Reviewerly/exHarmony huggingface dataset:
GPS2Vec

GPS coordinates are fine-grained location indicators that are difficult to be effectively utilized by classifiers in geo-aware applications. Previous GPS embedding methods are mostly tailored for specific problems that are taken place within areas of interest. When it comes to the scale of the entire planet, existing approaches always suffer from extensive computational cost and significant information loss. To solve these issues, we present a novel two-level grid based framework to learn semantic embeddings for geo-coordinates worldwide. The Earth's surface is first discretized by the Universal Transverse Mercator (UTM) coordinate system. Each UTM zone is next processed as a local area of interest that is further divided into fine-grained cells to perform the initial GPS encoding. We train a neural network in each UTM zone to learn the semantic embeddings from the initial GPS encoding. The training labels can be automatically derived from large-scale geotagged documents such as tweets, check-ins, and images that are available from social sharing platforms. We evaluate the effectiveness of our proposed GPS embeddings in geotagged image classification. Improved classification results have been obtained based on a simple early feature fusion technique.
The Implementation Of Viewboard Of The Head Of Department As A Media For Student Information Is Worth Doing Final Research

Submission of information is a very important thing that is owned by a system. The system that is made very well will be able to convey good and clear information to its users so that it can communicate well. The more rapid technology has been used by several educational institutions, especially universities, to support the existing learning system. For students who have fulfilled the requirements for conducting final research, it is appropriate to declare to carry out the final research. But the head of the department is still required to come to campus to declare that the student is eligible or not to do the final research. Therefore we need a system that can provide convenience for the Head of Department to declare that students are eligible to do final research that can be accessed online so that it can be accessed anywhere and anytime by the Head of Department and does not need to come to campus because the system still uses local networks. In order to provide convenience and effectiveness for the Head of Department to check and provide eligibility for students eligible for final research which then leads to the maximum service to students. In conducting research research methods used in this study namely observation, interviews and literature study In the process of developing the system the writer uses 4 stages, namely problem identification, planning, designing prototypes and reviewing prototypes. Evidenced by the viewboard of the head of the department of service to students, it was very well fulfilled.
GEM: Generative Enhanced Model for adversarial attacks

We present our Generative Enhanced Model (GEM) that we used to create samples awarded the first prize on the FEVER 2.0 Breakers Task.GEM is the extended language model developed upon GPT-2 architecture.The addition of novel target vocabulary input to the already existing context input enabled controlled text generation.The training procedure resulted in creating a model that inherited the knowledge of pretrained GPT-2, and therefore was ready to generate natural-like English sentences in the task domain with some additional control.As a result, GEM generated malicious claims that mixed facts from various articles, so it became difficult to classify their truthfulness.
Secure Key Distribution System Based on Optical Channel Physical Features

This paper proposes a novel key distribution system based on the measurement of the bit error rate (BER) in a fiber channel. By carrying out the loopback BER measurement at both the transmitting and the receiving end, the BER is quantified and codified to generate consistency keys. Alice and Bob respectively encrypt the signal with the help of the public key base at the transmitting end. At the receiving end the data are decrypted and then the BER is measured. The security of generated keys in the system is enhanced by the use of the encryption base and the randomness of the channel. Additionally, noise sources are added to the channel so that the random noise conceals signals and Eve cannot eavesdrop the useful information in it. Due to the randomness of the channel noise, the generated keys have good randomness. This system at a high key generation rate is compatible with the existing communication device and its measurement is simple. The 10G bit/s-200 km coherent optical communication system is used to measure the BER of the security feature information of the extracted channel. The experimental results show that the key distribution system obtains a consistency rate of 98% with the key generation rate 2 Mbps, both of which have been significantly improved.
Deep Bidirectional Transformers for Relation Extraction without Supervision

We present a novel framework to deal with relation extraction tasks in cases where there is complete lack of supervision, either in the form of gold annotations, or relations from a knowledge base.Our approach leverages syntactic parsing and pre-trained word embeddings to extract few but precise relations, which are then used to annotate a larger corpus, in a manner identical to distant supervision.The resulting data set is employed to fine tune a pre-trained BERT model in order to perform relation extraction.Empirical evaluation on four data sets from the biomedical domain shows that our method significantly outperforms two simple baselines for unsupervised relation extraction and, even if not using any supervision at all, achieves slightly worse results than the state-of-the-art in three out of four data sets.Importantly, we show that it is possible to successfully fine tune a large pretrained language model with noisy data, as opposed to previous works that rely on gold data for fine tuning.
Deep Blind Image Inpainting

Image inpainting is a challenging problem as it needs to fill the information of the corrupted regions. Most of the existing inpainting algorithms assume that the positions of the corrupted regions are known. Different from existing methods that usually make some assumptions on the corrupted regions, we present an efficient blind image inpainting algorithm to directly restore a clear image from a corrupted input. Our algorithm is motivated by the residual learning algorithm which aims to learn the missing information in corrupted regions. However, directly using existing residual learning algorithms in image restoration does not well solve this problem as little information is available in the corrupted regions. To solve this problem, we introduce an encoder and decoder architecture to capture more useful information and develop a robust loss function to deal with outliers. Our algorithm can predict the missing information in the corrupted regions, thus facilitating the clear image restoration. Both qualitative and quantitative experimental demonstrate that our algorithm can deal with the corrupted regions of arbitrary shapes and performs favorably against state-of-the-art methods.
Blockchain archival discourse: Trust and the imaginaries of digital preservation

From its origins in virtual financial transactions, emerging initiatives are seeking to acquire a new identity for blockchain as capable of addressing anxieties over the capacity of digital media to permanently and accurately store information. In this article, we explore the ensuing mediation between blockchain enterprises and new professional communities to which they are catering. Drawing on thematic analysis, we analyze how this process is being carried out through the discursive construction of trust, leveraged rhetorically in academic, trade, and news publications to extend an application for financial transactions to cultural institutions. We describe how trust is used not only to mediate the introduction of an application that prioritizes decentralization and cryptography, but is the turf on which traditional institutions are staking a claim as the trustworthy managers of digital records through their use of blockchain. The concept of the archival imaginary—a vision of what archives and blockchain should be and mean that pivots on imagined needs and technological capacities based on the current information ecology, institutional control, and expert systems—offers a way to illuminate this process.
A Wearable Textile 3D Gesture Recognition Sensor Based on Screen-Printing Technology

Research has developed various solutions in order for computers to recognize hand gestures in the context of human machine interface (HMI). The design of a successful hand gesture recognition system must address functionality and usability. The gesture recognition market has evolved from touchpads to touchless sensors, which do not need direct contact. Their application in textiles ranges from the field of medical environments to smart home applications and the automotive industry. In this paper, a textile capacitive touchless sensor has been developed by using screen-printing technology. Two different designs were developed to obtain the best configuration, obtaining good results in both cases. Finally, as a real application, a complete solution of the sensor with wireless communications is presented to be used as an interface for a mobile phone.
Practical issues of twin-field quantum key distribution

Abstract Twin-field quantum key distribution(TF-QKD) protocol and its variants, such as phase-matching QKD, sending-or-not-sending QKD and no phase post-selection TF-QKD(NPP-TFQKD), are very promising for long-distance applications. However, there are still some gaps between theory and practice in these protocols. Concretely, a finite-key size analysis is still missing, and the intensity fluctuations are not taken into account. To address the finite-key size effect, we first give the key rate of NPP-TFQKD against collective attack in finite-key size region and then prove it can be against coherent attack. To deal with the intensity fluctuations, we present an analytical formula of 4-intensity decoy state NPP-TFQKD and a practical intensity fluctuation model. Finally, through detailed simulations, we show NPP-TFQKD can still keep its superiority of high key rate and long achievable distance.
FALF ConvNets: Fatuous auxiliary loss based filter-pruning for efficient deep CNNs

Obtaining efficient Convolutional Neural Networks (CNNs) are imperative to enable their application for a wide variety of tasks (classification, detection, etc.). While several methods have been proposed to solve this problem, we propose a novel strategy for solving the same that is orthogonal to the strategies proposed so far. We hypothesize that if we add a fatuous auxiliary task, to a network which aims to solve a semantic task such as classification or detection, the filters devoted to solving this frivolous task would not be relevant for solving the main task of concern. These filters could be pruned and pruning these would not reduce the performance on the original task. We demonstrate that this strategy is not only successful, it in fact allows for improved performance for a variety of tasks such as object classification, detection and action recognition. An interesting observation is that the task needs to be fatuous so that any semantically meaningful filters would not be relevant for solving this task. We thoroughly evaluate our proposed approach on different architectures (LeNet, VGG-16, ResNet, Faster RCNN, SSD-512, C3D, and MobileNet V2) and datasets (MNIST, CIFAR, ImageNet, GTSDB, COCO, and UCF101) and demonstrate its generalizability through extensive experiments. Moreover, our compressed models can be used at run-time without requiring any special libraries or hardware. Our model compression method reduces the number of FLOPS by an impressive factor of 6.03X and GPU memory footprint by more than 17X for VGG-16, significantly outperforming other state-of-the-art filter pruning methods. We demonstrate the usability of our approach for 3D convolutions and various vision tasks such as object classification, object detection, and action recognition.
A Novel Multifeature Based On-Site Calibration Method for LiDAR-IMU System

Calibration is an essential prerequisite for the combined application of light detection and ranging (LiDAR) and inertial measurement unit (IMU). However, current LiDAR-IMU calibration usually relies on particular artificial targets or facilities and the intensive labor greatly limits the calibration flexibility. For these reasons, this article presents a novel multifeature based on-site calibration method for LiDAR-IMU system without any artificial targets or specific facilities. This new on-site calibration combines the point/sphere, line/cylinder, and plane features from LiDAR scanned data to reduce the labor intensity. The main contribution is that a new method is developed for LiDAR extrinsic parameters on-site calibration and this method could incorporate two or more calibration models to generate more accurate calibration results. First of all, the calibration of LiDAR extrinsic parameters is performed through estimating the geometric features and solving the multifeature geometric constrained optimization problem. Then, the relationships between LiDAR and IMU intrinsic calibration parameters are determined by the coordinate transformation. Lastly, the full information maximum likelihood estimation (FIMLE) method is applied to solve the optimization of the IMU intrinsic parameters calibration. A series of experiments are conducted to evaluate the proposed method. The analysis results demonstrate that the proposed on-site calibration method can improve the performance of the LiDAR-IMU.
Fully Convolutional CaptionNet: Siamese Difference Captioning Attention Model

The generation of the textual description of the differences in images is a relatively new concept that requires the fusion of both computer vision and natural language techniques. In this paper, we present a novel Fully Convolutional CaptionNet (FCC) that employs an encoder-decoder framework to perform visual feature extractions, compute the feature distances, and generate new sentences describing the measured distances. After extracting the features of the images, a contrastive function is used to compute their weighted L1 distance which is learned and selectively attended to determine salient sections of the feature at every time step. The attended feature region is adequately matched to corresponding words iteratively until a sentence is completed. We propose the application of upsampling network to enlarge the features' field of view, this provides a robust pixel-based discrepancy computation. Our extensive experiments indicate that the FCC model outperforms other learning models on the benchmark Spot-the-Diff datasets by generating succinct and meaningful textual differences in images.
How to use kernel density estimation as a diagnostic and forecasting tool for distributed volcanic vents

Volcanic activity often results in the formation of new volcanic vents.These new vents can create hazards in unexpected areas.Therefore, the probability of new vent formation should be assessed as part of volcanic hazard assessments.This paper describes our use of kernel density estimation (KDE) as a way to estimate the spatial density of future volcanic vents.The bivariate Gaussian kernel function is described step-by-step using pseudocode.Our computer code, written in PERL, is used to calculate the spatial density of existing vents and then create a contour map using GMT (Generic Mapping Tools).Application of this method and code relies on several assumptions about the definition of volcanic events, independence of events, the type of kernel function used, and the selection of kernel bandwidth.Three examples using the code are provided: (1) for volcanic vents located west of the city of Managua (Nicaragua), (2) for volcanic vents distributed within the Arsia Mons caldera (Mars), weighted by volume, and (3) for vents of the Lassen volcanic system (northern California), sub-divided by geochemistry.
Multimedia Teaching Quality Evaluation System in Colleges Based on Genetic Algorithm and Social Computing Approach

To improve the quality of classroom teaching and understand the effectiveness of teaching in a timely manner, the multimedia teaching quality evaluation system of colleges and universities was established. Combined with genetic algorithm, through the investigation, the evaluation process of multimedia teaching in colleges and the status quo of multimedia teaching were analyzed. Many teachers were interviewed. A set of evaluation indicators suitable for college multimedia teaching quality was discussed, and the indicators were visually displayed with data. From the aspects of multimedia courseware, multimedia classroom teaching process and multimedia teaching effect, teachers' basic information, students' basic information and evaluation relationship management are analyzed. The teaching quality evaluation system of college multimedia was designed and the results were tested. The research results showed that the evaluation of teaching quality was realized through the teaching evaluation system. Therefore, the system is of great significance to improve the effectiveness of classroom teaching.
Robust anomaly detection and backdoor attack detection via differential privacy

Outlier detection and novelty detection are two important topics for anomaly detection. Suppose the majority of a dataset are drawn from a certain distribution, outlier detection and novelty detection both aim to detect data samples that do not fit the distribution. Outliers refer to data samples within this dataset, while novelties refer to new samples. In the meantime, backdoor poisoning attacks for machine learning models are achieved through injecting poisoning samples into the training dataset, which could be regarded as “outliers” that are intentionally added by attackers. Differential privacy has been proposed to avoid leaking any individual’s information, when aggregated analysis is performed on a given dataset. It is typically achieved by adding random noise, either directly to the input dataset, or to intermediate results of the aggregation mechanism. In this paper, we demonstrate that applying differential privacy could improve the utility of outlier detection and novelty detection, with an extension to detect poisoning samples in backdoor attacks. We first present a theoretical analysis on how differential privacy helps with the detection, and then conduct extensive experiments to validate the effectiveness of differential privacy in improving outlier detection, novelty detection, and backdoor attack detection.
<i>TabSINT</i>: open-source mobile software for distributed studies of hearing

Objective: The recent emphasis on outcomes-based medical research has motivated a need for technology that allows researchers and clinicians to reach a larger and more diverse subject population for recruitment and testing.Design: This article reports on open-source mobile software (TabSINT) that enables researchers to administer customised hearing tests and questionnaires on tablets located across multiple sites. Researchers create and modify test protocols using text-based templates and deploy it to the tablets via a cloud-based repository or USB-computer connection. Results are exported locally to the tablet SD card and can also be automatically posted to a cloud-based database.Results: Between 2014 and 2019, TabSINT collected 25,000+ test results using more than 200+ unique test protocols for researchers located worldwide.Conclusions: TabSINT is a powerful software system with the potential to greatly enhance research across multiple disciplines by enabling access to subject cohorts in remote and disparate locations. Released open-source, this software is available to researchers across the world to use and adapt to their specific needs. Researchers with engineering resources can contribute to the repository to extend the capability and robustness of this software.
DeGAN: Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier

In this era of digital information explosion, an abundance of data from numerous modalities is being generated as well as archived everyday. However, most problems associated with training Deep Neural Networks still revolve around lack of data that is rich enough for a given task. Data is required not only for training an initial model, but also for future learning tasks such as Model Compression and Incremental Learning. A diverse dataset may be used for training an initial model, but it may not be feasible to store it throughout the product life cycle due to data privacy issues or memory constraints. We propose to bridge the gap between the abundance of available data and lack of relevant data, for the future learning tasks of a given trained network. We use the available data, that may be an imbalanced subset of the original training dataset, or a related domain dataset, to retrieve representative samples from a trained classifier, using a novel Data-enriching GAN (DeGAN) framework. We demonstrate that data from a related domain can be leveraged to achieve state-of-the-art performance for the tasks of Data-free Knowledge Distillation and Incremental Learning on benchmark datasets. We further demonstrate that our proposed framework can enrich any data, even from unrelated domains, to make it more useful for the future learning tasks of a given network.
A Spherical Convolution Approach for Learning Long Term Viewport Prediction in 360 Immersive Video

Viewport prediction for 360 video forecasts a viewer’s viewport when he/she watches a 360 video with a head-mounted display, which benefits many VR/AR applications such as 360 video streaming and mobile cloud VR. Existing studies based on planar convolutional neural network (CNN) suffer from the image distortion and split caused by the sphere-to-plane projection. In this paper, we start by proposing a spherical convolution based feature extraction network to distill spatial-temporal 360 information. We provide a solution for training such a network without a dedicated 360 image or video classification dataset. We differ with previous methods, which base their predictions on image pixel-level information, and propose a semantic content and preference based viewport prediction scheme. In this paper, we adopt a recurrent neural network (RNN) network to extract a user's personal preference of 360 video content from minutes of embedded viewing histories. We utilize this semantic preference as spatial attention to help network find the "interested'' regions on a future video. We further design a tailored mixture density network (MDN) based viewport prediction scheme, including viewport modeling, tailored loss function, etc, to improve efficiency and accuracy. Our extensive experiments demonstrate the rationality and performance of our method, which outperforms state-of-the-art methods, especially in long-term prediction.