Reviewerly/exHarmony huggingface dataset:
A Comparison of Word Embeddings to Study Complications in Neurosurgery

Our study aimed to compare the capability of different word embeddings to capture the semantic similarity of clinical concepts related to complications in neurosurgery at the level of medical experts. Eighty-four sets of word embeddings (based on Word2vec, GloVe, FastText, PMI, and BERT algorithms) were benchmarked in a clustering task. FastText model showed the best close to the medical expertise capability to group medical terms by their meaning (adjusted Rand index = 0.682). Word embedding models can accurately reflect clinical concepts' semantic and linguistic similarities, promising their robust usage in medical domain-specific NLP tasks.
Measurement for Blade Edge Based on Machine Vision

In view of problems that measurements of grinding blade edge mainly depend on manual detection, which is difficult to detect and has low measuring efficiency, a measurement method based on machine vision is designed. Through series of processing of blade edge image with software, the measurement results are displayed in real time, and the dynamic real-time monitoring of the grinding quality of blade is realized. Through the test, the system can successfully realize the measurement of blade edge size with high efficiency and accuracy, and realize the measurement automation, which has great application value.
Deliberation Model for On-Device Spoken Language Understanding

We propose a novel deliberation-based approach to end-to-end (E2E) spoken language understanding (SLU), where a streaming automatic speech recognition (ASR) model produces the first-pass hypothesis and a second-pass natural language understanding (NLU) component generates the semantic parse by conditioning on both ASR's text and audio embeddings. By formulating E2E SLU as a generalized decoder, our system is able to support complex compositional semantic structures. Furthermore, the sharing of parameters between ASR and NLU makes the system especially suitable for resource-constrained (on-device) environments; our proposed approach consistently outperforms strong pipeline NLU baselines by 0.60% to 0.65% on the spoken version of the TOPv2 dataset (STOP). We demonstrate that the fusion of text and audio features, coupled with the system's ability to rewrite the first-pass hypothesis, makes our approach more robust to ASR errors. Finally, we show that our approach can significantly reduce the degradation when moving from natural speech to synthetic speech training, but more work is required to make text-to-speech (TTS) a viable solution for scaling up E2E SLU.
Adaptive Aggregation-Transformation Decoupled Graph Convolutional Network for Semi-Supervised Learning

Graph Convolutional Network (GCN) has achieved significant success in many graph representation learning tasks. GCN usually learns graph representations by performing Neighbor Aggregation (NA) and Feature Transformation (FT) operations. Deep Adaptive Graph Neural Network (DAGNN) improves NA operation in the aggregation-transformation decoupled GCN, which enables the model to obtain a large receiver field. However, the problem with GCN is that when the model is trained to a deeper level, the performance decreases. In particular, the influence of NA and FT on model degradation has not been fully considered. In this work, we propose a new decoupled GCN architecture to enhance the performance of deep GCN. First, we conduct an experimental analysis of the impact of NA and FT operations on the degradation of the deep GCN model. Subsequently, we propose an Adaptive Aggregation-Transform Decoupled Graph Convolutional Network (AATD-GCN) which divides the model into two depths <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D_{NA}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D_{FT}$ </tex-math></inline-formula> , and proposes improved approaches in NA and FT, respectively. The AATD-GCN reduces the influence of NA and FT operations on performance degradation of deep GCN, and obtains a large receiver field while extracting complex feature information. We investigate the requirements of model depth <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D_{NA}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D_{FT}$ </tex-math></inline-formula> for graphs of different structures and sizes. Finally, the effectiveness of the proposed architecture is verified through extensive experiments on real-world datasets, the experimental results show that AATD-GCN is superior in terms of accuracy and robustness.
Clustering

Supervised algorithms use labeled data as an input for developing a prediction model. However, the amount of unlabeled data collected often far exceeds that of labeled data. Unsupervised algorithms, such as clustering algorithms, are algorithms that are able to make use of these unlabeled data to extract useful insights. One example is the K-means algorithm which clusters the data into K different clusters. The elbow method will be used to select a suitable number of clusters to select the value of K.
Dataset: Variable Message Signal Annotated Images for Object Detection

This publication presents a dataset consisting of Spanish road images taken from inside a vehicle, as well as annotations in XML files in PASCAL VOC format that indicate the location of Variable Message Signals within them. Additionally, a CSV file is attached with information regarding the geographic position, the folder where the image is located and the text in Spanish. This can be used to train supervised learning computer vision algorithms such as convolutional neural networks. Throughout this work, the process followed to obtain the dataset, image acquisition and labeling and its specifications are detailed. The dataset constitutes 1216 instances, 888 positives and 328 negatives, in 1152 jpg images with a resolution of 1280 × 720 pixels. These are divided into 756 real images and 756 images created from the data-augmentation technique. The purpose of this dataset is to help in road computer vision research since there is not one specifically for VMSs.
Adversarial Robustness of Neural-Statistical Features in Detection of Generative Transformers

The detection of computer-generated text is an area of rapidly increasing significance as nascent generative models allow for efficient creation of compelling human-like text, which may be abused for the purposes of spam, disinformation, phishing, or online influence campaigns. Past work has studied detection of current state-of-the-art models, but despite a developing threat landscape, there has been minimal analysis of the robustness of detection methods to adversarial attacks. To this end, we evaluate neural and non-neural approaches on their ability to detect computer-generated text, their robustness against text adversarial attacks, and the impact that successful adversarial attacks have on human judgement of text quality. We find that while statistical features underperform neural features, statistical features provide additional adversarial robustness that can be leveraged in ensemble detection models. In the process, we find that previously effective complex phrasal features for detection of computer-generated text hold little predictive power against contemporary generative models, and identify promising statistical features to use instead. Finally, we pioneer the usage of $\Delta$MAUVE as a proxy measure for human judgement of adversarial text quality.
Entity Cloze By Date: What LMs Know About Unseen Entities

Language models (LMs) are typically trained once on a large-scale corpus and used for years without being updated.However, in a dynamic world, new entities constantly arise.We propose a framework to analyze what LMs can infer about new entities that did not exist when the LMs were pretrained.We derive a dataset of entities indexed by their origination date and paired with their English Wikipedia articles, from which we can find sentences about each entity.We evaluate LMs' perplexity on masked spans within these sentences.We show that models more informed about the entities, such as those with access to a textual definition of them, achieve lower perplexity on this benchmark.Our experimental results demonstrate that making inferences about new entities remains difficult for LMs.Given its wide coverage on entity knowledge and temporal indexing, our dataset can be used to evaluate LMs and techniques designed to modify or extend their knowledge.Our automatic data collection pipeline can be easily used to continually update our benchmark.
Leveraging strategic connection migration-powered traffic splitting for privacy

Network-level adversaries have developed increasingly sophisticated techniques to surveil and control users’ network traffic. In this paper, we exploit our observation that many encrypted protocol connections are no longer tied to device IP address (e.g., the connection migration feature in QUIC, or IP roaming in WireGuard and Mosh), due to the need for performance in a mobile-first world. We design and implement a novel framework, Connection Migration Powered Splitting (CoMPS), that utilizes these performance features for enhancing user privacy. With CoMPS, we can split traffic mid-session across network paths and heterogeneous network protocols. Such traffic splitting mitigates the ability of a network-level adversary to perform traffic analysis attacks by limiting the amount of traffic they can observe. We use CoMPS to construct a website fingerprinting defense that is resilient against traffic analysis attacks by a powerful adaptive adversary in the open-world setting. We evaluate our system using both simulated splitting data and real-world traffic that is actively split using CoMPS. In our real-world experiments, CoMPS reduces the precision and recall of VarCNN to 29.9% and 36.7% respectively in the openworld setting with 100 monitored classes. CoMPS is not only immediately deployable with any unaltered server that supports connection migration, but also incurs little overhead, decreasing throughput by only 5-20%.
Low-resource automatic speech recognition and error analyses of oral cancer speech

In this paper, we introduce a new corpus of oral cancer speech and present our study on the automatic recognition and analysis of oral cancer speech. A two-hour English oral cancer speech dataset is collected from YouTube. Formulated as a low-resource oral cancer ASR task, we investigate three acoustic modelling approaches that previously have worked well with low-resource scenarios using two different architectures; a hybrid architecture and a transformer-based end-to-end (E2E) model: (1) a retraining approach; (2) a speaker adaptation approach; and (3) a disentangled representation learning approach (only using the hybrid architecture). The approaches achieve a (1) 4.7% (hybrid) and 7.5% (E2E); (2) 7.7%; and (3) 2.0% absolute word error rate reduction, respectively, compared to a baseline system which is not trained on oral cancer speech. A detailed analysis of the speech recognition results shows that (1) plosives and certain vowels are the most difficult sounds to recognise in oral cancer speech — this problem is successfully alleviated by our proposed approaches; (3) however these sounds are also relatively poorly recognised in the case of healthy speech with the exception of/p/. (2) recognition performance of certain phonemes is strongly data-dependent; (4) In terms of the manner of articulation, E2E performs better with the exception of vowels — however, vowels have a large contribution to overall performance. As for the place of articulation, vowels, labiodentals, dentals and glottals are better captured by hybrid models, E2E is better on bilabial, alveolar, postalveolar, palatal and velar information. (5) Finally, our analysis provides some guidelines for selecting words that can be used as voice commands for ASR systems for oral cancer speakers.
Hierarchical Attribute-based Encryption Scheme to Secure Fog and IoT Communications

Haze looking after is taken into consideration as a essentially virtualized trouble of view that could lure figuring on the internet of factors devices, residing inside the edge of the shape, to skip on establishments and applications notably greater proficiently and as it should be. at the same time as you understand that darkness figuring begins offevolved from and is a non-trifling augmentation of scattered enlisting, it gets particular safety and affirmation problems of dispersed managing, inflicting the an extended way wearing out troubles within the evaluation put together. To attach actual and puzzle correspondences amongst a celebration of haze focuses, in this paper, we recommend an awesome key exchange display relying on Hierarchical characteristic-based totally Encryption (HABE) to create comfy exchanges some of the human beings. To perform insurance, attestation, eccentricity, and get right of get entry to to manipulate, we be a part of HABE and impelled imprint frameworks. We separate the capability of our display like protection and execution. We besides execute our display and distinction it and the confirmation based completely genuinely plan to show its achievability.
Story builders

We study methods for constructing a story from partial evidence where a story is defined as a path along a finite directed graph from the origin to a terminal node. Each node in the graph represents a possible event. A story builder receives evidence, i.e. a subset of events consistent with at least one story, and expands it into a coherent story. The analysis focuses on a stickiness property whereby if the story builder believes in a particular story, given a certain set of facts, then he believes in it given a broader set of facts consistent with the story.
Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

Key point analysis is the task of extracting a set of concise and high-level statements from a given collection of arguments, representing the gist of these arguments.This paper presents our proposed approach to the Key Point Analysis shared task, collocated with the 8th Workshop on Argument Mining.The approach integrates two complementary components.One component employs contrastive learning via a siamese neural network for matching arguments to key points; the other is a graph-based extractive summarization model for generating key points.In both automatic and manual evaluation, our approach was ranked best among all submissions to the shared task.
Research on anomaly detection method for hybrid big data subarea based on ant colony algorithm

Due to the problems of low accuracy and poor degree of freedom of the existing big data anomaly detection methods, a mixed big data partition anomaly detection method based on ant colony algorithm is proposed. The number of common neighbourhood between nodes in weighted network is redefined and the mixed big data sub-region is realised. Combining the operation, vulnerability and threat of the database, the security situation value is substituted into the abnormal location part to form the coordinate matrix. The pheromone concentration of each region was calculated, and the region where the concentration was reduced was defined as the abnormal region to complete the big data anomaly detection. Experimental results show that this method has high accuracy, freedom of anomaly location and good accuracy performance, which is a great progress of big data anomaly detection technology. In the future, an effective method to repair abnormal data and improve the specific application scope of this method should be developed on the basis of this method.
Ai Healthcare Interactive Talking Agent using Nlp

Good nutrition plays an important part in leading an active lifestyle. Combined with physical exercises, the diet can benefit people to maintain their weight, reduce the possibility of diseases and improve overall health. A self-help motivational tool for weight maintenance is a good option. This paper presents an interactive talking agent that is a chatbot. A chatbot is a piece of software that operates a conversation using textual methods. Chatbot will start communication with the user and help to solve the concern by initiating a human way conversation using Language Understanding Intelligence Service (LUIS) concept. Natural language processing (NLP) is the capability of a computer application for understanding human dialect. It is one of the part of Artificial Intelligence (AI). Each language has a different morphology the chatbot has to be able to separate words into individual morphemes. Morphology is one of the tasks that NLP should be able to handle.
Quantum Cryptography

In this chapter we'll explore more of some of the most essential components of quantum-based cryptography, including the grandparent of quantum key distribution protocols, the formidable BB84. You'll see some familiar concepts and names, but you will probably also encounter several new ideas.
Insights on Video Compression Strategies using Machine Learning

With the rising advancement of the multimedia technology, video compression is becoming a challenging problem. Although, there is availability of various standard compression algorithms, yet robust compression performance is yet to be seen in existing compression techniques. This paper also highlights that machine learning plays a significant contributory role in improving the performance of the video compression. Therefore, this manuscript offers a technical insight about the performance of existing video compression technique using machine learning approach. The contribution of this paper is its findings which states that machine learning approach do have significant advantage but the advantageous features are limited by the inherent and unsolved research problem. The core findings of this paper are basically to highlight the strength and limitations of existing methods as well as to highlight the research gap in terms of open-end research problems which requires immediate attention.
FPGA Based Implementation of ImageZero Compression Algorithm

The main goal of information compression is to achieve the greatest reduction possible of the volume of data. This action affects both storage and transmission. However, the most used algorithms have considerable latency and are not practical for real-world applications. In the image compression field, the situation is more complex because the resolutions are constantly increasing. Nowadays, it is common to deal with uncompressed images that exceed 60 MB. Dedicated compressors present strong restrictions of time, space and power so a thorough design and development should be made. In this article, an architecture of ImageZero compression lossless algorithm is presented. The algorithm was selected for its low latency both in encoding and decoding. The novelty of this work is the hardware implementation for this algorithm, based on FPGA. The synthesis results demonstrate that an increase in input image resolution does not affect the compression speedup. Also, the throughput for this proposal is greatest than other hardware implementations of image compressors.
Quantifying Uncertainty and Correcting for Systematic Error on Alpha-Ejection and eU in Apatite (U-Th)/He Chronology Based on Realistic Grain Sizes and Shapes

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Quantifying Uncertainty and Correcting for Systematic Error on Alpha-Ejection and eU in Apatite (U-Th)/He Chronology Based on Realistic Grain Sizes and ShapesAuthorsSpencerZeigleriDJAMESMETCALFiDREBECCAFLOWERSJenniferCoulombeSee all authors Spencer ZeigleriDCorresponding Author• Submitting AuthorUniversity of Colorado BoulderiDhttps://orcid.org/0000-0001-9338-010Xview email addressThe email was not providedcopy email addressJAMES METCALFiDUniversity of Colorado BoulderiDhttps://orcid.org/0000-0001-5659-2728view email addressThe email was not providedcopy email addressREBECCA FLOWERSUniversity of Colorado Boulderview email addressThe email was not providedcopy email addressJennifer CoulombeUniversity of Colorado Boulderview email addressThe email was not providedcopy email address
Improved Fault Tolerant ALU Architecture

The birth to IC technology by Moore became driving force behind civilization and it spent almost 45 years successfully without any scruple in mind. It affected life of a mankind and brought pivotal moment in civilization. Now technology is hitting atomic levels and soon limits will be touched. Therefore time has come to rethink for an alternative solution that may slow down exponential rate demonstrated by Moore. Reversible computing is emerging as a superior technology and soon will be future of all smart computing applications. Although renowned physicists and computer scientists have investigated remarkable results in reversible logic based arithmetic logic unit (ALU) designing still research in the field of reversible ALU with add on fault tolerance is under progress and there is scope of further optimization. This paper aims in investigation of improved fault tolerant ALU architecture using parity preserving fault tolerant reversible adder (FTRA), double Feynman and conservative Fredkin gates. Performance evaluation of proposed architecture is done in respect of functionality, garbage lines, ancillary lines, quantum cost and number of gates. The quantum cost of all gates is verified using RCViewer+ tool. The proposed architecture is coded in Verilog HDL, Synthesized and simulated using EDA (Electronic Design Automation) tool-Xilinx ISE design suit 14.2.
Virtual reality based novel use case in remote sensing and GIS

Virtual reality (VR) is realistic, interactive and immersive 3D computer generated world that one can explore as if one is present in the scene.VR makes it possible to experience anything, anywhere and anytime.Head mount device (HMD) with headphones and hand controllers are used to provide fully immersive experience.There are three important I's in VR; interactivity, immersion and imagination.Virtual reality is stepping in each and every domain like engineering, science, military, education, medical, tourism and entertainment.We have explored its potential for usage in remote sensing and Geographical Information System (GIS).Usually, GIS users analyse remote sensing data such as high resolution images, digital elevation models and vector data in commercial and open sources 2D GIS software(s).Available GIS software(s) allow users to interact with remote sensing data as a third person only.We have developed in-house virtual reality solution to generate a 3D terrain using high resolution imagery and elevation models over Earth.User can move around or teleport to different locations inside the 3D scene and interact with real time objects available in the scene.