Reviewerly/exHarmony huggingface dataset:
The pdb2sql Python Package: Parsing, Manipulation and Analysis of PDB Files Using SQL Queries

The analysis of biomolecular structures is a crucial task for a wide range of applications ranging from drug design to protein engineering.The Protein Data Bank (PDB) file format (Burley et al., 2019) is the most popular format to describe biomolecular structures such as proteins and nucleic acids.In this text-based format, each line represents a given atom and entails its main properties such as atom name and identifier, residue name and identifier, chain identifier, coordinates, etc.Several solutions have been developed to parse PDB files into dedicated objects that facilitate the analysis and manipulation of biomolecular structures.This is, for example, the case for the BioPython parser (Cock et al., 2009,@biopdb) that loads PDB files into a nested dictionary, the structure of which mimics the hierarchical nature of the biomolecular structure.Selecting a given sub-part of the biomolecule can then be done by going through the dictionary and selecting the required atoms.Other packages, such as ProDy (Bakan, Meireles, & Bahar, 2011), BioJava (Lafita, 2019), MMTK (Hinsen, 2000) and MDAnalysis (Gowers et al., 2016) to cite a few, also offer solutions to parse PDB files.However, these parsers are embedded in large codebases that are sometimes difficult to integrate with new applications and are often geared toward the analysis of molecular dynamics simulations.Lightweight applications such as pdb-tools (Rodrigues, Teixeira, Trellet, & Bonvin, 2018) lack the capabilities to manipulate coordinates.
PSL: Exploiting Parallelism, Sparsity and Locality to Accelerate Matrix Factorization on x86 Platforms

Matrix factorization is a basis for many recommendation systems. Although alternating least squares with weighted-$$\lambda $$-regularization (ALS-WR) is widely used in matrix factorization with collaborative filtering, this approach unfortunately incurs insufficient parallel execution and ineffective memory access. Thus, we propose a solution for accelerating the ALS-WR algorithm by exploiting parallelism, sparsity and locality on x86 platforms. Our PSL can process 20 million ratings and the speedup using multi-threading is up to 14.5$$\times $$ on a 20-core machine.
Optimization of fuzzy logic controller parameters using modern meta-heuristic algorithm for gantry crane system (GCS)

Robust control of underactuated nonlinear systems is a challenging task using conventional methods because of uncertainties which may lead to failure especially for mechanical and robotic applications. Fuzzy logic controller (FLC) is one of the methods specifically used for designing a robust controller that is able to deal with nonlinearity and achieve satisfactory performance for control applications. FLC uses heuristic information or expert operator knowledge to create its mechanism. However, nonlinearities exist in the control systems make its rule-based FLC design a non-trivial task. Manual trial and error tuning method is common way used for tuning the parameters of FLC to achieve the desired performance of the system. Hence, an automatic tuning using optimization algorithm is necessary. Meta-heuristic optimization algorithms are applied to tune the parameters of FLC automatically. In this paper, three popular meta-heuristic algorithms are used to optimize the scaling factors of FLC, i.e. Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Differential Evolution (DE) to improve the effectiveness of FLC design. A gantry crane system, which is a nonlinear system, is used as test system for this project. The simulation of the optimization of FLC parameters by using meta-heuristics algorithms was successfully achieved. The simulation results show that the optimization techniques for FLC are effective to improve the performance of conventional FLC for gantry crane control, i.e. position control and anti-swing control. In addition, PSO performs better than CS and DE in optimization of FLC for gantry crane system.
The Improved Equilibrium Optimization Algorithm with Levy Flight

It is still remaining the enthusiasm for new algorithms to solve the functions formulated by scientists describing the real world. In this paper, we improve the newly raised equilibrium optimization algorithm called here with Levy flight. Three traditional ways are discussed and the Levy flight to the best candidate in the equilibrium pool for each iteration is proved to perform better and other two proved to be failed. Simulation experiments on representatives of the unimodal and multimodal benchmark functions are carried out and another scalable experiment was carried out. Results showed that the improved algorithm with Levy flight could improve the EOA performance. However, some of the classical benchmark functions such as the Schwefel function remain difficulty to be solved.
Investigating Query Expansion and Coreference Resolution in Question Answering on BERT

The Bidirectional Encoder Representations from Transformers (BERT) model produces state-of-the-art results in many question answering (QA) datasets, including the Stanford Question Answering Dataset (SQuAD). This paper presents a query expansion (QE) method that identifies good terms from input questions, extracts synonyms for the good terms using a widely-used language resource, WordNet, and selects the most relevant synonyms from the list of extracted synonyms. The paper also introduces a novel QE method that produces many alternative sequences for a given input question using same-language machine translation (MT). Furthermore, we use a coreference resolution (CR) technique to identify anaphors or cataphors in paragraphs and substitute them with the original referents. We found that the QA system with this simple CR technique significantly outperforms the BERT baseline in a QA task. We also found that our best-performing QA system is the one that applies these three preprocessing methods (two QE and CR methods) together to BERT, which produces an excellent $$F_1$$ score (89.8 $$F_1$$ points) in a QA task. Further, we present a comparative analysis on the performances of the BERT QA models taking a variety of criteria into account, and demonstrate our findings in the answer span prediction task.
Scheduling with Complex Consumptive Resources for a Planetary Rover

Generating and scheduling activities is particularly challenging when considering both consumptive resources and complex resource interactions such as time-dependent resource usage. We present three methods of determining valid temporal placement intervals for an activity in a temporally grounded plan in the presence of such constraints. We introduce the Max Duration and Probe algorithms which are sound, but incomplete, and the Linear algorithm which is sound and complete for linear rate resource consumption. We apply these techniques to the problem of scheduling awake and asleep episodes for a planetary rover where the awake durations are affected by scheduled activities. We demonstrate how the Probe algorithm performs competitively with the Linear algorithm given an advantageous problem space and well-defined heuristics. We show that the Probe and Linear algorithms outperform the Max Duration algorithm empirically. We then present the runtime differences between the three algorithms. The Probe algorithm is currently base-lined for use in the onboard scheduler for NASA's next planetary rover, the Mars 2020 rover.
A Study on the Design and Effect of Feedback for Virtual Reality Exercise Posture Training

It is important to exercise in the correct posture in order to increase the exercise effect of the core exercise.This paper introduces a system that can train an exercise posture by providing feedback so that a user who performs core exercise in a virtual reality environment can take an accurate posture.It targeted three core movement postures, such as squat, lunge, and bridge, and provides visual feedback and haptic feedback to the user to take an accurate posture.The reference posture is generated by adjusting the expert's posture to the user's body length, and the accuracy of the exercise posture is calculated by comparing the user's posture with the reference posture.The effectiveness of the feedback was verified through user experiments, and the training effects according to the design of the feedback were compared.
Ecotoxicology of altered fractal organization in cells

The continuous release of existing and new/exotic chemicals in the biosphere raises concerns for the sustainability of ecosystems. Ecotoxicology is broadly defined as the study of impacts of environmental contaminants to ecosystems (population and community) and organisms. This discipline is relatively new and is confronted to the daunting task of the understanding of toxicity of chemicals and their mixtures in various species of the food chain from bacteria, yeast, cyano/bacteria, algae, the diverse group of invertebrates (worms, micro crustacean, mollusks) and vertebrates.
A Multi-objective architecture search for generative adversarial networks

This study aimed to search for better network architectures and their hyper-parameters for generative adversarial networks (GANs). To this end, we employ an evolutionary algorithm to search for network architectures and their hyper-parameters. We applied our method to several datasets and demonstrate that our method can discover promising network architectures together with appropriate hyper-parameters.
Calibrated Surrogate Losses for Adversarially Robust Classification

Adversarially robust classification seeks a classifier that is insensitive to adversarial perturbations of test patterns. This problem is often formulated via a minimax objective, where the target loss is the worst-case value of the 0-1 loss subject to a bound on the size of perturbation. Recent work has proposed convex surrogates for the adversarial 0-1 loss, in an effort to make optimization more tractable. A primary question is that of consistency, that is, whether minimization of the surrogate risk implies minimization of the adversarial 0-1 risk. In this work, we analyze this question through the lens of calibration, which is a pointwise notion of consistency. We show that no convex surrogate loss is calibrated with respect to the adversarial 0-1 loss when restricted to the class of linear models. We further introduce a class of nonconvex losses and offer necessary and sufficient conditions for losses in this class to be calibrated. We also show that if the underlying distribution satisfies Massart's noise condition, convex losses can also be calibrated in the adversarial setting.
Combining Embeddings of Input Data for Text Classification

The problem of automatic text classification is an essential part of text analysis. The improvement of text classification can be done at different levels such as a preprocessing step, network implementation, etc. In this paper, we focus on how the combination of different methods of text encoding may affect classification accuracy. To do this, we implemented a multi-input neural network that is able to encode input text using several text encoding techniques such as BERT, neural embedding layer, GloVe, skip-thoughts and ParagraphVector. The text can be represented at different levels of tokenised input text such as the sentence level, word level, byte pair encoding level and character level. Experiments were conducted on seven datasets from different language families: English, German, Swedish and Czech. Some of those languages contain agglutinations and grammatical cases. Two out of seven datasets originated from real commercial scenarios: (1) classifying ingredients into their corresponding classes by means of a corpus provided by Northfork; and (2) classifying texts according to the English level of their corresponding writers by means of a corpus provided by ProvenWord. The developed architecture achieves an improvement with different combinations of text encoding techniques depending on the different characteristics of the datasets. Once the best combination of embeddings at different levels was determined, different architectures of multi-input neural networks were compared. The results obtained with the best embedding combination and best neural network architecture were compared with state-of-the-art approaches. The results obtained with the dataset used in the experiments were better than the state-of-the-art baselines.
The Open Data Challenge: An Analysis of 124,000 Data Availability Statements and an Ironic Lesson about Data Management Plans

Data availability statements can provide useful information about how researchers actually share research data. We used unsupervised machine learning to analyze 124,000 data availability statements submitted by research authors to 176 Wiley journals between 2013 and 2019. We categorized the data availability statements, and looked at trends over time. We found expected increases in the number of data availability statements submitted over time, and marked increases that correlate with policy changes made by journals. Our open data challenge becomes to use what we have learned to present researchers with relevant and easy options that help them to share and make an impact with new research data.
Attention-Based Knowledge Tracing with Heterogeneous Information Network Embedding

Knowledge tracing is a key area of research contributing to personalized education. In recent times, deep knowledge tracing has achieved great success. However, the sparsity of students’ practice data still limits the performance and application of knowledge tracing. An additional complication is that the contribution of the answer record to the current knowledge state is different at each time step. To solve these problems, we propose Attention-based Knowledge Tracing with Heterogeneous Information Network Embedding (AKTHE). First, we describe questions and their attributes with a heterogeneous information network and generate meaningful node embeddings. Second, we capture the relevance of historical data to the current state by using attention mechanism. Experimental results on four benchmark datasets verify the superiority of our method for knowledge tracing.
Memory-Based Deep Neural Attention (mDNA) for Cognitive Multi-Turn Response Retrieval in Task-Oriented Chatbots

One of the important criteria used in judging the performance of a chatbot is the ability to provide meaningful and informative responses that correspond with the context of a user’s utterance. Nowadays, the number of enterprises adopting and relying on task-oriented chatbots for profit is increasing. Dialog errors and inappropriate response to user queries by chatbots can result in huge cost implications. To achieve high performance, recent AI chatbot models are increasingly adopting the Transformer positional encoding and the attention-based architecture. While the transformer performs optimally in sequential generative chatbot models, recent studies has pointed out the occurrence of logical inconsistency and fuzzy error problems when the Transformer technique is adopted in retrieval-based chatbot models. Our investigation discovers that the encountered errors are caused by information losses. Therefore, in this paper, we address this problem by augmenting the Transformer-based retrieval chatbot architecture with a memory-based deep neural attention (mDNA) model by using an approach similar to late data fusion. The mDNA is a simple encoder-decoder neural architecture that comprises of bidirectional long short-term memory (Bi-LSTM), attention mechanism, and a memory for information retention in the encoder. In our experiments, we trained the model extensively on a large Ubuntu dialog corpus, and the results from recall evaluation scores show that the mDNA augmentation approach slightly outperforms selected state-of-the-art retrieval chatbot models. The results from the mDNA augmentation approach are quite impressive.
KmerGO: A Tool to Identify Group-Specific Sequences With k-mers

Capturing group-specific sequences between two groups of genomic/metagenomic sequences is critical for the follow-up identifications of singular nucleotide variants (SNVs), gene families, microbial species or other elements associated with each group. A sequence that is present, or rich, in one group, but absent, or scarce, in another group is considered a “group-specific” sequence in our study. We developed a user-friendly tool, KmerGO, to identify group-specific sequences between two groups of genomic/metagenomic long sequences or high-throughput sequencing datasets. Compared with other tools, KmerGO captures group-specific k-mers (k up to 40bps) with much lower requirements for computing resources in much shorter running time. For a 1.05TB dataset (.fasta), it takes KmerGO about 21.5 hours (including k-mer counting) to return assembled group-specific sequences on a regular stand-alone workstation with no more than 1GB memory. Furthermore, KmerGO can also be applied to capture trait-associated sequences for continuous-trait. Through multi-process parallel computing, KmerGO is implemented with both graphic user interface and command line on Linux and Windows free from any pre-installed supporting environments, packages, and complex configurations. The output group-specific k-mers or sequences from KmerGO could be the inputs of other tools for the downstream discovery of biomarkers, such as genetic variants, species, or genes. KmerGO is available at https://github.com/ChnMasterOG/KmerGO.
“Regulatory Sandboxes” in Russia: New Horizons and Challenges

“Regulatory sandboxes” are regarded as a special mechanism for setting up experimental regulation in the area of digital innovation (especially in financial technologies), creating a special regime for a limited number of participants and for a limited time.Russiahas its own method of experimental regulation, which is not typical but may be helpful for other jurisdictions. There are three approaches to legal experiments (including digital innovations) inRussia. The first approach is accepting special regulation on different issues. There are recent examples of special laws (e.g. Federal Law on the experiment with artificial intelligence technologies inMoscow). An alternative to this option is establishing experimental regulation by an act of the Government if legislation does not prohibit it (e.g. labeling with means of identification). The second approach deals only with Fintech innovations and provides a special mechanism to pilot models of innovative financial technologies. The participants of such a “sandbox” may create a close-to-life model in order to estimate the effects and risks. If the model works fine, the regulation may be amended. The third approach works with creating a universal mechanism of real-life experiments in the sphere of digital innovations based on the special Federal Law and the specific decision of the Government of theRussian Federationor the Bank of Russia in the financial sphere. The author compares the three approaches and their implementation within the framework of Russian legislation and practice and concludes that this experience may be used by developing countries with inflexible regulation, in order to facilitate the development of digital innovations.
Roman Urdu News Headline Classification Empowered With Machine Learning

Roman Urdu has been used for text messaging over the Internet for years especially in Indo-Pak Subcontinent. Persons from the subcontinent may speak the same Urdu language but they might be using different scripts for wri... | Find, read and cite all the research you need on Tech Science Press
A training algorithm for networks of high-variability reservoirs

Abstract Physical reservoir computing approaches have gained increased attention in recent years due to their potential for low-energy high-performance computing. Despite recent successes, there are bounds to what one can achieve simply by making physical reservoirs larger. Therefore, we argue that a switch from single-reservoir computing to multi-reservoir and even deep physical reservoir computing is desirable. Given that error backpropagation cannot be used directly to train a large class of multi-reservoir systems, we propose an alternative framework that combines the power of backpropagation with the speed and simplicity of classic training algorithms. In this work we report our findings on a conducted experiment to evaluate the general feasibility of our approach. We train a network of 3 Echo State Networks to perform the well-known NARMA-10 task, where we use intermediate targets derived through backpropagation. Our results indicate that our proposed method is well-suited to train multi-reservoir systems in an efficient way.
Group Decision-Making Based on Artificial Intelligence: A Bibliometric Analysis

Decisions concerning crucial and complicated problems are seldom made by a single person. Instead, they require the cooperation of a group of experts in which each participant has their own individual opinions, motivations, background, and interests regarding the existing alternatives. In the last 30 years, much research has been undertaken to provide automated assistance to reach a consensual solution supported by most of the group members. Artificial intelligence techniques are commonly applied to tackle critical group decision-making difficulties. For instance, experts’ preferences are often vague and imprecise; hence, their opinions are combined using fuzzy linguistic approaches. This paper reports a bibliometric analysis of the ample literature published in this regard. In particular, our analysis: (i) shows the impact and upswing publication trend on this topic; (ii) identifies the most productive authors, institutions, and countries; (iii) discusses authors’ and journals’ productivity patterns; and (iv) recognizes the most relevant research topics and how the interest on them has evolved over the years.
Learning Sample-Adaptive Intensity Lookup Table for Brain Tumor Segmentation

Intensity variation among MR images increases the difficulty of training a segmentation model and generalizing it to unseen MR images. To solve this problem, we propose to learn a sample-adaptive intensity lookup table (LuT) that adjusts each image’s contrast dynamically so that the resulting images could better serve the subsequent segmentation task. Specifically, our proposed deep SA-LuT-Net consists of an LuT module and a segmentation module, trained in an end-to-end manner: the LuT module learns a sample-specific piece-wise linear intensity mapping function under the guide of the performance of the segmentation module. We develop our SA-LuT-Nets based on two backbone networks, DMFNet and the modified 3D Unet, respectively, and validate them on BRATS2018 dataset for brain tumor segmentation. Our experiment results clearly show the effectiveness of SA-LuT-Net in the scenarios of both single and multi-modalities, which is superior over the two baselines and many other relevant state-of-the-art segmentation models.
Multi-Face Recognition Using CNN for Attendance System

There has been a great technological development in face recognition in the last two decades. Machines these days are verifying the identity automatically for secure transactions, for access control of buildings, surveillance and security tasks, etc. Attendance is recorded everywhere like library, schools and colleges. The professor calling out student names and recording attendance is the traditional approach. The proposed approach deviates from such systems by introducing a novel approach that uses image processing for taking attendance. The face recognition algorithm is utilized to identify the faces for accurate attendance. Student database consisting of images, register numbers and names is collected. Real-time class images and Avengers dataset are considered as sample images for identifying the person. Student faces are trained with the features extracted from them. In recognition of face, local binary pattern histogram algorithm (LBPH) classifier is utilized. If the system recognizes faces, the attendance gets marked immediately; else, it shows unknown.