Reviewerly/exHarmony huggingface dataset:
Local earthquakes detection: A benchmark dataset of 3-component seismograms built on a global scale

Machine learning is becoming increasingly important in scientific and technological progress, due to its ability to create models that describe complex data and generalize well. The wealth of publicly-available seismic data nowadays requires automated, fast, and reliable tools to carry out a multitude of tasks, such as the detection of small, local earthquakes in areas characterized by sparsity of receivers. A similar application of machine learning, however, should be built on a large amount of labeled seismograms, which is neither immediate to obtain nor to compile. In this study we present a large dataset of seismograms recorded along the vertical, north, and east components of 1487 broad-band or very broad-band receivers distributed worldwide; this includes 629,095 3-component seismograms generated by 304,878 local earthquakes and labeled as EQ, and 615,847 ones labeled as noise (AN). Application of machine learning to this dataset shows that a simple Convolutional Neural Network of 67,939 parameters allows discriminating between earthquakes and noise single-station recordings, even if applied in regions not represented in the training set. Achieving an accuracy of 96.7, 95.3, and 93.2% on training, validation, and test set, respectively, we prove that the large variety of geological and tectonic settings covered by our data supports the generalization capabilities of the algorithm, and makes it applicable to real-time detection of local events. We make the database publicly available, intending to provide the seismological and broader scientific community with a benchmark for time-series to be used as a testing ground in signal processing.
Improvement of the end-to-end scene text recognition method for “text-to-speech” conversion

Methods for text detection and recognition in images of natural scenes have become an active research topic in computer vision and have obtained encouraging achievements over several benchmarks. In this paper, we introduce a robust yet simple pipeline that produces accurate and fast text detection and recognition for the Uzbek language in natural scene images using a fully convolutional network and the Tesseract OCR engine. First, the text detection step quickly predicts text in random orientations in full-color images with a single fully convolutional neural network, discarding redundant intermediate stages. Then, the text recognition step recognizes the Uzbek language, including both the Latin and Cyrillic alphabets, using a trained Tesseract OCR engine. Finally, the recognized text can be pronounced using the Uzbek language text-to-speech synthesizer. The proposed method was tested on the ICDAR 2013, ICDAR 2015 and MSRA-TD500 datasets, and it showed an advantage in efficiently detecting and recognizing text from natural scene images for assisting the visually impaired.
Acceptability of Using a Robotic Nursing Assistant in Health Care Environments: Experimental Pilot Study

According to the US Bureau of Labor Statistics, nurses will be the largest labor pool in the United States by 2022, and more than 1.1 million nursing positions have to be filled by then in order to avoid a nursing shortage. In addition, the incidence rate of musculoskeletal disorders in nurses is above average in comparison with other occupations. Robot-assisted health care has the potential to alleviate the nursing shortage by automating mundane and routine nursing tasks. Furthermore, robots in health care environments may assist with safe patient mobility and handling and may thereby reduce the likelihood of musculoskeletal disorders.This pilot study investigates the perceived ease of use and perceived usefulness (acceptability) of a customized service robot as determined by nursing students (as proxies for nursing staff in health care environments). This service robot, referred to as the Adaptive Robotic Nurse Assistant (ARNA), was developed to enhance the productivity of nurses through cooperation during physical tasks (eg, patient walking, item fetching, object delivery) as well as nonphysical tasks (eg, patient observation and feedback). This pilot study evaluated the acceptability of ARNA to provide ambulatory assistance to patients.We conducted a trial with 24 participants to collect data and address the following research question: Is the use of ARNA as an ambulatory assistive device for patients acceptable to nurses? The experiments were conducted in a simulated hospital environment. Nursing students (as proxies for nursing staff) were grouped in dyads, with one participant serving as a nurse and the other acting as a patient. Two questionnaires were developed and administrated to the participants based on the Technology Acceptance Model with respect to the two subscales of perceived usefulness and perceived ease of use metrics. In order to evaluate the internal consistency/reliability of the questionnaires, we calculated Cronbach alpha coefficients. Furthermore, statistical analyses were conducted to evaluate the relation of each variable in the questionnaires with the overall perceived usefulness and perceived ease of use metrics.Both Cronbach alpha values were acceptably high (.93 and .82 for perceived usefulness and perceived ease of use questionnaires, respectively), indicating high internal consistency of the questionnaires. The correlation between the variables and the overall perceived usefulness and perceived ease of use metrics was moderate. The average perceived usefulness and perceived ease of use metrics among the participants were 4.13 and 5.42, respectively, out of possible score of 7, indicating a higher-than-average acceptability of this service robot.The results served to identify factors that could affect nurses' acceptance of ARNA and aspects needing improvement (eg, flexibility, ease of operation, and autonomy level).
Remote state preparation of two-component Bose-Einstein condensates

A protocol for remote state preparation is proposed for spin ensembles, where the aim is to prepare a state with a given set of spin expectation values on a remote spin ensemble using entanglement, local spin rotations, and measurements in the Fock basis. The spin ensembles could be realized by thermal atomic ensembles or spinor Bose-Einstein condensates. The protocol works beyond the Holstein-Primakoff approximation, such that spin expectation values for the full Bloch sphere can be prepared. The main practical obstacle is the preparation of the maximally entangled state between the spin ensembles. To overcome this, we examine the protocol using states based on the two-axis two-spin (2A2S) Hamiltonian in place of the maximally entangled state and examine its performance. We find that the version of the protocol with 2A2S squeezing well approximates the maximally entangled state, such that spin averages can be remotely prepared. We evaluate the errors that are introduced by using 2A2S squeezed states, and find that it decreases with the ensemble size. With postselection, errors can be systematically decreased further.
NN-EVCLUS: Neural network-based evidential clustering

Evidential clustering is an approach to clustering based on the use of Dempster-Shafer mass functions to represent cluster-membership uncertainty. In this paper, we introduce a neural-network based evidential clustering algorithm, called NN-EVCLUS, which learns a mapping from attribute vectors to mass functions, in such a way that more similar inputs are mapped to output mass functions with a lower degree of conflict. The neural network can be paired with a one-class support vector machine to make it robust to outliers and allow for novelty detection. The network is trained to minimize the discrepancy between dissimilarities and degrees of conflict for all or some object pairs. Additional terms can be added to the loss function to account for pairwise constraints or labeled data, which can also be used to adapt the metric. Comparative experiments show the superiority of N-EVCLUS over state-of-the-art evidential clustering algorithms for a range of unsupervised and constrained clustering tasks involving both attribute and dissimilarity data.
Teaching software engineering for AI-enabled systems

Software engineers have significant expertise to offer when building intelligent systems, drawing on decades of experience and methods for building systems that are scalable, responsive and robust, even when built on unreliable components. Systems with artificial-intelligence or machine-learning (ML) components raise new challenges and require careful engineering. We designed a new course to teach software-engineering skills to students with a background in ML. We specifically go beyond traditional ML courses that teach modeling techniques under artificial conditions and focus, in lecture and assignments, on realism with large and changing datasets, robust and evolvable infrastructure, and purposeful requirements engineering that considers ethics and fairness as well. We describe the course and our infrastructure and share experience and all material from teaching the course for the first time.
Professional Psychological Profile of a Modern Patrol Officer as the Basis of Efficient Official Activities

In today’s conditions of reforming the law enforcement agencies of Ukraine, it becomes relevant to study the psychological aspects of the professional activities of the National Police officers in the context of the fulfillment of their professional duties. The task of improving the psychological support of patrol police units is of particular importance. The aim of the study is to develop and substantiate the professional psychological profile of a modern patrol officer. On the basis of an empirical study, in which 290 police officers of the National Police of Ukraine participated, the main components of the psychogram of a patrol officer, which form a generalized professional psychological profile of a modern police officer, were identified. Having applied theoretical and empirical methods (the analysis and generalization of scientific literature, observation, questioning, psychological diagnostics, expert interrogation, mathematical statistics, and experiment), the theoretical principles of studying the professional activity of patrol officers, the requirements for their knowledge, skills and individual psychological characteristics were outlined. A number of valid multifactorial psychodiagnostic techniques were applied, in particular, in order to study the level and structure of intelligence; to explore creativity; to investigate the features of nervous processes; to identify the character and temperamental features; to study the features of interpersonal interaction. The study determined a list of professionally important qualities that are required for mastering the profession of a patrol officer, as well as psychological contraindications to acquiring this profession.
Video Frame Interpolation and Enhancement via Pyramid Recurrent Framework

Video frame interpolation aims to improve users' watching experiences by generating high-frame-rate videos from low-frame-rate ones. Existing approaches typically focus on synthesizing intermediate frames using high-quality reference images. However, the captured reference frames may suffer from inevitable spatial degradations such as motion blur, sensor noise, etc. Few studies have approached the joint video enhancement problem, namely synthesizing high-frame-rate and high-quality results from low-frame-rate degraded inputs. In this paper, we propose a unified optimization framework for video frame interpolation with spatial degradations. Specifically, we develop a frame interpolation module with a pyramid structure to cyclically synthesize high-quality intermediate frames. The pyramid module features adjustable spatial receptive field and temporal scope, thus contributing to controllable computational complexity and restoration ability. Besides, we propose an inter-pyramid recurrent module to connect sequential models to exploit the temporal relationship. The pyramid module integrates the recurrent module, thus can iteratively synthesize temporally smooth results. And the pyramid modules share weights across iterations, thus it does not expand the model's parameter size. Our model can be generalized to several applications such as up-converting the frame rate of videos with motion blur, reducing compression artifacts, and jointly super-resolving low-resolution videos. Extensive experimental results demonstrate that our method performs favorably against state-of-the-art methods on various video frame interpolation and enhancement tasks.
2D Linear Trap Array for Quantum Information Processing

We present an ion-lattice quantum processor based on a two-dimensional arrangement of linear surface traps. Our design features a tunable coupling between ions in adjacent lattice sites and a configurable ion-lattice connectivity, allowing one, e.g., to realize rectangular and triangular lattices with the same trap chip. We present detailed trap simulations of a simplest-instance ion array with $2\times9$ trapping sites and report on the fabrication of a prototype device in an industrial facility. The design and the employed fabrication processes are scalable to larger array sizes. We demonstrate trapping of ions in rectangular and triangular lattices and demonstrate transport of a $2\times2$ ion-lattice over one lattice period.
QED at Large: A Survey of Engineering of Formally Verified Software

Development of formal proofs of correctness of programs can increase actual and perceived reliability and facilitate better understanding of program specifications and their underlying assumptions. Tools supporting such development have been available for over 40 years, but have only recently seen wide practical use. Projects based on construction of machine-checked formal proofs are now reaching an unprecedented scale, comparable to large software projects, which leads to new challenges in proof development and maintenance. Despite its increasing importance, the field of proof engineering is seldom considered in its own right; related theories, techniques, and tools span many fields and venues. This survey of the literature presents a holistic understanding of proof engineering for program correctness, covering impact in practice, foundations, proof automation, proof organization, and practical proof development.
Implementation of quantum secret sharing and quantum binary voting protocol in the IBM quantum computer

Quantum secret sharing is a way to share secret messages among the clients in a group with complete security. For the first time, Hillery et al. (Phys Rev A 59:1829, 1999) proposed the quantum version of the classical secret sharing protocol using GHZ states. Here, we implement the above quantum secret sharing protocol in 'IBM Q 5 Tenerife' quantum processor and compare the experimentally obtained results with the theoretically predicted ones. Further, a new quantum binary voting protocol is proposed and implemented in the 14-qubit 'IBM Q 14 Melbourne' quantum processor. The results are analyzed through the technique of quantum state tomography, and the fidelity of states is calculated for a different number of executions made in the device.
Secure Multi-party Quantum Computation with a Dishonest Majority

The cryptographic task of secure multi-party (classical) computation has received a lot of attention in the last decades. Even in the extreme case where a computation is performed between k mutually distrustful players, and security is required even for the single honest player if all other players are colluding adversaries, secure protocols are known. For quantum computation, on the other hand, protocols allowing arbitrary dishonest majority have only been proven for $$k=2$$ . In this work, we generalize the approach taken by Dupuis, Nielsen and Salvail (CRYPTO 2012) in the two-party setting to devise a secure, efficient protocol for multi-party quantum computation for any number of players k, and prove security against up to $$k-1$$ colluding adversaries. The quantum round complexity of the protocol for computing a quantum circuit of $$\{\mathsf {CNOT}, \mathsf {T} \}$$ depth d is $$O(k \cdot (d + \log n))$$ , where n is the security parameter. To achieve efficiency, we develop a novel public verification protocol for the Clifford authentication code, and a testing protocol for magic-state inputs, both using classical multi-party computation.
Explicit programming strategies

Software developers solve a diverse and wide range of problems. While software engineering research often focuses on tools to support this problem solving, the strategies that developers use to solve problems are at least as important. In this paper, we offer a novel approach for enabling developers to follow explicit programming strategies that describe how an expert tackles a common programming problem. We define explicit programming strategies, grounding our definition in prior work both within software engineering and in other professions which have adopted more explicit procedures for problem solving. We then present a novel notation called Roboto and a novel strategy tracker tool that explicitly represent programming strategies and frame executing strategies as a collaborative effort between human abilities to make decisions and computer abilities to structure process and persist information. In a formative evaluation, 28 software developers of varying expertise completed a design task and a debugging task. We found that, compared to developers who are free to choose their own strategies, developers given explicit strategies experienced their work as more organized, systematic, and predictable, but also more constrained. Developers using explicit strategies were objectively more successful at the design and debugging tasks. We discuss the implications of Roboto and these findings, envisioning a thriving ecosystem of explicit strategies that accelerate and improve developers' programming problem solving.
Quantum Ising Hamiltonian Programming in Trio, Quartet, and Sextet Qubit Systems

Rydberg-atom quantum simulators are of keen interest because of their possibilities towards high-dimensional qubit architectures. Here we report three-dimensional conformation spectra of quantum-Ising Hamiltonian systems with programmed qubit connections. With a Rydberg-atom quantum simulator, various connected graphs, in which vertices and edges represent atoms and blockaded couplings, respectively, are constructed in two or three-dimensional space and their eigenenergies are probed during their topological transformations. Star, complete, cyclic, and diamond graphs, and their geometric intermediates, are tested for four atoms and antiprism structures for six atoms. Spectroscopic resolution (dE/E) less than 10% is achieved and the observed energy level shifts and merges through structural transformations are in good agreement with the model few-body quantum-Ising Hamiltonian.
Empirical Analysis of Rank Aggregation-Based Multi-Filter Feature Selection Methods in Software Defect Prediction

Selecting the most suitable filter method that will produce a subset of features with the best performance remains an open problem that is known as filter rank selection problem. A viable solution to this problem is to independently apply a mixture of filter methods and evaluate the results. This study proposes novel rank aggregation-based multi-filter feature selection (FS) methods to address high dimensionality and filter rank selection problem in software defect prediction (SDP). The proposed methods combine rank lists generated by individual filter methods using rank aggregation mechanisms into a single aggregated rank list. The proposed methods aim to resolve the filter selection problem by using multiple filter methods of diverse computational characteristics to produce a dis-joint and complete feature rank list superior to individual filter rank methods. The effectiveness of the proposed method was evaluated with Decision Tree (DT) and Naïve Bayes (NB) models on defect datasets from NASA repository. From the experimental results, the proposed methods had a superior impact (positive) on prediction performances of NB and DT models than other experimented FS methods. This makes the combination of filter rank methods a viable solution to filter rank selection problem and enhancement of prediction models in SDP.
Fast Facial Landmark Detection and Applications: A Survey

&#x0D; Dense facial landmark detection is a key element of face processing pipeline. In this paper we survey and analyze modern neural-network-based facial landmark detection algorithms, focus on approaches that have led to a significant increase in quality over the past few years on datasets with large pose and emotion variability, significant face occlusion, all of which are typical in real-world scenarios. We summarize the improvements into categories, provide quality comparison on difficult and modern in-the-wild datasets: 300 Faces in-the-wild (300W), Annotated Facial Landmarks in-the-wild (AFLW), Wider Facial Landmarks in-the-wild (WFLW), Caltech Occluded Faces in-the-wild (COFW). Additionally, we compare algorithm speed on desktop central and graphical processors, mobile devices. For completeness, we also briefly touch on established methods with open implementations available. Besides, we cover applications and vulnerabilities of the landmark detection algorithms. We hope that generalizations that we make will lead to further algorithm improvements.&#x0D;
Nelder-Mead Simplex Channel Estimation for the RF-DNA Fingerprinting of OFDM Transmitters Under Rayleigh Fading Conditions

The Internet of Things (IoT) is a collection of Internet connected devices capable of interacting with the physical world and computer systems. It is estimated that IoT will consist of more than seventy five billion devices by the year 2025. In addition to the sheer numbers, the need for IoT security is exacerbated by the fact that many of the edge devices employ weak to no encryption of the communication link. It has been estimated that almost 70% of IoT devices use no form of encryption. Previous research has suggested the use of Specific Emitter Identification (SEI), a physical layer technique, as a means of augmenting bit-level security mechanisms such as encryption. Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprinting is an SEI technique that has demonstrated success in discriminating radios operating within a noise only channel. This work extends RF-DNA fingerprinting to the discrimination of radios operating under Rayleigh fading conditions through the use of a Nelder-Mead (N-M) simplex-based channel estimator. The N-M estimator estimates the multipath channel directly from the received waveform; thus, eliminating the need for demodulation that is required when using constellation-based estimators. N-M estimator proves superior to three alternative waveform-based estimation approaches under increasing fading paths/reflections and decreasing Signal-to-Noise Ratio (SNR). Radio discrimination performance is maximized through the assessment of: (i) RF-DNA fingerprints generated from the magnitude versus phase representation of the Gabor transform's coefficients, (ii) a statistic-based classifier versus a neural network-based classifier, and (iii) the size of patch used to subdivide the Gabor-based time-frequency response prior to calculation of the RF-DNA fingerprint features. The resulting RF-DNA fingerprinting process achieves an average percent correct classification of 92.3% or greater for Rayleigh fading channels consisting of: two, three, or five reflections/paths at SNR≥15 dB.