Reviewerly/exHarmony huggingface dataset:
On the verification of mission-related properties in software-intensive systems-of-systems architectural design

One of the prominent domains of nowadays software engineering concerns on building and maintaining the so-called system-of-systems (SoS). The activity of integrating independent, potentially heterogeneous, and distributed systems has become remarkable. Designing SoS that consists on integrating independent and behaviorally unknown systems involves several difficulties, specially due to the inner dynamism that hovers SoS. Aiming to support the development of such kind of systems, mission models allows stakeholders to set objectives and resources for SoS, complementing the traditional requirements approach. In a mission model, designers specify the missions of the system and what the system is capable of doing to achieve them. On the other hand, one of the most desired feature of any modeling language is the possibility of formally verifying described properties. Enabling the stakeholders to check the degree of compliance of a model to a given set of properties fosters quality and simplifies the development process by shifting the problem resolution to the design level. The difficulties of SoS modeling also reflect on the verification processes: traditional verification methods were shown noneffective. In this context, this work proposes a method to verify SoS models based on mission-related properties, using the mission modeling language mKAOS and the DynBLTL formalism. Our proposal can be applied to any modeling language, although we exemplify using SosADL, an architectural description language for the SoS context.
MAIN CORE AND MEANING OF SHAPING PROFESSIONAL FAITH

This article provides an overview of the meaning and significance of the notion of faith, the role of faith in one's life and a program for the formation of professional beliefs for youth has been developed and comments on the stages of its implementation are developed and provides an overview of the implementation phases of the program. The article also outlines the criteria for building trust in future professionals as well as the Criteria for forming professional beliefs which are: professional knowledge, professional fidelity, professional self-sacrifice, and professional honesty.
Divisibility of Dynamical Maps with Time Independent Invariant State

A new condition for dynamical maps with time independent invariant state displaying Heisenberg divisibility is provided. This condition is trivially satisfied for a Markovian semigroup. However, beyond Markovian semigroups it provides a nontrivial characteristics of quantum Markovianity. Interestingly, when the invariant state is pure one recovers a condition based on Wigner–Yanase skew information.
Inter-Data Center Virtual Machine Migration in Federated Cloud

Cloud computing services have spread widely. This impels cloud providers (CPs) to scale up their resources. Federated cloud has emerged as a solution where many CPs collaborate forming a single platform [2]. It offers several advantages including scalability, high availability, cost optimization, and improved QoS [1].
Models induced from critical birth–death process with random initial conditions

In this work, we study a linear birth–death process starting from random initial conditions. First, we consider these initial conditions as a random number of particles following different standard probabilistic distributions – Negative-Binomial and its closest Geometric, Poisson or Pólya–Aeppli distributions. It is proved analytically and numerically that in these cases the random number of particles alive at any positive time follows the same probability law like the initial condition, but with different parameters depending on time. The random initial conditions cannot change the critical parameter of branching mechanism, but they impact the extinction probability. Finally, the numerical model is extended to an application for studying branching processes with more complex initial conditions. This is demonstrated with a linear birth–death process initialised with Pólya urn sampling scheme. The obtained preliminary results for particle distribution show close relation to Pólya–Aeppli distribution.
Trends in Deep Learning Applications

Deep Learning is a new area of Machine Learning which has gained popularity in the recent past. It has surpassed the conventional algorithms in accuracy as the features are learned from the data using a general purpose learning procedure instead of being designed by human engineers [1]. Deep learning is responsible for today’s explosion of AI. Deep networks have demonstrated dramatic improvements in computer vision and machine translation tasks. It has the ability to recognize spoken words nearly as good as humans can. It has demonstrated good generalization power and has achieved high accuracy in machine learning modeling, which has even attracted non-computer scientists. It is now being used as a guide to make key decisions in fields like medicine, finance, manufacturing, and beyond. Deep learning has succeeded in previously unsolved problems which were quite difficult to resolve using machine learning as well as other shallow networks. However, deep learning is still in its infancy, but it is likely that deep learning will have many successes in the near future as it requires little hand engineering and thus can take advantage of the vast amount of data and computation power. Various supervised and unsupervised deep architectures have been reported in [1]. This chapter outlines the use of deep learning technology in applications like game playing, medical applications, video analytics, regression/classification, object detection/recognition, and robotic automation.
Web Text Categorization: A LSTM-RNN Approach

Dhar, Ankita Mukherjee, Himadri Obaidullah, Sk. Md. Santosh, K. C. Dash, Niladri Sekhar Roy, KaushikCategorization of text documents has become a prime task with plenty of applications. It is a process that assigns the text documents to their individual categories based on the contents. In this paper, a long-short-term memory (LSTM) recurrent neural network (RNN) based system along with handcrafted graph-based features is proposed for text categorization. In comparison to the traditional bag of words model for representation of documents, this graph-based model represents each document by a weighted graph for determining the relationships among the tokens. The significance of a token to a text document is determined by the graph-based feature. Experiments were tested on 14,373 Bangla text documents (around 57,22,569 tokens) and a maximum accuracy of 99.21% has been obtained.
Fault Tolerance Based Load Balancing Approach for Web Resources in Cloud Environment

Cloud computing consists group of heterogeneous resources scattered around the world connected through the network. Since high performance computing is strongly interlinked with geographically distributed service to interact with each other in wide area network, Cloud computing makes the architecture consistent, low-cost, and well-suited with concurrent services. This paper presents a fault tolerance load balancing technique based on resource load and fault index value. The proposed technique works in two phases: resource selection and task execution. The resource selection phase selects the suitable resource for task execution. A resource with least resource load and fault index value is selected for task execution. Further task execution phase sets checkpoints at various intervals for saving the task state periodically. The checkpoints are set at various intervals based on resource fault index. When a task is executed on a resource, fault index value of selected resource is updated accordingly. This reduces the checkpoint overhead by avoiding unnecessary placements of checkpoints. The proposed model is validated on CloudSim and provides improved performance in terms of response time, makespan, throughput and checkpoint overhead in comparison to other state-of-the-art methods.
Comparative analysis of image pre-filtering techniques for phase-shifted noise-affected interferograms

Phase-shifting techniques are one of the most promising strategies to extract the phase information and retrieve the parameters of interest (e.g., refractive index, beam collimation, object shape, deformations, thickness, focal length, etc.) from interferograms. However, traditional phase-shifting techniques suffer from both internal and external noise, which reduce measurement accuracy. This paper reports a comparative analysis of the three commonly used filtering techniques, namely, Fourier, windowed Fourier, and wavelet filtering for noise reduction and accurate extraction of phase information from phase-shifted interferograms. Toward this, two basic types of noise (additive and multiplicative noise) are introduced in the simulated interferograms and processed using the pre-filtering strategies. The effect of second-order harmonics in the presence of noise is also examined. In addition, experimental demonstrations of the real-life applicability of the analyses are provided using the interferograms recorded on coherent (Talbot) and incoherent (Lau) grating shearing interferometers. High accuracy in the measurement of defocusing error of the lens is obtained using the filtering strategies. Further inferences and insights are drawn in favor of the pre-filtering techniques.
AuditChain: A Trading Audit Platform Over Blockchain

A blockchain architecture and solution is proposed to audit processing under exchange regulation for trading activity of exchanges. A particular focus is made on dark pools and periodic auctions. An architecture of the solution is described conceptually and an implementation of the proposed solution is made in .NET framework in C# via a RESTful API for chain interaction with the periodic auction venue. The framework proof of concept is tested for different efficiency and latency considerations. This opens the concept to significantly more detailed and extensive developments.
Region Driven Remote Sensing Image Captioning

Remote sensing Image Captioning is a special case of Image Captioning which solves the difficulties in processing the remote sensing images. Issues may arise due to translation, rotation and viewpoint of images and maintaining semantic consistency in the generated captions. This method of describing a remote sensing scene in the form of sentences plays an important role in a number of fields, such as image retrieval, scene classification and as a vision companion. A Domain-driven approach is developed, in which the domain probabilities are used for captioning the remote sensing images. This approach concentrates on the domain- based information available in the images. A new dataset, called UAVIC dataset is created for images captured using Unmanned Aerial Vehicle (UAV), which covers wide range of land having multiple terrains and gives a better view of the landscapes. The proposed domain driven approach is applied to UCM and UAVIC dataset and the quality of resulting captions are evaluated using BLEU scores.
Working Together: Ethnographic Observations on Participatory Design Involving Adults with Autism

Understanding and improving how diverse people work together is a core concern of applied social sciences. This article reports ethnographic observations on a participatory design project in which researchers and adults on the autism spectrum worked together on the design of a new technology—biomusic. Biomusic uses a smartphone application and a wearable sensor to measure physiological signals and translate them into auditory output. Ethnographers were involved in this project, both to facilitate eliciting perspectives of different stakeholders and to observe, record, and reflect on the process. This paper discusses the relationship between ethnography and participatory design in two ways. First, it describes the contribution of ethnography to achieving the goals of participatory design. Second, it draws on ethnographic observations to highlight different strategies people with and without autism used to work together, including strategies put forth by the researchers, strategies already in place in the community, and strategies emerging from the intersection of both. These strategies created a space that was more accessible to many different types of people. Documenting the way that this group worked together challenged several stereotypes about autism and highlighted the role of autistic collaborators as agents.
Dendritic Neuron Model and Its Capability of Approximation

Recently, an artificial neural network (ANN) named dendritic neuron model has been proposed which mimics the nonlinear interaction in the signal processing in a neuron. in order to research the approximation performance of this model, this paper use the mathematical theory to prove that single dendritic neuron model neuron is able to approximate any Borel measurable function to any desired degree of accuracy.
Communication Optimization Strategies for Distributed Deep Learning: A Survey

Recent trends in high-performance computing and deep learning lead to a proliferation of studies on large-scale deep neural network (DNN) training. However, the frequent communication requirements among computation nodes drastically slow down the overall training speed, which makes the bottleneck in distributed training, particularly in clusters with limited network bandwidth. To mitigate the drawbacks of distributed communication, researchers have proposed various optimization strategies. In this paper, we give a comprehensive survey of communication strategies from both algorithm and computer network perspectives. Algorithm optimizations focus on reducing the amount of communication in distributed training, while network optimizations focus on speeding up the communication between distributed devices. At the algorithm level, we describe how to reduce the number of communication rounds and transmitted bits per round, besides we shed light on how to overlap computation and communication. At the network level, we discuss the effect caused by network infrastructures, including communication schemes, network protocols, and topology. Finally, we extrapolate potential challenges and research directions for communication acceleration in distributed DNN training.
RETRACTED: Design of basketball robot based on behavior-based fuzzy control

Aiming at the strong dependence on environmental information in traditional algorithms, the path planning of basketball robots in an unknown environment, and improving the safety of autonomous navi...
The Blockchain Technology and Modern Ledgers Through Blockchain Accounting

Blockchain is one of the emerging technology in global. Blockchain is fundamentally an accounting and finance technology. In this paper, we discuss Blockchain technology in accounting and Distributed ledger technology (DLT), Triple-Entry Accounting and the Benefits of Blockchain-based accounting, a negative impact on Blockchain-based accounting. The focus on this paper is to explain an overview of the current Blockchain market size, leading countries in Blockchain technology, major companies using Blockchain Technology, Blockchain formation, types of Blockchain, and core components of Blockchain.
Outlier Removal for the Reliable Condition Monitoring of Telecommunication Services

Customer contentment plays an essential (if not the most important) role for the continuation and development of business relations. In this context, the functioning of goods and services acts as the crucial influencing factor. In the case of services, their providers willingly deploy CMSs (Condition Monitoring Systems) for the continuous CM (Condition Monitoring) of the service availability by means of various KPIs (Key Performance Indicators). A CMS must red-flag an abnormal condition. This happens if the recent value(s) of a KPI exceed(s) a predetermined threshold for a certain period. The pertinent literature contains a multiplicity of ways for automatic threshold computation, including a particular one for telecommunication services with time-varying load characteristic. Regrettably, the latter lacks in (distribution-independent) outlier extinction. Thus, this scholarly piece bridges this gap by applying an outlier detection algorithm based upon Walsh's nonparametric tests to a KPI history, removing the identified outliers, and comparing Pukelsheim's three sigma rule and the minimum or maximum value of the outlier-free array for threshold evaluation. The result of a corresponding field test assesses the reliability of the suggested methodology.
Value-Based Constraint Control Flow Integrity

Control flow integrity (CFI) is a generic technique that prevents a control flow hijacking attacks by verifying the legitimacy of indirect branches against a predefined set of targets. State-of-the-art CFI solutions focus on reducing the number of targets using the context of a program such as the path to the indirect branch and the origin of the code pointer. However, these solutions work with an impractical assumption that the attacker only compromises control data; non-control data such as condition data that can also be abused by attackers are not considered. To overcome these limitations, in this paper, we propose value-based constraint CFI (vCFI) to improve the effectiveness of CFI by retrieving and protecting all data that can potentially be manipulated for control flow hijacking. We first perform static analysis such as dependency, condition, and data analyses to derive all control flow-related data. Then, vCFI protects these data during runtime by instrumenting a program to be hardened. We implemented vCFI as a compiler extension and evaluated its performance using SPEC CPU2006. The performance degradation caused by adopting vCFI was reasonable, and the average overhead was 13.6%.
Threshold image target segmentation technology based on intelligent algorithms

This paper briefly introduces the optimal threshold calculation model and particle swarm optimization (PSO) algorithm for image segmentation and improves the PSO algorithm. Then the standard PSO algorithm and improved PSO algorithm were used in MATLAB software to make simulation analysis on image segmentation. The results show that the improved PSO algorithm converges faster and has higher fitness value; after the calculation of the two algorithms, it is found that the improved PSO algorithm is better in the subjective perspective, and the image obtained by the improved PSO segmentation has higher regional consistency and takes shorter time in the perspective of quantitative objective data. In conclusion, the improved PSO algorithm is effective in image segmentation.
DIFRINT: Deep Iterative Frame Interpolation for Full-Frame Video Stabilization

Video stabilization is a fundamental and important technique for higher quality videos. Prior works have extensively explored video stabilization, but most of them involve cropping of the frame boundaries and introduce moderate levels of distortion. We present a novel deep approach to video stabilization which can generate video frames without cropping and low distortion. The proposed framework utilizes frame interpolation techniques to generate in between frames, leading to reduced inter-frame jitter. Once applied in an iterative fashion, the stabilization effect becomes stronger. A major advantage is that our framework is end-to-end trainable in an unsupervised manner. In addition, our method is able to run in near real-time (15 fps). To the best of our knowledge, this is the first work to propose an unsupervised deep approach to full-frame video stabilization. We show the advantages of our method through quantitative and qualitative evaluations comparing to the state-of-the-art methods.
The growth of hidden champions in China: a cognitive explanation from integrated view

Purpose There is a research gap in strategic management regarding the complement from managerial cognition literature to the behavioral theory of firm, as well as linkage between cognitive structure and cognitive process of strategy formulation in the field of managerial cognition, which also calls for further exploration. The purpose of this paper is to construct a model from an integrated view for explaining the process of cognitive reconstruction under incremental changes. Design/methodology/approach Qualitative research is conducted in the form of 17 semi-structured interviews in four hidden champions operating in China. Based on the model generated from the literature review, this paper adopts the abductive logic for data analysis. Findings This paper draws the following conclusions. The cognitive structure is shaped by the changing environment and the performance feedback, the variance in structural attributes will affect whether the changing environment destructs the effectiveness of original cognitive structure or not, the centrality of cognitive structure will promote the efficiency of tried-and-true organizational adaptations to incremental changes, and cognitive structure reconstruction is the result of the recursive process of trial-and-error learning. Originality/value This paper proposes the model explaining the interaction mechanisms between cognitive structure and strategy formulation process. It also presents the iterative sense-making process for reconstructing cognitive structure in strategy formulation. Both of them extend the understanding on managerial cognition in organizational adaptations to incremental environmental changes.
A Double Fragmentation Approach for Improving Virtual Primary Key-Based Watermark Synchronization

Relational data watermarking techniques using virtual primary key schemes try to avoid compromising watermark detection due to the deletion or replacement of the relation's primary key.Nevertheless, these techniques face the limitations that bring high redundancy of the generated set of virtual primary keys, which often compromises the quality of the embedded watermark.As a solution to this problem, this paper proposes double fragmentation of the watermark by using the existing redundancy in the set of virtual primary keys.This way, we guarantee the right identification of the watermark despite the deletion of any of the attributes of the relation.The experiments carried out to validate our proposal show an increment between 81.04% and 99.05% of detected marks with respect to previous solutions found in the literature.Furthermore, we found out that our approach takes advantage of the redundancy present in the set of virtual primary keys.Concerning the computational complexity of the solution, we performed a set of scalability tests that show the linear behavior of our approach with respect to the processes runtime and the number of tuples involved, making it feasible to use no matter the amount of data to be protected.