Experimental results indicate that compared with traditional machine learning, n-gram, and contextualized word embedding methods, the efficient semantic similarity and correlation analysis allow eliminating word ambiguity and extracting useful features to improve the accuracy of semantic document classification for texts in Chinese.
Gold Exploration in Two and Three Dimensions: Improved and Correlative Insights from Microscopy and X-Ray Computed Tomography

In this contribution, we highlight a correlative approach in which three-dimensional structural/positional data are combined with two dimensional chemical and mineralogical data to understand a complex orogenic gold mineralization system; we use the Kirk Range (southern Malawi) as a case study. Three dimensional structures and semi-quantitative mineral distributions were evaluated using X-ray Computed Tomography (XCT) and this was augmented with textural, mineralogical and chemical imaging using Scanning Electron Microscopy (SEM) and optical microscopy as well as fire assay. Our results detail the utility of the correlative approach both for quantifying gold concentrations in core samples (which is often nuggety and may thus be misrepresented by quarter- or half-core assays), and for understanding the spatial distribution of gold and associated structures and microstructures in 3D space. This approach overlays complementary datasets from 2D and 3D analytical protocols, thereby allowing a better and more comprehensive understanding on the distribution and structures controlling gold mineralization. Combining 3D XCT analyses with conventional 2D microscopies derive the full value out of a given exploration drilling program and it provides an excellent tool for understanding gold mineralization. Understanding the spatial distribution of gold and associated structures and microstructures in 3D space holds vast potential for exploration practitioners, especially if the correlative approach can be automated and if the resultant spatially-constrained microstructural information can be fed directly into commercially available geological modelling software. The extra layers of information provided by using correlative 2D and 3D microscopies offer an exciting new tool to enhance and optimize mineral exploration workflows, given that modern exploration efforts are targeting increasingly complex and low-grade ore deposits.
Review-Based Recommender Systems: A Proposed Rating Prediction Scheme Using Word Embedding Representation of Reviews

Abstract Recommender systems nowadays play an important role in providing helpful information for users, especially in ecommerce applications. Many of the proposed models use rating histories of the users in order to predict unknown ratings. Recently, users’ reviews as a valuable source of knowledge have attracted the attention of researchers in this field and a new category denoted as review-based recommender systems has emerged. In this study, we make use of the information included in user reviews as well as available rating scores to develop a review-based rating prediction system. The proposed scheme attempts to handle the uncertainty problem of the rating histories, by fuzzifying the given ratings. Another advantage of the proposed system is the use of a word embedding representation model for textual reviews, instead of using traditional models such as binary bag of words and TFIDF 1 vector space. It also makes use of the helpfulness voting scores, in order to prune data and achieve better results. The effectiveness of the rating prediction scheme as well as the final recommender system was evaluated against the Amazon dataset. Experimental results revealed that the proposed recommender system outperforms its counterparts and can be used as a suitable tool in ecommerce environments.
The improved Harris hawk optimization algorithm with the Tent map

The hawks are proved to be amongst the most intelligent birds in nature based on their observed innovations in feeding behaviors. Their behaviors in hunting for the rabbit in swarms are modeled mathematically, and a novel Harris hawk optimization (HHO) algorithm is proposed recently. This paper reports the improved HHO algorithm based on the Tent map and simulation experiments are done. Results show that the Tent map is capable of improving the capabilities of the HHO algorithm based on their applications in solving the benchmark functions, according to a series of simple experiments on the fast convergence ratios.
Online path planning of mobile robot using grasshopper algorithm in a dynamic and unknown environment

The navigation of mobile robots using heuristic algorithms is one of the important issues in computer and control sciences. Path planning and obstacle avoidance are current topics of navigational challenges for mobile robots. The major drawbacks of conventional methods are the inability to plan motion in a dynamic and unknown environment, failure in crowded and complex environments, and inability to predict the velocity vector of obstacles and non-optimality of the synthesised path. This paper presents a novel path planning approach using a grasshopper algorithm for navigation of a mobile robot in dynamic and unknown environments. To accomplish this goal, two different approaches are presented. First, a sensory system is used to detect the obstacles and then a new method is developed to predict and avoid static and dynamic obstacles while the velocity of obstacles is unknown. The robot uses the obtained information and finds a collision-free, optimal and safe path. The controller proposed in this paper is tested in crowded and complex environments. Simulation results show that the approach is successful in all test environments. Also, the proposed controller is compared with several heuristic methods. The comparison work stipulates that the introduced controller here is promising in terms of running time, optimality, stability and failure rate.
Self-supervised on-line cumulative learning from video streams

We present a novel online self-supervised method for face identity learning from video streams. The method exploits deep face feature descriptors together with a memory based learning mechanism that takes advantage of the temporal coherence of visual data. Specifically, we introduce a discriminative descriptor matching solution based on Reverse Nearest Neighbor and a memory based cumulative learning strategy that discards redundant descriptors while time progresses. This allows building a comprehensive and cumulative representation of all the past visual information observed so far. It is shown that the proposed learning procedure is asymptotically stable and can be effectively used in relevant applications like multiple face identification and tracking from unconstrained video streams. Experimental results show that the proposed method achieves comparable results in the task of multiple face tracking and better performance in face identification with offline approaches exploiting future information.
Pan-Cancer Prognosis Prediction Using Multimodal Deep Learning

In the age of precision medicine, cancer prognosis assessment from high-dimensional multimodal data requires powerful computational methods. We present an end-to-end multimodal Deep Learning method, named MultiSurv, for automatic patient risk prediction for a large group of 33 cancer types. The method leverages histophatology microscopy slides combined with tabular clinical information and different types of high-throughput sequencing and microarray molecular data. MultiSurv has high predictive performance over all cancer types after training on different combinations of input data modalities and it can handle missing data seamlessly. MultiSurv thus has the potential to integrate the wide variety of available patient data and assist physicians with cancer patient prognosis.
Soft-Label Guided Semi-Supervised Learning for Bi-Ventricle Segmentation in Cardiac Cine MRI

Deep convolutional neural networks have been applied to medical image segmentation tasks successfully in recent years by taking advantage of a large amount of training data with golden standard annotations. However, it is difficult and expensive to obtain good-quality annotations in practice. This work aims to propose a novel semi-supervised learning framework to improve the ventricle segmentation from 2D cine MR images. Our method is efficient and effective by computing soft labels dynamically for the unlabeled data. Specifically, we obtain the soft labels, rather than hard labels, from a teacher model in every learning iteration. The uncertainty of the target label of unlabeled data is intrinsically encoded in the soft label. The soft label can be improved towards the ideal target in training. We use a separate loss to regularize the unlabeled data to produce similar probability distribution as the soft labels in each iteration. Experiments show that our method outperforms a state-of-the-art semi-supervised method.
Understanding the relationship of conflict and success in software development projects

Software development incorporates numerous people with diverse expertise and expectations. This makes conflict a common phenomenon in software development. Besides human causes, many conflicts in software development root in the tools and processes. Moreover, the growing role of software in any type of system is increasing the heterogeneity in software projects. The number and variety of tools and processes are increasing. Nevertheless, the relationship between conflicts, particularly rooted in non-human elements, and software project success is still unclear. We aim to understand the impact of conflict on the success of software development projects for different types of conflict and different environments. Particularly, we distinguish between human-rooted conflict (HRC) and non-human-rooted conflict (NHRC). Moreover, we investigate whether organization size and team size moderate the impact of conflict on software project success. First, we conduct a survey and analyze it using structural equation modeling (SEM) to investigate any correlation between conflict and software project success. Second, we explore the reasons behind the relationship between conflict and software project success by conducting 13 semi-structured expert interviews. HRC is always a threat to software project success for any organization or team size. Based on the interviews, resolving an HRC is regularly problematic. On the other hand, NHRC is negatively correlated with software project success only in corporate organizations and small teams. High coordination overhead and dependency on tools and processes make NHRC more influential in corporate organizations. In contrast, overlooking non-human elements and lack of experienced individuals in smaller teams make them more vulnerable to NHRC. While the detrimental impact of HRC is constant for software project success, NHRC can be controlled efficiently. Corporate organizations need to frequently improve the non-human elements in the development. Smaller teams should expect tools and processes to be significantly influential in their success.
Human-friendly control system design for two-wheeled service robot with optimal control approach

This paper proposes a novel control system design for a two-wheeled service robot that follows a person as an assistant without knowing the person's destination. For this kind of service robot, the key skill is to realize human-friendly movement. However, appropriate motion always changed depending on the situation. For instance, when the robot is close and person turns toward it, it is important to suppress the robot's acceleration. Likewise, if the person turns away from the robot, the robot should maintain its position within an appropriate area. Therefore, to deal with various required movements, our control system is able to change its properties automatically and suitably depending on the situation by using weights of the cost function in nonlinear model predictive control (NMPC) as a function of the relative distance between the person and the robot. Unlike previous methods, our design includes only one controller. Consequently, we are able to take into account system stability. Moreover, owing to proposing in NMPC framework, it is easy to extend our method by adopting other recognition or goal-setting methods. We conducted simulations using actual human walking data taken by the robot's laser range sensors. The experiments demonstrate that the robot can follow a person who performs U-turn, confirming that our method can produce human-friendly robot movement in a practical scene.
Transfer Learning Under Conditional Shift Based on Fuzzy Residual

Transfer learning has received much attention recently and has been proven to be effective in a wide range of applications, whereas studies on regression problems are still scarce. In this article, we focus on the transfer learning problem for regression under the situations of conditional shift where the source and target domains share the same marginal distribution while having different conditional probability distributions. We propose a new framework called transfer learning based on fuzzy residual (ResTL) which learns the target model by preserving the distribution properties of the source data in a model-agnostic way. First, we formulate the target model by adding fuzzy residual to a model-agnostic source model and reuse the antecedent parameters of the source fuzzy system. Then two methods for bias computation are provided for different considerations, which refer to two ResTL methods called ResTLLS and ResTLRD. Finally, we conduct a series of experiments both on a toy example and several real-world datasets to verify the effectiveness of the proposed method.
CODE reuse in practice: Benefiting or harming technical debt

During the last years the TD community is striving to offer methods and tools for reducing the amount of TD, but also understand the underlying concepts. One popular practice that still has not been investigated in the context of TD, is software reuse. The aim of this paper is to investigate the relation between white-box code reuse and TD principal and interest. In particular, we target at unveiling if the reuse of code can lead to software with better levels of TD. To achieve this goal, we performed a case study on approximately 400 OSS systems, comprised of 897 thousand classes, and compare the levels of TD for reused and natively-written classes. The results of the study suggest that reused code usually has less TD interest; however, the amount of principal in them is higher. A synthesized view of the aforementioned results suggest that software engineers shall opt to reuse code when necessary, since apart from the established reuse benefits (i.e., cost savings, increased productivity, etc.) are also getting benefits in terms of maintenance. Apart from understanding the phenomenon per se, the results of this study provide various implications to research and practice.
The Importance of Starting With Goals in N-of-1 Studies

Self-tracking offers the potential to support people in monitoring health and identifying individualized health management strategies. We argue that elicitation of individualized goals and customization of tracking to support those goals are a critical yet under-studied and under-supported aspect of self-tracking. We review examples of self-tracking from across a range of chronic conditions and self-tracking designs (e.g., self-monitoring, correlation analyses, self-experimentation). Together, these examples show how failure to elicit goals can lead to ineffective tracking routines, breakdowns in collaboration (e.g., between patients and providers, among families), increased burdens, and even designs that encourage behaviors counter to a person's goals. We discuss potential techniques for eliciting and refining goals, scaffolding the right tracking routine based on those goals, and presenting results in ways that advance individual goals while preserving individual agency. We then describe open challenges, including how to reconcile competing goals and support evolution of goals over time.