Reviewerly/exHarmony huggingface dataset:
Financial data processing and forecasting model analysis based on neural network

Financial data is the key to the survival of an enterprise. The analysis of these financial data can not only see the company's own competitive advantages, but also can see the position of the company in the same industry, which can make it easier for the company to formulate financial plans. Improve financial status, improve economic efficiency, and achieve sustainable development of enterprises. This paper constructs an evaluation index system of a company's financial risk from four aspects: solvency, operating ability, profitability and development ability, and establishes the entropy weight TOPSIS method and the SOM neural network financial risk evaluation model through the neural network model. It solves the disadvantage that the weight of the existing AHP and fuzzy evaluation theory is difficult to determine, which leads to the low accuracy of the evaluation result. At the same time, the financial risk evaluation score is calculated on the basis of systematic theoretical research and empirical analysis, which makes the evaluation and calculation of financial risk more scientific and effective.
A Luminance Expansion Method for Displaying HDR Video in SDR Display

HDR video is possible to display the maximum 1200% luminance, however, it is limited in SDR display. In this study, we expand high luminance area considering with perceptual performance to improve a presentation performance of HDR video in the SDR display. As results of objective experiments, it is recognized that the proposed method can improve the presentation performance maximally 0.8dB in WPSNR.
A Critical Analysis of the Attributes of Images Embedded in the Designed E-Content from the Learner's Perspective

An interactive e-text supported with effective images will lead to bringing learning more optimal as per learners’ point of view as it brings things more in visualize form in front of learners. However, very little e-content appears to be beneficial as per the interest and engagement of learners due to its more text description than the visual effect and explanatory power of the information. Furthermore, it is realized that the attention of students may be held with the addition of a suitable visual feature to be included together with the offered electronic text (e-text) in any electronic material. So, to make the e-content a practical learning material, the researcher tried to explore some important attributes of images so that it will be helpful for e-content designers to develop effective and efficacious material in their relevant subject. For this purpose, data from 40 pre-service teacher educators was recorded using a feedback schedule that included open-ended and closed-ended questions about learners' level of satisfaction and engagement. From the analysis, it is identified that specific characteristics of the inserted images like clarity, labeling, and relevancy of the images have emerged as an essential requirement for effective e-content development in the online learning environment.
Toward User Control over Information Access: A Sociotechnical Approach

We study the relationship between Web users and service providers, taking a sociotechnical approach and focusing particularly (but not exclusively) on privacy and security of personal data. Much conventional Web-security practice seeks to protect benevolent parties, both individuals and organizations, against purely malevolent adversaries in an effort to prevent catastrophic events such as data breaches, ransomware attacks, and denial of service. By contrast, we highlight the dynamics among the parties that much conventional security technology seeks to protect. We regard most interactions between users and providers as implicit negotiations that, like the interactions between buyers and sellers in a marketplace, have both adversarial and cooperative aspects. Our goal is to rebalance these negotiations in order to give more power to users; toward that end we advocate the adoption of two techniques, one technical and one organizational. Technically, we introduce the Platform for Untrusted Resource Evaluation (PURE), a content-labeling framework that empowers users to make informed decisions about service providers, reduces the ability of providers to induce behaviors that benefit them more than users, and requires minimal time and effort to use. On the organizational side, we concur with Gordon-Tapiero et al. [19] that a collective approach is necessary to rebalance the power dynamics between users and providers; in particular, we suggest that the data co-op, an organizational form suggested by Ligett and Nissim [25] and Pentland and Hardjono [28], is a natural setting in which to deploy PURE and similar tools.
ResNet Small Target Detection Algorithm Based on Deep Separable Sonvolution

Abstract: Because of the poor effect of traditional small target detection, by analyzing the characteristics of small targets, taking RESNET of deep separable convolution as the feature extraction network, a small target detection algorithm based on RESNET of deep separable convolution is proposed. In order to ensure that the network has good adaptability and strong small target feature extraction ability, the algorithm adopts the topology of the multi convolution kernel. The convolution form of the network is improved by packet convolution to reduce the number of network parameters and computation, and the improved channel shuffling is used to enhance the exchange of feature information between different packets and splice the output features. Finally, combined with the residual connection form, a deep separable packet convolution RESNET network (mower) with multiple convolution cores is formed. The experimental results of the DOTA data set show that the Top1 error rate and top5 error rate of the RESNET network based on deep separable convolution are 30.68% and 8.75%, respectively, which is 3.34% and 1.56% lower than that of traditional RESNET network. The complexity of the model is also reduced, which has obvious advantages over other network models.
Designing Through The Stack: The Case for a Participatory Digital Security By Design

Whilst participatory practice is increasingly adopted in end user studies, there has been far less use of a participatory approach when designing lower down the software stack. As a result, end users are often presented with security controls over which they have no control but for which they retain the responsibility. Conversely, hardware and software engineers struggle to innovate new security control designs that are resilient to new and emerging threats. In a study utilising ethnographic research and stakeholder interviews, we show that there is a siloing of communities of practice between hardware security engineers, software engineers and coders, manufacturers in the technology supply chain and end users. Our findings indicate that this siloing and a lack of participatory practice impedes the development of a more cohesive digital security design that integrates security through the stack from the hardware layer upwards to the OS and application layers. These barriers make difficult the negotiation between what is possible lower down the stack with what is needed and wanted higher up the stack. Our findings suggest that a more holistic and comprehensive participatory design approach is required to negotiate a digital security by design paradigm that more evenly distributes power over and responsibility for security controls throughout the stack. Working with the HCI literature on co-production in design, this paper will suggest that a pathway for breaking through this impasse is to utilise objects in the design process of the hardware secure instruction set architecture as a feedback mechanism to incorporate other sets of designers and users in the design process to create a more workable stack.
Passwords and Cryptwords

Computers get faster every year; brains don’t. Passwords and other memorized credentials have unique usability advantages over tokens and biometrics, so we desire to design secure systems that maintain lengths that users can memorize. Some passwords are subject primarily to online attacks, and are simple to defend with rate limits and lockouts. Others, used to generate encryption keys, must be secure against offline attacks. We coin the term “cryptword” to distinguish these from passwords subject primarily to online attacks.
GAN-based Image Translation Model with Self-Attention for Nighttime Dashcam Data Augmentation

High-performance deep learning-based object detection models can reduce traffic accidents using dashcam images during nighttime driving. Deep learning requires a large-scale dataset to obtain a high-performance model. However, existing object detection datasets are mostly daytime scenes and a few nighttime scenes. Increasing the nighttime dataset is laborious and time-consuming. In such a case, it is possible to convert daytime images to nighttime images by image-to-image translation model to augment the nighttime dataset with less effort so that the translated dataset can utilize the annotations of the daytime dataset. Therefore, in this study, a GAN-based image-to-image translation model is proposed by incorporating self-attention with cycle consistency and content/style separation for nighttime data augmentation that shows high fidelity to annotations of the daytime dataset. Experimental results highlight the effectiveness of the proposed model compared with other models in terms of translated images and FID scores. Moreover, the high fidelity of translated images to the annotations is verified by a small object detection model according to detection results and mAP. Ablation studies confirm the effectiveness of self-attention in the proposed model. As a contribution to GAN-based data augmentation, the source code of the proposed image translation model is publicly available at https://github.com/subecky/Image-Translation-With-Self-Attention
Side Auth: Synthesizing Virtual Sensors for Authentication

While the embedded security research community aims to protect systems by reducing analog sensor side channels, our work argues that sensor side channels can be beneficial to defenders. This work introduces the general problem of synthesizing virtual sensors from existing circuits to authenticate physical sensors’ measurands. We investigate how to apply this approach and present a preliminary analytical framework and definitions for sensors side channels. To illustrate the general concept, we provide a proof-of-concept case study to synthesize a virtual inertial measurement unit from a camera motion side channel. Our work also provides an example of applying this technique to protect facial recognition against silicon mask spoofing attacks. Finally, we discuss downstream problems of how to ensure that side channels benefit the defender, but not the adversary, during authentication.
An Effective Software Vulnerability Detection Method Based On Devised Deep-Learning Model To Fix The Vague Separation

SVD(Software Vulnerability Detection) methods based on automated deep learning is critical in software safety, they are designable and promising. Several function-level deep-learning SVD methods achieve an accuracy of up to 0.97 on open-source C/C++ datasets. However, as vulnerable samples have a low proportion in existing open-source datasets, these methods suffer from high false negative rate, they fail to identify cross-domain software vulnerabilities for neglecting the imbalance and vague separation of existing datasets. This paper proposes a novel framework based on the SeqGAN and TextCNN to fix the vague separation of aggregated 7 open-source C/C++ datasets, therefore improving the performance of SVD. As a result, SeqGAN&TextCNN scores 0.9385 of F1 score, compared with merely adopting the TextCNN, the method achieves an increase of 119% in recall and 31.31% in precision, and from the separations plotted by t-SNE, SeqGAN effectively improves the separation of original datasets. SeqGAN&TextCNN detects more vulnerable samples with low false negative rate, the method’ s F1 score is 79.58% higher than that of leveraging the VulDeePecker on 7 open-source C/C++ datasets.
More classes of permutation hexanomials and pentanomials over finite fields with even characteristic

In this paper, we further push the studies on permutation polynomials over the finite fields Fq2 with q=2m. We find the coefficients of f(x)=x+a1xs1(q−1)+1+a2xs2(q−1)+1+a3xs3(q−1)+1+a4xs4(q−1)+1+a5xs5(q−1)+1 over Fq2 that lead f(x) to be a permutation for (s1,s2,s3,s4)=(14,12,1,34) in case of a5=0, and find more new (si) such that f(x) is a permutation hexanomial of Fq2 with coefficients in F4. Some well-known results are covered by our results. The numerical results suggest that the sufficient conditions we find in the first class of permutation pentanomials are also necessary.
Design of a fault-alarm system based on hydro-meteorological automatic observation

The fault alarm system is to help the duty officer to check the current operation status of each equipment in the automatic hydro-meteorological observation system and whether the observation data are at normal range in real-time. The system monitors the operation status of automatic hydro-meteorological stations in real-time and customizes fault rules to check the quality of observation data. The system generates fault information via fault diagnosis and provides alarms via sound, animation, and SMS. This paper describes the hydro-meteorological automatic observation composition, data center fault alarm system design, fault diagnosis and alarm process, and demonstration applications.
Balance-Based Fuzzy Logic Approach for the Classification of Liver Diseases Due to Hepatitis C Virus

Fuzzy logic can be successfully applied in the many fields of artificial intelligence, such as control, classification, clustering, and prediction. However, assigning the optimal values for the control parameters and designing the optimal fuzzy rule table is vital to the method’s performance. Besides, increasing the number of variables increases the solution space combinatorically. This study proposes a novel method that uses the fuzzy logic approach. The method positions the inputs and outputs on the unit circle and aims to balance them at the circle origin. The efficiency and performance of the method were investigated in the problem of classification of Liver Diseases Due to Hepatitis C Virus. The results prove that the method can design successful systems for classification problems.
A two-branch deep learning with spatial and pose constraints for social group detection

Group detection is a crucial yet challenging task in various application domains, including video surveillance analysis and service robot interactions. Previous studies have struggled with complicated group structures and diverse human behaviors, resulting in low performance in multiple activity group detections. In this article, we propose a two-branch deep learning framework with spatial and pose constraints that combines physical proximity and social behavior to improve group detection performance. Specifically, we design an original Variable Group Detection Transform Network (VGDTN) that integrates dyad representations and contextual information to recognize complex group arrangements. Additionally, we present a novel pose-based refined clustering module that efficiently explores human behavior within multiple activity group detections by utilizing ternary postures classification. To address the computational intensity of DL-based methods in extensive surveillance video analysis, a designed lightweight VGDTN which consists of NIN blocks maintains good performance while reducing the computational burden. The experimental results show that our method outperforms the state-of-the-art methods by 7%, 5%, 2%, and 4% in F1_score (T =1) on four public datasets.
On the Different Perspectives of Management in Software Engineering Education and their Implications

In light of the significance of software to society and of software engineering to industry, this note approaches software engineering education from multiple perspectives of management. These perspectives are salient, but are not necessarily mutually exclusive, and occur commonly in teaching and learning. In doing so, it also considers a renewed understanding of these perspectives brought upon by a 'revitalization' of e-learning during and after the COVID-19 pandemic.
On r-primitive k-normal elements with prescribed norm and trace over finite fields

Let q=pm, where p is a prime and m∈N. Let Fqn be the field extension of degree n over Fq. Further, let r≥1 and k≥0 be integers such that r|qn−1 and there exists a k degree polynomial g(x)|xn−1. Let ξ∈Fq⁎ be an ω-primitive element i.e. the multiplicative order of ξ is (qn−1)/ω, where ω=gcd(r,q−1), and ζ∈Fq be any arbitrary element. In this article, we establish a sufficient condition for the existence of an r-primitive k-normal element α∈Fqn such that NFqn/Fq(α)=ξ and TrFqn/Fq(α)=ζ. Using this sufficient condition, we show that, for fixed integers r and k there always exists an r-primitive k-normal element with prescribed norm ξ and trace ζ in all but finitely many finite fields Fqn, for n≥4k+5.
Spatial distribution of porphyry copper deposits in Dehaj terrain: Implications for exploration

Magmatism is triggered and controlled by tectonic systems, and thus these processes play an irrefutable role in the deposition and emplacement of hydrothermal mineral systems. It is, therefore, paramount to recognize the tectonic processes that are genetically associated with hydrothermal mineral systems. This study seeks to address this caveat by recognizing the main tectonic processes that have controlled the distribution of porphyry copper deposits in Dehaj terrain, Central Iran. For this purpose, the spatial association of 31 known porphyry copper deposits, faults, and fractures were evaluated by fry, fractal, and distance-distribution analyses. The results of fry analysis revealed that mineralization has distributed through three different trends, namely NE, NW, and NS, which shows a clear association with the fault systems presented in the area. Also, the fractal method applied demonstrated that structural controls on mineralization have operated on two different scales, regional and local scales. Distance-Distribution analysis was further used to assess the spatial correlation between known porphyry Cu deposits and fault traces, supplementing the results of fry and fractal analyses by quantitative measurements. The synthesis of the results of these three methods shows that the NW-trending faults have plausibly controlled the magmatism at a regional scale; nevertheless, NE- and N-trending faults have probably operated at local scales controlling the channeling and emplacement of mineral-bearing fluids.
Mining the medication rules of traditional Chinese medicine for thyroid diseases based on improved association rule algorithm

In order to mine the medication rules of traditional Chinese medicine in treating thyroid diseases and analyze the performance of association rule algorithm in traditional Chinese medicine data, the improved Apiori algorithm based on transaction compression technology and hash technology is introduced. Furthermore, an improved FP-growth algorithm is proposed in view of the characteristics of more transaction duplicates and longer single transaction in the TCM data transaction database. Combined with these improved association rule algorithms, the data mining analysis of traditional Chinese medicine is carried out, and the algorithm time efficiency diagram is drawn to analyze the algorithm efficiency. The results show that the mining results of association rules of all algorithms are consistent, which reflects the basic treatment principles of TCM in treating thyroid diseases by regulating phlegm and eliminating galls.
Tree-based data filtering for online user-generated reviews

AbstractAnalysis of online user-generated reviews has attracted extensive attention with broad applications in recent years. However, the high-volume and low-value density of online reviews bring challenges to a timely and effective data utilization. To address that challenge, this work proposes an unsupervised review filtering method based on the inherent tree-structured hierarchies among review data that reflect the general-to-specific characteristics of various quality aspects discussed in reviews. In particular, the reviews with aspects distributed near the leaf nodes of the tree are capable of providing more specific and detailed information about the examined product, which is more likely to be reserved after the tree-based filtering. To enable an effective extraction of aspect hierarchies from a broad variety of review corpora, a Bayesian nonparametric hierarchical topic model has been constructed and incorporated with an enhanced Pólya urn scheme. The approximate inference of model parameters is obtained by an efficient collapsed Gibbs sampling procedure. The proposed method can enhance the layered effect of individual reviews according to their general-to-specific characteristics and reserve an information-rich subset filtered from the raw review corpus. The merits of the proposed method have been elaborated by case studies on two real-world data sets and an extensive simulation study.Keywords: Bayesian nonparametric modeldata filteringdata qualityhierarchical topic modelonline reviews AcknowledgmentsThe author would like to thank the editor and three anonymous referees for their helpful comments and suggestions.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are openly available at: ACOS data set by Cai et al. (Citation2021): https://github.com/NUSTM/ACOS/tree/main/data.Amazon review data set by Ni et al. (Citation2019):https://cseweb.ucsd.edu/∼jmcauley/datasets/amazon_v2.Yelp open data set: https://www.yelp.com/dataset.Additional informationFundingThis work was funded by the National Natural Science Foundation of China (Grant No. 72201212).Notes on contributorsQiao LiangQiao Liang is currently an assistant professor at the School of Statistics, Southwestern University of Finance and Economics, Chengdu, China. She received her Ph.D. and B.S. degrees in industrial engineering from Tsinghua University, Beijing, China. Her research interests are in the areas of statistical modeling and data analytics for manufacturing and service processes, with a focus on statistical process control based on text analytics.
The Weakest Link: Employee Cyber-Defense Behaviors While Working from Home

With the increase in the number of employees working remotely from home following the COVID-19 pandemic, cyberattacks have grown in quantity and strength. While companies invest tremendous resources in technical defense practices and protection tools, the main weak link is still the human factor. The current study aims to provide theoretical and empirical evidence of the antecedents that contribute to active cyber defense as well as cyber risk behaviors. Based on a sample of 338 employees who worked from home or on-site during and after COVID-19, we examined the effects of organizational training on employee defense behaviors. The results of the current study suggest that the workplace and the amount of confidence in the defense measures play an important role in contributing to cyber risk behavior. Therefore, it is crucial that managers raise employee awareness of the hazards and educate them on the different defense methods that they can apply.