Reviewerly/exHarmony huggingface dataset:
In Code(rs) We Trust: Software Developers as Fiduciaries in Public Blockchains

This chapter addresses the myth of decentralized governance of public blockchains, arguing that certain people who create, operate, or reshape them function much like fiduciaries of those who rely on these powerful data structures. Explicating the crucial functions that leading software developers perform, the chapter compares the role to Tamar Frankel’s conception of a fiduciary, and finds much in common, as users of these technologies place extreme trust in the leading developers to be both competent and loyal (ie, to be free of conflicts of interest). The chapter then frames the cost-benefit analysis necessary to evaluate whether, on balance, it is a good idea to treat these parties as fiduciaries, and outlines key questions needed to flesh out the fiduciary categorization. For example, which software developers are influential enough to resemble fiduciaries? Are all users of a blockchain ‘entrustors’ of the fiduciaries who operate the blockchain, or only a subset of those who rely on the blockchain? Finally, the chapter concludes with reflections on the broader implications of treating software developers as fiduciaries, given the existing accountability paradigm that largely shields software developers from liability for the code they create.
MobilityMirror: Bias-Adjusted Transportation Datasets

We describe customized synthetic datasets for publishing mobility data. Companies are providing new transportation modalities, and their data is of high value for integrative transportation research, policy enforcement, and public accountability. However, these companies are disincentivized from sharing data not only to protect the privacy of individuals (drivers and/or passengers), but also to protect their own competitive advantage. Moreover, demographic biases arising from how the services are delivered may be amplified if released data is used in other contexts. We describe a model and algorithm for releasing origin-destination histograms that removes selected biases in the data using causality-based methods. We compute the origin-destination histogram of the original dataset then adjust the counts to remove undesirable causal relationships that can lead to discrimination or violate contractual obligations with data owners. We evaluate the utility of the algorithm on real data from a dockless bike share program in Seattle and taxi data in New York, and show that these adjusted transportation datasets can retain utility while removing bias in the underlying data.
A simple proof of Pitman–Yor’s Chinese restaurant process from its stick-breaking representation

Abstract For a long time, the Dirichlet process has been the gold standard discrete random measure in Bayesian nonparametrics. The Pitman-Yor process provides a simple and mathematically tractable generalization, allowing for a very flexible control of the clustering behaviour. Two commonly used representations of the Pitman-Yor process are the stick-breaking process and the Chinese restaurant process. The former is a constructive representation of the process which turns out very handy for practical implementation, while the latter describes the partition distribution induced. Obtaining one from the other is usually done indirectly with use of measure theory. In contrast, we propose here an elementary proof of Pitman-Yor’s Chinese Restaurant process from its stick-breaking representation.
Complementary Lipschitz continuity results for the distribution of intersections or unions of independent random sets in finite discrete spaces

We prove that intersections and unions of independent random sets in finite spaces achieve a form of Lipschitz continuity. More precisely, given the distribution of a random set $\Xi$, the function mapping any random set distribution to the distribution of its intersection (under independence assumption) with $\Xi$ is Lipschitz continuous with unit Lipschitz constant if the space of random set distributions is endowed with a metric defined as the $L_k$ norm distance between inclusion functionals also known as commonalities. Moreover, the function mapping any random set distribution to the distribution of its union (under independence assumption) with $\Xi$ is Lipschitz continuous with unit Lipschitz constant if the space of random set distributions is endowed with a metric defined as the $L_k$ norm distance between hitting functionals also known as plausibilities. Using the epistemic random set interpretation of belief functions, we also discuss the ability of these distances to yield conflict measures. All the proofs in this paper are derived in the framework of Dempster-Shafer belief functions. Let alone the discussion on conflict measures, it is straightforward to transcribe the proofs into the general (non necessarily epistemic) random set terminology.
Magnetic-Field Enhancement of Performance of Superconducting Nanowire Single-Photon Detector

We present SNSPDs from NbN nanowires shaped after square-spiral that allows an increase not only in critical currents but also an extension of spectral detection efficiencies by just applying an external magnetic field. Using negative electron-beam lithography with the positive resist for shaping nanowires, made it possible to reduce the inner bend radius. Consequently, the effect of critical-current enhancement in the magnetic field becomes stronger than it was demonstrated earlier. Here we achieved a 13% increase of the critical current in the magnetic field. We measured spectra of the single-photon detection efficiency in the wavelength range from 400 to 1100 nm in the magnetic field. At zero field, the square spiral has the spectrum similar to that of a meander. At the field providing the maximum of the critical current, the detection efficiency and the cut-off wavelength in the spectrum increase by 20% and by 54%, correspondingly. The magnetic-field dependence of dark count rate is well described by proposed analytical model.
Agent Embeddings: A Latent Representation for Pole-Balancing Networks

We show that it is possible to reduce a high-dimensional object like a neural network agent into a low-dimensional vector representation with semantic meaning that we callagent embeddings, akin to word or face embeddings. This can be done by collecting examples of existing networks, vectorizing their weights, and then learning a generative model over the weight space in a supervised fashion. We investigate a pole-balancing task, Cart-Pole, as a case study and show that multiplenew pole-balancing networks can be generated from their agent embeddings without direct access to training data from the Cart-Pole simulator. In general, the learned embedding space is helpful for mapping out the space of solutions for a given task. We observe in the case of Cart-Pole the surprising finding that good agents make different decisions despite learning similar representations, whereas bad agents make similar (bad) decisions while learning dissimilar representations. Linearly interpolating between the latent embeddings for a good agent and a bad agent yields an agent embedding that generates a network with intermediate performance, where the performance can be tuned according to the coefficient of interpolation. Linear extrapolation in the latent space also results in performance boosts, up to a point.
Application of force and inertial sensors to monitor gait on legacy walkers

&lt;p class="Abstract"&gt;Walker assistive devices play an important role in extending the autonomy of elderly people and in recovering the mobility of people affected by locomotion disabilities. The next generation of walkers are hoped to include embedded sensors and data processing capabilities that will allow for the extraction of objective metrics (about gait and body posture) to assist the work of physiotherapists and to enable the self-control nature of walker usage. The paper presents the &lt;em&gt;Andante&lt;/em&gt;, our latest proposal of a smart walker intended to monitor and analyze gait in real time. The system makes use of e-textile electrodes to sense the heart rate of the user, load cells to measure the forces applied on the walker legs, and an inertial measurement unit to sense motion and orientation. These signals are sampled locally and transferred over a Bluetooth link to a remote host, where they are processed in real time. Data processing includes the detection, classification, and characterization of the steps. A rich set of parameters is presented for each step, including estimates of balance and motor coordination, step length and azimuth, and lift of the walker frame. This information can be used by physiotherapist to objectively assess the physical condition of the user and tune rehabilitation therapy if needed. The proposed solution can be easily integrated into any commercial walker without any loss of functionality.&lt;/p&gt;
Cooperative Set Homomorphic Proofs for Data Possession Checking in Clouds

Outsourcing an increasing amount of data to a third party raises a number of security and privacy challenges, namely remote data integrity verification. Indeed, proofs for data possession checking address the verification that some previously outsourced data blocks across multiple storing nodes are correctly stored and fully available. In this paper, we propose a new set homomorphic proof of data possession, referred to as SHoPS, supporting several operations like aggregation of proofs. SHoPS is a deterministic Proof of Data Possession (PDP) scheme, based on an interactive proof protocol. Our approach has several advantages. First, it enables several proofs to be aggregated and a subset of data files' proofs to be verified, while providing an attractive communication overhead. Second, it supports public verifiability where the verification process can be delegated to another entity, thus releasing the data owner from the cumbersome task of periodical verifications. Third, SHoPS is efficient and provably secure, as it is resistant to the fraudulence of the prover and the leakage of verified data. Finally, a theoretical performances' analysis shows that SHoPS performs better in terms of functionality, communication and computation overhead compared to closely related works and experimental results point out the applicability of the proposed scheme in real world scenarios.
Design of 4-Bit Reversible Johnson Counter with Optimized Quantum Cost, Delay, and Number of Gate

In this paper, we have proposed the design quantum cost, delay, and number of reversible logic gate optimized 4-bit Johnson counter using existing reversible logic gate. The proposed work is designed using HNF gate and Feynman gate. The proposed work is derived with quantum cost (QC), delay (D), constant inputs (CI), garbage output (GO), and number of gates (GC). The QC, D, and GC of the proposed Johnson counter are 21, 5, and 5, respectively. The percentage (%) of improvement is 12.5, 37.5, and 37.5% w.r.t. latest reported result.
Generalized Dirichlet-process-means for f-separable distortion measures

DP-means clustering was obtained as an extension of $K$-means clustering. While it is implemented with a simple and efficient algorithm, it can estimate the number of clusters simultaneously. However, DP-means is specifically designed for the average distortion measure. Therefore, it is vulnerable to outliers in data, and can cause large maximum distortion in clusters. In this work, we extend the objective function of the DP-means to $f$-separable distortion measures and propose a unified learning algorithm to overcome the above problems by selecting the function $f$. Further, the influence function of the estimated cluster center is analyzed to evaluate the robustness against outliers. We demonstrate the performance of the generalized method by numerical experiments using real datasets.
Multiplayer games as extension of misère games

We introduce i-misère play to multiplayer impartial games, which is an extension of Li’s rank-based play and, at the same time, which contains the normal play and the misère play as special cases when the number of players is two. We characterize losing positions of such plays for both NIM and Moore’s game. i-misère play is defined as a special case of a more general notion of preference-based play, and we also study properties of some other preference-based plays.
The Standard Model and Beyond

The 20th century brought us enormous advances in understanding the most basic constituents of space, time, and matter. All known particles and forces are assembled and described in what is known as "The Standard Model". This allows us to calculate precisely what happens when particles collide with energies up to the equivalent of a trillion Volt for every charged particle. But there still is unexplored terrain: what exactly happens beyond those trillion Volts, and how exactly does the gravitational force enter into the picture? Gravity can turn space and time into "black holes", and when these black holes interact with particles, our mathematical tools fall short.
Evolutionary Construction of Convolutional Neural Networks

Neuro-Evolution is a field of study that has recently gained significantly increased traction in the deep learning community. It combines deep neural networks and evolutionary algorithms to improve and/or automate the construction of neural networks. Recent Neuro-Evolution approaches have shown promising results, rivaling hand-crafted neural networks in terms of accuracy. A two-step approach is introduced where a convolutional autoencoder is created that efficiently compresses the input data in the first step, and a convolutional neural network is created to classify the compressed data in the second step. The creation of networks in both steps is guided by an evolutionary process, where new networks are constantly being generated by mutating members of a collection of existing networks. Additionally, a method is introduced that considers the trade-off between compression and information loss of different convolutional autoencoders. This is used to select the optimal convolutional autoencoder from among those evolved to compress the data for the second step. The complete framework is implemented, tested on the popular CIFAR-10 data set, and the results are discussed. Finally, a number of possible directions for future work with this particular framework in mind are considered, including opportunities to improve its efficiency and its application in particular areas.
Real-time Collision Avoidance Motion Planning using Vehicle Lateral Angular Velocity Constraints

This paper proposes a method for motion planning of a vehicle considering lateral angular velocity constraints. Motion planning is very important for the safe travel of an autonomous vehicle. In this work, the steering angle of the vehicle is included in the cost function in order to achieve smooth operation of the vehicle. and path optimization uses dynamic programming to select the paths that satisfy the constraint with the least cost. After selecting the optimal path, the longitudinal velocity profile is determined using a fuzzy speed controller. The steering input of the vehicle and the distance from the obstacle to follow the path are used as the input of the fuzzy speed controller, and the controller provides longitudinal acceleration and deceleration of the vehicle as the output. Experiments were conducted for three scenarios using vehicles equipped with Lidar, IMU, GPS, and cameras. Experimental results show that the proposed motion plan generates a path within the steering constraint condition of the vehicle and runs effectively.
Regularized inversion of flow size distribution

In this paper, we revisit the estimation of the size distribution of packet flows in Internet traffic through an inversion approach for several packet sampling schemes which are based on probabilistic sampling (PS). We first study the statistical properties of the previously introduced inversion estimator in its general form and make connections to the singular value decomposition. This motivates the use of a regularization technique in the estimation of the flow size distribution. More specifically, a penalized weighted least square approach is proposed. We compare theoretically the penalized estimator under simplified assumptions against the (non-penalized) inversion approach in order to explain differences in their statistical behaviors. A data study with two real traces shows that the proposed penalized estimator outperforms the inversion estimator for all sampling schemes, corroborating the theoretical analysis. This work reveals that the simplest sampling schemes based on PS, that do not work with small sampling probabilities under the inversion approach, can be used with the penalized approach. Furthermore, the penalized approach allows considering smaller packet sampling rates for all the other sampling schemes.
Research on error-correction algorithm of high-speed QKD system based on FPGA

In the process of quantum key distribution (QKD), error correction algorithm is used to correct the error bits of the key at both ends. The existing applied QKD system has a low key rate and is generally Kbps of magnitude. Therefore, the performance requirement of data processing such as error correction is not high. In order to cope with the development demand of high-speed QKD system in the future, this paper introduces the Winnow algorithm to realize high-speed parity and hamming error correction based on Field Programmable Gate Array (FPGA), and explores the performance limit of this algorithm. FPGA hardware implementation can achieve the scale of Mbps bandwidth, with choosing different group length of sifted key by different error rate, and can achieve higher error correction efficiency by reducing the information leakage in the process of error correction, and improves the QKD system’s secure key rate, thus helping the future high-speed QKD system.
Ecological Risk Assessment Of Heavy Metals In Topsoil Around Major Industries Of Ardakan City

Introduction:Today, the contamination caused by rapid rate of developing industries has been drawing attention from scientific and environmental communities to itself. This study was conducted to investigate the concentration and distribution of heavy metals in topsoil of industrial area in Ardakan city.&#x0D; Methods: For this porous, sampling of topsoil (0-5 cm) was performed using 5 transects in an area of 20000 hectares. Concentrations of As, Cd, Cr, Ni, Mo, Pb, V and Zn were measured in 45 samples based on ICP-OES method. Then, Indices including: Contamination level of heavy metal ( ), Potential Ecological Risk of Heavy Metals ( ) and Total Potential Ecological Risk (RI) were calculated. Also Pearson’s correlation was used to assess the relationship between heavy metal’s concentrations.&#x0D; Results: Results indicated that mean value concentrations of As, Cd, Cr, Mo, Ni, Pb, V and Zn were repectivley 9.85, 0.31, 35.33, 1.001, 43.28, 18.51, 44.8 and 78.3 (mg/kg) in topsoil of studied area. Maximum concentrations of all heavy metals was found in samples collected from downwind and near the major industries except for Mo and Ni. and RI indices also showed an increase downwind areas. Strongest correlations were found between Cr-Ni, Cd-Zn and As-Cr which indicates that these metals probably have the same source.&#x0D; Conclusion: Although the ecological risk of the whole area was low in the risk class, the increase in ecological risk in the surrounding and lower parts of factories such as steel and pelletizing, and tile and ceramic industries indicated the dominant role in the distribution and spread of pollution and the necessity of attention to the site selection studies of these industries. Also the results of the present study indicated the necessity of preventive measurements such as Phytoremediation in the lower parts of industrial area of this region.&#x0D;
Interactive Visualization of Transformer Diagnosis Based on Multiple Views

Transformer is one of the most expensive and critical equipment in power industry. The research on condition monitoring and fault diagnosis of transformer is significant for the stability of power system. However, transformer diagnosis involves many factors, so it is very difficult to evaluate transformer operation state effectively and accurately by single diagnosis method. In this paper, we present a novel interactive visualization model of transformer diagnosis based on multiple views.This model imitates multi-angle analysis by combining multi-source information including operation information, experiment information and environmental information through multi-view collaborative visualization. Data are displayed in different dimensional and a variety of interaction technology are used so that analysts can access the original data from multiple perspectives. With the help of visual perception of human eyes and intelligent cognitive ability of human brain, users can find useful information from large-scale, high-dimensional, or even incomplete data to make effective decisions.
MSFA: Multiple System Fingerprint Attack Scheme for IoT Anonymous Communication

For the past few years, Internet of Things (IoT) has developed rapidly and been extensively used. However, its transmission security and privacy protection are insufficient, which limits the development of IoT to a certain extent. As a technology of IoT information transmission, anonymous communication technology comes into being as an important means to ensure the security of healthcare data, which can better protect users’ privacy in some ways. Nowadays, a variety of attack techniques for anonymous communication systems have been proposed by the academic community to track senders and receivers or discover communications between two users. Thus, the MSFA (Multiple System Fingerprint Attack) scheme for anonymous communication systems is presented in this paper where the MSFA scheme architecture, implementation in the Tor environment, and experimental data processing are described. Through a comparative analysis between two traces of visiting the same website based on the edit distance, it is shown that the longer the length of the site traffic data, the greater the edit distance of the site access traffic and the larger the range.
TASS 2018: The Strength of Deep Learning in Language Understanding Tasks

The edition of TASS in 2018 was the edition of the evolution of TASS to a competitive evaluation workshop on semantic and text understanding tasks. Consequently, TASS has enlarged the coverage of tasks, and it goes beyond sentiment analysis. Thereby, two new tasks focused on semantic relation extraction in the health domain and emotion classification in the news domain were added to the two traditional tasks of TASS, namely sentiment analysis at tweet level and aspect level. Several systems were submitted, and most of them are based on state of the art classification methods, which highlight those ones grounded in Deep Learning. As addition contribution, TASS 2018 released two new corpora, specifically the ones of the two new tasks.
A profile for Resource Tagged Attestations (RTAs)

This document defines a Cryptographic Message Syntax (CMS) profile fora general purpose Resource Tagged Attestation (RTA), for use theResource Public Key Infrastructure (RPKI). The objective is to allowan attestation, in the form of an arbitrary digital object, to besigned with resources, and for validation to provide an outcome ofvalid The profile is intended to provide for thesigning of an attestation an arbitrary set of resources.