Reviewerly/exHarmony huggingface dataset:
Reservoir Computing Based on Two Parallel Reservoirs Under Identical Electrical Message Injection

In this work, we propose and numerically investigate a scheme for reservoir computing (RC) based on two parallel reservoirs under identical electrical message injection, in which two semiconductor lasers (SLs) under optical feedback are utilized as two parallel reservoirs. For simplifying the system, only one mask signal is employed in this scheme. After multiplying with input data, the masked information is injected into two parallel reservoir lasers (SL1 and SL2). The virtual node states can be obtained from the temporal outputs of two SLs. RC can be accomplished by three ways, namely RC1/RC2 (the virtual node states originating from SL1/SL2 are used for training and testing) and RCM (the merged virtual node states originating from two SLs are used for training and testing). Via chaotic time series prediction task and memory capacity (MC) test, the performance of the RC system is simulated and assessed. The results show that, for a given data processing rate, better prediction performance and higher MC can be realized by RCM through setting suitable mismatched parameters between the two SLs. Under satisfying the requirement for achieving a good performance, the highest data processing rate can be doubled for RCM with respect to that for RC1/RC2.
QFold: quantum walks and deep learning to solve protein folding

Abstract We develop quantum computational tools to predict the 3D structure of proteins, one of the most important problems in current biochemical research. We explain how to combine recent deep learning advances with the well known technique of quantum walks applied to a Metropolis algorithm. The result, QFold, is a fully scalable hybrid quantum algorithm that, in contrast to previous quantum approaches, does not require a lattice model simplification and instead relies on the much more realistic assumption of parameterization in terms of torsion angles of the amino acids. We compare it with its classical analog for different annealing schedules and find a polynomial quantum advantage, and implement a minimal realization of the quantum Metropolis in IBMQ Casablanca quantum system.
Measuring the Novelty of Natural Language Text using the Conjunctive Clauses of a Tsetlin Machine Text Classifier

Most supervised text classification approaches assume a closed world, counting on all classes being present in the data at training time. This assumption can lead to unpredictable behaviour during operation, whenever novel, previously unseen, classes appear. Although deep learning-based methods have recently been used for novelty detection, they are challenging to interpret due to their black-box nature. This paper addresses \emph{interpretable} open-world text classification, where the trained classifier must deal with novel classes during operation. To this end, we extend the recently introduced Tsetlin machine (TM) with a novelty scoring mechanism. The mechanism uses the conjunctive clauses of the TM to measure to what degree a text matches the classes covered by the training data. We demonstrate that the clauses provide a succinct interpretable description of known topics, and that our scoring mechanism makes it possible to discern novel topics from the known ones. Empirically, our TM-based approach outperforms seven other novelty detection schemes on three out of five datasets, and performs second and third best on the remaining, with the added benefit of an interpretable propositional logic-based representation.
A dynamic discarding technique to increase speed and preserve accuracy for YOLOv3

Abstract This paper proposes an acceleration technique to minimise the unnecessary operations on a state-of-the-art machine learning model and thus to improve the processing speed while maintaining the accuracy. After the study of the main bottlenecks that negatively affect the performance of convolutional neural networks, this paper designs and implements a discarding technique for YOLOv3-based algorithms to increase the speed and maintain accuracy. After applying the discarding technique, YOLOv3 can achieve a 22% of improvement in terms of speed. Moreover, the results of this new discarding technique were tested on Tiny-YOLOv3 with three output layers on an autonomous vehicle for pedestrian detection and it achieved an improvement of 48.7% in speed. The dynamic discarding technique just needs one training process to create the model and thus execute the approach, which preserves accuracy. The improved detector based on the discarding technique is able to readily alert the operator of the autonomous vehicle to take the emergency brake of the vehicle in order to avoid collision and consequently save lives.
Stigmergic Independent Reinforcement Learning for Multiagent Collaboration

With the rapid evolution of wireless mobile devices, there emerges an increased need to design effective collaboration mechanisms between intelligent agents to gradually approach the final collective objective by continuously learning from the environment based on their individual observations. In this regard, independent reinforcement learning (IRL) is often deployed in multiagent collaboration to alleviate the problem of a nonstationary learning environment. However, behavioral strategies of intelligent agents in IRL can be formulated only upon their local individual observations of the global environment, and appropriate communication mechanisms must be introduced to reduce their behavioral localities. In this article, we address the problem of communication between intelligent agents in IRL by jointly adopting mechanisms with two different scales. For the large scale, we introduce the stigmergy mechanism as an indirect communication bridge between independent learning agents, and carefully design a mathematical method to indicate the impact of digital pheromone. For the small scale, we propose a conflict-avoidance mechanism between adjacent agents by implementing an additionally embedded neural network to provide more opportunities for participants with higher action priorities. In addition, we present a federal training method to effectively optimize the neural network of each agent in a decentralized manner. Finally, we establish a simulation scenario in which a number of mobile agents in a certain area move automatically to form a specified target shape. Extensive simulations demonstrate the effectiveness of our proposed method.
Personalized Mobile Learning and Course Recommendation System

Mobile-based learning provides new experience to the learners to learn anything from anywhere and anytime by using their portable or mobile device. Vast educational contents and also different media formats can be supported by the mobile devices. Access speed of those materials has also improved a lot. With this advancement, providing required content or materials in the desired format to the learner is essential to the learning management system. Also, it is very important to guide the learner based on their interest in learning. With this outset, the proposed mobile learning system helps the learners to access different courses under different levels and different specializations. The course contents are in different formats called learning objects (LO). In order to provide personalized learning experience to the learner, the system finds the learner's preferences and selects the desired learning objects. It also recommends some specializations with level to the learners to achieve higher grades.
Workload in a group of Latin American teachers during the COVID-19 pandemic

Introduction. The viral disease Covid-19 -currently a pandemic- forced to accommodate measures of social distancing with the intention of limiting its expansion, including: teleworking and online (virtual) teaching. Would the sudden shift from face-to-face classroom to virtual education result in teacher work overload? If so, what are the influencing factors? Objective. Analyze the workload of teachers in Latin America, at three different educational levels, during the COVID-19 pandemic in June and July 2020. Methods. Quantitative, descriptive, cross-sectional study. A 37-question questionnaire was used, with a Hernández-Nieto content validity coefficient of 0.99 and a temporal stability coefficient 0.91. Statistical analysis was performed using Excel 2019, measures of central tendency and frequency analysis were used. Results. 887 teachers were surveyed for whom sociodemographic characteristics are detailed and their personal, technical, institutional and space load factors assessed. Student dropout percentage was also ascertained. Conclusion. The surveyed teachers perceived an increase in workload due to the factors studied, intertwined with an increase in student desertion. The abrupt change in the modality from face-to-face to virtual education has generated a reorganization of work and family life, teleworking from home requires a clear delimitation by teachers of the work schedules and functions involved in the various aspects of online education.
Random sketch learning for deep neural networks in edge computing

Despite the great potential of deep neural networks (DNNs), they require massive weights and huge computational resources, creating a vast gap when deploying artificial intelligence at low-cost edge devices. Current lightweight DNNs, achieved by high-dimensional space pre-training and post-compression, present challenges when covering the resources deficit, making tiny artificial intelligence hard to be implemented. Here we report an architecture named random sketch learning, or Rosler, for computationally efficient tiny artificial intelligence. We build a universal compressing-while-training framework that directly learns a compact model and, most importantly, enables computationally efficient on-device learning. As validated on different models and datasets, it attains substantial memory reduction of ~50–90× (16-bits quantization), compared with fully connected DNNs. We demonstrate it on low-cost hardware, whereby the computation is accelerated by >180× and the energy consumption is reduced by ~10×. Our method paves the way for deploying tiny artificial intelligence in many scientific and industrial applications. Developing lightweight deep neural networks, while essential for edge computing, still remains a challenge. Random sketch learning is a method that creates computationally efficient and compact networks, thus paving the way for deploying tiny machine learning (TinyML) in resource-constrained devices.
BLight: efficient exact associative structure for k-mers

A plethora of methods and applications share the fundamental need to associate information to words for high-throughput sequence analysis. Doing so for billions of k-mers is commonly a scalability problem, as exact associative indexes can be memory expensive. Recent works take advantage of overlaps between k-mers to leverage this challenge. Yet, existing data structures are either unable to associate information to k-mers or are not lightweight enough.We present BLight, a static and exact data structure able to associate unique identifiers to k-mers and determine their membership in a set without false positive that scales to huge k-mer sets with a low memory cost. This index combines an extremely compact representation along with very fast queries. Besides, its construction is efficient and needs no additional memory. Our implementation achieves to index the k-mers from the human genome using 8 GB of RAM (23 bits per k-mer) within 10 min and the k-mers from the large axolotl genome using 63 GB of memory (27 bits per k-mer) within 76 min. Furthermore, while being memory efficient, the index provides a very high throughput: 1.4 million queries per second on a single CPU or 16.1 million using 12 cores. Finally, we also present how BLight can practically represent metagenomic and transcriptomic sequencing data to highlight its wide applicative range.We wrote the BLight index as an open source C++ library under the AGPL3 license available at github.com/Malfoy/BLight. It is designed as a user-friendly library and comes along with code usage samples.
A robust and secure image watermarking scheme using SURF and improved Artificial Bee Colony algorithm in DWT domain

To improve the security, robustness and imperceptibility of watermark scheme, a novel watermark scheme is devised by fusing multiple techniques, including DWT-SVD, Speed-Up Robust Feature (SURF), Random Sample Consensus (RANSAC) algorithm and improved Artificial Bee Colony algorithm named as IABC. To solve the false positive problem in various SVD-based watermark schemes, DWT is employed on the host image and then block wise singular components (maximum) are used for watermark’s principal component insertion. On the one hand, IABC algorithm is applied to optimize the scaling factors to balance invisibility and robustness and on the other hand to improve the convergence speed. Moreover, SURF and RANSAC algorithm are used to correct probable geometrical distortion to maintain watermark synchronization. Experimental outcomes demonstrated that compared with the related existing schemes, this scheme exhibits an overall superior performance in the aspects of security, imperceptibility and robustness.
Named Entity Aware Transfer Learning for Biomedical Factoid Question Answering

Biomedical factoid question answering is an important task in biomedical question answering applications. It has attracted much attention because of its reliability. In question answering systems, better representation of words is of great importance, and proper word embedding can significantly improve the performance of the system. With the success of pretrained models in general natural language processing tasks, pretrained models have been widely used in biomedical areas, and many pretrained model-based approaches have been proven effective in biomedical question-answering tasks. In addition to proper word embedding, name entities also provide important information for biomedical question answering. Inspired by the concept of transfer learning, in this study, we developed a mechanism to fine-tune BioBERT with a named entity dataset to improve the question answering performance. Furthermore, we applied BiLSTM to encode the question text to obtain sentence-level information. To better combine the question level and token level information, we use bagging to further improve the overall performance. The proposed framework was evaluated on BioASQ 6b and 7b datasets, and the results have shown that our proposed framework can outperform all baselines.
Topic Model or Topic Twaddle? Re-evaluating Semantic Interpretability Measures

When developing topic models, a critical question that should be asked is: How well will this model work in an applied setting?Because standard performance evaluation of topic interpretability uses automated measures modeled on human evaluation tests that are dissimilar to applied usage, these models' generalizability remains in question.In this paper, we probe the issue of validity in topic model evaluation and assess how informative coherence measures are for specialized collections used in an applied setting.Informed by the literature, we propose four understandings of interpretability.We evaluate these using a novel experimental framework reflective of varied applied settings, including human evaluations using open labeling, typical of applied research.These evaluations show that for some specialized collections, standard coherence measures may not inform the most appropriate topic model or the optimal number of topics, and current interpretability performance validation methods are challenged as a means to confirm model quality in the absence of ground truth data.
De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention

Distant supervision tackles the data bottleneck in NER by automatically generating training instances via dictionary matching.Unfortunately, the learning of DS-NER is severely dictionary-biased, which suffers from spurious correlations and therefore undermines the effectiveness and the robustness of the learned models.In this paper, we fundamentally explain the dictionary bias via a Structural Causal Model (SCM), categorize the bias into intra-dictionary and inter-dictionary biases, and identify their causes.Based on the SCM, we learn de-biased DS-NER via causal interventions.For intra-dictionary bias, we conduct backdoor adjustment to remove the spurious correlations introduced by the dictionary confounder.For inter-dictionary bias, we propose a causal invariance regularizer which will make DS-NER models more robust to the perturbation of dictionaries.Experiments on four datasets and three DS-NER models show that our method can significantly improve the performance of DS-NER.
CurGraph: Curriculum Learning for Graph Classification

Graph neural networks (GNNs) have achieved state-of-the-art performance on graph classification tasks. Existing work usually feeds graphs to GNNs in random order for training. However, graphs can vary greatly in their difficulty for classification, and we argue that GNNs can benefit from an easy-to-difficult curriculum, similar to the learning process of humans. Evaluating the difficulty of graphs is challenging due to the high irregularity of graph data. To address this issue, we present the CurGraph (Curriculum Learning for Graph Classification) framework, that analyzes the graph difficulty in the high-level semantic feature space. Specifically, we use the infomax method to obtain graph-level embeddings and a neural density estimator to model the embedding distributions. Then we calculate the difficulty scores of graphs based on the intra-class and inter-class distributions of their embeddings. Given the difficulty scores, CurGraph first exposes a GNN to easy graphs, before gradually moving on to hard ones. To provide a soft transition from easy to hard, we propose a smooth-step method, which utilizes a time-variant smooth function to filter out hard graphs. Thanks to CurGraph, a GNN learns from the graphs at the border of its capability, neither too easy or too hard, to gradually expand its border at each training step. Empirically, CurGraph yields significant gains for popular GNN models on graph classification and enables them to achieve superior performance on miscellaneous graphs.
Readiness for digital transformation of higher education in the Covid-19 context: The dataset of Vietnam's students

With the development of digital technology, Vietnam's education has been undergoing significant changes. This is considered one of the eight important fields of the National Digital Transformation, so it needs to take advantage of opportunities to be able to train high-quality human resources according to international standards. Beside, the Covid-19 pandemic has quickly put pressure on the previously predicted trends in education such as the "Future University". This paper shows the data of an investigation on the factors affecting the readiness of Vietnamese students for digital transformation in the above context. The data is built based on the TAM model and sociological investigation method to collect multidimensional information from many perspectives of different individuals to have a basis for assessing the level of influence. The survey includes the main questions corresponding to the independent variables in the model: Self-study ability, Attitude, Perceived Usefulness, Perceived Ease of Use, and Covid-19. The authors distributed the questionnaire online and collected 913 valid responses.
Efficient PVO-Based Reversible Data Hiding by Selecting Blocks With Full-Enclosing Context

In reversible data hiding (RDH) schemes, how to select those smooth pixels, pixel pairs or pixel blocks in order to improve performance is an important issue. For pixel-value-ordering (PVO) based RDH schemes, two existing techniques appear to be defective since only two reference pixels in a block or the right and bottom neighbors of a block are exploited as the context for block selection, and their performance might be only adequate for those smooth images. For rough images, the embedding performance could be affected. In this paper, an efficient block selection method by computing a block’s smoothness with a full-enclosing context (FEC) way is proposed. Obtained results show that the proposed FEC strategy can better estimate a block’s smoothness for PVO-based schemes. Furthermore, a more scalable pairing way is presented for the recently reported location-based PVO predictor. The proposed PVO scheme can be implemented by dividing the cover image and embedding bits into two different types of blocks, respectively. Experimental results show that the marked images by the proposed two-stage PVO scheme have higher visual quality, e.g., the average PSNR for the Kodak image database is 63.31 dB after embedding 10,000 bits, and the gain is 0.16 dB against the best result in the literature. Compared with some state-of-the-art RDH works, the superiority of the proposed algorithm has been verified in extensive experiments.
A Permissioned Blockchain-Based Clinical Trial Service Platform to Improve Trial Data Transparency

The clinical research faces numerous challenges, from patient enrollment to data privacy concerns and regulatory requirements to spiraling costs. Blockchain technology has the potential to overcome these challenges, thus making clinical trials transparent and enhancing public trust in a fair and open process with all stakeholders because of its distinct features such as data immutability and transparency. This paper proposes a permissioned blockchain platform to ensure clinical data transparency and provides secure clinical trial-related solutions. We explore the core functionalities of blockchain applied to clinical trials and illustrate its general principle concretely. These clinical trial operations are automated using the smart contract, which ensures traceability, prevents a posteriori reconstruction, and securely automates the clinical trial. A web-based user interface is also implemented to visualize the data from the blockchain and ease the interaction with the blockchain network. A proof of concept is implemented on Hyperledger Fabric in the case study of clinical management for multiple clinical trials to demonstrate the designed approach's feasibility. Lastly, the experiment results demonstrate the efficiency and usability of the proposed platform.