Reviewerly/exHarmony huggingface dataset:
A GRL-compliant iStar extension for collaborative cyber-physical systems

Abstract Collaborative cyber-physical systems are capable of forming networks at runtime to achieve goals that are unachievable for individual systems. They do so by connecting to each other and exchanging information that helps them coordinate their behaviors to achieve shared goals. Their highly complex dependencies, however, are difficult to document using traditional goal modeling approaches. To help developers of collaborative cyber-physical systems leverage the advantages of goal modeling approaches, we developed a GRL-compliant extension to the popular iStar goal modeling language that takes the particularities of collaborative cyber-physical systems and their developers’ needs into account. In particular, our extension provides support for explicitly distinguishing between the goals of the individual collaborative cyber-physical systems and the network and for documenting various dependencies not only among the individual collaborative cyber-physical systems but also between the individual systems and the network. We provide abstract syntax, concrete syntax, and well-formedness rules for the extension. To illustrate the benefits of our extension for goal modeling of collaborative cyber-physical systems, we report on two case studies conducted in different industry domains.
The Role of Forensic Accounting Techniques in Reducing Cloud Based Accounting Risks in the Jordanian Five Stars Hotels

The current study investigated the role of forensic accounting techniques in reducing Cloud Based Accounting risks in the Jordanian five stars hotels. The study population consisted of all accountants in Jordanian five stars hotels. Five stars hotels accountants located in Amman the capital were selected subjects. The total is (170) accountants. A random sample amounting 118 accountants were selected. 118 questionnaires were distributed and 96ones were collected. The study concluded that: There is a significant role of Forensic Accounting Techniques ((FPDDS), (FAIIS), (LMAS), (CARDR)) in reducing Cloud Based Accounting risks in the Jordanian five stars hotels. Despite the fact that nature of computer crime is increasing daily, so there is a need for different types of efforts, to be made on all levels of cloud investigations. And in the same time all legislations and codification related to computer crimes must be updated and reviewed from time to time to keep up with emerging technology.
Vulnerability Analysis of DID Document’s Updating Process in the Decentralized Identifier Systems

In this paper, we analyzed the vulnerabilities that could occur when a third party who is not the owner of a Decentralized Identifier (DID) has the authority to modify a DID Document. DID is a technology that allows individuals to have data sovereignty. The DID Document contains information for authenticating the DID owner. A DID Controller has the authority to modify DID Document. This authority is being standardized so that it can be held by third parties as well as DID owners. It is a matter of changing the DID document against the will of the DID owner. To help owners assert their sovereignty even stronger, we propose a way for DID owners to use a policy, history, and service to verify the DID Document updating process.
The Danish National Energy Data Lake: Requirements, Technical Architecture, and Tool Selection

Renewable Energy Sources such as wind and solar do not emit CO2 but their production vary considerably depending on time and weather.Thus, it is important to use the flexibility in device loads to shift energy consumption to follow the production.For example, an Electrical Vehicle (EV) can be charged very flexibly between arriving home at 5PM and leaving again at 7AM.Utilizing all available energy flexibility requires applying machine learning and AI on massive amounts of Big Data from many different actors and devices, ranging from private consumers, over companies, to energy network operators, and using this to create digital solutions to enable and exploit flexibility.The project Flexible Energy Denmark (FED) is building the foundation for this for the entire Danish society.Specifically, FED collects data from a number of Living Labs (LLs) in representative real-life physical environments.The data is stored in the Danish National Energy Data, called FED Data Lake (FEDDL) to enable efficient and advanced analysis.FEDDL is built using only open source tools which can run both on-premise and in cloud settings.In this paper, we describe the requirements for FEDDL based on a representative LL case study, present its technical architecture, and provide a comparison of relevant tools along with the arguments for which ones we selected.
Research on Collision-Free Control and Simulation of Single-Agent Based on An Improved DDPG Algorithm

This technical note investigates a novel design of algorithmic models for single-agent obstacle avoidance based on deep reinforcement learning. In view of the traditional DDPG algorithm has many shortages such as many training rounds and requiring much time to learn. To this end, an improved deep deterministic policy gradient algorithm is proposed and used to improve the efficiency of obtaining obstacle avoidance path. In other words, the proposed strategy may be favor for overcoming the limitations of traditional DDPG algorithm by adopting two-way search of agents, improving the reward mechanism and adding real-time rewards and punishments as supplements. Different from the traditional human strategy obstacle avoidance algorithms such as artificial potential field method and grid method, the bidirectional DDPG algorithm has the abilities of independent decision-making, stronger adaptability and better obstacle avoidance effect in many practical applications.
Adaptive robust local online density estimation for streaming data

Accurate online density estimation is crucial to numerous applications that are prevalent with streaming data. Existing online approaches for density estimation somewhat lack prompt adaptability and robustness when facing concept-drifting and noisy streaming data, resulting in delayed or even deteriorated approximations. To alleviate this issue, in this work, we first propose an adaptive local online kernel density estimator (ALoKDE) for real-time density estimation on data streams. ALoKDE consists of two tightly integrated strategies: (1) a statistical test for concept drift detection and (2) an adaptive weighted local online density estimation when a drift does occur. Specifically, using a weighted form, ALoKDE seeks to provide an unbiased estimation by factoring in the statistical hallmarks of the latest learned distribution and any potential distributional changes that could be introduced by each incoming instance. A robust variant of ALoKDE, i.e., R-ALoKDE, is further developed to effectively handle data streams with varied types/levels of noise. Moreover, we analyze the asymptotic properties of ALoKDE and R-ALoKDE, and also derive their theoretical error bounds regarding bias, variance, MSE and MISE. Extensive comparative studies on various artificial and real-world (noisy) streaming data demonstrate the efficacies of ALoKDE and R-ALoKDE in online density estimation and real-time classification (with noise).
Android Forensic Tools Analysis for Unsend Chat on Social Media

This research discusses mobile phone forensics on the unsend message feature of social media. It assists investigators forensic or law enforcers in Indonesia to get digital evidence of cybercrime problems such as hoaxes, cyberbullying, illegal transactions, online protection, or other crimes on social media. This research uses Universal Forensic Extraction Device (UFED) and MOBILedit tools to get digital evidence. The selected social media that will be investigated by investigator forensic are Instagram, Line, Whatsapp, Facebook Messenger, Skype, Snapchat, Viber, and Telegram. Based on the results obtained, artifacts can only be found by UFED on social media such as Instagram, Whatsapp, Facebook Messenger, Skype, Viber, and Telegram, whereas digital evidence can not be found on social media such as Line and Snapchat.
Challenge report Recognizing Families In the Wild Data Challenge

This paper is a brief report to our submission to the Recognizing Families In the Wild Data Challenge (4th Edition), in conjunction with FG 2020 Forum. Automatic kinship recognition has attracted many researchers' attention for its full application, but it is still a very challenging task because of the limited information that can be used to determine whether a pair of faces are blood relatives or not. In this paper, we studied previous methods and proposed our method. We try many methods, like deep metric learning-based, to extract deep embedding feature for every image, then determine if they are blood relatives by Euclidean distance or method based on classes. Finally, we find some tricks like sampling more negative samples and high resolution that can help get better performance. Moreover, we proposed a symmetric network with a binary classification based method to get our best score in all tasks.
BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search

Relevance has significant impact on user experience and business profit for e-commerce search platform. In this work, we propose a data-driven framework for search relevance prediction, by distilling knowledge from BERT and related multi-layer Transformer teacher models into simple feed-forward networks with large amount of unlabeled data. The distillation process produces a student model that recovers more than 97% test accuracy of teacher models on new queries, at a serving cost that's several magnitude lower (latency 150x lower than BERT-Base and 15x lower than the most efficient BERT variant, TinyBERT). The applications of temperature rescaling and teacher model stacking further boost model accuracy, without increasing the student model complexity. We present experimental results on both in-house e-commerce search relevance data as well as a public data set on sentiment analysis from the GLUE benchmark. The latter takes advantage of another related public data set of much larger scale, while disregarding its potentially noisy labels. Embedding analysis and case study on the in-house data further highlight the strength of the resulting model. By making the data processing and model training source code public, we hope the techniques presented here can help reduce energy consumption of the state of the art Transformer models and also level the playing field for small organizations lacking access to cutting edge machine learning hardwares.
Computational Intelligence for Analysis of Traffic Data

This article presents a system developed for the collection and analysis of traffic data obtained from traffic camera videos using computational intelligence. The proposed system is developed using the modern object detection library Detectron2. A pipeline-type architecture is used for frame processing, where each step is an independent, configurable functional module, loosely coupled to the others. The validation of the proposed system is performed on real scenarios in Montevideo, Uruguay, under different conditions (daylight, nightlight, and different video qualities). Results demonstrate the effectiveness of the system in the considered scenarios.
ESinGAN: Enhanced Single-Image GAN Using Pixel Attention Mechanism for Image Super-Resolution

Recently, the SinGAN model emerged, and it was famous for generating from a single image. The SinGAN model achieves superior performance to other advanced models for image super-resolution task (trained on a dataset or a single image). However, SinGAN does not consider the importance of feature pixels on the feature map. In this paper, we propose an Enhanced SinGAN model (ESinGAN), an unconditional generative model that can improve the defects of SinGAN using the Pixel Attention mechanism. To evaluate the proposed ESinGAN model's performance, we carry out the experiments on a benchmark dataset. Through the experiments, we have achieved better performance than SinGAN and proved the effectiveness of the proposed method. Not only is the image significantly improved visually, but the PSNR and SSIM values of the model are also considerably increased. Besides, ESinGAN runs as fast as SinGAN under the same experimental environment.
A Novel Metric for Developing Easy-to-Use and Accurate Clinical Prediction Models

Guidelines recommend that clinicians use clinical prediction models to estimate future risk to guide decisions. For example, predicted fracture risk is a major factor in the decision to initiate bisphosphonate medications. However, current methods for developing prediction models often lead to models that are accurate but difficult to use in clinical settings.The objective of this study was to develop and test whether a new metric that explicitly balances model accuracy with clinical usability leads to accurate, easier-to-use prediction models.We propose a new metric called the Time-cost Information Criterion (TCIC) that will penalize potential predictor variables that take a long time to obtain in clinical settings. To demonstrate how the TCIC can be used to develop models that are easier-to-use in clinical settings, we use data from the 2000 wave of the Health and Retirement Study (n=6311) to develop and compare time to mortality prediction models using a traditional metric (Bayesian Information Criterion or BIC) and the TCIC.We found that the TCIC models utilized predictors that could be obtained more quickly than BIC models while achieving similar discrimination. For example, the TCIC identified a 7-predictor model with a total time-cost of 44 seconds, while the BIC identified a 7-predictor model with a time-cost of 119 seconds. The Harrell C-statistic of the TCIC and BIC 7-predictor models did not differ (0.7065 vs. 0.7088, P=0.11).Accounting for the time-costs of potential predictor variables through the use of the TCIC led to the development of an easier-to-use mortality prediction model with similar discrimination.
Sentiment analysis with genetic programming

With the advent of online social networks, people became more eager to express and share their opinions and sentiment about all kinds of targets. The overwhelming amount of opinion texts soon attracted the interest of many entities (industry, e-commerce, celebrities, etc.) that were interested in analyzing the sentiment people express about what they produce or communicate. This interest has led to the surge of the sentiment analysis (SA) field. One of the most studied subfields of SA is polarity detection, which is the problem of classifying a text as positive, negative, or neutral. This classification problem is difficult to solve automatically, and many hand-adjusted resources are needed to overcome the difficulties in detecting sentiment from text. These resources include hand-adjusted textual features as well as lexicons. Deciding which resource and which combination of resources are more appropriate to a given scenario is a time-consuming trial-and-error process. Thus, in this work, we propose the use of Genetic Programming (GP) as a tool for automatically choosing, combining, and classifying sentiment from text. We propose a series of functions that allow GP to deal with preprocessing tasks, handcrafted features, and automatic weighting of lexicons for a given training set. Our experiments show that our GP solution is competitive and sometimes better than SVM and superior to naïve Bayes, logistic regression, and stochastic gradient descent, which are methods used in SA competitions.
A reservoir computing approach for forecasting and regenerating both dynamical and time-delay controlled financial system behavior

Significant research in reservoir computing over the past two decades has revived interest in recurrent neural networks. Owing to its ingrained capability of performing high-speed and low-cost computations this has become a panacea for multi-variate complex systems having non-linearity within their relationships. Modelling economic and financial trends has always been a challenging task owing to their volatile nature and no linear dependence on associated influencers. Prior studies aimed at effectively forecasting such financial systems, but, always left a visible room for optimization in terms of cost, speed and modelling complexities. Our work employs a reservoir computing approach complying to echo-state network principles, along with varying strengths of time-delayed feedback to model a complex financial system. The derived model is demonstrated to act robustly towards influence of trends and other fluctuating parameters by effectively forecasting long-term system behavior. Moreover, it also re-generates the financial system unknowns with a high degree of accuracy when only limited future data is available, thereby, becoming a reliable feeder for any long-term decision making or policy formulations.
COVIs: Supporting Temporal Visual Analysis of Covid-19 Events Usable in Data-Driven Journalism

Figure 1: COVis's coordinated multiple view environment: (A) Control Panel: allows the user to change the graphs scale (linear / log), metric (absolute / per million cohabitants), and dimensions (cases / deaths).(B) Line charts: present four different line charts that are coordinated to support exploration of multiple narratives.Respectively, the charts display the relation between: (B1) time x cases/deaths, (B2) time x tests, (B3) total cases/deaths x last week cases/deaths, and (B4) time x cases/deaths projection length.(C) Events Panel: displays information and source references concerning main events occurred in certain time periods.(D) Events Time Chart: chart presenting the policy changes of a country over time.(E) Country Cards: show information concerning the analysed group of countries, allowing the exclusion and inclusion of different countries into the analysis.
Improving CNN-based colorization of B&amp;W photographs

Colorization is the process of converting a black and white image - lot of different shades of gray - into a realistic colour image. Our contribution highlights the fact that the CNNs used for colorization (and specifically the data set used for their training) are not adequate to colourize legacy B&W pictures. In fact the data sets are exclusively composed of colour images, and turning these colour images into greyscale pushes the CNN to learn the contribution of each colour to the resulting luminance. Anyway, CNN are quite good in classification task and can easily recognize skies, trees and foliage, faces and persons. The colorization works well for these elements, but it fails even on memorable objects such as flags and well-known monuments albeit their representation is present in the data sets. We make improvement by using a pretrained network and add hints in the colorization process. Global hints (a colour palette given to the CNN conjointly to the image) still leave a high degree of automation, and these hints could be provided once for a collection of images related to the same theme, which is very suitable for movie colorization. Adding manual hints by scribbling colours onto the B&W image leads to even better results but needs user interaction.
An Automated Approach to White Blood Cell Classification Using a Lightweight Convolutional Neural Network

White blood cell (WBC) count in our bloodstream plays a notable role in the diagnosis or prognosis of various blood diseases like acute lymphoblastic leukemia, heart diseases, or infections. While a radical change in white blood cell count comparative to the baseline provides us a sign that our body is being attacked by an antigen, it protects us from various infectious diseases as well. A particular type of white blood cell count tells us about being affected by a specific antigen. In medical diagnosis, it is of extreme importance to distinguish the different white blood cell components efficiently. An incorrect diagnosis may also become the cause of death. At present, in most of the medical centers, this classification is done manually by experts, which is time-consuming. Though some semi-automated procedures are being proposed where feature extraction is done manually before classifying automatically using microscopic blood smear images, it is still time-consuming and tedious. In recent years ANN, CNN-RNN, and also fused CNN models have been employed to perform the WBC classification. For making the procedure more effective, we propose a Deep Learning methodology to perform the classification applying a convolutional neural network model with a higher accuracy rate and a lower number of parameters compared to the state-of-the-art approaches.
A Novel Probabilistic Label Enhancement Algorithm for Multi-Label Distribution Learning

We propose a novel probabilistic label enhancement algorithm, called PLEA, to solve challenging label distribution learning (LDL) for multi-label classification problems.We adopt the well-known maximum entropy model based label distribution learner.However, unlike the existing LDL algorithms based on the maximum entropy model, we propose to use manifold learning to enhance the label distribution learner.Specifically, the supervised information in the label manifold is utilized in the feature manifold space construction to improve the accuracy of feature extraction, while dramatically reducing the feature dimension.Then the robust linear regression is employed to estimate the label distributions associated with the extracted reduced-dimension features.Using the enhanced reduced-dimension features and their associated estimated label distributions in the maximum entropy model, the unknown true label distributions can be estimated more accurately, while imposing considerably lower computational complexity.We evaluate the proposed PLEA method on a wide-range artificial and high-dimensional real-world datasets.Experimental results obtained demonstrate that our proposed PLEA method has advantages in LDL accuracy and runtime performance, compared to the latest multi-label LDL approaches.The results also show that our PLEA compares favourably with the state-ofthe-arts multi-label learning algorithms for classification tasks.
Customer Transaction Fraud Detection Using Random Forest

In the evolution of the electronic money system, frequent transaction fraud has been a shadow behind the prosperity. It not only endangers the property security of users, but also hinders the development of digital finance in the world. With the development of data mining and machine learning, some mature technologies are gradually applied to the detection of transaction fraud. This paper proposes a transaction fraud detection model based on random forest. The experimental results of IEEE CIS fraud dataset show that the method of this model is better than the benchmark model, such as logistic regression, support vector machine. Finally, the accuracy of our model reached 97.4%, and the AUC ROC score was 92.7%.