Reviewerly/exHarmony huggingface dataset:
Exercise Hierarchical Feature Enhanced Knowledge Tracing

Knowledge tracing is a fundamental task in the computer-aid educational system. In this paper, we propose a hierarchical exercise feature enhanced knowledge tracing framework, which could enhance the ability of knowledge tracing by incorporating knowledge distribution, semantic features, and difficulty features from exercise text. Extensive experiments show the high performance of our framework.
A Matrix-Free Likelihood Method for Exploratory Factor Analysis of High-Dimensional Gaussian Data

This technical note proposes a novel profile likelihood method for estimating the covariance parameters in exploratory factor analysis of high-dimensional Gaussian datasets with fewer observations than number of variables. An implicitly restarted Lanczos algorithm and a limited-memory quasi-Newton method are implemented to develop a matrix-free framework for likelihood maximization. Simulation results show that our method is substantially faster than the expectation-maximization solution without sacrificing accuracy. Our method is applied to fit factor models on data from suicide attempters, suicide ideators, and a control group. Supplementary materials for this article are available online.
Quantum computations on the ensemble of two-node cluster states obtained by sub-Poissonian lasers

In this study, we demonstrate the possibility of the implementation of universal Gaussian computation on a two-node cluster state ensemble. We consider the phase-locked sub-Poissonian lasers, which radiate the bright light with squeezed quadrature, as the resource to generate these states.
Hamiltonian Dynamical Systems: Symbolical, Numerical and Graphical Study

Hamiltonian dynamical systems can be studied from a variety of viewpoints. Our intention in this paper is to show some examples of usage of two Maxima packages for symbolical and numerical analysis (\texttt{pdynamics} and \texttt{poincare}, respectively), along with the set of scripts \KeTCindy\ for obtaining the \LaTeX\ code corresponding to graphical representations of Poincar\'e sections, including animation movies.
Quantum Advantage and the Y2K Bug: A Comparison

The quantum advantage helps speed up computations in many areas. However, quantum computer power can also be leveraged to break modern cryptographic algorithms. To maintain the cryptoagility of software, we must prepare for the era of quantum advantage now.
Self-planting: digging holes in rough landscapes

Motivated by a potential application in economics, we investigate a simple dynamical scheme to produce planted solutions in optimization problems with continuous variables. We consider the perceptron model as a prototypical model. Starting from random input patterns and perceptron weights, we find a locally optimal assignment of weights by gradient descent; we then remove misclassified patterns (if any), and replace them by new, randomly extracted patterns. This "remove and replace" procedure is iterated until perfect classification is achieved. We call this procedure "self-planting" because the "planted" state is not pre-assigned but results from a co-evolution of weights and patterns. We find an algorithmic phase transition separating a region in which self-planting is efficiently achieved from a region in which it takes exponential time in the system size. We conjecture that this transition might exist in a broad class of similar problems.
RoboCupSimData: Software and Data for Machine Learning from RoboCup Simulation League

The main goal of this work is to facilitate machine learning research for multi-robot systems as they occur in RoboCup, an international scientific robot competition. We describe our software (a simulator patch and scripts) and a larger research dataset from games of some of the top teams from 2016 and 2017 in Soccer Simulation League (2D), where teams of 11 agents compete against each other, recorded by this software. We used 10 different teams to play each other, resulting in 45 unique pairings. For each pairing, we ran 25 matches, leading to 1125 matches or more than 180 h of game play. The generated CSV files are 17 GB of data (zipped), or 229 GB (unzipped). The dataset is unique in the sense that it contains local, incomplete and noisy percepts (as sent to each player), in addition to the ground truth logfile that the simulator creates (global, complete, noise-free information of all objects on the field). These data are made available as CSV files, as well as in the original soccer simulator formats.
Face Recognition Using Siamese Network

Face recognition has become very popular in biometrics in recent time, especially after the availability of GPU-based processing technology and ease of implementing deep learning methodologies. Still, it is treated as computationally expensive when the training database is large and efficiency is in stake when little data is available for training. One such example is one-shot learning in which prediction has to be made correctly given only a single image for training. Though human with a good memory can recognize a face fairly in difficult conditions, it is challenging for a machine to identify if little data is available for training. In this paper, a framework, which leverages the convolutional neural network in Siamese architecture, is proposed to solve the problem. This network learns a mapping of input image from image space to a compact 4096-dimensional vector space where distance among points ranks similarity. Once the model is trained with an eye on optimization function’s behaviour it can utilize its discriminating potential to recognize the faces.
Altering the Conveyed Facial Emotion Through Automatic Reenactment of Video Portraits

Current facial reenactment techniques are able to generate results with a high level of photo-realism and temporal consistency. Although the technical possibilities are rapidly progressing, recent techniques focus on achieving fast, visually plausible results. Further perceptual effects caused by altering the original facial expressivity of the recorded individual are disregarded. By investigating the influence of altered facial movements on the perception of expressions we aim to generate not only physically possible but truly believable reenactments. In this paper we perform two experiments using a modified state-of-the-art technique to reenact a video portrait of a person with different expressions gathered from a validated database of motion captured facial expressions to better understand the impact of reenactments.
Improved whale optimization algorithm based on the tent chaotic mapping and nonlinear convergence factor

Abstract To overcome the shortcomings of whale optimization algorithm (WOA) such as the slow convergence rate, and low convergence accuracy, and being easy to fall into the local optimum, an improved whale optimization algorithm based on the tent chaotic mapping and nonlinear convergence factor (TNWOA)is proposed. Firstly, In this algorithm, tent chaotic mapping, which enhances the diversity of the initialization population, is introduced into the initialization of population, therefore, the search space can be searched more thoroughly; Secondly, trigonometric function and the beta distribution are introduced in the convergence factor ‘a’, which balance the global search ability as well as local optimization ability and speed up the convergence speed of the algorithm. Simulation experiments on the four kinds of common test functions on CEC2017 show that under the same experimental conditions, the improved whale optimization algorithm improves the solution accuracy and convergence speed significantly, and its performance is obviously better than other smart optimization algorithms and other improved WOA algorithms.
Evaluation of Naïve Bayes and Chi-Square performance for Classification of Occupancy House

Occupancy status is one indicator of the rehabilitation and reconstruction program to support eruption victims in Indonesia. It needs to establish the rehabilitation and reconstruction in digital system with structured database. In this paper, we provide dataset 2,146 occupied and 370 unoccupied houses. We utilize a naive Bayes classifier to classify the objects and implement a chi-square algorithm to measure comparison data to actual observed data. This research uses a combination of Naive Bayes and Chi-Square by applying weighting to the dataset attributes. Our study conclude that the combination of the algorithms can achieve a promosing result in classifying the occupancy houses status. The combination of the proposed technique gain 89.59% accuracy and ROC-AUC value 0.839. Therefore, our approach is better than the standard Naive Bayes without combination with the Chi-Square approach
Rotational Cryptanalysis of Salsa Core Function

This paper presents rotational cryptanalysis of the Salsa core function, and warns designers of symmetric-key primitives not to incorporate the Salsa core function into other encryption schemes. The core functions of Serpent, ChaCha, and AES are actually incorporated into SOSEMANUK, BLAKE2, and SNOW-V, respectively. We first construct a toy model of the Salsa core function, and observe the rotational characteristics in the toy model by conducting an experiment. Since our experimental observations differ from the theoretical results presented by Khovratovich et al. at FSE 2010 and FSE 2015, we provide their proofs. In addition, we then demonstrate the rotational distinguishers for the Salsa and ChaCha permutations, and compare their results. While the rotational distinguisher for the ChaCha permutation performs properly only up to 8 rounds with a probability of approximately $$2^{-489.6}$$ , the rotational distinguisher for the Salsa permutation performs properly up to 32 rounds with a probability of approximately $$2^{-506.752}$$ . Consequently, our study clarifies how weak the Salsa permutation is to rotational cryptanalysis. Finally, we remark that our results do not affect the security of Salsa.
Two-Server Verifiable Homomorphic Secret Sharing for High-Degree Polynomials

Homomorphic secret sharing (HSS) allows multiple input clients to secret-share their data among multiple servers such that each server is able to locally compute a function on its shares to obtain a partial result and all partial results enable the reconstruction of the function's value on the outsourced data by an output client. The existing HSS schemes for {\em high-degree} polynomials either {\em require a large number of servers} or {\em lack verifiability}, which is essential for ensuring the correctness of the outsourced computations. In this paper, we propose a two-server verifiable HSS (VHSS) model and construct a scheme that supports the computation of high-degree polynomials. The degree of the outsourced polynomials can be as high as a polynomial in the system's security parameter. Despite of using only 2 servers, our VHSS ensures that each single server learns no information about the outsourced data and no single server is able to persuade the client to output a wrong function value. Our VHSS is significantly more efficient. When computing degree-7 polynomials, our scheme could be 3-10 times faster than the previously best construction.
Distributional semantic modeling: a revised technique to train term/word vector space models applying the ontology-related approach

We design a new technique for the distributional semantic modeling with a neural network-based approach to learn distributed term representations (or term embeddings) – term vector space models as a result, inspired by the recent ontology-related approach (using different types of contextual knowledge such as syntactic knowledge, terminological knowledge, semantic knowledge, etc.) to the identification of terms (term extraction) and relations between them (relation extraction) called semantic pre-processing technology – SPT. Our method relies on automatic term extraction from the natural language texts and subsequent formation of the problem-oriented or application-oriented (also deeply annotated) text corpora where the fundamental entity is the term (includes non-compositional and compositional terms). This gives us an opportunity to changeover from distributed word representations (or word embeddings) to distributed term representations (or term embeddings). The main practical result of our work is the development kit (set of toolkits represented as web service APIs and web application), which provides all necessary routines for the basic linguistic pre-processing and the semantic pre-processing of the natural language texts in Ukrainian for future training of term vector space models.
Fine-Grained Context-aware Ad Targeting on Social Media Platforms

One of the most important sources of revenue for social media platforms, like, Twitter, Facebook, Reddit, etc., is advertising. An effective social media advertising plan moves people from awareness and interest in desire and action. Despite the potentiality, campaigns and marketing strategies should be improved. One of the challenges is to identify the right target audience at the right time, considering both communities of interests and locations and the development of these conditions along the timeline. This is crucial to create the right communication strategy and the right advertising message. This paper proposes a context-aware ad-targeting methodology using time, locations, and inferring users' interests by analyzing published content. The method relies on a fuzzy extension of Triadic Formal Concept Analysis for identifying Location-based and Content-based communities of users. Then, a task of community fusion takes place, named Join, for matching a target audience. The matching may be tuned for identifying a wide or narrow community and implementing a fine-grained ad targeting. Experimental results are given.
Combinatorial Algorithms for String Sanitization

String data are often disseminated to support applications such as location-based service provision or DNA sequence analysis. This dissemination, however, may expose sensitive patterns that model confidential knowledge (e.g., trips to mental health clinics from a string representing a user’s location history). In this article, we consider the problem of sanitizing a string by concealing the occurrences of sensitive patterns, while maintaining data utility, in two settings that are relevant to many common string processing tasks. In the first setting, we aim to generate the minimal-length string that preserves the order of appearance and frequency of all non-sensitive patterns. Such a string allows accurately performing tasks based on the sequential nature and pattern frequencies of the string. To construct such a string, we propose a time-optimal algorithm, TFS-ALGO. We also propose another time-optimal algorithm, PFS-ALGO, which preserves a partial order of appearance of non-sensitive patterns but produces a much shorter string that can be analyzed more efficiently. The strings produced by either of these algorithms are constructed by concatenating non-sensitive parts of the input string. However, it is possible to detect the sensitive patterns by “reversing” the concatenation operations. In response, we propose a heuristic, MCSR-ALGO, which replaces letters in the strings output by the algorithms with carefully selected letters, so that sensitive patterns are not reinstated, implausible patterns are not introduced, and occurrences of spurious patterns are prevented. In the second setting, we aim to generate a string that is at minimal edit distance from the original string, in addition to preserving the order of appearance and frequency of all non-sensitive patterns. To construct such a string, we propose an algorithm, ETFS-ALGO, based on solving specific instances of approximate regular expression matching. We implemented our sanitization approach that applies TFS-ALGO, PFS-ALGO, and then MCSR-ALGO, and experimentally show that it is effective and efficient. We also show that TFS-ALGO is nearly as effective at minimizing the edit distance as ETFS-ALGO, while being substantially more efficient than ETFS-ALGO.
Realization of the tradeoff between internal and external entanglement

We experimentally realize the internal and external entanglement tradeoff, which is a kind of entanglement monogamy relation different from that usually discussed. Using a source of twin photons, we find that the external entanglement in the twin photons and the path-polarization internal entanglement of one photon limit each other. In the extreme case, when the internal state is maximally entangled, the external entanglement must be vanishing, which illustrates entanglement monogamy. Our results of the experiment coincide with the theoretical predictions and therefore provide a direct experimental observation of the internal and external entanglement monogamy relations.Received 9 January 2019Revised 20 August 2020Accepted 25 August 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043068Published 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 measuresQuantum entanglementQuantum opticsQuantum Information
Digital Collaboration in Teaching and Learning Activities: The Reflexivity Study on Educational Digital Empowerment

This research examines digital empowerment in high schools in Indonesia. Based on the reflexivity perspective, digital empowerment is described as the effort to respond to changes in learning systems in digital-based schools. This research was conducted qualitatively through observations and in-depth interviews. The participants of this study included school principals and the teachers of information and communication technology in three Indonesian public senior high schools in different cities, namely Banda Aceh, Padang, and Banjarmasin. The results highlighted the need to understand the context and projections of the teachers' and student's ability and self-access to information and communication technology elements, in which appropriate measures can be formulated in response to the digitisation of the teaching-learning process in the schools. Self-awareness and information and communication technology competence shape digital empowerment efforts in schools based on their respective needs and abilities. As such, the digital-based collaboration of teaching and learning can be construed as a form of contextual digital empowerment, which is applied at the micro-level adapting to dynamic changes in digital competence, especially in teachers and students.
Simulation Study of a Detector Function for QTMS Radar and Noise Radar

A detector function for a newly proposed quantum radar known as quantum two-mode squeezing (QTMS) radar is analyzed in this paper. The detector function proposed in this paper behaves like a normal distribution function when the number of radar samples integrated for the detection is over 500 samples. A closed form expression for the receiver operating characteristic (ROC) curve for QTMS radar is derived under the assumption that the radar integrates more than 500 samples to detect the presence or absence of a target. The detector function can also be applied to noise radar.
An Intelligent Web App Chatbot

Chatbot is an Artificial Intelligence program that stimulates interactive human conversations with computers. It plays a vital role now-a days due to its 24/7 support for customer queries. Web app chatbot uses a Linguistic machine learning algorithm (NLP) for predicting correct responses for human queries. LUIS (Language Understanding Intelligent Service) is a natural Language processing Artificial Intelligence for predicting human queries. In this paper, chatbot is implemented on LUIS for predicting the user queries. Based on the highest prediction score, Luis detects the intents and entities, to solve user queries. A web app chatbot discussed in this paper is fast, accurate and secure with high performance. Using LUIS API's for automated LUIS training and publishing the endpoint to the chatbot. Enhanced authentication secures the bot from unauthorized persons accessing the chatbot.
Measurement of entropy and quantum coherence properties of two type-I entangled photonic qubits

Using the type-I SPDC process in BBO nonlinear crystal (NLC), we generate a polarization-entangled state near to the maximally-entangled Bell-state with high-visibility (high-brightness) $ 98.50 \pm 1.33 ~ \% $ ($ 87.71 \pm 4.45 ~ \% $) for HV (DA) basis. We calculate the CHSH version of the Bell inequality, as a nonlocal realism test, and find a strong violation from the classical physics or any hidden variable theory (HVT), $ S= 2.71 \pm 0.10 $. Via measuring the coincidence count (CC) rate in the SPDC process, we obtain the quantum efficiency of single-photon detectors (SPDs) around $ (25.5\pm 3.4) \% $, which is in good agreement to their manufacturer company. As expected, we verify the linear dependency of the CC rate vs. pump power of input CW-laser, which may yield to find the effective second-order susceptibility crystal. Using the theory of the measurement of qubits, includes a tomographic reconstruction of quantum states due to the linear set of 16 polarization-measurement, together with a maximum-likelihood-technique (MLT), which is based on the numerical optimization, we calculate the physical non-negative definite density matrices, which implies on the non-separability and entanglement of prepared state. By having the maximum likelihood density operator, we calculate precisely the entanglement measures such as Concurrence, entanglement of formation, tangle, logarithmic negativity, and different entanglement entropies such as linear entropy, Von-Neumann entropy, and Renyi 2-entropy. Finally, this high-brightness and low-rate entangled photons source can be used for short-range quantum measurements in the Lab.
Accelerating Lattice Based Proxy Re-encryption Schemes on GPUs

Proxy Re-Encryption (PRE) is an indispensable tool in many public-key cryptographic schemes that enables users to delegate decryption rights to other users via a proxy. In this work, we present a high performance implementation of PRE schemes on NVIDIA GPUs. We target two lattice based PRE schemes, BV-PRE and Ring-GSW PRE defined over polynomial rings. We design a parallel Number Theoretic Transform (NTT) procedure capable of working on arbitrary precision moduli (in CRT form) and demonstrate several low level and GPU optimizations techniques to accelerate the PRE schemes. For the same or higher security settings our results show 39x to 228x factors of improvement in performance with a peak throughput of 6.3 Mbps when compared to the CPU implementation of the BV-PRE scheme in the PALISADE lattice crypto software library. Similarly, for the Ring-GSW PRE scheme we achieve a peak throughput of 49 Mbps and up to 11x improvement in performance.