Reviewerly/exHarmony huggingface dataset:
Multi-Robot Task and Motion Planning With Subtask Dependencies

We present a multi-robot integrated task and motion method capable of handling sequential subtask dependencies within multiply decomposable tasks. We map the multi-robot pathfinding method, Conflict Based Search, to task planning and integrate this with motion planning to create TMP-CBS. TMP-CBS couples task decomposition, allocation, and planning to support cases where the optimal solution depends on robot availability and inter-team conflict avoidance. We show improved planning time for simpler task sets and generate optimal solutions w.r.t. the state space representation for a broader range of problems than prior methods.
Real Time Video Streaming From Multi-Source Using Client-Server for Video Distribution

The Real Time Video Distribution (RTVD) is now a day a subject of interest in various applications that support different fields, like video conferences, security systems, video broadcasting, etc. This paper presents a system for a multi-IP cameras based on the use of client/server to distribute video signals to many clients. There are three main functions in the proposed system: the first is that the server will receive the sent streams by all connected cameras. The second means that the server starts viewing video signals and distributes them to all connected clients in the network. Finally, the clients will receive video signals from the server. Hence, the proposed RTVD System (RTVDS) makes the Admin to focus on the received frames sent by IP cameras to/from the server within signals broadcasting. In addition, RTVDS has the capability of tracking and storing the frames stream and encode them during the broadcasting process. Different structures and algorithms have been depended in the proposed RTVDS using tools implemented by VC# programming language.
SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark

We release SVIRO, a synthetic dataset for sceneries in the passenger compartment of ten different vehicles, in order to analyze machine learning-based approaches for their generalization capacities and reliability when trained on a limited number of variations (e.g. identical backgrounds and textures, few instances per class). This is in contrast to the intrinsically high variability of common benchmark datasets, which focus on improving the state-of-the-art of general tasks. Our dataset contains bounding boxes for object detection, instance segmentation masks, keypoints for pose estimation and depth images for each synthetic scenery as well as images for each individual seat for classification. The advantage of our use-case is twofold: The proximity to a realistic application to benchmark new approaches under novel circumstances while reducing the complexity to a more tractable environment, such that applications and theoretical questions can be tested on a more challenging dataset as toy problems. The data and evaluation server are available under https://sviro.kl.dfki.de.
Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints

In this paper, we aim to reduce the footskate artifacts when reconstructing human dynamics from monocular RGB videos. Recent work has made substantial progress in improving the temporal smoothness of the reconstructed motion trajectories. Their results, however, still suffer from severe foot skating and slippage artifacts. To tackle this issue, we present a neural network based detector for localizing ground contact events of human feet and use it to impose a physical constraint for optimization of the whole human dynamics in a video. We present a detailed study on the proposed ground contact detector and demonstrate high-quality human motion reconstruction results in various videos.
Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis

An ongoing major challenge in computer vision is the task of person re-identification, where the goal is to match individuals across different, non-overlapping camera views. While recent success has been achieved via supervised learning using deep neural networks, such methods have limited widespread adoption due to the need for large-scale, customized data annotation. As such, there has been a recent focus on unsupervised learning approaches to mitigate the data annotation issue; however, current approaches in literature have limited performance compared to supervised learning approaches as well as limited applicability for adoption in new environments. In this paper, we address the aforementioned challenges faced in person re-identification for real-world, practical scenarios by introducing a novel, unsupervised domain adaptation approach for person re-identification. This is accomplished through the introduction of: i) k-reciprocal tracklet Clustering for Unsupervised Domain Adaptation (ktCUDA) (for pseudo-label generation on target domain), and ii) Synthesized Heterogeneous RE-id Domain (SHRED) composed of large-scale heterogeneous independent source environments (for improving robustness and adaptability to a wide diversity of target environments). Experimental results across four different image and video benchmark datasets show that the proposed ktCUDA and SHRED approach achieves an average improvement of +5.7 mAP in re-identification performance when compared to existing state-of-the-art methods, as well as demonstrate better adaptability to different types of environments.
Two-stage gold deposition in response to H2S loss from a single fluid in the Sizhuang deposit (Jiaodong, China)

The material source, fluid evolution and mineralizing mechanism in the Jiaodong gold province have been hotly debated for several decades. The Sizhuang gold deposit provides an excellent object for studying these issues. Gold in this deposit mainly precipitated in the quartz–Au–pyrite (stage II) and quartz–Au–polymetallic (stage III) stages. Stage III can be further subdivided into the early stage III (chalcopyrite, galena with minor amounts of electrum, pyrrhotite, pyrite, siderite and sphalerite) and the late stage III (chalcopyrite, magnetite, siderite with a little pyrrhotite). Pyrite from stage II and stage III contains consistent δ34S signatures (8.1‰–9.6‰ and 7.5‰–9.8‰, respectively) as well as trace elements (e.g. Co: 8.19 vs. 2.63 ppm, Ni: 3.54 vs. 0.74 ppm, As: 29.95 vs. 5.78 ppm, and Bi: 3.18 vs. 7.81 ppm). These consistent geochemical features combined with previous fluid inclusion and C-H-O isotope studies argue for a common source for ore-forming materials and fluids of the stage II and III at Sizhuang deposit. During the stage II and III, reduced sulfur species may have been lost from ore-forming fluids in response to pyrite precipitation (HS- being scavenged by pyrite) and fluid immiscibility (H2S entering vapor phase), which led to the total sulfur concentration (∑Stotal) decrease in the ore-forming fluids. Thermodynamic calculations at different ∑Stotal and constant pressure (2000 bar) and temperature (300 °C) in Fe-Cu-O-S-H system reveal that ∑Stotal decrease can lead to first enlargement and then shrinkage of pyrrhotite stability field with magnetite stability field being broadened to more acidic field; that sulfur species is dominated by H2S (>90 mol%) under the bornite–pyrite phase boundaries in the acidic ore-forming fluids; and that decreases in H2S concentration can efficiently cause gold precipitation by reducing gold bisulfide solubility up to 2–3 orders of magnitude while ∑Stotal just decreases ~50 times. Pyrrhotite and magnetite precipitated due to enlarged stability field during this physicochemical process, which is in line with geologic observations. It is proposed that the two-stage gold deposition by H2S loss from a single ore-forming fluid could account for gold precipitation coupling with occurrence of pyrrhotite and magnetite in the mineral assemblage at Sizhuang deposit.
Customization scenarios for de-identification of clinical notes

Automated machine-learning systems are able to de-identify electronic medical records, including free-text clinical notes. Use of such systems would greatly boost the amount of data available to researchers, yet their deployment has been limited due to uncertainty about their performance when applied to new datasets.We present practical options for clinical note de-identification, assessing performance of machine learning systems ranging from off-the-shelf to fully customized.We implement a state-of-the-art machine learning de-identification system, training and testing on pairs of datasets that match the deployment scenarios. We use clinical notes from two i2b2 competition corpora, the Physionet Gold Standard corpus, and parts of the MIMIC-III dataset.Fully customized systems remove 97-99% of personally identifying information. Performance of off-the-shelf systems varies by dataset, with performance mostly above 90%. Providing a small labeled dataset or large unlabeled dataset allows for fine-tuning that improves performance over off-the-shelf systems.Health organizations should be aware of the levels of customization available when selecting a de-identification deployment solution, in order to choose the one that best matches their resources and target performance level.
An efficient tensor completion method via truncated nuclear norm

Tensor completion aims to recover missing entries from partial observations for multi-dimensional data. Traditional tensor completion algorithms process the dimensional data by unfolding the tensor into matrices, which breaks the inherent correlation and dependencies in multiple channels and lead to critical information loss. In this paper, we propose a novel tensor completion model for visual multi-dimensional data completion under the tensor singular value decomposition (t-SVD) framework. In the proposed method, tensor is treated as a whole and a truncated nuclear norm regularization is employed to exploit the structural properties in a tensor and hidden information existing among the adjacent channels of a tensor. Besides, we introduce a weighted tensor to adjust the residual error of each frontal slices in consideration of their different recovery statistics. It does enhance the sparsity of all unfoldings of the tensor and accelerates the convergence of the proposed method. Experimental results on various visual datasets demonstrate the promising performance of the proposed method in comparison with the state-of-the-art tensor completion methods.
Using virtual edges to improve the discriminability of co-occurrence text networks

Word co-occurrence networks have been employed to analyze texts both in the practical and theoretical scenarios.Despite the relative success in several applications, traditional co-occurrence networks fail in establishing links between similar words whenever they appear distant in the text.Here we investigate whether the use of word embeddings as a tool to create virtual links in cooccurrence networks may improve the quality of classification systems.Our results revealed that the discriminability in the stylometry task is improved when using Glove, Word2Vec and FastText.In addition, we found that optimized results are obtained when stopwords are not disregarded and a simple global thresholding strategy is used to establish virtual links.Because the proposed approach is able to improve the representation of texts as complex networks, we believe that it could be extended to study other natural language processing tasks.Likewise, theoretical languages studies could benefit from the adopted enriched representation of word co-occurrence networks.
Proposing A Load Balancing Algorithm For The Optimization Of Cloud Computing Applications

Cloud Computing (CC) is a fast growing services that make use of pay per use model. The technology provides various services in terms of storage, deployment, web services etc. however the expand of these services and the tremendous increase of user demand has resulted in many challenges to keep up the performance in line with QoS measurement and SLA document provided by cloud providers to enterprises. This expand resulted in challenges such as load balancing. Besides that, user's requirements became hard to fulfil in terms of response time and deadline regarding task scheduling. To address these challenges, this research proposes an optimized algorithm with the use of Machine Learning Classification technique based on deadline constraints. The main objective of the proposed algorithm is to enhance the efficiency, optimize the server resources by considering the priority of different users' tasks and avoid server breakdown. Our proposed algorithm will address the mentioned issues and current research gap based on the recent literature.
GPOP

The past decade has seen the development of many shared-memory graph processing frameworks intended to reduce the effort of developing high-performance parallel applications. However, many of these frameworks, based on Vertex-centric or Edge-centric paradigms suffer from several issues, such as poor cache utilization, irregular memory accesses, heavy use of synchronization primitives, or theoretical inefficiency, that deteriorate over-all performance and scalability. Recently, we proposed a cache and memory-efficient partition-centric paradigm for computing PageRank [26]. In this article, we generalize this approach to develop a novel Graph Processing Over Parts (GPOP) framework that is cache efficient, scalable, and work efficient. GPOP induces locality in memory accesses by increasing granularity of execution to vertex subsets called “parts,” thereby dramatically improving the cache performance of a variety of graph algorithms. It achieves high scalability by enabling completely lock and atomic free computation. GPOP’s built-in analytical performance model enables it to use a hybrid of source and part-centric communication modes in a way that ensures work efficiency each iteration, while simultaneously boosting high bandwidth sequential memory accesses. Finally, the GPOP framework is designed with programmability in mind. It completely abstracts away underlying parallelism and programming model details from the user and provides an easy to program set of APIs with the ability to selectively continue the active vertex set across iterations. Such functionality is useful for many graph algorithms but not intrinsically supported by the current frameworks. We extensively evaluate the performance of GPOP for a variety of graph algorithms, using several large datasets. We observe that GPOP incurs up to 9×, 6.8×, and 5.5× less L2 cache misses compared to Ligra, GraphMat, and Galois, respectively. In terms of execution time, GPOP is up to 19×, 9.3×, and 3.6× faster than Ligra, GraphMat, and Galois, respectively.
Attacking and Protecting Tunneled Traffic of Smart Home Devices

The number of smart home IoT (Internet of Things) devices has been growing fast in recent years. Along with the great benefits brought by smart home devices, new threats have appeared. One major threat to smart home users is the compromise of their privacy by traffic analysis (TA) attacks. Researchers have shown that TA attacks can be performed successfully on either plain or encrypted traffic to identify smart home devices and infer user activities. Tunneling traffic is a very strong countermeasure to existing TA attacks. However, in this work, we design a Signature based Tunneled Traffic Analysis (STTA) attack that can be effective even on tunneled traffic. Using a popular smart home traffic dataset, we demonstrate that our attack can achieve an 83% accuracy on identifying 14 smart home devices. We further design a simple defense mechanism based on adding uniform random noise to effectively protect against our TA attack without introducing too much overhead. We prove that our defense mechanism achieves approximate differential privacy.
Cross domain knowledge learning with dual-branch adversarial network for vehicle re-identification

The widespread popularization of vehicles has facilitated all people’s life during the last decades. However, the emergence of a large number of vehicles poses the critical but challenging problem of vehicle re-identification (reID). Till now, for most vehicle reID algorithms, both the training and testing processes are conducted on the same annotated datasets under supervision. However, even a well-trained model will still cause fateful performance drop due to the severe domain bias between the trained dataset and the real-world scenes. To address this problem, this paper proposes a domain adaptation framework for vehicle reID (DAVR), which narrows the cross-domain bias by fully exploiting the labeled data from the source domain to adapt the target domain. DAVR develops an image-to-image translation network named Dual-branch Adversarial Network (DAN), which promotes the images from the source domain (well-labeled) to learn the style of the target domain (unlabeled). Specially, DAN doesn’t need any annotation and can preserve identity information from source domain before and after translation. Furthermore, the generated images are employed to train the vehicle reID model by a proposed attention-based feature learning network. Through the proposed framework, the well-trained reID model has better generalization ability for various scenes in real-world situations. Comprehensive experimental results have demonstrated that our proposed DAVR can achieve excellent performances on benchmark datasets VehicleID and VeRi-776.
An Online Robust Support Vector Regression for Data Streams

Since support vector regression (SVR) is a flexible regression algorithm, its computational complexity does not depend on the dimensionality of the input space, and it has excellent generalization capability. However, a central assumption with SVRs is that all the required data is available at the time of construction, which means these algorithms cannot be used with data streams. Incremental SVR has been offered as a potential solution, but its accuracy suffers with noise and learning speeds are slow. To overcome these two limitations, we propose a novel incremental regression algorithm, called online robust support vector regression (ORSVR). ORSVR solves nonparallel bound functions simultaneously. Hence, the large quadratic programming problem (QPP) in classical v-SVR are decomposed into two smaller QPPs. An incremental learning algorithm then solves each QPP step-by-step. The results of a series of comparative experiments demonstrate that the ORSVR algorithm efficiently solves regression problems in data streams, with or without noise, and speeds up the learning process.
Kennedy Receiver Using Threshold Detection and Optimized Displacement Under Thermal Noise

The performance of Kennedy receiver with threshold detection is analyzed in the presence of thermal noise. A lower bound is first derived for the error probability of the Kennedy receiver in the presence of thermal noise and it is shown that the performance of the Kennedy receiver is greatly affected by the thermal noise. Then we analyze the performance of the Kennedy receiver using the maximum a posteriori detection, which is proven to be equivalent to a threshold detection. We also optimize the displacement operation to improve further the receiver performance. Numerical results show that the Kennedy receiver with threshold detection can surpass the standard quantum limit achieved by homodyne detection when the average number of thermal photons is smaller than 0.1. The Kennedy receiver with threshold detection using optimized displacement can surpass the standard quantum limit when the average number of thermal photons is smaller than 0.2.
Non-Markovian Noise Characterization with the Transfer Tensor Method

We propose simple protocols for performing quantum noise spectroscopy based on the method of transfer tensor maps (TTMs) [Cerrillo and Cao, Phys. Rev. Lett. 112, 110401 (2014)]. The TTM approach is a systematic way to deduce the memory kernel of a time-nonlocal quantum master equation via quantum process tomography. With access to the memory kernel, it is possible to (1) assess the non-Markovianity of a quantum process, (2) reconstruct the noise spectral density beyond pure-dephasing models, and (3) investigate collective decoherence in multiqubit devices. We illustrate the usefulness of TTM spectroscopy on the IBM Quantum Experience platform, and demonstrate that the qubits in the IBM device are subject to mild non-Markovian dissipation with spatial correlations.
On the Morality of Artificial Intelligence [Commentary]

Examines ethical principles and guidelines that surround machine learning and artificial intelligence.
The automated grading of student open responses in mathematics

The use of computer-based systems in classrooms has provided teachers with new opportunities in delivering content to students, supplementing instruction, and assessing student knowledge and comprehension. Among the largest benefits of these systems is their ability to provide students with feedback on their work and also report student performance and progress to their teacher. While computer-based systems can automatically assess student answers to a range of question types, a limitation faced by many systems is in regard to open-ended problems. Many systems are either unable to provide support for open-ended problems, relying on the teacher to grade them manually, or avoid such question types entirely. Due to recent advancements in natural language processing methods, the automation of essay grading has made notable strides. However, much of this research has pertained to domains outside of mathematics, where the use of open-ended problems can be used by teachers to assess students' understanding of mathematical concepts beyond what is possible on other types of problems. This research explores the viability and challenges of developing automated graders of open-ended student responses in mathematics. We further explore how the scale of available data impacts model performance. Focusing on content delivered through the ASSISTments online learning platform, we present a set of analyses pertaining to the development and evaluation of models to predict teacher-assigned grades for student open responses.
HighEr-Resolution Network for Image Demosaicing and Enhancing

Neural-networks based image restoration methods tend to use low-resolution image patches for training. Although higher-resolution image patches can provide more global information, state-of-the-art methods cannot utilize them due to their huge GPU memory usage, as well as the instable training process. However, plenty of studies have shown that global information is crucial for image restoration tasks like image demosaicing and enhancing. In this work, we propose a HighEr-Resolution Network (HERN) to fully learning global information in high-resolution image patches. To achieve this, the HERN employs two parallel paths to learn image features in two different resolution, respectively. By combining global-aware features and multi-scale features, our HERN is able to learn global information with feasible GPU memory usage. Besides, we introduce a progressive training method to solve the instability issue and accelerate model convergence. On the task of image demosaicing and enhancing, our HERN achieves state-of-the-art performance on the AIM2019 RAW to RGB mapping challenge. The source code of our implementation is available at https://github.com/MKFMIKU/RAW2RGBNet.