Reviewerly/exHarmony huggingface dataset:
Fine-Grained Image-Text Retrieval via Discriminative Latent Space Learning

Fine-grained image-text retrieval aims at searching relevant images among fine-grained classes given a text query or in a reverse way. The challenges are not only bridging the gap between two heterogeneous modalities but also dealing with large inter-class similarity and intra-class variance existed in fine-grained data. To deal with the above challenges, we propose a Discriminative Latent Space Learning (DLSL) method for fine-grained image-text retrieval. Concretely, image and text features are extracted for capturing the subtle difference in fine-grained data. Subsequently, based on the extracted features, we perform couple dictionary learning to align the heterogeneous data in a uniform latent space. To make such alignment discriminative enough for the fine-grained task, the learned latent space is endowed with discriminative property via learning a discriminative map. Comprehensive experiments on fine-grained datasets demonstrate the effectiveness of our approach.
CNN as a feature extractor in gaze recognition

In this paper, we employ a Convolutional Neural Network (CNN) in predicting physician gaze. This paper focuses on two aspects – one comparison between hand-crafted features and CNN-based learned features, and two in investigating the impact of fully-connected layers in an end-to-end CNN model. The pre-trained CNN model based on VGG16 through transfer learning is used as a feature extractor and a K-Nearest Neighbor and a Random Forest (RF) algorithm were used as the classifier of physician gaze. The CNN-RF and CNN–K-NN models were compared with the traditional end-to-end CNN model and through a series of experiments and statistical tests of significance, we show that the power of CNN comes from the features extraction part and that the fully connected layers of the CNN have comparable performance to the random forest and the k-NN classifiers. We also show that the CNN-based learned features provide substantial distinguishable power in classifying physician gaze.
Study development of digital teaching materials support online learning during Covid-19

Abstract The implementation of education activity policy conducted at home by applying work from home (WFH) demand all of the each education institutions to perform the online learning by using any platforms provided in internet web. For college education, it is hoped to develop the information system of online learning based on learning management system. This research is intended to find out the information about the college students responds related to the online learning activity conducted in the Study Program of Language and Literature of Bahasa Indonesia Unimed since March 2020 until June 2020. The data obtained by answering the questionnaire submitted to the college students randomly. The results showed that 81.6% was in the very positive category related to the college students’ responses to the implementation of online learning. Besides that, there are some obstacles in the implementation of online learning as well, they are a small proportion of instructors and the college students who have not mastered the use of IT in the online learning process. The college students highly hope that the instructors can transform the teaching materials into digital form based on the applications so that it makes easier for them to understand the materials and can save any costs as complained by the college students when the online learning is carried out.
Constructionist Learning Tool for Acquiring Skills in Understanding Standardised Engineering Drawings of Mechanical Assemblies in Mobile Devices

The purpose of Graphic Design is to transfer information about design into reality and concerns the analysis, design and representation of mechanical components and assemblies. For the correct rendering of mechanical components, this discipline requires a command of, and the ability to, manage techniques and systems for graphical representation and standardisation; 3D models in a virtual environment enable engineering students to develop graphical skills and spatial awareness. The objective of the present study is the development of an application for smart devices (mobile phones and tablets), based on the constructionist theory of learning, which will enable first year engineering degree students to acquire the technical drawing knowledge and skills necessary to render mechanical assemblies. The mobile application tested and designed in this work is called ARPAID. It is a learning tool aimed at teaching students about the representation of mechanical assemblies as part of an engineering Graphic Design course. Teaching material and a process for evaluation have been designed. A detailed description is given of a classroom activity accompanied by a tabulation and analysis of the results obtained. This mobile application, when used in a Graphic Design course, promotes a more rapid understanding of spatial relationships and problems, fosters students’ learning and motivation, and develops higher order skills. Results from before and after the use of the application will be presented and do indeed show significant improvements in student performance.
Classification of Prostate Transitional Zone Cancer and Hyperplasia Using Deep Transfer Learning From Disease-Related Images

PurposeThe diagnosis of prostate transition zone cancer (PTZC) remains a clinical challenge due to their similarity to benign prostatic hyperplasia (BPH) on MRI.The Deep Convolutional Neural Networks (DCNNs) showed high efficacy in diagnosing PTZC on medical imaging but was limited by the small data size.A transfer learning (TL) method was combined with deep learning to overcome this challenge. Materials and methodsA retrospective investigation was conducted on 217 patients enrolled from our hospital database (208 patients) and The Cancer Imaging Archive (nine patients).Using T2-weighted images (T2WIs) and apparent diffusion coefficient (ADC) maps, DCNN models were trained and compared between different TL databases (ImageNet vs. disease-related images) and protocols (from scratch, fine-tuning, or transductive transferring). ResultsPTZC and BPH can be classified through traditional DCNN.The efficacy of TL from natural images was limited but improved by transferring knowledge from the disease-related images.Furthermore, transductive TL from disease-related images had comparable efficacy to the fine-tuning method.Limitations include retrospective design and a relatively small sample size. ConclusionDeep TL from disease-related images is a powerful tool for an automated PTZC diagnostic system.In developing regions where only conventional MR scans are available, the accurate diagnosis of PTZC can be achieved via transductive deep TL from disease-related images.
Automated analysis of microscopy images using deep convolutional neural networks

The general cell quantification and identification have technical limitations concerning the fast and accurate detection of complex morphological cells, especially for overlapping cells, irregular cell shapes, bad focal planes, among other factors. We use the deep convolutional neural networks (DCNN) to classify the annotated images of five types of white blood cells. The accuracy and performance of the proposed framework are evaluated for the blood cell classifications. The results demonstrate that the DCNN model performs close to the accuracy of 80% and provides an accurate and fast method for hematological laboratories.
Pre-Training Acquisition Functions by Deep Reinforcement Learning for Fixed Budget Active Learning

Abstract There are many situations in supervised learning where the acquisition of data is very expensive and sometimes determined by a user’s budget. One way to address this limitation is active learning. In this study, we focus on a fixed budget regime and propose a novel active learning algorithm for the pool-based active learning problem. The proposed method performs active learning with a pre-trained acquisition function so that the maximum performance can be achieved when the number of data that can be acquired is fixed. To implement this active learning algorithm, the proposed method uses reinforcement learning based on deep neural networks as as a pre-trained acquisition function tailored for the fixed budget situation. By using the pre-trained deep Q-learning-based acquisition function, we can realize the active learner which selects a sample for annotation from the pool of unlabeled samples taking the fixed-budget situation into account. The proposed method is experimentally shown to be comparable with or superior to existing active learning methods, suggesting the effectiveness of the proposed approach for the fixed-budget active learning.
Hybrid spatio-frequency domain global thresholding filter (HSFGTF) model for SAR image enhancement

Synthetic Aperture Radar (SAR) deploys single antenna for both transmitting and receiving radar signals since it comes under mono static category. Major research work based on airborne and orbital focused on moving vehicle capturing, base penetration, greenery or vegetation penetration, methodologies to improve the quality of the captured images etc. In general, synthetic aperture radar imaging provides high level resolution images of vast regions under coverage. The pixel intensity of images in synthetic aperture radar is measured based on roughness, dimensional orientation and dielectric constant of plane captured. Processing of SAR images is mapping of signal data received by the radar in to a spatial image. Synthetic Aperture Radar images are easily vulnerable to speckle noises. These kinds of noises disrupt the image elucidation. This research article proposed the hybrid spatio-frequency domain global thresholding filter based pre-processing for SAR image enhancement for removal of additive as well as speckle noise which is more characteristic of any SAR images. The proposed image enhancement algorithm has been experimented under different noise variances and the results obtained are compared with other traditional methodologies.
Balancing memory-accessing and computing over sparse DNN accelerator via efficient data packaging

Embedded devices are common carriers for deploying inference networks, which leverage the customized accelerator to achieve the promised performance with strict resource constraints. In the inference of Deep Neural Network(DNN), the sparsity existing in the activations and weights of every layer contributes massive non-effective memory accesses and computing operations. The data compression is adopted as a data pruning method for accelerator design, which eliminates the zero-valued data with a specific data packaging method. However, the data compression, in varying degrees, breaks the data regularity of the processing array DNN accelerators calculates with. The complexity of data access caused by irregular data organization will add extra control logic and decoding logic to compensate. The accelerator architecture that supports sparsity can use the sophisticated memory access scheming and parallel on-chip decoder structure via an efficient data packaging method to balance memory-accessing and computing for acceleration. In this paper, we propose a flexible and highly parallel accelerator architecture that uses a quantitative data packaging method which is efficient and stable for different degrees of sparsity and parallel optimization to explore the sparsity in DNN to achieve high performance with low energy consumption. The total DRAM accesses, performance and energy consumption of the proposed sparse architecture are evaluated with different inference networks. Experiments show that the DRAM accesses of the proposed efficient data packaging method is significantly lower than other commonly used sparse data compression storage methods, the improved performance and saved energy of the sparse accelerator architecture after adopting the optimization method proposed in this paper are up to 1.2x and 1.6x, respectively, over a comparably provisioned do not support sparsity accelerator. In addition, the accelerator architecture proposed has achieved energy efficiency and performance improvements of up to 1.70x and 1.56x, compared with the state-of-the-art architectures.
A Feature Selection Algorithm Performance Metric for Comparative Analysis

This study presents a novel performance metric for feature selection algorithms that is unbiased and can be used for comparative analysis across feature selection problems. The baseline fitness improvement (BFI) measure quantifies the potential value gained by applying feature selection. The BFI measure can be used to compare the performance of feature selection algorithms across datasets by measuring the change in classifier performance as a result of feature selection, with respect to the baseline where all features are included. Empirical results are presented to show that there is performance complementarity for a suite of feature selection algorithms on a variety of real world datasets. The BFI measure is a normalised performance metric that can be used to correlate problem characteristics with feature selection algorithm performance, across multiple datasets. This ability paves the way towards describing the performance space of the per-instance algorithm selection problem for feature selection algorithms.
Inductive Gaussian representation of user-specific information for personalized stress-level prediction

The accurate prediction of stress in a person’s life has a significant effect on improving personal health and the national economy. Since individuals have different historical circumstances and personality traits, stress symptoms and levels may vary from person to person. Thus, most studies on stress prediction pay attention to personalized models, which determine the personal stress level using user-specific information and heterogeneous stress-related data. However, these models cannot elaborately handle the uncertainty caused by the sparsity, data imbalance, irregularity, and high-dimensionality of user-specific information. In particular, out-of-sample users increase uncertainty. To cope with the problem, we propose a personalized stress-level prediction model with inductive Gaussian representation (PSP-IGR), which exploits heterogeneous inputs with a unified end-to-end approach. PSP-IGR extracts feature vectors from the heterogeneous inputs via Gaussian sampling, domain rules, and deep learning, depending on the characteristics of each input. Especially, PSP-IGR inductively generates a Gaussian feature vector called IGR by Gaussian sampling from the shared contents of user-specific information. Thus, PSP-IGR not only generalizes to both in-sample and out-of-sample users effectively but also deals with the uncertainty problem caused by limitations of healthcare datasets. Also, since we fuse the extracted feature vectors considering their characteristics (Gaussian and point vectors), we can preserve the expressiveness of each feature vector. Experiments on a real-world dataset, including survey results, wearable sensor signals, and contexts, demonstrate that PSP-IGR shows higher accuracy in predicting individual stress-level than previous models.
Entangled resource for interfacing single- and dual-rail optical qubits

Today&amp;apos;s most widely used method of encoding quantum information in optical qubits is the dual-rail basis, often carried out through the polarisation of a single photon. On the other hand, many stationary carriers of quantum information – such as atoms – couple to light via the single-rail encoding in which the qubit is encoded in the number of photons. As such, interconversion between the two encodings is paramount in order to achieve cohesive quantum networks. In this paper, we demonstrate this by generating an entangled resource between the two encodings and using it to teleport a dual-rail qubit onto its single-rail counterpart. This work completes the set of tools necessary for the interconversion between the three primary encodings of the qubit in the optical field: single-rail, dual-rail and continuous-variable.
Navigational Control and Path Optimization of Mobile Robot Using Updated Sine–Cosine Algorithm in Obscure Environment

The metaheuristic optimization technique, updated sine–cosine algorithm (USCA), is basically based on the mathematical explanation of sine and cosine functions. It is applied on navigational control of mobile robot to optimize the path. The application of USCA is validated with V-Rep simulation and laboratory experiments with Khepera-III robot in an unknown environment. Robot effectively achieved the specified target without any collision. The comparative result tables show a difference in each run of robot under 5% which is considerable range of deviation in results.
Transfer Deep Learning for Reconfigurable Snapshot HDR Imaging Using Coded Masks

Abstract High dynamic range (HDR) image acquisition from a single image capture, also known as snapshot HDR imaging, is challenging because the bit depths of camera sensors are far from sufficient to cover the full dynamic range of the scene. Existing HDR techniques focus either on algorithmic reconstruction or hardware modification to extend the dynamic range. In this paper we propose a joint design for snapshot HDR imaging by devising a spatially varying modulation mask in the hardware and building a deep learning algorithm to reconstruct the HDR image. We leverage transfer learning to overcome the lack of sufficiently large HDR datasets available. We show how transferring from a different large‐scale task (image classification on ImageNet) leads to considerable improvements in HDR reconstruction. We achieve a reconfigurable HDR camera design that does not require custom sensors, and instead can be reconfigured between HDR and conventional mode with very simple calibration steps. We demonstrate that the proposed hardware–software so lution offers a flexible yet robust way to modulate per‐pixel exposures, and the network requires little knowledge of the hardware to faithfully reconstruct the HDR image. Comparison results show that our method outperforms the state of the art in terms of visual perception quality.
Does it make you sad? A lexicon-based sentiment analysis on COVID-19 news tweets

Abstract This research utilizes a lexicon-based sentiment analysis to reveal the emotions conveyed by various news media and analyze the differences among the media. NRC Affect Intensity Lexicon is utilized; it provides the emotions of words in 8 categories of emotions. The lexicon is modified to reflect the context of the news, i.e., COVID-19 pandemic. Tweets are collected from four Indonesia’s news media and three international English-language news media. Each tweet from a news media is assigned a total emotion score for each of the 8 emotions. The scores are averaged for each day to obtain the Daily Emotion. Based on the Daily Emotion, the Dominant Daily Emotion may be identified, i.e., the emotion with the highest average score in a particular date. The Dominant Daily Emotion visualization shows that the dominant emotions in Indonesian-language media are Sadness and Trust, while Fear and Trust are considerably more pronounced in English-language media. Furthermore, among Indonesian-language media, kompascom is significantly more intense than the others in Joy, Sadness, Fear, and Trust. Among English-language media, XHNews is more intense than others in conveying Fear, Sadness, and Trust; while XHNews and timesofindia are both the most intense in Joy. The sentiment analysis in this research concludes that the Emotion Mix of news media are significantly different. Furthermore, news media also convey significantly different levels of emotion intensity in reporting COVID-19 news.
DeepFusion: A simple way to improve traditional multi-view stereo methods using deep learning

In this paper, we propose a simple deep learning based method, “DeepFusion”, to improve the results from traditional multi-view stereo (MVS) methods. In our method, we first design a deep convolutional neural network to predict the confidences of the depths produced by the traditional MVS method, which takes the colors, depths, normals and costs as inputs. In particular, we convert the depths into the normalized inverse depths to enhance the accuracy and generality of the network. In addition, we use the plane sweeps in the disparity space to get the top-k matching costs in order to determine the distinctiveness among all costs. Then we propose a novel approach to accurately fuse all depth maps into the final point cloud, which balances the geometric consistency and the predicted confidences. Experimental results on several well-known datasets demonstrate the effectiveness and the generalization ability of the proposed method.
Automatic clustering algorithm for interval data based on overlap distance

In this study, the improved overlap distance is used as a criterion in order to build clusters for interval data. This distance has shown the suitability, and given an outstanding advantage in evaluating the similarity for intervals with a lot of the considered data sets. Based on the overlap distance, we propose the Automatic Clustering Algorithm for Interval data (ACAI). One of the best advantages of the proposed algorithm is that ACAI figure out simultaneously the appropriate number of groups, and factors in every group. The proposed algorithm can be effectively performed through a Matlab procedure. Based on the extracted intervals from texture of images, we have applied ACAI to recognize the images, an interesting and challenging issue at present. Experimental data sets including the differences of the characteristics as well as the number of elements has shown the reasonableness of the proposed algorithm, and its advantages in comparing to the surviving ones. From the image recognition problem, this research has shown prospect in practical applications for many fields.
SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems

With the wide use of Automatic Speech Recognition (ASR) in applications such as human machine interaction, simultaneous interpretation, audio transcription, and so on, its security protection becomes increasingly important. Although recent studies have brought to light the weaknesses of popular ASR systems that enable out-of-band signal attack, adversarial attack, and so on, and further proposed various remedies (signal smoothing, adversarial training, etc.), a systematic understanding of ASR security (both attacks and defenses) is still missing, especially on how realistic such threats are and how general existing protection could be. In this article, we present our systematization of knowledge for ASR security and provide a comprehensive taxonomy for existing work based on a modularized workflow. More importantly, we align the research in this domain with that on security in Image Recognition System (IRS), which has been extensively studied, using the domain knowledge in the latter to help understand where we stand in the former. Generally, both IRS and ASR are perceptual systems. Their similarities allow us to systematically study existing literature in ASR security based on the spectrum of attacks and defense solutions proposed for IRS, and pinpoint the directions of more advanced attacks and the directions potentially leading to more effective protection in ASR. In contrast, their differences, especially the complexity of ASR compared with IRS, help us learn unique challenges and opportunities in ASR security. Particularly, our experimental study shows that transfer attacks across ASR models are feasible, even in the absence of knowledge about models (even their types) and training data.