Reviewerly/exHarmony huggingface dataset:
Density-based clustering using approximate natural neighbours

We propose a computationally efficient natural neighbour based metric for discovering clusters of arbitrary shape based on fuzzy measures. The approximate natural neighbours are found with the help of the Choquet integral with respect to a specially designed two-additive fuzzy measure. Fuzzy betweenness relation is used to construct such a measure and helps determine the natural neighbours of a query point. The natural neighbours of a datum allow the computation of point density estimate, which in turn defines a density-based metric suitable for clustering. The proposed method overcomes the exponential computational complexity of the Delaunay triangulation traditionally used to identify the natural neighbours. The run-time of the density estimate by this metric keeps the same quadratic trends as the Euclidean distance based estimates with respect to the data size. Empirical evaluation based on 20 synthetic and real-world datasets shows that this metric has a higher clustering accuracy for existing state-of-the-art density-based clustering algorithms, such as DBSCAN, SNN and DP. Furthermore, the proposed metric is easily combined with these algorithms, and the enhanced clustering algorithms inherit positive features such as resistance to noise and imbalanced data.
SLO-aware colocation: Harvesting transient resources from latency-critical services

An effective way to increase utilization in datacenters is to run batch jobs on the transient idle resources of latency-critical services. Such transient resources must be reclaimed once latency-critical services require them again, which severely hurts the performance of batch jobs. However, there is an increasing demand to provide predictable performance for users who have strict requirements for job completion times. In this paper, we present TCON, a transiency-aware controller that enables the safe colocations of batch jobs alongside latency-critical services while reducing job completion time SLO (Service Level Objective) misses. TCON makes some of the transient resources more valuable by providing reclaim probabilities to them. Since the transient resources left by different latency-critical services may have different capacities and reclaim probabilities, TCON formulates a cardinality-constrained portfolio model that can construct an optimal mix of transient resources for each batch job to achieve both high resource efficiency and low SLO misses. Furthermore, TCON exploits a partial checkpointing mechanism to minimize the re-computation cost in the case of reclaiming. Testbed experiments show that TCON is able to increase the average resource utilization to 45% and reduce the job completion time SLO misses to 16%.
Static generation of UML sequence diagrams

Abstract UML sequence diagrams are visual representations of object interactions in a system and can provide valuable information for program comprehension, debugging, maintenance, and software archeology. Sequence diagrams generated from legacy code are independent of existing documentation that may have eroded. We present a framework for static generation of UML sequence diagrams from object-oriented source code. The framework provides a query refinement system to guide the user to interesting interactions in the source code. Our technique involves constructing a hypergraph representation of the source code, traversing the hypergraph with respect to a user-defined query, and generating the corresponding set of sequence diagrams. We implemented our framework as a tool, StaticGen (supporting software: StaticGen ), analyzing a corpus of 30 Android applications. We provide experimental results demonstrating the efficacy of our technique (originally appeared in the Proceedings of Fundamental Approaches to Software Engineering—20th International Conference, FASE 2017, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2017, Uppsala, Sweden, April 22–29, 2017).
Design Embedded System Testbench Based on FPGA and Microcontrollers for TATU Smart Lab as Education Component of Industry 4.0

The article describes problems of embedded systems testing and development for Industry 4.0 ready system. Test benches based on FPGA and microcontrollers were analyzed. Education components for Industry 4.0 were considered. Embedded systems for Testbench based on FPGA and microcontrollers for TATU Smart Lab were designed. Testbench with several systems for training were proposed. The idea of combining different systems in one Testbench was successfully implemented.
STAR

In modern cities, complaining has become an important way for citizens to report emerging urban issues to governments for quick response. For ease of retrieval and handling, government officials usually organize citizen complaints by manually assigning tags to them, which is inefficient and cannot always guarantee the quality of assigned tags. This work attempts to solve this problem by recommending tags for citizen complaints. Although there exist many studies on tag recommendation for textual content, few of them consider two characteristics of citizen complaints, i.e., the spatio-temporal correlations and the taxonomy of candidate tags. In this paper, we propose a novel Spatio-Temporal Taxonomy-Aware Recommendation model (STAR), to recommend tags for citizen complaints by jointly incorporating spatio-temporal information of complaints and the taxonomy of candidate tags. Specifically, STAR first exploits two parallel channels to learn representations for textual and spatio-temporal information. To effectively leverage the taxonomy of tags, we design chained neural networks that gradually refine the representations and perform hierarchical recommendation under a novel taxonomy constraint. A fusion module is further proposed to adaptively integrate contributions of textual and spatio-temporal information in a tag-specific manner. We conduct extensive experiments on a real-world dataset and demonstrate that STAR significantly performs better than state-of-the-art methods. The effectiveness of key components in our model is also verified through ablation studies.
Author identification of short texts using dependency treebanks without vocabulary

Abstract How to classify short texts effectively remains an important question in computational stylometry. This study presents the results of an experiment involving authorship attribution of ancient Greek texts. These texts were chosen to explore the effectiveness of digital methods as a supplement to the author’s work on text classification based on traditional stylometry. Here it is crucial to avoid confounding effects of shared topic, etc. Therefore, this study attempts to identify authorship using only morpho-syntactic data without regard to specific vocabulary items. The data are taken from the dependency annotations published in the Ancient Greek and Latin Dependency Treebank. The independent variables for classification are combinations generated from the dependency label and the morphology of each word in the corpus and its dependency parent. To avoid the effects of the combinatorial explosion, only the most frequent combinations are retained as input features. The authorship classification (with thirteen classes) is done with standard algorithms—logistic regression and support vector classification. During classification, the corpus is partitioned into increasingly smaller ‘texts’. To explore and control for the possible confounding effects of, e.g. different genre and annotator, three corpora were tested: a mixed corpus of several genres of both prose and verse, a corpus of prose including oratory, history, and essay, and a corpus restricted to narrative history. Results are surprisingly good as compared to those previously published. Accuracy for fifty-word inputs is 84.2–89.6%. Thus, this approach may prove an important addition to the prevailing methods for small text classification.
PRISM

Containerized clusters of machines at scale that provision Cloud services are encountering substantive difficulties with stragglers -- whereby a small subset of task execution negatively degrades system performance. Stragglers are an unsolved challenge due to a wide variety of root-causes and stochastic behavior. While there have been efforts to mitigate their effects, few works have attempted to empirically ascertain how system operational scenarios precisely influence straggler occurrence and severity. This challenge is further compounded with the difficulties of conducting experiments within real-world containerized clusters. System maintenance and experiment design are often error-prone and time-consuming processes, and a large portion of tools created for workload submission and straggler injection are bespoke to specific clusters, limiting experiment reproducibility. In this paper we propose PRISM, a framework that automates containerized cluster setup, experiment design, and experiment execution. Our framework is capable of deployment, configuration, execution, performance trace transformation and aggregation of containerized application frameworks, enabling scripted execution of diverse workloads and cluster configurations. The framework reduces time required for cluster setup and experiment execution from hours to minutes. We use PRISM to conduct automated experimentation of system operational conditions and identify straggler manifestation is affected by resource contention, input data size and scheduler architecture limitations.
Cross-domain Beauty Item Retrieval via Unsupervised Embedding Learning

Cross-domain image retrieval is always encountering insufficient labelled data in real world. In this paper, we propose unsupervised embedding learning (UEL) for cross-domain beauty and personal care product retrieval to finetune the convolutional neural network (CNN). More specifically, UEL utilizes the non-parametric softmax to train the CNN model as instance-level classification, which reduces the influence of some inevitable problems (e.g., shape variations). In order to obtain better performance, we integrate a few existing retrieval methods trained on different datasets. Furthermore, a query expansion strategy (i.e., diffusion) is adopted to improve the performance. Extensive experiments conducted on a dataset including half million images of beauty and personal product items (Perfect-500K) manifest the effectiveness of our proposed method. Our approach achieves the 2nd place in the leader board of the Grand Challenge of AI Meets Beauty in ACM Multimedia 2019. Our code is available at: https://github.com/RetrainIt/Perfect-Half-Million-Beauty-Product-Image-Recognition-Challenge-2019.
Machine learning integrated credibilistic semi supervised clustering for categorical data

In real life, availability of correctly labeled data and handling of categorical data are often acknowledged as two major challenges in pattern analysis. Thus, clustering techniques are employed on unlabeled data to group them according to homogeneity. However, clustering techniques fail to make a decision while data are uncertain, ambiguous, vague, coincidental and overlapping in nature. Hence, in this case, the use of semi supervised technique can be useful. On the other hand, real life datasets are majorly categorical in nature, where natural ordering in attribute values is missing. This special property of categorical values with the inherent characteristics like uncertainty, ambiguity and vagueness makes clustering more complicated than numerical data. In recent times, credibilistic measure shows better performance over fuzzy and possibilistic measures while considering similar inherent characteristics in numerical data. Thus, these facts motivated us to propose a semi supervised clustering technique using credibilistic measure with the integration of machine learning techniques to address the above mentioned challenges of clustering categorical data. This semi supervised technique first clusters the dataset into K subsets with the proposed Credibilistic K-Mode, where credibilistic measure helps to determine the homogeneity by avoiding coincident clustering problem as well as finds the points those are certain to the clusters. Thereafter, in the second part of the semi supervised technique, clustered dataset is used to build a supervised model for classification of other unlabeled or uncertain data. This technique not only handles the unlabeled data better, but also yields improved results for uncertain or ambiguous data e.g, if the credibilistic measure is same for a data point in multiple classes. The results of the proposed technique are demonstrated quantitatively and visually in comparison with widely used state-of-the-art methods for eight synthetic and four real life datasets. Finally, statistical tests have been conducted to judge the statistical significance of the results produced by the proposed technique.
Registration with probabilistic correspondences — Accurate and robust registration for pathological and inhomogeneous medical data

The registration of two medical images is usually based on the assumption that corresponding regions exist in both images. If this assumption is violated by e. g. pathologies most approaches encounter problems. The registration approach proposed is based on probabilistic correspondences of sparse image representations and enables a robust handling of potentially missing correspondences. A maximum a-posteriori framework is used to derive an optimization criterion with respect to deformation parameters that aim to reduce the shape and appearance differences between the registered images. A multi-resolution approach speeds up the optimization and increases the robustness of the registration. The computed probabilistic correspondences enable the approach to deal with missing correspondences in the images. Furthermore, they provide additional information about the quality of fit and potentially non-corresponding/pathological image regions. The approach is compared to two state-of-the-art registration methods using MR brain and cardiac images: a variational intensity-based registration algorithm and a feature-based registration approach using a discrete optimization scheme. The comprehensive quantitative evaluation using additional simulated stroke lesions shows a significantly higher accuracy and robustness of the proposed approach. Furthermore, the correspondence probability maps were used to characterize pathological regions in the MRI brain data.
Towards an integrated methodology for the development of blockchain-based solutions supporting cross-organizational processes

Blockchain technology is argued to have the potential to facilitate the development and improvement of cross-organizational business processes, on various aspects of the business process management discipline. In this paper, we review existing work on the use of blockchain technology to support business processes with a focus on cross-organizational settings. Based on this, we suggest to view blockchain as a software connector helping organizations to integrate the IT systems they use to support business processes. In addition, this paper suggests to use a model-driven engineering approach for the development of such connectors. By doing so, the need to develop a methodology helping to model and implement blockchain-based software connectors supporting cross-organizational business processes is highlighted. By adopting such an approach, organizations would be able to facilitate and speed up the development of blockchain-based solutions. This in turn would facilitate experimentations with this technology, which is considered as an important phase for organizations to derive value from it.
Responsibility Analysis by Abstract Interpretation

Given a behavior of interest in the program, statically determining the corresponding responsible entity is a task of critical importance, especially in program security. Classical static analysis techniques (e.g. dependency analysis, taint analysis, slicing, etc.) assist programmers in narrowing down the scope of responsibility, but none of them can explicitly identify the responsible entity. Meanwhile, the causality analysis is generally not pertinent for analyzing programs, and the structural equations model (SEM) of actual causality misses some information inherent in programs, making its analysis on programs imprecise. In this paper, a novel definition of responsibility based on the abstraction of event trace semantics is proposed, which can be applied in program security and other scientific fields. Briefly speaking, an entity $$E_{R }$$ is responsible for behavior $$\mathcal {B}$$ , if and only if $$E_{R }$$ is free to choose its input value, and such a choice is the first one that ensures the occurrence of $$\mathcal {B}$$ in the forthcoming execution. Compared to current analysis methods, the responsibility analysis is more precise. In addition, our definition of responsibility takes into account the cognizance of the observer, which, to the best of our knowledge, is a new innovative idea in program analysis.
Research on data retrieval and analysis system based on Baidu reptile technology in big data era

With the rapid development of the Internet, the current Web has become the main platform for people to publish and retrieve information. How to quickly and accurately find the information required by users in a large amount of network information resources has become an urgent need of the people. W eb crawlers are research fields that appear to meet this demand. Based on this, the paper designs and implements a distributed web crawler system based on the existing research work, and its goal is to provide high quality data support for the network public opinion system. The web crawler system designed and implemented in this paper solves the problems of low efficiency, poor scalability and low automation of single-machine crawlers, which improves the speed of webpage collection and data extraction precision and expands the scale of webpage collection. At the end of the article, the system related interface screenshots and test results are displayed. It can be seen from the test results that the crawler system can effectively collect dynamic web pages, and the result of automatic extraction of web pages has high precision, and also realizes the entire crawling system.
Face Recognition and Detection Using Haars Features with Template Matching Algorithm

Finding facial component in face images is a significant arrangement for various facial image-understanding applications. The face detection is a process of detecting a region of the face from a picture, or image of one or multiple objects together. In this paper, we introduce Adaboost, viola-Jones, and Haar algorithms to detect faces either through mobile phone interface or from desktop computer UI. The application of the work has extended into an automated classroom attendance system using a handheld device. The results have shown the effectiveness of the proposed model.
A 8.93-TOPS/W LSTM Recurrent Neural Network Accelerator Featuring Hierarchical Coarse-Grain Sparsity With All Parameters Stored On-Chip

Long short-term memory (LSTM) networks are widely used for speech applications but pose difficulties for efficient implementation on hardware due to large weight storage requirements. We present an energy-efficient LSTM recurrent neural network (RNN) accelerator, featuring an algorithm-hardware co-optimized memory compression technique called hierarchical coarse-grain sparsity (HCGS). Aided by HCGS-based block-wise recursive weight compression, we demonstrate LSTM networks with up to 16× fewer weights while achieving minimal accuracy loss. The prototype chip fabricated in 65-nm LP CMOS achieves 8.93/7.22 TOPS/W for 2-/3-layer LSTM RNNs trained with HCGS for TIMIT/TED-LIUM corpora.
Hierarchical Semantic Labeling with Adaptive Confidence

In real-world applications with semantic classification labels ('dog', 'car', 'chair', etc.), it would be advantageous to identify any unconfident classification and then determine if a less specific label could instead be reliably established. In this work, we present a hierarchical estimation and inference approach using a semantic concept tree to provide an appropriate generalized label when needed. The proposed method has several advantages, including the ability to work with any logit/softmax-based semantic label classifier, the ability to correct many misclassified labels while not introducing any new errors, and a statistical guarantee of confidence for the final labels. We additionally provide a new set of hierarchical metrics to properly evaluate the approach. Multiple synthetic and real datasets are examined to demonstrate how the framework can quickly and efficiently resolve unconfident predictions.
Perceptual evaluation of bracewood and soundboard wood variations on the preference of a steel-string acoustic guitar

The wood of the spruce tree (Picea spp.) has been valued for centuries as an ideal soundboard for stringed instruments due to its material acoustic properties. There is large variability in these properties between individual trees of the same species and even within an individual log. It stands to reason that this variability would produce audible differences in the sound quality of otherwise identical musical instruments. Furthermore, there may be a suite of physical characteristics of the soundboard that would result in optimal sound quality for a given design. Nine steel-string guitars of the same model were produced. The guitars varied only in two parameters: the density and Young's modulus of the soundboard and bracewood. This variability was representative of the range of wood currently produced by Pacific Rim Tonewoods. A short music sequence was used for a pairwise preference evaluation in a listening test. The results suggested that, for this particular model (the Taylor 814ce Grand Auditorium), the low density and Young's modulus of the soundboard and bracewood had a positive impact on the sound quality. More generally, these results underscore the importance of integrating a given design with the physical characteristics of the component wood.
A Software Defined Network Based Research on Fairness in Multimedia

The demand for online distribution of high quality and high throughput content has led to non-cooperative competition of network resources between a growing number of media applications. This causes a significant impact on network efficiency, the quality of user experience (QoE) as well as a discrepancy of QoE across user devices. Within a multi-user multi-device environment, measuring and maintaining perceivable fairness becomes as critical as achieving the QoE on individual user applications. This paper discusses application- and human-level fairness over networked multimedia applications and how such fairness can be managed through novel network designs using programmable networks such as software-defined networks (SDN).