Reviewerly/exHarmony huggingface dataset:
Single Label Model for Confidentiality in Big Data

Data security is defined as preventing actions such as accessing, using, changing, and eliminating information without authorization and consists of specific basic elements called confidentiality, integrity and accessibility. Many studies have been carried out in the literature to ensure information security. However, no study has been carried out to provide these basic elements together. In this study, we created a model that provides three basic elements of information security for large data. With the single labeling model we propose, a more practical and flexible structure has been realized for all operations performed in the database (reading, writing, updating, deleting) on actual data. In previous studies with the labeling model, a separate label was used for only reading or writing only and a structure that will ensure both confidentiality and integrity at the same time has not been established. In our study, we have shown how to perform an authorization and access control between which operation and which user by looking at a single label for all operations performed on the data. In this way, unlike the studies in the literature, data confidentiality, data integrity and data consistency were provided for all processes. The results of the proposed single-label model have been shown in comparison with the application and experimental study we have carried out. The results obtained are promising for future studies.
A Grid Partition-Based Local Outlier Factor for Data Stream Processing

Outlier detection is getting significant attention in the research field of big data. Detecting the outlier is important in various applications such as communication, finance, fraud detection, and network intrusion detection. Data streams posed new challenges to the existing algorithms of outlier detection. Local Outlier Factor (LOF) is one of the most appropriate techniques used in the density-based method to determine the outlier. However, it faces some difficulties regarding data streams. First, LOF processes the data all at once, which is not suitable for data streams. Another issue appears when a new data point arrives. All the data points need to be recalculated again significantly. Therefore, it affects the execution time. A new algorithm is proposed in this research paper called Grid Partition-based Local Outlier Factor (GP-LOF). GP-LOF uses a grid for the LOF with a sliding window to detect outliers. The outcome of experiments with the proposed algorithm demonstrates the effectiveness in both performance accuracy and execution time in several real-world datasets compared to the state-of-the-art DILOF algorithm.
ASI-PRISMA Hyperspectral Mission for the Analysis of Geophysical Phenomena

The recent PRISMA (PRecursore IperSpettrale della Missione Applicativa) space mission, launched by the ASI (Italian Space Agency) on March 2019, brought in orbit a hyperspectral sensor (in the range <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.4-2.5 \mu\mathrm{m})$</tex> and a panchromatic camera with a spatial resolution of 30 and 5 meters, respectively. The PRISMA data are available for free (under license) and represent a unique orbiting source of hyperspectral data to investigate and analyze geophysical/geological phenomena. PRISMA works as the precursor of the next generation of ESA-SENTINEL satellites. In this work, a series of possible applications of PRISMA hyperspectral data are presented also considering data fusion with the panchromatic camera and the integration with other available satellite data as from Landsat and ASTER (Advanced Spaceborne Thermal Emission and Reflection).
Improved and Efficient Inter-Vehicle Distance Estimation Using Road Gradients of Both Ego and Target Vehicles

In advanced driver assistant systems and autonomous driving, it is crucial to estimate distances between an ego vehicle and target vehicles. Existing inter-vehicle distance estimation methods assume that the ego and target vehicles drive on a same ground plane. In practical driving environments, however, they may drive on different ground planes. This paper proposes an inter-vehicle distance estimation framework that can consider slope changes of a road forward, by estimating road gradients of both ego vehicle and target vehicles and using a 2D object detection deep net. Numerical experiments demonstrate that the proposed method significantly improves the distance estimation accuracy and time complexity, compared to deep learning-based depth estimation methods.
Spatiotemporal Rainfall Dynamics in Kosi Basin Using Wavelet Analysis

Spatial and temporal analysis of rainfall data were carried out along with wavelet analysis for seven rain gauge sites of Kosi basin, India during the time period from 1985 to 2017. Wavelet spectrum analysis and wavelet coherence analysis were performed to fully characterize the time-frequency rainfall variability of the rain gauge data in these areas. For all the selected gauge stations during the study period, the peak value of the wavelet power spectrum was identified for the 8-16 month band. The results of wavelet spectrum analysis reveal a good correlation of rainfall data in the rain gauge sites lying in the southwest of the Kosi basin. The spectrum analysis also differentiates the wet and dry periods and it was observed that in the majority of the selected sites, a dry period occurred from the year 2005 onwards. This was again confirmed with breakpoint analysis. The wavelet coherence analysis explicit is a good correlation between the rain gauges in the study area. Overall, the variability of the rainfall parameters was more vivid with the wavelet analysis and this can be extended to other climatological parameters.
Secure Health Information Exchange (S-HIE) Protocol with Reduced Round-Trip Count

Healthcare breaches pose a lucrative field for cyber criminals. These breaches affect a large population and costs a fortune for the healthcare industry. It is estimated that about 55% of the US population has been affected by a healthcare breach between 2010 and 2017. In 2019, a report by IBM indicated that the healthcare industry has the highest average cost of a data breach. As more protocols, frameworks and profiles are developed for the exchange of Electronic Health Records (EHRs), there is an increasing need to set standards to maintain the privacy of patient’s information, confidentiality, integrity of data transferred, and allow for auditing of requests. Current standards and frameworks (e.g. OpenHIE) seem to still think of the security of health information exchange as an afterthought. In this paper, we propose a secure health information exchange protocol that improves upon a previously proposed protocol and develops a new method to exchange electronic health records using a one round-trip exchange. The improvements allow for more efficient health information exchange while maintaining patients privacy and the security, integrity, and auditability of health information being exchanged. Patients authorize a certain type of health information to be exchanged and are actively participating in the exchange, which helps prevent leakage of information. The security analysis and exchange times of the proposed protocol demonstrate its security properties and efficiency.
Studio Based Learning of Instructional Design in an Online Environment

Online teaching requires moving beyond traditional models of pedagogy into new practices. There is no unique way to take traditional models and transfer them online. The research presented in this paper included the process of helping the teachers to develop teaching content in an online environment. This research aims to provide insight into experience gained from the design and delivery of an online course entitled ‘Instructional Design: Teaching for Teachers’, intended to improve teachers' knowledge and skills in using educational technology. The course was based on the studio-based learning approach, which implements the ADDIE model. A group of 6 primary and secondary school teachers from Bosnia and Herzegovina was involved in the research during July and August 2020.
Secure Audio-Visual Data Exchange for Android In-Vehicle Ecosystems

Mobile device pairing inside vehicles is a ubiquitous task which requires easy to use and secure solutions. In this work we exploit the audio-video domain for pairing devices inside vehicles. In principle, we rely on the widely used elliptical curve version of the Diffie-Hellman key-exchange protocol and extract the session keys from the acoustic domain as well as from the visual domain by using the head unit display. The need for merging the audio-visual domains first stems from the fact that in-vehicle head units generally do not have a camera so they cannot use visual data from smartphones, however, they are equipped with microphones and can use them to collect audio data. Acoustic channels are less reliable as they are more prone to errors due to environmental noise. However, this noise can be also exploited in a positive way to extract secure seeds from the environment and audio channels are harder to intercept from the outside. On the other hand, visual channels are more reliable but can be more easily spotted by outsiders, so they are more vulnerable for security applications. Fortunately, mixing these two types of channels results in a solution that is both more reliable and secure for performing a key exchange.
Strype: Frame-Based Editing tool for programming the micro:bit through Python

Frame-Based Editing (FBE) is a relatively new programming paradigm that sits between Block and Text based editing, by implementing good features of both practices. Moreover, FBE is designed as a middle step between block and text based editing, in order to ease the transition from the first to the later. Strype is a new online editor which implements FBE’s idea for Python and is used for programming the micro:bit.
XHAC

Explainable artificial intelligence (XAI) is a concept that has emerged and become popular in recent years. Even interpretation in machine learning models has been drawing attention. Human activity classification (HAC) systems still lack interpretable approaches. In this study, an approach, called eXplainable HAC (XHAC), was proposed in which the data exploration, model structure explanation, and prediction explanation of the ML classifiers for HAR were examined to improve the explainability of the HAR models' components such as sensor types and their locations. For this purpose, various internet of things (IoT) sensors were considered individually, including accelerometer, gyroscope, and magnetometer. The location of these sensors (i.e., ankle, arm, and chest) was also taken into account. The important features were explored. In addition, the effect of the window size on the classification performance was investigated. According to the obtained results, the proposed approach makes the HAC processes more explainable compared to the black-box ML techniques.
“Anyway,”: Two-player Defense Game via Voice Conversation

Conversation is a key element in maintaining social relationships. In contrast to soliloquy, conversation with other people gives various social effects to both speaker and listener. Most voice communication in games had been bound to achieving games goal, thus not fully facilitates social effects. Therefore, we tried to deliver the positive effects of the conversation by gamifying turn-taking communication. With "Anyway," we present a two-player conversational defense game that uses voice conversation as an input. This game aims to encourage wholesome in-game conversation and to expand the boundary of the game's use of voice interaction in game. In "Anyway," players have to proceed with their talk to protect their partner from incoming waves. Spoken words from each player are converted to tetrapod breakwaters that only protect their playmate, which leads to a cooperative game experience. We conducted a preliminary user test and observed positive responses, especially in empathy and behavioral involvement.
Blame and coercion: Together again for the first time

Abstract C#, Dart, Pyret, Racket, TypeScript, VB: many recent languages integrate dynamic and static types via gradual typing. We systematically develop four calculi for gradual typing and the relations between them, building on and strengthening previous work. The calculi are as follows: $\lambda{B}$ , based on the blame calculus of Wadler and Findler (2009); $\lambda{C}$ , inspired by the coercion calculus of Henglein (1994); $\lambda{S}$ inspired by the space-efficient calculus of Herman, Tomb, and Flanagan (2006); and $\lambda{T}$ based on the threesome calculus of Siek and Wadler (2010). While $\lambda{B}$ and $\lambda{T}$ are little changed from previous work, $\lambda{C}$ and $\lambda{S}$ are new. Together, $\lambda{B}$ , $\lambda{C}$ , $\lambda{S}$ , and $\lambda{T}$ provide a coherent foundation for design, implementation, and optimization of gradual types. We define translations from $\lambda{B}$ to $\lambda{C}$ , from $\lambda{C}$ to $\lambda{S}$ , and from $\lambda{S}$ to $\lambda{T}$ . Much previous work lacked proofs of correctness or had weak correctness criteria; here we demonstrate the strongest correctness criterion one could hope for, that each of the translations is fully abstract. Each of the calculi reinforces the design of the others: $\lambda{C}$ has a particularly simple definition, and the subtle definition of blame safety for $\lambda{B}$ is justified by the simple definition of blame safety for $\lambda{C}$ . Our calculus $\lambda{S}$ is implementation-ready: the first space-efficient calculus that is both straightforward to implement and easy to understand. We give two applications: first, using full abstraction from $\lambda{C}$ to $\lambda{S}$ to establish an equational theory of coercions; and second, using full abstraction from $\lambda{B}$ to $\lambda{S}$ to easily establish the Fundamental Property of Casts, which required a custom bisimulation and six lemmas in earlier work.
COVID-19 Digital Vaccination Passport Based on Blockchain with Its Own Cryptocurrency as a Reward and Mobile App for Its Use

Due to the recent outbreak of the COVID-19 pandemic, everybody's lives have changed dramatically and society went through new stages accompanied by new needs. This article details a solution for one of these needs, since it provides a system of digital certificates for the secure storage and sharing of medical data related to COVID-19, with the goal of demonstrating immunity or lack of viral infection in a unequivocal, unbreakable and secure way. The proposed system is based on Blockchain and its inherent benefits, developing also a decentralized, mobile and multiplatform app for its use and an incentive system with a customized cryptocurrency in order to reward the users who use it.
Secure Outsourced Attribute-Based Signatures with Perfect Anonymity in the Standard Model

Outsourced attribute-based signatures (OABS) enable users to sign messages without revealing specific identity information and are suitable for scenarios with limited computing power. Recently, Mo et al. proposed an expressive outsourced attribute-based signature scheme (Peer-to-Peer Networking and Applications, 11, 2017). In this paper, we show that Mo et al.’s scheme does not achieve any of the three security properties. Their scheme is incorrect. The adversary can collude with the malicious signing-cloud service provider (S-CSP) to forge valid signatures on any message and any attribute set. And the S-CSP could trace the access structures used to generate the signatures. Then, we treat the S-CSP as an adversary and present more accurate unforgeability and anonymity models for OABS to remedy the drawbacks of the previous ones. Finally, we propose a simple but significant improvement to fix our attacks. The improved scheme achieves correctness, unforgeability, and perfect anonymity while keeping the efficiency almost unchanged. We also prove the security of the improved scheme under the standard model.
Mix-order Attention Networks for Image Restoration

Convolutional neural networks (CNNs) have obtained great success in image restoration tasks, like single image denoising, demosaicing, and super-resolution. However, most existing CNN-based methods neglect the diversity of image contents and degradations in the corrupted images and treat channel-wise features equally, thus hindering the representation ability of CNNs. To address this issue, we propose deep mix-order attention networks (MAN) to extract features that capture rich feature statistics within networks. Our MAN is mainly built on simple residual blocks and our mix-order channel attention (MOCA) module, which further consists of feature gating and feature pooling blocks to capture different types of semantic information. With our MOCA, our MAN can be flexible to handle various types of image contents and degradations. Besides, our MAN can be generalized to different image restoration tasks, like image denoising, super-resolution, and demosaicing. Extensive experiments demonstrate that our method obtains favorably against state-of-the-art methods in terms of quantitative and qualitative metrics.
Multidocument Summarization using GloVe Word Embedding and Agglomerative Cluster Methods

This paper explores the method of extracting multi-document summary terminology that enhances single document summary methods using the information related to the document and perhaps even the relation between the documents. In the problems of fragmentation, density, duplication and selection of passages, the summarization of several documents ranges from the summarization of single documents to the creation of efficient summaries. Our approach addresses these issues by using Agglomerative cluster sentence, GloVe, TextRank, Cosine Similarity. In addition, this research also use NLTK as library filter word such as stopwords, numeric, punctuation, multiple_whitespaces, short_words in order Vectorizing the sentence when using GloVe. The result of this paper was evaluated using ROUGE; 41% for unigram, 17% for bigram, 57% for trigram.
Coding for Distributed Multi-Agent Reinforcement Learning

This paper aims to mitigate straggler effects in synchronous distributed learning for multi-agent reinforcement learning (MARL) problems. Stragglers arise frequently in a distributed learning system, due to the existence of various system disturbances such as slow-downs or failures of compute nodes and communication bottlenecks. To resolve this issue, we propose a coded distributed learning framework, which speeds up the training of MARL algorithms in the presence of stragglers, while maintaining the same accuracy as the centralized approach. As an illustration, a coded distributed version of the multi-agent deep deterministic policy gradient (MADDPG) algorithm is developed and evaluated. Different coding schemes, including maximum distance separable (MDS) code, random sparse code, replication-based code, and regular low density parity check (LDPC) code are also investigated. Simulations in several multi-robot problems demonstrate the promising performance of the proposed framework.
Deriving a Single Interpretable Model by Merging Tree-Based Classifiers

Decision tree classifiers have been proved to be among the most interpretable models due to their intuitive structure that illustrates decision processes in form of logical rules. Unfortunately, more complex tree-based classifiers such as oblique trees and random forests overcome the accuracy of decision trees at the cost of becoming non interpretable. In this paper, we propose a method that takes as input any tree-based classifier and returns a single decision tree able to approximate its behavior. Our proposal merges tree-based classifiers by an intensional and extensional approach and applies a post-hoc explanation strategy. Our experiments shows that the retrieved single decision tree is at least as accurate as the original tree-based model, faithful, and more interpretable.
Interpreting Super-Resolution CNNs for Sub-Pixel Motion Compensation in Video Coding

Machine learning approaches for more efficient video compression have been developed thanks to breakthroughs in deep learning. However, they typically bring coding improvements at the cost of significant increases in computational complexity, making them largely unsuitable for practical applications. In this paper, we present open-source software for convolutional neural network-based solutions which improve the interpolation of reference samples needed for fractional precision motion compensation. Contrary to previous efforts, the networks are fully linear, allowing them to be interpreted, with a full interpolation filter set derived from trained models, making it simple to integrate in conventional video coding schemes. When implemented in the context of the state-of-the-art Versatile Video Coding (VVC) test model, the complexity of the learned interpolation schemes is significantly reduced compared to the interpolation with full neural networks, while achieving notable coding efficiency improvements on lower resolution video sequences. The open-source software package is available at https://github.com/bbc/cnn-fractional-motion-compensation under the 3-clause BSD license.
Feature Enhanced Projection Network for Zero-shot Semantic Segmentation

In environmental perception of autonomous driving, zero-shot semantic segmentation that can make prediction of new categories without using any labeled training samples is considered as a challenging task. One key step in this task is to transfer knowledge across categories via auxiliary semantic word embeddings. In this paper, we propose a feature enhanced projection network (FEPNet) that takes full advantage of transferred knowledge to enrich semantic representations. In FEPNet, two projection layers are added to a segmentation network so as to map features into seen (S) and unseen (U) category spaces, respectively. During training, U-space features are transferred to S-space using similarity relations to enhance the representation of seen categories. In the inference stage, the representation of unseen categories is also strengthened by incorporating features transferred from S-space. Moreover, a novel strategy is proposed to effectively alleviate prediction bias by performing segmentation independently in separate areas that contain seen and unseen categories. We conduct extensive experiments on three benchmark datasets. The experimental results show that our FEPNet achieves new state-of-the-art results compared to existing approaches.