Reviewerly/exHarmony huggingface dataset:
RETRACTED ARTICLE: Does urban spatial structure affect labour income? – research based on 97 cities in China

We, the Editor and Publisher of Economic Research-Ekonomska Istraživanja, have retracted the following article:Yang Liu, ‘Does urban spatial structure affect labour income? – research based on 97 cities in China’, Economic Research-Ekonomska Istraživanja, Volume 34, 2021, Pages 545–569, DOI: 10.1080/1331677X.2020.1798265Since publication, the author has raised concerns about the data in the article having been used incorrectly by mistake, and confirmed there are therefore fundamental errors present.As this invalidates the reported results and conclusions the author alerted the issue to the Editor and Publisher and all have agreed to retract the article to ensure the integrity of the scholarly record.We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions.The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.
Automatic speckle region selection for digital image correlation

With the increase in digital image correlation (DIC) applications, it is an important task to realize the fully automatic measurement and provide an automatic deformation measurement sensor. Keeping this in mind, we propose an automatic speckle region selection method for DIC. This method can be divided into three main steps: initial contour selection, active contour computation, and morphological operation. The sum of modulus of the local intensity gradient vector of the local image is used to determine the initial contour, the active contour computation is driven by improved automatic local Gaussian distribution fitting energy, and the morphological closing operation is finally conducted to achieve better robustness. Experimental results show that the root mean squared error of the localization is less than 0.8260 pixels. We further integrate the proposed method into an open-source DIC software, and the fully automatic deformation analysis is realized. The fully automatic deformation measurement will further widen the application scope of DIC, especially in the field of smart robotics and smart city.
An Improved Complexity Measure in Hierarchical Fuzzy Systems

Interpretability is an important and necessary topic that needs to be discussed in relation to the fields of Artificial Intelligence and Machine Learning. Within fuzzy logic systems (FLSs), hierarchical fuzzy systems (HFSs) have been suggested as a key component to help improve the interpretability of FLSs. In this context, complexity is a key component in the interpretability of FLSs. In FLSs, the complexity is commonly expressed using in a rule-based manner, considering the number of rules, variables, and fuzzy terms. Several studies have used indicators (for example, the number of rules) to measure the complexity of FLSs. However, this is not a perfect way of assessing complexity in HFSs that have the structure of multiple subsystems, layers and different topologies. Thus far, complexity assessment associated with the structure of HFSs has not been discussed. In this paper, we aim to put forward a new approach in assessing the complexity of HFSs, which will combine rule-based complexity and structural complexity. A detailed measurement of complexity for different HFSs' topologies, namely parallel and serial, will be presented to showcase the features of the new approach. The contribution of this paper is the introduction of a combined rule-based and structural complexities-based approach in order to establish a comprehensive measurement of complexity in HFSs.
Randomized Pulse-Based Encoding for Secure Wireless Data Communications

This paper presents a randomized pulse-based data encoding scheme that enables high-speed and low-cost secure wireless communication. For high-speed wireless communication, we encode a 4-bit data as a symbol and compress the data volume by a total of 16 distinct orthogonal pulses. To ensure the security, we randomize the multi-order orthogonal pulse patterns to modulate the bit stream randomly. We randomize the pulses with emerging, energy-efficient, non-volatile, consumer-off-the-shelf Ferroelectric Random Access Memory (FRAM)-based high-quality random numbers. The generation of multi-order pulse sets, randomization of the pulses, and verification of the power requirements of individual pulses defined by the Federal Communication Commission (FCC) are performed through MATLAB Simulation. Simulation results show that the proposed data encoding scheme supports a total of M = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> *k bits data by N <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> different orthogonal pulses and ensures physical-layer security, where k is a nonnegative integer number.
An Analysis on the Opportunity of Introducing Blockchain Technology in Education - A Case Study

This paper presents the initiative to increase the flexibility of final year student activity and the results obtained. The didactic achievements illustrated by the grades obtained during the last 3 years show higher performance in students who adopted flexible ways to accomplish their academic activity. The students' opinion was determined through applying questionnaires comparing the activity in the classic way and in the flexible way. The conclusions show the opportunity for a flexible way of organizing the activity, while the organizational difficulties justify the need to use Blockchain technology in managing the attendance and the grades obtained by students.
Proof-of-stake at stake

There have been several 51% attacks on Proof-of-Work (PoW) blockchains recently, including Verge and GameCredits, but the most noteworthy has been the attack that saw hackers make off with up to $18 million after a successful double-spend was executed on the Bitcoin Gold network. For this reason, the Proof-of-Stake (PoS) algorithm, which already has advantages of energy efficiency and throughput, is attracting attention as an alternative to the PoW algorithm. With a PoS, the attacker needs to obtain 51% of the cryptocurrency to carry out a 51% attack. But unlike PoW, the attacker in a PoS system is highly discouraged from launching a 51% attack because he would have to risk losing his entire stake amount to do so. Moreover, even if a 51% attack succeeds, the value of PoS-based cryptocurrency will fall, and the attacker with the most stake will eventually lose the most. In this paper, we propose a predatory, destructive attack on PoS cryptocurrencies. The attacker destroys the PoS cryptocurrency system. Then, using the significant depreciation of cryptocurrency, our method can make a profit from a 51% attack on the PoS cryptocurrencies using the traditional stock market's short selling (or shorting) concept. Our findings are an example to show that the conventional myth that "a destructive attack that destroys the blockchain ecosystem totally will not occur because it is fundamentally unprofitable to the attacker itself" may be wrong.
HyBloSE

Although smart environments are a key component of the Internet of Things (IoT), it is also clear that billions connected doors, washing machines, ovens and others will ultimately raise security and privacy concerns. Early work in this area, as well as most of commercial solutions, has adopted a centralized client/server approach, neglecting the multitude of risks that are induced by an unfair control of the server side. This has made the adoption of a decentralized and trust-less framework quintessential to guarantee devices security. Nevertheless, decentralized proposals are hardly applicable due to costs, slowness and privacy issues. In this paper, we make the use of blockchain practical for smart environments by designing HyBloSE, a secure-by-design and lightweight blockchain-based framework, able to run on low-power devices without additional hardware. HyBloSE is built by using Delegated Proof of Authority and a Moving Window Blockchain. We evaluate HyBloSE through a network emulator and real experiments with different Raspberry Pi platforms. Results show that HyBloSE guarantees a higher security level in terms of resiliency to internal and external attacks compared to centralized solutions, with overhead below 0.38s per operation and less than $4 per month for unlimited operations. Furthermore, we show how Proof of Authority is more adapt then Proof of Work in IoT private scenarios.
A Framework for Detecting User’s Psychological Tendencies on Twitter Based on Tweets Sentiment Analysis

The more societies develop, people have less time for interacting face-to-face with each other. Therefore, more and more users express their opinions on many topics on Twitter. The sentiments contained in these opinions are becoming a valuable source of data for politicians, researchers, producers, and celebrities. Many studies have used this data source to solve a variety of practical problems. However, most of the previous studies only focused on using the sentiment in tweets to address the issues regarding commercial without considering the negative aspects related to user psychology, such as psychological disorders, cyberbullying, antisocial behaviors, depression, and negative thoughts. These problems have a significant effect on users and societies. This paper proposes a method to detect the psychological tendency that hides insides one person and to give the causations that lead to this psychological tendency based on analyzing sentiment of tweets by combining the feature ensemble model and the convolutional neural network model. The results prove the efficacy of the proposed approach in terms of the $$F_1$$ score and received information.
A Fast Algorithm to Initialize Cluster Centroids in Fuzzy Clustering Applications

The goal of partitioning clustering analysis is to divide a dataset into a predetermined number of homogeneous clusters. The quality of final clusters from a prototype-based partitioning algorithm is highly affected by the initially chosen centroids. In this paper, we propose the InoFrep, a novel data-dependent initialization algorithm for improving computational efficiency and robustness in prototype-based hard and fuzzy clustering. The InoFrep is a single-pass algorithm using the frequency polygon data of the feature with the highest peaks count in a dataset. By using the Fuzzy C-means (FCM) clustering algorithm, we empirically compare the performance of the InoFrep on one synthetic and six real datasets to those of two common initialization methods: Random sampling of data points and K-means++. Our results show that the InoFrep algorithm significantly reduces the number of iterations and the computing time required by the FCM algorithm. Additionally, it can be applied to multidimensional large datasets because of its shorter initialization time and independence from dimensionality due to working with only one feature with the highest number of peaks.
Simulated Annealing with Exploratory Sensing for Global Optimization

Simulated annealing is a well-known search algorithm used with success history in many search problems. However, the random walk of the simulated annealing does not benefit from the memory of visited states, causing excessive random search with no diversification history. Unlike memory-based search algorithms such as the tabu search, the search in simulated annealing is dependent on the choice of the initial temperature to explore the search space, which has little indications of how much exploration has been carried out. The lack of exploration eye can affect the quality of the found solutions while the nature of the search in simulated annealing is mainly local. In this work, a methodology of two phases using an automatic diversification and intensification based on memory and sensing tools is proposed. The proposed method is called Simulated Annealing with Exploratory Sensing. The computational experiments show the efficiency of the proposed method in ensuring a good exploration while finding good solutions within a similar number of iterations.
Face and Body-Based Human Recognition by GAN-Based Blur Restoration

The long-distance recognition methods in indoor environments are commonly divided into two categories, namely face recognition and face and body recognition. Cameras are typically installed on ceilings for face recognition. Hence, it is difficult to obtain a front image of an individual. Therefore, in many studies, the face and body information of an individual are combined. However, the distance between the camera and an individual is closer in indoor environments than that in outdoor environments. Therefore, face information is distorted due to motion blur. Several studies have examined deblurring of face images. However, there is a paucity of studies on deblurring of body images. To tackle the blur problem, a recognition method is proposed wherein the blur of body and face images is restored using a generative adversarial network (GAN), and the features of face and body obtained using a deep convolutional neural network (CNN) are used to fuse the matching score. The database developed by us, Dongguk face and body dataset version 2 (DFB-DB2) and ChokePoint dataset, which is an open dataset, were used in this study. The equal error rate (EER) of human recognition in DFB-DB2 and ChokePoint dataset was 7.694% and 5.069%, respectively. The proposed method exhibited better results than the state-of-art methods.
Formalising $$\varSigma $$-Protocols and Commitment Schemes Using CryptHOL

Abstract Machine-checked proofs of security are important to increase the rigour of provable security. In this work we present a formalised theory of two fundamental two party cryptographic primitives: $$\varSigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Σ</mml:mi></mml:math> -protocols and Commitment Schemes. $$\varSigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Σ</mml:mi></mml:math> -protocols allow a prover to convince a verifier that they possess some knowledge without leaking information about the knowledge. Commitment schemes allow a committer to commit to a message and keep it secret until revealing it at a later time. We use CryptHOL (Lochbihler in Archive of formal proofs, 2017) to formalise both primitives and prove secure multiple examples namely; the Schnorr, Chaum-Pedersen and Okamoto $$\varSigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Σ</mml:mi></mml:math> -protocols as well as a construction that allows for compound (AND and OR) $$\varSigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Σ</mml:mi></mml:math> -protocols and the Pedersen and Rivest commitment schemes. A highlight of the work is a formalisation of the construction of commitment schemes from $$\varSigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Σ</mml:mi></mml:math> -protocols (Damgard in Lecture notes, 2002). We formalise this proof at an abstract level using the modularity available in Isabelle/HOL and CryptHOL. This way, the proofs of the instantiations come for free.
Vulnerability and Health Care. Law and Medical Protocols

The article deals with the analysis of the criteria for the allocation of scarce health resources during the pandemic produced by the COVID 19 virus in Spain. It critically analyses the absence of a legal-constitutional perspective in the elaboration of such criteria and suggests the incorporation of the criterion of equity as a guarantee of the effective exercise of the constitutional right to health protection by vulnerable persons.
A Memory RAS System Design and Engineering Practice in High Temperature Ambient Data Center

Data center infrastructure uptime increasing, unplanned downtime reduction and data integrity main-taining are increasingly critical in today's real-time, service-level agreement (SLA)-driven cloud service business environment. Server, as backbone of cloud computing, has been developing and evolving with diverse challenges of preserving data integrity, increasing availability, minimizing planned downtime, especially in high temperature ambient (HTA) data center environment So, rock robust server system design for reliability, availability, and serviceability (RAS) are crucial for cloud service providers. Memory errors in server are among the most common hardware causes of machine crashes in production sites with large-scale systems. The higher temperature environment, the more errors. The typical response to memory failures is to replace any affected memory modules, which makes memory modules among the most commonly replaced server components. So, memory failures and their correction are very costly. Based on data collection and failure analysis from Baidu infrastructure maintenance group, server system memory (uncorrectable error) failure rate is Top 1 in data center. For reducing memory failure rate and related server downtime, Baidu developed an advanced server handling memory correctable and uncorrectable errors throughout a "6 pillars" complete application stack, from the underlying hardware to the scheduling system. Such solutions involve three components: (1) reliability, how the solution preserves data integrity; (2) availability, how it guarantees uninterrupted operation with minimal degradation; and (3) serviceability, how it simplifies proactively and reactively dealing with failed or potentially failed components. Availability is not an independent vector. This paper addresses Baidu rack server memory RAS architecture and design, scoping from Intel Xeon processor hardware errors avoidance, detection and correction RAS features for system reliability and improves fault tolerance; failure identification and reconfiguration such as leakage bucket based software-enhanced error recovery and error containment; extending to kernel level page retirement as well as high availability scheduler. Also, Memory RAS system design specific for HTA data center environment is detail introduced. Then, the related lab test procedure, data, and observations are summarized at the end of each section. Overall conclusion and future work plan are summarized in the end.
Histopathological imaging features- versus molecular measurements-based cancer prognosis modeling

Abstract For lung and many other cancers, prognosis is essentially important, and extensive modeling has been carried out. Cancer is a genetic disease. In the past 2 decades, diverse molecular data (such as gene expressions and DNA mutations) have been analyzed in prognosis modeling. More recently, histopathological imaging data, which is a “byproduct” of biopsy, has been suggested as informative for prognosis. In this article, with the TCGA LUAD and LUSC data, we examine and directly compare modeling lung cancer overall survival using gene expressions versus histopathological imaging features. High-dimensional penalization methods are adopted for estimation and variable selection. Our findings include that gene expressions have slightly better prognostic performance, and that most of the gene expressions are weakly correlated imaging features. This study may provide additional insight into utilizing the two types of important data in cancer prognosis modeling and into lung cancer overall survival.
Characteristics of Implementation of Distance Remote Access to Learning in Russian Universities

The most widespread digital services have been studied in addition to their effectiveness for the development of training materials that provide educational opportunities of distance learning in terms of the implementation of remote access to learning. The significance of the implementation of distance remote learning for future teachers training at universities is determined. The results of the study prove the necessity to develop educational and methodological materials for the implementation of new form of training in NEFU, RSPU and CSU.
PGLP: Customizable and Rigorous Location Privacy Through Policy Graph

Location privacy has been extensively studied in the literature. However, existing location privacy models are either not rigorous or not customizable, which limits the trade-off between privacy and utility in many real-world applications. To address this issue, we propose a new location privacy notion called PGLP, i.e., Policy Graph based Location Privacy, providing a rich interface to release private locations with customizable and rigorous privacy guarantee. First, we design a rigorous privacy for PGLP by extending differential privacy. Specifically, we formalize location privacy requirements using a location policy graph, which is expressive and customizable. Second, we investigate how to satisfy an arbitrarily given location policy graph under realistic adversarial knowledge, which can be seen as constraints or public knowledge about user’s mobility pattern. We find that a policy graph may not always be viable and may suffer location exposure when the attacker knows the user’s mobility pattern. We propose efficient methods to detect location exposure and repair the policy graph with optimal utility. Third, we design an end-to-end location trace release framework that pipelines the detection of location exposure, policy graph repair, and private location release at each timestamp with customizable and rigorous location privacy. Finally, we conduct experiments on real-world datasets to verify the effectiveness and the efficiency of the proposed algorithms.
Unsupervised Dual Learning for Feature and Instance Selection

Feature selection and instance selection are dual operations on a data matrix. Feature selection aims at selecting a subset of relevant and informative features from original feature space, while instance selection at identifying a subset of informative and representative instances. Most of previous studies address these two problems separately, such that irrelevant features (resp. outliers) may mislead the process of instance (resp. feature) selection. In this paper, we address the problem by doing feature and instance selection simultaneously. We propose a novel unified framework, which chooses instances and features simultaneously, such that 1)all the data can be reconstructed from the selected instances and features and 2) the global structure which is characterized by the sparse reconstruction coefficient is preserved. Experimental results on several benchmark data sets demonstrate the effectiveness of our proposed method.
Tracking Mixed Bitcoins

Mixer services purportedly remove all connections between the input (deposited) Bitcoins and the output (withdrawn) mixed Bitcoins, seemingly rendering taint analysis tracking ineffectual. In this paper, we introduce and explore a novel tracking strategy, called Address Taint Analysis, that adapts from existing transaction-based taint analysis techniques for tracking Bitcoins that have passed through a mixer service. We also investigate the potential of combining address taint analysis with address clustering and backward tainting. We further introduce a set of filtering criteria that reduce the number of false-positive results based on the characteristics of withdrawn transactions and evaluate our solution with verifiable mixing transactions of nine mixer services from previous reverse-engineering studies. Our findings show that it is possible to track the mixed Bitcoins from the deposited Bitcoins using address taint analysis and the number of potential transaction outputs can be significantly reduced with the filtering criteria.