Reviewerly/exHarmony huggingface dataset:
Modified Social Group Optimization—a meta-heuristic algorithm to solve short-term hydrothermal scheduling

Social Group Optimization (SGO), developed by Satapathy et al. in the year 2016, is a class of meta-heuristic optimization inspired by social behavior. It has two phases: improving phase and acquiring phase. In the improving phase, each individual improves its knowledge by interacting with the best person/solution and in acquiring phase, the individuals interact with randomly selected individuals and the best person simultaneously to acquire knowledge. Modified Social Group Optimization (MSGO) is the improved version of SGO, where the acquiring phase is modified. A self-awareness probability factor is added in the acquiring phase, which enhances the learning capability of an individual from the best-learned person in the societal setup. It is observed that this modification has improved both exploration and exploitation abilities in comparison with the conventional SGO. To analyze the performance of the MSGO, an exhaustive performance comparison is made with GA, PSO, DE, ABC, and a few newer algorithms of the years 2010–2019. The results are tabulated in six experiments. Later, MSGO is applied to solve the short-term hydrothermal scheduling (HTS) problem. The central objective of the HTS problem is to ascertain the optimal plan of action for hydro and thermal generation minimizing the fuel cost of thermal plants and, at the same time satisfying various operational and physical constraints. The valve point loading effect related to the thermal power plants, transmission loss, and other constraints lead HTS as a complex non-linear, non-convex, and non-smooth optimization problem. Simulation results clearly show that the MSGO method is capable of obtaining a better solution.
Concentration of tempered posteriors and of their variational approximations

While Bayesian methods are extremely popular in statistics and machine learning, their application to massive data sets is often challenging, when possible at all.The classical MCMC algorithms are prohibitively slow when both the model dimension and the sample size are large.Variational Bayesian methods aim at approximating the posterior by a distribution in a tractable family F .Thus, MCMC are replaced by an optimization algorithm which is orders of magnitude faster.VB methods have been applied in such computationally demanding applications as collaborative filtering, image and video processing or NLP to name a few.However, despite nice results in practice, the theoretical properties of these approximations are not known.We propose a general oracle inequality that relates the quality of the VB approximation to the prior π and to the structure of F .We provide a simple condition that allows to derive rates of convergence from this oracle inequality.We apply our theory to various examples.First, we show that for parametric models with log-Lipschitz likelihood, Gaussian VB leads to efficient algorithms and consistent estimators.We then study a high-dimensional example: matrix completion, and a nonparametric example: density estimation.
WareVision: CNN Barcode Detection-Based UAV Trajectory Optimization for Autonomous Warehouse Stocktaking

This letter presents a heterogeneous Unmanned Aerial Vehicle (UAV)-based robotic system for real-time barcode detection and scanning using Convolutional Neural Networks (CNN). The proposed approach improves the UAV's localization using scanned barcodes as landmarks in a real warehouse with low-light conditions. Instead of using the standard overlapping snake-based grid (OSBG) trajectory, we implement a novel approach for flight-path optimization based on barcode locations. This approach reduces the time of warehouse stocktaking and decreases the number of mistakes in barcode scanning.
TraceTogether: Pandemic Response, Democracy, and Technology

On 20th March 2020, in the midst of the COVID-19 pandemic, the Singapore government released a new app called "TraceTogether."Developed by the Ministry of Health, SG United, and GovTech Singapore, the app uses the Bluetooth capability of smartphones to store information about which other smartphones have come into close proximity with your own.These data facilitate the government's process of "contact tracing" through which they track those who have potentially come into contact with the virus and place them in quarantine.This essay attempts to understand what kinds of citizens and civic behaviour may be brought into being by this technology.By examining the workings and affordances of the TraceTogether app in detail, we argue that its peer-to-peer and open source technology features mobilize the rhetorics and ideals of citizens science and democratic participation.However, by deploying these within a context that centralizes data, the app turns ideals borne of dissent and protest on their head, using them to build trust not within a community but rather in government power and control.Rather than building social trust, TraceTogether becomes a technological substitute for it.The significant public support for TraceTogether shows both the possibilities and limitations of citizen science in less liberal political contexts and circumstances.
Half-Quadratic Minimization for Unsupervised Feature Selection on Incomplete Data

Unsupervised feature selection (UFS) is a popular technique of reducing the dimensions of high-dimensional data. Previous UFS methods were often designed with the assumption that the whole information in the data set is observed. However, incomplete data sets that contain unobserved information can be often found in real applications, especially in industry. Thus, these existing UFS methods have a limitation on conducting feature selection on incomplete data. On the other hand, most existing UFS methods did not consider the sample importance for feature selection, i.e., different samples have various importance. As a result, the constructed UFS models easily suffer from the influence of outliers. This article investigates a new UFS method for conducting UFS on incomplete data sets to investigate the abovementioned issues. Specifically, the proposed method deals with unobserved information by using an indicator matrix to filter it out the process of feature selection and reduces the influence of outliers by employing the half-quadratic minimization technique to automatically assigning outliers with small or even zero weights and important samples with large weights. This article further designs an alternative optimization strategy to optimize the proposed objective function as well as theoretically and experimentally prove the convergence of the proposed optimization strategy. Experimental results on both real and synthetic incomplete data sets verified the effectiveness of the proposed method compared with previous methods, in terms of clustering performance on the low-dimensional space of the high-dimensional data.
A survey on automatic infographics and visualization recommendations

Automatic infographics generators employ machine learning algorithms/user-defined rules and visual embellishments into the creation of infographics. It is an emerging topic in the field of information visualization that has requirements in many sectors, such as dashboard design, data analysis, and visualization recommendation. The growing popularity of visual analytics in recent years brings increased attention to automatic infographics. This creates the need for a broad survey that reviews and assesses the significant advances in this field. Automatic tools aim to lower the barrier for visually analyzing data by automatically generating visualizations for analysts to search and make a choice, instead of manually specifying. This survey reviews and classifies automatic tools and papers of visualization recommendations into a set of application categories including network-graph visualizations, annotation visualizations, and storytelling visualization. More importantly, this report presents several challenges and promising directions for future work in the field of automatic infographics and visualization recommendations.
NOMA-Enabled Cooperative Computation Offloading for Blockchain-Empowered Internet of Things: A Learning Approach

Blockchain technologies allow the Internet of Things (IoT) to build trust among various interest parties. For the resource-limited IoT devices, offloading computation-intensive tasks (blockchain verification and mining tasks, and data process tasks) to edge servers for execution is considered as a promising solution in mobile-edge computing. However, conventional methods (such as linear programming or game theory) for the computation offloading problem cannot achieve long-term performance while the existing deep reinforcement learning (DRL)-based algorithms suffer from slow convergence, lack of robustness, and unstable performance. In this article, we propose a multiagent DRL framework to achieve long-term performance for cooperative computation offloading, in which a scatter network is adopted to improve its stability and league learning is introduced for agents to explore the environment collaboratively for fast convergence and robustness. First, we study the nonorthogonal multiple access-enabled cooperative computation offloading problem and formulate the joint problem as a Markov decision process by considering both the blockchain mining tasks and data processing tasks. Second, to avoid useless exploration and unstable performance, we initially train an intelligent agent represented by scatter networks using conventional expert strategies. Third, in order to enhance the performance, we subsequently establish a hierarchical league where agents collaborate with others to explore the environment. Finally, our experimental results demonstrate that our algorithm could perform better in terms of reducing energy cost and delay cost, and shortening almost 60% of the training time compared with the state-of-the-art approaches.
Worldwide performance assessment of 95 direct and diffuse clear-sky irradiance models using principal component analysis

Accurate estimations of clear-sky direct normal irradiance (DNIcs) and diffuse horizontal irradiance (DIFcs) are crucial in solar resources assessment. This study examines 95 and 88 popular clear-sky irradiance models for their worldwide estimation capability of DNIcs and DIFcs, respectively. Atmospheric inputs are from MERRA-2 reanalysis and irradiance observations for validation are extracted from 100 ground stations across five major Köppen-Geiger climate zones. They consist of 24 million 1-min measurements of DINcs and 18.7 million 1-min measurements of DIFcs after quality control and clear-sky detection, during the 5-year period 2015–2019. Using principal component analysis, the performance of each clear-sky irradiance model is ranked separately among the five climate zones, as well as given a global rank. For the Equatorial, Arid, Temperate, Cold and Polar climates, it is found that Heliosat1-I, ESRA-I, REST2v9.1, REST2v9.1 and CLS, respectively, are the best DNIcs models, while Modified Iqbal-C, Heliosat1-R, Calinoiu, Modified Iqbal-C and PSI-REST are the best DIFcs models. On a global worldwide basis, the three top-ranking models are REST2v9.1, REST2v5 and MMAC-V2 for DNIcs, and Modified Iqbal-C, PSI-REST and MRMv5 for DIFcs. The results and rankings presented are strictly relative to MERRA-2 input data, and should not be extrapolated to results from alternate sources of atmospheric data. This detailed validation exercise revealed inconsistent performance of many models across different climates, possibly due to insufficient training and/or over-fitting of empirical relationships. Codes of all clear-sky irradiance models in the public domain are available online on the Github repository JamieMBright/clear-sky-models.
Fast reinforcement learning with generalized policy updates

The combination of reinforcement learning with deep learning is a promising approach to tackle important sequential decision-making problems that are currently intractable. One obstacle to overcome is the amount of data needed by learning systems of this type. In this article, we propose to address this issue through a divide-and-conquer approach. We argue that complex decision problems can be naturally decomposed into multiple tasks that unfold in sequence or in parallel. By associating each task with a reward function, this problem decomposition can be seamlessly accommodated within the standard reinforcement-learning formalism. The specific way we do so is through a generalization of two fundamental operations in reinforcement learning: policy improvement and policy evaluation. The generalized version of these operations allow one to leverage the solution of some tasks to speed up the solution of others. If the reward function of a task can be well approximated as a linear combination of the reward functions of tasks previously solved, we can reduce a reinforcement-learning problem to a simpler linear regression. When this is not the case, the agent can still exploit the task solutions by using them to interact with and learn about the environment. Both strategies considerably reduce the amount of data needed to solve a reinforcement-learning problem.
RayS: A Ray Searching Method for Hard-label Adversarial Attack

Deep neural networks are vulnerable to adversarial attacks. Among different attack settings, the most challenging yet the most practical one is the hard-label setting where the attacker only has access to the hard-label output (prediction label) of the target model. Previous attempts are neither effective enough in terms of attack success rate nor efficient enough in terms of query complexity under the widely used $L_\infty$ norm threat model. In this paper, we present the Ray Searching attack (RayS), which greatly improves the hard-label attack effectiveness as well as efficiency. Unlike previous works, we reformulate the continuous problem of finding the closest decision boundary into a discrete problem that does not require any zeroth-order gradient estimation. In the meantime, all unnecessary searches are eliminated via a fast check step. This significantly reduces the number of queries needed for our hard-label attack. Moreover, interestingly, we found that the proposed RayS attack can also be used as a sanity check for possible "falsely robust" models. On several recently proposed defenses that claim to achieve the state-of-the-art robust accuracy, our attack method demonstrates that the current white-box/black-box attacks could still give a false sense of security and the robust accuracy drop between the most popular PGD attack and RayS attack could be as large as 28%. We believe that our proposed RayS attack could help identify falsely robust models that beat most white-box/black-box attacks.
Online Hate Speech

This chapter examines the state of the literature–including scientific research, legal scholarship, and policy reports–on online hate speech. In particular it explores ongoing debates and limitations in current approaches to defining and detecting online hate speech; provides an overview of what social media data and surveys can tell us about the producers, targets, and overall prevalence of harmful language; reviews empirical evidence of the offline consequences of online hate speech; and offers quantitative insights into what interventions might be most effective in combating harmful rhetoric online.
A Probabilistic Approach for Cooperative Computation Offloading in MEC-Assisted Vehicular Networks

Mobile edge computing (MEC) has been an effective paradigm for supporting computation-intensive applications by offloading resources at network edge. Especially in vehicular networks, the MEC server, is deployed as a small-scale computation server at the roadside and offloads computation-intensive task to its local server. However, due to the unique characteristics of vehicular networks, including high mobility of vehicles, dynamic distribution of vehicle densities and heterogeneous capacities of MEC servers, it is still challenging to implement efficient computation offloading mechanism in MEC-assisted vehicular networks. In this article, we investigate a novel scenario of computation offloading in MEC-assisted architecture, where task upload coordination between multiple vehicles, task migration between MEC/cloud servers and heterogeneous computation capabilities of MEC/cloud severs, are comprehensively investigated. On this basis, we formulate cooperative computation offloading (CCO) problem by modeling the procedure of task upload, migration and computation based on queuing theory, which aims at minimizing the delay of task completion. To tackle the CCO problem, we propose a probabilistic computation offloading (PCO) algorithm, which enables MEC server to independently make online scheduling based on the derived allocation probability. Specifically, the PCO transforms the objective function into augmented Lagrangian and achieves the optimal solution in an iterative way, based on a convex framework called Alternating Direction Method of Multipliers (ADMM). Last but not the least, we implement the simulation model. The comprehensive simulation results show the superiority of the proposed algorithm under a wide range of scenarios.
How to Translate a Verbal Theory Into a Formal Model

Abstract. Turning verbal theories into formal models is an essential business of a mature science. Here I elaborate on taxonomies of models, provide ten lessons for translating a verbal theory into a formal model, and discuss the specific challenges involved in collaborations between modelers and non-modelers. It’s a start.
Qibo: a framework for quantum simulation with hardware acceleration

We present Qibo, a new open-source software for fast evaluation of quantum circuits and adiabatic evolution which takes full advantage of hardware accelerators. The growing interest in quantum computing and the recent developments of quantum hardware devices motivates the development of new advanced computational tools focused on performance and usage simplicity. In this work we introduce a new quantum simulation framework that enables developers to delegate all complicated aspects of hardware or platform implementation to the library so they can focus on the problem and quantum algorithms at hand. This software is designed from scratch with simulation performance, code simplicity and user friendly interface as target goals. It takes advantage of hardware acceleration such as multi-threading CPU, single GPU and multi-GPU devices.
The Effect of Broad and Specific Demographic Homogeneity on the Imposter Distributions and False Match Rates in Face Recognition Algorithm Performance

The growing adoption of biometric identity systems, notably face recognition, has raised questions regarding whether performance is equitable across demographic groups. Prior work on this issue showed that performance of face recognition systems varies with demographic variables. However, biometric systems make two distinct types of matching errors, which lead to different outcomes for users depending on the technology use case. In this research, we develop a framework for classifying biometric performance differentials that separately considers the effect of false positive and false negative outcomes, and show that oft-cited evidence regarding biometric equitability has focused on primarily on false-negatives. We then correlate demographic variables with false-positive outcomes in a diverse population using a commercial face recognition algorithm, and show that false match rate (FMR) at a fixed threshold increases >400-fold for broadly homogeneous groups (individuals of the same age, same gender, and same race) relative to heterogeneous groups. This was driven by systematic shifts in the tails of the imposter distribution impelled primarily by homogeneity in race and gender. For specific demographic groups, we observed the highest false match rate for older males that self-identified as White and the lowest for older males that self-identified as Black or African American. The magnitude of FMR differentials between specific homogeneous groups (<;3-fold) was modest in comparison with the FMR increase associated with broad demographic homogeneity. These results demonstrate the false positive outcomes of face recognition systems are not simply linked to single demographic factors, and that a careful consideration of interactions between multiple factors is needed when considering the equitability of these systems.
Automatic Registration of Optical and SAR Images Via Improved Phase Congruency Model

In this article, we propose an automatic and efficient method to solve optical and synthetic aperture radar (SAR) image registration using the improved phase congruency (PC) model. First, evenly distributed keypoints are extracted from the optical images via the block-Harris method. Complementary grid points are then selected in image regions with poor structural information and supplemented to the keypoint set. For each keypoint, a robust feature representation that captures the local spatial relationship is proposed based on the improved PC model. Specifically, we propose to use two different PC models, the classic PC and the SAR-PC, to construct features for optical and SAR images, respectively. The PC features of several directions are aggregated to construct the feature descriptors, and a similarity metric via the phase correlation of feature descriptors is obtained. The proposed similarity metric cannot only find accurate correspondence but also present efficient results without presetting the size of the search region. We compare the proposed method with two baselines and state-of-the-art (SOTA) methods, i.e., OS-SIFT, histogram of oriented PC, and channel features of oriented gradients, in various scenarios. The results show that the proposed method outperforms the baselines and shows comparable performance with SOTA methods in regions with abundant structural information and better performance in regions with less structural information. Moreover, we build a high-resolution optical and SAR image matching dataset, which consists of 10 692 nonoverlapping patch pairs of 256 × 256 pixels and 1-m resolution. Results of two benchmarks, Siamese deep matching network, and conditional generative adversarial networks show that this dataset is practical and challenging.
Paper recommendation based on heterogeneous network embedding

Researchers face millions of research papers on various digital libraries. Therefore, finding relevant research work that meets the preferences of a researcher is a challenging task. Hence, different paper recommendation models have been proposed to address this issue. However, these models lack in exploiting prominent information factors, namely: papers' citations proximity, authors' information, papers' topical relevance, venues' information, researchers' preference dynamics, and labels information to produce quality recommendations. Additionally, these models encounter problems such as cold start papers and data sparsity. To overcome these problems, this paper presents a weighted probabilistic paper recommendation model termed as PR-HNE, which jointly learns researchers' and papers' dynamics by encoding information from six information networks into a joint latent space. Specifically, it captures papers' citation proximity, authors' collaboration proximity, venues' information, labeled information, and topical relevance to generate personalized paper recommendations. Compared to state-of-the-art models, the results generated by PR-HNE over publicly available datasets prove 4% and 6% improvement in Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) metrics, respectively. Further, in the cold-start papers problem, the proposed model produced 8% better recall score than its counterparts.