Reviewerly/exHarmony huggingface dataset:
Time-series quantum reservoir computing with weak and projective measurements

Quantum machine learning represents a promising avenue for data processing, also for purposes of sequential temporal data analysis, as recently proposed in quantum reservoir computing (QRC). The possibility to operate on several platforms and noise intermediate-scale quantum devices makes QRC a timely topic. A challenge that has not been addressed yet, however, is how to efficiently include quantum measurement in realistic protocols, while retaining the reservoir memory needed for sequential time series processing and preserving the quantum advantage offered by large Hilbert spaces. In this work, we propose different measurement protocols and assess their efficiency in terms of resources, through theoretical predictions and numerical analysis. We show that it is possible to exploit the quantumness of the reservoir and to obtain ideal performance both for memory and forecasting tasks with two successful measurement protocols. One consists in rewinding part of the dynamics determined by the fading memory of the reservoir and storing the corresponding data of the input sequence, while the other employs weak measurements operating online at the trade-off where information can be extracted accurately and without hindering the needed memory. Our work establishes the conditions for efficient protocols, being the fading memory time a key factor, and demonstrates the possibility of performing genuine online time-series processing with quantum systems.
Stop using the elbow criterion for k-means and how to choose the number of clusters instead

A major challenge when using k-means clustering often is how to choose the parameter k, the number of clusters. In this letter, we want to point out that it is very easy to draw poor conclusions from a common heuristic, the "elbow method". Better alternatives have been known in literature for a long time, and we want to draw attention to some of these easy to use options, that often perform better. This letter is a call to stop using the elbow method altogether, because it severely lacks theoretic support, and we want to encourage educators to discuss the problems of the method - if introducing it in class at all - and teach alternatives instead, while researchers and reviewers should reject conclusions drawn from the elbow method.
Universal linear intensity transformations using spatially incoherent diffractive processors

Abstract Under spatially coherent light, a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view (FOVs) if the total number ( N ) of optimizable phase-only diffractive features is ≥~2 N i N o , where N i and N o refer to the number of useful pixels at the input and the output FOVs, respectively. Here we report the design of a spatially incoherent diffractive optical processor that can approximate any arbitrary linear transformation in time-averaged intensity between its input and output FOVs. Under spatially incoherent monochromatic light, the spatially varying intensity point spread function ( H ) of a diffractive network, corresponding to a given, arbitrarily-selected linear intensity transformation, can be written as H ( m , n ; m ′, n ′) = | h ( m , n ; m ′, n ′)| 2 , where h is the spatially coherent point spread function of the same diffractive network, and ( m , n ) and ( m ′, n ′) define the coordinates of the output and input FOVs, respectively. Using numerical simulations and deep learning, supervised through examples of input-output profiles, we demonstrate that a spatially incoherent diffractive network can be trained to all-optically perform any arbitrary linear intensity transformation between its input and output if N ≥ ~2 N i N o . We also report the design of spatially incoherent diffractive networks for linear processing of intensity information at multiple illumination wavelengths, operating simultaneously. Finally, we numerically demonstrate a diffractive network design that performs all-optical classification of handwritten digits under spatially incoherent illumination, achieving a test accuracy of &gt;95%. Spatially incoherent diffractive networks will be broadly useful for designing all-optical visual processors that can work under natural light.
Table Header Detection and Classification

In digital libraries, a table, as a specific document component as well as a condensed way to present structured and relational data, contains rich information and often the only source of .that information. In order to explore, retrieve, and reuse that data, tables should be identified and the data extracted. Table recognition is an old field of research. However, due to the diversity of table styles, the results are still far from satisfactory, and not a single algorithm performs well on all different types of tables. In this paper, we randomly take samples from the CiteSeerX to investigate diverse table styles for automatic table extraction. We find that table headers are one of the main characteristics of complex table styles. We identify a set of features that can be used to segregate headers from tabular data and build a classifier to detect table headers. Our empirical evaluation on PDF documents shows that using a Random Forest classifier achieves an accuracy of 92%.
Multi-objective and discrete Elephants Herding Optimization algorithm for QoS aware web service composition

The goal of QoS aware web service composition (QoS-WSC) is to provide new functionalities and find a best combination of services to meet complex needs of users. QoS of the resulting composite service should be optimized. QoS-WSC is a global multi-objective optimization problem belonging to NP-hard class given the number of available services. Most of existing approaches reduce this problem to a single-objective problem by aggregating different objectives, which leads to a loss of information. An alternative issue is to use Pareto-based approaches. The Pareto-optimal set contains solutions that ensure the best trade-off between conflicting objectives. In this paper, a new multi-objective meta-heuristic bio-inspired Pareto-based approach is presented to address the QoS-WSC, it is based on Elephants Herding Optimization (EHO) algorithm. EHO is characterised by a strategy of dividing and combining the population to sub population (clan) which allows exchange of information between local searches to get a global optimum. However, the application of others evolutionary algorithms to this problem cannot avoids the early stagnancy in a local optimum. In this paper a discrete and multi-objective version of EHO will be presented based on a crossover operator. Compared with SPEA2 (Strength Pareto Evolutionary Algorithm 2) and MOPSO (Multi-Objective Particle Swarm Optimization algorithm), the results of experimental evaluation show that our improvements significantly outperform the existing algorithms in term of Hypervolume, Set Coverage and Spacing metrics.
Secure Wavelet Matrix: Alphabet-Friendly Privacy-Preserving String Search for Bioinformatics

Biomedical data often includes personal information, and the technology is demanded that enables the searching of such sensitive data while protecting privacy. We consider a case in which a server has a text database and a user searches the database to find substring matches. The user wants to conceal his/her query and the server wants to conceal the database except for the search results. The previous approach for this problem is based on a linear-time algorithm in terms of alphabet size |Σ|, and it cannot search on the database of large alphabet such as biomedical documents. We present a novel algorithm that can search a string in logarithmic time of |Σ|. In our algorithm, named secure wavelet matrix (sWM), we use an additively homomorphic encryption to build an efficient data structure called a wavelet matrix. In an experiment using a simulated string of length 10,000 whose alphabet size ranges from 4 to 1024, the run time of the sWM was up to around two orders of magnitude faster than that of the previous method. sWM enables the searching of a private database efficiently and thus it will facilitate utilizing sensitive biomedical information.
Experimental comparison of various quantum key distribution protocols under reference frame rotation and fluctuation

In free-space quantum key distribution (QKD) between moving parties, e.g., free-space QKD via satellite, the reference frame rotation and fluctuation degrades the performance of QKD. Reference-frame-independent QKD (RFI-QKD) provides a simple but efficient way to overcome this problem. While there has been a number of theoretical and experimental studies on RFI-QKD, the experimental verification of the robustness of RFI-QKD over other QKD protocols under the reference frame rotation and fluctuation is still missing. Here, we have constructed a free-space QKD system which can implement BB84, six-state, and RFI-QKD protocols, and compared their performances under the reference frame rotation and fluctuation. With the theoretical analysis and experimental results, we have successfully verified the robustness of RFI-QKD protocol over other QKD protocols in the presence of reference frame rotation and fluctuation.
Uncertainty-based Human Motion Tracking with Stable Gaussian Process State Space Models

Data-driven approaches are well suited to represent human motion because arbitrary complex trajectories can be captured. Gaussian process state space models allow to encode human motion while quantifying uncertainty due to missing data. Such human motion models are relevant for many application domains such as learning by demonstration and motion prediction in human-robot collaboration. For goal-directed tasks it is essential to impose stability constraints on the model representing the human motion. Motivated by learning by demonstration applications, this paper proposes an uncertainty-based control Lyapunov function approach for goal-directed path tracking. We exploit the model fidelity which is related to the location of the training and test data: Our approach actively strives into regions with more demonstration data and thus higher model certainty. This achieves accurate reproduction of the human motion independent of the initial condition and we show that generated trajectories are uniformly globally asymptotically stable. The approach is validated in a nonlinear learning by demonstration task where human-demonstrated motions are reproduced by the learned dynamical system, and higher precision than competitive state of the art methods is achieved.
The Deep Weight Prior.

Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution. In this work, we propose a new type of prior distributions for convolutional neural networks, deep weight prior, that in contrast to previously published techniques, favors empirically estimated structure of convolutional filters e.g., spatial correlations of weights. We define deep weight prior as an implicit distribution and propose a method for variational inference with such type of implicit priors. In experiments, we show that deep weight priors can improve the performance of Bayesian neural networks on several problems when training data are limited. Also, we found that initialization of weights of conventional networks with samples from deep weight prior leads to faster training.
Learning Phenotypes and Dynamic Patient Representations via RNN Regularized Collective Non-Negative Tensor Factorization

Non-negative Tensor Factorization (NTF) has been shown effective to discover clinically relevant and interpretable phenotypes from Electronic Health Records (EHR). Existing NTF based computational phenotyping models aggregate data over the observation window, resulting in the learned phenotypes being mixtures of disease states appearing at different times. We argue that by separating the clinical events happening at different times in the input tensor, the temporal dynamics and the disease progression within the observation window could be modeled and the learned phenotypes will correspond to more specific disease states. Yet how to construct the tensor for data samples with different temporal lengths and properly capture the temporal relationship specific to each individual data sample remains an open challenge. In this paper, we propose a novel Collective Non-negative Tensor Factorization (CNTF) model where each patient is represented by a temporal tensor, and all of the temporal tensors are factorized collectively with the phenotype definitions being shared across all patients. The proposed CNTF model is also flexible to incorporate non-temporal data modality and RNN-based temporal regularization. We validate the proposed model using MIMIC-III dataset, and the empirical results show that the learned phenotypes are clinically interpretable. Moreover, the proposed CNTF model outperforms the state-of-the-art computational phenotyping models for the mortality prediction task.
Tool Embodiment: The Tool’s Output Must Match the User’s Input

The embodiment of tools and rubber hands is believed to involve the modification of two separate body representations: the body schema and the body image, respectively. It is thought that tools extend the capabilities of the body's action schema, whereas prosthetics like rubber hands are incorporated into the body image itself. Contrary to this dichotomy, recent research demonstrated that chopsticks can be embodied perceptually during a modified version of the rubber hand illusion (RHI) in which tools are held by the rubber hand and by the participant. In the present research, two experiments examined tool morpho-functional (tool output affordance, e.g., precision grasping) and sensorimotor (tool input, e.g., precision grip) match as a mechanism for this tool-use dependent change to the body image. Proprioceptive drift in the RHI occurred when the tool's output and the user's input matched, but not when this match was absent. This suggests that this factor may be necessary for tools to interact with the body image in the RHI.
High-performance superconducting photon-number-resolving detectors with 86% system efficiency at telecom range

The use of improved fabrication technology, highly disordered NbN thin films, and intertwined section topology makes it possible to create high-performance photon-number-resolving superconducting single-photon detectors (PNR SSPDs) that are comparable to conventional single-element SSPDs at the telecom range. The developed four-section PNR SSPD has simultaneously an 86±3% system detection efficiency, 35 cps dark count rate, ∼2 ns dead time, and maximum 90 ps jitter. An investigation of the PNR SSPD’s detection efficiency for multiphoton events shows good uniformity across sections. As a result, such a PNR SSPD is a good candidate for retrieving the photon statistics for light sources and quantum key distribution systems.
Cybercrime: an emerging threat to the banking sector of Pakistan

Purpose The purpose of this study is to gain more insight into the impact of cybercrime incidents in the banking sector of Pakistan. This study investigates the significant contribution of information security awareness on the relationship of cybercrimes and organizational performance. Design/methodology/approach The impact of cybercrime incidents on organizational performance is investigated by further exploring the moderating effects of information security awareness. A sample of 302 employees in the banking industry of Pakistan was studied by using survey design. Findings Cybercrime incidents have negative impact on organizational performance, but information security awareness weakens the negative impact of cybercrimes on organizational performance. Research limitations/implications The present study focuses on the banking sector so its finding cannot be generalized in other sectors. Further, in-depth comparative studies in other sectors with different cultural settings will help to authenticate the research findings. Practical implications Information security awareness weakens the negative impact of cybercrimes on organizational performance; therefore, it is important for banks’ HR managers to set up more security training courses to increase employees’ awareness on cybercrimes. Originality/value This study explores the impact of cybercrimes on banks’ performance with the moderating role of employees’ information security awareness. Linking these topics has created a new study within the cybercrimes discipline. The present study also enhances the understanding of employees’ role to combat the impact of cybercrimes on organizational performance.
Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization

Quantization of neural networks has become common practice, driven by the need for efficient implementations of deep neural networks on embedded devices. In this paper, we exploit an oft-overlooked degree of freedom in most networks - for a given layer, individual output channels can be scaled by any factor provided that the corresponding weights of the next layer are inversely scaled. Therefore, a given network has many factorizations which change the weights of the network without changing its function. We present a conceptually simple and easy to implement method that uses this property and show that proper factorizations significantly decrease the degradation caused by quantization. We show improvement on a wide variety of networks and achieve state-of-the-art degradation results for MobileNets. While our focus is on quantization, this type of factorization is applicable to other domains such as network-pruning, neural nets regularization and network interpretability.
Cataloging the visible universe through Bayesian inference in Julia at petascale

A key task in astronomy is to locate astronomical objects in images and to characterize them according to physical parameters such as brightness, color, and morphology. This task, known as cataloging, is challenging for several reasons: many astronomical objects are much dimmer than the sky background, labeled data is generally unavailable, overlapping astronomical objects must be resolved collectively, and the datasets are enormous – terabytes now, petabytes soon. In this work, we infer an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia. Using over 1.3 million threads on 650,000 Intel Xeon Phi cores of the Cori Phase II supercomputer, Celeste achieves a peak rate of 1.54 DP PFLOP/s. Celeste is able to jointly optimize parameters for 188 M stars and galaxies, loading and processing 178 TB across 8192 nodes in 14.6 min. To achieve this, Celeste exploits parallelism at multiple levels (cluster, node, and thread) and accelerates I/O through Cori's burst buffer. Julia's native performance enables Celeste to employ high-level constructs without resorting to hand-written or generated low-level code (C/C++/Fortran) while still achieving petascale performance.
Towards a Knowledge-Based Recommender System for Linking Electronic Patient Records With Continuing Medical Education Information at the Point of Care

Given the limits of human memory, clinicians have trouble recalling therapeutic recommendations, even when the clinician previously judged that the information relevant for the care of a specific patient. To tackle this problem, we present a knowledge-based recommender system prototype that links the electronic patient records to clinical information, previously delivered to the target physician and judged to be potentially beneficial. We developed this prototype within the context of RxTx, a Canadian continuing medical education program. We apply a constraint-based recommendation strategy as follows: (1) clinical experts (taggers) map a set of therapeutic recommendations (called Highlights) to a requirement statement built from the standard clinical codes and supplementary demographic information, when applicable; (2) a matching system identifies patient-Highlights recommendation pairs through requirement satisfaction; and (3) given a patient record being examined, the recommended Highlights can be retrieved online at the point of care. We tested this prototype using electronic medical records from the Canadian Primary Care Sentinel Surveillance Network and 87 therapeutic Highlights from the RxTx collection, evaluating the system's performance against a gold standard consisting of a two-expert consolidated patient-Highlight matching set for 150 patient records. The requirements-based recommendation system exhibits very high precision (mode: 1.0, 89% of the time; average precision: 0.95) and moderate recall (mode 1.0, 48.7% of the time; average recall: 0.61). The near-perfect precision minimizes the possibility of generating alert fatigue in physicians using the system. We note that more than half of the false negative results from the information being available in the text of the electronic medical records, but unavailable as a clinical code. The near-perfect precision over the tested patient set suggests that the system has the potential to deliver high-quality recommendations of clinical information at the point of care while being easily integrated within a continuing medical education program and the clinician's workflow.
Classification Spam Email with Elimination of Unsuitable Features with Hybrid of GA-Naive Bayes

Email spam is a security problem that involves different techniques in machine learning to solve this problem. The rise of this security issue makes organisation email service unreliable and has a direct relation with vulnerability of clients through unexpected spam mails, like ransomware. There are several methods to identifying spam emails. Most of these methods focused on feature selection; however, these models decreased the accuracy of the detection. This paper proposed a novel spam detection method that is not only to decrease the accuracy, but eliminates unsuitable features with less processing. The features are in the terms of contents, and the number of features is very big, so it can decrease the memory complexity. We use Hewlett-Packet (HP) laboratory samples text emails. First, GA algorithm is employed to select features without limited number of feature selection with the aid of Bayesian theory as a fitness function and checked with a different number of repetitions. The result improved with GA by increasing number of repetitions, and tested with distinctive selection method, Random selection and Tournament selection. In the second stage, the dataset classifies emails as Spam or Ham by Naive Bayes. The results show that Naive Bayes and hybrid GA-Naive Bayes are almost identical, but GA-Naive Bayes has a better performance.