Reviewerly/exHarmony huggingface dataset:
Incorporating side information into Robust Matrix Factorization with Bayesian Quantile Regression

Matrix Factorization is a widely used technique for modeling pairwise and matrix-like data. It is frequently used in pattern recognition, topic analysis and other areas. Side information is often available, however utilization of this additional information is problematic in the pure matrix factorization framework. This article proposes a novel method of utilizing side information by combining arbitrary nonlinear Quantile Regression model and Matrix Factorization under Bayesian framework. Gradient-free optimization procedure with the novel Surrogate Function is used to solve the resulting MAP estimator. The model performance has been evaluated on real data-sets.
Application Research of Display Design Based on Virtual Reality

Abstract This article expounds the concepts, characteristics, expressions, and basic types of virtual reality display design. It also discusses the design principles that virtual reality design should follow its practical significance for all design. By analysing its specific applications in commercial product display, built environment display, cultural genetic reincarnation and so on, its different application values and meanings are explained, and the two design categories of 3D modelling virtual reality display and panoramic virtual reality display are studied and discussed the design and implementation principles, application technology and design content in detail.
Private Distributed Mean Estimation.

Ever since its proposal, differential privacy has become the golden standard for rigorous privacy protection. Output perturbation is the most widely used differentially private mechanism. It works by adding calibrated noise drawn from the real domain to the output. However, the finite computers can only represent real numbers to a given machine precision. To guarantee the strictness of differential privacy under fixed precision, several pioneering works proposed to use noise drawn from discrete for differential privacy. However, discrete differentially private mechanisms are not used in dense tasks such as deep learning because of the lack of tight composition theorem like moments accountant. In this paper, we prove Renyi differential privacy for discrete Gaussian mechanism. Thus, we can directly use analytical moments accountant (\cite{wang2018subsampled}) to provide tight composition for distributed mean estimation.
CLASSROOM ACTIVITIES FOR TEACHING SPEAKING: VOICES OF INDONESIAN EFL LEARNERS

&lt;p&gt;Among the four English skills, oral communication becomes crucial for undergraduate students to be more competitive in the work-places. In a foreign language teaching, designing appropriate classroom activities is a prerequisite for the success of a language learning. Therefore, this study sheds some light on 92 EFL learners’ perceptions and insights of classroom activities for teaching speaking. Drawing on the data obtained from questionnaire and focus group discussion (FGD), the findings illustrated that the students held moderately positive perceptions toward the speaking activities in their classroom. In addition, the results of FGD revealed some insights from the students with regard to the ideal classroom activities for teaching speaking. The findings of this study provide insights for EFL teachers in designing appropriate classroom activities to achieve the success of teaching speaking.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords: &lt;/strong&gt;teaching speaking, classroom activities, students’ perception&lt;/p&gt;
B-Mode Ultrasound Breast Anatomy Segmentation

Breast Ultrasound has long been used to support diagnostic and exploratory procedures concerning breast cancer, with an interesting success rate, specially when complemented with other radiology information. This usability can further enhance visualization tasks during pre-treatment clinical analysis by coupling the B-Mode images to 3D space, as found in Magnetic Resonance Imaging (MRI) per instance. In fact, Lesions in B-mode are visible and present high detail when comparing with other 3D sequences. This coupling, however, would be largely benefited from the ability to match the various structures present in the B-Mode, apart from the broadly studied lesion. In this work we focus on structures such as skin, subcutaneous fat, mammary gland and thoracic region. We provide a preliminary insight to several structure segmentation approaches in the hopes of obtaining a functional and dependable pipeline for delineating these potential reference regions that will assist in multi-modal radiological data alignment. For this, we experiment with pre-processing stages that include Anisotropic Diffusion guided by Log-Gabor filters (ADLG) and main segmentation steps using K-Means, Meanshift and Watershed. Among the pipeline configurations tested, the best results were found using the ADLG filter that ran for 50 iterations and H-Maxima suppression of 20% and the K-Means method with $$K=6$$. The results present several cases that closely approach the ground truth despite overall having larger average errors. This encourages the experimentation of other approaches that could withstand the innate data variability that makes this task very challenging.
Learning Sparse Masks for Efficient Image Super-Resolution

Current CNN-based super-resolution (SR) methods process all locations equally with computational resources being uniformly assigned in space. However, since highfrequency details mainly lie around edges and textures, less computational resources are required for those flat regions. Therefore, existing CNN-based methods involve much redundant computation in flat regions, which increases their computational cost and limits the applications on mobile devices. To address this limitation, we develop an SR network (SMSR) to learn sparse masks to prune redundant computation conditioned on the input image. Within our SMSR, spatial masks learn to identify important locations while channel masks learn to mark redundant channels in those unimportant regions. Consequently, redundant computation can be accurately located and skipped while maintaining comparable performance. It is demonstrated that our SMSR achieves state-of-the-art performance with 41%/33%/27% FLOPs being reduced for x2/3/4 SR.
Comparative Analysis of Neural Models for Abstractive Text Summarization

Abstractive text summarization is the task of generating the summary of text documents like humans do. It’s completely laborious and time taking process to summarize the lengthy documents manually. Abstractive text summarization takes a document as input and produces a summary by combining a piece of information from different source sentences and paraphrase them while maintaining the overall meaning of the document. Here, the abstractive text summarization task is done using various sequence-to-sequence (seq2seq) models and their performance has been analyzed on the MSMO dataset. Seq2seq with attention, pointer generator network (PGN) and pointer generator with coverage models are used to generate the summary. To improve accuracy, hyper-parameters were tuned and successfully obtained good results. ROUGE and BLEU scores are used to evaluate the performance of these models. Seq2seq with attention, PGN, and PGN with coverage models achieved ROUGE-1 scores 35.49, 38.19, 37.68 respectively. These neural abstractive text summarization models have also performed effectively in terms of the BLEU score and achieved BLEU-1 scores 40.60, 44.50, 45.20 respectively.
Networked Multi-Agent Reinforcement Learning with Emergent Communication

We develop a Multi-Agent Reinforcement Learning (MARL) method that finds approximately optimal policies for cooperative agents that co-exist in an environment. Central to achieving this is how the agents learn to communicate with each other. Can they together develop a language while learning to perform a common task? We formulate and study a MARL problem where cooperative agents are connected via a fixed underlying network. These agents communicate along the edges of this network by exchanging discrete symbols. However, the semantics of these symbols are not predefined and have to be learned during the training process. We propose a method for training these agents using emergent communication. We demonstrate the applicability of the proposed framework by applying it to the problem of managing traffic controllers, where we achieve state-of-the-art performance (as compared to several strong baselines) and perform a detailed analysis of the emergent communication.
Text Embeddings for Retrieval from a Large Knowledge Base

Text embedding representing natural language documents in a semantic vector space can be used for document retrieval using nearest neighbor lookup. In order to study the feasibility of neural models specialized for retrieval in a semantically meaningful way, we suggest the use of the Stanford Question Answering Dataset (SQuAD) in an open-domain question answering context, where the first task is to find paragraphs useful for answering a given question. First, we compare the quality of various text-embedding methods on the performance of retrieval and give an extensive empirical comparison on the performance of various non-augmented base embedding with, and without IDF weighting. Our main results are that by training deep residual neural models, specifically for retrieval purposes, can yield significant gains when it is used to augment existing embeddings. We also establish that deeper models are superior to this task. The best base baseline embeddings augmented by our learned neural approach improves the top-1 paragraph recall of the system by $$14\%$$.
An Exploratory Visual Analytics Tool for Multivariate Dynamic Networks

Visualizing multivariate dynamic networks is a challenging task. The evolution of the dynamic network within the temporal axis must be depicted in conjunction with the associated multivariate attributes. In this paper, an exploratory visual analytics tool is proposed to display multivariate dynamic networks with spatial attributes. The proposed tool displays the distribution of multivariate temporal domain and network attributes in scattered views. Moreover, in order to expose the evolution of a single or a group of nodes in the dynamic network along the temporal axis, an egocentric approach is applied in which a node is represented with its neighborhood as an ego-network. This approach allows users to observe a node's surrounding environment along the temporal axis. On top of the traditional ego-network visualization methods, such as timelines, the proposed tool encodes ego-networks as feature vectors consisting of the domain and network attributes and projects them onto 2D views. As a result, the distance between projected ego-networks represents the dissimilarity across the temporal axis in a single view. The proposed tool is demonstrated with a real-world use case scenario on merchant networks obtained from a one-year-long credit card transactions.
Adversarially Robust Learning Could Leverage Computational Hardness.

Over recent years, devising classification algorithms that are robust to adversarial perturbations has emerged as a challenging problem. In particular, deep neural nets (DNNs) seem to be susceptible to small imperceptible changes over test instances. However, the line of work in provable robustness, so far, has been focused on information-theoretic robustness, ruling out even the existence of any adversarial examples. In this work, we study whether there is a hope to benefit from algorithmic nature of an attacker that searches for adversarial examples, and ask whether there is any learning task for which it is possible to design classifiers that are only robust against polynomial-time adversaries. Indeed, numerous cryptographic tasks can only be secure against computationally bounded adversaries, and are indeed impossible for computationally unbounded attackers. Thus, it is natural to ask if the same strategy could help robust learning. We show that computational limitation of attackers can indeed be useful in robust learning by demonstrating the possibility of a classifier for some learning task for which computational and information theoretic adversaries of bounded perturbations have very different power. Namely, while computationally unbounded adversaries can attack successfully and find adversarial examples with small perturbation, polynomial time adversaries are unable to do so unless they can break standard cryptographic hardness assumptions. Our results, therefore, indicate that perhaps a similar approach to cryptography (relying on computational hardness) holds promise for achieving computationally robust machine learning. On the reverse directions, we also show that the existence of such learning task in which computational robustness beats information theoretic robustness requires computational hardness by implying (average-case) hardness of NP.
Space-Efficient Data Structures for Lattices

A lattice is a partially-ordered set in which every pair of elements has a unique meet (greatest lower bound) and join (least upper bound). We present new data structures for lattices that are simple, efficient, and nearly optimal in terms of space complexity.Our first data structure can answer partial order queries in constant time and find the meet or join of two elements in O(n^{3/4}) time, where n is the number of elements in the lattice. It occupies O(n^{3/2}log n) bits of space, which is only a Θ(log n) factor from the Θ(n^{3/2})-bit lower bound for storing lattices. The preprocessing time is O(n²). This structure admits a simple space-time tradeoff so that, for any c ∈ [1/2, 1], the data structure supports meet and join queries in O(n^{1-c/2}) time, occupies O(n^{1+c}log n) bits of space, and can be constructed in O(n² + n^{1+3c/2}) time.Our second data structure uses O(n^{3/2}log n) bits of space and supports meet and join in O(d (log n)/(log d)) time, where d is the maximum degree of any element in the transitive reduction graph of the lattice. This structure is much faster for lattices with low-degree elements.This paper also identifies an error in a long-standing solution to the problem of representing lattices. We discuss the issue with this previous work.
Organizational Effectiveness of Non-Profit Organizations: Evidences from Coastal Region of China

Li, X. and Xie, X., 2020. Organizational effectiveness of non-profit organizations: Evidences from coastal region of China. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 74–78. Coconut Creek (Florida), ISSN 0749-0208.As the important contents of organizational effectiveness of non-profit organizations (NPOs), job satisfaction, organizational citizenship behavior (OCB) and organizational commitment interact with each other and act on organizational behavior together. In this paper, it is concluded through the questionnaire survey of employees in NPOs and structural equation analysis that employees' job satisfaction has a certain impact on OCB and organizational commitment; employees' organizational commitment has a significant negative impact on organizational destructive behavior; normative commitment and continuous commitment have a significant positive impact on identification with the company, while affective commitment has no significant positive impact on identification with the company.
Signaling Friends and Head-Faking Enemies Simultaneously: Balancing Goal Obfuscation and Goal Legibility

In order to be useful in the real world, an AI agent needs to plan and act in the presence of other agents, who may be helpful or disruptive. In this paper, we consider the problem where an autonomous agent needs to act in a manner that clarifies its objectives to cooperative agents while simultaneously preventing adversarial agents from inferring those objectives. We call it Mixed-Observer Controlled Observability Planning Problem (mo-copp). We develop two new solution approaches: one provides an optimal solution to the problem given a fixed time horizon by using an integer programming solver, the other provides a satisficing solution using heuristic-guided forward search to achieve prespecified amount of obfuscation and legibility for adversarial and cooperative agents respectively.
Cross-lingual transfer-learning approach to negation scope resolution

Detecting instances of negation in text is crucially important for several applications, yet it is often neglected. Several decades of research in automated negation detection have not yet provided a reliable solution, especially in a multilingual context. Negation scope resolution poses particular challenges since identifying the scope of influence of a negation cue in a sentence requires a deeper level of natural language understanding. Little work has been done on negation scope resolution in languages other than English. Meanwhile, transfer learning is in wide use and large multilingual models are available to the public. This paper explores the feasibility of a cross-lingual transfer-learning approach to negation scope resolution. Preliminary experiments with the Multilingual BERT model and data in English, French, and Spanish show solid results with the highest F1-score 84.73 on zero-shot transfer between English and French.
Causal Feature Selection via Orthogonal Search.

The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines. Recent work in the field of causal discovery exploits invariance properties of models across different experimental conditions for detecting direct causal links. However, these approaches generally do not scale well with the number of explanatory variables, are difficult to extend to nonlinear relationships, and require data across different experiments. Inspired by {\em Debiased} machine learning methods, we study a one-vs.-the-rest feature selection approach to discover the direct causal parent of the response. We propose an algorithm that works for purely observational data, while also offering theoretical guarantees, including the case of partially nonlinear relationships. Requiring only one estimation for each variable, we can apply our approach even to large graphs, demonstrating significant improvements compared to established approaches.
Multiclass Classification with an Ensemble of Binary Classification Deep Networks.

Deep neural network classifiers have been used frequently and are efficient. In multiclass deep network classifiers, the burden of classifying samples of different classes is put on a single classifier. As shown in this paper, the classification capability of deep networks can be further increased by using an ensemble of binary classification deep networks. In the proposed approach, a single (one-versus-all) deep network binary classifier is dedicated to each category classification. Subsequently, binary classification deep network ensembles have been investigated. Every network in an ensemble has been trained by a one-versus-all binary training technique using the Stochastic Gradient Descent with Momentum Algorithm. For classification of the test sample, the sample is presented to each network in the ensemble. After softmax-layer score voting, the network with the largest score is assumed to have classified the sample. Digit image recognition has been used for experimentation. Three datasets have been used for experimentation viz. the MATLAB Digit Image Dataset, the USPS+ Digit Image Dataset, and the MNIST Digit Image Dataset. The experiments demonstrate that given sufficient training, a Binary Classification Convolutional Neural Network (BCCNN) ensemble can outperform a conventional Multi-class Convolutional Neural Network (MCNN). In one of the experiments, it was noted that after training and testing of a BCCNN ensemble and an MCNN respectively on a subset of the MNIST Digit Image Dataset, the BCCNN ensemble gave a higher accuracy of 98.03% as compared to the MCNN which gave an accuracy of 97.90%. The architecture of the BCCNNs in an ensemble has also been modified in order to increase their recognition accuracy. On a large subset of the MNIST Digit Image Dataset, the modified BCCNN ensemble gave a higher accuracy of 98.50%, while as the MCNN gave an accuracy of 98.4875%.
Research on air cushion suspension support system with low speed and heavy load

Abstract In this paper, cushion suspension support system of low speed and heavy loading of the gas membrane flow field was in computational fluid dynamics analysis, explore the influence of air cushion suspension of performance support system air cushion structural parameters, and the air pressure in the process of air cushion lift proportional control system for mathematical modeling, thus to control the thickness of air cushion, based on pole assignment self-tuning controller, to improve the response and stability of the system, and the system in this paper the lifting process of air cushion suspension support system are analyzed and the gas film flow state, deepening the theoretical research of air cushion suspension technology.
The development of android-based learning media on vibrations and waves topic for junior high school students

Abstract Instilling abstract concept of vibrations and waves into students requires proper learning media and this research intended to develop such learning media. The Android-based learning media on Vibrations and Waves topic for VIII grade junior high school students have been successfully created through the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. The interactive research product entitled “Vibrations and Waves” packed in Application Package File (.apk) is downloadable on Google Play Store. The application contains texts, figures, and animations that can be accessed on the Objective, Materials, Simulation, Exercise, and Evaluation menu. This product has been through a validation test and product trial. The validation was performed by materials expert, media expert, and teachers while the trial was done to VIII grade junior high school students. There were three students in the One-to-One Trial, twelve students in the Small-Scale Trial, and twenty-eight students in the Field Trial. The validity and practicality of the product were respectively 4.24 (excellent); and 4.43 (excellent). Also, the students stated that this learning media could be used as an independent learning source while the teachers convinced that this product is a supportive school learning source.
Electric simulation of silicon drift detector for single photon measurement

The silicon drift detector (SDD) with high quantum efficiency, energy resolution and time resolution is an advanced single photon counting detector in the fields of quantum communication, X-ray communication and pulsar navigation. In this paper we design an optimum SDD structure with circular drift rings and linear-type voltage divider, the device electric characteristics are simulated using a 3D Sentaurus TCAD computational tool including electric potential, electric field, electron concentration, voltage-divider current, leakage current and saturation capacitance. Simulation results are comparable with those of conventional spiral SDDs and other shapes of ring SDDs. The circular ring-shaped structure can generate more even and symmetrical radial electric potential and field distribution, so electrons have shorter and more distinct drift channels, and SDD has ultra-fast time response and good time resolution to detect pulsed single photon signals.