Reviewerly/exHarmony huggingface dataset:
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning

Modern deep learning is primarily an experimental science, in which empirical advances occasionally come at the expense of probabilistic rigor. Here we focus on one such example; namely the use of the categorical cross-entropy loss to model data that is not strictly categorical, but rather takes values on the simplex. This practice is standard in neural network architectures with label smoothing and actor-mimic reinforcement learning, amongst others. Drawing on the recently discovered continuous-categorical distribution, we propose probabilistically-inspired alternatives to these models, providing an approach that is more principled and theoretically appealing. Through careful experimentation, including an ablation study, we identify the potential for outperformance in these models, thereby highlighting the importance of a proper probabilistic treatment, as well as illustrating some of the failure modes thereof.
On the possibility to detect quantum correlation regions with the variable optimal measurement angle

Quantum correlations described by quantum discord and one-way quantum deficit can contain ordinary regions with constant (i.e., universal) optimal measurement angle 0 or π/2 with respect to the z-axis and regions with a variable (state-dependent) angle of the optimal measurement. The latter regions which are absent in the Bell-diagonal states are very tiny for the quantum discord and cannot be observed experimentally due to various imperfections on the preparation and measurement steps of the experiment. On the contrary, for the one-way quantum deficit we succeeded in getting the special two-qubit X states which seem to allow one to reach all regions of quantum correlation exploiting available quantum optical techniques. These states give possibility to deep investigation of quantum correlations and related optimization problems at new region and it’s boundaries. In the paper, explicit theoretical calculations applicable to one-way deficit are reported, together with the design of the experimental setup for generating such selected family of states; moreover, there are presented numerical simulations showing that the most inaccessible region with the intermediate optimal measurement angle may be resolved experimentally.
SunBear at WNUT-2020 Task 2: Improving BERT-Based Noisy Text Classification with Knowledge of the Data domain

This paper proposes an improved custom model for WNUT task 2: Identification of Informative COVID-19 English Tweet.We improve experiment with the effectiveness of fine-tuning methodologies for state-of-the-art language model RoBERTa (Liu et al., 2019).We make a preliminary instantiation of this formal model for the text classification approaches.With appropriate training techniques, our model is able to achieve 0.9218 F1score on public validation set and the ensemble version settles at top 9 F1-score (0.9005) and top 2 Recall (0.9301) on private test set.
A Survey of Researches on Personalized Bundle Recommendation Techniques

The recommender system is widely used in various fields such as movies, music, and products. It has been an effective method to handle the preference matching problem by retrieving the most relevant information and services from a large amount of data. Most researches on recommender systems focus on improving the relevance of individual recommendation item. Nevertheless, in recommendation scenarios, users are often exposed to multiple items and may be interested in a collection of items. The platform can offer multiple items as a bundle to user in this case. That is called Bundle Recommendation problem, which refers to predict a user’s preference on a bundle rather than an individual item. The larger scale data of the bundle compared to individual item results in information overload, thus, it is crucial to develop personalized bundle recommendation technology to help users quickly match a high-quality bundle which can improve the user experience and benefit both consumers and product providers. In this paper, we first review the main model methods of bundle recommender systems in recent years, including integer programming methods, association analysis methods, traditional recommendation methods, and recommendation methods based on deep learning. And then, we analyze the relationships of personalized bundle recommender systems compared with the traditional recommender systems. Finally, we discuss the difficulties and future development trend of personalized bundle recommender systems.
Μελέτη της ορθογραφικής επεξεργασίας μέσω οφθαλμοκίνησης στην ελληνική γλώσσα

The present study investigated the differences between children with reading difficulties (RD) and their controls (Grade 3 and Grade 6) on an orthographic processing task (OP), using eye-tracking technology. Grade 3 typically developing readers were used as reading age controls for the Grade 6 RD group. During the OP task, the participants had to identify the correct word in pairs of correctly spelled and phonologically correct but misspelled words. Two oculomotor movements, fixations and saccades, were recorded using an eye-tracking technology (EyeLink® 1000 Plus eye-tracker). The results revealed that Grade 6 RD group produced significantly longer fixations, shorter first fixations, and more saccades than their CA controls, suggesting a lack of established orthographic representations in their mental lexicon. No differences were observed in eye movements between Grade 3 groups and between Grade 6 RD group and their RA controls, showing that these groups follow a similar strategy to identify the correctly spelled word. Discussion centers around the role of eye tracking technology in providing valuable information about the mechanisms involved in reading and spelling..
An Analysis of Capsule Networks for Part of Speech Tagging in High- and Low-resource Scenarios

Neural networks are a common tool in NLP, but it is not always clear which architecture to use for a given task.Different tasks, different languages, and different training conditions can all affect how a neural network will perform.Capsule Networks (CapsNets) are a relatively new architecture in NLP.Due to their novelty, CapsNets are being used more and more in NLP tasks.However, their usefulness is still mostly untested.In this paper, we compare three neural network architectures-LSTM, CNN, and CapsNet-on a part of speech tagging task.We compare these architectures in both high-and low-resource training conditions and find that no architecture consistently performs the best.Our analysis shows that our CapsNet performs nearly as well as a more complex LSTM under certain training conditions, but not others, and that our CapsNet almost always outperforms our CNN.We also find that our CapsNet implementation shows faster prediction times than the LSTM for Scottish Gaelic but not for Spanish, highlighting the effect that the choice of languages can have on the models.
Formalizing IMO Problems and Solutions in Isabelle/HOL

The International Mathematical Olympiad (IMO) is perhaps the most celebrated mental competition in the world and as such is among the greatest grand challenges for Artificial Intelligence (AI).The IMO Grand Challenge, recently formulated, requires to build an AI that can win a gold medal in the competition.We present some initial steps that could help to tackle this goal by creating a public repository of mechanically checked solutions of IMO Problems in the interactive theorem prover Isabelle/HOL.This repository is actively maintained by students of the Faculty of Mathematics, University of Belgrade, Serbia within the course "Introduction to Interactive Theorem Proving".
Answer Span Correction in Machine Reading Comprehension

Answer validation in machine reading comprehension (MRC) consists of verifying an extracted answer against an input context and question pair.Previous work has looked at re-assessing the "answerability" of the question given the extracted answer.Here we address a different problem: the tendency of existing MRC systems to produce partially correct answers when presented with answerable questions.We explore the nature of such errors and propose a post-processing correction method that yields statistically significant performance improvements over state-of-the-art MRC systems in both monolingual and multilingual evaluation.
Factors of the Detective Story and the Extraction of Plot Patterns Based on Japanese Detective Comics

In recent years, there have been attempts to generate stories automatically by utilizing computers and various other kinds of information technology. However, it remains unclear how story sequences can be made to look natural and appear interesting. This study conducted a data description of 134 famous Japanese comic detective stories and listed 37 types of plot elements, 10 types of tricks, 9 types of criminal motives, and 10 types of relationships between victims and criminals, in order to realize a computational narratological analysis. Based on these parameters, eleven factors have been extracted through factor analysis. The structures of the plot transition networks varied according to the factors present in the detective stories. Therefore, an elaborate, human-like, and understandable creation of plot transitions can be enabled based on the detailed differences between plot transition networks.
Syntactic and Semantic-driven Learning for Open Information Extraction

One of the biggest bottlenecks in building accurate, high coverage neural open IE systems is the need for large labelled corpora.The diversity of open domain corpora and the variety of natural language expressions further exacerbate this problem.In this paper, we propose a syntactic and semantic-driven learning approach, which can learn neural open IE models without any human-labelled data by leveraging syntactic and semantic knowledge as noisier, higher-level supervisions.Specifically, we first employ syntactic patterns as data labelling functions and pretrain a base model using the generated labels.Then we propose a syntactic and semantic-driven reinforcement learning algorithm, which can effectively generalize the base model to open situations with high accuracy.Experimental results show that our approach significantly outperforms the supervised counterparts, and can even achieve competitive performance to supervised stateof-the-art (SoA) model.
A General Model Based On The Dupont System Of Financial Analysis For Identification, Analysis And Solution Of A Potential Crisis In A Business

The purpose of this article is to create a general model using the data commonly available in the managerial accounting system for the signalization of a possible potential crisis.The critical level of the input variables influencing the return of equity (ROE) and return on sales (ROS) was theoretically determined based on the DuPont system of financial analysis.To determine actual amount of the input variables for the test of their critical amount in a real company, we considered their average amount based on a simple arithmetic mean.For the real analysis in a potentially threatened company, we considered one month as a time unit for the time interval in a range of 14 months.We determined the average actual amounts of the input variables using confidence intervals.Because of the relatively small sample, we used Student's t-distribution for the construction of confidence intervals of the input variables.An analytical model-based system approach based on analysis of the complex value chain was used.By the means of logical derivations and testing this analytical model-based system approach in a real company, we proved, that this model enables not only to determine the critical level of the input variables leading to a crisis in a business but also to calculate their new adjusted amount, which the business needs to reach so that a potential crisis can be solved.The presented theoretical model was successfully applied to solve a real potential crisis in a particular Czech company which supports the correctness and practical applicability of this general model.There are two main advantages of this model: 1) it can use the data commonly available in the managerial accounting system, 2) this model is generally valid, i.e., it can be used in any business producing goods or services.
Lower bounds on the localisation length of balanced random quantum walks

We consider the dynamical properties of Quantum Walks defined on the d-dimensional cubic lattice, or the homogeneous tree of coordination number 2d, with site-dependent random phases, further characterised by transition probabilities between neighbouring sites equal to 1/(2d). We show that the localisation length for these Balanced Random Quantum Walks can be expressed as a combinatorial expression involving sums over weighted paths on the considered graph. This expression provides lower bounds on the localisation length by restriction to paths with weight 1, which allows us to prove the localisation length diverges on the tree as $$d^2$$ . On the cubic lattice, the method yields the lower bound $$1/\ln (2)$$ for all d and allows us to bound the localisation length from below by the correlation length of self-avoiding walks computed at 1/(2d).
Single Image Dehazing Based on Enhanced Generative Adversarial Network

Single image dehazing is a classical and challenging problem in computer vision. However, existing GAN-based methods focus on designing more complex network to achieve better performance, which makes it difficult to converge stably during training in the discriminator. In this paper, we propose an enhanced generative adversarial network for single image dehazing. Specifically, we introduce ResNet in the generative network, which enhances the ability of feature extraction. Then, we use DenseNet in the discriminative network to improve the feature learning ability. Finally, we use perceptual loss to reduce the generated image and the real clear image spatial differences in the feature domain. Qualitative and quantitative comparison against several state-of-the-art methods on synthetic datasets demonstrate that our approach is effective and performs favorably for recovering a clear image from a hazy image.
Hybrid Recommendation Algorithm Based on Long-term and Short-term Interest and Matrix Factorization for Collaborative Filtering

This paper proposes a hybrid recommendation algorithm which based on user interests and matrix factorization. It solves problems such as spares data, the difference between long-term and short-term interest, overemphasis of time and ignoring user new interests. The proposed algorithm distinguished users' interest through time window. Then it obtained the distribution of user interests. Finally, it integrated into matrix factorization to explore more new interests. The result shows that short-term interest should be recommend first. Compared with the traditional matrix factorization for collaborative filtering, forgetting curve and time window, the proposed algorithm shows superior performance on precision, recall, and F1-Score. It extracts user's interest sets, user activity, item popularity and other related indicators, which can automatically tag users and provide more options for subsequent research on the dynamic evolution of user interests or the expansion of website functions.
Testing a Quantum Inequality with a Meta-analysis of Data for Squeezed Light

In quantum field theory, coherent states can be created that have negative energy density, meaning it is below that of empty space, the free quantum vacuum. If no restrictions existed regarding the concentration and permanence of negative energy regions, it might, for example, be possible to produce exotic phenomena such as Lorentzian transversable wormholes, warp drives, time machines, violations of the second law of thermodynamics, and naked singularities. Quantum Inequalities (QIs) have been proposed that restrict the size and duration of the regions of negative quantum vacuum energy that can be accessed by observers. However, QIs generally are derived for situations in cosmology and are very difficult to test. Direct measurement of vacuum energy is difficult and to date no QI has been tested experimentally. We test a proposed QI for squeezed light by a meta-analysis of published data obtained from experiments with optical parametric amplifiers (OPA) and balanced homodyne detection. Over that last three decades, researchers in quantum optics have been trying to maximize the squeezing of light and the quantum vacuum and have succeeded in reducing the variance in the quantum vacuum fluctuations to -15 dB. To apply the QI, a time sampling function is required. In our meta-analysis different time sampling functions for the QI were examined, but in all physically reasonable cases the QI is violated by much or all of the measured data. This brings into question the basis for QI. Possible explanations are given for this surprising result.
Automatic Isolated Speech Recognition System Using MFCC Analysis and Artificial Neural Network Classifier: Feasible For Diversity of Speech Applications

In this research, an Automatic Isolated Speech Recognition System (AISR) has been proposed to categorize four distinct word class pronounced as “GO” “STOP” “LEFT” and “RIGHT”. Speech data has investigated mainly employing Mel Frequency Cestrum analysis together with specific subsidiary discrete signal processing to generate compact and productive speech database. In this paper, an Artificial Neural Network (ANN) has been used as a classifier to categorize four distinct word classes. The ANN functions with the Feed Forward Multi-layer Perceptron (FFMP) with Back Propagation (BP) training algorithm. Trial and error procedure is adopted to determine an optimally performed ANN classifier. The presented method has achieved the best recognition rates 97.14% and 98.57% respectively for two training algorithms- Steepest Gradient Descent with Adaptive Learning and Scaled Conjugate Gradient by investigation on different configuration and parameters of the ANN. To validate the feasibility and efficiency of the proposed ASR system, a small-scale application has implemented where two filament bulbs have been efficiently operated with designated speech classes.
THE DIGITALIZATION OF THE LEARNING PROCESS IN SERBIA DURING THE COVID-19 CRISIS

The COVID-19 crisis significantly disturbed the teaching process in Serbia. The subject of this research is the reaction and response of Serbian educational system during this crisis. The aim of this paper is the analysis of modifications of the teaching process and distance learning activities in primary and secondary schools, and teaching using the national public television service. The use of the method of content analysis, legal acts in the field of education adopted during the state of emergency in Serbia, decisions and instructions for the implementation of teaching, the manner of implementation of teaching, as well as the results of research on distance learning were investigated. Measures and activities for improving the digital alternative for future educational practice are proposed.
Correcting the Misuse: A Method for the Chinese Idiom Cloze Test

The cloze test for Chinese idioms is a new challenge in machine reading comprehension: given a sentence with a blank, choosing a candidate Chinese idiom which matches the context.Chinese idiom is a type of Chinese idiomatic expression.The common misuse of Chinese idioms leads to error in corpus and causes error in the learned semantic representation of Chinese idioms.In this paper, we introduce the definition written by Chinese experts to correct the misuse.We propose a model for the Chinese idiom cloze test integrating various information effectively.We propose an attention mechanism called Attribute Attention to balance the weight of different attributes among different descriptions of the Chinese idiom.Besides the given candidates of every blank, we also try to choose the answer from all Chinese idioms that appear in the dataset as the extra loss due to the uniqueness and specificity of Chinese idioms.In experiments, our model outperforms the state-of-the-art model.
A systematic comparison of grapheme-based vs. phoneme-based label units for encoder-decoder-attention models

Following the rationale of end-to-end modeling, CTC, RNN-T or encoder-decoder-attention models for automatic speech recognition (ASR) use graphemes or grapheme-based subword units based on e.g. byte-pair encoding (BPE). The mapping from pronunciation to spelling is learned completely from data. In contrast to this, classical approaches to ASR employ secondary knowledge sources in the form of phoneme lists to define phonetic output labels and pronunciation lexica. In this work, we do a systematic comparison between grapheme- and phoneme-based output labels for an encoder-decoder-attention ASR model. We investigate the use of single phonemes as well as BPE-based phoneme groups as output labels of our model. To preserve a simplified and efficient decoder design, we also extend the phoneme set by auxiliary units to be able to distinguish homophones. Experiments performed on the Switchboard 300h and LibriSpeech benchmarks show that phoneme-based modeling is competitive to grapheme-based encoder-decoder-attention modeling.
Finite-key analysis for memory-assisted decoy-state quantum key distribution

Abstract Memory-assisted quantum key distribution (MA-QKD) systems are among novel promising solutions that can improve the key-rate scaling with channel loss. By using a middle node with quantum storage and measurement functionalities, they offer the same key-rate scaling with distance as a single-node quantum repeater. However, the distance at which they can surpass the nominal key rate of repeaterless systems, in terms of bits per second, is typically long, owing to the efficiency and/or interaction time issues when one deals with quantum memories. This crossover distance can be a few hundred kilometres, for instance, when one relies on the exchange of infinitely many key bits for the key-rate analysis. In a realistic setup, however, we should account for the finite-key effects in our analysis. Here, we show that accounting for such effects would actually favour MA-QKD setups, by reducing the crossover distance to the regime where realistic implementations can take place. We demonstrate this by rigorously analysing a decoy-state version of MA-QKD, in the finite-key regime, using memory parameters already achievable experimentally. This provides us with a better understanding of the advantages and challenges of working with memory-based systems.
Evaluation of the spectrum of a quantum system using machine learning based on incomplete information about the wavefunctions

We propose an effective approach for rapid estimation of the energy spectrum of quantum systems with the use of the machine learning (ML) algorithm. In the ML approach (backpropagation), the wavefunction data obtained from experiments are interpreted as the attribute class (input data), while the spectrum of quantum numbers establishes the label class (output data). To evaluate this approach, we employ two exactly solvable models with the random modulated wavefunction amplitude. The random factor allows modeling the incompleteness of information about the state of quantum system. The trial wave functions are fed into the neural network, with the goal of making prediction about the spectrum of quantum numbers. We found that in such a configuration, the training process occurs with rapid convergence if the number of analyzed quantum states is not too large. The two qubit entanglement is studied as well. The accuracy of the test prediction (after training) reached 98%. It is considered that the ML approach opens up important perspectives to plane the quantum measurements and optimal monitoring of complex quantum objects.