Reviewerly/exHarmony huggingface dataset:
MAP Inference for Probabilistic Logic Programming

In Probabilistic Logic Programming (PLP) the most commonly studied inference task is to compute the marginal probability of a query given a program. In this paper, we consider two other important tasks in the PLP setting: the Maximum-A-Posteriori (MAP) inference task, which determines the most likely values for a subset of the random variables given evidence on other variables, and the Most Probable Explanation (MPE) task, the instance of MAP where the query variables are the complement of the evidence variables. We present a novel algorithm, included in the PITA reasoner, which tackles these tasks by representing each problem as a Binary Decision Diagram and applying a dynamic programming procedure on it. We compare our algorithm with the version of ProbLog that admits annotated disjunctions and can perform MAP and MPE inference. Experiments on several synthetic datasets show that PITA outperforms ProbLog in many cases.
Classifier combination approach for question classification for Bengali question answering system

Question classification (QC) is a prime constituent of automated question answering system. The work presented here demonstrates that the combination of multiple models achieve better classification performance than those obtained with existing individual models for the question classification task in Bengali. We have exploited state-of-the-art multiple model combination techniques, i.e., ensemble, stacking and voting, to increase QC accuracy. Lexical, syntactic and semantic features of Bengali questions are used for four well-known classifiers, namely Na\"{\i}ve Bayes, kernel Na\"{\i}ve Bayes, Rule Induction, and Decision Tree, which serve as our base learners. Single-layer question-class taxonomy with 8 coarse-grained classes is extended to two-layer taxonomy by adding 69 fine-grained classes. We carried out the experiments both on single-layer and two-layer taxonomies. Experimental results confirmed that classifier combination approaches outperform single classifier classification approaches by 4.02% for coarse-grained question classes. Overall, the stacking approach produces the best results for fine-grained classification and achieves 87.79% of accuracy. The approach presented here could be used in other Indo-Aryan or Indic languages to develop a question answering system.
A Poisson Model for Entanglement Optimization in the Quantum Internet

We define a nature-inspired model for entanglement optimization in the quantum Internet. The optimization model aims to maximize the entanglement fidelity and relative entropy of entanglement for the entangled connections of the entangled network structure of the quantum Internet. The cost functions are subject of a minimization defined to cover and integrate the physical attributes of entanglement transmission, purification, and storage of entanglement in quantum memories. The method can be implemented with low complexity that allows a straightforward application in the quantum Internet and quantum networking scenarios.
Efficient Algorithms for Device Placement of DNN Graph Operators

Modern machine learning workloads use large models, with complex structures, that are very expensive to execute. The devices that execute complex models are becoming increasingly heterogeneous as we see a flourishing of domain-specific accelerators being offered as hardware accelerators in addition to CPUs. These trends necessitate distributing the workload across multiple devices. Recent work has shown that significant gains can be obtained with model parallelism, i.e, partitioning a neural network’s computational graph onto multiple devices. In particular, this form of parallelism assumes a pipeline of devices, which is fed a stream of samples and yields high throughput for training and inference of DNNs. However, for such settings (large models and multiple heterogeneous devices), we require automated algorithms and toolchains that can partition the ML workload across devices. In this paper, we identify and isolate the structured optimization problem at the core of device placement of DNN operators, for both inference and training, especially in modern pipelined settings. We then provide algorithms that solve this problem to optimality. We demonstrate the applicability and efficiency of our approaches using several contemporary DNN computation graphs.
Generalized approach to quantify correlations in bipartite quantum systems

In this work we developed a general approach to the problem of detecting and quantifying different kind of correlations in bipartite quantum systems. Our method is based on the use of distances between quantum states and processes. We rely upon the premise that total correlations can be separated into classical and quantum contributions due to their different nature. In addition, according to recently discussed criteria, we determined the requirements to be satisfied by distances in order to generate correlation measures physically well behaved. The proposed measures allow us to quantify quantum, classical and total correlations.
Continuous-variable source-device-independent quantum key distribution against general attacks

The continuous-variable quantum key distribution with entanglement in the middle, a semi-device-independent protocol, places the source at the untrusted third party between Alice and Bob, and thus has the advantage of high levels of security with the purpose of eliminating the assumptions about the source device. However, previous works considered the collective-attack analysis, which inevitably assumes that the states of the source has an identical and independently distributed (i.i.d) structure, and limits the application of the protocol. To solve this problem, we modify the original protocol by exploiting an energy test to monitor the potential high energy attacks an adversary may use. Our analysis removes the assumptions of the light source and the modified protocol can therefore be called source-device-independent protocol. Moreover, we analyze the security of the continuous-variable source-device-independent quantum key distribution protocol with a homodyne-homodyne structure against general coherent attacks by adapting a state-independent entropic uncertainty relation. The simulation results indicate that, in the universal composable security framework, the protocol can still achieve high key rates against coherent attacks under the condition of achievable block lengths.
FGD at MRP 2020: Prague Tectogrammatical Graphs

Prague Tectogrammatical Graphs (PTG) is a meaning representation framework that originates in the tectogrammatical layer of the Prague Dependency Treebank (PDT) and is theoretically founded in Functional Generative Description of language (FGD).PTG in its present form has been prepared for the CoNLL 2020 shared task on Cross-Framework Meaning Representation Parsing (MRP).It is generated automatically from the Prague treebanks and stored in the JSON-based MRP graph interchange format.The conversion is partially lossy; in this paper we describe what part of annotation was included and how it is represented in PTG.
FreeST: Context-free Session Types in a Functional Language

FreeST is an experimental concurrent programming language. Based on a core linear functional programming language, it features primitives to fork new threads, and for channel creation and communication. A powerful type system of context-free session types governs the interaction on channels. The compiler builds on a novel algorithm for deciding type equivalence of context-free session types. This abstract provides a gentle introduction to the language and discusses the validation process and runtime system.
Machine learning phase transition: An iterative proposal

We propose an iterative proposal to estimate critical points for statistical models based on configurations by combining machine-learning tools. Firstly, phase scenarios and preliminary boundaries of phases are obtained by dimensionality-reduction techniques. Besides, this step not only provides labeled samples for the subsequent step but also is necessary for its application to novel statistical models. Secondly, making use of these samples as training set, neural networks would be employed to assign labels to those samples between the phase boundaries in an iterative manner. Newly labeled samples would be put in the training set used in subsequent training and the phase boundaries would be updated as well. The average of the phase boundaries is expected to converge to the critical temperature in this proposal. In concrete examples, we implement this proposal to estimate the critical temperatures for two q-state Potts models with continuous and first order phase transitions. Techniques used in linear and manifold dimensionality-reductions are employed in the first step. Both a convolutional neural network and a bidirectional recurrent neural network with long short-term memory units perform well for two Potts models in the second step. The convergent behaviors of the estimations reflect the types of phase transitions. And the results indicate that our proposal may be used to explore phase transitions for new general statistical models.
Fully tunable and switchable coupler for photonic routing in quantum detection and modulation

Photonic routing is a key building block of many optical applications challenging its development. We report a 2$\times$2 photonic coupler with splitting ratio switchable by a low-voltage electronic signal with 10~GHz bandwidth and tens of nanoseconds latency. The coupler can operate at any splitting ratio ranging from 0:100 to 100:0 with the extinction ratio of 26 dB in optical bandwidth of 1.3 THz. We show sub-nanosecond switching between arbitrary coupling regimes including balanced 50:50 beam splitter, 0:100 switch, and a photonic tap. The core of the device is based on Mach-Zehnder interferometer in a dual-wavelength configuration allowing real-time phase lock with long-term sub-degree stability at single-photon level. Using the reported coupler, we demonstrate for the first time the perfectly balanced time-multiplexed device for photon-number-resolving detectors and also the active preparation of a photonic temporal qudit state up to four time bins. Verified long-term stable operation of the coupler at the single photon level makes it suitable for wide application range in quantum information processing and quantum optics in general.
Estimating the precision for quantum process tomography

Quantum tomography is a widely applicable tool for complete characterization of quantum states and processes. In the present work, we develop a method for precision-guaranteed quantum process tomography. With the use of the Choi-Jamiolkowski isomorphism, we generalize the recently suggested extended norm minimization estimator for the case of quantum processes. Our estimator is based on the Hilbert-Schmidt distance for quantum processes. Specifically, we discuss the application of our method for characterizing quantum gates of a superconducting quantum processor in the framework of the IBM Q Experience.
Optical Exfiltration of Data via Keyboard LED Status Indicators to IP Cameras

The ability of the light-emitting diodes (LEDs) on a keyboard to send data at a rate that is far greater than the human eye can perceive has been fully studied. However, an IP camera can only fetch high-resolution images at a low frame rate. It is unable to act as a sink of the optical covert channel directly. In this paper, a novel form of modulation is proposed to modulate the LEDs on a keyboard. The modulated signal can be received by a nearby IP camera. To verify its validity, we implement a prototype of exfiltration malware. Our experiment shows a significant improvement in the imperceptibility of covert communication. Against the background of the Internet of Things, it is possible to leak data covertly to IP cameras across air-gapped networks via LED keyboard status indicators.
Optical Hall response of bilayer graphene: Manifestation of chiral hybridized states in broken mirror symmetry lattices

The authors study the optical Hall response of bilayer graphene under sliding and twisting and, based on symmetry arguments, explain the effects of Kerr and Faraday rotations as well as the circular dichroism.
Measure of qubit-environment entanglement for pure dephasing evolutions

We propose a qubit-environment entanglement measure which is tailored for evolutions that lead to pure dephasing of the qubit, such as are abundant in solid-state scenarios. The measure can be calculated directly form the density matrix without minimization of any kind. In fact, it does not require knowledge of the full density matrix and it is enough to know the initial qubit state and the states of the environment conditional on qubit pointer states. This yields a computational advantage over standard entanglement measures, which becomes large when there are no correlations between environmental components in the conditional environmental states. In contrast to all other measures of mixed-state entanglement, the measure has a straightforward physical interpretation directly linking the amount of information about the qubit state which is contained in the environment to the amount of qubit-environment entanglement. This allows for a direct extension of the pure-state interpretation of entanglement generated during pure dephasing to mixed states, even though pure-state conclusions about qubit decoherence are not transferable.Received 20 December 2019Revised 11 August 2020Accepted 11 September 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043062Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEntanglement measuresOpen quantum systems & decoherenceQuantum information with solid state qubitsQuantum InformationCondensed Matter, Materials & Applied PhysicsGeneral Physics
A Deep Learning Algorithm for Solar Radiation Time Series Forecasting: A Case Study of El Kelaa Des Sraghna City

Nowadays, the studies that address solar radiation (SR) forecasting tend to focus on the implementation of conventional techniques. This provides good results, but researchers should focus on the creation of new methodologies that help us in going further and boost the prediction accuracy of SR data. The prime aim of this research study is to propose an efficient deep learning (DL) algorithm that can handle nonlinearities and dynamic behaviors of the meteorological data, and generate accurate real-time forecasting of hourly global solar radiation (GSR) data of the city of El Kelaa des Sraghna (32°2’53”N 7°24’30”W), Morocco. The proposed DL algorithm integrates the dynamic model named Elman neural network with a new input configuration-based autoregressive process in order to learn from the seasonal patterns of the historical SR measurements, and the actual measurements of air temperature. The attained performance proves the reliability and the accuracy of the proposed model to forecast the hourly GSR time series in case of missing values detection or pyranometer damage. Hence, electrical power engineers can adopt this forecasting tool to improve the integration of solar power resources into the power grid system.
The effects Of COVID-19 on restaurant industry: A perspective article.

This paper aims to discuss the impacts of COVID-19 on restaurant marketing and management practices and outlines a three-pronged research agenda to stimulate knowledge development in the restaurant sector. This paper is based on an overview of the relevant literature on social media in food advertising. The authors also investigated trends in hospitality services to suggest a research agenda. This paper presents a research agenda in three dimensions - Artificial Intelligence (AI) and digital media ads and the importance of social media ads during Covid 19. First, different types of artificial intelligence (mechanical, thinking and feeling) may open distinct research streams at the intersection of health crises and restaurants, in light of the COVID-19 pandemic. In addition, this paper recommends that researchers move beyond typical perspectives on precedents and the results of restaurant cleanliness and cleanliness to delve into. Moreover, to help restaurants recover from the COVID-19 pandemic. The proposed research streams are expected to provide actionable insights to promote development and sustainability in the restaurant sector. Authenticity / Value - This paper appears to be a frontier study, looking critically at the potential impacts of the COVID-19 pandemic, the impacts of COVID-19 on the restaurant industry and how restaurant owners can respond to such post-pandemic recovery challenges.
KATRec: Knowledge Aware aTtentive Sequential Recommendations

Sequential recommendation systems model dynamic preferences of users based on their historical interactions with platforms. Despite recent progress, modeling short-term and long-term behavior of users in such systems is nontrivial and challenging. To address this, we present a solution enhanced by a knowledge graph called KATRec (Knowledge Aware aTtentive sequential Recommendations). KATRec learns the short and long-term interests of users by modeling their sequence of interacted items and leveraging pre-existing side information through a knowledge graph attention network. Our novel knowledge graph-enhanced sequential recommender contains item multi-relations at the entity-level and users’ dynamic sequences at the item-level. KATRec improves item representation learning by considering higher-order connections and incorporating them in user preference representation while recommending the next item. Experiments on three public datasets show that KATRec outperforms state-of-the-art recommendation models and demonstrates the importance of modeling both temporal and side information to achieve high-quality recommendations.
Verification of Identity and Educational Certificates of Students Using Biometric and Blockchain

Blockchain technology is being adopted in various areas like finance, healthcare, and trade. It is adding value to the existing business methods with its features like transparency, security, and decentralization. It can also transform the working of education systems. In the current education systems, students identify themselves through multiple government-authorized identity proofs. Additionally, their previous degree certificates are manually verified by college authorities during the admission process. This requires lots of time and manpower. A blockchain-based system can offer more security, transparency and efficiency than the existing system. The proposed solution is the use of biometric to access all the previous degree certificates of a student using blockchain. The students will submit the hash of their biometric and a unique phrase. This hash will be stored on the blockchain. The degree certificate for a student will be issued by the college authorities. They will upload the hash of digitally signed certificate on the blockchain. For admission into higher studies, college authorities can fetch a student’s previous certificates from the blockchain using the hash of his/her biometric and a unique phrase. This concept can be extended to verify any documents by linking them to a person’s identity without involving a third party.
Dissipation-induced antiferromagneticlike frustration in coupled photonic resonators

We propose a photonic quantum simulator for anti-ferromagnetic spin systems based on reservoir engineering. We consider a scheme where quadratically driven dissipative Kerr cavities are indirectly coupled via lossy ancillary cavities. We show that the ancillary cavities can produce an effective dissipative and Hamiltonian anti-ferromagnetic-like coupling between the cavities. By solving the master equation for a triangular cavity configuration, we demonstrate that the non-equilibrium steady state of the system bears full analogy with the ground state of an antiferromagnetic Ising model, exhibiting key signatures of frustration. We show that when the effective photon hopping amplitude is zero, the engineered non-local dissipation alone is capable of inducing antiferromagnetic interaction and frustration. This simple scheme can be generalised to arbitrary lattice geometries, providing a fully controllable recipe for simulating antiferromagnetism and frustration on a controlled quantum optical platform.
Increasing the dimension of the maximal pure coherent subspace of a state via incoherent operations

Quantum states transformation under free operations plays a central role in the resource theory of coherence. In this paper, we investigate the transformation from a mixed coherent state into a pure one by using both incoherent operations and stochastic incoherent operations. We show that contrary to the strictly incoherent operations and the stochastic strictly incoherent operations, both the incoherent operations and the stochastic incoherent operations can increase the dimension of the maximal pure coherent subspace of a state. This means that the incoherent operations are generally stronger than the strictly incoherent operations when we want to transform a mixed coherent state into a pure coherent one. Our findings can also be interpreted as confirming the ability of incoherent operations to enhance the coherence of mixed states relative to certain coherence monotones under strictly incoherent operations.
A Improving Feature Selection on Heart Disease Dataset With Boruta Approach

Coronary artery disease (CAD) is one of the deadliest diseases in the entire world, including in Indonesia. CAD occurs due to narrowing or blockage of coronary arteries which is usually caused by atherosclerosis. Various studies have been conducted with the aim to predict the nature and characteristics of this disease. Some researches uses the Z-Alizadeh Sani dataset which consists of 54 attributes with two results of classification, CAD and Normal to classify its data. Feature selection is one way to reduce the number of attributes that exist by leaving the attributes that have a high effect on the dataset. In this study, the Boruta method is used as a feature selection to minimize the attributes and leave the attributes with high relative with the dataset. By reducing the attributes in the dataset through the feature selection process, sets of 17 and 18 attributes are selected as attributes with high relative with the dataset. These attributes then used to calculate the accuracy value of the dataset using the several classification methods and 90,3% accuracy is obtained from this study.
Asynchronous effects

We explore asynchronous programming with algebraic effects. We complement their conventional synchronous treatment by showing how to naturally also accommodate asynchrony within them, namely, by decoupling the execution of operation calls into signalling that an operation's implementation needs to be executed, and interrupting a running computation with the operation's result, to which the computation can react by installing interrupt handlers. We formalise these ideas in a small core calculus, called $\lambda_{\text{ae}}$. We demonstrate the flexibility of $\lambda_{\text{ae}}$ using examples ranging from a multi-party web application, to preemptive multi-threading, to remote function calls, to a parallel variant of runners of algebraic effects. In addition, the paper is accompanied by a formalisation of $\lambda_{\text{ae}}$'s type safety proofs in Agda, and a prototype implementation of $\lambda_{\text{ae}}$ in OCaml.