Reviewerly/exHarmony huggingface dataset:
Tunable Dynamics in Agent-Based Simulation using Multi-Objective Reinforcement Learning

Agent-based simulation is a powerful tool for studying complex systems of interacting agents. To achieve good results, the behavior models used for the agents must be of high quality. Traditionally ...
Collaborative Digital Forensic Investigations Model for Law Enforcement

Cybercrime has evolved rapidly in recent years since societies are becoming more reliant on digital communications with e-services inevitably replacing legacy systems. This has redefined our ecosystem to a highly interconnected Cyber-Physical-Natural (CPN) world where various aspects of our daily life including cybercrime are being realized online as part of the cyberspace. Therefore, it is crucial to perform Digital Forensic Investigations (DFI) as part of a modernized and collaborative Incident Response (IR) methodology. Conducting forensic investigations incorporates standardized and formalized processes to comply with legal (both local and international), professional and ethical requirements. In this study, we address the need for a standardized collaborative model to facilitate sound investigations for the Royal Oman Police (ROP). We propose the Collaborative Digital Forensics and Incident Response (CDFIR) model to mitigate risks associated with the utilization of inconsistent methodologies selected based on the various personal expertise of the local staff. Furthermore, we introduce an online portal to complement the investigation process and empower police officers with a tool to manage their allocated cases more efficiently.
Artificial intelligence-based analysis of payment system data can predict one-year mortality of hospitalized patients with heart failure

Abstract Background Individual risk stratification is fundamental in the care of heart failure (HF) patients. However, the prediction performance of risk scores, such as MAGGIC and SHFM, is not adequate and, more importantly, they need additional predictors including various biomarkers, imaging data, and environmental factors. Data from a case-mix payment system including diagnosis and procedures with outcomes can be used to develop the risk prediction models, allowing the use of big data for a more accurate prediction of mortality. Purpose This study aimed to develop artificial intelligence (AI) models for predicting 1-year mortality in patients hospitalized due to HF. Methods We analyzed the data from 10175 patients enrolled in the Japanese Registry Of Acute Decompensated Heart Failure (JROADHF). Candidate variables included the data obtained from a payment system introduced by the Japanese government, the Diagnosis Procedure Combination (DPC), which included each patient profile (age, sex, height, weight), principal diagnosis for hospitalization, comorbidities, procedures, length of hospital stay, and discharge status. They did not include clinical data available from patients such as vital status, laboratory data including bio-makers, electrocardiographic and echocardiographic data. The collected data were divided into the training set and the validation set (80%: 20%). With the training set, 5 AI models (logistic regression, random forest, support vector machine, neural network, and ensemble classifier) learned the one-year mortality results. AI models were evaluated by using the validation set with ROC analysis. The training and validation steps were repeated 10 times with different seed values to calculate the C-statistic of each model. We also identified the predictors for one-year prognosis acquired from the AI models. Results At 1-year of follow-up, a total of 1727 patients had died (17%). Among the machine learning models, the ensemble classifier showed the highest C-statistic of 0.76 (95% confidence interval: 0.75 to 0.77) for predicting mortality. Top predictors acquired from the random forest classifier was ADL (Barthel Index) at discharge, age, body mass index, and length of hospital stay. Conclusion By using AI-based analysis of a national case-mix payment system data, the present risk stratification model could predict the one-year mortality of hospitalized HF patients without any quantitative laboratory and physiological data. Furthermore, the present results could emphasize the advantage of this approach using the claim-based data that are routinely collected in a usual daily practice with no need to collect any additional information. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Japan Agency for Medical Research and Development
A Novel Pricing Mechanism for User Coalition in Blockchain

As the blockchain platform is widely used as a new trading way, both participants and transaction volume in the blockchain projects have been growing by leaps and bounds. The generic mechanisms of ranking transaction priorities are heavily dependent on the transaction fees the users append into each transaction; then, all transactions are ranked in the nonincreasing order according to the transaction fee amounts, and the selected transactions will be packed into a new created block in order based on the ranking results. However, more complex influence factors more than transaction fees on transaction priority ranking results are not taken into consideration in the generic transaction priority ranking mechanisms, and a single user is the objective to create transactions in these mechanisms. In order to optimize the generic transaction priority ranking mechanisms and enrich transaction creation modes, a novel user-coalition-based transaction pricing mechanism (UCTPM) is proposed, and the user coalition quality score, user coalition contribution degree, and the transaction type demand degree are formulated and introduced into the UCTPM mechanism. Our research findings indicate that the UCTPM mechanism satisfies the economic attributes of budget balanced, individual rationality, and incentive compatibility when the user coalition contribution degree increases through theoretical proof and experimental analysis. Moreover, the UCTPM mechanism allows all the transactions to be processed more efficiently by experimental analysis.
Feature Linkage Weight Based Feature Reduction using Fuzzy Clustering Method

In this paper, a novel Feature-Reduction Fuzzy C-means (FRFCM) with Feature Linkage Weight (FRFCM-FLW) algorithm is introduced. By the combination of FRFCM and feature linkage weight, a new feature selection model is developed, called a Feature Linkage Weight Based FRFCM using fuzzy clustering. The larger amounts of features are superior to the complication of the problem, and the larger the time that is exhausted in creating the outcome of the classifier or the model. Feature selection has been established as a high-quality method for preferring features that best describes the data under certain criteria or measure. The proposed method presents three stages namely, 1) Data Formation: The process of data collection and data cleaning; 2) FRFCM-FLW. The proposed method can decrease feature elements routinely, and also construct excellent clustering results. The proposed method calculates a novel weight for every feature by combining modified Mahalanobis distance with feature δm variance in FRFCM algorithm; 3) Fuzzy C-means (FCM) cluster. The proposed FRFCM-FLW method proves high Accuracy Rate (AR), Rand Index (RI) and Jaccard Index (JI) ratio when compared to other feature reduction algorithms like WFCM, EWKM, WKM, FCM and FRFCM algorithms.
Corpus in “Natural” Language Versus “Translation” Language: LBC Corpora, A Tool for Bilingual Lexicographic Writing

The aim of this paper is to describe the work done to exploit the LBC database for the purpose of translation analysis as a resource to edit the bilingual lexical sections of our dictionaries of cultural heritage (in nine languages). This database, made up of nine corresponding corpora, contains texts whose subject is cultural heritage, ranging from technical texts on art history to books on art appreciation, such as tour guides, and travel books highlighting Italian art and culture. We will illustrate the different questions with the SketchEngine LBC French corpus, made up at the moment of 3,000,000 words. Our particular interest here is in research that not only orients lexical choices for translators but that also precedes the selection of bilingual quotations (from our Italian/French parallel corpus) and that we rely on for editing an optional element of the file called “translation notes.” We will rely on this as much for works on “universals of translation” already described by Baker (Corpus linguistics and translation studies. Implications and applications. In Baker M et al (eds) Text and technology. Benjamins, Amsterdam/Philadelphia, pp 233–250 (1993)) as for studies aimed at improving translation quality assessment (TQA). We will show how a targeted consultation of different corpora and subcorpora that the database allows us to distinguish (“natural language” vs “translation,” “technical texts” vs “popularization texts” or “literary texts”) can help us identify approximations or translation errors, so as to build quality comparative lexicographical information.
LookOut! Interactive Camera Gimbal Controller for Filming Long Takes

The job of a camera operator is challenging, and potentially dangerous, when filming long moving camera shots. Broadly, the operator must keep the actors in frame while safely navigating around obstacles and while fulfilling an artistic vision. We propose a unified hardware and software system that distributes some of the camera operator’s burden, freeing the operator up to focus on safety and aesthetics during a take. Our real-time system provides solo operators with end-to-end control so that they can balance on-set responsiveness to action against planned storyboards and framing while looking where they are going. By default, we film without a field monitor. Our LookOut system is built around a lightweight commodity camera gimbal mechanism, with heavy modifications to the controller, which would normally just provide active stabilization. Our control algorithm reacts to speech commands, video, and a premade script. Specifically, our automatic monitoring of the live video feed saves the operator from distractions. In preproduction, an artist uses our graphical user interface (GUI) to design a sequence of high-level camera “behaviors.” Those can be specific, based on a storyboard, or looser objectives, such as “frame both actors.” Then, during filming, a machine-readable script, exported from the GUI, ties together with the sensor readings to drive the gimbal. To validate our algorithm, we compared tracking strategies, interfaces, and hardware protocols and collected impressions from (a) filmmakers who used all aspects of our system and (b) filmmakers who watched footage filmed using LookOut.
Secure Transfer Learning for Machine Fault Diagnosis Under Different Operating Conditions

The success of deep learning is largely due to the availability of big training data nowadays. However, data privacy could be a big concern, especially when the training or inference is done on untrusted third-party servers. Fully Homomorphic Encryption (FHE) is a powerful cryptography technique that enables computation on encrypted data in the absence of decryption key, thus could protect data privacy in an outsourced computation environment. However, due to its large performance and resource overheads, current applications of FHE to deep learning are still limited to very simple tasks. In this paper, we first propose a neural network training framework on FHE encrypted data, namely PrivGD. PrivGD leverages the Single-Instruction Multiple-Data (SIMD) packing feature of FHE to efficiently implement the Gradient Descent algorithm in the encrypted domain. In particular, PrivGD is the first to support training a multi-class classification network with double-precision float-point weights through approximated Softmax function in FHE, which has never been done before to the best of our knowledge. Then, we show how to apply FHE with transfer learning for more complicated real-world applications. We consider outsourced diagnosis services, as with the Machine-Learning-as-a-Service paradigm, for multi-class machine faults on machine sensor datasets under different operating conditions. As directly applying the source model trained on the source dataset (collected from source operating condition) to the target dataset (collect from the target operating condition) will lead to degraded diagnosis accuracy, we propose to transfer the source model to the target domain by retraining (fine-tuning) the classifier of the source model with data from the target domain. The target domain data is encrypted with FHE so that its privacy is preserved during the transfer learning process. We implement the secure transfer learning process with our PrivGD framework. Experiments results show that by fine-tuning a source model for fewer than 10 epochs with encrypted target domain data, the model can converge to an increased diagnosis accuracy by up to 20%, while the whole fine-tuning process takes approximate 3.85 h on our commodity server.
Beyond Conversational Artificial Intelligence

Humans have struggled for centuries to give voice to machines. Since conversational artificial intelligence (AI) emerged, it has changed the business models in many industries, and this is only the beginning. How did conversational AI develop, how does it work, and where is it going?
A Safe and Energy Efficient Robotic System for Industrial Automatic Tests on Domestic Appliances: Problem Statement and Proof of Concept

In this paper, the design and the development of a robotic platform conceived to perform accelerated life tests on a newly manufactured domestic appliances is presented. The proposed system aims at improving the safety of human operators that share the workspace with the robotic platform which is a common scenario of test laboratories. A deep learning algorithm is used for the human detection and pose estimation, while the integration between a conventional motion planning algorithm with a fast 3D collision checker has been implemented as a global planner plugin for the ROS navigation stack. With the twofold objective of improving safety and saving energy in the battery-powered mobile manipulator used in this project, the problem of minimizing the overall kinetic energy is addressed through a properly designed task priority controller, in which the manipulator inertia matrix is used to weight the joint speeds while satisfying multiple robotic tasks according to a hierarchy designed to interact with the appliances while preserving the safety of the human operators. Simulations are carried out to evaluate the overall control architecture and preliminary results indicate the effectiveness of the developed system in the test laboratory floors.
A Fully Symmetrical Quantum Key Distribution System Capable of Preparing and Measuring Quantum States*

We propose a fully symmetrical QKD system that enables quantum states to be prepared and measured simultaneously without compromising system performance. Over a 25.6 km fiber channel, we demonstrate point-to-point QKD operations with asymmetric Mach–Zehnder interferometer modules. Two interference visibilities of above 99% indicate that the proposed system has excellent stability. Consequently, the scheme not only improves the feasibility of distributing secret keys, but also enables QKD closer to more practical applications.
Multi‐scale patches based image denoising using weighted nuclear norm minimisation

As a prior knowledge, non-local self-similarity (NSS) has been widely utilised in ill-posed problems. Actually, similar textures appear not only in a single scale, but also in different scales. Unlike most existing patch-based methods that only explore NSS in the same scale, a multi-scale patches based image denoising algorithm is proposed in this study. The authors have designed a multi-scale strategy to expand the search space of block-matching, which will increase the probability of finding more similar patches. After that, the weighted nuclear norm minimisation (WNNM) algorithm is employed to reveal latent clean patches. With the join of the multi-scale framework, the performance of WNNM can be improved. The proposed algorithm can be used to solve NSS-based image restoration tasks. In this study, mainly image denoising is studied, and its effectiveness is derived through experiments on widely used test images.
SOCIAL MEDIA ANALYSIS WITH AI: SENTIMENT ANALYSISTECHNIQUES FOR THE ANALYSIS OF TWITTER COVID-19 DATA

Recently, In the current situation there has been an outbreak known as COVID-19 (corona virus) causingacute respiratory syndrome, first noticed in China and now a pandemic. Social media plays a crucial role in the currentscenario of the world being locked up and further leading to the social imbalance among people. The news scattered likeleaves about people attempting suicide.In this chapter, we aim at providing the sentiment analysis on covid-19, about the people's reaction towards the decisionsmade either by the government or the local authorities through Twitter. We propose a system which automates analyzingthe tweets and categorizing them into positive, negative or neutral sets. With the utilization of automata and NLP (naturallanguage processing) together the accuracy, quantization and prediction of the sets can be achieved. Classification can bewhether on a pattern based or a NLTK (Natural language toolkit). The classified results are further stored in thestructures that could be iterated while calling for the visualization.
A Decision Making Approach Based on Hesitant Fuzzy Linguistic-Valued Credibility Reasoning

Credibility reasoning has attracted a lot of attention due to its distinguished power and efficiency in representing uncertainty and vagueness within the process of reasoning and decision making. Aiming at the problem of inaccurate credibility estimation in uncertainty reasoning and making experts to express hesitant preferences better in evaluation reasoning process, this paper introduces hesitant fuzzy linguistic term set into credibility uncertainty reasoning. First, we propose hesitant fuzzy linguistic-valued credibility (HLCF), and establish the knowledge representation model of the hesitant fuzzy linguistic-valued credibility. Then, in order to solve the problem of incomplete information in the evaluation reasoning process, an information complement algorithm based on maximum similarity is constructed. After that, the algorithms of single rule and multiple rules of parallel relationship of hesitant fuzzy linguistic-valued credibility are proposed to enrich the reasoning rule base and get more accurate reasoning results. The closeness degrees between the conclusions of each alternative after reasoning and the expected value are calculated, so as to select the most suitable alternative. Finally, a practical example which concerned the social risk analysis is given to illustrate the applicability and effectiveness of the proposed approach.
Photo Content Classification Using Convolutional Neural Network

Abstract This research is about photo content classification using the TensorFlow framework and the deep neural network algorithm in deep learning. Machine learning is a multidisciplinary major whose main research object is artificial intelligence, which enables machines to have the learning ability like human beings. As a branch of machine learning, deep learning analyses and interprets texts, images, and sounds by learning the rules and representations of sample data. A convolutional neural network is an approach of a deep neural network, which is mostly applied to analyse the photo content and has good performance in photo content classification. It is also the main point that the study focuses on. The Visual Geometry Group 16 (VGG16) model in the convolutional neural network was used to achieve the research objectives and has excellent performance. The research used data from CelebFaces and Kaggle, including photos about people, food, animals, and architecture. The photo content classification model in this study can classify these four types of images. Moreover, the analysis of the work, summary, as well as the future work were provided at the end.
Recurrent Neural Networks

In Chapter 6 , we covered feedforward neural networks, which are the most basic artificial neural network types. Then, we covered convolutional neural networks in Chapter 7 as the type of artificial neural network architecture, which performs exceptionally good on image data. Now, it is time to cover another type of artificial neural network architecture, recurrent neural network, or RNN, designed particularly to deal with sequential data.
A data-driven approach for the identification of misconceptions in step-based tutoring systems

Math errors are an important part of the learning process. For this reason, diagnosing them can help teachers and intelligent learning environments to choose the most appropriate type of assistance for the learner. In particular, the identification of learner misconceptions can be of special importance because they represent a misunderstanding of math concepts. In this context, this paper proposes the use of clustering algorithms to automatically identify algebra misconceptions from learners' algebra problem-solving steps in an intelligent learning environment. The computing platform is an intelligent tutoring system that assists students when solving linear equations step by step, by giving minimal and error feedback. The results showed that the model was able to identify some misconceptions already known in the literature, which illustrates the appropriateness of our approach. The automatic identification of misconceptions can help in the identification of new conceptual misunderstanding from large datasets of math problem solving, besides give valuable information for teachers and intelligent learning environments to adapt their instruction and assistance.