Reviewerly/exHarmony huggingface dataset:
Reconsidering the Analytic Basis for Individuals and Situations: Invited Talk

The purpose of this short paper is to stimulate a community wide discussion on how situation awareness (SA) technologies may best support the decisions made during pandemics. Last year we discussed how much of SA is directed towards the critical information needed to make epidemiological and public health decisions; as important as this is, an epidemic also requires doctors and front line medical professional to make numerous decisions regarding individualized patient care. This year we emphasize some statistical analyses that better support individualized medicine. We believe that these methods will also support better analyses and understanding of the specific details of individual situations.
Machine learning algorithms for the activity monitoring of elders by home sensor network

The aim of this research is to measure and cluster the activity of ageing people, through a minimally invasive home sensor network, realised by movement and light sensors, installed in seven apartments. Unsupervised machine learning (UML) algorithms are used to cluster daily data in different pattern of activation of light and movement sensors. Data are grouped in 6 time slots per day of 4 hours each and the activations of each sensor are counted. The chosen number of clusters is set to 2 and best clustering results are obtained for the first time slot of House 4 and House 5 with a Silhouette score of 0.7 and 0.9, suggesting clear separation between the days belonging to 2 different clusters. In addition, to provide an explanation to the clustering algorithm, a supervised machine learning (SML) algorithm is used to establish which sensor, in the different time slots of the day, is ruling the clustering. Moreover, a Decision Tree algorithm (DT) is used to understand the clustering methodology adopted by the UML algorithm for each time window of the different houses. DT allows identifying which sensor may cause the assignment of a diversity condition between the days of the dataset.
Communication-aware Fairness in Blockchain Transaction Ordering

Blockchain leader-based protocols elect leaders for proposing the next block of transactions. Proposed blocks need to pass a validation routine in order to be added to the blockchain. Proposers may prioritize certain transactions based on their fees or accounts. A fair block selection follows a random selection of transactions among pending transactions that a proposer is aware of. The validators may only have partial knowledge of the network transactions making it challenging to validate the random selection. We propose a protocol to encourage fair block selection in a leader-based blockchain network. Our protocol offers two main contributions. First, suggesting an algorithm that evaluates the proposed blocks based on both their transactions’ issuance times and zone structure. Second, providing incentives for acting honestly and diminishing malicious and dishonest nodes. To accomplish this, we use a reputation system, whereby each node is given a reputation score based on its actions (i.e. latest proposals and evaluations). We demonstrate the improved accuracy of our protocol by implementing experiments based on Ethereum topology, comparing it with Helix [1], an existing consensus algorithm for a fair block selection.
Enhancing the Security of a Private Network by Using A Multi-level Hierarchical NAT Scheme

Nowadays, attacks coming from the Internet are posing serious threats to the hosts in an institution, campus, company, etc. The Network Address Translator (NAT) is a device that allows a host in a private network to interact with the hosts on the public Internet. Due to the property of NAT, unless a host that is behind a NAT actively contacts a host on the Internet, hosts on the Internet cannot actively reach the host behind the NAT. In this work, we exploit NATs and propose a multi-level hierarchical NAT scheme to protect and enhance the security of a private network. We have designed and implemented our scheme over P4 programmable hardware switches. Experimental results show that our scheme functions correctly and provides high throughput, low latency, and high stability. In addition, according to our tests, our scheme works correctly with most existing network applications.
A Data Analytics Based Approach to Cloud Resource Auto-Scaling

Multiplexing resources is the core savings principle upon which the economic model of the Cloud is built. Cloud customers can flexibly purchase additional resources when needed, and trim these down when the need has past, while Cloud providers can direct resources when and where customers might require. One aspect which poses a challenge to this capability is the allocation process itself, which can be costly in terms of time and energy. Indeed, both provider and customer would prefer if resource allocation would be continuous, fast and with low energy overhead. Since this is not the case, there is an inherent tension between limiting the number of allocation events and efficient resource utilization.This paper considers this tension using several different models, and proposes a history-based dynamic allocation scheme that minimizes the number of resource allocation transition points for both average and adversarial use cases. We prove performance bounds and use extensive simulation to study the performance of our scheme.
Sensors for detection of cyber threats on industrial environment using a high interaction ICS/SCADA Honeynet<sup>1</sup>

In the present work, investigate the use of honeypots and honeynets designed for real-time control environments as tools to prevent cyber-attacks and produce statistics that help refine cybersecurity and anomaly detection techniques in ICS/SCADA environments. High interaction ICS/SCADA Honeynets can be viewed as sensors that can maintain an updated base of attack signature techniques, helping the identification of vulnerabilities in Information and Industrial Automation Systems and promoting the preservation of evidence of attacks for future forensic analysis. The present work was the result of a strategic partnership between the Brazilian Army and the Itaipu Technological Park Foundation - Brazil (FPTI-BR). The project was based on free software and currently serves as an important instrument in Brazil for information exchange and threat intelligence sharing for the national electricity sector. The achieved results highlight the potential that honeynets have to address cybersecurity issues in ICS/SCADA environments.
Incremental Learning Assisted Dynamic Driver Identification: A New Perspective

With the popularity of intelligent and connected vehicles, driver identification based on driver behaviors, which has great significance in driving safety, vehicle alarm system and other situations, attracts more and more attention. However, the implementation of driver identification also faces some problems, such as insufficient computing power, slow transmission rate and so on. In order to solve these problems, people use mobile edge computing technology to transfer the computing process, which makes drive identification more feasible in practical scenarios. Many researchers have done a lot of works for the accuracy of driver identification. Nonetheless, few of them pay attention to the identification efficiency in the scenarios where drivers are constantly added to the model. Different from the works before, we propose a new driver identification scheme based on incremental learning for the first time, which can greatly improve the efficiency of model retraining when new driver is added. We extract the driver behavior characteristics data from two physical vehicles and train an incremental learning model in edge server. The experimental results show that the our scheme can accurately identify drivers. With the number of drivers in model increasing, although the identification accuracy slowly decreases, it remains above 93%, which means our scheme against the catastrophic forgetting well. Comparing with the existing identification schemes, our scheme requires less time and memory resources for model retraining, which is suitable for practical scenarios.
Didactic Model Aiming at the Use of Digital Information and Communication Technologies in Science Education

The scope of academic processes in which Information and Communication Technologies are incorporated (ICT) has increased in recent decades, same as the number of concerns about its contributions to learning itself. A predominantly instrumental sense of these technologies becomes evident as far as scientific education in Colombia is concerned. This work seeks the construction of a teaching model that may guide natural science teachers around the design of proposals for implementing Digital Information and Communication Technologies (DICT), based on science education challenges (Hodson, 2003; 2010) and the Critical Meaningful Learning Theory (Moreira, 2005; 2010). Thus, Design-Based Research (DBR) is regarded as a methodological standpoint, which, based in turn on a systemic and iterative process, allows the development of a model and validate the potential these technologies have to meet the challenges science teaching entails when their foray into the classroom is based on didactic, pedagogical and epistemological foundations.
Hawkeye: Eliminating Kernel Address Leakage in Normal Data Flows

The confidentiality of the operating system kernel addresses is crucial to keeping the kernel secure from malicious users. To avoid leaking this position, researchers have proposed various techniques to defeat the exploits of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">abnormal</i> data flows embedded in the kernel's memory safety loopholes, such as uninitialized memory read and buffer over-reads. However, this is far from complete. The kernel address can be leaked even in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">normal</i> data flows without exploiting memory safety loopholes. We have designed a static analysis tool named Hawkeye to fill the gap. It searches for kernel address leakages in normal data flows that reveal the kernel address clues. Hawkeye precisely identifies the kernel addresses with minimum manual annotation and scales to analyze the whole kernel source code. It requires nearly ten times fewer memory resources and 40 times less inspection time than the state-of-the-art tool that analyzes kernel address leakage. Hawkeye unveils hundreds of leakages in various versioned kernels. It has even discovered 20 bugs in the mainline Linux kernel with the kernel pointer hashing mechanism already deployed and three bugs in FreeBSD. All the corresponding patches have been accepted by the developers.
Adaptive Lighting for Curved and Nonuniform Objects in Optomechanical Inspection Systems

Visual inspection is omnipresent and critical in precision manufacturing. However, complex geometries of parts hinder uniform illumination, and high reflectivity challenges accurate focusing for digital visual data collection. This research provides a novel adaptive illuminance distribution for consistent lighting to facilitate quality imaging over complex-shaped, highly reflective surfaces. The central approach entails using arrays of independently controlled light sources to reliably generate different lighting patterns, structures, and colors. Such results consider the geometry, the 3-D pose of parts in the environment, and the surface topography of the workpiece to be inspected, hence amplifying the capabilities of an image capturing system. In this article, we discuss the mathematical problem formulation, analytic solution, optimality of the proposed lighting, and experimental results in imaging curved parts common in aerospace manufacturing. The efficacy of the resulting defect identification is tested using a deep neural network.
PreGNN: Hardware Acceleration to Take Preprocessing Off the Critical Path in Graph Neural Networks

In this paper, we observe that the main performance bottleneck of emerging graph neural networks (GNNs) is not the inference algorithms themselves, but their graph data preprocessing. To take such preprocessing off the critical path in GNNs, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PreGNN</i> , a novel hardware automation architecture that accelerates all the tasks of GNN preprocessing from the beginning to the end. Specifically, PreGNN accelerates graph generation in parallel, samples neighbor nodes of a given graph, and prepares graph datasets through all hardware. To reduce the long latency of GNN preprocessing over hardware, we also propose simple, efficient combinational logic that can perform radix sort and arrange the data in a self-governing manner. The evaluation results show that PreGNN can shorten the end-to-end latency of GNN inferences by 10.7× while consuming less energy by 3.3×, compared to a GPU-only system.
ConPhrase: Enhancing Context-Aware Phrase Mining from Text Corpora

Phrase mining is an essential step when transforming unstructured text into structured information, in which the aim is to extract high-quality phrases from given corpora automatically. Existing statistics-based methods have achieved state-of-the-art performance on this task. However, such methods often rely heavily on statistical signals to extract quality phrases, ignoring the effect of contextual information. In this paper, we propose a novel context-aware method, called ConPhrase, for quality phrase mining under distantly supervised settings. Specifically, ConPhrase formulates phrase mining as a sequence labeling problem by considering local contextual information, and also incorporates distant supervision methods to automatically generate labeled data. It comprises two modules designed to tackle global information scarcity and noisy data filtration: 1) a topic-aware phrase recognition network that incorporates domain-related topic information into word representation learning to identify quality phrases effectively; 2) an instance selection network that focuses on choosing correct sentences with reinforcement learning for improving the prediction performance of the phrase recognition network. Moreover, we also propose an extended variant of ConPhrase, called ConPhrase&#x002B;, that further enhances phrase recognition by utilizing document-level contextual information across sentences within the entire document. Experimental results show that contextual information is indispensable for phrase mining and our context-aware methods perform significantly better than state-of-the-art approaches on three publicly available datasets.
Quantum machine learning

This chapter covers machine learning (ML) and quantum machine learning (QML). It begins with a review of the classical supervised, unsupervised, and reinforcement ML algorithms and then discusses principal component analysis, support vector machines (SVMs), clustering, boosting, regression analysis, and neural networks. The focus then moves to QML algorithms, first describing the Ising model and relating it to quadratic unconstrained binary optimization (QUBO) problems. We then study how to solve QUBO problems by adiabatic quantum computing and quantum annealing. To perform QML using imperfect and noisy quantum circuits, we describe the variational quantum eigensolver and quantum approximate optimization algorithm (QAOA). The impact of the QAOA is illustrated through combinatorial optimization and MAX-CUT problems and solutions. Next, quantum boosting is discussed and related to QUBO problems. Quantum random access memory is described next, where we address the superposition of memory cells with the help of a quantum register. Quantum matrix inversion, also known as the Harrow–Hassidim–Lloyd algorithm, is then described. Quantum principal component analysis, one of the many QML algorithms for which quantum matrix inversion is a basic ingredient, is also described. In the quantum optimization-based clustering section, we describe how the MAX-CUT problem is related to clustering and can thus be solved by adiabatic computing, quantum annealing, and the QAOA. Grover algorithm-based quantum optimization is discussed next. The quantum K-means section describes how to calculate the dot product and quantum distance, followed by the Grover search-based K-means algorithm. In the quantum SVM section, we formulate the SVM problem using least-squares and solve it via quantum matrix inversion. The section on quantum neural networks (QNNs) describes feedforward QNNs, quantum perceptron, and quantum convolutional networks.
Quantum detection and quantum communication

This chapter focuses on quantum detection theory (QDT), quantum communication, and Gaussian quantum information theory. After briefly reviewing density operators, we describe QDT concepts and apply them to the binary detection problem. We introduce coherent states and describe their basic properties, followed by an introduction to quadrature operators and derivation of the uncertainty principle for continuous-variable systems. We then discuss binary quantum optical communication without noise, including photon-counting, on–off keying (OOK), binary phase-shift keying (BPSK), and Kennedy and Dolinar receivers. We discuss the P-representation and apply it to represent thermal noise and coherent states in the presence of thermal noise, followed by a discussion of binary optical communication in the presence of noise with a focus on OOK and BPSK. Gaussian and squeezed states are introduced, followed by definitions of the Wigner function and covariance matrices. Gaussian transformation and Gaussian channels are discussed, with beam splitter operation, phase rotation operation, and squeezing operator as examples. The thermal decomposition of Gaussian states is discussed, and the von Neumann entropy for thermal states is derived. Covariance matrices for two-mode Gaussian states are discussed in detail. Gaussian state measurements and detection are discussed, emphasizing homodyne detection, heterodyne detection, and partial measurements. The focus then moves to nonlinear quantum optics fundamentals; in particular, three-wave and four-wave mixing are described. The generation of Gaussian states is also described, and two-mode squeezed state generation is discussed in detail. Finally, we discuss multilevel quantum optical communication, wherein scenarios are described in the absence and presence of noise. Using square root measurements to analyze the performance of these schemes is described in detail.
Generalized Sketches for Streaming Sets

Many real-world datasets are given as a stream of user–interest pairs, where a user–interest pair represents a link from a user (e.g., a network host) to an interest (e.g., a website), and may appear more than once in the stream. Monitoring and mining statistics, including cardinality, intersection cardinality, and Jaccard similarity of users’ interest sets on high-speed streams, are widely employed by applications such as network anomaly detection. Although estimating set cardinality, set intersection cardinality, and set Jaccard similarity, respectively, is well studied, there is no effective method that provides a one-shot solution for estimating all these three statistics. To solve the above challenge, we develop a novel framework, SimCar. SimCar online builds an order-hashing (OH) sketch for each user occurring in the data stream of interest. At any time of interest, one can query the cardinalities, intersection cardinalities, and Jaccard similarities of users’ interest sets. Specially, using OH sketches, we develop maximum likelihood estimation (MLE) methods to estimate cardinalities and intersection cardinalities of users’ interest sets. In addition, we use OH sketches to estimate Jaccard similarities of users’ interest sets and build locality-sensitive hashing tables to search for users with similar interests with sub-linear time. We evaluate the performance of our methods on real-world datasets. The experimental results demonstrate the superiority of our methods.
A relaxed support vector data description algorithm based fault detection in distribution systems

The power detection of the distribution network is essential for reliable and secure distribution. In this paper, a flexible dual-threshold SVDD fault warning algorithm with fault samples is proposed to deal with problems concerning complex network topology, accessible data, and missing fault data in the power grid. For the problems of complicated network topology and a wide variety of signal types, we propose to combine wavelet packet energy features with Spearman to extract electric signal features, and finally achieve accurate feature extraction of multiple signal types. In the case of the problems of untimely judgement and low accuracy of the original SVDD, a relaxed SVDD fault warning algorithm with fault samples is correspondingly proposed. We turn the original SVDD boundary into a double-layer boundary, and divides the hypersphere space into three regions to increase the sensitivity to the fault samples and lessen the risk of missed detection. Besides, an adaptive update strategy is developed, which reduces the computational effort of the model and is proven more applicable to the distribution. Finally, the method is applied to numerical examples and fault detection experiments, and the experimental results in turn verified its effectiveness and superiority.
Identity Design and Identities Exhibited in Social Networks

Identity emerges as a flexible, multidimensional, variable, and slippery concept that cannot be defined through the processes of discussion and understanding. The new construction area of this concept, which is regarded as a process constructed on the social plane, is the social networking platforms. This is because these platforms are the most common communication environments where people and their lifestyles are presented to the outside world, in addition to the cheap and rapid satisfaction of their needs for information and entertainment. Face-to-face communication and language practices are not sufficient enough in the identity presentation anymore. Individuals choose to design and update their identities through social networks and to perform an image-based identity manifestation. This chapter examines how identity was established and manifested through social networks, and analyzes the identities the popular people in these networks designed and exhibited.
Feature Based Opinion Mining

Opinion mining is the estimated learning of user's beliefs, evaluation and sentiments about units, actions and its features. This method has several features matched with data mining techniques, language processing methods and feature oriented data abstraction. This seems to be extremely difficult to mine opinions from analysis those exist in common human used language. Views are very essentials when one desires to construct a judgment. Data abstraction is an important characteristic for decision making applicable to individuals and organization of different nature. While selecting and purchasing a particular product, it is always beneficial for an individual to collect other views for correct decision making. One association wants to conduct surveys and gather opinions to develop their product excellence. Internet as a source of information, having a number of websites available with the customer reviews as a number of products, it is easy to extract the features from these opinions, sentiments and view, is a task comes under feature-based opinion mining.