Reviewerly/exHarmony huggingface dataset:
One Step Away from Technology but One Step Towards Domain Experts—MDRBridge: A Template-Based ISO 11179-Compliant Metadata Processing Pipeline

Secondary use of routine medical data relies on a shared understanding of given information. This understanding is achieved through metadata and their interconnections, which can be stored in metadata repositories (MDRs). The necessity of an MDR is well understood, but the local work on metadata is a time-consuming and challenging process for domain experts.To support the identification, collection, and provision of metadata in a predefined structured manner to foster consolidation. A particular focus is placed on user acceptance.We propose a software pipeline MDRBridge as a practical intermediary for metadata capture and processing, based on MDRSheet, an ISO 11179-3 compliant template using popular spreadsheet software. It serves as a practical mediator for metadata acquisition and processing in a broader pipeline. Due to the different origins of the metadata, both manual entry and automatic extractions from application systems are supported. To enable the export of collected metadata into external MDRs, a mapping of ISO 11179 to Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) was developed.MDRSheet is embedded in the processing pipeline MDRBridge and delivers metadata in the CDISC ODM format for further use in MDRs. This approach is used to interactively unify core datasets, import existing standard datasets, and automatically extract all defined data elements from source systems. The involvement of clinical domain experts improved significantly due to minimal changes within their usual work routine.A high degree of acceptance was achieved by adapting the working methods of clinical domain experts. The designed process is capable of transforming all relevant data elements according to the ISO 11179-3 format. MDRSheet is used as an intermediate format to present the information at a glance and to allow editing or supplementing by domain experts.
Quantum key distribution with no shared reference frame

Any quantum communication task requires a common reference frame (i.e., phase, coordinate system). In particular, quantum key distribution requires different bases for preparation and measurements of states which are obviously based on the existence of a common frame of reference. Here, we show how QKD can be achieved in the absence of any common frame of reference. We study the coordinate reference frame, where the two parties do not even share a single direction, but the method can be generalized to other general frames of reference, pertaining to other groups of transformations.
The SweLL Language Learner Corpus

The article presents a new language learner corpus for Swedish, SweLL, and the methodology from collection and pesudonymisation to protect personal information of learners to annotation adapted to second language learning. The main aim is to deliver a well-annotated corpus of essays written by second language learners of Swedish and make it available for research through a browsable environment. To that end, a new annotation tool and a new project management tool have been implemented, – both with the main purpose to ensure reliability and quality of the final corpus. In the article we discuss reasoning behind metadata selection, principles of gold corpus compilation and argue for separation of normalization from correction annotation.
Otsu Multi-Threshold Image Segmentation Algorithm Based on Improved Particle Swarm Optimization

The Maximum Inter-Class Variance Method (Otsu) is a commonly used threshold segmentation method in image segmentation. It has a significant effect for single-threshold segmentation, but for multi-threshold segmentation, the computational complexity is large. To reduce complexity, further optimization techniques can be employed to find the optimal multi-level threshold. This paper proposes an improved multi-threshold segmentation algorithm based on improved particle swarm optimization (IPSO) for finding the optimal threshold. The general particle swarm optimization algorithm has two major problems: dimension disaster and easy to fall into local optimum. Improved particle swarm optimization decomposes high-dimensional groups into multiple one-dimensional groups. These one-dimensional groups exchange information with each other to generate overall fitness values, and then in each one-dimensional group, particles with fitness values smaller than the average fitness of the entire population. Wavelet variability is performed to prevent particles from falling into local optimum. Finally, a simulation experiment is carried out to compare the results with the existing particle swarm optimization maximum interclass variance algorithm. Experimental results show that the method has a faster convergence speed and a better Otsu threshold.
Single Real Goal, Magnitude-Based Deceptive Path-Planning

Deceptive path-planning is the task of finding a path so as to minimize the probability of an observer (or a defender) identifying the observed agent’s final goal before the goal has been reached. It is one of the important approaches to solving real-world challenges, such as public security, strategic transportation, and logistics. Existing methods either cannot make full use of the entire environments’ information, or lack enough flexibility for balancing the path’s deceptivity and available moving resource. In this work, building on recent developments in probabilistic goal recognition, we formalized a single real goal magnitude-based deceptive path-planning problem followed by a mixed-integer programming based deceptive path maximization and generation method. The model helps to establish a computable foundation for any further imposition of different deception concepts or strategies, and broadens its applicability in many scenarios. Experimental results showed the effectiveness of our methods in deceptive path-planning compared to the existing one.
Multimedia IPP codes with efficient tracing

Wu et al. (IEEE Signal Process Mag 21:15–27, 2004) showed that fingerprinting system can be designed according to different criteria: catching one, catching many and catching all. For the case of “catching many”, binary multimedia identifiable parent property codes (t-MIPPCs) were introduced to construct fingerprints resistant to the averaging collusion attack on multimedia contents. In this paper, we focus on such a case, and introduce the binary strongly multimedia identifiable parent property code (t-SMIPPC) whose tracing algorithm is more efficient than that of a binary t-MIPPC. Then a concatenation construction for binary t-SMIPPCs from q-ary t-SMIPPCs is provided. Moreover, we establish a probabilistic lower bound on q-ary t-SMIPPCs, whose code rate is asymptotically as good as that of q-ary t-MIPPCs. Finally, several infinite series of optimal q-ary t-SMIPPCs of length 2 with $$t = 2, 3$$ are derived from the relationships among t-SMIPPCs and other fingerprinting codes, such as t-MIPPCs and $$\overline{t}$$-separable codes.
Fraction-Order Total Variation Image Blind Restoration Based on Self-Similarity Features

To improve the artifacts of the restoration results restored by existing blind restoration method, an effective image blind restoration method using self-similarity as prior information is proposed for restoring the blurry images.Firstly, the fraction-order model is achieved by extending integer-order total variation, which is prone to reduce artifacts.Motivated by the fact that the introduction of prior information is beneficial to improve the restoration results, we found that natural images usually exhibit some texture features.Self-similarity is a popular texture features and well-defined in the statistics.Therefore, this texture feature is introduced as prior information for the restoration model and further improving the restoration results.Finally, the cost function is generated and solved by semi-quadratic regularization.Experiments on various natural images showed that the proposed method can improve the performance relative to other image blind restoration algorithms in terms of both subjective vision and objective evaluation.The subjective analysis revealed that the proposed algorithm resulted in improved translation and improved artifact appearance.The objective evaluation showed that the proposed algorithm showed the best evaluation values, including Structural Similarity and Peak Signal-to-noise ratio.The restoration results of various images reveal that the proposed method is practical and effective in image restoration.
From Service Role to Partnership: Faculty Voices on Collaboration with Librarians

Librarians at two research universities surveyed faculty practices and views about collaboration with librarians to gain insight into likely partners and strategies for information literacy (IL) instruction. Quantitative data on methods of collaboration revealed that the most often-practiced method of working together was having a librarian deliver an instruction session, followed by jointly developing an online course guide. Statistical differences by disciplinary area and years teaching were examined; experienced faculty reported a greater range of collaboration methods. Thematic analysis of open-ended responses on motives for collaboration, a factor less frequently studied, found that the top reason was to improve student IL skills, followed by to benefit from librarian expertise.
A Comparison of Random Forest Methods for Solving the Problem of Pulsar Search

Random Forests are an ensemble learning method that refers to train individual classifiers and aggregates their predictors to produce one optimal predictive model. In this paper, we compare the accuracy metric of six Random Forest methods implemented in the ‘CARET package’ of the R language. We explore the Time Resolution Universe (HTRU2) dataset collected from the UCI dataset.
Quiver: An Informed Storage Cache for Deep Learning

We introduce Quiver, an informed storage cache for deep learning training (DLT) jobs in a cluster of GPUs. Quiver employs domain-specific intelligence within the caching layer, to achieve much higher efficiency compared to a generic storage cache. First, Quiver uses a secure hash-based addressing to transparently reuse cached data across multiple jobs and even multiple users operating on the same dataset. Second, by co-designing with the deep learning framework (e.g. PyTorch), Quiver employs a technique of substitutable cache hits to get more value from the existing contents of the cache, thus avoiding cache thrashing when cache capacity is much smaller than the working set. Third, Quiver dynamically prioritizes cache allocation to jobs that benefit the most from the caching. With a prototype implementation in PyTorch, we show that Quiver can significantly improve throughput of deep learning workloads.
Ping-Pong Governance: Token Locking for Enabling Blockchain Self-governance

Updating blockchain-based protocols remains a significant challenge. If the community does not come to an agreement, a hard-fork can occur, splitting the blockchain’s community. Previous protocols have provided mechanisms to establish community consensus through the protocol itself, but these protocols either facilitate substantial, infrequent updates, or they allow more frequent but only minor changes. This work offers a mechanism that allows clients to vote by locking tokens, making the clients’ tokens temporarily unavailable in exchange for their vote. This design introduces an economic cost to voting, allowing us to measure both breadth and depth of support. Since there is an economic cost to voting, we wish to make non-contentious issues cheap to pass, but still allow the community to establish agreement on larger, more disputatious proposals. We achieve this property by a ping-pong governance model. An issue is tentatively accepted when it achieves enough votes within a fixed period. The proposal then enters a review period, where the opponents must gather enough votes to veto it. The supporters then have their own opportunity to overrule the veto. This process continues with new voting rounds until one side is unable to exceed the needed threshold, settling the issue. Our simulations show that this model allows the community to come to agreement quickly on popular changes, but still come to resolution when the community is more divided. Finally, we define the ideal properties of a blockchain governance protocol, and evaluate different governance protocols under these criteria.
Exploiting Siamese Neural Networks on Short Text Similarity Tasks for Multiple Domains and Languages

Semantic textual similarity algorithms are essential to several natural language processing tasks as clustering documents and text summarization. Many shared tasks regarding this subject were performed during the last few years, but generally, focused on a unique domain and/or language. Siamese Neural Network (SNN) is well known for its ability to compute similarity requiring less training data. We proposed a SNN architecture incorporated with language-independent features, aiming to perform short text similarity calculation in multiple languages and domains. We explored three different corpora from shared tasks: ASSIN 1 and ASSIN 2 with Portuguese journalistic texts and N2C2 (English clinical texts). We adapted the SNN proposed by Mueller and Thyagarajan (2016), in two ways: (i) the activation functions were changed to the ReLU, instead of the sigmoid function, and; (ii) we incorporated the architecture to accept three new lexical features and an embedding layer to infer the values of the pre-trained word embeddings. The evaluation was performed by the Pearson correlation (PC) and the Mean Squared Error (MSE) between the models’ predicted values and corpora’s gold standard. Our approach achieved better results than the baseline in both languages and domains.
Lightweight speaker verification for online identification of new speakers with short segments

Verifying if two audio segments belong to the same speaker has been recently put forward as a flexible way to carry out speaker identification, since it does not require to be re-trained when new speakers appear on the auditory scene. Although many of the current techniques have achieved high performances, they require a considerably high amount of memory, and a specific minimum length for their input audio segments. These requirements limit the applicability of these techniques in scenarios such as service robots, internet of things and virtual assistants, where computational resources are limited and the users tend to speak in short segments. In this work we propose a BLSTM-based model that reaches a level of performance comparable to the current state of the art when using short input audio segments, while requiring a considerably less amount of memory. Further, as far as we know, a complete speaker identification system has not been reported using this verification paradigm. Thus, we present a complete online speaker identifier, based on a simple voting system, that shows that the proposed BLSTM-based model achieves a similar performance at identifying speakers online compared to the current state of the art.
Optimal-size problem kernels for d-Hitting Set in linear time and space

The known linear-time kernelizations for d-Hitting Set guarantee linear worst-case running times using a quadratic-size data structure (that is not fully initialized). Getting rid of this data structure, we show that problem kernels of asymptotically optimal size O(kd) for d-Hitting Set are computable in linear time and space. Additionally, we experimentally compare the linear-time kernelizations for d-Hitting Set to each other and to a classical data reduction algorithm due to Weihe.
ICO Market Report 2018/2019 – Performance Analysis of 2018's Initial Coin Offerings

Initial Coin Offerings (ICOs) are currently one of the most fashionable topics in the area of financial markets. For token issuers, they are a success story. In 2018 alone, more than $ 14 billion dollars have been raised in ICOs. From the investors’ perspective, however, things do not look so shiny. Lots of them claim to have lost significant amounts of money invested in tokens issued by more or less reputable companies.In this market report, we analyze the ICOs of 2018. We illustrate the ICO market of that year and scrutinize how prices of the issued tokens have developed until mid 2019. Thereby, we take the perspective of an investor that bought tokens initially from the issuer. More than 90% of 2018's ICOs were based on the Ethereum Blockchain. We found that investors, which invested in ICOs during 2018, had only roughly an 8% chance that their tokens traded above their ICO issue price after six months. These numbers have not changed by July 2019. Nine in ten tokens trade below their ICO price. More than 70% of tokens have lost substantially all their value. This could indicate that the market gets rid of the those ICOs which are initiated by companies without a proper product or without a serious intention to develop a sustainable business. This could help the ICO market as a whole to become mature. We will continue this research in an ICO Market Report 2019/2020.
Quantum-clustered two-photon walks

We demonstrate a previously unknown two-photon effect in a discrete-time quantum walk. Two identical bosons with no mutual interactions nonetheless can remain clustered together as they walk on a lattice of directionally reversible optical four-ports acting as Grover coins; both photons move in the same direction at each step due to a two-photon quantum interference phenomenon reminiscent of the Hong-Ou-Mandel effect. The clustered two-photon amplitude splits into two localized parts, one oscillating near the initial point and the other moving ballistically without spatial spread, in solitonlike fashion. But the two photons are always clustered in the same part of the superposition, leading to potential applications for transport of entanglement and opportunities for novel two-photon interferometry experiments.
Raindrop Removal With Light Field Image Using Image Inpainting

In this paper, we propose a method that removes raindrops with light field image using image inpainting. We first use the depth map generated from light field image to detect raindrop regions which are then expressed as a binary mask. The original image with raindrops is improved by refocusing on the far regions and filtering by a high-pass filter. With the binary mask and the enhanced image, image inpainting is then utilized to eliminate raindrops from the original image. We compare pre-trained models of several deep learning based image inpainting methods. A light field raindrop dataset is released to verify our method. Image quality analysis is performed to evaluate the proposed image restoration method. The recovered images are further applied to object detection and visual localization tasks.
Permutation polynomials of degree 8 over finite fields of characteristic 2

Up to linear transformations, we obtain a classification of permutation polynomials (PPs) of degree 8 over F2r with r>3. By Bartoli et al. (2017) [1], a polynomial f of degree 8 over F2r is exceptional if and only if f−f(0) is a linearized PP, which has already been classified. So it suffices to search for non-exceptional PPs of degree 8 over F2r, which exist only when r⩽9 by a previous result. This can be exhausted by the SageMath software running on a personal computer. To facilitate the computation, some requirements after linear transformations and explicit equations by Hermite's criterion are provided for the polynomial coefficients. The main result is that a non-exceptional PP f of degree 8 over F2r (with r>3) exists if and only if r∈{4,5,6}, and such f is explicitly listed up to linear transformations.
FREEDOM OF EXPRESSION IN SOCIAL MEDIA AND CRIMINALIZATION OF HATE SPEECH IN SPAIN: EVOLUTION, IMPACT AND EMPIRICAL ANALYSIS OF NORMATIVE COMPLIANCE AND SELF-CENSORSHIP

The popularization of social media as a forum for the expression of ideas and for political debate has increased in recent years the, always present, tension between freedom of expression and public control of offensive or dangerous speech. Many democratic states criminalise hate speech and other types of offensive expression, and now are social Media themselves that can exercise prior censorship over content on the basis of similar rules but much more restrictive and ambiguous. Combining normative analysis with empirical methodologies, this paper analyses, focusing on Spain, the implications that Criminal laws and "community guidelines" have on citizens' decisions to express themselves freely, which is fundamental for the configuration of a real democracy. It is made up of two studies: the first traces the evolution of judicial resolutions in Spain that prosecute possible crimes of terrorism and hate for political expression on social networks, as a result of the popularisation of these since 2014 in concurrence with a broad and ambiguous legislation. The results show a significant growth of state control over expressions that are essentially offensive but that, in most cases, do not represent a clear and present danger and could go beyond the doctrine of the ECHR. In the second part, an empirical study is carried out to analyse how the existence of hate speech crimes and social media rules affects the decision to express political ideas on social networks or to self-censor. The results show that a large part of the sample is self-censoring but that criminal law and content rules, in particular the severity of the law and the certainty that it is applied, do not have a direct effect on the decision to express ideas on the Internet, while the social perception of what others do is decisive. It is discussed how this calls into question the legitimacy of the evident limitation of freedom of expression of some crimes in Spain.
Single-photon limit of dispersive readout of a qubit with a photodetector

We study the dispersive readout of a qubit in the ultimate limit of a single-photon probe. The use of a single-photon probe avoids the errors due to nonorthogonality of coherent states. A photodetector is used in the scheme we consider. The dynamics of the system is studied using the Heisenberg-Langevin equations. We treat the counter-rotating terms in the Hamiltonian perturbatively, which leads to the Bloch-Siegert shift in the resonator frequency. It is shown how the shift can improve the readout. The theory of photon transport through the qubit and the resonator it couples to is provided while taking the effect of the counter-rotating terms into account. To calculate the readout contrast, we use two approaches. The first one neglects the qubit relaxation and allows us to derive a compact expression for the contrast. Also, we obtain simple estimates for the system parameters to maximize the contrast. The second approach accounts for the qubit relaxation, which allows us to further improve the contrast. We demonstrate that for a readout time of $1\phantom{\rule{4pt}{0ex}}\ensuremath{\mu}\mathrm{s}$, a contrast of more than 75% can be achieved for an ideal detector and single-photon source.