Reviewerly/exHarmony huggingface dataset:
La formula di saluto «salve» nell’insegnamento della lingua italiana come lingua straniera (livello di competenza A1)

The greeting formula “salve” in teaching Italian as a foreign language at reference level A1 The aim of this this paper is to examine the greeting formula “salve” in the context of teaching Italian as a foreign language at A1 level of competence. First, the topic is investigated from the perspective of pragmatics and communicative competence. Then we analyse a range of teaching materials to see how “salve” is realized on a more practical level and to check how the authors of the coursebooks understand its function.
Error Diffusion Halftoning Against Adversarial Examples

Adversarial examples contain carefully crafted perturbations that can fool deep neural networks (DNNs) into making wrong predictions. Enhancing the adversarial robustness of DNNs has gained considerable interest in recent years. Although image transformation-based defenses were widely considered at an earlier time, most of them have been defeated by adaptive attacks. In this paper, we propose a new image transformation defense based on error diffusion halftoning, and combine it with adversarial training to defend against adversarial examples. Error diffusion halftoning projects an image into a 1-bit space and diffuses quantization error to neighboring pixels. This process can remove adversarial perturbations from a given image while maintaining acceptable image quality in the meantime in favor of recognition. Experimental results demonstrate that the proposed method is able to improve adversarial robustness even under advanced adaptive attacks, while most of the other image transformation-based defenses do not. We show that a proper image transformation can still be an effective defense approach. Code: https://github.com/shaoyuanlo/Halftoning-Defense
Aggregation Delayed Federated Learning

Federated learning is a distributed machine learning paradigm where multiple data owners (clients) collaboratively train one machine learning model while keeping data on their own devices. The heterogeneity of client datasets is one of the most important challenges of federated learning algorithms. Studies have found performance reduction with standard federated algorithms, such as FedAvg, on non-IID data. Many existing works on handling non-IID data adopt the same aggregation framework as FedAvg and focus on improving model updates either on the server side or on clients. In this work, we tackle this challenge in a different view by introducing redistribution rounds that delay the aggregation. With delayed aggregations, local models are trained on data that are more representative to the global distribution. The proposed algorithm can also be used as a federated learning paradigm, as an alternative to FedAvg, where other methods can be plugged in. We perform experiments on multiple tasks and show that the proposed framework significantly improves the performance on non-IID data.
Foundations of Programmable Secure Computation

This paper formalises the security of programmable secure computation focusing on simplifying security proofs of new algorithms for existing computation frameworks. Security of the frameworks is usually well established but the security proofs of the algorithms are often more intuitive than rigorous. This work specifies a transformation from the usual hybrid execution model to an abstract model that is closer to the intuition. We establish various preconditions that are satisfied by natural secure computation frameworks and protocols, thus showing that mostly the intuitive proofs suffice. More elaborate protocols might still need additional proof details.
Visual Arrangements of Bar Charts Influence Comparisons in Viewer Takeaways

Well-designed data visualizations can lead to more powerful and intuitive processing by a viewer. To help a viewer intuitively compare values to quickly generate key takeaways, visualization designers can manipulate how data values are arranged in a chart to afford particular comparisons. Using simple bar charts as a case study, we empirically tested the comparison affordances of four common arrangements: vertically juxtaposed, horizontally juxtaposed, overlaid, and stacked. We asked participants to type out what patterns they perceived in a chart, and coded their takeaways into types of comparisons. In a second study, we asked data visualization design experts to predict which arrangement they would use to afford each type of comparison and found both alignments and mismatches with our findings. These results provide concrete guidelines for how both human designers and automatic chart recommendation systems can make visualizations that help viewers extract the 'right' takeaway.
A noise-based privacy preserving model for Internet of Things

Abstract With the ever-increasing number of devices, the Internet of Things facilitates the connection between the devices in the hyper-connected world. As the number of interconnected devices increases, sensitive data disclosure becomes an important issue that needs to be addressed. In order to prevent the disclosure of sensitive data, effective and feasible privacy preservation strategies are necessary. A noise-based privacy-preserving model has been proposed in this article. The components of the noise-based privacy-preserving model include Multilevel Noise Treatment for data collection; user preferences-based data classifier to classify sensitive and non-sensitive data; Noise Removal and Fuzzification Mechanism for data access and user-customized privacy preservation mechanism. Experiments have been conducted to evaluate the performance and feasibility of the proposed model. The results have been compared with existing approaches. The experimental results show an improvement in the proposed noise-based privacy-preserving model in terms of computational overhead. The comparative analysis indicates that the proposed model without the fuzzifier has around 52–77% less computational overhead than the Data access control scheme and 46–70% less computational overhead compared to the Dynamic Privacy Protection model. The proposed model with the fuzzifier has around 48–73% less computational overhead compared to the Data access control scheme and 31–63% less computational overhead compared to the Dynamic Privacy Protection model. Furthermore, the privacy analysis has been done with the relevant approaches. The results indicate that the proposed model can customize privacy as per the users’ preferences and at the same time takes less execution time which reduces the overhead on the resource constraint IoT devices.
DIGITALIZATION IN CONSTRUCTION: BLOCKCHAIN APPLICABILITY IN THE INDUSTRY

Blockchain, as a Distributed Ledger Technology (DLT), could disrupt the traditional hierarchy way in which information is shared and processes are developed in the construction industry. The fragmentation of people, processes and contracts has been considered as the main responsible for the sector negative trend. The information asymmetry characterizing the dynamics of the processes denies both the information traceability and the disciplines interoperability. The recent digital transformation boosted by Building Information Modeling (BIM) has promised a more transparent and collaborative environment. However, its adoption has gradually shown some trust issues in the information flow, discouraging participants from collaborating to achieve project goals. For these reasons, the research explores Blockchain in the construction industry, highlighting its potentials in supporting transparent, reliable and distributed information sharing. In particular, the research intends to combine the features of the digitalization, namely BIM and Blockchain, in the industry. To accomplish this, the first goal consists of an overall literature review on the current level of the Blockchain investigation in the built environment. Secondly, based on that analysis, two proposals for potential application of Blockchain in AECO sector are presented. This preliminary exploration intends to endorse Blockchain as a suitable system for governing the trust issues, supporting an efficient contract execution and a better sharing of all project information.
Modular Transformation of Java Exceptions Modulo Errors

Deductive verifiers are used more and more in both academia and industry to prevent costly bugs. Their capabilities of verifying concurrent programs are getting better, but they are still lagging behind with regard to many major programming language features such as exceptions. To improve the situation, this work presents a semantics of Java exceptions which reduces the annotation burden on the user, while still allowing verification of exceptions. This is accomplished by ignoring sources of errors which are irrelevant to functional verification. Additionally, to deal with the complex control flow introduced by finally, a transformation is proposed that simplifies verification of exceptional postconditions and finally into postconditions and goto. We implement the approach and evaluate it against several common exception patterns.
Korean researchers’ motivations for publishing in data journals and the usefulness of their data: a qualitative study

Purpose: This study investigated the usefulness and limitations of data journals by analyzing motivations for submission, review and publication processes according to researchers with experience publishing in data journals.Methods: Among 79 data journals indexed in Web of Science, we selected four data journals where data papers accounted for more than 20% of the publication volume and whose corresponding authors belonged to South Korean research institutes. A qualitative analysis was conducted of the subjective experiences of seven corresponding authors who agreed to participate in interviews. To analyze interview transcriptions, clusters were created by restructuring the theme nodes using Nvivo 12.Results: The most important element of data journals to researchers was their usefulness for obtaining credit for research performance. Since the data in repositories linked to data papers are screened using journals’ review processes, the validity, accuracy, reusability, and reliability of data are ensured. In addition, data journals provide a basis for data sharing using repositories and data-centered follow-up research using citations and offer detailed descriptions of data.Conclusion: Data journals play a leading role in data-centered research. Data papers are recognized as research achievements through citations in the same way as research papers published in conventional journals, but there was also a perception that it is difficult to attain a similar level of academic recognition with data papers as with research papers. However, researchers highly valued the usefulness of data journals, and data journals should thus be developed into new academic communication channels that enhance data sharing and reuse.
An Efficient Image Compression Method Based On Neural Network: An Overfitting Approach

Over the past decade, nonlinear image compression techniques based on neural networks have been rapidly developed to achieve more efficient storage and transmission of images compared with conventional linear techniques. A typical nonlinear technique is implemented as a neural network trained on a vast set of images, and the latent representation of a target image is transmitted. In contrast to the previous nonlinear techniques, we propose a new image compression method in which a neural network model is trained exclusively on a single target image, rather than a set of images. Such an overfitting strategy enables us to embed fine image features in not only the latent representation but also the network parameters, which helps reduce the reconstruction error against the target image. The effectiveness of our method is validated through a comparison with conventional image compression techniques in terms of a rate-distortion criterion.
Research on Risk Assessment of Science and Technology Data in Network Finance Based on Portfolio Empowerment

At present, the proposed network finance technology data risk assessment time is too long, leading to low accuracy. In order to solve the above problems, this paper puts forward the research on the risk assessment of network financial S&amp;T data based on portfolio weighting, determines the risk index of network financial S&amp;T data, calculates the weight of risk data in network S&amp;T data, searches the risk data characteristic quantity in networks according to network S&amp;T risk index, and completes the extraction of risk data. According to the risk data characteristics of network finance, a decision tree is constructed, the data entropy involved in the decision tree is calculated, the types of risk data characteristics are induced, the nodes of the decision tree are created, and the status of risk data of network finance is obtained. The state of risk data is brought into the definition of Bayesian network probability, and the risk degree of risk data is analyzed to improve the precision of risk data analysis. The experimental results show that the risk assessment of network financial S&amp;T data based on portfolio weighting can effectively shorten the assessment time and improve the accuracy.
Research on Computer Application Technology Based on Big Data Environment

Abstract After the emergence of big data, people have a deeper understanding of data segmentation. With the support of computer hardware, people can face huge amounts of data and extract useful information in a short time. This is particularly important for countless industries. As for the lives of ordinary people, because of the emergence of big data, people’s work and life efficiency has become more efficient, so we can say that big data is a new and epoch-making technology. This article focuses on analyzing the significance and development of current big data processing technology, and proposes the challenge of computer processing technology under big data. I believe that the promotion of computer applications in the age of Big Data growth has some significance.
Examining the role of blockchain technology against fraud in SMEs

The study examined the use of blockchain technology for the prevention of fraud in small and medium enterprises. Fraud in businesses is a significant problem that stifles the growth of businesses. The methodology used is a review of relevant and extant literature, after which a conclusion was made. The transparency, security, and traceability of blockchain enhance its credibility as the most valuable technology to stem fraud in a business organization. The study contributed to knowledge by evaluating how blockchain can be adopted to stem the tide of fraud in businesses in Nigeria.
Data Security and Privacy in Cloud Computing: Concepts and Emerging Trends

Millions of users across the world leverages data processing and sharing benefits from cloud environment. Data security and privacy are inevitable requirement of cloud environment. Massive usage and sharing of data among users opens door to security loopholes. This paper envisages a discussion of cloud environment, its utilities, challenges, and emerging research trends confined to secure processing and sharing of data.
METHOD FOR DEVELOPING PSEUDO-RANDOM NUMBER GENERATORS FOR CRYPTOGRAPHIC APPLICATIONS IN 5G NETWORKS

Today, pseudo-random number generators are used in various systems and applications, including as key generators in stream ciphers. The implementation of the latest information and communication technologies (in particular, 5G networks) strengthens the requirements for ensuring the confidentiality of critical data and forces the development of new methods and means for cryptographic protection. Existing generators, like other cryptographic algorithms, do not meet the requirements for processing speed and security against known types of attacks. From this position, in the paper a method for constructing pseudo-random sequence generators was developed. It allows to build efficient generators for cryptographic applications. Based on this method, software generators of pseudo-random numbers have been developed and implemented. These will be useful for cryptographic applications in modern 5G networks. The developed pseudo-random number generators have passed complex statistical testing by the NIST STS technique (showed results not worse than the results of known pseudo-random sequence generators used in practice to solve similar problems). Besides, they are faster in comparison with analogues used today in 5G networks (for example, with algorithms SNOW and Trivium). In further works it is planned to investigate the security of the developed pseudo-random generators against different types of cryptanalytic attacks, as well as to simulate the work of the developed pseudo-random sequence generators using the base station equipment of modern 5G networks.
Application of Multiscale Facial Feature Manifold Learning Based on VGG-16

Purpose. In order to solve the problems of small face image samples, high size, low structure, no label, and difficulty in tracking and recapture in security videos, we propose a popular multiscale facial feature manifold (MSFFM) algorithm based on VGG16. Method. We first build the VGG16 architecture to obtain face features at different scales and construct a multiscale face feature manifold with face features at different scales as dimensions. At the same time, the recognition rate, accuracy rate, and running time are used to evaluate the performance of VGG16, LeNet-5, and DenseNet on the same database. Results. From the results of comparative experiments, it can be seen that the recognition rate and accuracy of VGG16 are the highest among the three networks. The recognition rate of VGG16 is 97.588%, and the accuracy is 95.889%. And the running time is only 3.5 seconds, which is 72.727% faster than LeNet-5 and 66.666% faster than DenseNet. Conclusion. The model proposed in this paper breaks through the key problem in the face detection and tracking problem in the public security field, predicts the position of the face target image in the time dimension manifold space, and improves the efficiency of face detection.
Fast speech adversarial example generation for keyword spotting system with conditional GAN

Deep network-based keyword spotting (KWS) has embraced great success in many speech assistant applications. However, such network-based KWS systems were demonstrated vulnerable to adversarial attacks. In this work, we propose to utilize a conditional generative adversarial network (CGAN) to efficiently craft targeted speech adversarial examples. Specifically, we first transform the attacking target label into a vector, which is treated as the condition input of CGAN. The generator in CGAN is tasked to generate perturbation that could make the adversarial example misclassified as the pre-specified target keyword, while simultaneously deceiving the discriminator to misclassify the adversarial example as genuine. The discriminator aims to differentiate the crafted adversarial examples from the legitimate samples. Secondly, the target network-based KWS classifier(s) are ensembled and integrated into the proposed CGAN framework to enforce the generator to construct model-independent perturbation. The classification error loss of the target KWS is back-propagated through gradients for guiding the weight update of the generator. Finally, with properly devised network architecture and training procedure, we obtain a well-trained generator that generates the adversarial perturbation for a given speech clip and target label. Experimental results show that the crafted adversarial examples could effectively attack the state-of-the-art KWS system with quite a high attack success rate, while attaining acceptable perception quality.
Geo-Quantities: A Framework for Automatic Extraction of Measurements and Spatial Context from Scientific Documents

Quantitative information derived from scientific documents provides an important source of data for studies in almost all domains, however, manual extraction of this information is very time consuming. In this paper we will introduce a system Geo-Quantities that supports the automatic extraction of quantitative, spatial and temporal information of a given measurement entity from scientific literature using text mining techniques. The difficulty of automatic measurement recognition is mainly caused by the diverse expressions in the papers. Geo-Quantities offers an interactive interface for the visualization of extracted user-defined information, in particular spatial and temporal context. In our demonstration, we will showcase the capabilities of our system by retrieving measurements such as “mass accumulation rates” and “sedimentation rates” from scientific publications in the field of marine geology, which could have high impact in studies for building global mass accumulation rate maps. For training and evaluation of Geo-Quantities we use a corpus of domain-relevant papers.
CRITERIA-BASED ASSESSMENT AS THE WAY OF FORMING STUDENTS FUNCTIONAL LITERACY IN COMPUTER SCIENCE

For a long time, the primary approach to assessment was the normative approach when the individual achievements of students were compared with a particular norm (the results of most students). Recently, domestic pedagogical research has been developing a criteria-based approach to assessing academic achievement when students' achievements are compared with the amount of knowledge that needs to be acquired at a particular stage of training. This study aimed to determine the role of criteria-based assessment in the formation of students functional literacy in computer science and to build a criteria-based assessment model in the development of functional literacy beyond to demonstrate the effectiveness of the methods of formation of students functional literacy in computer science. The leading research methods were the criteria-based assessment methodology developed by the authors and the method of formative assessment. Some elements of the methodology of forming evaluation were considered. For further development of the methods for the formation of students functional literacy in computer science, a criteria-based assessment model has been built. The introduction of criteria-based assessment will allow to switch to a formative evaluation aimed at developing student competence. The evaluation, consisting of criteria that a student understands, stimulates him and makes the learning process meaningful. Based on practical experiments and the proposed criteria-based assessment, the effectiveness of methods for the formation of students functional literacy in computer science has been proved.