Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Long-Term Time-Stamping Scheme

Traditional time-stamping services confirm the existence time of data items by using a time-stamping authority. To eliminate trust requirements on this authority, decentralized Blockchain-based Time-Stamping (BTS) services have been proposed. In these services, a hash digest of users’ data is written into a blockchain transaction. The security of such services relies on the security of hash functions used to hash the data, and of the cryptographic algorithms used to build the blockchain. It is well-known that any single cryptographic algorithm has a limited lifespan due to the increasing computational power of attackers. This directly impacts the security of the BTS services from a long-term perspective. However, the topic of long-term security has not been discussed in the existing BTS proposals. In this paper, we propose the first formal definition and security model of a Blockchain-based Long-Term Time-Stamping (BLTTS) scheme. To develop a BLTTS scheme, we first consider an intuitive solution that directly combines the BTS services and a long-term secure blockchain, but we prove that this solution is vulnerable to attacks in the long term. With this insight, we propose the first BLTTS scheme supporting cryptographic algorithm renewal. We show that the security of our scheme over the long term is not limited by the lifespan of any underlying cryptographic algorithm, and we successfully implement the proposed scheme under existing BTS services.
New Unbounded Verifiable Data Streaming for Batch Query with Almost Optimal Overhead

Verifiable Data Streaming (VDS) enables a resource-limited client to continuously outsource data to an untrusted server in a sequential manner while supporting public integrity verification and efficient update. However, most existing VDS schemes require the client to generate all proofs in advance and store them at the server, which leads to a heavy computation burden on the client. In addition, all the previous VDS schemes can perform batch query (i.e., retrieving multiple data entries at once), but are subject to linear communication cost l, where l is the number of queried data. In this paper, we first introduce a new cryptographic primitive named Double-trapdoor Chameleon Vector Commitment (DCVC), and then present an unbounded VDS scheme $$\mathsf {VDS_1}$$ with optimal communication cost in the random oracle model from aggregatable cross-commitment variant of DCVC. Furthermore, we propose, to our best knowledge, the first unbounded VDS scheme $$\textsf{VDS}_2$$ with optimal communication and storage overhead in the standard model by integrating Double-trapdoor Chameleon Hash Function (DCH) and Key-Value Commitment (KVC). Both of our schemes enjoy constant-size public key. Finally, we demonstrate the efficiency of our two VDS schemes with a comprehensive performance evaluation.
Zero-Knowledge Age Restriction for GNU Taler

We propose a design for a privacy-friendly method of age restriction in e-commerce that is aligned with the principle of subsidiarity. The design is presented as an extension of a privacy-friendly payment protocol with a zero-knowledge scheme that cryprographically augments coins for this purpose. Our scheme enables buyers to prove to be of sufficient age for a particular transaction without disclosing it. Our modification preserves the privacy and security properties of the payment system such as the anonymity of minors as buyers as well as unlinkability of transactions. We show how our scheme can be instantiated with ECDSA as well with a variant of EdDSA, respectively, and how it can be integrated with the GNU Taler payment system. We provide formal proofs and implementation of our proposal. Key performance measurements for various CPU architectures and implementations are presented.
Privacy Leakage in Privacy-Preserving Neural Network Inference

The community has seen many attempts to secure machine learning algorithms from multi-party computation or other cryptographic primitives. An interesting 3-party framework (SCSDF hereafter) for privacy-preserving neural network inference was presented at ESORICS 2020. SCSDF defines several protocols for non-linear activation functions including ReLU, Sigmoid, etc. In particular, these protocols reckon on a protocol DReLU (derivative computation for ReLU function) they proposed as a building block. All protocols are claimed secure (against one single semi-honest corruption and against one malicious corruption). Unfortunately, the paper shows that there exists grievous privacy leakage of private inputs during SCSDF executions. This would completely destroy the framework security. We first give detailed cryptanalysis on SCSDF from the perspective of the real-ideal simulation paradigm and indicate that these claimed-secure protocols do not meet the underlying security model. We then go into particular steps in SCSDF and demonstrate that the signs of input data would be inevitably revealed to the (either semi-honest or malicious) third party responsible for assisting protocol executions. To show such leakage more explicitly, we perform plenteous experiment evaluations on the MNIST dataset, the CIFAR-10 dataset, and CFD (Chicago Face Database) for both ReLU and Sigmoid non-linear activation functions. All experiments succeed in disclosing original private data of the data owner in the inference process. Potential countermeasures are recommended and demonstrated as well.
Rapid Pre-Characterization of Fine-Grained EM Side-Channel (In)Vulnerability of AES Modules

A statistical method that rapidly identifies near-field probe configurations that are ineffective for leaking information from advanced encryption standard (AES) implementations is proposed. The method can be used as a pre-characterization stage to accelerate a recently introduced multistage analysis of variance (ANOVA)-based measurement protocol for evaluating crypto-systems’ vulnerability to fine-grained EM side-channel analysis attacks.
Philosophical problems of the Metaverse. How real is the Virtual World and how Virtual is the real one?

With this article, the authors propose to temporarily complete the cycle of texts devoted to metaverses and virtual reality – before &amp;quot;retiring to the study room&amp;quot; to systematize new data and new phenomena in this area, because in any study, the time naturally comes to test the hypothesis. This does not mean avoiding the study of the problem – on the contrary, it is about the development of research together with the qualitative development of the topic itself. And the fact that there is a qualitative change in the subject and process of research is evident from several signs that the authors have tried to reveal in this article.
Agile Methodology

This chapter discusses the Agile methodology which is a popular lightweight approach to software development. Agile provides opportunities to assess the direction of a project throughout the development lifecycle, and ongoing changes to requirements are considered normal in the Agile world. It has a strong collaborative style of working, and it advocates adaptive planning and evolutionary development.
Capability Maturity Model Integration

This chapter gives an overview of the CMMI model and discusses its five maturity levels and their constituent process areas. We discuss both the staged and continuous representations of the CMMI, and SCAMPI appraisals that indicate the extent to which the CMMI has been implemented in the organization, as well as identifying opportunities for improvement.
Smart Contracts: A Valuable Technology in ERP e-Commerce Systems and for Customer Experience

In the past few years, more and more e-commerce companies are progressively looking for business opportunities and ways to evolve in terms of their informatic system in such a crowded market and gain a unique customer experience to earn the client’s trust. This need is exacerbated, taking into account the competition generated by the major market leaders, by constantly introducing innovations in planning the business resources, and by the results-oriented for stimulating the competition. This study theoretically systematizes how blockchain technology and smart contracts are slowly becoming a necessity in the ERP systems from the big companies that offer online shopping services, whether B2B or B2C, providing extra security for its clients. The models defined in this study highlight the technical efficiency in a resource system planning of three companies, market leaders in Romania, in terms of supply-chain, secure payments, and forecast, for comparing the costs of the change when using blockchain technology. Moreover, for a better shopping experience in online stores, the efficiency and effectiveness scores of applying these technologies to the customer review section, so that frauds are analyzed and can be significantly reduced by making in this way a secured and more reliable system. The study analysis of these aspects shows that the efficiency of minimizing frauds and improving the technology of ERP development by creating a safe and stable environment varies substantially between models and leads to a new era in e-commerce that is so needed nowadays.
Software Process Improvement

This chapter discusses software process improvement. It begins with a discussion of a software process, and discusses the benefits that may be gained from a software process improvement initiative. Various models that support software process improvement are discussed, and these include the Capability Maturity Model Integration (CMMI), ISO 9000, Personal Software Process (PSP) and Team Software Process (TSP).
Non-parametric Rank Correlation Trained Single-Hidden Layer Feedforward Neural Networks for Medical Data

Massive amounts of medical data led to a faster, more accurate diagnosis and course of treatment for cancer. Machine learning (ML) algorithms have been developed increasingly in order to process medical data. By embedding knowledge from data into the algorithm’s architecture we customize MLs to provide personalized diagnosis and personalized treatment plan. The goal of this study is to statistically analyze non-parametric correlation coefficients for initializing the input-hidden weight matrix of three single hidden layer feedforward neural networks (SLFNs). A SLFN is able to approximate a continuous function given a training dataset as input for a desired output. Using different non-parametric correlation coefficients, $$\rho , \tau $$ , and $$\gamma $$ , we developed three novel SLFNs that have their input hidden layer weight matrix initialized using the above-mentioned correlation coefficients computed between the input and the output. The matrix that contains the hidden output weights is computed in just one step, using the Moore-Penrose pseudoinverse matrix. A statistical analysis is performed for assessing the models’ results when applied on three medical datasets that regard differentiating lung, breast, and liver cancer. The statistical benchmarking showed that the $$\rho $$ -SLFN is superior to the other two. All three models were overall competitive to other state-of-the-art algorithms.
Software Metrics and Problem Solving

This chapter is concerned with metrics and problem solving, and this includes a discussion of the balanced score card which assists in identifying appropriate metrics for the organization. The Goal, Question, Metrics (GQM) approach is discussed, and this allows metrics related to the organization goals to be defined. A selection of sample metrics for an organization is presented, and problem-solving tools such as fishbone diagrams, pareto charts, trend charts are discussed.
Integrated Solutions and Computerized Human Gesture Control

Recognition of human actions is a topical issue due to its applicability in different domains such as security, medicine, behavioral analysis, and education. The development of the computer processing power, as well as the quality of image capture equipment, has increased the accessibility of larger and more accurate data sets. This permanently improves the performance of the developed programs. Convolutional neural networks are the starting point for identifying facial analysis and gesture recognition. By their nature, they can recognize ideal Spatio-temporal features for RGB image processing. The proposed model with 3D Kernel is useful in certain situations where the gestures are very different from each other and must be recognized by a system that reacts quickly and with high processing speed (emergencies or critical situations). The main objective of this paper is to build a reliable and easy-to-implement human gesture recognition classifier. It must be able to easily classify a large number of images so that it can operate in real-time, processing at least 30 frames per second. This is possible by using other hardware components of a shelf computer connected to a normal CCTV-Closed Circuit TV infrastructure. The selection of the gesture class must be not related to the environment, person, background, appearance, or viewing angle. The decision should be related only to the person to whom the gesture is attributed and to the context in which he reacts.
Software Engineering Tools

This chapter discusses various tools to support the various software engineering activities. The focus is first to define the process, and then to find tools to support the process. Tools to support project management are discussed as well as tools to support requirements engineering, configuration management, design and development activities and software testing.
Software Inspections

This chapter discusses software inspections, which play an important role in building quality into a product. The well-known Fagan inspection process that was developed at IBM in the 1970s is discussed, as well as lighter review and walkthrough methodologies.
Cybersecurity and Cybercrime

This chapter discusses cybersecurity and cybercrime. Computer crime (or cybercrime) is a crime that involves a computer and a network. The computer may be the vehicle by which the crime was conducted, or it may be the target of the crime. Cybersecurity is the protection of information through good security practices, including the protection of confidentiality, integrity, and availability of data. It is achieved through policies that ensure consistency in employee behaviour in the use of computer resources, as well as training and awareness of security in the workplace.
Formal Methods

This chapter discusses formal methods, which consist of a set of mathematic techniques that provide an extra level of confidence in the correctness of the software. They consist of a formal specification language, and employ a collection of tools to support the syntax checking of the specification, as well as the proof of properties of the specification. They allow questions to be asked about what the system does independently of the implementation, and they may be employed to formally state the requirements of the proposed system, and to derive a program from its mathematical specification. They may be employed to provide a rigorous proof that the implemented program satisfies its specification, and they have been applied mainly to the safety critical field.
A Miscellany of Innovation

This chapter discusses innovation in the software field including miscellaneous topics such as distributed systems, service-oriented architecture, software as a service, cloud computing and embedded systems. We discuss the need for innovation in software engineering and discuss some recent innovations including aspect-oriented software engineering.
Software Project Management

This chapter provides an introduction to project management for traditional software engineering, and we discuss project estimation, project planning and scheduling, project monitoring and control, risk management, managing communication and change, and managing project quality.
Fundamentals of Software Engineering

This chapter presents a broad overview of software engineering and discusses various software lifecycles and the phases in software development. We discuss requirements gathering and specification, software design, implementation, testing and maintenance. The lightweight Agile methodology is introduced, and it has become very popular in industry. Mathematics may potentially assist software engineers in delivering high-quality software products that are safe to use, and the extent to which mathematics should be employed remains a topic of active debate.
The Nature of stage Transformation as a Problem (L.S. Vygotsky's virtual dialogue with a modern theater psychologist)

The article raises the question of the nature of stage transformation. The psychological concept of N.V. Rozhdestvenskaya's explanation of reincarnation is analyzed, which distinguishes in this phenomenon as an ideal object of science: a) acceptance and emotional assimilation of motives that are dictated by the stage role, b) formation of an attitude to an imaginary situation that allows the artist to act and live in "given circumstances", c) restructuring and functioning of the personality already during the performance in these given circumstances. For comparison, the author analyzes I.I. Silantieva's art criticism concept, in which reincarnation is explained within the framework of the new humanitarian discipline "virtualistics", as well as the explanation of this phenomenon proposed by the author within the framework of the concept of "artistic reality". The similarity of the main differences in all three interpretations of reincarnation is fixed. Then the place is given to L.S. Vygotsky, who sharply objected to the analysis of scenic phenomena in logic that does not take into account their historical development and social nature. The author notes that although psychologists are well acquainted with Vygotsky's cultural-historical theory, they, nevertheless, realizing the view of psychology as an exact science, analyze the psyche, ignoring the cultural-historical approach. He undertakes to fill this gap in relation to reincarnation, showing that this concept was formed quite late, is not characteristic of the director's theater, but in theoretical terms is associated with a certain interpretation of the subject and personality.
A hybrid model for efficient decision-making in self-adaptive systems

Engineering self-adaptive systems to guarantee the required quality properties is challenging and particularly in presence of uncertainties. Such uncertainties may occur in a variety of situations, ranging from variations in the system’s operating environment to ambiguity while selecting the appropriate adaptation option. Formal methods provide a rigorous means to specify and verify the behaviour of self-adaptive systems. They are applied both during system design and at runtime to provide guarantees on the required properties of self-adaptive systems. However, existing approaches generally use exhaustive verification at runtime to pick adaptation options and achieve adaptation objectives, which is time and resource consuming. Aiming to tackle this shortcoming, we target a twofold objective. Firstly, we reduce the adaptation space, and then we predict the impact of each adaptation plan on the rest of system qualities, to assist the decision-making process in determining the most suitable adaptation plans and side effects. An Adaptation-space Reducer component is added to the analyser element; it uses deep learning to reduce the adaptation space. Furthermore, the planner element has been extended with a Decision Impact Predictor component, which employs quantitative analysis to forecast the impact of a decision. The DLA4EDM is defined as an approach for providing self-adaptive systems (SASs) with an efficient decision-making process. Our approach is applied on a self-adaptive Internet of Things application and the obtained results are compared to those of other approaches. Results show that the adaptation space is reduced by 97.57%, and the error rate in the decision-making is very low. Reducing the adaptation space and resolving uncertainties to be faced in the decision-making of self-adaptive systems contribute considerably to enhance the efficiency and quality of the adaptation process and hence ensure that the quality requirements are met. Evaluating the impact of the identified adaptation plans on the obtained guarantees ensures that the system is effectively operational.
Decoupled dynamic group equivariant filter for saliency prediction on omnidirectional image

Current saliency prediction models based on convolutional neural networks (CNNs) achieve solid improvement in predicting human attention on omnidirectional image (ODI). However, these models that employ standard convolution have two main shortcomings: content-agnostic and computation-intensive. To address these two shortcomings, we propose a decoupled dynamic group equivariant filter (DDGF). Specifically, inspired by the attention mechanism that adopts light-weight branches for estimating spatial and channel attention, we decouple group equivariant convolution (i.e. p4 convolution) into spatial and channel dynamic group equivariant filters. Such a design not only makes p4 convolution filter adaptive to ODI content, but also considerably reduces computational cost. To our best knowledge, the DDGF is the first decoupled dynamic convolution filter that applied to the task of saliency prediction. Meanwhile, we observe that it is effective and efficient when replacing standard group equivariant convolution with DDGF in ODI saliency prediction. Experimental results show that the proposed DDGF can achieve superior performance in comparison with other state-of-the-art methods. Additionally, we conduct ablation experiments to verify the effectiveness of each component of the proposed DDGF.
SSMTReID-Net: Multi-Target Unsupervised Domain Adaptation for Person Re-Identification

• Person re-identification problem statement. • Novel unsupervised single-source multiple-target domain adaptation setup. • Loss function modeled around triplet loss. • Tackling catastrophic forgetting in source domain using domain adaptive methods. • Ensuring competitive performance across source and all target domains. The task of unsupervised domain adaptive (DA) person re-identification (reID) has gained prominence in recent years. Current reID works mostly leverage off-the-shelf single-target domain adaptive (STDA) techniques to reduce any domain gap between a labeled source dataset and an unlabeled target dataset in a single-source single-target training setup in their reID framework. However, directly extending such STDA techniques to multiple target domains suffers from two inherent drawbacks: a) the performance on the source domain deteriorates once the model is fine-tuned for a target domain, and b) posing the single-source multi-target (SSMT) setup as an independent single-source single-target case by blending all target domains to form a single target domain fails to utilize the complementary information present in the different target domains. Hence, to tackle this problem of unsupervised multi-target domain adaptation (MTDA), we propose a novel architecture called SSMTReID-Net. SSMTReID-Net employs the elastic weight consolidation (EWC) regularizer to ensure competitive performance on the source domain after adaptation, and the notion of information bottleneck (IB) to highlight domain invariant target features while suppressing any domain-irrelevant artifacts . Our model is end-to-end trainable, and extensive results on different single-source multi-target combinations of challenging person reID datasets like DukeMTMC-reID, Market-1501 and CUHK03 datasets confirm the superiority of SSMTReID-Net over the other baselines.