Reviewerly/exHarmony huggingface dataset:
A Taxonomy and Survey of Power Models and Power Modeling for Cloud Servers

Due to the increasing demand of cloud resources, the ever-increasing number and scale of cloud data centers make their massive power consumption a prominent issue today. Evidence reveals that the behaviors of cloud servers make the major impact on data centers’ power consumption. Although extensive research can be found in this context, a systematic review of the models and modeling methods for the entire hierarchy (from underlying hardware components to the upper-layer applications) of the cloud server is still missing, which is supposed to cover the relevant studies on physical and virtual cloud server instances, server components, and cloud applications. In this article, we summarize a broad range of relevant studies from three perspectives: power data acquisition, power models, and power modeling methods for cloud servers (including bare-metal, virtual machine (VM), and container instances). We present a comprehensive taxonomy on the collection methods of server-level power data, the existing mainstream power models at multiple levels from hardware to software and application, and commonly used methods for modeling power consumption including classical regression analysis and emerging methods like reinforcement learning. Throughout the work, we introduce a variety of models and methods, illustrating their implementation, usability, and applicability while discussing the limitations of existing approaches and possible ways of improvement. Apart from reviewing existing studies on server power models and modeling methods, we further figure out several open challenges and possible research directions, such as the study on modeling the power consumption of lightweight virtual units like unikernel and the necessity of further explorations toward empowering server power estimation/prediction with machine learning. As power monitoring is drawing increasing attention from cloud service providers (CSPs), this survey provides useful guidelines on server power modeling and can be inspiring for further research on energy-efficient data centers.
Block Proposal Neural Architecture Search

The existing neural architecture search (NAS) methods usually restrict the search space to the pre-defined types of block for a fixed macro-architecture. However, this strategy will limit the search space and affect architecture flexibility if block proposal search (BPS) is not considered for NAS. As a result, block structure search is the bottleneck in many previous NAS works. In this work, we propose a new evolutionary algorithm referred to as latency EvoNAS (LEvoNAS) for block structure search, and also incorporate it to the NAS framework by developing a novel two-stage framework referred to as Block Proposal NAS (BP-NAS). Comprehensive experimental results on two computer vision tasks demonstrate the superiority of our newly proposed approach over the state-of-the-art lightweight methods. For the classification task on the ImageNet dataset, our BPN-A is better than 1.0-MobileNetV2 with similar latency, and our BPN-B saves 23.7% latency when compared with 1.4-MobileNetV2 with higher top-1 accuracy. Furthermore, for the object detection task on the COCO dataset, our method achieves significant performance improvement than MobileNetV2, which demonstrates the generalization capability of our newly proposed framework.
Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements

Neurodegenerative diseases are particular diseases whose decline can partially or completely compromise the normal course of life of a human being. In order to increase the quality of patient's life, a timely diagnosis plays a major role. The analysis of neurodegenerative diseases, and their stage, is also carried out by means of gait analysis. Performing early stage neurodegenerative disease assessment is still an open problem. In this paper, the focus is on modeling the human gait movement pattern by using the kinematic theory of rapid human movements and its sigma-lognormal model. The hypothesis is that the kinematic theory of rapid human movements, originally developed to describe handwriting patterns, and used in conjunction with other spatio-temporal features, can discriminate neurodegenerative diseases patterns, especially in early stages, while analyzing human gait with 2D cameras. The thesis empirically demonstrates its effectiveness in describing neurodegenerative patterns, when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. The solution developed achieved 99.1% of accuracy using velocity-based, angle-based and sigma-lognormal features and left walk orientation.
Client-specific anomaly detection for face presentation attack detection

One-class anomaly detection approaches are particularly appealing for use in face presentation attack detection (PAD), especially in an unseen attack scenario, where the system is exposed to novel types of attacks. This work builds upon an anomaly-based formulation of the problem and analyses the merits of deploying client-specific information for face spoofing detection. We propose training one-class client-specific classifiers (both generative and discriminative) using representations obtained from pre-trained deep Convolutional Neural Networks (CNN). In order to incorporate client-specific information, a distinct threshold is set for each client based on subject-specific score distributions, which is then used for decision making at the test time. Through extensive experiments using different one-class systems, it is shown that the use of client-specific information in a one-class anomaly detection formulation (both in model construction as well as decision boundary selection) improves the performance significantly. We also show that anomaly-based solutions have the capacity to perform as well or better than two-class approaches in the unseen attack scenarios. Moreover, it is shown that CNN features obtained from models trained for face recognition appear to discard discriminative traits for spoofing detection and are less capable for PAD compared to the CNNs trained for a generic object recognition task.
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension

Value function approximation has demonstrated phenomenal empirical success in reinforcement learning (RL). Nevertheless, despite a handful of recent progress on developing theory for RL with linear function approximation, the understanding of general function approximation schemes largely remains missing. In this paper, we establish a provably efficient RL algorithm with general value function approximation. We show that if the value functions admit an approximation with a function class $\mathcal{F}$, our algorithm achieves a regret bound of $\widetilde{O}(\mathrm{poly}(dH)\sqrt{T})$ where $d$ is a complexity measure of $\mathcal{F}$ that depends on the eluder dimension [Russo and Van Roy, 2013] and log-covering numbers, $H$ is the planning horizon, and $T$ is the number interactions with the environment. Our theory generalizes recent progress on RL with linear value function approximation and does not make explicit assumptions on the model of the environment. Moreover, our algorithm is model-free and provides a framework to justify the effectiveness of algorithms used in practice.
Designerly Ways of Knowing in HRI

Interest in design methods and tools has been steadily growing in HRI. Yet, design is not acknowledged as a discipline with specific epistemology and methodology. Designerly HRI work is validated through user studies which, we argue, provide a limited account of the knowledge design produces. This paper aims to broaden current understanding of designerly HRI work and its contributions by unpacking what designerly knowledge is and how to produce it. Through a critical analysis of current HRI design literature, we identify a lack of work dedicated to understanding the conceptual implications of robotic artifacts. These, in fact, are implicit carriers of crucial HRI knowledge that can challenge established assumptions about how a robot should look, act, and be like. We conclude by discussing a set of practices desirable to legitimize designerly HRI work, and calling for further research addressing the conceptual implications designerly HRI work.
Quantum Machine Learning: A tutorial

This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together concepts from Machine Learning (ML), Quantum Computing (QC) and Quantum Information (QI). The great development experienced by QC, partly due to the involvement of giant technological companies as well as the popularity and success of ML have been responsible of making QML one of the main streams for researchers working on fuzzy borders between Physics, Mathematics and Computer Science. A possible, although arguably coarse, classification of QML methods may be based on those approaches that make use of ML in a quantum experimentation environment and those others that take advantage of QC and QI to find out alternative and enhanced solutions to problems driven by data, oftentimes offering a considerable speedup and improved performances as a result of tackling problems from a complete different standpoint. Several examples will be provided to illustrate both classes of methods.
Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization

Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet promising task relieving the need for laborious annotation works. However, the domain shifts/discrepancies problem in this task compromise the final segmentation performance. Based on our observation, the main causes of the domain shifts are differences in imaging conditions, called image-level domain shifts, and differences in object category configurations called category-level domain shifts. In this paper, we propose a novel UDA pipeline that unifies image-level alignment and category-level feature distribution regularization in a coarse-to-fine manner. Specifically, on the coarse side, we propose a photometric alignment module that aligns an image in the source domain with a reference image from the target domain using a set of image-level operators; on the fine side, we propose a category-oriented triplet loss that imposes a soft constraint to regularize category centers in the source domain and a self-supervised consistency regularization method in the target domain. Experimental results show that our proposed pipeline improves the generalization capability of the final segmentation model and significantly outperforms all previous state-of-the-arts.
The transparency challenge of blockchain in organizations

This position paper discusses the challenges of blockchain applications in businesses and the public sector related to an excessive degree of transparency. We first point out the types of sensitive data involved in different patterns of blockchain use cases. We then argue that the implications of blockchains' information exposure caused by replicated transaction storage and execution go well beyond the often-mentioned conflicts with the GDPR's "right to be forgotten" and may be more problematic than anticipated. In particular, we illustrate the trade-off between protecting sensitive information and increasing process efficiency through smart contracts. We also explore to which extent permissioned blockchains and novel applications of cryptographic technologies such as self-sovereign identities and zero-knowledge proofs can help overcome the transparency challenge and thus act as catalysts for blockchain adoption and diffusion in organizations.
Impact of climate change on solar monofacial and bifacial Photovoltaics (PV) potential in Qatar

The considerable growth of the renewables’ shares in the energy market paves the way towards United Nations Sustainable Development Goal 7. Solar photovoltaics (PV) are more economically viable amongst other renewables because of their lower, easier installation and reliability. However, the PV, like other renewables, is more vulnerable to climatic conditions. Currently, the monofacial leads the PV market; however, it is expected that the bifacial PV will be dominant as it offers more energy yield at a lower cost. It is thus crucial to analyze the PV performance concerning climatic conditions. The merits of this study include: (i) the performance of bifacial PV for the climate change scenarios is analyzed, which is lacking in the literature; (ii) detailed monofacial and bifacial PV mathematical models are developed to estimate the PV output; and (iii) the comparison is made between monofacial and bifacial PVs performance and their dependence on climatic conditions. The climate model is used to predict the ambient conditions for the years 2050 and 2080. The average hourly responses of solar irradiance and energy yield are presented and analyzed.
Neural Style Transfer for image within images and conditional GANs for destylization

In this paper, the feature representation of an image by CNN is used to hide the secret image into the cover image. The style of the cover image hides the content of the secret image and produce a stego image using Neural Style Transfer (NST) algorithm, which resembles the cover image and also contains the semantic content of secret image. The main technical contributions are to hide the content of the secret image in the in-between hidden layered style features of the cover image, which is the first of its kind in the present state-of-art-technique. Also, to recover the secret image from the stego image, destylization is done with the help of conditional generative adversarial networks (GANs) using Residual in Residual Dense Blocks (RRDBs). Further, stego images from different layer combinations of content and style features are obtained and evaluated. Evaluation is based on the visual similarity and quality loss between the cover-stego pair and the secret-reconstructed secret pair of images. From the experiments, it has been observed that the proposed algorithm has 43.95 dB Peak Signal-to-Noise Ratio (PSNR)), .995 Structural Similarity Index (SSIM), and .993 Visual Information Fidelity (VIF) for the ImageNet dataset. The proposed algorithm is found to be more robust against StegExpose than the traditional methods. • Neural Style Transfer algorithm based steganographic framework. • Hidden layers of a network represents the features of an image. • Embedding of the secret content information in the cover style image. • Destylization of the neural styled image using an image to image translation. • Stego images from different combinations of layers are evaluated.
CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields

Tremendous progress in deep generative models has led to photorealistic image synthesis. While achieving compelling results, most approaches operate in the two-dimensional image domain, ignoring the three-dimensional nature of our world. Several recent works therefore propose generative models which are 3D-aware, i.e., scenes are modeled in 3D and then rendered differentiably to the image plane. While this leads to impressive 3D consistency, the camera needs to be modelled as well and we show in this work that these methods are sensitive to the choice of prior camera distributions. Current approaches assume fixed intrinsics and predefined priors over camera pose ranges, and parameter tuning is typically required for real-world data. If the data distribution is not matched, results degrade significantly. Our key hypothesis is that learning a camera generator jointly with the image generator leads to a more principled approach to 3D-aware image synthesis. Further, we propose to decompose the scene into a background and foreground model, leading to more efficient and disentangled scene representations. While training from raw, unposed image collections, we learn a 3D- and camera-aware generative model which faithfully recovers not only the image but also the camera data distribution. At test time, our model generates images with explicit control over the camera as well as the shape and appearance of the scene.
COVID 19: Identification of Masked Face using CNN Architecture

Tackling with the Covid-19 pandemic has been a grueling task from the day one of its rise. To prevent the spread of the virus, one of the major steps taken by governments of the world is to mandate the use of face masks in public places. This has made face recognition process in today’s era quite a hideous process as masked faces are difficult to analyze because most of the key parts of the face get covered by the mask. Thus, it raises a matter of great concern when security and individual identification comes into play. Therefore, this paper proposes a reliable system to recognize the individual even when the face is covered with mask. The first step aims to convert all the images of the database to grayscale and then convert them into vectors. This reduces the contributing factor of the masked region in the prediction process. The vectors are then fed to a Convolutional Neural Network. A sequential model is defined to design multiple layers of the network and generate output. The proposed model showed maximum accuracy of 90% after tuning the key parameters of the model.
LAtools: A data analysis package for the reproducible reduction of LA-ICPMS data

Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICPMS) is an increasingly popular analytical technique, that is able to provide spatially resolved, minimally destructive analyses of heterogeneous materials. The data produced by this technique are inherently complex, and require extensive processing and subjective expert interpretation to produce useful compositional data. At present, laboratories employ diverse protocols for data processing, and the reporting of these protocols is usually insufficient to allow data processing to be independently replicated, rendering the resulting data untraceable. Importantly, different expert users can obtain significantly different results from the same raw data using nominally identical processing workflows, depending on how 'contaminants' are identified and excluded, and which regions of signal are selected as representative of the composition of the sample. The irreproducibility of LA-ICPMS is a significant problem for the technique, but the complexity of the raw data has been a major hindrance to developing traceable data processing workflows. Here, we present LAtools – a free, open-source Python package for LA-ICPMS data processing designed with reproducibility at its core. The software performs basic data processing with similar efficacy to existing software, and brings a number of new data selection algorithms to facilitate reproducible reduction of LA-ICPMS data. We discuss the key advances of LAtools, and compare its output to trace metal analysis of marine CaCO3 (foraminifera) processed both manually and with Iolite, and to manually processed trace element data from zircon grains.
Read Between the Lines

This paper introduces the Visual Inspection Tool (VIT) which supports researchers in the annotation of multimodal data as well as the processing and exploitation for learning purposes. While most of the existing Multimodal Learning Analytics (MMLA) solutions are tailor-made for specific learning tasks and sensors, the VIT addresses the data annotation for different types of learning tasks that can be captured with a customisable set of sensors in a flexible way. The VIT supports MMLA researchers in 1) triangulating multimodal data with video recordings; 2) segmenting the multimodal data into time-intervals and adding annotations to the time-intervals; 3) downloading the annotated dataset and using it for multimodal data analysis. The VIT is a crucial component that was so far missing in the available tools for MMLA research. By filling this gap we also identified an integrated workflow that characterises current MMLA research. We call this workflow the Multimodal Learning Analytics Pipeline, a toolkit for orchestration, the use and application of various MMLA tools.
Intelligent Mobile Drone System Based on Real-Time Object Detection

Drone also known as unmanned aerial vehicle (UAV) has drawn lots of attention in recent years. Quadcopter as one of the most popular drones has great potential in both industrial and academic fields. Quadcopter drones are capable of taking off vertically and flying towards any direction. Traditional researches of drones mainly focus on their mechanical structures and movement control. The aircraft movement is usually controlled by a remote controller manually or the trajectory is pre-programmed with specific algorithms. Consumer drones typically use mobile device together with remote controllers to realize flight control and video transmission. Implementing different functions on mobile devices can result in different behaviors of drones indirectly. With the development of deep learning in computer vision field, commercial drones equipped with camera can be much more intelligent and even realize autonomous flight. In the past, running deep learning based algorithms on mobile devices is highly computational intensive and time consuming. This paper utilizes a novel real-time object detection method and deploys the deep learning model on the modern mobile device to realize autonomous object detection and object tracking of drones.
Bit Prudent In-Cache Acceleration of Deep Convolutional Neural Networks

We propose Bit Prudent In-Cache Acceleration of Deep Convolutional Neural Networks - an in-SRAM architecture for accelerating Convolutional Neural Network (CNN) inference by leveraging network redundancy and massive parallelism. The network redundancy is exploited in two ways. First, we prune and fine-tune the trained network model and develop two distinct methods - coalescing and overlapping - to run inferences efficiently with sparse models. Second, we propose an architecture for network models with a reduced bit width by leveraging bit-serial computation. Our proposed architecture achieves a 17.7×/3.7× speedup over server class CPU/GPU, and a 1.6× speedup compared to the relevant in-cache accelerator, with 2% area overhead each processor die, and no loss on top-1 accuracy for AlexNet. With a relaxed accuracy limit, our tunable architecture achieves higher speedups.
Mining Labor Market Requirements Using Distributional Semantic Models and Deep Learning

This article describes a new method for analyzing labor market requirements by matching job listings from online recruitment platforms with professional standards to weigh the importance of particular professional functions and requirements and enrich the general concepts of professional standards using real labor market requirements. Our approach aims to combat the gap between professional standards and reality of fast changing requirements in developing branches of economy. First, we determine professions for each job description, using the multi-label classifier based on convolutional neural networks. Secondly, we solve the task of concept matching between job descriptions and standards for the respective professions by applying distributional semantic models. In this task, the average word2vec model achieved the best performance among other vector space models. Finally, we experiment with expanding general vocabulary of professional standards with the most frequent unigrams and bigrams occurring in matching job descriptions. Performance evaluation is carried out on a representative corpus of job listings and professional standards in the field of IT.