Reviewerly/exHarmony huggingface dataset:
A direct product theorem for quantum communication complexity with applications to device-independent QKD.

We give a direct product theorem for the entanglement-assisted interactive quantum communication complexity of an $l$-player predicate $\mathsf{V}$. In particular we show that for a distribution $p$ that is product across the input sets of the $l$ players, the success probability of any entanglement-assisted quantum communication protocol for computing $n$ copies of $\mathsf{V}$, whose communication is $o(\log(\mathrm{eff}^*(\mathsf{V},p))\cdot n)$, goes down exponentially in $n$. Here $\mathrm{eff}^*(\mathsf{V}, p)$ is a distributional version of the quantum efficiency or partition bound introduced by Laplante, Lerays and Roland (2014), which is a lower bound on the distributional quantum communication complexity of computing a single copy of $\mathsf{V}$ with respect to $p$. As an application of our result, we show that it is possible to do device-independent quantum key distribution (DIQKD) without the assumption that devices do not leak any information after inputs are provided to them. We analyze the DIQKD protocol given by Jain, Miller and Shi (2017), and show that when the protocol is carried out with devices that are compatible with $n$ copies of the Magic Square game, it is possible to extract $\Omega(n)$ bits of key from it, even in the presence of $O(n)$ bits of leakage. Our security proof is parallel, i.e., the honest parties can enter all their inputs into their devices at once, and works for a leakage model that is arbitrarily interactive, i.e., the devices of the honest parties Alice and Bob can exchange information with each other and with the eavesdropper Eve in any number of rounds, as long as the total number of bits or qubits communicated is bounded.
Modular adaptive optics solution for a QKD receiver on a fork mount telescope system

Quantum key distribution (QKD) is a promising field within telecommunication and cryptography. QKD allows two parties to generate a shared secret key, which only the two communicating parties know, and can later be used to encrypt and decrypt messages. This leads to unbreakable data transmission. QKD was experimentally already tested on a Chinese mission called MICIUS. Future QKD protocols may also benefit from coupling into an optical single-mode fiber. Adaptive optics is, from a quantum communication perspective, very interesting. In this paper, we present the overall system design, the optical and mechanical design, and the factory performance test results, of an adaptive optics module for post-compensation of the quantum signal in a QKD ground station. The set-up is integrated in a compact and transportable “AO-box”. The AO-box has been designed to be directly mounted on the flange of an 80 cm fork mount telescope and delivers the fiber-coupled quantum signal at its output interface.
Structural Causal Models Are (Solvable by) Credal Networks.

A structural causal model is made of endogenous (manifest) and exogenous (latent) variables. We show that endogenous observations induce linear constraints on the probabilities of the exogenous variables. This allows to exactly map a causal model into a credal network. Causal inferences, such as interventions and counterfactuals, can consequently be obtained by standard algorithms for the updating of credal nets. These natively return sharp values in the identifiable case, while intervals corresponding to the exact bounds are produced for unidentifiable queries. A characterization of the causal models that allow the map above to be compactly derived is given, along with a discussion about the scalability for general models. This contribution should be regarded as a systematic approach to represent structural causal models by credal networks and hence to systematically compute causal inferences. A number of demonstrative examples is presented to clarify our methodology. Extensive experiments show that approximate algorithms for credal networks can immediately be used to do causal inference in real-size problems.
Neuro-Symbolic Techniques for Description Logic Reasoning (Student Abstract)

With the goal to find scalable reasoning approaches, neuro-symbolic techniques have gained significant attention. However, the existing approaches do not take into account the inference capabilities of ontology languages that are based on expressive description logic (such as OWL 2). To fill this gap, we propose two approaches: an ontology-based embedding model for theories in EL++ description logic and a reinforcement learning-based solution for efficient tableau-based reasoning on description logic. We describe promising initial results of our efforts towards these directions and lay down the direction for future work.
Performance Comparison of Image Processing Techniques on Various Filters

Image processing plays a crucial role in a large number of applications including fields of medical, watermarking in images, spatial data analysis applications. When images are static, generally, users can get good performance, though processing of real-time images are dependent on various parameters like efficacy of algorithm and filtering techniques. Researchers have observed high variation in performance during processing of real-life images; therefore, efficient filtering techniques play a vital role in determining the implemented processing algorithm's performance as well as the quality of captured images taken into consideration. Thus, the focus of this study is to discuss various widely used filtering techniques and efficient performance analysis in outdoor environmental scenarios. A real-time efficiency system is made to conclude each filter type's effectiveness in different environmental conditions with comparison and evaluation, highlighting merits and demerits of different algorithms based on application needs along with external factors.
Visual Object Tracking using Surface Fitting for Scale and Rotation Estimation

Since correlation filter appeared in the field of object tracking, it plays an increasingly vital role due to its excellent performance.Although many sophisticated trackers have been successfully applied to track the object accurately, very few of them attaches importance to the scale and rotation estimation.In order to address the above limitation, we propose a novel method combined with Fourier-Mellin transform and confidence evaluation strategy for robust object tracking.In the first place, we construct a correlation filter to locate the target object precisely.Then, a log-polar technique is used in the Fourier-Mellin transform to cope with the rotation and scale changes.In order to achieve subpixel accuracy, we come up with an efficient surface fitting mechanism to obtain the optimal calculation result.In addition, we introduce a confidence evaluation strategy modeled on the output response, which can decrease the impact of image noise and perform as a criterion to evaluate the target model stability.Experimental experiments on OTB100 demonstrate that the proposed algorithm achieves superior capability in success plots and precision plots of OPE, which is 10.8% points and 8.6% points than those of KCF.Besides, our method performs favorably against the others in terms of SRE and TRE validation schemes, which shows the superiority of our proposed algorithm in scale and rotation evaluation.
A Comparison Between Naïve Bayes and The K-Means Clustering Algorithm for The Application of Data Mining on The Admission of New Students

The process of admitting new students at Universitas Islam Negeri Raden Fatah each year produces a lot of new student data. so that there is an accumulation of student data continuously. The purpose of this study is to compare the K-Means Clustering Algorithm and Naïve Bayes on the admission of new students as well as being one of the bases for making decisions to determine the promotion strategy of each study program. The data mining method used is Knowledge Discovery in Database (KDD). The tools used are Rapid Miner. The attributes used are national examination score, school origin, and study programs. The new student data used from 2016 to 2019 was an 18.930 item. The results of this study used the K-Means Clustering Algorithm to produce 3 clusters, while the Naïve Bayes results resulted in an accuracy value of 9.08%.
Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction

Recently, question answering (QA) based on machine reading comprehension has become popular.This work focuses on generative QA which aims to generate an abstractive answer to a given question instead of extracting an answer span from a provided passage.Generative QA often suffers from two critical problems: (1) summarizing content irrelevant to a given question, (2) drifting away from a correct answer during generation.In this paper, we address these problems by a novel Rationale-Enriched Answer Generator (REAG), which incorporates an extractive mechanism into a generative model.Specifically, we add an extraction task on the encoder to obtain the rationale for an answer, which is the most relevant piece of text in an input document to a given question.Based on the extracted rationale and original input, the decoder is expected to generate an answer with high confidence.We jointly train REAG on the MS MARCO QA+NLG task and the experimental results show that REAG improves the quality and semantic accuracy of answers over baseline models.
Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision Processes

We present a novel approach to hierarchical reinforcement learning for linearly-solvable Markov decision processes. Our approach assumes that the state space is partitioned, and defines subtasks for moving between the partitions. We represent value functions on several levels of abstraction, and use the compositionality of subtasks to estimate the optimal values of the states in each partition. The policy is implicitly defined on these optimal value estimates, rather than being decomposed among the subtasks. As a consequence, our approach can learn the globally optimal policy, and does not suffer from non-stationarities induced by high-level decisions. If several partitions have equivalent dynamics, the subtasks of those partitions can be shared. We show that our approach is significantly more sample efficient than that of a flat learner and similar hierarchical approaches when the set of boundary states is smaller than the entire state space.
Living Without Beth and Craig: Definitions and Interpolants in Description Logics with Nominals and Role Inclusions.

The Craig interpolation property (CIP) states that an interpolant for an implication exists iff it is valid. The projective Beth definability property (PBDP) states that an explicit definition exists iff a formula stating implicit definability is valid. Thus, the CIP and PBDP transform potentially hard existence problems into deduction problems in the underlying logic. Description Logics with nominals and/or role inclusions do not enjoy the CIP nor PBDP, but interpolants and explicit definitions have many potential applications in ontology engineering and ontology-based data management. In this article we show the following: even without Craig and Beth, the existence of interpolants and explicit definitions is decidable in description logics with nominals and/or role inclusions such as ALCO, ALCH and ALCHIO. However, living without Craig and Beth makes this problem harder than deduction: we prove that the existence problems become 2ExpTime-complete, thus one exponential harder than validity. The existence of explicit definitions is 2ExpTime-hard even if one asks for a definition of a nominal using any symbol distinct from that nominal, but it becomes ExpTime-complete if one asks for a definition of a concept name using any symbol distinct from that concept name.
The specifics of the implementation of the concept of “Antichrist” in the novel by D. S. Merezhkovsky “Peter and Alexey”

The article is devoted to the study of the concept of “Antichrist” in the language picture of the world by D. S. Merezhkovsky. The relevance of the study is due to the significant role of the writer in the literature of the late 19th-early 20th century and the dominance of religious and philosophical ideas in his work. The work is aimed at identifying the specifics of the implementation of the concept of “Antichrist” as one of the key concepts for D.S. Merezhkovsky, analyzing its structure and main representations. The material for the study was the text of the novel “Peter and Alexey”, which was studied using the methods of component, frame and distributional analysis. The paper considers three layers of the concept: the outer layer, which corresponds to the biblical text and represents an literary reconstruction of the eschatological popular consciousness of the 23th and 18th centuries, the middle layer, which contains the idea of Peter as the Antichrist, and the deep layer, formed by the idea of the Antichrist as an entity inextricably linked with Christ in the struggle and unity at the same time. As a result of comparison with one of the first texts that allows us to reconstruct this concept, the Revelation of St. John the Baptist. The differentiating features that make up the peculiarity of the implementation of the concept of “Antichrist” in the text under study are highlighted. It is determined that the outer layer of the concept has a frame structure, and the slots included in it are identified. The semes included in the middle layer of the concept, which has a field structure, are analyzed, the organizing dominant and distributional connections between individual semes are established, which allow combining them into hypersemes. The togetherness of the signification in the third, deep layer of the concept of “Antichrist’s beginning” and “Christ’s beginning” is revealed, which is especially clearly manifested in the fragments that have a mystical character. Based on the analysis of the concept of “Antichrist”, which is widely and variously represented in the text of the novel” Peter and Alexey”, the author concludes that it affects the text and the system of images.
iReserve: An Online Event Reservation for Lipa City Cultural with SMS Notification

The study proposed a web-based research iReserve: An Online Event Reservation for Lipa City Cultural, to integrate SMS Notification. In the existing manual system of reservation, the major challenge on the administration was conflicts of schedule with the event reservation. Thus, iReserve: An Online Event Reservation for Lipa City Cultural was proposed to serve as a useful tool for booking events and send updated notifications about the status of the request event details by integrating SMS technology. iReserve is designed to process the booking request and search for clients’ available target event schedules from the system database. The study used the software development life cycle in developing the proposed application. The web application structure was designed using workflow, entity-relationship, context, and use case diagrams. The web application was developed using different open sources such as HTML, CSS, JavaScript, PHP, and MySQL. iReserve was evaluated using ISO 9126 software evaluation tool assessing the web applications’ functionality, reliability, usability, efficiency, portability, and maintainability. The study concluded that using several diagrams and a combination of open source technologies, the web application designed and developed would meet the specified functionalities.
Heart Disease Simulation with Mixed Reality Technology

Abstract Heart disease is one of the leading causes of death in the world. Various types of heart disease include Coronary Heart Disease, Arrhythmias, Heart Failure, Heart Valves and Heart Muscle. The Ministry of Health of the Republic of Indonesia calls on the public to be smarter in responding to the terror of heart disease. One of them through the introduction and early prevention of heart disease. As time has evolved, technology has become part of society. One of the things that can be done to prevent and recognize heart disease is to utilize the use of Mixed Reality technology as a medium for learning and learning. The use of Mixed Reality technology used is a device from Microsoft is namely Hololens. By using this tool, the developer can build applications to display 3D objects and their interactions. To interact, hololens use a targetting mechanism based on the view from the user’s side, a circular viewfinder as a cursor that can be directed with head movements and 2 gestures namely airtap and bloom by the user directing the hand forward until the sensor is detected from Hololens. In this case, the researchers visualize the type of heart disease in 3D and interact by applying hand gestures or voice input as interactions.
Implementation of Minkowski-Chebyshev Distance in Fuzzy Subtractive Clustering

Clustering is a method of the grouping which is done by looking at the similarities between data in a data set. Fuzzy clustering is a clustering method that uses fuzzy set membership values as the basis for grouping data. Fuzzy Subtractive Clutering (FSC) is a fuzzy clustering method where the number of clusters to be formed is unknown. The concept of FSC is to determine the highest data density and the data with the most number of neighbors will be selected as the center of the cluster. Thus, the size of the proximity or distance between points is needed to determine the members of each cluster. The distance used in this study is a combination of the Minkowski and Chebyshev distances. The number of clusters formed will be evaluated using the Partition Coefficient (PC) value where the highest PC value indicates the best number of clusters. The results obtained indicate that the best clusters are three clusters with a PC value of 0.7422
Particularities of the Forensic Investigation of Software Piracy and Online Piracy

Starting from the analysis of the Law no 8/1996 on copyright and related rights in Romania, and continuing with the analysis of the main provisions of the European Union Directive 2001/29/EC on copyright and related rights in the information society and the European Union Directive 2009/24/EC on the legal protection of computer programs, the article presents and analyzes aspects of the criminal investigation of software piracy and online piracy. The article analyzes both some of the criminal investigation acts commonly used in software piracy such as technical-scientific findings and forensic expertise of copyrighted software or related rights, and some methodological issues related to forensic investigation of software piracy and online piracy.
Adaptation of young people from foreign countries based on the study of spiritual and moral subjects

The analysis of the adaptation of the students' youth from foreign countries within Russian society and the educational environment is presented in the current research. The authors study the problem of adaptation based on the case of the Don State Technical university which is providing activities on increasing the number of foreign students. Logically this university's politics lead to the engagement of the students from many foreign countries and the gradual formation of the multicultural and multi-confessional community within the campus. Despite the engagement of a large number of the representatives of different cultures the university, namely, the Orthodox culture and theology department has established the number of innovative educational methods and educational techniques which contribute to the adaptation of the foreign youth into Russian society. These new methods are based on the establishment of spiritual and moral subjects which were integrated into the educational process. The results of the cycle of the research activities have confirmed that these methods can be effectively used for the adaptation of the foreign students within Russian society.
Roman Urdu sentiment analysis using Machine Learning with best parameters and comparative study of Machine Learning algorithms

People talks on the social media as they feel good and easy way to express their feelings about topic, post or product on the ecommerce websites. In the Asia mostly the people use the Roman Urdu language script for expressing their opinion about the topic. The Sentiment analysis of the Roman Urdu (Bilal et al. 2016)language processes is a big challenging task for the researchers because of lack of resources and its non-structured and non-standard syntax / script. We have collected the Dataset from Kaggle containing 21000 values with manually annotated and prepare the data for machine learning and then we apply different machine learning algorithms(SVM , Logistic regression , Random Forest, Naïve Bayes ,AdaBoost, KNN )(Bowers et al. 2018) with different parameters and kernels and with TFIDF(Unigram , Bigram , Uni-Bigram)(Pereira et al. 2018) from the algorithms we find the best fit algorithm , then from the best algorithm we choose 4 algorithms and combined them to deploy on the data set but after the deployment of the hyperparameters we get the best model build by the Support Vector Machine with linear kernel which are 80% accuracy and F1 score 0.79 precision 0.79 and recall is 0.78 with (Ezpeleta et al. 2018)Grid Search CV and CV is 5 fold. Then we perform experiments on the Robust linear Regression model estimation using (Huang, Gao, and Zhou 2018)(Chum and Matas 2008)RANSAC(random sample Consensus) that gives us the best estimators with 82.19%.
Semantic Constraint Based Unsupervised Domain Adaptation for Cardiac Segmentation

The segmentation of unlabeled medical images is troublesome due to the high cost of annotation, and unsupervised domain adaptation is one solution to this.In this paper, an improved unsupervised domain adaptation method was proposed.The proposed method considered both global alignment and category-wise alignment.First, we aligned the appearance of two domains by image transformation.Second, we aligned the output maps of two domains in a global way.Then, we decomposed the semantic prediction map by category, aligning the prediction maps in a category-wise manner.Finally, we evaluated the proposed method on the 2017 Multi-Modality Whole Heart Segmentation Challenge dataset, and obtained 82.1 on the dice similarity coefficient and 4.6 on the average symmetric surface distance, demonstrating the effectiveness of the combination of global alignment and category-wise alignment.
Competences in the quality of education and their organization

This article discusses the nature and interrelationships of competencies related to the quality of education. The narrow and broad definition of the quality of education is also explained, as well as analytical views on the educational and methodological relationship between the departments responsible for graduates of higher education institutions and consumers. The concept of quality of education is one of the most pressing issues that has become the subject of research of pedagogical scientists of many developed countries and our country, as well as the discussion of the general pedagogical community.
Preparing the Data

The most important step in data science is to prepare the data. Data preparation is the process of cleaning, processing, and transforming the raw data for analysis. From this stage, the errors in the data can be effectively handled by cleaning, identifying the missing values, handling outliers, etc. Hence, this chapter discusses the methodologies used to prepare the data using the Pandas package in Python.