Reviewerly/exHarmony huggingface dataset:
Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples

Recent efforts have shown that neural text processing models are vulnerable to adversarial examples, but the nature of these examples is poorly understood.In this work, we show that adversarial attacks against CNN, LSTM and Transformer-based classification models perform word substitutions that are identifiable through frequency differences between replaced words and their corresponding substitutions.Based on these findings, we propose frequency-guided word substitutions (FGWS), a simple algorithm exploiting the frequency properties of adversarial word substitutions for the detection of adversarial examples.FGWS achieves strong performance by accurately detecting adversarial examples on the SST-2 and IMDb sentiment datasets, with F 1 detection scores of up to 91.4% against RoBERTa-based classification models.We compare our approach against a recently proposed perturbation discrimination framework and show that we outperform it by up to 13.0% F 1 .
Neural Transfer Learning for Repairing Security Vulnerabilities in C Code

In this paper, we address the problem of automatic repair of software vulnerabilities with deep learning. The major problem with data-driven vulnerability repair is that the few existing datasets of known confirmed vulnerabilities consist of only a few thousand examples. However, training a deep learning model often requires hundreds of thousands of examples. In this work, we leverage the intuition that the bug fixing task and the vulnerability fixing task are related and that the knowledge learned from bug fixes can be transferred to fixing vulnerabilities. In the machine learning community, this technique is called transfer learning. In this paper, we propose an approach for repairing security vulnerabilities named VRepair which is based on transfer learning. VRepair is first trained on a large bug fix corpus and is then tuned on a vulnerability fix dataset, which is an order of magnitude smaller. In our experiments, we show that a model trained only on a bug fix corpus can already fix some vulnerabilities. Then, we demonstrate that transfer learning improves the ability to repair vulnerable C functions. We also show that the transfer learning model performs better than a model trained with a denoising task and fine-tuned on the vulnerability fixing task. To sum up, this paper shows that transfer learning works well for repairing security vulnerabilities in C compared to learning on a small dataset.
Surfacing the Voices of People with Dementia: Strategies for Effective Inclusion of Proxy Stakeholders in Qualitative Research

Best practices for conducting HCI research on dementia care increasingly involve multiple stakeholders and incorporate diverse viewpoints. When done effectively, involving proxy stakeholders such as family members and professionals can help bring forward the voices of people with dementia. However, concrete practical guidance for navigating the challenges of integrating different perspectives is lacking. We critically reflect on our own recent qualitative fieldwork involving participants with dementia, family caregivers, and facilitators at a local social program for people with dementia, re-examining our interview transcripts and observation notes through content analysis. We illustrate practical approaches to prioritizing participants’ voices through concrete excerpts that demonstrate strategies for better managing dynamics, intervening effectively, and engaging all stakeholders in the research process. Our reflections and proposed guidelines can benefit HCI researchers and practitioners working with vulnerable populations. We hope this work will spur further discussion and critique to strengthen and improve research practices in this domain.
Object Tracking in Hyperspectral-Oriented Video with Fast Spatial-Spectral Features

This paper presents a correlation filter object tracker based on fast spatial-spectral features (FSSF) to realize robust, real-time object tracking in hyperspectral surveillance video. Traditional object tracking in surveillance video based only on appearance information often fails in the presence of background clutter, low resolution, and appearance changes. Hyperspectral imaging uses unique spectral properties as well as spatial information to improve tracking accuracy in such challenging environments. However, the high-dimensionality of hyperspectral images causes high computational costs and difficulties for discriminative feature extraction. In FSSF, the real-time spatial-spectral convolution (RSSC) kernel is updated in real time in the Fourier transform domain without offline training to quickly extract discriminative spatial-spectral features. The spatial-spectral features are integrated into correlation filters to complete the hyperspectral tracking. To validate the proposed scheme, we collected a hyperspectral surveillance video (HSSV) dataset consisting of 70 sequences in 25 bands. Extensive experiments confirm the advantages and the efficiency of the proposed FSSF for object tracking in hyperspectral video tracking in challenging conditions of background clutter, low resolution, and appearance changes.
Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation

Deep learning has achieved promising segmentation performance on 3D left atrium MR images. However, annotations for segmentation tasks are expensive, costly and difficult to obtain. In this paper, we introduce a novel hierarchical consistency regularized mean teacher framework for 3D left atrium segmentation. In each iteration, the student model is optimized by multi-scale deep supervision and hierarchical consistency regularization, concurrently. Extensive experiments have shown that our method achieves competitive performance as compared with full annotation, outperforming other state-of-the-art semi-supervised segmentation methods.
Cognitive load influences oculomotor behavior in natural scenes

Cognitive neuroscience researchers have identified relationships between cognitive load and eye movement behavior that are consistent with oculomotor biomarkers for neurological disorders. We develop an adaptive visual search paradigm that manipulates task difficulty and examine the effect of cognitive load on oculomotor behavior in healthy young adults. Participants (N = 30) free-viewed a sequence of 100 natural scenes for 10 s each, while their eye movements were recorded. After each image, participants completed a 4 alternative forced choice task in which they selected a target object from one of the previously viewed scenes, among 3 distracters of the same object type but from alternate scenes. Following two correct responses, the target object was selected from an image increasingly farther back (N-back) in the image stream; following an incorrect response, N decreased by 1. N-back thus quantifies and individualizes cognitive load. The results show that response latencies increased as N-back increased, and pupil diameter increased with N-back, before decreasing at very high N-back. These findings are consistent with previous studies and confirm that this paradigm was successful in actively engaging working memory, and successfully adapts task difficulty to individual subject's skill levels. We hypothesized that oculomotor behavior would covary with cognitive load. We found that as cognitive load increased, there was a significant decrease in the number of fixations and saccades. Furthermore, the total duration of saccades decreased with the number of events, while the total duration of fixations remained constant, suggesting that as cognitive load increased, subjects made fewer, longer fixations. These results suggest that cognitive load can be tracked with an adaptive visual search task, and that oculomotor strategies are affected as a result of greater cognitive demand in healthy adults.
An end-to-end trainable hybrid classical-quantum classifier

We introduce a hybrid model combining a quantum-inspired tensor network and a variational quantum circuit to perform supervised learning tasks. This architecture allows for the classical and quantum parts of the model to be trained simultaneously, providing an end-to-end training framework. We show that compared to the principal component analysis, a tensor network based on the matrix product state with low bond dimensions performs better as a feature extractor for the input data of the variational quantum circuit in the binary and ternary classification of MNIST and Fashion-MNIST datasets. The architecture is highly adaptable and the classical-quantum boundary can be adjusted according the availability of the quantum resource by exploiting the correspondence between tensor networks and quantum circuits.
Biology begins to tangle with quantum computing

Quantum computing promises plenty, such as how it can massively accelerate some bioinformatics calculations.
An efficient anonymous authentication and key agreement scheme with privacy-preserving for smart cities

Internet of Things devices are responsible for collecting and transmitting data in smart cities, assisting smart cities to release greater potential. As Internet of Things devices are increasingly connected to smart cities, security and privacy have gradually become important issues. Recently, research works on mitigating security challenges of Internet of Things devices in smart cities mainly focused on authentication. However, in most of the existing authentication protocols, the trustworthiness evaluation of Internet of Things devices in smart cities is ignored. Considering the trustworthiness evaluation of Internet of Things devices is an important constituent of data source authentication, in this article, a cloud-aided trustworthiness evaluation mechanism is first designed to improve the credibility of the Internet of Things devices in smart cities. Furthermore, aiming at the problem that the user’s privacy is easy to leak in the process of authentication, an anonymous authentication and key agreement scheme based on non-interactive zero knowledge argument is proposed. The proposed scheme can ensure the privacy preservation and data security of Internet of Things devices in smart cities. The security analysis demonstrates that the proposed scheme is secure under q-SDH problem. The experimental simulation indicates that the performance of the proposal is greatly improved compared with other similar schemes.
What’s in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus

Whereas much of the success of the current generation of neural language models has been driven by increasingly large training corpora, relatively little research has been dedicated to analyzing these massive sources of textual data.In this exploratory analysis, we delve deeper into the Common Crawl, a colossal web corpus that is extensively used for training language models.We find that it contains a significant amount of undesirable content, including hate speech and sexually explicit content, even after filtering procedures.We discuss the potential impacts of this content on language models and conclude with future research directions and a more mindful approach to corpus collection and analysis.
Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study

Over the past several years there has been a considerable research investment into learning-based approaches for tasks inspired by industrial manufacturing, but despite significant progress, these techniques have yet to be adopted by in the realworld.We argue that it is the prohibitively large design space for Deep Reinforcement Learning (DRL), rather than algorithmic limitations per se, that are truly responsible for this lack of adoption.Pushing these techniques into the industrial mainstream requires a paradigm which differs significantly from the academic mindset.In this paper we define criteria for industryoriented DRL, and perform a thorough comparison according to these criteria of one family of learning approaches, DRL from demonstration, against results of a professional industrial integrator on the recently established NIST assembly benchmark.We explain the design choices, representing several years of investigation, which enabled our DRL system to consistently outperform the integrator's baseline in terms of both speed and reliability.Finally, we conclude with a competition between our DRL system and a human on a challenge task of insertion into a randomly moving target.This study suggests that DRL is capable of outperforming not only established engineered approaches, but the human motor system as well, and that there remains significant room for improvement.Videos can be found on our project website:https://sites.google.com/view/shield-nist.
Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction

Document-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences.Yet, human annotators usually use a small number of sentences to identify the relationship between a given entity pair.In this paper, we present an embarrassingly simple but effective method to heuristically select evidence sentences for document-level RE, which can be easily combined with BiLSTM to achieve good performance on benchmark datasets, even better than fancy graph neural network based methods.We have released our code at https://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need.
Quantum random number cloud platform

Abstract Randomness lays the foundation for information security. Quantum random number generation based on various quantum principles has been proposed to provide true randomness in the last two decades. We integrate four different types of quantum random number generators on the Alibaba Cloud servers to enhance cybersecurity. Post-processing modules are integrated into the quantum platform to extract true random numbers. We employ improved authentication protocols where original pseudo-random numbers are replaced with quantum ones. Users from the Alibaba Cloud, such as Ant Financial and Smart Access Gateway, request random numbers from the quantum platform for various cryptographic tasks. For cloud services demanding the highest security, such as Alipay at Ant Financial, we combine the random numbers from four quantum devices by XOR the outputs to enhance practical security. The quantum platform has been continuously run for more than a year.
The Growing Security Concerns of Cloud Computing

The emergence of cloud computing has allowed for a more efficient use of system and business resources for many users and organizations. The market of the cloud computing business has grown worldwide. The reason for this rapid adoption rate is related to the many advantages of moving to the cloud as opposed to hosting computing resources internally in a privately owned cloud datacenter. However, there is one glaring drawback to cloud computing: the housing of your data is in the hands of an outside entity. In this paper, we will expand on publicly known cloud data breaches, how they were carried out, where the implemented security controls failed, and the effects of the breaches. By this paper's end, we look to inform the reader of the recent data breaches related to cloud datacenters and their technology, and how future computer security analysts can work with the cloud infrastructure to optimize cloud resources while implementing strong security controls.
Integration of path planning, trajectory generation and trajectory tracking control for aircraft mission autonomy

Integrating multiple features for autonomous flight of aircraft requires extensive research over wide categories, high-level of know-how, and experiences. To contribute to these issues, this paper treats the realizable integration techniques of the path planning, trajectory generation, and trajectory-tracking control for the mission autonomy of the aircraft. A collision-free path over the three-dimensional digital terrain is efficiently generated using the P-RRT* algorithm and optimized using the line-of-sight path optimizer proposed in this paper. After additional waypoints are inserted into the planned straight-line path segments, a flyable trajectory for the position and heading angle can be built using the spline trajectory generator with reference to the prescribed velocity distribution. Finally, the incremental backstepping-control design has been adopted to provide accurate trajectory-tracking control. The paper addresses in detail the underlying rationales of the present selection of each technique from the realizability point of view. The integrated designs are validated through simulation studies for a series of autonomous mission scenarios of the rotorcraft. The results show that the P-RRT* algorithm combined with the line-of-sight path optimizer can provide a fast and efficient solution for path planning. Also, the extremely accurate trajectory-tracking performance has been achieved by applying the incremental backstepping control to the flyable trajectory obtained using the spline trajectory generator.
Using topic models with browsing history in hybrid collaborative filtering recommender system: Experiments with user ratings

Personalizing user experience in recommender systems is possible when there is sufficient information about the user. But when new users join the system, the unavailability of information about these users, referred to as cold-start, inhibits the functionality of a recommender system. We propose an enhancement to the user-based approaches, which are extensively used in the recommender system literature. Our approach combines Wikipedia data and browsing history into the recommendation algorithm. Specifically, we generate topics by using the Latent Dirichlet Allocation (LDA) models on the Wikipedia data, and then use the topics on user browsing history to extract user preferences. Our evaluation employs five approaches and tests their performance in terms of prediction and classification accuracy. We conduct experiments in two domains (movies and restaurants), to gather user ratings and their browsing history for evaluation. Results from both experiments favor our proposed enhancement.
Communication-Efficient Federated Learning With Binary Neural Networks

Federated learning (FL) is a privacy-preserving machine learning setting that enables many devices to jointly train a shared global model without the need to reveal their data to a central server. However, FL involves a frequent exchange of the parameters between all the clients and the server that coordinates the training. This introduces extensive communication overhead, which can be a major bottleneck in FL with limited communication links. In this paper, we consider training the binary neural networks (BNNs) in the FL setting instead of the typical real-valued neural networks to fulfill the stringent delay and efficiency requirement in wireless edge networks. We introduce a novel FL framework of training BNNs, where the clients only upload the binary parameters to the server. We also propose a novel parameter updating scheme based on the Maximum Likelihood (ML) estimation that preserves the performance of the BNN even without the availability of aggregated real-valued auxiliary parameters that are usually needed during the training of the BNN. Moreover, for the first time in the literature, we theoretically derive the conditions under which the training of BNN is converging. Numerical results show that the proposed FL framework significantly reduces the communication cost compared to the conventional neural networks with typical real-valued parameters, and the performance loss incurred by the binarization can be further compensated by a hybrid method.
Structural context-based knowledge graph embedding for link prediction

Knowledge graph embedding, which aims to address the limitation of symbolic representation of knowledge, has become an effective method for many AI downstream tasks, such as relation extraction, question answering. Existing knowledge graph embedding models mainly consider triples individually, and ignore the structural information connected with other entities. However, the connectivity between entities not only provides explicit structural information represented in triples, but also embodies a lot of implicit structure information. In this paper, a new knowledge graph embedding model is proposed, which can capture both the information of relational structure-context and edge structure-context by two-interaction. In addition, in order to model complex relations, we define different score function for different relation types. Moreover, the four relation connectivity types in knowledge graph (i.e. symmetry/antisymmetry, inversion, and composition) also can be modeled and inferred by StructurE. We evaluate our StructurE for knowledge graph link prediction task. Benefiting from the structural context and the relation-type-specific score function, compared with conventional geometric transformation-based knowledge graph embedding models StructurE achieves state-of-the-art results for link prediction. Moreover, compared with GCN-based models StructurE also achieves state-of-the-art results on more challenging dataset WN18RR which contains more symmetric relations.
Survival prediction of heart failure patients using machine learning techniques

The goal of this research is to develop a reliable decision-support system for the survival prediction of heart failure patients by utilizing their clinical records and laboratory test results. Forecasting heart-failure related events in clinical practice tend to be quite inaccurate and highly variable. Identifying the key drivers of heart failure is also clinically very important. In this regard, we develop a model to accurately identify the patients who are at risk utilizing machine learning techniques. This can help clinicians make informed decisions regarding the intensity of treatment required for a patient. For this study, we have utilized a heart failure dataset originally collected from the Faisalabad Institute of Cardiology and the Allied Hospital in Faisalabad, Pakistan. Sampling strategy is incorporated into the ensemble learning framework to develop a more robust Random Forest Classifier that can effectively deal with the imbalanced nature of the data and provide a more generalizable result with higher accuracy. Two different feature selection techniques - Chi-square test and Recursive Feature Elimination are utilized to identify the features that are most significant in terms of survival prediction of heart failure patients. Using our proposed approach, a maximum G-mean score of 76.83% with a sensitivity score of 80.21% was achieved, which is significantly higher than what has been reported by other researchers. Thus, our proposed framework has the potential to be an effective tool to identify the patients who are at risk and guide clinicians accordingly to take pertinent measures.