Reviewerly/exHarmony huggingface dataset:
Description of gold and copper anomalies using fractal and stepwise factor analysis according to stream sediments in NW Iran

Major aim of this study is to define the copper and gold anomalies using the combination of the Concentration-Number (C-N) fractal and the stepwise factor analysis, based on stream sediment samples in Lahrud 1:100,000 sheet, NW Iran. The case study is situated in Alborz-Azerbaijan structural zone. Elements related to Au and Cu hydrothermally mineralization were selected for this study, according to ore deposit types of the study region. Staged factor analysis was carried out in four steps. Consequently, all noise elements were removed after applying four steps of factor analysis and As, Sb, Sn, W, Co, Mn, Zn, Ti, Ba, Ni, Cr, Au, Cu, and B were grouped in the five factors. Cu and Au are grouped on factor 4 of the last step (FAC4-4). The C-N log-log plots for factor scores, Au and Cu were plotted and threshold values were determined in order to a generation of elemental distribution maps. The anomalous areas for metallic elements specifically copper and gold, are located in the NW and SW parts of the region which show a correlation between alteration zones, faults, and intrusions.
Hand Gesture Vocalizer for Dumb and Deaf People

The main purpose of this paper is to confer the system that converts a given sign used by disabled person into its appropriate textual, audio, and pictorial form using components such as Arduino Mega, Flex sensors, Accelerometer, which could be under standby a common person. A wearable glove controller is design with fl ex sensors attached on each finger, which allows the system to sense the finger movements, and aGy-61 accelerometer, which are uses to sense the hand movement of the disabled person. The wearable input glove controller sends the collected input signal to the system for processing. The system uses Random forest algorithm to predict the correct output to an accuracy of 85% on current training model.
A Bayesian Approach to Direct and Inverse Abstract Argumentation Problems

This paper studies a fundamental mechanism of how to detect a conflict between arguments given sentiments regarding acceptability of the arguments. We introduce a concept of the inverse problem of the abstract argumentation to tackle the problem. Given noisy sets of acceptable arguments, it aims to find attack relations explaining the sets well in terms of acceptability semantics. It is the inverse of the direct problem corresponding to the traditional problem of the abstract argumentation that focuses on finding sets of acceptable arguments in terms of the semantics given an attack relation between the arguments. We give a probabilistic model handling both of the problems in a way that is faithful to the acceptability semantics. From a theoretical point of view, we show that a solution to both the direct and inverse problems is a special case of the probabilistic inference on the model. We discuss that the model provides a natural extension of the semantics to cope with uncertain attack relations distributed probabilistically. From en empirical point of view, we argue that it reasonably predicts individuals sentiments regarding acceptability of arguments. This paper contributes to lay the foundation for making acceptability semantics data-driven and to provide a way to tackle the knowledge acquisition bottleneck.
Hybrid Neural Models For Sequence Modelling: The Best Of Three Worlds

We propose a neural architecture with the main characteristics of the most successful neural models of the last years: bidirectional RNNs, encoder-decoder, and the Transformer model. Evaluation on three sequence labelling tasks yields results that are close to the state-of-the-art for all tasks and better than it for some of them, showing the pertinence of this hybrid architecture for this kind of tasks.
Robustness of Haar Feature-Based Cascade Classifier for Face Detection Under Presence of Image Distortions

In this paper examines effectiveness of HAAR feature-based cascade classifier for face detection in the presence of various image distortions. In the article we have focused on picture distortions that are likely to be met in everyday life, namely blurring, salt and pepper noise, contrast and brightness shifts and “fisheye” type distortion typical for wide-angle lens. In the paper present the mathematical model of the classifier and distortions, the training procedure and finally results of segmentation under various level of distortion. The test dataset is a large publicly available “Labelled Faces in the Wild” (LFW). Results show that Cascade Classifier finds it most difficult to recognize images that contain 70% noise type salt and pepper. The least impact on the effectiveness of the method use of blurred images even though the high parameter of blurring. From the obtained results it appears that the effectiveness of face detection is also affected by the adequate parameters of contrast and brightness.
Characterizing Strongly First Order Dependencies: The Non-Jumping Relativizable Case

Team Semantics generalizes Tarski's Semantics for First Order Logic by allowing formulas to be satisfied or not satisfied by sets of assignments rather than by single assignments.Because of this, in Team Semantics it is possible to extend the language of First Order Logic via new types of atomic formulas that express dependencies between different assignments.Some of these extensions are much more expressive than First Order Logic proper; but the problem of which atoms can instead be added to First Order Logic without increasing its expressive power is still unsolved.In this work, I provide an answer to this question under the additional assumptions (true of most atoms studied so far) that the dependency atoms are relativizable and non-jumping.Furthermore, I show that the global (or Boolean) disjunction connective can be added to any strongly first order family of dependencies without increasing the expressive power, but that the same is not true in general for non strongly first order dependencies.
Data-Specific Functions: A Comment on Kindel et al.

In this issue, Kindel et al. describe a new approach to managing survey data in service of the Fragile Families Challenge, which they call “treating metadata as data.” Although the approach they present is a good first step, a more ambitious proposal could improve survey data analysis even more substantially. The author recommends that data collection efforts distribute an open-source set of tools for working with a particular data set the author calls data-specific functions. The goal of these functions is to codify best practices for working with the data in a set of functions for commonly used statistical software. These functions would be jointly developed by the users and distributers of the data. Building such functions would both shorten the learning curve for new users and improve the quality of the data, by making tacit knowledge about problems with the data explicit and easy to act on.
PROMOTING TOTAL PHYSICAL RESPONSE (TPR) METHOD ON EARLY CHILDHOOD ENGLISH LANGUAGE TEACHING

The purpose of this study was to describe the use of Total Physical Response (TPR) method on early childhood English Language Teaching (ELT) at Panti Asuhan Yauma Jakarta and to know the students’ response after learning English using TPR method. The method of the research is descriptive qualitative. In this research the researcher describes the activities carried out by teachers and students by using TPR method. The data resources are from teacher and students in Panti Asuhan Yauma Jakarta . The class consist of 6 students which their age was around 5 to 11 years old. The data collected through observation and interview both teacher and students. The instruments were pre-test and post-test with 2 (two) criterion being assessed, namely vocabulary and comprehension. They were divided into 6 (six) elements such as accuracy of word, understanding each word, word choice, understanding the meaning, speaking easily,and intonation. The results showed that there was any improvement on vocabulary score about 26.16 and comprehension score about 27.16. The research finding firstly showed the TPR method was able to be applied to children because they enjoyed and were also very active in learning English. The researcher hopes that the study contributes to the activities of teaching and learning English, especially teaching English for early childhood. It was proved by the score from pre-test and post-test.
Explaining Aggregate Behaviour in Cognitive Agent Simulations Using Explanation

We consider the problem of obtaining useful (and actionable) insight into the behaviour of agent-based simulation (using cognitive agents). When such simulations are being developed and refined, it can be useful to gain understanding of the simulation’s behaviour. In particular, such understanding often needs to be specific to a given scenario (not just high-level generic information about the simulation dynamics), and about the aggregate behaviour of multiple agents. We describe a method for taking explanations of behaviour produced by individual agents, and aggregating them to obtain useful information about the aggregate behaviour of multiple agents. The method, which has been implemented, is illustrated in the context of a traffic simulation.
Encoder–decoder with densely convolutional networks for monocular depth estimation

We propose an encoder–decoder with densely convolutional networks model to recover the depth information from a single RGB image without the need for depth sensors. The encoder part serves to extract the most representative information from the original data through a series of convolution operations and to reduce the resolution of the spatial input feature. We use the decoder section to produce an upsampling structure that improves the output resolution. Our model is trained from scratch, without any special tuning process, and uses a new optimization function to adaptively learn the rate. We demonstrate the effectiveness of the method by evaluating both indoor and outdoor scenes, and the experimental results show that our proposed approach is more accurate than competing methods.
Designing Laboratory Forensics

Recently, the topic of research data management (RDM) has emerged at the forefront of Open Science. Funders and publishers posit new expectations on data management planning and transparent reporting of research. At the same time, laboratories rely upon undocumented files to record data, process results and submit manuscripts which hinders repeatable and replicable management of experimental resources. In this study, we design a forensic process to reconstruct and evaluate data management practices in scientific laboratories. The process we design is named Laboratory Forensics (LF) as it combines digital forensic techniques and the systematic study of experimental data. We evaluate the effectiveness and usefulness of Laboratory Forensics with laboratory members and data managers. Our preliminary evaluation indicates that LF is a useful approach for assessing data management practices. However, LF needs further developments to be integrated into the information systems of scientific laboratories.
Spatial Attention Gated Variational Autoencoder Enhanced Cycle-Consistent Generative Adversarial Networks for MRI to CT Translation

Cycle-consistent generative adversarial networks (CycleGANs) have achieved state-of-the-art image-to-image translation accuracy in many domains . In order to facilitate training within a cycle-consistent framework, current CycleGANs are restricted to relatively simple discriminator architectures. Since CycleGANs rely on pure adversarial loss, a restricted discriminator network could limit the overall accuracy of MR to CT translation. In this study we present an attention-aware cycle-consistent generative adversarial network (GAN) enhanced with variational autoencoding as a superior alternative to current state-of-the-art MR to CT image translation methods. A spatial attention gating mechanism is incorporated into a discriminator network to encourage a more parsimonious use of network parameters, while variational autoencoding enhancement enables deeper discrimination architectures without inhibiting model convergence. The attention mechanism helps the discriminator attend to specific anatomy within the image by selectively enhancing portions of the network during training. However, training attention gated classifier networks is non-trivial due to the gradient saturation problem. Variational autoencoding is used to facilitate model convergence of the core network and attention mechanism, while simultaneously allowing the discriminator network to progress in synchrony with the generator network. 60 head and neck and brain cancer patients were used to train and validate all methods. An additional 20 hold out test patients were used to report final evaluation metric results using mean absolute error (MAE) and peak signal to noise ratio (PSNR). A two sample one-sided Mann-Whitney U test was used to compare attention-aware cycle-consistent GANs with each alternative method and p-values of less than 0.05 were considered to be significant. Our method was compared to a UNet approach, a conventional GAN, and a cycle-consistent GAN. Our method achieved statistically superior results compared to all alternative methods for MAE (19.33 +/- 1.77, p-value=0.002351), and PSNR (62.76 +/- 0.97, p-value=0.000208). This is the first-time attention-aware cycle-consistent GANs have been used to solve MR to CT translation. This is also the first ever implementation of variational autoencoder enhanced attention-aware discrimination in any problem space, including non-medical applications. We demonstrated that attention-aware cycle-consistent GANs enhanced with variational autoencoding is a superior alternative to current state-of-the-art MR to CT translation methods.
Optoelectronic Reservoir Computing Using a Mixed Digital-Analog Hardware Implementation

Optoelectronic systems have proven to be an attractive platform for the realization of hardware-based Reservoir Computing (RC). These unconventional computers can perform nonlinear prediction of chaotic timeseries and generate arbitrary input/output functions, after training. One of the main advantages of a delay-based optoelectronic reservoir is that it only requires a single nonlinear hardware node and a feedback loop. To implement the RC scheme experimentally, we use photonic and electronic components and an FPGA to drive the system. The resulting optoelectronic RC is a hybrid analog and digital system with great versatility. We show that this set-up can perform a chaotic timeseries prediction task, in which the output is computed online, with low prediction errors. Ultimately, this testbed system will allow to test several strategies to improve the prediction performance, including the addition of multiple delays and the tailoring of the input mask for noise mitigation.
Aspect Level Sentiment Analysis with Aspect Attention

Aspect level sentiment classification is a fundamental task in the field of sentiment analysis, which goal is to inferring sentiment on entities mentioned within texts or aspects of them. Since it performs finer-grained analysis, aspect level sentiment classification is more challenging. Recently, neural network approaches, such as LSTMs, have achieved much progress in sentiment analysis. However, most neural models capture little aspect information in sentences. Aspect level sentiment of a sentence is determined not only by the content but also by the concerned aspect. In this paper, we propose a novel LSTM with Aspect Attention model (LSTM_AA) for aspect level sentiment classification. Our model introduces aspect attention to relate the aspect level sentiment of a sentence closely to the concerned aspect, as well as to explore the connection between an aspect and the content of a sentence. We experiment on the SemEval 2014 datasets and results show that our model performs comparable to state-of-the-art deep memory network, and substantially better than other neural network approaches. Besides, our approach is more robust than deep memory network which performance heavily depends on the hops.
Automated cleaning of identity label noise in a large face dataset with quality control

For face recognition, some very large-scale datasets are publicly available in recent years, which are usually collected from the Internet using search engines, and thus have many faces with wrong identity (ID) labels (outliers). Additionally, the face images in these datasets have different qualities because of uncontrolled situations. The authors propose a novel approach for cleaning the ID label error, handling face images in different qualities. The face ID labels cleaned by their method can train better models for low-quality face recognition since more low-quality images are correctly labelled for training a deep model. In their low-to-high-quality face verification experiments, the deep model trained on their cleaning results of MS-Celeb-1M.v1 face dataset outperforms the same model trained on the same dataset cleaned by the semantic bootstrapping method. They also apply their ID label cleaning method on a subset of the cross-age celebrity dataset (CACD) face dataset, in which their quality-based cleaning can deliver higher precision and recall than a previous method on detecting the ID label errors.
Neural Architecture Search for Class-incremental Learning

In class-incremental learning, a model learns continuously from a sequential data stream in which new classes occur. Existing methods often rely on static architectures that are manually crafted. These methods can be prone to capacity saturation because a neural network's ability to generalize to new concepts is limited by its fixed capacity. To understand how to expand a continual learner, we focus on the neural architecture design problem in the context of class-incremental learning: at each time step, the learner must optimize its performance on all classes observed so far by selecting the most competitive neural architecture. To tackle this problem, we propose Continual Neural Architecture Search (CNAS): an autoML approach that takes advantage of the sequential nature of class-incremental learning to efficiently and adaptively identify strong architectures in a continual learning setting. We employ a task network to perform the classification task and a reinforcement learning agent as the meta-controller for architecture search. In addition, we apply network transformations to transfer weights from previous learning step and to reduce the size of the architecture search space, thus saving a large amount of computational resources. We evaluate CNAS on the CIFAR-100 dataset under varied incremental learning scenarios with limited computational power (1 GPU). Experimental results demonstrate that CNAS outperforms architectures that are optimized for the entire dataset. In addition, CNAS is at least an order of magnitude more efficient than naively using existing autoML methods.
Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data

This research proposes a hybrid method for improving the segmentation accuracy of reconstructed 3D models from computed tomography/ magnetic resonance imaging (CT/MRI) data.A semi-automatic hybrid method based on combination of Fuzzy C-Means clustering (FCM) and region growing (RG) is proposed.In this approach, FCM is used in the first stage as a preprocessing step in order to classify and improve images by assigning pixels to the clusters for which they have the maximum membership, and manual selection of the membership intensity map with the best contrast separation.Afterwards, automatic seed selection is performed for RG, for which a new parameter standard deviation (STD) of pixel intensities, is included.It is based on the selection of an initial seed inside a region with maximum value of STD.To evaluate the performance of the proposed method, it was compared to several other segmentation methods.Experimental results show that the proposed method overall provides better results compared to other methods in terms of accuracy.The average sensitivity and accuracy rates for cone-beam computed tomography CBCT 1 and CBCT 2 datasets are 99 %, 98.4 %, 47.2 % and 89.9 %, respectively.For MRI 1 and MRI 2 datasets, the average sensitivity and accuracy values are 99.1 %, 100 %, 75.6 % and 99.6 %, respectively.The average values for the Dice coefficient and Jaccard index for the CBCT 1 and CBCT 2 datasets are 95.88,0.88, 0.6, and 0.51, respectively, while for MRI 1 and MRI 2 datasets, average values are 0.96, 0.93, 0.81 and 0.7, respectively, which confirms the high accuracy of the proposed method.
Formal Specification of Continuum Deformation Coordination

Continuum deformation is a leader-follower multiagent cooperative control approach. Previous work showed a desired continuum deformation can be uniquely defined based on trajectories of d+1 leaders in a d-dimensional motion space and acquired by followers through local inter-agent communication. This paper formally specifies continuum deformation coordination in an obstacle-laden environment. Using linear temporal logic (LTL), continuum deformation liveness and safety requirements are defined. Safety is prescribed by providing conditions on (i) agent deviation bound, (ii) inter-agent collision avoidance, (iii) agent containment, (iv) motion space containment, and (v) obstacle collision avoidance. Liveness specifies a reachability condition on the desired final formation.
Fill the GAP: Exploiting BERT for Pronoun Resolution

In this paper, we describe our entry in the gendered pronoun resolution competition which achieved fourth place without data augmentation.Our method is an ensemble system of BERTs which resolves co-reference in an interaction space.We report four insights from our work: BERT's representations involve significant redundancy; modeling interaction effects similar to natural language inference models is useful for this task; there is an optimal BERT layer to extract representations for pronoun resolution; and the difference between the attention weights from the pronoun to the candidate entities was highly correlated with the correct label, with interesting implications for future work.
Text to Image Synthesis Based on Multiple Discrimination

We propose a novel and simple text-to-image synthesizer (MD-GAN) using multiple discrimination. Based on the Generative Adversarial Network (GAN), we introduce segmentation images to the discriminator to ensure the improvement of discrimination ability. The improvement of discrimination ability will enhance the generator’s generating ability, thus obtaining high-resolution results. Experiments well validate the outstanding performance of our algorithm. On CUB dataset, our inception score is 27.7% and 1.7% higher than GAN-CLS-INT and GAWWN, respectively. On the flower dataset, it further outplays GAN-CLS-INT and StackGAN by 21.8% and 1.25%, respectively. At the same time, our model is more concise in structure, and its training time is only half that of StackGAN.
Efficient similarity computation for collaborative filtering in dynamic environments

The problem of computing all pairwise similarities in a large collection of vectors is a well-known and common data mining task. As the number and dimensionality of these vectors keeps increasing, however, currently existing approaches are often unable to meet the strict efficiency requirements imposed by the environments they need to perform in. Real-time neighbourhood-based collaborative filtering (CF) is one example of such an environment in which performance is critical.