Reviewerly/exHarmony huggingface dataset:
Imparting interpretability to word embeddings while preserving semantic structure

Abstract As a ubiquitous method in natural language processing, word embeddings are extensively employed to map semantic properties of words into a dense vector representation. They capture semantic and syntactic relations among words, but the vectors corresponding to the words are only meaningful relative to each other. Neither the vector nor its dimensions have any absolute, interpretable meaning. We introduce an additive modification to the objective function of the embedding learning algorithm that encourages the embedding vectors of words that are semantically related to a predefined concept to take larger values along a specified dimension, while leaving the original semantic learning mechanism mostly unaffected. In other words, we align words that are already determined to be related, along predefined concepts. Therefore, we impart interpretability to the word embedding by assigning meaning to its vector dimensions. The predefined concepts are derived from an external lexical resource, which in this paper is chosen as Roget’s Thesaurus. We observe that alignment along the chosen concepts is not limited to words in the thesaurus and extends to other related words as well. We quantify the extent of interpretability and assignment of meaning from our experimental results. Manual human evaluation results have also been presented to further verify that the proposed method increases interpretability. We also demonstrate the preservation of semantic coherence of the resulting vector space using word-analogy/word-similarity tests and a downstream task. These tests show that the interpretability-imparted word embeddings that are obtained by the proposed framework do not sacrifice performances in common benchmark tests.
A quantum active learning algorithm for sampling against adversarial attacks

Abstract Adversarial attacks represent a serious menace for learning algorithms and may compromise the security of future autonomous systems. A theorem by Khoury and Hadfield-Menell (KH), provides sufficient conditions to guarantee the robustness of active learning algorithms, but comes with a caveat: it is crucial to know the smallest distance among the classes of the corresponding classification problem. We propose a theoretical framework that allows us to think of active learning as sampling the most promising new points to be classified, so that the minimum distance between classes can be found and the theorem KH used. Additionally, we introduce a quantum active learning algorithm that makes use of such framework and whose complexity is polylogarithmic in the dimension of the space, m , and the size of the initial training data n , provided the use of qRAMs; and polynomial in the precision, achieving an exponential speedup over the equivalent classical algorithm in n and m .
DISK: Learning local features with policy gradient

Local feature frameworks are difficult to learn in an end-to-end fashion, due to the discreteness inherent to the selection and matching of sparse keypoints. We introduce DISK (DIScrete Keypoints), a novel method that overcomes these obstacles by leveraging principles from Reinforcement Learning (RL), optimizing end-to-end for a high number of correct feature matches. Our simple yet expressive probabilistic model lets us keep the training and inference regimes close, while maintaining good enough convergence properties to reliably train from scratch. Our features can be extracted very densely while remaining discriminative, challenging commonly held assumptions about what constitutes a good keypoint, as showcased in Fig. 1, and deliver state-of-the-art results on three public benchmarks.
Subspace-Based Representation and Learning for Phonotactic Spoken Language Recognition

Phonotactic constraints can be employed to distinguish languages by representing a speech utterance as a multinomial distribution or phone events. In the present study, we propose a new learning mechanism based on subspace-based representation, which can extract concealed phonotactic structures from utterances, for language verification and dialect/accent identification. The framework mainly involves two successive parts. The first part involves subspace construction. Specifically, it decodes each utterance into a sequence of vectors filled with phone-posteriors and transforms the vector sequence into a linear orthogonal subspace based on low-rank matrix factorization or dynamic linear modeling. The second part involves subspace learning based on kernel machines, such as support vector machines and the newly developed subspace-based neural networks (SNNs). The input layer of SNNs is specifically designed for the sample represented by subspaces. The topology ensures that the same output can be derived from identical subspaces by modifying the conventional feed-forward pass to fit the mathematical definition of subspace similarity. Evaluated on the “General LR” test of NIST LRE 2007, the proposed method achieved up to 52%, 46%, 56%, and 27% relative reductions in equal error rates over the sequence-based PPR-LM, PPR-VSM, and PPR-IVEC methods and the lattice-based PPR-LM method, respectively. Furthermore, on the dialect/accent identification task of NIST LRE 2009, the SNN-based system performed better than the aforementioned four baseline methods.
Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes

Stochastic Approximation (SA) is a popular approach for solving fixed-point equations where the information is corrupted by noise. In this paper, we consider an SA involving a contraction mapping with respect to an arbitrary norm, and show its finite-sample error bounds while using different stepsizes. The idea is to construct a smooth Lyapunov function using the generalized Moreau envelope, and show that the iterates of SA have negative drift with respect to that Lyapunov function. Our result is applicable in Reinforcement Learning (RL). In particular, we use it to establish the first-known convergence rate of the V-trace algorithm for off-policy TD-learning. Importantly, our construction results in only a logarithmic dependence of the convergence bound on the size of the state-space.
Finite-size effects in continuous-variable quantum key distribution with Gaussian postselection

In a continuous-variable quantum key distribution (CV-QKD) protocol, which is based on heterodyne detection at the receiver, the application of a noiseless linear amplifier (NLA) on the received signal before the detection can be emulated by the post-selection of the detection outcome. Such a post-selection, which is also called a measurement-based NLA, requires a cut-off to produce a normalisable filter function. Increasing the cut-off with respect to the received signals results in a more faithful emulation of the NLA and nearly Gaussian output statistics at the cost of discarding more data. While recent works have shown the benefits of post-selection via an asymptotic security analysis, we undertake the first investigation of such a post-selection utilising a composable security proof in the realistic finite-size regime, where this trade-off is extremely relevant. We show that this form of post-selection can improve the secure range of a CV-QKD over lossy thermal channels if the finite block size is sufficiently large and that the optimal value for the filter cut-off is typically in the non-Gaussian regime. The relatively modest improvement in the finite-size regime as compared to the asymptotic case highlights the need for new tools to prove the security of non-Gaussian cryptographic protocols. These results also represent a quantitative assessment of a measurement-based NLA with an entangled-state input in both the Gaussian and non-Gaussian regime.
A novel homomorphic RASD framework for secured data access and storage in cloud computing

Abstract In the distributed computing worldview, the client and association’s information is put away remotely on the cloud server. Clients and associations can get to applications, administrations, and foundation on-request from a cloud server through the internet, withstanding the various advantages, numerous difficulties, and issues that endure verifying cloud information access and capacity. These difficulties have featured additional security and protection issues as cloud specialist co-ops are exclusively in charge of the capacity and handling of the association’s information out of its physical limits. Hence, a robust security plan is required in order to ensure the association’s touchy information emerges to keep the information shielded and distant from programmers. Over the globe, specialists have proposed fluctuated security structures having an alternate arrangement of security standards with changing computational expense. Down to earth usage of these structures with low calculation cost remains an extreme test to tackle, as security standards have not been characterized. Methodology – To verify the cloud and deal with all security standards, we propose a REGISTRATION AUTHENTICATION STORAGE DATA ACCESS (RASD) structure for giving security to authoritative information put away on cloud catalogs utilizing a novel security plot, for example, H E E T P S . A RASD system involves a stage by stage process-Enlistment of clients, Authentication of the client secret key, and Capacity of information just as information access on cloud registry. When the system is connected to cloud servers, all the delicate information put away on the cloud will end up being accessible just to verified clients. The essential favourable position of the proposed RASD structure is its simple usage, high security, and overall less computational expense. Moreover, we propose a homomorphic-private-practical uniformity testing-based plan structured under a schematic calculation C he A et D Ps . This calculation executes homomorphic encryption with subtractive fairness testing, notwithstanding low computational intricacy. To test the security ability, we tried the proposed RASD system with other existing conventions like Privacy-protecting examining convention, group reviewing convention, verified system coding convention, and encoded information preparing with homomorphic re-encryption convention. Findings – Experimentation-based outcomes demonstrated that the RASD structure not only gives a high-security layer for delicate information but also enables a decrease in computational expense and performs better when compared with existing conventions for distributed computing.
Big data actionable intelligence architecture

Abstract The amount of data produced by sensors, social and digital media, and Internet of Things (IoTs) are rapidly increasing each day. Decision makers often need to sift through a sea of Big Data to utilize information from a variety of sources in order to determine a course of action. This can be a very difficult and time-consuming task. For each data source encountered, the information can be redundant, conflicting, and/or incomplete. For near-real-time application, there is insufficient time for a human to interpret all the information from different sources. In this project, we have developed a near-real-time, data-agnostic, software architecture that is capable of using several disparate sources to autonomously generate Actionable Intelligence with a human in the loop. We demonstrated our solution through a traffic prediction exemplar problem.
An exploratory analysis of the impact of learners’ first language on vocabulary recall using immersive technologies

This exploratory post-hoc analysis examined the impact of learners’ first language (L1) on learning vocabulary annotated in immersive 360º pictures. This analysis is a part of a larger, between-subjects study (Papin &amp; Kaplan-Rakowski, 2020) in which learners (N=63) of French as a second language (L2) studied vocabulary annotated in (1) Two-Dimensional (2D) pictures viewed on a desktop monitor, (2) 360º pictures viewed on a desktop monitor, and (3) 360º pictures viewed using a Virtual Reality (VR) headset. A multiple regression linear model revealed that native speakers of English benefited significantly more from immersive technologies compared with L1 Chinese speakers. When low-immersion and high-immersion technologies were used, Chinese L1 speakers were significantly disadvantaged by high-immersion VR. This study has implications in the field of L2 vocabulary research and learning materials design.
Ontology Learning from Relational Database: Opportunities for Semantic Information Integration

Along with the rapidly growing scale of relational database (RDB), how to construct domain-related ontologies from various databases effectively and efficiently has been a bottleneck of the ontology-based integration. The traditional methods for constructing ontology from RDB are mainly based on the manual mapping and transformation, which not only requires a lot of human experience but also easily leads to the semantic loss during the transformation. Ontology learning from RDB is a new paradigm to (semi-)automatically construct ontologies from RDB by borrowing the techniques of machine learning, it provides potential opportunities for integrating heterogeneous data from various data sources efficiently. This paper surveys the recent methods and tools of the ontology learning from RDB, and highlights the potential opportunities and challenges of using ontology learning in semantic information integration. Initially, the previous surveys on the topic of the ontology-based integration and ontology learning were summarized, and then the limitations of previous surveys were identified and analyzed. Furthermore, the methods and techniques of ontology learning from RDB were investigated by classifying into three categories: reverse engineering, mapping, and machine learning. Accordingly, the opportunities and possibility of using ontology learning from RDB in semantic information integration were discussed based on the mapping results between the bottlenecks of ontology-based integration and the features of ontology learning. a a This paper is an extension of our prior work [C. Ma and B. Molnár, Use of ontology learning in information system integration: A literature survey, in Intelligent Information and Database Systems, eds. P. Sitek, M. Pietranik, M. Krótkiewicz and C. Srinilta, Communications in Computer and Information Science, Vol. 1178 (Springer, Singapore, 2020), pp. 342–353]
The (In)Efficiency of interaction

Evaluating higher-order functional programs through abstract machines inspired by the geometry of the interaction is known to induce space efficiencies, the price being time performances often poorer than those obtainable with traditional, environment-based, abstract machines. Although families of lambda-terms for which the former is exponentially less efficient than the latter do exist, it is currently unknown how general this phenomenon is, and how far the inefficiencies can go, in the worst case. We answer these questions formulating four different well-known abstract machines inside a common definitional framework, this way being able to give sharp results about the relative time efficiencies. We also prove that non-idempotent intersection type theories are able to precisely reflect the time performances of the interactive abstract machine, this way showing that its time-inefficiency ultimately descends from the presence of higher-order types.
The Future of Fashion: How the Quest for Digitization and the Use of Artificial Intelligence and Extended Reality Will Reshape the Fashion Industry After COVID-19

The aim of this paper is to analyze how extended reality (formed by virtual and augmented reality), and artificial intelligence are influencing the fashion field. During and after the Covid19 pandemic, a radical shift happened: most activities, from shopping to showrooms and fashion weeks had to move online. While e-commerce was already a consolidated pillar of fashion shopping, it recently started to evolve to achieve through AR (augmented reality) and VR (virtual reality) what customers could before find in physical stores: interesting experiences and sensory stimuli. Online traffic peaked, creating even more room for technology based on artificial intelligence. With its disrupting powers, AI (artificial intelligence) has all the potentialities to take over each step of the fashion value chain. Furthermore, with the quarantine the production of fashion was slowed down by the absence of workers in production sites. Artificial intelligence and automation could be an answer. In this paper AR, VR and AI technologies are analyzed with practical examples on what has happened and is happening in the industry, from a perspective based on the challenges imposed by Covid-19. Results indicate that AR, VR and AI have the potential to become in the near future the norm in the fashion media and technology ecosystem, reinforcing the digitization process of the industry.
On the semantic expressiveness of recursive types

Recursive types extend the simply-typed lambda calculus (STLC) with the additional expressive power to enable diverging computation and to encode recursive data-types (e.g., lists). Two formulations of recursive types exist: iso-recursive and equi-recursive. The relative advantages of iso- and equi-recursion are well- studied when it comes to their impact on type-inference. However, the relative semantic expressiveness of the two formulations remains unclear so far. This paper studies the semantic expressiveness of STLC with iso- and equi-recursive types, proving that these formulations are equally expressive. In fact, we prove that they are both as expressive as STLC with only term-level recursion. We phrase these equi-expressiveness results in terms of full abstraction of three canonical compilers between these three languages (STLC with iso-, with equi-recursive types and with term-level recursion). Our choice of languages allows us to study expressiveness when interacting over both a simply-typed and a recursively-typed interface. The three proofs all rely on a typed version of a proof technique called approximate backtranslation. Together, our results show that there is no difference in semantic expressiveness between STLCs with iso- and equi-recursive types. In this paper, we focus on a simply-typed setting but we believe our results scale to more powerful type systems like System F.
A new solution model for cardiac medical image segmentation

Calculation methods have a critical role in the precise sorting of medical images. Particle swarm optimization (PSO) is a widely used approach in the clinical centers and for other medical applications as it can disentangle optimization errors in attached spaces. In this work, a new model for image segmentation is proposed through an improved optimization algorithm.A novel multi-objective algorithm was configured, named "multi-objective mathematical programming" (MOMP), based on the normalized normal constraint method (NNCM). In this model, the proposed algorithm was applied to evaluate the robustness of the suggested model through including the synthetic images of objects with various concavities and Gaussian noise. This model segments the individuals' heart and the left ventricle from data sets of sequentially evaluated tomography and magnetic resonance images. To objectively and quantifiably assess the presentation of the medical image segmentations based on regions outlined by experts and the graph cut method, a set of distance and resemblance metrics were implemented.The numerical results obtained in experimental test cases demonstrate the validity and superiority of the proposed model through better segmentation accuracy and stability.The results indicated that the proposed MOMP method can outperform all traditional models in terms of segmentation accuracy and stability, and is thus appropriate for use in medical imaging.
Cue Me In: Content-Inducing Approaches to Interactive Story Generation

Automatically generating stories is a challenging problem that requires producing causally related and logical sequences of events about a topic. Previous approaches in this domain have focused largely on one-shot generation, where a language model outputs a complete story based on limited initial input from a user. Here, we instead focus on the task of interactive story generation, where the user provides the model mid-level sentence abstractions in the form of cue phrases during the generation process. This provides an interface for human users to guide the story generation. We present two content-inducing approaches to effectively incorporate this additional information. Experimental results from both automatic and human evaluations show that these methods produce more topically coherent and personalized stories compared to baseline methods.
Detecting and quantifying entanglement on near-term quantum devices

Abstract Quantum entanglement is a key resource in quantum technology, and its quantification is a vital task in the current noisy intermediate-scale quantum (NISQ) era. This paper combines hybrid quantum-classical computation and quasi-probability decomposition to propose two variational quantum algorithms, called variational entanglement detection (VED) and variational logarithmic negativity estimation (VLNE), for detecting and quantifying entanglement on near-term quantum devices, respectively. VED makes use of the positive map criterion and works as follows. Firstly, it decomposes a positive map into a combination of quantum operations implementable on near-term quantum devices. It then variationally estimates the minimal eigenvalue of the final state, obtained by executing these implementable operations on the target state and averaging the output states. Deterministic and probabilistic methods are proposed to compute the average. At last, it asserts that the target state is entangled if the optimized minimal eigenvalue is negative. VLNE builds upon a linear decomposition of the transpose map into Pauli terms and the recently proposed trace distance estimation algorithm. It variationally estimates the well-known logarithmic negativity entanglement measure and could be applied to quantify entanglement on near-term quantum devices. Experimental and numerical results on the Bell state, isotropic states, and Breuer states show the validity of the proposed entanglement detection and quantification methods.
Generalization, Representation, and Recovery in a Self-Organizing Feature-Map Model of Language Acquisition

This study explores the self-organizing neural network as a model of lexical and morphological acquisition. We examined issues of generalization, representation, and recovery in a multiple feature-map model. Our results indicate that self-organization and Hebbian learning are two important computational principles that can account for the psycho- linguistic processes of semantic representation, morphological generalization, and recovery from generalizations in the acquisition of reversive prefixes such as un- and dis-. These results attest to the utility of self-organizing neural networks in the study of language acquisition.
Resource Management for Cloud Functions with Memory Tracing, Profiling and Autotuning

Application software provisioning evolved from monolithic designs towards differently designed abstractions including serverless applications. The promise of that abstraction is that developers are free from infrastructural concerns such as instance activation and autoscaling. Today's serverless architectures based on FaaS are however still exposing developers to explicit low-level decisions about the amount of memory to allocate for the respective cloud functions. In many cases, guesswork and ad-hoc decisions determine the values a developer will put into the configuration. We contribute tools to measure the memory consumption of a function in various Docker, OpenFaaS and GCF/GCR configurations over time and to create trace profiles that advanced FaaS engines can use to autotune memory dynamically. Moreover, we explain how pricing forecasts can be performed by connecting these traces with a FaaS characteristics knowledge base.