Reviewerly/exHarmony huggingface dataset:
A Makespan-optimized Task-Level Scheduling Strategy for Cloud Workflow Systems

Cloud-based workflow systems is a platform which can be embedded in a cloud computing infrastructure. Virtual machine has the ability to run multi-tasks simultaneously and time-sharing characteristic, but cannot take advantage of VM’s benefit, therefore, the makespan of scheduling strategy in datacenter cannot be reduced availably. In this paper, we bring up a new makespan model which take advantage of VM’s time-shared characteristic to schedule cloud workflow in task-layer. Furthermore, a novel Ant Colony Optimization (ACO) scheduling strategy is designed to obtain the optimal makespan. This scheduling strategy is implemented in Swinburne Decentralized Workflow for Cloud. The results suggest that by exploiting a time-sharing characteristic of VM, our scheduling strategy offers a significant improvement over the existing approaches including the makespan optimization by scheduling strategy within a datacenter, ACO scheduling strategy can converge fast with different task sets. The makespan is smaller than its counterpart without time-sharing of VMs.
Self-attention Based Text Matching Model with Generative Pre-training

Text matching is an important method to judge the semantic similarity of different sentences. Improving the efficiency and accuracy of text matching is the most focus in the field of information matching. In recent years, deep learning has been widely applied to text matching tasks and achieved good results. However, the different models have different limitations, such as CNN cannot learn global semantic information well, RNN cannot be parallelized well, and large pre-training language models have too many parameters to be deployed on hardware well. To address these problems, this paper propose a self-attention based text matching model with generative pre-training. Self-attention mechanism is adopted to learn the semantic information between words in a sentence, and can achieve better parallelization. We use the deep separable convolution model to obtain local features. In the pretraining stage of this model, a generative model variational autoencoder is used to learn the semantic relationship between similar sentences. And in the downstream text matching model, we employ Siamese Network structure, combine depth-wise separable convolutions and self-attention mechanism for feature extraction, and use attention mechanism for text interaction, in which the parameters in the pre-training phase will be shared. At last, we evaluate our model on three datasets: LCQMC, QQP, and a securities dataset. Experiment results show that our method achieves pretty good performance.
Securing Remote Policy Enforcement by a Multi-Enclave based Attestation Architecture

The concept of usage control goes beyond traditional access control by regulating not only the retrieval but also the processing of data. To be able to remotely enforce usage control policy the processing party requires a trusted execution environ-ment such as Intel SGX which creates so-called enclaves. In this paper we introduce Multi Enclave based Code from Template (MECT), an SGX-based architecture for trusted remote policy enforcement. MECT uses a multi-enclave approach in which an enclave generation service dynamically generates enclaves from pre-defined code and dynamic policy parameters. This approach leads to a small trusted computing base and highly simplified attestation while preserving functionality benefits. Our proof of concept implementation consumes customisable code from templates. We compare the implementation with other architectures regarding the trusted computing base, flexibility, performance, and modularity. This comparison highlights the security benefits for remote attestation of MECT.
An approach to the mobile social services recommendation algorithm based on association rules mining

With the continuous development of social network, more and more research areas have emerged. Among them, service recommendation has always been a research hotspot. Yet achieving accurate personalised recommendation is challenging because of the large amount of data generated in social networks as the data available for each user is highly sparse. In this paper, it focuses on the association rules in the mobile social network recommendation algorithms. By introducing the mobile users' location information to the collaborative filtering recommendation process, the association rules between the items are minded. Then the association rules are filtered and split, which are integrated into the similarity matrix to combine user location information with them. Also, an association rule mining model is proposed, which considers time factor to further improved the recommendation accuracy. Experimental results show that the performance of our algorithm is better than the baseline.
Multi Classification of Static Security Assessment using Teaching Learning based Optimization enhanced Support Vector Machine

the assessment of static security is very essential for reliable and safe functioning of the power system. The primary drawbacks of standard power flow techniques are lengthy calculation time and large memory need. The composite security index has been implemented here to avoid the problem of masking involved with the previously used performance index for assessment of static security. Multi-classifier for assessment of static security in power system has been implemented utilizing support vector machine. The parameters of Radial Basis Function are selected using Grid Search method and Teaching Learning based Optimization. Sequential forward selection method has been used to select features which are much lesser and best features. The results of the proposed Teaching learning based binary classifier are validated using two IEEE standard test systems.
Extractive and Abstractive Text Summarization Techniques

Text summarization generates an abstract version of information on a particular topic from various sources without modifying its originality. It is essential to dig information from the large repository of data, thereby eliminating the irrelevant information. The manual summarization consumes a large amount of time and hence an automated text summarization model is required. The summarization can be performed from a single source or multiple sources. The Natural Language Processing (NLP) based text summarization can be generally categorized as abstractive and extractive methods. The extractive methods mine the essential text from the document whereas the abstractive methods summarize the document by rewriting. The extractive summarization methods rely on topics and centrality of the document. The abstractive techniques transform the sentences based on the language resources available. This paper deals with the study of extractive as well as abstractive strategies in text summarization. Overall objective of this paper is to provide a significant direction to the researchers to learn about different strategies applied in text summarization.
Optimization of sensor model for solar radiation measurement with a pyranometer

Abstract Solar energy is electromagnetic energy produced in a thermonuclear process by the Sun from the nuclear fusion of the Sun’s core. This process produces solar radiation. In the lives of living beings on this Earth, solar radiation plays a significant role. As mentioned earlier, the uses are when there is a certain level required for the solar radiation received and vice versa. A more specific measurement of solar radiation is, therefore, needed. Unfortunately, until now, measurement equipment for solar radiation was limited to a few parameters that could be observed. On the other hand, conventional mechanisms and communication media are still used in the current measurement monitoring system, so that the monitoring and analysis process is ineffective. Therefore, this paper provides a solar radiation sensor model that uses only a sensor but can produce four parameters. The system contains a Pyranometer and a realtime controller that can generate all of the solar radiation measurements needed. The proposed model system is hoped that this sensor system will work more accessible and more efficiently.
A Channel-aware Attention Network for Crowd Counting

With the rapid increase of urban population, crowd counting is a popular yet difficult topic. However, the problem of scale variation in high-density scenario remains under-explored. To address this problem, we propose a channel-aware attention network in this paper. The channel attention module attempts to handle the relations between channel maps and highlight the discriminative information in specific channels. Thus, it alleviates the misestimation for background regions. Experimental results on ShanghaiTech and UCF-QNRF benchmark datasets prove that our approach achieves compelling performance compared to the state-of-the-art methods.
A singular(ity) preoccupation

Some futurists predict that machines will soon – that is, by the time the "Singularity" arrives – replace human translators. Whether that prediction is accurate or not, translator trainers must prepare students to enter a profession in which machine translation plays an increasingly important role. This paper argues that modern translation students must prepare themselves to also be language-services advisors (LSAs). LSAs recommend translation solutions before a project reaches a project manager. Based upon project specifications, LSAs recommend solutions along a spectrum ranging from unedited machine translation to professional human translation. Translator trainers must help students gain the knowledge necessary to be effective LSAs. Students who master the LSA role will be successful regardless of the evolution of "neural" networks and artificial intelligence.
Artificial Intelligence Chat Bot for Counselling Therapy

Counseling is a psychotherapeutic process of guiding and assisting needful people, by a trained professional, to resolve personal, social, or psychological problems and difficulties. People in the present century undergoes so much mental pressure, which reduces the productivity of a person and ultimately affects a countryâ€™s growth. Maximum productivity is an important goal for any developing country. A chatbot is a service, powered by rules of Artificial Intelligence (AI) that citizens can interact via a chat interface. The proposed system Artificial Intelligence Chat Boat for Counselling judges the internal turmoil of a citizen from facial expression, voice modulation, and text messages and each of the modules produces a confidence value that is being fused and a composite confidence value is generated regarding the emotional state of the citizen. Counseling the citizen to resolve problems faced more efficiently.
The Integration of Innovation in Education Technology to Improve The Quality of Website Learning in Industrial Revolution Era 4.0 Using Waterfall Method

Abstract The integration of educational technology is one of the necessary ways to improve the competence of qualified graduates. This study aims to create an integration of website learning model to improve the quality of students. The Current Study Provided does not indicate the incorporation of existing websites so that students are less aware of the benefits of this lesson. The Participants of this study were lecturers, undergraduate and postgraduate students and educational practitioners throughout Indonesia using online form. Methods Data collection used is direct interview and online survey and needs analysis using waterfall method. The results obtained show the merging of learning that will lead to the needs in the industry era 4.0. This study resulted in a complete innovative learning incorporation as required by Policy Makers.
Personality Prediction from Stack Overflow by using Naïve Bayes Theorem in Data Mining

This paper presents a system that provides an automatic process which defines how to evaluate designation and predict personality of individuals as wel for any jobl. This system is being set up via a web application, where the system manipulates data from social media sites like Twitter and Blog-Forums i.e. “Stack Overflow” of individuals. Myers-Briggs personality type is used for predicting the personality behavior of individuals. The technical skills in the form of raw data have been classified by using Gaussian Naive Bayes classification and for personal behavior; data mining &amp; machine learning are used. This system is used to analyze the personality of an individual and hence using this information to build a team for a job.
Aircraft Dynamic Parameters Identification Based on Adaptive Weight Kalman Filter

An incremental dynamic parameter identification model is proposed based on the small disturbance equation of pitch channel of the aircraft. The Kalman filter (KF) is used to identify the aircraft dynamic parameters representing the static stability and elevator efficiency. In order to solve the problem of the divergence of identification results using traditional KF, an Adaptive Weight Kalman filter (AWKF) is proposed using rated aerodynamic parameters and prior knowledge. The simulation results show that the identification results of this filter is significantly better than the traditional KF, and can effectively suppress the divergence of the identification results.
Usable Security

The increased availability of information and services has led to the affirmation of the internet involvement for a large segment of the population. This implies a paradigm shift for computer security: users become less skilled and security aware, requiring easier interface to communicate with “the machine” and more specific and comprehensible security measures. These two aspects, which are complex and challenging, have significant reciprocal influence. In practice, it has proven very intriguing to study and propose effective trade-offs among them. This chapter focus on these aspects by analyzing the goals and state of the art of usability and security to understand where and how they might be effectively “aligned.”
Cybersecurity &amp; Insurance

A related conversation and potential area of revenue and advisory services that financial services professionals may have traditionally overlooked is the importance of cybersecurity advisory services in a business environment that is increasingly digitized. Cybersecurity policies and training may have, in the past, focused on ensuring that the organization had in place sufficient password controls and safeguard to protect data against unauthorized access to client information and ensuring that anti-virus software was kept up to date. While these are still, rather obviously, still important for every organization, they now simply represent a good starting point in the conversation rather than the end point in the debate. Stated differently, cybersecurity has moved beyond merely an IT or technical concern, and is increasingly a concern for the entire organization at large (McLane 2018). None of this is news, however; every professional is going to be aware of just how important data integrity and cybersecurity it. Let’s connect this trend and topic area to the same topics we have been discussing throughout this book; blockchain, artificial intelligence, and cybersecurity tools (Fig. 15.1).
Forecasting audit opinion based on multilevel perceptron neural network model using one-goal particle swarm optimisation

Audit opinion is not necessarily absolute due to constraints inherent in any audit work, including sampling, different convincing degrees of audit evidence, accounting system and internal control characteristics, as well as inherent constraints of accounting measurement. The information contained in audit reports is used to forecast corporate bankruptcy; therefore, the auditor's opinion influences the views of users of financial statements. Financial status of a business can be assessed using financial ratios. The circumstances considered by an auditor in commenting on financial distress including financials (in particular, short-term liquidity, ability to repay and debt tolerability), and operations (profitability and ability to generate cash from operations). This study uses an integrated ANN-PSO to forecast audit opinion. The results show that ANN-PSO is more useful, because PSO algorithm can optimise hidden layers and neurons in ANN simultaneously. Finally, ANN-PSO outperforms ANN.
Risk-Based Privacy-Aware Information Disclosure

Risk-aware access control systems grant or deny access to resources based on the notion of risk. It has many advantages compared to classical approaches, allowing for more flexibility, and ultimately supporting for a better exploitation of data. The authors propose and demonstrate a risk-aware access control framework for information disclosure, which supports run-time risk assessment. In their framework access-control decisions are based on the disclosure-risk associated with a data access request and, differently from existing models, adaptive anonymization operations are used as risk-mitigation method. The inclusion of on-the-fly anonymization allows for extending access to data, still preserving privacy below the maximum tolerable risk. Risk thresholds can be adapted to the trustworthiness of the requester role, so a single access control framework can support multiple data access use cases, ranging from sharing data among a restricted (highly trusted) group to public release (low trust value). The authors have developed a prototype implementation of their framework and have assessed it by running a number of queries against the Adult Data Set from the UCI Machine Learning Repository, a publicly available dataset that is widely used by the research community. The experimental results are encouraging and confirm the feasibility of the proposed approach.
Hybrid nature-inspired algorithms for the optimal tuning of fuzzy controllers

This chapter first describes the hybridization of particle swarm optimization (PSO) and gravitational search algorithms (GSAs) and later introduces a gray wolf optimization (GWO) using PSO's search mechanism. The operating algorithm of a hybrid PSOGSA and GWOPSO are presented. Combining two nature-inspired algorithms is necessary in order to reduce one's search process drawbacks by using the other's fortes. In the case of the PSOGSA, the PSO's exploitation capabilities and GSA's exploration abilities are combined to avoid getting trapped in local minima situations, while in GWOPSO's case, the exploitation advantages of PSO are employed in order to speed the GWO's convergence. The hybrid algorithms are inserted in the design approach dedicated to the optimal tuning of simple Takagi-Sugeno proportional-integral fuzzy controllers for the position control of servo systems. Four optimization problems are solved and some results concerning the hybrid PSOGSA's behavior are presented.
Interoperability Frameworks for Distributed Systems

One of the fundamental problems to tackle when interconnecting distributed systems is to entail the minimum coupling possible while ensuring the minimum interoperability requirements. This chapter presents a solution to the coupling problem based on the concepts of compliance and conformance, in which compatibility between interacting services does not rely on a shared schema, but rather on the features that are actually used. To help systematizing the various aspects relevant to interoperability, this chapter proposes a multidimensional interoperability framework, which includes the following dimensions: Lifecycle (with typical development stages), interoperability (based on compliance and conformance, with various layers of abstraction), and concerns (to deal with non-functional aspects such as security, quality of service, and social and legal issues).
A “Social Bitcoin” Could Sustain a Democratic Digital World

Abstract A multidimensional financial system could provide benefits for individuals, companies, and states. Instead of top-down control, which is destined to eventually fail in a hyperconnected world, a bottom-up creation of value can unleash creative potential and drive innovations. Multiple currency dimensions can represent different externalities and thus enable the design of incentives and feedback mechanisms that foster the ability of complex dynamical systems to self-organize and lead to a more resilient society and sustainable economy. Modern information and communication technologies play a crucial role in this process, as Web 2.0 and online social networks promote cooperation and collaboration on unprecedented scales. Within this contribution, we discuss how one dimension of a multidimensional currency system could represent socio-digital capital (Social Bitcoins) that can be generated in a bottom-up way by individuals who perform search and navigation tasks in a future version of the digital world. The incentive to mine Social Bitcoins could sustain digital diversity, which mitigates the risk of totalitarian control by powerful monopolies of information and can create new business opportunities needed in times where a large fraction of current jobs is estimated to disappear due to computerization.
CSOD: Context-Sensitive Overflow Detection

Buffer overflow is possibly the most well-known memory issue. It can cause erratic program behavior, such as incorrect outputs and crashes, and can be exploited to issue security attacks. Detecting buffer overflows has drawn significant research attention for almost three decades. However, the prevalence of security attacks due to buffer overflows indicates that existing tools are still not widely utilized in production environments, possibly due to their high performance overhead or limited effectiveness. This paper proposes CSOD, a buffer overflow detection tool designed for the production environment. CSOD proposes a novel context-sensitive overflow detection technique that can dynamically adjust its detection strategy based on the behavior of different allocation calling contexts, enabling it to effectively detect overflows in millions of objects via four hardware watchpoints. It can correctly report root causes of buffer over-writes and over-reads, without any additional manual effort. Furthermore, CSOD only introduces 6.7% performance overhead on average, which makes it appealing as an always-on approach for production software.
Trust Management in Cloud Computing

Cloud computing is a model for enabling everywhere, suitable, on-demand network access. There are a number of challenges to provide cloud computing services and to accomplish this, it is necessary to establish trust across the cloud, between the user and the service provider. It is becoming increasingly complex for cloud users to make distinction among service providers offering similar kinds of services. There must be some mechanisms in the hands of users to determine trustworthiness of service providers so that they can select service providers with confidence and with some degree of assurance that service provider will not behave unpredictably or maliciously. An effective trust management system helps cloud service providers and consumers reap the benefits brought about by cloud computing technologies. Hence the objective of this chapter is to describe existing mechanisms that are used to determine a trust worthiness of a cloud service, various models that are used for calculating a trust value and method to establish trust management system.