bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of the efficacy of dapagliflozin and linagliptin in the treatment of type 2 diabetes patients with normal body mass index and poor metformin monotherapy
Authors: He Mingli , Jiang Li , Zhang Su , Yang Xinrong
Published: June 2024

Objective: To explore the efficacy and safety of dapagliflozin and linagliptin in the treatment of type 2 diabetes patients (T2DM) with normal body mass index (BMI) who have poor single drug efficacy of metformin. Method: 102 T2DM patients with normal BMI who visited our hospital from December 2021 to March 2023 were randomly divided into an observation group (51 cases) and a control group (51 cases) using a random number table method. The control group patients were treated with linagliptin in addition to metformin treatment, while the observation group was treated with dapagliflozin in addition to metformin treatment. The general indicators, blood glucose metabolism, biochemical indicators, and adverse reactions of the two groups of patients were compared. Result: After treatment, the body weight and BMI of both the observation group and the control group slightly decreased, while the systolic blood pressure of the observation group decreased. The changes in systolic blood pressure of the control group were not significant, and the differences were not statistically significant ([Formula: see text]); after treatment, the fasting blood glucose (FBG) and glycated hemoglobin (HbA1c) of both groups of patients decreased, and the FPG and HbA1c of the control group were not significantly lower than those of the observation group ([Formula: see text]). After treatment, the values of blood creatinine, blood uric acid, urinary microalbumin (UAlb), and urinary microalbumin/creatinine (UACR) in both groups of patients decreased, and the decrease in various biochemical indicators in the control group was not as significant as that in the observation group. The comparison of blood uric acid, UAlb, and UACR between the two groups showed statistical significance ([Formula: see text]). The incidence of adverse reactions in the observation group and control group patients was 8.70% and 13.04%, respectively, and there was no statistically significant difference ([Formula: see text]) after comparison. Conclusion: Both dapagliflozin and linagliptin have shown good efficacy in the treatment of T2DM patients with normal BMI and poor efficacy in monotherapy with metformin. Compared to linagliptin, dapagliflozin can cause slight weight loss in patients, but its control effect on blood glucose and blood pressure is better than that of linagliptin. The risk of hypoglycemia is lower, and it has a protective effect on the kidneys. The disadvantage is that it slightly increases the risk of urogenital tract infections, Overall, it has high security and can be promoted for use.
Caputo fractional model for the predator–prey relation with sickness in prey and refuge to susceptible prey
Authors: Sharma Shivani , Dubey Ravi Shanker , Chaudhary Arun
Published: June 2024

This research develops a model describing a relation between a predator and a prey where the prey species are divided into two classes i.e., susceptible prey species and the infected prey species. Furthermore, the susceptible prey species are also provided with a refuge. After this, the research expands the formulated model into a model with Caputo fractional derivative. A detailed analysis of the solution of Caputo fractional model for the predator–prey relation with a sickness in prey and a refuge to susceptible prey is done. Numerical solutions along with the simulations are briefed for the Caputo fractional model. Lastly, the phase portrait diagrams are also given for better understanding.
Impact of Language-Specific Training on Image Caption Synthesis: A Case Study on Low-Resource Assamese Language
Authors: Choudhury Pankaj , Guha Prithwijit , Nandi Sukumar
Published: June 2024

Automatic Image Captioning (AIC) refers to the process of synthesizing semantically and syntactically correct descriptions for images. Existing research on AIC has predominantly focused on the English language. Comparatively, lower numbers of works have focused on developing captioning systems for low-resource Indian languages like Assamese. This paper investigates AIC for the Assamese language using two distinct approaches. The first approach involves utilizing state-of-the-art AIC model pretrained on an English image-caption dataset to generate English captions for input images. Next, these English captions are translated to the Assamese language using a publicly available automatic translator. The second approach involves exclusively training the AIC model using an Assamese image-caption dataset to predict captions directly in Assamese. The experiments are performed on two types of state-of-art models, one which uses LSTM as a decoder and the other one uses a transformer. Through extensive experimentation, the performance of these approaches is evaluated both quantitatively and qualitatively. The quantitative results are obtained using automatic metrics such as BLEU-n and CIDEr. For qualitative analysis, human evaluation is performed. The comparative performances between the two approaches reveal that models trained exclusively on Assamese image-caption datasets achieve superior results both in terms of quantitative measures and qualitative assessment when compared to models pretrained on English and subsequently translated into Assamese.
Roemerine, a Phytoconstituent of Annona senegalensis, Targets MAO-A in Alzheimer’s Disease: Network Pharmacology Integrated with Molecular Docking and Dynamics Studies
Authors: Kumar Hitesh , Datusalia Ashok Kumar , Kumar Anoop , Khatik Gopal L.
Published: June 2024

Alzheimer’s disease (AD) is a common factor contributing to cognitive decline in aged people. The etiology of AD involves several mechanisms, including the breakdown of acetylcholine, the accumulation of amyloid beta, the formation of neurofibrillary tangles and inflammation. Several targets have been discovered in the study that are useful in reducing the cognitive decline associated with AD. By employing network pharmacology, molecular docking and MD simulations, we have ascertained that Roemerine can specifically interact with MAO-A. Network pharmacology identified a set of genes with a higher degree of interactions. MAO-A was identified as the gene with the highest interaction level with 37 other genes. MAO-A gene was interconnected with other top 10 genes with overall higher degrees of interactions. Roemerine molecule showed a binding energy of −5.27 kcal/mol, which was better than the reference ligand found to be −4.43 kcal/mol. MD simulations demonstrated that the average RMSD for Roemerine was 4.67 Å, whereas the average RMSF was 1.06 Å. Conversely, the unbound protein and the reference chemical exhibited average RMSD values of 5.57 Å and 5.74 Å, respectively. In addition, the average RMSF values for them were 1.30 Å and 1.00 Å, respectively. Based on these results, it may be inferred that Roemerine can inhibit MAO-A and potentially be used as a lead for AD.
Computational Exploration of Atriplex halimus Phytocompounds: A Targeted Approach Toward Inhibiting SARS-CoV-2
Authors: Roubi Mohammed , Dalli Mohammed , Azizi Salah-eddine , Mahdi Youness , Mothana Ramzi A. , Alanzi Abdullah R. , Conte Raffaele , Gseyra Nadia
Published: June 2024

The primary aim of this investigation is to shed light on potential targets within the main protease Mpro, spike protein of SARS-CoV-2, and Angiotensine converting enzyme 2 (ACE2) for the exploration of novel inhibitors derived from therapeutic natural compounds originating from Atriplex halimus (AH). Many constituents were discerned within AH extracts through comprehensive gas chromatography/mass spectrometry (GC/MS). Notably, compounds, such as oleocanthal and methyl stearate, exhibited promising attributes across various biological activities, including inhibition of key proteins associated with SARS-CoV-2. Furthermore, absorption, distribution, metabolism, excretion, and toxicity (ADMET) settings were evaluated to explore the potential safety and viability of these compounds for pharmaceutical application, affirming their non-interference with human physiology and thus suggesting their suitability for pharmaceutical use. Catechin, epigallocatechin, and methyl stearate were discovered to be the compounds with the highest affinity to the studied protein showing a great potential to be inhibitors to COVID-19. Consequently, this discovery highlights Atriplex halimus as a promising candidate for utilization in combating the virus.
Machine learning Kronecker coefficients
Authors: Lee Kyu-Hwan
Published: June 2024

The Kronecker coefficients are the decomposition multiplicities of the tensor product of two irreducible representations of the symmetric group. Unlike the Littlewood–Richardson coefficients, which are the analogues for the general linear group, there is no known combinatorial description of the Kronecker coefficients, and it is an NP-hard problem to decide whether a given Kronecker coefficient is zero or not. In this paper, we show that standard machine learning algorithms such as Nearest Neighbors, Convolutional Neural Networks and Gradient Boosting Decision Trees may be trained to predict whether a given Kronecker coefficient is zero or not. Our results show that a trained machine can efficiently perform this binary classification with high accuracy ([Formula: see text]0.98).
Iterative Prompt Refinement for Mining Gene Relationships from ChatGPT
Authors: Chen Yibo , Gao Jeffrey , Petruc Marius , Hammer Richard D. , Popescu Mihail , Xu Dong
Published: June 2024

ChatGPT has demonstrated its potential as a surrogate knowledge graph. Trained on extensive data sources, including open-access publications, peer-reviewed research articles, and biomedical websites, ChatGPT extracted information on gene relationships and biological pathways so that it can be used to predict them. However, a major challenge is model hallucination, that is, high false positive rates. To assess and address this challenge, we systematically evaluated ChatGPT’s capacity for predicting gene relationships using GPT-3.5-turbo, GPT-4, and GPT-4o. Benchmarking against the KEGG Pathway Database as the ground truth, we experimented with diverse prompting strategies, targeting gene relationships of activation, inhibition, and phosphorylation. We introduced an innovative iterative prompt refinement technique. By assessing prompt efficacy using metrics such as F-1 score, precision, and recall, GPT-4 suggested improved prompts. A refined prompt, which combines a specialized role with explanatory text, significantly enhanced the performance. Going beyond pairwise gene relationships, we also deciphered complex gene interplays, such as gene interaction chains and pathways pertinent to diseases such as non-small cell lung cancer. Direct prompts showed limited success, but “least-to-most” prompting exhibited significant potentials for such network constructions. The methods in this study may be used for other bioinformatics prediction problems.
Late Paleozoic intra-oceanic arc and its accretionary complex in East Junggar (NW China): implications for multiple arc amalgamation in the southern Altaids
Authors: Tan Yiying , Xiao Wenjiao , Song Dongfang , Mao Qigui , Gao Mengqi , Ao Songjian
Published: June 2024

The East Junggar is a key to understanding the accretionary history of the southern Altaids. However, the tectonic setting and amalgamation history of the East Junggar during the Carboniferous–Permian remain controversial. Here we report new field, geochronological and geochemical data for the Carboniferous–Permian volcanic and sedimentary rocks from the Jiangjun unit and the Hongliugou ophiolitic complex of East Junggar to constrain its tectonic evolution. Carboniferous volcanic rocks, with zircon and apatite U–Pb ages from 337 ± 2 to 303 ± 32 Ma, display arc-related geochemical characteristics. Carboniferous–Permian sedimentary rocks predominantly contain Carboniferous–Permian detrital zircons and have maximum depositional ages from 318 ± 5 to 281 ± 6 Ma. Provenance analysis indicates that the sediments from the west Jiangjun unit were sourced from the Yemaquan arc and those from the east Jiangjun unit may be sourced solely from a new intra-oceanic arc. Integrated data indicate that Carboniferous–Permian strata in the Jiangjun unit were formed in the intra-oceanic arc setting that remained until the middle Permian. These analyses suggest an amalgamation process of multiple convergence and accretion for the southern Altaids during the formation of NE Pangaea in the late Paleozoic.
An integrated convolutional neural network prediction framework for
in situ
shale oil content based on conventional logging data
Authors: Qiao Lu , Yang Shengyu , Hu Qinhong , Wang Huijun , He Taohua
Published: June 2024

The quantification of total organic carbon (TOC) and the free hydrocarbon content (S
1
) is crucial for evaluating the shale oil generation and bearing properties of source rocks. This study aimed to enhance the accuracy of TOC and S
1
quantification in shale oil evaluations. The scope encompassed the development of a novel deep learning framework to overcome the limitations of traditional physical and machine learning or deep learning methods. This paper proposes an integrated swarm optimization algorithm–convolutional neural network/machine learning framework. This framework uses a swarm optimization algorithm for the hyperparameter optimization of a convolutional neural network or machine learning framework, utilizing experimental data from core samples preserved in liquid nitrogen alongside well logging data. The application of the proposed framework to the H11 well in the Subei Basin, China, using 110 core samples, demonstrated a superior performance. The results validate the framework's effectiveness in predicting the TOC and S
1
contents at various depths. The proposed framework stands out for its convenient methodology, wide application range and high precision in prediction. These attributes contribute significantly to the field of petroleum engineering and development, offering a novel approach that promises to enhance both the efficiency and accuracy of well logging evaluation.
Stochastic Optimization and Learning for Two-Stage Supplier Problems
Authors: Brubach Brian , Grammel Nathaniel , Harris David G. , Srinivasan Aravind , Tsepenekas Leonidas , Vullikanti Anil
Published: June 2024

The main focus of this article is radius-based (supplier) clustering in the two-stage stochastic setting with recourse, where the inherent stochasticity of the model comes in the form of a budget constraint. In addition to the standard (homogeneous) setting where all clients must be within a distance

\(R\)

of the nearest facility, we provide results for the more general problem where the radius demands may be
inhomogeneous
(i.e., different for each client). We also explore a number of variants where additional constraints are imposed on the first-stage decisions, specifically matroid and multi-knapsack constraints, and provide results for these settings.

We derive results for the most general distributional setting, where there is only black-box access to the underlying distribution. To accomplish this, we first develop algorithms for the
polynomial scenarios
setting; we then employ a novel
scenario-discarding
variant of the standard
Sample Average Approximation
method, which crucially exploits properties of the restricted-case algorithms. We note that the scenario-discarding modification to the SAA method is necessary to optimize over the radius.
Prototyping a Zoomorphic Interactive Robot Companion with Emotion Recognition and Affective Voice Interaction for Elderly People
Authors: Schnitzer Benjamin , Vural Umut Can , Schnitzer Bastian , Sardar Muhammad Usman , Fuerst Oren , Korn Oliver
Published: June 2024

An aging society paired with a skilled labor shortage, particularly in European countries, requires a rethinking of deprecated structures. Intelligent assistive technologies, specifically socially assistive robots, addressing the gap between caretakers and elderly people in need of care have moved into the focus of debate due to their potentials to reduce costs, improve independence, and eventually raise quality of life. In this work, we outline the potentials of zoomorphic robot companions combining intelligent conversational abilities and emotion recognition. We then describe the prototyping of an emotion-sensing zoomorphic interactive robot companion including the development and implementation of a multimodal emotion recognition framework. This framework uses speech emotion recognition, sentiment analysis, and affective voice interaction based on a large language model. The prototyping has been accompanied by two studies on elderly peoples' design preferences regarding the proposed feature set as well as different embodiments to find the appropriate casing for the robot companion. This work provides valuable insights into the prototyping and can thus support future research endeavors in this area.
Integration of Extended Reality with a Cyber-Physical Factory Environment and its Digital Twins
Authors: Emporio Marco , Caputo Ariel , Pintani Deborah , Cheng Dong Seon , De Marchi Thomas , Forte Gianmaria , Fummi Franco , Giachetti Andrea
Published: June 2024

In this paper, we present an example of complete integration of eXtended Reality technologies within a demonstration laboratory showcasing Industry 4.0/5.0 compliant machinery in realistic scenarios of use. We describe the design choices and the implementation of the augmented and virtual reality applications developed and potentially usable to support different real-world tasks, featuring advanced gesture-based interaction modes. We also describe the optimized communication architecture used to synchronize data between the cyber-physical factory environment with all its components, its industrial digital twin, and the augmented and virtual replica of the factory.

Example tasks supported with the tools in public demonstrations allow users wearing Microsoft HoloLens 2 or Meta Quest 2 headsets to monitor the status of the prototype production line and operate on it, locally or remotely.

An example video showing the applications is available in the supplementary material.
A Type System for Flexible User Interactions Handling
Authors: Blouin Arnaud
Published: June 2024

Engineering user interfaces involves the use of multiple user interactions. Developers may struggle with programming and using those user interactions because of a lack of flexibility that affects the current user interface programming approaches. First, developers may want to switch from one user interaction to another close one or combine multiple user interactions without changing much code. Second, developers may also want to use several user interactions to concisely produce the same user command. Third, developers may want to be warned about conflicts between involved user interactions. Currently, developers can hardly perform these first two cases without applying numerous code changes or producing boilerplate code. Regarding the third case, developers can only observe such issues during the execution of the interactive systems, which prolongs the detection time. To overcome these three issues this paper proposes a user interaction type system. This user interaction type system reifies user interactions as first-class concerns with typing facilities for enabling user interactions substitutability and union. It also allows the writing of type checking rules to check for possible issues related to user interactions at compile time. We implemented the type system within the TypeScript version of Interacto, a framework for processing user interactions. We evaluated the soundness and the expressiveness of our approach through several implemented use cases. This demonstrates the feasibility of the proposed approach and its ability to overcome the three mentioned issues.
Does the Medium Matter? A Comparison of Augmented Reality Media in Instructing Novices to Perform Complex, Skill-Based Manual Tasks.
Authors: Dhiman Hitesh , Röcker Carsten
Published: June 2024

Past research comparing augmented reality (AR) media such as in-situ projection and head-mounted devices (HMD) has usually considered simple manual activities. It is unknown whether previously reported differences between different AR media also apply to complex, skill-driven tasks. In this paper, we explore the feasibility and challenges in designing AR instructions for expertise-driven, skilled activities. We present findings from a real-world, between-subjects experiment in which novices were instructed to trim and bone sub-primal cuts of pork using two interactive AR prototypes, one utilizing in-situ projection and a second using the Hololens 2. The prototypes and instructions were designed in consultation with experts. We compared novices' task performance and subjective perceptions and gathered experts' feedback. Although both users and experts indicated a subjective preference for in-situ projection, results indicate that when tasks require knowledge, skill and expertise, the choice of the AR medium itself may not be consequential. Rather, in our experiment, the instruction quality influenced comprehension, knowledge retention and task performance. Hence, from an engineering perspective, emphasis ought to be laid on gathering and structuring expert performance and knowledge to create effective instructions, which could be delivered using any AR medium suited to the task and work environment.
Inline Visualization and Manipulation of Real-Time Hardware Log for Supporting Debugging of Embedded Programs
Authors: Bianchi Andrea , Yap Zhi Lin , Lertjaturaphat Punn , Henley Austin Z. , Moon Kongpyung Justin , Kim Yoonji
Published: June 2024

The advent of user-friendly embedded prototyping systems, exemplified by platforms like Arduino, has significantly democratized the creation of interactive devices that combine software programs with electronic hardware. This interconnection between hardware and software, however, makes the identification of bugs very difficult, as problems could be rooted in the program, in the circuit, or at their intersection. While there are tools to assist in identifying and resolving bugs, they typically require hardware instrumentation or visualizing logs in serial monitors. Based on the findings of a formative study, we designed Inline a programming tool that simplifies debugging of embedded systems by making explicit the internal state of the hardware and the program's execution flow using visualizations of the hardware logs directly within the user's code. The system's key characteristics are 1) an inline presentation of logs within the code, 2) real-time tracking of the execution flow, and 3) an expression language to manipulate and filter the logs. The paper presents the detailed implementation of the system and a study with twelve users, which demonstrates what features were adopted and how they were leveraged to complete debugging tasks.
TARPS: A Toolbox for Enhancing Privacy and Security for Collaborative AR
Authors: Krings Sarah Claudia , Yigitbas Enes
Published: June 2024

Modern AR applications collect a wide range of data to leverage context-specific functionalities. This includes data that might be private or security-critical (e.g., the camera view of a private home), calling for protective measures, especially in collaborative settings where data is inherently shared. A literature research revealed a lack of development support for privacy and security in collaborative AR. This makes it difficult for developers to find the time and resources to include protection mechanisms, leading to very limited options for end-users to control what data about them is shared. To address this problem, we present TARPS, a development Toolbox for enhancing collaborative AR applications with Privacy and Security protection mechanisms. TARPS is an out-of-the-box solution to add protection features to collaborative AR applications in a configurable manner. In developer interviews, the idea of TARPS was well received and an end-user study with an application created using TARPS showed that the included protection features were usable and accepted by end-users.
Evaluation of Code Generation for Simulating Participant Behavior in Experience Sampling Method by Iterative In-Context Learning of a Large Language Model
Authors: Khanshan Alireza , Van Gorp Pieter , Markopoulos Panos
Published: June 2024

The Experience Sampling Method (ESM) is commonly used to understand behaviors, thoughts, and feelings in the wild by collecting self-reports. Sustaining sufficient response rates, especially in long-running studies remains challenging. To avoid low response rates and dropouts, experimenters rely on their experience, proposed methodologies from earlier studies, trial and error, or the scarcely available participant behavior data from previous ESM protocols. This approach often fails in finding the acceptable study parameters, resulting in redesigning the protocol and repeating the experiment. Research has shown the potential of machine learning to personalize ESM protocols such that ESM prompts are delivered at opportune moments, leading to higher response rates. The corresponding training process is hindered due to the scarcity of open data in the ESM domain, causing a cold start, which could be mitigated by simulating participant behavior. Such simulations provide training data and insights for the experimenters to update their study design choices. Creating this simulation requires behavioral science, psychology, and programming expertise. Large language models (LLMs) have emerged as facilitators for information inquiry and programming, albeit random and occasionally unreliable. We aspire to assess the readiness of LLMs in an ESM use case. We conducted research using GPT-3.5 turbo-16k to tackle an ESM simulation problem. We explored several prompt design alternatives to generate ESM simulation programs, evaluated the output code in terms of semantics and syntax, and interviewed ESM practitioners. We found that engineering LLM-enabled ESM simulations have the potential to facilitate data generation, but they perpetuate trust and reliability challenges.
End-User Development for Human-Robot Interaction: Results and Trends in an Emerging Field
Authors: Vaiani Giacomo , Paternò Fabio
Published: June 2024

This paper presents a comprehensive survey on End-User Development for Human-Robot Interaction, examining existing literature to validate findings and identify unexplored areas for future research. It explores the importance of End-User Development in allowing non-expert users to customise robots, covering methodologies, evaluation methods, robot types, and application contexts. The findings reveal various End-User Development approaches, evaluation practices, and robots application domains, leading to discussions on the untapped potential of End-User Development in enhancing Human-Robot Interaction across diverse fields. The document aims to provide groundwork for future studies, highlighting the necessity for new evaluation standards and greater customisation in robotic technologies.
PACMHCI - Engineering Interactive Computing Systems, June 2024: Editorial Introduction
Authors: Santoro Carmen , Dittmar Anke
Published: June 2024

Welcome to this issue of the Proceedings of the ACM on Human-Computer Interaction, bringing together contributions from the community on Engineering Interactive Computing Systems (EICS). The EICS track of the PACM-HCI is the primary venue for research contributions at the intersection of Human-Computer Interaction (HCI) and Software Engineering.
Design Goals for End-User Development of Robot-Assisted Physical Training Activities: A Participatory Design Study
Authors: De La Rosa Gutierrez Jose Pablo , Silva Thiago Rocha , Dittrich Yvonne , S⊘rensen Anders Stengaard
Published: June 2024

Programming robots presents significant challenges, including high costs, extensive time commitments and steep learning curves, particularly for individuals lacking technical background in engineering. These barriers have been partially mitigated by the emergence of end-user development methodologies. Yet existing approaches often fall short in equipping users with the necessary software engineering competencies to develop comprehensive robot behaviors or to effectively maintain and re-purpose their creations. In this paper, we introduce a novel end-user development approach designed to empower physical therapists to independently specify robot-assisted physical training exercises, eliminating the need for robotics experts' intervention. Our approach is based on a set of design goals obtained through a participatory design study with experts in the field. It utilizes a textual domain-specific language (DSL) that enables users to define expected robot behaviors through Behaviour-Driven Development (BDD) scenarios. This paper discusses key themes, design objectives, and the evolution of requirements that emerged from an evaluative workshop.
Supporting Mixed-Presence Awareness across Wall-Sized Displays Using a Tracking Pipeline based on Depth Cameras
Authors: Coppens Adrien , Hermen Johannes , Schwartz Lou , Moll Christian , Maquil Valérie
Published: June 2024

One of the main benefits of large interactive surfaces (e.g. wall-sized displays) lies in their support for collocated collaboration by facilitating simultaneous interactions with the displays and high awareness of other group members' actions. In the context of remote collaboration, this awareness information needs to be acquired through digital means such as video feeds, which typically offer very limited information on non-verbal communication aspects, including on workspace awareness. We describe a new approach we have implemented to tackle that challenge through a multimodal pipeline that deals with tracking, attributing, transmitting, and visualising non-verbal information through what we refer to as workspace awareness cues, across wall-sized displays placed at distant locations. Our approach relies on commodity depth cameras combined with screen configuration information to generate deictic cues such as pointing targets and gaze direction. It also leverages recent artificial intelligence breakthroughs to attribute such cues to identified individuals and augment them with additional gestural interactions. In the present paper, we expand on the details and rationale behind our approach, describe its technical implementation, validate its novelty with regards to the existing literature, and report on early but promising results from an evaluation we conducted based on a mixed-presence decision-making scenario across two distant wall-sized displays.
All in One Place: Ensuring Usable Access to Online Shopping Items for Blind Users
Authors: Prakash Yash , Nayak Akshay Kolgar , Sunkara Mohan , Jayarathna Sampath , Lee Hae-Na , Ashok Vikas
Published: June 2024

Perusing web data items such as shopping products is a core online user activity. To prevent information overload, the content associated with data items is typically dispersed across multiple webpage sections over multiple web pages. However, such content distribution manifests an unintended side effect of significantly increasing the interaction burden for blind users, since navigating to-and-fro between different sections in different pages is tedious and cumbersome with their screen readers. While existing works have proposed methods for the context of a single webpage, solutions enabling usable access to content distributed across multiple webpages are few and far between. In this paper, we present InstaFetch, a browser extension that dynamically generates an alternative screen reader-friendly user interface in real-time, which blind users can leverage to almost instantly access different item-related information such as description, full specification, and user reviews, all in one place, without having to tediously navigate to different sections in different webpages. Moreover, InstaFetch also supports natural language queries about any item, a feature blind users can exploit to quickly obtain desired information, thereby avoiding manually trudging through reams of text. In a study with 14 blind users, we observed that the participants needed significantly lesser time to peruse data items with InstaFetch, than with a state-of-the-art solution.
Fundamental Logic Is Decidable
Authors: Aguilera Juan , Bydžovský Jan
Published: June 2024

It is shown that Holliday’s propositional Fundamental Logic is decidable in polynomial time and that first-order Fundamental Logic is decidable in double-exponential time. The proof also yields a double-exponential–time decision procedure for first-order orthologic.
ZTA-IoT: A Novel Architecture for Zero-Trust in IoT Systems and an Ensuing Usage Control Model
Authors: Ameer Safwa , Praharaj Lopamudra , Sandhu Ravi , Bhatt Smriti , Gupta Maanak
Published: June 2024

Recently, several researchers motivated the need to integrate Zero Trust (ZT) principles when designing and implementing authentication and authorization systems for IoT. An integrated Zero Trust IoT system comprises the network infrastructure (physical and virtual) and operational policies in place for IoT as a product of a ZT architecture plan. This article proposes a novel Zero Trust architecture for IoT systems called ZTA-IoT. Additionally, based on different types of interactions between various layers and components in this architecture, we present ZTA-IoT-ACF, an access control framework that recognizes different interactions that need to be controlled in IoT systems. Within this framework, the article then refines its focus to object-level interactions, i.e., interactions where the target resource is a device (equivalently a thing) or an information file generated or stored by a device. Building on the recently proposed Zero Trust score-based authorization framework (ZT-SAF), we develop the object-level Zero Trust score-based authorization framework for IoT systems, denoted as ZTA-IoT-OL-SAF, to govern access requests in this context. With this machinery in place, we finally develop a novel usage control model for users-to-objects and devices-to-objects interactions, denoted as UCON

\(_{IoT}\)

. We give formal definitions, illustrative use cases, and a proof-of-concept implementation of UCON

\(_{IoT}\)

. This article is a first step toward establishing a rigorous formally defined score-based access control framework for Zero Trust IoT systems.
Multimodal Fusion for Talking Face Generation Utilizing Speech-Related Facial Action Units
Authors: Liu Zhilei , Liu Xiaoxing , Chen Sen , Liu Jiaxing , Wang Longbiao , Bi Chongke
Published: June 2024

Talking face generation is to synthesize a lip-synchronized talking face video by inputting an arbitrary face image and corresponding audio clips. The current talking face model can be divided into four parts: visual feature extraction, audio feature processing, multimodal feature fusion, and rendering module. For the visual feature extraction part, existing methods face the challenge of complex learning task with noisy features, this article introduces an attention-based disentanglement module to disentangle the face into Audio-face and Identity-face using speech-related facial action unit (AU) information. For the multimodal feature fusion part, existing methods ignore not only the interaction and relationship of cross-modal information but also the local driving information of the mouth muscles. This study proposes a novel generative framework that incorporates a dilated non-causal temporal convolutional self-attention network as a multimodal fusion module to enhance the learning of cross-modal features. The proposed method employs both audio- and speech-related facial AUs as driving information. Speech-related AU information can facilitate more accurate mouth movements. Given the high correlation between speech and speech-related AUs, we propose an audio-to-AU module to predict speech-related AU information. Finally, we present a diffusion model for the synthesis of talking face images. We verify the effectiveness of the proposed model on the GRID and TCD-TIMIT datasets. An ablation study is also conducted to verify the contribution of each component. The results of quantitative and qualitative experiments demonstrate that our method outperforms existing methods in terms of both image quality and lip-sync accuracy. Code is available at https://mftfg-au.github.io/Multimodal_Fusion/.
KPAR: Knowledge-aware Path-based Attentive Recommender with Interpretability
Authors: Eytan Leigh , Bogina Veronika , Ben-Gal Irad , Koenigstein Noam
Published: June 2024

Knowledge Graph (KG)-based recommender systems utilize both Collaborative Filtering (CF) data and informative KG data to improve prediction accuracy. Path-based KG methods are a family of KG-based recommendation models that explore the interlinks within a knowledge graph in order to enhance the connectivity between users and items with rich complementary information. A key advantage of path-based KG methods stems from their ability to enable intuitive explanations naturally. In this work, we present a novel path-based algorithm that employs neural attention in order to better extract the relevant information from the unified graph. Evaluations based on public KG recommendation datasets indicate a clear advantage to the proposed method compared to state-of-the-art path-based alternatives. Furthermore, we show that this advantage also extends to cold items where a better utilization of the KG leads to improved predictions in cases where no CF data is available. Finally, by performing attention-score analysis, we demonstrate the ability of our approach to provide better interpretability into the model’s inner workings as well as extract more intuitive explanations. The code for this work is publicly available on GitHub: https://github.com/DeltaLabTLV/KPAR.
Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation
Authors: Cheng Zhiyong , Dong Jianhua , Liu Fan , Zhu Lei , Yang Xun , Wang Meng
Published: June 2024

Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user preferences across different behaviors and fail to account for diverse item preferences within behaviors. Various user preference factors (such as price or quality) entangled in the behavior may lead to sub-optimization problems. Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items. To tackle these challenges, we propose the Disentangled Cascaded Graph Convolutional Network (Disen-CGCN), a novel multi-behavior recommendation model. Disen-CGCN employs disentangled representation techniques to effectively separate factors within user and item representations, ensuring their independence. In addition, it incorporates a multi-behavioral meta-network, enabling personalized feature transformation across user and item behaviors. Furthermore, an attention mechanism captures user preferences for different item factors within each behavior. By leveraging attention weights, we aggregate user and item embeddings separately for each behavior, computing preference scores that predict overall user preferences for items. Our evaluation of benchmark datasets demonstrates the superiority of Disen-CGCN over state-of-the-art models, showcasing an average performance improvement of 7.07% and 9.00% on respective datasets. These results highlight Disen-CGCN’s ability to effectively leverage multi-behavioral data, leading to more accurate recommendations.
Central Bank Independence: Views from History and Machine Learning
Authors: Dincer Nergiz , Eichengreen Barry , Martinez Joan J.
Published: June 2024

We assemble an almost complete set of central bank statutes since 1800 to assess the legal independence of central banking institutions. We use these to extend existing indices of legal independence backward and forward in time. We document the trend toward increased independence post 1980 as well as an earlier, more limited movement in the direction of enhanced independence in the 1920s. We apply natural language processing to current statutes to corroborate our human-reader assessment. Using machine-learning methods, we quantify the extent to which topics in those statutes contribute to the independence measure based on our reading of the statutes. The topic with the largest positive contribution to explaining the cross-country variation in central bank independence encompasses disclosure, transparency, and reporting obligations. The topic with the largest negative contribution covers regulatory powers over inter alia securities markets that complicate the central bank's mandate, make accountability more complex, and render independence problematic.
Protests
Authors: Cantoni Davide , Kao Andrew , Yang David Y. , Yuchtman Noam
Published: June 2024

Citizens have long taken to the streets to demand change, expressing political views that may otherwise be suppressed. Protests have produced change at local, national, and international scales, including spectacular moments of political and social transformation. We document five new empirical patterns describing 1.2 million protest events across 218 countries between 1980 and 2020. First, autocracies and weak democracies experienced a trend break in protests during the Arab Spring. Second, protest movements also rose in importance following the Arab Spring. Third, protest movements geographically diffuse over time, spiking to their peak before falling off. Fourth, a country's year-to-year economic performance is not strongly correlated with protests; individual values are predictive of protest participation. Fifth, the United States, China, and Russia are the most overrepresented countries in academic studies. We discuss each pattern's connections to the existing literature and anticipate paths for future work.
Government Intervention in Innovation
Authors: Howell Sabrina T.
Published: June 2024

Governments' powerful imperative to support innovation has only grown more urgent amid slowing growth in the developed world and a rapidly changing climate. In this review, I describe the important role that economics and finance play in rigorously evaluating innovation policy. I organize government intervention in innovation into arenas, agendas, and instruments. The arenas are firms, financial intermediaries, universities, and government laboratories. The key agendas are entrepreneurship, defense, climate, health, and education. The instruments fall into three categories: supply-push, demand-pull, and legal. I provide theoretical rationales for government intervention in innovation and discuss how they intersect with government agendas and empirical evidence in practice. One takeaway is that the government has a key role to play in the type of failure-tolerant, open-ended research that yields breakthrough inventions. In contrast, there is less evidence that subsidizing financial intermediaries is useful.
Galaxy Formation in ΛCDM Cosmology
Authors: Primack Joel R.
Published: June 2024

This is a golden age for galaxy formation: Existing and especially new telescopes are providing observations that challenge and illuminate rapidly improving theory and simulations. This review describes the formation of the cosmic web and the structure of the dark matter halos that provide the scaffolding of the Universe. It then summarizes how empirical models, semianalytic models, and hydrodynamic simulations attempt to account for key properties of the galaxy population, including the main sequence of star-forming galaxies, the inefficiency of star formation, the shape evolution and color bimodality of galaxies, and the phenomena that cause galaxies to quench their star formation. It concludes with a summary of observations that have challenged the cosmological constant cold dark matter (ΛCDM) paradigm of galaxy formation—including the Hubble and S
8 tensions, bright galaxies in the early Universe, an extragalactic background light mystery, missing satellite galaxies, the diversity of dwarf galaxies, the cusp–core problem, the too-big-to-fail problem, stellar clumps, planes of satellite galaxies, and galaxies without dark matter—and solutions that have been proposed.
Molecular Dialogue During Host Manipulation by the Vascular Wilt Fungus Fusarium oxysporum
Authors: Srivastava Vidha , Patra Kuntal , Pai Hsuan , Aguilar-Pontes Maria Victoria , Berasategui Aileen , Kamble Avinash , Di Pietro Antonio , Redkar Amey
Published: June 2024

Vascular wilt fungi are a group of hemibiotrophic phytopathogens that infect diverse crop plants. These pathogens have adapted to thrive in the nutrient-deprived niche of the plant xylem. Identification and functional characterization of effectors and their role in the establishment of compatibility across multiple hosts, suppression of plant defense, host reprogramming, and interaction with surrounding microbes have been studied mainly in model vascular wilt pathogens Fusarium oxysporum and Verticillium dahliae. Comparative analysis of genomes from fungal isolates has accelerated our understanding of genome compartmentalization and its role in effector evolution. Also, advances in recent years have shed light on the cross talk of root-infecting fungi across multiple scales from the cellular to the ecosystem level, covering their interaction with the plant microbiome as well as their interkingdom signaling. This review elaborates on our current understanding of the cross talk between vascular wilt fungi and the host plant, which eventually leads to a specialized lifestyle in the xylem. We particularly focus on recent findings in F. oxysporum, including multihost associations, and how they have contributed to understanding the biology of fungal adaptation to the xylem. In addition, we discuss emerging research areas and highlight open questions and future challenges.
High Performance Ti3+ Self-Doped Li4Ti5O12 Anode Materials Synthesized by an Electrochemical Method in Molten Salt
Authors: Liang Tianzi , Yang Yagang , Dong Hanqing , Yu Kai , Song Qiushi , Ning Zhiqiang , Xie Hongwei
Published: June 2024

A simple synthesis for the Ti3+-doped, small Li4Ti5O12 is favorable for its application as an anode material of lithium-ion batteries. This study presents a direct synthesis method of high-performance Li4Ti5O12 (LTO-Ti) with the Ti3+ self-doped and uniformly small in size. It is carried out by the redox of an electrochemical electrode pair of Ti and Fe2O3 at a constant potential in molten KCl-LiCl salt with Li2CO3. The synthesis mechanism and the effect of synthesis conditions on the formation and properties of LTO-Ti were investigated in detail. The LTO-Ti with an average size of approximately 400 nm is prepared by the ionic level synthesis method, and exhibits a superior specific capacity of 130.5 mAh·g−1 at 10 C current density, which is 73.6% of its average specific capacity at 1 C. Moreover, it also shows good cycling stability with a specific capacity of 127.2 mAh·g−1 after 1000 cycles at 5 C (capacity retention of 96.9%). This synthesis is secure and prospective.
Numerical Modeling for Hydrogen Permeation Through Steel Under Sour-Corrosion and Tensile Stress
Authors: Kim Sung Jin
Published: June 2024

New perspectives arise from the application of the finite difference method (FDM), which incorporates the dynamic change in sub-surface hydrogen concentration (C

S
) on the steel surface, for analyzing hydrogen permeation behaviors under the combined action of sour corrosion and tensile loading. With this modified-FDM modeling and quantified C

S
values, it can be mechanistically understood that the loaded steel undergoes a cycle consisting of a higher hydrogen evolution rate on the surface, weakening the stability of the corrosion scale (FeSX) with more defects, and increased electrical conductivity. This leads to maintaining higher C

S
level, based on a higher cathodic reduction reaction.