Reviewerly/exHarmony huggingface dataset:
Bang-bang control as a design principle for classical and quantum optimization algorithms

Physically motivated classical heuristic optimization algorithms such as simulated annealing (SA) treat the objective function as an energy landscape, and allow walkers to escape local minima. It has been argued that quantum properties such as tunneling may give quantum algorithms advantage in finding ground states of vast, rugged cost landscapes. Indeed, the Quantum Adiabatic Algorithm (QAO) and the recent Quantum Approximate Optimization Algorithm (QAOA) have shown promising results on various problem instances that are considered classically hard. Here, building on previous observations from \cite{mcclean2016, Yang2017}, we argue that the type of control strategy used by the optimization algorithm may be crucial to its success. Along with SA, QAO, and QAOA, we define a new, bang-bang version of simulated annealing, BBSA, and study the performance of these algorithms on two well-studied problem instances from the literature. Both classically and quantumly, the successful control strategy is found to be bang-bang, exponentially outperforming the quasistatic analogues on the same instances. Lastly, we construct O(1)-depth QAOA protocols for a class of symmetric cost functions, and provide an accompanying physical picture.
Does Unsupervised Architecture Representation Learning Help Neural  Architecture Search?

Existing Neural Architecture Search (NAS) methods either encode neural architectures using discrete encodings that do not scale well, or adopt supervised learning-based methods to jointly learn architecture representations and optimize architecture search on such representations which incurs search bias. Despite the widespread use, architecture representations learned in NAS are still poorly understood. We observe that the structural properties of neural architectures are hard to preserve in the latent space if architecture representation learning and search are coupled, resulting in less effective search performance. In this work, we find empirically that pre-training architecture representations using only neural architectures without their accuracies as labels considerably improve the downstream architecture search efficiency. To explain these observations, we visualize how unsupervised architecture representation learning better encourages neural architectures with similar connections and operators to cluster together. This helps to map neural architectures with similar performance to the same regions in the latent space and makes the transition of architectures in the latent space relatively smooth, which considerably benefits diverse downstream search strategies.
Blockchain technology and its application in libraries

Blockchain technology is mainly used for trading securely in a digital environment by verifying and storing a transaction record in a distributed and time stamp manner. However, there are several other areas also where blockchain technology can be implemented. This technology is distributed, making it difficult for transactions to be counterfeit, duplicated, or faked. This article provides insight into how blockchain technology works and examines its innovative current and future uses in various branches of libraries.
Quantum Technology &amp; Quantum Neural Networks in Smart Grids Control: Premier Perspectives

This paper presents a practical field of quantum technology and its contribution to smart grids, and their future in smart sustainable cities (SSCs). Smart grids are among the important basis of an SSC, so its factors such as forecasting, optimization, turbine design have a significant role. Based on quantum technology's amazing features, it has conspicuous applications in smart grids including weather prediction, or demand response forecasting. This paper represents SSCs, smart grids, and quantum technology and its use in the ladders. Then, defines the areas of smart grids in which quantum technology can make a noticeable development. Respectively, it compares quantum technology with other regular supercomputing technologies. Ultimately, quantum technology is a kind of technology that has a noticeable contribution potentiality to other technologies to make the world to be processed in a faster, and much more accurate way, and smart grids, SSCs are, also, in this future.
Detecting Outliers in High Dimensional Data Sets Using Z-Score Methodology

Outlier detection is an interesting research area in machine learning. With the recently emergent tools and varied applications, the attention of outlier recognition is growing significantly. Recently, a significant number of outlier detection approaches have been observed and effectively applied in a wide range of fields, comprising medical health, credit card fraud and intrusion detection. They can be utilized for conservative data analysis. However, Outlier recognition aims to discover sequence in data that do not conform to estimated performance. In this paper, we presented a statistical approach called Z-score method for outlier recognition in high-dimensional data. Z-scores is a novel method for deciding distant data based on data positions on charts. The projected method is computationally fast and robust to outliers’ recognition. A comparative Analysis with extant methods is implemented with high dimensional datasets. Exploratory outcomes determines an enhanced accomplishment, efficiency and effectiveness of our projected methods.
Peer to Peer Federated Learning: Towards Decentralized Machine Learning on Edge Devices

Federated Learning (FL) is an emerging technique that assures user privacy and data integrity in distributed machine learning environments. To perform so, chunks of data are trained across edge devices and a high performance cluster server maintains a local copy without exchanging it with other parties. In this work, we investigate a FL scenario in a real-world case study using 5, 10 and 20 Raspberry Pi devices acting as clients. Under this setup, we employ the widely known FedAvg algorithm which trains each client for several local epochs and then the weight of each model is aggregated. Moreover we perform experiments across imbalanced and noisy data so as to explore scalability and robustness based on real-world datasets were noise is present and we also propose two innovative algorithms where the FL scenario is considered as a peer-to-peer formulation. Ultimately, to ensure that each device is not oversampled a client-balancing Dirichlet sampling algorithm with probabilistic guarantees is proposed.
An automated conflict of interest based greedy approach for conference paper assignment system

Reviewer Assignment Problem (RAP) is a crucial problem for the conference due to time constraints and inadequate availability of expert reviewers. A fair evaluation of paper is key to an author's success, paper quality, conference reputation, and productive usage of funds. Recent studies reflect on the issue of reviewer bias in bids favoring authors belonging to the top institution and higher authority. Existing Conference Management Systems (CMS) are solely dependent upon self-declared Conflict of Interest (CoI) made by the authors, and reviewers. In literature, existing studies considers topic similarity, potential CoI, and reviewer's workload as trivial factors for ensuring review quality. Other factors include the diversity and authority of a reviewer. Past studies propose several theoretical optimization models. In this paper, we first individually model the factors using the best possible strategy in a constrained-based optimization framework. We tried to propose a completely novel framework that can be practically implemented to improve upon the performance of existing CMS. We map the RAP to an equilibrium multi-job assignment problem. Moreover, we propose a meta-heuristic greedy solution to solve it using weighted matrix factorization. We re-define an assignment quality metric required to validate such assignments. A real conference assignment data set collected from EasyChair is used for a comparative study. The TPMS is used as a baseline because it also uses similar factors, and due to its integration with widely used Microsoft CMS. The results show that the mean assignment quality of the proposed method is superior to other benchmark RAP systems.
Augmenting the Sensing Performance of Entangled Photon Pairs through Asymmetry

We analyze theoretically and experimentally cases of asymmetric detection, stimulation, and loss within a quantum nonlinear interferometer of entangled pairs. We show that the visibility of the SU(1,1) interference directly discerns between loss on the measured mode (signal) and the conjugated mode (idler). This asymmetry also affects the phase sensitivity of the interferometer, where coherent seeding is shown to mitigate losses that are suffered by the conjugated mode; therefore increasing the maximum threshold of loss that permits sub-shot-noise phase detection. Our findings can improve the performance of setups that rely on direct detection of entangled pairs, such as quantum interferometry and imaging with undetected photons.
Improved Two Hidden Layers Extreme Learning Machines for Node Localization in Range Free Wireless Sensor Networks

Wireless Sensor Network (WSN) architectures are widely used in a variety of practical applications. In most cases of application, the event information transmitted by a sensor node via the network has no significance without the knowledge of its accurate geographical localization. In this paper, a method based on Machine Learning Technique (MLT) is proposed to improve node accuracy localization in WSN. We propose a Single Hidden Layer Extreme Learning Machine (SHL-ELM) and a Two Hidden Layer Extreme Learning Machine (THL-ELM) based methods for nodes localization in WSN. The suggested methods are applied in different Multi-hop WSN deployment cases. We focused on range-free localization algorithm in isotropic case and irregular environments. Simulation results demonstrate that the proposed THL-ELM algorithm greatly minimizes the average localization errors when compared to the Single Hidden Layer Extreme Learning Machine and the Distance Vector Hop (DV- Hop) algorithm.
Opinion Mining Classification u sing Naive Bayes Algorithm

With the recent advancement in the field of online services, the importance of a review for a product has also gone up. In this paper we focus on the aspect of reducing the time and effort for the user by recommending the best product to him. For this to be achieved, this paper proposes a Naive Bayes Classifier which labels the reviews accurately and combines the reviews to give a final rating to the product. The amazon product review data consisting of both negative and positive reviews was used for training and testing purposes. The model’s performance is evaluated, and results are analysed.
Constrained Predictive Filters for Single Image Bokeh Rendering

Bokeh rendering is a technique used to take pictures with out-of-focus areas to highlight regions of interest. Due to limitations in hardware and shooting condition, rendering a bokeh image from a full-focus image has attracted a lot of interest. In this paper, we model bokeh rendering as the combination of salient region retention and bokeh blurring, and propose a neural network to generate a realistic bokeh image from a single full-focus image through end-to-end training. Specifically, we propose a gate fusion block to estimate the salient area, and introduce a constrained predictive filter for salient region retention and bokeh blurring within a unified architecture. Further, we utilize a pixel coordinate-based map to enhance the training. Experimental results illustrate the effectiveness of our model. The comparison with state-of-the-art methods (PyNET [1], DMSHN [2], BGGAN [3], etc.) shows that our model produces better bokeh effects and retains salient objects.
Advantages of Asynchronous Measurement-Device-Independent Quantum Key Distribution in Intercity Networks

The new variant of measurement-device-independent quantum key distribution (MDI-QKD), called asynchronous MDI-QKD or mode-pairing MDI-QKD, offers similar repeater-like rate-loss scaling but has the advantage of simple technology implementation by exploiting an innovative post-measurement pairing technique. We herein present an evaluation of the practical aspects of decoy-state asynchronous MDI-QKD. To determine its effectiveness, we analyze the optimal method of decoy-state calculation and examine the impact of asymmetrical channels and multi-user networks. Our simulations show that, under realistic conditions, aynchronous MDI-QKD can furnish the highest key rate with MDI security as compared to other QKD protocols over distances ranging from 50 km to 480 km. At fiber distances of 50 km and 100 km, the key rates attain 6.02 Mbps and 2.29 Mbps respectively, which are sufficient to facilitate real-time one-time-pad video encryption. Our findings indicate that experimental implementation of asynchronous MDI-QKD in intercity networks can be both practical and efficient.
Few-Shot Learning with Embedded Class Models and Shot-Free Meta Training

We propose a method for learning embeddings for few-shot learning that is suitable for use with any number of ways and any number of shots (shot-free). Rather than fixing the class prototypes to be the Euclidean average of sample embeddings, we allow them to live in a higher-dimensional space (embedded class models) and learn the prototypes along with the model parameters. The class representation function is defined implicitly, which allows us to deal with a variable number of shots per each class with a simple constant-size architecture. The class embedding encompasses metric learning, that facilitates adding new classes without crowding the class representation space. Despite being general and not tuned to the benchmark, our approach achieves state-of-the-art performance on the standard few-shot benchmark datasets.
Exploring the Role of xR in Visualisations for Use in Medical Education

Emerging technologies have the potential to transform our approach to medical education. A goal in this chapter is to inspire researchers, educators and scholars in the bio-medical visualisation field who can benefit from integrating wearable Augmented Reality (AR) technologies, like the HoloLens into their existing teaching and learning environments. We draw from case studies, existing research and the educational technology literature, to propose the design of purposeful learner-centered experiences that might benefit from wearable AR technologies in the classroom.
Improving Naive Bayes for Regression with Optimized Artificial Surrogate Data

Can we evolve better training data for machine learning algorithms? To investigate this question we use population-based optimization algorithms to generate artificial surrogate training data for naive Bayes for regression. We demonstrate that the generalization performance of naive Bayes for regression models is enhanced by training them on the artificial data as opposed to the real data. These results are important for two reasons. Firstly, naive Bayes models are simple and interpretable but frequently underperform compared to more complex “black box” models, and therefore new methods of enhancing accuracy are called for. Secondly, the idea of using the real training data indirectly in the construction of the artificial training data, as opposed to directly for model training, is a novel twist on the usual machine learning paradigm.
Learning Fair Policies in Multi-Objective (Deep) Reinforcement Learning with Average and Discounted Rewards

As the operations of autonomous systems generally affect simultaneously several users, it is crucial that their designs account for fairness considerations. In contrast to standard (deep) reinforcement learning (RL), we investigate the problem of learning a policy that treats its users equitably. In this paper, we formulate this novel RL problem, in which an objective function, which encodes a notion of fairness that we formally define, is optimized. For this problem, we provide a theoretical discussion where we examine the case of discounted rewards and that of average rewards. During this analysis, we notably derive a new result in the standard RL setting, which is of independent interest: it states a novel bound on the approximation error with respect to the optimal average reward of that of a policy optimal for the discounted reward. Since learning with discounted rewards is generally easier, this discussion further justifies finding a fair policy for the average reward by learning a fair policy for the discounted reward. Thus, we describe how several classic deep RL algorithms can be adapted to our fair optimization problem, and we validate our approach with extensive experiments in three different domains.
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse

In narrow asymptotic settings Gaussian VAE models of continuous data have been shown to possess global optima aligned with ground-truth distributions. Even so, it is well known that poor solutions whereby the latent posterior collapses to an uninformative prior are sometimes obtained in practice. However, contrary to conventional wisdom that largely assigns blame for this phenomena on the undue influence of KL-divergence regularization, we will argue that posterior collapse is, at least in part, a direct consequence of bad local minima inherent to the loss surface of deep autoencoder networks. In particular, we prove that even small nonlinear perturbations of affine VAE decoder models can produce such minima, and in deeper models, analogous minima can force the VAE to behave like an aggressive truncation operator, provably discarding information along all latent dimensions in certain circumstances. Regardless, the underlying message here is not meant to undercut valuable existing explanations of posterior collapse, but rather, to refine the discussion and elucidate alternative risk factors that may have been previously underappreciated.
Using machine learning to predict children’s reading comprehension from linguistic features extracted from speech and writing.

Advances in machine learning (ML) are poised to contribute to our understanding of the linguistic processes associated with successful reading comprehension, which is a critical aspect of children’s educational success. We used ML techniques to investigate and compare associations between children’s reading comprehension and 260 linguistic features extracted from their speech and writing. Language samples were gathered from 99 linguistically diverse children in grades 4–6 using Talk2Me, Jr., an online language and literacy assessment platform. Lexical and syntactic features were extracted via a consolidated natural language processing pipeline. We compared five machine learning models predicting reading comprehension from the linguistic features and then, using the best models, analyzed the 20 top predictive features for both the oral-elicited and text-elicited data. The findings suggest that variance in children’s reading comprehension can be predicted by grammatical and lexical features extracted from productive written and spoken language. The study highlights how ML methodologies can enable nuanced examination of the language processes associated with reading comprehension. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
Analysis of Security and Privacy Aspects of Blockchain Technologies from Smart Era' Perspective: The Challenges and a Way Forward to Future

In 2008–2009, a distributed technology “blockchain” was introduced in cryptocurrency (as Bitcoin). Blockchain is a distributed consensus scheme which allows any transactions or any other data, and stores it securely with verifying by peer users (users available in a network), i.e. without the need of any centralized authority (CA). The blockchain concept is purely distributed and decentralized in nature, and is receiving attraction from many areas and sectors (for protecting data or providing reliable communications). Initially, Bitcoin (a popular cryptocurrency) used well-known proof of work hash-based mechanism to make respective Bitcoin (a cryptocurrency) more foolproof. Blockchain provides different functional and security guarantees, so it can bring more change to a large number of systems and businesses (or applications). In general, essential building blocks of blockchain security has decentralization structure, security by block, and encryption via consensus mechanisms or proof of work—but due to lot of popularity of this technology among many applications, several serious issues like security and privacy are raised in this technology. Security and privacy are general problems in blockchain technology of today’s era (raised in every computing environment). Scalability, security, privacy, and sustainability are essential issues in blockchain technology. The future world requires distributed trust and everything in distributed form, including security and privacy issues and the need to provide a state of art work (for each available technology or application). Our work will be more useful to academia and the blockchain community to do some innovative work to solve complex problems and to improve industries and businesses productivity. Among all existing technologies (in communication), blockchain technology guarantees more reliable and expedient services. In blockchain technology, it is important to consider the security and privacy issues and challenges behind the innovative technology. In this chapter, we provide a complete introduction of blockchain and its uses in several applications, analyzing problems by deploying newer cryptographic techniques and mechanisms to enabling use cases like privacy, preserving file storage, and security of user information. Also, several research gaps, as well as the challenges and opportunities in blockchain technology, are discussed. In summary, we provide comprehensive flow regarding blockchain technology which has not been provided yet by anyone. In this chapter, challenges and opportunities from the prospective of security and privacy of data in blockchain will be discussed (with respect to the security and privacy community point of view). Furthermore, we will provide some future trends which blockchain technology may adapt in near future (in brief).
Drug Abuse Detection in Twitter-sphere: Graph-Based Approach

The rate of non-medical use of opioid drugs has increased markedly since the early 2000s. Due to this non-medical use, abusers suffer from different adverse effects that include physical and psychological problems. Many studies have been done to detect Drug Abuse (DA) events from social media data using machine learning and deep learning concepts. Moreover, Graph Neural Networks (GNNs) have recently become popular in text classification tasks due to their high accuracy and capability to handle complex structures. In this work, we collect drugs-related Twitter data (tweets) and build text graphs (corpus-level and document-level) to capture word-word, document-word, and document-document relations. Then we apply different GNN models on those text graphs and thus turn the text classification task into a node classification (for corpus-level graph) and graph classification (for document-level graph) task to detect DA events. Finally, we compare our graph-based DA detection models with different types of baselines models, including rule-based, traditional machine learning, and deep learning models. Our result shows graph-based models outperform the traditional machine learning and deep learning-based models.