Reviewerly/exHarmony huggingface dataset:
KAMA-NNs: low-dimensional rotation based neural networks

We present new architectures for feedforward neural networks built from products of learned or random low-dimensional rotations that offer substantial space compression and computational speedups in comparison to the unstructured baselines. Models using them are also competitive with the baselines and often, due to imposed orthogonal structure, outperform baselines accuracy-wise.We propose to use our architectures in two settings. We show that in the non-adaptive scenario (random neural networks) they lead to asymptotically more accurate, space-efficient and faster estimators of the so-called PNG-kernels (for any activation function defining the PNG). This generalizes several recent theoretical results about orthogonal estimators (e.g. orthogonal JLTs, orthogonal estimators of angular kernels and more). In the adaptive setting we propose efficient algorithms for learning products of low-dimensional rotations and show how our architectures can be used to improve space and time complexity of state of the art reinforcement learning (RL) algorithms (e.g. PPO, TRPO). Here they offer up to 7x compression of the network in comparison to the unstructured baselines and outperform reward-wise state of the art structured neural networks offering similar computational gains and based on low displacement rank matrices.
Design, analysis and performance evaluation of a hand gesture platform for navigation

Prevailing technological solutions that address the problems that are experienced by the infirm and elderly people in terms of locomotion needs, offer limited options when it comes to control mechanism and customization. For more than a decade, joysticks have served the purpose of steering and navigation of autonomous wheelchairs. An alternative gesture-based method for navigation of wheelchairs by the physically impaired could very well replace the conventional joystick controls. A prototype system, 'Mudra' (Sanskrit word for gesture), incorporates a gesture capture module, developed for recognition and identification of hand gestures. Mudra is a no-nonsense user-friendly system that enables control of the navigational platform, merely by four gestures of the human hand. This paper presents a comprehensive report on the implementation of the Mudra system's hardware and software, performance analysis and safety evaluation. Verification tests of the conceptual design show promising results, where 97.8% of the hand gestures were recognized accurately. Furthermore, the response timings of wheelchairs with Mudra controls were noticeably faster than the joystick-based wheelchairs, as affirmed by live testing with wheelchair-users. Pursuant to the positive feedback from the wheelchair-user experience, we conclude that Mudra's gesture controlled wheelchairs would be a preferable alternative to joystick-controlled wheelchairs.
Parameter Tuning for S-ABCPK

QoS-aware service composition problem has been drawn great attention in recent years. As an NP-hard problem, high time complexity is inevitable if global optimization algorithms (such as integer programming) are adopted. Researchers applied various evolutionary algorithms to decrease the time complexity by looking for a near-optimum solution. However, each evolutionary algorithm has two or more parameters, the values of which are to be assigned by algorithm designers and likely have impacts on the optimization results (primarily time complexity and optimality). The authors' experiments show that there are some dependencies between the features of a service composition problem, the values of an evolutionary algorithm's parameters, and the optimization results. In this article, the authors propose an improved algorithm called Service-Oriented Artificial Bee Colony algorithm considering Priori Knowledge (S-ABCPK) to solve service composition problem and focus on the S-ABCPK's parameter turning issue. The objective is to identify the potential dependency for designers of a service composition algorithm easily setting up the values of S-ABCPK parameters to obtain a preferable composition solution without many times of tedious attempts. Eight features of the service composition problem and the priori knowledge, five S-ABCPK parameters and two metrics of the final solution are identified. Based on a large volume of experiment data, S-ABCPK parameter tuning for a given service composition problem is conducted using C4.5 algorithm and the dependency between problem features and S-ABCPK parameters are established using the neural network method. An experiment on a validation dataset shows the feasibility of the approach.
Decentralizing the Social Web

For over a decade, standards bodies like the IETF and W3C have attempted to prevent the centralization of the Web via the use of open standards for ‘permission-less innovation.’ Yet today, these standards, from OAuth to RSS, seem to have failed to prevent the massive centralization of the Web at the hands of a few major corporations like Google and Facebook. We’ll delve deep into the lessons of failed attempts to replace DNS like XRIs, identity systems like OpenID, and metadata formats like the Semantic Web, all of which were re-cuperated by centralized platforms like Facebook as Facebook Connect and the “Like” Button. Learning from the past, a new generation of blockchain standards and governance mechanisms may be our last, best chance to save the Web.
Data Selection to Improve Anomaly Detection in a Component-Based Robot

The rise in complexity of robotic systems usually leads to an increase in failures of such systems. To improve the maintenance of this type of systems and thus reducing economic costs and downtime, present paper addresses anomaly detection in a component-based robot. To do so, the problem of anomaly detection is modelled as a classification problem, being Support Vector Machine (SVM) the selected classifier. It is applied to a publicly-available and recent dataset containing useful information about the performance of the software system in a component-based robot when certain anomalies are induced. Different preprocessing strategies and data sources are compared to get the best scores for some classification metrics through cross-validation.
The Philosophical Status of “Metaphilosophy of Science”

Interdisciplinary studies of science form a “living” organism, in which every part performs its function and is connected with other parts. Philosophy of science plays a role of the “think-tank” of that organism. It is a generator of the sense that connects the functions of its separate parts into a systematic unity. It can be called the consciousness of science. Metaphilosophy of science is related to philosophy of science in the same way as philosophy of science itself is related to science. Within metaphilosophy of science the propensity of philosophy of science to self-reflection is implemented. Metaphilosophy of science makes relevant the issues that relate to the philosophical significance of the processes taking place in the so-called “trading zone” (in the sense of P. Galison). These trading zone is a place where scientists, science-of-science theorists and philosophers exchange their ideas. The interaction between philosophy and metaphilosophy of science takes place in the course of a competition among various philosophical interpretations of the results received within the studies of these “trading” processes. Institutional, methodological, historical and culturological studies get a philosophical interpretation and become the source of metaphilosophical ideas. Metaphorically, one may say, that metaphilosophy of science is the self-consciousness of philosophy of science. The very idea that metaphilosophy of science is a participant of the process occurring in the “trading zone” puts an end to a meaningless pile of “metalevels” that very often characterize philosophical discussions of science.
Supervised Hierarchical Clustering with Exponential Linkage.

In supervised clustering, standard techniques for learning a pairwise dissimilarity function often suffer from a discrepancy between the training and clustering objectives, leading to poor cluster quality. Rectifying this discrepancy necessitates matching the procedure for training the dissimilarity function to the clustering algorithm. In this paper, we introduce a method for training the dissimilarity function in a way that is tightly coupled with hierarchical clustering, in particular single linkage. However, the appropriate clustering algorithm for a given dataset is often unknown. Thus we introduce an approach to supervised hierarchical clustering that smoothly interpolates between single, average, and complete linkage, and we give a training procedure that simultaneously learns a linkage function and a dissimilarity function. We accomplish this with a novel Exponential Linkage function that has a learnable parameter that controls the interpolation. In experiments on four datasets, our joint training procedure consistently matches or outperforms the next best training procedure/linkage function pair and gives up to 8 points improvement in dendrogram purity over discrepant pairs.
A Simple and Robust Approach to Detecting Subject-Verb Agreement Errors

Simon Flachs, Ophélie Lacroix, Marek Rei, Helen Yannakoudakis, Anders Søgaard. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
High-dimension experimental tomography of a path-encoded photon quantum state

Quantum information protocols often rely on tomographic techniques to determine the state of the system. A popular method of encoding information is on the different paths a photon may take, e.g., parallel waveguides in integrated optics. However, reconstruction of states encoded onto a large number of paths is often prohibitively resource intensive and requires complicated experimental setups. Addressing this, we present a simple method for determining the state of a photon in a superposition of d paths using a rotating one-dimensional optical Fourier transform. We establish the theory and experimentally demonstrate the technique by measuring a wide variety of six-dimensional density matrices. The average fidelity of these with the expected state is as high as 0.9852±0.0008. This performance is comparable to or exceeds established tomographic methods for other types of systems.
Looking to the future: Learning from experience, averting catastrophe

As humans go through life sifting vast quantities of complex information, we extract knowledge from settings that are more ambiguous than our early homes and classrooms. Learning from experience in an individual’s unique context generally improves expert performance, despite the risks inherent in brain dynamics that can transform previously reliable expectations. Designers of twenty-first century technologies face the challenges and responsibilities posed by fielded systems that continue to learn on their own. The neural model Self-supervised ART, which can acquire significantly new knowledge in unpredictable contexts, is an example of one such system.
Transferable Neural Projection Representations

Chinnadhurai Sankar, Sujith Ravi, Zornitsa Kozareva. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
A Deep-learning-based approach to VM behavior Identification in Cloud Systems

Cloud computing data centers are growing in size and complexity to the point where monitoring and management of the infrastructure become a challenge due to scalability issues. A possible approach to cope with the size of such data centers is to identify VMs exhibiting a similar behavior. Existing literature demonstrated that clustering together VMs that show a similar behavior may improve the scalability of both monitoring andmanagement of a data center. However, available techniques suffer from a trade-off between accuracy and time to achieve this result. Throughout this paper we propose a different approach where, instead of an unsupervised clustering, we rely on classifiers based on deep learning techniques to assigna newly deployed VMs to a cluster of already-known VMs. The two proposed classifiers, namely DeepConv and DeepFFT use a convolution neural network and (in the latter model) exploits Fast Fourier Transformation to classify the VMs. Our proposal is validated using a set of traces describing the behavior of VMs from a realcloud data center. The experiments compare our proposal with state-of-the-art solutions available in literature, demonstrating that our proposal achieve better performance. Furthermore, we show that our solution issignificantly faster than the alternatives as it can produce a perfect classification even with just a few samples of data, making our proposal viable also toclassify on-demand VMs that are characterized by a short life span.
Degradation of entanglement in Markovian noise

The entanglement survival time is defined as the maximum time a system which is evolving under the action of local Markovian, homogeneous-in-time noise is capable of preserving the entanglement it had at the beginning of the temporal evolution. In this paper we study how this quantity is affected by the interplay between the coherent preserving and dissipative contributions of the corresponding dynamical generator. We report the presence of a counterintuitive, nonmonotonic behavior in such a functional, capable of inducing sudden death of entanglement in models which, in the absence of unitary driving, are capable of sustaining entanglement for arbitrarily long times.
A Data Protection by Design Model for Privacy Management in Electronic Health Records

Privacy by design (PbD) is considered an international principle for privacy protection. For understanding and applying a PbD legal provision, the context of the data processing is essential. This paper intends to analyse the data protection by design (DPbD) legal obligation in the European framework and investigate how it can be implemented in the context of e-health for Electronic Health Records. The PbD approach may play a pivotal role in this sector to fulfil the requirements of the law and to better protect the rights of the data subjects. To fulfil these goals, to understand the deeper meaning of the concept and to evaluate the approach itself, the paper conducts a theoretical legal analysis on PbD and critically compares the edges, the benefits, the challenges and the disadvantages. As the chosen legal framework is that of the European Union, the DPbD legal obligation established by the GDPR will be examined. The paper first gives a brief overview of the applicable EU legal framework for EHRs. Settled this context, the paper proposes a comprehensive DPbD model for the privacy management with technical and organisational measures to be implemented in EHRs. The purpose is to provide more guidance for data controllers and developers on how to comply with the DPbD obligation.
Fuzzy Side Information Clustering-Based Framework for Effective Recommendations

Collaborative filtering (CF) is the most successful and widely implemented algorithm in the area of recommender systems (RSs). It generates recommendations using a set of user-product ratings by matching similarity between the profiles of different users. Computing similarity among user profiles efficiently in case of sparse data is the most crucial component of the CF technique. Data sparsity and accuracy are the two major issues associated with the classical CF approach. In this paper, we try to solve these issues using a novel approach based on the side information (user-product background content) and the Mahalanobis distance measure. The side information has been incorporated into RSs to further improve their performance, especially in the case of data sparsity. However, incorporation of side information into traditional two-dimensional recommender systems would increase the dimensionality and complexity of the system. Therefore, to alleviate the problem of dimensionality, we cluster users based on their side information using k-means clustering algorithm and each user's similarity is computed using the Mahalanobis distance method. Additionally, we use fuzzy sets to represent the side information more efficiently. Results of the experimentation with two benchmark datasets show that our framework improves the recommendations quality and predictive accuracy of both traditional and clustering-based collaborative recommendations.
Effect of Vibrotactile Feedback on Simulator Sickness, Performance, and User Satisfaction with Virtual Reality Glasses

This study investigated the effects of vibrotactile feedback on simulator sickness, human performance, and user satisfaction when wearing virtual reality glasses. A total of 36 participants were recruited and 30 of them finished the experiments. They were asked to wear virtual reality glasses to play a car race game with a vibrotactile vest and a vibrotactile gamepad. The vest and gamepad provided vibration feedback, which was turned on in one task and turned off in the other task. The performance was measured during the tasks and simulator sickness and satisfaction were measured through questionnaires. The results showed that the participants suffered severe simulator sickness without vibrotactile feedback and they were more satisfied with vibrotactile feedback. However, there was no significant difference observed in terms of performance. This study suggested that virtual reality glasses with vibrotactile feedback would improve the user experience. The result can help VR developers to choose proper vibrotactile feedback in VR applications.
Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis

Adjective phrases like “a little bit surprised”, “completely shocked”, or “not stunned at all” are not handled properly by current state-of-the-art emotion classification and intensity prediction systems. Based on this finding, we analyze differences between embeddings used by these systems in regard to their capability of handling such cases and argue that intensifiers in context of emotion words need special treatment, as is established for sentiment polarity classification, but not for more fine-grained emotion prediction. To resolve this issue, we analyze different aspects of a post-processing pipeline which enriches the word representations of such phrases. This includes expansion of semantic spaces at the phrase level and sub-word level followed by retrofitting to emotion lexicons. We evaluate the impact of these steps with ‘A La Carte and Bag-of-Substrings extensions based on pretrained GloVe,Word2vec, and fastText embeddings against a crowd-sourced corpus of intensity annotations for tweets containing our focus phrases. We show that the fastText-based models do not gain from handling these specific phrases under inspection. For Word2vec embeddings, we show that our post-processing pipeline improves the results by up to 8% on a novel dataset densly populated with intensifiers while it does not decrease the performance on the established EmoInt dataset.
Thick Line Segment Detection with Fast Directional Tracking

This paper introduces a fully discrete framework for a new straight line detector in gray-level images, where line segments are enriched with a thickness parameter intended to provide a quality criterion on the extracted feature. This study is based on a previous work on interactive line detection in gray-level images. At first, a better estimation of the segment thickness and orientation is achieved through two main improvements: adaptive directional scans and control of assigned thickness. Then, these advances are exploited for a complete unsupervised detection of all the line segments in an image. The new thick line detector is left available in an online demonstration.
On derivatives of smooth functions represented in multiwavelet bases

We construct high-order derivative operators for smooth functions represented via discontinuous multiwavelet bases. The need for such operators arises in order to avoid artifacts when computing functionals involving high-order derivatives of solutions of integral equations. Previously high-order derivatives had to be formed by repeated application of a first-derivative operator that, while uniquely defined, has a spectral norm that grows quadratically with polynomial order and, hence, greatly amplifies numerical noise (truncation error) in the multiwavelet computation. The new constructions proceed via least-squares projection onto smooth bases and provide substantially improved numerical properties as well as permitting direct construction of high-order derivatives. We employ either b-splines or bandlimited exponentials as the intermediate smooth basis, with the former maintaining the concept of approximation order while the latter preserves the pure imaginary spectrum of the first-derivative operator and provides more direct control over the bandlimit and accuracy of computation. We demonstrate the properties of these new operators via several numerical tests as well as application to a problem in nuclear physics.
Parallelizable Stack Long Short-Term Memory

Stack Long Short-Term Memory (StackLSTM) is useful for various applications such as parsing and string-to-tree neural machine translation, but it is also known to be notoriously difficult to parallelize for GPU training due to the fact that the computations are dependent on discrete operations. In this paper, we tackle this problem by utilizing state access patterns of StackLSTM to homogenize computations with regard to different discrete operations. Our parsing experiments show that the method scales up almost linearly with increasing batch size, and our parallelized PyTorch implementation trains significantly faster compared to the Dynet C++ implementation.
Scale-Space Splatting: Reforming Spacetime for Cross-Scale Exploration of Integral Measures in Molecular Dynamics

Understanding large amounts of spatiotemporal data from particle-based simulations, such as molecular dynamics, often relies on the computation and analysis of aggregate measures. These, however, by virtue of aggregation, hide structural information about the space/time localization of the studied phenomena. This leads to degenerate cases where the measures fail to capture distinct behaviour. In order to drill into these aggregate values, we propose a multi-scale visual exploration technique. Our novel representation, based on partial domain aggregation, enables the construction of a continuous scale-space for discrete datasets and the simultaneous exploration of scales in both space and time. We link these two scale-spaces in a scale-space space-time cube and model linked views as orthogonal slices through this cube, thus enabling the rapid identification of spatio-temporal patterns at multiple scales. To demonstrate the effectiveness of our approach, we showcase an advanced exploration of a protein-ligand simulation.