Reviewerly/exHarmony huggingface dataset:
FLUID: A common model for semantic structural graph summaries based on equivalence relations

Summarization is a widespread method for handling very large graphs. The task of structural graph summarization is to compute a concise but meaningful synopsis of the key structural information of a graph. As summaries may be used for many different purposes, there is no single concept or model of graph summaries. We have studied existing structural graph summaries for large-scale (semantic) graphs. Despite their different concepts and purposes, we found commonalities in the graph structures they capture. We use these commonalities to provide for the first time a formally defined common model, FLUID (FLexible graph sUmmarIes for Data graphs), that allows us to flexibly define structural graph summaries. FLUID allows graph summaries to be quickly defined, adapted, and compared for different purposes and datasets. To this end, FLUID provides features of structural summarization based on equivalence relations such as distinction of types and properties, direction of edges, bisimulation, and inference. We conduct a detailed complexity analysis of the features provided by FLUID. We show that graph summaries defined with FLUID can be computed in the worst case in time $\mathcal{O}(n^2)$ w.r.t. $n$, the number of edges in the data graph. An empirical analysis of large-scale web graphs with billions of edges indicates a typical running time of $\Theta(n)$. Based on the formal FLUID model, one can quickly define and modify various structural graph summaries from the literature and beyond.
Numerical Implementation of Just-In-Time Decoding in Novel Lattice Slices Through the Three-Dimensional Surface Code

We build on recent work by B. Brown (Sci. Adv. 6, eaay4929 (2020)) to develop and simulate an explicit recipe for a just-in-time decoding scheme in three 3D surface codes, which can be used to implement a transversal (non-Clifford) $\overline{CCZ}$ between three 2D surface codes in time linear in the code distance. We present a fully detailed set of bounded-height lattice slices through the 3D codes which retain the code distance and measurement-error detecting properties of the full 3D code and admit a dimension-jumping process which expands from/collapses to 2D surface codes supported on the boundaries of each slice. At each timestep of the procedure the slices agree on a common set of overlapping qubits on which $CCZ$ should be applied. We use these slices to simulate the performance of a simple JIT decoder against stochastic $X$ and measurement errors and find evidence for a threshold $p_c \sim 0.1\%$ in all three codes. We expect that this threshold could be improved by optimisation of the decoder.
Performance based algorithm for DWT and DCT for ISL

Communication plays a vital role in world. It is mainly carried out in from of speech or through non-verbal movements. Though the communication through speech is the origin for communication, other body expressions such hand movement and facial expressions also help to communicate our thoughts to others. The main aim of this research is as follows; The main objective of image compression is to reduce the size of data i.e used to represent a digital media in an efficient manner without causing major degradation to the quality of the image. To increase the efficiency and performance of digital data, image compression is used in various fields like security industries, federal government agencies and health industries. The reduced size may facilitate the quick transfer of the data over the communication channels. There are two types of compression methods. One is lossy and second is lossless compression. In lossless compression, no information is lost that is reduces bits by finding and eliminating redundancy. Lossy compression technique reduces size of data by finding non-essential information and destroys it. Therefore, the process of decreasing the size of data file is termed as data compression.
Power side channel attack analysis and detection

Side Channel Attack (SCA) is a serious threat to the hardware implementation of cryptographic protocols. Various side channels such as, power, timing, electromagnetic emission and acoustic noise have been explored to extract the secret keys. Machine Learning (ML)-based detection of SCA have been proposed in past which incur high design overheads and, require digitization that reduce their accuracy under process variations. We propose a real-time power SCA detection technique using on-chip sensors based on a thorough analysis. The dependency of phase/frequency of Ring Oscillator (RO) on supply voltage is exploited to detect the insertion of a SCA resistance in the power rail. The proposed approach is validated using simulation with a detailed model of Power Delivery Network (PDN) and power grid. The technique can detect a minimum resistance of 1 Ω within 2 μs of attack initiation and incurs a tiny fraction of area/power (0.044%/0.1065%, respectively) compared to ML-based techniques.
Information management during a crisis – providing an open and reliable information infrastructure for a sustainable world

Purpose The purpose of this paper is to analyze the impact of the corona situation on the academic library services, policies and activities. Design/methodology/approach The methodology of the paper is opinion piece based on experiences. Findings There is a need for global policymaking for both the open academic resources dissemination and crisis management. Originality/value The originality of the paper is based on recent experiences.
Fuzzy Infinite-Step Opacity Measure of Discrete Event Systems and Its Applications

Opacity is an important security notion for discrete event systems (DESs) to measure the secret behaviors leaked to an intruder. The intruder is usually modeled as a malicious observer who always attempts to infer secret behaviors based on his dynamical observations of the system. In our earlier research, we proposed several opacity measures for DESs from the perspective of fuzzy logic, in which the intruder is assumed to only infer the current secret behaviors. Considering that modern computers usually have a powerful computation ability and rich memory resources, this article presents a new opacity measure of DESs, called as fuzzy infinite-step opacity (FIO) measure, where the intruder is assumed to have the ability of inferring all the secret behaviors that occurred in the past. In addition, the properties and the calculation of the FIO measure are also investigated. The FIO measure can be used to assess the secret-leakage situations of fuzzy DESs attacked by powerful intruders. For the purpose of illustration, we present a practical application of the FIO measure in evaluating the privacy protection mechanisms in location-based services.
Child welfare policies and services during the COVID-19 pandemic in South Korea

Emergency situations render children vulnerable; hence, this study reviewed child-related policies and services in South Korea during the COVID-19 pandemic. Thus far, the government has proposed online health promotion programmes for children, emergency care services and allowances, and school meal delivery services. Based on these findings, we recommend the establishment of mental health, sexual abuse, and child abuse online messaging services, allocation of additional financial and educational support to low-income families, and prioritisation of childcare services.
A Process Convergence Approach for Crossover Services based on Message Flow Partition and Merging

Crossover service is a new type of services in the modern service industry, which aims to provide users with value-added services through converging services across the boundaries of organizations, industries, value chains, and spaces. Convergence is an essential topic in the evolution from traditional services to crossover services. Where and how to converge to achieve user goals is a challenging issue in the convergence process. Towards this issue, in this paper, we focus our attention on the process-level convergence and propose a framework to converge business processes with consideration of the effect of involved roles and goals. In our framework, convergence rules and corresponding convergence operations are defined based on message flow partition and merging. Finally, a case study of a crossover service by converging services from a travel domain and an insurance domain, and an experiment conducted on five use cases, are presented to show the feasibility and efficiency of the proposed approach.
A Secure Distributed Blockchain Platform for Use in AI-Enabled IoT Applications

The increased implementation of Edge Computing technology has provided The Internet of Things (IoT) with the ability of real-time data processing and tasks execution requested by smart devices.To support this processing the integration of Artificial Intelligence (AI) into IoT is considered one of the most promising approach.While AI helps in the analyses of the data, blockchain technology provides a robust environment within which to create a secure, distributed way to share and store data.This paper proposes an architecture that combines the strengths provided by edge computing, AI, and blockchain technologies to provide robust, secure, and intelligent solutions for secure and faster data processing and sharing.The pandemic created by the rapid spread of the novel Coronavirus COVID-19, as well as the tracking of viruses in water sewage to help control the spread of such viruses, were used as our case study for exploring this architecture.To secure the proposed architecture a new concept for consensus mechanism based on Honesty-Based Distributed Proof of Work (DPOW) were devised and tested.
Widely Reused and Shared, Infrequently Updated, and Sometimes Inherited: A Holistic View of PIN Authentication in Digital Lives and Beyond

Personal Identification Numbers (PINs) are widely used as an access control mechanism for digital assets (e.g., smartphones), financial assets (e.g., ATM cards), and physical assets (e.g., locks for garage doors or homes). Using semi-structured interviews (n=35), participants reported on PIN usage for different types of assets, including how users choose, share, inherit, and reuse PINs, as well as behaviour following the compromise of a PIN. We find that memorability is the most important criterion when choosing a PIN, more so than security or concerns of reuse. Updating or changing a PIN is very uncommon, even when a PIN is compromised. Participants reported sharing PINs for one type of asset with acquaintances but inadvertently reused them for other assets, thereby subjecting themselves to potential risks. Participants also reported using PINs originally set by previous homeowners for physical devices (e.g., alarm or keypad door entry systems). While aware of the risks of not updating PINs, this did not always deter participants from using inherited PINs, as they were often missing instructions on how to update them. Given the expected increase in PIN-protected assets (e.g., loyalty cards, smart locks, and web apps), we provide suggestions and future research directions to better support users with multiple digital and non-digital assets and more secure human-device interaction when utilizing PINs.
Author Identification of Micro-Messages via Multi-Channel Convolutional Neural Networks

With the emergence use of social media, millions of micro-messages are exchanged daily. Although micro-messages are a powerful and efficient way to communicate among individuals, their anonymity and short-length characteristics give rise to a real challenge for Author Identification studies. In this paper, we tackled the Author Identification of micro-messages problem via Convolutional Neural Networks (CNNs). Specifically, we introduce a novel Multi-Channel CNN architecture that processes different features of text via word and character embedding layers, and utilizes both pre-trained word embedding and character bigram embeddings. We examine the usefulness of different feature types and show that the combination of embedding layers can capture different stylometric features. We conduct extensive experiments with a varying number of authors and writing samples per author. Our results show that our proposed method outperforms the state-of-the-art system on a Twitter dataset that contains 1,000 authors.
A Blockchain-based Testing Approach for Collaborative Software Development

Development of large-scale and complex software systems requires multiple teams, including software development teams, domain experts, user representatives, and other project stakeholders, to work collaboratively to achieve software development goals. These teams rely on the use of agreed software development processes, knowledge management tools, and communication channels collaboratively in the software development project. Software testing is an important and complicated process due to reasons such as difficulties in achieving testing goals with the given time constraint, absence of efficient data sharing policies, vague testing acceptance criteria at various levels of testing, and lack of trusted coordination among the teams involved in software testing. The efficiency of the software testing relies on efficient, reliable, and trusted information sharing among these teams. Existing approaches to software testing for collaborative software development use centralized or decentralize tools for software testing, knowledge management, and communication channels. Existing approaches have the limitations of centralized authority, a single point of failure/compromise, lack of automatic requirement compliance checking and transparency in information sharing, and lack of unified data sharing policy, and reliable knowledge management repositories for sharing and storing past software testing artifacts and data. In this paper, a software testing approach for collaborative software development using private blockchain is presented, and the desirable properties of private blockchain, such as distributed data management, tamper-resistance, auditability and automatic requirement compliance checking, are incorporated to greatly improve the quality of software testing for collaborative software development.
Model Decision Support System For Diagnosis COVID-19 Using Forward Chaining: A Case in Indonesia

The government in Indonesia and its staff work together to make tactical steps to prevent the spread of COVID-19 in the community.From the ministerial level to the heads of the provinces, regencies, and even the government.Therefore, this study aims to make a model decision support system to diagnose patients exposed to Covid-19, such as people in control, patients in oversight, and those who are positive for the Covid-19 Virus.Model decision support system development aims to provide information about the development of COVID-19 and help the community in diagnosing themselves related to COVID-19 infection.In this study, the authors use the forward chaining method in application to get conclusions from the symptoms of the Covid-19.This research resulted in an application that patients exposed to the Covid-19, and it's also provided a solution for healing from patients.And this could be a reference for patients before consulting further with the doctor.
Deep Learning-Based Cell-Level and Beam-Level Mobility Management System

The deployment with beamforming-capable base stations in 5G New Radio (NR) requires an efficient mobility management system to reliably operate with minimum effort and interruption. In this work, we propose two artificial neural network models to optimize the cell-level and beam-level mobility management. Both models consist of convolutional, as well as dense, layer blocks. Based on current and past received power measurements, as well as positioning information, they choose the optimum serving cell and serving beam, respectively. The obtained results show that the proposed cell-level mobility model is able to sustain a strong serving cell and reduce the number of handovers by up to 94.4% compared to the benchmark solution when the uncertainty (representing shadowing, interference, etc.) is introduced to the received signal strength measurements. The proposed beam-level mobility management model is able to proactively choose and sustain the strongest serving beam, even when high uncertainty is introduced to the measurements.
A novel fuzzy ARTMAP with area of influence

Fuzzy ARTMAP (FAM) is a neural network model based on the adaptive resonance theory (ART). Its main advantage relies on its capability to successfully deal with the stability-plasticity dilemma. Even though FAM models have been used in many applications with remarkable performance, such model suffers from a well-known problem, named category proliferation, which results in the creation of a large number of categories in the training step. In that case, the FAM model may present lower generalization capability for unseen data. In this work, we aim to handle the category proliferation problem by proposing a new model that modifies the vigilance criterion and the weight update rule to pursue the generation of a sparse set of categories. Our model named FAM with Area of Influence (FAM-AI) is compared to the original FAM and some proposed variants. We perform several computational experiments and verify that, in general, the proposed FAM-AI achieves higher accuracy with a lower number of categories.
Cooperative search planning in wide area via multi-UAV formations based on distance probability

Cooperative search technology based on Multi-UAV is an important research content of the future development for the Navy. This paper focuses on the research of cooperative search planning method in wide area, and analyzes the uncertain factors of the targets and environment during missions. Considering the distance between the probability of the target found and the area center determined, search planning model is proposed based on distance probability function to develop the corresponding search and warning models for the static and dynamic area types combining the technology of multi-UAV formation.
Automatic path generation for tractor-trailers according to the ramp slope of Ro-Ro ships

Abstract Roll-on and roll-off (Ro-Ro) ships are economical transportation vessels that are very useful for transporting large freights, such as plant equipment and aircraft bodies. To load a large freight on a Ro-Ro ship, a tractor is generally used to pull a trailer on which the cargo is secured via a ramp from the port berth to the ship deck. In the case of a long cargo, the trajectory of the trailer is not the same as that of the tractor, which can cause a collision with the ramp or the gate or obstacles on the deck. Also, if the freight is too high, it may overturn when the trailer is tilted due to the ramp slope. This research proposes a method for calculating the best and safest trajectories for large freights to be loaded on Ro-Ro ships without the occurrence of any collisions or overturns. By reasonably assuming a low speed of the tractor, the proposed method generates all the possible tractor paths based on the Bezier curve and also calculates the trajectories of trailers when moving on inclined ramps; thus, it is the first method to calculate 3D trajectories for trailers when the tractor and trailer move on different planes. Besides, it searches for the best paths without any collisions or overturns and with the minimum shipping time possible. Since the proposed method calculates the paths by taking the ramp angle as a variable input, the trajectories can be automatically generated based on the change in the waterline of the ship according to the loading of the cargoes. Overall, the proposed method can be useful for transportation companies when planning the shipping of large freights, and it can also be beneficial for future autonomous driving systems.
Evaluating the NSF broader impacts with the Inclusion-Immediacy Criterion: A retrospective analysis of nanotechnology grants

A major goal of government and non-profit scientific funding agencies is to support research and development (R&D) that has broad impacts. This study proposes a new framework, called the Inclusion-Immediacy Criterion (IIC), to determine whether research benefits marginalized communities, reduces inequality, and encourages inclusive innovation. To test the framework, the study analyzes NSF sponsored nanotechnology grant abstracts from 2013 to 2017. We find that 109 out of the 300 grants feature research and grant activities that are inclusive, while 235 out of the 300 grants have research and grant activities that either maintain the status quo or predominately target advantaged groups. Of the 109 grants with inclusive broader impacts, 9 of them involve inclusive research that is intrinsic to the underlying work. In comparison there are 102 grants that feature inclusive research that is directly related to the research. Of those 102 direct-inclusive grants, 99 of them relate to broadening participation of women and underrepresented minority populations is science fields.
A Two-Stage Minimum Cost Multicut Approach to Self-supervised Multiple Person Tracking

Multiple Object Tracking (MOT) is a long-standing task in computer vision. Current approaches based on the tracking by detection paradigm either require some sort of domain knowledge or supervision to associate data correctly into tracks. In this work, we present a self-supervised multiple object tracking approach based on visual features and minimum cost lifted multicuts. Our method is based on straight-forward spatio-temporal cues that can be extracted from neighboring frames in an image sequences without supervision. Clustering based on these cues enables us to learn the required appearance invariances for the tracking task at hand and train an AutoEncoder to generate suitable latent representations. Thus, the resulting latent representations can serve as robust appearance cues for tracking even over large temporal distances where no reliable spatio-temporal features can be extracted. We show that, despite being trained without using the provided annotations, our model provides competitive results on the challenging MOT Benchmark for pedestrian tracking.