Reviewerly/exHarmony huggingface dataset:
Evaluating Energy Differences on a Quantum Computer with Robust Phase Estimation

We adapt the robust phase estimation algorithm to the evaluation of energy differences between two eigenstates using a quantum computer. This approach does not require controlled unitaries between auxiliary and system registers or even a single auxiliary qubit. As a proof of concept, we calculate the energies of the ground state and low-lying electronic excitations of a hydrogen molecule in a minimal basis on a cloud quantum computer. The denominative robustness of our approach is then quantified in terms of a high tolerance to coherent errors in the state preparation and measurement. Conceptually, we note that all quantum phase estimation algorithms ultimately evaluate eigenvalue differences.
Machine Learning based anomaly detection for IoT Network: (Anomaly detection in IoT Network)

As the world is leading towards having everything smart, like smart home, smart grid smart irrigation, there is the major concern of attack and anomaly detection in the Internet of Things (IoT) domain. There is an exponential increase in the use of IoT infrastructure in every field leads to an increase in threats and attacks too. There can be many types of possible attacks and anomaly that can affect the IoT system which can lead to failure of the IoT system. In this paper, different anomalies are predicted based on a different feature in the data set. Two machine learning classification models are used and comparisons between the performance of these used models are shown. Logistic regression and artificial neural network classification algorithms are applied. Since there are more than 3.5 lakh data set, two different approaches are experimented. In the first case, the classification algorithm stated above is applied on the whole 3.5 lakh dataset, and in the second case, all the classification algorithms are applied after omitting the feature “value” having data as 0 and 1. Data is divided into two sets, training and test set where the training set is 75% of total data available and the rest are test set, 99.4% accuracy is obtained for ANN for the first case while 99.99% accuracy is obtained for the algorithm stated above for the second case. This work can be used for identifying threats and anomaly occurring in a smart device and IoT solutions and prevent attacks.
On the PV Tracker Performance: Tracking the Sun Versus Tracking the Best Orientation

This article models the performance of photovoltaic tracking algorithms worldwide, based on the overall insolation collection, by comparing two tracking algorithms, namely tracking the sun (TS) and tracking the best orientation (TBO). In general, the latter is expected to receive higher irradiance with the drawback of requiring a higher installation and maintenance cost (due to the extra sensors). The aim of this research is then to quantify how big the difference is on irradiance collection from these two trackers worldwide by analyzing the data from 61 ground weather stations. In addition, three different tracking mounting structures are considered in this work: first, horizontal single axis tracker (HSAT); second, tilted single axis tracker (TSAT); and third, dual axis tracker (2T). Furthermore, the irradiance collection from front and rear sides are estimated for installations of monofacial and bifacial modules. The simulations reveal that, although the TBO results are higher than the TS ones, there is not a big difference on their insolation collection for latitude locations below 60° (<;1.8%). Nevertheless, for higher latitude locations, TBO tracking systems can achieve a considerably higher performance reaching values of up to 3.3%, 7.1%, and 2.9% for HSAT, TSAT, and 2T systems, respectively. It was also observed that for bifacial and 2T systems in particular, high albedo values would produce a considerable enhancement on the TBO performance with respect to TS.
What are the Actual Flaws in Important Smart Contracts (And How Can We Find Them)?

An important problem in smart contract security is understanding the likelihood and criticality of discovered, or potential, weaknesses in contracts. In this paper we provide a summary of Ethereum smart contract audits performed for 23 professional stakeholders, avoiding the common problem of reporting issues mostly prevalent in low-quality contracts. These audits were performed at a leading company in blockchain security, using both open-source and proprietary tools, as well as human code analysis performed by professional security engineers. We categorize 246 individual defects, making it possible to compare the severity and frequency of different vulnerability types, compare smart contract and non-smart contract flaws, and to estimate the efficacy of automated vulnerability detection approaches.
Data Compression Accelerator on IBM POWER9 and z15 Processors : Industrial Product

Lossless data compression is highly desirable in enterprise and cloud environments for storage and memory cost savings and improved utilization I/O and network. While the value provided by compression is recognized, its application in practice is often limited because it's a processor intensive operation resulting low throughput and high elapsed time for compression intense workloads.The IBM POWER9 and IBM z15 systems overcome the shortcomings of existing approaches by including a novel on-chip integrated data compression accelerator. The accelerator reduces processor cycles, I/O traffic, memory and storage footprint of many applications practically with zero hardware cost. The accelerator also eliminates the cost and I/O slots that would have been necessary with FPGA/ASIC based compression adapters. On the POWER9 chip, a single accelerator uses less than 0.5% of the processor chip area, but provides a 388x speedup factor over the zlib compression software running on a general-purpose core and provides a 13x speedup factor over the entire chip of cores. On a POWER9 system, the accelerators provide an end-to-end 23% speedup to Apache Spark TPC-DS workload compared to the software baseline. The z15 chip doubles the compression rate of POWER9 resulting in even much higher speedup factors over the compression software running on general-purpose cores. On a maximally configured z15 system topology, on-chip compression accelerators provide up to 280 GB/s data compression rate, the highest in the industry. Overall, the on-chip accelerators significantly advance the state of the art in terms of area, throughput, latency, compression ratio, reduced processor utilization, power/energy efficiency, and integration into the system stack.This paper describes the architecture, and novel elements of the POWER9 and z15 compression/decompression accelerators with emphasis on trade-offs that made the on-chip implementation possible.
Survey on Individual Differences in Visualization

Abstract Developments in data visualization research have enabled visualization systems to achieve great general usability and application across a variety of domains. These advancements have improved not only people's understanding of data, but also the general understanding of people themselves, and how they interact with visualization systems. In particular, researchers have gradually come to recognize the deficiency of having one‐size‐fits‐all visualization interfaces, as well as the significance of individual differences in the use of data visualization systems. Unfortunately, the absence of comprehensive surveys of the existing literature impedes the development of this research. In this paper, we review the research perspectives, as well as the personality traits and cognitive abilities, visualizations, tasks, and measures investigated in the existing literature. We aim to provide a detailed summary of existing scholarship, produce evidence‐based reviews, and spur future inquiry.
Deep neural network to extract high-level features and labels in multi-label classification problems

Pooling layers help reduce redundancy and the number of parameters in deep neural networks without the need of performing additional learning processes. Although these operators are able to deal with both single-label and multi-label problems they are specifically aimed at reducing feature space. However, in the case of multi-label data, this should also be done in the label space. On the other hand, in spite of their success, existing pooling operators are not ideal when handling (multi-label) datasets that do not have an explicit topological organization. In this paper, we present a deep neural architecture using bidirectional association-based pooling layers to extract high-level features and labels in multi-label classification problems. Our approach uses an association function to detect distinct pairs of neurons that will be aggregated into pooled neurons. In the first pooling layer, our proposal computes the Pearson correlation among the variables as the basis to quantify the association values. In addition, we propose an iterative procedure that allows estimating the association degree among pooled neurons in deeper layers without the need of recomputing the correlation matrix. The main advantage of this deep neural architecture is that it allows extracting high-level features and labels on datasets with no specific topological organization. The numerical results show that our bidirectional neural network helps reduce the number of problem features and labels while preserving network’s discriminatory power.
Motion Planning for Dual-Arm Manipulation of Elastic Rods

We present a novel motion planning strategy for the manipulation of elastic rods with two robotic arms. In previous work, it has been shown that the free configuration space of an elastic rod, i.e., the set of equilibrium shapes of the rod, is a smooth manifold of a finite dimension that can be parameterized by one chart. Thus, a sampling-based planning algorithm is straightforward to implement in the product space of the joint angles and the equilibrium configuration space of the elastic rod. Preliminary results show that planning directly in this product space is feasible. However, solving for the elastic rod's shape requires the numerical solution of differential equations, resulting in an excessive and impractical runtime. Hence, we propose to pre-compute a descriptor of the rod, i.e., a roadmap in the free configuration space of the rod that captures its main-connectivity. By doing so, we can plan the motion of any dual-arm robotic system over this roadmap with dramatically fewer solutions of the differential equations. Experiments using the Open Motion Planning Library (OMPL) show significant runtime reduction by an order of magnitude.
Robust RL-Based Map-Less Local Planning: Using 2D Point Clouds as Observations

In this letter, we propose a robust approach to train map-less navigation policies that rely on variable size 2D point clouds, using Deep Reinforcement Learning (Deep RL). The navigation policies are trained in simulations using the DDPG algorithm. Through experimental evaluations in simulated and real-world environments, we showcase the benefits of our approach when compared to more classical RL-based formulations: better performance, the possibility to interchange sensors at deployment time, and to easily augment the environment observability through sensor preprocessing and/or sensor fusion. Videos showing trajectories traversed by agents trained with the proposed approach can be found in https://youtu.be/AzvRJyN6rwQ.
Maximum Information Exploitation Using Broad Learning System for Large-Scale Chaotic Time-Series Prediction

How to make full use of the evolution information of chaotic systems for time-series prediction is a difficult issue in dynamical system modeling. In this article, we propose a maximum information exploitation broad learning system (MIE-BLS) for extreme information utilization of large-scale chaotic time-series modeling. An improved leaky integrator dynamical reservoir is introduced in order to capture the linear information of chaotic systems effectively. It can not only capture the information of the current state but also achieve the compromise with historical states in the dynamical system. Furthermore, the feature is mapped to the enhancement layer by nonlinear random mapping to exploit nonlinear information. The cascading mechanism promotes the information propagation and achieves feature reactivation in dynamical modeling. Discussions about maximum information exploration and the comparisons with ResNet, DenseNet, and HighwayNet are presented in this article. Simulation results on four large-scale data sets illustrate that MIE-BLS could achieve better performance of information exploration in large-scale dynamical system modeling.
An adaptive training-less framework for anomaly detection in crowd scenes

Anomaly detection in crowd videos has become a popular area of research for the computer vision community. Several existing methods have determined anomaly as a deviation from scene normalcy learned via separate training with/without labeled information. However, owing to rare and sparse nature of anomalous events, any such learning can be misleading as there exist no hardcore segregation between anomalous and non-anomalous events. To address such challenge, we propose an adaptive training-less system capable of detecting anomaly on-the-fly. Our solution pipeline consists of three major components, namely, adaptive 3D-DCT model for multi-object detection-based association, local motion descriptor generation through an improved saliency guided optical flow, and anomaly detection based on Earth mover’s distance (EMD). The proposed model, despite being training-free, is found to achieve comparable performance with several state-of-the-art methods on publicly available UCSD, UMN, CUHK-Avenue and ShanghaiTech datasets.
An efficient approach for rice prediction from authenticated Block chain node using machine learning technique

With the advent of Internet of Things (IoT), even devices communicate without human intervention. Even though many cryptographic schemes provide secure communication between devices, errors happen through wireless link. Wireless communications are prone to errors such as session hijacking, masquerading, impersonation attack, Distributed Denial of Service (DDoS) attack, non-repudiation attack, tampering and many more. Block chain provides immutable transaction with authorized communication. To address the problem of non-repudiation attack in IoT and log false information, block chain based authentication using Rivest–Shamir–Adleman (RSA) digital signature is proposed. This method prevents the existence and active participation of fake nodes. The performance of a public key operation is also measured with the crypto metrics of latency (204.2ms, 1642.7ms) and throughput metric (37.2, 12) connections per sec for RSA key sizes of 1024 and 2048 . The environmental factors such as temperature, humidity, pH (potential for hydrogen) concentration and rainfall are collected from sensors deployed at agricultural lands through node Micro Controller Unit (MCU) and transferred to Block chain enabled edge device. Furthermore, edge intelligence is performed to predict rice production with Machine Learning (ML) algorithm and the best appropriate model is selected from the regression metric which includes, Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Coefficient of Determination (R2).
Hyperspectral Image Denoising Using SURE-Based Unsupervised Convolutional Neural Networks

Hyperspectral images (HSIs) are useful for many remote sensing applications. However, they are usually affected by noise that degrades the HSIs quality. Therefore, HSI denoising is important to improve the performance of subsequent HSI processing and analysis. In this article, we propose an HSI denoising method called Stein's unbiased risk estimate-convolutional neural network (SURE-CNN). The method is based on an unsupervised CNN and SURE. The main difference of SURE-CNN from existing supervised learning methods is that the SURE-based loss function can be computed only from noisy data. Since SURE is an unbiased estimate of the mean squared error (MSE) of an estimator, training a CNN using the SURE loss can yield similar results as using the MSE with ground truth in supervised learning. Also, a subspace version of SURE-CNN is proposed to reduce the running time. Extensive experimental results with both simulated and real data sets show that the SURE-CNN method outperforms the competitive methods in both objective and subjective assessments.
Joint Cross-Modal and Unimodal Features for RGB-D Salient Object Detection

RGB-D salient object detection is one of the basic tasks in computer vision. Most existing models focus on investigating efficient ways of fusing the complementary information from RGB and depth images for better saliency detection. However, for many real-life cases, where one of the input images has poor visual quality or contains affluent saliency cues, fusing cross-modal features does not help to improve the detection accuracy, when compared to using unimodal features only. In view of this, a novel RGB-D salient object detection model is proposed by simultaneously exploiting the cross-modal features from the RGB-D images and the unimodal features from the input RGB and depth images for saliency detection. To this end, a Multi-branch Feature Fusion Module is presented to effectively capture the cross-level and cross-modal complementary information between RGB-D images, as well as the cross-level unimodal features from the RGB images and the depth images separately. On top of that, a Feature Selection Module is designed to adaptively select those highly discriminative features for the final saliency prediction from the fused cross-modal features and the unimodal features. Extensive evaluations on four benchmark datasets demonstrate that the proposed model outperforms the state-of-the-art approaches by a large margin.
Sentiment Polarity Detection on Bengali Book Reviews Using Multinomial Naïve Bayes

Recently, sentiment polarity detection has increased attention to NLP researchers due to the massive availability of customer’s opinions or reviews in the online platform. Due to the continued expansion of e-commerce sites, the rate of purchase of various products, including books, is growing enormously among the people. Reader’s opinions/reviews affect the buying decision of a customer in most cases. This work introduces a machine learning-based technique to determine sentiment polarities (either positive or negative category) from Bengali book reviews. To assess the effectiveness of the proposed technique, a corpus with 2000 reviews on Bengali books is developed. A comparative analysis with various approaches (such as logistic regression, naive Bayes, SVM, and SGD) also performed by taking into consideration of the unigram, bigram, and trigram features, respectively. Experimental result reveals that the multinomial naive Bayes with unigram feature outperforms the other techniques with \(84\%\) accuracy on the test set.
Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption

Abstract Background Privacy-preserving computations on genomic data, and more generally on medical data, is a critical path technology for innovative, life-saving research to positively and equally impact the global population. It enables medical research algorithms to be securely deployed in the cloud because operations on encrypted genomic databases are conducted without revealing any individual genomes. Methods for secure computation have shown significant performance improvements over the last several years. However, it is still challenging to apply them on large biomedical datasets. Methods The HE Track of iDash 2018 competition focused on solving an important problem in practical machine learning scenarios, where a data analyst that has trained a regression model (both linear and logistic) with a certain set of features, attempts to find all features in an encrypted database that will improve the quality of the model. Our solution is based on the hybrid framework Chimera that allows for switching between different families of fully homomorphic schemes, namely TFHE and HEAAN . Results Our solution is one of the finalist of Track 2 of iDash 2018 competition. Among the submitted solutions, ours is the only bootstrapped approach that can be applied for different sets of parameters without re-encrypting the genomic database, making it practical for real-world applications. Conclusions This is the first step towards the more general feature selection problem across large encrypted databases.
Partial attention and multi-attribute learning for vehicle re-identification

Intelligent monitoring systems are in increasing need with the rapid growth of traffic nowadays. Vehicle re-identification has vital applications in digital forensics to track suspected vehicles in camera network. It is very challenging to learn discriminative information because of violent changes including the illumination and the viewpoint when a vehicle appears in different cameras, which will lead to the difficulty in distinguishing different vehicles and confusing the same vehicle. To improve the discrimination and the robustness of vehicle re-identification, we propose a partial attention and multi-attribute learning network. Focusing on the local areas which contain abundant discriminative information, we employ partial attention based on vehicle keypoint detection model. Moreover, because the color and the model of a vehicle are relatively stable in different viewpoints, we employ the branch networks to extract multi-attribute features which will improve the robustness. To validate our approach, experiments are carried out on VeRi and VehicleID datasets, and results show that the proposed method achieves higher accuracy compared with other methods.
Deconstructing tourist scams: A social-practice-theory perspective

Tourist scams have been somewhat overlooked due to definitional ambiguities. By focusing initially on conceptual distinctions among a family of related terms, and by introducing the theory of social practice, we considered tourist scams as a practice bundle. Through content analysis of online tourist-generated information and 40 key-informant interviews, we found that this bundle is composed of two interactive practices typically enacted by two parties. Furthermore, we applied the zooming-in technique to deconstruct tourist scams into five crucial elements: materials, meanings and competences in practising scams, meanings and competences in being scammed. By zooming-out, we also discussed the inter-practice relationships surrounding tourist scams. Our work contributes theoretically to tourist safety and security by clarifying conceptual confusions, as well as to tourist behaviour research by depicting the behavioural pattern of tourist scams, and to future studies by revealing opportunities to offer protection to tourists against scammers.