Reviewerly/exHarmony huggingface dataset:
Improved spectral gaps for random quantum circuits: Large local dimensions and all-to-all interactions

Random quantum circuits are a central concept in quantum information theory with applications ranging from demonstrations of quantum computational advantage to descriptions of scrambling in strongly interacting systems and black holes. The utility of random quantum circuits in these settings stems from their ability to rapidly generate quantum pseudorandomness. In a seminal paper by Brand\~ao, Harrow, and Horodecki [Commun. Math. Phys. 346, 397 (2016)] it was proven that the $t\mathrm{th}$ moment operator of local random quantum circuits on $n$ qudits with local dimension $q$ has a spectral gap of at least $\mathrm{\ensuremath{\Omega}}({n}^{\ensuremath{-}1}{t}^{\ensuremath{-}5\ensuremath{-}3.1/ln(q)})$, which implies that they are efficient constructions of approximate unitary designs. As a first result, we use Knabe bounds for the spectral gaps of frustration-free Hamiltonians to show that one-dimensional random quantum circuits have a spectral gap scaling as $\mathrm{\ensuremath{\Omega}}({n}^{\ensuremath{-}1})$, provided that $t$ is small compared to the local dimension: ${t}^{2}\ensuremath{\le}O(q)$. This implies a (nearly) linear scaling of the circuit depth in the design order $t$. Our second result is an unconditional spectral gap bounded below by $\mathrm{\ensuremath{\Omega}}[{n}^{\ensuremath{-}1}{ln}^{\ensuremath{-}1}(n){t}^{\ensuremath{-}\ensuremath{\alpha}(q)}]$ for random quantum circuits with all-to-all interactions. This improves both the $n$ and $t$ scaling in design depth for the nonlocal model. We show this by proving a recursion relation for the spectral gaps involving an auxiliary random walk. Lastly, we solve the smallest nontrivial case exactly and combine with numerics and Knabe bounds to improve the constants involved in the spectral gap for small values of $t$.
Clustering-Based Hybrid Approach for Multivariate Missing Data Imputation

In the era of big data, a significant amount of data is produced in many applications areas.However due to various reasons including sensor failures, communication failures, environmental disruptions, and human errors, missing values are found frequently These missing data in the observed data make a challenge for other data mining approaches, requiring the missed data to be handled at the preprocessing stage of data mining.Several approaches for handling the missing data have been proposed in the past.These approaches consider the whole dataset for making a prediction, making the whole imputation approach to be cumbersome.This paper proposes the procedure which makes use of the local similarity structure of the dataset for making an Imputation.The K-means clustering technique along with the weighted KNN makes efficient imputation of the missed value.The results are compared against imputations by mean substitution and Fuzzy C Means (FCM).The proposed imputation technique shows that it performs better than other imputation procedures.
Attitudes in the motivational sphere and Change readiness of university students

The article examines the interrelations between socio-psychological attitudes in the motivational sphere and change readiness of students of the liberal arts profile university. Change readiness is understood as an important component of innovative activity. Also, attitudes in the motivational sphere were identified as predictors of change readiness. The following methods were used: O.F. Potemkina’s diagnostics of socio-psychological attitudes of personality and PCRS methodology - "Personal change readiness in the context of "expectation" phenomenon study (Rollnick, Heather, Gold, Hall). The study involved 80 university students, aged 18 to 23. The results describe the interrelations between different socio-psychological attitudes and the components of change readiness, and also show which attitudes act as predictors of the integral indicator of change readiness. The reliability of the data is confirmed by the use of a set of mathematical statistics methods.
Evolution of Human Computer Interaction

The work is devoted to the review of the development the human-computer interaction.In the first sections the history of computing in the "pre-computer" era is briefly described and then the early history of modern computing, methods of the first computers controlling and the tasks of programmers at this stage are described.It describes the methods of interaction with the first -generation computers using the remote control elements, punched cards and punched tapes.The section, devoted to the second generation computers, describes the emergence of high-level operating systems and programming languages.At this point, there are such means of interaction with the computer as the displays and, respectively, such programming tools as interactive languages and interactive debuggers.Research is also beginning on principles of human-computer interaction the infancy of the discipline "computer graphics", the development of computer graphics packages and the emergence of interactive computer graphics standards are considered.In the section "Revolutions in computer science" describes the appearance of a large number of the same series computers and the first super-computers in the context of human-computer interaction.Revolutionary changes are considered in computer graphics and emerging of the science discipline "computer visualization" with its parts "scientific visualization", "software visualization", "information visualization" and also "programming by demonstration".The information about the attempt to create a fifth generation computer based on logical programming is given.It is told about the initial period of teaching programming.The creation of computer networks and the emergence of personal computing as well as the creation the tools of modern parallel computing have become the important stages in the development of modern computing.The virtual reality becomes an important computer visualization tool .The modern state of human-computer interfaces is characterized primarily by emerging of natural interfaces which can be attributed Brain-Computer Interface (Neurocomputer interface, Brain-Computer Interfaces), interfaces based on the direct use of nerve impulses, speech recognition, recognition of lip movement, mimic recognition and eye tracking (Eye Gaze or Eye Tracking), haptic interfaces and also interfaces giving tactile feedback (allowing you to feel the touch),motion capture interfaces the entire human body or individual organs (head, entire arm, hands, fingers, legs), motion capture toolkits ,in particular, interfaces based on leg movements (foot-operated computer interfaces), sign interfaces, sign languages.We briefly describe the activity approach to the design of interfaces and also some problems concerning the problem of mass interfaces.Finally, we discuss a number of problems arising from the increasing capabilities of modern computers.The work is in the nature of a popular science article and it largely reflects the subjective impressions of the author.
Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map

For the indoor navigation of service robots, human–robot interaction and adapting to the environment still need to be strengthened, including determining the navigation goal socially, improving the success rate of passing doors, and optimizing the path planning efficiency. This paper proposes an indoor navigation system based on object semantic grid and topological map, to optimize the above problems. First, natural language is used as a human–robot interaction form, from which the target room, object, and spatial relationship can be extracted by using speech recognition and word segmentation. Then, the robot selects the goal point from the target space by object affordance theory. To improve the navigation success rate and safety, we generate auxiliary navigation points on both sides of the door to correct the robot trajectory. Furthermore, based on the topological map and auxiliary navigation points, the global path is segmented into each topological area. The path planning algorithm is carried on respectively in every room, which significantly improves the navigation efficiency. This system has demonstrated to support autonomous navigation based on language interaction and significantly improve the safety, efficiency, and robustness of indoor robot navigation. Our system has been successfully tested in real domestic environments.
Noise2Kernel: Adaptive Self-Supervised Blind Denoising Using a Dilated Convolutional Kernel Architecture

With the advent of unsupervised learning, efficient training of a deep network for image denoising without pairs of noisy and clean images has become feasible. Most current unsupervised denoising methods are built on self-supervised loss with the assumption of zero-mean noise under the signal-independent condition, which causes brightness-shifting artifacts on unconventional noise statistics (i.e., different from commonly used noise models). Moreover, most blind denoising methods require a random masking scheme for training to ensure the invariance of the denoising process. In this study, we propose a dilated convolutional network that satisfies an invariant property, allowing efficient kernel-based training without random masking. We also propose an adaptive self-supervision loss to increase the tolerance for unconventional noise, which is specifically effective in removing salt-and-pepper or hybrid noise where prior knowledge of noise statistics is not readily available. We demonstrate the efficacy of the proposed method by comparing it with state-of-the-art denoising methods using various examples.
Use of Digital Learning Technologies in Education on the Example of Smart Education

Currently, the main task of teachers is to develop the needs of students for self-development and self-improvement, which is a decisive factor in the upbringing and formation of personality. Practice has shown that modern teachers better assimilate the studied topics as a result of using SMART technologies to effectively determine the goals of the lesson, compared to the indicators of topics studied traditionally. This article discuss about analyzes the methods of using smart learning apps in the classroom and the expected results.
Quantifying the Difference between Many-Body Quantum States

The quantum state overlap is the textbook measure of the difference between two quantum states. Yet, it is inadequate to compare the complex configurations of many-body systems. The problem is inherited by the widely employed quantum state fidelity and related distances. We introduce the weighted distances, a new class of information-theoretic measures that overcome these limitations. They quantify how hard it is to discriminate between two quantum states of many particles, factoring in the structure of the required measurement apparatus. Therefore, they can be used to evaluate both the theoretical and the experimental performances of complex quantum devices. We also show that the newly defined ``weighted Bures length'' between the input and output states of a quantum process is a lower bound to the experimental cost of the transformation. The result uncovers an exact quantum limit to our ability to convert physical resources into computational ones.
Part-of-Speech Tagging via Deep Neural Networks for Northern-Ethiopic Languages

Deep Neural Networks (DNNs) have proven to be especially successful in the area of Natural Language Processing (NLP) and Part-Of-Speech (POS) tagging—which is the process of mapping words to their corresponding POS labels depending on the context. Despite recent development of language technologies, low-resourced languages (such as an East African Tigrinya language), have received too little attention. We investigate the effectiveness of Deep Learning (DL) solutions for the low-resourced Tigrinya language of the Northern-Ethiopic branch. We have selected Tigrinya as the testbed example and have tested state-of-the-art DL approaches seeking to build the most accurate POS tagger. We have evaluated DNN classifiers (Feed Forward Neural Network – FFNN, Long Short-Term Memory method – LSTM, Bidirectional LSTM, and Convolutional Neural Network – CNN) on a top of neural word2vec word embeddings with a small training corpus known as Nagaoka Tigrinya Corpus. To determine the best DNN classifier type, its architecture and hyper-parameter set both manual and automatic hyper-parameter tuning has been performed. BiLSTM method was proved to be the most suitable for our solving task: it achieved the highest accuracy equal to 92% that is 65% above the random baseline.
Evaluation of Student Core Drives on e-Learning during the Covid-19 with Octalysis Gamification Framework

Learning activities during the Covid-19 pandemic were carried out with an online system even though in reality many institutions had not prepared their systems and infrastructure properly.Some e-learning media that are generally used based on survey results include 53.81% google classrooms combined with other applications that are not integrated with the institution's Learning Management System.This condition provides research opportunities to evaluate the effectiveness of online learning, especially how students are motivated to learn the method, where the results can be used as a reference in developing and refining the method.Based on many studies, that the gamification model can increase individual motivation in carrying out activities, this study uses a gamification octalysis framework to analyze the extent of the role of gamification in the learning process and measure the amount of student motivation in online learning activities.The evaluation results show that the conclusion of the Likert scale results in a "High" level, while the highest score is "Very High".As for the octalysis test scale, the average score of 6.5 on a scale of 1 to 10.The conclusion from the results of this evaluation is that the motivation to learn e-learning during the Covid-19 period is quite high and has the potential to be developed.While the results of the Octalysis framework with 8 core drives are still average, for that we need innovation in E-learning which aims to increase student motivation based on Octalysis's 8 core drives.The results of this study recommend that gamification is needed to increase student learning motivation in order to improve learning outcomes.
CoinLayering: An Efficient Coin Mixing Scheme for Large Scale Bitcoin Transactions

Coin mixing can be used to preserve identity privacy of Bitcoin owners, by engaging a set of middlepersons (i.e., <inline-formula><tex-math notation="LaTeX">$Mix$</tex-math></inline-formula> ) to temporarily hold the transacting Bitcoins and remove the linkage between the transacting parties. However, existing schemes are generally not scalable due to limitations associated with the anonymity set and self-credibility. In this article, we propose an efficient coin mixing scheme (hereafter referred to as CoinLayering). To achieve strong anonymity, CoinLayering randomly selects two sets of middlepersons to respectively execute Bitcoin holding and Bitcoin trading. The seller can also select lower-loaded sets of middlepersons in the shortest time possible. We also design two coin mixing protocols, CoinLayering-PA and CoinLayering-PB, to mitigate the risk due to misbehaving middlepersons and <inline-formula><tex-math notation="LaTeX">$Supervisor$</tex-math></inline-formula> . We then mathematically prove that CoinLayering achieves both strong anonymity and self-credibility, and evaluate its performance to demonstrate its scalability.
Pengembangan Multimedia Pembelajaran Interaktif untuk Mata Pelajaran Matematika untuk Kelas 5 Sekolah Dasar

The learning process during a pandemic requires teachers to be more interactive in making online learning media. The government has designed the Covid Emergency Curriculum, which is that educational units in their current condition can choose one of the several components needed in the learning process, namely still referring to the national curriculum, using the emergency curriculum, or making curriculum simplification independently. For curriculum simplification, only the main material in learning activities needs to be conveyed, while for practice it can be independently or through an application media. This study aims to create an augmented reality-based learning media on geometry in mathematics subjects for grade 5 elementary school. This media can make it easier for students to understand the shapes with augmented reality and there is a learning video feature that also explains the material presented by the teacher. With the black box white box method, the application is developed by adjusting the curriculum simplification planned by the government. Features in the application include objectives and materials, learning videos, augmented reality simulations and integrated with Google Classroom as a learning evaluation platform. The results of this research are digital applications that can be accessed from the web or android applications.
ThreatZoom: Hierarchical Neural Network for CVEs to CWEs Classification

The Common Vulnerabilities and Exposures (CVE) represent standard means for sharing publicly known information security vulnerabilities. One or more CVEs are grouped into the Common Weakness Enumeration (CWE) classes for the purpose of understanding the software or configuration flaws and potential impacts enabled by these vulnerabilities and identifying means to detect or prevent exploitation. As the CVE-to-CWE classification is mostly performed manually by domain experts, thousands of critical and new CVEs remain unclassified, yet they are unpatchable. This significantly limits the utility of CVEs and slows down proactive threat mitigation tremendously. This paper presents ThreatZoom, as the first automatic tool to classify CVEs to CWEs. ThreatZoom uses a novel learning algorithm that employs an adaptive hierarchical neural network that adjusts its weights based on text analytic scores and classification errors. It automatically estimates the CWE classes corresponding to a CVE instance using both statistical and semantic features extracted from the description of a CVE. This tool is rigorously tested by various datasets provided by MITRE and the National Vulnerability Database (NVD). The accuracy of classifying CVE instances to their correct CWE classes is $$92\%$$ (fine-grain) and $$94\%$$ (coarse-grain) for NVD dataset, and $$75\%$$ (fine-grain) and $$90\%$$ (coarse-grain) for MITRE dataset, despite the small corpus.
HDEC: A Heterogeneous Dynamic Ensemble Classifier for Binary Datasets

In recent years, ensemble classification methods have been widely investigated in both industry and literature in the field of machine learning and artificial intelligence. The main advantage of this approach is to benefit from a set of classifiers instead of using a single classifier with the aim of improving the prediction performance, such as accuracy. Selecting the base classifiers and the method for combining them are the most challenging issues in the ensemble classifiers. In this paper, we propose a heterogeneous dynamic ensemble classifier (HDEC) which uses multiple classification algorithms. The main advantage of using heterogeneous algorithms is increasing the diversity among the base classifiers as it is a key point for an ensemble system to be successful. In this method, we first train many classifiers with the original data. Then, they are separated based on their strength in recognizing either positive or negative instances. For doing this, we consider the true positive rate and true negative rate, respectively. In the next step, the classifiers are categorized into two groups according to their efficiency in the mentioned measures. Finally, the outputs of the two groups are compared with each other to generate the final prediction. For evaluating the proposed approach, it has been applied to 12 datasets from the UCI and LIBSVM repositories and calculated two popular prediction performance metrics, including accuracy and geometric mean. The experimental results show the superiority of the proposed approach in comparison to other state-of-the-art methods.
Surpassing the classical limit in magic square game with distant quantum dots coupled to optical cavities

Abstract The emergence of quantum technologies is heating up the debate on quantum supremacy, usually focusing on the feasibility of looking good on paper algorithms in realistic settings, due to the vulnerability of quantum systems to myriad sources of noise. In this vein, an interesting example of quantum pseudo-telepathy games that quantum mechanical resources can theoretically outperform classical resources is the Magic Square game (MSG), in which two players play against a referee. Due to noise, however, the unit winning probability of the players can drop well below the classical limit. Here, we propose a timely and unprecedented experimental setup for quantum computation with quantum dots inside optical cavities, along with ancillary photons for realizing interactions between distant dots to implement the MSG. Considering various physical imperfections of our setup, we first show that the MSG can be implemented with the current technology, outperforming the classical resources under realistic conditions. Next, we show that our work gives rise to a new version of the game. That is, if the referee has information on the physical realization and strategy of the players, he can bias the game through filtered randomness, and increase his winning probability. We believe our work contributes to not only quantum game theory, but also quantum computing with quantum dots.
TAC at SemEval-2020 Task 12: Ensembling Approach for Multilingual Offensive Language Identification in Social Media

Usage of offensive language on social media is getting more common these days, and there is a need of a mechanism to detect it and control it.This paper deals with offensive language detection in five different languages; English, Arabic, Danish, Greek and Turkish.We presented an almost similar ensemble pipeline comprised of machine learning and deep learning models for all five languages.Three machine learning and four deep learning models were used in the ensemble.In the OffensEval-2020 competition our model achieved F1-score of 0.85, 0.74, 0.68, 0.81, and 0.9 for Arabic, Turkish, Danish, Greek and English language tasks respectively.
A Gradient-Based Recurrent Neural Network for Visual Servoing of Robot Manipulators with Acceleration Command

Recent decades have witnessed the rapid evolution of robotic applications and their expansion into a variety of spheres with remarkable achievements. This article researches a crucial technique of robot manipulators referred to as visual servoing, which relies on the visual feedback to respond to the external information. In this regard, the visual servoing issue is tactfully transformed into a quadratic programming problem with equality and inequality constraints. Differing from the traditional methods, a gradient-based recurrent neural network (GRNN) for solving the visual servoing issue is newly proposed in this article in the light of the gradient descent method. Then, the stability proof is presented in theory with the pixel error convergent exponentially to zero. Specifically speaking, the proposed method is able to impel the manipulator to approach the desired static point while maintaining physical constraints considered. After that, the feasibility and superiority of the proposed GRNN are verified by simulative experiments. Significantly, the proposed visual servo method can be leveraged to medical robots and rehabilitation robots to further assist doctors in treating patients remotely.