Reviewerly/exHarmony huggingface dataset:
Memory and CPU utilization-aware Energy-Efficient VM Placement and Consolidation in Cloud Data Centers

Virtualization is one of the key techniques applied in Cloud data centers for seamlessly hosting and managing several applications together. However, the system performance level may get deteriorated if there exist co-located high resource utilization virtual machines (VMs). On the other hand, in order to reduce the growing energy consumption of the data centers, VMs are typically consolidated. This further deteriorates the system performance. Therefore, to tackle such problems, an efficient VM consolidation method using multiple parameters such as CPU, and memory associated with the VM is proposed. Our main contribution includes considering a weighted summation of CPU and memory utilization for VM consolidation with the objective to reduce energy consumption. The approach is based on the symbiotic association between hosts and VMs which is analogous to the biological interaction between parasites and host organisms, also termed symbionts in a biological ecosystem. A comparative analysis with the existing method states that a considerable amount of energy consumption got reduced using our proposed technique.
Transformation of the Education System in the Context of Digitalization

The article examines the transformation of the education system under the conditions of digitalization. Analyzes the international and Russian experience of implementing digital technologies in educational processes. The positive aspects of this process, as well as the difficulties with which this process is accompanied. Possible negative trends associated with the digitalization of education are also analyzed.
Modeling the Development of Russian Universities

The main problems of the development of the higher education system in the Russian Federation, characteristic of both the federal system as a whole and its regional components, are considered. The directions of solving these difficulties in order to improve and effectively develop, ensure the state order for education are proposed. This includes the development of communication systems, distance learning, issues of expanding the training of foreign students. The possibilities of modeling the processes of functioning and development of educational institutions based on the use of the methodology of scenario research of complex systems are considered. A basic simulation model of an educational institution based on the method of constructing operator graphs is proposed.
Aspects of Teaching IT Cources for Metallurgy-Bachelor Students under the Joint Program between Russian and Chinese Universities

In the paper the experience of teaching the ”Python Programming” discipline in the joint educational program in metallurgy between Lipetsk State Technical University (LSTU) and Liaoning University of Science and Technology (USTL) for USTL students is presented. The structure of the discipline program and features of its implementation in online and offline formats are provided. The structure of the program takes into account the possibilities of future usage of the obtained competences in professional activity. The specific feature of online classes is using the learning management system (LMS) Moodle. The feature of the offline educational process is the semester training course implementation in one month.
User Classification Based On Mouse Dynamic Authentication Using K-Nearest Neighbor

Mouse dynamics authentication is a method for identifying a person by analyzing the unique pattern or rhythm of their mouse movement.Owing to its distinctive properties, such mouse movements can be used as the basis for security.The development of technology is followed by the urge to keep private data safe from hackers.Therefore, increasing the accuracy of user classification and reducing the false acceptance rate (FAR) are necessary to improve data security.In this study, we propose to combine the K-nearest neighbor method and simple random sampling and obtain a sample from a dataset to improve the classification of users and attackers.The results show that our proposed method has high accuracy for implement to practical system and reports the best results than previous research with a FAR of 0.037.Therefore, this method can be implemented in a real login system.The high false rejection rate of our proposed method will not be a problem because the most important thing in the login system is denying the attacker system access.
A Hybrid Laptop Recommendation System for Engineering Undergraduates

Today, recommendation systems are widely used to solve the problem of finding the most relevant information from the abundant amount of data present. The literature findings suggest that there have been studies in various domains of recommendation systems such as skin care recommendations, movie recommendations, course recommendations, and others. However, limited studies exist on recommending products such as laptops which are a necessity these days and have become an important part of day-to-day life. From office workers to homemakers to undergraduate students this product is required by all. The undergraduate community faces challenges in identifying the right laptop due to lack of knowledge and expertise. Purchasing a laptop that matches the desired specifications for a particular specialization is tedious. The question in hand is “Do online reviews aid their decision-making for product (laptop) purchase?”. They all use different methods and approaches in order to form recommendations best suit their problem at hand. This study reviews the extant literature on product recommendation systems and proposes a Laptop Recommendation System based on a hybrid model, combining both Content-Based Filtering and Collaborative Filtering. The study captures data through the use of an online survey administered to undergraduate engineering respondents. The factors considered were processor, operating system, graphic card, and RAM (Random Access Memory). The data is analyzed through panda's library of python programming. The proposed specialization-based laptop recommendation system would be of benefit to engineering undergraduates to make an informed decision.
Addressing inclusive education in the context of the pandemic: a systematic review

The research focused on the development of a systematic review of different studies carried out by other authors in which the barriers, limitations and actions implemented to address this problem have been analyzed. A non-experimental methodological design with a qualitative approach was applied; In this case, techniques were applied for the search, selection, and analysis of the different referential articles so that they met the criteria of scientific relevance. A total of ten articles were reviewed, among which similar results were found that reflected the prevalence of educational inequalities that affect students with disabilities to a greater extent; In addition, it is suggested that although policies and actions aimed at guaranteeing inclusive education have been implemented, they are still minimal or insufficient to face different scenarios. It was concluded that in order to guarantee inclusion it is essential to meet the specific needs of students with disabilities, recognize the barriers that still prevail and take measures to reduce and gradually eradicate them, for this the involvement of the State, all actors of the educational system and society in general is required.
The digitalisation of higher education: the transformative role of EdTech

Higher education is being digitalised more and faster than ever before, making it even more important to understand the platforms and surrounding systems which are underpinning this change. This chapter examines the role of education technology in contemporary and emerging education transformations. It discusses platform and application ecosystems, significant financial arrangements, the nature of integration so far, contemporary innovation frontiers, and steps ahead for education design and development. The analysis draws on available research, distils environmental scans, and presents case studies. Achieving at least basic knowledge of digitalisation, the chapter concludes, is essential to understanding and navigating higher education.
Research on Decision Tree Method of Medical Text Based on Information Extraction

Extracting diagnosis and treatment decision trees from medical texts is a very meaningful thing. Recently, research in this area has just started. The general direction is to use pipeline extraction methods, which can be divided into two steps: triplet extraction and decision tree generation. However, in the previous research method, there are some problems in triplet extraction and decision tree generation, which lead to poor effect of the whole decision extraction. This paper improves in the following three directions: (1) adopts the pre-training method on the medical data set; (2) uses named entity recognition and biaffine to judge the relationship between entities in terms of triplet extraction; (3) adopts the pattern method to make the triples generate a decision tree. Through the above three improvements, it has achieved excellent performance on the 2022CHIP evaluation three data sets (Text2MDT) The medical pre-training model allows the model to have a deeper under-standing of medical vocabulary and the dependencies between vocabulary; The triplet ex-traction method uses biaffine to judge that the entity relationship is suitable for the triplet extraction of the evaluation data set; The method using the pattern triplet is more expressive.
Estimating (Miner) Extractable Value is Hard, Let’s Go Shopping!

The term miner extractable value (MEV) has been coined to describe the value which can be extracted by a miner, e.g., from manipulating the order of transactions within a given timeframe. $$\textsc {MEV}$$ has been deemed an important factor to assess the overall economic stability of a cryptocurrency. This stability also influences the economically rational choice of the security parameter k, by which a merchant defines the number of required confirmation blocks in cryptocurrencies based on Nakamoto consensus. Unfortunately, although being actively discussed within the cryptocurrency community, no exact definition of $$\textsc {MEV}$$ was given when the term was originally introduced. In this paper, we outline the difficulties in defining different forms of extractable value, informally used throughout the community. We show that there is no globally unique $$\textsc {MEV}$$ / $$\textsc {EV}$$ which can readily be determined, and that a narrow definition of $$ \textsc {MEV}$$ fails to capture the extractable value of other actors like users, or the probabilistic nature of permissionless cryptocurrencies. We describe an approach to estimate the minimum extractable value that would incentivize actors to act maliciously and thus can potentially lead to consensus instability. We further highlight why it is hard, or even impossible, to precisely determine the extractable value of other participants, considering the uncertainties in real world systems. Finally, we outline a peculiar yet straightforward technique for choosing the individual security parameter k, which can act as a workaround to transfer the risk of an insufficiently chosen k to another merchant.
UTS: The Universal Token Swapper

We propose the Universal Token Swapper (UTS), a smart contract acting as a payment processor between buyers and merchants in a multi-token environment for EVM compatible blockchains. UTS gives merchants the ability to accept any token by performing instant conversions. This way, the buyer does not have to convert the tokens before a payment and the merchant is not subject to financial ruins if he does not want bear the risk on speculation on the value of the token received. In addition, UTS can be implemented to follow any regulation by linking the contract to on-chain oracles, bringing regulatory compliance to DeFi and helping merchants take advantage of the new possibilities.
Privacy-Preserving Post-quantum Credentials for Digital Payments

Digital payments and decentralized systems enable new financial products and services for users. A core challenge stems from the need to protect users from fraud and abuse while retaining privacy in individual transactions. Proposed herein is a pseudonymous credential scheme for use in payment systems. The scheme is privacy-preserving, efficient for practical applications, and hardened against quantum-compute attacks. A constant-round, interactive, zero-knowledge proof of knowledge (ZK-POK), relying on a one-way function and an asymmetric encryption primitive, both of which need to support at most one homomorphic addition, is presented. The scheme is instantiated with SWIFFT as a post-quantum one-way function and Ring Learning With Errors (RLWE) as a post-quantum asymmetric encryption primitive, with the protocol deriving its quantum-hardness from the properties of the underlying primitives. Performance of the ZK-POK instantiated with the chosen primitive was evaluated to reveal a memory footprint of 85 kB to achieve 200 bits of security. Comparison reveals that our scheme is more efficient than equivalent, state-of-the-art post-quantum schemes. A practical, interactive, credential mechanism was constructed from the proposed building blocks, in which users are issued pseudonymous credentials against their personally identifiable information (PII) that can be used to register with financial service providers without revealing personal information. The protocol is shown to be secure and free of information leakage, preserving the user’s privacy regardless of the number of registrations.
Stakechain: A Bitcoin-Backed Proof-of-Stake

We propose an energy-efficient solution to the double-spending problem using a bitcoin-backed proof-of-stake. Stakers vote on sidechain blocks forming a record that cannot be changed without destroying their collateral. Every user can become a staker by locking Bitcoins in the bitcoin blockchain. One-time signatures guarantee that stakers lose their bitcoin stake for publishing conflicting histories. As long as 34% of the stakers are honest the sidechain provides safety, and with a 67%-majority it provides liveness. Overwriting a finalized block costs at least 34% of the total stake. Checkpoints in Bitcoin’s blockchain mitigate classical attacks against conventional proof-of-stake algorithms. A stakechain’s footprint within the mainchain is minimal. The protocol is a generic consensus mechanism allowing for arbitrary sidechain architectures. Spawning multiple, independent instances scales horizontally to a free market of sidechains which can potentially serve billions of users.
Data Portability

Data portability has emerged as another important inclusion to the data legal framework. It enables a data subject to request that their personal data be transferred from one entity to another. Data portability has been established to provide data subjects with a higher level of control over their personal data. However, as this chapter will demonstrate there are few member countries of the Commonwealth that have established law for data portability. At the time of writing this book, the limited nation states adopting this important concept, that originated out of the EU is underwhelming and more needs to be done by the Commonwealth to expand the take up of this concept.
Medical Causality Extraction: A Two-Stage Based Nested Relation Extraction Model

The extraction of medical causality contributes to constructing medical causal knowledge graphs, and enhancing the interpretability of modern medical consultation process. In this paper, we present our approach to medical causal entity and relation extraction in the 8th China Health Information Processing Conference (CHIP 2022) Open Shared Task. Nested relations and overlapping relations with shared entities are two major challenges in this task. We propose a two-stage model to achieve nested relation extraction. In the first stage, we extract traditional non-nested relations and explore how to utilize causal relational signals in entity recognition module to alleviate the problem of overlapping relations. In the second stage, we identify entities in nested relations through the method of machine reading comprehension and design a span-based contrastive learning method (SpanCL) with under-sampling strategy to determine whether causality is nested. The experiment results show that the method we proposed can achieve 43.23% in terms of macro-averaged F1-score.
Teaching PoW Algorithm to a Classroom Environment

In this note we explain our method to teach various consensus algorithms in the classroom. We accomplish that by creating slightly modifying PoS algorithm to create a version which is equivalent to PoW consensus algorithms.
Localisation

Data localisation is a new addition to data and privacy law(s) around the world. This is no different for Commonwealth member countries. This chapter highlights how most members of the Commonwealth members have no data localisation laws. That said, data localisation law have been established to restrict the movement of data. The negative effect from these laws is the restrictions have an impact on the growth and development of the digital economy. Moreover, because the digital economy needs data to flow freely across all international borders. On the other hand, localisation rules may be of some benefit, particularly in enforcement situations where the unauthorised access to data is in breach of cybersecurity law. More work is needed by the Commonwealth to determine what level of locations provisions should be developed or otherwise.
Explainable AI and Pricing Algorithms: A Case For Accountability in Pricing

As artificial intelligence (AI) continues to shape various industries, concerns arise regarding its potential anti-competitive implications. This research paper delves into the intersection of AI and competition law, with a specific focus on the role of explainability in identifying and addressing anti-competitive AI practices under the purview of Indian competition law. The paper begins by analyzing the challenges posed by the lack of transparency and explainability in AI models deployed by companies in market competition by analyzing 3 possibilities. It highlights the risks associated with opaque AI algorithms regarding tacit collusion that hinder the detection of anti competitive behavior, limit accountability, and impede fair competition. It also analyzes the possibility of pro-competitive behavior using algorithms. To propose solutions, the pape advocates for enhanced transparency and algorithmic accountability, emphasizing the need for companies to disclose information about their AI algorithms and justify their decisions. It highlights the need for antitrust authorities to develop expertise in assessing AI algorithms and enforcing competition laws effectively by assessing EU’s Competition and market authority and India’s Competition commission and their experiences in dealing with digital antitrust issues. Overall, this research aims to contribute to the evolving field of AI and competition law by shedding light on the significance of explainability, proposing solutions to address anti-competitive AI practices, and considering the specific context of Competition law. By ensuring transparency, accountability, and fairness in AI systems, we can promote competition, protect consumer welfare, and foster innovation in the AI-driven marketplace.
Simulations of Ballot Polling Risk-Limiting Audits

In this paper we present simulation results comparing the risk, stopping probability, and number of ballots required over multiple rounds of ballot polling risk-limiting audits (RLAs) Minerva, Selection-Ordered (SO) Bravo, and End-of-Round (EoR) Bravo . Bravo is the most commonly used ballot polling RLA and requires the smallest expected number of ballots when ballots are drawn one at a time and the (true) underlying election is as announced. In real audits, multiple ballots are drawn at a time, and Bravo is implemented as SO Bravo or EoR Bravo . Minerva is a recently proposed ballot polling RLA that requires fewer ballots than either implementation of Bravo in a first round with stopping probability 0.9 but requires a predetermined round schedule. It is an open question how these audits compare over multiple rounds and for lower stopping probabilities. Our simulations use stopping probabilities of 0.9 and 0.25. The results are consistent with predictions of the R2B2 open-source library for ballot polling audits. We observe that both Bravo audits are more conservative than Minerva, which stops with fewer ballots, for both first round stopping probabilities. However, the advantage of using Minerva decreases considerably for the smaller first round stopping probability, as one would expect.
Regulator

The creation of an independent Regulator is an important step to ensuring the promotion and implementation of data laws within a country. This chapter determines whether Commonwealth members through their respective national laws have established an independent Regulator according to the MBP and MBPPI. It does not compare the formal and functional arrangements of each Regulator. Generally, the Regulator has the title of Privacy, Information or Data Protection Commissioner who has been afforded legal powers to investigate and make decisions on breaches of the law. In Commonwealth countries, the specific title of the Commissioner will vary from nation state to nation state. Commissioners also have some responsibility for educating stakeholders and raising the awareness of data protection and data flows within their respective nation state, and for collaborating and cooperating with other Commissioners.
Ransom-Extortion

The increased cyber attacks on government and the private sector has seen large quantities of personal data being stolen over the past five years. This illegal activity is often coupled with a level of ransom or extortion that, results in an individual or organisation requesting a level of payment from a data subject or entity for the return of the data. This chapter explores how the Commonwealth members have addressed the issue within the national laws. It will highlight how there is a lack of coherence amongst member states, and there is a need for an urgent review and consideration for this provision to be included into national laws.
Breaking and Fixing Vote Privacy of the Estonian E-Voting Protocol IVXV

We revisit the e-voting protocol IVXV that is used for legally-binding political elections in Estonia from a privacy perspective. We demonstrate that IVXV is vulnerable to attacks against vote privacy in those threat scenarios that were considered for IVXV originally. We explain how to improve IVXV so that it protects against the privacy issues we discovered.
AI and Robotics in Futuristic Hospitals

Messy galleries oil painted walls stained bathrooms inefficient staff dim light and noise... This is how we identify a typical Indian hospital. Hospital chains are indeed making their presence felt but for an average Indian branded healthcare is still a luxury. The scenario has to change. The 21st century marks the incoming of technology where everything is changing. Hospitals are no exception. However, the problem is with the strategic perspective.