Reviewerly/exHarmony huggingface dataset:
On Studying CPU Performance of CloudLab Hardware

Empirical performance measurements of computer systems almost always exhibit variability and anomalies. Run-to-run and server-to-server variations are common for CPU, memory, disk, and network performance characteristics. In our previous work, we focused on taming performance variability for memory, disk, and network [1] and established an interactive analysis service at: https://confirm.fyi/ to help users of the CloudLab testbed [2] better plan and conduct their experiments. In this paper, we describe our analysis of CPU variability based on over 1.3M performance measurements from nearly 1,800 servers and present our initial findings.
Development of First-Person Shooting Games Using Human Voice Command and its Potential Use for Serious Game Engines

Computer games have become a special attraction for some people. Every computer game has the same important structure including the use of images and sounds, objects that interact visually on the screen, and repeatedly update the display per second according to the simulated situation. One component in computer games is having objects that interact visually so that players can understand the game's storyline. One important structure in modern games is the presence of sound to enhance emotional feelings that can trigger feelings so that it can be used as an alternative for media rehabilitation for disabled people. However, for people with disabilities they have problems playing games that they like because of the limitations of the game control system so that in the development of game control systems biological and physiological sensors are added, one of which is voice control technology. Voice recognition is a system that has the ability to understand human speech in this study. We make computer games with the FPS genre using a sound control system created using the Unity Windows Speech Library engine from Unity3D 5.5 as a game engine to create games with voice recognition system facilities in it.
ECHO: Efficiently Overbooking Applications to Create a Highly Available Cloud

Ensuring high availability for applications despite unpredictable cloud component failure events is a well-known problem in managing cloud infrastructure. One proposed solution uses a VM redundancy approach, reserving cloud resources for backup VMs that can substitute for primary ones in case of a failure event. However, this solution decreases the cloud resource utilization, since the backup resources usually remain idle. In this paper, we propose ECHO, a cloud resource management system that overbooks these backup VMs by optimizing the overbooking rate tradeoff between maximizing the cloud resource utilization, and thus maximizing the cloud provider's revenue; and improving application availability, thus satisfying users. Specifically, ECHO first obtains the optimal overbooking rate required to achieve a cloud provider's desired resource utilization level. It then computes the optimal (required) number of backup VMs that are required to maintain a given application availability level. Our extensive experimental and simulation results show that using ECHO can increase the number of accepted applications with satisfied availability by about 30%, while increasing the defined resource utilization at the same time.
Real‐time multi‐trajectory matching for dynamic hand gesture recognition

Focus on the field of dynamic gesture recognition, there is a problem of the action, is difficult to recognise when multiple fingertips move in a small range (rotation, grabbing), the authors proposed a method to get the recognition results with high robustness in real-time.Firstly, they proposed the concavity kernel accumulation algorithm (CKA) to cluster corners in an image.Secondly, they deem CKA as a region proposal generator and combined it with convolutional neural network to detect fingertips.Thirdly, they proposed the global nearest neighbour point matching algorithm to match fingertips from two frames.Finally, the long short-term memory is used in multi-trajectory recognition to get the results of gesture recognition.Experiments show that their method could recognise multi-trajectory gestures accurately, furthermore, it can run in real time (20 FPS) without graphics processing unit (GPU).
Web Citation Indicators for Wider Impact Assessment of Articles

There are many ways in which academic articles can be used outside research contexts for teaching, culture, medical practice, business, policy making, or knowledge communication. Articles that have significant wider benefits may therefore be undervalued if they are assessed through conventional citation indicators and sources (e. g., the Web of Science ( Web of Science (WoS) ) and Scopus). A range of online document genres and sources may help to evaluate these broader impacts of articles, including academic syllabi, textbooks, clinical trials or guidelines, patents, encyclopedia articles, and grey literature publications. Web citations can be used as a quantitative impact indicator for monitoring the wider impact of articles, especially in the arts, humanities, and social sciences where many research outputs have value beyond academia. This article reviews literature about the web citation analysis of articles and explains different methods to capture web citations from a range of online sources via commercial search engines. The applications and limitations of web citation analysis for wider impact assessment of articles are discussed, in addition to practical advice for data gathering. New web citation indicators can help research evaluation peer review and citation analysis by giving additional information about the wider benefits of published research when a type of impact (e. g., teaching, commercial, or clinical impact) is required to be assessed by authors, research funders, or evaluators in addition to their academic research impact.
Processing An Effective Method For Clock Tree Synthesis

The following article represents the method of clock tree synthesis (CTS). The method gives the advantage to synthesis clock tree with small clock skew and a smaller difference in the time of data and synchro signal spreading. The following method is applicable for each modern software of place and route (PR).
Associated Task Scheduling Based on Dynamic Finish Time Prediction for Cloud Computing

Cloud computing has emerged as an increasingly indispensable and highly demanded platform for various applications, as cloud computing allows for on demand resource provisioning and allocation. The associated tasks composing a job processed by cloud computing need to be executed under ordering constraints for correctness or consistency. The associated tasks are executed on different servers and communication is required to transfer the data between the servers, while the processing capacity of and the communication capacity between different components underlying the cloud computing platform may show great heterogeneity. Therefore, efficient scheduling for the associated tasks is critical for achieving high performance in cloud computing systems. In this paper, we tackle the problem of associated task scheduling for cloud computing with the aim to minimize the makespan of the job, when excessive diversities are present in the computing and communication components. We propose a Dynamic Priority List Scheduling (DPLS) algorithm based on dynamic task finish time prediction. The algorithm dynamically predicts the remaining execution time for each task to be scheduled according to the server allocation of the previously scheduled tasks, and decides the next task to be scheduled and the server allocation based on the previously task scheduling result. We conduct experiments through simulations on randomly generated associated tasks and real-world applications. Experimental results demonstrate that the proposed algorithm is promising.
Novel privacy preserving system for Cloud Data security using Signature Hashing Algorithm

Cloud computing is a vital part of any little or huge organization. With cloud storage service clients can remotely store their information to the cloud and realize the information sharing to others. Information redistributing has the danger of delicate information getting breached. Remote information trustworthiness auditing is proposed to ensure the integrity of data stored in the cloud. In some regular cloud storage frameworks, for example, Electronic Health Records (EHR) frameworks, the cloud document may contain some delicate data which must not be modified. This paper proposes a novel privacy preserving system that supports reviewing on shared information stored in the cloud. Specifically, hashing algorithm is utilized to compute verification metadata expected to audit the correctness of shared information. A hash signature is created for each record by the data owner and afterward it is transferred. After a document is transferred to the database by the data owner, the auditing mechanism recompute’s signature and contrasts with the local copy signature of the file in data owner side. On the off chance that an interloper or a client breaks the security and alters the open cloud information, the Hashing calculation is performed, and the interloper or client is ended from further accessing the cloud information. This strategy guarantees data integrity in shared cloud data especially for EHR frameworks, substantially more efficient and less calculation cost contrasted with the current framework. Therefore, the information in the cloud is solid and credible.
Robustifying Relative Orientations With Respect to Repetitive Structures and Very Short Baselines for Global SfM

Recently, global SfM has been attracting many researchers, mainly because of its time efficiency. Most of these methods are based on averaging relative orientations (ROs). Therefore, eliminating incorrect ROs is of great significance for improving the robustness of global SfM. In this paper, we propose a method to eliminate wrong ROs which have resulted from repetitive structure (RS) and very short baselines (VSB). We suggest two corresponding criteria that indicate the quality of ROs. These criteria are functions of potentially conjugate points resulting from local image matching of image pairs, followed by a geometry check using the 5-point algorithm combined with RANSAC. RS is detected based on counts of corresponding conjugate points of the various pairs, while VSB is found by inspecting the intersection angles of corresponding image rays. Based on these two criteria, incorrect ROs are eliminated. We demonstrate the proposed method on various datasets by inserting our refined ROs into a global SfM pipeline. The experiments show that compared to other methods we can generate the better results in this way.
Optimal Continuous-Discrete Linear Filter and Moment Equations for Nonlinear Diffusions

In this article, we consider the estimation problem of continuous-time stochastic systems with discrete measurements, having linear drift and nonlinear diffusion term. We build the infinite-dimensional linear system equivalent to this class of systems by means of a Carleman linearization approach. Based on this embedding, we investigate the properties of the moment equations of the original system, and we show that it is possible to write the optimal linear filter, for which a finite-dimensional approximation can be implemented. We validate the approach by showing that the resulting algorithm may outperform widely used continuous-discrete filters without increasing the computational burden.
Dimensionality reduction of image feature based on geometric parameter adaptive LLE algorithm

Locally linear embedding (LLE) is a classical nonlinear dimensionality reduction algorithm, and it has been widely used in image feature selection. LLE reduces the dimensions of a data set only by exploring the geometric structure, which is calculated by Euclidean distance and makes the embedding r esult be sensitive to noise. Moreover, the choice of the number of nearest neighbors is fixed for all data points and only given by human experience. In order to overcome these problems, a geometric parameter adaptive LLE (PALLE) algorithm is proposed in this paper. This algorithm jointly uses Geodesic distance and Cosine similarity to replace Euclidean distance, and then the number of neighbors is adaptable selected by weak-σ rule. Extensive experimental results over various real-life data sets have demonstrated the superiority of the proposed algorithm in terms of image feature dimensionality reduction compared with classical LLE and other well-known algorithms.
Searching for Pareto-Optimal Solutions

The problem of narrowing the Pareto set is considered. The existing approaches aimed at solving the problem of finding a set of Pareto optimal solutions has been analyzed. One approach for solving multi-objective problems based on complex using methods for the construction of the Pareto optimal set and evidence theory has been proposed in this paper. The proposed technique allows to evaluate the obtained set of non-dominated alternatives by methods of the evidence theory to find the best (optimal) solution. The proposed approach allows us to obtain a more formalized procedure for narrowing the Pareto set to obtaining a single optimal solution (a single-element Pareto set). The use of the mathematical apparatus of the evidence theory makes it possible to model uncertainty in expert or decision makers judgments (the strict requirement of the “unambiguous” preference of one alternative over the other is removed). Using the proportional conflict redistribution rules for aggregating group expert assessments makes it possible to process expert evidence generated under conflicting, contradiction expert information. Numerical examples of the proposed methodology for integrated application of evidence theory and methods for Pareto set construction to find optimal solutions are given. The results obtained make it possible to improve the quality and effectiveness of finding optimal solutions.
Dense attentional network for pancreas segmentation in abdominal CT scans

Deep neural networks have been widely used in medical image segmentation and they can achieve good results in segmentation of some big organs. However, for some small organs, such as the pancreas in 3D CT images, the segmentation results are usually not satisfactory due to the low proportion. In this paper, we present a novel network --- Dense Attentional Network (DA-Net), to improve the pancreas segmentation in abdominal CT scans. In DA-Net, dense connection is used to combine low-level features of encoder with corresponding features of decoder, which can help to improve the utilization of feature maps (FMs). In addition, a new module for recombination and recalibration of feature maps (RRFM) and a new attentional mechanism --- deep attentional features (DAF), are used, which can excite the most discriminating features. Pancreas is segmented from 3D CT images by a coarse-to-fine mode, in which pancreas is firstly located from 3D CT images by a coarse segmentation network, and then pancreas is further finely segmented by the DA-Net. We evaluate the proposed method with 129 CT images from NIH pancreas dataset and BTCV segmentation challenge, and compare it with several mainstream segmentation networks. Comparing with these networks, our DA-Net has the higher mean DSC of 81.39%. This shows the effectiveness and advantage of the proposed method.
Cluster-Based Sample Selection for Document Image Binarization

The current state-of-the-art, in terms of performance, for solving document image binarization is training artificial neural networks on pre-labelled ground truth data. As such, it faces the same issues as other, more conventional, classification problems; requiring a large amount of training data. However, unlike those conventional classification problems, document image binarization involves having to either manually craft or estimate the binarized ground truth data, which can be error-prone and time-consuming. This is where sample selection, the act of selecting training samples based on some method or metric, might help. By reducing the size of the training dataset in such a way that the binarization performance is not impacted, the required time spent creating the ground truth is also reduced. This paper proposes a cluster-based sample selection method that uses image similarity metrics and the relative neighbourhood graph to reduce the underlying redundancy of the dataset. The method, implemented with affinity propagation and the structural similarity index, reduces the training dataset on average by 49.57% while reducing the binarization performance only by 0.55%.
Web Explainer for Children’s Education with Image Recognition Based on Deep Learning

The web-based children's learning system is very common to talk about. One of the characteristic of learning system for the children is object recognition. But the problem is that it still requires a manual checking process by humans, so it is not efficient. We propose a web explainer for children's education with image recognition based on deep learning. The web system is able to capture and recognize objects from the camera using a deep learning algorithm. Our system consists of web management for the administration of user and video content. Image recognition system is based on Deep Learning algorithms using tensorflow.js. From the experiments, we found that our system accuracy is 0.95 and provide lessons for children and also increase efficiency 15x faster for classes with 30 students.
A Standard Test Method for Evaluating Navigation and Obstacle Avoidance Capabilities of AGVs and AMRs

Automatic guided vehicles (AGVs) and autonomous mobile robots (AMRs) are now ubiquitous in industrial manufacturing environments. These systems all must possess a similar set of core capabilities, including navigation, obstacle avoidance, and localization. However, there are few standard methods to evaluate the capabilities and limitations of these systems in a way that is comparable. In this article, a standard test method is presented that can be used to evaluate these capabilities and can be easily scaled and augmented according to the characteristics of the system under test. The test method can be configured in a variety of ways to exercise different capabilities, all using a common test apparatus to ease test setup and increase versatility. For each test configuration, conditions are specified with respect to the a priori knowledge provided to the system (e.g., boundary or obstacle locations) and the obstacles in the environment. Robustness of system capabilities is evaluated by purposefully introducing misalignment between the characteristics of the physical and virtual environments (e.g., providing representations of obstacles in the system’s map when they are not physically present). Example test performance data from an AMR are provided. The goal of this work is to provide a common method to characterize the performance of mobile systems in industrial environments that is easily comparable and communicated for both commercial and developmental purposes. This work is driven by existing standards and those in development by the ASTM F45 Committee on Driverless Automatic Guided Industrial Vehicles and will influence the development of new standards within the committee.
POSTER: A Memory-Access-Efficient Adaptive Implementation of kNN on FPGA through HLS

Implementing an efficient k-Nearest Neighbors(kNN) algorithm on FPGA is becoming challenging due to the fact that both the size and dimensionality of datasets that kNN is working on have been rapidly growing, which may incur a performance bottleneck on the memory-access. To reduce the impact of the memory-access constraint, in this paper we implement two kNN kernels through high-level synthesis (HLS) on FPGA by employing two data access reduction methods: low-precision data representation and principal component analysis based filtering (PCAF). One kernel is called MBFSkNN (Memory-efficient Brute-Force Searching kNN) and the other is called MPCAF-kNN (Memory-efficient PCAF kNN). Both kernels have been highly optimized to fully exploit the characteristics of FPGA. Besides, they are adaptive to the number of dimensions (D), number of data points in a database (N), number of nearest neighbors (k), number of bits per feature (B), and number of principal components (d). We evaluate the two kernels by comparing them with two state-of-the-art kNN implementations on a high-end CPU server, an existing BFS-kNN kernel on FPGA, and an existing BFS-kNN kernel on GPU. Our results show that the external memory accesses of these two kernels are greatly reduced and our design outperforms the existing ones.
Hand-hygiene activity recognition in egocentric video

Food safety is affected by the conditions and practices during different manufacturing steps to prevent contamination and food-borne illnesses. In this paper, we focus on detecting hand-hygiene actions in Egocentric videos. We create a two-stage system to localize and recognize all the hand-hygiene actions in each untrimmed video. In the first stage, we apply a low-cost hand mask and motion histogram features to localize the temporal regions of hand-hygiene actions. In the second stage, we use the two-stream network model combined with a search algorithm to recognize all types of hand-hygiene actions that happen in the untrimmed video. The system achieves a detection accuracy close to 80% on our dataset with 100 participants.
Cluster-Based Focused Retrieval

The focused retrieval task is to rank documents' passages by their presumed relevance to a query. Inspired by work on cluster-based document retrieval, we present a novel cluster-based focused retrieval method. The method is based on ranking clusters of similar passages using a learning-to-rank approach and transforming the cluster ranking to passage ranking. Empirical evaluation demonstrates the clear merits of the method.
Integrating manifold ranking with boundary expansion and corners clustering for saliency detection of home scene

In this paper, we propose a novel framework for saliency detection of home scene by exploiting manifold ranking, boundary expansion, and corners clustering. Our proposed method firstly combines color cues in RGB and CIELab to select image boundary seeds, and exclude the ones which might be located at salient objects as much as possible. Then, we utilize the boundary seeds on each image boundary as the queries of manifold ranking to compute saliency and integrate them for a background-based saliency map. For the foreground-based saliency detection. Boundary expansion combined with background-based saliency map highlights foreground regions, which are regarded as queries for a foreground-based saliency map. Moreover, we achieve center prior saliency map through multi-scale Harris corner detection and corners clustering to further highlight salient regions and suppress background regions. Finally, we integrate the three saliency maps via the proposed unified framework for a more accurate and smooth saliency map. Both qualitative and quantitative experimental results indicate that our proposed method can deliver better performance than several state-of-the-art saliency detection methods as a whole.