Reviewerly/exHarmony huggingface dataset:
Network Big Data Mining Algorithm Based on Association Rules of Computer Technology

Abstract With the development of cloud computing, it has provided a platform for big data presentation and sharing. These data often contain artificially added uncertain factors to prevent privacy leaks. How to perform mining of these big data is a problem to be solved urgently in big data sharing. In the big data for sharing, it is implemented through the generalization process of precise data, with the feature of uniform distribution, which is not conducive to exact query, but it can provide convenience for the mining of association rules. Firstly, based on the possible intersection or inclusion relationship, hierarchical clustering is performed on the generalized values. To save the essential information related to big dataset mining, we proposed an algorithm for building an uncertain frequent pattern tree. An improved algorithm of network big data mining based on association rules of computer technology is proposed in the big data environment, and its application in landslide monitoring and early warning is discussed.
A Multi-Neural Network Fusion Based Method for Financial Event Subject Extraction

Event extraction is a fundamental task in the domain of public opinion monitoring and financial risk control. Subject extraction of events with specific types is the kernel of event extraction. At present, there are some problems still existing in the mainstream event subject extraction methods, such as the inadequate use of semantic relationship between Chinese characters and the weak ability of feature learning. In order to solve these problems, this paper introduces the BERT (Bidirectional Encoder Representations from Transformers) pre-training model to enhance the semantic representation of characters, then proposes a novel event subject extraction method combing convolutional neural network (CNN) and long short-term memory (LSTM) to improve the ability of feature learning in the model. Experimental results show that the F1 score of the method proposed in this paper can reach 86.99%, which greatly improves the identification accuracy of the event subject in the financial domain.
Distributed random walks for fitness landscape analysis

Fitness landscape analysis is used to mathematically characterize optimization problems. In order to perform fitness landscape analysis on continuous-valued optimization problems, a sample of the fitness landscape needs to be taken. A common way to perform this sampling is to use random walk algorithms. This paper proposes a new random walk algorithm for continuous-valued optimization problems, called the distributed random walk algorithm. The algorithm is based on the premise that multiple short random walks of the same type will provide better coverage of the decision space and more robust fitness landscape measures than a single long random walk. The distributed random walk algorithm is simple to implement, and the computational overhead is insignificant compared to random walk algorithms in the literature. The results of the study indicate that the distributed random walk algorithm achieves both of these objectives. Furthermore, the benefits of the distributed random walk algorithm are shown to be much more significant when small step sizes are used in the random walks.
Squeeze Interaction in Physical &amp; Occupational Therapy

Squeeze interaction is a promising interaction technique for interactive systems in health care. Through the presented PhD research we aim to answer the question of how to make an accessible squeeze interaction toolkit, that empowers both clients and therapists. Our methodology combines research through design with ethnographic practice, leaning to critical making. In our research, we focus on the 'feel' and 'aesthetic' of squeeze interaction as affective interaction technique (cfr. phenomenology). In this extended abstract, we reflect on the research process by means of three created prototypes: A first prototype enabled us to grasp the interaction gestalt of squeeze interaction, a second moved our understanding of squeeze interaction beyond the screen, and through a third prototype we situate squeeze interaction in special education. To conclude, we outline future work and introduce a second case study situated in a residential care facility.
A Two-Tier Blockchain Architecture for the Digital Transformation of Multilateralism

Multilateral organizations have been a major force for economic development and cooperation, but also face several challenges. We believe innovative technologies such as blockchain are an excellent fit for multilateralism and have the potential to deliver substantial economic impacts to all parties at all levels. This paper proposes a two-tier blockchain architecture to facilitate both the governance of multi-lateral agreements, and the execution of projects and services that are governed by such agreements. The blockchain-based architecture is meant to enhance trust among participating entities by having a shared, transparent, and immutable record, automated and traceable execution of the mutual agreements using smart contracts, and the opportunity for each participant to not only have a fair voting power on decisions, but can also get rewarded by running a blockchain node and thus making the overall architecture more robust. Technically, our prototype explored the various ways for the different blockchain platforms in the two-tier architecture to interoperate. We have implemented two different interoperability solutions, and plan to continue our exploration. Overall, we believe such a technology architecture is ideally suited for multilateral initiatives, and plan to explore its implementation in the field.
The Architecture Design of Government Microblog Matrix Management System Based on Unified Modeling Language

Government microblog is one of the important online communication modes of socialized participation, discussion and inquiry in politics in contemporary China. The matrix mode of government microblog is helpful to strengthen the inter-departmental, cross-level and cross-regional linkage of government microblog, which is one of the hot research directions at present. Based on the operation process, we used UML technology to propose a system architecture and database design method of microblog matrix management system, which established the reasonable granularity of functional modules and found the balance between data redundancy and data correlation to ensure the efficiency and reliability of the whole system. The architecture design method proposed in this paper has certain reference value for the research of related fields.
Leveraging Arguments in User Reviews for Generating and Explaining Recommendations

Abstract Review texts constitute a valuable source for making system-generated recommendations both more accurate and more transparent. Reviews typically contain statements providing argumentative support for a given item rating that can be exploited to explain the recommended items in a personalized manner. We propose a novel method called Aspect-based Transparent Memories (ATM) to model user preferences with respect to relevant aspects and compare them to item properties to predict ratings, and, by the same mechanism, explain why an item is recommended. The ATM architecture consists of two neural memories that can be viewed as arrays of slots for storing information about users and items. The first memory component encodes representations of sentences composed by the target user while the second holds an equivalent representation for the target item based on statements of other users. An offline evaluation was performed with three datasets, showing advantages over two baselines, the well-established Matrix Factorization technique and a recent competitive representative of neural attentional recommender techniques.
Impact of Trust and Reputation Based Brokerage on the CloudAnchor Platform

This paper analyses the impact of trust and reputation modelling on CloudAnchor, a business-to-business brokerage platform for the transaction of single and federated resources on behalf of Small and Medium Sized Enterprises (SME). In CloudAnchor, businesses act as providers or consumers of Infrastructure as a Service (IaaS) resources. The platform adopts a multi-layered multi-agent architecture, where providers, consumers and virtual providers, representing provider coalitions, engage in trust & reputation-based provider look-up, invitation, acceptance and resource negotiations. The goal of this work is to assess the relevance of the distributed trust model and centralised fuzzified reputation service in the number of resources successfully transacted, the global turnover, brokerage fees, losses, expenses and time response. The results show that trust and reputation based brokerage has a positive impact on the CloudAnchor performance by reducing losses and the execution time for the provision of both single and federated resources and increasing considerably the number of federated resources provided.
Collaborative Use of a Shared System Interface: The Role of User Gaze—Gaze Convergence Index Based on Synchronous Dual-Eyetracking

Gaze convergence of multiuser eye movements during simultaneous collaborative use of a shared system interface has been proposed as an important albeit sparsely explored construct in human-computer interaction literature. Here, we propose a novel index for measuring the gaze convergence of user dyads and address its validity through two consecutive eye-tracking studies. Eye-tracking data of user dyads were synchronously recorded while they simultaneously performed tasks on shared system interfaces. Results indicate the validity of the proposed gaze convergence index for measuring the gaze convergence of dyads. Moreover, as expected, our gaze convergence index was positively associated with dyad task performance and negatively associated with dyad cognitive load. These results suggest the utility of (theoretical or practical) applications such as synchronized gaze convergence displays in diverse settings. Further research perspectives, particularly into the construct’s nomological network, are warranted.
Preparing the Digital Piano: Introducing bitKlavier

Abstract This article describes a new kind of digital musical instrument, a novel assemblage of the familiar MIDI keyboard with custom interactive software. Inspired by John Cage's prepared piano, our instrument both takes advantage of and subverts the pianist's hard-earned, embodied training, while also inviting an extended configuration stage that “prepares” the instrument to behave in composition-specific, idiosyncratic ways. Through its flexible—though constrained—design, the instrument aims to inspire a playful approach to instrument building, composition, and performance. We outline the development history of our instrument, called bitKlavier, its current design, and some of its musical possibilities.
Didactic conditions for the formation of digital competence of students of pedagogical universities

The article is devoted to the formation of digital competence of pedagogical university students. The scientific analysis of the essence of the “digital competence” concept is carried out, its components are defined. In the course of the research the components of digital competence of pedagogical university students are determined, namely: motivational-value (target), cognitive, operational-activity and personal-reflexive components. The article defines and substantiates didactic conditions of digital formation competences of students of pedagogical universities: actualization of motivational value training of students of pedagogical universities; organization of interaction between students and teachers of pedagogical universities on the Internet through the creation of digital information educational environment; creation of individual educational trajectories of students.
A Novel Approach to Handle Huge Data for Refreshment Anomalies in Near Real-Time ETL Applications

Real-time analysis of data is the new trend to get useful insights in very less time spend on data preprocessing. Analysis of data requires the movement of data from various heterogeneous/homogenous sources to a common place known as the data warehouse. Data source for data warehouse is the transaction processing systems. Movement of data from the transactional database to the data warehouse is done using the process of extract, transform, and load (ETL). ETL previously was done during of peak hours like a night load or on weekends. The requirement of real-time analysis demands the ETL to be fast and not wait for off-peak hours. This leads to the concept of near real-time ETL, and here techniques are employed to identify the potential changed data at the transaction database and move it to the analysis database with a very minimal delay. This movement of data in real time from multiple sources in an incremental form could lead to anomalies in the data warehouse. This work discusses the various causes of anomalies and solutions to overcome them. Our main contribution is the application of loading data into temporary tables for reducing query execution time in case of overcoming refreshment anomalies.
Neural Network Equalisation and Symbol Detection for 802.11p V2V Communication at 5.9GHz

Neural networks are shown to be a viable implementation for joint channel equalisation and symbol detection in a vehicular network. Experimental results using a hardware-in-the-loop approach at 5.9GHz validate the efficacy of the proposed implementation following the 802.11p parameters using orthogonal frequency division multiplexing (OFDM). Further results are obtained using a more spectrally efficient waveform, namely spectrally efficient frequency division multiplexing (SEFDM), to show a trade-off between loss of orthogonality, and therefore bit-error rate (BER) performance, versus increased spectral efficiency to enable higher data rates or the ability to service more users. SEFDM is tested with compression factors ranging from 20% up to 60% bandwidth compression. The results show the neural network is able to achieve an acceptable BER performance in a highway non-line-of-sight (NLOS) channel which is a well established harsh and dynamic vehicular channel. This is further validated via measurements of the error vector magnitude.
An Empirical Study of Agile Testing in A Distributed Software Development Project

The software engineering industry has witnessed an increasing number of innovative methods and practices in the last decade at different levels, ranging from development processes to software projects and from testing to verification software products. Extensive studies have been conducted empirically to investigate and discuss the impact of using agile principles in the testing process on distributed teams across geographical boundaries. This empirical study has a similar focus, using a real case study in a distributed domain and applying agile testing to a selected team, compares their outcome with another three teams to determine the impact of involving a client in a testing process to overcome distributed development challenges. The findings indicate a highly positive impact on team productivity when using agile tests as compared with other groups using central distributed team testing. All teams met a 90% testing requirement. However, the group applying agile testing verified more than 99% of all requests entered into the testing process, a notable difference supporting the productivity of any development project.
Modeling wildfire using evolutionary cellular automata

With the increased size and frequency of wildfire events worldwide, accurate real-time prediction of evolving wildfire fronts is a crucial component of firefighting efforts and forest management practices. We propose a cellular automaton (CA) that simulates the spread of wildfire. We embed the CA inside of a genetic program (GP) that learns the state transition rules from spatially registered synthetic wildfire data. We demonstrate this model's predictive abilities by testing it on unseen synthetically generated landscapes. We compare the performance of a genetic program (GP) based on a set of primitive operators and restricted expression length to null and logistic models. We find that the GP is able to closely replicate the spreading behavior driven by a balanced logistic model. Our method is a potential alternative to current benchmark physics-based models.
Error control scheme for malicious and natural faults in cryptographic modules

Abstract Today’s electronic systems must simultaneously fulfill strict requirements on security and reliability. In particular, their cryptographic modules are exposed to faults, which can be due to natural failures (e.g., radiation or electromagnetic noise) or malicious fault-injection attacks. We present an architecture based on a new class of error-detecting codes that combine robustness properties with a minimal distance. The new architecture guarantees (with some probability) the detection of faults injected by an intelligent and strategic adversary who can precisely control the disturbance. At the same time it supports automatic correction of low-multiplicity faults. To this end, we discuss an efficient technique to correct single nibble/byte errors while avoiding full syndrome analysis. We also examine a Compact Protection Code (CPC)-based system level fault manager that considers this code an inner code (and the CPC as its outer code). We report experimental results obtained by physical fault injection on the SAKURA-G FPGA board. The experimental results reconfirm the assumption that faults may cause an arbitrary number of bit flips. They indicate that a combined inner–outer coding scheme can significantly reduce the number of fault events that go undetected due to erroneous corrections of the inner code.
On providing systematized access to consolidated principles, guidelines and patterns for usable security research and development†

Abstract We present a systematization of usable security principles, guidelines and patterns to facilitate the transfer of existing knowledge to researchers and practitioners. Based on a literature review, we extracted 23 principles, 11 guidelines and 47 patterns for usable security and identified their interconnection. The results indicate that current research tends to focus on only a subset of important principles. The fact that some principles are not yet addressed by any design patterns suggests that further work on refining these patterns is needed. We developed an online repository, which stores the harmonized principles, guidelines and patterns. The tool enables users to search for relevant guidance and explore it in an interactive and programmatic manner. We argue that both the insights presented in this article and the web-based repository will be highly valuable for students to get a good overview, practitioners to implement usable security and researchers to identify areas of future research.
Wearing the Invisible

This PhD research is concerned with the human body. The body is constantly changing, diverse, complex - and to design for it requires to understand its needs, desires and experiences. In my research, I reflect on existing processes for creating for the body, and aim, through building on critical design, participatory design and feminist theory, to identify a corporeal-centered and diverse approach to designing for the body, such as when creating wearables. To reach that aim it is crucial to overcome an assumption-based assessment of the body of others, and instead deeply engage with the personal, bodily, idiosyncratic experiences of diverse people. Therefore, I am creating wearable artefacts that resemble lived experiences of others, and make them touchable, discussable and wearable. Based on the process of making those wearable artefacts, I will define design sensitivities for a body-diverse, body-centered and inclusive design process.
Fully persistent B-trees

We present efficient fully persistent B-trees in the I/O model with block size B that support range searches on t reported elements at any accessed version of size n in O(logB⁡n+t/B) I/Os and updates at any accessed version in O(logB⁡n+log2⁡B) amortized I/Os, using O(m/B) disk blocks after m updates. This improves both the query and update I/O-efficiency of the previous fully persistent B-trees of Lanka and Mays (ACM SIGMOD ICMD 1991). To achieve the result, we introduce an implementation for ephemeral B-trees that supports searches and updates in O(logB⁡n) I/Os, using O(n/B) blocks, where moreover every update makes a worst-case constant number of modifications to the structure. We make these B-trees fully persistent using an I/O-efficient method for full persistence, inspired by the node-splitting method of Driscoll et al. (JCSS 1989). Interesting in its own right, the method is generic enough to be applied to any external memory pointer-based data structure with maximum in-degree din and out-degree O(B), where every node occupies a constant number of blocks on disk. For a user-specified parameter π=Ω(din), we achieve O(πB+log2⁡π) I/O-overhead per access to a field of an ephemeral block and amortized O(πB+log2⁡π+dinπlog2⁡B) I/O-overhead and O(1/B) block space-overhead per modification to the ephemeral structure.
WP-SGD: Weighted parallel SGD for distributed unbalanced-workload training system

Stochastic gradient descent (SGD) is a popular stochastic optimization method in machine learning. Traditional parallel SGD algorithms, e.g., SimuParallel SGD (Zinkevich, 2010), often require all nodes to have the same performance or to consume equal quantities of data. However, these requirements are difficult to satisfy when the parallel SGD algorithms run in a heterogeneous computing environment; low-performance nodes will exert a negative influence on the final result. In this paper, we propose an algorithm called weighted parallel SGD (WP-SGD). WP-SGD combines weighted model parameters from different nodes in the system to produce the final output. WP-SGD makes use of the reduction in standard deviation to compensate for the loss from the inconsistency in performance of nodes in the cluster, which means that WP-SGD does not require that all nodes consume equal quantities of data. We also propose the methods of running two other parallel SGD algorithms combined with WP-SGD in a heterogeneous environment. The experimental results show that WP-SGD significantly outperforms the traditional parallel SGD algorithms on distributed training systems with an unbalanced workload.
On finite length nonbinary sequences with large nonlinear complexity over the residue ring $ \mathbb{Z}_{m} $

&lt;p style='text-indent:20px;'&gt;In this paper, we characterize all nonbinary sequences of length &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ n $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; with nonlinear complexity &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ n-4 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; for &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ n\geq9 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and establish a formula on the number of such sequences. More generally, we characterize other finite length nonbinary sequences with large nonlinear complexity over &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ \mathbb{Z}_{m} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;.&lt;/p&gt;