Miracle-dz/UniFAR-FTUs huggingface dataset:
Key Node Ranking in Complex Networks: A Novel Entropy and Mutual Information-Based Approach

Numerous problems in many fields can be solved effectively through the approach of modeling by complex network analysis. Finding key nodes is one of the most important and challenging problems in network analysis. In previous studies, methods have been proposed to identify key nodes. However, they rely mainly on a limited field of local information, lack large-scale access to global information, and are also usually NP-hard. In this paper, a novel entropy and mutual information-based centrality approach (EMI) is proposed, which attempts to capture a far wider range and a greater abundance of information for assessing how vital a node is. We have developed countermeasures to assess the influence of nodes: EMI is no longer confined to neighbor nodes, and both topological and digital network characteristics are taken into account. We employ mutual information to fix a flaw that exists in many methods. Experiments on real-world connected networks demonstrate the outstanding performance of the proposed approach in both correctness and efficiency as compared with previous approaches.
The road to data maturity - Why publishing has to stay relevant in the digital age and why it needs to start now

The world is rapidly changing driven by the rise of data and Artiﬁcial Intelligence (AI). Many industries are facing wholesale change. Customers now expect tailored, digital solutions and personalized insights delivered at the point of need - and structural transformation is simmering as a result. This paper looks at the sea change and opportunities in the academic publishing sector. Driven by technology and changes in user experience, publishers need to harness technologies such as AI to help them evolve from being traditional content providers to data-driven product companies. Data is at the heart of everything. Increasing their data maturity will fuel innovation and power the transformed products and services that customers demand going forward. As part of this feature 67 Bricks will also introduce the Product Development Data Maturity model for scholarly publishers to help plot a course from where they are now to where they need to be to survive.
Spectrum Sensing Method Based on Information Geometry and Deep Neural Network

Due to the scarcity of radio spectrum resources and the growing demand, the use of spectrum sensing technology to improve the utilization of spectrum resources has become a hot research topic. In order to improve the utilization of spectrum resources, this paper proposes a spectrum sensing method that combines information geometry and deep learning. Firstly, the covariance matrix of the sensing signal is projected onto the statistical manifold. Each sensing signal can be regarded as a point on the manifold. Then, the geodesic distance between the signals is perceived as its statistical characteristics. Finally, deep neural network is used to classify the dataset composed of the geodesic distance. Simulation experiments show that the proposed spectrum sensing method based on deep neural network and information geometry has better performance in terms of sensing precision.
Robust DNN-Based Speech Enhancement with Limited Training Data

In conventional speech enhancement, statistical models for speech and noise are used to derive clean speech estimators. The parameters of the models are estimated blindly from the noisy observation using carefully designed algorithms. These algorithms generalize well to unseen acoustic conditions, but are unable to reduce highly non-stationary noise types. This shortcoming motivated the usage of machine-learning-based (ML-based) algorithms, in particular deep neural networks (DNNs). But if only limited training data are available, the noise reduction performance in unseen acoustic conditions suffers. In this paper, motivated by conventional speech enhancement, we propose to use the a priori and a posteriori signal-to-noise ratios (SNRs) for DNN-based speech enhancement systems. Instrumental measures show that the proposed features increase the robustness in unknown noise types even if only limited training data are available.
Complexity Changes in the US and China’s Stock Markets: Differences, Causes, and Wider Social Implications

How different are the emerging and the well-developed stock markets in terms of efficiency? To gain insights into this question, we compared an important emerging market, the Chinese stock market, and the largest and the most developed market, the US stock market. Specifically, we computed the Lempel–Ziv complexity (LZ) and the permutation entropy (PE) from two composite stock indices, the Shanghai stock exchange composite index (SSE) and the Dow Jones industrial average (DJIA), for both low-frequency (daily) and high-frequency (minute-to-minute)stock index data. We found that the US market is basically fully random and consistent with efficient market hypothesis (EMH), irrespective of whether low- or high-frequency stock index data are used. The Chinese market is also largely consistent with the EMH when low-frequency data are used. However, a completely different picture emerges when the high-frequency stock index data are used, irrespective of whether the LZ or PE is computed. In particular, the PE decreases substantially in two significant time windows, each encompassing a rapid market rise and then a few gigantic stock crashes. To gain further insights into the causes of the difference in the complexity changes in the two markets, we computed the Hurst parameter H from the high-frequency stock index data of the two markets and examined their temporal variations. We found that in stark contrast with the US market, whose H is always close to 1/2, which indicates fully random behavior, for the Chinese market, H deviates from 1/2 significantly for time scales up to about 10 min within a day, and varies systemically similar to the PE for time scales from about 10 min to a day. This opens the door for large-scale collective behavior to occur in the Chinese market, including herding behavior and large-scale manipulation as a result of inside information.
Context-Aware Superpixel and Bilateral Entropy—Image Coherence Induces Less Entropy

Superpixel clustering is one of the most popular computer vision techniques that aggregates coherent pixels into perceptually meaningful groups, taking inspiration from Gestalt grouping rules. However, due to brain complexity, the underlying mechanisms of such perceptual rules are unclear. Thus, conventional superpixel methods do not completely follow them and merely generate a flat image partition rather than hierarchical ones like a human does. In addition, those methods need to initialize the total number of superpixels, which may not suit diverse images. In this paper, we first propose context-aware superpixel (CASP) that follows both Gestalt grouping rules and the top-down hierarchical principle. Thus, CASP enables to adapt the total number of superpixels to specific images automatically. Next, we propose bilateral entropy, with two aspects conditional intensity entropy and spatial occupation entropy, to evaluate the encoding efficiency of image coherence. Extensive experiments demonstrate CASP achieves better superpixel segmentation performance and less entropy than baseline methods. More than that, using Pearson’s correlation coefficient, a collection of data with a total of 120 samples demonstrates a strong correlation between local image coherence and superpixel segmentation performance. Our results inversely support the reliability of above-mentioned perceptual rules, and eventually, we suggest designing novel entropy criteria to test the encoding efficiency of more complex patterns.
Reducing saturation and congestion in VANET networks: Alliance‐based approach and comparisons

A vehicular ad hoc network (VANET) is composed mainly of fixed roadside entities (RSUs) and mobile entities (vehicles). In order to exchange information and data relating to the safety and comfort of road users, these different entities must establish communications between them. In these communications, one of the main problems is related to congestion and saturation of RSUs. In this paper, we first study the main protocols that involve RSUs in their strategy of routing by classifying them according to four levels. Furthermore, to deal with the problem of saturation of RSUs, we present a new approach of cooperation between the RSUs of a VANET in order to reduce its congestion and avoid as much as possible the saturation of these entities. This approach, called “D2A2RS” (defensive alliance–based approach for reducing RSUs saturation), is based on the concept of defensive alliances in graphs that ensures effective collaboration between RSUs. To evaluate the performance of the proposed approach, we conduct a comparative analysis by using both analytical models and simulations. The obtained comparison results have shown the efficiency and the performance of our approach compared with other concurrent approaches in the literature in terms of packet loss/success rate, end‐to‐end transmission delay, and network scalability.
Robust Speaker Identification by Fusing Classification Scores with a Neural Network

Score-based fusion of multiple independent models for the purpose of identifying speakers is widely used as it reduces the identiﬁcation error rate signiﬁcantly. In this work, a speaker identiﬁcation system for low-quality speech which has been propagated through telephone and communication channels is proposed. The system consists of 15 models based on 5 features as well as a Neural Network structure for the task of fusing the classiﬁcation scores resulting from the individual models. Its performance is evaluated in closed-set speaker identiﬁcation experiments conducted on the Switchboard corpus. Furthermore, the proposed Neural Network architecture is compared to other fusion techniques such as taking the mean, a Majority Voting, an Evolutionary Algorithm and Logistic Regression.
Identifying the Ideal Types of Online Shoppers: A Qualitative Analysis of Online Shopping

The tremendous increase in online shopping has created a growing demand to understand online shopping behavior. This study contributes to this understanding by identifying ideal types among online shoppers. An ideal type is an analytical construct used to ascertain similarities and deviations to concrete cases in an individual phenomenon. Theoretically, the study draws from different perspectives to create a multifaceted view of online shoppers. The purpose is not to categorize online shoppers under a specific category but rather to help understand different typically occurring online shopping behaviors. Through thematic analysis of the data from 31 participants, this study presents five ideal types of online shoppers: conservative shoppers, rational shoppers, hedonistic shoppers, spontaneous shoppers, and vanguard shoppers. The formed ideal types serve as the main theoretical contribution of this study. From a practical standpoint, implications for online shop providers on how to accommodate the needs of each ideal type are provided.
MONTE CARLO DEEP NEURAL NETWORK ARITH-

Quantization is a crucial technique for achieving low-power, low latency and high throughput hardware implementations of Deep Neural Networks. Quantized floating point representations have received recent interest due to their hardware efficiency benefits and ability to represent a higher dynamic range than fixed point representations, leading to improvements in accuracy. We present a novel technique, Monte Carlo Deep Neural Network Arithmetic (MCA), for determining the sensitivity of Deep Neural Networks to quantization in floating point arithmetic. We do this by applying Monte Carlo Arithmetic to the inference computation and analyzing the relative standard deviation of the neural network loss. The method makes no assumptions regarding the underlying parameter distributions. We evaluate our method on pre-trained image classification models on the CIFAR10 and ImageNet datasets. For the same network topology and dataset, we demonstrate the ability to gain the equivalent of bits of precision by simply choosing weight parameter sets which demonstrate a lower loss of significance from the Monte Carlo trials. Additionally, we can apply MCA to compare the sensitivity of different network topologies to quantization effects.1
Study on Magnet Eddy Current Losses in Interior Permanent Magnet Machines for Aircraft Electric Green Taxiing Systems

The magnet eddy current of multi-layered interior permanent magnet (IPM) machines is studied in the paper for the aircraft electric green taxiing systems. The armature reaction flux density distribution, eddy current density and harmonic spectrum at the end of permanent magnet are analysed. A method to reduce the magnet eddy current of multi-layered IPM machines is presented. That is to increase the distance between outer permanent magnet and rotor surface and the distance between two layers of permanent magnet so as to provide a larger core path for armature reactive flux. This method can effectively reduce the magnet eddy-current loss of the multi-layered IPM machines and improve the operation efficiency.
Global Practices of Higher Education Financing: Approaches and Models

The primary purpose of this paper is to determine the model of higher education financing that is the most effective, flexible and easiest to adapt to the market demands. The most successful HEIs of the world have the best practices for attracting and managing additional funds to finance the educational and research activities of the HEIs. The objective of the article is to summarize the existing progressive practices of attracting and managing additional funds to finance HEIs’ activities. The object of the study is the framework for the formation of endowment funds to support the activities of HEIs. Using comparative and system analysis methods, we looked at the most common models of funding for higher education and the mechanisms for attracting additional funds. It was discovered that most countries use three main models of financing higher education – bureaucratic, collegial and market models. Based on the experience of the OECD countries we determined that at the moment the share of private funds varies but tends to increase. The most successful HEIs use the market model to manage their finances and use additional sources of funds to supplement government support. The most wide-spread instruments of attraction additional funding are issuance of bonds, private equity and donations. The most effective ways to manage additional financing are asset management, securitization and endowment funds. Based on the best practices of the HEIs we proposed a general model of managing the endowment funds that uses such principles and provisions that can be applied in the activities of the HEIs, which will allow them to use fundamentally new source of financing.
Metabolic Syndrome in Rural Community

According to our observation, prevalence of metabolic syndrome is rising in urban populations as well as rural communities. However, this rising problem is largely being ignored and according to preliminary results quality adjusted life years can be increased by preventing Metabolic Syndrome. The first attempt to give a proper definition of Metabolic Syndrome was done by World Health Organization (WHO) in 1998. 1 According to WHO criteria metabolic syndrome consists of diabetes mellitus along with any two of the following: BP greater than 140/90 mm Hg, hypertriglyceridemia, decreased HDL, central obesity and microalbumenemia. 2 Metabolic syndrome is a major use of morbidity in diabetic patients and medical establishments.
Okun’s law in an emerging country: An empirical analysis in Indonesia

Objective: Labor economics policy instruments, such as gross domestic product (GDP) and unemployment are persistent challenges for every country. As an emerging country, Indonesia seeks to reduce unemployment and increase gross domestic product. The aim of this study is to investigate the relationship between GDP and the open unemployment rate in Indonesia starting from 1987 to 2017. Research Design & Methods: The data were obtained from Indonesia’s Central Bureau of Statistics website. Moreover, the econometric model of the Granger causality and Structural Vector Autoregression capture the causality of GDP and the open unemployment rate. Findings: The Granger causality test suggested the open unemployment rate does Granger cause gross domestic product, but not vice versa. In line with the Structural Vector Auto-regression, there was a negative relationship between GDP and the open unemployment rate. Contribution & Value Added: This study is in line with Okun’s law which explains the more labor used in production will have implications for increasing GDP in the economy. It means this study presents evidence to support the existence of Okun’s law in Indonesia. Also, this study contributes to the literature in an emerging country by using the Structural Vector Autoregression model.
Sulfiredoxin as a Potential Therapeutic Target for Advanced and Metastatic Prostate Cancer

The incidence of prostate cancer (PCa) is increasing, and it is currently the second most frequent cause of death by cancer in men. Despite advancements in cancer therapies, new therapeutic approaches are still needed for treatment-refractory advanced metastatic PCa. Cross-species analysis presents a robust strategy for the discovery of new potential therapeutic targets. This strategy involves the integration of genomic data from genetically engineered mouse models (GEMMs) and human PCa datasets. Considering the role of antioxidant pathways in tumor initiation and progression, we searched oxidative stress-related genes for a potential therapeutic target for PCa. First, we analyzed RNA-sequencing data from Pb-Cre4; Ptenf/f mice and discovered an increase in sulfiredoxin (Srxn1) mRNA expression in high-grade prostatic intraepithelial neoplasia (PIN), well-differentiated adenocarcinoma (medium-stage tumors), and poor-differentiated adenocarcinoma (advanced-stage prostate tumors). The increase of SRXN1 protein expression was confirmed by immunohistochemistry in mouse prostate tumor paraffin samples. Analyses of human databases and prostate tissue microarrays demonstrated that SRXN1 is overexpressed in a subset of high-grade prostate tumors and correlates with aggressive PCa with worse prognosis and decreased survival. Analyses in vitro showed that SRXN1 expression is also higher in most PCa cell lines compared to normal cell lines. Furthermore, siRNA-mediated downregulation of SRXN1 led to decreased viability of PCa cells LNCaP. In conclusion, we identified the antioxidant enzyme SRXN1 as a potential therapeutic target for PCa. Our results suggest that the use of specific SRXN1 inhibitors may be an effective strategy for the adjuvant treatment of castration-resistant PCa with SRXN1 overexpression.
Sustainable High-Ductility Concrete with Rapid Self-Healing Characteristic by Adding Magnesium Oxide and Superabsorbent Polymer

As a class of high-ductility concrete, engineered cementitious composites (ECC) have wide application prospects in engineering fields. However, the occurrence of cracks and the limited self-healing ability hinder the development of ECC. Rapid self-healing has important significance for ECC in reducing maintenance costs and prolonging service life, which are conducive to sustainable development of ECC. Therefore, the aim of this paper is to enhance the self-healing property of ECC by adding light-burned magnesium oxide (MgO) and superabsorbent polymer (SAP) on the premise of maintaining the high ductility. First, the effect of MgO and SAP on the ductility property of ECC which is the most important feature was explored with the uniaxial tensile test. The results indicated that MgO is helpful to the strength but not conducive to the ductility of ECC, while SAP has an opposite effect. The effects of MgO and SAP on the ductility of ECC can be balanced. Later, the permeability test, scanning electron microscopy (SEM), and X-ray diffraction (XRD) were used to evaluate the effects of MgO and SAP on the self-healing property of ECC. The results showed that the combined addition of MgO and SAP shows much better effect than the individual addition and can cut the healing time by half. Overall, it is concluded that ECC with MgO and SAP have the potential for self-healing, and the ductility can also be reconciled.
CONSUMER SURVEY DATA

This paper extracts information on ination expectations, the real interest rate, and various risk premiums by exploring the underlying common factors among the actual ination, University of Michigan consumer survey ination forecast, yields on U.S. nominal Treasury bonds, and particularly, yields on Treasury Ination Protected Securities (TIPS). Our ndings suggest that a signi cant liquidity risk premium on TIPS exists, which leads to ination expectations that are generally higher than the ination compensation measure at the 10-year horizon. On the other hand, the estimated expected ination is mostly lower than the consumer survey ination forecast at the 12-month horizon. Survey participants slowly adjust their ination forecasts in response to ination changes. The nominal interest rate adjustment lags ination movements too. Our model also edges out a parsimonious seasonal AR(2) time series model in the one-step-ahead forecast of ination. JEL classi cation: E43, G12, C32
Kunibert Raffer

The neo-liberal tide of privatisation targeted public pension schemes quite some time ago. While decreasing the role of the State as such is part and parcel of this ideology, another aspect of privatising basic needs does not seem to have received appropriate attention. Government intervention and trade union strife for higher income have created high levels of relatively wide-spread wealth, which could profitably be tapped by private business. This is similar to state property such as railways whose privatisation offers golden business opportunities. The necessity to provide for old age and outright fear of misery during this stage of one's life are powerful incentives to provide financially, even if fees and charges for doing so are relatively high – elasticity of demand will be low. Naturally, private pension schemes are likely to remain a comparatively marginal factor of interest mainly to a relatively small layer of the well heeled if and as long as public schemes offer relatively good levels of income for most people. By contrast, if public schemes were abolished altogether, a huge market would be opened to private business. Reducing or abolishing public systems allow private firms to tap this promising market more profitably. Re-channelling parts of mandatory contributions into private funds could strike a bonanza at little or no risk. Such funds only administer accumulated money for a fee. Huge losses may occur when stock prices fall dramatically as has happened recently but the risk is fully borne by pensioners or governments.