Reviewerly/exHarmony huggingface dataset:
Implicit Authentication Approach by Generating Strong Password through Visual Key Cryptography

In this era of digitization where literally everything is available at the tip of the finger. Huge amount of data used to flow day in day out, where users used to work with various applications like internet websites, cloud applications, various data servers, web servers, etc. This paper provide idea about access control or authentication used to be acting as first line of defense for preserving data secrecy and its integrity, so far it is learned that the usual login password based methods are easy to implement and to use as well but it is also observed that they are more subjected to be get attacked therefore to preserve authentication on the basis of simple alphanumeric passwords is a challenging task now a days. Hence new methods which bring more strength for authentication and access control are so very expected and desirable.
The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation

Recent text generation research has increasingly focused on open-ended domains such as story and poetry generation.Because models built for such tasks are difficult to evaluate automatically, most researchers in the space justify their modeling choices by collecting crowdsourced human judgments of text quality (e.g., Likert scores of coherence or grammaticality) from Amazon Mechanical Turk (AMT).In this paper, we first conduct a survey of 45 open-ended text generation papers and find that the vast majority of them fail to report crucial details about their AMT tasks, hindering reproducibility.We then run a series of story evaluation experiments with both AMT workers and English teachers and discover that even with strict qualification filters, AMT workers (unlike teachers) fail to distinguish between model-generated text and human-generated references.We show that AMT worker judgments improve when they are shown model-generated output alongside human-generated references, which enables the workers to better calibrate their ratings.Finally, interviews with the English teachers provide deeper insights into the challenges of the evaluation process, particularly when rating model-generated text.
Programmable and sequential Gaussian gates in a loop-based single-mode photonic quantum processor

A quantum processor to import, process, and export optical quantum states is a common core technology enabling various photonic quantum information processing. However, there has been no photonic processor that is simultaneously universal, scalable, and programmable. Here, we report on an original loop-based single-mode versatile photonic quantum processor that is designed to be universal, scalable, and programmable. Our processor can perform arbitrarily many steps of programmable quantum operations on a given single-mode optical quantum state by time-domain processing in a dynamically controlled loop-based optical circuit. We use this processor to demonstrate programmable single-mode Gaussian gates and multistep squeezing gates. In addition, we prove that the processor can perform universal quantum operations by injecting appropriate ancillary states and also be straightforwardly extended to a multimode processor. These results show that our processor is programmable, scalable, and potentially universal, leading to be suitable for general-purpose applications.
Learning Prototype Representations Across Few-Shot Tasks for Event Detection

We address the sampling bias and outlier issues in few-shot learning for event detection, a subtask of information extraction.We propose to model the relations between training tasks in episodic few-shot learning by introducing cross-task prototypes.We further propose to enforce prediction consistency among classifiers across tasks to make the model more robust to outliers.Our extensive experiment shows a consistent improvement on three fewshot learning datasets.The findings suggest that our model is more robust when labeled data of novel event types is limited.
Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction

Event time is one of the most important features for event-event temporal relation extraction.However, explicit event time information in text is sparse.For example, only about 20% of event mentions in TimeBank-Dense have event-time links.In this paper, we propose a joint model for event-event temporal relation classification and an auxiliary task, relative event time prediction, which predicts the event time as real numbers.We adopt a Stack-Propagation framework to incorporate predicted relative event time for temporal relation classification and keep the differentiability.Our experiments on MA-TRES dataset show that our model can significantly improve the RoBERTa-based baseline and achieve state-of-the-art performance. 1
Efficacy and Usefulness of an Independent Public Earthquake Early Warning System: A Case Study—The Earthquake Network Initiative in Peru

Abstract Public earthquake early warning (PEEW) systems are intended to reduce individual risk by warning people ahead of shaking and allowing them to take protective action. Yet very few studies have assessed their actual efficacy from a risk-reduction perspective. Moreover, according to these studies, a majority of people do not undertake safety actions when receiving the warning. The spectrum of PEEW systems has expanded, with a greater diversity of actors (from citizens to private companies), increased independence from national authorities, and greater internationality. Beyond differences in warning and messaging strategies, systems’ characteristics may impact the way the public perceive, trust, understand, and respond to these warnings, which in turn will influence PEEW systems’ efficacy and perceived usefulness, enhancing the need for additional research. We take the example of earthquake network, an independent, voluntary, community-based and free system that offers a PEEW service. Through a quantitative survey (n = 2625), we studied users’ perception and reaction to a warning sent related to an M 8.0 earthquake in Peru (where no national system existed). We observed that even though only a minority of users actually took protective action, the system was appreciated and perceived as useful by the majority because it enabled mental preparation before the shaking. We found evidence for a tolerance for perceived late, missed, and false alerts. However, because it is a voluntary and independent system, the social dimension of the warning was incomplete because only a fringe of the population benefited from the warning. Therefore, many users’ first reaction was to warn their relatives. We discuss the need for partnerships between PEEW operators and national authorities to guarantee universal access to the service and maximize PEEW system efficacy.
Science Teachers' Views on the Use and Effectiveness of Interactive Simulations in Science Teaching and Learning

The objective of this article is to identify and discuss the views of teachers of physics-chemistry (Ph-Ch) and Life and Earth Sciences (LES) on the use and effectiveness of interactive simulations PhET (Physics Education Technology) in student teaching and learning.A survey research method was used in this study.114 Moroccan science teachers (67 Ph-Ch; 47 LES) at middle and high schools were surveyed.The quantitative analysis was performed to provide insight into who uses PhET simulations, why they use them, and how they are used.The data collected was analyzed using Statistical Package for the Social Sciences (SPSS) software.The results indicate that the use of interactive simulations in investigative science teaching and learning is very effective for both teachers and students, despite the presence of a set of obstacles that hinder the use of classroom simulations.The implication of this research is that the use of interactive simulations in science teaching in Morocco can enhance learning activities and help students to understand scientific concepts effectively.
Charge-Noise Insensitive Chiral Photonic Interface for Waveguide Circuit QED

A chiral photonic interface is a quantum system that has different probabilities for emitting photons to the left and right. An on-chip compatible chiral interface is attractive for both fundamental studies of light-matter interactions and applications to quantum information processing. We propose such a chiral interface based on superconducting circuits, which has wide bandwidth, rich tunability, and high tolerance to fabrication variations. The proposed interface consists of a core that uses Cooper-pair boxes (CPBs) to break time-reversal symmetry, and two superconducting transmons that connect the core to a waveguide in the manner reminiscent of a ``giant atom.'' The transmons form a state decoupled from the core, akin to dark states of atomic physics, rendering the whole interface insensitive to the CPB charge noise. The proposed interface can be extended to realize a broadband fully passive on-chip circulator for microwave photons.
Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study

A tumor is an abnormal tissue classified as either benign or malignant. A breast tumor is one of the most common tumors in women. Radiologists use mammograms to identify a breast tumor and classify it, which is a time-consuming process and prone to error due to the complexity of the tumor. In this study, we applied machine learning-based techniques to assist the radiologist in reading mammogram images and classifying the tumor in a very reasonable time interval. We extracted several features from the region of interest in the mammogram, which the radiologist manually annotated. These features are incorporated into a classification engine to train and build the proposed structure classification models. We used a dataset that was not previously seen in the model to evaluate the accuracy of the proposed system following the standard model evaluation schemes. Accordingly, this study found that various factors could affect the performance, which we avoided after experimenting all the possible ways. This study finally recommends using the optimized Support Vector Machine or Naïve Bayes, which produced 100% accuracy after integrating the feature selection and hyper-parameter optimization schemes.
Complex Deep Neural Networks from Large Scale Virtual IMU Data for Effective Human Activity Recognition Using Wearables

Supervised training of human activity recognition (HAR) systems based on body-worn inertial measurement units (IMUs) is often constrained by the typically rather small amounts of labeled sample data. Systems like IMUTube have been introduced that employ cross-modality transfer approaches to convert videos of activities of interest into virtual IMU data. We demonstrate for the first time how such large-scale virtual IMU datasets can be used to train HAR systems that are substantially more complex than the state-of-the-art. Complexity is thereby represented by the number of model parameters that can be trained robustly. Our models contain components that are dedicated to capture the essentials of IMU data as they are of relevance for activity recognition, which increased the number of trainable parameters by a factor of 1100 compared to state-of-the-art model architectures. We evaluate the new model architecture on the challenging task of analyzing free-weight gym exercises, specifically on classifying 13 dumbbell execises. We have collected around 41 h of virtual IMU data using IMUTube from exercise videos available from YouTube. The proposed model is trained with the large amount of virtual IMU data and calibrated with a mere 36 min of real IMU data. The trained model was evaluated on a real IMU dataset and we demonstrate the substantial performance improvements of 20% absolute F1 score compared to the state-of-the-art convolutional models in HAR.
ISEEQ: Information Seeking Question Generation Using Dynamic Meta-Information Retrieval and Knowledge Graphs

Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and satisfy the users' needs. If realized, such a CIS system has far-reaching benefits in the real world; for example, CIS systems can assist clinicians in pre-screening or triaging patients in healthcare. A key open sub-problem in CIS that remains unaddressed in the literature is generating Information Seeking Questions (ISQs) based on a short initial query from the end-user. To address this open problem, we propose Information SEEking Question generator (ISEEQ), a novel approach for generating ISQs from just a short user query, given a large text corpus relevant to the user query. Firstly, ISEEQ uses a knowledge graph to enrich the user query. Secondly, ISEEQ uses the knowledge-enriched query to retrieve relevant context passages to ask coherent ISQs adhering to a conceptual flow. Thirdly, ISEEQ introduces a new deep generative-adversarial reinforcement learning-based approach for generating ISQs. We show that ISEEQ can generate high-quality ISQs to promote the development of CIS agents. ISEEQ significantly outperforms comparable baselines on five ISQ evaluation metrics across four datasets having user queries from diverse domains. Further, we argue that ISEEQ is transferable across domains for generating ISQs, as it shows the acceptable performance when trained and tested on different pairs of domains. A qualitative human evaluation confirms that ISEEQ generated ISQs are comparable in quality to human-generated questions, and it outperformed the best comparable baseline.
Improving Human-Object Interaction Detection via Phrase Learning and Label Composition

Human-Object Interaction (HOI) detection is a fundamental task in high-level human-centric scene understanding. We propose PhraseHOI, containing a HOI branch and a novel phrase branch, to leverage language prior and improve relation expression. Specifically, the phrase branch is supervised by semantic embeddings, whose ground truths are automatically converted from the original HOI annotations without extra human efforts. Meanwhile, a novel label composition method is proposed to deal with the long-tailed problem in HOI, which composites novel phrase labels by semantic neighbors. Further, to optimize the phrase branch, a loss composed of a distilling loss and a balanced triplet loss is proposed. Extensive experiments are conducted to prove the effectiveness of the proposed PhraseHOI, which achieves significant improvement over the baseline and surpasses previous state-of-the-art methods on Full and NonRare on the challenging HICO-DET benchmark.
FedSoft: Soft Clustered Federated Learning with Proximal Local Updating

Traditionally, clustered federated learning groups clients with the same data distribution into a cluster, so that every client is uniquely associated with one data distribution and helps train a model for this distribution. We relax this hard association assumption to soft clustered federated learning, which allows every local dataset to follow a mixture of multiple source distributions. We propose FedSoft, which trains both locally personalized models and high-quality cluster models in this setting. FedSoft limits client workload by using proximal updates to require the completion of only one optimization task from a subset of clients in every communication round. We show, analytically and empirically, that FedSoft effectively exploits similarities between the source distributions to learn personalized and cluster models that perform well.
Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval

We present Mr. TYDI, a multi-lingual benchmark dataset for mono-lingual retrieval in eleven typologically diverse languages, designed to evaluate ranking with learned dense representations.The goal of this resource is to spur research in dense retrieval techniques in non-English languages, motivated by recent observations that existing techniques for representation learning perform poorly when applied to out-of-distribution data.As a starting point, we provide zero-shot baselines for this new dataset based on a multi-lingual adaptation of DPR that we call "mDPR".Experiments show that although the effectiveness of mDPR is much lower than BM25, dense representations nevertheless appear to provide valuable relevance signals, improving BM25 results in sparse-dense hybrids.In addition to analyses of our results, we also discuss future challenges and present a research agenda in multi-lingual dense retrieval.Mr.
A Multifractal Analysis and Machine Learning Based Intrusion Detection System with an Application in a UAS/RADAR System

The rapid development of Internet of Things (IoT) technology, together with mobile network technology, has created a never-before-seen world of interconnection, evoking research on how to make it vaster, faster, and safer. To support the ongoing fight against the malicious misuse of networks, in this paper we propose a novel algorithm called AMDES (unmanned aerial system multifractal analysis intrusion detection system) for spoofing attack detection. This novel algorithm is based on both wavelet leader multifractal analysis (WLM) and machine learning (ML) principles. In earlier research on unmanned aerial systems (UAS), intrusion detection systems (IDS) based on multifractal (MF) spectral analysis have been used to provide accurate MF spectrum estimations of network traffic. Such an estimation is then used to detect and characterize flooding anomalies that can be observed in an unmanned aerial vehicle (UAV) network. However, the previous contributions have lacked the consideration of other types of network intrusions commonly observed in UAS networks, such as the man in the middle attack (MITM). In this work, this promising methodology has been accommodated to detect a spoofing attack within a UAS. This methodology highlights a robust approach in terms of false positive performance in detecting intrusions in a UAS location reporting system.
Amendable Generation for Dialogue State Tracking

In task-oriented dialogue systems, recent dialogue state tracking methods tend to perform one-pass generation of the dialogue state based on the previous dialogue state.The mistakes of these models made at the current turn are prone to be carried over to the next turn, causing error propagation.In this paper, we propose a novel Amendable Generation for Dialogue State Tracking (AG-DST), which contains a two-pass generation process: (1) generating a primitive dialogue state based on the dialogue of the current turn and the previous dialogue state, and (2) amending the primitive dialogue state from the first pass.With the additional amending generation pass, our model is tasked to learn more robust dialogue state tracking by amending the errors that still exist in the primitive dialogue state, which plays the role of reviser in the double-checking process and alleviates unnecessary error propagation.Experimental results show that AG-DST significantly outperforms previous works in two active DST datasets (MultiWOZ 2.2 and WOZ 2.0), achieving new state-of-the-art performances.
Efficient Nearest Neighbor Language Models

Non-parametric neural language models (NLMs) learn predictive distributions of text utilizing an external datastore, which allows them to learn through explicitly memorizing the training datapoints.While effective, these models often require retrieval from a large datastore at test time, significantly increasing the inference overhead and thus limiting the deployment of non-parametric NLMs in practical applications.In this paper, we take the recently proposed k-nearest neighbors language model (Khandelwal et al., 2019) as an example, exploring methods to improve its efficiency along various dimensions.Experiments on the standard WikiText-103 benchmark and domain-adaptation datasets show that our methods are able to achieve up to a 6x speed-up in inference speed while retaining comparable performance.The empirical analysis we present may provide guidelines for future research seeking to develop or deploy more efficient non-parametric NLMs. 1
YOLO MDE: Object Detection with Monocular Depth Estimation

This paper presents an object detector with depth estimation using monocular camera images. Previous detection studies have typically focused on detecting objects with 2D or 3D bounding boxes. A 3D bounding box consists of the center point, its size parameters, and heading information. However, predicting complex output compositions leads a model to have generally low performances, and it is not necessary for risk assessment for autonomous driving. We focused on predicting a single depth per object, which is essential for risk assessment for autonomous driving. Our network architecture is based on YOLO v4, which is a fast and accurate one-stage object detector. We added an additional channel to the output layer for depth estimation. To train depth prediction, we extract the closest depth from the 3D bounding box coordinates of ground truth labels in the dataset. Our model is compared with the latest studies on 3D object detection using the KITTI object detection benchmark. As a result, we show that our model achieves higher detection performance and detection speed than existing models with comparable depth accuracy.
Context-Aware Human Activity Recognition in Industrial Processes

The automatic, sensor-based assessment of human activities is highly relevant for production and logistics, to optimise the economics and ergonomics of these processes. One challenge for accurate activity recognition in these domains is the context-dependence of activities: Similar movements can correspond to different activities, depending on, e.g., the object handled or the location of the subject. In this paper, we propose to explicitly make use of such context information in an activity recognition model. Our first contribution is a publicly available, semantically annotated motion capturing dataset of subjects performing order picking and packaging activities, where context information is recorded explicitly. The second contribution is an activity recognition model that integrates movement data and context information. We empirically show that by using context information, activity recognition performance increases substantially. Additionally, we analyse which of the pieces of context information is most relevant for activity recognition. The insights provided by this paper can help others to design appropriate sensor set-ups in real warehouses for time management.