Reviewerly/exHarmony huggingface dataset:
ETAS: Energy and thermal‐aware dynamic virtual machine consolidation in cloud data center with proactive hotspot mitigation

Summary Data centers consume an enormous amount of energy to meet the ever‐increasing demand for cloud resources. Computing and Cooling are the two main subsystems that largely contribute to energy consumption in a data center. Dynamic Virtual Machine (VM) consolidation is a widely adopted technique to reduce the energy consumption of computing systems. However, aggressive consolidation leads to the creation of local hotspots that has adverse effects on energy consumption and reliability of the system. These issues can be addressed through efficient and thermal‐aware consolidation methods. We propose an Energy and Thermal‐Aware Scheduling (ETAS) algorithm that dynamically consolidates VMs to minimize the overall energy consumption while proactively preventing hotspots. ETAS is designed to address the trade‐off between time and the cost savings and it can be tuned based on the requirement. We perform extensive experiments by using the real‐world traces with precise power and thermal models. The experimental results and empirical studies demonstrate that ETAS outperforms other state‐of‐the‐art algorithms by reducing overall energy without any hotspot creation.
CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation

We analyze clouds in the earth's atmosphere using ground-based sky cameras.An accurate segmentation of clouds in the captured sky/cloud image is difficult, owing to the fuzzy boundaries of clouds.Several techniques have been proposed that use color as the discriminatory feature for cloud detection.In the existing literature, however, analysis of daytime and nighttime images is considered separately, mainly because of differences in image characteristics and applications.In this paper, we propose a light-weight deep-learning architecture called CloudSegNet.It is the first that integrates daytime and nighttime (also known as nychthemeron) image segmentation in a single framework, and achieves state-of-the-art results on public databases.
The Log-Structured Merge-Bush &amp; the Wacky Continuum

Data-intensive key-value stores based on the Log-Structured Merge-Tree are used in numerous modern applications ranging from social media and data science to cloud infrastructure. We show that such designs exhibit an intrinsic contention between the costs of point reads, writes and memory, and that this trade-off deteriorates as the data size grows. The root of the problem is that in all existing designs, the capacity ratio between any pair of levels is fixed. This causes write cost to increase with the data size while yielding exponentially diminishing returns for point reads and memory. We introduce the Log-Structured Merge-Bush (LSM-Bush), a new data structure that sets increasing capacity ratios between adjacent pairs of smaller levels. As a result, smaller levels get lazier by gathering more runs before merging them. By using a doubly-exponential ratio growth rate, LSM-bush brings write cost down from \small$O(łog N)$ to \small$O(łogłog N)$, and it can trade this gain to either improve point reads or memory. Thus, it enables more scalable trade-offs all around. We further introduce Wacky, a design continuum that includes LSM-Bush as well as all state-of-the-art merge policies, from laziest to greediest, and can assume any of them within a single implementation. Wacky encompasses a vast space of performance properties, including ones that favor range reads, and it can be searched analytically to find the design that performs best for a given workload in practice.
Sentiment Classification Using Document Embeddings Trained with Cosine Similarity

In document-level sentiment classification, each document must be mapped to a fixed length vector.Document embedding models map each document to a dense, lowdimensional vector in continuous vector space.This paper proposes training document embeddings using cosine similarity instead of dot product.Experiments on the IMDB dataset show that accuracy is improved when using cosine similarity compared to using dot product, while using feature combination with Naïve Bayes weighted bag of n-grams achieves a competitive accuracy of 93.68% † .
Stable Prediction with Model Misspecification and Agnostic Distribution Shift

For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the same distribution as the training data, and the other is that the model is correctly specified. In real applications, however, we often have little prior knowledge on the test data and on the underlying true model. Under model misspecification, agnostic distribution shift between training and test data leads to inaccuracy of parameter estimation and instability of prediction across unknown test data. To address these problems, we propose a novel Decorrelated Weighting Regression (DWR) algorithm which jointly optimizes a variable decorrelation regularizer and a weighted regression model. The variable decorrelation regularizer estimates a weight for each sample such that variables are decorrelated on the weighted training data. Then, these weights are used in the weighted regression to improve the accuracy of estimation on the effect of each variable, thus help to improve the stability of prediction across unknown test data. Extensive experiments clearly demonstrate that our DWR algorithm can significantly improve the accuracy of parameter estimation and stability of prediction with model misspecification and agnostic distribution shift.
Post-Quantum Authentication in TLS 1.3: A Performance Study

The potential development of large-scale quantum computers is raising concerns among IT and security research professionals due to their ability to solve (elliptic curve) discrete logarithm and integer factorization problems in polynomial time.All currently used public key algorithms would be deemed insecure in a post-quantum (PQ) setting.In response, the National Institute of Standards and Technology (NIST) has initiated a process to standardize quantum-resistant crypto algorithms, focusing primarily on their security guarantees.Since PQ algorithms present significant differences over classical ones, their overall evaluation should not be performed out-of-context.This work presents a detailed performance evaluation of the NIST signature algorithm candidates and investigates the imposed latency on TLS 1.3 connection establishment under realistic network conditions.In addition, we investigate their impact on TLS session throughput and analyze the trade-off between lengthy PQ signatures and computationally heavy PQ cryptographic operations.Our results demonstrate that the adoption of at least two PQ signature algorithms would be viable with little additional overhead over current signature algorithms.Also, we argue that many NIST PQ candidates can effectively be used for less timesensitive applications, and provide an in-depth discussion on the integration of PQ authentication in encrypted tunneling protocols, along with the related challenges, improvements, and alternatives.Finally, we propose and evaluate the combination of different PQ signature algorithms across the same certificate chain in TLS.Results show a reduction of the TLS handshake time and a significant increase of a server's TLS tunnel connection rate over using a single PQ signature scheme.
Curriculum Pre-training for End-to-End Speech Translation

End-to-end speech translation poses a heavy burden on the encoder because it has to transcribe, understand, and learn cross-lingual semantics simultaneously.To obtain a powerful encoder, traditional methods pre-train it on ASR data to capture speech features.However, we argue that pre-training the encoder only through simple speech recognition is not enough, and high-level linguistic knowledge should be considered.Inspired by this, we propose a curriculum pre-training method that includes an elementary course for transcription learning and two advanced courses for understanding the utterance and mapping words in two languages.The difficulty of these courses is gradually increasing.Experiments show that our curriculum pre-training method leads to significant improvements on En-De and En-Fr speech translation benchmarks.
Function-as-a-Service performance evaluation: A multivocal literature review

Function-as-a-Service (FaaS) is one form of the serverless cloud computing paradigm and is defined through FaaS platforms (e.g., AWS Lambda) executing event-triggered code snippets (i.e., functions). Many studies that empirically evaluate the performance of such FaaS platforms have started to appear but we are currently lacking a comprehensive understanding of the overall domain. To address this gap, we conducted a multivocal literature review (MLR) covering 112 studies from academic (51) and grey (61) literature. We find that existing work mainly studies the AWS Lambda platform and focuses on micro-benchmarks using simple functions to measure CPU speed and FaaS platform overhead (i.e., container cold starts). Further, we discover a mismatch between academic and industrial sources on tested platform configurations, find that function triggers remain insufficiently studied, and identify HTTP API gateways and cloud storages as the most used external service integrations. Following existing guidelines on experimentation in cloud systems, we discover many flaws threatening the reproducibility of experiments presented in the surveyed studies. We conclude with a discussion of gaps in literature and highlight methodological suggestions that may serve to improve future FaaS performance evaluation studies.
Cross-Modal Attentional Context Learning for RGB-D Object Detection

Recognizing objects from simultaneously sensed photometric (RGB) and depth channels is a fundamental yet practical problem in many machine vision applications, such as robot grasping and autonomous driving. In this paper, we address this problem by developing a cross-modal attentional context (CMAC) learning framework, which enables the full exploitation of the context information from both RGB and depth data. Compared to existing RGB-D object detection frameworks, our approach has several appealing properties. First, it consists of an attention-based global context model for exploiting adaptive contextual information and incorporating this information into a region-based CNN (e.g., fast RCNN) framework to achieve improved object detection performance. Second, our CMAC framework further contains a fine-grained object part attention module to harness multiple discriminative object parts inside each possible object region for superior local feature representation. While greatly improving the accuracy of RGB-D object detection, the effective cross-modal information fusion as well as attentional context modeling in our proposed model provide an interpretable visualization scheme. Experimental results demonstrate that the proposed method significantly improves upon the state of the art on all public benchmarks.
Debiased Contrastive Learning

A prominent technique for self-supervised representation learning has been to contrast semantically similar and dissimilar pairs of samples. Without access to labels, dissimilar (negative) points are typically taken to be randomly sampled datapoints, implicitly accepting that these points may, in reality, actually have the same label. Perhaps unsurprisingly, we observe that sampling negative examples from truly different labels improves performance, in a synthetic setting where labels are available. Motivated by this observation, we develop a debiased contrastive objective that corrects for the sampling of same-label datapoints, even without knowledge of the true labels. Empirically, the proposed objective consistently outperforms the state-of-the-art for representation learning in vision, language, and reinforcement learning benchmarks. Theoretically, we establish generalization bounds for the downstream classification task.
HateCheck: Functional Tests for Hate Speech Detection Models

Detecting online hate is a difficult task that even state-of-the-art models struggle with.Typically, hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score.However, this approach makes it difficult to identify specific model weak points.It also risks overestimating generalisable model performance due to increasingly well-evidenced systematic gaps and biases in hate speech datasets.To enable more targeted diagnostic insights, we introduce HATECHECK, a suite of functional tests for hate speech detection models.We specify 29 model functionalities motivated by a review of previous research and a series of interviews with civil society stakeholders.We craft test cases for each functionality and validate their quality through a structured annotation process.To illustrate HATECHECK's utility, we test near-state-of-the-art transformer models as well as two popular commercial models, revealing critical model weaknesses.
Blockchain Consensus: An Overview of Alternative Protocols

Blockchain networks are based on cryptographic notions that include asymmetric-key encryption, hash functions and consensus protocols. Despite their popularity, mainstream protocols, such as Proof of Work or Proof of Stake still have drawbacks. Efforts to enhance these protocols led to the birth of alternative consensus protocols, catering to specific areas, such as medicine or transportation. These protocols remain relatively unknown despite having unique merits worth investigating. Although past reviews have been published on popular blockchain consensus protocols, they do not include most of these lesser-known protocols. Highlighting these alternative consensus protocols contributes toward the advancement of the state of the art, as they have design features that may be useful to academics, blockchain practitioners and researchers. In this paper, we bridge this gap by providing an overview of alternative consensus protocols proposed within the past 3 years. We evaluate their overall performance based on metrics such as throughput, scalability, security, energy consumption, and finality. In our review, we examine the trade-offs that these consensus protocols have made in their attempts to optimize scalability and performance. To the best of our knowledge, this is the first paper that focuses on these alternative protocols, highlighting their unique features that can be used to develop future consensus protocols.
Quantum State Tomography with Conditional Generative Adversarial Networks

Quantum state tomography (QST) is a challenging task in intermediate-scale quantum devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the CGAN framework, two dueling neural networks, a generator and a discriminator, learn multimodal models from data. We augment a CGAN with custom neural-network layers that enable conversion of output from any standard neural network into a physical density matrix. To reconstruct the density matrix, the generator and discriminator networks train each other on data using standard gradient-based methods. We demonstrate that our QST-CGAN reconstructs optical quantum states with high fidelity, using orders of magnitude fewer iterative steps, and less data, than both accelerated projected-gradient-based and iterative maximum-likelihood estimation. We also show that the QST-CGAN can reconstruct a quantum state in a single evaluation of the generator network if it has been pretrained on similar quantum states.
Algorithmic and Human Teaching of Sequential Decision Tasks

A helpful teacher can significantly improve the learning rate of a learning agent. Teaching algorithms have been formally studied within the field of Algorithmic Teaching. These give important insights into how a teacher can select the most informative examples while teachinga new concept. However the field has so far focused purely on classification tasks. In this paper we introducea novel method for optimally teaching sequential decision tasks. We present an algorithm that automatically selects the set of most informative demonstrations andevaluate it on several navigation tasks. Next, we explore the idea of using this algorithm to produce instructions for humans on how to choose examples when teaching sequential decision tasks. We present a user study that demonstrates the utility of such instructions.
Heterogeneous Multilayer Generalized Operational Perceptron

The traditional multilayer perceptron (MLP) using a McCulloch-Pitts neuron model is inherently limited to a set of neuronal activities, i.e., linear weighted sum followed by nonlinear thresholding step. Previously, generalized operational perceptron (GOP) was proposed to extend the conventional perceptron model by defining a diverse set of neuronal activities to imitate a generalized model of biological neurons. Together with GOP, a progressive operational perceptron (POP) algorithm was proposed to optimize a predefined template of multiple homogeneous layers in a layerwise manner. In this paper, we propose an efficient algorithm to learn a compact, fully heterogeneous multilayer network that allows each individual neuron, regardless of the layer, to have distinct characteristics. Based on the complexity of the problem, the proposed algorithm operates in a progressive manner on a neuronal level, searching for a compact topology, not only in terms of depth but also width, i.e., the number of neurons in each layer. The proposed algorithm is shown to outperform other related learning methods in extensive experiments on several classification problems.
Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation

Most previous work on outfit recommendation focuses on designing visual features to enhance recommendations. Existing work neglects user comments of fashion items, which have been proved to be effective in generating explanations along with better recommendation results. We propose a novel neural network framework, neural outfit recommendation (NOR), that simultaneously provides outfit recommendations and generates abstractive comments. NOR consists of two parts: outfit matching and comment generation. For outfit matching, we propose a convolutional neural network with a mutual attention mechanism to extract visual features. The visual features are then decoded into a rating score for the matching prediction. For abstractive comment generation, we propose a gated recurrent neural network with a cross-modality attention mechanism to transform visual features into a concise sentence. The two parts are jointly trained based on a multi-task learning framework in an end-to-end back-propagation paradigm. Extensive experiments conducted on an existing dataset and a collected real-world dataset show NOR achieves significant improvements over state-of-the-art baselines for outfit recommendation. Meanwhile, our generated comments achieve impressive ROUGE and BLEU scores in comparison to human-written comments. The generated comments can be regarded as explanations for the recommendation results. We release the dataset and code to facilitate future research.
Robustness of Measurement, Discrimination Games, and Accessible Information

We introduce a resource theory of measurement informativeness. This allows us to define an associated quantifier, which we call the robustness of measurement. It describes how much "noise" must be added to a measurement before it becomes completely uninformative. We show that this geometric quantifier has operational significance in terms of the advantage the measurement provides over guessing at random in a suitably chosen state discrimination game and that it is the single-shot generalization of the accessible information of a certain quantum-to-classical channel. Using this insight, we further show that the recently introduced robustness of asymmetry or coherence is the single-shot generalization of the accessible information of an ensemble. Finally, we discuss more generally the connection between robustness-based measures, discrimination problems, and information-theoretic quantities.Received 29 October 2018DOI:https://doi.org/10.1103/PhysRevLett.122.140403© 2019 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum channelsQuantum measurementsResource theoriesQuantum InformationGeneral Physics
Digital quantum simulation of molecular vibrations

Molecular vibrations underpin important phenomena such as spectral properties, energy transfer, and molecular bonding. However, obtaining a detailed understanding of the vibrational structure of even small molecules is computationally expensive. While several algorithms exist for efficiently solving the electronic structure problem on a quantum computer, there has been comparatively little attention devoted to solving the vibrational structure problem with quantum hardware. In this work, we discuss the use of quantum algorithms for investigating both the static and dynamic vibrational properties of molecules. We introduce a physically motivated unitary vibrational coupled cluster ansatz, which also makes our method accessible to noisy, near-term quantum hardware. We numerically test our proposals for the water and sulfur dioxide molecules.
Improving the McClear model estimating the downwelling solar radiation at ground level in cloud-free conditions – McClear‑v3

The fast McClear clear-sky model estimates the downwelling shortwave direct and diffuse irradiances received at ground level under cloud-free conditions.Several improvements are presented.They focus on the modeling of changes in irradiances with the solar zenithal angle and on a better exploitation of the aerosol properties offered by the Copernicus Atmosphere Monitoring Service (CAMS).Irradiances from this new version McClear-v3 were compared to 1 min measurements made in cloud-free conditions at 11 stations belonging to the Baseline Surface Radiation Network and being located in various climates.The correlation coefficient ranges between 0.982 and 0.999 for the global irradiance.The bias is positive (overestimation) and ranges between 1 W m -2 (0.1 % of the mean observed irradiance) and 20 W m -2 (3.2 %), with the exception of Barrow in Alaska (18 W m -2 ).The standard deviation ranges between 16 W m -2 (2.3 %) and 30 W m -2 (3.8 %).The correlation coefficient for the direct irradiance ranges between 0.902 and 0.995.As expected, since the direct in McClear does not comprise any circumsolar contribution, the bias is negative (underestimation) and ranges between 49 W m -2 (7.7 %) and 5 W m -2 (0.7 %), with two exceptions: Sede Boqer (79 W m -2 ) and Brasilia (13 W m -2 ).The standard deviation is comprised between 34 W m -2 (5.3 %) and 69 W m -2 (10.7 %).These results are similar to those obtained with McClear version 2. Compared to the latter, McClear-v3 removes several artifacts and its estimates are continuous in space and time.
Domain Adaptation with BERT-based Domain Classification and Data Selection

The performance of deep neural models can deteriorate substantially when there is a domain shift between training and test data.For example, the pre-trained BERT model can be easily fine-tuned with just one additional output layer to create a state-of-the-art model for a wide range of tasks.However, the fine-tuned BERT model suffers considerably at zero-shot when applied to a different domain.In this paper, we present a novel two-step domain adaptation framework based on curriculum learning and domain-discriminative data selection.The domain adaptation is conducted in a mostly unsupervised manner using a small target domain validation set for hyper-parameter tuning.We tested the framework on four large public datasets with different domain similarities and task types.Our framework outperforms a popular discrepancy-based domain adaptation method on most transfer tasks while consuming only a fraction of the training budget.