Reviewerly/exHarmony huggingface dataset:
Consumer Customs Analysis Using the Association Rule and Apriori Algorithm for Determining Sales Strategies in Retail Central

The sustainability of a company will not be separated from the role of consumers in conducting transactions. In fact, a consumer has different behaviour and character, therefore as a company owner must be able to analyze the patterns or habits of consumers in making transactions. This also happens in the retail center X, which has problems in the sales process, such as products running out of stock and unsold products and the most popular products and products that are not in demand by consumers. Therefore we need an analysis of consumer habits in conducting transactions. The method of association rule with Apriori algorithm is able to be applied well in the analysis of the habits of consumer transactions in the central retail X. The results of the calculation obtained an average percentage of the value of support 33%-40% and the value of confidence 43%-80%. The results of applying the association rule method with Apriori algorithm can help recommend central retail X owners in structuring product and determine strategic steps in increasing sales, such as providing discounts or promos for certain products.
Sensor-To-Sensor Data Association and Preliminary Estimation of Coordinates of Air Targets in Multisensory Radar in Multitarget Conditions

The new method of data association and preliminary estimation of coordinates of air targets in multisensory radar is offered. The method operates in the multitarget conditions when trajectories of the targets are not tracked yet and all targets are observed simultaneously. The method is based on virtual survey of space, uses only total-range measurements and does not demand measurement of additional parameters
Fast search real‐time face recognition based on DCT coefficients distribution

The authors propose an adaptive face recognition algorithm based on the discrete cosine transform (DCT) coefficients approach. For the database's establishment, the face images are pre-processed with colour transform, hair cutting, and background removing to eliminate non-face information. The recognised kernel applied the weights of DCT coefficient distribution with the entire image transformation, to avoid position mismatch and reduce the light effect. The key coefficients of DCT are chosen from the training database by maximum variance. The fast search mode can reject 90% weak candidates with few coefficients to fasten the processing speed. The significant coefficients weighting methods are used to enhance face features. Only using 50 coefficients per picture, the recognition rate can achieve 95% for ORL face database testing. For real-time recognition, camera imaging is processed with algorithms using C-programming based on Windows system. The recognition rate can achieve 95% and the speed is about nine frames per second for real-time recognition in practice.
Coherence-based Modeling of Clinical Concepts Inferred from Heterogeneous Clinical Notes for ICU Patient Risk Stratification

In hospitals, critical care patients are often susceptible to various complications that adversely affect their morbidity and mortality.Digitized patient data from Electronic Health Records (EHRs) can be utilized to facilitate risk stratification accurately and provide prioritized care.Existing clinical decision support systems are heavily reliant on the structured nature of the EHRs.However, the valuable patient-specific data contained in unstructured clinical notes are often manually transcribed into EHRs.The prolific use of extensive medical jargon, heterogeneity, sparsity, rawness, inconsistent abbreviations, and complex structure of the clinical notes poses significant challenges, and also results in a loss of information during the manual conversion process.In this work, we employ two coherence-based topic modeling approaches to model the free-text in the unstructured clinical nursing notes and capture its semantic textual features with the emphasis on human interpretability.Furthermore, we present FarSight, a long-term aggregation mechanism intended to detect the onset of disease with the earliest recorded symptoms and infections.We utilize the predictive capabilities of deep neural models for the clinical task of risk stratification through ICD-9 code group prediction.Our experimental validation on MIMIC-III (v1.4) database underlined the efficacy of FarSight with coherence-based topic modeling, in extracting discriminative clinical features from the unstructured nursing notes.The proposed approach achieved a superior predictive performance when benchmarked against the structured EHR data based state-of-the-art model, with an improvement of 11.50% in AUPRC and 1.16% in AUROC.
Memory Augmented Recursive Neural Networks

Recursive neural networks have shown an impressive performance for modeling compositional data compared to their recurrent counterparts. Although recursive neural networks are better at capturing long range dependencies, their generalization performance starts to decay as the test data becomes more compositional and potentially deeper than the training data. In this paper, we present memory-augmented recursive neural networks to address this generalization performance loss on deeper data points. We augment Tree-LSTMs with an external memory, namely neural stacks. We define soft push and pop operations for filling and emptying the memory to ensure that the networks remain end-to-end differentiable. In order to assess the effectiveness of the external memory, we evaluate our model on a neural programming task introduced in the literature called equation verification. Our results indicate that augmenting recursive neural networks with external memory consistently improves the generalization performance on deeper data points compared to the state-of-the-art Tree-LSTM by up to 10%.
Incremental bit-quads count in component trees: Theory, algorithms, and optimization

Abstract Component tree is a full image representation which encodes all connected components from upper (resp. lower) level sets of a given image through the inclusion relation. Information from this representation can be used in many image processing and computational vision applications, e.g. connected filtering, image segmentation, feature extraction, among others. In general, each node of a component tree represents a connected component of a level set and stores attributes which describes features of this connected component. This paper presents a review of a previously published method to compute attributes such as area, perimeter, and number of Euler by incrementally counting patterns while traversing nodes of a component tree. This method foundation is further detailed in this paper by presenting a novel theoretical background and algorithm correctness intuition. We also present a novel approach for this algorithm showing improvements for run-time execution and precision analysis.
SOCIETY-BASED ENGLISH COMMUNITY (SOBAT): EFL LEARNERS’ STRATEGY IN LEARNING AND PRACTICING ENGLISH OUTSIDE THE WALLS

As for most Indonesian learners of English, classroom is the only place to learn and to practice English either with classmates or teachers. However, they do not to have plenty of opportunities to practice in English when they leave the classrooms. This has encouraged many of the learners seek strategies that allow them to learn and to use English outside the walls of the classrooms. This paper aims to introduce an approach which can be used as an English learning strategy for EFL learners that they can learn and practice English outside the classroom. This approach is hereinafter referred to as the Society-Based English Community (SOBAT). Therefore, the discussion of this paper focuses on three main points. First, it presents a theoretical discussion on the SOBAT concept. Second, it introduces procedures that can be used by EFL learners to establish SOBAT. Third, it outlines several proposed activities that can be carried out in this community. Forming this community is believed to be able to provide four main benefits: providing the EFL learners with plenty of opportunities not only to learn but also to practice English collectively, forming a habit of using English as intensive as possible for the sake of establishing a community of English whose members actively communicate in English and use English the first lingua franca, giving the members a chance to become interactive, creative, active and effective EFL learners, developing their assertiveness, critical thinking, leadership, initiative as well as inclusiveness by taking a part in discussions and debates on important issues of this country.
A Stealthy Hardware Trojan Exploiting the Architectural Vulnerability of Deep Learning Architectures: Input Interception Attack (IIA)

Deep learning architectures (DLA) have shown impressive performance in computer vision, natural language processing and so on. Many DLA make use of cloud computing to achieve classification due to the high computation and memory requirements. Privacy and latency concerns resulting from cloud computing has inspired the deployment of DLA on embedded hardware accelerators. To achieve short time-to-market and have access to global experts, state-of-the-art techniques of DLA deployment on hardware accelerators are outsourced to untrusted third parties. This outsourcing raises security concerns as hardware Trojans can be inserted into the hardware design of the mapped DLA of the hardware accelerator. We argue that existing hardware Trojan attacks highlighted in literature have no qualitative means how definite they are of the triggering of the Trojan. Also, most inserted Trojans show a obvious spike in the number of hardware resources utilized on the accelerator at the time of triggering the Trojan or when the payload is active. In this paper, we introduce a hardware Trojan attack called Input Interception Attack (IIA). In this attack, we make use of the statistical properties of layer-by-layer output to ensure that asides from being stealthy. Our IIA is able to trigger with some measure of definiteness. Moreover, this IIA attack is tested on DLA used to classify MNIST and Cifar-10 data sets. The attacked design utilizes approximately up to 2% more LUTs respectively compared to the un-compromised designs. Finally, this paper discusses potential defensive mechanisms that could be used to combat such hardware Trojans based attack in hardware accelerators for DLA.
Improved Image Compression Scheme Using Hybrid Encoding Algorithm

A color image compression is the most challenging task in the field of multimedia. During last decades several techniques are developed for improving the quality, coding time and compression ratio using different coding strategies. In this work, an effective compression method for hybrid images is proposed based on the discrete wavelet transformation and hybrid encoding algorithm (Huffman and SPIHT). This paper's primary participation is to take advantage of the hybrid encoding technique to maintain the quality of the reconstructed image and the reduction of time complexity. The sample test images are taken from both standard image database and high-quality images (SD and HD). The performance of the proposed scheme is evaluated by using different metrics such as (PSNR, compression ratio and encoding time). Test results indicate that the time and compression ratio of encoding are improved in the expense of the image quality.
Formation of the properties of the structure of disperse-filled polymer composites

The influence of the structure on the properties of the metal-polymer composite consisting of a polymer matrix in the form of epoxy resin (ED-20) with butadiene-styrene rubber (BSK), dispersedly filled with copper nanoparticles, is investigated. In the framework of fractal analysis, the real diameter of the aggregates of the initial filler particles was calculated for various concentrations and size of the filling particles and for different compositions of the polymer matrix. The concept of the structure of a polymer composite as a combination of two fractals (multifractals) was substantially used, which allows to determine the principles of the polymer matrix plasticity changes and to reveal the main factors affecting the degree of perturbation of its structure. Using fractal analysis methods, the influence of factors on the fractal dimension of the surface of initial filler particles aggregates and on the pattern of its dependence both on the degree of aggregation and on the fractal dimension of the frame of the particle aggregate is investigated The proposed approach enables prediction the fi nal parameters of aggregates of nanoparticles as a function of the size of the initial particles, their concentration and chemical properties of the surface of the polymer matrix.
How Trainees Use the Information from Telepointers in Remote Instruction

Researchers have shown both performance drawbacks and benefits of using telepointers or similar display overlay-technologies in remote instruction; however, there is not a clear understanding of why there are these performance effects. This poses a challenge in knowing how and when to successfully use or design telepointing technologies in remote instruction. A better understanding is needed with the rise of remote workers in a wide array of industries from oil rig repair to surgery, and the proliferation of heads-up displays or telecommunications devices to support these future work practices. In this study, we explore how the information conveyed through a telepointer is taken up and acted upon by surgical trainees in a laparoscopic surgical telementoring setting. We collected audio and video data of 12 surgical trainees who performed standard laparoscopic surgical tasks on a physical model under the guidance of a surgical trainer. We investigated both action and talk to determine how the telepointer-based information was used. Our findings reveal three main challenges in using the instructional information conveyed through the telepointer including the trainees' tendency of attending to the telepointer instruction as the primary source of information. We argue that the found challenges are socio-technical in nature and require a redesign of the mentoring context as well as the technological tools.
Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction

Predicting which patients are more likely to be readmitted to a hospital within 30 days after discharge is a valuable piece of information in clinical decision-making.Building a successful readmission risk classifier based on the content of Electronic Health Records (EHRs) has proved, however, to be a challenging task.Previously explored features include mainly structured information, such as sociodemographic data, comorbidity codes and physiological variables.In this paper we assess incorporating additional clinically interpretable NLP-based features such as topic extraction and clinical sentiment analysis to predict early readmission risk in psychiatry patients.
A Computational-Hermeneutic Approach for Conceptual Explicitation

We present a computer-supported approach for the logical analysis and conceptual explicitation of argumentative discourse. Computational hermeneutics harnesses recent progresses in automated reasoning for higher-order logics and aims at formalizing natural-language argumentative discourse using flexible combinations of expressive non-classical logics. In doing so, it allows us to render explicit the tacit conceptualizations implicit in argumentative discursive practices. Our approach operates on networks of structured arguments and is iterative and two-layered. At one layer we search for logically correct formalizations for each of the individual arguments. At the next layer we select among those correct formalizations the ones which honor the argument's dialectic role, i.e. attacking or supporting other arguments as intended. We operate at these two layers in parallel and continuously rate sentences' formalizations by using, primarily, inferential adequacy criteria. An interpretive, logical theory will thus gradually evolve. This theory is composed of meaning postulates serving as explications for concepts playing a role in the analyzed arguments. Such a recursive, iterative approach to interpretation does justice to the inherent circularity of understanding: the whole is understood compositionally on the basis of its parts, while each part is understood only in the context of the whole (hermeneutic circle). We summarily discuss previous work on exemplary applications of human-in-the-loop computational hermeneutics in metaphysical discourse. We also discuss some of the main challenges involved in fully-automating our approach. By sketching some design ideas and reviewing relevant technologies, we argue for the technological feasibility of a highly-automated computational hermeneutics.
Effect of fuzziness on the stability of inertial neural networks with mixed delay via non-reduced-order method

In this paper, without transforming the original inertial neural networks into the first-order differential equation by some variable substitutions, fuzziness, time-varying and distributed delays are introduced into inertial networks and the existence, the uniqueness and the asymptotic stability for the neural networks are investigated. The existence of a unique equilibrium point is proved by using inequality techniques, and the properties of an M-matrix. By finding a new Lyapunov–Krasovskii functional, some sufficient conditions are derived ensuring the asymptotic stability. Finally, three numerical examples with simulation are presented to show the effectiveness of our theoretical results.
Towards Procedural Generation of Narrative Puzzles for Adventure Games

Narrative puzzles involve exploration, logical thinking and progressing a story. This paper presents a narrative design innovation in the form of a system for the procedural generation of such puzzles for use in story-rich games or games with large open worlds. The approach uses an extended type of context-free grammar as the basis for both the generation algorithm and the puzzle solving. Each designer-defined rule in the grammar defines a possible behavior of item types in the game world. Puzzles are generated at runtime on a per area basis, through recursive generation of inputs for outputs. Given a valid grammar, the system guarantees that its puzzles are solvable.
Learning to Flip the Sentiment of Reviews from Non-Parallel Corpora

Canasai Kruengkrai. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Regularized super-resolution restoration algorithm for single medical image based on fuzzy similarity fusion

Abstract Medical images are blurred and noised due to various reasons in the acquirement, transmission and storage. In order to improve the restoration quality of medical images, a regular super-resolution restoration algorithm based on fuzzy similarity fusion is proposed. Based on maintained similarity in multiple scales, the fused similarity of the medical images is computed by fuzzy similarity fusion. First, fuzzy similarity is determined by the regional features. The images with certain similarity are obtained according to the maximum value, and the fused image is obtained by all obvious regional features. Then, an adaptive regularized restoration algorithm is employed. In order to ensure the objective function has a global optimal solution, regularized parameters of the global minimum solution of nonlinear function are solved iteratively. Finally, experimental results show that mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the restored image are visibly improved. The restored image also has an obvious improvement in the burr of local edge. Moreover, the algorithm has good stability with significantly enhanced PSNR.
Convergence Analysis of a Momentum Algorithm with Adaptive Step Size for Non Convex Optimization

Although ADAM is a very popular algorithm for optimizing the weights of neural networks, it has been recently shown that it can diverge even in simple convex optimization examples. Several variants of ADAM have been proposed to circumvent this convergence issue. In this work, we study the ADAM algorithm for smooth nonconvex optimization under a boundedness assumption on the adaptive learning rate. The bound on the adaptive step size depends on the Lipschitz constant of the gradient of the objective function and provides safe theoretical adaptive step sizes. Under this boundedness assumption, we show a novel first order convergence rate result in both deterministic and stochastic contexts. Furthermore, we establish convergence rates of the function value sequence using the Kurdyka-Lojasiewicz property.
The Quantum Strong Exponential-Time Hypothesis

The strong exponential-time hypothesis (SETH) is a commonly used conjecture in the field of complexity theory. It states that CNF formulas cannot be analyzed for satisfiability with a speedup over exhaustive search. This hypothesis and its variants gave rise to a fruitful field of research, fine-grained complexity, obtaining (mostly tight) lower bounds for many problems in P whose unconditional lower bounds are hard to find. In this work, we introduce a framework of Quantum Strong Exponential-Time Hypotheses, as quantum analogues to SETH. Using the QSETH framework, we are able to translate quantum query lower bounds on black-box problems to conditional quantum time lower bounds for many problems in BQP. As an example, we illustrate the use of the QSETH by providing a conditional quantum time lower bound of $Ω(n^{1.5})$ for the Edit Distance problem. We also show that the $n^2$ SETH-based lower bound for a recent scheme for Proofs of Useful Work, based on the Orthogonal Vectors problem holds for quantum computation assuming QSETH, maintaining a quadratic gap between verifier and prover.
Hate Speech Detection on Vietnamese Social Media Text using the Bi-GRU-LSTM-CNN Model

In recent years, Hate Speech Detection has become one of the interesting fields in natural language processing or computational linguistics. In this paper, we present the description of our system to solve this problem at the VLSP shared task 2019: Hate Speech Detection on Social Networks with the corpus which contains 20,345 human-labeled comments/posts for training and 5,086 for public-testing. We implement a deep learning method based on the Bi-GRU-LSTM-CNN classifier into this task. Our result in this task is 70.576% of F1-score, ranking the 5th of performance on public-test set.
Graph Neural Network for Symbol Detection on Document Images

In this paper, we propose a new method to simultaneously detect and classify symbols in floorplan images. This method relies on the very recent developments of Graph Neural Networks (GNN). In the proposed approach, floorplan images are first converted into Region Adjacency Graphs (RAGs). Within those graphs, each node corresponds to a white region in the original image, and each edge indicates an adjacency relationship between two regions encoded by incident nodes. Nodes are attributed using Zernike moments, and edges are characterised using the distance between centers of gravity of connected components. Then, graphs are fed to a dedicated neural network which has been learned to classify the nodes of unknown graphs using both node attributes and topology. The method is evaluated on the ILPIso dataset and obtains very promising results. These results show the interest of using graphs for such a task, especially when input data are noisy.