Reviewerly/exHarmony huggingface dataset:
DynamicsExplorer: Visual Analytics for Robot Control Tasks involving Dynamics and LSTM-based Control Policies

Deep reinforcement learning (RL), where a policy represented by a deep neural network is trained, has shown some success in playing video games and chess. However, applying RL to real-world tasks like robot control is still challenging. Because generating a massive number of samples to train control policies using RL on real robots is very expensive, hence impractical, it is common to train in simulations, and then transfer to real environments. The trained policy, however, may fail in the real world because of the difference between the training and the real environments, especially the difference in dynamics. To diagnose the problems, it is crucial for experts to understand (1) how the trained policy behaves under different dynamics settings, (2) which part of the policy affects the behaviors the most when the dynamics setting changes, and (3) how to adjust the training procedure to make the policy robust.This paper presents DynamicsExplorer, a visual analytics tool to diagnose the trained policy on robot control tasks under different dynamics settings. DynamicsExplorer allows experts to overview the results of multiple tests with different dynamics-related parameter settings so experts can visually detect failures and analyze the sensitivity of different parameters. Experts can further examine the internal activations of the policy for selected tests and compare the activations between success and failure tests. Such comparisons help experts form hypotheses about the policy and allows them to verify the hypotheses via DynamicsExplorer. Multiple use cases are presented to demonstrate the utility of DynamicsExplorer.
An Empirical Evaluation of Correlation Based Feature Selection for Tweet Sentiment Classification

This paper presents a study on Twitter sentiment analysis where tweets are gathered and sentiments behind the tweet are evaluated by using various machine learning techniques. It presents an empirical evaluation of correlation based feature selection for sentiment classification on twitter data. The data is extracted from twitter in real time and text preprocessing and feature extraction is applied on the textual data. Correlation based attribute selection methods are used and machine learning classifiers (SVM, Naïve Bayes, Random Forest, Meta classifier, SGD, Logistic Regression) are compared on various performance parameters to show which classifier gives better results. The results show that when STWV with Attribute Selection methods are used together in the same setup, the classifiers give accuracy between 78 and 88% with about 0.88 true positive rate and 0.15 false positive rate which is far better when no attribute selection method is used.
ExtremeReader: An interactive explorer for customizable and explainable review summarization

Building summarization systems have become a necessity due to the extensive volume and growth of online reviews. Despite extensive research on this topic, existing summarization systems generally fall short on two aspects. First, existing techniques generate static summaries which cannot be tailored to specific user needs. Second, most existing systems generate extractive summaries which selects only certain salient aspects from the summaries. Hence, they do not completely depict the overall opinion of the reviews. In this paper, we demonstrate a novel summarization system, ExtremeReader, that overcomes the limitations of existing summarization systems described above. ExtremeReader allows summaries to be tailored and explored interactively so that users can quickly find the desired information. In addition, ExtremeReader generates abstractive summaries with an underlying structure that helps users understand, explore, and seek explanations to the generated summaries.
EM algorithm for mixture of skew-normal distributions fitted to grouped data

Grouped data are frequently used in several fields of study. In this work, we use the expectation-maximization (EM) algorithm for fitting the skew-normal (SN) mixture model to the grouped data. Implementing the EM algorithm requires computing the one-dimensional integrals for each group or class. Our simulation study and real data analyses reveal that the EM algorithm not only always converges but also can be implemented in just a few seconds even when the number of components is large, contrary to the Bayesian paradigm that is computationally expensive. The accuracy of the EM algorithm and superiority of the SN mixture model over the traditional normal mixture model in modelling grouped data are demonstrated through the simulation and three real data illustrations. For implementing the EM algorithm, we use the package called ForestFit developed for R environment available at https://cran.r-project.org/web/packages/ForestFit/index.html.
Card-Based Cryptographic Protocols with the Minimum Number of Rounds Using Private Operations

This paper shows new card-based cryptographic protocols with the minimum number of rounds using private operations under the semi-honest model. Physical cards are used in card-based cryptographic protocols instead of computers. Operations that a player executes in a place where the other players cannot see are called private operations. Using three private operations called private random bisection cuts, private reverse cuts, and private reveals, calculations of two variable boolean functions and copy operations were realized with the minimum number of cards. Though the number of cards has been discussed, the efficiency of these protocols has not been discussed. This paper defines the number of rounds to evaluate the efficiency of the protocols using private operations. Most of the meaningful calculations using private operations need at least two rounds. This paper shows a new two-round committed-input, committed-output logical XOR protocol using four cards. Then we show new two-round committed-input, committed-output logical AND and copy protocols using six cards. This paper then shows the relationship between the number of rounds and available private operations. Even if private reveal operations are not used, logical XOR can be executed with the minimum number of cards in two rounds. On the other hand, logical AND and copy operations can be executed with the minimum number of cards in three rounds without private reveal operations. Protocols that preserves an input are also shown.
Sensory Interactive Table (SIT)—Development of a Measurement Instrument to Support Healthy Eating in a Social Dining Setting

This paper presents the Sensory Interactive Table (SIT): an instrumented, interactive dining table. Through the use of load cells and LEDs that are embedded in the table surface, SIT allows us to study: (1) the eating behaviors of people in a social setting, (2) the social interactions around the eating behaviors of people in a social setting, and (3) the continuous cycle of feedback through LEDs on people’s eating behavior and their response to this feedback in real time, to ultimately create an effective dietary support system. This paper presents the hard- and software specifications of the system, and it shows the potential of the system to capture mass-related dimensions in real time and with high accuracy and spatial resolution.
Multi-Resolution POMDP Planning for Multi-Object Search in 3D

Robots operating in households must find objects on shelves, under tables, and in cupboards. In such environments, it is crucial to search efficiently at 3D scale while coping with limited field of view and the complexity of searching for multiple objects. Principled approaches to object search frequently use Partially Observable Markov Decision Process (POMDP) as the underlying framework for computing search strategies, but constrain the search space in 2D. In this paper, we present a POMDP formulation for multi-object search in a 3D region with a frustum-shaped field-of-view. To efficiently solve this POMDP, we propose a multi-resolution planning algorithm based on online Monte-Carlo tree search. In this approach, we design a novel octree-based belief representation to capture uncertainty of the target objects at different resolution levels, then derive abstract POMDPs at lower resolutions with dramatically smaller state and observation spaces. Evaluation in a simulated 3D domain shows that our approach finds objects more efficiently and successfully compared to a set of baselines without resolution hierarchy in larger instances under the same computational requirement. We demonstrate our approach on a mobile robot to find objects placed at different heights in two 10m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ×2m regions by moving its base and actuating its torso.
A Survey of Digital Eye Strain in Gaze-Based Interactive Systems

Display-based interfaces pose high demands on users' eyes that can cause severe vision and eye problems, also known as digital eye strain (DES). Although these problems can become even more severe if the eyes are actively used for interaction, prior work on gaze-based interfaces has largely neglected these risks. We offer the first comprehensive account of DES in gaze-based interactive systems that is specifically geared to gaze interaction designers. Through an extensive survey of more than 400 papers published over the last 46 years, we first discuss the current role of DES in interactive systems. One key finding is that DES is only rarely considered when evaluating novel gaze interfaces and neglected in discussions of usability. We identify the main causes and solutions to DES and derive recommendations for interaction designers on how to guide future research on evaluating and alleviating DES.
Revisiting Multi-Task Learning in the Deep Learning Era

Despite the recent progress in deep learning, most approaches still go for a silo-like solution, focusing on learning each task in isolation: training a separate neural network for each individual task. Many real-world problems, however, call for a multi-modal approach and, therefore, for multi-tasking models. Multi-task learning (MTL) aims to leverage useful information across tasks to improve the generalization capability of a model. In this survey, we provide a well-rounded view on state-of-the-art MTL techniques within the context of deep neural networks. Our contributions concern the following. First, we consider MTL from a network architecture point-of-view. We include an extensive overview and discuss the advantages/disadvantages of recent popular MTL models. Second, we examine various optimization methods to tackle the joint learning of multiple tasks. We summarize the qualitative elements of these works and explore their commonalities and differences. Finally, we provide an extensive experimental evaluation across a variety of datasets to examine the pros and cons of different methods, including both architectural and optimization based strategies.
Characterizing the Effects of Random Subsampling on Lexicase Selection

Lexicase selection is a proven parent-selection algorithm designed for genetic programming problems, especially for uncompromising test-based problems where many distinct test cases must all be passed. Previous work has shown that random subsampling techniques can improve lexicase selection's problem-solving success; here, we investigate why. We test two types of random subsampling lexicase variants: down-sampled lexicase, which uses a random subset of all training cases each generation; and cohort lexicase, which collects candidate solutions and training cases into small groups for testing, reshuffling those groups each generation. We show that both of these subsampling lexicase variants improve problem-solving success by facilitating deeper evolutionary searches; that is, they allow populations to evolve for more generations (relative to standard lexicase) given a fixed number of test-case evaluations. We also demonstrate that the subsampled variants require less computational effort to find solutions, even though subsampling hinders lexicase's ability to preserve specialists. Contrary to our expectations, we did not find any evidence of systematic loss of phenotypic diversity maintenance due to subsampling, though we did find evidence that cohort lexicase is significantly better at preserving phylogenetic diversity than down-sampled lexicase.
A novel deformable body partition model for MMW suspicious object detection and dynamic tracking

In recent years, millimeter wave (MMW) imaging techniques have developed rapidly and been widely used in public security field. Traditional security techniques on MMW images mostly based on manual analysis and simple image processing, which cannot achieve real-time and accurate performance. This paper proposes a deep learning-based deformable body partition (DBP) model for detecting suspicious objects hidden in human body on MMW image dataset. Considering the characteristics of MMW security images, we combine the location information of concealed object with the corresponding human body part for object detection. According to the human body structure, 17 deformable regions are partitioned with 12 body keypoints to address body region misalignment problems of different people. By training convolutional neural network (CNN) with different body regions, the suspicious objects can be detected, together with their location information. To make full use of spatio-temporal context information of MMW image sequences, we design an object dynamic tracking method to lock the concealed targets moving from different angles’ MMW images of the same still body. Compared with conventional global-based object detection methods, the proposed method can not only accurately detect suspicious objects but also output the location information of them. Importantly, DBP model is more adaptive than the fixed coordinate-based body partition method and can automatically change the size of each body region according to each person’s body shape. Moreover, our object dynamic tracking method can utilize the positional relationship of suspicious objects in image sequences to reduce the object searching area of each image. Experimental results prove the effectiveness of the proposed method. The detection accuracy and speed on MMW images are favorable in practical application.
A Visual Analytics Framework for Reviewing Streaming Performance Data

Understanding and tuning the performance of extreme-scale parallel computing systems demands a streaming approach due to the computational cost of applying offline algorithms to vast amounts of performance log data. Analyzing large streaming data is challenging because the rate of receiving data and limited time to comprehend data make it difficult for the analysts to sufficiently examine the data without missing important changes or patterns. To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization. The data management module collects various computing and communication performance metrics from the monitored system using streaming data processing techniques and feeds the data to the other two modules. The analysis module automatically identifies important changes and patterns at the required latency. In particular, we introduce a set of online and progressive analysis methods for not only controlling the computational costs but also helping analysts better follow the critical aspects of the analysis results. Finally, the interactive visualization module provides the analysts with a coherent view of the changes and patterns in the continuously captured performance data. Through a multi-faceted case study on performance analysis of parallel discrete-event simulation, we demonstrate the effectiveness of our framework for identifying bottlenecks and locating outliers.
On the Improvement Attack Upon Some Variants of RSA Cryptosystem via the Continued Fractions Method

Let N = pq be an RSA modulus where p and q are primes not necessarily of the same bit size.Previous cryptanalysis results on the difficulty of factoring the public modulus N = pq deployed on variants of RSA cryptosystem are revisited.Each of these variants share a common key relation utilizing the modified Euler quotient (p 2 -1)(q 2 -1), given by the key relation edk(p 2 -1)(q 2 -1) = 1 where e and d are the public and private keys respectively.By conducting continuous midpoint subdivision analysis upon an interval containing (p 2 -1)(q 2 -1) together with continued fractions on the key relation, we increase the security bound for d exponentially.
Analysis of the Communication Competence Dynamics in Integrated Learning

In this paper we analyze the level of communication competence of students enrolled to the course based on the integrated learning approach. In our case an integrated learning approach means the combination of learning techniques (CLIL, blended learning, flipped classroom, team-based and problem-based learning) in the process of teaching one discipline in English. To evaluate the dynamics of communication competence we measured its components – knowledge, skills, values and personal traits, each of which included a set of special indicators. To collect the data for research we conducted several surveys and an interview with students. According to the results obtained, students raised their communication competence in four components, but the most significant difference was in skills category. Students noted that the format of active learning in developed integrated course and participation in X-culture project (a part of the course) allowed them to master negotiation skills, self-control skills and persuasion skills. Also we conducted a correlation analysis of four components of communication competence and students course outcomes (English testing and professional discipline testing). The results showed a positive correlation between all indicators. It was a strong relationship between English and professional discipline knowledge and such category as skills, while the correlation between the knowledge indicators and other two components of communication competence was weak. To conclude we can confirm the efficiency of integrated learning approach for improving the communication competence.
Quantum fast hitting on glued trees mapped on a photonic chip

Quantum walks on graphs play an important role in the field of quantum algorithms. Fast hitting is one of the properties that quantum walk algorithms can utilize to outperform classical random walk algorithms. Fast hitting refers to a particle starting from the entrance node on a graph and trying to hit the exit node quickly. Especially, continuous-time quantum walks on random glued binary trees have been investigated in theories extensively for their exponentially faster hitting speed over classical random walks. Here, using heralded single photons to represent quantum walkers and laser-written waveguide arrays to simulate the theoretical graph, we are able to demonstrate the hitting efficiency of quantum walks with tree depth as high as 16 layers for the first time. Furthermore, we expand the graph’s branching rate from 2 to 5, revealing that quantum walks can exhibit more superiority over classical random walks as the branching rate increases. Our results may shed light on the physical implementation of quantum walk algorithms as well as quantum computation and quantum simulation.
A Survey on Blockchain-based Applications in Education

Researchers and practitioners continue to draw varied interest on blockchain (BC) technology. The concept is generally attributed to its outstanding features such as decentralization, reliability, security and data veracity. However, even with the growing interest, a lot has to be done concerning the modern state of knowledge and practice in relation to the use of BC technology in education. Therefore, this survey presents a review of the existing studies on BC-based learning applications. The survey is centered on three major themes: learning applications that have been made using BC technology, the contribution and merits of blockchain technology to the education sector, and the problems experienced while integrating BC technology in learning. An intensive discussion is presented as well as a comprehensive result analysis of each theme. The survey also highlights the key areas in education that stand to benefit from the incorporation of the blockchain technology.
The Model and The Method for Forming a Mosaic Sustainable Marker of Augmented Reality

The paper proposes the model and the method of forming a mosaic sustainable marker of augmented reality. The advantages and disadvantages of existing augmented reality markers have been analyzed. The basic requirements for augmented reality marker are formulated. The basic stages of building a mosaic sustainable marker of augmented reality are offered. The conformity of the marker with the formulated requirements was checked. Marker recognition accuracy is substantiated, the minimum value of the parameter under which the robustness condition is ensured. The relation, which defines the accuracy of recognition of the AR-marker, taking into account the fact that only part of the pixels are recognized correctly, is specified.
Learning from discrete Gaussian label distribution and spatial channel-aware residual attention for head pose estimation

Recent head pose estimation techniques are advanced by performing bin classification, where the predicted result is compared against a one-hot classification vector. We argue that the head poses may better be modelled by discrete distribution sampled from a smooth continuous curve rather than one-hot coding or some other kinds of binned classification vector, since pose angles in practice are arbitrary. In this paper, we propose a deep head pose estimation scheme by regressing between predicted probabilistic labels and discrete Gaussian distribution. Such Gaussian distribution aims at modelling the arbitrary state of true head poses and supervises the deep network through maximum mean discrepancy loss. Besides, we also propose a spatial channel-aware residual attention structure for enhancing intrinsic pose features to further improve the prediction accuracy and speed up training convergence. Experiments on two public datasets AFLW2000 and BIWI show the proposed method outperforms all previous methods, and its individual components yield substantial improvements.
Multi-Scale Aerial Target Detection Based on Densely Connected Inception ResNet

With the rapid development of unmanned aerial vehicles (UAVs), aerial targets detection has attracted extensive attention from researchers.The difficulty of aerial image detection lies in the small proportion of ground targets in aerial images and the wide variety of target sizes.After multiple downsampling, the features of small targets are almost not available on the feature maps.To address these drawbacks, a densely connected Inception ResNet (RIDNet) is proposed.RIDNet is a lightweight multi-scale fusion detection network constructed with two residual inception units (RI): the RI-Dense model and the RI-Deconv model.The RI-Dense model consists of densely connected layers and shortcut connections.Each convolutional layer in RI-Dense has access to all the subsequent layers and passes on the information that needs to be preserved.The RI-Deconv fuses the global feature in a residual and hierarchical way, which continuously deconvolutes the output of RI-Dense and concatenates the result with the original output to get fusion layers.The fused layers absorb semantic information and detailed information from deep layers and shallow layers, respectively.Extensive experiments show the effectiveness of the proposed RIDNet.Ablation experiments also demonstrate that the RI-Dense model and RI-Deconv model can improve the mAP by 7.8% and 6.8%, respectively.