Reviewerly/exHarmony huggingface dataset:
Strengths and Weaknesses of Persona Creation Methods: Guidelines and Opportunities for Digital Innovations

Persona is a technique for enhancing user understanding and improving the user-centered design of digital products.Persona creation has traditionally been divided into Qualitative, Quantitative, and Mixed Methods approaches.However, no literature systematically contrasts the strengths and weaknesses of these approaches.We review the literature to map the strengths and weaknesses of these approaches and evaluate the potential of personas for the domain of digital innovation.We provide insights for better creation and use of personas by both researchers and practitioners, especially those that are new to personas, deploying personas in a new domain, or familiar with only one of the persona creation approaches.
Key features of information management systems (IMSs) for automation in university libraries: a view point of information professionals in Pakistan

Purpose The purpose of this study is to investigate the key features of information management systems (IMSs) for automation in university libraries. This study also highlights the use of library information management systems in university libraries and the satisfaction of university librarians in using various IMS. Design/methodology/approach This study is quantitative in nature. A survey research method has been applied to achieve the research objectives. Purposive sampling has been used to select the targeted population which consisted of 157 librarians working in university libraries. Data were collected through a web-based questionnaire. Descriptive and inferential statistics were applied to analyze the collected data by using SPSS software. Findings Free and open source software are widely used in university libraries to manage the bibliographic information of library material. KOHA is the most used software for library automation in university libraries of Pakistan. The key features of library information management system (IMS) include reliability and security of the software, user-friendly interface, advance searching options, use of library standards (MARC, Uni MARC, RDA), online upgradation, technical support of developing company, shared cataloging, multilingual features of software, etc (See Figure 4). The study recommends that the above-mentioned features must be considered by university librarians when selecting any software for library automation. Results showed that apart from the use of KOHA software, there are also some other software which are being used for library automation in university libraries of Pakistan. These systems include; Virtua, Library Management System (LMS), and Library Information Management Systems (LIMSs). Practical implications This study has practical implications for university librarians in Pakistan as well as in other countries. Librarians can use the results of this study as a blueprint before selecting any information management system to automate the library record. Originality/value This study identifies the core features of IMS for automation in libraries. These features have been recommended by informational professionals who have been working in automated libraries and possess adequate professional experience in using library automation software.
SGX-UAM: A Secure Unified Access Management Scheme With One Time Passwords via Intel SGX

With the convergence of fixed and mobile networks, heterogeneous networks are becoming ubiquitous.Internet giants are seeing the plight of identity authentication.To address this issue, unified access management (UAM) was conceived.This paper provides a novel unified access management scheme, named SGX-UAM, with one-time passwords (OTPs) based on Intel software guard extensions (SGX).SGX-UAM outperforms generic UAM for providing resistance to most client attacks, man-in-the-middle (MITM) attacks, phishing attacks, most replay attacks and most denial of service (DoS) attacks to which generic UAM implementaions are vulnerable.Specifically, client attacks are prevented by ensuring input security and memory security, where the former is achieved through shuffle mapping and ''periodic hooking'' strategy, the latter is mainly guaranteed by Intel SGX; MITM attacks are prevented by transferring ciphertext rather than plaintext; phishing attacks are avoided by authorization control; replay attacks cannot succeed because we adopts OTPs, which contain time-related dynamic factors that expire in a few seconds; as for DoS attack, we blunted its edge by blocking-invocation for identical user connection.SGX-UAM also differs from generic UAM in that it relieves the security concerns of sevice providers (SPs) and protects users' privacy at little cost of performance.An exceptional value of SGX-UAM is that it brings a lightweight OTP solution that eliminates the need of additional hardware devices, thus reducing the costs.The experimental results show that SGX-UAM consumes almost the same time with OpenID and OAuth2.0 for one login request and performs steadily when handling sequential login requests.Furthermore, the resource usage for SGX-UAM is acceptable.
Local optimization on pure Gaussian state manifolds

We exploit insights into the geometry of bosonic and fermionic Gaussian states to develop an efficient local optimization algorithm to extremize arbitrary functions on these families of states. The method is based on notions of gradient descent attuned to the local geometry which also allows for the implementation of local constraints. The natural group action of the symplectic and orthogonal group enables us to compute the geometric gradient efficiently. While our parametrization of states is based on covariance matrices and linear complex structures, we provide compact formulas to easily convert from and to other parametrization of Gaussian states, such as wave functions for pure Gaussian states, quasiprobability distributions and Bogoliubov transformations. We review applications ranging from approximating ground states to computing circuit complexity and the entanglement of purification that have both been employed in the context of holography. Finally, we use the presented methods to collect numerical and analytical evidence for the conjecture that Gaussian purifications are sufficient to compute the entanglement of purification of arbitrary mixed Gaussian states.
A Hierarchy Distributed-Agents Model for Network Risk Evaluation Based on Deep Learning

Deep Learning presents a critical capability to be geared into environments being constantly changed and ongoing learning dynamic, which is especially relevant in Network Intrusion Detection. In this paper, as enlightened by the theory of Deep Learning Neural Networks, Hierarchy Distributed-Agents Model for Network Risk Evaluation, a newly developed model, is proposed. The architecture taken on by the distributed-agents model are given, as well as the approach of analyzing network intrusion detection using Deep Learning, the mechanism of sharing hyper-parameters to improve the efficiency of learning is presented, and the hierarchical evaluative framework for Network Risk Evaluation of the proposed model is built. Furthermore, to examine the proposed model, a series of experiments were conducted in terms of NSL-KDD datasets. The proposed model was able to differentiate between normal and abnormal network activities with an accuracy of 97.60% on NSL-KDD datasets. As the results acquired from the experiment indicate, the model developed in this paper is characterized by high-speed and high-accuracy processing which shall offer a preferable solution with regard to the Risk Evaluation in Network.
Modeling word and morpheme order in natural language as an efficient trade-off of memory and surprisal.

Memory limitations are known to constrain language comprehension and production, and have been argued to account for crosslinguistic word order regularities. However, a systematic assessment of the role of memory limitations in language structure has proven elusive, in part because it is hard to extract precise large-scale quantitative generalizations about language from existing mechanistic models of memory use in sentence processing. We provide an architecture-independent information-theoretic formalization of memory limitations which enables a simple calculation of the memory efficiency of languages. Our notion of memory efficiency is based on the idea of a memory-surprisal trade-off: A certain level of average surprisal per word can only be achieved at the cost of storing some amount of information about the past context. Based on this notion of memory usage, we advance the Efficient Trade-off Hypothesis: The order of elements in natural language is under pressure to enable favorable memory-surprisal trade-offs. We derive that languages enable more efficient trade-offs when they exhibit information locality: When predictive information about an element is concentrated in its recent past. We provide empirical evidence from three test domains in support of the Efficient Trade-off Hypothesis: A reanalysis of a miniature artificial language learning experiment, a large-scale study of word order in corpora of 54 languages, and an analysis of morpheme order in two agglutinative languages. These results suggest that principles of order in natural language can be explained via highly generic cognitively motivated principles and lend support to efficiency-based models of the structure of human language. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
The role of mathematics in transdisciplinarity content of modern education

Introduction . In the context of modernisation in modern education, a deeper (by contrast to interdisciplinary) transdisciplinary level of cognition is becoming prevalent. Transdisciplinary level generates a universal methodology capable of addressing the complex multi-factorial interdisciplinary problems of nature and society. The result is transdisciplinary branches of science such as cybernetics, disaster theory, synergetics, artificial intelligence, big data, etc. All these concepts have been developed on the basis of the achievements of mathematics over the past 70-80 years, the era of mathematical sciences. As a consequence, mathematics has become the basis of the language of information technologies and processes, and, thereby, this science has given rise to a global digital transformation of society based on the use of the unique computer capabilities. The aim of the present research was to explore the role of mathematics in the transdisciplinary trend in updating the content of education with a view to bringing education to a higher (by contrast to interdisciplinary) level, based on the inclusion of modern mathematical theories and methods in the content of education and their applications depending on the direction and profile of the student training. Methodology and research methods . In the course of research, the systemic, cultural, and meta-subject approaches were employed to analyse the role of mathematics in education and to solve the transdisciplinary problems of education content modernisation (based on the most striking manifestations of modern mathematical culture). As a result of the synthesis of these approaches, a holistic scientific worldview emerges, which not only goes beyond the traditional disciplines and methods, but also appears above them. Results and scientific novelty . The analysis of transdisciplinary trend in postindustrial education was carried out. Mathematical and pedagogical aspects of the implementation of systemic, cultural and meta-subject approaches were investigated in order to achieve a higher level of educational process. At the same time, the authors justified the use of mathematical modelling, discrete mathematics, computational processes and artificial intelligence in the training, i.e. formation of a new superdisciplinary way of thinking in students, acquisition of a general cultural cognitive strategy to perform professional and transprofessional tasks. Practical importance . The findings of the current publication contribute to the realisation of the transdisciplinary trend in the content of student training, and will be of interest to both educational theorists and teachers, who train students in many fields. Moreover, this work will be useful for all those interested in the future advancement of the system of education.
Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering

Commonly used information retrieval methods such as TF-IDF in open-domain question answering (QA) systems are insufficient to capture deep semantic matching that goes beyond lexical overlaps.Some recent studies consider the retrieval process as maximum inner product search (MIPS) using dense question and paragraph representations, achieving promising results on several informationseeking QA datasets.However, the pretraining of the dense vector representations is highly resource-demanding, e.g., requires a very large batch size and lots of training steps.In this work, we propose a sample-efficient method to pretrain the paragraph encoder.First, instead of using heuristically created pseudo questionparagraph pairs for pretraining, we use an existing pretrained sequence-to-sequence model to build a strong question generator that creates high-quality pretraining data.Second, we propose a simple progressive pretraining algorithm to ensure the existence of effective negative samples in each batch.Across three opendomain QA datasets, our method consistently outperforms a strong dense retrieval baseline that uses 6 times more computation for training.On two of the datasets, our method achieves more than 4-point absolute improvement in terms of answer exact match.
ML-KELM: A Kernel Extreme Learning Machine Scheme for Multi-Label Classification of Real Time Data Stream in SIoT

Social Internet of Things is the fusion carrier of social network and Internet of Things. In the social Internet of Things, millions of different intelligent objects connect and communicate with each other, and social data emerge rapidly, which puts forward higher requirements for the rapid dissemination of valuable information. As an important means of information retrieval, the research and application of multi-label classification technology in social Internet of Things environment is relatively few. Characterized by multi-labels cognitive learning, fast response, concept drifting and huge solution space, the multi-label learning becomes more complicated under data stream environment. To overcome the problems above, this paper proposed a multi-label algorithm based on kernel extreme learning machine. In the training phase, both Cholesky matrix decomposition inverse method and matrix block method were deployed on the Spark platform to solve the inverse problem of large matrix, thus improving the modeling efficiency. In addition, incremental learning of the model was realized from the two aspects of example increment and class increment. The online sequential extreme learning machine is improved to adjust the network weights and realize incremental updating of input dimension. Meanwhile, the output layer nodes were divided and assembled in the way of “concept group,” which made the single classifier model structure scalable. And the label incremental learning is realized in the output dimension. Experimental results on large-scale datasets and practical datasets under stream environment demonstrate that the proposed method provides an efficient solution to multi-label classification.
Information Leakages in Code-based Masking: A Unified Quantification Approach

This paper presents a unified approach to quantifying the information leakages in the most general code-based masking schemes. Specifically, by utilizing a uniform representation, we highlight first that all code-based masking schemes’ side-channel resistance can be quantified by an all-in-one framework consisting of two easy-tocompute parameters (the dual distance and the number of conditioned codewords) from a coding-theoretic perspective. In particular, we use signal-to-noise ratio (SNR) and mutual information (MI) as two complementary metrics, where a closed-form expression of SNR and an approximation of MI are proposed by connecting both metrics to the two coding-theoretic parameters. Secondly, considering the connection between Reed-Solomon code and SSS (Shamir’s Secret Sharing) scheme, the SSS-based masking is viewed as a particular case of generalized code-based masking. Hence as a straightforward application, we evaluate the impact of public points on the side-channel security of SSS-based masking schemes, namely the polynomial masking, and enhance the SSS-based masking by choosing optimal public points for it. Interestingly, we show that given a specific security order, more shares in SSS-based masking leak more information on secrets in an information-theoretic sense. Finally, our approach provides a systematic method for optimizing the side-channel resistance of every code-based masking. More precisely, this approach enables us to select optimal linear codes (parameters) for the generalized code-based masking by choosing appropriate codes according to the two coding-theoretic parameters. Summing up, we provide a best-practice guideline for the application of code-based masking to protect cryptographic implementations.
The Eye of Horus: Spotting and Analyzing Attacks on Ethereum Smart Contracts

In recent years, Ethereum gained tremendously in popularity, growing from a daily transaction average of 10K in January 2016 to an average of 500K in January 2020. Similarly, smart contracts began to carry more value, making them appealing targets for attackers. As a result, they started to become victims of attacks, costing millions of dollars. In response to these attacks, both academia and industry proposed a plethora of tools to scan smart contracts for vulnerabilities before deploying them on the blockchain. However, most of these tools solely focus on detecting vulnerabilities and not attacks, let alone quantifying or tracing the number of stolen assets. In this paper, we present Horus, a framework that empowers the automated detection and investigation of smart contract attacks based on logic-driven and graph-driven analysis of transactions. Horus provides quick means to quantify and trace the flow of stolen assets across the Ethereum blockchain. We perform a large-scale analysis of all the smart contracts deployed on Ethereum until May 2020. We identified 1,888 attacked smart contracts and 8,095 adversarial transactions in the wild. Our investigation shows that the number of attacks did not necessarily decrease over the past few years, but for some vulnerabilities remained constant. Finally, we also demonstrate the practicality of our framework via an in-depth analysis on the recent Uniswap and Lendf.me attacks.
Continuous-Variable Measurement-Device-Independent Quantum Key Distribution with One-Time Shot-Noise Unit Calibration

Imperfections in practical detectors, including limited detection efficiency, and inherent electronic noise, can seriously decrease the transmission distance of continuous-variable measurement-device-independent quantum key distribution systems. Owing to the difficulties inherent in realizing a high-efficiency fiber homodyne detector, challenges still exist in continuous-variable measurement-device-independent quantum key distribution system implementation. We offer an alternative approach in an attempt to solve these difficulties and improve the potential for system implementation. Here, a novel practical detector modeling method is utilized, which is combined with a one-time shot-noise-unit calibration method for the purpose of system realization. The new modeling method benefits greatly from taking advantage of one-time shot-noise-unit calibration methods, such as measuring electronic noise and shot noise directly to a novel shot-noise unit, so as to eliminate the statistical fluctuations found in previous methods; this makes the implementation of such systems simpler, and the calibration progress more accurate. We provide a simulation of the secret key rate versus distance with different parameters. In addition, the minimal detection efficiency required at each distance, as well as the contrast between the two methods, are also shown, so as to provide a reference in terms of system realization.
Improved Accuracy on Noisy Devices by Nonunitary Variational Quantum Eigensolver for Chemistry Applications

We propose a modification of the Variational Quantum Eigensolver algorithm for electronic structure optimization using quantum computers, named nonunitary Variational Quantum Eigensolver (nu-VQE), in which a nonunitary operator is combined with the original system Hamiltonian leading to a new variational problem with a simplified wave function ansatz. In the present work, as nonunitary operator, we use the Jastrow factor, inspired from classical Quantum Monte Carlo techniques for simulation of strongly correlated electrons. The method is applied to prototypical molecular Hamiltonians for which we obtain accurate ground-state energies with shallower circuits, at the cost of an increased number of measurements. Finally, we also show that this method achieves an important error mitigation effect that drastically improves the quality of the results for VQE optimizations on today’s noisy quantum computers. The absolute error in the calculated energy within our scheme is 1 order of magnitude smaller than the corresponding result using traditional VQE methods, with the same circuit depth.
Linear Transformers Are Secretly Fast Weight Programmers

We show the formal equivalence of linearised self-attention mechanisms and fast weight controllers from the early '90s, where a ``slow neural net learns by gradient descent to program the ``fast weights of another net through sequences of elementary programming instructions which are additive outer products of self-invented activation patterns (today called keys and values). Such Fast Weight Programmers (FWPs) learn to manipulate the contents of a finite memory and dynamically interact with it. We infer a memory capacity limitation of recent linearised softmax attention variants, and replace the purely additive outer products by a delta rule-like programming instruction, such that the FWP can more easily learn to correct the current mapping from keys to values. The FWP also learns to compute dynamically changing learning rates. We also propose a new kernel function to linearise attention which balances simplicity and effectiveness. We conduct experiments on synthetic retrieval problems as well as standard machine translation and language modelling tasks which demonstrate the benefits of our methods.