Reviewerly/exHarmony huggingface dataset:
Kuru Üzüm Tanelerinin Makine Görüşü ve Yapay Zeka Yöntemleri Kullanılarak Sınıflandırılması

In this study, machine vision system was developed in order to distinguish between two different variety of raisins (Kecimen and Besni) grown in Turkey.Firstly, a total of 900 pieces raisin grains were obtained, from an equal number of both varieties.These images were subjected to various preprocessing steps and 7 morphological feature extraction operations were performed using image processing techniques.In addition, minimum, mean, maximum and standard deviation statistical information was calculated for each feature.The distributions of both raisin varieties on the features were examined and these distributions were shown on the graphs.Later, models were created using LR, MLP, and SVM machine learning techniques and performance measurements were performed.The classification achieved 85.22% with LR, 86.33% with MLP and 86.44% with the highest classification accuracy obtained in the study with SVM.Considering the number of data available, it is possible to say that the study was successful.
Few-shot Pseudo-Labeling for Intent Detection

In this paper, we introduce a state-of-the-art pseudo-labeling technique for few-shot intent detection.We devise a folding/unfolding hierarchical clustering algorithm which assigns weighted pseudo-labels to unlabeled user utterances.We show that our two-step method yields significant improvement over existing solutions.This performance is achieved on multiple intent detection datasets, even in more challenging situations where the number of classes is large or when the dataset is highly imbalanced.Moreover, we confirm these results on the more general text classification task.We also demonstrate that our approach nicely complements existing solutions, thereby providing an even stronger state-of-the-art ensemble method.We make our code publicly available 1 for future research.
MONITORING THE COVID-19 DIFFUSION BY COMBINING WEARABLE BIOSENSORS AND SMARTPHONES.

The management of the current pandemic COVID-19 has been challenging and complex.The main and only successes have been achieved with non-pharmacological interventions (NPI).When tracking, monitoring, and early intervention at home have been delivered to citizens, the contagion can be controlled.In the current pandemic, various methods have been applied to track the COVID-19 virus, such as Korea's mobile phone tracking system.We propose a method based on a wearable bracelet prototype able to detect biomedical parameters, which can be very useful to monitor the virus infection when the patient develops symptoms, such as a high temperature or low blood oxygenation.In particular, the prototype bracelet can measure the blood oxygenation using an infrared optical sensor and measure the temperature of the patient.The bracelet can record the identification number of other bracelet devices that came in proximity.The bracelet is equipped with a built-in low power Bluetooth, aimed to send the recorded data to a smartphone or another device in order to connect them with proper geo-localization and to the web.The identification number of the patient device can be used to trace the number of people and whom he has been in contact with, immediately by the sanitary authorities.Moreover, the bracelet can be used for monitoring the patient's health at home, avoiding hospital's overcrowding.The proposed system not only can effectively localize the trace path of patients positive to the COVID-19 virus or to other respiratory diseases, but also can provide an evolution of the patient symptoms and monitor people in-home quarantine.The system is simple and could be an efficient tool to track any other future pandemics.
I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning

CommonsenseQA is a task in which a correct answer is predicted through commonsense reasoning with pre-defined knowledge.Most previous works have aimed to improve the performance with distributed representation without considering the process of predicting the answer from the semantic representation of the question.To shed light upon the semantic interpretation of the question, we propose an AMR-ConceptNet-Pruned (ACP) graph.The ACP graph is pruned from a full integrated graph encompassing Abstract Meaning Representation (AMR) graph generated from input questions and an external commonsense knowledge graph, ConceptNet (CN).Then the ACP graph is exploited to interpret the reasoning path as well as to predict the correct answer on the CommonsenseQA task.This paper presents the manner in which the commonsense reasoning process can be interpreted with the relations and concepts provided by the ACP graph.Moreover, ACP-based models are shown to outperform the baselines.
Ask to Learn: A Study on Curiosity-driven Question Generation

We propose a novel text generation task, namely Curiosity-driven Question Generation.We start from the observation that the Question Generation task has traditionally been considered as the dual problem of Question Answering, hence tackling the problem of generating a question given the text that contains its answer.Such questions can be used to evaluate machine reading comprehension.However, in real life, and especially in conversational settings, humans tend to ask questions with the goal of enriching their knowledge and/or clarifying aspects of previously gathered information.We refer to these inquisitive questions as Curiosity-driven: these questions are generated with the goal of obtaining new information (the answer) which is not present in the input text.In this work, we experiment on this new task using a conversational Question Answering (QA) dataset; further, since the majority of QA dataset are not built in a conversational manner, we describe a methodology to derive data for this novel task from non-conversational QA data.We investigate several automated metrics to measure the different properties of Curious Questions, and experiment different approaches on the Curiosity-driven Question Generation task, including model pre-training and reinforcement learning.Finally, we report a qualitative evaluation of the generated outputs.
Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement

In recent years, the plentiful information contained in Chinese legal documents has attracted a great deal of attention because of the large-scale release of the judgment documents on China Judgments Online.It is in great need of enabling machines to understand the semantic information stored in the documents which are transcribed in the form of natural language.The technique of information extraction provides a way of mining the valuable information implied in the unstructured judgment documents.We propose a Legal Triplet Extraction System for drug-related criminal judgment documents.The system extracts the entities and the semantic relations jointly and benefits from the proposed entity feature and multi-task learning framework.Furthermore, we manually annotate a dataset for Named Entity Recognition and Relation Extraction in Chinese legal domain, which contributes to training supervised triplet extraction models and evaluating the model performance.Our experimental results show that the entity feature introduction and multi-task learning framework are feasible and effective for the Legal Triplet Extraction System.
Selected topics of quantum computing for nuclear physics*

Nuclear physics, whose underling theory is described by quantum gauge field coupled with matter, is fundamentally important and yet is formidably challenge for simulation with classical computers. Quantum computing provides a perhaps transformative approach for studying and understanding nuclear physics. With rapid scaling-up of quantum processors as well as advances on quantum algorithms, the digital quantum simulation approach for simulating quantum gauge fields and nuclear physics has gained lots of attention. In this review, we aim to summarize recent efforts on solving nuclear physics with quantum computers. We first discuss a formulation of nuclear physics in the language of quantum computing. In particular, we review how quantum gauge fields (both Abelian and non-Abelian) and their coupling to matter field can be mapped and studied on a quantum computer. We then introduce related quantum algorithms for solving static properties and real-time evolution for quantum systems, and show their applications for a broad range of problems in nuclear physics, including simulation of lattice gauge field, solving nucleon and nuclear structures, quantum advantage for simulating scattering in quantum field theory, non-equilibrium dynamics, and so on. Finally, a short outlook on future work is given.
Sentence Analogies: Linguistic Regularities in Sentence Embeddings

While important properties of word vector representations have been studied extensively, far less is known about the properties of sentence vector representations.Word vectors are often evaluated by assessing to what degree they exhibit regularities with regard to relationships of the sort considered in word analogies.In this paper, we investigate to what extent commonly used sentence vector representation spaces as well reflect certain kinds of regularities.We propose a number of schemes to induce evaluation data, based on lexical analogy data as well as semantic relationships between sentences.Our experiments consider a wide range of sentence embedding methods, including ones based on BERT-style contextual embeddings.We find that different models differ substantially in their ability to reflect such regularities.
The State of the Art in Empirical User Evaluation of Graph Visualizations

While graph drawing focuses more on the aesthetic representation of node-link diagrams, graph visualization takes into account other visual metaphors making them useful for graph exploration tasks in information visualization and visual analytics. Although there are aesthetic graph drawing criteria that describe how a graph should be presented to make it faster and more reliably explorable, many controlled and uncontrolled empirical user studies flourished over the past years. The goal of them is to uncover how well the human user performs graph-specific tasks, in many cases compared to previously designed graph visualizations. Due to the fact that many parameters in a graph dataset as well as the visual representation of them might be varied and many user studies have been conducted in this space, a state-of-the-art survey is needed to understand evaluation results and findings to inform the future design, research, and application of graph visualizations. In this article, we classify the present literature on the topmost level into graph interpretation, graph memorability, and graph creation where the users with their tasks stand in focus of the evaluation, not the computational aspects. As another outcome of this work, we identify the white spots in this field and sketch ideas for future research directions.
Quantitative Assessment Method of Image Stitching Performance Based on Estimation of Planar Parallax

While parallax-tolerant image stitching is a relatively mature field, the performances of image stitching methods have been assessed subjectively and qualitatively.These methods primarily provide the stitched image itself to demonstrate the performance, rather than quantitative data.Although several objective assessment methods have been proposed for quantifying the quality of stitched images, only the stitched output images have been analyzed, without considering the parallax level in each input image.We propose a method for quantifying the parallax level of the input images and clustering them accordingly.This facilitates a quantitative assessment of the various stitching methods for each parallax level.The parallax levels of the images are grouped based on the magnitude and variation in the planar parallax, as estimated with the proposed metric using matching errors and patch similarity.The existing image stitching methods are compared experimentally in terms of the residual misalignment errors, based on 73 pairs of different levels of parallax images originally classified in this study.Among the existing methods, the elastic local alignment method exhibits the least error.The shape-preserving half-projective method produces a larger misalignment error, but creates a natural panorama with less geometric distortion.We introduce a quantitative assessment method for considering the parallax of input images in image stitching methods.It can aid in specifying their performances, and in finding an appropriate method depending on the parallax level of the input images.
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging

Influence functions approximate the "influences" of training data-points for test predictions and have a wide variety of applications.Despite the popularity, their computational cost does not scale well with model and training data size.We present FASTIF, a set of simple modifications to influence functions that significantly improves their run-time.We use k-Nearest Neighbors (kNN) to narrow the search space down to a subset of good candidate data points, identify the configurations that best balance the speed-quality trade-off in estimating the inverse Hessian-vector product, and introduce a fast parallel variant.Our proposed method achieves about 80X speedup while being highly correlated with the original influence values.With the availability of the fast influence functions, we demonstrate their usefulness in four applications.First, we examine whether influential data-points can "explain" test time behavior using the framework of simulatability.Second, we visualize the influence interactions between training and test data-points.Third, we show that we can correct model errors by additional fine-tuning on certain influential data-points, improving the accuracy of a trained MultiNLI model by 2.5% on the HANS dataset.Finally, we experiment with a similar setup but fine-tuning on datapoints not seen during training, improving the model accuracy by 2.8% and 1.7% on HANS and ANLI datasets respectively.Overall, our fast influence functions can be efficiently applied to large models and datasets, and our experiments demonstrate the potential of influence functions in model interpretation and correcting model errors. 1
Random and Synthetic Over-Sampling Approach to Resolve Data Imbalance in Classification

High accuracy value is one of the parameters of the success of classification in predicting classes. The higher the value, the more correct the class prediction. One way to improve accuracy is dataset has a balanced class composition. It is complicated to ensure the dataset has a stable class, especially in rare cases. This study used a blood donor dataset; the classification process predicts donors are feasible and not feasible; in this case, the reward ratio is quite high. This work aims to increase the number of minority class data randomly and synthetically so that the amount of data in both classes is balanced. The application of SOS and ROS succeeded in increasing the accuracy of inappropriate class recognition from 12% to 100% in the KNN algorithm. In contrast, the naïve Bayes algorithm did not experience an increase before and after the balancing process, which was 89%.
EFL Students’ Challenges towards Home Learning Policy During Covid-19 Outbreak

Obviously, the world wide is shocking with pandemic of Covid-19 where it effects to every single part of human lives and everyday activities for sure. Additionally, the Covid-19 pandemic also has its influence toward education system from lower to upper levels. Both teachers and students feel the impact from the recent pandemic especially in Indonesian context where of course it triggers barriers due to the educational adjustment they have to follow. Therefore, this research is particulalrly aimed to investigate perception of EFL students toward home learning during pandemic of Covid-19. In-dept interview was applied to eight students of English Department students of University of Widya Gama Mahakam Samarinda. Specifically, the in-dept interview is consisted of the students’ perception mainly regarding with challenges they faced when having home learning activities during the Covid-19 pandemic up to the recent time. As a result, the researcher found that the students face three major types of challenges including internet connection, healthy reason, and the use of certain online application. Further discussion and and fruitful suggestions also provided in this research.
K-Nearest Neighbor with K-Fold Cross Validation and Analytic Hierarchy Process on Data Classification&#x0D;

This study analyzes the performance of the k-Nearest Neighbor method with the k-Fold Cross Validation algorithm as an evaluation model and the Analytic Hierarchy Process method as feature selection for the data classification process in order to obtain the best level of accuracy and machine learning model. The best test results are in fold-3, which is getting an accuracy rate of 95%. Evaluation of the k-Nearest Neighbor model with k-Fold Cross Validation can get a good machine learning model and the Analytic Hierarchy Process as a feature selection also gets optimal results and can reduce the performance of the k-Nearest Neighbor method because it only uses features that have been selected based on the level of importance for decision making.
Motion tracking of iris features to detect small eye movements

&#x0D; &#x0D; &#x0D; &#x0D; The inability of current video-based eye trackers to reliably detect very small eye movements has led to confusion about the prevalence or even the existence of monocular microsaccades (small, rapid eye movements that occur in only one eye at a time). As current methods often rely on precisely localizing the pupil and/or corneal reflection on successive frames, current microsaccade-detection algorithms often suffer from signal artifacts and a low signal-to-noise ratio. We describe a new video-based eye tracking methodology which can reliably detect small eye movements over 0.2 degrees (12 arcmin) with very high confidence. Our method tracks the motion of iris features to estimate velocity rather than position, yielding a better record of microsaccades. We provide a more robust, detailed record of miniature eye movements by relying on more stable, higher-order features (such as local features of iris texture) instead of lower-order features (such as pupil center and corneal reflection), which are sensitive to noise and drift. &#x0D; &#x0D; &#x0D; &#x0D;
Do Transparency and Anti-Monopoly Policies Matter for Financial Development? Evidence from a Panel ARDL-PMG Approach

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
The role of knowledge sharing, trust as mediation on servant leadership and job performance

This study analyzes the effect of servant leadership on job performance with, trust and knowledge sharing as mediating variables. The research is classified in the quantitative analysis used to test the significance between latent variables. The study sample includes 164 of the STIE Malangkucecwara postgraduate program alumni from 2010 to 2019. Data are collected through an online questionnaire and the SmartPls processes the results. The results show that job performance was directly affected by servant leadership. Trust and knowledge sharing are proven as variables that mediate the influence between servant leadership and job performance with the nature of full mediation. Serving leadership tends to increase employee trust in leaders and organizations and stimulates a culture of sharing knowledge to increase employee job performance. The results can be a basis for further studies in the field of management, especially those related to servant leadership, and as material for practical understanding for leaders in various areas of the organization.
The Computational Complexity of Understanding Binary Classifier Decisions

For a d-ary Boolean function Φ: {0, 1}d → {0, 1} and an assignment to its variables x = (x1, x2, . . . , xd) we consider the problem of finding those subsets of the variables that are sufficient to determine the function value with a given probability δ. This is motivated by the task of interpreting predictions of binary classifiers described as Boolean circuits, which can be seen as special cases of neural networks. We show that the problem of deciding whether such subsets of relevant variables of limited size k ≤ d exist is complete for the complexity class NPPP and thus, generally, unfeasible to solve. We then introduce a variant, in which it suffices to check whether a subset determines the function value with probability at least δ or at most δ − γ for 0 &lt; γ &lt; δ. This promise of a probability gap reduces the complexity to the class NPBPP. Finally, we show that finding the minimal set of relevant variables cannot be reasonably approximated, i.e. with an approximation factor d1−α for α &gt; 0, by a polynomial time algorithm unless P = NP. This holds even with the promise of a probability gap.
2D Positional Embedding-based Transformer for Scene Text Recognition

Recent state-of-the-art scene text recognition methods are primarily based on Recurrent Neural Networks (RNNs), however, these methods require one-dimensional (1D) features and are not designed for recognizing irregular-text instances due to the loss of spatial information present in the original two-dimensional (2D) images. In this paper, we leverage a Transformer-based architecture for recognizing both regular and irregular text-in-the-wild images. The proposed method takes advantage of using a 2D positional encoder with the Transformer architecture to better preserve the spatial information of 2D image features than previous methods. The experiments on popular benchmarks, including the challenging COCO-Text dataset, demonstrate that the proposed scene text recognition method outperformed the state-of-the-art in most cases, especially on irregular-text recognition.
npTrack: A n-Position Single Axis Solar Tracker Model for Optimized Energy Collection

The single axis solar tracker based on flat panels is used in large solar plants and in distribution-level photovoltaic systems. In order to achieve this, the solar tracking systems generally need to work by tracking the sun’s position with dozens, maybe hundreds of movements along the day with a maximal known tracking error within the specifications. A novel model is proposed along this work based on the control of the angle deviation within a (polar) single axis configuration. This way an optimization of the harnessing of solar energy can be achieved with as few panel displacements as possible in order to decrease the wear in the mechanical parts of the equipment and the energy consumed by it. This tracking approach was implemented with as few as seven positions along the day and got an estimated theoretical value of 99.27% of the total collected energy in a continuous tracking system. Regarding an annual average basis, it would be about 96.5% of a dual axis system according to the proposed model. The novelty of the model is related to a tradeoff between the gain with the simplicity of a single axis n-position tracking and the solar energy loss associated.