Reviewerly/exHarmony huggingface dataset:
Observation of interaction induced blockade and local spin freezing in a NMR quantum simulator

We experimentally emulate interaction induced blockade and local spin freezing in two and three qubit Nuclear Magnetic Resonance (NMR) architecture. These phenomena are identical to the Rydberg blockade and Rydberg biased freezing. In Rydberg blockade, the simultaneous excitation of two or more atoms is blocked due to the level shift induced by the strong Van der Waal's interaction. In such a strong interaction regime, one can also observe Rydberg biased freezing, wherein the dynamics is confined to a subspace, with the help of multiple drives with unequal amplitudes. Here we drive NMR qubits with specific transition-selective radio waves, while intermittently characterizing the quantum states via quantum state tomography. This not only allows us to track the population dynamics, but also helps to probe quantum correlations, by means of quantum discord, evolving under blockade and freezing phenomena. While, our work constitutes the first experimental simulations of these phenomena in the NMR platform, it is also the first experimental demonstration of Rydberg biased freezing. Moreover, these studies open up interesting quantum control perspectives in exploiting the above phenomena for entanglement generation as well as subspace manipulations.
Multi‐mode neural network for human action recognition

Video data are of two different intrinsic modes, in‐frame and temporal. It is beneficial to incorporate static in‐frame features to acquire dynamic features for video applications. However, some existing methods such as recurrent neural networks do not have a good performance, and some other such as 3D convolutional neural networks (CNNs) are both memory consuming and time consuming. This study proposes an effective framework that takes the advantage of deep learning on the static image feature extraction to tackle the video data. After extracting in‐frame feature vectors using a pretrained deep network, the authors integrate them and form a multi‐mode feature matrix, which preserves the multi‐mode structure and high‐level representation. They propose two models for follow‐up classification. The authors first introduce a temporal CNN, which directly feeds the multi‐mode feature matrix into a CNN. However, they show that characteristics of the multi‐mode features differ significantly in distinct modes. The authors therefore further propose the multi‐mode neural network (MMNN), in which different modes deploy different types of layers. They evaluate their algorithm with the task of human action recognition. The experimental results show that the MMNN achieves a much better performance than the existing long short‐term memory‐based methods and consumes far fewer resources than the existing 3D end‐to‐end models.
Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network

In this paper, two algorithms are proposed for short-term PV power forecasting using a long short-term memory (LSTM) neural network (NN). The first algorithm is designed to predict a single step ahead PV power, whereas the latter is capable of forecasting time horizons with variable starting points, which makes it very useful for rolling horizon based energy management algorithms. The effect of the input sequence length on the performance of the single-step model is investigated. The prediction accuracy of the multi-step model is examined with different lengths of rolling prediction horizons. It is shown that in the case of intraday rolling horizons, adding certain new predictors can effectively improve the machine performance. Hourly and half hourly data from different seasons are used to train and test the performance of the forecasting machine. Moreover, to demonstrate the superiority of the proposed LSTM based algorithms, the performance of other neural networks, namely the generalized recurrent neural network (GRNN) and the nonlinear autoregressive exogenous (NARX) neural network, is also explored.
Motion Planning for Reconfigurable Mobile Robots Using Hierarchical Fast Marching Trees

Reconfigurable mobile robots are versatile platforms that may safely traverse cluttered environments by morphing their physical geometry. However, planning paths for these robots is challenging due to their many degrees of freedom. We propose a novel hierarchical variant of the Fast Marching Tree (FMT*) algorithm. Our algorithm assumes a decomposition of the full state space into multiple sub-spaces, and begins by rapidly finding a set of paths through one such sub-space. This set of solutions is used to generate a biased sampling distribution, which is then explored to find a solution in the full state space. This technique provides a novel way to incorporate prior knowledge of sub-spaces to efficiently bias search within the existing FMT* framework. Importantly, probabilistic completeness and asymptotic optimality are preserved. Experimental results are provided for a reconfigurable wheel-on-leg platform that benchmark the algorithm against state-of-the-art samplingbased planners. In minimizing an energy objective that combines the mechanical work required for platform locomotion with that required for reconfiguration, the planner produces intuitive behaviors where the robot dynamically adjusts its footprint, varies its height, and clambers over obstacles using legged locomotion. These results illustrate the generality of the planner in exploiting the platform’s mechanical ability to fluidly transition between various physical geometric configurations, and wheeled/legged locomotion modes.
A novel Pooling Block for improving lightweight deep neural networks

Since lightweight neural networks such as MobileNet, SqueezeNet were designed, it has been possible to run deep neural networks on mobile devices. However, the performance of these lightweight networks is not as good as that of the general deeper networks, especially for image classification and object detection. To solve this issue, we design a general subnetwork, called Pooling Block, to extract the information from input, and fuse it with the information from the original backbone network. Our study shows that the main backbone network combined with the Pooling Block gains better performance with a few extra parameters and computation cost for the lightweight networks. Meanwhile, we find that the Pooling Block can evidently accelerate the speed of convergence during the training phase. We adopt Pooling Block into MobileNet V2 to conduct the image classification, object detection and pedestrian detection experiments on many widely used datasets, including Animal with Attributes 2, Caltech-101, Caltech-256, PASCAL VOC 2007, MS COCO 2017, Caltech Pedestrian and Citypersons. Experimental results show that adding Pooling Block leads to 2% improvement on image classification datasets, and more than 0.5% improvement on object detection datasets, with little or no loss of computational speed.
Private membership test protocol with low communication complexity

We introduce a practical method to perform private membership tests. In this method, clients are able to test whether an item is in a set controlled by the server without revealing their query item to the server. After executing the queries, the content of the server's set remains secret. One use case for a private membership test is to check whether a file contains any malware by checking its signature against a database of malware samples in a privacy-preserving way. We apply the Bloom filter and the Cuckoo filter in the membership test procedure. In order to achieve privacy properties, we present a novel protocol based on some homomorphic encryption schemes. In our protocol, we rearrange the data in the set into N-dimensional hypercubes. We have implemented our method in a realistic scenario where a client of an anti-malware company wants to privately check whether a hash value of a given file is in the malware database of the company. The evaluation shows that our method is feasible for real-world applications. We also have tested the performance of our protocol for databases of different sizes and data structures with different dimensions: 2-dimensional, 3-dimensional, and 4-dimensional hypercubes. We present formulas to estimate the cost of computation and communication in our protocol.
Software Defined Demodulation of Multiple Frequency Shift Keying with Dense Neural Network for Weak Signal Communications

In this paper we present the symbol and bit error rate performance of the weak signal digital communications system. We investigate orthogonal multiple frequency shift keying modulation scheme with supervised machine learning demodulation approach using simple dense end-to-end artificial neural network. We focus on the interference immunity over an additive white Gaussian noise with average signal-to-noise ratios from -20 dB to 0 dB.
Local probabilistic model for Bayesian classification: A generalized local classification model

In Bayesian classification, it is important to establish a probability distribution model, e.g., a Gaussian distribution for each class for probability estimation. Most of the previous methods modeled the probability distribution in the whole sample space. However, real-world problems are usually too complex to model in the whole sample space; some fundamental assumptions are required to simplify the global model, for example, the class conditional independence assumption for naive Bayesian classification. In this paper, with the insight that the distribution in a local sample space should be simpler than that in the whole sample space, a local probabilistic model established for a local region is expected much simpler and can relax the fundamental assumptions that may not be true in the whole sample space. Based on these advantages we propose establishing local probabilistic models for probability estimation in Bayesian classification. In addition, a Bayesian classifier adopting a local probabilistic model can even be viewed as a generalized local classification model; by tuning the size of the local region and the corresponding local model assumption, a fitting model can be established for a particular classification problem. The experimental results on several real-world datasets demonstrate the effectiveness of local probabilistic models for Bayesian classification.
A novel network traffic classification approach via discriminative feature learning

Network traffic classification plays an important role in many network monitoring and security tasks. More recently, with the development of deep learning techniques, the performance of network traffic classification has been significantly improved due to the powerful feature representations learned by deep neural networks. Despite the great success that has been achieved, the problems of within-class diversity and between-class similarity are still big challenges. In this paper, we propose to train a CNN model by optimizing a new discriminative objective function, where apart from minimizing the empirical risk, a metric learning regularization term is also imposed on the learned features. This metric learning regularization term enforces the CNN model to learn more discriminative features in the mapped feature space, where the instances from the same class are closer together while the instances of different classes are farther apart. We conduct extensive experiments to evaluate the proposed method on three traffic datasets. The experimental results demonstrate that our proposed method outperforms the existing baseline methods and obtains state-of-the-art results on all the three datasets.
Compact NIZKs from Standard Assumptions on Bilinear Maps

A non-interactive zero-knowledge (NIZK) protocol enables a prover to convince a verifier of the truth of a statement without leaking any other information by sending a single message. The main focus of this work is on exploring short pairing-based NIZKs for all $$\mathbf{NP} $$ languages based on standard assumptions. In this regime, the seminal work of Groth, Ostrovsky, and Sahai (J.ACM'12) (GOS-NIZK) is still considered to be the state-of-the-art. Although fairly efficient, one drawback of GOS-NIZK is that the proof size is multiplicative in the circuit size computing the $$\mathbf{NP} $$ relation. That is, the proof size grows by $$O(|C|\kappa )$$, where C is the circuit for the $$\mathbf{NP} $$ relation and $$\kappa $$ is the security parameter. By now, there have been numerous follow-up works focusing on shortening the proof size of pairing-based NIZKs, however, thus far, all works come at the cost of relying either on a non-standard knowledge-type assumption or a non-static q-type assumption. Specifically, improving the proof size of the original GOS-NIZK under the same standard assumption has remained as an open problem. Our main result is a construction of a pairing-based NIZK for all of $$\mathbf{NP} $$ whose proof size is additive in |C|, that is, the proof size only grows by $$|C| +\mathsf {poly}(\kappa )$$, based on the decisional linear (DLIN) assumption. Since the DLIN assumption is the same assumption underlying GOS-NIZK, our NIZK is a strict improvement on their proof size. As by-products of our main result, we also obtain the following two results: (1) We construct a perfectly zero-knowledge NIZK (NIPZK) for $$\mathbf{NP} $$ relations computable in $$\mathbf{NC} ^1$$ with proof size $$|w| \cdot \mathsf {poly}(\kappa )$$ where |w| is the witness length based on the DLIN assumption. This is the first pairing-based NIPZK for a non-trivial class of $$\mathbf{NP} $$ languages whose proof size is independent of |C| based on a standard assumption. (2) We construct a universally composable (UC) NIZK for $$\mathbf{NP} $$ relations computable in $$\mathbf{NC} ^1$$ in the erasure-free adaptive setting whose proof size is $$|w| \cdot \mathsf {poly}(\kappa )$$ from the DLIN assumption. This is an improvement over the recent result of Katsumata, Nishimaki, Yamada, and Yamakawa (CRYPTO'19), which gave a similar result based on a non-static q-type assumption. The main building block for all of our NIZKs is a constrained signature scheme with decomposable online-offline efficiency. This is a property which we newly introduce in this paper and construct from the DLIN assumption. We believe this construction is of an independent interest.
Automatic argumentation mining and the role of stance and sentiment

Abstract Argumentation mining is a subfield of Computational Linguistics that aims (primarily) at automatically finding arguments and their structural components in natural language text. We provide a short introduction to this field, intended for an audience with a limited computational background. After explaining the subtasks involved in this problem of deriving the structure of arguments, we describe two other applications that are popular in computational linguistics: sentiment analysis and stance detection. From the linguistic viewpoint, they concern the semantics of evaluation in language. In the final part of the paper, we briefly examine the roles that these two tasks play in argumentation mining, both in current practice, and in possible future systems.
The Mathematical Model, Implementation and the Parameter-Tuning of the African Buffalo Optimization Algorithm

This paper presents the mathematical model, implementation guidelines, tuning of the control parameters and the practical implementation of the newly-designed African Buffalo Optimization (ABO) algorithm to solve the benchmark Rosenbrock function. Mathematical modeling, a detailed implementation information as well as the tuning of the controlling parameters of a newly-developed optimization algorithms are vital steps in helping the research community to better understand the inner working of a newly-designed meta-heuristic technique. This study aims to equip the research community with the needed tools to investigate the capacity of the ABO in attempting solutions to optimization problems. After an exhaustive discussion of the major themes of this paper as well as the practical implementation of the ABO with a review to unraveling the effects of iteration number and search population, it is our belief that many researchers will appreciate the workings of the African Buffalo Optimization algorithm.
The Adapters Public Institute as a Means of First-Year Students’ Pedagogical Support During the Period of Adaptation to Studying at a University

The problem of first-year students’ adaptation to studying at a university forces higher education institutions to search for various effective tools and methods, which could help integrate first-year students, both native and international, into university’s academic environment. The Adapters public institute, launched in 2015, is the outcome of such search in Peter the Great St. Petersburg Polytechnic University. The given article aims at analysing the effectiveness of work of the Adapters by comparing the group of students surveyed in 2014, a year before the launch of the Adapters public institute, and the group surveyed in 2019, four years after its launch. The study takes into consideration psychosocial sphere of adaptation. First-year students’ adaptation level in this sphere is measured by means of tests and questionnaires “The methodology for determining the psychological climate of the group” by A. N. Lutoshkin and R. Cattell’s questionnaire.The results of the study reveal considerable changes and high level of adaptation in psychosocial sphere in general and in psychological climate of the group in particular.The article also considers the first-year students’ attitudes to Adapters institute and suggestions concerning its further development and improvement.
An Extended Isomap Approach for Nonlinear Dimension Reduction

Nowadays, Isomap is one of the most popular nonlinear manifold dimension reductions which have applied to the real-world datasets. However, it has various limitations for the high-dimensional and large-scale dataset. Two main limitations of the Isomap are: it may make incorrect links in the neighborhood graph G and high computational cost. In this paper, we have introduced a novel framework, which we called the FastIsomap. The main purpose of the FastIsomap is to increase the accuracy of the graph by using two state-of-the-art algorithms: a randomized division tree and NN-Descent. The basic idea of FastIsomap is to construct an accurate approximated KNN graph from millions and hundreds of dimensional’ data points and then project the graph into low-dimensional space. We have compared the FastIsomap framework with the existing Isomap algorithm to verify its efficiency and performance, which provided accurate results of the high and large-dimensional datasets.
Pragmatic Industrial Augmented Reality in Electric Power Industry

Organizing and presenting necessary information to electric power workforces just-in-time is a challenging issue. Today Electric Power Industry (EPI) severely suffers from the lack of sufficient knowledge of young workforces at the field. Current offline paper-based training methods or voice call assistants rarely able to help these workforces in a disaster situation. Recently industrial Augmented Reality (AR) tools open new insight for remote assistance through virtualizing necessary information on the real environment. That is, using AR tools, field workforces can observe required information just-in-time and in-location in front of their eyes. Here, one question is how AR technology will change the EPI. To answer this question and shape the power stakeholders' viewpoint in the adaption of industrial AR, we precisely review the potential use cases and case studies of pragmatic industrial AR in the EPI in this paper. To the best of our knowledge, this paper is one of the first comprehensive studies of AR opportunities in the EPI. Studies in this paper show that AR applications in the EPI is in the infancy stage and needs remarkable consideration to develop AR tools in accordance with the electric power requirements.
Dual-attentional Factorization-Machines based Neural Network for User Response Prediction

This paper proposes Dual-attentional Factorization-Machines (DFM), which incorporates global-wise attention and element-wise attention with FM for user response prediction. We further extend DFM with a deep neural network and name this new model Dual-attentional Factorization-machines based Network (DFNet). Comprehensive experiments are conducted on two real-world datasets to demonstrate the effectiveness of DFM and DFNet over the state-of-the-art models for user response prediction.
Algorithms and Applications to Weighted Rank-one Binary Matrix Factorization

Many applications use data that are better represented in the binary matrix form, such as click-stream data, market basket data, document-term data, user-permission data in access control, and others. Matrix factorization methods have been widely used tools for the analysis of high-dimensional data, as they automatically extract sparse and meaningful features from data vectors. However, existing matrix factorization methods do not work well for the binary data. One crucial limitation is interpretability, as many matrix factorization methods decompose an input matrix into matrices with fractional or even negative components, which are hard to interpret in many real settings. Some matrix factorization methods, like binary matrix factorization, do limit decomposed matrices to binary values. However, these models are not flexible to accommodate some data analysis tasks, like trading off summary size with quality and discriminating different types of approximation errors. To address those issues, this article presents weighted rank-one binary matrix factorization, which is to approximate a binary matrix by the product of two binary vectors, with parameters controlling different types of approximation errors. By systematically running weighted rank-one binary matrix factorization, one can effectively perform various binary data analysis tasks, like compression, clustering, and pattern discovery. Theoretical properties on weighted rank-one binary matrix factorization are investigated and its connection to problems in other research domains are examined. As weighted rank-one binary matrix factorization in general is NP-hard, efficient and effective algorithms are presented. Extensive studies on applications of weighted rank-one binary matrix factorization are also conducted.
Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

Recommender systems are efficient tools for online applications; these systems exploit historical user ratings on items to make recommendations of items to users.This paper aims to enhance dynamic collaborative filtering on recommender systems under volatile conditions in which both users' preferences and item properties dynamically change over time.Moreover, existing collaborative filtering models mainly rely on solving data sparsity by adding side information to improve performance.We propose a model to capture the user preference dynamics in the rating matrix by using a joint decomposition method to extract user latent transition patterns and combine latent factors together with the associated topic evolution of review texts by using topic modeling based on the dynamic environment.We evaluate the accuracy on real datasets, and the experimental results show that the model leads to a significant improvement compared with the state-of-the-art dynamic CF models.