Reviewerly/exHarmony huggingface dataset:
A New Hybrid Online and Offline Multi-Factor Cross-Domain Authentication Method for IoT Applications in the Automotive Industry

Connected vehicles have emerged as the latest revolution in the automotive industry, utilizing the advent of the Internet of Things (IoT). However, most IoT-connected cars mechanisms currently depend on available network services and need continuous network connections to allow users to connect to their vehicles. Nevertheless, the connectivity availability shortcoming in remote or rural areas with no network coverage makes vehicle sharing or any IoT-connected device problematic and undesirable. Furthermore, IoT-connected cars are vulnerable to various passive and active attacks (e.g., replay attacks, MiTM attacks, impersonation attacks, and offline guessing attacks). Adversaries could all use these attacks to disrupt networks posing a threat to the entire automotive industry. Therefore, to overcome this issue, we propose a hybrid online and offline multi-factor authentication cross-domain authentication method for a connected car-sharing environment based on the user’s smartphone. The proposed scheme lets users book a vehicle using the online booking phase based on the secured and trusted Kerberos workflow. Furthermore, an offline authentication phase uses the OTP algorithm to authenticate registered users even if the connectivity services are unavailable. The proposed scheme uses the AES-ECC algorithm to provide secure communication and efficient key management. The formal SOV logic verification was used to demonstrate the security of the proposed scheme. Furthermore, the AVISPA tool has been used to check that the proposed scheme is secured against passive and active attacks. Compared to the previous works, the scheme requires less computation due to the lightweight cryptographic algorithms utilized. Finally, the results showed that the proposed system provides seamless, secure, and efficient authentication operation for the automotive industry, specifically car-sharing systems, making the proposed system suitable for applications in limited and intermittent network connections.
Character-level HyperNetworks for Hate Speech Detection

The massive spread of hate speech, hateful content targeted at specific subpopulations, is a problem of critical social importance. Automated methods of hate speech detection typically employ state-of-the-art deep learning (DL)-based text classifiers—large pretrained neural language models of over 100 million parameters, adapting these models to the task of hate speech detection using relevant labeled datasets. Unfortunately, there are only a few public labeled datasets of limited size that are available for this purpose. We make several contributions with high potential for advancing this state of affairs. We present HyperNetworks for hate speech detection, a special class of DL networks whose weights are regulated by a small-scale auxiliary network. These architectures operate at character-level, as opposed to word or subword-level, and are several orders of magnitude smaller compared to the popular DL classifiers. We further show that training hate detection classifiers using additional large amounts of automatically generated examples is beneficial in general, yet this practice especially boosts the performance of the proposed HyperNetworks. We report the results of extensive experiments, assessing the performance of multiple neural architectures on hate detection using five public datasets. The assessed methods include the pretrained language models of BERT, RoBERTa, ALBERT, MobileBERT and CharBERT, a variant of BERT that incorporates character alongside subword embeddings. In addition to the traditional setup of within-dataset evaluation, we perform cross-dataset evaluation experiments, testing the generalization of the various models in conditions of data shift. Our results show that the proposed HyperNetworks achieve performance that is competitive, and better in some cases, than these pretrained language models, while being smaller by orders of magnitude.
Gated Transformer for Robust De-noised Sequence-to-Sequence Modelling

Robust sequence-to-sequence modelling is an essential task in the real world where inputs are often noisy.Both user-generated and machine generated inputs contain various kinds of noises in the form of spelling mistakes, grammatical errors, character recognition errors etc, all of which impact downstream tasks and affect interpretability of texts.In this work, we devise a novel sequence-to-sequence architecture for detecting and correcting different real world and artificial noises (adversarial attacks) from English texts.Towards that we propose a modified transformer-based encoderdecoder architecture that uses a gating mechanism to detect types of corrections required and accordingly corrects texts.Experimental results show that our gated architecture with pre-trained language models perform significantly better that the non-gated counterparts and other state-of-the-art error correction models in correcting spelling and grammatical errors.Extrinsic evaluation of our model on Machine Translation (MT) and Summarization tasks show the competitive performance of the model against other generative sequence-tosequence models under noisy inputs.
Pollen productivity estimates from KwaZulu-Natal Drakensberg, South Africa

Pollen assemblages from sedimentary sequences reflect the landscapes surrounding the basin at the time of deposition, however the translation of pollen records into estimates or maps of past landcover is complex.Models of the pollen-vegetation relationship can help, but need to be calibrated first against modern data on pollen and vegetation for the pollen types of interest.This study presents initial estimates of Relative Pollen Productivity in the KwaZulu-Natal Drakensberg, South Africa, a palaeoecologically significant region of southern Africa, using the Extended R-Value approach.Modern pollen spectra from soil surface samples were extracted from three dominant vegetation communities, and vegetation data were collected around each sample point using a 3-tiered ring surveying approach.Fall speeds of key taxa were calculated using Stokes Law for spherical pollen grains and Falck's assumption for ellipsoidal grains.Pollen and vegetation data were subjected to Extended R-value analysis to calculate Relevant Source Area of Pollen (RSAP) and Relative Pollen Productivity estimates (RPP).RSAPs were found to be approximately 100-150 m in all three communities.Asteraceae and Proteaceae have low RPPs relative to Poaceae, the RPP of Ericaceae is comparable to Poaceae, and Pteridophyte spores and Rosaceae and Podocarpaceae pollen have significantly higher RPP values than Poaceae.These results imply that the cover of some important shrub taxa may well be underestimated in palaeoecological records from the area relative to forested communities.The number of modern pollen samples collected and analysed was small, and therefore these results can only be considered initial estimates, however they demonstrate that it is possible to use soil surface samples to obtain RSAP and PPE estimates in this important ecological and cultural region of southern Africa.
Saliency-based Multi-View Mixed Language Training for Zero-shot Cross-lingual Classification

Recent multilingual pre-trained models, like XLM-RoBERTa (XLM-R), have been demonstrated effective in many cross-lingual tasks.However, there are still gaps between the contextualized representations of similar words in different languages.To solve this problem, we propose a novel framework named Multi-View Mixed Language Training (MVMLT), which leverages code-switched data with multi-view learning to fine-tune XLM-R.MVMLT uses gradient-based saliency to extract keywords which are the most relevant to downstream tasks and replaces them with the corresponding words in the target language dynamically.Furthermore, MVMLT utilizes multiview learning to encourage contextualized embeddings to align into a more refined language-invariant space.Extensive experiments with four languages show that our model achieves state-of-the-art results on zeroshot cross-lingual sentiment classification and dialogue state tracking tasks, demonstrating the effectiveness of our proposed model 1 .
The Use of Straw String Media to Improve Pre-Writing Skills for Children With Intellectual Disability

The purpose of this study was to determine the use of straw string media to improve pre-writing skills for children with intellectual disabilities. The research method used is Classroom Action Research (CAR) using demonstration learning methods and assignments. The research subjects were three children with intellectual disabilities at the Special School. The results showed that students experienced an increase in pre-writing skills. This is proved by the average acquisition of students' pre-writing skills which increases in each cycle. The results of the post-test showed that the average score of the students increased by 43% compared to the results of the pre-test. The success of this method is due to the use of straw string media which helps students improve pre-writing skills. This study demonstrates that the use of straw string media can make it easier for students to improve pre-writing skills and also makes it easier for teachers to guide students, especially children with intellectual disabilities.
Theoretical Bounds on Data Requirements for the Ray-Based Classification

The problem of classifying high-dimensional shapes in real-world data grows in complexity as the dimension of the space increases. For the case of identifying convex shapes of different geometries, a new classification framework has recently been proposed in which the intersections of a set of one-dimensional representations, called rays, with the boundaries of the shape are used to identify the specific geometry. This ray-based classification (RBC) has been empirically verified using a synthetic dataset of two- and three-dimensional shapes (Zwolak et al. in Proceedings of Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada [December 11, 2020], arXiv:2010.00500 , 2020) and, more recently, has also been validated experimentally (Zwolak et al., PRX Quantum 2:020335, 2021). Here, we establish a bound on the number of rays necessary for shape classification, defined by key angular metrics, for arbitrary convex shapes. For two dimensions, we derive a lower bound on the number of rays in terms of the shape’s length, diameter, and exterior angles. For convex polytopes in $$\mathbb {R}^N$$ , we generalize this result to a similar bound given as a function of the dihedral angle and the geometrical parameters of polygonal faces. This result enables a different approach for estimating high-dimensional shapes using substantially fewer data elements than volumetric or surface-based approaches.
Single-photon pulse induced giant response in N &gt; 100 qubit system

The temporal dynamics of large quantum systems perturbed weakly by a single excitation can give rise to unique phenomena at the quantum phase boundaries. Here, we develop a time-dependent model to study the temporal dynamics of a single photon interacting with a defect within a large system of interacting spin qubits (N$>$100). Our model predicts a new quantum resource, giant susceptibility, when the system of qubits is engineered to simulate a first-order quantum phase transition (QPT). We show that the absorption of a single photon pulse by an engineered defect in the large qubit system can nucleate a single shot quantum measurement through spin noise read-out. This concept of a single-shot detection event ("click") is different from parameter estimation which requires repeated measurements. The crucial step of amplifying the weak quantum signal occurs by coupling the defect to a system of interacting qubits biased close to a QPT point. The macroscopic change in long-range order during the QPT generates amplified magnetic noise, which can be read out by a classical device. Our work paves the way for studying the temporal dynamics of large quantum systems interacting with a single-photon pulse.
Algorithmic or Human Source? Examining Relative Hostile Media Effect With a Transformer-Based Framework

The relative hostile media effect suggests that partisans tend to perceive the bias of slanted news differently depending on whether the news is slanted in favor of or against their sides. To explore the effect of an algorithmic vs. human source on hostile media perceptions, this study conducts a 3 (author attribution: human, algorithm, or human-assisted algorithm) x 3 (news attitude: pro-issue, neutral, or anti-issue) mixed factorial design online experiment (&lt;em&gt;N&lt;/em&gt; = 511). This study uses a transformer-based adversarial network to auto-generate comparable news headlines. The framework was trained with a dataset of 364,986 news stories from 22 mainstream media outlets. The results show that the relative hostile media effect occurs when people read news headlines attributed to all types of authors. News attributed to a sole human source is perceived as more credible than news attributed to two algorithm-related sources. For anti-Trump news headlines, there exists an interaction effect between author attribution and issue partisanship while controlling for people’s prior belief in machine heuristics. The difference of hostile media perceptions between the two partisan groups was relatively larger in anti-Trump news headlines compared with pro-Trump news headlines.
A Generative Framework for Simultaneous Machine Translation

We propose a generative framework for simultaneous machine translation.Conventional approaches use a fixed number of source words to translate or learn dynamic policies for the number of source words by reinforcement learning.Here we formulate simultaneous translation as a structural sequence-tosequence learning problem.A latent variable is introduced to model read or translate actions at every time step, which is then integrated out to consider all the possible translation policies.A re-parameterised Poisson prior is used to regularise the policies which allows the model to explicitly balance translation quality and latency.The experiments demonstrate the effectiveness and robustness of the generative framework, which achieves the best BLEU scores given different average translation latencies on benchmark datasets. 1 1 2 3 4 2 3 4 5 (a) Wait-k (k=3).
A Range and Performance Optimized Version of the Computer-Aided Speckle Interferometry Algorithm for Real-Time Displacement-Strain Field Monitoring

Abstract This work presents an optimized implementation of the Computer-Aided Speckle Interferometry algorithm which enables full-field determination of displacements and strains on commodity Graphics Processing Units at high resolution and frame rates. By combining careful control of the average speckle size in a laser speckle pattern with a simple sampling rate conversion scheme, a compact representation of the optical speckle is achieved. This allows for optimal use of Graphics Processing Unit architecture with robust range extension. The optimal mapping of the Computer-Aided Speckle Interferometry algorithm to Graphics Processing Unit architecture is shown in detail, and a straightforward method for disambiguating large displacements is illustrated. Lastly, this paper demonstrates a two-step subimage-tapering modification to the original algorithm that enables robust range enhancement while maintaining resolution. Results from numerical simulations on synthetic speckle patterns are shown, and runtime performance metrics are provided, with performance ranging up to 60 frames per second in some cases. The method is suitable for interactive experimental mechanics research, process and testing or any application where real-time high-resolution displacement-strain monitoring is needed. A .NET Framework class library enabling the incorporation of the algorithm into 3rd -party applications is available for download.
Image colorization using Scaled-YOLOv4 detector

Image Colorization is the problem of defining colors for grayscale images. Recently many research works have been conducted to propose fully-automatic colorization methods. However, many of these papers failed in colorizing images with multiple objects accurately. This might be because of dealing with the whole multi-object image as a single input. Following the efforts made in the last few years, this paper aims at studying the effect of preceding the image colorization with an object detection phase, such that the colorization will be made for each object individually as well as the full image. After the colorization of each object and the full image, they are fused together to reach a more accurate colorized image. In our work, we used a more accurate detector (Scaled-YOLOv4) than that used by the state of the art to increase the quality of the colorization results. Comparing our results to literature, it is found that using Scaled-YOLOv4 increases the Peak signal-to-noise ratio (PSNR) by 2.6%. Results of colorized images with different extensions are compared, and png extension got 5.8% better value of Learned Perceptual Image Patch Similarity (LPIPS) metric than JPEG.
#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention

Millions of hashtags are created on social media every day to cross-refer messages concerning similar topics.To help people find the topics they want to discuss, this paper characterizes a user's hashtagging preferences via predicting how likely they will post with a hashtag.It is hypothesized that one's interests in a hashtag are related to what they said before (user history) and the existing posts present the hashtag (hashtag contexts).These factors are married in the deep semantic space built with a pre-trained BERT and a neural topic model via joint training.In this way, user interests learned from the past can be customized to match future hashtags, which is beyond the capability of existing methods assuming unchanged hashtag semantics.Furthermore, we propose a novel personalized topic attention to capture salient contents to personalize hashtag contexts.Experiments on a large-scale Twitter dataset show that our model significantly outperforms the state-of-the-art recommendation approach without exploiting latent topics. 1 * Jing Li is the corresponding author.† This work was mainly conducted before Ziyan Jiang joined Amazon.1
Geologic structures associated with gold mineralization in the Kirk Range area in Southern Malawi

Abstract In this contribution, we use a newly acquired high-resolution airborne geophysical data set and field geological investigations in the Kirk Range area in southern Malawi to understand structures that control gold mineralization. Gold in this area is alluvial, mined by artisanal miners and detailed information regarding the structures controlling primary mineralization remains sparse. Structural interpretations are afforded by in-depth investigation of airborne magnetic and radiometric data, which are then supported by ground geological mapping and by microscopic observations using X-ray computed tomography (XCT) and optical microscopy. The results show that the Kirk Range displays extensive faulting and shearing with a NE–SW trend as the prevalent structural grain of the region. Gold mineralization is hosted in NE–SW trending structures. The wall rock alteration associated with gold mineralization results in a pronounced K/Th anomaly, which is suggested as an important radiometric guide for future exploration efforts. Exploration in the Kirk Range region should focus on the NE–SW structures, which represent potential conduits for fluid flow.
Learning to Rank in the Age of Muppets: Effectiveness–Efficiency Tradeoffs in Multi-Stage Ranking

It is well known that rerankers built on pretrained transformer models such as BERT have dramatically improved retrieval effectiveness in many tasks.However, these gains have come at substantial costs in terms of efficiency, as noted by many researchers.In this work, we show that it is possible to retain the benefits of transformer-based rerankers in a multi-stage reranking pipeline by first using feature-based learning-to-rank techniques to reduce the number of candidate documents under consideration without adversely affecting their quality in terms of recall.Applied to the MS MARCO passage and document ranking tasks, we are able to achieve the same level of effectiveness, but with up to 18× increase in efficiency.Furthermore, our techniques are orthogonal to other methods focused on accelerating transformer inference, and thus can be combined for even greater efficiency gains.A higher-level message from our work is that, even though pretrained transformers dominate the modern IR landscape, there are still important roles for "traditional" LTR techniques, and that we should not forget history.
Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning

The High Efficiency Video Coding Standard (HEVC) is one of the most advanced coding schemes at present, and its excellent coding performance is highly suitable for application in the navigation field with limited bandwidth. In recent years, the development of emerging technologies such as screen sharing and remote control has promoted the process of realizing the virtual driving of unmanned ships. In order to improve the transmission and coding efficiency during screen sharing, HEVC proposes a new extension scheme for screen content coding (HEVC-SCC), which is based on the original coding framework. SCC has improved the performance of compressing computer graphics content and video by adding new coding tools, but the complexity of the algorithm has also increased. At present, there is no delay in the compression optimization method designed for radar digital video in the field of navigation. Therefore, our paper starts from the perspective of increasing the speed of encoded radar video, and takes reducing the computational complexity of the rate distortion cost (RD-cost) as the goal of optimization. By analyzing the characteristics of shipborne radar digital video, a fast encoding algorithm for shipborne radar digital video based on deep learning is proposed. Firstly, a coding tree unit (CTU) division depth interval dataset of shipborne radar images was established. Secondly, in order to avoid erroneously skipping of the intra block copy (IBC)/palette mode (PLT) in the coding unit (CU) division search process, we designed a method to divide the depth interval by predicting the CTU in advance and limiting the CU rate distortion cost to be outside the traversal calculation depth interval, which effectively reduced the compression time. The effect of radar transmission and display shows that, within the acceptable range of Bjøntegaard Delta Bit Rate (BD-BR) and Bjøntegaard Delta Peak Signal to Noise Rate (BD-PSNR) attenuation, the algorithm proposed in this paper reduces the coding time by about 39.84%, on average, compared to SCM8.7.
Prototyping the variability of computer games plots

The paper presents a solution to the issue of visualizing the branched structures of the narrative of computer games, as well as generating the continuation of the development of events. In this regard, the existing solutions are analyzed, as well as our results are presented. As a result, a conception was formed, and a technology stack was chosen, which made it possible to implement a simple but effective solution for computer game script writers. The main message of the work is to optimize labor, to free up resources for solving development problems by automating the routine.