Reviewerly/exHarmony huggingface dataset:
Eye tracking for work-related visual search: a cognitive task analysis

Cognitive Task Analysis (CTA) is an important methodology in ergonomics for studying workplaces and work patterns. Using eye tracking as a CTA methodology, this article explores visual search patterns in complex work environments and situations. It presents a simulated crime scene case study that applies eye tracking-based experiments in foraging and sense-making loops to elicit and represent knowledge on expert versus novice search patterns for complex work. The case probes the visual search task of preliminarily evaluating and documenting potential crime scene evidence. The experimental protocol relies on the ASL Mobile Eye and the analyses of experimental data include preliminary inspections of live-viewing data on eye-movements, precedence matrices detailing scan paths, and gaze charts that illustrate participants' attention based on fixation counts and durations. In line with the CTA methodology, the article uses concept maps to represent knowledge derived from different phases of the study. The article also discusses the research implications and methodologically reflects on the case study. Practitioner summary: This study offers valuable insights for work design. The use of eye tracking as a CTA methodology offers potentials for translating visual search tasks into defined visual search concepts for complex work environments and situations. The ability to model visual attention is valuable for work designs that improve complex work performance, reduce work stress, and promote work satisfaction.
Design and Control of Automated Guided Vehicle

Autonomous Guided Vehicles (AGV) have the ability to operate on their own, perform tasks without human intervention. However, the investment cost is very high, so it is not suitable for countries with cheap workers like Vietnam. Therefore, this article will aim to build a complete AGV model that can move to the given coordinates. Model of AGV is 6-wheeled vehicles driven by 4 engines with the advantage of good movement in soft terrain, subsidence, ruggedness, slope, easy to balance and limit the vibration. Furthermore, we propose a method to improve the quality of the traditional A* algorithm by eliminating unnecessary intermediate points, which is Shortcut Path Reduction (SPR). Experimental results show that the vehicle can perform the bending technique, following straight lines and broken lines. Besides, the SPR not only met the requirements but also reduced the length of the journey.
Where are you?

This paper describes our submission as Team-Petrichor to the competition that was organized by the SHL recognition challenge dataset authors. We compared multiple machine learning approach for classifying eight different activities (Still, Walk, Run, Bike, Car, Bus, Train, Subway). The first step was feature engineering, a wide set of statistical domain features were computed and their quality was evaluated. Finally, the appropriate machine learning model was chosen. The recognition result for the testing dataset will be presented in the summary paper of the SHL recognition challenge.
A Suite of Metrics for Calculating the Most Significant Security Relevant Software Flaw Types

The Common Weakness Enumeration (CWE) is a prominent list of software weakness types. This list is used by vulnerability databases to describe the underlying security flaws within analyzed vulnerabilities. This linkage opens the possibility of using the analysis of software vulnerabilities to identify the most significant weaknesses that enable those vulnerabilities. We accomplish this through creating mashup views combining CWE weakness taxonomies with vulnerability analysis data. The resulting graphs have CWEs as nodes, edges derived from multiple CWE taxonomies, and nodes adorned with vulnerability analysis information (propagated from children to parents). Using these graphs, we develop a suite of metrics to identify the most significant weakness types (using the perspectives of frequency, impact, exploitability, and overall severity).
A proposed approach to determining expertise level of StackOverflow programmers based on mining of user comments

The popularity of Question–Answer websites such as StackOverflow, Ask and Yahoo! Answers is gradually increasing. Considering this increased popularity, the quality of the questions and answers is necessary to be taken into account. The reason for this necessity is that there could be many answers to any question, and some of these answers have a low level in terms of quality. The credibility and expertise of the questioner and the respondents in the field of the question is one of the solutions to get around this problem. In other words, individuals with a high level of expertise ask more difficult and high-quality questions in their field of expertise, and individuals with a high level of expertise can answer these questions appropriately. The present paper aims at finding the expertise level of the individuals based on available statistical data about questions and answers. For this purpose, two methods were tested. In the first method, which is performed through scoring in this study, the emphasis is on the scores of the questions and answers. The basic assumption of the scoring method is that the scores of the questions and the answers are related to each other, because individuals with a higher level of expertise raise questions with higher scores and, on the other hand, respondents also provide answers with higher scores. Therefore, it is possible to determine the level of expertise of individuals by examining the scores of the questions and answers. In the second method, which is named comment-mining here, the expertise of an individual is determined through scoring positive and negative words in the comments and ultimately obtaining a final score for the questions and answers of a user. The actual data on the StackOverflow website were used to perform these methods. The results of the scoring method show that there is no significant relationship between the scores of the questions and answers, so this method cannot be used to determine the level of expertise of users. But the results from the comment-mining method suggest that the method determines the expertise level of user to an acceptable level.
One‐shot video‐based person re‐identification with variance subsampling algorithm

Abstract Previous works propose the distance‐based sampling for unlabeled datapoints to address the few‐shot person re‐identification task, however, many selected samples may be assigned with wrong labels due to poor feature quality in these works, which negatively affects the learning procedure. In this article, we propose a novel sampling strategy to improve the quality of assigned pseudo‐labels, thus promoting the final performance. To illustrate, we first propose the concept of variance confidence to measure the credibility of pseudo‐labels, then we apply a novel variance subsampling algorithm to improve the accuracy of the selected sample labels. Our method combines distance confidence and variance confidence as a two‐round sampling criterion. Meanwhile, a variation decay strategy is used in our sampling process in combination with the actual distribution of features. We evaluate our approach on two publicly available datasets, MARS and DukeMTMC‐VideoReID, and achieve state‐of‐the‐art one‐shot performance.
HTN Plan Repair via Model Transformation

To make planning feasible, planning models abstract from many details of the modeled system. When executing plans in the actual system, the model might be inaccurate in a critical point, and plan execution may fail. There are two options to handle this case: the previous solution can be modified to address the failure (plan repair), or the planning process can be re-started from the new situation (re-planning). In HTN planning, discarding the plan and generating a new one from the novel situation is not easily possible, because the HTN solution criteria make it necessary to take already executed actions into account. Therefore all approaches to repair plans in the literature are based on specialized algorithms. In this paper, we discuss the problem in detail and introduce a novel approach that makes it possible to use unchanged, off-the-shelf HTN planning systems to repair broken HTN plans. That way, no specialized solvers are needed.
Collaborative Sequential State Estimation Under Unknown Heterogeneous Noise Covariance Matrices

We study the problem of distributed sequential estimation of common states and measurement noise covariance matrices of hidden Markov models by networks of collaborating nodes. We adopt a realistic assumption that the true covariance matrices are possibly different (heterogeneous) across the network. This setting is frequent in many distributed real-world systems where the sensors (e.g., radars) are deployed in a spatially anisotropic environment, or where the networks may consist of sensors with different measuring principles (e.g., using different wavelengths). Our solution is rooted in the variational Bayesian paradigm. In order to improve the estimation performance, the measurements and the posterior estimates are communicated among adjacent neighbors within one network hop distance using the information diffusion strategy. The resulting adaptive algorithm selects neighbors with compatible information to prevent degradation of estimates.
First discovery of gold in kimberlite in Xuzhou, Northern Jiangsu Province

Several individual particle native golds were observed in two Mesozoic Group II kimberlites dikes in the southeastern margin of North China Craton (NCC). This discovery provides new information of the nature of the Mesozoic lithospheric mantle beneath the southeastern NCC, the metallogenic setting of gold deposits in the Jiaodong peninsula and the metallogenic theory of decratonic gold deposits.
Imbalanced Learning for Hospital Readmission Prediction using National Readmission Database

In this paper, we propose to use imbalanced learning for hospital readmission prediction. The goal is to predict whether a patient, based on his/her current hospital visit records, is likely going to be re-admitted or not within 30-days after being discharged from the current hospital visit. The main challenge of hospital readmission prediction is twofold: (1) the readmission visits (i.e., the positive class) are a small portion of the total hospital visits, representing a severe class imbalance problem for learning; (2) due to privacy and health regulation, the information available for patient characterization is limited; and is often only limited to the payment level information. However, there are over 80,000 procedures code, representing a high dimensionality and high sparsity problem for learning. Motivated by the above challenges, in this paper, we design an imbalanced learning strategy to create features from patient hospital visit, by combining patient demographic information, ICD-10 clinical modification (CM) and procedure codes (PCS), and Clinical Classification Software Refined (CCSR) conversion. Instead of directly using ICD-10-CM/PCS code to characterize patients, we convert each patient's visit to CCSR code space with a smaller feature space. By using random sampling approach to balance the sample distributions in the training set, our method achieves good performance to predict patient readmission.
Blind compression artifact reduction using dense parallel convolutional neural network

Different artifacts will manifest, whenever an image is compressed by a lossy compression algorithm. Higher frequency details present in the image may tend to be eliminated by compression. In certain cases, compression may introduce small image structures and noise. This phenomenon will limit the image quality thereby making images to appear much less pleasant to the human eye. Furthermore, other machine learning tasks like object detectors performance will be reduced due to compression. In this paper, we introduce a novel deep neural network with densely connected parallel convolutions to remove such artifacts and to recover the original image from its perturbed version. The proposed algorithm is named as densely connected parallel convolutional neural network in short DPCNN. Parallel convolution provides model parallelism and reduce the training burden. Furthermore, the dense skip connections provide short paths for gradient back-propagation and alleviates the gradient vanishing problem. Moreover, skip connections reduce the feature redundancy by combining features from different levels and increases the learning efficiency. However, these skip connections increase the model complexity. A bottleneck layer is used to keep the model compactness and to reduce the model complexity. The proposed approach can be used as a preprocessing step in different computer vision tasks where images are degraded by compression. Different from other methods, the proposed method is able to remove compression artifacts generated at any quality factor (QF). The experiments on benchmark datasets show the superiority of the proposed method over other methods quantitatively and qualitatively.
Incept_LSTM : Accession for human activity concession in automatic surveillance

AbstractAbstractAutomatic monitoring is increasingly being used today, as it helps to detect various unforeseen events that may cause threat. To provide basic classification data, the surveillance operation is monitored via sensors and cameras. Any unfair situation / person / event is a complex activity to detect. It includes various external as well as internal factors such as: context environment, gestures of the person – movement of the body and the hand, muscle strain, personal identity and demography. Automatic testing uses certain variables for identification in literature .In this paper, Incept_LSTM used for video surveillance using deep learning method called Inception-based LSTM for Human Activity Recognition (HAR). The proposed system aims at improving the system performance by monitoring human activities closely. The approach proposed is using Inception v3 as a deep learning model for extraction of the features. And other unit, LSTM is used to capture the temporal or time series data. The training and evaluation data was empirically tested with an accuracy of 91 per cent on UCF crime dataset.Subject Classification: 68U10Keywords: Face recognitionHuman activity recognitionDeep learningBehavioral analysis
Analyzing Psychological Similarity Spaces for Shapes

The cognitive framework of conceptual spaces [3] proposes to represent concepts and properties such as apple and round as convex regions in perception-based similarity spaces.
A Self-identifying Checkerboard-like Pattern for Camera Calibration

This paper proposes a new type of planar pattern for camera calibration featuring its checkerboard-like structure and self-identity. White and black blocks are arranged by a novel method to ensure the uniqueness of each local pattern, and meanwhile maintain all checkerboard corners. The pattern is generated by combining identification units based on a random search strategy. Positions of local patterns can be acquired by searching the global pattern, achieving the benefit of self-identity. Because of the checkerboard-like structure, no burden is introduced in sampling compared to traditional calibrations. Experimental results show the effectiveness of the proposed calibration method. The conceived pattern is especially suitable for the calibration of multi-camera system with limited overlapping scope.
Securing Graph Steganography over Social Networks via Interaction Remapping

The modern social networks are huge and complex, with lots of users and connections, and are well suited for steganography. Recently, Wu et al. introduce a novel steganographic approach through graph structure, which is represented by a series of sequential interactions over online social networking service (SNS). However, since the SNS is public to social users, according to the Kerckhoffs’s principle, if an attacker masters the procedure to reconstruct the graph structure with interactions, the directly embedded data may be exposed. In order to address this problem, we put forward a new approach to remap the corresponding interactions of the vertices of the graph structure by a key, ensuring that even if the attacker reconstructs the graph structure perfectly, he cannot retrieve the directly embedded data. Compared with Wu et al.’s method, our method has a larger capacity for the same number of interactions, which has demonstrated the superiority and applicability.
Genetic Algorithms with the Crossover-Like Mutation Operator for the k-Means Problem

Progress in the development of automatic grouping (clustering) methods, based on solving the p-median and similar problems, is mainly aimed at increasing the computational efficiency of the algorithms, their applicability to larger problems, accuracy, and stability of their results. The researchers' efforts are focused on the development of compromise heuristic algorithms that provide a fairly quick solution with minimal error. The Genetic Algorithms (GAs) with greedy agglomerative crossover procedure and other special GAs for the considered problems demonstrate the best values of the objective function (sum of squared distances) for many practically important problems. Usually, such algorithms do not use any mutation operator, which is common for other GAs.
Optimizing autocatalysis with uncertainty by derivative‐free estimators

Summary A derivative‐free estimator was introduced to alleviate the drawbacks of the conventional Kalman filter when performing nonlinear analyses under different circumstances. In this work, the scaled Unscented Kalman Filter, Divided Difference Kalman filter, and Cubature Kalman filter (CKF) were selected to investigate the effectiveness of these filters in predicting the states of a complex semi‐batch reaction between propionic anhydride and 2‐butanol. The estimator's performance was evaluated under four different case studies, that is, under normal condition, under poor estimator initialization, under disturbances, and under parameter uncertainty. Results from the study show that CKF was the best option for an online dynamic optimization because of its highest degree of accuracy and stability under the normal and noisy conditions. Under normal condition, CKF yielded the lowest root mean square error of 0.61 × 10 −2 . Under uncertain initial condition, disturbance and parameter uncertainty, the lowest error of 1.83 × 10 −2 , 1.04 × 10 −2 , and 0.81 × 10 −2 were obtained, respectively.
The Unmusical Ear: Georg Simon Ohm and the Mathematical Analysis of Sound

This essay presents a detailed analysis of Georg Simon Ohm’s acoustical research between 1839 and 1844. Because of its importance in Hermann von Helmholtz’s subsequent study of sound and hearing, this work is rarely considered on its own terms. A thorough assessment of Ohm’s articles, however, can greatly enrich our understanding of later developments. Based on study of Ohm’s published writings, as well as a lengthy unpublished manuscript, the essay argues that his acoustical research foreshadows an important paradigmatic shift at a time of discursive instability prior to Helmholtz’s influential contributions. Using Ohm’s own dismissal of his supposedly “unmusical ears” as a conceptual frame, the essay describes this shift as a move away from understanding sound primarily in a musical context and toward an increasingly mathematical approach to sound and hearing. As such, Ohm’s work also anticipates a more general change in the role of the senses in nineteenth-century scientific research.
Performance Evaluation and Optimisation for Kyber on the MULTOS IoT Trust-Anchor

The Internet of Things (IoT) may be considered as a distributed, critical infrastructure, consisting of billions of devices, many of which having limited processing capability. However, the security of IoT must not be compromised by these limitations, and defenses need to protect against today's threats, and those predicted for the future. This requires protection against implementation attacks, as well as the ability to load, replace and run, best-practice cryptographic algorithms. Post-Quantum cryptographic algorithms are attracting great interest, and NIST standardization has a competition to find the best. Prior research demonstrated that a Learning With Errors candidate algorithm could be implemented on a smart card chip, however this was a low-level implementation, and not representative of loading the algorithm onto a secured IoT chip platform. In this paper we present analysis from a practical implementation of the Kyber768 CPAPKE public key encryption component on a MULTOS IoT Trust-Anchor chip. The investigation considered memory and speed requirements, and optimizations, and compared the NTT transform version of Kyber, presented in Round 1 of the NIST competition, with the Kroenecker multiplier technique that exploits a hardware crypto-coprocessor. The work began with a generic multi-round multiplier approach, which was then improved using a novel modification of the input data, allowing a built-in modular multiply function to be used, significantly increasing the speed of a multiplication round, and doubling the useable size of the hardware multiplier.
Research on the Construction of a Student Model of an Adaptive Learning System Based on Cognitive Diagnosis Theory

With the promotion of online education, the adaptive learning system has attracted attention due to its good curriculum recommendation function. The student model is an important interface between the adaptive learning system and the user, reflecting the individual characteristics, knowledge status, and cognitive ability of the student. The accuracy of the information in the student model directly affects the quality of the system recommendation service. The traditional student model only judges students based on the basic information and simple test scores. This paper introduces the self-adaptive item bank and adaptive item selection strategy based on the cognitive diagnosis theory that dynamically detects the students' knowledge and analyzes the state according to the answering habits and knowledge mastering status of different students. This paper analyzes and contrasts a variety of traditional cognitive diagnosis theories and proposes a mixed cognitive diagnosis question bank and a selection strategy model to provide strong support for the construction of student models.
Semi-supervised active learning image classification method based on Tri-Training algorithm

This paper proposes an improved Cost-Effective Active Learning (CEAL) method for Deep Image Classification: Tri-CEAL, which was based on the Tri-training algorithm. By implementing the semi-supervised learning Tri-Training algorithm in CEAL, Tri-CEAL can use semi-supervised classification to select high-confidence samples in unlabeled samples for feature learning. At the same time, the active learning strategy in CEAL was improved to an active learning algorithm based on voting entropy, in which unlabeled samples with high information value are selected for manual labeling based on voting entropy. The classification experiments of Tri-CEAL algorithm and CEAL algorithm on CIFAR-10 indicate that the Tri-CEAL significantly reduces the workload of manually labeling samples and has better generalization performance on image classification problems.