Reviewerly/exHarmony huggingface dataset:
Robust auxiliary learning with weighting function for biased data

Deep neural networks easily suffer from weak generalization caused by overfitting on biased data. One popular remedy to alleviate this issue is sample reweighting methods that adaptively adjust the importance of biased samples. Separate from the effort to reduce bias, recent works show that the generalization power can be improved by auxiliary tasks. Inspired by the two lines of works, we extend the sample reweighting methods to auxiliary tasks. In this paper, we propose a novel auxiliary learning framework that improves the primary task by adaptively adjusting the weights of samples from multiple tasks rather than samples from a single task using a weighting function. The weighting function is optimized by meta-learning along the gradient of the loss for meta-data, which is a small unbiased validation data. We also present a task-activation score that indicates the correlation between the learning tendency of the training samples and meta-data samples. This score is utilized as a regularizer for meta-learning objective. Our framework can obtain powerful representations for the primary task on biased data by automatically identifying effective combinations of tasks. Our experiments demonstrate that our proposed method consistently outperforms all baselines and state-of-the-art methods on both corrupted labels and class imbalance settings.
On the Logical Expressive Power of Natural Languages

Various constructions found in natural languages whose semantics cannot be represented by first-order logic are recalled. In addition, constraints on denotations of quantifiers and quantifier-like expressions are discussed. Finally, some arguments are given from which it follows that many natural language (verbal) predicates do not denote sets but sets of quantifiers.
Conversational AI Chatbots in Digital Engagement

Digital transformation and globalisation have taken the online business to the next frontier, embracing the customer engagements with conversational artificial intelligence or chatbots. Chatbots are deployed across several industries ranging from e-commerce to healthcare. While the advantages of using chatbots are enormous, chatbots also introduce certain pitfalls. A lack of diversity among creators may result in biased responses from the chatbot. Though chatbots are widely used, not all of their security issues are satisfactorily resolved. It causes significant security issues and risks, which needs immediate attention. Many chatbots are built on top of social/messaging platforms, which has its own set of terms and conditions governing data collection and usage. This work gives a detailed analysis of security considerations in the context of communication with bots. This chapter has the potential to spark a debate and draw attention to the issues surrounding data storage and usage of chatbots to protect users.
The Gendered Nature of Chatbots

The majority of chatbots are built, by default, as women. In doing so, dangerous stereotypes and behaviors are perpetuated by those responsible for designing the chatbots, and ultimately the users. It is therefore crucial that gender identity and expression are well understood by all those involved in designing the chatbots. This chapter explores this alongside a literature survey regarding feminist methodologies, anthropomorphism, and authenticity to put forward three recommendations. That those responsible for building chatbots should keep up to date with research, look to widen the diversity of their own team, and to integrate ethics in their design processes. Only in doing so will chatbots that are fit for purpose be built.
New necessary conditions for the existence of finite non-Desarguesian flag-transitive projective planes

This paper studies the existence of finite non-Desarguesian flag-transitive projective planes, giving necessary conditions in terms of polynomial equations over finite fields of characteristic 3. This sheds light on the longstanding conjecture that every finite flag-transitive projective plane is Desarguesian.
Design variations and evaluation of loanable technology web pages in academic library websites

Loanable technology is an emerging specialized collection in academic libraries. An effective collection depends on several moving parts, including funding; space; a collection management strategy; and information systems, including web pages, to support access to the collection. The current study surveys websites from institutions carrying the Carnegie classification of “Doctoral University: Very High Research” to determine the existence and design variations of loanable technology websites. The paper notes tendencies and exceptions in the designs and develops a general design framework to guide future design decisions. The results offer design options and recommendations based on variations of affordances; the scope and variety of the items in collections; and the use of images, catalog records, and supporting documents. A structural analysis of design variations can thus enhance the coherence, completeness, and overall effectiveness of loanable technology websites.
Top secret: Integrating 20 years of research on secrecy

Secrets are a double-edged sword. They are crucial for protecting valuable knowledge and appropriating value from innovation; they also invite consumer curiosity and in doing so may generate demand. On the other side, secrecy may invite suspicions, distrust, and miscommunication. In this paper, we review the growing body of research on secrets and secrecy. Our review reconciles the various theoretical perspectives on secretive protective mechanisms and their consequences, both positive and potentially detrimental. We adopt a dynamic relational perspective towards secrecy and develop a multilevel framework to categorize and define four major types of secrets: trade secrets, reputation secrets, power secrets, and marketing mix secrets. Finally, we outline a research agenda by pointing out understudied themes and underemployed theoretical perspectives on secrecy.
Inexact tree pattern matching with 1-degree edit distance using finite automata

Given an input tree and a tree pattern, the inexact tree pattern matching problem is about finding all subtrees in the input tree that match the tree pattern with up to k errors. To measure the number of errors between two labeled ordered trees, we use the 1-degree edit distance that uses operations node relabel, leaf insert, and leaf delete. The proposed solution is based on a finite automaton that reads a given input tree represented in linear, prefix bar, notation. First, we show its construction for a constrained variant of 1-degree edit distance where leaf insert/delete operations cannot be applied to the tree pattern recursively. Then, we extend this approach to both unit cost and non-unit cost 1-degree edit distance. The deterministic version of the proposed finite automaton finds all inexact occurrences of the tree pattern in time linear to the size of the input tree. However, since the size of such automaton can be exponential in the number of nodes of the tree pattern, it is practical only for small patterns. Therefore, we also consider the dynamic programming approach as a way of simulating the nondeterministic finite automaton. This approach comes with the space complexity O(mk) and time complexity O(kmn) where m is the number of nodes of the tree pattern, n is the number of nodes of the input tree, and k is the maximum number of errors allowed.
Chapter 11. Customer support agents in Spanish live chats

Little research has been addressed to multimodal politeness in professional-layperson interactions such as between agents and customers in online platforms such as live chats. Customer support plays a significant role in commercial firms and institutions given two functions: achieving successful interactions with customers to maintain their loyalty and, the public image of the company. Thus, this ethnographic study focuses on the effects of live-chat customer support agents including or omitting affective communicative cues such as emoticons, among others, as multimodal politeness strategies in interactions with customers. The illustrative examples here show that when live-chat agents use non-verbal affective communicative markers, interactions become more personal. This improves the level of service satisfaction in customers and the public image of the company.
Sulfide disseminations and hydrothermal alteration haloes in the Gabal Monqul area, Egypt: Field, mineralogical, and remote sensing studies

Field and mineralogical studies are integrated with multispectral and radar satellite data to unravel the occurrence of sulfide dissemination in the Gabal Monqul area (Eastern Desert, Egypt). This study was motivated by the strong belief that the widespread hydrothermal alteration haloes and sulfide disseminations in this area were ambiguously deciphered as a porphyry-style mineralisation. NE-Elongate bodies of silicified and sulfidized rocks frequently occur at the contacts between andesite-dacite, granodiorite, and porphyritic biotite granite intrusions. Hydrothermal alteration zones are best identified and outlined by employing band ratios (BR) and independent component analysis (ICA) techniques for the distinction between ferric and ferrous iron minerals. If fused with the clay and OH-bearing minerals maps, ferric iron oxides may characterise epithermal and porphyry mineralisation in which ore formation occurs under oxidising conditions. Iron-hydroxide, copper hydro-carbonate (malachite), and hydrous copper phosphate (pseudomalachite) are supergene mineral phases. The resultant processed images show that alteration extends beyond unit boundaries and overlaps different crosscutting lithologies. This observation implies that hydrothermal alteration of the host rock represents a secondary overprint, yet gypsum (±anhydrite) and barite in association with iron oxides and pyrite disseminations pinpoint a porphyry-related alteration assemblage. The poor metal endowment in the potassic alteration zones goes against the supergene enrichment typical of the porphyry mineralisation system and is coupled with the lack of hydrothermal breccia bodies, implying a rather epithermal sulfidation genesis. However, a thorough assay program is required to outline the potential metal-rich lithological unit(s) using XRD and remote sensing investigations that point to zonal alteration anomalies.
Exploring the Linkage Amid Dimensions of Virtual Learning Service Quality, Virtual learning Learner Satisfaction, and Loyalty

The core intention of this study is to examine the impact of virtual learning service quality attributes on the virtual learning learner's satisfaction and loyalty in India. The current study was conducted in universities of India's best cities for higher education according to the IIRF ranking 2022. Data were specifically collected from the universities who have implemented virtual courses. Three constructs with 35 statements are identified using the exploratory factor analysis. Virtual learning system quality, virtual learning instructor and content quality, and virtual learning management service quality were the three extracted factors. The findings of study revealed that the most significant factor is virtual learning system quality, followed by virtual learning instructor and content quality and virtual learning management quality among the numerous attributes of virtual learning service quality.
Multigraph learning for parallelism discovery in sequential programs

Summary Parallelization is an optimization technique that is playing an increasingly vital role in software applications. Discovering the potential parallelism in sequential programs is of primary importance for parallelization. To this end, various tools have been created to obtain parallelizable targets by analyzing the dependencies in sequential programs. However, the majority are constructed based on manually designed dependence analysis rules. Consequently, they must follow some constraints, which limit the scope of their program analysis capability. Furthermore, constructing a dependency analysis rule is a complicated and highly sophisticated endeavor. In this study, we exploit the fact that neural network models can learn the potential features of programs to tackle the parallelism discovery task in an end‐to‐end manner. Specifically, we developed a multigraph learning architecture on top of multiple abstract code representations in a complementary manner. In the architecture, we equip code representations with different neural network models in a targeted manner, including a deep convolutional neural network for analyzing both control and data flows and a novel neural network for learning the abstract syntax trees. Experimental results obtained on a common parallelism discovery dataset indicate that the developed multigraph learning architecture can learn potential parallelism patterns in sequential programs with high accuracy and efficiency.
Shigeru Miyagawa, Syntax in the treetops (Linguistic Inquiry Monographs). Cambridge, MA &amp; London: MIT Press, 2022. Pp. xviii + 234.

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Multilayer self‐attention residual network for code search

Summary Software developers usually search existing code snippets in open source code repositories to modify and reuse them. Therefore, how to get the right code snippet from the open‐source code repository quickly and accurately is the focus of current software development research. Nowadays, code search is one of the solutions. To improve the accuracy of source code feature information representation and the accuracy of code search. A multilayer self‐ attention residual network‐based code search model (MSARN‐CS) is proposed in this paper. In the MSARN‐CS model, not only the weight of each word in the code sequence unit is considered but also the effect of embedding between code sequence units is calculated. In addition, an optimization model of residuals is introduced to compensate for the loss of information in the code sequences during the model training. To verify the search effectiveness of the MSARN‐CS model, three other baseline models are compared on the basis of extensive source code data. The experimental results show that the MSARN‐CS model has better search results compared with the baseline model. For parameter Recall@1, the experimental result of MSARN‐CS model was 9.547, which as 100.90%, 73.87%, 60.37%, and 2.55% better compared to CODEnn, CRLCS, SAN‐CS‐ and SAN‐CS, respectively. For the parameter Recall@5, the results improved by 26.67%, 36.23%, 36.21%, and 1.63%, respectively, and for the parameter Recall@10, the results improved by 13.92%, 25.70%, 20.78%, and 2.23%, respectively. For the parameter mean reciprocal rank, the results improved by 52.89%, 76.17%, 63.38%, and 3.88%, respectively. For the parameter normalized discounted cumulative gain, the results improved by 54.22%, 60.55%, 50.28%, and 3.30%, respectively. The MSARN‐CS model proposed in the paper can effectively improve the accuracy of code search and enhance the programming efficiency of developers.
Batch classifier with adaptive update for backbone traffic classification

Network traffic analysis is an important method for ISPs to know the status of the network. However, the high speed and rapid evolution of backbone network traffic have brought new challenges to traffic analysis. Most existing traffic classifiers are based on full traffic, while processing all the large-scale backbone network traffic is time-intensive and resource-consuming. Moreover, the relationship between feature vectors and classification labels drifts with the rapid evolution of backbone network traffic, which leads to the degradation of the performance of the classification model. This paper presents a model that can implement real-time classification of sampled backbone network traffic and adaptively update the classification model when concept drift occurs. To accurately classify the backbone network traffic in real time, we propose the Multiple Counter Sketch (MC Sketch) to quickly extract feature vectors from the sampled traffic and design an Adaptive Batch Classifier based on Agglomerative Clustering (A-BCAC) to perform unsupervised batch clustering of feature vectors. Using the labels obtained from A-BCAC, we trained the classifier to classify the backbone traffic. In addition, we design an adaptive batch update model based on concept drift detection to solve the concept drift problem. The experimental results on sampled traffic collected on a 10Gbps link and the comparisons with state-of-the-art studies demonstrate the performance and efficiency of our method.
Why Healthcare Needs Blockchain

As Healthcare evolves into a more patient-centric and digitised experience, we need a foundational technology layer that enables trust. Data exchange is at the heart of how health systems succeed or fail. Trust in how this data currency is transferred is critical to patient care, administrative efficiency, commercial relationships, and the advancement of medical science in general. The fundamental value proposition of blockchain is that it enables disparate, potentially competing entities to transact peer-to-peer via a shared foundational infrastructure that brokers the trust between them. It manages the rules and builds the roads upon which a trusted data economy can exist. Our current healthcare systems are broken - precisely because the flow of critical information cannot occur without clinical or commercial risk. Blockchain is transforming the financial services industry through disintermediation and friction reduction with blockchain. Healthcare must similarly be transformed. This chapter explores this claim, examines the reality of what has been developed thus far and offers a vision for how we might achieve a better future for healthcare. There will most certainly be barriers, but we believe that these are worth overcoming. This is what drives us forward. It is, we believe, essential for good healthcare.
Machine Learning for Decision Support Systems: Prediction of Clinical Deterioration

In-hospital clinical deterioration could lead to unfavorable adverse events, such as mortality, cardiac arrest, or unplanned admission to the intensive care unit. Early detection of deterioration allows clinical staff to respond in a timely manner in order to avoid such events. Advancements in data digitization and computational power enable the development of new algorithms for the prediction of clinical deterioration. Such algorithms, traditionally based on simple aggregate-based Early Warning Score (EWS) systems, seek to drive the inference engine of clinical decision support systems. They geared even more attention since the coronavirus disease 2019 pandemic to support the prognosis of in-hospital patients. The purpose of this chapter is to provide a brief overview of classical EWS systems as well as systems based on state-of-the-art machine learning and deep learning. We also compare their strengths and limitations, summarize current findings on the clinical impact of EWS systems in practice, and provide a future outlook on early warning clinical decision support systems based on current needs.
A Study on Self-Supervised Object Detection Pretraining

In this work, we study different approaches to self-supervised pretraining of object detection models. We first design a general framework to learn a spatially consistent dense representation from an image, by randomly sampling and projecting boxes to each augmented view and maximizing the similarity between corresponding box features. We study existing design choices in the literature, such as box generation, feature extraction strategies, and using multiple views inspired by its success on instance-level image representation learning techniques [6, 7]. Our results suggest that the method is robust to different choices of hyperparameters, and using multiple views is not as effective as shown for instance-level image representation learning. We also design two auxiliary tasks to predict boxes in one view from their features in the other view, by (1) predicting boxes from the sampled set by using a contrastive loss, and (2) predicting box coordinates using a transformer, which potentially benefits downstream object detection tasks. We found that these tasks do not lead to better object detection performance when finetuning the pretrained model on labeled data.
Internet Curiosity: Directed Unsupervised Learning on Uncurated Internet Data

We show that a curiosity-driven computer vision algorithm can learn to efficiently query Internet text-to-image search engines for images that improve the model's performance on a specified dataset. In contrast to typical self-supervised computer vision algorithms, which learn from static datasets, our model actively expands its training set with the most relevant images. First, we calculate an image-level curiosity reward that encourages our model to find the most useful images for pre-training. Second, we use text similarity scores to propagate observed curiosity rewards to untried text queries. This efficiently identifies relevant semantic clusters without any need for class labels or label names from the targeted dataset. Our method significantly outperforms models that require 1–2 orders of magnitude more compute and data.
A Survey on Automatic Delineation of Radiotherapy Target Volume based on Machine Learning

ABSTRACT Radiotherapy is one of the main treatment methods for cancer, and the delineation of the radiotherapy target area is the basis and premise of precise treatment. Artificial intelligence technology represented by machine learning has done a lot of research in this area, improving the accuracy and efficiency of target delineation. This article will review the applications and research of machine learning in medical image matching, normal organ delineation and treatment target delineation according to the procudures of doctors to delineate the target volume, and give an outlook on the development prospects.
Expertise and information: an epistemic logic perspective

In this paper we present a modal logic framework to reason about the expertise of information sources. A source is considered an expert on a proposition φ if they are able to correctly refute φ in any possible world where φ is false. Closely connected with expertise is a notion of soundness of information: φ is said to be "sound" if it is true up to lack of expertise of the source. That is, any statement logically weaker than φ on which the source has expertise must in fact be true. This is relevant for modelling situations in which sources make claims beyond their domain of expertise. Particular attention is paid to the connection between expertise and knowledge: we show that expertise and soundness admit precise interpretations in terms of S4 and S5 epistemic logic, under certain conditions. We go on to extend the framework to multiple sources, defining two notions of collective expertise. These also have epistemic interpretations via distributed and common knowledge from multi-agent epistemic logic. On the technical side, we give several sound and complete axiomatisations of various classes of expertise models.