Reviewerly/exHarmony huggingface dataset:
Crowd-Sourced Chemistry: Considerations for Building a Standardized Database to Improve Omic Analyses

Mass spectrometry (MS) is used in multiple omics disciplines to generate large collections of data. This data enables advancements in biomedical research by providing global profiles of a given system. One of the main barriers to generating these profiles is the inability to accurately annotate omics data, especially small molecules. To complement pre-existing large databases that are not quite complete, research groups devote efforts to generating personal libraries to annotate their data. Scientific progress is impeded during the generation of these personal libraries because the data contained within them is often redundant and/or incompatible with other databases. To overcome these redundancies and incompatibilities, we propose that communal, crowd-sourced databases be curated in a standardized fashion. A small number of groups have shown this model is feasible and successful. While the needs of a specific field will dictate the functionality of a communal database, we discuss some features to consider during database development. Special emphasis is made on standardization of terminology, documentation, format, reference materials, and quality assurance practices. These standardization procedures enable a field to have higher confidence in the quality of the data within a given database. We also discuss the three conceptual pillars of database design as well as how crowd-sourcing is practiced. Generating open-source databases requires front-end effort, but the result is a well curated, high quality data set that all can use. Having a resource such as this fosters collaboration and scientific advancement.
Relaxing the strong triadic closure problem for edge strength inference

Social networks often provide only a binary perspective on social ties: two individuals are either connected or not. While sometimes external information can be used to infer the strength of social ties, access to such information may be restricted or impractical. Sintos and Tsaparas (KDD 2014) first suggested to infer the strength of social ties from the topology of the network alone, by leveraging the Strong Triadic Closure (STC) property. The STC property states that if person A has strong social ties with persons B and C, B and C must be connected to each other as well (whether with a weak or strong tie). Sintos and Tsaparas exploited this to formulate the inference of the strength of social ties as NP-hard optimization problem, and proposed two approximation algorithms. We refine and improve upon this landmark paper, by developing a sequence of linear relaxations of this problem that can be solved exactly in polynomial time. Usefully, these relaxations infer more fine-grained levels of tie strength (beyond strong and weak), which also allows to avoid making arbitrary strong/weak strength assignments when the network topology provides inconclusive evidence. One of the relaxations simultaneously infers the presence of a limited number of STC violations. An extensive theoretical analysis leads to two efficient algorithmic approaches. Finally, our experimental results elucidate the strengths of the proposed approach, and sheds new light on the validity of the STC property in practice.
Digital literacy deficiencies in digital learning among undergraduates

Digital technology have changed the way university students approach learning because it has become a necessity and an integral part of their lives. University students are accustomed to using their digital devices for almost anything such as communication, collaboration, accessing multiple source of information for solutions, etc. Although these digital generation are undeniably engaged with technologies and they are very comfortable and confident using technology to accomplish tasks, many educators tend to assume that students today have digital literacy. Surprisingly, current studies reveal that students do not have the required digital literacy skills for digital learning. Furthermore, scholars mentioned that individual’s level of digital literacy affects a student’s performance positively. This paper presents the deficiencies of digital literacy among university students in a digital learning environment in higher education, implications of these deficiencies and suggests some measures to reduce these deficiencies.
Implementing an Insect Brain Computational Circuit Using III–V Nanowire Components in a Single Shared Waveguide Optical Network

Recent developments in photonics include efficient nanoscale optoelectronic components and novel methods for subwavelength light manipulation. Here, we explore the potential offered by such devices as a substrate for neuromorphic computing. We propose an artificial neural network in which the weighted connectivity between nodes is achieved by emitting and receiving overlapping light signals inside a shared quasi 2D waveguide. This decreases the circuit footprint by at least an order of magnitude compared to existing optical solutions. The reception, evaluation, and emission of the optical signals are performed by neuron-like nodes constructed from known, highly efficient III-V nanowire optoelectronics. This minimizes power consumption of the network. To demonstrate the concept, we build a computational model based on an anatomically correct, functioning model of the central-complex navigation circuit of the insect brain. We simulate in detail the optical and electronic parts required to reproduce the connectivity of the central part of this network using previously experimentally derived parameters. The results are used as input in the full model, and we demonstrate that the functionality is preserved. Our approach points to a general method for drastically reducing the footprint and improving power efficiency of optoelectronic neural networks, leveraging the superior speed and energy efficiency of light as a carrier of information.
Autonomous ESP Learning of Prospective Teachers of Mathematics

The paper deals with theoretical substantiation and construction of the methodological system of autonomous learning of the course "English for Specific Purposes (ESP)" for prospective teachers of Mathematics.The purpose of the study is to prove theoretically the methodological system of autonomous ESP learning for prospective teachers of Mathematics and to test empirically the current students" level of autonomous learning competence.The main components of the methodological system are observed, among them: target component (social demand, requirements, goals, and objectives); methodological component (approaches and principles); procedure component (content and technologies); assessment component (control and results).The main features such as functionality, complexity, openness and conceptual positions of the methodological system are described in the paper.It is said that the designed methodological system of autonomous learning of ESP of prospective teachers of Mathematics can be implemented in the educational process effectively in the presence of grounded pedagogical conditions, among them: training of students" autonomy, pedagogical support, and effective learning environment.The analysis of the study results reveals that during the initial phase of the implementation of autonomous ESP learning, the students have a moderate level of autonomous learning competence and it needs improvement in the process of implementation of the developed system.
Development of Invisible Sensors and a Machine-Learning-Based Recognition System Used for Early Prediction of Discontinuous Bed-Leaving Behavior Patterns

This paper presents a novel bed-leaving sensor system for real-time recognition of bed-leaving behavior patterns. The proposed system comprises five pad sensors installed on a bed, a rail sensor inserted in a safety rail, and a behavior pattern recognizer based on machine learning. The linear characteristic between loads and output was obtained from a load test to evaluate sensor output characteristics. Moreover, the output values change linearly concomitantly with speed to attain the sensor with the equivalent load. We obtained benchmark datasets of continuous and discontinuous behavior patterns from ten subjects. Recognition targets using our sensor prototype and their monitoring system comprise five behavior patterns: sleeping, longitudinal sitting, lateral sitting, terminal sitting, and leaving the bed. We compared machine learning algorithms of five types to recognize five behavior patterns. The experimentally obtained results revealed that the proposed sensor system improved recognition accuracy for both datasets. Moreover, we achieved improved recognition accuracy after integration of learning datasets as a general discriminator.
Identifying quantum phase transitions via geometric measures of nonclassicality

It is shown that divergences in the susceptibility of any geometric measure of nonclassicality are sufficient conditions to identify phase transitions at arbitrary temperature. This establishes that geometric measures of nonclassicality, in any quantum resource theory, are generic tools to investigate phase transitions in quantum systems. For the zero-temperature case, we show that geometric measures of quantum coherence are especially useful for identifying first-order quantum phase transitions and can be a particularly robust alternative to other approaches employing measures of quantum correlations.
Automatic recognition of image of abnormal situation in scenic spots based on Internet of things

Internet of things is an emerging information-aware technology that combines computer vision and artificial intelligence technology to ensure the safety of personnel and facilities in tourist attractions by discovering real-time alarms of abnormal conditions in the monitoring of tourist attractions. Therefore, this paper proposes an automatic image algorithm for tourism scene anomaly based on Internet of things. The algorithm uses the Internet of things intelligent camera in the image acquisition preprocessing platform to collect the tourist scenic spot image. Based on the traditional image segmentation technology, according to the neighborhood-related characteristics of the Markov random field, the dynamic characteristics of continuous frames are added. Reconstruct the Gibbs energy function. The location of the abnormal situation is determined by coding, and a treatment opinion is given. The experimental results show that the proposed algorithm not only considers the spatial information of each pixel and neighboring points, but also adds the time information of successive frames, accumulates the energy values of all the pixels in the whole image and analyzes the data with the energy curve. It can accurately and efficiently identify the abnormal situation images of tourist attractions.
How Far Are We From Quantifying Visual Attention in Mobile HCI?

With an ever-increasing number of mobile devices competing for attention, quantifying when, how often, or for how long users look at their devices has emerged as a key challenge in mobile human-computer interaction. Encouraged by recent advances in automatic eye contact detection using machine learning and device-integrated cameras, we provide a fundamental investigation into the feasibility of quantifying overt visual attention during everyday mobile interactions. In this article, we discuss the main challenges and sources of error associated with sensing visual attention on mobile devices in the wild, including the impact of face and eye visibility, the importance of robust head poses estimation, and the need for accurate gaze estimation. Our analysis informs future research on this emerging topic and underlines the potential of eye contact detection for exciting new applications toward next-generation pervasive attentive user interfaces.
MILPIBEA: Algorithm for Multi-objective Features Selection in (Evolving) Software Product Lines

Software Product Lines Engineering (SPLE) proposes techniques to model, create and improve groups of related software systems in a systematic way, with different alternatives formally expressed, e.g., as Feature Models. Selecting the ‘best’ software system(s) turns into a problem of improving the quality of selected subsets of software features (components) from feature models, or as it is widely known, Feature Configuration. When there are different independent dimensions to assess how good a software product is, the problem becomes even more challenging – it is then a multi-objective optimisation problem. Another big issue for software systems is evolution where software components change. This is common in the industry but, as far as we know, there is no algorithm designed to the particular case of multi-objective optimisation of evolving software product lines. In this paper we present MILPIBEA, a novel hybrid algorithm which combines the scalability of a genetic algorithm (IBEA) with the accuracy of a mixed-integer linear programming solver (IBM ILOG CPLEX). We also study the behaviour of our solution (MILPIBEA) in contrast with SATIBEA, a state-of-the-art algorithm in static software product lines. We demonstrate that MILPIBEA outperforms SATIBEA on average, especially for the most challenging problem instances, and that MILPIBEA is the one that continues to improve the quality of the solutions when SATIBEA stagnates (in the evolving context).
Training with Quantization Noise for Extreme Fixed-Point Compression

We tackle the problem of producing compact models, maximizing their accuracy for a given model size. A standard solution is to train networks with Quantization Aware Training, where the weights are quantized during training and the gradients approximated with the Straight-Through Estimator. In this paper, we extend this approach to work beyond int8 fixed-point quantization with extreme compression methods where the approximations introduced by STE are severe, such as Product Quantization. Our proposal is to only quantize a different random subset of weights during each forward, allowing for unbiased gradients to flow through the other weights. Controlling the amount of noise and its form allows for extreme compression rates while maintaining the performance of the original model. As a result we establish new state-of-the-art compromises between accuracy and model size both in natural language processing and image classification. For example, applying our method to state-of-the-art Transformer and ConvNet architectures, we can achieve 82.5% accuracy on MNLI by compressing RoBERTa to 14MB and 80.0 top-1 accuracy on ImageNet by compressing an EfficientNet-B3 to 3.3MB.
Building Search Engine Using Machine Learning Technique

The web is the huge and most extravagant wellspring of data. To recover the information from World Wide Web, Search Engines are commonly utilized. Search engines provide a simple interface for searching for user query and displaying results in the form of the web address of the relevant web page, but using traditional search engines has become very challenging to obtain suitable information. This paper proposed search engine using Machine Learning technique that will give more relevant web pages at top for user queries.
Dirac Formulation for Universal Quantum Gates and Shor’s Integer Factorization in High-frequency Electric Circuits

Quantum computation may well be performed with the use of electric circuits. Especially, the Schrödinger equation can be simulated by a lumped-element model of transmission lines, which is applicable to low-frequency electric circuits. In this paper, we show that the Dirac equation is simulated by a distributed-element model, which is applicable to high-frequency electric circuits. Then, a set of universal quantum gates (the Hadamard, phase-shift and CNOT gates) is proposed by networks made of transmission lines. We demonstrate Shor’s prime factorization based on electric circuits. It will be possible to simulate any quantum algorithms simply by designing networks of metallic wires, although the scalability is absent in the present scheme.
Masked convolutional neural network for supervised learning problems

Convolutional neural networks (CNNs) have exhibited superior performance in various types of classification and prediction tasks, but their interpretability remains to be low despite years of research effort. It is crucial to improve the ability of existing models to interpret deep neural networks from both theoretical and practical perspectives and to develop new neural network models with interpretable representations. The aim of this paper is to propose a set of novel masked CNN (MCNN) models with better ability to interpret networks and more accurate prediction. The key ideas behind MCNNs are to introduce a latent binary network to extract informative regions of interest that contain important signals for prediction and to integrate the latent binary network with CNNs to achieve better prediction in various supervised learning problems. Extensive numerical studies demonstrate the competitive performance of the proposed MCNN models.
Towards a New Indicator for Evaluating Universities Based on Twitter Sentiment Analysis

Due to the rapid growth of social media platforms as a medium for providing opinions such as Twitter, a tremendous amount of informal statements based on opinions for various university related topics are created. Investigating tweet sentiments in the context of educational institutions can act as a vital complementary source to compare and evaluate universities. This paper deploys sentiment analysis methods to analyze the collected tweets about four chosen universities in Michigan, where each tweet conveys an opinion or feedback. The tweets were classified according to their sentiment into "Positive," "Negative," or "Neutral" using Machine Learning classifiers. Accordingly, the percentage of "Positive" tweets was used to carry out a comparison between the chosen universities. The results can be utilized by institutions policy makers to improve their educational environments. In addition, universities comparison and evaluation results can be enhanced using such vital indicators.
Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication

Symbolic regression is a powerful system identification technique in industrial scenarios where no prior knowledge on model structure is available. Such scenarios often require specific model properties such as interpretability, robustness, trustworthiness and plausibility, that are not easily achievable using standard approaches like genetic programming for symbolic regression. In this chapter we introduce a deterministic symbolic regression algorithm specifically designed to address these issues. The algorithm uses a context-free grammar to produce models that are parameterized by a non-linear least squares local optimization procedure. A finite enumeration of all possible models is guaranteed by structural restrictions as well as a caching mechanism for detecting semantically equivalent solutions. Enumeration order is established via heuristics designed to improve search efficiency. Empirical tests on a comprehensive benchmark suite show that our approach is competitive with genetic programming in many noiseless problems while maintaining desirable properties such as simple, reliable models and reproducibility.
Approximal operator with application to audio inpainting

In their recent evaluation of time-frequency representations and structured sparsity approaches to audio inpainting, Lieb and Stark (2018) have used a particular mapping as a proximal operator. This operator serves as the fundamental part of an iterative numerical solver. However, their mapping is improperly justified. The present article proves that their mapping is indeed a proximal operator, and also derives its proper counterpart. Furthermore, it is rationalized that Lieb and Stark's operator can be understood as an approximation of the proper mapping. Surprisingly, in most cases, such an approximation (referred to as the approximal operator) is shown to provide even better numerical results in audio inpainting compared to its proper counterpart, while being computationally much more effective.
Elpis, an accessible speech-to-text tool

Elpis is a speech-to-text tool which has been designed to give language workers, including linguists and speech scientists, access to cutting-edge automatic speech recognition software, without the specialist training typically required to run these systems. Our presentation would demonstrate local and server-based versions of Elpis using sample data from the Abui (ISO 639: abz) language, about 17,000 speakers in Indonesia. Attendees would gain a sense of the benefits that a first-pass ASR transcription can bring to transcription workflows.
Continuous-time quantum walks in the presence of a quadratic perturbation

We address the properties of continuous-time quantum walks with Hamiltonians of the form $\mathcal{H}=L+\ensuremath{\lambda}{L}^{2}$, with $L$ the Laplacian matrix of the underlying graph and the perturbation $\ensuremath{\lambda}{L}^{2}$ motivated by its potential use to introduce next-nearest-neighbor hopping. We consider cycle, complete, and star graphs as paradigmatic models with low and high connectivity and/or symmetry. First, we investigate the dynamics of an initially localized walker. Then we devote attention to estimating the perturbation parameter $\ensuremath{\lambda}$ using only a snapshot of the walker dynamics. Our analysis shows that a walker on a cycle graph spreads ballistically independently of the perturbation, whereas on complete and star graphs one observes perturbation-dependent revivals and strong localization phenomena. Concerning the estimation of the perturbation, we determine the walker preparations and the simple graphs that maximize the quantum Fisher information. We also assess the performance of position measurement, which turns out to be optimal, or nearly optimal, in several situations of interest. Besides fundamental interest, our study may find applications in designing enhanced algorithms on graphs.
The Binary Equilibrium Optimization Algorithm with Sigmoid Transfer Functions

The binary version of bio-inspired approaches is used frequently in modern digital world. We therefore propose a new kind of binary version of the equilibrium optimization algorithm which was just raised before. Causing the universal usage for Sigmoid function, we carried out the simulation experiments on the values of β parameter for Sigmoid function. Results show that the value of β should be large in the interval of 2 to 5 for most unimodal and multimodal, and even bottom flat-like benchmark functions. Unfortunately, some of the multimodal and bottom flat-like benchmark functions need smaller value of β. Even more, some of them such as Salomon function lack the discipline for β parameter. Conclusions could be made that specific experiment should be carried out for given problems before real application.
Extracting Smoking Status from Electronic Health Records Using NLP and Deep Learning.

Half a million people die every year from smoking-related issues across the United States. It is essential to identify individuals who are tobacco-dependent in order to implement preventive measures. In this study, we investigate the effectiveness of deep learning models to extract smoking status of patients from clinical progress notes. A Natural Language Processing (NLP) Pipeline was built that cleans the progress notes prior to processing by three deep neural networks: a CNN, a unidirectional LSTM, and a bidirectional LSTM. Each of these models was trained with a pre- trained or a post-trained word embedding layer. Three traditional machine learning models were also employed to compare against the neural networks. Each model has generated both binary and multi-class label classification. Our results showed that the CNN model with a pre-trained embedding layer performed the best for both binary and multi- class label classification.