Reviewerly/exHarmony huggingface dataset:
A challenge for historical research: Making data FAIR using a collaborative ontology management environment (OntoME)

This paper addresses the issue of interoperability of data generated by historical research and heritage institutions in order to make them re-usable for new research agendas according to the FAIR principles. After introducing the symogih.org project’s ontology, it proposes a description of the essential aspects of the process of historical knowledge production. It then develops an epistemological and semantic analysis of conceptual data modelling applied to factual historical information, based on the foundational ontologies Constructive Descriptions and Situations and DOLCE, and discusses the reasons for adopting the CIDOC CRM as a core ontology for the field of historical research, but extending it with some relevant, missing high-level classes. Finally, it shows how collaborative data modelling carried out in the ontology management environment OntoME makes it possible to elaborate a communal fine-grained and adaptive ontology of the domain, provided an active research community engages in this process. With this in mind, the Data for history consortium was founded in 2017 and promotes the adoption of a shared conceptualization in the field of historical research.
Accelerating variance-reduced stochastic gradient methods

Abstract Variance reduction is a crucial tool for improving the slow convergence of stochastic gradient descent. Only a few variance-reduced methods, however, have yet been shown to directly benefit from Nesterov’s acceleration techniques to match the convergence rates of accelerated gradient methods. Such approaches rely on “negative momentum”, a technique for further variance reduction that is generally specific to the SVRG gradient estimator. In this work, we show for the first time that negative momentum is unnecessary for acceleration and develop a universal acceleration framework that allows all popular variance-reduced methods to achieve accelerated convergence rates. The constants appearing in these rates, including their dependence on the number of functions n , scale with the mean-squared-error and bias of the gradient estimator. In a series of numerical experiments, we demonstrate that versions of SAGA, SVRG, SARAH, and SARGE using our framework significantly outperform non-accelerated versions and compare favourably with algorithms using negative momentum.
Performance analysis of out-of-distribution detection on trained neural networks

Deep Neural Networks (DNN) have shown great promise in various domains, for example to support pattern recognition in medical imagery. However, DNNs need to be tested for robustness before being deployed in safety critical applications. One common challenge occurs when the model is exposed to data samples outside of the training data domain, which can yield to outputs with high confidence despite no prior knowledge of the given input. The aim of this paper is to investigate how the performance of detecting out-of-distribution (OOD) samples changes for outlier detection methods (e.g., supervisors) when DNNs become better on training samples. Supervisors are components aiming at detecting out-of-distribution samples for a DNN. The experimental setup in this work compares the performance of supervisors using metrics and datasets that reflect the most common setups in related works. Four different DNNs with three different supervisors are compared during different stages of training, to detect at what point during training the performance of the supervisors begins to deteriorate. Found that the outlier detection performance of the supervisors increased as the accuracy of the underlying DNN improved. However, all supervisors showed a large variation in performance, even for variations of network parameters that marginally changed the model accuracy. The results showed that understanding the relationship between training results and supervisor performance is crucial to improve a model’s robustness. Analyzing DNNs for robustness is a challenging task. Results showed that variations in model parameters that have small variations on model predictions can have a large impact on the out-of-distribution detection performance. This kind of behavior needs to be addressed when DNNs are part of a safety critical application and hence, the necessary safety argumentation for such systems need be structured accordingly.
Generation Analysis of Blockchain Technology: Bitcoin and Ethereum

In this paper, the importance of blockchain technology have been discussed and the generations of blockchain (Bitcoin and Ethereum) have been compared provided different aspects.The blockchain is a technology which allows direct transaction without involving third party.Also, it offers many facilities like high translucency, high safety and security, improved trace-ability, greater proficient and transactions' speed, and reduced costs.Moreover, the cryptocurrencies provide advance security level.The basic purpose of this study is to highlight different aspects of Blockchain, Bitcoin and Ethereum and to show which cryptocurrency is better approach.The research contributes to show the impact of this technology in different fields and a comparison of bitcoin and ethereum is presented to analyze and furnish a decision regarding the best among them.The use of blochchain technology in government applications can bring a drastic change in the world because it is safer and faster.Also, the comparison shows that ethereum is better than bitcoin as it is efficient and has more applications as compared to bitcoin.It offers more advanced services such as smart contracts.All in all, the analysis has concluded with Ethereum as faster and securer approach.
Tightly Secure Hierarchical Identity-Based Encryption

Abstract We construct the first tightly secure hierarchical identity-based encryption (HIBE) scheme based on standard assumptions, which solves an open problem from Blazy, Kiltz, and Pan (CRYPTO 2014). At the core of our constructions is a novel randomization technique that enables us to randomize user secret keys for identities with flexible length. The security reductions of previous HIBEs lose at least a factor of Q, which is the number of user secret key queries. Different to that, the security loss of our schemes is only dependent on the security parameter. Our schemes are adaptively secure based on the Matrix Diffie-Hellman assumption, which is a generalization of standard Diffie-Hellman assumptions such as k-Linear. We have two tightly secure constructions, one with constant ciphertext size, and the other with tighter security at the cost of linear ciphertext size. Among other things, our schemes imply the first tightly secure identity-based signature scheme by a variant of the Naor transformation.
Human engagement providing evaluative and informative advice for interactive reinforcement learning

Abstract Interactive reinforcement learning proposes the use of externally sourced information in order to speed up the learning process. When interacting with a learner agent, humans may provide either evaluative or informative advice. Prior research has focused on the effect of human-sourced advice by including real-time feedback on the interactive reinforcement learning process, specifically aiming to improve the learning speed of the agent, while minimising the time demands on the human. This work focuses on answering which of two approaches, evaluative or informative, is the preferred instructional approach for humans. Moreover, this work presents an experimental setup for a human trial designed to compare the methods people use to deliver advice in terms of human engagement. The results obtained show that users giving informative advice to the learner agents provide more accurate advice, are willing to assist the learner agent for a longer time, and provide more advice per episode. Additionally, self-evaluation from participants using the informative approach has indicated that the agent’s ability to follow the advice is higher, and therefore, they feel their own advice to be of higher accuracy when compared to people providing evaluative advice.
Low-complexity CNN with 1D and 2D filters for super-resolution

Abstract This paper proposes a low-complexity convolutional neural network (CNN) for super-resolution (SR). The proposed deep-learning model for SR has two layers to deal with horizontal, vertical, and diagonal visual information. The front-end layer extracts the horizontal and vertical high-frequency signals using a CNN with one-dimensional (1D) filters. In the high-resolution image-restoration layer, the high-frequency signals in the diagonal directions are processed by additional two-dimensional (2D) filters. The proposed model consists of 1D and 2D filters, and as a result, we can reduce the computational complexity of the existing SR algorithms, with negligible visual loss. The computational complexity of the proposed algorithm is 71.37%, 61.82%, and 50.78% lower in CPU, TPU, and GPU than the very-deep SR (VDSR) algorithm, with a peak signal-to-noise ratio loss of 0.49 dB.
High-Level Parallel Ant Colony Optimization with Algorithmic Skeletons

Abstract Parallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA can be a difficult and error-prone task even for experienced programmers. To overcome this issue, the parallel programming model of Algorithmic Skeletons simplifies parallel programs by abstracting from low-level features. This is realized by defining common programming patterns (e.g. map, fold and zip) that later on will be converted to efficient parallel code. In this paper, we show how algorithmic skeletons formulated in the domain specific language Musket can cope with the development of a parallel implementation of ACO and how that compares to a low-level implementation. Our experimental results show that Musket suits the development of ACO. Besides making it easier for the programmer to deal with the parallelization aspects, Musket generates high performance code with similar execution times when compared to low-level implementations.
Modern Era of the Theory and Practice of Learning Activity: Key Issues and Perspectives

The paper describes the main stages and transitions in the unfolding of the Learning Activity which is reviewed within the framework of D.B. Elkonin and V.V. Davydov’s concepts about the meaning and content of education. The unfolding of teaching/learning is explained through the emergence of a joint action of teacher and students, that is, the Mediative Action. The concept of the Learning Task as a basis for constructing the general method of action, central to the system of D.B. Elkonin and V.V. Davydov, is also considered as a source of learning motivation. The learning task is seen as the distinct opposition between the search for the method of acting (possible scaffolds of acting) and the very achievement of the result, i.e. the opposition between the consideration (of situation) and the immediate achievement (of result), and, therefore, the opposition between the intriguing (interesting) and the required. The paper also focuses on the evolution of modeling as the evolution of sign mediation in the student’s activity. As the student progresses from understanding and configuring the model to using it as a means of acting, the model evolves. Having gained the function of the means, the model becomes a resource of possible action, and that is how the experience of acting appears before the student him/herself. The unfolding of the learning task and modeling is conceptualized as the correlation between functional genesis and ontogenesis.
Focus First: Coarse-to-Fine Traffic Sign Detection With Stepwise Learning

Traffic sign detection is a very important part of intelligent assisted driving system.However, with the interference of various target sizes, geometric distortion, occlusion and motion blur, fast and accurate detection on large-size car camera image is extremely hard.To achieve both high efficient and accurate detection, we present a traffic sign detection method within a coarse-to-fine framework, which sequentially detects the targets in grid-level and image-level.We demonstrate that focusing first is a more effective detection strategy for small targets in wide detection space.We propose a target grid prediction network, which is a fully convolutional network for binary classification, to realize rapid coarse localization of the target and effectively guide the clipping and scaling of the target area.With the flexible potential target region extracting strategy, the detecting space can be significantly reduced.At the same time, the correctly extracted local areas for the targets can further facilitate the accurate detection of the subsequent traffic sign detector.In the experiments, our method achieves impressive performance in terms of both efficiency and accuracy.On the challenging Tsinghua-Tencent 100K (TT100K) dataset, our method achieves 20.9 FPS detection speed for 1600 × 1600 images and the F 1 -score of the proposed method for all-scale targets is 91.55.
Optimal search and rescue route design using an improved ant colony optimization

In this paper, an improved ant colony algorithm is proposed for the route design of maritime emergency search and rescue. To solve the problem that the ant colony algorithm is easy to fall into local optimal solutions in the process of searching, the pheromone concentration updating strategy of the original ant colony algorithm is provided. According to the actual situation of maritime search and rescue, the path weight based on the time of falling into the water is introduced into the algorithm to obtain the optimal route. The simulation results show that the improved algorithm can be used for route design, and obtain the optimal route suitable for sea search and rescue.
Forming and Assessing Student Teams in Software Engineering Courses

Abstract In software development projects, working in teams is essential. Therefore, software engineering courses often require the students to be working in teams to learn about team work behaviors and practices. The instructors of software engineering courses are presented with several challenges when teaching courses that require teamwork. For example, how to form high-performing student teams, and how to assess their work. The aim of this study is to evaluate whether there are differences in performance whether the students form the teams themselves, or if the teams are formed by the instructor. We evaluated a course involving agile software development by 200 students working in 39 teams. A total of 76% of the students chose to form their own teams, the remaining 24% were placed in teams by the instructors. Our findings indicate that teams formed by the students perform slightly better than the teams formed by the instructors.
EvalDNN

Recent studies have shown that the performance of deep learning models should be evaluated using various important metrics such as robustness and neuron coverage, besides the widely-used prediction accuracy metric. However, major deep learning frameworks currently only provide APIs to evaluate a model's accuracy. In order to comprehensively assess a deep learning model, framework users and researchers often need to implement new metrics by themselves, which is a tedious job. What is worse, due to the large number of hyper-parameters and inadequate documentation, evaluation results of some deep learning models are hard to reproduce, especially when the models and metrics are both new.
Exploring the meaning of usable security – a literature review

Purpose For decades, literature has reported on the perceived conflict between usability and security. This mutual trade-off needs to be considered and addressed whenever security products are developed. Achieving well-balanced levels of both is a precondition for sufficient security as users tend to reject unusable solutions. To assess it correctly, usability should be evaluated in the context of security. This paper aims to identify and describe universally applicable and solution-independent factors that affect the perceived usability of security mechanisms. Design/methodology/approach The selected methodology was a systematic literature review during which multiple database resources were queried. Application of predefined selection criteria led to the creation of a bibliography before backward snowballing was applied to minimize the risk of missing material of importance. All 70 included publications were then analyzed through thematic analysis. Findings The study resulted in the identification of 14 themes and 30 associated subthemes representing aspects with reported influence on perceived usability in the context of security. While some of them were only mentioned sparsely, the most prominent and thus presumably most significant ones were: simplicity, information and support, task completion time, error rates and error management. Originality/value The identified novel themes can increase knowledge about factors that influence usability. This can be useful for different groups: end users may be empowered to choose appropriate solutions more consciously, developers may be able to avoid common usability pitfalls when designing new products and system administrators may benefit from a better understanding of how to configure solutions and how to educate users efficiently.
Program debloating via stochastic optimization

Programs typically provide a broad range of features. Because different typologies of users tend to use only a subset of these features, and unnecessary features can harm performance and security, program debloating techniques, which can reduce the size of a program by eliminating (possibly) unneeded features, are becoming increasingly popular. Most existing debloating techniques tend to focus on program-size reduction alone and, although effective, ignore other important aspects of debloating. We believe that program debloating is a multifaceted problem that must be addressed in a more general way. In this spirit, we propose a general approach that allows for formulating program debloating as a multi-objective optimization problem. Given a program to be debloated, our approach lets users specify (1) a usage profile for the program (i.e., a set of inputs with associated usage probabilities), (2) the factors of interest for debloating, and (3) the relative importance of these factors. Based on this information, the approach defines a suitable objective function for associating a score to every possible reduced program and aims to generate an optimal solution that maximizes the objective function. We also present and evaluate Debop, a specific instance of our approach that considers three objectives: size reduction, attack-surface reduction, and generality (i.e., the extent to which the reduced program handles inputs in the usage profile provided). Our results, albeit still preliminary, are promising and show that our approach can be effective at generating debloated programs that achieve a good trade-off between the different de-bloating objectives considered. Our results also provide insights on the performance of our general approach when compared to a specialized single-goal technique.
Single-shot number-resolved detection of microwave photons with error mitigation

Single-photon detectors are ubiquitous and integral components of photonic quantum cryptography, communication, and computation. Many applications, however, require not only detecting the presence of any photons, but distinguishing the number present with a single shot. Here, we implement a single-shot, high-fidelity photon number-resolving detector of up to 15 microwave photons in a cavity-qubit circuit QED platform. This detector functions by measuring a series of generalized parity operators which make up the bits in the binary decomposition of the photon number. Our protocol consists of successive, independent measurements of each bit by entangling the ancilla with the cavity, then reading out and resetting the ancilla. Photon loss and ancilla readout errors can flip one or more bits, causing nontrivial errors in the outcome, but these errors have a traceable form which can be captured in a simple hidden Markov model. Relying on the independence of each bit measurement, we mitigate biases in ensembles of measurements, showing good agreement with the predictions of the model. The mitigation improves the average total variation distance error of Fock states from $13.5\%$ to $1.1\%$. We also show that the mitigation is efficiently scalable to an $M$-mode system provided that the errors are independent and sufficiently small. Our work motivates the development of new algorithms that utilize single-shot, high-fidelity PNR detectors.
Splitting Gaussian processes for computationally-efficient regression

Gaussian processes offer a flexible kernel method for regression. While Gaussian processes have many useful theoretical properties and have proven practically useful, they suffer from poor scaling in the number of observations. In particular, the cubic time complexity of updating standard Gaussian process models can be a limiting factor in applications. We propose an algorithm for sequentially partitioning the input space and fitting a localized Gaussian process to each disjoint region. The algorithm is shown to have superior time and space complexity to existing methods, and its sequential nature allows the model to be updated efficiently. The algorithm constructs a model for which the time complexity of updating is tightly bounded above by a pre-specified parameter. To the best of our knowledge, the model is the first local Gaussian process regression model to achieve linear memory complexity. Theoretical continuity properties of the model are proven. We demonstrate the efficacy of the resulting model on several multi-dimensional regression tasks.
pirecorder: Controlled and automated image and video recording with the raspberry pi

A fundamental component of empirical research is the acquisition of accurate, consistent, and often significant amounts of data.Specifically, researchers often require large numbers of controlled and often parallel image and video recordings.For this the raspberry pi, a small, single-board computer that brings together open-source principles with sensor and controller interfaces, and highly customisable programming capabilities, provides a great, low cost solution.Indeed, in recent years, the raspberry pi has been increasingly taken up by the scientific community (Fletcher & Mura, 2019) and used in a wide range of projects that required the collection of high quality image data, from sub-micron resolution microscopy (Aidukas, Eckert, Harvey,