Reviewerly/exHarmony huggingface dataset:
Audio-Aware Spoken Multiple-Choice Question Answering With Pre-Trained Language Models

Spoken multiple-choice question answering (SMCQA) requires machines to select the correct choice to answer the question by referring to the passage, where the passage, the question, and multiple choices are all in the form of speech. While the audio could contain useful cues for SMCQA, usually only the auto-transcribed text is utilized in model development. Thanks to the large-scaled pre-trained language representation models, such as the bidirectional encoder representations from Transformers (BERT), systems with only auto-transcribed text can still achieve a certain level of performance. However, previous studies have evidenced that acoustic-level statistics can offset text inaccuracies caused by the automatic speech recognition systems or representation inadequacy lurking in word embedding generators, thereby making the SMCQA system robust. Along the line of research, in this study, an audio-aware SMCQA framework is proposed. Two different mechanisms are introduced to distill the useful cues from speech, and then a BERT-based SMCQA framework is presented. In other words, the proposed SMCQA framework not only inherits the advantages of contextualized language representations learned by BERT but integrates the complementary acoustic-level information distilled from audio with the text-level information. A series of experiments demonstrates remarkable improvements in accuracy over selected baselines and SOTA systems on a published Chinese SMCQA dataset.
Hierarchical Bayesian modeling of ocean heat content and its uncertainty

The accurate quantification of changes in the heat content of the world’s oceans is crucial for our understanding of the effects of increasing greenhouse gas concentrations. The Argo program, consisting of Lagrangian floats that measure vertical temperature profiles throughout the global ocean, has provided a wealth of data from which to estimate ocean heat content. However, creating a globally consistent statistical model for ocean heat content remains challenging due to the need for a globally valid covariance model that can capture complex nonstationarity. In this paper, we develop a hierarchical Bayesian Gaussian process model that uses kernel convolutions with cylindrical distances to allow for spatial nonstationarity in all model parameters while using a Vecchia process to remain computationally feasible for large spatial datasets. Our approach can produce valid credible intervals for globally integrated quantities that would not be possible using previous approaches. These advantages are demonstrated through the application of the model to Argo data, yielding credible intervals for the spatially varying trend in ocean heat content that accounts for both the uncertainty induced from interpolation and from estimating the mean field and other parameters. Through cross-validation, we show that our model outperforms an out-of-the-box approach as well as other simpler models. The code for performing this analysis is provided as the R package BayesianOHC.
CP-ABE with Efficient Revocation Based on the KEK Tree in data Outsourcing System

CP-ABE (ciphertext-policy attribute-based encryption) is a promising encryption scheme. In this paper, a highly expressive revocable scheme based on the key encryption keys (KEK) tree is proposed. In this method, the cloud server realizes the cancellation of attribute-level users and effectively reduces the computational burden of the data owner and attribute authority. This scheme embeds a unique random value associated with the user in the attribute group keys. The attribute group keys of each user are different, and it is impossible to initiate a collusion attack. Computing outsourcing makes most of the decryption work done by the cloud server, and the data user only need to perform an exponential operation; in terms of security, the security proof is completed under the standard model based on simple assumptions. Under the premise of ensuring security, the scheme in this paper has the functions of revocation and traceability, and the speed of decryption calculation is also improved.
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification

Semi-supervised learning (SSL) has proven to be effective at leveraging large-scale unlabeled data to mitigate the dependency on labeled data in order to learn better models for visual recognition and classification tasks. However, recent SSL methods rely on unlabeled image data at a scale of billions to work well. This becomes infeasible for tasks with relatively fewer unlabeled data in terms of runtime, memory and data acquisition. To address this issue, we propose noisy semi-supervised transfer learning, an efficient SSL approach that integrates transfer learning and self-training with noisy student into a single framework, which is tailored for tasks that can leverage unlabeled image data on a scale of thousands. We evaluate our method on both binary and multi-class classification tasks, where the objective is to identify whether an image displays people practicing sports or the type of sport, as well as to identify the pose from a pool of popular yoga poses. Extensive experiments and ablation studies demonstrate that by leveraging unlabeled data, our proposed framework significantly improves visual classification, especially in multi-class classification settings compared to state-of-the-art methods. Moreover, incorporating transfer learning not only improves classification performance, but also requires 6x less compute time and 5x less memory. We also show that our method boosts robustness of visual classification models, even without specifically optimizing for adversarial robustness.
Conceptualizing Hyperlocal Information Systems for Developing Countries

Hyperlocal information systems cater to the needs of the people in a well-defined geographical area. Such information systems are ubiquitously accessible to the users in the developed world through their mobile phones. In developing countries hyperlocal information is valued, but often dispersed, constantly evolving, and poorly accessible. Additionally, most of the social information systems in developing countries are adopted from the developed world, and often fail to take into consideration local socio-geographical settings and cultural complexities. As a result, there is a strong reliance on word of mouth through known and unknown networks. In this paper, we present foundational insights on user needs, behaviors and motivations for the design of hyperlocal information systems in developing countries based on a multi-year, multi-method research study with around 5000 participants in three such countries - India, Indonesia and Nigeria. We also discuss how the complexity of real-world sociocultural phenomena and heterogeneity of neighborhoods impact hyperlocal information, which can further inform design of such systems in the future.
Synchronous Ideation Workshop with Rural Library Professionals to Envision Future Makerspaces

Abstract Ideation can support the development of educational programs and services at public libraries. Prior studies on ideation workshops focus on face‐to‐face settings; less is known about synchronous ideation workshops with library professionals with limited design experience. This paper reports on the results of a reflective case study of an online ideation workshop with three rural library professionals to envision future makerspaces. Preliminary findings suggest design considerations for a synchronous ideation workshop around librarian experience, design space, and technology.
A BAS-Based Strategy for Redundant Manipulator Tracking Continuous Moving Object in 3D Relative Velocity Coordinate

The ability of real-time tracking of manipulator end-effect is critical for intelligent manipulators. This paper presents a control strategy for tracking the continuous moving object. Our research aims at converting the problem of tracking moving object to a planning problem of relative velocity vector by establishing Relative Velocity Coordinate (RVC) on manipulator end-effect. The key novelty of the proposed strategy about constructing the optimization problem lies in considering both relative velocity and relative position simultaneously. The Beetle Antennae Search (BAS) algorithm is introduced to tackle the formulated optimization function. Theory analysis and different simulation experiments are provided to illustrate the feasibility and efficiency of the proposed control strategy.
Reconstruction of Torn Document by Moore Algorithm

Mosaic is a creative art which is used for combining number of torn document or books pages piece to get completed stitch document. Mosaicing technique can be applied to videos, photos images and documents images. Image mosaic has a wide application such as image data security, biomedical image processing, image forensic, historic artifact processing, image combining and image restoration. In this paper, proposed work on boundary extracted. Experimented torn documents as input on proposed algorithm which work on documents mosaic techniques and results is successfully stitched output get.
A Hybrid Deep Learning Approach for Detecting and Classifying Breast Cancer Using Mammogram Images

Next to skin cancer, the most common cancer disease found in women is the breast cancer and which very rarely can found in men. This type of cancer will develop on the upper part of the breast on the lobules which causes lobular carcinoma or in the milk ducts which causes ductal carcinoma which will be usually in form of tumor cells which is visible in mammogram X-ray image or feel like lumps. The cancer becomes deadly when the cell starts growing and it spreads and invade the skin tissues which are around the breast. About 15% of the cancer deaths are because of breast cancer as per the survey of World Health Organization (WHO). At the same time, all the lumps cannot be as deadly as the breast cancer. So identifying the disease and classifying its type in the early stage will reduce the impact. In this research, we propose a hybrid deep learning–based approach for detecting and classify the breast cancer cells in the early stage. The algorithm used in this proposed work is a hybrid approach, which is a combination of Convolutional Neural Networks (CNN) and Random Forest (RF) algorithms that are the most popular deep learning algorithms for detection of breast cancer. The mammogram dataset for this research was collected from various online dataset repositories. Experimental results show that the proposed method gains an overall accuracy of 98.6% with sensitivity and specificity of 96.5% and 98% respectively. While compared with the other state-of-the-art methods, the superior performance of the proposed hybrid technique is visible. The overall system was implemented in MATLAB 2018b software with deep learning toolbox.
CFMNet: Coarse-to-Fine Cascaded Feature Mapping Network for Hair Attribute Transfer

Recently, GAN-based manipulation methods have been proposed to effectively edit and transfer facial attributes. However, these state-of-the-art methods usually fail to delicately manipulate hair attributes because hair does not own a concrete shape and varies a lot with flexible structure. Therefore, how to achieve high-fidelity hair attribute transfer becomes a challenging task. In this paper, we propose a coarse-to-fine cascaded feature mapping network (CFMNet), which can disentangle hair into coarse-grained and fine-grained attributes, and transform hair feature in latent space according to a reference image. The disentangled hair attributes consist of the coarse-grained labels, including length, waviness and bangs, and the fine-grained 3D model, including geometry and color. Next we design a cascaded feature mapping network to manipulate the attributes in a coarse-to-fine way between source and reference images, which can adjust and control hair feature more delicately. Moreover, we also construct an identity loss to avoid the destruction of identity information in source image. A variety of experimental results demonstrate the effectiveness of our proposed method.
Entertaining Geophysics 4. The Study of Seismic Phenomena as the Process of Fortune-Telling on the Coffee Grounds. What Does the School Curriculum Say about Earthquake Energy?

This article represents the second work in a series of articles under the general title Funny Geophysics, the purpose of which is to popularize the earth sciences, and this abstract will be common to the works of the entire cycle. Answering the question of why we call serious science entertaining, we answer that at the moment there are no rigorous scientific disciplines such as seismology, tectonics, volcanology, etc., that explain the process of earthquakes and other catastrophic rock movements. Instead, there are pseudo-scientific fantasies of modern geophysicists, built on mythical ideas about the structure of the earth's crust and mantle. Figuratively speaking, we can safely say that the modern reasoning of geophysicists boils down to the fact that our Earth stands on three turtles and earthquakes are the result of the movement of these animals. This situation is partly due to the fact that our knowledge of the internal structure of the Earth is limited to a depth of 12 km, and what is deeper than this mark still remains a mystery to us. And what happens at a depth of hundreds of kilometers turns out to be a secret behind seven locks for us. But an even greater mystery for mankind is the question of why modern geophysics ossified at the level of the beginning of the 20th century and operates with the knowledge of that period, in every possible way ignoring the achievements of modern sciences? Tell me what's wrong? Then why all modern geophysics is based on the speculations of Mr. Reid and his mythical elastic stresses, which supposedly arise during the deformation of rocks? We will try to answer this question, and at the same time, using simple examples of catastrophic earthquakes and other seismic events, we will show the unforgivable errors of modern geophysics.
How changing API terms changed Instagram's domain? A bibliometric analysis

research-article How changing API terms changed Instagram's domain? A bibliometric analysis Share on Authors: Richard Hartman Czech University of Life Sciences Prague, CZ Czech University of Life Sciences Prague, CZView Profile , Tereza Simova Czech University of Life Sciences Prague, CZ Czech University of Life Sciences Prague, CZView Profile Authors Info & Claims ICCTA 2021: 2021 7th International Conference on Computer Technology ApplicationsJuly 2021 Pages 73–79https://doi.org/10.1145/3477911.3477923Online:15 October 2021Publication History 0citation19DownloadsMetricsTotal Citations0Total Downloads19Last 12 Months19Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Synthesis and Sensitivity Analysis of a Prosthetic Finger

A prosthetic finger based on a one degree of freedom mechanism is synthesized in this paper using a genetic algorithm optimization method. The features of this method are defined so that the mechanism performs an anatomical movement of a hand finger. Then, a sensitivity analysis is proposed to identify which links most affect the end-effector position. Some simulation results are presented to analyze this proposal.
Improving Micro-Extended Belief Rule-Based System Using Activation Factor for Classification Problems

The micro-extended belief rule-based system (Micro-EBRBS) is an advanced rule-based system and has shown its superior ability in solving big data problems. To overcome the activation rule incompleteness and inconsistency of Micro-EBRBS, a new concept, named activation factor (AF), is introduced to revise the calculation of individual matching degree and, furthermore, an AF-based inference (AFI) method is proposed for improving Micro-EBRBS. A comparative analysis study is conducted using three classification datasets. Results demonstrate that the proposed AFI method can not only improve the accuracy of Micro-EBRBS, but also reduce the number of failed data in the process of rule inference.
Study on Situation Evaluation Assessment of Cooperative Searching Target Based on Cloud Theory and Bayesian Network

In the process of ASW patrol aircraft cooperative search target, there is a lot of uncertainty in situation evaluation problem. In order to deal with the uncertain information, cloud theory and Bayesian network are introduced, and a method based on cloud Bayesian network is proposed to to deduce the enemy’s operational intention and form the battlefield situation. Firstly, a situation evaluation model of cooperative search target is established. Secondly, the cloud model is used for discretization. After determining the conditional probability table(CPT) of each node and the conversion between certainty and probability, the final situation grade is obtained through the probability composition formula. The simulation results show that the proposed method is reasonable and effective.
Nonparametric estimation of marginal distributions for unordered pairs

In this article, we consider the problem of estimating the marginal distributions when pairs of data are recorded, with unobserved order in each pair. A Glivenko–Cantelli theorem is obtained, together with a functional central limit result and we illustrate the applicability of the proposed procedure to the homologous chromosomes data set.
Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning

We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly. Rather than learning an individual policy for each particular task, the proposed framework, DISH, distills a hierarchical policy from a set of tasks by representation and reinforcement learning. The framework is based on the idea of latent variable models that represent high-dimensional observations using low-dimensional latent variables. The resulting policy consists of two levels of hierarchy: (i) a planning module that reasons a sequence of latent intentions that would lead to an optimistic future and (ii) a feedback control policy, shared across the tasks, that executes the inferred intention. Because the planning is performed in low-dimensional latent space, the learned policy can immediately be used to solve or adapt to new tasks without additional training. We demonstrate the proposed framework can learn compact representations (3- and 1-dimensional latent states and commands for a humanoid with 197- and 36-dimensional state features and actions) while solving a small number of imitation tasks, and the resulting policy is directly applicable to other types of tasks, i.e., navigation in cluttered environments.
Image Manipulation Detection Through Laterally Linked Pixels and Kernel Algorithms

In this paper, copy-move forgery in image is detected for single image with multiple manipulations such as blurring, noise addition, gray scale conversion, brightness modifications, rotation, Hu adjustment, color adjustment, contrast changes and JPEG Compression. However, traditional algorithms detect only copy-move attacks in image and never for different manipulation in single image. The proposed LLP (Laterally linked pixel) algorithm has two dimensional arrays and single layer is obtained through unit linking pulsed neural network for detection of copied region and kernel tricks is applied for detection of multiple manipulations in single forged image. LLP algorithm consists of two channels such as feeding component (F-Channel) and linking component (L channel) for linking pixels. LLP algorithm linking pixels detects image with multiple manipulation and copy-move forgery due to one-to-one correspondence between pixel and neuron, where each pixel’s intensity is taken as input for F channel of neuron and connected for forgery identification. Furthermore, neuron is connected with neighboring field of neuron by L channel for detecting forged images with multiple manipulations in the image along with copy-move, through kernel trick classifier (KTC). From experimental results, proposed LLP algorithm performs better than traditional algorithms for multiple manipulated copy and paste images. The accuracy obtained through LLP algorithm is about 90% and further forgery detection is improved based on optimized kernel selections in classification algorithm.
Server-Side Computation of Package Dependencies in Package-Management Systems

Package managers are often used in recent software development to obtain directly-dependent packages recursively. Typically, package managers make requests to the package registry more than once when computing indirect dependencies. Moreover, much amount of computations are duplicated by clients of package managers. This duplication can be avoided by computing indirect dependencies in advance on the server-side of package-management systems. Therefore, we propose two algorithms functioning in parallel on the server-side: one to compute the indirect dependencies when copying all packages in existing package managers to the server and one to add packages to the server. Based on these parallelized algorithms, we implement a server fpms-server and a client fpms for npm packages. By our experiments, our client obtains dependencies of some package more than two times faster than clients in existing npm and yarn systems.
Judging Emotion from EEGs Using Pseudo Data

For a robot to converse naturally with a human, it must be able to accurately gauge the emotional state of the person. Techniques for estimating the emotions of a person from facial expressions, intonation and speech content have been proposed. This chapter presents a technique for judging the emotion of a person from EEGs by SVM using pseudo extended data. The accuracy of emotion judgment from EEG features using at random pseudo extended data was 25.0% and using proposed extension technique was 30.0%. However, performance accuracy remains low, and continued development is required through further development of methods for both reducing noise mixed with EEGs.
A Parametric Approach to Detect Isomorphism and Inversion in the Planar Kinematic Chains

This work deals with the problem of detection of isomorphism and inversions among planar kinematic chains. To detect isomorphism and inversions a least distance matrix (LDM) is formed using the values on the basis of the parameter. Isomorphism is detected on the basis of chain string (CS) of least distance matrix (LDM). An inversion is generated by grounding a different links in the kinematic chain. Here, link string (LS) is generated for the different kinematic inversions of the chains so that we can detect kinematic inversions with the help of link string (LS).