Reviewerly/exHarmony huggingface dataset:
Estimation of the Quality of Experience During Video Streaming From Facial Expression and Gaze Direction

This article investigates the possibility to estimate the perceived Quality of Experience (QoE) automatically and unobtrusively by analyzing the face of the consumer of video streaming services, from which facial expression and gaze direction are extracted. If effective, this would be a valuable tool for the monitoring of personal QoE during video streaming services without asking the user to provide feedback, with great advantages for service management. Additionally, this would eliminate the bias of subjective tests and would avoid bothering the viewers with questions to collect opinions and feedback. The performed analysis relies on two different experiments: i) a crowdsourcing test, where the videos are subject to impairments caused by long initial delays and re-buffering events; ii) a laboratory test, where the videos are affected by blurring effects. The facial Action Units (AU) that represent the contractions of specific facial muscles together with the position of the eyes' pupils are extracted to identify the correlation between perceived quality and facial expressions. An SVM with a quadratic kernel and a k-NN classifier have been tested to predict the QoE from these features. These have also been combined with measured application-level parameters to improve the quality prediction. From the performed experiments, it results that the best performance is obtained with the k-NN classifier by combining all the described features and after training it with both the datasets, with a prediction accuracy as high as 93.9% outperforming the state of the art achievements.
Predicting Breast Cancer via Supervised Machine Learning Methods on Class Imbalanced Data

A widespread global health concern among women is the incidence of the second most leading cause of fatality which is breast cancer.Predicting the occurrence of breast cancer based on the risk factors will pave the way to an early diagnosis and an efficient treatment in a quicker time.Although there are many predictive models developed for breast cancer in the past, most of these models are generated from highly imbalanced data.The imbalanced data is usually biased towards the majority class but in cancer diagnosis, it is crucial to diagnose the patients with cancer correctly which are oftentimes the minority class.This study attempts to apply three different class balancing techniques namely oversampling (Synthetic Minority Oversampling Technique (SMOTE)), undersampling (SpreadSubsample) and a hybrid method (SMOTE and SpreadSubsample) on the Breast Cancer Surveillance Consortium (BCSC) dataset before constructing the supervised learning methods.The algorithms employed in this study include Naïve Bayes, Bayesian Network, Random Forest and Decision Tree (C4.5).The balancing method which yields the best performance across all the four classifiers were tested using the validation data to determine the final predictive model.The performances of the classifiers were evaluated using a Receiver Operating Characteristic (ROC) curve, sensitivity, and specificity.
Forced shift to distance learning as an impetus to technological changes in the Russian higher education

The objective request for the compliance of all spheres of life with the requirements of the information society has accelerated processes determined by the restrictive measures of the covid-19 pandemic. Almost all sectors of the economy were forced to switch to remote working; however, the education system was the most affected. The mass transition to distance learning revealed a number of challenges and contradictions in distant forms of the higher education programs and made the Russian education system respond to the challenges of the digital society. This required a deep analysis of various aspects of distance learning, readiness of all education actors to work remotely, availability of material-technical facilities, methodological support and software, and also an assessment of prospects and scenarios for the development of the Russian education system after the pandemic. In May 2020, the Center for Education Development Strategy at Lomonosov Moscow State University and the RUDN Department of Sociology, with the support of the Russian Professors Assembly, conducted a survey of the academic personnel to study universities transition to distance learning. The survey data reveled the real situation in the education system, features of the forced transition to remote working and challenges of such an educational format, and allowed to reconstruct a portrait of the teacher most skeptical to distance learning. The study helped to make a number of recommendations on improving forms and methods of learning and on developing education technologies in universities; to identify the main risks of distance education and to suggest possible forms of mixed learning in Russian universities.
THE EFFECT OF TIME CONSTRAINT ON STUDENT READING COMPREHENSION TEST PERFORMANCE IN NARRATIVE TEXT

The objective of this study was to know whether there is any effect between the time constraints of students when doing exams using (extended time) with students who take exams using (non-extended time). The sample used in this study amounted to 60 grade 11 students in SMA 03 Kaur. The method used in this research was the quantitative method and uses ex post facto design. In collecting data the author gives multiple-choice exam questions consisting of 50 questions, these are questions in the form of narrative texts. In analyzing the data, the authors used the Mann Whitney U-Test in SPSS 16.0. The results of this study indicate that the influence of time constraints on student examinations using extended time.
Collision Free Smooth Path for Mobile Robots in Cluttered Environment Using an Economical Clamped Cubic B-Spline

Mobile robots have various applications in agriculture, autonomous cars, industrial automation, planetary exploration, security, and surveillance. The generation of the optimal smooth path is a significant aspect of mobile robotics. An optimal path for a mobile robot is measured by various factors such as path length, path smoothness, collision-free curve, execution time, and the total number of turns. However, most of the planners generate a non-smooth less optimal and linear piecewise path. Post processing smoothing is applied at the cost of increase in path length. Moreover, current research on post-processing path smoothing techniques does not address the issues of post smoothness collision and performance efficiency. This paper presents a path smoothing approach based on clamped cubic B-Spline to resolve the aforementioned issues. The proposed approach has introduced an economical point insertion scheme with automated knot vector generation while eliminating post smoothness collisions with obstacles. It generates C2 continuous path without any stitching point and passes more closely to the originally planned path. Experiments and comparison with previous approaches have shown that the proposed approach generates better results with reduced path length, and execution time. The test cases used for experiments include a simple structure environment, complex un-structured environment, an environment full of random cluttered narrow obstacles, and a case study of an indoor narrow passage.
Quantum circuits design for evaluating transcendental functions based on a function-value binary expansion method

Quantum arithmetic in the computational basis constitutes the fundamental component of many circuit-based quantum algorithms. There exist a lot of studies about reversible implementations of algebraic functions, while research on the higher-level transcendental functions is scant. We propose to evaluate the transcendental functions using a novel methodology, which is called quantum function-value binary expansion (qFBE) method. This method transforms the evaluation of transcendental functions to the computation of algebraic functions, and output the binary solution digit-by-digit in a simple recursive way. The quantum circuits for solving the logarithmic, exponential, trigonometric and inverse trigonometric functions are presented based on the qFBE method. The efficiency of the circuits is demonstrated on a quantum virtual computing system installed on the Sunway TaihuLight supercomputer. The qFBE method provides a unified and programmed solution for the evaluation of transcendental functions, and it can be the essential building block for many quantum algorithms.
SHREC 2020: 3D point cloud semantic segmentation for street scenes

Scene understanding of large-scale 3D point clouds of an outer space is still a challenging task. Compared with simulated 3D point clouds, the raw data from LiDAR scanners consist of tremendous points returned from all possible reflective objects and they are usually non-uniformly distributed. Therefore, its cost-effective to develop a solution for learning from raw large-scale 3D point clouds. In this track, we provide large-scale 3D point clouds of street scenes for the semantic segmentation task. The data set consists of 80 samples with 60 for training and 20 for testing. Each sample with over 2 million points represents a street scene and includes a couple of objects. There are five meaningful classes: building, car, ground, pole and vegetation. We aim at localizing and segmenting semantic objects from these large-scale 3D point clouds. Four groups contributed their results with different methods. The results show that learning-based methods are the trend and one of them achieves the best performance on both Overall Accuracy and mean Intersection over Union. Next to the learning-based methods, the combination of hand-crafted detectors are also reliable and rank second among comparison algorithms.
Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate Layers

With the growth of computing power neural machine translation (NMT) models also grow accordingly and become better.However, they also become harder to deploy on edge devices due to memory constraints.To cope with this problem, a common practice is to distill knowledge from a large and accurately-trained teacher network (T ) into a compact student network (S).Although knowledge distillation (KD) is useful in most cases, our study shows that existing KD techniques might not be suitable enough for deep NMT engines, so we propose a novel alternative.In our model, besides matching T and S predictions we have a combinatorial mechanism to inject layer-level supervision from T to S. In this paper, we target low-resource settings and evaluate our translation engines for Portuguese→English, Turkish→English, and English→German directions.Students trained using our technique have 50% fewer parameters and can still deliver comparable results to those of 12-layer teachers.
Collective photon routing improvement in a dissipative quantum emitter chain strongly coupled to a chiral waveguide QED ladder

This work shows that collective effects arising due to infinitely long-ranged dipole-dipole interaction among thirty quantum emitters can protect the routing of single photons from spontaneous emission loss in a chiral waveguide quantum electrodynamics ladder.
An Efficient Method for Detection of Fake Accounts on the Instagram Platform

In recent years, social media platforms such as Instagram, Twitter, and Facebook have gradually become important ways to disseminate information. One of these social platforms that have attracted more attention in past years is Instagram. Instagram has widely used for sharing photos and videos and is profitable for celebrities, businesses, and people with a considerable number of followers. In the meantime, this high profit made this platform prone to be the potential place to be used for malicious activities. One of the essential malicious activities in the Instagram platform is fake accounts. However, in this paper, an efficient method for identifying Instagram fake accounts is proposed. In the presented model. First, a dataset of legitimate and fake accounts is created. Then, the collected dataset has been used as input of the bagging classifier to classify fake users on the dataset. Furthermore, the proposed method compared to the five well-known machine-learning classifiers in terms of classification accuracy to better evaluate effectiveness of the method. The experimental results show that the proposed method performs better than other considered algorithms and correctly classified over 98% of the accounts with a low error rate.
Detecting Shilling Attacks Using Hybrid Deep Learning Models

Recommendation systems play a significant role in alleviating information overload in the digital world. They provide suggestions to users based on past symmetric activities or behaviors. Being heavily dependent on users’ behavior, they tend to be vulnerable to shilling attacks. Therefore, protecting them from attacks’ effects is highly important. As shilling attacks have features of a large number of ratings and increasing complexity in attack models, deep learning methods become proper alternatives for more accurate attack detections. This paper proposes a hybrid model of two different neural networks, convolutional and recurrent neural networks, to detect shilling attacks efficiently. The proposed deep learning model utilizes the transformed network architecture for undertaking the attributes derived from user-rated profiles. This architecture enables modeling of the temporal and spatial information in the recommendation system’s ratings. The hybrid model overcomes the limitations of the existing shilling attack deep-learning methods to enhance the recommendation systems’ efficiency and robustness. Experimental results show that the hybrid model results in better predictions on the Movie-Lens 100 K and Netflix datasets by accurately detecting most of the obfuscated attacks compared to the state-of-art deep learning algorithms used for investigation.
Four Approaches to Supposition

Suppositions can be introduced in either the indicative or subjunctive mood. There are also two types of judgments that may be initiated by suppositions of either sort: qualitative (binary) judgments and quantitative (numerical) ones. The former are judgments about whether a given proposition is acceptable and the latter are about how acceptable it is. We systematically explicate the relationships between canonical representatives of each of the four available types of theories. For the qualitative accounts, our representative theories of indicative and subjunctive supposition are based on the belief change operations given by AGM revision and KM update respectively; for the quantitative ones, we consider those given by conditionalization and imaging. This choice is motivated by the familiar approach of understanding supposition as ‘provisional belief revision’ wherein one temporarily treats the supposition as true and forms judgments by making appropriate changes to their other opinions. To compare the numerical judgments recommended by the quantitative theories with the binary ones recommended by the qualitative accounts, we appeal to a suitably adapted version of the Lockean thesis. Ultimately, we establish a number of new results that we interpret as vindicating the often-repeated claim that conditionalization is a probabilistic version of revision, while imaging is a probabilistic version of update.
Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement Learning

Deep Reinforcement Learning (RL) is a promising approach for adaptive robot control, but its current application to robotics is currently hindered by high sample requirements. To alleviate this issue, we propose to exploit the symmetries present in robotic tasks. Intuitively, symmetries from observed trajectories define transformations that leave the space of feasible RL trajectories invariant and can be used to generate new feasible trajectories, which could be used for training. Based on this data augmentation idea, we formulate a general framework, called Invariant Transform Experience Replay that we present with two techniques: (i) Kaleidoscope Experience Replay exploits reflectional symmetries and (ii) Goal-augmented Experience Replay which takes advantage of lax goal definitions. In the Fetch tasks from OpenAI Gym, our experimental results show significant increases in learning rates and success rates. Particularly, we attain a 13, 3, and 5 times speedup in the pushing, sliding, and pick-and-place tasks respectively in the multi-goal setting. Performance gains are also observed in similar tasks with obstacles and we successfully deployed a trained policy on a real Baxter robot. Our work demonstrates that invariant transformations on RL trajectories are a promising methodology to speed up learning in deep RL.
Attention Is All You Need for Chinese Word Segmentation

Taking greedy decoding algorithm as it should be, this work focuses on further strengthening the model itself for Chinese word segmentation (CWS), which results in an even more fast and more accurate CWS model.Our model consists of an attention only stacked encoder and a light enough decoder for the greedy segmentation plus two highway connections for smoother training, in which the encoder is composed of a newly proposed Transformer variant, Gaussian-masked Directional (GD) Transformer, and a biaffine attention scorer.With the effective encoder design, our model only needs to take unigram features for scoring.Our model is evaluated on SIGHAN Bakeoff benchmark datasets.The experimental results show that with the highest segmentation speed, the proposed model achieves new state-of-the-art or comparable performance against strong baselines in terms of strict closed test setting.
FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformations

It is widely known that very small datasets produce overfitting in Deep Neural Networks (DNNs), i.e., the network becomes highly biased to the data it has been trained on. This issue is often alleviated using transfer learning, regularization techniques and/or data augmentation. This work presents a new approach, independent but complementary to the previous mentioned techniques, for improving the generalization of DNNs on very small datasets in which the involved classes share many visual features. The proposed model, called FuCiTNet (Fusion Class inherent Transformations Network), inspired by GANs, creates as many generators as classes in the problem. Each generator, k, learns the transformations that bring the input image into the k-class domain. We introduce a classification loss in the generators to drive the leaning of specific k-class transformations. Our experiments demonstrate that the proposed transformations improve the generalization of the classification model in three diverse datasets.
HDSR-Flor: A Robust End-to-End System to Solve the Handwritten Digit String Recognition Problem in Real Complex Scenarios

Automatic handwriting recognition systems are of interest for academic research fields and for commercial applications.Recent advances in deep learning techniques have shown dramatic improvement in relation to classic computer vision problems, especially in Handwritten Text Recognition (HTR).However, several approaches try to solve the problem of deep learning applied to Handwritten Digit String Recognition (HDSR ), where it has to deal with the low number of trainable data, while learning to ignore any writing symbol around the digits (noise).In this context, we present a new optical model architecture (Gated-CNN-BGRU), based on HTR workflow, applied to HDSR.The International Conference on Frontiers of Handwriting Recognition (ICFHR) 2014 competition on HDSR were used as baselines to evaluate the effectiveness of our proposal, whose metrics, datasets and recognition methods were adopted for fair comparison.Furthermore, we also use a private dataset (Brazilian Bank Check -Courtesy Amount Recognition), and 11 different approaches from the state-of-the-art in HDSR, as well as 2 optical models from the state-of-the-art in HTR.Finally, the proposed optical model demonstrated robustness even with low data volume (126 trainable data, for example), surpassing the results of existing methods with an average precision of 96.50%, which is equivalent to an average percentage of improvement of 3.74 points compared to the state-of-the-art in HDSR.In addition, the result stands out in the competition's CVL HDS set, where the proposed optical model achieved a precision of 93.54%, while the best result so far had been from Beijing group (from the competition itself), with 85.29%.
Local Popularity and Time in top-N Recommendation

Items popularity is a strong signal in recommendation algorithms. It strongly affects collaborative filtering approaches and it has been proven to be a very good baseline in terms of results accuracy. Even though we miss an actual personalization, global popularity can be effectively used to recommend items to users. In this paper we introduce the idea of a time-aware personalized popularity in recommender systems by considering both items popularity among neighbors and how it changes over time. An experimental evaluation shows a highly competitive behavior of the proposed approach, compared to state of the art model-based collaborative approaches, in terms of results accuracy.
Improving Grammatical Error Correction with Machine Translation Pairs

We propose a novel data synthesis method to generate diverse error-corrected sentence pairs for improving grammatical error correction, which is based on a pair of machine translation models (e.g., Chinese to English) of different qualities (i.e., poor and good). The poor translation model can resemble the ESL (English as a second language) learner and tends to generate translations of low quality in terms of fluency and grammaticality, while the good translation model generally generates fluent and grammatically correct translations. With the pair of translation models, we can generate unlimited numbers of poor to good English sentence pairs from text in the source language (e.g., Chinese) of the translators. Our approach can generate various error-corrected patterns and nicely complement the other data synthesis approaches for GEC. Experimental results demonstrate the data generated by our approach can effectively help a GEC model to improve the performance and achieve the state-of-the-art single-model performance in BEA-19 and CoNLL-14 benchmark datasets.
Faster integer multiplication using short lattice vectors

We prove that $n$-bit integers may be multiplied in $O(n \log n \, 4^{\log^* n})$ bit operations. This complexity bound had been achieved previously by several authors, assuming various unproved number-theoretic hypotheses. Our proof is unconditional, and depends in an essential way on Minkowski's theorem concerning lattice vectors in symmetric convex sets.
End to End Binarized Neural Networks for Text Classification

Deep neural networks have demonstrated their superior performance in almost every Natural Language Processing task, however, their increasing complexity raises concerns.A particular concern is that these networks pose high requirements for computing hardware and training budgets.The state-of-the-art transformer models are a vivid example.Simplifying the computations performed by a network is one way of addressing the issue of the increasing complexity.In this paper, we propose an end to end binarized neural network for the task of intent and text classification.In order to fully utilize the potential of end to end binarization, both the input representations (vector embeddings of tokens statistics) and the classifier are binarized.We demonstrate the efficiency of such a network on the intent classification of short texts over three datasets and text classification with a larger dataset.On the considered datasets, the proposed network achieves comparable to the state-of-the-art results while utilizing ∼ 20-40% lesser memory and training time compared to the benchmarks.