Reviewerly/exHarmony huggingface dataset:
High-Utility Interval-Based Sequences

Sequential pattern mining is an interesting research area with broad range of applications. Most prior research on sequential pattern mining has considered point-based data where events occur instantaneously. However, in many application domains, events persist over intervals of time of varying lengths. Furthermore, traditional frameworks for sequential pattern mining assume all events have the same weight or utility. This simplifying assumption neglects the opportunity to find informative patterns in terms of utilities, such as cost. To address these issues, we incorporate the concept of utility into interval-based sequences and define a framework to mine high utility patterns in interval-based sequences i.e., patterns whose utility meets or exceeds a minimum threshold. In the proposed framework, the utility of events is considered while assuming multiple events can occur coincidentally and persist over varying periods of time. An algorithm named High Utility Interval-based Pattern Miner (HUIPMiner) is proposed and applied to real datasets. To achieve an efficient solution, HUIPMiner is augmented with a pruning strategy. Experimental results show that HUIPMiner is an effective solution to the problem of mining high utility interval-based sequences.
FedORAM: A Federated Oblivious RAM Scheme

Instant messaging (IM) applications, even with end-to-end encryption enabled, pose privacy issues due to metadata and pattern leakage. Our goal is to develop a model for a privacy preserving IM application, by designing an IM application that focuses on hiding metadata and discussion patterns. To solve the issue of privacy preservation through the obfuscation of metadata, cryptographic constructions like Oblivious Random Access Machines (ORAM) have been proposed in recent years. However, although they completely hide the user access patterns, they incur high computational costs, often resulting in excessively slow performance in practice. We propose a new federated model, FedORAM, which is the first ORAM scheme that uses a federation of servers to hide metadata for an IM use case. In order to investigate the trade-off between security and performance, we propose two versions of FedORAM: Weak FedORAM and Strong FedORAM. Strong FedORAM uses a tree-based federation architecture to ensure strong obliviousness, but with an increased overhead cost. Weak FedORAM has a more simple federated architecture that only uses Oblivious Transfer (OT) to increase communication speed, but with security consequences. Our results show that both constructions are faster than a similar client-server ORAM scheme. Furthermore, Weak FedORAM has a response time of less than 2 seconds per message for a middle-sized federation.
Blockchain Technology Enabled Digital Identity Management in Smart Cities

With the improvement of technology and use of the emerging technologies, the evolution of Smart Cities has become smoother. In a smart city environment the automation strategy is based on the deployed IoT devices that stream big data for the betterment of the services. But while doing this, the security of the data is becoming a major concern. In order to monitor daily to daily activities of the users, they are compromising their privacy rights. The Blockchain Technology has emerged as the most secured distributed Peer to Peer Network. In this chapter, we present an approach to manage our online identities using highly secured blockchain technology. The chapter deals with privacy and soverignity of the user identities along with ensuring their security so that identities can’t be misused. On the other hand the approach ensures the transparency and control of the user’s identity. In the result analysis the chapter presents the output of the functions depicting the flow of the application. We discuss the architecture integrating blockchain and accordingly we also discuss the related challenges and future work.
Constraint-Based Software Diversification for Efficient Mitigation of Code-Reuse Attacks

Modern software deployment process produces software that is uniform, and hence vulnerable to large-scale code-reuse attacks. Compiler-based diversification improves the resilience and security of software systems by automatically generating different assembly code versions of a given program. Existing techniques are efficient but do not have a precise control over the quality of the generated code variants. This paper introduces Diversity by Construction (DivCon), a constraint-based compiler approach to software diversification. Unlike previous approaches, DivCon allows users to control and adjust the conflicting goals of diversity and code quality. A key enabler is the use of Large Neighborhood Search (LNS) to generate highly diverse assembly code efficiently. Experiments using two popular compiler benchmark suites confirm that there is a trade-off between quality of each assembly code version and diversity of the entire pool of versions. Our results show that DivCon allows users to trade between these two properties by generating diverse assembly code for a range of quality bounds. In particular, the experiments show that DivCon is able to mitigate code-reuse attacks effectively while delivering near-optimal code ( $${<}10\%$$ optimality gap). For constraint programming researchers and practitioners, this paper demonstrates that LNS is a valuable technique for finding diverse solutions. For security researchers and software engineers, DivCon extends the scope of compiler-based diversification to performance-critical and resource-constrained applications.
Optimisation of a Siamese Neural Network for Real-Time Energy Efficient Object Tracking

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations – from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover, quantisation of weights positively affects the network training by decreasing overfitting.
A Photonic Accelerator for Feature Map Generation in Convolutional Neural Networks

We propose a photonic accelerator for convolutional neural networks carrying out linear, nonlinear, and pooling operations. It achieves an excellent accuracy in TensorFlow simulations and is more energy efficient than state-of-the-art electronics.
Quantum Computing: Exploiting a Single‐Crystal Environment to Minimize the Charge Noise on Qubits in Silicon (Adv. Mater. 40/2020)

Atom qubits in silicon provide a promising platform for realising a scalable quantum computer. In article number 2003361, Michelle Simmons and co-workers present a qubit environment with a notably low level of charge noise achieved by hosting phosphorus atoms in an all-crystalline silicon substrate well-separated from surface and interface states. The level of charge noise experienced by the phosphorus atoms is sufficiently low to enable two-qubit gate fidelities above 99.99%, well within the 99% threshold required for error-correction.
Convolutional Neural Networks With Integrated Optics

We present an architecture to realize a convolutional neural network with integrated optics by using the Fourier transform property of star couplers. We show, in computer simulation, high accuracy image classification using the MNIST dataset.
A survey on clustering techniques in pattern recognition

In the recent era, technological approaches to a problem can be made to solve it easily. There are many technical ways to solve according to the types of problem. Classification makes that identify the problem of character. Clustering is one of the technique to classify and grouping the objects. Clustering has hundreds of different approaches to group the objects. Clustering has more than 50 years of history behind it. In this article, we take a study on cluster's definitions, types, algorithms and try to analyze the results. As well as give a literature survey based on data clustering, pattern recognition, and image classifications.
Variational Item Response Theory: Fast, Accurate, and Expressive

Item Response Theory (IRT) is a ubiquitous model for understanding humans based on their responses to questions, used in fields as diverse as education, medicine and psychology. Large modern datasets offer opportunities to capture more nuances in human behavior, potentially improving test scoring and better informing public policy. Yet larger datasets pose a difficult speed / accuracy challenge to contemporary algorithms for fitting IRT models. We introduce a variational Bayesian inference algorithm for IRT, and show that it is fast and scaleable without sacrificing accuracy. Using this inference approach we then extend classic IRT with expressive Bayesian models of responses. Applying this method to five large-scale item response datasets from cognitive science and education yields higher log likelihoods and improvements in imputing missing data. The algorithm implementation is open-source, and easily usable.
Learning a Low-Dimensional Representation of a Safe Region for Safe Reinforcement Learning on Dynamical Systems

For the safe application of reinforcement learning algorithms to high-dimensional nonlinear dynamical systems, a simplified system model is used to formulate a safe reinforcement learning (SRL) framework. Based on the simplified system model, a low-dimensional representation of the safe region is identified and used to provide safety estimates for learning algorithms. However, finding a satisfying simplified system model for complex dynamical systems usually requires a considerable amount of effort. To overcome this limitation, we propose a general data-driven approach that is able to efficiently learn a low-dimensional representation of the safe region. By employing an online adaptation method, the low-dimensional representation is updated using the feedback data to obtain more accurate safety estimates. The performance of the proposed approach for identifying the low-dimensional representation of the safe region is illustrated using the example of a quadcopter. The results demonstrate a more reliable and representative low-dimensional representation of the safe region compared with previous works, which extends the applicability of the SRL framework.
Parametric synthesis of the controller by metaheuristic algorithm in the SimInTech environment

Efficiency and quality of operation of local automatic control systems as part of an autonomous object is mainly determined by the regulator in their composition and the used control law, for the synthesis of which is necessary to use modern computer-aided design systems. The article presents the results of the application of genetic algorithm as a method of parametric synthesis of the PID-controller implemented in the SimInTech visual dynamic modeling environment, which currently is used by enterprises in the rocket and space sectors of Russian Federation. Object of research is the dynamic negative feedback system on the example of the automatic angular position of artificial satellite control system. The functional scheme and the simplified mathematical model of the system in the form of the transfer functions of its links are presented. The computer model of the system as well as a process of synthesis of the controller are implemented as a package of projects, based on standard blocks and submodels SimInTech. Projects interact using a common signal base, which provides information exchange between projects, making the system model flexible and versatile. An interactive computing environment for programming language Python, Jupyter Notebook, is used as a third-party software. The organization of interaction between SimInTech and Jupyter Notebook is described, scripts of programs for its implementation are presented.
Carnival Time: Russian Higher Education and Science in the Postmodern Era

Since the end of the twentieth Century to the present, society has been undergoing deep systemic changes, which are accompanied by crisis phenomena. But the crisis affects society as a whole and science and education, as its components, in different ways. What hopes of the society should be associated with science and education? Communities of intellectuals (universities) go through the following stages in their formation: classical, modern, postmodern. Postmodernism brings polycentrism, fragmentation, deconstruction, stylization, collage, modeling and simulacra, and serialization to universities and research organizations. If classical and modernist views on intellectuals and Universities assumed that their activities were aimed at producing new knowledge and a new person, the postmodern transformation significantly changed academic communities and their environment. In order to better understand the essence of what happened, the authors refer to the phenomenon of carnival in the interpretation of M. M. Bakhtin. The most important sign of carnival is that everything is not what it seems. According to the sophists of antiquity, intellectual activity becomes a paid service. Is it appropriate to say that in a modern University teaching becomes a form of spiritual prostitution? Yes, it is appropriate, provided that the carnival of what is happening in the Russian University of the XXI century is determined by the change of roles and priorities of teachers, students, and support staff. The intensity and specificity of the carnival in a particular University depends on the specifics of the decision about the purpose of the University: issuance (sale) of a state-issued diploma, production of knowledge and/or training of qualified specialists. A serious external factor that reduces the authority of teachers and scientists is strengthening of trends towards relatively autonomous development of technologies and technics, which is clearly expressed in the phenomenon of the “revolution of interfaces”. Society is also changing, becoming the “society of the spectacle”, where the development of science, theater and professional sports has more and more similar characteristics. The crisis in science and education has always been a feature of postmodernism, an existential problem of intellectuals, which leads many of them to leave the walls of universities and scientific institutions.
An Ingenious Application-Specific Quality Assessment Methods for Compressed Wireless Capsule Endoscopy Images

&lt;p&gt;Image quality assessment methods are used in different image processing applications. Among them, image compression and image super-resolution can be mentioned in wireless capsule endoscopy (WCE) applications. The existing image compression algorithms for WCE employ the generalpurpose image quality assessment (IQA) methods to evaluate the quality of the compressed image. Due to the specific nature of the images captured by WCE, the general-purpose IQA methods are not optimal and give less correlated results to that of subjective IQA (visual perception). This paper presents improved image quality assessment techniques for wireless capsule endoscopy applications. The proposed objective IQA methods are obtained by modifying the existing full-reference image quality assessment techniques. The modification is done by excluding the noninformative regions, in endoscopic images, in the computation of IQA metrics. The experimental results demonstrate that the proposed IQA method gives an improved peak signal-tonoise ratio (PSNR) and structural similarity index (SSIM). The proposed image quality assessment methods are more reliable for compressed endoscopic capsule images.&lt;/p&gt;
A Unified Decision-Theoretic Model for Information Gathering and Communication Planning

We consider the problem of communication planning for human-machine cooperation in stochastic and partially observable environments. Partially Observable Markov Decision Processes with Information Rewards (POMDPs-IR) form a powerful framework for information-gathering tasks in such environments. We propose an extension of the POMDP-IR model, called a Communicating POMDP-IR (com-POMDP-IR), that allows an agent to proactively plan its communication actions by using an approximation of the human's beliefs. We experimentally demonstrate the capability of our com-POMDPIR agent to limit its communication to relevant information and its robustness to lost messages.
Moving from blocker to enabler: cloud security and the modern CISO

The role of the chief information security officer (CISO) is changing. It's an exciting time for many working in information security as the new world of public cloud comes to fruition and is driving an evolution. Yet this pace of change for some can feel unsettling, so it is not uncommon for CISOs and security teams to want to revert to older models and this has become more prevalent with the increasing shift to public cloud. The role of the chief information security officer (CISO) is changing. It's an exciting time for many as the new world of public cloud comes to fruition. But change often brings fear and anxiety, and it's not unusual for the IT security function to act as ‘blockers’. Yet with the right changes, modern CISOs can act as a springboard to demonstrate relevance and the importance of their role while shaping a more secure IT landscape, says Olivier Subramanian of Contino.
Data Auditing for the Internet of Things Environments Leveraging Smart Contract

Cloud storage has been proposed to be integrated with the Internet of Things (IoT) to provide more intelligent and powerful services through sharing and analyzing the IoT data. However, how to assure the integrity of IoT data outsourced in the cloud is still an open challenge. A number of auditing schemes have been proposed in recent years, but most of them introduce a credible auditor to perform auditing work instead of the user. However, the absolutely credible auditor is an ideal hypothesis. Thus, we propose a new public auditing scheme, which introduces the smart contract to replace the auditor to perform the auditing task. Since the essence of smart contracts is a piece of public code posted on the blockchain, our scheme can solve the credit problem of the auditor compared with the traditional public auditing model. The proposed scheme streamlines the auditing processes to reduce the computational overheads of the user and cloud. The security of the proposed scheme is rigorously proved. In addition, the results of the performance evaluation show that our scheme can effectively realize the security auditing, and outperforms the existing schemes in computational overheads.
LINE WEBTOON SEBAGAI INDUSTRI KOMIK DIGITAL

Because of internet and smartphones, various industries and their products have changed. Comics as one form of the reading industry also experienced this impact. Conventional comics are changing into digital comics, which can be read via cellphones. One of the digital comic services is LINE Webtoon, which has successfully become a popular comic application in various countries, including Indonesia. The purpose of this study is to provide an overview, deepen and develop the concept of Webtoon, especially LINE Webtoon. This research using qualitative descriptive as method. The results showed that the Webtoon was developed from a conventional form of comics in South Korea called manhwa. Manhwa change into digital because the decline of conventional comic market and technological advancements that developed in South Korea. As an industry, LINE Webtoon expanded its business with presenting paid content, creating advertisements, looking for talented comics, and adapting across media to popular Webtoon works.
Monolingual Data Selection Analysis for English-Mandarin Hybrid Code-Switching Speech Recognition

In this paper, we conduct data selection analysis in building an English-Mandarin code-switching (CS) speech recognition (CSSR) system, which is aimed for a real CSSR contest in China. The overall training sets have three subsets, i.e., a code-switching data set, an English (LibriSpeech) and a Mandarin data set respectively. The code-switching data are Mandarin dominated. First of all, it is found using the overall data yields worse results, and hence data selection study is necessary. Then to exploit monolingual data, we find data matching is crucial. Mandarin data is closely matched with the Mandarin part in the code-switching data, while English data is not. However, Mandarin data only helps on those utterances that are significantly Mandarin-dominated. Besides, there is a balance point, over which more monolingual data will divert the CSSR system, degrading results. Finally, we analyze the effectiveness of combining monolingual data to train a CSSR system with the HMM-DNN hybrid framework. The CSSR system can perform within-utterance code-switch recognition, but it still has a margin with the one trained on code-switching data.