Reviewerly/exHarmony huggingface dataset:
Quantum-safe cryptography: crossroads of coding theory and cryptography

Abstract We present an overview of quantum-safe cryptography (QSC) with a focus on post-quantum cryptography (PQC) and information-theoretic security. From a cryptographic point of view, lattice and code-based schemes are among the most promising PQC solutions. Both approaches are based on the hardness of decoding problems of linear codes with different metrics. From an information-theoretic point of view, lattices and linear codes can be constructed to achieve certain secrecy quantities for wiretap channels as is intrinsically classical- and quantum-safe. Historically, coding theory and cryptography are intimately connected since Shannon’s pioneering studies but have somehow diverged later. QSC offers an opportunity to rebuild the synergy of the two areas, hopefully leading to further development beyond the NIST PQC standardization process. In this paper, we provide a survey of lattice and code designs that are believed to be quantum-safe in the area of cryptography or coding theory. The interplay and similarities between the two areas are discussed. We also conclude our understandings and prospects of future research after NIST PQC standardisation.
Pedestrian Re-identity Based on ResNet Lightweight Network

Abstract With the development of deep learning technology, pedestrian re-identity technology has been widely used in multi-target tracking and cross mirror tracking tasks. In this paper, the classical deep learning ResNet18 network is used for pedestrian re-identity tasks. The advantage of the network is that it can easily realize lightweight deployment. In addition, the labeled smooth cross entropy loss function and migration learning technology are used in the process of training the network, which can realize the accuracy of map 67.8 on the Market1501 data set while lightening the network, and promote the engineering landing of pedestrian re-identity network.
Develop with the Microsoft Quantum Development Kit

After reading the previous chapters, you have gained just enough theoretical knowledge about quantum mechanics and quantum computing to get started with actual quantum software development. Different companies, cloud providers, and software vendors are preparing for a future where quantum computers will be generally available and used to solve real-world problems. One of these companies is Microsoft. To prepare for quantum computing, Microsoft has already invested in tools and a programming language that provides developers with an opportunity to play around with quantum software. The collection of these tools and programming language is named the Microsoft Quantum Development Kit.
Danimer concludes Novomer purchase

Certificateless cryptography eliminates the need of certificates in the Public Key Infrastructure and solves the inherent key escrow problem in the ID-based cryptography. In this paper, we point out security pitfalls on the restrictions of an adversary’s final output in security models of certificateless signature schemes by demonstrating key replacement attacks on three certificateless signature schemes in the different security models.
Sentiment Analysis in Portuguese Texts from Online Health Community Forums: Data, Model and Evaluation

This study introduces novel data and models for the task of Sentiment Analysis in Portuguese texts about Diabetes Mellitus. The corpus contains 1290 posts retrieved from online health community forums in Portuguese and annotated by two annotators according to 3 sentiment categories (e.g. Positive, Neutral and Negative). Evaluation of traditional (Support Vector Machine, Decision Tree, Random Forest and Logistic Regression classifiers) and state-ofthe-art (BERT-based models) machine learning classifiers for the task showed the advantage in performance of the latter models as expected. Data and models are available to the community upon request.
Understanding the Practices of Global Censorship through Accurate, End-to-End Measurements

It is challenging to conduct a large scale Internet censorship measurement, as it involves triggering censors through artificial requests and identifying abnormalities from corresponding responses. Due to the lack of ground truth on the expected responses from legitimate services, previous studies typically require a heavy, unscalable manual inspection to identify false positives while still leaving false negatives undetected. In this paper, we propose Disguiser, a novel framework that enables end-to-end measurement to accurately detect the censorship activities and reveal the censor deployment without manual efforts. The core of Disguiser is a control server that replies with a static payload to provide the ground truth of server responses. As such, we send requests from various types of vantage points across the world to our control server, and the censorship activities can be recognized if a vantage point receives a different response. In particular, we design and conduct a cache test to pre-exclude the vantage points that could be interfered by cache proxies along the network path. Then we perform application traceroute towards our control server to explore censors' behaviors and their deployment. With Disguiser, we conduct 58 million measurements from vantage points in 177 countries. We observe 292 thousand censorship activities that block DNS, HTTP, or HTTPS requests inside 122 countries, achieving a 10^-6 false positive rate and zero false negative rate. Furthermore, Disguiser reveals the censor deployment in 13 countries.
Foreign Remittances, Good Governance and Human Development in Sub-Saharan Africa: Evidence from FMOLS and DOLS

Even though some progress has been recorded in terms of GDP growth in Sub-Saharan Africa, still the region has high level of extreme poverty, income inequality, insecurity, consequently low human development. This study examined the effect of foreign remittances and governance on human development for 20 countries of sub-Saharan Africa, using data from 1996 to 2019. The study used FMOLS and DOLS methods to estimates the long run coefficients. All the variables are I(1), have cross section dependency and cointegrated. The results revealed that foreign remittances and governance promote long run human development in sub-Saharan Africa. The results also show that inflation, population growth and military expenditures found to have negative long run effect on human development, while financial development have positive long run effect on human development in sub-Saharan Africa. Therefore, to promote human development, policies that will attract and encourage sending remittances through official channel should be put in place, especially related to the cost of sending remittances to sub-Saharan Africa. Also, policies to strengthen institutions and provide quality regulatory and legal framework for equitable distribution of resources to reduce poverty, and the cost of doing business, thereby promoting business activities, human capital development, consequently higher human development.
Publishing ESTS

In our previous editorial (Khandekar et al. 2021), we noted the blackboxing of scholarly publication infrastructure that we encountered when we assumed editorship of the journal. We outlined several aspects of infrastructuring that we have undertaken since, with an explicit goal of supporting transnational workflows and participation in ESTS. In this editorial, we continue describing our infrastructural work, highlighting especially the work of content production at ESTS. We also discuss the relevance of our infrastructural work for open access (OA) scholarly publishing.
The influence of information configuration on mobile game download

Purpose The study aims to show how several factors interact to promote mobile game download: the number of games released by a publisher, the quality of the games released, the popularity of a game's genre, the quality of borrowed intellectual property, the frequency of recommendations, intragenre ranking, consumer rating and review quantity. Design/methodology/approach Signaling theory was used to classify the mobile game information displayed on the Apple App Store into four groups. A conceptual model was proposed to illustrate the complex relationship between the information and download. Based on information on 203 mobile games in the seven days following their release, the model was empirically tested to identify the influence of information configuration on game download by combining fuzzy qualitative comparative analysis (fsQCA) and a fuzzy cognitive map (FCM). Findings Three solutions were identified for high game download and two for low/medium. The number of previous games released by a publisher, intragenre ranking, consumer rating and review quantity are core conditions that reinforce high game download. The effects of one information type on another and on downloads change as coexisting information types change. Originality/value This study enriches existing knowledge about how combinations of multiple types of game information lead to game download and extends previous variance-based research. Combining an FCM with fsQCA can facilitate one’s understanding of the complex causal relationships between game information and download.
Quantum Watermarking Schemes for QHSL Images in Spatial Domain

Three spatial-domain-based quantum watermarking schemes (QWSs) are proposed for quantum hue, saturation, and lightness (QHSL) color model images. Among them, QWS-visible is a visible watermarking scheme in which only the copyright owner who owns both the image mask and key image can remove the visible watermark from the protected image; QWS-lossless is an invisible watermarking scheme that can embed a watermark image in some constrained areas of the image without destroying the image quality; QWS-QRcode embeds a QR code with a color pattern into a carrier image, and uses the error correction mechanism of the QR code to resist a certain degree of cropping attacks. Based on simulation experiments, the QWSs’ visual effect, security, applicability, and ability to resist cropping attacks are analyzed. The proposed watermarking schemes set the stage for advancing the research of quantum image watermarking based on the HSL color model.
COLBAC: Shifting Cybersecurity from Hierarchical to Horizontal Designs

Cybersecurity suffers from an oversaturation of centralized, hierarchical systems and a lack of exploration in the area of horizontal security, or security techniques and technologies which utilize democratic participation for security decision-making. Because of this, many horizontally governed organizations such as activist groups, worker cooperatives, trade unions, not-for-profit associations, and others are not represented in current cybersecurity solutions, and are forced to adopt hierarchical solutions to cybersecurity problems. This causes power dynamic mismatches that lead to cybersecurity and organizational operations failures. In this work we introduce COLBAC, a collective based access control system aimed at addressing this lack. COLBAC uses democratically authorized capability tokens to express access control policies. It allows for a flexible and dynamic degree of horizontality to meet the needs of different horizontally governed organizations. After introducing COLBAC, we finish with a discussion on future work needed to realize more horizontal security techniques, tools, and technologies.
A Recommendation Scheme with Reputation-Based Incentive Mechanism on Consortium Blockchain

Aiming at the problem of privacy leakage and lack of trust in centralized recommendation system architecture, we propose a recommendation scheme with reputation-based incentive mechanism on consortium blockchain, which uses the auditable and transparent feature of consortium blockchain to enhance the credibility of the recommendation system. Multiple users are allowed to participate in the recommendation process fairly through the certification before joining the blockchain network. Besides, the scheme introduces a reputation-based incentive mechanism, which can curb users’ malicious behaviors and attract more users to participate in the recommendation process, thus optimizing the recommendation results. Then, we design and implement an education resources recommendation system based on the proposed scheme, which provides a new solution idea for the design of highly credible recommendation systems. Finally, the experimental analysis of the proposed scheme shows that it can achieve fair and credible recommendations and significantly improve the accuracy of the recommendation results.
Compositional data analysis (CoDA) as a tool to evaluate a new low-cost settling-based PM&amp;lt;sub&amp;gt;10&amp;lt;/sub&amp;gt; sampling head in a desert dust source region

Abstract. This paper presents a new sampling head design and the method used to evaluate it. The elemental composition of aerosols collected by two different sampling devices in a semi-arid region of Tunisia is compared by means of compositional perturbation vectors and biplots. This set of underused mathematical tools belongs to a family of statistics created specifically to deal with compositional data. The two sampling devices operate at a flow rate in the range of 1 m3 h−1, with a cut-off diameter of 10 µm. The first device is a low-cost laboratory-made system, where the largest particles are removed by gravitational settling in a vertical tube. This new system will be compared to the second device, a brand-new standard commercial PM10 sampling head, where size segregation is achieved by particle impaction on a metal surface. A total of 44 elements (including rare earth elements, REEs, together with Al, As, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Rb, S, Sc, Se, Sr, Ti, Tl, U, V, Zn, and Zr) were analysed in 16 paired samples, collected during a 2-week field campaign in Tunisian dry lands, close to source areas, with high levels of large particles. The contrasting meteorological conditions encountered during the field campaign allowed a broad range of aerosol compositions to be collected, with very different aerosol mass concentrations. The compositional data analysis (CoDA) tools show that no compositional differences were observed between samples collected simultaneously by the two devices. The mass concentration of the particles collected was estimated through chemical analysis. Results for the two sampling devices were very similar to those obtained from an online aerosol weighing system, TEOM (tapered element oscillating microbalance), installed next to them. These results suggest that the commercial PM10 impactor head can therefore be replaced by the decanter, without any measurable bias, for the determination of chemical composition and for further assessment of PM10 concentrations in source regions.
Breast Cancer Detection in Mammogram Images by MapReduce Based Deep Convolutional Neural Networks

Imaging techniques using mammographic pictures are the most efficient and straightforward way to diagnose Breast Cancer (BC).The accurate discovery can significantly lower the mortality rate caused by BC.Machine Learning (ML) techniques were utilized for the prediction of BC in images.The ML approaches offered a unique way of making decisions, and they were able to aid the medical expert in providing a second perspective on more precise nodule detection.However, the over-fitting problem in ML degraded the performance of BC detection.To solve this issue, Deep Learning (DL) algorithms were utilized to detect BC early using mammographic images, which is a huge help in detecting BC at an early stage.The Deep Convolutional Neural Networks (DCNN) are being used in DL to help with an accurate BC diagnosis and better BC image outcome detection.However, the time complexity of DCNN is high for a large-scale dataset.In this paper, The DCNN is parallelized in the Mappers of MapReduce (MR) programming model in which the network weights can be iteratively adjusted via determining their fractional gradients following all collections of learning images are propagated throughout the system.Accordingly, the Mapper will parallelize the training phase by distributing the normal, benign, and malignant images that have been preprocessed using the K-Means (KM) algorithm.Each image can be provided to various DCNNs and each DCNN is trained independently in parallel.The output weight of the training stage of DCNNs is aggregated in Reducer and then the weights are then updated for the next iteration.After the completion of the training process, the updated weight is utilized in the all distributed DCNN for classifying the test images of BC.The whole process is termed MapReduce-KM-DCNN (MR-KM-DCNN).Finally, the experimental outcome demonstrates that the proposed MR-KM-DCNN method achieves 88.35 percent accuracy, which is 13.48%, 8.02 %, and 4.85 % higher than the existing SSL-DCNN, AN-DCNN, and KM-DCNN methods.In addition, the running time is reduced by 60% on average compared to previous methods.As a result, it has been verified that the MR-KM-DCNN attained the highest BC detection rate while requiring the smallest amount of computational time.
Çevik Yazılım Geliştirme Metodolojisi Üzerine Bir Üçüncül Çalışma ve Sosyal Ağ Analizi

In recent years, there has been an increasing interest in research and application of agile software development. In academic literature, the number of systematic literature reviews (SLRs), systematic mapping studies, and unsystematic reviews has also increased. One of the aims of this article is to create a tertiary study of SLRs on agile software development research topics between 2013 and 2018. Our second goal is to provide an in-depth analysis with data including reference, author, institution, etc. belonging to this tertiary study, and to provide an in-depth analysis for software engineering researchers and practitioners to reveal the relationships of developments in this field. We applied a two-stage research method within the scope of the study. First, we obtained and examined the systematic literature reviews published in the field of agile software development from Web of Science, Science Direct, Scopus, and IEEE academic databases. Second, we visualized the publications examined within the scope of the tertiary study, and revealed the relationships between publications, researchers, institutions, publication sources, and countries with VOSviewer and Gephi social network analysis tools. The findings obtained as a result of the study can be summarized as the fact that developing countries have more studies on the subject, the cited publications are mostly from developed countries, contrary to what is expected, distant countries are more in cooperation, and the number of citations is not directly proportional to the number of publications.
Aligning a University English Language Proficiency Measurement Tool with the CEFR: A Case in Malaysia

The Common European Framework of Reference (CEFR) describes the capability of learners’ language skills at six reference levels. It is internationally recognised as the standard language proficiency framework for describing language learning, teaching and assessment. Many countries, including Malaysia, have attempted and invested tremendous efforts to adopt the CEFR as a reference for language ability at all levels of education. However, there are many ways of adopting CEFR, and it is a continuous process of alignment between curriculum and assessment. In this regard, this study is carried out to examine how a Malaysian university attempts to demonstrate this alignment by correlating the scores obtained from English language proficiency courses in the university, called the English Language Competence Score Average (ELCSA), to a CEFR-aligned English language proficiency test (Linguaskill). The results showed an overall significant positive correlation that varied in strength. The overall correlation was 0.371, a positive but weak correlation whereby the strongest correlation was seen between ELCSA and CEFR Writing score with a correlation of 0.417, which is positive and moderate in strength. Therefore, it could be identified that a score of 3.25 and 3.5 on the ELCSA can be considered equivalent to a Linguaskill score of 160 (CEFR Band B2). It could be considered that the B2 CEFR level could be subdivided into lower and higher B2. However, there is a need to correlate ELCSA with other CEFR-aligned tests and perform further revisions to the English language proficiency programme at the university to successfully benchmark the programme and its assessment tool, ELCSA, with the CEFR.
Educational Methodologies for Hearing Impaired Children Supported by Mobile Technology and Extended Reality: Systematic Analysis of Literature

This study aims to identify Teaching-Learning (TL) methodologies applied in conjunction with mobile and extended reality applications developed for the hearing impaired community. A systematic literature review of mixed type, cross-sectional between 2016 and 2020, focused on educational mobile applications for hearing impaired children in basic school age is presented, emphasizing the type of methodology and support strategies used, as well as the type of extended reality. development and use technology. The results show little literature that records TL applications for children, the most used communication system is sign language. There are few applications that record the use of collaborative strategies, but playful ones that combine memory, cognitive, metacognitive, metacognitive and affective techniques. Regarding the type of development technology, Android applications supported by multimedia elements stand out, followed by the use of Augmented Reality and a minority for other XR technologies.
Session interest model for CTR prediction based on self-attention mechanism

Abstract Click-through rate prediction, which aims to predict the probability of the user clicking on an item, is critical to online advertising. How to capture the user evolving interests from the user behavior sequence is an important issue in CTR prediction. However, most existing models ignore the factor that the sequence is composed of sessions, and user behavior can be divided into different sessions according to the occurring time. The user behaviors are highly correlated in each session and are not relevant across sessions. We propose an effective model for CTR prediction, named Session Interest Model via Self-Attention (SISA). First, we divide the user sequential behavior into session layer. A self-attention mechanism with bias coding is used to model each session. Since different session interest may be related to each other or follow a sequential pattern, next, we utilize gated recurrent unit (GRU) to capture the interaction and evolution of user different historical session interests in session interest extractor module. Then, we use the local activation and GRU to aggregate their target ad to form the final representation of the behavior sequence in session interest interacting module. Experimental results show that the SISA model performs better than other models.