Reviewerly/exHarmony huggingface dataset:
Using blockchains to protect critical infrastructures: a comparison between Ethereum and Hyperledger Fabric

The monitoring and protection of critical infrastructures, especially the ones involving physical assets (e.g., dams, nuclear energy facilities, governmental buildings), constitute a challenging problem. The failure and collapse of these infrastructures can cause untold consequences. Recent works have proposed blockchains as a tool to improve monitoring systems in different critical infrastructures. However, most previous works lack on presenting a more in-depth discussion about how to implement these solutions. In this paper, we develop a practical approach. We propose a comprehensive framework that describes how to implement a blockchain-based system to monitor and protect critical infrastructures. We implement our framework in two distinct blockchain platforms: Ethereum and Hyperledger Fabric. We compare both implementations and discuss their differences in terms of performance, easiness of development, security, privacy, complexity, and costs. We believe that our results can be valuable for professionals interested in applying blockchain-based solutions to protect critical infrastructures.
Properties of System W and Its Relationships to Other Inductive Inference Operators

System W is a recently introduced inference method for conditional belief bases with some notable properties like capturing system Z and thus rational closure and, in contrast to system Z, fully satisfying syntax splitting. This paper further investigates properties of system W. We show how system W behaves with respect to postulates put forward for nonmonotonic reasoning like rational monotony, weak rational monotony, or semi-monotony. We develop tailored postulates ensuring syntax splitting for any inference operator based on a strict partial order on worlds. By showing that system W satisfies these axioms, we obtain an alternative and more general proof that system W satisfies syntax splitting. We explore how syntax splitting affects the strict partial order underlying system W and exploit this for answering certain queries without having to determine the complete strict partial order. Furthermore, we investigate the relationships among system W and other inference methods, showing that, for instance, lexicographic inference extends both system W and c-inference, and leading to a full map of interrelationships among various inductive inference operators.
Cross-Cutting Support of Making and Explaining Decisions in Intelligent Tutoring Systems Using Cognitive Maps of Knowledge Diagnosis

The paper concerns the problem of visual support of decision-making in intelligent tutoring systems and generating natural-language explanations of these decisions. A model of interpreting a learning situation and decision making about further learning on operational, tactical, and strategic levels considered from topic, competency, and learning-goal points of view. We show a case study of learning-situation visualization of analysis and synthesis of explanatory feedback for making operational, tactical, and strategic decisions. The method was evaluated with 3 groups of graduate students; the groups that received simplified Cognitive Maps of Knowledge Diagnosis along with textual explanations demonstrated higher trust in the system’s decisions and were more likely to follow the recommendations. We conclude with recommendations for using cognitive maps of knowledge diagnosis for cross-cutting analysis of learning situations.
Metaverse: A Matter of eXperience

In this brief article I discuss the nature of the Metaverse from the perspective of the expanded experience (eXperience or XE) it can provide to its users. I then focus on the challenges, risks, and opportunities brought about by the Metaverse. I conclude with a note of caution about the possible future success or failure of this new technology.
An Essential Vigilance Analysis on Security of Web Applications

Modern web applications are heavily influenced by security advancements. Protecting open web applications is a common term for these programs, which are often referred to as the protection project. A mechanism for establishing security cooperation would be: When providing services through the internet, this is what you'd use. According to a recent study, millions of online services and applications produced using digital technology would be utilized by millions of websites and people every day. They want to adopt a safe method of building web applications that are used by people and people together.
The Usage of Artificial Intelligence in Journalism

The study aims to identify the techniques of the Fourth Industrial Revolution, in many new technologies produced by that revolution, which will form artificial intelligence journalism. The dependence of some media on a “robot” in newsrooms or media institutions, which represents open data journalism, big data journalism, blockchain journalism, cloud journalism, and other tools that constitute the era of artificial intelligence journalism. The results also revealed that the provision of big data for press and media content seems astonishing, if the right moment comes to expand it, the press of artificial intelligence based on big data can provide contents that are difficult for human effort to come up with the same effort, time and cost.
Image Fusion-Based Watermarking in IWT-SVD Domain

To protect the copyright of the digital media, a robust watermarking algorithm based on image fusion, integer wavelet transform (IWT)-singular value decomposition (SVD) and hybrid of optimization scheme is developed. The algorithm considers IWT to decompose the carrier image into non-overlapping sub-bands, and then SVD is performed on lower frequency sub-bands to compute the singular matrices. Further, the algorithm utilizes the stationary wavelet transform (SWT) to decompose the multi-focus images into several sub-bands, and obtain the fused image as mark image. To provide the solution of false positive problem (FPP), we conceal principal component (PC) of the fused mark the carrier image with optimal embedding factor. Prior to this, fusion of Firefly and PSO optimization scheme is used to determine the embedding strength, which provide a good relationship between robustness and quality. Experimental demonstration shows that compared with the traditional algorithms, robustness of the proposed algorithm is significantly improved.
Fake Product Review Detection Using Machine Learning

Online reviews play a crucial role in determining whether a product will be sold on e-commerce websites or applications. Because so many people rely on internet evaluations, unethical actors may fabricate reviews in order to artificially boost or devalue items and services. To detect false product reviews, this research provides a semi-supervised machine learning approach. Furthermore, feature engineering techniques are used in this work to extract diverse reviewer behaviors. This study examines the outcomes of numerous experiments on a real food review dataset of restaurant reviews with attributes collected from user behavior. In terms off-score, the results indicate that Random Forest surpasses another classifier, with the best f-score of 98 %. In addition, the data reveals that taking into account the reviewers' behavioral characteristics raises the f-score and the final accuracy has come out 97.7%. In the current technique, not all reviewers' behavioral characteristics have been considered. Other low-level features such as frequent time or date dependency, the reviewer's timing for giving a review, and how common it is to deliver favorable or poor reviews will be added further in order to improve the efficacy of the offered fake review detecting algorithm.
Workflow scheduling optimisation for distributed environment using artificial neural networks and reinforcement learning

The growing volumes of information and multifaceted nature of information processing, workflow scheduling in distributed environment are a prominent component for computing operations to diminish the amount of information transferring, computation load allocation to resources, reducing the task's waiting time and execution time. The basic objective of this article is to find an optimal schedule (Sopt) which can reduce the makespan of workflow. Artificial intelligence and neural network (NN) systems are the main-stream, but they were not effectively employed nevertheless for workflow scheduling. Hence, we enhance the scheduling by realising artificial neural networks and reinforcement Q-learning standards. An optimised NN-based scheduling algorithm (WfSo_ANRL) that represents an agent which can effectively schedule the tasks among computational nodes was provided in this article. The agent interacts with the external environment, i.e., the computing environment and collects the current status of load encoded in the form of a state vector. The agent then predicts an action and efficiently allocates the tasks on to the attainable resources. The external computing environment then awards incentives to the agent. The agent then learns to produce optimal schedules for reducing the makespan. In this way, the WfSo_ANRL produces optimal solution for workload.
Chapter 13. The Rise of Cloud Computing

Cloud computing—computing done on an off-site network of resources accessed through the Internet—is revolutionizing how computing services are used. However, because cloud is so new and it largely is an intermediate input to other industries, it is difficult to track in the U.S. statistical system. Moreover, there is a paucity of systematic information on the prices of cloud services. To begin filling this gap, this chapter does three things. First, we define the different segments of cloud computing and document its explosive expansion. Second, we develop new hedonic price indexes for cloud services based on quarterly data for compute, database, and storage services offered by Amazon Web Services (AWS) from 2009 to 2016. These indexes fall rapidly over the sample period, with quickening (and double digit) rates of decline for all three products starting at the beginning of 2014. Finally, we highlight the puzzle of why investment in IT equipment in the NIPAs has been so weak while capital expenditures have exploded for IT equipment associated with cloud infrastructure. We suggest that cloud service providers are undertaking large amounts of own-account investment in IT equipment and that some of this investment may not be captured in GDP.
Lexicographic Multi-Objective Reinforcement Learning

In this work we introduce reinforcement learning techniques for solving lexicographic multi-objective problems. These are problems that involve multiple reward signals, and where the goal is to learn a policy that maximises the first reward signal, and subject to this constraint also maximises the second reward signal, and so on. We present a family of both action-value and policy gradient algorithms that can be used to solve such problems, and prove that they converge to policies that are lexicographically optimal. We evaluate the scalability and performance of these algorithms empirically, and demonstrate their applicability in practical settings. As a more specific application, we show how our algorithms can be used to impose safety constraints on the behaviour of an agent, and compare their performance in this context with that of other constrained reinforcement learning algorithms.
Interpretable AMR-Based Question Decomposition for Multi-hop Question Answering

Effective multi-hop question answering (QA) requires reasoning over multiple scattered paragraphs and providing explanations for answers. Most existing approaches cannot provide an interpretable reasoning process to illustrate how these models arrive at an answer. In this paper, we propose a Question Decomposition method based on Abstract Meaning Representation (QDAMR) for multi-hop QA, which achieves interpretable reasoning by decomposing a multi-hop question into simpler subquestions and answering them in order. Since annotating the decomposition is expensive, we first delegate the complexity of understanding the multi-hop question to an AMR parser. We then achieve decomposition of a multi-hop question via segmentation of the corresponding AMR graph based on the required reasoning type. Finally, we generate sub-questions using an AMR-to-Text generation model and answer them with an off-the-shelf QA model. Experimental results on HotpotQA demonstrate that our approach is competitive for interpretable reasoning and that the sub-questions generated by QDAMR are well-formed, outperforming existing question-decomposition-based multihop QA approaches.
Assessing the Impact of AI Solutions’ Ethical Issues on Performance in Managerial Accounting

In the contemporary, constantly changing business environment characterized by globalization, openness, and competitiveness, implementing different processes of new information technologies has become a competitive advantage. The field of managerial accounting is a successful example of the implementation of artificial intelligence in operations and the decision-making process based on accounting information. However, ethical issues within managerial accounting and those added through the implementation of artificial intelligence need to be addressed carefully. In this paper, the main objective is to investigate these ethical issues regarding the perception of accountants on the usefulness, efficiency, and effectiveness of implementing artificial intelligence in managerial accounting. To investigate these effects, we conducted a study based on a questionnaire among 396 accountants in Romania who use various artificial intelligence solutions in their activities in managerial accounting. The results of structural equation modeling showed that the ethical issues of autonomy, responsibility, and trust significantly influence the perceived usefulness and the performance of artificial intelligence solutions. The research concludes that artificial intelligence solutions solve many ethical issues in managerial accounting. Still, through their design and application, artificial intelligence solutions can create other ethical problems specific to managerial accounting and business ethics. Therefore, despite all the barriers and reluctance of professionals, artificial intelligence will substantially impact managerial accounting in the years to come.
USRP RIO-Based Testbed for Real-Time Blind Digital Modulation Recognition in MIMO Systems

Modulation recognition is one of the key elements in the cognitive radio (CR) technology. Chiefly, automatic modulation recognition (AMR) is a challenging task in such systems. It refers to blindly identifying the modulation type by a CR receiver which is highly recommended for military as well as civil applications, e.g. security, threat analysis. In this letter, a comprehensive testbed is provided. It aims at assessing the AMR performance in near real-world multiple-input multiple-output (MIMO) communication scenario, as a first step for industrial integration and commercialization. Our testbed is based on the Software-Defined Radio (SDR) technology where the radio reconfigurability feature is warranted by integrating the NI-2954R Universal Software Radio Peripheral (USRP) reconfigurable I/O (RIO) boards. The different processing modules of the designed testbed have been implemented within a scalable and flexible architecture to realize real-time recognition. It also serves as an efficient tool to bridge the gap between theory and practice. Empirical results showed that the proposed testbed yields high recognition accuracy and strong robustness.
Animating Portrait Line Drawings from a Single Face Photo and a Speech Signal

Animating a single face photo is an important research topic which receives considerable attention in computer vision and graphics. Yet line drawings for face portraits, which is a longstanding and popular art form, have not been explored much in this area. Simply concatenating a realistic talking face video generation model with a photo-to-drawing style transfer module suffers from severe inter-frame discontinuity issues. To address this new challenge, we propose a novel framework to generate artistic talking portrait-line-drawing video, given a single face photo and a speech signal. After predicting facial landmark movements from the input speech signal, we propose a novel GAN model to simultaneously handle domain transfer (from photo to drawing) and facial geometry change (according to the predicted facial landmarks). To address the inter-frame discontinuity issues, we propose two novel temporal coherence losses: one based on warping and the other based on a temporal coherence discriminator. Experiments show that our model produces high quality artistic talking portrait-line-drawing videos and outperforms baseline methods. We also show our method can be easily extended to other artistic styles and generate good results. The source code is available at https://github.com/AnimatePortrait/AnimatePortrait .
Evaluating the Expediency of Smartphone Applications for Indian Farmers and Other Stakeholders

Smartphone application usage has increased exponentially over the past decade. The potentiality of smartphone applications as a tool for various decision-making processes is not fully explored, especially in the field of agriculture. This work systematically evaluates smartphone applications developed by research institutes and non-profit organizations and made available to Indian agriculture stakeholders, who have the world’s largest user base. The study analyzed 25 smartphone applications developed for the agriculture and allied sectors available to the Indian farming community. The usability, accessibility, frequency of updates, user ratings, and number of downloads of smartphone applications are systematically evaluated. Furthermore, this article assesses the divergence between existing smartphone applications and the needs of agricultural stakeholders. This research necessitates the importance of systematic evaluation of digital applications available to the end-users and offers guidelines to application developers, researchers, and policymakers on the potential shortcomings of prevailing smartphone applications and warrants features for future smartphone applications.
MHAttnSurv: Multi-head attention for survival prediction using whole-slide pathology images

Whole slide images (WSI) based survival prediction has attracted increasing interest in pathology. Despite this, extracting prognostic information from WSIs remains a challenging task due to their enormous size and the scarcity of pathologist annotations. Previous studies have utilized multiple instance learning approach to combine information from several randomly sampled patches, but this approach may not be adequate as different visual patterns may contribute unequally to prognosis prediction. In this study, we introduce a multi-head attention mechanism that allows each attention head to independently explore the utility of various visual patterns on a tumor slide, thereby enabling more comprehensive information extraction from WSIs. We evaluated our approach on four cancer types from The Cancer Genome Atlas database. Our model achieved an average c-index of 0.640, outperforming three existing state-of-the-art approaches for WSI-based survival prediction on these datasets. Visualization of attention maps reveals that the attention heads synergistically focus on different morphological patterns, providing additional evidence for the effectiveness of multi-head attention in survival prediction.
Semantic‐aware spatial regularization correlation filter for visual tracking

Correlation filters with convolutional neural network (CNN) features have been successfully applied to visual tracking owing to their impressive combined capability for object representation. Unfortunately, further performance improvement is limited due to unwanted boundary effects of the circular structure. In this work, through an in-depth study of the features’ characteristics, the authors propose a novel tracking strategy to achieve simultaneous filter matching and regularization with CNN features when tracking is on the fly. With a feature decomposed transform matrix, a spatial semantic regularization is generated to reduce the boundary effect effectively during filter optimization. Before each output, the regularized filter is then back performed to match with the extracted features of a search region to find the optimum candidate. Specifically, the most important advantage of the proposed spatial semantic map is to initialize only in the first frame as all the other tracking strategies. Besides, the authors design a novel updating strategy to tackle the cases where the object is occluded or disappeared in the scene. At this time, the maximum of the map is small, even negative. A substantial experiment has been carried out on the popular benchmark tracking datasets; the reliable results have demonstrated that the authors’ method is able to outperform most of the state-of-the-art tracking works in both accuracy and robustness.
Blockchain iLearning Platform in Education

The development of technology is currently growing very rapidly, especially in education where there is currently an ilearning system. i-learning is a learning process that utilizes electronic media in the form of computers and networks as a means to exchange information. But in this case data security is minimal, which makes it easy for data to be widely circulated. Blockchain is a set of data records maintained by a group of computers that do not belong to a single entity. A computer command that facilitates an agreement between one party and another without going through a third party is a Smart Contract. In this study, the existence of Blockchain and smart contracts that will be carried out can bring a level of security and transparency to the ilearning platform. Platform Learning blockchain-based will be developed to improve evaluation and facilitate curriculum personalization in an educational context. Focused on the platform to be able to automate evaluation and credential issues and to demonstrate the benefits of the blockchain backend to end users such as college students and university students. To collect data using the application of the SUS method with sampling which gets a data sample value of 93 and has a SUS score of 75, it shows that the blockchain platform for education is included in the Acceptable category.
An Analysis of Students’ Achievement in Reading Comprehension through Higher Order Thinking Skills (HOTS)

This research aims to find out the students’ achievement by using HOTS as learning strategy in reading comprehension at grade VIII of SMP Yayasan Perguruan Keluaraga Pematangsiantar. The aspect of students’ achievement overwhelming, remembering, understanding, applying, analyzing, evaluating, and creating. These aspects were seen in Descriptive text. The research subject belongs to students in class VIIIA and VIIIB. The data are collected using qualitative method, namely the type of basic interpretative studies in which the data obtained used questionnaires to the student in the form of a Google form. In collecting data questionnaires were shared to 65 respondents. It was used to describe the topic related to the phenomenon happen in reading comprehension through HOTS. The research findings highlight C1-C6 phase which almost of students gave positive response to each statement of criteria HOTS. The achievement in six aspects of reading comprehension used HOTS, they are remembering text with strongly agree scale 58,4%, understanding supporting sentence with strongly agree 50,2%, applying decoding process with agree scale 49,2%, analyzing main ideas with strongly agree 50,2%, evaluating inference with agree scale 51,7%, and creating new ideas with agree scale 53,3%.