Reviewerly/exHarmony huggingface dataset:
Workload Characterization for a Non-Hyperscale Public Cloud Platform

The improvement of automated resource management techniques for cloud computing platforms requires a deep understanding of the workload. Previous works focused on virtual machines (VMs), and neglected complementary virtual resources such as images, volumes, snapshots and security groups. Besides, most attention went to public hyperscale platforms with more than ten thousand servers, or small on-premise platforms. To fill the gap, we perform a holistic workload characterization of a non-hyperscale platform. We have collected a three-month-long trace allowing us to characterize the correlated utilization of virtual resources; the consumption of CPU, memory and disk by VMs; and the CPU interferences between VMs.
Autonomous Vehicle Path Planning Considering Dwarf or Negative Obstacles

Autonomous vehicle path planning involves finding a collision-free and kinematic-feasible path from the start to the goal. Evidently, collision-free and kinematic-feasible are the two most important parts of the path planning process. In this paper, we propose a collision checking method that considers dwarf or negative obstacles, which can improve the traversability and stability under the promise of ensuring collision-free, and we use a kind of motion primitives which considering non-holonomic constraints for path planning to generate a kinematic-feasible path. Specifically, some existing approaches of collision checking deal with lofty and dwarf obstacles, or wide and narrow, negative obstacles in the same manner that require the entire vehicle body to steer away from them. However, some dwarf obstacles are shorter than the chassis of the vehicle while some negative obstacles are narrower than the vehicle track. In those cases, overstriding maneuvers will prove much more efficient and reasonable. Moreover, dealing with these obstacles in the same way as lofty obstacles can dramatically reduce the flexibility and disturb the stability of path planning under clustered environments, especially when the vehicle moves at higher speed. This paper proposes a double layer collision checking (DLCC) method that deals with lofty and dwarf obstacles, or wide and narrow, negative obstacles separately, effectively increasing the flexibility under the complex scenarios where dwarf and narrow negative obstacles exist.
Prediction of Study Period Students (Bachelor Degree) Muhammadiyah University of Sidoarjo Based on Decision Tree Method using C4.5 Algorithm

Abstract The implementation of the study of stratum 1 (one) students is taken for 4 (four) years at the Muhammadiyah University of Sidoarjo. There are 10 faculty consisting of 27 departments with multi discipline and characteristic of study program of differentiate. Where in the study period is reached within the period of at least 4 years or 8 semesters. Students complete their study periods step by step. During the study period of 8 semesters, students’ time cannot run in a timely manner, so the graduation projections will be delayed and the study period will be more than 8 semesters. Many of the factors behind this condition both on the student’s initial ability, finance, and achievement of GPA in each semester. To know the factors that cause the condition of the student is not on time in taking the study period underlying various conditions, then conducted the study prediction period of study by using method C 4.5. It is expected that the condition underlying delayed graduation student of Muhammadiyah University of Sidoarjo can be anticipated through this research. With this, between providers of higher education can control each other over the condition of graduation students. The prediction achievement of study period using data mining decision tree method C 4.5 can produce approach or factors that influence student graduation with degree percentage to 85%.
Application of Reinforcement Learning in the Design of Microwave Circuit Matching Structure

In the context of the rapid development of machine learning and intelligent manufacturing, we try to introduce intelligent mechanism into the design and production of microwave components. In this paper, an automatic design method for matching structure of microwave components based on reinforcement learning (Q-learning) is proposed, by using the return value extracted from the Smith Chart and optimization of algorithm parameters with microwave theory. Its feasibility is verified by the combined use of the software Ansoft HFSS and Matlab. Compared with random search, Q-learning algorithm can help us to analyze each simulation result and accumulate experience, shorten the simulation time and reduce the burden of designers greatly. Besides, we introduce an empirical factor ε to improve the convergence efficiency. The algorithm can be further optimized with the empirical factor, especially in the first-round learning, in the absence of simulation experience, the prior experience helps us reduce the time of simulation obviously.
Exploring the Impact of Purity Gap Gain on the Efficiency and Effectiveness of Random Forest Feature Selection

The Random Forest (RF) classifier has the capacity to facilitate both wrapper and embedded feature selection through the Mean Decrease Accuracy (MDA) and Mean Decrease Impurity (MDI) methods, respectively. MDI is known to be biased towards predictor variables with multiple values whilst MDA is stable in this regard. As such, MDA is the predominantly preferred option for RF-based feature selection, despite its higher computational overhead in comparison to MDI. This research seeks to simultaneously reduce the computational overhead and improve the effectiveness of RF feature selection. We propose two improvements to the MDI method to overcome its shortcomings. The first is using our proposed Purity Gap Gain (PGG) measure which has an emphasis on computational efficiency, as an alternative to the Gini Importance (GI) metric. The second is incorporating a Relative Mean Decrease Impurity (RMDI) score, which aims to offset the bias towards multi-valued predictor variables through random feature value permutations. Experiments are conducted on UCI datasets to establish the impact of PGG and RMDI on RF performance.
Sequential User Profiling from Newspapers’ Access Log

With the spread of social media and e-commerce websites, the technology of user profiling from users’ action history has attracted a lot of interest. Users’ action history can be acquired both in the passive way and in the active (interactive) way, and previous studies have found out how to presume the users’ profile from their action history which is acquired passively. The purpose of this study was to find out the best way to interact with users for efficient user profiling. First, we constructed a CNN (Convolutional Neural Network) model which presumes user’s profile. Next, we proposed three ways to interact with users for efficient user profiling and compared them. Consequently, two ways of the three were discovered to be effective to streamline the user profiling. And we provided some analyses of the ways, which revealed that the CNN model’s output for an item could be utilized for efficient user profiling , and considering the probability that each item is consumed by the target user could provide more efficient ways.
Multi-Branch Context-Aware Network for Person Re-Identification

Most existing methods on person re-identification ignore contextual dependencies which are important in representing pedestrian images. In this paper, we propose a Multi-Branch Context-Aware Network (MBCAN) for person re-identification to exploit rich context information. MBCAN learns global features and local-part features in two separate branches to take full advantages of both coarse-grained and fine-grained features. Additionally, two types of attention modules are introduced to capture contextual dependencies in spatial dimension and channel dimension, respectively. A module called feature vector extraction block is designed to find an efficient way to integrate features from coarse to fine. Extensive experiments with ablation analysis show the effectiveness of our method, and state-of-the-art results are achieved on Market-1501, DukeMTMC-reID and CUHK03 datasets.
Incorporating Word Significance into Aspect-Level Sentiment Analysis

Aspect-level sentiment analysis has drawn growing attention in recent years, with higher performance achieved through the attention mechanism. Despite this, previous research does not consider some human psychological evidence relating to language interpretation. This results in attention being paid to less significant words especially when the aspect word is far from the relevant context word or when an important context word is found at the end of a long sentence. We design a novel model using word significance to direct attention towards the most significant words, with novelty decay and incremental interpretation factors working together as an alternative for position based models. The interpretation factor represents the maximization of the degree each new encountered word contributes to the sentiment polarity and a counter balancing stretched exponential novelty decay factor represents decaying human reaction as a sentence gets longer. Our findings support the hypothesis that the attention mechanism needs to be applied to the most significant words for sentiment interpretation and that novelty decay is applicable in aspect-level sentiment analysis with a decay factor β = 0.7 .
Reducing Feature Embedding Data for Discovering Relations in Big Text Data

Relation extraction is a critical task in building a knowledge base from unstructured text documents. Most works in automatic relation extraction have applied deep learning techniques such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in large text corpora. However, they require a large amount of human labelling data, which is labour intensive and is hardly applied in a new domain of document without human supervision. This paper proposes a novel framework to extract relations in multi-domain texts effectively. In particular, we construct the framework in three phases including preprocessing, feature embedding and relation extraction. We show that a small proportion of training data is sufficient to train our relation extraction framework and achieve a good accuracy in relation extraction works.
Detection-by- Tracking Boosted Online 3D Multi-Object Tracking

In the Multi-Object Tracking (MOT) scenario, on top of existing image-based approaches, 3D information can also be essential to improving tracking performance. This paper introduces a way to leverage 3D information, and establishes a Bayesian model that can easily be integrated with 2D and 3D features. Furthermore, to remedy fragmented trajectories due to detection failures in the tracking-by-detection framework, we propose a novel detection-by-tracking method that prevents trajectory interruption. We evaluate our online tracking approach on the KITTI tracking benchmark, and show that by using our proposed 2D and 3D features along with detection-by-tracking, we are able to achieve state-of-the-art results on both cars and pedestrians with a runtime as fast as 16-50 FPS.
VCA-Optimizer: SOA-Based Customizable Virtual Cluster Allocation in the Cloud Datacenter

As greater numbers of distributed cloud applications are required to deploy in virtual clusters (VCs) in the datacenter, the VC allocation problem is facing various QoS requirements and different resource optimizations. In this paper, we propose an SOA-based Optimizer, name as VCA-optimizer, for solving customizable VC allocation problems with different constraint conditions and different optimization objectives in the datacenter.
Semi-supervised Learning on Network Using Structure Features and Graph Convolution

Since several types of data can be represented as graphs, there has been a demand for generalizing neural network models for graph data. Graph convolution is a recent scalable method for performing deep feature learning on attributed graphs by aggregating local node information over multiple layers. Such layers only consider attribute information of node neighbors in the forward model and do not incorporate knowledge of global network structure in the learning task. In this paper, we present a scalable semi-supervised learning method for graph-structured data which considers not only neighbors information, but also the global network structure. In our method, we add a term preserving the network structural features such as centrality to the objective function of Graph Convolutional Network and train for both node classification and network structure preservation simultaneously. Experimental results showed that our method outperforms state-of-the-art baselines for the node classification tasks in the sparse label regime.
Alignment and test of the Wide Field Infrared Survey Telescope (WFIRST) Engineering Design Unit (EDU) grism

The WFIRST wide field instrument (WFI) includes a slitless spectrometer, which plays an important role in the WFIRST mission for the survey of emission-line galaxies. WFI is building engineering design and environmental test (EDU and ETU) units to reduce risk for the flight grism unit. We report here on successful build and test of the EDU grism. The four-element EDU grism consists of two prism elements and two diffractive elements that provide R700 dispersion. The elements were fabricated with alignment fiducials and integral flats to allow opto-mechanical alignment in six-degrees of freedom. Each element in turn, was installed onto a hexapod and positioned to its nominal orientation relative to the grism deck, then bonded into individual cells. Alignment measurements were performed in situ using theodolites to set tip/tilt and a Micro-vu non-contact Multisensor Measurement System was used to set despace, decenter and clocking of each element using the hexapod. After opto-mechanical alignment, the grism wavefront was measured using an Infrared ZYGO interferometer at various field points extending over a 20 by 14- degree (local) field of view. Using modeled alignment sensitivities, we determined the alignment correction required on our Element 2 prism compensator and successfully minimized the field dependent wavefront error and confocality. This paper details the alignment and testing of the EDU grism at ambient and cold operating temperatures.
“I Kept Browsing and Browsing, But Still Couldn’t Find the One”: Salient Factors and Challenges in Online Typeface Selection

Web fonts quickly gained popularity among practitioners. Despite their wide-spread usage and critical role in design, there is a lack of empirical research regarding how practitioners select web fonts and what problems they encounter in the process. To fill this knowledge gap, we took a mixed-method approach to examine the salient factors and common issues in the typeface selection process. To understand the landscape of the problem, we first analyzed adoption data for Google Fonts, a representative online fonts directory. Then, we interviewed practitioners regarding their experience selecting web fonts and the problems they encountered. Finally, we issued a follow-up survey to validate the qualitative findings. Our study uncovered how practitioners operationalized three salient factors—affordability, functionality, and personality—in the typeface selection process. Participants reported difficulty in finding typefaces that satisfy the functionality and personality needs. We discuss patterns that led to this difficulty and offered practical design guidelines that alleviated the identified issues.
Implementation of CAPTCHA suitable for mobile devices

CAPTCHA is a technology designed to prevent automated programs (known as bots) from acquiring access to on-line accounts to send spam mail, manipulate vote numbers in on-line polls, or take other malicious actions. In addition, access to Web services has been incorporated into mobile devices, such as smartphones. However, because most CAPTCHAs are not designed for mobile devices, user-friendly CAPTCHA for mobile devices is required. Thus, we implemented CAPTCHA on mobile devices and evaluated its resistance to bots. Our CAPTCHA showed robustness against bots, with good usability.
An Exploratory Study of Naturalistic Decision Making in Complex Software Architecture Environments

Architects always make decisions in some context. That context shifts and changes dynamically. Different decision-making strategies are appropriate in different contexts. Architecture decisions are at times made under conditions of time pressure, high stakes, uncertainty, and with too little information. At other times, decision-makers have sufficient time to reflect on the decision and consider alternatives. Understanding context is critical to choosing appropriate approaches to architecture decision making. Naturalistic Decision Making (NDM) explains how people make decisions under real-world conditions. This paper investigates NDM in software architecture and studies architecture decisions in their environment and decision-making context. The research approach includes a case study of large technology organizations consisting of a survey, multiple focus groups, and participant observation. Previous studies that touch on NDM in software architecture have mainly focused on decision-making processes or tools or developing decision models. This paper provides three contributions. First, we build on previous studies by other researchers to produce an in-depth exploration of NDM in the context of software architecture. We focus on Recognition-Primed Decision (RPD) making as an implementation of NDM. Second, we present an examination of the decisions made by experienced architects under conditions that can be considered naturalistic. Third, we provide examples and recommendations that help software architects determine when an NDM approach is appropriate for their context.
A Study on the Influence of Regional Competency in Science and Technology Policy on Performance

Purpose - Recently, the fourth industrial revolution is rapidly progressing, and the central government-led innovation system is not able to flexibly cope with changes in science and the economy and society. To solve these problems, it is necessary for local governments, which can easily identify and flexibly respond to local sites, to become self-centered and ready to respond more quickly to massive changes. Through this research, this study investigated the awareness of the elements of Jeonnam Province's capabilities in the field of science and technology policy, the importance of R&D, and how network cooperation among the base institutions might affect performance. Research design, data, and methodology - In fact, the data used in this study only 115 people were polled, excluding five who did not respond to the necessary variables. The methods of the survey were sampled, and the means of the survey were investigated via a self-contained electronic file (e-mail). Statistical analysis, including hypothesis verification, was performed by SPSS 19. The regression analysis was used. Results - All factors significantly affect performance by dividing them into five sub-fields: R&D strategic establishment, R&D demand survey, R&D planning, R&D evaluation, and R&D project management. These results suggest the importance and need for local scientific technology policy capabilities. Besides, it was confirmed that the relationship between regional science and technology policy capabilities and performance was moderated by the recognition of the importance of science technology and network cooperation among the core organizations. Conclusions - As a result, independent variables regarding the capabilities of each scientific technology policy were found to be statistically significant and have a significant effect on performance. Second, the regression analysis has shown the moderation effects of R&D importance awareness between the capabilities of science and technology policies and their performance. On the other hand, a regression analysis showing that the capabilities of science and technology policies and network cooperation between the base regions were not significant, indicating that there is no effect of moderation of network cooperation between the base regions between the capabilities of science and technology policies and performance.
A Method of Online Motion Generation Using Swept Volumes Collected in Advance

This paper describes a method to efficiently generate a reaching motion to the target posture by using the experience of the past motion. The key point of the proposed method is the practical use of swept volumes. As pre-processing, various target postures of the end-effector are given under various environments, and robot movements to reach to the posture are generated. In each reaching motion, swept volume, which is the volume that the robot occupies in its workspace during the reaching motion, is recorded combined with the target posture of the end-effector. When a new target posture of the end-effector is given, past end-effector postures that are close to the new posture are searched. Then swept volumes associated with each posture are remembered, and the interference between each volume and the current environment is checked. This enables to select a feasible reaching motion with only one collision check procedure per one motion sequence. The effectiveness of the proposed method was confirmed by the experiments with a daily assistive robot in a simulated environment.
LOL: Learning To Optimize Loss Switching Under Label Noise

Deep convolutional neural networks excel in image recognition, but they are also known to be fragile to label corruption. To mitigate this problem, we propose to dynamically switch two loss functions, categorical cross entropy and mean absolute error, to exploit their complementary advantages. We employ the bilevel programming approach to simultaneously optimize base CNNs and the weights of two loss functions. Our proposed method only requires little modification in the optimization process of the original supervised problem and is applicable to a wide variety of networks under label corruption. Further, our approach achieves on-par results with other state-of-the-art noise-tolerant learning methods.
Playing with Shadows: Reinjection Loops and Historical Allusion in Georg Friedrich Haas’s Live Electronic Music

Recent scholarship suggests that the music of Austrian composer Georg Friedrich Haas can be understood as a dramatic confrontation between “clashing harmonic systems” (Hasegawa, 2015). Building on this observation, the present article focuses on Haas’s recent endeavors in the genre of live electronic music, showing how the composer deploys a relatively straightforward technical procedure—the reinjection loop, or the delayed playback of recorded sound at various speeds—to juxtapose different modes of pitch organization, including twelve-tone equal temperament, ultrachromatic microtonality, and just intonation. After surveying the composer’s relationship to these historical idioms, the article presents detailed analyses of two pieces ( Ein Schattenspiel and String Quartet No. 4) to illustrate how the reinjection loop operates at multiple registers simultaneously, bringing the performers into contact with their immediate past, while also bringing Haas into dialogue with the shadows of his own compositional predecessors.