Reviewerly/exHarmony huggingface dataset:
Judging Actions on the Basis of Prima Facie Duties. The case of self-driving cars

The need for a logic that allows us to reason about conflicting and non-conflicting norms has recently emerged in the domain of self-driving cars. In this paper we propose a formal model that supports moral decisions making by autonomous agents such as for example autonomous vehicles. Such a model – which we call a “Deontic Machine” – helps resolve both typical and atypical moral and legal situations that agents may encounter. The Deontic Machine has two sources of inspiration. The first one is W.D. Ross’s theory of prima facie norms and the other one is a deontic multi-valued logic. The main contribution of this paper is bringing together conceptual and technical tools of deontic logic to show how they can be used to control or assess the behaviour of a self-driving car.
A Practical Approach to Optical Switching in Data Centers

Optical switching can address the energy and bandwidth scaling challenges facing data center networks, but its adoption is impeded by the cost and complexity of associated hardware and control mechanisms. This paper discusses an approach that reduces cost and complexity by co-designing the hardware and overall network architecture.
A diagram of confidentiality of information during a traffic offence

In this paper we introduce one of the most famous problems on confidentiality of information during a traffic offence. In particular, we will propose a new diagram of confidentiality of the data captured by a radar during a traffic offence. The distribution of the data is organized by an authority which ensures the authentication between the radar which captures an image of the infraction automobile, the penalizer which receives the masked image of the automobile to be able to penalize it and the automobile which will receive a message of penalisation; all communications are made in real time via an unsecured channel.
Efficient Contextual Representation Learning Without Softmax Layer

Contextual representation models have achieved great success in improving various downstream tasks. However, these language-model-based encoders are difficult to train due to the large parameter sizes and high computational complexity. By carefully examining the training procedure, we find that the softmax layer (the output layer) causes significant inefficiency due to the large vocabulary size. Therefore, we redesign the learning objective and propose an efficient framework for training contextual representation models. Specifically, the proposed approach bypasses the softmax layer by performing language modeling with dimension reduction, and allows the models to leverage pre-trained word embeddings. Our framework reduces the time spent on the output layer to a negligible level, eliminates almost all the trainable parameters of the softmax layer and performs language modeling without truncating the vocabulary. When applied to ELMo, our method achieves a 4 times speedup and eliminates 80% trainable parameters while achieving competitive performance on downstream tasks.
A Hybrid Formal Verification System in Coq for Ensuring the Reliability and Security of Ethereum-based Service Smart Contracts

This paper reports on the development of a formal symbolic process virtual machine (FSPVM) denoted as FSPVM-E for verifying the reliability and security of Ethereum-based services at the source code level of smart contracts, and a Coq proof assistant is employed for both programming the system and for proving its correctness. The current version of FSPVM-E adopts execution-verification isomorphism, which is an application extension of Curry-Howard isomorphism, as its fundamental theoretical framework to combine symbolic execution and higher-order logic theorem proving. The four primary components of FSPVM-E include a general, extensible, and reusable formal memory framework, an extensible and universal formal intermediate programming language denoted as Lolisa, which is a large subset of the Solidity programming language using generalized algebraic datatypes, the corresponding formally verified interpreter of Lolisa, denoted as FEther, and assistant tools and libraries. The self-correctness of all components is certified in Coq. Currently, FSPVM-E supports the ERC20 token standard, and can automatically and symbolically execute Ethereum-based smart contracts, scan their standard vulnerabilities, and verify their reliability and security properties with Hoare-style logic in Coq. To the best of authors' knowledge, the present work represents the first hybrid formal verification system implemented in Coq for Ethereum smart contracts that is applied at the Solidity source code level.
Peer-to-Peer Trading and Blockchains: Enabling Regional Energy Markets and Platforms for Energy Transactions

Decarbonization has driven the uptake of variable renewable energy resources, both utility scale and distributed. This is driving the development of new business models of how electricity is distributed and managed on the network. One such model, peer-to-peer trading, allows consumers, prosumers, and prosumagers to trade energy between themselves, both lowering costs while providing balancing services to the network. Enabling technologies such as blockchains can help build trust and track transactions efficiently, securely and at low cost. This chapter examines the factors governing successful deployment of such systems and technical and regulatory challenges.
The Use of Tangible User Interface in Interactive System to Learn about Countries

Tangible User Interface (TUI) is an interface that allows user to interact with digital information through physical environment. We have developed an interactive system serves the educational field within the geographical domain. The system allows children to discover and learn about countries from different continents using tangible objects. In our project we selected so far six countries that represent six continents in the world. The selected countries are: Saudi Arabia, Egypt, United States of America, France, Brazil, and Australia. With the system, children are required to manipulate tangible objects. Their task is to select a landmark from a country and placing it on the table top. The system will recognize the object and provides users with feedback. The system involves the recognition of six elements for each country: (capital, flag, climate, currency, population and industries). This application has been evaluated in a field study with children 9–11 years of age. The initial results are promising and show that such an interactive system can support interaction and collaboration among young children, as well as enriches the learning process and makes it more enjoyable.
QLMC-HD: Quasi Large Margin Classifier based on Hyperdisk

In the area of data classification, the different classifiers have been developed by their own strengths and weaknesses. Among these classifiers, we propose a method that is based on the maximum margin between two classes. One of the main challenges in this area is dealt with noisy data. In this paper, our aim is to optimize the method of large margin classifiers based on hyperdisk (LMC-HD) and combine it into a quasisupport vector data description (QSVDD) method. In the proposed method, the bounding hypersphere is calculated based on the QSVDD method. So our convex class model is more robust compared with the support vector machine (SVM) and less tight than LMC-HD. Large margin classifiers aim to maximize the margin and minimizing the risk. Since our proposed method ignores the effect of outliers and noises, so this method has the widest margin compared with other large margin classifiers. In the end, we compare our proposed method with other popular large margin classifiers by the experiments on a set of standard data which indicates our results are more efficient than the others
CyberPaths

The primary purpose of this workshop is to help undergraduate liberal arts colleges integrate cyber security seamlessly into the curriculum with little to no additional investment. We believe Liberal Arts Colleges can provide to the cyber security workforce well-rounded individuals, equipped to tackle new and complex problems. However, liberal arts colleges rarely have the resources needed to provide the hands-on labs critical to attracting students to cyber security classes and for preparing students for the workforce. Our NSF funded project 'CyberPaths" addresses this by devising several active learning labs on topics such as ransomware, authentication, Distributed Denial of Service (DDoS), and remote network attacks. These labs are implemented on the cloud infrastructure GENI, an NSF funded free academic cloud infrastructure for realistic networking experimentation. We have developed a variety of labs that are suitable for non-CS majors as well as CS upper level courses. In addition, we propose interdisciplinary cyber security modules and multiple paths that can lead to the cyber security professions, starting from the general education courses, moving to an intro to cyber security course, and upper level CS courses. Our goal is to offer solutions to liberal arts institutions with cyber security active learning and introducing cyber security early in the curriculum with low overhead. This workshop will be highly interactive using a combination of hands-on labs and discussions. If you wish to do the hands-on exercises please bring a laptop with a browser and ssh client installed.
Graph and String Parameters: Connections Between Pathwidth, Cutwidth and the Locality Number

We investigate the locality number, a recently introduced structural parameter for strings (with applications in pattern matching with variables), and its connection to two important graph-parameters, cutwidth and pathwidth. These connections allow us to show that computing the locality number is NP-hard but fixed parameter tractable (when the locality number or the alphabet size is treated as a parameter), and can be approximated with ratio $O(\sqrt{\log{opt}} \log n)$. As a by-product, we also relate cutwidth via the locality number to pathwidth, which is of independent interest, since it improves the currently best known approximation algorithm for cutwidth. In addition to these main results, we also consider the possibility of greedy-based approximation algorithms for the locality number.
Design education can change the world

column Share on Design education can change the world Author: Kristian T. Simsarian California College of the Arts California College of the ArtsView Profile Authors Info & Claims InteractionsVolume 26Issue 2March - April 2019 pp 36–43https://doi.org/10.1145/3305362Published:22 February 2019Publication History 1citation1,181DownloadsMetricsTotal Citations1Total Downloads1,181Last 12 Months267Last 6 weeks36 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Experimental demonstration of an optical neuron with a logistic Sigmoid activation function

We experimentally demonstrate an optical neuron using an optical logistic Sigmoid activation function. Successful thresholding at 4 different power levels was achieved yielding a 100% improvement compared to state-of-the-art, employing a sequence of 100psec long pulses.
SAFE ML: Surrogate Assisted Feature Extraction for Model Learning

Complex black-box predictive models may have high accuracy, but opacity causes problems like lack of trust, lack of stability, sensitivity to concept drift. On the other hand, interpretable models require more work related to feature engineering, which is very time consuming. Can we train interpretable and accurate models, without timeless feature engineering? In this article, we show a method that uses elastic black-boxes as surrogate models to create a simpler, less opaque, yet still accurate and interpretable glass-box models. New models are created on newly engineered features extracted/learned with the help of a surrogate model. We show applications of this method for model level explanations and possible extensions for instance level explanations. We also present an example implementation in Python and benchmark this method on a number of tabular data sets.
An Information Criterion for Auxiliary Variable Selection in Incomplete Data Analysis

Statistical inference is considered for variables of interest, called primary variables, when auxiliary variables are observed along with the primary variables. We consider the setting of incomplete data analysis, where some primary variables are not observed. Utilizing a parametric model of joint distribution of primary and auxiliary variables, it is possible to improve the estimation of parametric model for the primary variables when the auxiliary variables are closely related to the primary variables. However, the estimation accuracy reduces when the auxiliary variables are irrelevant to the primary variables. For selecting useful auxiliary variables, we formulate the problem as model selection, and propose an information criterion for predicting primary variables by leveraging auxiliary variables. The proposed information criterion is an asymptotically unbiased estimator of the Kullback-Leibler divergence for complete data of primary variables under some reasonable conditions. We also clarify an asymptotic equivalence between the proposed information criterion and a variant of leave-one-out cross validation. Performance of our method is demonstrated via a simulation study and a real data example.
Entrepreneurial Competencies in Higher Education

Entrepreneurship education has a great success in education field. During the last decades, it became widely spread in the USA, Asian and European countries. As a result, the number of various pedagogic approaches and courses that are devoted to the field-specific themes within entrepreneurial activity, drastically increased. Nevertheless, the majority of the existing programs and techniques in different countries (including Russian Federation) have some deficiencies in their theoretical and practical parts that influence the students’ attitude towards entrepreneurial activity. That is why, they need to be corrected. Guided by this purpose, the authors conducted a survey to identify the students’ attitude towards entrepreneurial activity and the level of their readiness to participate in it. Besides, the purpose of the research was to reveal the deficiencies of educational programs that can result in the students’ lack of readiness for entrepreneurial activity. The research is oriented towards the audience that is more universal in its composition. This will allow to reveal general problem that characterizes the domestic education. That is why, the research was conducted in different universities of four Russian cities. The survey was focused upon three aspects: the students’ attitude towards entrepreneurship, the factors of their business-development and the students’ evaluation of the practical and theoretical content of education programs in universities. 710 four-year students took part in the survey. The results of the research pointed to the students’ general lack of readiness for entrepreneurial activity. Besides, the research allowed to identify a low effectiveness level of education programs as the reason of this situation. It is conditioned by a small number of field-specific disciplines, by the absence of the programs and probations, by the lack of techniques that develop entrepreneurial spirit. These results will allow the administration of higher education institutions to revise all the critical remarks that were formulated by the students, to introduce the lacking techniques, to correct the content of the education program in order to exclude the disciplines that do not directly relate to the students’ specialization and to address the problem of the advanced training of the faculty staff in order to increase the quality of the education process.
Efficient Private Algorithms for Learning Halfspaces.

We present new differentially private algorithms for learning a large-margin halfspace. In contrast to previous algorithms, which are based on either differentially private simulations of the statistical query model or on private convex optimization, the sample complexity of our algorithms depends only on the margin of the data, and not on the dimension.
Using Raspberry Pi as a Platform for Teaching Cybersecurity Concepts

This workshop aims to assist high school and college educators with implementing cybersecurity into their existing curriculum. The workshop will introduce Raspberry Pi as a teaching platform for topics such as network traffic analysis, cryptography and web security. We will discuss the Raspberry Pi hardware and software requirements, and will demonstrate network traffic analysis using Wireshark. Participants will receive a fully configured Raspberry Pi kit to use during the workshop and then take home at the end. Manuals on Raspberry Pi configuration will be shared for both Windows and Mac platforms, along with sample lesson plans on network analysis and its security implications. The workshop will send all participants home with flexible tools and new expertise in implementing cybersecurity curricula. Bring a laptop with an Ethernet port or adapter, and ensure that you have full administrator privileges to configure settings and install software on your device. Seats are limited.implementing cybersecurity curricula. Bring a laptop with an Ethernet port or adapter, and ensure that you have full administrator privileges to configure settings and install software on your device. Seats are limited.
Quantum Information Processing with Superconducting Nanowire Single-Photon Detectors

Superconducting nanowire single-photon detector(SNSPD) has been one of the important ingredients for photonic quantum information processing (QIP). In order to see the potential of SNSPDs, I briefly review recent progresses of the photonic QIP with SNSPDs implemented for various purposes and present a possible direction for the development of SNSPDs.
The Arboricity Captures the Complexity of Sampling Edges

In this paper, we revisit the problem of sampling edges in an unknown graph $G = (V, E)$ from a distribution that is (pointwise) almost uniform over $E$. We consider the case where there is some a priori upper bound on the arboriciy of $G$. Given query access to a graph $G$ over $n$ vertices and of average degree $d$ and arboricity at most $α$, we design an algorithm that performs $O\!\left(\fracα{d} \cdot \frac{\log^3 n}{\varepsilon}\right)$ queries in expectation and returns an edge in the graph such that every edge $e \in E$ is sampled with probability $(1 \pm \varepsilon)/m$. The algorithm performs two types of queries: degree queries and neighbor queries. We show that the upper bound is tight (up to poly-logarithmic factors and the dependence in $\varepsilon$), as $Ω\!\left(\fracα{d} \right)$ queries are necessary for the easier task of sampling edges from any distribution over $E$ that is close to uniform in total variational distance. We also prove that even if $G$ is a tree (i.e., $α= 1$ so that $\fracα{d}=Θ(1)$), $Ω\left(\frac{\log n}{\log\log n}\right)$ queries are necessary to sample an edge from any distribution that is pointwise close to uniform, thus establishing that a $\mathrm{poly}(\log n)$ factor is necessary for constant $α$. Finally we show how our algorithm can be applied to obtain a new result on approximately counting subgraphs, based on the recent work of Assadi, Kapralov, and Khanna (ITCS, 2019).
An assessment of decoverslipping methods on histology slides

Histology laboratories are often required to decoverslip and restain older slides when the original staining on the section has faded or the mountant used for coverslipping has caused discoloration on the section. The current method of decoverslipping is time-consuming and three possible techniques for rapid coverslip removal have been compared for use in histopathology laboratories. Slides aged between 14 and 22 years were randomly selected to have their coverslip removed by liquid nitrogen, ultrasonic vibration, or freezing at −80°C. Both liquid nitrogen and −80°C cooling required a microtome blade to pry the coverslip from the slide following the rapid freezing. Liquid nitrogen and ultrasonic vibrations were both deemed unacceptable options for histological use due to cracking of the slide and loss of tissue. The cooling of slides in a −80°C freezer for 5–10 minutes had no tissue loss and the histological morphology remained intact. Therefore, freezing slides in a −80°C freezer is a suitable alternative from the commonly used xylene immersion removal.
Statistics for products of traces of high powers of the Frobenius class of hyperelliptic curves in even characteristic

We study the averages of products of traces of high powers of the Frobenius class of real hyperelliptic curves of genus [Formula: see text] over a fixed finite field [Formula: see text] in both odd and even characteristic cases.
Asymmetric Single Magnitude Four Error Correcting Codes

Limited magnitude asymmetric error model is well suited for flash memory. In this paper, we consider the construction of asymmetric codes correcting single error over $\mathbb{Z}_{2^{k}r}$ and which are based on so called $B_{1}[4](2^{k}r)$ set. In fact, we reduce the construction of a maximal size $B_{1}[4](2^{k}r)$ set for $k\geq3$ to the construction of a maximal size $B_{1}[4](2^{k-3}r)$ set. Finally, we give a explicit formula of a maximal size $B_{1}[4](4r)$ set and some lower bounds of a maximal size $B_{1}[4](2r)$ set. By computer searching up to $q\leq106$, we conjecture that those lower bounds are tight.
Accelerating Training of Deep Neural Networks with a Standardization Loss

A significant advance in accelerating neural network training has been the development of normalization methods, permitting the training of deep models both faster and with better accuracy. These advances come with practical challenges: for instance, batch normalization ties the prediction of individual examples with other examples within a batch, resulting in a network that is heavily dependent on batch size. Layer normalization and group normalization are data-dependent and thus must be continually used, even at test-time. To address the issues that arise from using explicit normalization techniques, we propose to replace existing normalization methods with a simple, secondary objective loss that we term a standardization loss. This formulation is flexible and robust across different batch sizes and surprisingly, this secondary objective accelerates learning on the primary training objective. Because it is a training loss, it is simply removed at test-time, and no further effort is needed to maintain normalized activations. We find that a standardization loss accelerates training on both small- and large-scale image classification experiments, works with a variety of architectures, and is largely robust to training across different batch sizes.
Active Estimation of 3D Lines in Spherical Coordinates

Straight lines are common features in human made environments, which makes them a frequently explored feature for control applications. Many control schemes, like Visual Servoing, require the 3D parameters of the features to be estimated. In order to obtain the 3D structure of lines, a nonlinear observer is proposed. However, to guarantee convergence, the dynamical system must be coupled with an algebraic equation. This is achieved by using spherical coordinates to represent the line's moment vector, and a change of basis, which allows to introduce the algebraic constraint directly on the system's dynamics. Finally, a control law that attempts to optimize the convergence behavior of the observer is presented. The approach is validated in simulation, and with a real robotic platform with a camera onboard.