Reviewerly/exHarmony huggingface dataset:
Integrating IoT platforms using the INTER-IoT approach: A case study of the CasAware project

CasAware is an Ambient Assisted Living platform, developed within an Italian research project, with the aim to improve the level of comfort and well-being of inhabitants of a house, while optimizing the energy consumption. A key feature, for successful realization of such a platform, is its capability to interoperate with other IoT platforms, which can augment CasAware with additional services. Indeed, this capability facilitates smooth communication between CasAware devices and external devices connected to other IoT platforms, thus allowing efficient exchange of messages among them. However, such integration is hindered by the heterogeneity of data models used in different platforms, which is also related to lack of common standards. In order to realize integration needed for CasAware, this paper presents an approach which exploits results of the INTER-IoT project. Specifically, the INTER-IoT methodology and a set of software tools for achieving IoT interoperability are applied. In the presented study, it is shown how the INTER-IoT based approach can facilitate interoperability between CasAware and two other platforms, which use completely different data models.
Lossless Image Compression Using a Multi-scale Progressive Statistical Model

Lossless image compression is an important technique for image storage and transmission when information loss is not allowed. With the fast development of deep learning techniques, deep neural networks have been used in this field to achieve a higher compression rate. Methods based on pixel-wise autoregressive statistical models have shown good performance. However, the sequential processing way prevents these methods to be used in practice. Recently, multi-scale autoregressive models have been proposed to address this limitation. Multi-scale approaches can use parallel computing systems efficiently and build practical systems. Nevertheless, these approaches sacrifice compression performance in exchange for speed. In this paper, we propose a multi-scale progressive statistical model that takes advantage of the pixel-wise approach and the multi-scale approach. We developed a flexible mechanism where the processing order of the pixels can be adjusted easily. Our proposed method outperforms the state-of-the-art lossless image compression methods on two large benchmark datasets by a significant margin without degrading the inference speed dramatically.
Neural crossbreed

We propose Neural Crossbreed, a feed-forward neural network that can learn a semantic change of input images in a latent space to create the morphing effect. Because the network learns a semantic change, a sequence of meaningful intermediate images can be generated without requiring the user to specify explicit correspondences. In addition, the semantic change learning makes it possible to perform the morphing between the images that contain objects with significantly different poses or camera views. Furthermore, just as in conventional morphing techniques, our morphing network can handle shape and appearance transitions separately by disentangling the content and the style transfer for rich usability. We prepare a training dataset for morphing using a pre-trained BigGAN, which generates an intermediate image by interpolating two latent vectors at an intended morphing value. This is the first attempt to address image morphing using a pre-trained generative model in order to learn semantic transformation. The experiments show that Neural Crossbreed produces high quality morphed images, overcoming various limitations associated with conventional approaches. In addition, Neural Crossbreed can be further extended for diverse applications such as multi-image morphing, appearance transfer, and video frame interpolation.
Knowledge Discovery in Databases

The huge amount of data, generated by daily-life data sources, represents a big opportunity for the development and advancement in several fields: scientific research, social life and industry. At the same time, analyzing these big repositories is a hard challenge, since the overload of information can overwhelm our capability of reading and understanding data, making finding useful pieces of information a difficult task. In this discussion we give a general overview about Knowledge Discovery in Databases as a scientific discipline that provides methodologies, techniques and tools for dealing with Big Data in order to find underlying knowledge that can be exploited in decision making processes.
Systematic Literature Review of Blockchain based Smart Contracts Platforms

Blockchain-based smart contracts are contracts that can be executed in whole or in part without human interaction. Nevertheless, all processes can be trusted by anyone associated with the contract platform. The purpose of this study was to discover the latest field of study related to smart contracts. This paper also offered a broad perspective on blockchain-based smart contract platforms. This review will help pinpoint gaps to be marked in future research. This research was conducted by extracting 134 digital library articles, namely IEEE, ACM, Scopus, and Springer. After a detailed review, the 47 publications deemed worthy were current, popular, and distinctive. The extraction results show that the platforms needed for trading transactions are smart contract platforms. This platform must have the characteristics of being fast (creating blocks), reliable, safe, stable, and user-friendly.
Training with synthetic images for object detection and segmentation in real machinery images

Over the last years, Convolutional Neural Networks have been extensively used for solving problems such as image classification, object segmentation, and object detection. However, deep neural networks require a great deal of data correctly labeled in order to perform properly. Generally, generation and labeling processes are carried out by recruiting people to label the data manually. To overcome this problem, many researchers have studied the use of data generated automatically by a renderer. To the best of our knowledge, most of this research was conducted for general-purpose domains but not for specific ones. This paper presents a methodology to generate synthetic data and train a deep learning model for the segmentation of pieces of machinery. For doing so, we built a computer graphics synthetic 3D scenery with the 3D models of real pieces of machinery for rendering and capturing virtual photos from this 3D scenery. Subsequently, we train a Mask R-CNN using the pre-trained weights of COCO dataset. Finally, we obtained our best averages of 85.7% mAP for object detection and 84.8% mAP for object segmentation, over our real test dataset and training only with synthetic images filtered with Gaussian Blur.
AIRC: Attentive Implicit Relation Recommendation Incorporating Content Information for Bipartite Graphs

With users being exposed to the growing volume of online information, the recommendation system aiming at mining the important or interesting information is becoming a modern research topic. One approach of recommendation is to integrate the graph neural network with deep learning algorithms. However, some of them are not tailored for bipartite graphs, which is a unique type of heterogeneous graph having two entity types. Others, though customized, neglect the importance of implicit relation and content information. In this paper, we propose the attentive implicit relation recommendation incorporating content information (AIRC) framework that is designed for bipartite graphs based on the GC–MC algorithm. First, through reconstructing the bipartite graphs, we obtain the implicit relation graphs. Then we analyze the content information of users and items with a CNN process, so that each user and item has its feature-tailored embeddings. Besides, we expand the GC–MC algorithms by adding a graph attention mechanism layer, which handles the implicit relation graph by highlighting important features and neighbors. Therefore, our framework takes into consideration both the implicit relation and content information. Finally, we test our framework on Movielens dataset and the results show that our framework performs better than other state-of-art recommendation algorithms.
Model-Free Lossless Data Compression for Real-Time Low-Latency Transmission in Smart Grids

This article proposes a model-free lossless data compression method for time series in smart grids (SGs), namely, Lossless Coding considering Precision (LCP) method. The LCP method encodes the current datapoint only using the immediate previous datapoint by differential coding, XOR coding, and variable length coding and transmits the encoded data once generated. It does not use the dynamics (e.g., many previous datapoints) or prior knowledge (e.g., mathematical models) of the time series. It considers the patterns, potential applications, and associated precision to preprocess the time series and especially suits high-resolution time series with long steady periods. The LCP method features low-latency and generalizability which enables real-time data communication for different time-critical tasks. Sub-metered load profiles in REDD dataset, high-resolution LIFTED dataset, AMPds dataset and PMU dataset are used to evaluate the performance of the LCP method. The results show that the LCP method demonstrates high compression ratio, low latency, and low complexity compared to state-of-the-art Resumable Data Compression (RDC) method, DEFLATE based on LZ77 & Huffman coding, and Lempel-Ziv-Markov Chain Algorithm (LZMA).
Usability Evaluation of the Website Services Using the WEBUSE Method (A Case Study: covid19.go.id)

Abstract The purpose of this study is to analyze the site usability evaluation system using the WEBUSE method on one of the information sites about COVID-19 namely the website covid19.go.id. The basic analysis was carried out on 24 usability criteria classified into 5 usability categories, namely: content, organization, and readability, navigation and links, user interface design, performance and effectiveness, educational purpose. Each category deals with one aspect of usability. Data from respondents’ answers were analyzed using the WEBUSE method to get the usage point and usability level of the website that has been studied. This research produces a usability evaluation information system that provides an online evaluation questionnaire and this analysis is useful to present high-quality system information and user satisfaction aspects of the website covid19.go.id. The case study on the website covid19.go.id, shows that the level of usability and overall level of user satisfaction is good and acceptable for the general public from all walks of life. And in terms of satisfaction in the user interface design category, it is possible that some people are still uncomfortable with the look of the website.
Keep your communities clean

BGP communities are widely used to tag prefix aggregates for policy, traffic engineering, and inter-AS signaling. Because individual ASes define their own community semantics, many ASes blindly propagate communities they do not recognize. Prior research has shown the potential security vulnerabilities when communities are not filtered. This work sheds light on a second unintended side-effect of communities and permissive propagation: an increase in unnecessary BGP routing messages. Due to its transitive property, a change in the community attribute induces update messages throughout established routes, just updating communities. We ground our work by characterizing the handling of updates with communities, including when filtered, on multiple real-world BGP implementations in controlled laboratory experiments. We then examine 10 years of BGP messages observed in the wild at two route collector systems. In 2020, approximately 25% of all announcements modify the community attribute, but retain the AS path of the most recent announcement; an additional 25% update neither community nor AS path. Using predictable beacon prefixes, we demonstrate that communities lead to an increase in update messages both at the tagging AS and at neighboring ASes that neither add nor filter communities. This effect is prominent for geolocation communities during path exploration: on a single day, 63% of all unique community attributes are revealed exclusively due to global withdrawals.
Critical faults of leakage errors on the surface code

Leakage is a particularly damaging error that occurs when a qubit leaves the defined computational subspace. Leakage errors limit the effectiveness of quantum error correcting codes by spreading additional errors to other qubits and corrupting syndrome measurements. The effects of leakage errors on the surface code has been studied in various contexts. However, the effects of a leaked data qubit versus a leaked ancilla qubit can be quite different. Here, we study the effects of data leakage and ancilla leakage separately. We find that data leakage is much less damaging. We show that the surface code maintains its distance in the presence of leakage by either confining leakage to data qubits or eliminating ancilla qubit leakage at the critical fault location. To demonstrate this, we introduce new techniques for handling leakage: taking advantage of gates with one-sided leakage, effectively isolating leakage events to data qubits alone and mixing two types of leakage reducing circuits, to handle data leakage and ancilla leakage separately.
Fall Detection on Multimodal Dataset using Convolutional Neural Netwok and Long Short Term Memory

Fall is the second leading cause of accidental injury and death worldwide. This event often occurs in the elderly and the frequency is increasing every year. Reliable fall activity detection system can reduce the risk of injuries suffered. Since falls are unwanted events or occur suddenly, it is difficult to collect actual fall data. It is also difficult because of the similarity to some activities such as squatting and picking up objects from the floor. In addition, in recent years the publicly available fall datasets are limited. Therefore, in 2019, some researchers tried to create a comprehensive fall dataset that simulates the actual events using camera and sensor devices. The experiment produced a multimodal dataset UP-Fall. Using this dataset, this work tries to detect falling activity using Convolutional Neural Network and Long Short-Term Memory approaches. CNN is used to detect spatial information from image data, while LSTM is used to exploit temporal information from signal data. Then, the results of the two models are combined with the majority voting strategy. Based on the evaluation results, CNN obtained an accuracy of 98.49% and LSTM 98.88%. Both models contribute to the performance of the majority voting strategy with the result that the accuracy (98.31%) exceeds baseline accuracy (96.4%). Other evaluation metrics also improved such as precision goes up to 11%, recall 14%, and F1-score 12% in comparison with baseline.
Automatic Detection and Mapping of Solar Photovoltaic Arrays with Deep Convolutional Neural Networks in High Resolution Satellite Images

The locations and capacities of household rooftop solar photovoltaic (PV) arrays are important for power grid planning. However, it is hard to collect such information manually as a significant n umber of PV arrays are distributed dispersedly in residential areas. With the development of deep learning model on image recognition, it brings an opportunity to build an intelligent detector that is able to automatically identify and delineate PV arrays in satellite images. Convolutional neural networks (CNN) are ideally suited for this task as CNN has capability of capturing spatial information of digital images by convolution operation. In this work, we trained a deep CNN with manually annotated satellite images taken from a region of Manchester (UK), and validated our detector with another set of satellite images taken from the same city. Our results indicate that the detector is capable to identify PV arrays with a high accuracy and delineate them in pixel-wise with high precision, showing the feasibility of our approach.
Ultra-Dense Denoising Network: Application to Cardiac Catheter-Based X-Ray Procedures

Reducing radiation dose in cardiac catheter-based X-ray procedures increases safety but also image noise and artifacts. Excessive noise and artifacts can compromise vital image information, which can affect clinical decision-making. Developing more effective X-ray denoising methodologies will be beneficial to both patients and healthcare professionals by allowing imaging at lower radiation dose without compromising image information. This paper proposes a framework based on a convolutional neural network (CNN), namely Ultra-Dense Denoising Network (UDDN), for low-dose X-ray image denoising. To promote feature extraction, we designed a novel residual block which establishes a solid correlation among multiple-path neural units via abundant cross connections in its representation enhancement section. Experiments on synthetic additive noise X-ray data show that the UDDN achieves statistically significant higher peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) than other comparative methods. We enhanced the clinical adaptability of our framework by training using normally-distributed noise and tested on clinical data taken from procedures at St. Thomas' hospital in London. The performance was assessed by using local SNR and by clinical voting using ten cardiologists. The results show that the UDDN outperforms the other comparative methods and is a promising solution to this challenging but clinically impactful task.
Budgeted Passive-Aggressive Learning for Online Multiclass Classification

Online multiclass classification is a specific problem of online learning that performs a sequence of multiclass classification tasks given the knowledge of previous tasks. The goal is to make correct predictions for this sequence. It is generally considered a more complicated problem than its binary counterpart, online binary classification. A popular algorithm, called the passive-aggressive algorithm, was primarily proposed for binary problems and later extended as the multiclass passive-aggressive (MPA) algorithm for multiclass problems. The nature of MPA allows itself to implement the kernel trick, which enables us to make better predictions with a kernel-based model. However, this approach suffers from the curse of kernelization that causes unbounded growth of the model in memory usage and runtime. To solve the growth problem, we first introduce a resource perspective that gives an alternative and equivalent interpretation of the kernel-based MPA algorithm. Based on the resource perspective, we propose the budgeted MPA (BMPA) algorithm, which approximates the kernel-based MPA algorithm. BMPA limits the maximum number of available resources by removal and fully exploits them through a constrained optimization. We study three removal strategies and give a relative mistake bound that provides a unified analysis. Simulation experiments on various datasets are conducted to demonstrate that BMPA is effective and competitive with state-of-the-art budgeted online algorithms.
Privacy-aware Cold-Start Recommendation based on Collaborative Filtering and Enhanced Trust

The ever-increasing popularity of the recommender system provides a convenient way for users to find their interesting items among plenty of candidate services. However, on account of the enhancement of user privacy protection consciousness in recent years, users tend to conceal their evaluation information from the public. Thus, a large number of users with little explicit rating information are generated (i.e., cold-start users), which makes it challenging to implement high-quality recommendations. It has become a serious barrier to further and broader applications of the recommender system. In response to this issue, we take social network information into account and first propose TeCF (Trust-enhanced Collaborative Filtering). Our proposal integrates user-based, item-based, and trust-based collaborative filtering methods harmoniously and achieves a good trade-off between privacy preservation and service recommendation accuracy. A case study is conducted to validate the feasibility and comprehensiveness of our research.
Understanding the Role of Visualizations on Decision Making: A Study on Working Memory

The use of data visualization is increasing; however, there is little empirical explanation for how it supports users. Our goal in this paper is to deepen our understanding of the role of interactive visualizations in a particular context of decision making. Specifically, we attempt to understand the role of the working memory system, which is a concept to understand the mechanism of the processing and temporary storage of information in variety of cognitive tasks. We compared two interfaces, SimulSort and its non-visual counterpart Typical Sorting, with a multi-attribute decision-making problem. Because decision outcomes are known to be affected by the limitations of a person’s working memory, we conducted a crowdsourcing-based user study using SimulSort to understand how working memory, especially the phonological loop, can benefit from the using visualizations. We examined the impact on working memory with a well known dual-task methodology by designing a concurrent task to tap into the main decision-making task. The experiment was conducted with a total of 137 participants and an ordered logistic regression using a proportional odds model was applied to analyze the decision quality. The results supported the hypothesis that when using SimulSort, participants required less working memory than they required with Typical Sorting to accomplish the multi-attribute decision-making task even though SimulSort outperformed Typical Sorting in terms of decision quality. We also provide methodologies to conduct working memory studies by implementing an articulatory suppression task on crowdsourcing platforms in which experimenters have less control over the participants.
Data mining for cryptocurrencies price prediction

Abstract Electronic payments is something that is currently in high demand by investors today, but transactions are often constrained due to various problems, especially from third parties. For this reason, cryptocurrency emerged, which is one of the solutions for conducting electronic payment transactions. Some types of cryptocurrency that are most in demand by investors are bitcoin, ethereum, and ripple. The fluctuation value of cryptocurrency is very difficult to predict so that investors often experience losses when making transactions. This study aims to predict cryptocurrency prices such as bitcoin, ethereum and ripple using data mining algorithms. The data mining algorithm used in this prediction process is K-NN, Neural Network, SVM, Linear Regression, Random Forest and Decision Tree. Data mining modeling is done by dividing the dataset into each type of commodity and then analyzed using each algorithm. The results of this study indicate that the accuracy value obtained from some data mining algorithms is good enough to predict cryptocurrency prices