Reviewerly/exHarmony huggingface dataset:
Rewriting the infinite chase

Guarded tuple-generating dependencies (GTGDs) are a natural extension of description logics and referential constraints. It has long been known that queries over GTGDs can be answered by a variant of the chase ---a quintessential technique for reasoning with dependencies. However, there has been little work on concrete algorithms and even less on implementation. To address this gap, we revisit Datalog rewriting approaches to query answering, where GTGDs are transformed to a Datalog program that entails the same base facts on each base instance. We show that the rewriting can be seen as containing "shortcut" rules that circumvent certain chase steps, we present several algorithms that compute the rewriting by simulating specific types of chase steps, and we discuss important implementation issues. Finally, we show empirically that our techniques can process complex GTGDs derived from synthetic and real benchmarks and are thus suitable for practical use.
Disorder-induced enhancement of precision in quantum metrology

We find that a quantum device having an accessory involving precision measurement can have an enhancement of its metrological precision in estimating an unknown parameter of the quantum system by insertion of glassy disorder, accidental or engineered. We clearly mention how an unbiased estimator can also be identified in a disordered situation, and how the precision thereof can be bounded by the quantum Cramer-Rao inequality. We compare the Fisher information-based lower bound of the minimum standard deviation of an unbiased estimator, in presence of glassy disorder in the system, with the same of an ideal, viz. disorder-free, situation. The phenomenon can boost the efficiency of certain measuring devices, such as atomic clocks. The precision of these clocks, when measuring time, hinges on the precise determination of the frequency of a two-level atom. In cases where impurities are present in the atom, and can be modeled as a disorder parameter, it is possible for the measurement of frequency to be more accurate than in an ideal, disorder-free scenario. Moreover, disorder insertion can reduce the requirement of entanglement content of the initial probes, which are copies of two-qubit states, along with providing a disorder-induced enhancement.
Semantic Sensing and Communications for Ultimate Extended Reality

As a key technology in metaversa, wireless ultimate extended reality (XR) has attracted extensive attentions from both industry and academia. However, the stringent latency and ultra-high data rates requirements have hindered the development of wireless ultimate XR. Instead of transmitting the original source data bit-by-bit, semantic communications focus on the successful delivery of semantic information contained in the source, which have shown great potentials in reducing the data traffic of wireless systems. Inspired by semantic communications, this article develops a joint semantic sensing, rendering, and communication framework for wireless ultimate XR. In particular, semantic sensing is used to improve the sensing efficiency by exploring the spatial-temporal distributions of semantic information. Semantic rendering is designed to reduce the costs on semantically-redundant pixels. Next, semantic communications are adopted for high data transmission efficiency in wireless ultimate XR. Then, two case studies are provided to demonstrate the effectiveness of the proposed framework. Finally, potential research directions are identified to boost the development of semantic-aware wireless ultimate XR.
Is it Required? Ranking the Skills Required for a Job-Title

In this paper, we describe our method for ranking the skills required for a given job title. Our analysis shows that important/relevant skills appear more frequently in similar job titles. We train a Language-agnostic BERT Sentence Encoder (LaBSE) model to predict the importance of the skills using weak supervision. We show the model can learn the importance of skills and perform well in other languages. Furthermore, we show how the Inverse Document Frequency factor of skill boosts the specialised skills.
A Comprehensive Survey of Benchmarks for Automated Improvement of Software's Non-Functional Properties

Performance is a key quality of modern software. Although recent years have seen a spike in research on automated improvement of software's execution time, energy, memory consumption, etc., there is a noticeable lack of standard benchmarks for such work. It is also unclear how such benchmarks are representative of current software. Furthermore, frequently non-functional properties of software are targeted for improvement one-at-a-time, neglecting potential negative impact on other properties. In order to facilitate more research on automated improvement of non-functional properties of software, we conducted a survey gathering benchmarks used in previous work. We considered 5 major online repositories of software engineering work: ACM Digital Library, IEEE Xplore, Scopus, Google Scholar, and ArXiV. We gathered 5000 publications (3749 unique), which were systematically reviewed to identify work that empirically improves non-functional properties of software. We identified 386 relevant papers. We find that execution time is the most frequently targeted property for improvement (in 62% of relevant papers), while multi-objective improvement is rarely considered (5%). Static approaches are prevalent (in 53% of papers), with exploratory approaches (evolutionary in 18% and non-evolutionary in 14% of papers) increasingly popular in the last 10 years. Only 40% of 386 papers describe work that uses benchmark suites, rather than single software, of those SPEC is most popular (covered in 33 papers). We also provide recommendations for choice of benchmarks in future work, noting, e.g., lack of work that covers Python or JavaScript. We provide all programs found in the 386 papers on our dedicated webpage at https://bloa.github.io/nfunc_survey/ We hope that this effort will facilitate more research on the topic of automated improvement of software's non-functional properties.
Quantum control: Teaching Schrödinger’s cat to compute the impossible

Quantum mechanics has revolutionised the way we understand the world around us, and has already produced technologies that have had an enormous impact on our daily lives. From medical imaging such as MRI to the mobile device in your pocket, quantum physics has played a key role in their design and manufacture. As our understanding of quantum physics has matured, a second generation of quantum technologies are now becoming a reality. This second generation of quantum technology pushes even further the boundary of what is possible, from new sensors that measure beyond classical limits, to quantum computers that can solve problems no classical computer can. However, a major roadblock to developing these new technologies is fast and stable quantum control. Of the many quantum control techniques available, analytic techniques have several advantages. Our research has extended a class of analytic control methods called shortcuts to adiabaticty (STA) to control problems where STA could not be used before. Our new technique is called enhanced shortcuts to adiabaticity (eSTA), and offers new analytic control protocols that have applications across a wide range of practical quantum technologies, from quantum computers to quantum sensors and thermal devices.
Grothendieck inequalities characterize converses to the polynomial method

A surprising 'converse to the polynomial method' of Aaronson et al. (CCC'16) shows that any bounded quadratic polynomial can be computed exactly in expectation by a 1-query algorithm up to a universal multiplicative factor related to the famous Grothendieck constant. Here we show that such a result does not generalize to quartic polynomials and 2-query algorithms, even when we allow for additive approximations. We also show that the additive approximation implied by their result is tight for bounded bilinear forms, which gives a new characterization of the Grothendieck constant in terms of 1-query quantum algorithms. Along the way we provide reformulations of the completely bounded norm of a form, and its dual norm.
Can artificial intelligence infringe copyright? Some reflections

These examples clearly raise a wide range of intellectual property (IP) issues, ranging from questions of copyright subsistence and core patentability requirements to issues concerning IP theory more broadly.Rather than examining all pertinent IP issues, this chapter will focus on copyright and machine-induced infringement in particular.AI is capable of consuming large amounts of creative works and data as part of its learning process, which clearly raises serious risks of infringement.At the same time, this is an area which has not been fully explored yet (as most academic works have focused on the issue of protecting AI-generated works).In terms of scope, we will examine key jurisdictions including the UK, EU and US which are leading the academic debate (and scientific research) in this area.Before looking at the critical issues of infringement and exceptions to infringement, we will first provide a brief overview of AI creativity, which will help to explain why and how infringement may occur. CAN AI INFRINGE COPYRIGHT?Creative AI uses substantial amounts of input data-images, videos, text and other artistic content-as part of its learning process. 8Music-generating AI, for instance, utilises significant amounts of source material to find patterns and create new melodies based on various elements including tempo, chords and length. 9Similar rules apply in the context of visual art-The Next Rembrandt project involved 350 scanned images and over 150 gigabytes of data. 10While the final output was not universally acclaimed (labelled by some as 'fan-fiction'), 11 it clearly demonstrated that modern AI can produce sophisticated creative output that resembles the works of professional (human) artists.Large amounts of source text are required to generate literature and creative writing too.Deep learning language models such as GPT-3-AI that produces human-like text across a range of categories, including creative writing, parodies and
Reduce++: Unsupervised Content-Based Approach for Duplicate Result Detection in Search Engines

Search engines are among the most popular web services on the World Wide Web. They facilitate the process of finding information using a query-result mechanism. However, results returned by search engines contain many duplications. In this paper, we introduce a new content-type-based similarity computation method to address this problem. Our approach divides the webpage into different types of content, such as title, subtitles, body, etc. Then, we find for each type a suitable similarity measure. Next, we add the different calculated similarity scores to get the final similarity score between the two documents, using a weighted formula. Finally, we suggest a new graph-based algorithm to cluster search results according to their similarity. We empirically evaluated our results with the Agglomerative Clustering, and we achieved about 61% reduction in web pages, 0.2757 Silhouette coefficient, 0.1269 Davies Bouldin Score, and 85 Calinski Harabasz Score.
Visually Similar Products Retrieval for Shopsy

Visual search is of great assistance in reseller commerce, especially for non-tech savvy users with affinity towards regional languages. Product attributes available in e-commerce have potential for building better visual search systems [2, 20, 29]. We design a visual search system for reseller commerce using a multi-task learning approach and address challenges like image compression, cropping, etc, faced in reseller commerce. Our model consists of three tasks: attribute classification, triplet ranking and variational autoencoder (VAE). We introduce an offline triplet mining technique which utilizes information from multiple attributes to capture relative order within data. This technique displays better performance compared to traditional triplet mining [27] baseline. We compare and report incremental gain achieved by our unified multi-task model over each individual task separately. The efsfectiveness of our method is demonstrated using in-house dataset of images from the Lifestyle business-unit of Flipkart. To efficiently retrieve images in production, we use Approximate Nearest Neighbor (ANN) index.
Alpha Stable Random Fields and Additive Error

This work studies the estimation of spectral density for random field (two-dimensional signal) when the spectral measure have certain mixture and the process is observed with a constant error. The objective of this paper is to give an estimator of the constant error by using the Jackson polynomial kernel. We show that the rate of convergence depends of size of sample and the behaviours of the spectral density at origin. Indeed the estimator converges rapidly when the spectral density is null at origin. Few long memory signals are taken here as example.
A Deep Learning Framework for Predicting Signals in OFDM-NOMA with various Algorithms

The non-orthogonal multiple access (NOMA) approaches have increasingly attracted much interest. It has also been a potential method for wireless communication systems beyond the fifth generation (5G). The successive interference cancellation (SIC) procedure in NOMA systems is often carried out at the receiver, where several users are sequentially decoded. The successful detection of prior users will significantly influence the detection accuracy due to the effects of interferences. A deep learning-based NOMA receiver is analyzed to detect signals for multiple users in a single application without determining channels. This paper analyzes deep learning (DL)- based receiver for NOMA signal detection concerning several DL-aided sequence layersbased algorithms and optimizers by training orthogonal frequency division multiplexing (OFDM) symbols. The simulation outcomes illustrate the various DL-based receiver characteristics using the traditional SIC approach. It also demonstrates that the effect of the different DL-based models is more predictable than the SIC approach.
Word Embedding Interpretation using Co-Clustering

Word embedding is the foundation of modern language processing (NLP). In the last few decades, word representation has evolved remarkably resulting in an impressive performance in NLP downstream applications. Yet, word embedding's interpretability remains a challenge. In this paper, We propose a simple technique to interpret word embedding. Our method is based on post-processing technique to improve the quality of word embedding and reveal the hidden structure in these embeddings. We deploy Co-clustering method to reveal the hidden structure of word embedding and detect sub-matrices between word meaning and specific dimensions. Empirical evaluation on several benchmarks shows that our method achieves competitive results compared to original word embedding.
Streamline Border Control with Blockchain Towards Self-Sovereign Identity

Since the mid-2000s, the digitalisation of border checks has often referred to the increased adoption of automated border control (ABC) solutions at border crossing points in all border environments from air- ports and seaports to land border crossings. Key prerequisites for the operational implementations of the so-called eGates have been the electronic machine-readable travel document together with biometric technologies that have facilitated the automation of much of the tasks performed by border guards at manual control booths for selected groups of nationalities. Now, the next wave of major changes is emerging with the development of electronic identification (eID), with certain implementations particularly designed for crossborder use cases supplementing and possibly replacing the traditional physical identity document in a long-term future. The evolution of eID strongly aligns with the increased demands for data privacy to ensure that individuals can better control how much information is shared about themselves, with whom and for what purpose. One possible technology to provide the so-called data self-sovereignty is distributed ledger technology (DLT), including blockchains. DLT is being developed for instance by the Linux foundation, dispensing several distributed ledger projects and associated solutions for digital and self-sovereign identity. One of these projects is Hyperledger Indy. In this study, we present a distributed ledger implementation based on Hyperledger Indy applied as a border check use case. Our aim is to investigate the suitability of DLT in providing data self-sovereign facility in border checks, and to discuss the benefits and disadvantages the technology might entail for this security domain.
A Cyber Threat Intelligence Management Platform for Industrial Environments

Developing intelligent, interoperable Cyber Threat Information (CTI) sharing technologies can help build strong defences against modern cyber threats. CTIs allow the community to share information about cybercriminals' threats and vulnerabilities and countermeasures to defend themselves or detect malicious activity. A crucial need for success is that the data connected to cyber risks be understandable, organized, and of good quality. The receiving parties may grasp its content and utilize it effectively. This article describes an innovative cyber threat intelligence management platform (CTIMP) for industrial environments, one of the Cyber-pi project's significant elements. The suggested architecture, in particular, uses cyber knowledge from trusted public sources and integrates it with relevant information from the organization's supervised infrastructure in an entirely interoperable and intelligent way. When combined with an advanced visualization mechanism and user interface, the services mentioned above provide administrators with the situational awareness they require while also allowing for extended cooperation, intelligent selection of advanced coping strategies, and a set of automated selfhealing rules for dealing with threats.
Enhancing a Student Productivity Model for Adaptive Problem-Solving Assistance

Research on intelligent tutoring systems has been exploring data-driven methods to deliver effective adaptive assistance. While much work has been done to provide adaptive assistance when students seek help, they may not seek help optimally. This had led to the growing interest in proactive adaptive assistance, where the tutor provides unsolicited assistance upon predictions of struggle or unproductivity. Determining when and whether to provide personalized support is a well-known challenge called the assistance dilemma. Addressing this dilemma is particularly challenging in open-ended domains, where there can be several ways to solve problems. Researchers have explored methods to determine when to proactively help students, but few of these methods have taken prior hint usage into account. In this paper, we present a novel data-driven approach to incorporate students' hint usage in predicting their need for help. We explore its impact in an intelligent tutor that deals with the open-ended and well-structured domain of logic proofs. We present a controlled study to investigate the impact of an adaptive hint policy based on predictions of HelpNeed that incorporate students' hint usage. We show empirical evidence to support that such a policy can save students a significant amount of time in training, and lead to improved posttest results, when compared to a control without proactive interventions. We also show that incorporating students' hint usage significantly improves the adaptive hint policy's efficacy in predicting students' HelpNeed, thereby reducing training unproductivity, reducing possible help avoidance, and increasing possible help appropriateness (a higher chance of receiving help when it was likely to be needed). We conclude with suggestions on the domains that can benefit from this approach as well as the requirements for adoption.
A COMPARATIVE STUDY OF FALL RISK ASSESSMENT IN COMMUNITY DWELLING OLDER ADULTS IN INDIA

Abstract Falls in older adults can be prevented with early identification. Recent research investigates the use of wearable sensors to provide quantitative data on mobility parameters to assess risk of fall. However, the efficiency of these new methods in comparison to conventional fall risk assessment tools is unknown. Therefore, the current study compares the sensor-based fall risk assessment with conventional fall risk measures. The study included 659 community-dwelling older adults (&amp;gt;60 years) who were followed for 12 months after baseline. The American Geriatric Society Fall Risk Assessment tool was used to assess risk of fall conventionally and Kinesis QTUG was used to assess the fall risk using sensors. Receiver operating characteristics curve (ROC) was used to compare the sensor-based method and the conventional method of assessing fall risk. Out of the 659 community-dwelling older adults, 24% (163) of older adults reported a fall in 12 months. According to the sensor-based assessment, 23% of older adults had low risk, 50% had medium risk and 27% had high risk of fall and according to the conventional method 66.1% were in low risk, 27.1% were in medium risk and 6.8% were in high risk. The ROC analysis showed that the sensor-based methods (AUC-78%; sensitivity- 93.8% specificity – 56.3%) outperformed the conventional method (AUC – 61.2%; sensitivity – 82.8% and specificity - 67.2%) of identifying older adults at risk of fall. Therefore, use of simple wearable sensors can determine the risk of future falls.
Quantum phase estimation in presence of glassy disorder

We investigate the response to noise, in the form of glassy disorder present in circuit elements, in the success probability of the quantum phase estimation algorithm, a subroutine used to determine the eigenvalue - a phase - corresponding to an eigenvector of a unitary gate. We prove that when a large number of auxiliary qubits are involved in the circuit, the probability does not depend on the actual type of disorder but only on the mean and strength of the disorder. For further analysis, we consider three types of disorder distributions: Haar-uniform with a circular cut-off, Haar-uniform with an elliptical or squeezed cut-off, and spherical normal. There is generally a depreciation of the disorder-averaged success probability in response to the disorder incorporation. Even in the presence of the disorder, increasing the number of auxiliary qubits helps to get a better precision of the phase, albeit to a lesser extent (probability) than that in the clean case. We find a concave to convex transition in the dependence of probability on the strength of disorder, and a log-log dependence is witnessed between the point of inflection and the number of auxiliary qubits used.