Reviewerly/exHarmony huggingface dataset:
Quantum Annealing Based Optimization of Robotic Movement in Manufacturing

Recently, considerable attention has been paid to planning and scheduling problems for multiple robot systems (MRS). Such attention has resulted in a wide range of techniques being developed in solving more complex tasks at ever increasing speeds. At the same time, however, the complexity of such tasks has increased as such systems have to cope with ever increasing business requirements, rendering the above mentioned techniques unreliable, if not obsolete. Quantum computing is an alternative form of computation that holds a lot of potential for providing some advantages over classical computing for solving certain kinds of difficult optimization problems in the coming years. Motivated by this fact, in this paper we demonstrate the feasibility of running a particular type of optimization problem on existing quantum computing technology. The optimization problem investigate arises when considering how to optimize a robotic assembly line, which is one of the keys to success in the manufacturing domain. A small improvement in the efficiency of such an MRS can lead to huge saving in terms of time of manufacturing, capacity, robot life, and material usage. The nature of the quantum processor used in this study imposes the constraint that the optimization problem be cast as a quadratic unconstrained binary optimization (QUBO) problem. For the specific problem we investigate, this allows situations with one robot to be modeled naturally, meanwhile modeling the multi-robot generalization is less obvious and left as a topic for future research. The results show that for simple 1-robot tasks, the optimization problem can be straightforwardly solved within a feasible time span on existing quantum computing hardware.
A Study of Usage and Usability of Intelligent Personal Assistants in Denmark

Intelligent personal assistants (IPA), such as Siri, Google Assistant, Alexa, and Cortana, are rapidly becoming a popular way of interacting with our smart devices. As a result, there has been a wealth of research on all aspects of IPAs in recent years, such as studies of usage of and user satisfaction with IPAs. However, the overwhelming majority of these studies have focused on English as the interaction language. In this paper, we investigate the usage and perceived usability of IPAs in Denmark. We conduct a questionnaire with 357 Danish-speaking respondents that sheds light on how IPAs are used in Denmark. We find they are only used regularly by 19.9% of respondents and that most people do not find IPAs to be reliable. We also conduct a usability study of Siri and find that Siri suffers from several issues when used in Danish: poor voice recognition, unnatural dialogue responses, and an inability to support mixed-language speech recognition. Our findings shed light on both the current state of usage and adoption of IPAs in Denmark as well as the usability of its most popular IPA in a foreign-language setting.
The present state of global scientific debate in the field of financial development

The article attempts to summarize the results of a global scientific discussion in the field of financial development. Four areas of research on financial development have been identified and analyzed, which seem to be the most noticeable and significant both for the global economic discussion and for Russian experts: the impact of financial development on economic growth; the problem of assessing financial development; financial inclusion; sustainable finance concept. It is noted that the last two areas are closely related to the concepts of socio-economic development, respectively, of inclusive growth (“growth for all”) and of sustainable development. The necessity of taking into account the latest achievements of the world scientific thought in the Russian state policy concerning the financial sector is substantiated.
Spatio-Temporal Dynamics of Soil Geochemical Anomalies in the Zone of Impact of Slag Residuals

The 13-year-old (from 2003 to 2016) dynamics of soil contamination for the content of heavy metals in the area of the village of Bolshoye Dumchino of the Mtsensk District in the territory adjacent to the slag dump of Mtsensk Foundry was studied. It is shown that on the territory of the placement of slag residuals a techno geochemical anomaly is formed, in which the contained heavy metals are of technogenic and genetic (natural geochemical) nature. To identify the effect of anthropogenic factor on soil pollution with heavy metals, was determined the refined enrichment factor of heavy metals (EFHM) in light gray forest soils at different distances from the slag dump, allowing to calculate the proportion of technogeneity of metals as a percentage of its total content. The technogenic origin of heavy metals as pollutants and the role of humus soil horizons in fixing these metals in the soil profile have been proven. Analysis of the results characterizing the degree of enrichment of the genetic horizons of light gray forest soils with heavy metals for the period 2003–2016 convincingly proves the effect of maximum accumulation of slag residuals in the dump on the accumulation intensity and fixation of the studied metals, both in the upper humus layer and their distribution in the profile soil. The established patterns in changing the degree of enrichment and technogenicity of heavy metals in the profile of light-gray forest soil are caused not only by the impact of the slag dump as a source of pollution, but also by the peculiarities of using the soils of the studied territories. It is shown that the toxic load for the analyzed period not only did not decrease, but even increased.
Context-Free Session Type Inference

Some interesting communication protocols can be precisely described only by context-free session types, an extension of conventional session types supporting a general form of sequential composition. The complex metatheory of context-free session types, however, hinders the definition of corresponding checking and inference algorithms. In this work, we study a new syntax-directed type system for context-free session types that is easy to embed into a host programming language. We also detail 2 OCaml embeddings that allow us to piggyback on OCaml’s type system to check and infer context-free session types.
On the Power and Limitations of Random Features for Understanding Neural Networks

Recently, a spate of papers have provided positive theoretical results for training over-parameterized neural networks (where the network size is larger than what is needed to achieve low error). The key insight is that with sufficient over-parameterization, gradient-based methods will implicitly leave some components of the network relatively unchanged, so the optimization dynamics will behave as if those components are essentially fixed at their initial random values. In fact, fixing these explicitly leads to the well-known approach of learning with random features. In other words, these techniques imply that we can successfully learn with neural networks, whenever we can successfully learn with random features. In this paper, we first review these techniques, providing a simple and self-contained analysis for one-hidden-layer networks. We then argue that despite the impressive positive results, random feature approaches are also inherently limited in what they can explain. In particular, we rigorously show that random features cannot be used to learn even a single ReLU neuron with standard Gaussian inputs, unless the network size (or magnitude of the weights) is exponentially large. Since a single neuron is learnable with gradient-based methods, we conclude that we are still far from a satisfying general explanation for the empirical success of neural networks.
Variable-Length Representation for EC-Based Feature Selection in High-Dimensional Data

Feature selection is a challenging problem, especially when hundreds or thousands of features are involved. Evolutionary Computation based techniques and in particular genetic algorithms, because of their ability to explore large and complex search spaces, have proven to be effective in solving such kind of problems. Though genetic algorithms binary strings provide a natural way to represent feature subsets, several different representation schemes have been proposed to improve the performance, with most of them needing to a priori set the number of features. In this paper, we propose a novel variable length representation, in which feature subsets are represented by lists of integers. We also devised a crossover operator to cope with the variable length representation. The proposed approach has been tested on several datasets and the results compared with those achieved by a standard genetic algorithm. Results of comparisons demonstrated the effectiveness of the proposed approach in improving the performance obtainable with a standard genetic algorithm when thousand of features are involved.
Multi-View Classification via a Fast and Effective Multi-View Nearest-Subspace Classifier

Multi-view data represented in multiple views contains more complementary information than a single view, whereby multi-view learning explores and utilizes the multi-view data. In general, most existing multi-view learning methods consider the correlation between multiple views. However, the relationship between classes and views which is also important in multi-view learning has never been involved in the existing works. In this paper, we propose a fast and effective multi-view nearest-subspace classifier (MV-NSC) by taking advantage of both the two relationships simultaneously. MV-NSC consists of four main parts: (1) projection residual, (2) view-dependent class separability, (3) view similarity, and (4) final decision. The last part combines the first three parts in one final decision matrix, while the first three parts utilize the information of the multi-view data in various aspects. Our proposed method is evaluated on four benchmark datasets and compared with seven other classifiers including both multi- and single-view algorithms. According to the experimental results, it shows that our proposed method is effective, efficient, and robust in multi-view classification.
Relax Yourself - Using Virtual Reality to Enhance Employees' Mental Health and Work Performance

This paper presents work-in-progress aiming to develop an actively adapting virtual reality (VR) relaxation application. Due to the immersive nature of VR technologies, people can escape from their real environment and get into a relaxing state. Goal of the application is to adapt to the users' physiological signals to foster the positive effect. Until now, a first version of the VR application was constructed and is currently evaluated in an experiment. Preliminary results of this study demonstrate that people appreciate the immersion into the virtual environment and escape from reality. Moreover, participants highlighted the option to adapt users' needs and preferences. Based on the final study data, the constructed application will be enhanced with regard to adoption and surrounding factors.
Priority-based Parameter Propagation for Distributed DNN Training

Data parallel training is widely used for scaling distributed deep neural network (DNN) training. However, the performance benefits are often limited by the communication-heavy parameter synchronization step. In this paper, we take advantage of the domain specific knowledge of DNN training and overlap parameter synchronization with computation in order to improve the training performance. We make two key observations: (1) the optimal data representation granularity for the communication may differ from that used by the underlying DNN model implementation and (2) different parameters can afford different synchronization delays. Based on these observations, we propose a new synchronization mechanism called Priority-based Parameter Propagation (P3). P3 synchronizes parameters at a finer granularity and schedules data transmission in such a way that the training process incurs minimal communication delay. We show that P3 can improve the training throughput of ResNet-50, Sockeye and VGG-19 by as much as 25%, 38% and 66% respectively on clusters with realistic network bandwidth
Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning.

A membership inference attack (MIA) against a machine learning model enables an attacker to determine whether a given data record was part of the model's training data or not. The effectiveness of these attacks is reported using metrics computed across the whole population (e.g., average attack accuracy). In this paper, we show that the attack success varies across different subgroups of the data (e.g., race, gender), i.e., there is \emph{disparate vulnerability}. Even if MIA's success looks no better than random guessing over the whole population, subgroups can still be vulnerable. We study the necessary and sufficient conditions for a classifier to exhibit disparate vulnerability, and we determine to what extent certain learning techniques (e.g., fairness constraints, differential privacy) can prevent it. Our work provides a theoretical framework for studying MIA attacks from a new perspective.
Untangling the ‘Dark Web’: an emerging technological challenge for the criminal law

The Dark Web, and the technology which underpins it, is fundamentally changing how crime is conducted. It is an enabler of cross-border, truly international crime where each of the major actors, evidence, and the proceeds of crime can all be in different jurisdictions. The technologies utilised mask the identity of individuals and the nature of the crimes committed. It is these complexities, and law’s inability to deal with them, which this paper will focus on. It critically analyses six intersecting and overlapping themes in order to highlight the technological challenges posed by the Dark Web to the criminal law. The paper argues that the current approaches, regulatory structures, legislation and investigative methods are all unfit for purpose. There is little to suggest the law is any closer to restricting Dark Web crime, particularly given a substantial amount of the challenges posed are unsolved traditional issues, in a new form.
Towards Full Proof Automation in Frama-C Using Auto-active Verification

While deductive verification is increasingly used on real-life code, making it fully automatic remains difficult. The development of powerful SMT solvers has improved the situation, but some proofs still require interactive theorem provers in order to achieve full formal verification. Auto-active verification relies on additional guiding annotations (assertions, ghost code, lemma functions, etc.) and provides an important step towards a greater automation of the proof. However, the support of this methodology often remains partial and depends on the verification tool. This paper presents an experience report on a complete functional verification of several C programs from the literature and real-life code using auto-active verification with the C software analysis platform Frama-C and its deductive verification plugin Wp. The goal is to use automatic solvers to verify properties that are classically verified with interactive provers. Based on our experience, we discuss the benefits of this methodology and the current limitations of the tool, as well as proposals of new features to overcome them.
Learning Morphosyntactic Analyzers from the Bible via Iterative Annotation Projection across 26 Languages

A large percentage of computational tools are concentrated in a very small subset of the planet’s languages. Compounding the issue, many languages lack the high-quality linguistic annotation necessary for the construction of such tools with current machine learning methods. In this paper, we address both issues simultaneously: leveraging the high accuracy of English taggers and parsers, we project morphological information onto translations of the Bible in 26 varied test languages. Using an iterative discovery, constraint, and training process, we build inflectional lexica in the target languages. Through a combination of iteration, ensembling, and reranking, we see double-digit relative error reductions in lemmatization and morphological analysis over a strong initial system.
Optimizing clinical trials recruitment via deep learning

Abstract Objective Clinical trials, prospective research studies on human participants carried out by a distributed team of clinical investigators, play a crucial role in the development of new treatments in health care. This is a complex and expensive process where investigators aim to enroll volunteers with predetermined characteristics, administer treatment(s), and collect safety and efficacy data. Therefore, choosing top-enrolling investigators is essential for efficient clinical trial execution and is 1 of the primary drivers of drug development cost. Materials and Methods To facilitate clinical trials optimization, we propose DeepMatch (DM), a novel approach that builds on top of advances in deep learning. DM is designed to learn from both investigator and trial-related heterogeneous data sources and rank investigators based on their expected enrollment performance on new clinical trials. Results Large-scale evaluation conducted on 2618 studies provides evidence that the proposed ranking-based framework improves the current state-of-the-art by up to 19% on ranking investigators and up to 10% on detecting top/bottom performers when recruiting investigators for new clinical trials. Discussion The extensive experimental section suggests that DM can provide substantial improvement over current industry standards in several regards: (1) the enrollment potential of the investigator list, (2) the time it takes to generate the list, and (3) data-informed decisions about new investigators. Conclusion Due to the great significance of the problem at hand, related research efforts are set to shift the paradigm of how investigators are chosen for clinical trials, thereby optimizing and automating them and reducing the cost of new therapies.
Robust Multi-View 2-D/3-D Registration Using Point-To-Plane Correspondence Model

In minimally invasive procedures, the clinician relies on image guidance to observe and navigate the operation site. In order to show structures which are not visible in the live X-ray images, such as vessels or planning annotations, X-ray images can be augmented with pre-operatively acquired images. Accurate image alignment is needed and can be provided by 2-D/3-D registration. In this paper, a multi-view registration method based on the point-to-plane correspondence model is proposed. The correspondence model is extended to be independent of the used camera coordinates and different multi-view registration schemes are introduced and compared. Evaluation is performed for a wide range of clinically relevant registration scenarios. We show for different applications that registration using correspondences from both views simultaneously provides accurate and robust registration, while the performance of the other schemes varies considerably. Our method also outperforms the state-of-the-art method for cerebral angiography registration, achieving a capture range of 18 mm and an accuracy of 0.22±0.07 mm. Furthermore, investigations on the minimum angle between the views are performed in order to provide accurate and robust registration, while minimizing the obstruction to the clinical workflow. We show that small angles around 30° are sufficient to provide reliable registration results.
Does violation of a Bell inequality always imply quantum advantage in a communication complexity problem?

Quantum correlations which violate a Bell inequality are presumed to power better-than-classical protocols for solving communication complexity problems (CCPs). How general is this statement? We show that violations of correlation-type Bell inequalities allow advantages in CCPs, when communication protocols are tailored to emulate the Bell no-signaling constraint (by not communicating measurement settings). Abandonment of this restriction on classical models allows us to disprove the main result of, inter alia, \cite{BZ02}; we show that quantum correlations obtained from these communication strategies assisted by a small quantum violation of the CGLMP Bell inequalities do not imply advantages in any CCP in the input/output scenario considered in the reference. More generally, we show that there exists quantum correlations, with nontrivial local marginal probabilities, which violate the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>I</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>3322</mml:mn></mml:mrow></mml:msub></mml:math> Bell inequality, but do not enable a quantum advantange in any CCP, regardless of the communication strategy employed in the quantum protocol, for a scenario with a fixed number of inputs and outputs
Electronic Medical Information Encryption Using Modified Blowfish Algorithm

Security and privacy of patients' information remains a major issue of concern among health practitioners. Therefore, measures must be put in place to ensure that unauthorized individual do not have access to this information. However, the adoption of digital alternative of retrieving and documenting medical information has further opened it up to more attacks. This article presents a modified blowfish algorithm for securing textual and graphical medical information. The F-function used in generating round sub-keys was strengthened so as to produce a strong key that could resist differential attacks. Number of Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI) of 98.85% and 33.65% revealed that the modified algorithm is sensitive to changes in its key and also resistive to differential attacks. Furthermore, the modified algorithm demonstrated a better encryption and decryption time than the existing blowfish algorithm.
Existence results on $k$-normal elements over finite fields

An element $\alpha \in \mathbb{F}\_{q^n}$ is normal over $\mathbb{F}q$ if $\alpha$ and its conjugates $\alpha, \alpha^q, \dots, \alpha^{q^{n-1}}$ form a basis of $\mathbb{F}{q^n}$ over $\mathbb{F}\_q$. In 2013, Huczynska, Mullen, Panario and Thomson introduced the concept of $k$-normal elements, generalizing the normal elements. In the past few years, many questions concerning the existence and number of $k$-normal elements with specified properties have been proposed. In this paper, we discuss some of these questions and, in particular, we provide many general results on the existence of $k$-normal elements with additional properties like being primitive or having large multiplicative order. We also discuss the existence and construction of $k$-normal elements in finite fields, providing a connection between $k$-normal elements and the factorization of $x^n-1$ over $\mathbb{F}\_q$.
Train One Get One Free: Partially Supervised Neural Network for Bug Report Duplicate Detection and Clustering

Lahari Poddar, Leonardo Neves, William Brendel, Luis Marujo, Sergey Tulyakov, Pradeep Karuturi. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers). 2019.