Reviewerly/exHarmony huggingface dataset:
Moving Target Defense - Challenges, Problems and Suitability for Internet of Things

Traditionally cyber attackers have always had an upper hand over defenders as the latter can only react in the event of an attack. Moving Target Defense (MTD) has emerged as a potential game changer by reversing the advantage from attackers to defenders. Together with traditional defenses, MTD is expected to take cyber security up a notch. MTD solutions have been proposed for various levels, namely operating system, network, runtime platform, etc. However, improvement of existing solutions and development of new solutions are required. This paper presents an overview of MTD, the various MTD approaches, attacks mitigated, open research problems in MTD and MTD for the Internet of Things (IoT).
BiLSTM with CRF Part-of-Speech Tagging for Khasi language

Part-of-speech (POS) tagging is one of the tasks involved in sequence labeling which recognizes different word classes or tags in a sentence. In this paper, a deep learning approach is used, and to perform a comparative performance analysis, each model is first executed in its setting. These settings include Long-Short-Term Memory (LSTM), its variant, Bidirectional Long-Short-Term Memory (BiLSTM), and a Conditional Random Field (CRF) network. Additionally, these models are executed by combining LSTM and CRF to create an LSTM-CRF network and BiLSTM and CRF (BiLSTM-CRF) network. And although some word classes in the Khasi language are context-sensitive, the adopted models, supported by a pretrained word embedding model, can contextually categorize words into their appropriate categories. Word embedding is also used to aid in the part-of-speech (PoS) tagging task, where words are encoded with real-valued vectors, with similar vector representations of words having similar meanings; this minimizes the likelihood that the words in a sentence will be incorrectly tagged. In addition, the capability of the BiLSTM layer to effectively exploit both past and future input features, in conjunction with the CRF layer’s ability to apply sentencelevel tags, contributes to the model’s achievement with an encouraging degree of accuracy.
Ner4Opt: Named Entity Recognition for Optimization Modelling from Natural Language

Solving combinatorial optimization problems involves a two-stage process that follows the model-and-run approach. First, a user is responsible for formulating the problem at hand as an optimization model, and then, given the model, a solver is responsible for finding the solution. While optimization technology has enjoyed tremendous theoretical and practical advances, the overall process has remained the same for decades. To date, transforming problem descriptions into optimization models remains a barrier to entry. To alleviate users from the cognitive task of modeling, we study named entity recognition to capture components of optimization models such as the objective, variables, and constraints from free-form natural language text, and coin this problem as Ner4Opt. We show how to solve Ner4Opt using classical techniques based on morphological and grammatical properties and modern methods leveraging pre-trained large language models and fine-tuning transformers architecture with optimization-specific corpora. For best performance, we present their hybridization combined with feature engineering and data augmentation to exploit the language of optimization problems. We improve over the state-of-the-art for annotated linear programming word problems, identify several next steps and discuss important open problems toward automated modeling.
Supporting Students in Team-Based Software Development Projects: An Exploratory Study

Team-based software development projects (TBSDP) are a useful instrument to expose students to teamwork in an industry-like working context. However, TBSDP exposes students to a number of challenges. This paper has a twofold objective. First, understand the practices and challenges that students face in TBDSP in an Agile context. Second, investigate whether the use of a teaching domain-specific information system, a learning dashboard, could help them in improving these practices and facing these challenges. We conducted a multi-instrument exploratory study at the Polytechnical University of Catalunya. We gathered information about the progress of 39 students organised in 6 teams during one semester by mining two software repositories and conducting a number of questionnaires and interviews related both to working practices and to students' perception of learning dashboard adoption. Results show that many students do not follow adequate practices for some TBSDP activities. On the other hand, metrics informing about the use of code repositories and task management were generally well understood and perceived as potentially useful by students when shown in a learning dashboard. We conclude that the adoption of learning dashboards is a viable approach to improve student practices in TBSDP, but it needs to be carefully considered which metrics provide the most value to face the identified challenges.
Cloud Migration High-Level Requirements

With the increasing adoption of Cloud Computing in the industry, new challenges have emerged for information systems design. In this context, many requirements are met by choosing adequate cloud providers, cloud services, and service configurations. To provide design support, it is necessary to understand what drives the selection of each of these elements. Therefore, it may be interesting to elicitate these high-level requirements, to have an overview of what significantly impacts the cloud environment selection. Here we focus on a particular case of cloud system design: migrations. Through a qualitative study with cloud migration experts, we identify eleven high-level requirements that drive design decisions. We propose an analysis of these results and two classifications to support elicitation and analysis of requirements in cloud migrations.
Goal Modelling: Design and Manufacturing in Aeronautics

In aeronautics, the development of a new aircraft is usually organised in sequence. That means the aircraft is designed first, then its industrial system. Therefore, the industrial system may endure stringent constraints due to aircraft design choices. This can result in suboptimal performance with respect to manufacturing. But approaches such as Collaborative Engineering or Concurrent Engineering invite different engineering teams to work simultaneously and together in order to open up new prospects for a product design. In the context of a project that aims at developing methods and tools for co-designing an aircraft and its industrial system, we use Goal-Oriented Requirements Engineering (GORE) to model and to understand their respective expectations but also their dependencies. In this paper, we describe our application of goal modelling based on three iterative attempts. We start from an exploratory stage to have a global picture of the dependencies between the design of an aircraft nose section and its industrial system. We finish with a focus on a smaller problem in which we understand the key elements of the assembly line performance based on a nose design. For each attempt, we describe our results and feedback, and show how we overcame issues raised at the previous stage. We also highlight the links with known issues about GORE practical application.
Generating Random Instances of Weighted Model Counting

Weighted model counting (WMC) is an extension of propositional model counting with applications to probabilistic inference and other areas of artificial intelligence. In recent experiments, WMC algorithms perform similarly overall but with significant differences on specific subsets of benchmarks. A good understanding of the differences in the performance of algorithms requires identifying key characteristics that favour some algorithms over others. In this paper, we introduce a random model for WMC instances with a parameter that influences primal treewidth—the parameter most commonly used to characterise the difficulty of an instance. We then use this model to experimentally compare the performance of WMC algorithms c2d, Cachet, d4, DPMC, and miniC2D. Using these random instances, we show that the easy-hard-easy pattern is different for algorithms based on dynamic programming and algebraic decision diagrams than for all other solvers. We also show how all WMC algorithms scale exponentially with respect to primal treewidth and how this scalability varies across algorithms and densities. Finally, we combine insights from experiments involving both random and competition instances to determine how the best-performing WMC algorithm varies depending on clause density and primal treewidth.
DECENT: A Domain Specific Language to Design Governance Decisions

Decentralized ecosystems, such as the Bitcoin, claim to be decentralized to avoid power concentrations such as commercial banks. This is perhaps true for their operations, but often not the case for their governance, which is about deciding the rules of monitoring and controlling protocols. In previous work, we have developed DECENT a domain specific language (DSL) to conceptualize the domain of decentralized governance design. In this paper, we focus on deriving governance design decisions based on the DECENT language. We do so by taking the case of Fractional Reserve Banking (FRB), which is about governance rules for commercial banks to create and destroy money. As many banks are licensed to do FRB, under the control of national central banks and the European Central Bank (ECB), this is already a case of a decentralized ecosystem. The governance design decisions are developed in close cooperation with our case study partner, a commercial bank.
Towards Creating a Secure Framework for Building Mirror World Applications

The growing interest in Virtual Reality and Augmented Reality technologies has led to the development of the concept of Mirror Worlds, the Metaverse, and the AR Cloud. Different companies are creating their own version of the Metaverse and Mirror Worlds serve as a foundation for these experiences. As these technologies continue to grow, there is a need for the design of a systematic way for designing and implementing applications in virtual spaces. There are emerging technologies and standards that could form the basis of such a framework, however, privacy concerns like data privacy and location tracking need to be addressed while designing any potential framework or solution.
Adaptive Neuro Fuzzy Control of Triple Inverted Pendulum System

This study presents proportional-integral-derivative (PID) and adaptive neuro fuzzy inference system (ANFIS) control strategy to stabilize of highly nonlinear triple inverted pendulum system. A nonlinear dynamic representation of the system has been proposed and simulated in MATLAB/Simulink platform. The result indicates better performance of ANFIS controller compared to PID controller. Both ANFIS and PID controllers were able to stabilize complete system with desirable overshoot and steady state response. In order to minimize if–then fuzzy rules, the ANFIS controller has been designed using only three membership functions of triangular shape. The proposed ANFIS controller aided in solving the problem of fuzzy rule explosion commonly associated with fuzzy controllers.
Distributed Encoding and Updating for SAZD Coded Distributed Training

Linear combination (LC) based coded distributed computing (CDC) suffers from the problem of poor numerical stability. Therefore, LC-CDC based model parallel (MP) training for a deep nueral network (DNN) may have poor accuracy. To enhance accuracy, we propose to replace LC by shift-and-addition (SA) and replace matrix inversion by zigzag decoding (ZD) in the encoding and decoding process of each layer, respectively, and call the scheme Naive SAZD-CDC based MP training (N-SAZD-CDC-MP-T). However, N-SAZD-CDC-MP-T encounters the problem of bottleneck at the master node, which is caused by frequent encoding/decoding at the master node and frequent huge volume of data delivery between master and worker node. This bottleneck problem may pull down the training speed significantly. To alleviate this bottleneck problem, we further design an enhanced version, by offloading certain processing from master node to distributed encoding and updating (DEU) at the worker nodes and call it DEU-SAZD-CDC-MP-T. A proof that DEU-SAZD-CDC-MP-T automatically maitains the code structure during each iteration is provided. Extensive numerical studies show that the prediction accuracy of SAZD-CDC-MP-T improves significantly over that of Poly (which is representative of LC) based scheme. In addition, the training speed of DEU-SAZD-CDC-MP-T over N-SAZD-CDC-MP-T is improved significantly.
A Cooperative PoW and Incentive Mechanism for Blockchain in Edge Computing

More and more works use blockchain to improve the data security of integrated edge computing and Internet of Things (IoT) system. However, there are some problems of Proof-of-Work (PoW) that hinder the application, such as high energy consumption, low resource utilization efficiency, and insufficient incentive. In this article, we present a cooperative PoW named relay mining-based PoW (Relay-PoW) to reduce energy consumption and improve resource utilization efficiency, where the nodes can mine blocks together under the management of edge server. We further propose parallel relay mining method to increase the throughput, where the nodes can mine blocks with multiple heights in a pipeline manner. In addition, we design supervision group mechanism to ensure the security, where the edge server evaluates the trust values of the nodes according to the capability and quality, and eliminates abnormal nodes timely. Finally, we propose a Shapley-based reward allocation strategy (SRAS) to encourage node to participate in Relay-PoW. Experimental results show that Relay-PoW can effectively decrease the energy consumption, improve the throughput and resource utilization efficiency, SRAS can motivate nodes to cooperate, and they all have a better performance than other methods.
The Role of Standards and Benchmarking in Technology-Enhanced Learning

Standards and benchmarking frameworks are key enablers of the future virtual university. This chapter reviews key concepts of organizational quality improvement using technology to stimulate and enable change. It presents a model of four key elements, TEL policy and procedures, the institutional TEL framework, baseline standards, and standards for fully online education. This model is used to analyze the major current TEL quality frameworks providing a guide to institutional leaders working to enact quality standards and benchmarking for the improvement of TEL as they lead their institutions toward the future virtual university.
Dual Discriminative Low-Rank Projection Learning for Robust Image Classification

Numerous methods have exploited projection learning to extract low-dimensional features for image classification. Some projection learning methods integrate low-rank matrix recovery into classification models to equip the projection subspace with discrimination and robustness against corruption. However, these methods cannot directly recover “clean” components from the new datum in a low-dimensional subspace. Additionally, they are sensitive to the selection of projection dimensions. To overcome these shortcomings, we propose a dual discriminative low-rank projection learning framework for robust image classification. Specifically, the proposed method learns a low-rank projection and a semi-orthogonal projection to recover “clean” components from the original data and simultaneously obtain a low-dimensional subspace. Thereafter, to preserve discriminative information in the low-dimensional subspace, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2,1}$ </tex-math></inline-formula> -norm term is constructed by concentrating the projected intra-class samples around their adaptive class centroids. Regression-based terms are appended using the low-dimensional features extracted from the recovered clean data and the class centroids for more accurate classification. Experiments on five public databases with various corruptions demonstrate that the proposed method can robustly classify image data despite a small training sample sizes and gross corruption. The superiority of the proposed method is further verified on the large-scale PubFig83 database, on which it achieves an 87.58% classification accuracy.
Introduction

In this chapter, we will introduce the background information of cross-technology coexistence, then review and discuss the related works on heterogeneous signal identification, cross-technology interference management and cross-technology communication. Finally, we will present the organization of this book.
TransPose Re-ID: transformers for pose invariant person Re-identification

Person re-identification (Re-ID) is a computer vision task that involves recognizing and tracking individuals across multiple non-overlapping cameras or over time within the same camera view. It is particularly important in surveillance systems, where it can help in identifying potential threats or tracking suspects. Convolutional neural networks (CNNs) have been used to extract invariant person representation for this challenging task. However, CNNs do not consider global dependencies in their initial layers, causing some vital information to be lost during the convolution process. The development of vision-based transformers has opened up new research avenues for person re-identification. This work proposes a purely transformer-based solution, called TansPose Re-ID, that learns pose-invariant person representations. The proposed system uses a vision transformer baseline and enhances its architecture by introducing multiple streams to learn global and local dependencies as well as pose invariance in person images. The architecture includes a Global Self-Attention Module (GSM) and a Local Self-Attention Module (LSM) that jointly learn global and local patch-based person embeddings. The LSM is further improved by stochastically grouping local patches and aligning them. Additionally, an attention feature learning module (AFLM) is introduced in the LSM to handle pose and viewpoint variations. The proposed method is evaluated on two public Re-ID benchmarks, Market1501 and DukeMTMC-ReID, and demonstrates superior performance compared to existing transformer baselines.
IOT Computation Integration into Business Process Management

Integrating the internet of things (IoT) with Business Process Management is the need of hour. When any system which are interrelated are being given an unique identifiers (UIDs) and the same time it ensured that data is getting transferred without the physical presence of human being it is called IOT. This technology can be of huge support in business process management (BPM) and hence integration must be given special attention. Despite the broad range of BPM, which encompasses both business and IT disciplines, as well as the significant effect of IT on business process innovation, the relationship among BPM and IOT has received little attention. In this context, the digital marketing and human resource trends that are presently forming and developing have the greatest potential to dominate and revolutionise the future of businesses. Therefore, the business process contributes to the creation of a digital overlay on top of the physical business infrastructure by doing so. In this paper the influence of important digital changes on dominating business models has been reviewed.
Personalized recommendation system of modern science and technology resources based on hybrid filtering

With the rapid development of the science and technology service industry, the number of modern science and technology resources is increasing. In the current science and technology resource recommendation process, the content-based recommendation method is mainly applied for resource filtering. The resource scoring results are easily affected by data sparsity so that the F-measure value of the personalized recommendation results of the system is low. Therefore, this paper proposes a personalized recommendation system of modern science and technology resources based on the hybrid filtering algorithm. Starting from the user's basic information, browsing time, and other data, the multi-dimensional user characteristics are obtained. A bipartite network containing several users and resource items is constructed, and a user-resource rating matrix is defined. Based on the concept of hybrid filtering, the dynamic weighted calculation is carried out on the predictive scoring results of collaborative filtering and content filtering to obtain a reliable comprehensive scoring prediction result, based on which all modern scientific and technological resources are filtered. The recommended resources are sorted in descending order according to the scores of multiple users. The top resources are selected as the personalized recommendation results. The system test results verify that the F-measure value of the proposed system is 0. 85, which meets the requirements of personalized recommendation of science and technology resources.
Research on the application of virtual simulation technology in the IoT programming class

With the development of information technology, the teaching of IoT technology has gradually become popular, but as an important part of hands-on training, the practical aspects of the course are restricted by various factors such as equipment and venue, resulting in unsatisfactory results. Through the introduction of virtual simulation technology in teaching, the IoT programming course is no longer constrained by the above factors, so that the course can reflect the characteristics of IoT applications, improve the implementation effect and teaching quality of the IoT programming course, and stimulate students' interest in learning.
Almost lossless compression of noisy images

Abstract An almost lossless compression method for images is introduced adapted to the temporal noise of image sensors. In a first step, a non-linear gray value transform is applied to generate an image with a gray value independent temporal noise and less bits than the original image. The chosen value for the standard deviation of the temporal noise in the transformed image determines how accurately mean values and the standard deviation of temporal noise can be computed and to which extent the image can be compressed further by a lossless compression in a second step. Just a measurement of the noise characteristics according to the open and international EMVA standard 1288, a non-linear gray value transform for noise equalization, and an open source lossless compression algorithm are required to use this new compression method.
From Fiction to Function: Imagining New Instruments through Design Workshops

Abstract This article introduces a series of workshop activities carried out with expert musicians to imagine new musical instruments through design fiction. At the workshop, participants crafted nonfunctional prototypes of instruments they would want to use in their own performance practice. Through analysis of the workshop activities, a set of design specifications was developed that can be applied to the design of new digital musical instruments intended for use in real-world artistic practice. In addition to generating tangible elements for instrument design, the theories and models utilized, drawn from human–computer interaction and human-centered design, are offered as a possible model for merging the generation of creative ideas with functional design outputs in a variety of applications within and beyond music and the arts.