Reviewerly/exHarmony huggingface dataset:
Breaking the 𝒪(<i>n</i>)-Barrier in the Construction of Compressed Suffix Arrays and Suffix Trees

The suffix array, describing the lexicographical order of suffixes of a given text, and the suffix tree, a path-compressed trie of all suffixes, are the two most fundamental data structures for string processing, with plethora of applications in data compression, bioinformatics, and information retrieval. For a length-n text, however, they use Θ(n log n) bits of space, which is often too costly. To address this, Grossi and Vitter [STOC 2000] and, independently, Ferragina and Manzini [FOCS 2000] introduced space-efficient versions of the suffix array, known as the compressed suffix array (CSA) and the FM-index. Sadakane [SODA 2002] then showed how to augment them to obtain the compressed suffix tree (CST). For a length-n text over an alphabet of size σ, these structures use only 𝒪(n log σ) bits. Nowadays, these structures are part of the standard toolbox: modern textbooks spend dozens of pages describing their applications, and they almost completely replaced suffix arrays and suffix trees in space-critical applications. The biggest remaining open question is how efficiently they can be constructed. After two decades, the fastest algorithms still run in 𝒪(n) time [Hon et al., FOCS 2003], which is Θ(logσ n) factor away from the lower bound of Ω(n/ logσ n) (following from the necessity to read the input).In this paper, we make the first in 20 years improvement in n for this problem by proposing a new compressed suffix array and a new compressed suffix tree which admit o(n)-time construction algorithms while matching the space bounds and the query times of the original CSA/CST and the FM-index. More precisely, our structures take 𝒪(n log σ) bits, support SA queries and full suffix tree functionality in 𝒪(logε n) time per operation, and can be constructed in time using 𝒪(n log σ) bits of working space. (For example, if σ = 2, the construction time is We derive this result as a corollary from a much more general reduction: We prove that all parameters of a compressed suffix array/tree (query time, space, construction time, and construction working space) can essentially be reduced to those of a data structure answering new query types that we call prefix rank and prefix selection. Using the novel techniques, we also develop a new index for pattern matching.* The full version of the paper can be accessed at https://arxiv.org/abs/2106.12725.
Logic programming for deliberative robotic task planning

Abstract Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application.
The main tasks of a semiotics of artificial intelligence

Abstract The article indicates the essential tasks of a semiotics of artificial intelligence: studying the way it simulates the expression of intelligence; the way it produces content that is creatively endowed; the ideological assumptions of artificial intelligence within the culture that produces it. Artificial intelligence is, from a semiotic point of view, the predominant technology of fakery in the current era. On the strength of its studies on the false, semiotics can therefore also be applied to the analysis of the fake that, in increasingly sophisticated forms, is produced through artificial intelligence and through the deep learning of neural networks. The article focuses on the adversarial ones, trying to highlight their ideological assumptions and cultural developments, which seem to indicate the entry of human societies and cultures into the ‘realm of the absolute fake’.
Robust Copyright Protection Technique with High-embedding Capacity for Color Images

Copyright violation issues have a growing impact on applications of the digital era, especially images. It is not easy to guarantee the copyright protection of essential information. This paper presents a robust copyright protection technique with high embedding capacity for color images. The technique first fuses the multi-focus images using non-subsampled contourlet transform (NSCT) to ensure that the fused image have rich information than a single image. Further, hash value of the cover media is computed for authentication purposes. The fused image and hash value of the cover is then embedded into the cover media with the help of transformed-domain schemes. To achieve higher security, we apply an encryption scheme on fused media watermark before embedding into the cover. Lastly, a hybrid optimization algorithm is employed to compute an optimal factor, which makes a good imperceptibility and robustness at the same time. We demonstrate that the proposed technique is effective and resistant to common attacks on image datasets. Compared to existing works, this work increases the 9.5% robustness, 8.8% quality with high embedding capacity.
Experimental evaluation of simulated quantum annealing with MTJ-augmented p-bits

The slowing down of Moore’s Law has created an exciting new era of electronics, leading to the emergence of various types of CMOS+X devices and architectures. Here, we present the first experimental demonstration of a probabilistic computer where a stochastic magnetic tunnel junction (sMTJ) drives a powerful CMOS-based field programmable gate array (FPGA) in a heterogeneous compute fabric. We use our machine to experimentally evaluate the simulated quantum annealing (SQA) algorithm, known to closely mimic the behavior of D-Wave’s quantum annealers which implement the transverse field Ising model (TFIM). Our machine matches the exact solution of the TFIM where p-bits in the FPGA are asynchronously driven by the stochastic dynamics of a magnetic tunnel junction. To compare the performance of SQA against classical annealing (CA) in hard combinatorial optimization at large scale, we also design a fully digital emulator of our asynchronous architecture in the FPGA. Our digital system uses 7,085 p-bits to factor up to 26-bit integers and is about 10X faster than optimized Tensor (TPU) and Graphics Processing Units (GPU) at lower power. Surprisingly, we find that the additional replica networks necessary for SQA do not lead to appreciably better performance over an optimized CA that is using the same computational resources. The systematic evaluation of the SQA algorithm we present will be relevant for other types of accelerators, such as photonic or electronic Ising machines and the integrated scaling of our CMOS + sMTJ architecture could lead to orders of magnitude further improvements over TPU and GPUs, according to experimentally-validated projections.
Sentiment Analysis of Health Protocol Policy Using K-Nearest Neighbor and Cosine Similarity

As an effort to handle COVID-19 transmission, the Indonesian government introduced various regulations such as Health Protocol Policy.Meanwhile, the policy has become a public discussion on social media, such as Twitter.To classify people's opinions on the social media, sentiment analysis technique is used.This method is widely utilized in natural language processing (NLP), data mining, and information retrieval.Therefore, this study aimed to analyze Twitter users' opinions toward health protocol policy.There were several approaches to conducting sentiment analysis, including the TF-IDF method for word weighting and the K-Nearest Neighbor algorithm for data classification.Also, the similarity between two documents or texts was measured using cosine similarity.The processed data were categorized into three, namely positive, negative, and neutral.The system classification results showed 43.94% positive sentiment, 9.06% negative, and 46.99% neutral assuming the value of K = 4.
Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database

Introduction Many researchers used machine learning (ML) to predict the prognosis of breast cancer (BC) patients and noticed that the ML model had good individualized prediction performance. Objective The cohort study was intended to establish a reliable data analysis model by comparing the performance of 10 common ML algorithms and the the traditional American Joint Committee on Cancer (AJCC) stage, and used this model in Web application development to provide a good individualized prediction for others. Methods This study included 63145 BC patients from the Surveillance, Epidemiology, and End Results database. Results Through the performance of the 10 ML algorithms and 7th AJCC stage in the optimal test set, we found that in terms of 5-year overall survival, multivariate adaptive regression splines (MARS) had the highest area under the curve (AUC) value (0.831) and F1-score (0.608), and both sensitivity (0.737) and specificity (0.772) were relatively high. Besides, MARS showed a highest AUC value (0.831, 95%confidence interval: 0.820–0.842) in comparison to the other ML algorithms and 7th AJCC stage (all P &lt; 0.05). MARS, the best performing model, was selected for web application development ( https://w12251393.shinyapps.io/app2/ ). Conclusions The comparative study of multiple forecasting models utilizing a large data noted that MARS based model achieved a much better performance compared to other ML algorithms and 7th AJCC stage in individualized estimation of survival of BC patients, which was very likely to be the next step towards precision medicine.
Large-scale genealogical information extraction from handwritten Quebec parish records

This paper presents a complete workflow designed for extracting information from Quebec handwritten parish registers. The acts in these documents contain individual and family information highly valuable for genetic, demographic and social studies of the Quebec population. From an image of parish records, our workflow is able to identify the acts and extract personal information. The workflow is divided into successive steps: page classification, text line detection, handwritten text recognition, named entity recognition and act detection and classification. For all these steps, different machine learning models are compared. Once the information is extracted, validation rules designed by experts are then applied to standardize the extracted information and ensure its consistency with the type of act (birth, marriage and death). This validation step is able to reject records that are considered invalid or merged. The full workflow has been used to process over two million pages of Quebec parish registers from the 19–20th centuries. On a sample comprising 65% of registers, 3.2 million acts were recognized. Verification of the birth and death acts from this sample shows that 74% of them are considered complete and valid. These records will be integrated into the BALSAC database and linked together to recreate family and genealogical relations at large scale.
Multi-modal Multi-view Clustering based on Non-negative Matrix Factorization

By combining related objects, unsupervised machine learning techniques aim to reveal the underlying patterns in a data set. Non-negative Matrix Factorization (NMF) is a data mining technique that splits data matrices by imposing restrictions on the elements' non-negativity into two matrices: one representing the data partitions and the other to represent the cluster prototypes of the data set. This method has attracted a lot of attention and is used in a wide range of applications, including text mining, clustering, language modeling, music transcription, and neuroscience (gene separation). The interpretation of the generated matrices is made simpler by the absence of negative values. In this article, we propose a study on multi-modal clustering algorithms and present a novel method called multi-modal multi-view non-negative matrix factorization, in which we analyze the collaboration of several local NMF models. The experimental results show the value of the proposed approach, which was evaluated using a variety of data sets, and the obtained results are very promising compared to state of art methods.
MLHandwrittenRecognition: Handwritten Digit Recognition using Machine Learning Algorithms

Handwritten digit recognition has remained a topic of interest to computer vision scientists. Its origination precedes the emergence of the machine as it is a crucial component of the digital transformation of the majority of institutions in numerous fields. With the uprising of machine models, choosing a satisfactory and fit algorithm for this multi-class (0-9) classification problem became challenging. This paper aims to compare seven machine learning algorithms in terms of their performance metrics in recognizing handwritten digits employing two datasets. The - Nearest Neighbors (kNN), Support Vector Machine (SVM), Logistic Regression, Neural Network, Random Forest (RF), Naive Bayes, and Decision Tree models are accordingly evaluated concerning the Area Under the Curve (AUC), accuracy (ACC), F1-score (F1), precision (PREC), and recall (REC). The widely used Modified National Institute of Standards and Technology database (MNIST) dataset and the Handwritten Digit Classification dataset (HDC) have been the providers of the images on which this research is conducted. The results confirm that the Neural Networks model is a great classifier for this problem; however, it presents similar results to other machine learning classifiers in several cases. Therefore, this paper does not provide an absolute choice of a classifier for the handwritten digit recognition problem but rather explains the reason behind the performance of each model.
A tutorial on Federated Learning methodology for indoor localization with non-IID fingerprint databases

This paper presents a tutorial on Deep Learning (DL) with Federated Learning (FL)-based indoor localization method for non-Independently and Identically Distributed (non-IID) fingerprinting databases. To this end, this paper explains systematic approaches for addressing privacy concerns and performance degradation issues in non-IID fingerprinting databases. The method presented in this tutorial entails the application of a personalized layer, model reliability, and Layer-wise local model's Weight Change (LWC) information to FL. This tutorial provides intuitions to be considered by future researchers to improve the performance of FL-based fingerprinting localization by summarizing the above-mentioned methods into three FL-based techniques: high-complexity training for performance improvement of local training models, exact characteristics of the local model for global model aggregation, and Bayesian data fusion for probabilistic clustering, to improve FL-based indoor localization performance.
Countering Modal Redundancy and Heterogeneity: A Self-Correcting Multimodal Fusion

Fusing multimodal heterogeneous data plays a vital role in recognition and prediction tasks in various fields, e.g., action recognition and traffic accident forecast. Yet, there remain some key challenges, such as heterogeneous feature interaction and feature redundancies, that significantly affect the performance of multimodal fusion. To tackle these challenges, we first devise a Unified Feature Interaction Module (UFIM) in which a novel orthogonal attention component is designed to obtain fine-grained inter-modal interaction information among heterogeneous features. Then, we propose a novel Self-Correcting Transformer Module (SCTM) which employs a modified transformer to obtain the one-to-many correlation information between the current modal feature and the merged features of other modalities to alleviate the redundancy problem. Extensive experiments on four cross-domain tasks demonstrate the effectiveness and generalization ability of our proposed method.
Student-Generated Videos to Promote Understanding of Chemical Reactions

Students were tasked with the creation of videos of ordinary reactions to promote significant learning of complex concepts underlying chemical transformations. Interactive infographics were used to deliver instructions. Afterward, students planned the experimental setup for the reaction execution and video recording using their mobile phones. The videos and an online questionnaire, also created by the students, were shared with other class members using the visual platform Padlet. The reasoning required to elaborate the questions contributed to a better understanding of the principles underpinning the chemical equation. An exit survey showed that planning and performing the activity were not time-consuming for the students. Marks attained by the students in questions related to chemical reactions improved after completing the activity.
Real quantum amplitude estimation

Abstract We introduce the Real Quantum Amplitude Estimation (RQAE) algorithm, an extension of Quantum Amplitude Estimation (QAE) which is sensitive to the sign of the amplitude. RQAE is an iterative algorithm which offers explicit control over the amplification policy through an adjustable parameter. We provide a rigorous analysis of the RQAE performance and prove that it achieves a quadratic speedup, modulo logarithmic corrections, with respect to unamplified sampling. Besides, we corroborate the theoretical analysis with a set of numerical experiments.
Meta-Auxiliary Learning for Future Depth Prediction in Videos

We consider a new problem of future depth prediction in videos. Given a sequence of observed frames in a video, the goal is to predict the depth map of a future frame that has not been observed yet. Depth estimation plays a vital role for scene understanding and decision-making in intelligent systems. Predicting future depth maps can be valuable for autonomous vehicles to anticipate the behaviours of their surrounding objects. Our proposed model for this problem has a two-branch architecture. One branch is for the primary task of future depth prediction. The other branch is for an auxiliary task of image reconstruction. The auxiliary branch can act as a regularization. Inspired by some recent work on test-time adaption, we use the auxiliary task during testing to adapt the model to a specific test video. We also propose a novel meta-auxiliary learning that learns the model specifically for the purpose of effective test-time adaptation. Experimental results demonstrate that our proposed approach outperforms other alternative methods.
Knowledge extraction from textual data and performance evaluation in an unsupervised context

Among the incoming challenges in monitoring systems, the aggregation, synthesis and management of knowledge through ontological structures hold an essential place. Existing knowledge extraction systems often use a supervised approach that relies on annotated data, inducing implicitly a fastidious annotation process. Current research is towards the definition of unsupervised or semi-supervised systems, allowing a wider range of knowledge extraction. The evaluation of such systems, performing knowledge extraction using natural language processing methods requires performance indicators. The indicators usually used in such evaluations have limitations in the specific context of knowledge extraction for unsupervised ontology population. Thus, the definition of new evaluation methods becomes a need arising from the singularity of the harvested data, especially when these are not annotated. Hence, this article proposes a method for measuring performance in unsupervised context where reference data and extracted data do not overlap optimally. The proposed evaluation method is based on the exploitation of data that serve as a reference but are not specifically linked to the data used for extraction, which makes it an original evaluation method. To apply the performance measure on concrete cases, this paper also presents an unsupervised self-feeding rule-based approach for domain-independent ontology population from textual data.
Application of LSTM Method Combined with Feature Optimization in Chiller Failure Detection

Abstract Aiming at the characteristics of a long short-term memory network (LSTM) which is suitable for processing high-dimensional, strongly coupled, and highly time-dependent data, it combines the advantages of feature selection to reduce the difficulty of learning tasks and improve the performance of model fault diagnosis. This paper proposes an LSTM method combining sequential floating forward search with integrated feature selection for chiller sensor deviation fault detection. The detection results of the proposed method are compared with those of the single feature selection method, and it is concluded that the detection efficiency of the proposed method in chiller sensor deviation fault detection is significantly better than that of the single feature selection method.
Neural network enhanced measurement efficiency for molecular groundstates

Abstract It is believed that one of the first useful applications for a quantum computer will be the preparation of groundstates of molecular Hamiltonians. A crucial task involving state preparation and readout is obtaining physical observables of such states, which are typically estimated using projective measurements on the qubits. At present, measurement data is costly and time-consuming to obtain on any quantum computing architecture, which has significant consequences for the statistical errors of estimators. In this paper, we adapt common neural network models (restricted Boltzmann machines and recurrent neural networks) to learn complex groundstate wavefunctions for several prototypical molecular qubit Hamiltonians from typical measurement data. By relating the accuracy ɛ of the reconstructed groundstate energy to the number of measurements, we find that using a neural network model provides a robust improvement over using single-copy measurement outcomes alone to reconstruct observables. This enhancement yields an asymptotic scaling near ɛ −1 for the model-based approaches, as opposed to ɛ −2 in the case of classical shadow tomography.
A Lightweight Low-dose PET Image Super-resolution Reconstruction Method based on Convolutional Neural Network

PET imaging is one of the most widely used neurological disease screening and diagnosis techniques.Since PET involves the radiation and tolerance of different people, the improvement that has always been focused on is to cut down radiation, in the meantime, ensuring that the generated images with low-dose tracer and generated images with standard-dose tracer have the same details of images.We propose a lightweight low-dose PET super-resolution network (SRPET-Net) based on a convolutional neural network. In this research, We propose a method for accurately recovering highresolution (HR) PET images from low-resolution (LR) PET images. The network learns the details and structure of the image between low-dose PET images and standard-dose PET images and, afterward, reconstructs the PET image by the trained network model.The experiments indicate that the SRPET-Net can achieve a superior peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) values. Moreover, our method has less memory consumption and lower computational cost.In our follow-up work, the technology can be applied to medical imaging in many different directions.