Reviewerly/exHarmony huggingface dataset:
Is working memory inherently more “precise” than long-term memory? Extremely high fidelity visual long-term memories for frequently encountered objects.

Long-term memory is often considered easily corruptible, imprecise, and inaccurate, especially in comparison to working memory. However, most research used to support these findings relies on weak long-term memories: those where people have had only one brief exposure to an item. Here we investigated the fidelity of visual long-term memory in more naturalistic setting, with repeated exposures, and ask how it compares to visual working memory fidelity. Using psychophysical methods designed to precisely measure the fidelity of visual we demonstrate that long-term memory for the color of frequently seen objects is as accurate as working memory for the color of a single item seen 1 s ago. In particular, we show that repetition greatly improves long-term including the ability to discriminate an item from a very similar item (fidelity), in both a lab setting (Experiments 1-3) and a naturalistic setting (brand logos, Experiment 4). Overall, our results demonstrate the impressive nature of visual long-term memory fidelity, which we find is even higher fidelity than previously indicated in situations involving repetitions. Furthermore, our results suggest that there is no distinction between the fidelity of visual working memory and visual long-term but instead both memory systems are capable of storing similar incredibly high-fidelity memories under the right circumstances. Our results also provide further evidence that there is no fundamental distinction between the precision of memory and the likelihood of retrieving a memory, instead suggesting a single continuous measure of memory strength best accounts for working and long-term memory. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Making a case for (Hyper-)parameter tuning as benchmark problems

One of the biggest challenges in evolutionary computation concerns the selection and configuration of a best-suitable heuristic for a given problem. While in the past both of these problems have primarily been addressed by building on experts' experience, the last decade has witnessed a significant shift towards automated decision making, which capitalizes on techniques proposed in the machine learning literature.
Using Semantic Similarity as Reward for Reinforcement Learning in Sentence Generation

Traditional model training for sentence generation employs cross-entropy loss as the loss function.While cross-entropy loss has convenient properties for supervised learning, it is unable to evaluate sentences as a whole, and lacks flexibility.We present the approach of training the generation model using the estimated semantic similarity between the output and reference sentences to alleviate the problems faced by the training with crossentropy loss.We use the BERT-based scorer fine-tuned to the Semantic Textual Similarity (STS) task for semantic similarity estimation, and train the model with the estimated scores through reinforcement learning (RL).Our experiments show that reinforcement learning with semantic similarity reward improves the BLEU scores from the baseline LSTM NMT model.
Visual Percepts Quality Recognition Using Convolutional Neural Networks

In visual recognition systems, it is necessary to identify between good or bad quality images. Visual perceptions are discrete representation of observable objects. In typical systems, visual parameters are adjusted for optimal detection of good quality images. However, over a wide range of visual context scenarios, these parameters are usually not optimized. This study focused on the learning and detection of good and bad percepts from a given visual context using a convolutional neural network. The system utilized a perception-action model with memory and learning mechanism which is trained and validated in four different road traffic locations (DS0, DS3-1, DS4-1, DS4-3). The training accuracy for DS0, DS3-1, DS4-1, and DS4-3 are 93.53%, 91.16%, 93.39%, and 95.76%, respectively. The validation accuracy for DS0, DS3-1, DS4-1, and DS4-3 are 88.73%, 77.40%, 95.21%, and 83.56%, respectively. Based from these results, the system can adequately learn to differentiate between good or bad quality percepts.
Volumes of Blurred-Invariant Gaussians for Dynamic Texture Classification

An effective model, which jointly captures shape and motion cues, for dynamic texture (DT) description is introduced by taking into account advantages of volumes of blurred-invariant features in three main following stages. First, a 3-dimensional Gaussian kernel is used to form smoothed sequences that allow to deal with well-known limitations of local encoding such as near uniform regions and sensitivity to noise. Second, a receptive volume of the Difference of Gaussians (DoG) is figured out to mitigate the negative impacts of environmental and illumination changes which are major challenges in DT understanding. Finally, a local encoding operator is addressed to construct a discriminative descriptor of enhancing patterns extracted from the filtered volumes. Evaluations on benchmark datasets (i.e., UCLA, DynTex, and DynTex++) for issue of DT classification have positively validated our crucial contributions.
BASED ON MAGNETOTELLURIC DATA (METHODOLOGICAL ASPECT)

Based on the field depth magnetotelluric measurements performed in the Tien Shan region, examples of the extraction of an electromagnetic field of endogenous origin are considered. The research was based on the following working hypothesis: irreversible rock deformations (cracking (and)) caused by the development of modern geodynamic processes form sources of the electromagnetic field of endogenous origin. The authors have introduced an approach that suggests the electromagnetic field of endogenous origin to be included in the impedance relations in an additive manner, which allows us to separate the electromagnetic field recorded on the surface, according to the position of the sources. The main point of this algorithm is that the known impedance of the lower half-space requires finding the difference between the tangential components of the electromagnetic field registered on the day surface. To implement the algorithm, a data processing programme for magnetotelluric sensing was compiled in order to identify sources of electromagnetic fields of endogenous origin.
Age Estimation on Low Quality Face Images

In this paper, we contribute an age estimation method towards dealing with low quality face images. This is a practical and important problem because an image we received may have low resolution or be affected by some noise via transmission. Upon reviewing the literature on facial age estimation, we notice that few articles tackle this low quality image based facial age estimation problem. In our framework, we propose a newly designed deep convolutional neural networks architecture, consisting of five major steps. Firstly, we propose to use a super-resolution method to enhance the input images. Secondly, a data augmentation step is utilized to ease the training procedure. Thirdly, we use a deep network to conduct gender grouping. Fourthly, two recently proposed deep networks are modified with depthwise separable convolutions to perform age estimation within male and female groups. Finally, a fusion procedure is added to further boost age estimation accuracy. In the experiment, we use two benchmark datasets, IMDB-WIKI and MORPH-II, to verify our proposed method and also show a significantly performance improvement over two state-of-the-art deep CNN models.
Library Linked Data Models: Library Data in the Semantic Web

This exploratory study examined Linked Data (LD) schemas/ontologies and data models proposed or in use by libraries around the world using MAchine Readable Cataloging (MARC) as a basis for comparison of the scope and extensibility of these potential new standards. The researchers selected 14 libraries from national libraries, academic libraries, government libraries, public libraries, multi-national libraries, and cultural heritage centers currently developing Library Linked Data (LLD) schemas. The choices of models, schemas, and elements used in each library's LD can create interoperability issues for LD services because of substantial differences between schemas and data models evolving via local decisions. The researchers observed that a wide variety of vocabularies and ontologies were used for LLD including common web schemas such as Dublin Core (DC)/DCTerms, Schema.org and Resource Description Framework (RDF), as well as deprecated schemas such as MarcOnt and rdagroup1elements. A sharp divide existed as well between LLD schemas using variations of the Functional Requirements for Bibliographic Records (FRBR) data model and those with different data models or even with no listed data model. Libraries worldwide are not using the same elements or even the same ontologies, schemas and data models to describe the same materials using the same general concepts.
multimatch-gaze: The MultiMatch algorithm for gaze path comparison in Python

multimatch-gaze is a Python package for computing the similarity of eye-movement sequences, so called scan paths.Scan paths are the trace of eye-movements in space and time, usually captured with eye tracking devices.Scan path similarity is a measure that is used in a variety of disciplines ranging from cognitive psychology, medicine, and marketing to humanmachine interfaces.In addition to quantifying position and order of a series of eye-movements, comparing their temporo-spatial sequence adds an insightful dimension to the traditional analysis of eye tracking data.It reveals commonalities and differences of viewing behavior within and between observers, and is used to study how people explore visual information.For example, scan path comparisons are used to study analogy-making (French, Glady, & Thibaut,
FI-Net: A Lightweight Video Frame Interpolation Network Using Feature-Level Flow

Video frame interpolation is a classic computer vision task that aims to generate in-between frames given two consecutive frames.In this paper, a flow-based interpolation method (FI-Net) is proposed.FI-Net is a lightweight end-to-end neural network that takes two frames in arbitrary size as input and outputs the estimated intermediate frame.Novelly, it computes optical flow at feature level instead of image level.Such practice can increase the accuracy of estimated flow.Multi-scale technique is utilized to handle large motions.For training, a comprehensive loss function that contains a novel content loss (Sobolev loss) and a semantic loss is introduced.It forces the generated frame to be close to the ground truth one at both pixel level and semantic level.We compare FI-Net with previous methods and it achieves higher performance with less time consumption and much smaller model size.
Reproducibility dataset for a large experimental survey on word embeddings and ontology-based methods for word similarity

This data article introduces a reproducibility dataset with the aim of allowing the exact replication of all experiments, results and data tables introduced in our companion paper (Lastra-Díaz et al., 2019), which introduces the largest experimental survey on ontology-based semantic similarity methods and Word Embeddings (WE) for word similarity reported in the literature. The implementation of all our experiments, as well as the gathering of all raw data derived from them, was based on the software implementation and evaluation of all methods in HESML library (Lastra-Díaz et al., 2017), and their subsequent recording with Reprozip (Chirigati et al., 2016). Raw data is made up by a collection of data files gathering the raw word-similarity values returned by each method for each word pair evaluated in any benchmark. Raw data files were processed by running a R-language script with the aim of computing all evaluation metrics reported in (Lastra-Díaz et al., 2019), such as Pearson and Spearman correlation, harmonic score and statistical significance p-values, as well as to generate automatically all data tables shown in our companion paper. Our dataset provides all input data files, resources and complementary software tools to reproduce from scratch all our experimental data, statistical analysis and reported data. Finally, our reproducibility dataset provides a self-contained experimentation platform which allows to run new word similarity benchmarks by setting up new experiments including other unconsidered methods or word similarity benchmarks.
Cryptanalysis of Round-Reduced KECCAK Using Non-linear Structures

In this paper, we present new preimage attacks on KECCAK-384 and KECCAK-512 for 2, 3 and 4 rounds. The attacks are based on non-linear structures (structures that contain quadratic terms). These structures were studied by Guo et al. [13] and Li et al. [18, 19] to give preimage attacks on round reduced KECCAK. We carefully construct non-linear structures such that the quadratic terms are not spread across the whole state. This allows us to create more linear equations between the variables and hash values, leading to better preimage attacks. As a result, we present the best theoretical preimage attack on KECCAK-384 and KECCAK-512 for 2 and 3-rounds and also KECCAK-384 for 4-rounds.
Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models

Hyperbolic embeddings achieve excellent performance when embedding hierarchical data structures like synonym or type hierarchies, but they can be limited by numerical error when ordinary floating-point numbers are used to represent points in hyperbolic space. Standard models such as the Poincar{\'e} disk and the Lorentz model have unbounded numerical error as points get far from the origin. To address this, we propose a new model which uses an integer-based tiling to represent \emph{any} point in hyperbolic space with provably bounded numerical error. This allows us to learn high-precision embeddings without using BigFloats, and enables us to store the resulting embeddings with fewer bits. We evaluate our tiling-based model empirically, and show that it can both compress hyperbolic embeddings (down to $2\%$ of a Poincar{\'e} embedding on WordNet Nouns) and learn more accurate embeddings on real-world datasets.
Security framework for cloud based electronic health record (EHR) system

Health records are an integral aspect of any Hospital Management System. With newer innovations in technology, there has been a shift in the way of recording health information. Medical records which used to be managed using various paper charts have now become easier to organize and maintain, thereby increasing the efficiency of medical staff. The Electronic Health Records (EHR) System is becoming a high-tech medical management technology developed for the economic or emerging economic countries like India. In a national health system, the EHR integrates the Electronic Medical Records (EMR) in all collaborating hospitals through different networks. EHR gives healthcare professionals a way to share and manage patient data quickly and effectively. Due to the mass storage of confidential patient data, healthcare organizations are considered as one of the most targeted sectors by intruders. This paper proposes a security framework for EHR system, which takes into consideration the integrity, availability, and confidentiality of health records. The threats posed to the EHR system are modeled by STRIDE modeling tool, and the amount of risk is calculated using DREAD. The paper also suggests the security mechanism and countermeasures based on security standards, which can be utilized in an EHR environment. The paper shows that the utilization of the proposed methods effectively addresses security concerns such as breach of sensitive medical information.
Importance Resampling for Off-policy Prediction

Importance sampling (IS) is a common reweighting strategy for off-policy prediction in reinforcement learning. While it is consistent and unbiased, it can result in high variance updates to the weights for the value function. In this work, we explore a resampling strategy as an alternative to reweighting. We propose Importance Resampling (IR) for off-policy prediction, which resamples experience from a replay buffer and applies standard on-policy updates. The approach avoids using importance sampling ratios in the update, instead correcting the distribution before the update. We characterize the bias and consistency of IR, particularly compared to Weighted IS (WIS). We demonstrate in several microworlds that IR has improved sample efficiency and lower variance updates, as compared to IS and several variance-reduced IS strategies, including variants of WIS and V-trace which clips IS ratios. We also provide a demonstration showing IR improves over IS for learning a value function from images in a racing car simulator.
Towards modular and programmable architecture search

Neural architecture search methods are able to find high performance deep learning architectures with minimal effort from an expert. However, current systems focus on specific use-cases (e.g. convolutional image classifiers and recurrent language models), making them unsuitable for general use-cases that an expert might wish to write. Hyperparameter optimization systems are general-purpose but lack the constructs needed for easy application to architecture search. In this work, we propose a formal language for encoding search spaces over general computational graphs. The language constructs allow us to write modular, composable, and reusable search space encodings and to reason about search space design. We use our language to encode search spaces from the architecture search literature. The language allows us to decouple the implementations of the search space and the search algorithm, allowing us to expose search spaces to search algorithms through a consistent interface. Our experiments show the ease with which we can experiment with different combinations of search spaces and search algorithms without having to implement each combination from scratch. We release an implementation of our language with this paper.
On Understanding the Relation between Expert Annotations of Text Readability and Target Reader Comprehension

Automatic readability assessment aims to ensure that readers read texts that they can comprehend.However, computational models are typically trained on texts created from the perspective of the text writer, not the target reader.There is little experimental research on the relationship between expert annotations of readability, reader's language proficiency, and different levels of reading comprehension.To address this gap, we conducted a user study in which over a 100 participants read texts of different reading levels and answered questions created to test three forms of comprehension.Our results indicate that more than readability annotation or reader proficiency, it is the type of comprehension question asked that shows differences between reader responses -inferential questions were difficult for users of all levels of proficiency across reading levels.The data collected from this study is released with this paper 1 , which will, for the first time, provide a collection of 45 reader bench marked texts to evaluate readability assessment systems developed for adult learners of English.It can also potentially be useful for the development of question generation approaches in intelligent tutoring systems research.
ELPKG: A High-Accuracy Link Prediction Approach for Knowledge Graph Completion

Link prediction in knowledge graph is the task of utilizing the existing relations to infer new relations so as to build a more complete knowledge graph. The inferred new relations plus original knowledge graph is the symmetry of completion knowledge graph. Previous research on link predication only focuses on path or semantic-based features, which can hardly have a full insight of features between entities and may result in a certain ratio of false inference results. To improve the accuracy of link predication, we propose a novel approach named Entity Link Prediction for Knowledge Graph (ELPKG), which can achieve a high accuracy on large-scale knowledge graphs while keeping desirable efficiency. ELPKG first combines path and semantic-based features together to represent the relationships between entities. Then it adopts a probabilistic soft logic-based reasoning method that effectively solves the problem of non-deterministic knowledge reasoning. Finally, the relation between entities is completed based on the entity link prediction algorithm. Extensive experiments on real dataset show that ELPKG outperforms baseline methods on hits@1, hits@10, and MRR.
Lookup Table-Based Consensus Algorithm for Real-Time Longitudinal Motion Control of Connected and Automated Vehicles

Connected and automated vehicle (CAV) technology is one of the promising solutions to addressing the safety, mobility and sustainability issues of our current transportation systems. Specifically, the control algorithm plays an important role in a CAV system, since it executes the commands generated by former steps, such as communication, perception, and planning. In this study, we propose a consensus algorithm to control the longitudinal motion of CAVs in real time. Different from previous studies in this field where control gains of the consensus algorithm are pre-determined and fixed, we develop algorithms to build up a lookup table, searching for the ideal control gains with respect to different initial conditions of CAVs in real time. Numerical simulation shows that, the proposed lookup table-based consensus algorithm outperforms the authors' previous work, as well as van Arem's linear feedback-based longitudinal motion control algorithm in all four different scenarios with various initial conditions of CAVs, in terms of convergence time and maximum jerk of the simulation run.
Motion Planning of Autonomous Road Vehicles by Particle Filtering: Implementation and Validation

Autonomous driving in urban and highway scenarios involves a set of predefined requirements that the vehicle should obey, such as lane following, safety distances to surrounding vehicles, and speed preferences. We have previously shown that by interpreting the motion-planning problem as a nonlinear non-Gaussian estimation problem, we can leverage particle filtering to determine suitable vehicle trajectories. In this paper, we validate our proposed motion planner using scaled vehicles. We show that our motion planner is capable of determining safe and drivable trajectories for a number of challenging scenarios, and that the trajectories can be accurately tracked by a lower-level nonlinear model predictive control scheme.
Investigation of knowledge transfer approaches to improve the acoustic modeling of Vietnamese ASR system

It is well known that automatic speech recognition (ASR) is a resource consuming task. It takes sufficient amount of data to train a state-of-the-art deep neural network acoustic model. As for some low-resource languages where scripted speech is difficult to obtain, data sparsity is the main problem that limits the performance of speech recognition system. In this paper, several knowledge transfer methods are investigated to overcome the data sparsity problem with the help of high-resource languages. The first one is a pre-training and fine-tuning (PT/FT) method, in which the parameters of hidden layers are initialized with a well-trained neural network. Secondly, the progressive neural networks (Prognets) are investigated. With the help of lateral connections in the network architecture, Prognets are immune to forgetting effect and superior in knowledge transferring. Finally, bottleneck features (BNF) are extracted using cross-lingual deep neural networks and serves as an enhanced feature to improve the performance of ASR system. Experiments are conducted in a low-resource Vietnamese dataset. The results show that all three methods yield significant gains over the baseline system, and the Prognets acoustic model performs the best. Further improvements can be obtained by combining the Prognets model and bottleneck features.