Reviewerly/exHarmony huggingface dataset:
A Neighborhood Regression Optimization Algorithm for Computationally Expensive Optimization Problems

Expensive optimization problems arise in diverse fields, and the expensive computation in terms of function evaluation poses a serious challenge to global optimization algorithms. In this article, a simple yet effective optimization algorithm for computationally expensive optimization problems is proposed, which is called the neighborhood regression optimization algorithm. For a minimization problem, the proposed algorithm incorporates the regression technique based on a neighborhood structure to predict a descent direction. The descent direction is then adopted to generate new potential offspring around the best solution obtained so far. The proposed algorithm is compared with 12 popular algorithms on two benchmark suites with up to 30 decision variables. Empirical results demonstrate that the proposed algorithm shows clear advantages when dealing with unimodal and smooth problems, and is better than or competitive with other peer algorithms in terms of the overall performance. In addition, the proposed algorithm is efficient and keeps a good tradeoff between solution quality and running time.
CLTS: A New Chinese Long Text Summarization Dataset

We present CLTS, a Chinese long text summarization dataset, in order to solve the problem that large-scale and high-quality datasets are scarce in automatic summarization, which is a limitation for further research. To the best of our knowledge, it is the first long text summarization dataset in Chinese. Extracted from the Chinese news website ThePaper.cn (https://www.thepaper.cn/), the corpus contains more than 180,000 Chinese long articles and corresponding summaries written by professional editors and authors, which is available online (CLTS dataset is available to download online at https://github.com/lxj5957/CLTS-Dataset). We train and evaluate several existing methods on CLTS to verify the utility and challenges of the dataset, and the results show that the corpus proposed in this paper is useful to set some baselines to contribute to the further research on automatic text summarization.
Knowledge Representation in Modeling and Simulation: A survey for the production and logistic domain

Recently, ontologies and semantic data technologies have increasingly come back into the focus of research due to the emerging use of knowledge graphs. However, even though the modeling and simulation community has recognized the potential of using this technology for the modeling process, for example for automatic model generation, adaptation or to represent simulation expert knowledge, a general and reusable approach for the aforementioned purposes is still missing. Therefore, in this paper a state of the art review for using knowledge representation during modeling and simulation processes of complex technical systems is conducted, such as factories or process plants with specific focus on the production and logistic domain. Based on that, requirements and benefits of knowledge graphs in this specific domain are evaluated.
Extraction of information from bill receipts using optical character recognition

This paper presents an application of optical character recognition(OCR) which can extract information from images of bills and receipts; store it as machine-processable text; in an organized manner for ease of access. It can do this efficiently even in the presence of watermarks on the bills or any shadows in the images of the bills. In developing this application, OpenCV has been used for the processing of the images and the Tesseract OCR engine has been used for optical character recognition and text extraction. The image is first processed using OpenCV for the removal of any shadows or watermarks present in it. For longer invoices, by employing the image bifurcation process, the data can be easily extracted which was not possible earlier. Furthermore, the processed image is passed on to the Tesseract OCR engine for the retrieving of text present in it. The text is then searched for important information, such as the total amount spent and the date on the receipt, using string processing.
Dadu-CD: Fast and Efficient Processing-in-Memory Accelerator for Collision Detection

Collision detection is a fundamental task in motion planning of robotics. Typically, the performance of collision detection is the bottleneck of an entire motion planning, and so does the energy consumption. Several hardware accelerators have been proposed for collision detection, which achieves higher performance and energy efficiency than general-purpose CPUs and GPUs. However, existing accelerators are still facing the limited memory bandwidth bottleneck, due to the large data volume required by the parallel processing cores and the limited DRAM bandwidth. In this work, we propose a novel collision detection accelerator by employing the processing-in-memory technique. We elaborate the in-memory processing architecture to fully utilize the internal bandwidth of DRAM banks. To make the algorithm and hardware suitable for in-memory processing to be highly efficient, a set of innovative software and hardware techniques are also proposed. Compared with a state-of-the-art ASIC-based collision detection accelerator, both performance and energy efficiency of our accelerator are significantly improved.
Design Space Exploration in the Mapping of Reversible Circuits to IBM Quantum Computers

With more and more powerful quantum computers becoming available, there is an increasing interest in the efficient mapping of a given quantum circuit to a particular quantum computer (so-called technology mapping). In most cases, the limitations of the targeted quantum hardware have not been taken into account when generating these quantum circuits in the first place. Thus, the technology mapping is likely to induce a considerable overhead for such circuits. In this paper, we consider the realization of reversible circuits consisting of multiple-controlled Toffoli gates on IBM quantum computers. We show that choosing different quantum-level decompositions can indeed have a significant impact on the mapping overhead. Based on this observation, we present an approach to perform design space exploration to obtain quantum circuits with reduced overhead by exploiting information about the targeted quantum hardware as well as the reversible circuit. An experimental evaluation shows that this approach often leads to considerable reductions of the technology mapping overhead with negligible runtime.
Detection of Spinal Fracture Lesions based on Improved Yolov2

Yolo algorithm has a good detection effect in target detection. Because of its high detection accuracy and fast detection speed, it is widely used in practice. Because of the problem that the complexity of spine CT images, the irregular shape of vertebral boundary, meanwhile there are artificial deviations and low efficiencies in clinic, which needs doctors' prior knowledge and clinical experience to determine lesions location in CT images, so it can not meet the clinical real-time needs. In this paper, We use deep learning to process the CT images of spine, and to detect and locate lesion of (cervical fracture, cfracture), (thoracic fracture, tfracture), (lumbar fracture, lfracture) by the improved YOLOv2. Through using lesions bounding box dimensional cluster and multiscale transformation of input CT images to improve detection efficiency and accuracy. The experiment shows the results are more accurate, and mAP (mean average precision) of detection algorithm is 75.30%, detection rate is 0.027 seconds per detection, and IOU((Intersection-over-Union) is 77.3, which can basically meet the clinical real-time needs.
Dynamic Job Scheduling Strategy Using Jobs Characteristics in Cloud Computing

A crucial performance concern in distributed decentralized environments, like clouds, is how to guarantee that jobs complete their execution within the estimated completion times using the available resources’ bandwidth fairly and efficiently while considering the resource performance variations. Formerly, several models including reservation, migration, and replication heuristics have been implemented to solve this concern under a variety of scheduling techniques; however, they have some undetermined obstacles. This paper proposes a dynamic job scheduling model (DTSCA) that uses job characteristics to map them to resources with minimum execution time taking into account utilizing the available resources bandwidth fairly to satisfy the cloud users quality of service (QoS) requirements and utilize the providers’ resources efficiently. The scheduling algorithm makes use of job characteristics (length, expected execution time, expected bandwidth) with regards to available symmetrical and non-symmetrical resources characteristics (CPU, memory, and available bandwidth). This scheduling strategy is based on generating an expectation value for each job that is proportional to how these job’s characteristics are related to all other jobs in total. That should make their virtual machine choice closer to their expectation, thus fairer. It also builds a feedback method which deals with reallocation of failed jobs that do not meet the mapping criteria.
Deep Learning based Anomaly Detection for a Vehicle in Swarm Drone System

In this paper, we perform the actual verification of the anomaly detection (AD) model of each drone that indicates the anomaly in swarm drone flight using the actual flight data. For this purpose, we use a model-based AD method that uses data accumulated through actual flight tests. The AD model uses a deep neural network-based generation model to create a training model with normal data and perform tests with abnormal data. As a result, the diagnostic results of mainly three cases are derived and analyzed for validity. The proposed AD method can be integrated with a machine learning based framework that can immediately detect abnormal behavior of swarm drone flights, which can be utilized to improve the reliability of swarm drone flight operations.
A consensus mechanism based on multi-round concession negotiation

Consensus is the rule that maintains the normal operation of the blockchain. The formation of consensus is a process that allows each node in the blockchain to maintain consistent data. However, the existing consensus formation methods lack concession negotiation. The process of consensus formation is too rigid, which leads to the consensus reached rate is not high, and the satisfaction of nodes across the network decreases. In this paper, we propose a proposition consensus mechanism based on multiple rounds of concession negotiation. This mechanism quantifies the node’s satisfaction with the proposition by weighted voting at first, and then designs the proposition consensus algorithm to describe the concession negotiation process of blockchain nodes. Finally, through experiments and theoretical analysis, the effectiveness of the mechanism proposed in this paper is verified, which significantly improves the consensus reached rate and the overall satisfaction of nodes across the network.
Factors Affecting Sustainable Purchase Intentions of SNS Emojis: Modeling the Impact of Self-Presentation

To promote more sustainable consumption of Social Networking Service (SNS) emojis, it is necessary to understand the relationships between social psychological antecedents and SNS users’ intention to purchase emojis. This study aimed to investigate motivational factors affecting users’ intentions to purchase SNS emojis. We examined SNS users of Kakao Talk, the most widely used instant messaging service in South Korea, and investigated the relationships among five latent constructs: Self-presentation, symbolic values, playfulness, purchase intentions, and need for uniqueness. The results of this study indicate that self-presentation does not directly affect the intention to purchase emojis (β = 0.10, p &gt; 0.05). Rather, self-presentation positively influences purchase intention through the mediating path of SNS users’ perceived symbolic value (β = 0.60, p &lt; 0.001) and playfulness (β = 0.35, p &lt; 0.001) to purchase intention (β = 0.29, p &lt; 0.001; β = 0.37, p &lt; 0.001). Further, symbolic value affects playfulness (β = 0.56, p &lt; 0.001), which in turn leads to consumers’ intentions to purchase emojis. This study highlights that SNS users’ need for uniqueness functions as a moderator in the relationship between self-presentation and their intentions to purchase emojis.
Collision Detection and Avoidance using Optical Flow for Multicopter UAVs

This paper presents the collision avoidance system for multicopter UAVs for autonomous navigation around obstacles using a simple camera as a main sensor and optical flow as the primary machine vision technique for obstacle detection. This is a very affordable and easily accessible combination to be used on UAVs of any type. Optical flow is a method that is used to detect the motion of pixels between pairs of images. If the clusters of like-colored pixels of an object move in a similar direction, the object is in a relative motion. An S1000 Octocopter was used as a test platform for the project. The vehicle is equipped with a Pixhawk 2 flight controller for autonomous navigation. Intel NUC processor was used as an onboard flight computer to process the video input from the sensor (camera) for collision detection, and to run the collision avoidance algorithms. The developed algorithm was tested in multiple flight tests in a flight test site with both natural and man-made obstacles present. Prior to flight tests, the algorithm was tested in simulation. Simulation and flight test results are shown.
Suppressing Mislabeled Data via Grouping and Self-attention

Deep networks achieve excellent results on large-scale clean data but degrade significantly when learning from noisy labels. To suppressing the impact of mislabeled data, this paper proposes a conceptually simple yet efficient training block, termed as Attentive Feature Mixup (AFM), which allows paying more attention to clean samples and less to mislabeled ones via sample interactions in small groups. Specifically, this plug-and-play AFM first leverages a group-to-attend module to construct groups and assign attention weights for group-wise samples, and then uses a mixup module with the attention weights to interpolate massive noisy-suppressed samples. The AFM has several appealing benefits for noise-robust deep learning. (i) It does not rely on any assumptions and extra clean subset. (ii) With massive interpolations, the ratio of useless samples is reduced dramatically compared to the original noisy ratio. (iii) It jointly optimizes the interpolation weights with classifiers, suppressing the influence of mislabeled data via low attention weights. (iv) It partially inherits the vicinal risk minimization of mixup to alleviate over-fitting while improves it by sampling fewer feature-target vectors around mislabeled data from the mixup vicinal distribution. Extensive experiments demonstrate that AFM yields state-of-the-art results on two challenging real-world noisy datasets: Food101N and Clothing1M.
Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs

The overview-driven visual analysis of large-scale dynamic graphs poses a major challenge. We propose Multiscale Snapshots, a visual analytics approach to analyze temporal summaries of dynamic graphs at multiple temporal scales. First, we recursively generate temporal summaries to abstract overlapping sequences of graphs into compact snapshots. Second, we apply graph embeddings to the snapshots to learn low-dimensional representations of each sequence of graphs to speed up specific analytical tasks (e.g., similarity search). Third, we visualize the evolving data from a coarse to fine-granular snapshots to semi-automatically analyze temporal states, trends, and outliers. The approach enables us to discover similar temporal summaries (e.g., reoccurring states), reduces the temporal data to speed up automatic analysis, and to explore both structural and temporal properties of a dynamic graph. We demonstrate the usefulness of our approach by a quantitative evaluation and the application to a real-world dataset.
Efficient verifiable databases with additional insertion and deletion operations in cloud computing

Verifiable database (VDB) schemes allow the data owner to outsource a large database to the cloud so that any resource-constraint client could later retrieve the database records and check whether the cloud returns valid records or not. Meanwhile, the database can be updated by the data owner. To the best of our knowledge, there is no secure and efficient VDB scheme supporting public verification and all kinds of update operations (i.e., insertion, modification, and deletion) simultaneously. To resolve this problem, we propose an efficient VDB scheme by incorporating vector commitment (VC) with Merkle Interval Hash Tree (MIHT). To enhance the security (i.e., resist the forward automatic update attack), we re-formalize the syntax of VC and present a new construction of VC based on modified Generalized Bilinear Inversion (mGBI) assumption. Our proposed VC scheme is used for guaranteeing the integrity of data. Like the traditional VC schemes, our proposed scheme remains publicly verifiable. MIHT is a new primitive introduced by us, which is mainly used to support all update operations. Security analysis shows that our proposed VDB scheme can achieve real-world security requirements. The detailed performance analyses and simulations show that our proposed schemes are more practical in the real world.
Partial Volume Segmentation of Brain MRI Scans of Any Resolution and Contrast

Partial voluming (PV) is arguably the last crucial unsolved problem in Bayesian segmentation of brain MRI with probabilistic atlases. PV occurs when voxels contain multiple tissue classes, giving rise to image intensities that may not be representative of any one of the underlying classes. PV is particularly problematic for segmentation when there is a large resolution gap between the atlas and the test scan, e.g., when segmenting clinical scans with thick slices, or when using a high-resolution atlas. Forward models of PV are realistic and simple, as they amount to blurring and subsampling a high resolution (HR) volume into a lower resolution (LR) scan. Unfortunately, segmentation as Bayesian inference quickly becomes intractable when "inverting" this forward PV model, as it requires marginalizing over all possible anatomical configurations of the HR volume. In this work, we present PV-SynthSeg, a convolutional neural network (CNN) that tackles this problem by directly learning a mapping between (possibly multi-modal) LR scans and underlying HR segmentations. PV-SynthSeg simulates LR images from HR label maps with a generative model of PV, and can be trained to segment scans of any desired target contrast and resolution, even for previously unseen modalities where neither images nor segmentations are available at training. PV-SynthSeg does not require any preprocessing, and runs in seconds. We demonstrate the accuracy and flexibility of our method with extensive experiments on three datasets and 2,680 scans. The code is available at https://github.com/BBillot/SynthSeg.
Flow-Aware Adaptive Pacing to Mitigate TCP Incast in Data Center Networks

In data center networks, many network-intensive applications leverage large fan-in and many-to-one communication to achieve high performance. However, the special traffic patterns, such as micro-burst and high concurrency, easily cause TCP Incast problem and seriously degrade the application performance. To address the TCP Incast problem, we first reveal theoretically and empirically that alleviating packet burstiness is much more effective in reducing the Incast probability than controlling the congestion window. Inspired by the findings and insights from our experimental observations, we further propose a general supporting scheme Adaptive Pacing (AP), which dynamically adjusts burstiness according to the flow concurrency without any change on switch. Additionally, a sender-based approach is proposed to estimate the flow concurrency. Another feature of AP is its broad applicability. We integrate AP transparently into different TCP protocols (i.e., DCTCP, L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DCT and D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TCP). Through a series of large-scale NS2 simulations and testbed experiments, we show that AP significantly reduces the Incast probability across different TCP protocols and the network goodput can be increased consistently by on average 7× under severe congestion.
Practical dynamic reconstruction of control flow graphs

Abstract The automatic recovery of a program's high‐level representation from its binary version is a well‐studied problem in programming languages. However, most of the solutions to this problem are based on purely static approaches: techniques such as dataflow analyses or type inference are used to convert the bytes that constitute the executable code back into a control flow graph (CFG). This article departs from such a modus operandi to show that a dynamic analysis can be effective and useful, both as a standalone technique, and as a way to enhance the precision of static approaches. The experimental results provide evidence that completeness, that is, the ability to conclude that the entire CFG has been discovered, is achievable on many functions that are part of industry‐strong benchmarks. Experiments also indicate that dynamic information greatly enhances the ability of DynInst , a state‐of‐the‐art binary reconstructor, to deal with code stripped of debugging information. These results were obtained with CFGgrind , a new implementation of a dynamic code reconstructor, built on top of Valgrind . When applied to cBench , CFGgrind is 9% faster than callgrind , Valgrind 's tool used to track targets of function calls; and 7% faster in Spec Cpu2017 . CFGgrind recovers the complete CFG of 40% of all the procedures invoked during the standard execution of programs in Spec Cpu2017 , and 37% in cBench . When combined with CFGgrind , DynInst finds 15% more CFGs for cBench , and 7% more CFGs for Spec Cpu2017 . Finally, CFGgrind is more than 7 times faster than DCFG, a CFG reconstructor from Intel, and 1.30 times faster than bfTrace , a CFG reconstructor used in research. CFGgrind is also more precise than these two tools, handling operating system signals, shared code in functions, and unaligned instructions; besides supporting multithreaded programs, exact profiling and incremental refinements.