Reviewerly/exHarmony huggingface dataset:
Full-Stack, Real-System Quantum Computer Studies: Architectural Comparisons and Design Insights

In recent years, Quantum Computing (QC) has progressed to the point where small working prototypes are available for use. Termed Noisy Intermediate-Scale Quantum (NISQ) computers, these prototypes are too small for large benchmarks or even for Quantum Error Correction, but they do have sufficient resources to run small benchmarks, particularly if compiled with optimizations to make use of scarce qubits and limited operation counts and coherence times. QC has not yet, however, settled on a particular preferred device implementation technology, and indeed different NISQ prototypes implement qubits with very different physical approaches and therefore widely-varying device and machine characteristics. Our work performs a full-stack, benchmark-driven hardware-software analysis of QC systems. We evaluate QC architectural possibilities, software-visible gates, and software optimizations to tackle fundamental design questions about gate set choices, communication topology, the factors affecting benchmark performance and compiler optimizations. In order to answer key cross-technology and cross-platform design questions, our work has built the first top-to-bottom toolflow to target different qubit device technologies, including superconducting and trapped ion qubits which are the current QC front-runners. We use our toolflow, TriQ, to conduct {\em real-system} measurements on 7 running QC prototypes from 3 different groups, IBM, Rigetti, and University of Maryland. From these real-system experiences at QC's hardware-software interface, we make observations about native and software-visible gates for different QC technologies, communication topologies, and the value of noise-aware compilation even on lower-noise platforms. This is the largest cross-platform real-system QC study performed thus far; its results have the potential to inform both QC device and compiler design going forward.
Multithread Multistring Burrows–Wheeler Transform and Longest Common Prefix Array

Indexing huge collections of strings, such as those produced by the widespread sequencing technologies, heavily relies on multistring generalizations of the Burrows–Wheeler transform (BWT) and the longest common prefix (LCP) array, since solving efficiently both problems are essential ingredients of several algorithms on a collection of strings, such as those for genome assembly. In this article, we explore a multithread computational strategy for building the BWT and LCP array. Our algorithm applies a divide and conquer approach that leads to parallel computation of multistring BWT and LCP array.
Innovative InterLabs System for Smart Learning Analytics in Engineering Education

Learning analytics is a fast growing area in academia; it is focused on collecting, analysis, cleaning, processing, and visualization of data from various academic sources that are related to course, student, faculty, program director and chair/department levels. This paper presents the features and functions, architectural models, software engineering diagrams, main components, inputs and outputs, hierarchical levels and smartness features, mathematical methods and algorithms used in a design, development, testing and implementation of the innovative InterLabs smart learning analytics system into the Computer Science and Information Systems curriculum at Bradley University (Peoria, IL, USA). Conceptually the InterLabs system is based on (1) smartness features such as adaptivity, sensing, inferring, anticipation, self-learning, self-organization, and (2) the Gartner's Analytics Ascendancy Model. The InterLabs system is focused on descriptive, diagnostic, predictive and prescriptive types of analytics of student academic performance.
Locating secret messages based on quantitative steganalysis

Steganography poses a serious challenge to forensics because investigators cannot identify even traces of secret messages embedded using a steganographer. Contrarily, the objective of locating steganalysis is to locate the embedded message, which should help extract the secret message. In this paper, a methodology of locating steganalysis using quantitative steganalysis is presented for multiple stego images with embedded messages along the same embedding path. Three typical quantitative steganalysis methods are applied to the methodology to locate the messages embedded using LSB re-placement. Experimental results show that the presented methods can reliably estimate the embedding positions, which verifies the validity of the presented methodology. The presented methodology points out a new use of quantitative steganalysis, and further demonstrates that it is necessary to design more precise quantitative steganalysis methods.
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs. In addition, we also combined several AMR-to-text alignments with an attention mechanism and we supplemented the parser with pre-processed concept identification, named entities and contextualized embeddings. We achieve a highly competitive performance that is comparable to the best published results. We show an in-depth study ablating each of the new components of the parser
Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning

Time series anomaly detection plays a critical role in automated monitoring systems. Most previous deep learning efforts related to time series anomaly detection were based on recurrent neural networks (RNN). In this paper, we propose a time series segmentation approach based on convolutional neural networks (CNN) for anomaly detection. Moreover, we propose a transfer learning framework that pretrains a model on a large-scale synthetic univariate time series data set and then fine-tunes its weights on small-scale, univariate or multivariate data sets with previously unseen classes of anomalies. For the multivariate case, we introduce a novel network architecture. The approach was tested on multiple synthetic and real data sets successfully.
Energy-Efficient Hardware Implementation of Road-Lane Detection Based on Hough Transform with Parallelized Voting Procedure and Local Maximum Algorithm

Efficient road-lane detection is expected to be achievable by application of the Hough transform (HT) which realizes high-accuracy straight-line extraction from images. The main challenge for HT-hardware implementation in actual applications is the trade-off optimization between accuracy maximization, power-dissipation reduction and real-time requirements. We report a HT-hardware architecture for road-lane detection with parallelized voting procedure, local maximum algorithm and FPGA-prototype implementation. Parallelization of the global design is realized on the basis of θ-value discretization in the Hough space. Four major hardware modules are developed for edge detection in the original video frames, computation of the characteristic edge-pixel values (ρ,θ) in Hough-space, voting procedure for each (ρ,θ) pair with parallel local-maximum-based peak voting-point extraction in Hough space to determine the detected straight lines. Implementation of a prototype system for real-time road-lane detection on a low-cost DE1 platform with a Cyclone II FPGA device was verified to be possible. An average detection speed of 135 frames/s for VGA (640x480)-frames was achieved at 50 MHz working frequency.
Towards the full classification of exceptional scattered polynomials

Let $f(X) \in \mathbb{F}_{q^r}[X]$ be a $q$-polynomial. If the $\mathbb{F}_q$-subspace $U=\{(x^{q^t},f(x)) \mid x \in \mathbb{F}_{q^n}\}$ defines a maximum scattered linear set, then we call $f(X)$ a scattered polynomial of index $t$. The asymptotic behaviour of scattered polynomials of index $t$ is an interesting open problem. In this sense, exceptional scattered polynomials of index $t$ are those for which $U$ is a maximum scattered linear set in ${\rm PG}(1,q^{mr})$ for infinitely many $m$. The complete classifications of exceptional scattered monic polynomials of index $0$ (for $q&gt;5$) and of index 1 were obtained by Bartoli and Zhou. In this paper we complete the classifications of exceptional scattered monic polynomials of index $0$ for $q \leq 4$. Also, some partial classifications are obtained for arbitrary $t$. As a consequence, the complete classification of exceptional scattered monic polynomials of index $2$ is given.
Accelerating evolutionary computation using a convergence point estimated by weighted moving vectors

We introduce weighted moving vectors to increase the accuracy of estimating a convergence point of population and evaluate its efficiency. Key point is to weight moving vectors according to their reliability when a convergence point is calculated instead of equal weighting of the original method. We propose two different methods to evaluate the reliability of moving vectors. The first approach uses the fitness gradient information between starting points and terminal points of moving vectors for their weights. When a fitness gradient is bigger, the direction of a moving vector may have more potential, and a higher weight is given to it. The second one uses the fitness of parents, i.e., starting points of moving vectors, to give weights for moving vectors. Because an individual with higher fitness may have a high probability of being close to the optimal area, it should be given a higher weight, vice versa. If the estimated point is better than the worst individual in current population, it is used as an elite individual and replace the worst one to accelerate the convergence of evolutionary algorithms. To evaluate the performance of our proposal, we employ differential evolution and particle swarm optimization as baseline algorithms in our evaluation experiments and run them on 28 benchmark functions from CEC 2013. The experimental results confirmed that introducing weights can further improve the accuracy of an estimated convergence point, which helps to make EC search faster. Finally, some open topics are given to discuss.
Social Media News Classification in Healthcare Communication

The trend of news transmission is rapidly shifting from electronic media to social media. Currently, news channels in general, while health news channels specifically send health related news on social media sites. These news are beneficial for the patients, medical professionals and the general public. A lot of health related data is available on the social media that may be used to extract significant information and present several predictions from it to assist physicians, patients and healthcare organizations for decision making. However, A little research is found on health news data using machine learning approaches, thus in this paper, we have proposed a framework for the data collection, modeling, and visualization of the health related patterns. For the analysis, the tweets of 13 news channels are collected from the Twitter. The dataset holds approximately 28k tweets available under 280 hashtags. Furthermore, a comprehensive set of experiments are performed to extract patterns from the data. A comparative analysis is carried among the baseline method and four classification algorithms which include Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (J48). For the evaluation of the results, the standard measures accuracy, precision, recall and f -measure have been used. The results of the study are encouraging and better than the other studies of such kind.
Exploiting Optical Chaos for Color Image Encryption and Secure Resource Sharing in Cloud

Cloud as a developing technology provides convenient services to sharing and usage of images resource, but brings a threat to images security and privacy authentication. Optical chaos possess distinct superiority for image secure sharing, therefore, based on two mutually coupled semiconductor lasers (MC-SL1 and MC-SL2), we theoretically propose a color image encryption system for secure resource sharing and introduce a watermarking method for images privacy authentication in cloud. In our paper, after achieving chaos synchronization under proper parameters, two MC-SLs are used to transmit encryption/decryption key space generated by chaotic signal of MC-SL1 and user's key. We carry out digital watermarking and encryption of a color image before secure resource sharing in cloud. On the other side of cloud, we make some tests for decryption of color image by received key space and watermark extraction. Meanwhile, we discuss security performance of color image encryption/decryption and key space transmission process, and analyze secure resource sharing of color image in cloud.
Quantum metrology enhanced by coherence-induced driving in a cavity-QED setup

We propose a quantum metrology scheme in a cavity QED setup to achieve the Heisenberg limit. In our scheme, a series of identical two-level atoms randomly pass through and interact with a dissipative single-mode cavity. Different from the entanglement-based Heisenberg limit metrology scheme, we do not need to prepare the atomic entangled states before they enter into the cavity. We show that the initial atomic coherence will induce an effective driving to the cavity field, whose steady state is an incoherent superposition of orthogonal states, with the superposition probabilities being dependent on the atom-cavity coupling strength. By measuring the average photon number of the cavity in the steady state, we demonstrate that the root mean square of the fluctuation of the atom-cavity coupling strength is proportional to $1/{N}_{c}^{2}$ (${N}_{c}$ is the effective atom number interacting with the photon in the cavity during its lifetime). It implies that we have achieved the Heisenberg limit in our quantum metrology process. We also discuss the experimental feasibility of our theoretical proposal. Our findings may find potential applications in quantum metrology technology.
Foreign Language and Individualization of Student Training: Realities, Trends, Options

The article discusses the features of the modern system of Russian higher education, reveals its advantages and disadvantages. In the light of the Federal Target Program for the De velopment of Education for 2016–2020, the authors conclude that the multifunctional foreign language communicative competence plays a special role in the process of forming a holistic competence-based portrait of a graduate of a higher educational institution, considering the three main trends characteristic of the transition to an updated educational system: direct participation in the educational process of all stakeholders (students, teachers, representatives of professional communications, employers); the need to create and operate an innovative information and educational environment, taking into account the possibility of modeling real professional tasks; personal orientation and individualization of the preparation of students with the growing role of the learner in the educational process. The task is to find the best methodological tools and organizational forms of educational activities in the context of foreign language training of university students. In accordance with this, the authors propose options for implementing the formulated trends. Particular attention is paid to the possibilities of interactive learning of foreign languages; the peculiarities of «immersion» of students in a model professional environment with the involvement of employers and representatives of professional communities, including as experts, in direct participation in the educational process; the importance of personal communication between students and other subjects of the educational process; work in small groups. The emphasis is on the need to create favorable conditions for self-education and self-realization of future graduates and an individual approach to each of them.
Emerging unitary evolutions in dissipatively coupled systems

Having a broad range of methods available for implementing unitary operations is crucial for quantum information tasks. We study a dissipative process commonly used to describe dissipatively coupled systems and show that the process can lead to pure unitary dynamics on one part of a bipartite system, provided that the process is strong enough. As a consequence of these findings, we discuss within the framework of quantum control theory how the dissipative process can enable universal control of the considered part, thereby turning parts of the system into a system capable of universal quantum information tasks. We characterize the time scales necessary to implement gates with high fidelity through the dissipative evolution. The considered dissipative evolution is of particular importance since it can be engineered in the laboratory in the realm of superconducting circuits. Based on a reservoir that is formed by a lossy microwave mode we present a detailed study of how our theoretical findings can be realized in an experimental setting.
The ML Data Prep Zoo

Data preparation (prep) time is a major bottleneck for many ML applications. It is often painful grunt work that is handled manually by data scientists, reducing their productivity and raising costs. It is also a roadblock for emerging AutoML platforms. We envision a new line of community-driven research to tackle this bottleneck based on a simple philosophy: use ML to semi-automate data prep for ML. For impactful research on this problem, we believe the major impediment is not new algorithms or theory but rather common task definitions and benchmark labeled datasets. To this end, we formalize a few major data prep tasks for ML over structured data as applied ML tasks. We discuss research challenges in scaling up data labeling, defining accuracy metrics, and creating practical tool support. We present a case study of our progress on a key data prep task: ML schema inference. Finally, we propose a public "zoo" of labeled datasets and pre-trained ML models for data prep tasks to act as a community-led repository for further research on this problem.
Significance of Patterns in Data Visualisations

In this paper we consider the following important problem: when we explore data visually and observe patterns, how can we determine their statistical significance? Patterns observed in exploratory analysis are traditionally met with scepticism, since the hypotheses are formulated while viewing the data, rather than before doing so. In contrast to this belief, we show that it is, in fact, possible to evaluate the significance of patterns also during exploratory analysis, and that the knowledge of the analyst can be leveraged to improve statistical power by reducing the amount of simultaneous comparisons. We develop a principled framework for determining the statistical significance of visually observed patterns. Furthermore, we show how the significance of visual patterns observed during iterative data exploration can be determined. We perform an empirical investigation on real and synthetic tabular data and time series, using different test statistics and methods for generating surrogate data. We conclude that the proposed framework allows determining the significance of visual patterns during exploratory analysis.
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study

Learning about many things can provide numerous benefits to a reinforcement learning system. For example, learning many auxiliary value functions, in addition to optimizing the environmental reward, appears to improve both exploration and representation learning. The question we tackle in this paper is how to sculpt the stream of experience---how to adapt the learning system's behavior---to optimize the learning of a collection of value functions. A simple answer is to compute an intrinsic reward based on the statistics of each auxiliary learner, and use reinforcement learning to maximize that intrinsic reward. Unfortunately, implementing this simple idea has proven difficult, and thus has been the focus of decades of study. It remains unclear which of the many possible measures of learning would work well in a parallel learning setting where environmental reward is extremely sparse or absent. In this paper, we investigate and compare different intrinsic reward mechanisms in a new bandit-like parallel-learning testbed. We discuss the interaction between reward and prediction learners and highlight the importance of introspective prediction learners: those that increase their rate of learning when progress is possible, and decrease when it is not. We provide a comprehensive empirical comparison of 14 different rewards, including well-known ideas from reinforcement learning and active learning. Our results highlight a simple but seemingly powerful principle: intrinsic rewards based on the amount of learning can generate useful behavior, if each individual learner is introspective.
The Limited Multi-Label Projection Layer

We propose the Limited Multi-Label (LML) projection layer as a new primitive operation for end-to-end learning systems. The LML layer provides a probabilistic way of modeling multi-label predictions limited to having exactly k labels. We derive efficient forward and backward passes for this layer and show how the layer can be used to optimize the top-k recall for multi-label tasks with incomplete label information. We evaluate LML layers on top-k CIFAR-100 classification and scene graph generation. We show that LML layers add a negligible amount of computational overhead, strictly improve the model's representational capacity, and improve accuracy. We also revisit the truncated top-k entropy method as a competitive baseline for top-k classification.
Optimization of stereoscopic imager performance by computer simulation of geometrical calibration using optical design software

Stereoscopic imagers are widely used in machine vision for three-dimensional (3D) visualization as well as in non-destructive testing for quantitative characterization of cracks, delamination and other defects. Measurement capability in these systems is provided by a proper combination of the optical parameters and data processing techniques. Conventional approach to their design consists of two sequential stages: optical system design and optimization of calibration and image processing algorithms. Such two-stage procedure often complicates both the hardware and the software, and results in a time-ineffective design procedure and cost-ineffective solution. We demonstrate a more effective approach and propose to estimate errors of 3D measurements at the early optical design stage. We show that computer simulation using optical design software allows not only optimizing optical parameters of the imager but also choosing the most effective mathematical model of the system and the equipment necessary for calibration. We tested the proposed approach on the design of miniature prism-based stereoscopic system and analyzed the impact of various factors (aberrations, tolerances, etc.) as on the image quality, so on the quality of calibration and 3D measurements accuracy. The proposed joint design approach may be highly effective for various measurement systems and applications when both optical parameters and image processing algorithms are not defined in advance and are necessary to be optimized.