Reviewerly/exHarmony huggingface dataset:
Crowd Counting by Using Top-k Relations: A Mixed Ground-Truth CNN Framework

Crowd counting has important applications in the environments of smart cities, such as intelligent surveillance. In this paper, we propose a novel convolutional neural network (CNN) framework for crowd counting with mixed ground-truth, called top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> relation-based network (TKRNet). Specifically, the estimated density maps generated in a coarse-to-fine manner are treated as coarse locations for crowds so as to assist our TKRNet to regress the scattered point-annotated ground truth. Moreover, an adaptive top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> relation module (ATRM) is proposed to enhance feature representations by leveraging the top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> dependencies between the pixels with an adaptive filtering mechanism. Specifically, we first compute the similarity between two pixels so as to select the top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> relations for each position. Then, a weight normalization operation with an adaptive filtering mechanism is proposed to make the ATRM adaptively eliminate the influence from the low correlation positions in the top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> relations. Finally, a weight attention mechanism is introduced to make the ATRM pay more attention to the positions with high weights in the top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> relations. Extensive experimental results demonstrate the effectiveness of our proposed TKRNet on several public datasets in comparison to state-of-the-art methods.
SummN: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents

Text summarization helps readers capture salient information from documents, news, interviews, and meetings.However, most stateof-the-art pretrained language models (LM) are unable to efficiently process long text for many summarization tasks.In this paper, we propose SUMM N , a simple, flexible, and effective multi-stage framework for input texts that are longer than the maximum context length of typical pretrained LMs.SUMM N first splits the data samples and generates a coarse summary in multiple stages and then produces the final fine-grained summary based on it.Our framework can process input text of arbitrary length by adjusting the number of stages, while keeping the LM input size fixed.Moreover, it can deal with both single-source documents and dialogues, and it can be used on top of different backbone abstractive summarization models.To the best of our knowledge, SUMM N is the first multi-stage split-then-summarize framework for long input summarization.Our experiments demonstrate that SUMM N outperforms previous state-of-the-art methods by improving ROUGE scores on three long meeting summarization datasets AMI, ICSI, and QMSum, two long TV series datasets from SummScreen, and a long document summarization dataset GovReport.
Cross-modal attention for multi-modal image registration

In the past few years, convolutional neural networks (CNNs) have been proven powerful in extracting image features crucial for medical image registration. However, challenging applications and recent advances in computer vision suggest that CNNs are limited in their ability to understand the spatial correspondence between features, which is at the core of image registration. The issue is further exaggerated when it comes to multi-modal image registration, where the appearances of input images can differ significantly. This paper presents a novel cross-modal attention mechanism for correlating features extracted from the multi-modal input images and mapping such correlation to image registration transformation. To efficiently train the developed network, a contrastive learning-based pre-training method is also proposed to aid the network in extracting high-level features across the input modalities for the following cross-modal attention learning. We validated the proposed method on transrectal ultrasound (TRUS) to magnetic resonance (MR) registration, a clinically important procedure that benefits prostate cancer biopsy. Our experimental results demonstrate that for MR-TRUS registration, a deep neural network embedded with the cross-modal attention block outperforms other advanced CNN-based networks with ten times its size. We also incorporated visualization techniques to improve the interpretability of our network, which helps bring insights into the deep learning based image registration methods. The source code of our work is available at https://github.com/DIAL-RPI/Attention-Reg.
Affordances and challenges of artificial intelligence in K-12 education: a systematic review

Artificial Intelligence in Education (AIEd) has experienced a rapid rise in the past decade. This systematic review is the first examining the use of AIEd in K-12 including 169 extant studies from 2011 to 2021. This study provides contextual information from the research, such as the educational disciplines, educational levels, research purposes, methodologies, year published and who the AI was intended to support. The grounded coding revealed affordances fit into three main themes of AIEd connecting to pedagogies (e.g., gaming, personalization), administration (e.g., diagnostic tools), and subject content. Challenges in AIEd K-12 included issues toward negative perceptions, lack of student and teacher technology skills, ethical concerns, and issues directly with the ease of use and design of the AI tools.
Hugging Face

If you have even a passing familiarity with the advancements that have been made in the fields of machine learning and artificial intelligence in the years since 2018, you have almost certainly become aware of the tremendous strides that have been taken in the field of natural language processing (also known as NLP). Most of the progress in this area can be attributed to large language models, also known as LLMs. The architecture behind these LLMs is the transformer’s encoder-decoder, which we discussed in Chapter 2 .
Snapshot multispectral imaging using a diffractive optical network

Abstract Multispectral imaging has been used for numerous applications in e.g., environmental monitoring, aerospace, defense, and biomedicine. Here, we present a diffractive optical network-based multispectral imaging system trained using deep learning to create a virtual spectral filter array at the output image field-of-view. This diffractive multispectral imager performs spatially-coherent imaging over a large spectrum, and at the same time, routes a pre-determined set of spectral channels onto an array of pixels at the output plane, converting a monochrome focal-plane array or image sensor into a multispectral imaging device without any spectral filters or image recovery algorithms. Furthermore, the spectral responsivity of this diffractive multispectral imager is not sensitive to input polarization states. Through numerical simulations, we present different diffractive network designs that achieve snapshot multispectral imaging with 4, 9 and 16 unique spectral bands within the visible spectrum, based on passive spatially-structured diffractive surfaces, with a compact design that axially spans ~72 λ m , where λ m is the mean wavelength of the spectral band of interest. Moreover, we experimentally demonstrate a diffractive multispectral imager based on a 3D-printed diffractive network that creates at its output image plane a spatially repeating virtual spectral filter array with 2 × 2 = 4 unique bands at terahertz spectrum. Due to their compact form factor and computation-free, power-efficient and polarization-insensitive forward operation, diffractive multispectral imagers can be transformative for various imaging and sensing applications and be used at different parts of the electromagnetic spectrum where high-density and wide-area multispectral pixel arrays are not widely available.
Reproducibility in ecology and evolution: Minimum standards for data and code

We call for journals to commit to requiring open data be archived in a format that will be simple and clear for readers to understand and use. If applied consistently, these requirements will allow contributors to be acknowledged for their work through citation of open data, and facilitate scientific progress.
Towards Human-Bot Collaborative Software Architecting with ChatGPT

Architecting software-intensive systems can be a complex process. It deals with the daunting tasks of unifying stakeholders' perspectives, designers' intellect, tool-based automation, pattern-driven reuse, and so on, to sketch a blueprint that guides software implementation and evaluation. Despite its benefits, architecture-centric software engineering (ACSE) suffers from a multitude of challenges. ACSE challenges could stem from a lack of standardized processes, socio-technical limitations, and scarcity of human expertise etc. that can impede the development of existing and emergent classes of software. Software Development Bots (DevBots) trained on large language models can help synergise architects' knowledge with artificially intelligent decision support to enable rapid architecting in a human-bot collaborative ACSE. An emerging solution to enable this collaboration is ChatGPT, a disruptive technology not primarily introduced for software engineering, but is capable of articulating and refining architectural artifacts based on natural language processing. We detail a case study that involves collaboration between a novice software architect and ChatGPT to architect a service-based software. Future research focuses on harnessing empirical evidence about architects' productivity and explores socio-technical aspects of architecting with ChatGPT to tackle challenges of ACSE.
SoK: Decentralized Finance (DeFi) Attacks

Within just four years, the blockchain-based Decentralized Finance (DeFi) ecosystem has accumulated a peak total value locked (TVL) of more than 253 billion USD. This surge in DeFi’s popularity has, unfortunately, been accompanied by many impactful incidents. According to our data, users, liquidity providers, speculators, and protocol operators suffered a total loss of at least 3.24 billion USD from Apr 30, 2018 to Apr 30, 2022. Given the blockchain’s transparency and increasing incident frequency, two questions arise: How can we systematically measure, evaluate, and compare DeFi incidents? How can we learn from past attacks to strengthen DeFi security?In this paper, we introduce a common reference frame to systematically evaluate and compare DeFi incidents, including both attacks and accidents. We investigate 77 academic papers, 30 audit reports, and 181 real-world incidents. Our data reveals several gaps between academia and the practitioners’ community. For example, few academic papers address "price oracle attacks" and "permissonless interactions", while our data suggests that they are the two most frequent incident types (15% and 10.5% correspondingly). We also investigate potential defenses, and find that: (i) 103 (56%) of the attacks are not executed atomically, granting a rescue time frame for defenders; (ii) bytecode similarity analysis can at least detect 31 vulnerable/23 adversarial contracts; and (iii) 33 (15.3%) of the adversaries leak potentially identifiable information by interacting with centralized exchanges.
GeoPyTool: A cross-platform software solution for common geological calculations and plots

GeoPyTool is an open source application developed for geological calculations and plots, such as geochemical classification, parameter calculation, basic statistical analysis and diagrams for structural geology. More than acting as a link from raw data stored in Microsoft Excel® (MS Excel) files to vector graphic files, GeoPyTool includes recently developed routines that have not been included in previous software, such as the calculation of the Ce(IV)/Ce(III) ratio for zircons as a method to examine the temporal evolution of oxygen fugacity in the magmatic source for igneous rocks, and the temperature calculator with titanium in zircon and zirconium in rutile. Besides these routines, GeoPyTool also allows users to load any figure from articles or books as a base map. As a Python-based cross-platform program, GeoPyTool works on Windows®, MacOS X® and GNU/Linux. GeoPyTool can do the whole process from data to results without the dependence of Microsoft Excel®, CorelDraw® and other similar software. It takes Excel® XLSX and CSV (Comma Separated Value) as the formats of both the input data source files and the output calculation results files. The figures generated by GeoPyTool can be saved as portable network graphics (PNG), scalable vector graphics (SVG) or portable document format (PDF). Another highlight of GeoPyTool is the multilingual support, the official version of GeoPyTool supports both Chinese and English, and additional languages can be loaded through interface files. GeoPyTool is still in the development stage and will be expanded with further geochemical and structural geology routines. As an open source project, all source code of GeoPyTool are accessible on Github (https://github.com/GeoPyTool/GeoPyTool). Users with Python experience can join in the development team and build more complex functions expanding the capabilities of GeoPyTool.
Hemodynamic Analysis for Cognitive Load Assessment and Classification in Motor Learning Tasks Using Type-2 Fuzzy Sets

This paper addresses a novel approach to assess and classify the cognitive load of subjects from their hemodynamic response while engaged in motor learning tasks, such as vehicle driving. A set of complex motor-activity-learning stimuli for braking, steering-control, and acceleration is prepared to experimentally measure and classify the cognitive load of the car drivers in three distinct classes: high, medium, and low. New models of general and interval type-2 fuzzy classifiers are proposed to reduce the scope of uncertainty in cognitive load classification due to the fluctuation of the hemodynamic features within and across sessions. The proposed classifiers offer high classification accuracy over 96%, leaving behind the traditional type-1/type-2 fuzzy and other standard classifiers. Experiments undertaken also offer a deep biological insight concerning the shift of brain activations from the orbitofrontal to the ventrolateral prefrontal cortex during the high-to-low transition in cognitive load. Furthermore, the activation of the dorsolateral prefrontal cortex is also reduced during the low cognitive load of subjects. The proposed research work outcome may directly be utilized to identify driving learners with a low cognitive load for difficult motor learning tasks, such as taking a U-turn in a narrow space and motion control on the top of a bridge to avoid possible collision with the car ahead.
Cloak of Invisibility: Privacy-Friendly Photo Capturing and Sharing System

The wide adoption of smart devices with onboard cameras facilitates photo capturing and sharing, but greatly increases people's concern on privacy infringement. Here, we seek a solution to respect the privacy of persons being photographed in a smarter way that they can be automatically erased from photos captured by smart devices according to their intention. To make this work, we need to address three challenges: 1) how to enable users explicitly express their intentions without wearing any visible specialized tag, and 2) how to associate the intentions with persons in captured photos accurately and efficiently. Furthermore, 3) the association process itself should not cause portrait information leakage and should be accomplished in a privacy-preserving way. In this work, we design, develop, and evaluate a system, called COIN (Cloak Of INvisibility), that enables a user to flexibly express her privacy requirement and empowers the photo service provider (or image taker) to exert the privacy protection policy. Leveraging the visual distinguishability of people in the field-of-view and the dimension-order-independent property of vector similarity measurement, COIN achieves high accuracy and low overhead. We implement a prototype system, and our evaluation results on both the trace-driven and real-life experiments confirm the feasibility and efficiency of our system.
Pseudonymization risk analysis in distributed systems

In an era of big data, online services are becoming increasingly data-centric; they collect, process, analyze and anonymously disclose growing amounts of personal data in the form of pseudonymized data sets. It is crucial that such systems are engineered to both protect individual user (data subject) privacy and give back control of personal data to the user. In terms of pseudonymized data this means that unwanted individuals should not be able to deduce sensitive information about the user. However, the plethora of pseudonymization algorithms and tuneable parameters that currently exist make it difficult for a non expert developer (data controller) to understand and realise strong privacy guarantees. In this paper we propose a principled Model-Driven Engineering (MDE) framework to model data services in terms of their pseudonymization strategies and identify the risks to breaches of user privacy. A developer can explore alternative pseudonymization strategies to determine the effectiveness of their pseudonymization strategy in terms of quantifiable metrics: i) violations of privacy requirements for every user in the current data set; ii) the trade-off between conforming to these requirements and the usefulness of the data for its intended purposes. We demonstrate through an experimental evaluation that the information provided by the framework is useful, particularly in complex situations where privacy requirements are different for different users, and can inform decisions to optimize a chosen strategy in comparison to applying an off-the-shelf algorithm.
Attention-Based Deep Learning System for Negation and Assertion Detection in Clinical Notes

Natural language processing (NLP) has been recently used to extract clinical information from free text in Electronic Health Record (EHR). In clinical NLP one challenge is that the meaning of clinical entities is heavily affected by assertion modifiers such as negation, uncertain, hypothetical, experiencer and so on.Incorrect assertion assignment could cause inaccurate diagnosis of patients’ condition or negatively influence following study like disease modelling. Thus, high-performance clinical NLP systems which can automatically detect negation and other assertion status of given target medical findings (e.g. disease,symptom) in clinical context are highly demanded. Here in this work, we propose a deep-learning system based on word embedding and Attention-based idirectional Long Short-Term Memory networks (AttBiLSTM) for assertion detection in clinical notes. Unlike previous state-of-art methods which requireknowledge input, our system is a knowledge poor machine learning system and can be easily extended or transferred to other domains. The evaluation of our system on public benchmarking corpora demonstrates that a knowledge poor deep-learning system can also achieve high performance for detecting negation and assertions comparing to state-of-the-art systems
Self Beyond the Body: Action-Driven and Task-Relevant Purely Distal Cues Modulate Performance and Body Ownership

Our understanding of body ownership largely relies on the Rubber Hand Illusion (RHI) paradigm where synchronous stroking of the real and fake hands leads to an illusion of ownership of RH provided its physical, anatomical, and spatial plausibility. Self-attribution of a fake hand also occurs during visuomotor synchrony, when the visual feedback of self-initiated movements follows the trajectory of the instantiated motor command. In both cases, the experience of ownership is established through bottom-up integration and top-down prediction of multisensory (proximodistal) cues within the peripersonal space. It seems, however, that depending on whether the signals are externally or self-generated, top-down expectation signals are qualitatively different. On the one hand, in RHI, the sensory correlations are modulated by empirically induced priors about the internal model of the body. On the other hand, in mRHI, body ownership is actively shaped by processes allowing for continuous comparison between the actual and predicted sensory consequences of the actions. Ample research demonstrates that the differential processing of the predicted and the actual reafferent information is addressed by the central nervous system via an internal Forward Model (FM). Indeed, results from mRHI suggest that in action-contexts mechanism underlying body ownership could be similar to the FM. Crucially FMs integrate across all sensory input including not only proximodistal (visuomotor) but also purely distal sensory signals (visuoauditory). Thus if body ownership results from a consistency of FM, it would be affected by the (in)congruency of purely distal signals provided that they inform about the action-consequences relevant to the task. Here we explicitly address this question. To test our hypothesis, we devise an embodied VR-based task where action outcomes were signaled by distinct auditory cues. By manipulating the cues with respect to their spatial, temporal and semantic congruency, we show that purely distal feedback violating predictions about action outcomes compromises both body ownership and performance. These results demonstrate that body ownership is influenced by cues which not only pertain to the body itself but also those arising outside of the body, and suggest that in goal-oriented tasks body ownership might result from the consistency of forward models.
Dual-Personality DICOM-TIFF for Whole Slide Images: A Migration Technique for Legacy Software

Despite recently organized Digital Imaging and Communications in Medicine (DICOM) testing and demonstration events involving numerous participating vendors, it is still the case that scanner manufacturers, software developers, and users continue to depend on proprietary file formats rather than adopting the standard DICOM whole slide microscopic image object. Many proprietary formats are Tag Image File Format (TIFF) based, and existing applications and libraries can read tiled TIFF files. The sluggish adoption of DICOM for whole slide image encoding can be temporarily mitigated by the use of dual-personality DICOM-TIFF files. These are compatible with the installed base of TIFF-based software, as well as newer DICOM-based software. The DICOM file format was deliberately designed to support this dual-personality capability for such transitional situations, although it is rarely used. Furthermore, existing TIFF files can be converted into dual-personality DICOM-TIFF without changing the pixel data. This paper demonstrates the feasibility of extending the dual-personality concept to multiframe-tiled pyramidal whole slide images and explores the issues encountered. Open source code and sample converted images are provided for testing.