Reviewerly/exHarmony huggingface dataset:
Characterizing and Synthesizing Task Dependencies of Data-Parallel Jobs in Alibaba Cloud

Cluster schedulers routinely face data-parallel jobs with complex task dependencies expressed as DAGs (directed acyclic graphs). Understanding DAG structures and runtime characteristics in large production clusters hence plays a key role in scheduler design, which, however, remains an important missing piece in the literature. In this work, we present a comprehensive study of a recently released cluster trace in Alibaba. We examine the dependency structures of Alibaba jobs and find that their DAGs have sparsely connected vertices and can be approximately decomposed into multiple trees with bounded depth. We also characterize the runtime performance of DAGs and show that dependent tasks may have significant variability in resource usage and duration---even for recurring tasks. In both aspects, we compare the query jobs in the standard TPC benchmarks with the production DAGs and find the former inadequately representative. To better benchmark DAG schedulers at scale, we develop a workload generator that can faithfully synthesize task dependencies based on the production Alibaba trace. Extensive evaluations show that the synthesized DAGs have consistent statistical characteristics as the production DAGs, and the synthesized and real workloads yield similar scheduling results with various schedulers.
Sketching &amp; Drawing as Future Inquiry in HCI

Creating visual imagery helps us to situate ourselves within unknown worlds, processes, make connections, and find solutions. By exploring drawn ideas for novel technologies, we can examine the implications of their place in the world. Drawing, or sketching, for future inquiry in Human Computer Interaction (HCI) can be a stand-alone investigative approach, part of a wider 'world-building' in design fiction, or simply ideation around a concept. By examining instances of existing practice in HCI, in this paper we establish recommendations and rationales for those wishing to utilise sketching and drawing within their research. We examine approaches ranging from ideation, diagramming, scenario building, comics creation and artistic representation to create a model for sketching and drawing as future inquiry for HCI. This work also reflects on the ways in which these arts can inform and elucidate research and practice in HCI, and makes recommendations for the field, within its teaching, processes and outcomes.
Scavenger

Resource under-utilization is common in cloud data centers. Prior works have proposed improving utilization by running provider workloads in the background, colocated with tenant workloads. However, an important challenge that has still not been addressed is considering the tenant workloads as a black-box. We present Scavenger, a batch workload manager that opportunistically runs containerized batch jobs next to black-box tenant VMs to improve utilization. Scavenger is designed to work without requiring any offline profiling or prior information about the tenant workload. To meet the tenant VMs' resource demand at all times, Scavenger dynamically regulates the resource usage of batch jobs, including processor usage, memory capacity, and network bandwidth. We experimentally evaluate Scavenger on two different testbeds using latency-sensitive tenant workloads colocated with Spark jobs in the background and show that Scavenger significantly increases resource usage without compromising the resource demands of tenant VMs.
EvoloPy-FS: An Open-Source Nature-Inspired Optimization Framework in Python for Feature Selection

Feature selection is a necessary critical stage in data mining process. There is always an arm race to build frameworks and libraries that ease and automate this process. In this chapter, an EvoloPy-FS framework is proposed, which is a Python open-source optimization framework that includes several well-regarded swarm intelligence (SI) algorithms. It is geared toward feature selection optimization problems. It is an easy to use, reusable, and adaptable framework. The objective of developing EvoloPy-FS is providing a feature selection engine to help researchers even those with less knowledge in SI in solving their problems and visualizing rapid results with a less programming effort. That is why the orientation of this work was to build an open-source, white-box framework, where algorithms and data structures are being explicit, transparent, and publicly available. EvoloPy-FS comes to continue our path for building an integrated optimization environment, which was started by the original EvoloPy for global optimization problems, then EvoloPy-NN for training multilayer perception neural network, and finally the new EvoloPy-FS for feature selection optimization. EvoloPy-FS is freely hosted on ( www.evo-ml.com ) with a helpful documentation.
A robust clustering algorithm using spatial fuzzy C-means for brain MR images

Magnetic Resonance Imaging (MRI) is a medical imaging modality that is commonly employed for the analysis of different diseases. However, these images come with several problems such as noise and other imaging artifacts added during acquisition process. The researchers have actual challenges for segmentation under the consideration of these effects. In medical images, a well-known clustering approach like Fuzzy C-Means widely used for segmentation. The performance of FCM algorithm is fast in noise-free images; however, this method did not consider the spatial context of the image due to which its performance suffers when images corrupted with noise and other imaging relics. In this paper, a weighted spatial Fuzzy C-Means (wsFCM) segmentation method is proposed that considered the spatial information of image. Moreover, a spatial function is also developed that integrate a membership function. In order assess this function, a neighborhood window is established around a pixel and more weights have been assigned to those pixels which have greater correlation with central pixel in local neighborhood. By integration of this spatial function in membership function, the modified membership function strengthens the original membership function in handling the noise and intensity inhomogeneity, which has the ability to preserves and maintains structural information like edges. A comprehensive set of experimentation is performed on publicly accessible simulated and real standard brain MRI datasets. The performance of the proposed method has been compared with existing state-of-the-art methods. The results show that the performance of the proposed method is better and robust in handling noise and intensity inhomogeneity than of the existing works.
DeMiCPU: Device Fingerprinting with Magnetic Signals Radiated by CPU

With the widespread use of smart devices, device authentication has received much attention. One popular method for device authentication is to utilize internally-measured device fingerprints, such as device ID, software or hardware-based characteristics. In this paper, we propose DeMiCPU, a stimulation-response-based device fingerprinting technique that relies on externally-measured information, i.e., magnetic induction (MI) signals emitted from the CPU module that consists of the CPU chip and its affiliated power supply circuits. The key insight of DeMiCPU is that hardware discrepancies essentially exist among CPU modules and thus the corresponding MI signals make promising device fingerprints, which are difficult to be modified or mimicked. We design a stimulation and a discrepancy extraction scheme and evaluate them with 90 mobile devices, including 70 laptops (among which 30 are of totally identical CPU and operating system) and 20 smartphones. The results show that DeMiCPU can achieve 99.1% precision and recall on average, and 98.6% precision and recall for the 30 identical devices, with a fingerprinting time of 0.6 s. In addition, the performance can be further improved to 99.9% with multi-round fingerprinting.
Human–robot interaction based on gesture and movement recognition

Human–robot interaction (HRI) has become a research hotspot in computer vision and robotics due to its wide application in human–computer interaction (HCI) domain. Based on the explored algorithms of gesture recognition and limb movement recognition in somatosensory interaction, an HRI model of a robotic arm is proposed for robot arm manipulation. More specifically, 3D SSD architecture is used for the location and identification of gesture and arm movement. Then, DTW template matching algorithm is adopted to trace the dynamic gestures. The interactive scenarios and interactive modes are designed for experiment and implementation. Virtual interactive experimental results have demonstrated the usefulness of our method.
Attentional Feature-Pair Relation Networks for Accurate Face Recognition

Human face recognition is one of the most important research areas in biometrics. However, the robust face recognition under a drastic change of the facial pose, expression, and illumination is a big challenging problem for its practical application. Such variations make face recognition more difficult. In this paper, we propose a novel face recognition method, called Attentional Feature-pair Relation Network (AFRN), which represents the face by the relevant pairs of local appearance block features with their attention scores. The AFRN represents the face by all possible pairs of the 9x9 local appearance block features, the importance of each pair is considered by the attention map that is obtained from the low-rank bilinear pooling, and each pair is weighted by its corresponding attention score. To increase the accuracy, we select top-K pairs of local appearance block features as relevant facial information and drop the remaining irrelevant. The weighted top-K pairs are propagated to extract the joint feature-pair relation by using bilinear attention network. In experiments, we show the effectiveness of the proposed AFRN and achieve the outstanding performance in the 1:1 face verification and 1:N face identification tasks compared to existing state-of-the-art methods on the challenging LFW, YTF, CALFW, CPLFW, CFP, AgeDB, IJB-A, IJB-B, and IJB-C datasets.
Fast and Accurate Network Embeddings via Very Sparse Random Projection

We present FastRP, a scalable and performant algorithm for learning distributed node representations in a graph. FastRP is over 4,000 times faster than state-of-the-art methods such as DeepWalk and node2vec, while achieving comparable or even better performance as evaluated on several real-world networks on various downstream tasks. We observe that most network embedding methods consist of two components: construct a node similarity matrix and then apply dimension reduction techniques to this matrix. We show that the success of these methods should be attributed to the proper construction of this similarity matrix, rather than the dimension reduction method employed. FastRP is proposed as a scalable algorithm for network embeddings. Two key features of FastRP are: 1) it explicitly constructs a node similarity matrix that captures transitive relationships in a graph and normalizes matrix entries based on node degrees; 2) it utilizes very sparse random projection, which is a scalable optimization-free method for dimension reduction. An extra benefit from combining these two design choices is that it allows the iterative computation of node embeddings so that the similarity matrix need not be explicitly constructed, which further speeds up FastRP. FastRP is also advantageous for its ease of implementation, parallelization and hyperparameter tuning. The source code is available at https://github.com/GTmac/FastRP.
An Adaptive Image Contrast Enhancement Technique for Low-Contrast Images

Contrast enhancement is important and plays vital role in many applications. Histogram equalization-based techniques are widely used techniques for contrast enhancement. However, it faces the contrast over-stretching, which in return causes the loss of details and unnatural look to the target image. To address this issue, this work presents a novel scheme for image contrast enhancement. The contribution of the proposed scheme is twofold. First, the image can lose many important information when an image size is decreased. For that, the image is transformed from spatial to wavelet domain so that the multi-resolution can be achieved. Second, Gamma correction is a proven technique that produces natural look and preserves mean brightness of an image with the choice of optimal gamma values. Here, Particle Swarm Optimization (PSO) is utilized to select the optimal gamma values. In this study, an effective fitness function is proposed to maximize the performance of PSO. Experimental findings show that the proposed approach improve the image contrast up to a greater extent without introducing any artifacts.
SecTEE

Secure enclaves provide a practical solution to secure computation, and current approaches to secure enclaves are implemented by extending hardware security mechanisms to the CPU architecture. Therefore, it is hard for a platform to offer secure computation if its CPU architecture is not equipped with any secure enclave features. Unfortunately, ARM CPUs, dominating mobile devices and having increasing momentum in cloud markets, do not provide any security mechanisms achieving the security equivalent to modern secure enclave architectures. In this paper, we propose SecTEE, a software-based secure enclave architecture which is based on the CPU's isolation mechanism and does not require specialized security hardware of the CPU architecture such as memory encryption engines. SecTEE achieves a high level of security even compared with hardware-based secure enclave architectures: resistance to privileged host software attacks, lightweight physical attacks, and memory access based side-channel attacks. Besides, SecTEE provides rich trusted computing primitives for enclaves: integrity measurement, remote attestation, data sealing, secrets provisioning, and life cycle management. We implement a SecTEE prototype based on the ARM TrustZone technology, but our approach can be applied to other CPU architectures with isolation mechanisms. The evaluation results show that most overhead comes from the software encryption and the runtime overhead imposed by trusted computing primitives is acceptable.
Face De-Occlusion Using 3D Morphable Model and Generative Adversarial Network

In recent decades, 3D morphable model (3DMM) has been commonly used in image-based photorealistic 3D face reconstruction. However, face images are often corrupted by serious occlusion by non-face objects including eyeglasses, masks, and hands. Such objects block the correct capture of landmarks and shading information. Therefore, the reconstructed 3D face model is hardly reusable. In this paper, a novel method is proposed to restore de-occluded face images based on inverse use of 3DMM and generative adversarial network. We utilize the 3DMM prior to the proposed adversarial network and combine a global and local adversarial convolutional neural network to learn face de-occlusion model. The 3DMM serves not only as geometric prior but also proposes the face region for the local discriminator. Experiment results confirm the effectiveness and robustness of the proposed algorithm in removing challenging types of occlusions with various head poses and illumination. Furthermore, the proposed method reconstructs the correct 3D face model with de-occluded textures.
Low light image enhancement with adaptive sigmoid transfer function

Low light image enhancement algorithms intent to produce visually pleasant images and target to extract valuable information for computer vision applications. The task of improving the quality of low light images is a challenging one. The existing methods for quality improvement undeniably annoy the visual aesthetics and suffer the major drawback of high computational complexity and less efficiency. To improve the visual quality and lower the distortions, a simple and computationally efficient low light image enhancement framework is presented in this study. To achieve this, an adaptive sigmoid transfer function (ASTF) is used and is derived from the sigmoid activation function of neural networks. By combining ASTF with a Laplacian operator, colour and contrast-enhanced images are obtained. Experiments show the effectiveness of the proposed method with state-of-the-art methods.
Multiobjective big data optimization based on a hybrid salp swarm algorithm and differential evolution

This paper developed a multiobjective Big Data optimization approach based on a hybrid salp swarm algorithm and the differential evolution algorithm. The role of the differential evolution algorithm is to enhance the capability of the feature exploitation of the salp swarm algorithm because the operators of the differential evolution algorithm are used as local search operators. In general, the proposed method contains three stages. In the first stage, the population is generated, and the archive is initialized. The second stage updates the solutions using the hybrid salp swarm algorithm and the differential evolution algorithm, and the final stage determines the nondominated solutions and updates the archive. To assess the performance of the proposed approach, a series of experiments were performed. A set of single-objective and multiobjective problems from the 2015 Big Data optimization competition were tested; the dataset contained data with and without noise. The results of our experiments illustrated that the proposed approach outperformed other approaches, including the baseline nondominated sorting genetic algorithm, on all test problems. Moreover, for single-objective problems, the score value of the proposed method was better than that of the traditional multiobjective salp swarm algorithm. When compared with both algorithms, that is, the adaptive DE algorithm with external archive and the hybrid multiobjective firefly algorithm, its score was the largest. In contrast, for the multiobjective functions, the scores of the proposed algorithm were higher than that of the fireworks algorithm framework.
Radar remote sensing image retrieval algorithm based on improved Sobel operator

Aiming at the time-consuming problem caused by large computational load of radar image retrieval, based on blocking histogram, Sobel edge detection operator and gray level co-occurrence matrix (GLCCM), new radar remote sensing image retrieval algorithm based on improved Sobel operator is proposed. Firstly, the Sobel edge detection algorithm is used to process the image, the edge image is acquired, the radar remote sensing image is analyzed from different angles, and then the different radar remote sensing images are transformed. Then, based on the above processing, Radar Remote Sensing Image Retrieval Algorithm is acquired; finally, the plurality of statistic of the matrix is recorded as a feature vector describing the radar image, and the image is retrieved according to the feature vector of the radar image. Through a large number of experiments, Radar Remote Sensing Image Retrieval algorithm can greatly reduce the retrieval time, and it also has a good retrieval effect for images with rich texture.
Generalized support vector data description for anomaly detection

Traditional anomaly detection procedures assume that normal observations are obtained from a single distribution. However, due to the complexities of modern industrial processes, the observations may belong to multiple operating modes with different distributions. In such cases, traditional anomaly detection procedures may trigger false alarms while the process is indeed in another normally operating mode. We propose a generalized support vector-based anomaly detection procedure called generalized support vector data description which can be used to determine the anomalies in multimodal processes. The proposed procedure constructs hyperspheres for each class in order to include as many observations as possible and keep other class observations as far apart as possible. In addition, we introduce a generalized Bayesian framework which does not only consider the prior information from each mode, but also highlights the relationships among the modes. The effectiveness of the proposed procedure is demonstrated through various simulation studies and real-life applications.
Evaluation of AVIRIS-NG hyperspectral images for mineral identification and mapping

Advancement of airborne hyperspectral remote sensing techniques provides subtle variations to identify minerals and to make distinctions between rock formations. These techniques clearly define barren land versus economically viable zones containing ores and minerals. As we know profitable mineral zones are commonly associated with hydrothermal alteration zones, hyperspectral remote sensing techniques have the capability to identify and distinguish between altered, weathered, and clay minerals. In this research work, airborne hyperspectral remote sensing image from AVIRIS-NG is used to identify hydrothermally altered minerals in the Jahajpur region of Bhilwara district, Rajasthan, India. The purpose of this study is the identification of the minerals through spectral features of image spectra in corroboration with field sample spectra and USGS laboratory spectra. The spectral angle mapper (SAM) and Spectral Feature Fitting (SFF) algorithms were used for the mapping of the minerals. This study was conducted to prove the capability of AVIRIS-NG hyperspectral remote sensing data to identify zones of profitable mineral deposits.
Res-DNN: A Residue Number System-Based DNN Accelerator Unit

In this article, a technique, based on using Residue Number System (RNS) is suggested to improve the energy efficiency of Deep Neural Networks (DNNs). In the DNN architecture, which is fully RNS-based, only weights and the primary inputs in the main memory are in the binary number system (BNS). The architecture, which is called Res-DNN, offers a high energy saving while requiring higher bit count for data to handle the overflow compared to that of a BNS one. Scaling techniques in the processing elements are employed in the RNS-based computations to make the computation bit widths the same as the BNS bit width. In this architecture, the MAX pooling and ReLU activation function are implemented in the RNS format. To lower the memory usage and required memory bandwidth, we suggest a Huffman-based coding. Additionally, for accessing the weights stored in the main memory, to obtain further energy reduction, we propose a structural modification to the memory hierarchy where a lower level register file is added to the data path of these accesses. The effectiveness of the proposed architecture is evaluated under seven state-of-the-art DNNs with the datasets of ImageNet and CIFAR-10. The obtained results show that Res-DNN leads to $2.5\times $ lower energy for computations and an average of 30% overall energy reduction compared to those of the binary counterpart.