Reviewerly/exHarmony huggingface dataset:
Research Support Services in STEM Libraries: A Scoping Review

As science and technology libraries continue to evolve, specialized research support services are developed and offered at academic institutions or research organizations. Making sense of this changing landscape and determining the best programs for an institution can be a daunting task, especially for early-career librarians. This article aims to provide an overview of various small to medium size non-traditional or specialized research support services in academic and special libraries serving Science, Technology, Engineering and Mathematics (STEM) disciplines. A systematic search of five databases was conducted for articles that described the development and implementation of research support services. Non-traditional or specialized research support services identified in this scoping review fall in the following areas: bibliometrics/altmetrics, data management services, geographic information systems, patents, and systematic reviews. The paper provides a detailed foundation for novice and experienced STEM librarians to offer innovative library services or enhance existing research support services.
Forward and Backward Bellman Equations Improve the Efficiency of the EM Algorithm for DEC-POMDP

Decentralized partially observable Markov decision process (DEC-POMDP) models sequential decision making problems by a team of agents. Since the planning of DEC-POMDP can be interpreted as the maximum likelihood estimation for the latent variable model, DEC-POMDP can be solved by the EM algorithm. However, in EM for DEC-POMDP, the forward–backward algorithm needs to be calculated up to the infinite horizon, which impairs the computational efficiency. In this paper, we propose the Bellman EM algorithm (BEM) and the modified Bellman EM algorithm (MBEM) by introducing the forward and backward Bellman equations into EM. BEM can be more efficient than EM because BEM calculates the forward and backward Bellman equations instead of the forward–backward algorithm up to the infinite horizon. However, BEM cannot always be more efficient than EM when the size of problems is large because BEM calculates an inverse matrix. We circumvent this shortcoming in MBEM by calculating the forward and backward Bellman equations without the inverse matrix. Our numerical experiments demonstrate that the convergence of MBEM is faster than that of EM.
THE USE OF FLASHCARDS FOR TEACHING WRITING TO ENGLISH YOUNG LEARNERS (EYL)

&lt;p&gt;The aims of this research are to know about the use of flashcards to improve the English Young Learners’ writing skills. English has four skills to be learned; they are listening, speaking, reading, and writing, and writing skill is the most difficult skill to be taught and learnt. Flashcards are one of the ways to improve the learners’ skill, especially EYL writing skill. The participants of this research are English Young Learners (EYL) in of the English Course at Gading Serpong, Tangerang. Classroom Action Research (CAR) was used as the method of the research and the English Young Learners’ (EYL) story paper are used as the instruments of the research. The results of the research are: (1) Most learners were excited when they are asked to see the colorful flashcard; (2) Learners who have good writing skill could make the story by completing the missing words and answer the questions then arrange the answer sentences well and (3) Learners who are not really master in writing, got some difficulties not only in completing the missing words but also answering then arranging the answer sentences well.&lt;/p&gt;
Hand Gesture Recognition Using Semi Vectorial Multilevel Segmentation Method with Improved ReliefF Algorithm

Currently, hand gesture recognition becomes a promising system for the digital entertainment field due to the recent development of sensor innovations and machine learning.However, hand gesture recognition is a challenging task in most of the existing models, due to background clutter, motion blur, illumination variations, and occlusions.In this research, a dynamic hand gesture recognition system is proposed to improve the performance of hand gesture recognition.At first, the normalization method is used to enhance the visibility level of the gesture images which are collected from the Indian sign language dataset.Then, semi vectorial multilevel segmentation method is employed to segment the exact gesture regions from the normalized images.Further, improved reliefF algorithm and K-nearest neighbour classifiers are used to select the optimal features and to classify the 16 gesture classes or symbols.In the experimental phase, the proposed improved reliefF-K-nearest neighbour model performance is analysed in light of Matthew's correlation coefficient, accuracy, sensitivity, specificity, and f-score.For overall 16 distinct alphabets; A, B, D, E, F, G, H, K, P, R, T, U, W, X, Y, Z, the proposed improved reliefF-K-nearest neighbour model achieves average accuracy of 98.95%.Hence, the proposed improved reliefF-K-nearest neighbour model showed maximum of 7.97% and minimum of 0.15% improvement in recognition accuracy compared to k means-neural network model, and discriminant correlation analysis based unimodal feature-level fusion model.
On Logics and Semantics for Interpretability

Abstract We study various properties of formalised relativised interpretability. In the central part of this thesis we study for different interpretability logics the following aspects: completeness for modal semantics, decidability and algorithmic complexity. In particular, we study two basic types of relational semantics for interpretability logics. One is the Veltman semantics, which we shall refer to as the regular or ordinary semantics; the other is called generalised Veltman semantics. In the recent years and especially during the writing of this thesis, generalised Veltman semantics was shown to be particularly well-suited as a relational semantics for interpretability logics. In particular, modal completeness results are easier to obtain in some cases; and decidability can be proven via filtration in all known cases. We prove various new and reprove some old completeness results with respect to the generalised semantics. We use the method of filtration to obtain the finite model property for various logics. Apart from results concerning semantics in its own right, we also apply methods from semantics to determine decidability (implied by the finite model property) and complexity of provability (and consistency) problems for certain interpretability logics. From the arithmetical standpoint, we explore three different series of interpretability principles. For two of them, for which arithmetical and modal soundness was already known, we give a new proof of arithmetical soundness. The third series results from our modal considerations. We prove it arithmetically sound and also characterise frame conditions w.r.t. ordinary Veltman semantics. We also prove results concerning the new series and generalised Veltman semantics. Abstract prepared by Luka Mikec. E-mail : luka.mikec1@gmail.com URL : http://hdl.handle.net/2445/177373
PointDrop: Improving Object Detection from Sparse Point Clouds via Adversarial Data Augmentation

Current 3D object detection methods achieve accurate and efficient results on the standard point cloud dataset. However, in real-world applications, the point cloud samples obtained in the real-time running may be much sparser due to various reasons (occlusion, low reflectivity of objects and fewer laser beams) and existing methods do not consider the limitations of their models on sparse point clouds. To improve the robustness of an object detector to sparser point clouds, we propose PointDrop, which learns to drop the features of some key points in the point clouds to generate challenging sparse samples for data augmentation. Moreover, PointDrop is able to adjust the difficulty of the generated samples based on the capacity of the detector and thus progressively improve the performance of the detector. We create two sparse point clouds datasets from the KITTI dataset to evaluate our method, and the experimental results show that PointDrop significantly improves the robustness of the detector to sparse point clouds.
Density-Aware Differentially Private Textual Perturbations Using Truncated Gumbel Noise

Deep Neural Networks, despite their success in diverse domains, are provably sensitive to small perturbations which cause the models to return erroneous predictions to minor transformations. Recently, it was proposed that this effect can be addressed in the text domain by optimizing for the worst case loss function over all possible word substitutions within the training examples. However, this approach is prone to weighing semantically unlikely word replacements higher, resulting in accuracy loss. In this paper, we study robustness to adversarial perturbations by using differentially private randomized substitutions while training the model. This approach has two immediate advantages: (1) by ensuring that the word replacement likelihood is weighted by its proximity to the original word in a metric space, we circumvent optimizing for worst case guarantees thereby achieve performance gains; and (2) the calibrated randomness results in training a privacy preserving model, while also guaranteeing robustness against adversarial attacks on the model outputs. Our approach uses a novel density-based differentially private mechanism based on truncated Gumbel noise. This ensures training on substitutions of words in dense and sparse regions of a metric space while maintaining semantic similarity for model robustness. Our experiments on two datasets suggest an improvement of up to 10% on the accuracy metrics.
Phase squeezing of quantum hypergraph states

Corresponding to a hypergraph $G$ with $d$ vertices, a quantum hypergraph state is defined by $|G\rangle = \frac{1}{\sqrt{2^d}}\sum_{n = 0}^{2^d - 1} (-1)^{f(n)} |n \rangle$, where $f$ is a $d$-variable Boolean function depending on the hypergraph $G$, and $|n \rangle$ denotes a binary vector of length $2^d$ with $1$ at $n$-th position for $n = 0, 1, \dots (2^d - 1)$. The non-classical properties of these states are studied. We consider annihilation and creation operator on the Hilbert space of dimension $2^d$ acting on the number states $\{|n \rangle: n = 0, 1, \dots (2^d - 1)\}$. The Hermitian number and phase operators, in finite dimensions, are constructed. The number-phase uncertainty for these states leads to the idea of phase squeezing. We establish that these states are squeezed in the phase quadrature only and satisfy the Agarwal-Tara criterion for non-classicality, which only depends on the number of vertices of the hypergraphs. We also point out that coherence is observed in the phase quadrature.
Multilingual Automatic Term Extraction in Low-Resource Domains

With the emergence of the neural networks-based approaches, research on information extraction has benefited from large-scale raw texts by leveraging them using pre-trained embeddings and other data augmentation techniques to deal with challenges and issues in Natural Language Processing tasks. In this paper, we propose an approach using sequence-to-sequence neural networks-based models to deal with term extraction for low-resource domain. Our empirical experiments, evaluating on the multilingual ACTER dataset provided in the LREC-TermEval 2020 shared task on automatic term extraction, proved the efficiency of deep learning approach, in the case of low-data settings, for the automatic term extraction task.
Peran Orang Tua Terhadap Hasil Belajar Siswa Pada Pembelajaran Daring

This article aims to find out: 1) what is the role of parents on student learning outcomes? 2) what is the role of parents in online learning ?, 3) What are the efforts made by parents in improving student learning outcomes in online learning ?. Parents are one of the people who are very responsible in the process of children's education. The active role of parents during the Covid-19 pandemic by implementing online learning will have an impact on student learning outcomes. The research method used in this research is qualitative research methods and in data collection using the phenomenological method and the research subjects are parents. From the research that has been carried out, the results of this study are: The role of parents on student learning outcomes is very important, with the responsibility of that person, student learning outcomes will affect especially learning outcomes related to affective and psychomotor, parental war in learning online during the Covid-19 pandemic is very influential and very important, with the role of parents in this online learning, the child will complete the task that has been given, and the child's ability to continue to develop even though not with the accompanying teacher. The efforts made by parents are always to motivate and encourage children to be able to carry out online learning.&#x0D;
Accelerated K-Means Algorithms for Low-Dimensional Data on Parallel Shared-Memory Systems

This paper considers the problem of exact accelerated algorithms for the K-means clustering of low-dimensional data on modern multi-core systems. A version of the filtering algorithm parallelized using the OpenMP (Open Multi-Processing) standard is proposed. The algorithm employs a kd-tree structure to skip some unnecessary calculations between cluster centroids and feature vectors. In our approach, both the kd-tree construction and the iterations of the K-means are parallelized using the OpenMP tasking mechanism. A new task is created for a recursive call performed during kd-tree construction and traversal. The tasks are executed in parallel by the cores of a shared-memory system. In computational experiments, we evaluated the parallel efficiency of our approach and compared its performance to the parallel Lloyd's method, a GPU (Graphics Processing Unit) formulation of the K-means algorithm, and two parallel triangle inequality-based algorithms intended for low-dimensional data. The evaluation was performed on six synthetic datasets from two distributions and seven real-life datasets. The experiments, executed on a 24-core system, indicated that our version of the filtering algorithm had satisfactory or high parallel efficiency. Its runtime was much shorter than those of competing algorithms. However, the advantage of the parallel filtering algorithm decreased rapidly as the dimension of data increased.
Gray level co-occurrence matrix feature extraction and histogram in breast cancer classification with ultrasonographic imagery

One way to detect breast cancer is using the Ultrasonography (USG) procedure, but the ultrasound image is susceptible to the noise speckles so that the interpretation and diagnosis results are different. This paper discusses the classification of breast cancer ultrasound images that aims to improve the accuracy of the identification of the type and level of cancer malignancies based on the features of its texture. The feature extraction process uses a &lt;em&gt;histogram&lt;/em&gt; which then the results are calculated using the Gray Level Co-Occurrence Matrix (GLCM). The results of the two extraction features are then classified using K-Nearest Neighbors (KNN) to obtain accurate figures from those images. The results of this study is that the accuracy in detecting cancer types is 80%.
An Exploration On-demand Article Recommender System for Cancer Patients Information Provisioning

&#x0D; &#x0D; &#x0D; Information provision plays an important role in ed- ucating patients with serious illnesses, like cancer, to cope with their disease conditions and to actively partic- ipate in shared-decision making process. Recent stud- ies suggest that there is a lack of appropriate educa- tional resources for such patients, specifically prostate cancer patients. To address this issue, in this paper, a Knowledge-based Exploration on-demand article Rec- ommender System (called KERS) is proposed that can provide evidence-based information for patients. Rec- ognizing the fact that exploration is expensive when the user of the system is a human, the main idea in KERS is to minimize exploration while achieving the maximum long-term satisfaction. Therefore, using a knowledge- base developed by an expert in the field, KERS learns user interests as quickly as possible and then it ex- ploits this knowledge to recommend the best articles. Furthermore, KERS needs no information from users beforehand and it learns them through interacting with users. The system will help patients make informed de- cisions, and at the same time, will reduce the burden on the healthcare providers. The results of experiments have confirmed the effectiveness of the proposed system compared to baseline methods.&#x0D; &#x0D; &#x0D;
Earth-Bound Study as a Post-Critical Response to the Corona-Crisis

In this paper, we want to flesh out what a post-critical way of dealing with the pandemic we face today could consist of. In the first part, we give an account of the current situation in terms of the remarkably ambiguous experience brought about by the intrusion of the COVID-19 virus, which has confronted us with a threat that is radically transcendent and immanent at the same time. In the second part, we go deeper into specific insights that have come about in the wake of this crisis, showing that these insights can be taken in two directions. We can try and align them with what we already know, or we can try to take seriously the unforeseen novelty of the situation. However, in the third part, we argue that we can still answer in a different, more fundamental and truly educational manner. This consists of “being taught” by the virus, thereby re-establishing our relation with the world and its human and non-human “indwellers”. This comes down to reorienting our educational endeavours towards earth-bound study practices, with a view to coming up with new answers to the “old” question of how to live well together.
A discriminative self‐attention cycle GAN for face super‐resolution and recognition

Face image captured via surveillance videos in an open environment is usually of low quality, which seriously affects the visual quality and recognition accuracy. Most image super-resolution methods adopt paired high-quality and its interpolated low-resolution version to train the super-resolution network. It is difficult to achieve contented visual quality and restoring discriminative features in real scenarios. A discriminative self-attention cycle generative adversarial network is proposed for real-world face image super-resolution. Based on the cycle GAN framework, unpaired samples are adopted to train a degradation network and a reconstruction network simultaneously. A self-attention mechanism is employed to capture the contextual information for details restoring. A Siamese face recognition network is introduced to provide a constraint on identify consistency. In addition, an asymmetric perceptual loss is introduced to handle the imbalance between the degradation model and the reconstruction model. Experimental results show that the observation model achieved more realistic low-quality face images, and the super-resolved face images have shown better subjective quality and higher face recognition performance.
Flipping the lab: Using consumer electronics for high-quality data collection

The second decade of the 21st Century has seen the rise of consumer technology capable of producing auditory signals at a level of precision only available to laboratory scientists and engineers just a few years earlier. This has allowed researchers to “flip the lab” and begin creating testing systems that can be taken to the participants rather than bringing the participants to the systems. Freely available tablet and smart-phone applications, as well as programmable virtual and augmented reality systems, are all poised to change that way that research is conducted. Research validating the existing approaches is ongoing and a task force has been established to track what is available and help guide potential users through potential pitfalls.
Deep Loving - The Friend of Deep Learning

Artificial Intelligence and Deep Learning are good fields of research. Recently, the brother of Artificial Intelligence titled "Artificial Satisfaction" was introduced in literature [10]. In this article, we coin the term “Deep Loving”. After the publication of this article, "Deep Loving" will be considered as the friend of Deep Learning. Proposing a new field is different from proposing a new algorithm. In this paper, we strongly focus on defining and introducing "Deep Loving Field" to Research Scientists across the globe. The future of the "Deep Loving" field is predicted by showing few future opportunities in this new field. The definition of Deep Learning is shown followed by a literature review of the "Deep Loving" field. The World's First Deep Loving Algorithm (WFDLA) is designed and implemented in this work by adding Deep Loving concepts to Particle Swarm Optimization Algorithm. Results obtained by WFDLA are compared with the PSO algorithm.
VIFECO: An Open-Source Software for Counting Features on a Video

The aim of this article is to describe an open-source application (Vifeco) that makes it possible to manually identify features on a video. Vifeco also allows to: manage the number of users, create a category (feature) and a collection of categories, read video and identify the features on it, and analyze the counting concordance between two users. Written in Java 11 with the JavaFX UI toolkit, Vifeco is a stand-alone, multiplatform (Windows, Mac and Linux) and multi-language (3 languages supported) application. The software is available under Apache Licence on GitHub (<em><a href="https://github.com/LAEQ/vifeco" target="_blank">https://github.</a></em><em><a href="https://github.com/LAEQ/vifeco" target="_blank">com/LAEQ/vifeco</a></em>).
Anonymous Quantum Sensing

A lot of attention has been paid to a quantum-sensing network for detecting magnetic fields in different positions. Recently, cryptographic quantum metrology was investigated where the information of the magnetic fields is transmitted in a secure way. However, sometimes, the positions where non-zero magnetic fields are generated could carry important information. Here, we propose an anonymous quantum sensor where an information of positions having non-zero magnetic fields is hidden after measuring magnetic fields with a quantum-sensing network. Suppose that participants are located in different positions and they have quantum sensors. After the quantum sensors are entangled, the participants implement quantum sensing that provides a phase information if non-zero magnetic fields exist, and POVM measurement is performed on quantum sensors. Importantly, even if the outcomes of the POVM measurement is stolen by an eavesdropper, information of the positions with non-zero magnetic fields is still unknown for the eavesdropper in our protocol. Here, tracelessness is guaranteed, and so our protocol is considered as anonymous. In addition, we evaluate the sensitivity of our proposed quantum sensors by using Fisher information when there are at most two positions having non-zero magnetic fields. We show that the sensitivity is finite unless these two (non-zero) magnetic fields have exactly the same amplitude. Our results pave the way for new applications of quantum-sensing network.