Reviewerly/exHarmony huggingface dataset:
Out of Sight, Out of Mind: Detecting Orphaned Web Pages at Internet-Scale

Security misconfigurations and neglected updates commonly lead to systems being vulnerable. Especially in the context of websites, we often find pages that were forgotten, that is, they were left online after they served their purpose and never updated thereafter. In this paper, we introduce new methodology to detect such forgotten or orphaned web pages. We combine historic data from the Internet Archive with active measurements to identify pages no longer reachable via a path from the index page, yet stay accessible through their specific URL. We show the efficacy of our approach and the real-world relevance of orphaned web-pages by applying it to a sample of 100,000 domains from the Tranco Top 1M.
Detecting Out-of-Distribution Data in Wireless Communications Applications of Deep Learning

Deep learning-based classification algorithms offer no performance guarantees when deployed on testing data not generated by the same process as the training data. Such out-of-distribution (OOD) data often cause classification errors that are hard to detect since they do not generate explicit errors in the model. In real-world applications, there is no way to ensure that the testing data and the training data are drawn from the same or sufficiently similar distributions. This problem is especially challenging in wireless communications applications. Because the radio propagation channel is highly dynamic, it is very difficult to ensure that a deep learning model is not tested on OOD data. In this paper, we propose a novel deep learning model called FOOD (Feature representation for detecting OOD data) to detect OOD data in wireless communications applications. FOOD incorporates a new model architecture to detect OOD data accurately and minimizes the instances of normal data being recognized as OOD. We evaluated the performance of FOOD extensively using transmitter classification and modulation recognition tasks, with both experimental datasets and simulation-generated datasets. As far as we know, this is the first systematic study on the impact and detection of OOD data in deep learning-based wireless communications applications.
Semantic-Oriented Labeled-to-Unlabeled Distribution Translation for Image Segmentation

Automatic medical image segmentation plays a crucial role in many medical applications, such as disease diagnosis and treatment planning. Existing deep learning based models usually regarded the segmentation task as pixel-wise classification and neglected the semantic correlations of pixels across different images, leading to vague feature distribution. Moreover, pixel-wise annotated data is rare in medical domain, and the scarce annotated data usually exhibits the biased distribution against the desired one, hindering the performance improvement under the supervised learning setting. In this paper, we propose a novel Labeled-to-unlabeled Distribution Translation (L2uDT) framework with Semantic-oriented Contrastive Learning (SoCL), mainly for addressing the aforementioned issues in medical image segmentation. In SoCL, a semantic grouping module is designed to cluster pixels into a set of semantically coherent groups, and a semantic-oriented contrastive loss is advanced to constrain group-wise prototypes, so as to explicitly learn a feature space with intra-class compactness and inter-class separability. We then establish a L2uDT strategy to approximate the desired data distribution for unbiased optimization, where we translate the labeled data distribution with the guidance of extensive unlabeled data. In particular, a bias estimator is devised to measure the distribution bias, then a gradual-paced shift is derived to progressively translate the labeled data distribution to unlabeled one. Both labeled and translated data are leveraged to optimize the segmentation model simultaneously. We illustrate the effectiveness of the proposed method on two benchmark datasets, EndoScene and PROSTATEx, and our method achieves state-of-the-art performance, which clearly demonstrates its effectiveness for medical image segmentation. The source code is available at https://github.com/CityU-AIM-Group/L2uDT.
Homeostasis phenomenon in conformal prediction and predictive distribution functions

Conformal prediction is an attractive framework for prediction that is distribution free. In this article, we study in details its homeostasis property under a general regression setup and also introduce the concepts of upper and lower predictive distributions and predictive curve to establish connections to left-, right- and two-tailed hypothesis testing problems as well as the developments in confidence distributions. The homeostasis property is very attractive, since it states that under some conditions the prediction results remain valid even if the model used for learning is completely wrong. We show explicitly why the property holds in a model-based setup and also explore the boundary when the property breaks down. Beside the typical assumption used in conformal prediction that the response and covariate pairs ( y , x ) of all subjects are iid distributed, we also study the classical regression setting in which the design is fixed with given (non-random) covariates x . The trade-offs among learning model accuracy, prediction valid and prediction efficiency are discussed, leading to an emphasis of more efforts on developing better learning models.
An Elaboration of Service Views within the UAF

Abstract Services in the Unified Architecture Framework (UAF) are not just software services. Services can include transport, surveillance, communications, providing healthcare and medical services, etc. The UAF implements DoDAF using the Systems Modeling Language (SysML) as well as the British MODAF and NATO NAF. The DoDAF Service views implement services by duplicating the systems views and labeling the systems elements as services. This causes some confusion with engineers who either implement solution‐based service views or ignore them completely. Even when implemented, they can cause confusion in the model as it becomes difficult to tell if a model element describes a service or a system implementing a service. The UAF implementation of the MODAF services views provides a distinct set of views, concepts and traceability. The Service Oriented Views do not specify how the service is to be implemented, but the requirements for the services. The Resources (Systems) Views implement services in various phases and their deployment will modify the configurations of the system at the very highest level. This paper will show how services views trace from capabilities and how that can be used to define system resource requirements.
Data InStance Prior (DISP) in Generative Adversarial Networks

Recent advances in generative adversarial networks (GANs) have shown remarkable progress in generating high-quality images. However, this gain in performance depends on the availability of a large amount of training data. In limited data regimes, training typically diverges, and therefore the generated samples are of low quality and lack diversity. Previous works have addressed training in low data setting by leveraging transfer learning and data augmentation techniques. We propose a novel transfer learning method for GANs in the limited data domain by leveraging informative data prior derived from self-supervised/supervised pre-trained networks trained on a diverse source domain. We perform experiments on several standard vision datasets using various GAN architectures (BigGAN, SNGAN, StyleGAN2) to demonstrate that the proposed method effectively transfers knowledge to domains with few target images, outperforming existing state-of-the-art techniques in terms of image quality and diversity. We also show the utility of data instance prior in large-scale unconditional image generation.
How Can the [MASK] Know? The Sources and Limitations of Knowledge in BERT

We explore the idea of using the pre-trained BERT as a source of factual knowledge, analyze which components of the model are responsible for its ability to answer questions requiring factual knowledge, and study the transferability of the knowledge to downstream tasks. Our experiments show that the Language Modeling Head is indispensable for predicting facts, implying that transferability of any knowledge captured in the model is limited. While the dominant approach to researching how knowledge is stored in language models focuses on tailoring question formulation to optimize the retrieval quality, we find question patterns easily understood by humans that confuse BERT to the point that the answer does not make sense. The nature of the found patterns implies that the stored knowledge is fragile and based on token co-occurrence in the training set used during pre-training, rather than generalization or inference. Moreover, using a novel, hand-crafted dataset we show that BERT is vulnerable to common misconceptions, which could have fatal effects on downstream applications. Overall, we conclude that BERT offers low and unreliable performance out of the box. Jupyter notebooks with experiments are available on GitHub. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Project URL: https://github.com/tenpercent/knowledge-and-confusion
α-MeanShift++: Improving MeanShift++ for Image Segmentation

MeanShift is one of the popular clustering algorithms and can be used to partition a digital image into semantically meaningful regions in an unsupervised manner.However, due to its prohibitively high computational complexity, a grid-based approach, called MeanShift++, has recently been proposed and succeeded to surprisingly reduce the computational complexity of MeanShift.Nevertheless, we found that MeanShift++ still has computational redundancy and there is room for improvement in terms of accuracy and runtime; thus, we propose an improvement to MeanShift++, named α-MeanShift++.We first attempt to minimize the computational redundancy by using an additional hash table.Then, we introduce a speedup factor (α) to reduce the number of iterations required until convergence, and we use more neighboring grid cells for the same bandwidth to improve accuracy.Through intensive experiments on image segmentation benchmark datasets, we demonstrate that α-MeanShift++ can run 4.1-4.6xfaster on average (but up to 7x) than MeanShift++ and achieve better image segmentation quality.
Lattice-Based Secret Handshakes with Reusable Credentials

Secret handshake, as a fundamental privacy-preserving primitive, allows members in the same organization to anonymously authenticate each other. Since its proposal in 2003, numerous schemes have been presented in terms of various security, efficiency, and functionality. Unfortunately, all of the contemporary designs are based on number theoretic assumptions and will be fragile in the setting of quantum computations. In this paper, we fill this gap by presenting the first lattice-based secret handshake scheme with reusable credentials. More precisely, we utilize the verifier-local revocation techniques for member secession, such that users’ credentials support reusability rather than one-time usage. To build an interactive authentication protocol, we subtly modify a Stern-type zero-knowledge argument by use of a key exchange protocol, which enables users to negotiate a session key for further communication. The security of our scheme relies on the Short Integer Solution (\(\mathsf {SIS}\)) and Learning With Errors (\(\mathsf {LWE}\)) assumptions.
An archaeology of digital things: social, political, polemical

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Location Privacy Protection via Delocalization in 5G Mobile Edge Computing Environment

In this paper, we propose LBS@E, a new architecture for location-based services (LBSs) facilitated by the mobile edge computing paradigm. LBS@E tackles the location privacy problem innovatively by delocalizing LBSs so that mobile users of LBSs implemented based on LBS@E do not have to reveal their locations. They retrieve local information from nearby edge servers around them instead of the cloud. In this way, we resolve the root cause of the conventional location privacy problem. However, LBS@E raises new challenges to location privacy. A mobile user can still be localized to a particular privacy area co-covered by the edge servers accessed by the mobile user. A small privacy area puts the mobile users location at the risk of being approximated. In the meantime, the size of the utility area, which determines the amount of local information retrievable for the mobile user, is positively correlated with the number of edge servers accessed by the mobile user. We model this problem as a constrained optimization problem and propose an optimal approach for solving it based on integer programming. Extensive experiments are conducted on a widely-used real-world dataset to demonstrate effectiveness and efficiency.
Open Access and Data Sharing of Nucleotide Sequence Data

Open access, free access, and the public domain are different concepts. The International Nucleotide Sequence Database Collaboration (INSDC) permanently guarantees free and unrestricted access to nucleotide sequence data for all researchers, irrespective of nationality or affiliation. However, recent virus information is primarily distributed via the restricted-access repository known as the Global Initiative on Sharing Avian Flu Data (GISAID) supported by the World Health Organization. As compensation for the restriction, GISAID needs to meet its initial goal of benefit-sharing among countries and to curb ongoing vaccine diplomacy campaigns.
Generating Math Word Problems from Equations with Topic Consistency Maintaining and Commonsense Enforcement

Data-to-text generation task aims at generating text from structured data. In this work, we focus on a relatively new and challenging equation-to-text generation task – generating math word problems from equations and propose a novel equation-to-problem text generation model. Our model first utilizes a template-aware equation encoder and a Variational AutoEncoder (VAE) model to bridge the gap between abstract math tokens and text. We then introduce a topic selector and a topic controller to prevent topic drifting problems. To avoid the commonsense violation issues, we design a pre-training stage together with a commonsense enforcement mechanism. We construct a dataset to evaluate our model through both automatic metrics and human evaluation. Experiments show that our model significantly outperforms baseline models. Further analysis shows our model is effective in tackling topic drifting and commonsense violation problems.
ConvFiT: Conversational Fine-Tuning of Pretrained Language Models

Transformer-based language models (LMs) pretrained on large text collections are proven to store a wealth of semantic knowledge.However, 1) they are not effective as sentence encoders when used off-the-shelf, and 2) thus typically lag behind conversationally pretrained (e.g., via response selection) encoders on conversational tasks such as intent detection (ID).In this work, we propose CON-VFIT, a simple and efficient two-stage procedure which turns any pretrained LM into a universal conversational encoder (after Stage 1 CONVFIT-ing) and task-specialised sentence encoder (after Stage 2).We demonstrate that 1) full-blown conversational pretraining is not required, and that LMs can be quickly transformed into effective conversational encoders with much smaller amounts of unannotated data; 2) pretrained LMs can be fine-tuned into task-specialised sentence encoders, optimised for the fine-grained semantics of a particular task.Consequently, such specialised sentence encoders allow for treating ID as a simple semantic similarity task based on interpretable nearest neighbours retrieval.We validate the robustness and versatility of the CON-VFIT framework with such similarity-based inference on the standard ID evaluation sets: CONVFIT-ed LMs achieve state-of-the-art ID performance across the board, with particular gains in the most challenging, few-shot setups.Stage 1 loss (c, r) = (context, response) Input LM Input LM Pooling
An improved CRNN for Vietnamese Identity Card Information Recognition

This paper proposes an enhancement of an automatic text recognition system for extracting information from the front side of the Vietnamese citizen identity (CID) card.First, we apply Mask-RCNN to segment and align the CID card from the background.Next, we present two approaches to detect the CID card's text lines using traditional image processing techniques compared to the EAST detector.Finally, we introduce a new end-to-end Convolutional Recurrent Neural Network (CRNN) model based on a combination of Connectionist Temporal Classification (CTC) and attention mechanism for Vietnamese text recognition by jointly train the CTC and attention objective functions together.The length of the CTC's output label sequence is applied to the attention-based decoder prediction to make the final label sequence.This process helps to decrease irregular alignments and speed up the label sequence estimation during training and inference, instead of only relying on a data-driven attention-based encoder-decoder to estimate the label sequence in long sentences.We may directly learn the proposed model from a sequence of words without detailed annotations.We evaluate the proposed system using a real collected Vietnamese CID card dataset and find that our method provides a 4.28% in WER and outperforms the common techniques.
SWADAPT1: assessment of an electric wheelchair-driving robotic module in standardized circuits: a prospective, controlled repeated measure design pilot study

Abstract Objectives The objective of this study is to highlight the effect of a robotic driver assistance module of powered wheelchair (PWC), using infrared sensors and accessorizing a commercial wheelchair) on the reduction of the number of collisions in standardized circuit in a population with neurological disorders by comparing driving performance with and without assistance. Methods This is a prospective, single-center, controlled, repeated measure design, single-blind pilot study including patients with neurological disabilities who are usual drivers of electric wheelchairs. The main criterion for evaluating the device is the number of collisions with and without the assistance of a prototype anti-collision system on three circuits of increasing complexity. Travel times, cognitive load, driving performance, and user satisfaction are also analyzed. Results 23 Patients, 11 women and 12 men with a mean age of 48 years old completed the study. There was a statistically significant reduction in the number of collisions on the most complex circuit: 61% experienced collisions without assistance versus 39% with assistance (p = 0.038). Conclusion This study concludes that the PWC driving assistance module is efficient in terms of safety without reducing the speed of movement in a population of people with disabilities who are habitual wheelchair drivers. The prospects are therefore to conduct tests on a target population with driving failure or difficulty who could benefit from this device so as to allow them to travel independently and safely.
Feature flow: In-network feature flow estimation for video object detection

Optical flow, which expresses pixel displacement, is widely used in many computer vision tasks to provide pixel-level motion information. However, with the remarkable progress of the convolutional neural network, recent state-of-the-art approaches are proposed to solve problems directly on feature-level. Since the displacement of feature vector is not consistent with the pixel displacement, a common approach is to forward optical flow to a neural network and fine-tune this network on the task dataset. With this method, they expect the fine-tuned network to produce tensors encoding feature-level motion information. In this paper, we rethink about this de facto paradigm and analyze its drawbacks in the video object detection task. To mitigate these issues, we propose a novel network (IFF-Net) with an In-network Feature Flow estimation module (IFF module) for video object detection. Without resorting to pre-training on any additional dataset, our IFF module is able to directly produce feature flow which indicates the feature displacement. Our IFF module consists of a shallow module, which shares the features with the detection branches. This compact design enables our IFF-Net to accurately detect objects, while maintaining a fast inference speed. Furthermore, we propose a transformation residual loss (TRL) based on self-supervision, which further improves the performance of our IFF-Net. Our IFF-Net outperforms existing methods and achieves new state-of-the-art performance on ImageNet VID.
Gaps Between Continuous Measurement Methods: A Longitudinal Study of Perceived Usability

Abstract The available continuous measurement designs for longitudinal research on perceived usability include immediate and retrospective measurement methods. The present study was conducted to examine the gap between different continuous measurement designs. Thirty users completed a longitudinal study on typesetting software. We used two measurement methods to design three stages of testing. Each stage consists of one immediate measurement and two retrospective measurements. A long-term retrospective test was also conducted and SUS questionnaire measurements were gathered. We observed significant differences in perceived usability between participants with varying quantities of event experiences (i.e. the number of times they used the system). Within short periods of time (3 days), there may be significant differences between retrospective and immediate assessments—especially when users make negative evaluations. In the 3-day period, the user’s perceived usability retrospective evaluation is also based on the most recent experience as opposed to the most unpleasant. The recency effect is lost, however, over a long-term retrospective assessment (1 month later). This study is significant for two main reasons: (i) its unique methodology and (ii) its unprecedented level of consistency between measured results and actual user experiences. We hope that the results presented here provide a workable basis for continuous measurement design in future longitudinal studies. We found that in order to secure accurate measurements, it is necessary to set limits on the measurement time and the number of uses (events).