Miracle-dz/UniFAR-FTUs huggingface dataset:
The data management of heterogeneous resources in Belle II

The Belle II experiment at the SuperKEKB collider in Tsukuba, Japan, has started taking physics data in early 2018 and plans to accumulate 50 ab-1, which is approximately 50 times more data than the Belle experiment. The collaboration expects it will require managing and processing approximately 200 PB of data. Computing at this scale requires efficient and coordinated use of the geographically distributed compute resources in North America, Asia and Europe and will take advantage of high-speed global networks. We present the general Belle II the distributed data management system and computing results from the first phase of data taking.
Operational security, threat intelligence & distributed computing: the WLCG Security Operations Center Working Group

The modern security landscape for distributed computing in High Energy Physics (HEP) includes a wide range of threats employing different attack vectors. The nature of these threats is such that the most effective method for dealing with them is to work collaboratively, both within the HEP community and with partners further afield - these can, and should, include institutional and campus security teams. In parallel with this work, an appropriate technology stack is essential, incorporating current work on Big Data analytics. The work of the Worldwide LHC Computing Grid (WLCG) Security Operations Center (SOC) Working Group (WG) [1] is to pursue these goals to form a reference design (or guidelines) for WLCG sites of different types. The strategy of the group is to identify necessary components - starting with threat intelligence (MISP [2]) and network intrusion detection (Bro [3]), building a working model over time. We present on the progress of the working group thus far, in particular on the programme of workshops now underway. These workshops give an opportunity to engage with sites to allow the development of advice and procedures for deployment, as well as facilitating wider discussions on how to best work with trust groups at different levels. These trust groups vary in scope but can include institutes, National Grid Infrastructures and the WLCG as a whole.
Improving SGD convergence by tracing multiple promising directions and estimating distances to their extrema

—Deep neural networks are usually trained with stochastic gradient descent (SGD), which optimizes θ ∈ R D parameters to minimize objective function using very rough approximations of gradient, only averaging to the real gradient. Standard approaches like momentum or ADAM only consider single direction, and do not try to model distance from extremum - neglecting valuable information from calculated gradients. It can be improved by second order methods, but they are costly, need inverse of Hessian - problematic especially in the stochastic setting. Proposed general framework should overcome these dif-ﬁculties by directly evolving local second order parametrization in d ≪ D directions: as P di =1 λ i ( θ · v i − p i ) 2 modelling local information we are interested in, and relatively simple to update for better agreement with calculated gradients. It allows for θ update by simultaneously attracting toward modelled directional minima ( λ i > 0) , and repulsing from maxima ( λ i < 0) , correspondingly to distances from p i (and uncertainty), what allows to also handle problematic saddles. Calculated gradients can be used to slowly evolve this parametrization to improve agreement with local behavior of objective function, accumulating their statistical trends: 1) update λ, p parameters for more accurate description of parabola in corresponding directions (also uncertainty), 2) rotate considered subspace toward recently statistically signiﬁcant directions (replacing the less frequent ones), and 3) rotate ( v i ) inside the subspace to improve diagonal form of Hessian in this basis. Presented general framework leaves many customization options for optimizations to speciﬁc tasks.
Malicious Code Detection for Trusted Execution Environment Based on Paillier Homomorphic Encryption

SUMMARY Currently, mobile terminals face serious security threats. A Trusted Execution Environment (TEE) which can provide an isolated execution environment for sensitive workloads, is seen as a trusted relay for providing security services for any mobile application. However, mobile TEE’s architecture design and implementation strategy are not unbreakable at present. The existing researches lack of detect mechanisms for attack behaviour and malicious software. This paper proposes a Malicious code Detection scheme for Trusted Execution Environment based on Homomor-phic Encryption (HE-TEEMD), which is a novel detection mechanism for data and code in the trusted execution environment. HE-TEEMD uses the Paillier additive homomorphic algorithm to implement the signature matching and transmits the ciphertext information generated in the TEE to the normal world for detection by the homomorphism and randomness of the homomorphic encryption ciphertext. An experiment and security analysis proves that our scheme can achieve malicious code detection in the secure world with minimal cost. Furthermore, evaluation parameters are introduced to address the known plaintext attack problem of privileged users.
Approximate inference: new visions

Nowadays machine learning (especially deep learning) techniques are being incorporated to many intelligent systems affecting the quality of human life. The ultimate purpose of these systems is to perform automated decision making, and in order to achieve this, predictive systems need to return estimates of their confidence. Powered by the rules of probability, Bayesian inference is the gold standard method to perform coherent reasoning under uncertainty. It is generally believed that intelligent systems following the Bayesian approach can better incorporate uncertainty information for reliable decision making, and be less vulnerable to attacks such as data poisoning. Critically, the success of Bayesian methods in practice, including the recent resurgence of Bayesian deep learning, relies on fast and accurate approximate Bayesian inference applied to probabilistic models. These approximate inference methods perform (approximate) Bayesian reasoning at a relatively low cost in terms of time and memory, thus allowing the principles of Bayesian modelling to be applied to many practical settings. However, more work needs to be done to scale approximate Bayesian inference methods to big systems such as deep neural networks and large-scale dataset such as ImageNet. In this thesis we develop new algorithms towards addressing the open challenges in approximate inference. In the first part of the thesis we develop two new approximate inference algorithms, by drawing inspiration from the well known expectation propagation and message passing algorithms. Both approaches provide a unifying view of existing variational methods from different algorithmic perspectives. We also demonstrate that they lead to better calibrated inference results for complex models such as neural network classifiers and deep generative models, and scale to large datasets containing hundreds of thousands of data-points. In the second theme of the thesis we propose a new research direction for approximate inference: developing algorithms for fitting posterior approximations of arbitrary form, by rethinking the fundamental principles of Bayesian computation and the necessity of algorithmic constraints in traditional inference schemes. We specify four algorithmic options for the development of such new generation approximate inference methods, with one of them further investigated and applied to Bayesian deep learning tasks.
Forecasting Financial Budget Time Series: ARIMA Random Walk vs LSTM Neural Network

Financial time series are volatile, non-stationary and non-linear data that are affected by external economic factors. There is several performant predictive approaches such as univariate ARIMA model and more recently Recurrent Neural Network. The accurate forecasting of budget data is a strategic and challenging task for an optimal management of resources, it requires the use of the most accurate model. We propose a predictive approach that uses and compares the Machine Learning ARIMA model and Deep Learning Recurrent LSTM model. The application and the comparative analysis shows that the LSTM model outperforms the ARIMA model, mainly thanks to the LSTMs ability to learn non-linear relationship from data.
Blog Reliability Analysis With Conflicting Interests of Contexts in the Extended Branch for Cyber-Security

There are many blogs that recommend places and foods and on the web. In addition, there are various fake news that provide false information. They both are written by a blogger; bloggers can write on any topic of their choice. Web visitors read these blogs and decide if place or food item is satisfactory. This implies that the decision is based on the blogger’s prejudice. This is not objective because all the decisions depend on the blogger’s disposition. Other visitors, who had followed the bloggers recommendation, may have disagree with the blogger. To avoid this conflict, all the words and sentences in the posts must be analyzed objectively. All entries such as direction, address, excessive compliments, and monophonic are analyzed. This study also analyzed the entries to see their correlation; finally, it can make the decision if a blog is trustable with an anomaly sign.
Community detection in complex networks using a new agglomerative approach

Complex networks are used for the representation of complex systems such as social networks. Graph analysis comprises various tools such as community detection algorithms to uncover hidden data. Community detection aims to detect similar subgroups of networks that have tight interconnections with each other while, there is a sparse connection among different subgroups. In this paper, a greedy and agglomerative approach is proposed to detect communities. The proposed method is fast and often detects high-quality communities. The suggested method has several steps. In the first step, each node is assigned to a separated community. In the second step, a vertex is selected randomly and then its neighbors are determined. Then the selected node and its best neighbor will be merged if their merging brings positive gain. The merging of the selected vertex and its best neighbor has more gain than other neighbors. Whenever the merging occurs, the graph will be updated and this process will be continued until all the vertexes are assessed. Furthermore, the computational complexity of the proposed method is O(nm) , where n and m refer to the total number of vertexes and edges, respectively. In addition, our proposed method is compared with the Girvan–Newman algorithm and the fast divisive method for community detection. Results show that the proposed method is much faster than them and can detect high-quality communities. Finally, the accuracy of the proposed method is evaluated by using four different measures of purity, F-measure, NMI, and ARI.
BIM business value generation theory: a grounded theory approach

Theoretical developments in the Architectural Engineering and Construction (AEC) industry have been deficient, especially for the operations and use phase. The need for asset owners to understand the Building Information Modelling (BIM) process and to realise business value from BIM implementation cannot be over emphasised. This study investigates a BIM-based Asset Management (AM) system to examine how business value can be created for the asset owner. The BIM-based AM system is evaluated from the Transformation-Flow-Value (TFV) generation view to define the characteristics and functions. This is followed by the grounding of data to develop a novel explanatory theory through the development of conceptual categories. A qualitative research approach based on a grounded theory methodology is utilised to generate a proposition on how BIM can create value in an AM system. The study involves a five-stage research design using interviews and document analysis to inform the phenomenon of BIM business value creation in AM. The paper highlights that BIM business value generation in AM is dependent on three main categories, which are: development of the information requirements, creation of the information content and management of the information content. From the grounded data, the study finds that it is crucial for asset owners to develop their information requirements since the information requirements development category guides the data creation, data management and value generation. Furthermore, the development of a sound rationale that appropriately translates the organisational business objectives is critical to value generation. The paper studies how BIM can create business value in an AM system using a grounded theory methodology. The results show that the rationale for developing BIM-based information requirements determines the value that will be generated by the AM system. The key contribution of the paper is that it presents a novel theory for BIM-based AM. One of the contributions of this study is the demonstration of the application of grounded theory within the AEC industry. Also, the study demonstrates the relationships between concepts and how they emerge from the grounded data.
Control Design and Stability Analysis of Power Converters: The MIMO Generalized Bode Criterion

Three-phase dynamic systems and multiphase generators are frequently modeled and controlled in the synchronous reference frame. To properly model the cross-coupling terms in this reference frame, complex vector theory and transfer function matrices are commonly applied, obtaining multiple-input multiple-output (MIMO) dynamic models. The stability of MIMO systems can be assessed through the Nyquist generalized stability criterion. However, the use of the Nyquist diagram complicates the controller design. The Bode diagram is a more intuitive tool for the controller design; however, the Bode stability criterion is not applicable to MIMO systems. In this article, the MIMO generalized Bode criterion is proposed. Since this stability criterion is based on the Nyquist generalized stability criterion, it can be applied to any system. Furthermore, it is simple to use, as it only requires information contained in the open-loop transfer matrix and the Bode diagram. The proposed stability criterion thus offers an interesting tool for the controller design procedure in MIMO systems, as it is shown in this article for two common applications: the current control loop of a power converter, a $2\times2$ system, and the current control loop of two independent power converters in parallel, a $4\times4$ system.
The 60-Minute MVP

The 60-minute minimum viable product (MVP) exercise teaches critical aspects of the entrepreneurial mind-set and lean start-up methodology, namely, the iterative process of hypothesis testing through the creation of MVPs. In 60 minutes, with no prior technical expertise, students will work in teams to design a landing page, create a teaser video, and set up a way to gather information from prospective customers. The resulting low-fidelity MVP can subsequently be shared with prospective customers to gauge interest and be used as a starting point for the hypothesis testing process used in the lean start-up methodology. This is an immersive exercise that activates students, builds confidence, and teaches important entrepreneurial principles.
Method of distribution network reconfiguration at daily operation scheduling

The paper suggests a two-stage optimization algorithm of daily load curves of a distribution network to decrease electricity purchase costs and determine a primary distribution network configuration in presence of renewable power generation or without it. The first stage includes optimization of the daily load curves of active consumers, which ensures decrease of electricity purchase costs considering its price and daily consumption invariability. At the second stage the problem distribution network reconfiguration is solved to decrease power losses in it. The numerical calculations for the test distribution networks confirm the efficiency of the suggested algorithms. DOI: 10.12736/issn.2300-3022.2017205 Received: 03.02. 2017 Accepted: 22.03. 2017 Available online: 30.06. 2017
Generating specifications for JSON APIs for formal and semantic use

This thesis presents our research on generating type specifications for JSON-based APIs in an automated manner. Such specifications describe the type of a JSON document in such a way that they are usable for formal applications such as static type checking. Additionally, we aimed to let the specifications convey the semantics of the API to a programmer. We noticed that despite the availability of API specification languages, many APIs do not have specifications. This may be because it is costly to create them. With our automated method to create such specifications we hope to alleviate the hurdle to create them, and thereby increasing adoption. The basis of our solution is type inference on JSON data that resides in responses to an API request. We present a minimisation algorithm to decrease the size of inferred types, and transform a purely formal specification into one that can be interpreted better by a programmer as well. It identifies recurring parts of a type and makes this explicit in the specification. It also has a heuristic-based method for identifying semantically equivalent types, which can be fused together, which is then also made explicit. Furthermore, an automated clustering algorithm is presented, which attempts to group JSON data by API operation. We conclude that our automated method can be of use for formal purposes, because generated specifications reliably detect typing errors in a program (which we call the completeness property), even though it should be taken as a serious consideration that absence of errors can not be proved (which we call the lack of the soundness property). The extent to which the generated specifications convey semantics of APIs varies, but the techniques we present in this thesis increase usefulness of specifications for programmers.
Systematic review of congestion handling techniques for 802.11 wireless networks

IEEE 802.11 is a matured standard for wireless local area networks (WLANs). The cellular network providers had made every possible effort to offload their traffic over WLANs whenever it was possible, owing to the benefits that this technology provides. IEEE 802.11 is gaining greater prominence with the arrival of 802.11ax standard, which is backed by the Wi‐Fi alliance by branding as Wi‐Fi 6. The issue of congestion has been studied well for various networks. Particularly, in wireless networks, it needs to be tackled in a peculiar manner owing to the uncertain nature of the wireless medium. This study explores and evaluates the improvements done at various protocol layers to handle the problem of congestion in 802.11‐based wireless networks. An attempt is made to understand the implication of different parameters and the significance of cross‐layer interaction. Further, an endeavor is made to assess the influence on congestion control mechanisms in the different architectures of 802.11 networks. Lastly, the challenges that might be confronted at different protocol layers, while dealing with the issue of congestion, are classified.
Optimization and Parallelization of MRF Community Detection Algorithm for a Specific Network

Research on the optimization and parallelization of the MRF network community detection algorithm for a specific network is carried out in this paper. Firstly, the principle of the existing algorithm is expounded, the algorithm is analyzed, and some problems are pointed out. Some optimization strategies and rules are proposed, including the extraction of variables and operations from inner loops to outer loops, the merging of related operations in loops, the removal of redundant loops, and the split of loops. In order to achieve better parallelism, OpenMP parallel computing of this method is realized by reversing the order of inner and outer loops. The influence of the density of network edges on the algorithm efficiency is also analyzed in this paper. The optimization and parallel algorithm can be applied to the module partition of Alzheimer's disease gene data, and the efficiency of the algorithm is greatly improved. The optimization strategies and rules proposed in this paper can be further extended to general situations. It is significant in practical applications.
Effects of regulation on interaction pattern in web-based collaborative writing activity

Abstract Although regulation has become a critical method to improve the effect of collaborative writing in L2 classes, it is still not clear how the regulation activities influence peer interactions, which is important for improvement of regulation itself. This case study examined dynamic interactions of several small teams of ESL students when they performed two collaborative argument writing tasks without and with regulation. Interaction patterns were identified in terms of ‘equality’ and ‘mutuality’ and different interaction patterns occurred when these learners switched between tasks. The dynamic features of peer interactions were explained according to an extended activity model. This study enriched the observation of how regulation affected peer interactions through sociocultural factors. The result can provide insights into how regulation activity should be designed and improved for web-based collaborative writing activities in L2 classes.
The role of flow for mobile advergaming effectiveness

Purpose Based on flow theory, the purpose of this paper is to explain why the use of mobile advergames can enhance players’ brand perceptions and purchase intentions, as well as the factors that affect players’ flow experience. Design/methodology/approach Data from 212 participants who played a mobile advergame was analysed. Structural equation modelling with PLS was used to test the research model. Findings The results reveal that challenge, interactivity, focused attention and telepresence significantly influence the flow experience while playing mobile advergames. Results also show that the greater the flow, the more positive the attitude towards the featured brand and the greater the purchase intention. Practical implications The findings of this study are important for advertising practitioners and advergames developers as understanding the key game features that promote flow is crucial to designing engaging mobile advergames that persuade players most. Originality/value This study contributes to the literature in two ways. First, it provides new insights into the effectiveness of mobile advergames, which is an under-researched area. Second, it offers a conceptual framework based on flow theory for understanding why the use of mobile advergames can enhance players’ brand perceptions and purchase intentions.