Reviewerly/exHarmony huggingface dataset:
Problem Solving and Risk Management Methodology

Intending to lead organizations to continuous improvement, this chapter proposes a methodology that involves three axes: risk management, problem- solving, and feedback experience. This methodology allows organizations to characterize the experiences they have already confronted, as well as new experiences (which can be risks or problems) with the use of taxonomies established by the organization. It also enables them to capitalize and exploit their knowledge base. This work proposes a best-use approach of the past experiences that are similar to a current event and facilitate their treatment and provide solutions. The authors take the feedback as a point of articulation between the two methodologies because it is a mechanism that offers knowledge where it can be found that the organizations must avoid and take advantage of.
Average error determination in the calculation of earthquake epicenter coordinates

Objective. The study is aimed at determining the dependence of the average error in calculating the epicenter coordinates of an earthquake on errors in measuring the velocities of seismic waves for various methods of seismic event localization. Error distribution investigation for the method for determining the earthquake hypocenter coordinates using the Cassinian oval. Methods. The problem was solved using statistical methods: methods of frequency and regression analyzes, means comparison method, and uniform search method. Results. A relationship between the accuracy of measuring the velocities of seismic waves when determining the coordinates of an earthquake epicenter were established for four different earthquake hypocenter coordinates calculation methods. A method for determining the earthquake hypocenter coordinates using the fourth-order figure of the Cassinian oval was proposed. The error distribution density of the Cassinian oval method was compared with the ones of other methods. Conclusion. The results obtained make it possible to choose one or another method for calculating the hypocenter coordinates depending on the specific area in which a seismic event occurred and the locations of seismic sensors.
An Adaptable and Extensible Code Smell Detection Approach

A Code Smell in computer programming is a surface sign that there may be an issue with respect to your framework and the nature of your code. This issue may require refactoring to be fixed. Code smell is an unpleasant usage decision in the plan stage. Code smells are markers of value issues that make a product difficult to keep up and develop. Given the significance of scents in the source code's viability, numerous examinations have investigated the attributes of scents and dissected their impacts on the product's quality. Code smells are attributes of programming thatdemonstrate issue in the code construction and framework plan that bring about the product is being hard to create and keep up. Large, code smells can be recognized through the code structure. The proposed framework approves code smell for foreseeing class change inclination to discover mistake in expectation of progress inclination utilizing code smell.
Autonomy Reconsidered: Towards Developing Multi-agent Systems

An agent’s autonomy can be viewed as the set of physically and computationally grounded algorithms that can be performed by the agent. This view leads to two useful notions related to autonomy: behavior potential and success potential, which can be used to measure of how well an agent fulfills its potential, call fulfillment. Fulfillment and success potential induce partial and total orderings of possible agent algorithms, leading to algorithm-based, capability-centered definitions of levels of autonomy that complement common uses of this phrase. Because the success potential of a multi-agent system can exceed the success potentials of individual agents through synergy effects, the fulfillment of an individual can be augmented through interactions with others, though it can possibly also interfere in the fulfillment of the other agents. Interaction algorithms thus enable multiple agents to coordinate, communicate, or exchange information; these algorithms enable and constrain tradeoffs between augmenting and diminishing other agents. Short case studies are presented to illustrate how the algorithm-based definitions can be used to understand existing systems.
Context Incorporation Techniques for Social Recommender Systems

The problem of information overloading is prevalent in recommendations websites and social networks. Users seek relevant recommendations from like-minded connections. User-item interactions (i.e., ratings) are prevalent in recommendation websites such as Netflix, whereas user-user connections are the interaction sought in social websites such as Twitter. Social recommender systems seek to generate recommendations for users based on similar preferences of their close friends. Because social networks do not normally contain user-item interactions, social recommender systems are typically hybridized with other recommenders (e.g., website recommenders such as Netflix) that provide such interaction. However, current systems are unaware of the user’s additional contextual information when coupled with social counterparts. In this paper, we propose a context-aware deep learning-based recommender system, US-NCF, in support for social recommender systems. Our experiments show US-NCF outperforms state-of-art counterparts.
Customs Risk Assessment Based on Unsupervised Anomaly Detection Using Autoencoders

In this paper we describe our initial findings on a method for improving anomaly detection on a dataset with scarcely labeled data, based on an ongoing use-case with the Belgian Customs Administration (BCA). Data on shipping containers is used to predict the level of risk associated with a shipment, as well as the probability that the shipment is fraudulent. The absence of labeled data prevents the use of supervised machine learning techniques and calls for unsupervised analysis. We employ an autoencoder to learn the distribution of the dataset and detect anomalies, under the assumption that only a fraction of all shipments is fraudulent. The absence of labels in the dataset complicates the evaluation of the autoencoder’s performance. A qualitative approach is taken to assess the assess the properties of the detected anomalies. The variable distributions of the anomalies differ significantly from variable distributions in the complete dataset and are marked interesting by domain experts. To obtain an impression of the quantitative performance in the absence of ground-truths, synthetic data is generated using a variational autoencoder. The preliminary qualitative and quantitative results suggest that autoencoders can provide value for customs risk assessment.
Graph Semantics Based Neighboring Attentional Entity Alignment for Knowledge Graphs

Entity alignment is the task of matching entities in different knowledge graphs if they refer to the same real-world identity. A promising method for entity alignment is to use embedding methods to learn knowledge graph representations and align entities by measuring their embedding distance. However, when dealing with the challenge of structural heterogeneity between knowledge graphs, most existing entity alignment methods ignored the potential evidence provided by entity and relation semantics. In this paper, an entity alignment framework that incorporates graph semantic information with neighboring attention is proposed. The framework leverages both entity and relation semantic information by introducing the attention mechanism into a graph convolutional network module. In particular, an attention mechanism about neighboring relation semantic information is developed in the proposed framework to learn entity representations as well as to ignore unimportant neighborhoods. The experimental results on the real-world dataset WK3L demonstrates that the proposed framework consistently outperforms other state-of-the-art models.
A Semiphysical Approach of Haze Removal for Landsat Image

The presence of haze could seriously contaminate the observations of optical satellite imagery. Haze not only significantly affects the visual interpretation but also reduces the accuracy of map products. In this article, a semiphysical approach is proposed to reduce the haze effects for Landsat image. The proposed approach is based on the physical model of radiative transfer theory and the presence of dark objects. As the depth map of satellite remotely sensed image is almost a constant value, the coarse transmission map of atmosphere is estimated by the haze thickness, other than the scene depth map. The derived coarse transmission is utilized to correct the color shift induced by airlight. For haze veiled textural information, the guided-filter based approach is adopted to refine the coarse transmission map to restore the textural information. Experiments are conducted upon the images acquired by Landsat at different dates and spatial locations. The visual interpretation upon the dehazed results suggests that the proposed approach could generate visually promising results and preserve spectral properties of land surfaces. Moreover, it also performs favorably against several state-of-the-art deep learning based methods and a classic algorithm. The results of quantitative assessments demonstrate that the developed approach could enhance the information of Landsat scene with minimum spectrum changes. With the visual and quantitative assessments, we can conclude that the proposed approach is a promising solution for Landsat image dehazing. It is expected to improve the data quality of hazy scene and expand the usability of Landsat data.
Construction of Ternary Bent Functions by FFT-Like Permutation Algorithms

Binary bent functions have a strictly specified number of non-zero values. In the same way, ternary bent functions satisfy certain requirements on the elements of their value vectors. These requirements can be used to specify six classes of ternary bent functions. Classes are mutually related by encoding of function values. Given a basic ternary bent function, other functions in the same class can be constructed by permutation matrices having a block structure similar to that of the factor matrices appearing in the Good-Thomas decomposition of Cooley-Tukey Fast Fourier transform and related algorithms.
Generative adversarial networks for open information extraction

Open information extraction (Open IE) is a core task of natural language processing (NLP). Even many efforts have been made in this area, and there are still many problems that need to be tackled. Conventional Open IE approaches use a set of handcrafted patterns to extract relational tuples from the corpus. Secondly, many NLP tools are employed in their procedure; therefore, they face error propagation. To address these problems and inspired by the recent success of Generative Adversarial Networks (GANs), we employ an adversarial training architecture and name it Adversarial-OIE. In Adversarial-OIE, the training of the Open IE model is assisted by a discriminator, which is a (Convolutional Neural Network) CNN model. The goal of the discriminator is to differentiate the extraction result generated by the Open IE model from the training data. The goal of the Open IE model is to produce high-quality triples to cheat the discriminator. A policy gradient method is leveraged to co-train the Open IE model and the discriminator. In particular, due to insufficient training, the discriminator usually leads to the instability of GAN training. We use the distant supervision method to generate training data for the Adversarial-OIE model to solve this problem. To demonstrate our approach, an empirical study on two large benchmark dataset shows that our approach significantly outperforms many existing baselines.
Automated reflective optical system alignment: analysis and experiments

We previously demonstrated automated alignment of a multi-element reflective system using existing internal imaging components and model-based optimal estimation in simulation. Using multiple versions of Kalman filtering, we were able to consistently bring the linear misalignments from around 1 mm to &lt; 5 &mu;m and the angular misalignments from around 500 arcsec to &lt; 6 arcsec. This paper presents our preliminary experimental results and further analysis on the system. Specifically, we conduct information-based observability analysis to investigate the multi-state coupling effect and to evaluate our algorithm design, which provides insight into future improvements. We also discuss the current error sources in the experiments.
A Comparative Study of Filters for Denoising Mammograms

Mammogram is an effective tool for analyzing breast images for screening breast cancer and other abnormalities. The preprocessing of mammogram includes eliminating the noise effects which may be added during image acquisition. In this work, Mean, Gaussian, Wiener, Median, Weighted median and Hybrid median filters performances are analyzed as denoising methods applied to mammograms. The quantitative measures like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index Measure (SSIM) are used as means of performance comparison parameters.
Single-shot dual-wavelength Polarized Microscope to detect Malaria-infected Erythrocytes via birefringence response

The implementation of a dual-wavelength polarized microscope to obtain the birefringence response of Malaria-infected Erythrocytes is presented. By retrieving the dichroism images of blood samples, those containing the Plasmodium parasite can be detected.
Detecting shilling groups in recommender systems based on hierarchical topic model

In a group shilling attack, attackers work collaboratively to inject fake profiles aiming to obtain desired recommendation result. This type of attacks is more harmful to recommender systems than individual shilling attacks. Previous studies pay much attention to detect individual attackers, and little work has been done on the detection of shilling groups. In this work, we introduce a topic modeling method of natural language processing into shilling attack detection and propose a shilling group detection method on the basis of hierarchical topic model. First, we model the given dataset to a series of user rating documents and use the hierarchical topic model to learn the specific topic distributions of each user from these rating documents to describe user rating behaviors. Second, we divide candidate groups based on rating value and rating time which are not involved in the hierarchical topic model. Lastly, we calculate group suspicious degrees in accordance with several indicators calculated through the analysis of user rating distributions, and use the k-means clustering algorithm to distinguish shilling groups. The experimental results on the Netflix and Amazon datasets show that the proposed approach performs better than baseline methods.
Detection and Correction of Multi-Warping Document Image

In this work, we aim to solve the multi-warping document image problems. We can overcome the limitations of the previous dewarping algorithms to recover the shape of the document. The proposed method is based on a well-defined pattern to simulate the distorted and undistorted connected component of document images. Some pairs of control points are selected for each connected component and its ground truth pattern to define the mapping function between them. The dewarping process transforms the warping connected component according to the geometric transformation defined by the calculated mapping function. Results on document dewarping dataset CBDAR demonstrate the effectiveness of our method. OCR error metrics are also used to evaluate the performance of the proposed approach.
TSRRNet: two-stage reflection removal network with reflective guidance

Due to the shortage of paired images, the training of reflection removal networks relies heavily on synthesized samples, for which the ground truths of transmission and reflection are both known. But most existing CNN-based models cannot fully utilize the reflection information, which may cause performance limitations. In this paper, our goal is to design a novel, to the best of our knowledge, network that can take the reflection layer to refine the transmission layer. To this end, we propose a two-stage generative-adversarial-network-based network, where the first stage is used to obtain the coarse estimation of transmission and reflection, and the second stage is used to achieve the refinement. In addition, instead of just applying two penalty terms on the two coarse predictions in previous works, we consider the coarse reflection as a soft mask overlapped on the transmission and apply the recently proposed gated convolution into the second stage for further refinement. The network is trained with an adversarial frame using WGAN. The experimental results with benchmark datasets indicate that our method outperforms several state-of-the-art networks.
Permutation properties of Dickson and Chebyshev polynomials with connections to number theory

The $k$th Dickson polynomial of the first kind, $D_k(x) \in {\mathbb Z}[x]$, is determined by the formula: $D_k(u+1/u) = u^k + 1/u^k$, where $k \ge 0$ and $u$ is an indeterminate. These polynomials are closely related to Chebyshev polynomials and have been widely studied. Leonard Eugene Dickson proved in 1896 that $D_k(x)$ is a permutation polynomial on ${\mathbb F}_{p^n}$, $p$ prime, if and only if GCD$(k,p^{2n}-1)=1$, and his result easily carries over to Chebyshev polynomials when $p$ is odd. This article continues on this theme, as we find special subsets of ${\mathbb F}_{p^n}$ that are stabilized or permuted by Dickson or Chebyshev polynomials. Our analysis also leads to a factorization formula for Dickson and Chebyshev polynomials and some new results in elementary number theory. For example, we show that if $q$ is an odd prime power, then $\prod\left\{ a \in{\mathbb F}_q^\times : \text{$a$ and $4-a$ are nonsquares} \right\} = 2$.
Automatically Constructing Peer Slices via Semanticand Context-Aware Security Checks in the Linux Kernel

OS kernels enforce many security checks to validate system states. We observe that paths containing security checks are in fact very informative in inferring critical semantics in OS kernel. In particular, Such slices are valuable for detecting kernel semantic bugs because understanding semantics is typically required by the detection. However, there are few studies that address security checks, and constructing these slices is challenging due to not only a lack of clear criteria but also the large and complex OS. In this paper, combining security checks with program slicing, we first systematically study security check peer slices and propose an automatic approach to construct security check peer slices in OS kernel. Using an inter-procedural, semantic- and context-aware analysis, we can find slices sharing similar semantics in similar contexts. Based on the information offered by security check peer slices, we then introduce the Scenarios for semantic vulnerability detection by security check peer slices: missing security check and inaccurate security check. The evaluation results show that our approach can accurately constructing security check peer slices.
Prediction of Emotional Tendency of COVID-19 Speech Based on Pre-training Model

In 2020, the World Health Organization confirmed the new coronavirus (COVID-19) as a pandemic infectious disease. This virus affects the lives of tens of millions of people. Many countries have adopted compulsory measures to require residents to work and live at home and avoid unnecessary contact with the outside world. During this period, people expressed their feelings about the epidemic through various online platforms. In this article, we conducted an effective sentiment analysis on Weibo. These Weibo data are from January 1, 2020 to February 20, 2020, all related to COVID-19. The sentiment analysis of epidemic speech is to analyze people's views on COVID-19 during the epidemic. The results of the study concluded that although the epidemic is gradually getting worse, most people don't take a negative attitude.
THE IMPORTANCE OF GENDER-AWARE DESIGN IN DIGITAL HEALTH WEARABLES: A CO-DESIGN STUDY FOSTERING SUN PROTECTION BEHAVIOUR IN YOUNG MEN

Abstract The rise in the number of people living with long-term conditions is precipitating a shift towards the development of wearable technologies that can monitor our health remotely. This shift makes it possible to rethink the healthcare structure towards awareness and proactive prevention instead of treatment through empowering users, e.g. patients for self-management of their health. In a sector towards individual specialization and relying on personal healthcare management, design has a key role in user participation and engagement. This paper reports a co-design study that empowered the participants as designers to design human-centred interventions that foster sun protection behaviour in young men. The findings from this study show the following: 1) sun protection concepts to enhance young men's engagement in sun protection behaviour, 2) the integration of gender-aware design in digital health wearables contributes towards better user engagement in wearable technologies of the future.
Semantic segmentation with step-by-step upsampling of the fusion context

The existing semantic segmentation network deelabv3+ has the problem of weak segmentation ability to small-scale target objects and rough edge segmentation. The method of parallel connection of multiple resolution subnets in HRNet network is introduced. After deeplabv3+ down sampling, the network layers of different sizes were fused with features, and the decode side was fused with up sampling step by step to improve the edge segmentation accuracy. Attention mechanism is added before feature fusion to improve the recognition ability of small object. At the end, the edge is refined again by using CRF random vector field. The test is carried out on Pascal VOC 2012, the experimental results show that: the segmentation is more detailed from the image edge details, the recognition of small objects is more accurate, the Pixel Accuracy (PA) and Mean Intersection over Union (MIOU) are improved compared with deeplabv3+.
Intelligent mining vulnerabilities in python code snippets

In the software development process, many developers learn from code snippets in the open-source community to implement specific functions. However, few people think about whether these code have vulnerabilities, which provides channels for developing unsafe programs. To this end, this paper constructs a source code snippets vulnerability mining system named PyVul based on deep learning to automatically detect the security of code snippets in the open source community. PyVul builds abstract syntax tree (AST) for the source code to extract its code feature, and then introduces the bidirectional long-term short-term memory (BiLSTM) neural network algorithm to detect vulnerability codes. If it is vulnerable code, the further constructed a multi-classification model could analyze the context discussion contents in associated threads, to classify the code vulnerability type based the content description. Compared with traditional detection methods, this method can identify vulnerable code and classify vulnerability type. The accuracy of the proposed model can reach 85%. PyVul also found 138 vulnerable code snippets in the real public open-source community. In the future, it can be used in the open-source community for vulnerable code auditing to assist users in safe development.