Reviewerly/exHarmony huggingface dataset:
Dynamic Deduplication Algorithm for Cross-User Duplicate Data in Hybrid Cloud Storage

The escalating growth of distributed big data in hybrid cloud storage architecture introduces a new set of challenges. Constantly, content enrichment puts pressure on capacity. Nonetheless, the explosion of user data places a significant strain on broadband and storage capacity. Consequently, many cloud storage providers will implement deduplication to compress data, reduce transfer bandwidth, and reduce cloud storage space. In cloud storage systems, it is a data compression and storage optimization method. By locating and removing redundant data, it can save storage space and bandwidth. An MTHDedup deduplication strategy based on the Merkle hash tree is presented in a hybrid cloud environment to address the issue of convergent encryption algorithms being susceptible to brute-force attacks and ciphertext computation time overhead. Merkle hash trees are constructed using additional encryption algorithms to generate encryption keys during file- and block-level deduplication, ensuring that generated ciphertexts are unpredictable. The method is effective against both internal and external brute-force attacks, thereby increasing data security. Our method reduces the computational burden of ciphertext generation and the key storage space, and the performance advantage increases with the number of privilege sets.
Scale-Invariant Feature Transform (SIFT)

Many real applications require the localization of reference positions in one or more images, for example, for image alignment, removing distortions, object tracking, 3D reconstruction, etc. We have seen that corner points1 can be located quite reliably and independent of orientation. However, typical corner detectors only provide the position and strength of each candidate point, they do not provide any information about its characteristic or “identity” that could be used for matching. Another limitation is that most corner detectors only operate at a particular scale or resolution, since they are based on a rigid set of filters.
LPYOLO: Low Precision YOLO for Face Detection on FPGA

In recent years, number of edge computing devices and artificial intelligence applications on them have advanced excessively.In edge computing, decision making processes and computations are moved from servers to edge devices.Hence, cheap and low power devices are required.FPGAs are very low power, inclined to do parallel operations and deeply suitable devices for running Convolutional Neural Networks (CNN) which are the fundamental unit of an artificial intelligence application.Face detection on surveillance systems is the most expected application on the security market.In this work, TinyYolov3 architecture is redesigned and deployed for face detection.It is a CNN based object detection method and developed for embedded systems.PYNQ-Z2 is selected as a target board which has low-end Xilinx Zynq 7020 System-on-Chip (SoC) on it.Redesigned TinyYolov3 model is defined in numerous bit width precisions with Brevitas library which brings fundamental CNN layers and activations in integer quantized form.Then, the model is trained in a quantized structure with WiderFace dataset.In order to decrease latency and power consumption, onchip memory of the FPGA is configured as a storage of whole network parameters and the last activation function is modified as rescaled HardTanh instead of Sigmoid.Also, high degree of parallelism is applied to logical resources of the FPGA.The model is converted to an HLS (High-Level-Synthesis) based application with using FINN framework and FINN-HLS library which includes the layer definitions in C++.Later, the model is synthesized and deployed.CPU of the SoC is employed with multithreading mechanism and responsible for preprocessing, postprocessing and TCP/IP streaming operations.Consequently, 2.4 Watt total board power consumption, 18 Frames-Per-Second (FPS) throughput and 0.757 Mean-Average-Precision (mAP) accuracy rate on Easy category of the WiderFace are achieved with 4 bits precision model.
An Implementation of Traffic Signs and Road Objects Detection Using Faster R-CNN

Traffic signs and road objects detection is significant issue for driver safety. It has become popular with the development of autonomous vehicles and driver-assistant systems. This study presents a real-time system that detects traffic signs and various objects in the driving environment with a camera. Faster R-CNN architecture was used as a detection method in this study. This architecture is a well-known two-stage approach for object detection. Dataset was created by collecting various images for training and testing of the model. The dataset consists of 1880 images containing traffic signs and objects collected from Turkey with the GTSRB dataset. These images were combined and divided into the training set and testing set with the ratio of 80/20. The model's training was carried out in the computer environment for 8.5 hours and approximately 10000 iterations. Experimental results show the real-time performance of Faster R-CNN for robustly traffic signs and objects detection.
An effective short-text topic modelling with neighbourhood assistance-driven NMF in Twitter

Social media such as Twitter connect billions of people by allowing them to exchange their thoughts via short-text communication. Topic modelling is a widely used technique for analysing short texts. Discovering topic clusters in short-text collections faces issues with distance-based, density-based and dimensionality reduction-based methods due to their higher dimensionality and short length which results in extremely sparse text representation matrices. We propose the 'neighbourhood-based assistance'-driven non-negative matrix factorization (NMF) method to handle high-dimensional sparse short-text representation with lower-dimensional projection effectively. We utilized NMF that aligned with the natural non-negativity of text data coupled with the symmetric document affinity information to identify topic distribution in the short text. Neighbourhood information within documents is captured using Jaccard similarity to assist information loss, resulting in higher-to-lower-dimensional projection. Experimental results with Twitter data sets show that the proposed approach is able to attain high accuracy compared to state-of-the-art methods quantitatively, while qualitative analysis with case studies validates the ability of the proposed approach in generating meaningful topic clusters.
lambeq: An Efficient High-Level Python Library for Quantum NLP

We present lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP). The open-source toolkit offers a detailed hierarchy of modules and classes implementing all stages of a pipeline for converting sentences to string diagrams, tensor networks, and quantum circuits ready to be used on a quantum computer. lambeq supports syntactic parsing, rewriting and simplification of string diagrams, ansatz creation and manipulation, as well as a number of compositional models for preparing quantum-friendly representations of sentences, employing various degrees of syntax sensitivity. We present the generic architecture and describe the most important modules in detail, demonstrating the usage with illustrative examples. Further, we test the toolkit in practice by using it to perform a number of experiments on simple NLP tasks, implementing both classical and quantum pipelines.
Universal Deterministic Quantum Operations in Microwave Quantum Links

We propose a realistic setup, inspired by already existing experiments, within which we develop a general formalism for the implementation of distributed quantum gates. Mediated by a quantum link that establishes a bidirectional quantum channel between distant nodes, our proposal works both for inter- and intranode communication and handles scenarios ranging from the few to the many modes limit of the quantum link. We are able to design fast and reliable state transfer protocols in every regime of operation, which, together with a detailed description of the scattering process, allows us to engineer two sets of deterministic universal distributed quantum gates: gates whose implementation in quantum networks does not need entanglement distribution nor measurements. By employing a realistic description of the physical setup we identify the most relevant imperfections in the quantum links as well as optimal points of operation with resulting infidelities of $1\ensuremath{-}F\ensuremath{\approx}{10}^{\ensuremath{-}2}$--${10}^{\ensuremath{-}3}$.
SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval

In neural Information Retrieval (IR), ongoing research is directed towards improving the first retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using efficient approximate nearest neighbors methods has proven to work well. Meanwhile, there has been a growing interest in learning \emph{sparse} representations for documents and queries, that could inherit from the desirable properties of bag-of-words models such as the exact matching of terms and the efficiency of inverted indexes. Introduced recently, the SPLADE model provides highly sparse representations and competitive results with respect to state-of-the-art dense and sparse approaches. In this paper, we build on SPLADE and propose several significant improvements in terms of effectiveness and/or efficiency. More specifically, we modify the pooling mechanism, benchmark a model solely based on document expansion, and introduce models trained with distillation. We also report results on the BEIR benchmark. Overall, SPLADE is considerably improved with more than $9$\% gains on NDCG@10 on TREC DL 2019, leading to state-of-the-art results on the BEIR benchmark.
Video Crowd Localization With Multifocus Gaussian Neighborhood Attention and a Large-Scale Benchmark

Video crowd localization is a crucial yet challenging task, which aims to estimate exact locations of human heads in the given crowded videos. To model spatial-temporal dependencies of human mobility, we propose a multi-focus Gaussian neighborhood attention (GNA), which can effectively exploit long-range correspondences while maintaining the spatial topological structure of the input videos. In particular, our GNA can also capture the scale variation of human heads well using the equipped multi-focus mechanism. Based on the multi-focus GNA, we develop a unified neural network called GNANet to accurately locate head centers in video clips by fully aggregating spatial-temporal information via a scene modeling module and a context cross-attention module. Moreover, to facilitate future researches in this field, we introduce a large-scale crowd video benchmark named VSCrowd (https://github.com/HopLee6/VSCrowd), which consists of 60K+ frames captured in various surveillance scenes and 2M+ head annotations. Finally, we conduct extensive experiments on three datasets including our VSCrowd, and the experiment results show that the proposed method is capable to achieve state-of-the-art performance for both video crowd localization and counting.
Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows

Normalizing flows are generative models that provide tractable density estimation via an invertible transformation from a simple base distribution to a complex target distribution. However, this technique cannot directly model data supported on an unknown low-dimensional manifold, a common occurrence in real-world domains such as image data. Recent attempts to remedy this limitation have introduced geometric complications that defeat a central benefit of normalizing flows: exact density estimation. We recover this benefit with Conformal Embedding Flows, a framework for designing flows that learn manifolds with tractable densities. We argue that composing a standard flow with a trainable conformal embedding is the most natural way to model manifold-supported data. To this end, we present a series of conformal building blocks and apply them in experiments with synthetic and real-world data to demonstrate that flows can model manifold-supported distributions without sacrificing tractable likelihoods.
Solving Sokoban with Forward-Backward Reinforcement Learning

Despite seminal advances in reinforcement learning in recent years, many domains where the rewards are sparse, e.g. given only at task completion, remain quite challenging. In such cases, it can be beneficial to tackle the task both from its beginning and end, and make the two ends meet. Existing approaches that do so, however, are not effective in the common scenario where the strategy needed near the end goal is very different from the one that is effective earlier on. In this work we propose a novel RL approach for such settings. In short, we first train a backward-looking agent with a simple relaxed goal, and then augment the state representation of the forward-looking agent with straightforward hint features. This allows the learned forward agent to leverage information from backward plans, without mimicking their policy. We demonstrate the efficacy of our approach on the challenging game of Sokoban, where we substantially surpass learned solvers that generalize across levels, and are competitive with SOTA performance of the best highly-crafted systems. Impressively, we achieve these results while learning from a small number of practice levels and using simple RL techniques.
A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients

Abstract Background Various machine learning and artificial intelligence methods have been used to predict outcomes of hospitalized COVID-19 patients. However, process mining has not yet been used for COVID-19 prediction. We developed a process mining/deep learning approach to predict mortality among COVID-19 patients and updated the prediction in 6-h intervals during the first 72 h after hospital admission. Methods The process mining/deep learning model produced temporal information related to the variables and incorporated demographic and clinical data to predict mortality. The mortality prediction was updated in 6-h intervals during the first 72 h after hospital admission. Moreover, the performance of the model was compared with published and self-developed traditional machine learning models that did not use time as a variable. The performance was compared using the Area Under the Receiver Operator Curve (AUROC), accuracy, sensitivity, and specificity. Results The proposed process mining/deep learning model outperformed the comparison models in almost all time intervals with a robust AUROC above 80% on a dataset that was imbalanced. Conclusions Our proposed process mining/deep learning model performed significantly better than commonly used machine learning approaches that ignore time information. Thus, time information should be incorporated in models to predict outcomes more accurately.
Low-resource Languages: A Review of Past Work and Future Challenges

A current problem in NLP is massaging and processing low-resource languages which lack useful training attributes such as supervised data, number of native speakers or experts, etc. This review paper concisely summarizes previous groundbreaking achievements made towards resolving this problem, and analyzes potential improvements in the context of the overall future research direction.
SPECTER: Document-level Representation Learning using Citation-informed Transformers

Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, the embeddings power strong performance on end tasks. We propose SPECTER, a new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, SPECTER can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that SPECTER outperforms a variety of competitive baselines on the benchmark.
Machine Learning in Artificial Intelligence: Towards a Common Understanding

The application of "machine learning" and "artificial intelligence" has become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings. In this work, we aim to clarify the relationship between these terms and, in particular, to specify the contribution of machine learning to artificial intelligence. We review relevant literature and present a conceptual framework which clarifies the role of machine learning to build (artificial) intelligent agents. Hence, we seek to provide more terminological clarity and a starting point for (interdisciplinary) discussions and future research.
Person Re-Identification on a Mobile Robot Using a Depth Camera

In this paper, we designed and implemented a real-time person re-identification API on a mobile robot, for a closed-and open-world setting, using only the IR gray value image of a depth camera. Since common datasets are not usable we created our own dataset using the IR gray value images, the pose and image processing techniques. Then we trained the state-of-the-art neural network for person re-identification with common parameters and methods. For running it in real-time, we sped up the model as well as the application. It is possible to re-identify three persons at once, at around 10 FPS. Our model reaches as closed-world setting a rank-1-accuracy of 95.5 %. With an additional threshold, coming from rank-1-accuracy of closed-world setting, our real-time application reaches as open-world setting a f'l-score of 79.44 % and a recall of 68.44 %.
Graphicon Evolution on the Chinese Social Media Platform BiliBili

This study examines the evolutionary trajectory of graphicons in a 13-year corpus of comments from BiliBili, a popular Chinese video-sharing platform.Findings show that emoticons (kaomoji) rose and fell in frequency, while emojis and stickers are both presently on the rise.Graphicon distributions differ in comments and replies to comments.There is also a strong correlation between the types of graphicons used in comments and their corresponding replies, suggesting a priming effect.Finally, qualitative analysis of the 10 most-frequent kaomojis, emojis, and stickers reveals a trend for each successive graphicon type to become less about emotion expression and more integrated with platformspecific culture and the Chinese language.These findings lend partial support to claims in the literature about graphicon evolution.
SIGMORPHON–UniMorph 2022 Shared Task 0: Modeling Inflection in Language Acquisition

This year's iteration of the SIGMORPHON-UniMorph shared task on "human-like" morphological inflection generation focuses on generalization and errors in language acquisition.Systems are trained on data sets extracted from corpora of child-directed speech in order to simulate a natural learning setting, and their predictions are evaluated against what is known about children's developmental trajectories for three well-studied patterns: English past tense, German noun plurals, and Arabic noun plurals.Three submitted neural systems were evaluated together with two baselines.Performance was generally good, and all systems were prone to human-like over-regularization.However, all systems were also prone to non-human-like over-irregularization and nonsense productions to varying degrees.We situate this behavior in a discussion of the Past Tense Debate. 1
Informativeness and Invariance: Two Perspectives on Spurious Correlations in Natural Language

Spurious correlations are a threat to the trustworthiness of natural language processing systems, motivating research into methods for identifying and eliminating them.However, addressing the problem of spurious correlations requires more clarity on what they are and how they arise in language data.Gardner et al. ( 2021) argue that the compositional nature of language implies that all correlations between labels and individual "input features" are spurious.This paper analyzes this proposal in the context of a toy example, demonstrating three distinct conditions that can give rise to feature-label correlations in a simple PCFG.Linking the toy example to a structured causal model shows that (1) feature-label correlations can arise even when the label is invariant to interventions on the feature, and (2) feature-label correlations may be absent even when the label is sensitive to interventions on the feature.Because input features will be individually correlated with labels in all but very rare circumstances, domain knowledge must be applied to identify spurious correlations that pose genuine robustness threats.
User-Driven Research of Medical Note Generation Software

A growing body of work uses Natural Language Processing (NLP) methods to automatically generate medical notes from audio recordings of doctor-patient consultations.However, there are very few studies on how such systems could be used in clinical practice, how clinicians would adjust to using them, or how system design should be influenced by such considerations.In this paper, we present three rounds of user studies, carried out in the context of developing a medical note generation system.We present, analyse and discuss the participating clinicians' impressions and views of how the system ought to be adapted to be of value to them.Next, we describe a three-week test run of the system in a live telehealth clinical practice.Major findings include (i) the emergence of five different note-taking behaviours; (ii) the importance of the system generating notes in real time during the consultation; and (iii) the identification of a number of clinical use cases that could prove challenging for automatic note generation systems.
Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries

Current pre-trained models applied for summarization are prone to factual inconsistencies that misrepresent the source text.Evaluating the factual consistency of summaries is thus necessary to develop better models.However, the human evaluation setup for evaluating factual consistency has not been standardized.To determine the factors that affect the reliability of the human evaluation, we crowdsource evaluations for factual consistency across stateof-the-art models on two news summarization datasets using the rating-based Likert Scale and ranking-based Best-Worst Scaling.Our analysis reveals that the ranking-based Best-Worst Scaling offers a more reliable measure of summary quality across datasets and that the reliability of Likert ratings highly depends on the target dataset and the evaluation design.To improve crowdsourcing reliability, we extend the scale of the Likert rating and present a scoring algorithm for Best-Worst Scaling that we call value learning.Our crowdsourcing guidelines will be publicly available to facilitate future work on factual consistency in summarization.